CN118138661A - A network aggregation switching device and method based on multi-source data protocol - Google Patents

Abstract

The invention discloses a network convergence switching device and method based on a multi-source data protocol, and relates to the technical field of network convergence. The aggregation degree among different data sources can be evaluated by calculating the data source aggregation index, so that whether the data sources are suitable for aggregation is judged, the consistency and the reliability of data in the aggregation process are guaranteed, the efficiency and the accuracy of data aggregation are improved, centralized management, unified monitoring and comprehensive analysis of the data can be realized by aggregating the data of the plurality of data sources, the utilization value and the application effect of the data are improved, the data format conversion and the standardization processing are carried out by the network aggregation switching device, the data can be transmitted to target equipment in the form of a target protocol, the condition that the target equipment needs to analyze the data of various different protocols one by one is avoided, and the efficiency of data transmission is improved.

Description

A network aggregation switching device and method based on multi-source data protocol

Technical Field

The present invention relates to the technical field of network convergence, and in particular to a network convergence switching device and method based on a multi-source data protocol.

Background technique

With the continuous development of information technology, data exchanges between enterprises, organizations and individuals are becoming more frequent, and the number and types of data sources are increasing. Different data sources may use different data protocols, resulting in data that cannot be seamlessly integrated and transmitted, which brings great challenges to data management and application.

For example, announcement number: CN103402262B discloses a linear wireless sensor network aggregation method, wherein the linear duty cycle wireless sensor network aggregation includes time slot allocation and aggregation scheduling. In the time slot allocation stage, the initialization working cycle Tinit is a multiple of 3, and the working time slots of any three consecutive nodes except the BS are continuous and different on the time axis; in the aggregation scheduling stage, the working cycle of any node except the BS in the initial mode is Tinit, and the working time slot is set to τ. The working cycle Twork in the working mode is 3, and the working time slot is ((τ-1)mod3)+1. By setting a timer to dynamically switch between the two modes, the nodes in the network are scheduled in a duty cycle manner, which saves node energy, prolongs the network life, and determines the theoretical upper limit of the time to complete the aggregation. The non-duty cycle wireless sensor network aggregation method periodically searches and schedules all nodes in the network except the BS, and completes the aggregation in the shortest time under the linear wireless sensor network model. It is used for collision-free aggregation applications in linear wireless sensor networks.

Based on the above solutions, it is found that there are still some shortcomings in the current network convergence methods, which are specifically reflected in the following aspects: it is impossible to effectively process and integrate data from different sources and support decision-making and operation execution in complex network environments.

Summary of the invention

In view of the deficiencies of the prior art, the present invention provides a network convergence switching device and method based on a multi-source data protocol, which solves the problems of the above-mentioned background technology.

To achieve the above objectives, the present invention is implemented through the following technical solutions: a network convergence and switching method based on a multi-source data protocol, comprising the following steps: S1. Identifying and connecting data sources of different protocols, the data sources include network devices, sensors and embedded devices, acquiring network device data, sensor data and embedded device data, and transmitting the network device data, sensor data and embedded device data to a convergence node; S2. Based on the network device data, sensor data and embedded device data, calculating a data source aggregation index, which is used to measure the degree of data source aggregation, evaluate the degree of aggregation of data sources of different protocols, and obtain evaluation information of the degree of aggregation of data sources of different protocols; S3. Based on the evaluation information of the degree of aggregation of data sources of different protocols, performing data format conversion and standardization processing on the network device data, sensor data and embedded device data, converting them into a target device protocol, and transmitting the network device data, sensor data and embedded device data to the target device using the target protocol through a network convergence and switching device; S4. Integrating and storing the network device data, sensor data and embedded device data that have undergone data format conversion and standardization processing through a network convergence and switching device.

