CN116055346B

CN116055346B - Data processing method, device, electronic device and storage medium

Info

Publication number: CN116055346B
Application number: CN202111261686.6A
Authority: CN
Inventors: 胡通; 刘云霞; 陈磊; 蒋晔; 陈晨
Original assignee: China Mobile Communications Group Co Ltd; China Mobile Hangzhou Information Technology Co Ltd
Current assignee: China Mobile Communications Group Co Ltd; China Mobile Hangzhou Information Technology Co Ltd
Priority date: 2021-10-28
Filing date: 2021-10-28
Publication date: 2025-08-26
Anticipated expiration: 2041-10-28
Also published as: CN116055346A

Abstract

The present application discloses a data processing method, device, electronic device and storage medium. The method includes: obtaining southbound data and corresponding northbound data to be analyzed from an operation and maintenance center (OMC); using the obtained southbound data and northbound data to be analyzed, performing at least one of the following operations: determining the northbound data corresponding to the southbound data based on a first model, and performing a consistency rate test on the northbound data to be analyzed based on the corresponding northbound data to obtain a first test result; for each performance indicator in at least one performance indicator, using a dynamically determined first threshold, performing a volatility test on the performance data in the northbound data to be analyzed to obtain a second test result; and outputting the obtained test result. The solution provided in the present application automatically detects the consistency rate and/or volatility of the northbound data obtained from the OMC, and outputs the test result. In this way, the efficiency and accuracy of data detection can be improved, and the quality of northbound data can be guaranteed.

Description

Data processing method, device, electronic equipment and storage medium

Technical Field

The present application relates to the field of communications, and in particular, to a data processing method, apparatus, electronic device, and storage medium.

Background

With the continuous progress of communication technology, the complexity of the network scale is higher and higher, and the carried services are also continuously expanded and upgraded, such as the full development of the fifth generation mobile communication technology (5G) service and the upgrading and transformation of network function virtualization (NFV, networl Functions Virtualization). In this process, there are operations of upgrading, cutting and changing network elements, which may cause problems of inconsistent data, large time delay, data loss and the like of the network element data transmitted from each network element to the Operation and maintenance center (OMC, operation AND MAINTENANCE CENTER) and the network element data transmitted from the OMC to the service platform. In this case, quality detection needs to be performed on the data to be transmitted to the service platform by the OMC, so that related personnel can find out data problems in time and perform corresponding processing, thereby guaranteeing the quality of network data, and supporting stable operation of the upper service platform.

In the related art, the quality of network data is usually collected and detected by combining manual or manual information technology (IT, information Technology), however, the above technical scheme has the problems of low detection efficiency and low accuracy, so that an operation and maintenance person cannot timely and accurately locate the problems of data based on the detection result, namely, the quality of the data cannot be guaranteed. Because the quality of the data cannot be guaranteed, the upper layer service platform may influence the normal operation of the related service when analyzing and deciding by using the received data.

In summary, there is a lack of detection methods in the related art that can efficiently and accurately detect OMC data quality.

Disclosure of Invention

In order to solve the related technical problems, the embodiment of the application provides a data processing method, a data processing device, electronic equipment and a storage medium.

The technical scheme of the embodiment of the application is realized as follows:

the embodiment of the application provides a data processing method, which comprises the following steps:

acquiring southbound data and corresponding northbound data to be analyzed from an OMC;

And executing at least one of the following operations by using the acquired southbound data and northbound data to be analyzed:

determining corresponding northbound data of the southbound data based on a first model, and detecting the consistency rate of the northbound data to be analyzed based on the corresponding northbound data to obtain a first detection result;

Aiming at each performance index in at least one performance index, performing fluctuation rate detection on the performance data in the northbound data to be analyzed by utilizing a first threshold value which is dynamically determined to obtain a second detection result;

and outputting the obtained detection result.

In the above solution, the determining, based on the first model, the north data corresponding to the south data includes:

determining a field in north data corresponding to the field in south data based on a first sub-model;

determining a south computing formula corresponding to the count in the south data based on a second sub-model, wherein one field corresponds to one count;

And determining the north data corresponding to the south data by utilizing the fields in the determined north data and the corresponding calculation formulas.

In the above scheme, the method further comprises:

generating a first sub-model with a tree structure and/or generating a second sub-model with a tree structure;

the generated submodel is saved in the form of JSON file.

In the above solution, the determining, based on the first sub-model, a field in the northbound data corresponding to the field in the southbound data includes:

And determining the fields in the northbound data corresponding to the fields in the southbound data based on the first submodel and the regular expression.

In the above scheme, the method further comprises:

selecting N pieces of sampling data from the currently acquired northbound data to be analyzed according to each performance index to obtain a first data set, and selecting N pieces of sampling data from the historical northbound data to obtain a second data set, wherein the value of N is an integer greater than or equal to 1;

Performing linear fitting on the first data set and the second data set to obtain a fitting result;

Obtaining a first parameter based on the (n+1) th sampling data in the historical northbound data and the fitting result;

and determining a first threshold value based on the first parameter and the (n+1) th sampling data in the currently acquired north data to be analyzed.

In the above scheme, the acquiring the southbound data and the corresponding northbound data to be analyzed from the OMC includes:

obtaining the southbound data from the OMC based on DB;

Acquiring resources and performance data in the northbound data to be analyzed from the OMC based on FTP;

And acquiring alarm data in the northbound data to be analyzed from the OMC based on Socket.

In the above scheme, the southbound data is periodically acquired from the OMC based on DB;

and/or the number of the groups of groups,

And periodically acquiring the resource and performance data in the northbound data to be analyzed from the OMC based on FTP.

In the above scheme, the method further comprises:

And further performing at least one of the following operations by using the acquired southbound data and northbound data to be analyzed:

detecting the resource and performance integrity rate of the resource and performance data in the north data to be analyzed by utilizing the south data, and detecting the integrity rate of the alarm data in the north data to be analyzed to obtain a fourth detection result;

Utilizing the southbound data to perform compliance rate detection on resources and performance data in the northbound data to be analyzed, and performing compliance rate detection on alarm data in the northbound data to be analyzed to obtain a fifth detection result;

and detecting the time rate of the resources and the performance data in the north data to be analyzed by using the south data, and detecting the time rate of the alarm data in the north data to be analyzed to obtain a sixth detection result.

The embodiment of the application also provides a data processing device, which comprises:

the acquisition unit acquires southbound data and corresponding northbound data to be analyzed from the OMC;

the execution unit is used for executing at least one of the following operations by using the acquired southbound data and northbound data to be analyzed:

Aiming at each performance index in at least one performance index, judging that performance data in the northbound data to be analyzed is subjected to fluctuation rate detection by utilizing a first threshold value which is dynamically determined, so as to obtain a second detection result;

And the result unit is used for outputting the obtained detection result.

The embodiment of the application also provides an electronic device comprising a processor and a memory for storing a computer program capable of running on the processor,

Wherein the processor is configured to implement, when executing, the steps of any one of the data processing methods when executing the computer program.

The embodiment of the present application also provides a storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of any of the data processing methods.

