List generation method and device, computer equipment and storage medium

Abstract

The invention relates to a list generation method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring event information corresponding to a target event behavior from a target platform, and acquiring power grid data from a power grid system; processing the event information and the power grid data to obtain processed event information and processed power grid data; performing matching processing on the processed event information and the processed power grid data to obtain matched power grid data in the processed power grid data; and generating a target risk list of the power grid system according to the matched power grid data. By adopting the method, the recognition accuracy of the target event behavior in the power grid data can be improved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a list generation method, apparatus, computer equipment, storage medium, and computer program product. Background Art

[0002] As the power grid industry continues to gain informatization, electricity bill payment methods are becoming increasingly diverse, creating opportunities for illegal and irregular risk events. To address this issue, the power grid industry is continuously strengthening information collection and event analysis on risk events related to illegal and irregular activities, leveraging big data technology to improve the identification of risk events.

[0003] At present, learning and analysis mainly rely on information on risk events within the power grid industry. However, the amount of this information is limited and the type is single, which makes it difficult to provide sufficient data for data-driven model learning, resulting in difficulty for data-driven models to accurately and comprehensively identify risk events. Summary of the Invention

[0004] Based on this, it is necessary to provide a list generation method, device, computer equipment, computer-readable storage medium and computer program product that can improve the accuracy of identifying specific risk events in power grid data in response to the above technical problems.

[0005] In a first aspect, the present application provides a method for generating a list. The method comprises:

[0006] Obtain event information corresponding to target event behavior from the target platform, and obtain power grid data from the power grid system;

[0007] Processing the event information and the power grid data to obtain processed event information and processed power grid data;

[0008] performing matching processing on the processed event information and the processed power grid data to obtain matching power grid data in the processed power grid data;

[0009] A target risk list of the power grid system is generated based on the matched power grid data.

[0010] In one embodiment, generating a target risk list of the power grid system based on the matched power grid data includes:

[0011] Performing a risk assessment on the matching power grid data for the target event behavior to obtain a risk assessment result of the matching power grid data;

[0012] According to the risk assessment result, marking the matching grid data in the power grid system to obtain risk identification information of the matching grid data;

[0013] A target risk list for the power grid system is generated according to the matching power grid data, the risk identification information and the risk assessment result.

[0014] In one embodiment, performing a risk assessment on the matching power grid data for the target event behavior to obtain a risk assessment result of the power grid data includes:

[0015] determining data similarity between the matching grid data and event information corresponding to the matching grid data;

[0016] Through the risk assessment model for the target event behavior, based on the data similarity, the matching power grid data is risk assessed and processed to obtain a risk assessment result of the matching power grid data; the risk assessment model is trained on the initial risk assessment model based on the historical event information for the target event behavior and the historical matching power grid data associated with the historical event information.

[0017] In one embodiment, matching the processed event information with the processed power grid data to obtain matching power grid data in the processed power grid data includes:

[0018] Generate a database collision script based on the field information in the processed event information;

[0019] The database collision script is used to perform a database collision comparison process on the processed event information and the processed power grid data to obtain matching power grid data in the processed power grid data.

[0020] In one embodiment, after generating the target risk list of the power grid system according to the matched power grid data, the method further includes:

[0021] When a preset cycle time is reached, obtaining updated event information corresponding to the target event behavior from the target platform;

[0022] Processing the updated event information to obtain processed updated event information;

[0023] performing matching processing on the processed updated event information and the processed power grid data to obtain updated matching power grid data in the processed power grid data;

[0024] The target risk list is updated according to the updated matching grid data.

[0025] In one embodiment, processing the event information and the power grid data to obtain processed event information and processed power grid data includes:

[0026] performing data cleaning on the event information and the power grid data to obtain cleaned event information and cleaned power grid data;

[0027] Data preprocessing is performed on the cleaned event information and the cleaned power grid data to obtain the processed event information and the processed power grid data.