Furthermore, the specific process of transmitting network device data, sensor data and embedded device data to the aggregation node is as follows: use an adapter to connect data sources of different protocols to the network aggregation switching device, and securely transmit the network device data, sensor data and embedded device data to the aggregation node; the network device data includes the network device data volume, network device data update frequency, and network device data integrity; the sensor data includes sensor data volume, sensor data update frequency, and sensor data integrity; the embedded device data includes embedded device data volume, embedded device data update frequency, and embedded device data integrity.

Furthermore, the specific calculation process of calculating the data source aggregation index based on network device data, sensor data and embedded device data is as follows: based on network device data, analyzing the aggregation compliance of network device data, and calculating the network device data source aggregation index; based on sensor data, analyzing the aggregation compliance of sensor data, and calculating the sensor data source aggregation index; based on embedded device data, analyzing the aggregation compliance of embedded device data, and calculating the embedded device data source aggregation index; based on the network device data source aggregation index, sensor data source aggregation index, and embedded device data source aggregation index, assigning weights to the network device data source aggregation index, sensor data source aggregation index, and embedded device data source aggregation index, and calculating the data source aggregation index.

Furthermore, the specific process of assigning weights to the network device data source aggregation index, the sensor data source aggregation index, and the embedded device data source aggregation index is as follows: constructing a data source aggregation hierarchy, performing feature extraction on the network device data source aggregation index, the sensor data source aggregation index, and the embedded device data source aggregation index, and comparing the degree of influence of the network device data source aggregation index, the sensor data source aggregation index, and the embedded device data source aggregation index on the network device data source aggregation index; normalizing the network device data source aggregation index, the sensor data source aggregation index, and the embedded device data source aggregation index through a comparison matrix to obtain weight factors of the network device data source aggregation index, the sensor data source aggregation index, and the embedded device data source aggregation index.

Furthermore, the calculation formula of the data source aggregation index is as follows:

χ＝Cnet*λ ₁ +Csen*λ ₂ +Cemb*λ ₃ ;

Where χ represents the data source aggregation index, Cnet represents the network device data source aggregation index, Csen represents the sensor data source aggregation index, Cemb represents the embedded device data source aggregation index, _λ1 represents the weight factor of the network device data source aggregation index corresponding to the data source aggregation index, _λ2 represents the weight factor of the sensor data source aggregation index corresponding to the data source aggregation index, and _λ3 represents the weight factor of the embedded device data source aggregation index corresponding to the data source aggregation index.

Furthermore, the specific process of evaluating the degree of aggregation of data sources of different protocols is as follows: based on the data source aggregation index, compare it with the threshold of the data source aggregation index, calculate the difference between the data source aggregation index and the threshold of the data source aggregation index, compare the difference between the data source aggregation index and the threshold of the data source aggregation index with the allowable deviation value of the data source aggregation index, and calculate the data source aggregation compliance coefficient.

Furthermore, the specific calculation formula of the data source aggregation compliance coefficient is as follows:

In the formula, β represents the data source aggregation compliance coefficient, χ represents the data source aggregation index, _χ1 represents the threshold of the data source aggregation index, and Δχ represents the allowable deviation value of the data source aggregation index.

Furthermore, the specific process of obtaining evaluation information on the degree of aggregation of data sources of different protocols is as follows: based on the data source aggregation compliance coefficient, compare with the standard value of the data source aggregation compliance coefficient. If the data source aggregation compliance coefficient is less than the standard value of the data source aggregation compliance coefficient, it means that the data sources of different protocols are not aggregated, and the data sources of different protocols are converted into data formats and standardized; if the data source aggregation compliance coefficient is greater than the standard value of the data source aggregation compliance coefficient, it means that the data sources of different protocols are aggregated.

Furthermore, the specific process of converting and standardizing the data format of network device data, sensor data and embedded device data based on the evaluation information of the degree of aggregation of data sources of different protocols is as follows: based on the evaluation information of the degree of aggregation of data sources of different protocols, the data format of network device data, sensor data and embedded device data is converted using the scripting language Python; the time fields in data sources of different protocols are unified into the same date and time format, and text data is converted into numeric data to ensure that the data types of all data fields are consistent, and missing values are processed by filling in default values and interpolation.