The data processing method, the device, the electronic equipment and the storage medium provided by the embodiment of the application perform at least one operation of determining the corresponding north-oriented data of the south-oriented data based on a first model and detecting the consistency rate of the north-oriented data to be analyzed based on the corresponding north-oriented data to obtain a first detection result after acquiring the south-oriented data and the corresponding north-oriented data to be analyzed from the OMC, performing fluctuation rate detection on the performance data in the north-oriented data to be analyzed by utilizing a dynamically determined first threshold value according to each performance index in at least one performance index to obtain a second detection result, and outputting the obtained detection result. According to the technical scheme provided by the embodiment of the application, based on the southbound data obtained from the OMC and the northbound data to be analyzed, the northbound data to be analyzed is subjected to automatic detection of the coincidence rate and/or the fluctuation rate, and the detection result is output. Through the automatic detection of the index of the consistency rate and/or the fluctuation rate of the northbound data, the problems existing in the current northbound data can be rapidly reflected, so that maintenance personnel can rapidly position and solve the problems according to the output detection result. In addition, through the automatic detection of the northbound data consistency rate and/or the fluctuation rate, human errors caused by manual operation can be reduced, the detection accuracy of the northbound data is improved, and the quality of the northbound data is further improved.

Drawings

FIG. 1 is a flow chart of data processing according to an embodiment of the application;

FIG. 2 is a diagram of an OMC data quality detection system architecture for an embodiment of the application;

FIG. 3 is a schematic flow chart of the consistency rate detection according to an embodiment of the present application;

FIG. 4 is a schematic diagram of a data processing apparatus according to an embodiment of the present application;

fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the application.

Detailed Description

The application is described in further detail below with reference to the accompanying drawings and specific examples.

Before explaining the present application in further detail, terms and terminology involved in the embodiments of the present application are explained, and the terms and terminology involved in the embodiments of the present application are applicable to the following explanation.

(1) The OMC north interface is an interface between the OMC system and a Network management system (NMS, network MANAGEMENT SYSTEM), which can also be called a service platform, for communication interaction, and is used for transmitting data to the NMS so as to support the operation of upper-layer applications.

(2) And the OMC southbound interface is an interface for communication interaction between the OMC system and the OMC database and is used for storing the received original data of the network element into the OMC database.

At present, in the process of transmitting data to the NMS through the northbound interface by the OMC, the problems of insufficient data integrity, low accuracy, poor timeliness and insufficient stability of the northbound interface of the OMC exist. The specific analysis is as follows:

1. The integrity is insufficient, namely, the possibility that a data field is lost in the process of data transmission possibly exists, so that the integrity of the data received by an upper layer application is insufficient, and the data is inconsistent with the original data sent by a network element.

2. In the related accuracy detection method, due to lack of comparison of historical values and empirical values, abnormal data (also called bad data) cannot be found accurately, and therefore the operation of upper analysis class and decision class applications can be affected.

3. In the related technology, due to lack of supervision of data quality detection, the problem that OMC delays reporting of data through a northbound interface exists, so that the situation of abnormal operation of the application, such as interface data loss and abnormal display, caused by failure of timely receiving the data by upper layer analysis and report application is caused.

4. The stability is insufficient, namely, as the network element is increasingly large in scale, the processing load of the OMC is increased, so that the OMC cannot timely process the data uploaded by the network element, and thus, the situation of missing processing or long waiting time of processing of the data may exist. In other words, the data quality of the OMC northbound interface cannot be guaranteed at present, and the problem of unstable data quality exists.

In the related art, aiming at the data of the OMC northbound interface, the data acquisition and quality detection are mainly carried out by combining a manual mode or a manual mode with an IT (information technology) means. Specifically, based on manual operation and maintenance experience and data quality requirements on the OMC northbound interface data, timeliness, integrity, effectiveness and accuracy of the northbound interface data are detected. By establishing a data quality monitoring system, the problem of the data quality of the northbound interface can be found.

However, in the above process, although the quality of the data can be detected to some extent, in the process of detecting the data, it is necessary to manually set and adjust related parameters, which makes the manual operation costly. Meanwhile, manual operation can influence the detection efficiency to a certain extent. In addition, because the detection of the data is required to be realized according to manual experience, when the network data changes, particularly the performance data, the existing detection method cannot be adaptively adjusted, and the data quality problem, namely the accuracy of the detection result is reduced, may not be accurately detected.

Based on this, in various embodiments of the present application, after acquiring southbound data from a southbound interface of an OMC and northbound data to be analyzed from a northbound interface of the OMC, automatic detection of a coincidence rate and/or a fluctuation rate is automatically performed on the northbound data to be analyzed, and a corresponding detection result is generated. Therefore, the detection efficiency of the northbound data can be improved, human errors caused by manual operation can be reduced, the detection accuracy of the northbound data is improved, and the stable operation of upper-layer application business is facilitated.

The embodiment of the application provides a data processing method, which is applied to electronic equipment, as shown in fig. 1, and comprises the following steps:

Step 101, acquiring southbound data and corresponding northbound data to be analyzed from OMC;

102, executing at least one of the following operations by using the acquired southbound data and northbound data to be analyzed:

and 103, outputting the obtained detection result.

The electronic device may include a terminal device, such as a server.

In practical application, after a plurality of network elements under the network generate original data, the generated original data is sent to the OMC. After receiving the original data sent by the network element, on one hand, the OMC will store the received original data (i.e. the southbound data) into the DB through the southbound interface, and on the other hand, the OMC will process the original data according to the preset OMC specification requirement, and then transmit the processed data (i.e. the northbound data to be analyzed) to the upper service platform through the northbound interface.

In practical application, the southbound data may include resource data, performance data, alarm data and other data, where the resource data mainly includes configuration information of a network element, the performance data mainly includes service information associated with the network element, and the alarm data mainly includes abnormal state information of the network element.

Correspondingly, the north data to be analyzed can comprise resource data, performance data, alarm data and the like after the received south data is processed. Meanwhile, the north data to be analyzed can also comprise alarm data generated in the process of processing the received south data by the OMC.

Here, when the OMC transmits the northbound data to be analyzed to the upper service platform through the northbound interface, the northbound data can be transmitted in the form of a file. The OMC can generate a corresponding resource file according to the resource data in the northbound data to be analyzed, generate a corresponding performance file according to the performance data in the northbound data to be analyzed, and generate a corresponding alarm file according to the alarm data in the northbound data to be analyzed.

The alarm file may include a plurality of alarm messages, where each alarm message includes corresponding alarm data.

Accordingly, when the OMC stores southbound data into the DB through the southbound interface, the OMC may also store the southbound data in the form of a file. The OMC can generate a corresponding resource file according to the resource data in the southbound data, generate a corresponding performance file according to the performance data in the southbound data, and generate a corresponding alarm file according to the alarm data in the southbound data.

In practical application, since the southbound data of OMC local and the northbound data to be analyzed can contain different types of data, aiming at the characteristics of different types of data, the electronic equipment can acquire the data in different modes.

Based on this, in an embodiment, the acquiring southbound data, corresponding northbound data to be analyzed, from the OMC includes:

obtaining the southbound data from the OMC based on DB;

In practical application, under the current network scale, the network element equipment has more types and can comprise different types of service equipment such as a core network, a signaling network, a wireless network, a transmission network and the like, wherein the signaling network is used for transmitting control signals except user information, and the transmission network is used for transmitting and converting signals. Meanwhile, for the network element equipment of the same type, products of a plurality of different manufacturers can exist, so that more resource data and performance data, particularly performance data, are in northbound data corresponding to different manufacturers.