[0028] In a second aspect, the present application also provides a list generation device. The device includes:

[0029] The data acquisition module is used to obtain event information corresponding to the target event behavior from the target platform and to obtain grid data from the grid system;

[0030] a data processing module, configured to process the event information and the power grid data to obtain processed event information and processed power grid data;

[0031] a data matching module, configured to perform matching processing on the processed event information and the processed power grid data to obtain matching power grid data in the processed power grid data;

[0032] A list generation module is used to generate a target risk list of the power grid system based on the matching power grid data.

[0033] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are performed:

[0034] Obtain event information corresponding to target event behavior from the target platform, and obtain power grid data from the power grid system;

[0035] Processing the event information and the power grid data to obtain processed event information and processed power grid data;

[0036] performing matching processing on the processed event information and the processed power grid data to obtain matching power grid data in the processed power grid data;

[0037] A target risk list of the power grid system is generated based on the matched power grid data.

[0038] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the following steps:

[0039] Obtain event information corresponding to target event behavior from the target platform, and obtain power grid data from the power grid system;

[0040] Processing the event information and the power grid data to obtain processed event information and processed power grid data;

[0041] performing matching processing on the processed event information and the processed power grid data to obtain matching power grid data in the processed power grid data;

[0042] A target risk list of the power grid system is generated based on the matched power grid data.

[0043] In a fifth aspect, the present application further provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the following steps:

[0044] Obtain event information corresponding to target event behavior from the target platform, and obtain power grid data from the power grid system;

[0045] Processing the event information and the power grid data to obtain processed event information and processed power grid data;

[0046] performing matching processing on the processed event information and the processed power grid data to obtain matching power grid data in the processed power grid data;

[0047] A target risk list of the power grid system is generated based on the matched power grid data.

[0048] The aforementioned list generation method, apparatus, computer device, storage medium, and computer program product obtain event information corresponding to target event behaviors from a target platform and grid data from a power grid system; process the event information and grid data to obtain processed event information and processed grid data; match the processed event information and processed grid data to obtain matching grid data within the processed grid data; and generate a target risk list for the power grid system based on the matching grid data. This method utilizes event information from the target platform that confirms the occurrence of target event behaviors to match the grid data, thereby accurately screening out matching grid data with risks within the grid data and generating a corresponding target risk list, effectively improving the accuracy of identifying target event behaviors within the grid data. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A diagram showing an application environment of a list generation method according to an embodiment;

[0050] Figure 2 1. A flowchart of a list generation method according to an embodiment of the present invention;

[0051] Figure 3 A flowchart illustrating steps of generating a target risk list for a power grid system based on matching power grid data in one embodiment;

[0052] Figure 4 A flowchart of a list generation method according to another embodiment;

[0053] Figure 5 1 is a flow chart of a method for generating a list in another embodiment;

[0054] Figure 6 It is a structural block diagram of a list generating device in one embodiment;

[0055] Figure 7 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0057] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0058] The list generation method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. The power grid system 101 communicates with the target platform 102 through the network. The data storage system can store the data that the power grid system 101 needs to process. The data storage system can be integrated on the server, or it can be placed on the cloud or other network servers. The power grid system 101 can be, but is not limited to, various personal computers, laptops, smart phones, tablets, Internet of Things devices and portable wearable devices, and can also be implemented with an independent server or a server cluster consisting of multiple servers. The target platform 102 refers to an external third-party system that provides a large amount of event information to the power grid system 101. The target platform 102 can be the platform of the anti-fraud center.

[0059] In one embodiment, Figure 2 As shown, a list generation method is provided, which is applied to Figure 1The power grid system in FIG is taken as an example to illustrate the process, which includes the following steps:

[0060] Step S201 : acquiring event information corresponding to a target event behavior from a target platform, and acquiring grid data from a grid system.

[0061] Among them, the target event behavior refers to the behavior of executing a specific illegal or irregular event.