A network convergence switching device based on multi-source data protocols comprises a data acquisition and transmission module, a data processing module, a data conversion module and a data integration storage module; the data acquisition module is used to identify and connect data sources of different protocols, the data sources include network devices, sensors and embedded devices, obtain network device data, sensor data and embedded device data, and transmit the network device data, sensor data and embedded device data to a convergence node; the data transmission module is used to calculate a data source aggregation index based on the network device data, sensor data and embedded device data, which is used to measure the degree of data source aggregation, evaluate the degree of aggregation of data sources of different protocols, and obtain evaluation information of the degree of aggregation of data sources of different protocols; the data conversion module is used to perform data format conversion and standardization processing on the network device data, sensor data and embedded device data based on the evaluation information of the degree of aggregation of data sources of different protocols, convert them into a target device protocol, and transmit the network device data, sensor data and embedded device data to the target device using the target protocol through the network convergence switching device; the data integration storage module is used to integrate and store the network device data, sensor data and embedded device data that have undergone data format conversion and standardization processing through the network convergence switching device.

The present invention has the following beneficial effects:

(1) This network aggregation and exchange method based on a multi-source data protocol can evaluate the aggregation degree between different data sources by calculating the data source aggregation index, thereby judging whether these data sources are suitable for aggregation. This helps to ensure the consistency and reliability of data during the aggregation process, improves the efficiency and accuracy of data aggregation, and can achieve centralized management, unified monitoring and comprehensive analysis of data by aggregating data from multiple data sources, thereby improving the utilization value and application effect of data.

(2) The network convergence switching device based on a multi-source data protocol performs data format conversion and standardization processing through the network convergence switching device, and can transmit data to the target device in the form of a target protocol, thereby avoiding the situation where the target device needs to parse data of various different protocols one by one, thereby improving the efficiency of data transmission.

Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a flow chart of a network convergence exchange method based on a multi-source data protocol according to the present invention.

FIG. 2 is a flow chart of a network convergence switching device based on a multi-source data protocol according to the present invention.

Detailed ways

The embodiment of the present application uses a network convergence switching device and method based on a multi-source data protocol to effectively process and integrate data from different sources to support decision-making and operation execution in a complex network environment.

The overall idea of the problem in the embodiment of this application is as follows:

First, identify and connect data sources of different protocols, including network devices, sensors and embedded devices, obtain network device data, sensor data and embedded device data, and transmit the network device data, sensor data and embedded device data to the aggregation node.

Based on network device data, sensor data and embedded device data, the data source aggregation index is calculated to measure the degree of data source aggregation, evaluate the degree of aggregation of data sources of different protocols, and obtain evaluation information of the degree of aggregation of data sources of different protocols.

Based on the evaluation information of the degree of aggregation of data sources of different protocols, the network device data, sensor data and embedded device data are converted into data formats and standardized, and converted into the target device protocol. The network device data, sensor data and embedded device data are transmitted to the target device using the target protocol through the network aggregation switching device.

The network equipment data, sensor data and embedded device data that have been converted and standardized are integrated and stored through the network aggregation switching device.

Please refer to Figure 1. An embodiment of the present invention provides a technical solution: a network convergence and exchange method based on a multi-source data protocol, comprising the following steps: S1. Identify and connect data sources of different protocols, the data sources include network devices, sensors and embedded devices, obtain network device data, sensor data and embedded device data, and transmit the network device data, sensor data and embedded device data to a convergence node; S2. Based on the network device data, sensor data and embedded device data, calculate the data source aggregation index to measure the degree of data source aggregation, evaluate the degree of aggregation of data sources of different protocols, and obtain evaluation information of the degree of aggregation of data sources of different protocols; S3. Based on the evaluation information of the degree of aggregation of data sources of different protocols, perform data format conversion and standardization on the network device data, sensor data and embedded device data, convert them into a target device protocol, and transmit the network device data, sensor data and embedded device data to the target device using the target protocol through a network convergence and exchange device; S4. Integrate and store the network device data, sensor data and embedded device data that have undergone data format conversion and standardization through a network convergence and exchange device.