In a wireless network, for example, tens of base stations and hundreds of cells are arranged on average, and the data size of the generated performance data can reach millions of records.

Aiming at the characteristic that the data volume of the performance data and the resource data is large, the electronic equipment can acquire the resource and the performance data in the northbound data to be analyzed from the OMC in an FTP mode so as to ensure that the acquired data has higher integrity and fewer data loss conditions.

In practical application, the OMC can generate corresponding resource files and performance files according to the data of different network element devices, so that the independence among the network elements is ensured. In this way, the electronic device can acquire the resources and the performance files in the northbound data to be analyzed from the OMC in an FTP mode, and further obtain the corresponding resources and performance data.

In addition, because the alarm data usually represents abnormal conditions, the real-time requirement on the acquisition of the alarm data is high, and therefore, the electronic equipment can acquire the alarm data in the northbound data to be analyzed from the OMC in a SOCKET mode. The alarm information in the north data to be analyzed may include alarm data of the network element device and alarm data generated in the process of OMC processing the received south data. The electronic device may establish a connection with the OMC by using a SOCKET protocol through a NETTY framework, so that alarm data in the north data to be analyzed can be obtained from the OMC in real time.

Here, in practical application, for different network element devices, the OMC may generate corresponding alert files according to alert data of the different network element devices. In this way, the electronic device can acquire the alarm file in the northbound data to be analyzed from the OMC by adopting SOCKET, and further obtain the corresponding alarm data.

In addition, since the OMC southbound data is stored in the database, the electronic device may acquire southbound data from the OMC database in a DB manner, wherein the southbound data may include resource data, performance data, and alert data. Specifically, the DB can acquire the resource file, the performance file and the alarm file in the southbound data, thereby acquiring the corresponding resource data, performance data and alarm data.

In summary, the electronic device acquires three types of data including the south data and the resources, the performances and the alarms in the north data to be analyzed from the OMC together through three acquisition modes of the FTP, the DB and the socks, so that the richness of the acquired data types is ensured, and the subsequent detection of the north data is facilitated.

In practical application, in order to avoid that the data size obtained from the OMC is unlimited and large, so as to influence the efficiency of quality detection on the data, the electronic device can periodically obtain corresponding data from the OMC.

Based on this, in an embodiment, the southbound data may be obtained from the OMC on a DB periodic basis.

For northbound data, resource and performance data in the northbound data to be analyzed may be periodically obtained from the OMC based on FTP.

In practical application, the electronic device may periodically obtain the resource and performance file in the southbound data from the OMC based on the DB, so as to obtain the resource data and performance data in the southbound data in the corresponding period, where the period may be set as required, for example, 5 minutes.

Correspondingly, the electronic device can periodically acquire the resource and the performance file in the northbound data to be analyzed from the OMC based on the FTP, so that the resource data and the performance data in the northbound data to be analyzed in the corresponding period can be obtained.

In actual application, in step 102, the obtained southbound data and the northbound data to be analyzed are utilized to detect the coincidence rate and/or the fluctuation rate of the northbound data to be analyzed. Specifically, since the performance data in the northbound data to be analyzed is generally higher in correlation with the upper layer service, when the consistency rate and the fluctuation rate of the northbound data to be analyzed are detected, the method mainly comprises the step of detecting the performance data in the northbound data to be analyzed. Therefore, maintenance personnel can rapidly judge the problem of data in the OMC northbound data according to the detection result, and further normal operation of upper-layer business is guaranteed.

The uniformity rate of the performance data in the north data to be analyzed can be understood as the uniformity of the performance data in the north data to be analyzed and the performance data in the south data, and the fluctuation rate of the performance data in the north data to be analyzed can be understood as the fluctuation condition of the performance data in the north data to be analyzed.

When the OMC detects consistency, the private counting algorithm of each manufacturer is adopted to calculate the southbound data, and then the southbound data is compared with northbound data to be analyzed to detect, and in the process, on one hand, the private counting algorithm of each manufacturer is different, so that the calculation step is complex, and the detection efficiency is low. On the other hand, the southbound data has a large number of fields of performance data, and the processing difficulty and the time consumption are long when the private counting algorithm is adopted for calculation.

Based on this, in an embodiment, the determining, based on the first model, north data corresponding to the south data includes:

determining a calculation formula corresponding to the count in the southbound data based on a second sub-model, wherein one field corresponds to one count;

The field can be understood as an attribute or a feature of the southbound data, and the calculation formula corresponding to the count can be understood as a calculation formula adopted by a value corresponding to the attribute or the feature of the southbound data.

Here, the electronic device may construct the first model in advance based on mapping relationships between the southbound data corresponding to each manufacturer and the northbound data to be analyzed, and private counting algorithms corresponding to each manufacturer.

Specifically, the electronic device may construct a first sub-model based on a mapping relationship between a performance data field in southbound data and a performance data field in northbound data corresponding to each manufacturer, and may construct a second sub-model based on a private counting algorithm for counting performance data in southbound data, where the performance data field in southbound data and the performance data field in northbound data may be in a one-to-one correspondence relationship, or may be one performance data field in northbound data of a plurality of performance data fields in southbound data.

Then, the electronic device can determine fields in the north data corresponding to the performance fields in the south data based on the first sub-model, and can determine calculation formulas corresponding to counts in the south data based on the second sub-model. The electronic equipment calculates the count in the southbound data by using the corresponding calculation formula, the count corresponding to the field in the determined northbound data can be obtained, and the performance data in the northbound data corresponding to the performance data in the northbound data can be determined by combining the field in the determined northbound data.

In practical application, when the first model is constructed, the electronic device can also construct a visual model interface corresponding to the first model so that the OMC can edit the mapping relation between the corresponding southbound data neutral data field and the northbound data neutral data field to be analyzed. In addition, if special requirements exist in the network element field, the electronic equipment can also be revised on the visual model interface in the form of manual identification.

The electronic device may employ an orgchart. Js plug-in and a jquery. Js plug-in to construct a visual model interface.

The method for constructing the first model increases the compatibility and flexibility of the first model, can be suitable for the consistency rate detection of different manufacturers and different private counting algorithms, and has high detection efficiency.

In practical application, the electronic device may determine, based on the first sub-model and the regular expression, a performance data field in the north direction data corresponding to the performance data field in the south direction data obtained.

Based on this, in an embodiment, the determining, based on the first sub-model, a field in the northbound data corresponding to the field in the southbound data includes:

In practical application, aiming at a plurality of associated performance data fields in southbound data, the electronic device can splice the performance data fields, and then determine the performance data fields in northbound data corresponding to the performance data fields in the southbound data after splicing based on the first submodel and the regular expression, so as to meet the mapping and searching scene requirements of many-to-one fields.

And after determining a calculation formula corresponding to the performance data field value in the acquired southbound data based on the second sub-model, calculating the performance data field value in the acquired southbound data by using the corresponding calculation formula to obtain a value corresponding to the performance data field in the corresponding northbound data. Then, the electronic device compares the corresponding north data performance data field and the corresponding field value with the north data performance data field and the corresponding field value to be analyzed to detect the consistency rate, and further a first detection result is obtained.