[0062] Incident information refers to the content describing the cause of the target incident, the circumstances of the incident, and the resolution measures. Incident information includes, but is not limited to, the names, ID numbers, contact information, case types, and the number of resources involved.

[0063] Grid data refers to the internal data in the grid system, including user data and power data of grid users.

[0064] Specifically, after authorization by the target platform, the power grid system can establish a communication and data connection with the target platform. This allows the power grid system to obtain real-time event information (such as event time) corresponding to specific target event behaviors from the target platform. The power grid system can also extract power grid data (such as user data and power consumption data of power grid users) from the database.

[0065] Step S202 : Process the event information and the power grid data to obtain processed event information and processed power grid data.

[0066] Specifically, after obtaining event information and grid data, the power grid system can also perform data cleaning and data preprocessing on the event information and grid data to remove duplicate data, erroneous data and invalid data in the event information and grid data, and thus obtain processed event information and processed grid data.

[0067] It should be noted that even if the power grid system obtains the event information of the target platform, the processed event information and the processed power grid data are still stored in different libraries of the database, laying the foundation for performing special matching operations in the subsequent step S203.

[0068] Step S203 : performing matching processing on the processed event information and the processed power grid data to obtain matching power grid data in the processed power grid data.

[0069] The matching power grid data refers to the power grid data that matches the processed event information.

[0070] Specifically, since the processed event information and the processed power grid data are stored in libraries of different sources, the power grid system can use the database script to perform database matching on the processed power grid data and the processed event information, and finally output the matching power grid data in the processed power grid data.

[0071] It should be noted that database collision matching differs significantly from traditional data matching. The differences are as follows:

[0072] (1) Traditional data matching usually uses fields for precise field matching, while database collision matching combines fuzzy matching and variation rules for matching. Therefore, database collision matching can more accurately identify variant descriptions and similar words, synonyms, etc. of some texts. Among them, fuzzy matching can match fields in power grid data that have similar meanings to event information; variation rules can match fields in power grid data that have similar pronunciations to event information. For example, assuming that the event information to be matched is "profit", then the matching power grid data obtained by the traditional data matching for precise matching is "profit", while the matching power grid data obtained by the database collision matching in this application includes "profit, HL, Hl, hL, profit, benefit, profit, earn", etc.

[0073] (2) Traditional data matching is single-threaded matching, and the matching speed is limited by system resource constraints. However, database collision matching uses a distributed architecture and can match databases from different sources at the same time, with extremely fast matching speed.

[0074] (3) Traditional data matching mainly aims to discover superficial and intuitive associations in data, while database matching can also discover deep database structural associations, such as table associations, field associations, primary key associations, and foreign key associations. Moreover, database matching can more accurately and comprehensively identify and verify user identities due to its large-scale data leakage characteristics and cross-database recognition advantages. If a user uses the same username and password on multiple platforms, database matching can more easily identify these identical accounts, thereby achieving a high matching rate.

[0075] Step S204: Generate a target risk list for the power grid system based on the matched power grid data.

[0076] Among them, the target risk list refers to a list of power grid users that may have target event behaviors, not a list of power grid users who have been confirmed to have committed target event behaviors.

[0077] Specifically, based on matching grid data, corresponding grid users can be tagged and a target risk list for the grid system can be generated based on the matching grid data. The grid system can also further analyze the target risk list and generate visual reports (such as heat maps, pie charts, and trend graphs) to facilitate understanding and decision-making by grid system personnel. Based on the visual reports and target risk list, the grid system can also implement appropriate management measures to reduce the risk of target event behaviors.

[0078] In the above-mentioned list generation method, event information corresponding to the target event behavior is obtained from the target platform, and power grid data is obtained from the power grid system; the event information and power grid data are processed to obtain processed event information and processed power grid data; the processed event information and processed power grid data are matched to obtain matching power grid data within the processed power grid data; and a target risk list for the power grid system is generated based on the matching power grid data. Using this method, event information confirming the occurrence of the target event behavior on the target platform can be matched with the power grid data, thereby accurately screening out matching power grid data that presents risks within the power grid data and generating a corresponding target risk list, effectively improving the accuracy of identifying the target event behavior within the power grid data.