Specifically, the specific process of transmitting network device data, sensor data and embedded device data to the aggregation node is as follows: use an adapter to connect data sources of different protocols to the network aggregation switching device, and securely transmit network device data, sensor data and embedded device data to the aggregation node; network device data includes network device data volume, network device data update frequency, and network device data integrity; sensor data includes sensor data volume, sensor data update frequency, and sensor data integrity; embedded device data includes embedded device data volume, embedded device data update frequency, and embedded device data integrity.

In this implementation scheme, the process of transmitting network device data, sensor data and embedded device data to the aggregation node provides a network aggregation and switching method based on a multi-source data protocol with assistance in adapting data sources of different protocols, securely transmitting data, monitoring data quality and improving data processing efficiency, thereby achieving efficient aggregation and processing of data and improving the overall performance of the system.

Specifically, the specific calculation process of the data source aggregation index based on network device data, sensor data and embedded device data is as follows: based on network device data, analyze the aggregation compliance of network device data, and calculate the network device data source aggregation index; based on sensor data, analyze the aggregation compliance of sensor data, and calculate the sensor data source aggregation index; based on embedded device data, analyze the aggregation compliance of embedded device data, and calculate the embedded device data source aggregation index; based on the network device data source aggregation index, sensor data source aggregation index, and embedded device data source aggregation index, assign weights to the network device data source aggregation index, sensor data source aggregation index, and embedded device data source aggregation index, and calculate the data source aggregation index.

In this implementation scheme, based on the network device data, the network devices are numbered 1, 2, 3...i...n in sequence, where n represents the total number of network devices. The calculation formula for the network device data source aggregation index is as follows:

In the formula, Cnet represents the network device data source aggregation index, which is used to measure the degree of aggregation of network device data sources, Nnet _i represents the amount of data of the ith network device, Unet _i represents the update frequency of the ith network device data, Ynet _i represents the integrity of the ith network device data, Nnet ₁ represents the standard value of the amount of network device data, Unet ₁ represents the standard value of the update frequency of network device data, Ynet ₁ represents the standard value of the integrity of network device data, ω ₁ represents the weight factor of the amount of network device data, ω ₂ represents the weight factor of the update frequency of network device data, and ω ₃ represents the weight factor of the integrity of network device data. Based on sensor data, sensors are numbered 1, 2, 3...j...m in sequence, and m represents the total number of sensors. The calculation formula of the sensor data source aggregation index is as follows:

Wherein, Csen represents the aggregation index of sensor data source, which is used to measure the aggregation degree of sensor data source, Nsen _j represents the data volume of the jth sensor, Usen _j represents the update frequency of the jth sensor data, Ysen _j represents the completeness of the jth sensor data, Nsen ₁ represents the standard value of the sensor data volume, Usen ₁ represents the standard value of the sensor data update frequency, Ysen ₁ represents the standard value of the sensor data completeness, ξ ₁ represents the weight factor of the sensor data volume, ξ ₂ represents the weight factor of the sensor data update frequency, and ξ ₃ represents the weight factor of the sensor data completeness;

Based on the embedded device data, the embedded devices are numbered 1, 2, 3...t...d in sequence, where d represents the total number of embedded devices. The calculation formula for the embedded device data source aggregation index is as follows:

Wherein, Cemb represents the aggregation index of embedded device data source, which is used to measure the aggregation degree of embedded device data source, Nemb _t represents the amount of data of the tth embedded device, Uemb _t represents the update frequency of the tth embedded device data, Yemb _t represents the completeness of the tth embedded device data, Nemb ₁ represents the standard value of the amount of embedded device data, Uemb ₁ represents the standard value of the update frequency of embedded device data, Yemb ₁ represents the standard value of the completeness of embedded device data, δ ₁ represents the weight factor of the amount of embedded device data, δ ₂ represents the weight factor of the update frequency of embedded device data, and δ ₃ represents the weight factor of the completeness of embedded device data.

