CN117934135A

CN117934135A - Network operation management method and device, electronic equipment and storage medium

Info

Publication number: CN117934135A
Application number: CN202410096419.5A
Authority: CN
Inventors: 赵惊
Original assignee: Agricultural Bank of China
Current assignee: Agricultural Bank of China
Priority date: 2024-01-23
Filing date: 2024-01-23
Publication date: 2024-04-26

Abstract

The invention discloses a website operation management method, a website operation management device, electronic equipment and a storage medium. The method comprises the following steps: performing data processing on historical operation data of the network points to obtain a data set; model training, model verification and updating are carried out on the constructed digital twin model based on the data set to obtain a target model; evaluating the target model based on real-time website operation data, and optimizing the model according to an evaluation result to obtain a final model; integrating the final model into a working system, and determining an optimal network point operation strategy through the final model so as to apply the optimal network point operation strategy to a real network point to realize network point operation management. The method can optimize the layout and operation strategy of the network points by combining the digital twin technology with the operation of the network points of the bank, and improve the operation efficiency and the customer satisfaction, thereby realizing more efficient and personalized service.

Description

Network operation management method and device, electronic equipment and storage medium

Technical Field

The embodiment of the invention relates to the technical field of digital twinning, in particular to a network operation management method, a network operation management device, electronic equipment and a storage medium.

Background

In modern banking, banking outlets remain an important place to provide services to customers. Despite the rapid development of digital banking, many customers still tend to conduct certain complex or large transactions at physical banking sites. Therefore, optimizing the operation management of banking sites is important for improving customer satisfaction and achieving efficient operation of banking.

The traditional banking website management mode often depends on manual experience, and the behavior, transaction condition and the like of clients are analyzed through statistical reports. This approach may be effective in handling simple, conventional situations, but appears to catch up with elbows in dealing with complex market changes, customer needs, and emerging financial products.

In recent years, as digitization technology has evolved, some advanced banks have begun to attempt to analyze and predict the operation of sites using more complex data analysis tools and methods, such as machine learning and artificial intelligence. These methods can provide more in-depth analysis results, but the methods generally require a large amount of data and high-performance computing resources. In addition, this method is generally based on predictive analysis of past data, resulting in lower prediction accuracy.

Disclosure of Invention

The invention provides a website operation management method, a website operation management device, electronic equipment and a storage medium, which are used for solving the problem of low prediction accuracy in the prior art.

According to an aspect of the present invention, there is provided a network operation management method, including:

performing data processing on historical operation data of the network points to obtain a data set;

model training, model verification and updating are carried out on the constructed digital twin model based on the data set to obtain a target model;

evaluating the target model based on real-time website operation data, and optimizing the model according to an evaluation result to obtain a final model;

integrating the final model into a working system, and determining an optimal network point operation strategy through the final model so as to apply the optimal network point operation strategy to a real network point to realize network point operation management.

According to another aspect of the present invention, there is provided a network operation management apparatus including:

the processing module is used for carrying out data processing on the historical operation data of the network points to obtain a data set;

The determining module is used for carrying out model training, model verification and updating on the constructed digital twin model based on the data set to obtain a target model;

the tuning module is used for evaluating the target model based on real-time website operation data and tuning the model according to an evaluation result to obtain a final model;

and the application module is used for integrating the final model into a working system, and determining an optimal network point operation strategy through the final model so as to apply the optimal network point operation strategy to a real network point to realize network point operation management.

According to another aspect of the present invention, there is provided an electronic apparatus including: at least one processor; and

A memory communicatively coupled to the at least one processor; wherein,

The memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the website operation management method according to any one of the embodiments of the present invention.

According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to implement the method for website operation management according to any of the embodiments of the present invention when executed.