Here, in practical application, the first sub-model and the second sub-model may be constructed as a tree structure to increase the speed of detecting the coincidence rate of the north data to be analyzed by using the first sub-model and the second sub-model.

Based on this, in an embodiment, the method further comprises:

the generated submodel is saved in the form of JSON file.

Here, by generating the first sub-model and/or the second sub-model of the tree structure including the sub-nodes, the traversing speed of the performance data field in the southbound data can be increased, and thus the detecting speed of the coincidence rate can be increased.

In addition, the generated first sub-model and/or the generated second sub-model are stored in the form of JSON files, so that the degree of dependence on the third-party middleware of the database can be reduced, and the subsequent flexible migration and deployment are facilitated.

In practical application, when the electronic equipment detects the fluctuation rate of the performance data in the north data to be analyzed, the electronic equipment can dynamically detect each performance index to judge whether the performance data in the north data to be analyzed is abnormal or not.

In an embodiment, the method further comprises:

In practical application, aiming at a performance index, the electronic device can select N pieces of sampling data from currently acquired north data to be analyzed to obtain a first data set as a set of data of a historical Y value. Meanwhile, the electronic equipment can also select N pieces of sampling data from the historical northbound data to obtain a second data set which is used as a group of data of the historical X value.

For one performance index, the electronic device may select N pieces of sample data from among the north data to be analyzed in one period currently acquired to obtain a first data set, and may also select N pieces of sample data from among the north data to be analyzed in the corresponding period acquired N1 days ago to obtain a second data set, where the value of N1 is an integer greater than or equal to 1.

Here, based on the first data set and the second data set, the electronic device may perform fitting using a linear regression prediction formula to obtain a fitting result, where the linear regression prediction formula may include a function FORECAST, and the function FORECAST calculation formula may be expressed as:

q'=a+bx' (1)

Wherein, the X represents the average value of the sampled data in the second data set, y represents the average value of the sampled data in the first data set, and n represents the number of the sampled data in the first data set and the second data set.

Illustratively, by calculating the average value of N sampled data in the first data set and the second data set, respectively, the values of the parameters a and b can be determined, and the fitted result can be obtained.

Then, the electronic device acquires the (n+1) th sampling data from the historical northbound data, and uses the n+1 th sampling data as an input value of x 'in a fitted result to determine a predicted value q' (i.e., a first parameter) of the (n+1) th sampling data in the currently acquired northbound data to be analyzed. Then, the electronic device may acquire the (n+1) th sampling data from the currently acquired north data to be analyzed, to obtain an actual value q of the (n+1) th sampling data. The first threshold can be determined by calculating the difference between q and q'.

In practical application, if the value corresponding to the performance data in the north data to be analyzed at the current moment is larger in fluctuation amplitude than the value corresponding to the performance data in the north data to be analyzed at the previous moment, that is, the difference between the value of the performance data at the current moment and the value of the performance data at the previous moment exceeds a first threshold, the electronic device can consider that the performance data in the north data to be analyzed at the current moment is abnormal. If the value corresponding to the performance data in the north data to be analyzed at the current moment is smaller than the value fluctuation range corresponding to the performance data in the north data to be analyzed at the previous moment, that is, the difference value between the data of the performance data at the current moment and the value of the performance data at the previous moment is smaller than the first threshold, the electronic equipment can consider that the performance data in the north data to be analyzed at the current moment is normal.

Here, by performing the fluctuation rate detection on the performance data in the north data to be analyzed by the above method, the situations of setting the threshold value and setting the fixed threshold value frequently by manpower can be avoided. Based on the dynamically determined threshold value, the change of network performance can be self-adapted, and the detection accuracy of the performance data fluctuation rate in the northbound data is improved.

In practical application, in order to ensure the integrity of data quality detection, the electronic equipment can also detect the integrity rate, the compliance rate and the timeliness rate of OMC data, so that the coverage of the detected indexes is improved, and the accuracy of the detection result is further improved.

Based on this, in an embodiment, the method may further include:

Here, when the acquired southbound data is used for detecting the integrity rate of the northbound data to be analyzed, the electronic device may respectively detect the integrity rate of the resources and the performance data in the northbound data to be analyzed, and detect the integrity rate of the alarm data in the northbound data to be analyzed.

Specifically, the resource and performance integrity rate is a result obtained by comparing the number of resource and performance file records in the southbound data with the number of resource and performance file records in the northbound data to be analyzed, expressed by a formula, then there is:

resource and performance integrity ratio = number of resource and performance file records in northbound data to be analyzed/number of resource and performance file records in southbound data;

that is, when acquiring the north data and the south data to be analyzed from the OMC through the FTP and the DB, the electronic device records the number of the resources and the performance files in the north data and the south data to be analyzed respectively, so as to obtain the record numbers of the resources and the performance files in the north data and the south data to be analyzed.

In practical application, when the integrity rate of the alarm data in the northbound data to be analyzed is detected, the electronic device can compare the number of alarm files in the northbound data to be analyzed (including alarms reported by network elements and OMCs) with the number of alarm files in the southbound data (including alarms reported by network elements and OMCs), in other words, the alarm integrity rate can be understood as the ratio of the number of alarms received by the upper layer application to the number of alarms stored in the OMC database.

For example, the calculation formula of the alarm integrity rate can be expressed as:

Alarm integrity rate = number of alarms received by upper layer application/number of alarms stored by OMC;

in practical application, when the obtained southbound data is used for detecting the compliance rate of the northbound data to be analyzed, the electronic equipment can respectively detect the compliance rate of the resources and the performance data in the northbound data to be analyzed and detect the compliance rate of the alarm data in the northbound data to be analyzed.

Here, the compliance rate characterizes compliance of a field in the resource, performance, or alert data.

Specifically, when the compliance rate is detected on the resource and the performance data in the north data to be analyzed, the electronic device may construct a corresponding specification model file stored in JSON form based on the field requirements of the resource and the performance. Then, the electronic device detects the resources in the northbound data to be analyzed and the number of fields in the performance data that do not meet the requirements of the fields. The field non-compliance in the resource and performance data may specifically include filling-in-necessary item or conditional filling-in-necessary item non-compliance (such as null value, negative value, etc.), field type non-compliance, field format non-compliance, or field range non-compliance, etc.

Then, the electronic device respectively determines the record number of the resources in the northbound data to be analyzed and the field in the performance data, wherein the fields in the record number meet the field requirements (namely the record number of the resources in the northbound data to be analyzed and the field compliance in the performance data), and the total record number of the resources in the northbound data to be analyzed and the total record number of the resources in the performance data to be analyzed, so that the resources and the performance compliance rate are obtained. The resource and performance compliance calculation formula can be expressed as:

Resource and performance compliance = number of field compliance records in the northbound data to be analyzed/total number of records of the resource and performance data in the northbound data to be analyzed;

for the alarm compliance rate, because the fields in the alarm data are relatively fixed, the electronic device can determine the alarm compliance rate by detecting whether a specific field in the alarm data is compliant or not and counting the record number of the alarm data with the non-compliant field, wherein the name of the specific field can be set according to the requirement, and the name of the specific field is not limited in the embodiment.