[0079] In one embodiment, Figure 3 As shown, the above step S204 generates a target risk list of the power grid system based on the matched power grid data, which specifically includes the following contents:

[0080] Step S301 : performing risk assessment on the matching power grid data for target event behavior to obtain a risk assessment result of the matching power grid data.

[0081] The risk assessment results are used to describe the risk data related to the target event behavior that matches the power grid data. For example, the risk assessment results include information such as the probability of the target event behavior that matches the power grid data, the scope of the event impact, and the risk level.

[0082] Specifically, the power grid system can also input the matching power grid data and the event information matching the matching power grid data into the risk assessment model corresponding to the target event behavior, so as to perform risk assessment processing on the matching power grid data and event information for the target event behavior through the risk assessment model, and output risk assessment results such as the probability of event occurrence, event impact range and risk level risk of the matching power grid data.

[0083] Step S302 : Based on the risk assessment result, the matching grid data is marked in the grid system to obtain risk identification information of the matching grid data.

[0084] The risk identification information refers to identification information that marks the risk that the power grid user may have target event behavior.

[0085] Specifically, the power grid system can tag the grid users corresponding to the matching grid data, for example, tagging the grid users with risk identification information, thereby obtaining grid users with risk identification information. The risk identification information tagging content includes, but is not limited to, the risk assessment results of the target event behavior and the event type of the target event behavior.

[0086] Step S303: Generate a target risk list for the power grid system based on the matching power grid data, risk identification information and risk assessment results.

[0087] In practical applications, the target risk list can also be considered a "blacklist." By monitoring and restricting the services and permissions of grid users on the target risk list, the target event behavior can be avoided or the negative impact caused by the target event behavior can be promptly reduced.

[0088] Specifically, the power grid system can comprehensively match the user information and power data of power grid users corresponding to the power grid data, match the power grid data, risk identification information and risk assessment results, and generate a target risk list for the power grid system. For example, the user information and power data of power grid users, match the power grid data, risk identification information and risk assessment results can be input into the corresponding positions in the risk list template, and the filled target risk list can be output.

[0089] In this embodiment, the power grid system performs a risk assessment on the matching power grid data for the target event behavior to obtain the risk assessment results of the matching power grid data. According to the risk assessment results, the matching power grid data is marked in the power grid system to obtain risk identification information of the matching power grid data, which can timely identify high-risk events and power grid users, strengthen their monitoring efforts and authority restrictions, thereby reducing the potential probability of accidents and ensuring the safety of power grid operation; based on the matching power grid data, risk identification information and risk assessment results, a target risk list of the power grid system is generated, so that power grid managers can more effectively allocate monitoring and control resources to the power grid users corresponding to the target risk list, and make targeted management decisions for them, so that they can quickly respond to abnormal situations, formulate countermeasures, and deal with potential risks in a timely manner.

[0090] In one embodiment, the above-mentioned step S302 performs a risk assessment on the matching power grid data for the target event behavior to obtain a risk assessment result of the power grid data, which specifically includes the following contents: determining the data similarity between the matching power grid data and the event information corresponding to the matching power grid data; performing risk assessment processing on the matching power grid data based on the data similarity through a risk assessment model for the target event behavior to obtain a risk assessment result of the matching power grid data; the risk assessment model is obtained by training an initial risk assessment model based on historical event information for the target event behavior and historical matching power grid data associated with the historical event information.

[0091] Among them, the risk assessment model refers to a model used to evaluate the risk size of the target event behavior matching the power grid data.