According to the technical scheme, the data set is obtained by carrying out data processing on historical operation data of the network points; model training, model verification and updating are carried out on the constructed digital twin model based on the data set to obtain a target model; evaluating the target model based on real-time website operation data, and optimizing the model according to an evaluation result to obtain a final model; and integrating the final model into a working system, determining an optimal network point operation strategy through the final model, so that the optimal network point operation strategy is applied to real network points to realize network point operation management, the layout and the operation strategy of the optimal network points are obtained, and the operation efficiency and the customer satisfaction are improved, thereby realizing the beneficial effects of more efficient and personalized service.

It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1 is a flow chart of a website operation management method according to a first embodiment of the present invention;

Fig. 2 is a schematic diagram of final model generation in a website operation management method according to a first embodiment of the present invention;

fig. 3 is a flow chart of a website operation management method according to a second embodiment of the present invention;

fig. 4 is a schematic structural diagram of a website operation management device according to a third embodiment of the present invention;

Fig. 5 is a schematic structural diagram of an electronic device of a website operation management method according to an embodiment of the present invention.

Detailed Description

In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention. It should be understood that the various steps recited in the method embodiments of the present invention may be performed in a different order and/or performed in parallel. Furthermore, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the invention is not limited in this respect.

The term "including" and variations thereof as used herein are intended to be open-ended, i.e., including, but not limited to. The term "based on" is based at least in part on. The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments. Related definitions of other terms will be given in the description below.

It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

It should be noted that references to "one", "a plurality" and "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those skilled in the art will appreciate that "one or more" is intended to be construed as "one or more" unless the context clearly indicates otherwise.

The names of messages or information interacted between the devices in the embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of such messages or information.

It will be appreciated that prior to using the technical solutions disclosed in the embodiments of the present disclosure, the user should be informed and authorized of the type, usage range, usage scenario, etc. of the personal information related to the present disclosure in an appropriate manner according to the relevant legal regulations.

For example, in response to receiving an active request from a user, a prompt is sent to the user to explicitly prompt the user that the operation it is requesting to perform will require personal information to be obtained and used with the user. Thus, the user can autonomously select whether to provide personal information to software or hardware such as an electronic device, an application program, a server or a storage medium for executing the operation of the technical scheme of the present disclosure according to the prompt information.

As an alternative but non-limiting implementation, in response to receiving an active request from a user, the manner in which the prompt information is sent to the user may be, for example, a popup, in which the prompt information may be presented in a text manner. In addition, a selection control for the user to select to provide personal information to the electronic device in a 'consent' or 'disagreement' manner can be carried in the popup window.

It will be appreciated that the above-described notification and user authorization process is merely illustrative and not limiting of the implementations of the present disclosure, and that other ways of satisfying relevant legal regulations may be applied to the implementations of the present disclosure.

It will be appreciated that the data (including but not limited to the data itself, the acquisition or use of the data) involved in the present technical solution should comply with the corresponding legal regulations and the requirements of the relevant regulations.

Traditional banking management relies primarily on human experience and periodic statistical reporting. Banking managers and employees rely on past data and experience to determine customer needs, schedule employee work hours, and conduct marketing campaigns. The application of traditional banking outlets to data analysis has focused mainly on the following aspects:

transaction analysis: the transaction amount, the transaction amount and the transaction type of each day, each week and each month are statistically analyzed, and the business condition of the website is known.

Customer behavior analysis: the customer's needs and behavior patterns are known through the customer's transaction records and service requests.

And (3) resource optimization: based on the results of the transaction analysis and the customer behavior analysis, the staff's work arrangement and resource allocation are optimized.

As technology advances and financial markets become more competitive, banks are beginning to seek new modes and techniques of management to improve efficiency and customer satisfaction. Digital transformation becomes an important direction. This transformation mainly includes:

real-time data collection and analysis: and collecting operation data of banking sites in real time through various sensors and systems, and carrying out real-time analysis through a data analysis tool.

Intelligent decision support: and the machine learning and artificial intelligence technology is utilized to help the bank manager and staff make more accurate and timely decisions.