Then, the electronic device calculates the corresponding alarm compliance rate by using an alarm compliance rate calculation formula, wherein the alarm compliance rate calculation formula can be expressed as:

alarm compliance = 1-record number of alarm data with non-compliance field in north data to be analyzed/total record number of alarm data in north data to be analyzed;

In practical application, when the electronic device utilizes the acquired southbound data to perform time rate detection on northbound data to be analyzed, the electronic device mainly detects whether the time delay generated by resource data and performance data in the northbound data to be analyzed and the time delay reported by alarm data exceed set thresholds.

Specifically, when the north data to be analyzed is obtained from the OMC, the electronic device may first obtain the resource file, the performance file, and the alarm file corresponding to the north data to be analyzed from the OMC, so as to obtain the resource data, the performance data, and the alarm data in the north data to be analyzed. In the process, the electronic equipment can obtain the corresponding file start generation time and the corresponding file end generation time according to the acquired information carried by the resource file and the performance file, and can obtain the corresponding alarm generation time according to the acquired information carried by the alarm file.

For the resource and performance time-lapse rate, the electronic device can calculate the time difference between the start generation time and the end generation time of the resource file and the performance file by using the time difference between the two, and the time difference can be understood as the time delay of the resource file and the performance file. Then, the electronic equipment determines the quantity of the time delay exceeding the time delay threshold value of the resource file and the performance file in the northbound data to be analyzed by comparing the time delay sizes of the resource file and the performance file with the set time delay threshold value, and further calculates to obtain the resource and the performance time rate, wherein a calculation formula of the resource and the performance time rate can be expressed as follows:

resource and performance time rate = 1-the time delay of the resource and performance files in the northbound data to be analyzed exceeds the number of time delay thresholds/the total number of the resource and performance files in the northbound data to be analyzed;

the time delay threshold of the resources and the performances can be set according to the needs, for example, 5 minutes.

For the alarm time-out rate, the electronic device can respectively determine the time for acquiring each alarm in the north data to be analyzed and the generation time of each alarm in the north data to be analyzed, and calculate the time difference between the two, wherein the time difference can be understood as the time delay of the alarm. Then, the electronic equipment determines the quantity of alarm delay exceeding a threshold value in the north data to be analyzed by comparing the alarm delay with a set delay threshold value, and further calculates to obtain an alarm time-rate, wherein a calculation formula of the alarm time-rate can be expressed as follows:

Alarm time rate=1-the number of alarm delays exceeding a delay threshold in the northbound data to be analyzed/the total number of alarms in the northbound data to be analyzed;

In practical application, after at least one item of data index is detected by using the acquired southbound data and the northbound data to be analyzed, the electronic device can flexibly store and output the obtained detection result in a mode of combining SQLITE with Excel. Specifically, the electronic device may generate a corresponding quality report based on at least one of the first detection result, the second detection result, the third detection result, the fourth detection result, and the fifth detection result, so that an operation and maintenance person can analyze according to the quality report.

Illustratively, the content of each detection result in the quality report may be as follows:

1. integrity rate

The integrity rate of the resource and performance data is the result of comparing the original network element data stored in the OMC database with the data of the OMC northbound interface in one cycle. For each cycle, when the integrity rate of the resource data and the performance data reaches 99%, the operation and maintenance personnel can determine that the integrity rate of the resource and the performance data reaches the requirement, and the content of the integrity rate of the resource and the performance data can be referred to table 1.

TABLE 1

The integrity rate of alarms is the result of comparing alarms in OMC (including alarms generated by OMC itself and alarms reported by network elements) with alarms received by upper layer applications (including alarms reported by OMC and alarms generated by network elements) in one period. For each period, the integrity rate of the alarm data reaches 99.99%, and an operation and maintenance person can determine that the integrity rate of the alarm data reaches the requirement, wherein the period can be set according to the requirement, such as 1 hour. The content of the alarm integrity rate may be referred to in table 2.

TABLE 2

2. Compliance rate

The compliance rate of the resource and performance data is the result of comparing the number of compliance records in the OMC northbound interface data with the total number of records in the OMC northbound interface data in one cycle. For each cycle, when the compliance rate of the resource data and the performance data reaches 95%, the operation and maintenance personnel can determine that the compliance rate of the resource data and the performance data reaches the requirement, and the content of the compliance rate of the resource data and the performance data can be referred to table 3.

TABLE 3 Table 3

The alarm compliance rate is a result obtained by comparing the number of alarms of the missing field in the OMC with the number of alarms of all alarms in the OMC in one period. For each period, the compliance rate of the alarm data reaches 99.99%, and the operation and maintenance personnel can determine that the compliance rate of the alarm data reaches the requirement, wherein the period can be set according to the requirement, for example, 1 hour, and the content of the alarm compliance rate can be referred to table 4.

TABLE 4 Table 4

3. Time rate

The timeliness of the resource and performance data is a result of comparing the number of times the OMC resource and performance data are generated to meet the threshold value in one cycle with the total number of OMC resources and performance data. For each period, when the timeliness rate of the resource data and the performance data reaches 95%, the operation and maintenance personnel can determine that the timeliness rate of the resource data and the performance data reaches the requirement, and the content of the timeliness rate of the resource data and the performance data can be referred to

Table 5.

TABLE 5

The alarm timeliness rate is a result obtained by comparing the number of alarms received by the upper layer application in one period and the number of all alarms received by the upper layer application, wherein the number meets the threshold requirement. For each period, the time rate of the alarm data reaches 99%, and the operation and maintenance personnel can determine that the time rate of the alarm data reaches the requirement, wherein the period can be set according to the requirement, such as 1 hour, and the threshold can be set according to the requirement, such as 10s. The content of the alarm and time rate can be referred to in table 6.

TABLE 6

4. Consistency rate of

The consistency of the performance data is the result of comparing the OMC northbound data with the southbound data over a period. For each cycle, when the consistency rate of the performance data reaches 100%, the operation and maintenance personnel can determine that the consistency rate of the performance data reaches the requirement, and the content of the consistency rate of the performance data can be referred to in table 7.

TABLE 7

5. Fluctuation ratio

The fluctuation rate of the performance data is a result obtained by dynamically detecting the north-orientation performance data of the OMC. For performance data, when the fluctuation rate of the performance data is less than 5%, the operation and maintenance personnel can determine that no abnormality occurs in the performance data, and the content of the consistency rate of the performance data can be referred to in table 8.

TABLE 8

The data processing method provided by the embodiment of the application comprises the steps of determining the corresponding northbound data of the southbound data based on a first model, detecting the consistence rate of the northbound data to be analyzed based on the corresponding northbound data to obtain a first detection result, wherein the first model represents the mapping relation between the northbound data and the southbound data, detecting the fluctuation rate of the performance data in the northbound data to be analyzed by utilizing a dynamically determined first threshold value according to each performance index in at least one performance index to obtain a second detection result, and outputting the obtained detection result after the southbound data is obtained from the OMC and the corresponding northbound data to be analyzed. According to the technical scheme provided by the embodiment of the application, based on the southbound data obtained from the OMC and the northbound data to be analyzed, the northbound data to be analyzed is subjected to automatic detection of the coincidence rate and/or the fluctuation rate, and the detection result is output. Through the automatic detection of the index of the consistency rate and/or the fluctuation rate of the northbound data, the problems existing in the current northbound data can be rapidly reflected, so that maintenance personnel can rapidly position and solve the problems according to the output detection result. In addition, through the automatic detection of the northbound data consistency rate and/or the fluctuation rate, the problem of human error caused by human operation can be avoided, the detection accuracy of the northbound data is improved, and the quality of the northbound data is further improved.