Specifically, the specific process of assigning weights to the network device data source aggregation index, sensor data source aggregation index, and embedded device data source aggregation index is as follows: construct a data source aggregation hierarchy, extract features from the network device data source aggregation index, sensor data source aggregation index, and embedded device data source aggregation index, and compare the degree of influence of the network device data source aggregation index, sensor data source aggregation index, and embedded device data source aggregation index on the network device data source aggregation index; normalize the network device data source aggregation index, sensor data source aggregation index, and embedded device data source aggregation index through a comparison matrix to obtain the weight factors of the network device data source aggregation index, sensor data source aggregation index, and embedded device data source aggregation index.

In this implementation, based on the calculated weight factors, the weights of different data sources in data aggregation can be reasonably allocated, so that the information of each data source can be more fully utilized in the data aggregation process; reasonable weight factors can make the data aggregation results more in line with the contribution of each data source, thereby improving the accuracy and reliability of the data aggregation results; through weight factors, reasonable data aggregation strategies can be formulated, more attention can be paid to data sources that contribute more to the data, and their data can be used more in the data aggregation process, thereby optimizing the effect of data aggregation.

Specifically, the calculation formula of the data source aggregation index is as follows:

χ＝Cnet*λ ₁ +Csen*λ ₂ +Cemb*λ ₃ ;

Where χ represents the data source aggregation index, which is used to measure the degree of data source aggregation; Cnet represents the network device data source aggregation index; Csen represents the sensor data source aggregation index; Cemb represents the embedded device data source aggregation index; _λ1 represents the weight factor of the network device data source aggregation index corresponding to the data source aggregation index; _λ2 represents the weight factor of the sensor data source aggregation index corresponding to the data source aggregation index; and _λ3 represents the weight factor of the embedded device data source aggregation index corresponding to the data source aggregation index.

In this implementation scheme, the data source aggregation index is obtained by multiplying the network device data source aggregation index, the sensor data source aggregation index, the embedded device data source aggregation index, and the weight factor of the network device data source aggregation index, the weight factor of the sensor data source aggregation index, and the weight factor of the embedded device data source aggregation index. This improves the accuracy of the data aggregation results and optimizes the data aggregation strategy, so that the data aggregation index is more in line with the actual situation and provides more reliable data support for decision-making.

Specifically, the specific process of evaluating the degree of aggregation of data sources of different protocols is as follows: based on the data source aggregation index, compare it with the threshold of the data source aggregation index, calculate the difference between the data source aggregation index and the threshold of the data source aggregation index, compare the difference between the data source aggregation index and the threshold of the data source aggregation index with the allowable deviation value of the data source aggregation index, and calculate the data source aggregation compliance coefficient.

In this implementation scheme, evaluating the degree of aggregation of data sources of different protocols can help determine the importance and contribution of different data sources, optimize the selection and allocation of data sources, and improve data utilization and aggregation effect. At the same time, by setting the aggregation index threshold, the aggregation requirements can be finely controlled according to the specific application scenario to meet different application requirements. In short, evaluating the degree of aggregation of data sources of different protocols can help improve the efficiency and reliability of the network aggregation and exchange method of multi-source data protocols, and realize more intelligent and automated data aggregation processing.