Customer behavior prediction: by analyzing the historical data, the needs and behavior of the customer are predicted, thereby providing a more personalized service.

Although conventional data analysis methods and digital transformation can provide certain decision support for banking outlets, they have the following drawbacks and limitations:

Reaction dullness: these methods are based primarily on past data and do not react in time enough to sudden events and future changes.

The analysis depth is limited: these methods often only provide surface analysis results without going deep into the various details of the site operation due to the lack of sufficient data and analysis tools.

The resource consumption is large: advanced data analysis methods, such as machine learning and artificial intelligence, require large amounts of data and high performance computing resources.

Traditional banking data analysis methods rely primarily on past data to make decisions. Because of the complete reliance on historical data, these methods may fail or make erroneous predictions when encountering new, non-occurring contexts. For example, new financial policies, market changes, or events such as certain holidays may result in changes in customer behavior, and analysis methods that rely entirely on historical data may not accurately predict such changes.

Traditional banking data analysis often only provides surface results without going deep into the details of the website operation. Due to the lack of sufficient data and advanced analysis tools, banks cannot get in depth knowledge of the customer's real needs and behavior patterns, resulting in an inability to maximize marketing strategies and quality of service.

Advanced data analysis methods, such as machine learning and artificial intelligence, require large amounts of data and high performance computing resources. Although these methods can provide more accurate analysis results, in practical applications, banks may be faced with limitations on hardware equipment, data storage and computing power, and other resources, resulting in reduced or no analysis efficiency.

In view of the above background and problems, the present invention is directed to digitally twinning banking outlets according to outlet operation data by digital twinning techniques. Therefore, the bank can simulate and forecast the running condition of the network points and simulate and update the operation strategy, thereby realizing more efficient and intelligent network point management.

Example 1

Fig. 1 is a flow chart of a website operation management method according to an embodiment of the present invention, which is applicable to predicting operation conditions and customer behaviors of a banking website in a future time period, and the method may be performed by a website operation management device, where the device may be implemented by software and/or hardware and is generally integrated on an electronic device, and in this embodiment, the electronic device includes but is not limited to: a computer device.

As shown in fig. 1, a method for managing operation of a website according to a first embodiment of the present invention includes the following steps:

s110, performing data processing on historical operation data of the network points to obtain a data set.

The website may include a banking website, which refers to an entity location established by a bank for providing financial services, such as branch, business, etc. Banking outlets are the primary sites where banks and customers perform face-to-face services, and the primary services include providing various financial services such as deposit and withdrawal, transfer, loan, and financing.

The historical operation data may be understood as operation data of a banking website in the past time, including customer behavior data, where the customer behavior data refers to data generated when the customer performs a transaction at the banking website, including but not limited to transaction time, transaction amount, transaction type, service window used, waiting time, and the like. These data can be used to analyze the customer's transaction habits, needs and satisfaction.

In this embodiment, after the authorization of the client, the historical operation data may be obtained from the banking system, where the sources of the historical operation data may include: customer transaction records, customer service records, website operation data, and other relevant data. Wherein, the customer transaction records can include transfer, storage, withdrawal, loan application, etc.; customer service records may include queries, complaints, suggestions, and the like; the website operation data may include daily costs, staff attendance, equipment status, etc.; other relevant data may include financial market data, policy changes, and the like.

In this embodiment, the data processing method is not particularly limited, and a general data processing method may be selected. The data in the data set is time-series data.

And S120, performing model training, model verification and updating on the constructed digital twin model based on the data set to obtain a target model.

In this embodiment, the digital twin model can be constructed without additional sensors or devices by using historical operation data of the website. And in combination with the deep learning technology, a digital twin model is created for each website according to the daily data of banking website operation.