[0092] Specifically, the power grid system can pre-collect historical event information for the target event behavior and historical matching power grid data associated with the historical event information to construct a sample set. The sample set is then used to iteratively train an initial risk assessment model (e.g., a deep learning model such as a neural network) to obtain a risk assessment model corresponding to the target event behavior. The power grid system uses a similarity function to calculate the data similarity between the matching power grid data and the event information corresponding to the matching power grid data. Then, during the data inference phase, the power grid system inputs the matching power grid data, the event information corresponding to the matching power grid data, and the data similarity into the risk assessment model corresponding to the target event behavior. This risk assessment model then performs risk assessment processing on the matching power grid data and the event information of the target event behavior based on data similarity. For example, data similarity is used as an enhanced feature in the data inference phase to further improve the comprehensiveness and accuracy of the risk assessment processing of the matching power grid data and event information, ultimately obtaining a risk assessment result for the matching power grid data.

[0093] In this embodiment, by calculating the data similarity between the matching grid data and the event information corresponding to the matching grid data, the correlation between the matching grid data and the event information can be accurately identified; and then, through the risk assessment model for the target event behavior, the data similarity is used to provide the risk assessment model with richer feature information, which can assist the risk assessment model to have a deeper understanding of the dynamic change correlation between the matching grid data and the event information of the target event behavior, thereby obtaining a more accurate risk assessment result.

[0094] In one embodiment, the above step S203 matches the processed event information and the processed power grid data to obtain matching power grid data in the processed power grid data, which specifically includes the following contents: generating a database collision script based on the field information in the processed event information; performing a database collision comparison process on the processed event information and the processed power grid data through the database collision script to obtain matching power grid data in the processed power grid data.

[0095] Among them, the database collision script refers to the script program used to implement database collision processing.

[0096] Field information is a data storage unit in a database and is the core element of the database table structure. Field information includes data such as field name, data type, and constraints.

[0097] Specifically, the power grid system can generate a database collision script based on the field information in the processed event information; through the database collision script, the processed event information and the processed power grid data are subjected to collision comparison processing. For example, a distributed architecture can be adopted to perform fuzzy matching and mutation rule-based matching processing on the processed event information and the processed power grid data, and the fuzzy matching data and rule matching data in the processed power grid data are output; the power grid system merges the fuzzy matching data and the rule matching data to obtain matching power grid data.

[0098] In this embodiment, by generating a database collision script and comparing the processed event information with the power grid data, the accuracy and efficiency of data analysis can be significantly improved, potential matching data can be quickly identified, and a reliable data basis is provided for the subsequent generation of target risk lists, which helps to improve the accuracy of identifying target event behaviors in power grid data.

[0099] In one embodiment, in step S204, after generating a target risk list of the power grid system based on the matching power grid data, the method further includes: obtaining updated event information corresponding to the target event behavior from the target platform when a preset cycle time is reached; processing the updated event information to obtain processed updated event information; matching the processed updated event information with the processed power grid data to obtain updated matching power grid data in the processed power grid data; and updating the target risk list based on the updated matching power grid data.

[0100] Specifically, the power grid system can also periodically obtain the latest event information of the target event behavior from the target platform. For example, the power grid system can set a timer for a periodic time. When the periodic time is reached, the power grid system actively pulls the latest event information of the target event behavior from the target platform and sets it as the updated event information. The power grid system can also perform data cleaning and data preprocessing on the updated event information according to the data processing method in step S202 above, thereby obtaining the processed updated event information; refer to the matching processing in step S203 above, perform database matching processing on the processed updated event information and the processed power grid data, and obtain updated matching power grid data in the processed power grid data that matches the processed updated event information; finally, refer to the risk list generation method in step S204 above, use the updated matching power grid data to update the target risk list, and finally process to obtain the updated target risk list.

[0101] Furthermore, the power grid system also continuously monitors the power grid users on the target risk list. If its risk status is found to have changed (such as the risk situation is lifted, new target event behavior occurs, etc.), the target risk list information and risk level will be adjusted in a timely manner.

[0102] In this embodiment, the timeliness and accuracy of the target risk list are effectively ensured by regularly obtaining the latest case information from the target platform and re-comparing and updating it with the grid data within the grid system.