The present application will be described in further detail with reference to examples of application.

In this application embodiment, an OMC data quality automatic detection system in a communication network manager is provided. Specifically, as shown in fig. 3, the OMC data quality automatic detection system includes an acquisition module, an analysis module, and a presentation module.

The acquisition module is used for acquiring the original data of the network element side and the northbound interface data of the OMC side;

The analysis module is used for detecting the quality index of the OMC northbound interface data;

And the presentation module is used for processing the quality index detection results of the OMC northbound interface data and generating a quality report containing the quality index detection results.

In this application embodiment, the OMC data quality automatic detection system performs the process of OMC northbound interface data quality detection, including the following steps:

step 1, an acquisition module acquires network element original data (namely southbound data) and OMC northbound interface data (namely northbound data to be analyzed);

in practical application, the acquisition module can acquire network element original data and OMC northbound interface data from the OMC at the same time, wherein the network element original data comprises resources, performance and alarm data, the network element original data is stored in a database of the OMC, and the OMC northbound interface data comprises the resources, the performance and the alarm data.

Specifically, because the resource data and the performance data have the characteristics of large data volume and multiple types, the acquisition module can periodically acquire the resource data and the performance data in the OMC northbound interface data in an FTP mode. Therefore, the data volume acquired in each period can be ensured not to be unlimited and large, and the detection of a subsequent analysis module is facilitated. The resource data and the performance data acquired by the acquisition module are acquired in the form of files.

In addition, since the network element original data is stored in the OMC database, the acquisition module can periodically acquire the resource data and the performance data in the network element original data in a DB mode.

And for the alarm data, the acquisition module can acquire the alarm data in the network element original data and the alarm data in the OMC northbound interface data in real time in a SOCKET mode.

The method has the advantages that the method collects the source, performance and alarm 3 types of data in the network element original data and the OMC side northbound interface data in FTP, DB, SOCKET modes, ensures the richness of the collected data to the greatest extent, and lays a foundation for the quality detection of subsequent data.

Step 2, the acquisition module sends the acquired network element original data and OMC side northbound interface data to the analysis module;

And 3, respectively detecting the integrity rate, the compliance rate, the timeliness rate, the consistency rate and the fluctuation rate of the OMC side northbound interface data by the analysis module according to the received network element original data and the OMC side northbound interface data.

1) Integrity rate

Here, the integrity rate of OMC-side northbound interface data may include resource and performance integrity rates, as well as alert integrity rates.

The resource and performance integrity rate is a result obtained by comparing the record number of the resource and performance data in the OMC-side northbound interface data with the record number of the resource and performance data in the network element original data. The calculation formula of the resource and performance integrity rate can be expressed as:

resource and performance integrity ratio = OMC side northbound interface data record number/network element original data record number;

In practical application, the analysis module can count the recording rate of the data collected by the FTP and the DB to calculate the resource and the performance integrity rate.

The alarm integrity rate is a result obtained by comparing the number of alarms stored in the OMC (including the number of network element alarms and the number of OMC alarms) with the number of alarms received by the analysis module (including the number of network element alarms and the number of OMC alarms). The calculation formula of the alarm integrity rate can be expressed as:

alarm integrity rate = number of alarms received by the analysis module/number of alarms stored in OMC;

Specifically, the OMC data quality automatic detection system establishes connection through NETTY frames by adopting a SOCKET protocol and OMC, so that an analysis module can receive an alarm reported by the OMC in real time.

2) Compliance rate

Here, the compliance rate of OMC-side northbound interface data may include resource and performance compliance rate, as well as alarm compliance rate.

The resource and performance compliance rate is a result obtained by comparing the number of records of resource and performance data compliance in the OMC-side northbound interface data with the total number of records of resource and performance data in the OMC-side northbound interface data. The calculation formula of the resource and performance compliance rate can be expressed as:

resource and performance compliance = number of resource and performance data field compliance records in OMC-side northbound interface data/total number of resource and performance data records in OMC-side northbound interface data;

The resource and performance data field non-compliance comprises the types of non-compliance of field types, non-compliance of field formats, non-compliance of field ranges and the like of filling-in items or conditions (null negative value 0 value), wherein the resource and performance data field non-compliance represents that a data field is not in hit of any of the non-compliance types.

The alarm compliance rate is a result obtained by comparing the number of alarm bars with missing fields in the OMC-side northbound interface data with the total number of alarm bars in the OMC-side northbound interface data. The formula of the alarm compliance rate can be expressed as:

Alarm compliance = 1-number of alarm bars with missing fields in OMC-side northbound interface data/total number of alarm bars in OMC-side northbound interface data;

In practical application, since the alarm field is relatively fixed, whether the fields such as AlarmTitle、AlarmStatus、AlarmId、AlarmSeq、AlarmType、OrigSeverity、EventTime、SpecificProblemID、SpecificProblem、ObjectType、SubObjectType、ObjectUID、ObjectName、SubObjectUID、SubObjectName、DataSource、SourceID、VendorName are missing or not can be detected to obtain the alarm compliance rate.

3) Time rate

Here, the timeliness of the OMC-side northbound interface data may include resource and performance timeliness, as well as alarm timeliness.

The resource and performance time rate is a result obtained by comparing the time delay of the resource and performance files in the OMC side northbound interface data with a time delay threshold. The calculation formula of the resource and performance time rate can be expressed as:

Resource and performance time rate=1-number of resource and performance files exceeding time delay threshold/total number of resource and performance files in OMC-side northbound interface data;

The time delay threshold values of the resource and the performance file can be set according to different professional requirements.

Specifically, the analysis module can respectively determine the start generation time and the end generation time of the file according to the acquired resources and performance files in the OMC side northbound interface data, and the two time differences are the time delay of the file.

The alarm time rate is a result obtained by comparing the time delay of the alarm file in the OMC northbound interface data with a time delay threshold. The calculation formula of the alarm time rate can be expressed as follows:

Alarm time rate=1-number of alarm time delay timeout delay thresholds received by analysis module/total number of alarms received by analysis module

Here, the analysis module can determine the occurrence time and the receiving time of each alarm in the alarm file according to the acquired alarm file in the OMC side northbound interface data, and the two time differences are the time delay of the alarm.

4) Consistency rate of

Here, in practical application, before performing consistency rate detection on performance data in OMC northbound interface data, the analysis module may construct an intelligent model based on a fixed template. And the intelligent model is utilized to intelligently identify an operation formula and a mapping relation corresponding to the data, so that performance data consistency calculation, comparison and detection between the OMC northbound interface data and network element original data can be completed.

In practical application, as shown in fig. 3, the step of constructing an intelligent model may include:

step 301, an analysis module establishes a private count specification template and a private field specification template;

here, since the private algorithm field names and the calculation formulas of the respective manufacturers are different, one template of a fixed format can be unified, and the templates include a private count specification template and a private field specification template.