Specifically, the specific calculation formula for the data source aggregation compliance coefficient is as follows:

In the formula, β represents the data source aggregation compliance coefficient, which is used to measure the degree of data source aggregation compliance, χ represents the data source aggregation index, _χ1 represents the threshold of the data source aggregation index, and Δχ represents the allowable deviation value of the data source aggregation index.

In this implementation, the data source aggregation compliance coefficient can help evaluate the aggregation degree of data sources, optimize data source selection and allocation, and control aggregation requirements, thereby improving the efficiency and reliability of the network aggregation exchange method based on the multi-source data protocol.

Specifically, the specific process of obtaining evaluation information on the degree of aggregation of data sources of different protocols is as follows: based on the data source aggregation compliance coefficient, compare with the standard value of the data source aggregation compliance coefficient. If the data source aggregation compliance coefficient is less than the standard value of the data source aggregation compliance coefficient, it means that the data sources of different protocols are not aggregated, and the data sources of different protocols are converted into data formats and standardized; if the data source aggregation compliance coefficient is greater than the standard value of the data source aggregation compliance coefficient, it means that the data sources of different protocols are aggregated.

In this implementation scheme, by setting and comparing standard values of data source aggregation compliance coefficients, the aggregation requirements can be controlled, thereby improving data utilization and aggregation effects; obtaining evaluation information on the degree of aggregation of data sources of different protocols can help optimize the selection and allocation strategies of data sources, improve the efficiency and reliability of data aggregation processing, and thereby improve data utilization and aggregation effects.

Specifically, based on the evaluation information of the degree of aggregation of data sources of different protocols, the specific process of data format conversion and standardization of network device data, sensor data and embedded device data is as follows: based on the evaluation information of the degree of aggregation of data sources of different protocols, the data format of network device data, sensor data and embedded device data is converted using the scripting language Python; the time fields in data sources of different protocols are unified into the same date and time format, and text data is converted into numeric data to ensure that the data types of all data fields are consistent, and missing values are processed by filling in default values and interpolation.

In this implementation scheme, based on the evaluation information of the degree of aggregation of data sources of different protocols, the format conversion and standardization of network device data, sensor data and embedded device data are performed through Python scripts, which helps to improve the efficiency, reliability and operability of the data aggregation and exchange method, and further promotes the further optimization and application of the network aggregation and exchange method based on multi-source data protocols.

A network convergence switching device based on multi-source data protocols comprises a data acquisition and transmission module, a data processing module, a data conversion module and a data integration storage module; the data acquisition module is used to identify and connect data sources of different protocols, the data sources include network devices, sensors and embedded devices, obtain network device data, sensor data and embedded device data, and transmit the network device data, sensor data and embedded device data to a convergence node; the data transmission module is used to calculate the data source aggregation index based on the network device data, sensor data and embedded device data, which is used to measure the degree of data source aggregation, evaluate the degree of aggregation of data sources of different protocols, and obtain evaluation information of the degree of aggregation of data sources of different protocols; the data conversion module is used to convert and standardize the network device data, sensor data and embedded device data based on the evaluation information of the degree of aggregation of data sources of different protocols, convert them into a target device protocol, and transmit the network device data, sensor data and embedded device data to the target device using the target protocol through the network convergence switching device; the data integration storage module is used to integrate and store the network device data, sensor data and embedded device data that have been converted and standardized through the network convergence switching device.

In this implementation scheme, through the convergence and switching device, data from different data sources and different protocols can be integrated and unified, so that the data has a consistent format and structure, which is convenient for subsequent processing and analysis; the convergence and switching device can convert the data into the target device protocol so that the data can be recognized and applied by the target device, thereby improving the availability and applicability of the data; through the convergence and switching device, the data transmission process can be optimized, the conversion and processing time between data of different protocols can be reduced, and the efficiency of data transmission can be improved; the convergence and switching device can reduce the complexity of data processing, unify the data format and processing flow, and reduce the cost and workload of data processing; through the convergence and switching device, standardized processing and encrypted transmission of data can be achieved, enhancing data security and privacy protection.