The digital twin technology provides new possibilities for digital transformation of banking outlets. By creating a digital model for each banking site, the bank can simulate the operating conditions of the site, and forecast future changes and demands, thereby making more accurate and timely decisions. In addition, the digital twin model can also be used as an experimental platform to help banks test and optimize various strategies and schemes. Although analog technology has application in many fields, digital twinning differs from traditional analog technology primarily by:

Real-time synchronization: the digital twin model is synchronous with the actual entity in real time, and can reflect the state of the actual entity in real time.

Higher precision: because digital twin models are based on a large amount of real-time data, their simulation and prediction results are generally more accurate.

Wider application range: digital twinning can be used not only for simulation and prediction, but also for testing and optimizing various strategies and schemes.

In this embodiment, in order to ensure that the model can accurately simulate and predict the customer behavior in the banking website, a deep learning method is adopted, and a Long Short-Term Memory (LSTM) is adopted to capture the time sequence characteristics of the customer behavior. LSTM is a special form of RNN designed specifically to solve the long-term dependency problem, and by its special structure (forget gate, input gate and output gate), LSTM can memorize or forget information for a long time.

The digital twin model combines the transaction data and the behavior data of the client, and the client demand and the possible behavior can be predicted more accurately by predicting through the deep learning model, so that more personalized service is provided for the client, and the client satisfaction is improved.

In this embodiment, after the data set is divided into the training set, the verification set, and the test set, model training may be performed using the training set, and the model may be verified and updated using the verification set and the test set.

Further, the model training, model verification and updating are performed on the constructed digital twin model based on the data set to obtain a target model, which comprises the following steps: dividing the data set into a training set, a verification set and a test set according to a preset proportion; model training is carried out on the constructed digital twin model by using the training set, and model weight is updated by adopting a back propagation algorithm and an optimizer in the model training process; and evaluating the prediction performance of the model by using the verification set and the test set, and realizing model verification and updating to obtain a target model.

The data set can be divided into a training set, a verification set and a test set according to the proportion of 7:2:1, the training set is used for carrying out model training on the constructed digital twin model, a counter propagation algorithm and an optimizer are adopted for weight updating, a loss function is selected to be used for regression tasks, such as average distribution errors, and cross entropy is used for classification tasks; and evaluating the performance of the model on the verification set, such as accuracy, loss value and the like, and performing strategies such as early stop, learning rate adjustment and the like to optimize the model.

The weight update formula is as follows:

Wherein W _ij represents the weight from the ith node to the jth node, alpha represents the learning rate, and L represents the loss function.

And S130, evaluating the target model based on real-time website operation data, and optimizing the model according to an evaluation result to obtain a final model.

The digital twin model can receive new data in real time and update itself so as to ensure accuracy and timeliness of the model. The method solves the problem that the prediction accuracy is low due to the fact that prediction analysis is carried out based on past data in the prior art.

In this embodiment, the predicted result may be obtained by using the latest acquired website operation data at the current time through the target model; according to the prediction result and the real result, the prediction accuracy of the model can be determined; the model may be tuned based on the prediction accuracy.

It should be noted that, the digital twin model of the banking website can simulate the operation condition of the real website in real time, and simulate and verify various strategies on the digital model without implementation in the real environment, thereby saving the cost and time of operation.

The above steps are shown in fig. 2, and fig. 2 is a schematic diagram of final model generation in a website operation management method according to a first embodiment of the present invention, and the specific process includes data preprocessing, model construction, model training, model verification and test, and model tuning.

And S140, integrating the final model into a working system, and determining an optimal network point operation strategy through the final model so as to apply the optimal network point operation strategy to a real network point to realize network point operation management.

The final model may be integrated with other systems of the bank, such as customer relationship management systems and core banking systems, among others. The digital twin technology is applied to the actual banking outlets, the operation condition of the outlets is monitored and predicted in real time, and data support is provided for decision making.