[0103] In one embodiment, the above step S202 processes the event information and the power grid data to obtain the processed event information and the processed power grid data, which specifically includes the following contents: performing data cleaning processing on the event information and the power grid data to obtain the cleaned event information and the cleaned power grid data; performing data preprocessing on the cleaned event information and the cleaned power grid data to obtain the processed event information and the processed power grid data.

[0104] Data cleaning refers to the process of identifying and correcting (or deleting) inaccurate, incomplete, or inconsistent data.

[0105] Among them, data preprocessing refers to further processing of the cleaned data in order to perform data inference or model training.

[0106] It should be noted that data cleaning focuses on improving the quality of data and eliminating errors and inconsistencies, while data preprocessing emphasizes adjusting and preparing data for more effective analysis or model training.

[0107] Specifically, the power grid system performs data cleansing on event information and power grid data to obtain cleaned event information and cleaned power grid data. Data cleansing can include: 1) processing missing values: filling missing data, deleting records containing missing values, or interpolating missing values; 2) correcting errors: identifying and correcting input errors (e.g., spelling errors, data format errors); 3) eliminating duplicates: finding and removing duplicate records to ensure the uniqueness of each data entry; and 4) checking consistency: ensuring consistency in data format and unit. The power grid system performs data preprocessing on the cleaned event information and cleaned power grid data to obtain processed event information and processed power grid data. Data preprocessing can include: 1) normalization / standardization: adjusting the data range to make it suitable for a specific algorithm (e.g., scaling the data to a specific range); 2) feature selection / extraction: selecting the most useful features for the model or extracting new features from existing features; 3) encoding categorical variables: converting categorical data into numerical data for easier model processing (e.g., one-hot encoding); and 4) splitting the data set: dividing the data into training and test sets, which is usually used for model validation and evaluation.

[0108] In this embodiment, by performing data cleaning processing on event information and power grid data, the data quality and availability of the cleaned event information and cleaned power grid data are effectively improved, and outliers and missing values in the original data are removed; and by further data preprocessing of the cleaned event information and cleaned power grid data, the processed event information and processed power grid data can be analyzed more effectively, which helps to improve the reasoning effect of the model.

[0109] In one embodiment, Figure 4 As shown, another list generation method is provided, which is applied to Figure 1 The power grid system in FIG is taken as an example to illustrate the process, which includes the following steps:

[0110] Step S401: acquiring event information corresponding to a target event behavior from a target platform, and acquiring grid data from a grid system.

[0111] Step S402 : performing data cleaning on the event information and the power grid data to obtain cleaned event information and cleaned power grid data.

[0112] Step S403 : Processing the cleaned event information and the cleaned power grid data to obtain processed event information and processed power grid data.

[0113] Step S404: Generate a database collision script based on the field information in the processed event information; perform a collision comparison process on the processed event information and the processed power grid data through the database collision script to obtain matching power grid data in the processed power grid data.

[0114] Step S405 , performing risk assessment on the matching power grid data for the target event behavior to obtain a risk assessment result of the matching power grid data.

[0115] Step S406 : Based on the risk assessment result, the matching grid data is marked in the grid system to obtain risk identification information of the matching grid data.

[0116] Step S407: Generate a target risk list for the power grid system based on the matching power grid data, risk identification information and risk assessment results.

[0117] The above-mentioned list generation method can achieve the following beneficial effects: it can match the event information of the target event behavior confirmed to have occurred in the target platform with the power grid data, thereby accurately screening out the matching power grid data with risks in the power grid data and generating a corresponding target risk list, effectively improving the accuracy of identifying the target event behavior in the power grid data.