In summary, this application has at least the following effects:

A network aggregation and exchange method based on a multi-source data protocol can evaluate the aggregation degree between different data sources by calculating the data source aggregation index, so as to determine whether these data sources are suitable for aggregation. This helps to ensure the consistency and reliability of data in the aggregation process, improves the efficiency and accuracy of data aggregation, and can achieve centralized management, unified monitoring and comprehensive analysis of data by aggregating data from multiple data sources, thereby improving the utilization value and application effect of data.

Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

The present invention is described with reference to the flowchart and/or block diagram of the system, device (system), and computer program product according to the embodiment of the present invention. It should be understood that each process and/or box in the flowchart and/or block diagram, as well as the combination of the process and/or box in the flowchart and/or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for implementing the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device that implements the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

These computer program instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims (10)

1. A network convergence switching method based on a multi-source data protocol, characterized in that it comprises the following steps: S1. Identify and connect data sources of different protocols, including network devices, sensors, and embedded devices, obtain network device data, sensor data, and embedded device data, and transmit the network device data, sensor data, and embedded device data to the aggregation node; S2. Based on the network device data, sensor data and embedded device data, calculate the data source aggregation index to measure the degree of data source aggregation, evaluate the degree of aggregation of data sources of different protocols, and obtain evaluation information of the degree of aggregation of data sources of different protocols; S3. Based on the evaluation information of the aggregation degree of data sources of different protocols, the network device data, sensor data and embedded device data are converted and standardized into the target device protocol, and the network device data, sensor data and embedded device data are transmitted to the target device using the target protocol through the network aggregation switching device; S4. Integrate and store the network device data, sensor data and embedded device data that have undergone data format conversion and standardization processing through a network aggregation and switching device. 2. According to the network convergence and switching method based on multi-source data protocol of claim 1, it is characterized in that: the specific process of transmitting network device data, sensor data and embedded device data to the convergence node is as follows: Use adapters to connect data sources of different protocols to the network aggregation switch device, and securely transmit network device data, sensor data, and embedded device data to the aggregation node; The network device data includes network device data volume, network device data update frequency, and network device data integrity; The sensor data includes sensor data volume, sensor data update frequency, and sensor data integrity; The embedded device data includes the amount of embedded device data, the frequency of updating the embedded device data, and the completeness of the embedded device data. 3. According to the network convergence exchange method based on multi-source data protocol of claim 2, it is characterized in that: the specific calculation process of calculating the data source aggregation index based on network device data, sensor data and embedded device data is as follows: Based on the network device data, analyze the aggregation conformity of the network device data and calculate the aggregation index of the network device data source; Based on the sensor data, analyze the aggregation conformity of the sensor data and calculate the aggregation index of the sensor data source; Based on the embedded device data, analyze the aggregation conformity of the embedded device data and calculate the aggregation index of the embedded device data source; Based on the network device data source aggregation index, the sensor data source aggregation index, and the embedded device data source aggregation index, weights are assigned to the network device data source aggregation index, the sensor data source aggregation index, and the embedded device data source aggregation index to calculate the data source aggregation index. 4. According to the network convergence exchange method based on multi-source data protocol of claim 3, it is characterized in that: the specific process of allocating weights to the network device data source aggregation index, the sensor data source aggregation index, and the embedded device data source aggregation index is as follows: Construct a data source aggregation hierarchy, extract features from the network device data source aggregation index, sensor data source aggregation index, and embedded device data source aggregation index, and compare the influence of the network device data source aggregation index, sensor data source aggregation index, and embedded device data source aggregation index on the network device data source aggregation index; The network device data source aggregation index, sensor data source aggregation index and embedded device data source aggregation index are normalized by comparing the matrix to obtain the weight factors of the network device data source aggregation index, sensor data source aggregation index and embedded device data source aggregation index. 