In this embodiment, policy simulation is performed on the digital twin model, and effects of different operation policies are evaluated, so that an optimal network point operation policy is selected for practical application. The policy simulation means that different operation policies are simulated on a digital model to evaluate the effect of the policies, which can help banks to perform sufficient tests and optimization before the policies are actually applied, and ensure the effectiveness of the policies.

The first embodiment of the invention provides a website operation management method, which comprises the steps of firstly, performing data processing on historical operation data of a website to obtain a data set; then, carrying out model training, model verification and updating on the constructed digital twin model based on the data set to obtain a target model; then, evaluating the target model based on real-time website operation data, and optimizing the model according to an evaluation result to obtain a final model; and finally, evaluating the target model based on real-time website operation data, and optimizing the model according to an evaluation result to obtain a final model. The method utilizes the digital twin technology in combination with the actual operation data of the network points to create a digital model corresponding to the real network points of the bank, and the model not only can simulate the daily operation condition of the network points of the bank, but also can perform simulation tests of various strategies, such as resource allocation, personnel scheduling and the like; the bank can test and optimize various strategies on the digital model, and the optimized operation strategy is applied to the real banking sites, so that the digitization and the intellectualization of the banking site management are realized.

On the basis of the above embodiments, modified embodiments of the above embodiments are proposed, and it is to be noted here that only the differences from the above embodiments are described in the modified embodiments for the sake of brevity of description.

In one embodiment, the data processing the historical operation data of the website to obtain a data set includes: collecting historical operation data of the network points; carrying out missing value processing, abnormal value detection and data standardization on the historical operation data to obtain processed data; converting the processed data into a time sequence format to obtain time sequence data; the time series data is constructed as a dataset.

The missing value processing comprises filling missing data by adopting a mean value, a median value or a mode; outlier detection includes detection of outlier data using a quartile range or Z-Score method; data normalization includes normalization using MinMaxScaler or STANDARDSCALER.

Wherein, the rows in the time series data represent time points, and represent transaction amounts, transaction types, transaction times and the like.

Further, the digital twin model comprises an input layer, a hidden layer and an output layer;

the input layer comprises a node number matched with the characteristic number of the time sequence data;

the hidden layer comprises a plurality of long and short time memory network layers, and each layer comprises a plurality of long and short time memory network units used for capturing time dependence;

the output layer comprises the node number corresponding to the prediction task.

Wherein the model output may include:

Prediction result: predicting future behavior and transaction mode of the client according to the input data;

model parameters: the weight and the bias value obtained after training;

Model evaluation report: including accuracy, recall, score index, etc.

Through these outputs, banking outlets can predict, simulate and optimize customer behavior.

Example two

Fig. 3 is a flow chart of a website operation management method according to a second embodiment of the present invention, where the second embodiment is optimized based on the foregoing embodiments. For details not yet described in detail in this embodiment, refer to embodiment one.

As shown in fig. 3, a website operation management method provided in the second embodiment of the present invention includes the following steps:

S210, performing data processing on historical operation data of the network points to obtain a data set.

S220, performing model training, model verification and updating on the constructed digital twin model based on the data set to obtain a target model.

S230, acquiring website operation data at the current moment, inputting the website operation data into the target model, and performing website operation simulation and client behavior prediction through the target model to obtain a prediction result.

In the digital twin technology, simulation and prediction are two core functions, so that the bank can be helped to deeply analyze and optimize the operation condition of the network. Based on the digital twin model, the operation condition, customer behavior and the like of the banking website can be simulated and predicted, wherein the simulation refers to the reproduction of the behavior of the real event on the digital twin model, and the prediction refers to the estimation of the future behavior according to the existing data and model.

The prediction results may include, among other things, the future behavior of the customer, transaction patterns, etc.

S240, comparing the prediction result with the real result to evaluate the prediction accuracy of the target model.

The actual results are understood to be actual results, i.e. actual customer behavior data.

Illustratively, the predicted result is that 5 people come from the A website to transact B business in the future one hour, and the real result is that only 3 people come from the A website to transact B business in the future one hour.