The private field specification comprises a mapping relation between a data field in network element original data and a data field in OMC northbound interface data, and the private count specification template is a specific calculation formula of a data field value in the network element original data.

In practical application, each manufacturer can automatically generate an intelligent model only by filling in contents according to a specified private field specification template and a private count specification template. The configurable generation mode is adopted, so that the flexibility is high, and the compatibility is strong.

Step 302, an analysis module intelligently analyzes private field specifications and private count specifications of manufacturers;

here, the analysis module can identify the north-south mapping relation of the data fields in the original data of the network element according to the private field specification of the manufacturer. Specifically, a plurality of associated performance data fields in the original data of the network element can be spliced together, and the corresponding northbound data attribute data fields can be determined through a regular expression.

In addition, the analysis module can intelligently identify a calculation formula of the data field value in the original data of the network element according to the input private counting specification, then the calculation formula can be split into codes with letters and calculation formulas, and the performance data field value to be calculated in the original data of the network element can be automatically determined according to the mapping relation. The text formula variable codes are identified in an automatic mode, and corresponding values to be participated in calculation are matched according to the mapping relation, so that the situation that the corresponding values of the text formula variable codes are manually searched is avoided, and the labor cost is reduced.

And 303, generating a model with a tree structure by the analysis module according to the intelligent analysis result.

Here, the analysis module automatically generates a data model with a tree structure according to the intelligently analyzed north-south mapping relation and a calculation formula of the data field in the network element original data. In this way, the detection speed of field traversal can be increased.

Meanwhile, the generated tree-shaped structure model can be stored in the form of JSON files, so that dependence on third-party middleware of a database is reduced, and migration and deployment are facilitated.

Step 304, judging whether special requirements exist or not by the analysis module through the constructed visual model interface;

here, when the analysis module determines that there is a special requirement, step 305 is performed, and when the analysis module determines that there is no special requirement, step 306 is performed;

Here, the analysis module may use the orgchart. Js plugin and jquery. Js plugin to construct a visual model interface to facilitate secondary editing.

Step 305, the analysis module receives the content edited on the visual model interface;

In practical application, if special requirements exist for the data fields in the original data of the network element, the analysis module can revise the data fields in a manual labeling mode on the visual model interface and then store the revised data fields to update the private model.

Step 306, the analysis module generates a private model.

After the private model is built, the consistency of the performance data between the OMC northbound interface data and the network element original data can be automatically detected based on the built private model.

5) Fluctuation ratio

The performance data is generally related to the service, so it is important to analyze the performance data and judge the volatility, but in practical application, the performance fluctuation of the data is not necessarily a problem, so a certain detection means is needed to judge whether the fluctuation data is abnormal.

For performance indexes with larger variation amplitude, for example, when the indexes fluctuate instantaneously, and then the normal scene is recovered, the difficulty of judging whether the data is abnormal or not by maintenance personnel according to experience is higher.

In this case, the analysis module may dynamically predict the threshold coefficient according to the above formula (1) for each performance index, and dynamically determine whether the performance data in the OMC northbound interface data is abnormal according to the threshold coefficient.

Specifically, for performance data in OMC northbound interface data, if the fluctuation value of the performance data at the current moment is larger than the predicted threshold coefficient compared with the performance data at the last moment, the performance data is considered to be abnormal data, and if the fluctuation value of the performance data at the current moment is smaller than the predicted threshold coefficient compared with the performance data at the last moment, the performance data is considered to be normal data.

Step 4, the analysis module sends the detection results corresponding to the quality indexes to the presentation module;

and 5, generating a quality report containing the detection results of each quality index by the presentation module according to the received detection results in a mode of combining EXCEL with SQLITE so as to facilitate analysis and processing by maintenance personnel according to the quality report.

In this embodiment of the application, an acquisition module of the OMC data quality automatic detection system collects network element side and original data and northbound data of the OMC. And then, performing intelligent analysis such as modeling, analysis, comparison, calculation and the like on the quality indexes of the 5 types of core data such as the integrity rate, the compliance rate, the time-lapse rate, the consistency rate, the fluctuation rate and the like of the northbound data through an analysis module. And finally, a presentation module is used for conveniently and efficiently displaying a detection result report by adopting a mode of combining EXCEL with SQLITE. In the process, the OMC data quality automatic detection system builds a comprehensive data quality index detection system, can rapidly detect the quality of the OMC northbound data automatically, achieves the purpose of reducing human input and human errors, namely improves the detection efficiency and accuracy of the northbound data, and can ensure the normalization and actual effect of the data quality detection work.

And secondly, for performance data in the OMC northbound data, the OMC data quality automatic detection system dynamically determines a correlation threshold coefficient through a correlation dynamic detection algorithm, and can adapt to the change of network performance. Therefore, the detection accuracy of the performance data fluctuation rate in the northbound data is improved, the step of judging by manually setting a fixed threshold value in the prior art is improved, and the manual operation cost is reduced.

In addition, the OMC data quality automatic detection system is used for realizing the consistency rate detection between the performance data in the OMC northbound data and the performance data in the southbound original data by intelligently constructing private models of various manufacturers and adopting a tree structure and a configurable mode. By adopting the mode, the method can be suitable for detecting northbound data of different manufacturers, namely, the detection method has high compatibility and wide application range. Meanwhile, through the detection mode, the content of each field in the performance data can be flexibly and efficiently traversed, and the corresponding data operation formula and the mapping relation of the fields in the north-south data are intelligently identified and determined, so that the consistency rate of the performance data in the north-south data is detected, and the consistency rate detection efficiency is improved.

In order to implement the solution of the embodiment of the present application, an embodiment of the present application further provides a data processing apparatus, as shown in fig. 4, where the apparatus includes:

an acquiring unit 401, configured to acquire southbound data and corresponding northbound data to be analyzed from the OMC;

the execution unit 402 performs at least one of the following operations using the acquired southbound data and northbound data to be analyzed:

A result unit 403 for outputting the obtained detection result.

Here, it should be noted that the function of the acquiring unit 401 corresponds to the function of the acquisition module in the application embodiment, the function of the executing unit 402 corresponds to the function of the analysis module in the application embodiment, and the function of the result unit 403 corresponds to the function of the presentation module in the application embodiment.

Wherein, in an embodiment, the execution unit 402 is configured to:

In an embodiment, the execution unit 402 is further configured to:

the generated submodel is saved in the form of JSON file.

In an embodiment, the execution unit 402 determines a field in the northbound data corresponding to the field in the southbound data based on the first sub-model and a regular expression.

In an embodiment, the execution unit 402 is further configured to:

In an embodiment, the obtaining unit 401 is configured to:

obtaining the southbound data from the OMC based on DB;

In an embodiment, the obtaining unit 401 periodically obtains the southbound data from the OMC based on DB;

and/or the number of the groups of groups,

In an embodiment, the execution unit 402 is further configured to:

In practical applications, the acquisition unit 401 may be implemented by a communication interface in a data processing device, and the execution unit 402 and the result unit 403 may be implemented by a processor in the data processing device in combination with the communication interface.

It should be noted that, in the data processing apparatus provided in the foregoing embodiment, only the division of each program unit is used for illustration, and in practical application, the processing allocation may be performed by different program units according to needs, that is, the internal structure of the apparatus is divided into different program units to complete all or part of the processing described above. In addition, the data processing apparatus and the data processing method embodiment provided in the foregoing embodiments belong to the same concept, and specific implementation processes of the data processing apparatus and the data processing method embodiment are detailed in the method embodiment, which is not described herein again.