5. According to the network convergence exchange method based on multi-source data protocol of claim 3, it is characterized in that: the calculation formula of the data source aggregation index is as follows: χ＝Cnet*λ ₁ +Csen*λ ₂ +Cemb*λ ₃ ; Where χ represents the data source aggregation index, Cnet represents the network device data source aggregation index, Csen represents the sensor data source aggregation index, Cemb represents the embedded device data source aggregation index, _λ1 represents the weight factor of the network device data source aggregation index corresponding to the data source aggregation index, _λ2 represents the weight factor of the sensor data source aggregation index corresponding to the data source aggregation index, and _λ3 represents the weight factor of the embedded device data source aggregation index corresponding to the data source aggregation index. 6. A network convergence and exchange method based on multi-source data protocols according to claim 5, characterized in that: the specific process of evaluating the aggregation degree of data sources of different protocols is as follows: Based on the data source aggregation index, it is compared with the threshold of the data source aggregation index, the difference between the data source aggregation index and the threshold of the data source aggregation index is calculated, the difference between the data source aggregation index and the threshold of the data source aggregation index is compared with the allowable deviation value of the data source aggregation index, and the data source aggregation compliance coefficient is calculated. 7. A network convergence and exchange method based on a multi-source data protocol according to claim 6, characterized in that: the specific calculation formula of the data source aggregation compliance coefficient is as follows: Where β represents the data source aggregation compliance coefficient, χ represents the data source aggregation index, _χ1 represents the threshold of the data source aggregation index, and Δχ represents the allowable deviation value of the data source aggregation index. 8. A network convergence and exchange method based on a multi-source data protocol according to claim 7, characterized in that: the specific process of obtaining evaluation information of the aggregation degree of data sources of different protocols is as follows: Based on the data source aggregation compliance coefficient, it is compared with the standard value of the data source aggregation compliance coefficient. If the data source aggregation compliance coefficient is less than the standard value of the data source aggregation compliance coefficient, it means that the data sources of different protocols are not aggregated, and the data formats of the data sources of different protocols are converted and standardized. If the data source aggregation compliance coefficient is greater than the standard value of the data source aggregation compliance coefficient, it indicates that data sources of different protocols are aggregated. 9. A network convergence and exchange method based on a multi-source data protocol according to claim 8, characterized in that: the specific process of converting and standardizing the network device data, sensor data and embedded device data based on the evaluation information of the aggregation degree of data sources of different protocols is as follows: Based on the evaluation information of the aggregation degree of data sources with different protocols, the scripting programming language Python is used to convert the data formats of network device data, sensor data and embedded device data; Unify the time fields in data sources of different protocols into the same date and time format, convert text data into numeric data, ensure that the data types of all data fields are consistent, and handle missing values by filling in default values and interpolation. 10. A network convergence switching device based on a multi-source data protocol, using a network convergence switching method based on a multi-source data protocol as described in any one of claims 1 to 9, characterized in that: it includes a data acquisition and transmission module, a data processing module, a data conversion module, and a data integration and storage module; The data acquisition module is used to identify and connect data sources of different protocols, including network devices, sensors and embedded devices, obtain network device data, sensor data and embedded device data, and transmit the network device data, sensor data and embedded device data to the aggregation node; The data transmission module is used to calculate the data source aggregation index based on the network device data, sensor data and embedded device data, which is used to measure the degree of data source aggregation, evaluate the degree of aggregation of data sources of different protocols, and obtain evaluation information of the degree of aggregation of data sources of different protocols; The data conversion module is used to convert and standardize the network device data, sensor data and embedded device data based on the evaluation information of the aggregation degree of data sources of different protocols, convert them into the target device protocol, and transmit the network device data, sensor data and embedded device data to the target device through the network convergence switching device using the target protocol; The data integration storage module is used to integrate and store network device data, sensor data and embedded device data that have been processed through data format conversion and standardization through a network convergence switching device.