The prediction accuracy of the target model can be determined by comparing the prediction result with the real result.

In this embodiment, the digital twin model can simulate the operation condition of the banking website in real time, compare with the actual data, know the operation condition of the website in real time, adjust the strategy in time, and obtain timely early warning when abnormality occurs.

S250, optimizing the model according to the prediction accuracy to obtain a final model.

And the model obtained after the tuning is used as a final model.

And S260, integrating the final model into a working system, simulating the network point operation strategy through the final model, and optimizing the simulated network point operation strategy to obtain an optimal network point operation strategy so as to apply the optimal network point operation strategy to the real network point to realize network point operation management.

Specifically, the simulating the network point operation policy through the final model, and optimizing the simulated network point operation policy to obtain the optimal network point operation policy includes: creating a virtual dot environment based on the final model; simulating an operation strategy in the virtual network point environment; acquiring an influence result of the operation strategy on the network operation after the operation strategy is executed; and optimizing the operation strategy according to the influence result to obtain an optimal network point operation strategy.

In this embodiment, the simulation of various operation strategies is performed on the digital twin model, and there is no need to worry about risks of the real environment, so as to find an optimal operation strategy.

The second embodiment of the invention provides a website operation management method, which embodies the process of obtaining a final model and determining an optimal website operation strategy. The method can simulate and forecast the running condition of the network points and simulate and manage the updating of the strategy, thereby realizing the more efficient and intelligent network point management.

Example III

Fig. 4 is a schematic structural diagram of a website operation management device according to a third embodiment of the present invention, which is suitable for predicting the operation condition and the customer behavior of a banking website in a future time period, wherein the device may be implemented by software and/or hardware and is generally integrated on an electronic device.

As shown in fig. 4, the apparatus includes: a processing module 110, a determining module 120, a tuning module 130, and an application module 140.

A processing module 110, configured to perform data processing on historical operation data of the website to obtain a data set;

the determining module 120 is configured to perform model training, model verification and updating on the constructed digital twin model based on the data set to obtain a target model;

The tuning module 130 is configured to evaluate the target model based on real-time website operation data, and tune the model according to an evaluation result to obtain a final model;

And the application module 140 is used for integrating the final model into a working system, and determining an optimal network point operation strategy through the final model so as to apply the optimal network point operation strategy to a real network point to realize network point operation management.

In this embodiment, the device first performs data processing on historical operation data of the website through the processing module 110 to obtain a data set; then model training, model verification and updating are carried out on the constructed digital twin model based on the data set through a determining module 120 to obtain a target model; then, evaluating the target model based on real-time website operation data through an optimizing module 130, and optimizing the model according to an evaluation result to obtain a final model; and finally integrating the final model into a working system through an application module 140, and determining an optimal network point operation strategy through the final model so as to apply the optimal network point operation strategy to a real network point to realize network point operation management.

The embodiment provides a website operation management device, which can optimize layout and operation strategies of the website, improve operation efficiency and customer satisfaction, and therefore achieve more efficient and personalized service.

Further, the processing module 110 includes:

the collecting unit is used for collecting historical operation data of the network points;

The processing unit is used for carrying out missing value processing, abnormal value detection and data standardization on the historical operation data to obtain processed data;

the conversion unit is used for converting the processed data into a time sequence format to obtain time sequence data;

and the construction unit is used for constructing the time sequence data into a data set.

Further, the determining module 120 includes:

The dividing unit is used for dividing the data set into a training set, a verification set and a test set according to a preset proportion;

The training unit is used for carrying out model training on the constructed digital twin model by using the training set, and updating model weights by adopting a back propagation algorithm and an optimizer in the model training process;

And the updating unit is used for evaluating the prediction performance of the model by using the verification set and the test set, realizing model verification and updating, and obtaining a target model.