Based on the hardware implementation of the program modules, and in order to implement the method for processing data according to the embodiment of the present application, an embodiment of the present application further provides an electronic device, as shown in fig. 5, where the electronic device 500 includes:

a communication interface 501 capable of interacting with other devices;

A processor 502, connected to the communication interface 501, for interacting with other devices, and for executing the methods provided by one or more of the above technical solutions when running a computer program;

A memory 503, the computer program being stored on the memory 503.

Specifically, the processor 502 is configured to:

acquiring southbound data and corresponding northbound data to be analyzed from an OMC through the communication interface 501;

The obtained detection result is output through the communication interface 501.

Wherein, in one embodiment, the processor 502 is configured to:

In an embodiment, the processor 502 is further configured to:

the generated submodel is saved in the form of JSON file.

In one embodiment, the processor 502 is configured to determine a field in the northbound data corresponding to the field in the southbound data based on the first submodel and the regular expression.

In an embodiment, the processor 502 is further configured to:

In one embodiment, the processor 502 is configured to:

acquiring the southbound data from the OMC based on DB through the communication interface 501;

Acquiring resources and performance data in the northbound data to be analyzed from the OMC based on FTP through the communication interface 501;

and acquiring alarm data in the northbound data to be analyzed from the OMC based on Socket through the communication interface 501.

In an embodiment, the processor 502 periodically obtains the southbound data from the OMC via the communication interface 501 based on DB;

and/or the number of the groups of groups,

Resources and performance data in the northbound data to be analyzed are periodically acquired from the OMC based on FTP through the communication interface 501.

In an embodiment, the processor 502 is further configured to:

It should be noted that the specific processing procedure of the processor 502 may be understood by referring to the above method.

Of course, in actual practice, the various components in electronic device 500 are coupled together via bus system 504. It is to be appreciated that bus system 504 is employed to enable connected communications between these components. The bus system 504 includes a power bus, a control bus, and a status signal bus in addition to the data bus. But for clarity of illustration, the various buses are labeled as bus system 504 in fig. 5.

The memory 503 in embodiments of the present application is used to store various types of data to support the operation of the electronic device 500. Examples of such data include any computer program for operating on electronic device 500.

The method disclosed in the above embodiment of the present application may be applied to the processor 502 or implemented by the processor 502. The processor 502 may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the method described above may be performed by integrated logic circuitry in hardware or instructions in software in the processor 502. The Processor 502 described above may be a general purpose Processor, a digital signal Processor (DSP, digital Signal Processor), or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. The processor 502 may implement or perform the methods, steps and logic blocks disclosed in embodiments of the present application. The general purpose processor may be a microprocessor or any conventional processor or the like. The steps of the method disclosed in the embodiment of the application can be directly embodied in the hardware of the decoding processor or can be implemented by combining hardware and software modules in the decoding processor. The software modules may be located in a storage medium in a memory 503, and the processor 502 reads information in the memory 503, in combination with its hardware, to perform the steps of the method described above.

In an exemplary embodiment, the electronic device 500 may be implemented by one or more Application Specific Integrated Circuits (ASICs), DSPs, programmable logic devices (PLDs, programmable Logic Device), complex Programmable logic devices (CPLDs, complex Programmable Logic Device), field-Programmable gate arrays (FPGAs), general purpose processors, controllers, microcontrollers (MCUs, micro Controller Unit), microprocessors (microprocessors), or other electronic elements for performing the aforementioned methods.

In an exemplary embodiment, the present application also provides a storage medium, i.e. a computer storage medium, in particular a computer readable storage medium, for example comprising a memory 503 storing a computer program executable by the processor 502 of the electronic device 500 for performing the aforementioned data processing steps. The computer readable storage medium may be a Read Only Memory (ROM), a programmable Read Only Memory (PROM, programmable Read-Only Memory), an erasable programmable Read Only Memory (EPROM, erasable Programmable Read-Only Memory), an electrically erasable programmable Read Only Memory (EEPROM, ELECTRICALLY ERASABLE PROGRAMMABLE READ-Only Memory), a magnetic random access Memory (FRAM, ferromagnetic random access Memory), a Flash Memory (Flash Memory), a magnetic surface Memory, an optical disk, or a compact disk-Only Memory (CD-ROM, compact Disc Read-Only Memory), and the magnetic surface Memory may be a magnetic disk Memory or a tape Memory.

It should be noted that "first," "second," etc. are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order.

In addition, the embodiments of the present application may be arbitrarily combined without any collision.

The above description is not intended to limit the scope of the application, but is intended to cover any modifications, equivalents, and improvements within the spirit and principles of the application.

Claims

1.A method of data processing, comprising:

acquiring southbound data and corresponding northbound data to be analyzed from an operation maintenance center OMC;

using the acquired southbound data and northbound data to be analyzed, performing the following operations:

Determining corresponding northbound data of the southbound data based on a first model, and detecting the northbound data to be analyzed based on the corresponding northbound data to obtain a first detection result; the first model represents a mapping relation between north data and south data, and the first detection result represents consistency of performance data in the north data to be analyzed and performance data in the south data;

outputting the obtained detection result, wherein,

The determining, based on the first model, north data corresponding to the south data includes:

2. The method according to claim 1, wherein the method further comprises:

the generated submodel is saved in the form of JSON file.

3. The method of claim 1, wherein determining the field in the northbound data corresponding to the field in the southbound data based on the first submodel comprises:

4. The method according to claim 1, wherein the method further comprises:

5. The method of claim 1, wherein the obtaining southbound data, corresponding northbound data to be analyzed, from an OMC comprises:

acquiring the southbound data from the OMC based on a database DB;

acquiring resources and performance data in the northbound data to be analyzed from the OMC based on a File Transfer Protocol (FTP);

6. The method of claim 5, wherein the southbound data is obtained from the OMC on a DB periodic basis;

and/or the number of the groups of groups,

7. The method of claim 5, wherein the method further comprises:

8. A data processing apparatus, comprising:

the execution unit is used for executing the following operations by using the acquired southbound data and northbound data to be analyzed:

Determining corresponding northbound data of the southbound data based on a first model, and detecting the consistency rate of the northbound data to be analyzed based on the corresponding northbound data to obtain a first detection result, wherein the first model represents the mapping relation between the northbound data and the southbound data, and the first detection result represents the consistency of performance data in the northbound data to be analyzed and performance data in the southbound data;

A result unit for outputting the obtained detection result, wherein,

The execution unit is used for determining the field in the north data corresponding to the field in the south data based on the first sub-model, determining the calculation formula corresponding to the counting in the south data based on the second sub-model, wherein one field corresponds to one counting, and determining the north data corresponding to the south data by utilizing the determined field in the north data and the corresponding calculation formula.

9. An electronic device comprising a processor and a memory for storing a computer program capable of running on the processor,

Wherein the processor is adapted to perform the steps of the method of any of claims 1 to 7 when the computer program is run.

10. A storage medium having stored thereon a computer program, which when executed by a processor performs the steps of the method according to any of claims 1 to 7.