[0118] In order to more clearly illustrate the list generation method provided by the embodiment of the present disclosure, the above-mentioned list generation method is specifically described below with a specific embodiment. Figure 5 As shown, another list generation method is provided, which can be applied to Figure 1 The power grid system in the project includes the following contents:

[0119] (1) Obtaining event information from the Anti-Fraud Center

[0120] Event information obtained from the Anti-Fraud Center to confirm that it is a violation incident. Event information includes but is not limited to the name, ID number (such as ID number, bank account number), contact information (mobile phone number), event type, and the number of resources involved (amount).

[0121] (2) Data cleaning and data preprocessing

[0122] The event information obtained from the anti-fraud center and the power grid data within the power grid system are cleaned and preprocessed to remove duplicate, erroneous and invalid data, ensure the accuracy and completeness of the data, and finally obtain the processed event information and processed power grid data.

[0123] (3) Data comparison and labeling

[0124] Using a database SQL (Structured Query Language) function script, the processed event information is compared with the processed power grid data. The comparison fields include but are not limited to name, ID number, contact information, etc. Through this comparison, matching power grid data that matches the processed event information is obtained.

[0125] For matching power grid data, marking is performed within the power grid system, and the marking content includes but is not limited to violation events, risk levels, event types, etc.

[0126] (4) Generation of target risk list

[0127] Based on the marked matching grid data, an electricity fee blacklist is generated, and the grid system obtains a target risk list. The target risk list contains the user information and related risk information of the marked grid users, so that it can be monitored and managed in the subsequent electricity fee collection and business processing.

[0128] (5) Update and maintenance of target risk list

[0129] Regularly obtain the latest incident information from the anti-fraud center and re-compare and update it with the grid data within the power grid system to ensure the timeliness and accuracy of the target risk list. Continuously monitor grid users on the target risk list and promptly adjust the target risk list and risk level if any changes are detected in their risk status (such as the resolution of a violation, the occurrence of a new violation, etc.).

[0130] In this embodiment, by comparing event information from the anti-fraud center with internal grid data within the power grid system, it is possible to accurately identify offending grid users, improving the accuracy and effectiveness of identifying target illegal events within the power grid system. Through database collision comparison and internal grid system tagging, the workload of manual review is greatly reduced, improving both accuracy and efficiency of identification.

[0131] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0132] Based on the same inventive concept, embodiments of the present application also provide a list-generating device for implementing the aforementioned list-generating method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more list-generating device embodiments provided below can be found in the aforementioned limitations of the list-generating method and will not be further elaborated here.

[0133] In one embodiment, Figure 6 As shown, a list generating device 600 is provided, comprising: a data acquisition module 601, a data processing module 602, a data matching module 603 and a list generating module 604, wherein:

[0134] The data acquisition module 601 is used to acquire event information corresponding to target event behavior from the target platform, and to acquire power grid data from the power grid system.

[0135] The data processing module 602 is used to process the event information and the power grid data to obtain the processed event information and the processed power grid data.

[0136] The data matching module 603 is configured to perform matching processing on the processed event information and the processed power grid data to obtain matching power grid data in the processed power grid data.

[0137] The list generation module 604 is used to generate a target risk list of the power grid system based on the matching power grid data.

[0138] In one embodiment, the list generation module 604 is also used to perform risk assessment on the matching power grid data for target event behavior to obtain risk assessment results of the matching power grid data; based on the risk assessment results, the matching power grid data is marked in the power grid system to obtain risk identification information of the matching power grid data; based on the matching power grid data, risk identification information and risk assessment results, a target risk list of the power grid system is generated.

[0139] In one embodiment, the list generation device 600 also includes a risk assessment module for determining the data similarity between the matching power grid data and the event information corresponding to the matching power grid data; based on the data similarity, the matching power grid data is risk assessed and processed by a risk assessment model for the target event behavior to obtain a risk assessment result for the matching power grid data; the risk assessment model is based on the historical event information for the target event behavior and the historical matching power grid data associated with the historical event information, and the initial risk assessment model is trained.

[0140] In one embodiment, the data matching module 603 is also used to generate a database collision script based on the field information in the processed event information; through the database collision script, the processed event information and the processed power grid data are subjected to a collision comparison process to obtain matching power grid data in the processed power grid data.