Based on the above optimization, the tuning module 130 includes:

the acquisition unit is used for acquiring the website operation data at the current moment;

The prediction unit is used for inputting the website operation data into the target model, and performing website operation simulation and client behavior prediction through the target model to obtain a prediction result;

The comparison unit is used for comparing the prediction result with the real result and evaluating the prediction accuracy of the target model;

And the tuning unit is used for tuning the model according to the prediction accuracy to obtain a final model.

Further, the application module 140 includes:

an integration unit for integrating the final model into a working system;

And the optimization unit is used for simulating the network point operation strategy through the final model, and optimizing the simulated network point operation strategy to obtain an optimal network point operation strategy so as to apply the optimal network point operation strategy to the real network point to realize network point operation management.

Based on the above technical scheme, the optimizing unit is specifically configured to: creating a virtual dot environment based on the final model; simulating an operation strategy in the virtual network point environment; acquiring an influence result of the operation strategy on the network operation after the operation strategy is executed; and optimizing the operation strategy according to the influence result to obtain an optimal network point operation strategy.

The network point operation management device can execute the network point operation management method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.

Example IV

Fig. 5 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.

As shown in fig. 5, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.

Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.

The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as a website operation management method.

In some embodiments, the website operation management method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. One or more of the steps of the website operation management method described above may be performed when the computer program is loaded into the RAM 13 and executed by the processor 11. Alternatively, in other embodiments, processor 11 may be configured to perform the website operation management method in any other suitable manner (e.g., by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.

A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.

The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.

The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.

Claims

1. A method for managing operation of a website, the method comprising:

2. The method according to claim 1, wherein the data processing the historical operating data of the website to obtain a data set includes:

collecting historical operation data of the network points;

Carrying out missing value processing, abnormal value detection and data standardization on the historical operation data to obtain processed data;

Converting the processed data into a time sequence format to obtain time sequence data;

The time series data is constructed as a dataset.

3. The method of claim 1, wherein the digital twin model comprises an input layer, a hidden layer, and an output layer;

4. The method of claim 1, wherein model training, model verification and updating the constructed digital twin model based on the dataset to obtain a target model comprises:

Dividing the data set into a training set, a verification set and a test set according to a preset proportion;

Model training is carried out on the constructed digital twin model by using the training set, and model weight is updated by adopting a back propagation algorithm and an optimizer in the model training process;

And evaluating the prediction performance of the model by using the verification set and the test set, and realizing model verification and updating to obtain a target model.

5. The method according to claim 1 or 4, wherein the estimating the target model based on the real-time website operation data and optimizing the model according to the estimation result to obtain a final model includes:

Acquiring network point operation data at the current moment;

inputting the website operation data into the target model, and performing website operation simulation and client behavior prediction through the target model to obtain a prediction result;

Comparing the predicted result with the real result to evaluate the prediction accuracy of the target model;

And optimizing the model according to the prediction accuracy to obtain a final model.

6. The method of claim 1, wherein determining an optimal website operation strategy by the final model comprises:

And simulating the network point operation strategy through the final model, and optimizing the simulated network point operation strategy to obtain the optimal network point operation strategy.

7. The method of claim 6, wherein the performing the simulation of the network point operation policy by the final model and optimizing the simulated network point operation policy to obtain the optimal network point operation policy comprises:

Creating a virtual dot environment based on the final model;

simulating an operation strategy in the virtual network point environment;

acquiring an influence result of the operation strategy on the network operation after the operation strategy is executed;

and optimizing the operation strategy according to the influence result to obtain an optimal network point operation strategy.

8. A website operation management apparatus, the apparatus comprising:

9. An electronic device, the electronic device comprising:

At least one processor; and

A memory communicatively coupled to the at least one processor; wherein,

The memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the website operation management method of any one of claims 1 to 7.

10. A computer readable storage medium storing computer instructions for causing a processor to implement the website operation management method of any one of claims 1 to 7 when executed.