[0141] In one embodiment, the list generation device 600 also includes a list update module, which is used to obtain updated event information corresponding to the target event behavior from the target platform when a preset cycle time is reached; process the updated event information to obtain processed updated event information; match the processed updated event information with the processed power grid data to obtain updated matching power grid data in the processed power grid data; and update the target risk list based on the updated matching power grid data.

[0142] In one embodiment, the data matching module 603 is further used to perform data cleaning on the event information and the power grid data to obtain cleaned event information and cleaned power grid data; and to perform data preprocessing on the cleaned event information and the cleaned power grid data to obtain processed event information and processed power grid data.

[0143] Each module in the aforementioned list generation device may be implemented in whole or in part through software, hardware, or a combination thereof. Each module may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in the computer device in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0144] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 7 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store data such as power grid data, event information, and a target risk list. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a list generation method is implemented.

[0145] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0146] In one embodiment, a computer device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0147] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0148] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0149] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), data processing logic devices based on quantum computing, and the like.

[0150] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0151] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A list generation method, characterized in that: The method comprises: Obtain event information corresponding to target event behavior from the target platform, and obtain grid data from the power grid system; Processing the event information and the power grid data to obtain processed event information and processed power grid data; performing matching processing on the processed event information and the processed power grid data to obtain matching power grid data in the processed power grid data; A target risk list of the power grid system is generated based on the matched power grid data.

2. The method according to claim 1, characterized in that Generating a target risk list of the power grid system according to the matched power grid data includes: Performing a risk assessment on the matching power grid data for the target event behavior to obtain a risk assessment result of the matching power grid data; According to the risk assessment result, marking the matching grid data in the power grid system to obtain risk identification information of the matching grid data; A target risk list for the power grid system is generated according to the matching power grid data, the risk identification information and the risk assessment result.

3. The method according to claim 2, characterized in that The performing risk assessment on the matching power grid data for the target event behavior to obtain a risk assessment result of the power grid data includes: determining data similarity between the matching grid data and event information corresponding to the matching grid data; Through the risk assessment model for the target event behavior, based on the data similarity, the matching power grid data is risk assessed and processed to obtain a risk assessment result of the matching power grid data; the risk assessment model is trained on the initial risk assessment model based on the historical event information for the target event behavior and the historical matching power grid data associated with the historical event information.

4. The method according to claim 1, wherein The matching processing of the processed event information and the processed power grid data to obtain matching power grid data in the processed power grid data includes: Generate a database collision script based on the field information in the processed event information; The database collision script is used to perform a database collision comparison process on the processed event information and the processed power grid data to obtain matching power grid data in the processed power grid data.

5. The method according to claim 1, characterized in that After generating a target risk list of the power grid system according to the matched power grid data, the method further includes: When a preset cycle time is reached, obtaining updated event information corresponding to the target event behavior from the target platform; Processing the updated event information to obtain processed updated event information; performing matching processing on the processed updated event information and the processed power grid data to obtain updated matching power grid data in the processed power grid data; The target risk list is updated according to the updated matching grid data.

6. The method according to claim 1, characterized in that The processing of the event information and the power grid data to obtain the processed event information and the processed power grid data includes: performing data cleaning on the event information and the power grid data to obtain cleaned event information and cleaned power grid data; Data preprocessing is performed on the cleaned event information and the cleaned power grid data to obtain the processed event information and the processed power grid data.

7. A list generating device, characterized in that: The device comprises: The data acquisition module is used to obtain event information corresponding to the target event behavior from the target platform and to obtain grid data from the grid system; a data processing module, configured to process the event information and the power grid data to obtain processed event information and processed power grid data; a data matching module, configured to perform matching processing on the processed event information and the processed power grid data to obtain matching power grid data in the processed power grid data; A list generation module is used to generate a target risk list of the power grid system based on the matching power grid data.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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