Power consumption data information risk assessment method, device, equipment, storage medium and program product

Abstract

The invention discloses a risk assessment method, device and equipment for power consumption data information, a storage medium and a program product. The method comprises the following steps: acquiring a power consumption data information set corresponding to a high-risk user in a power grid database and a transaction data information set corresponding to a third-party blacklist user; each piece of power utilization data information corresponds to one first label; comparing the user identifier corresponding to each piece of power consumption data information with the user identifier corresponding to each piece of transaction data information, and determining a second tag corresponding to each piece of power consumption data information based on a comparison result; based on a target risk assessment model, determining a third label corresponding to each power consumption data information; when a selection operation for the target power utilization data information is detected, a first label, a second label and a third label corresponding to the target power utilization data information are output, and the first label, the second label and the third label are used for determining a risk assessment result corresponding to the target power utilization data information. Therefore, the accuracy of risk assessment of the power consumption data information can be improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method, device, equipment, storage medium and program product for risk assessment of electricity consumption data information. Background Art

[0002] With the continuous development of the electricity market and the increasing frequency of electricity transactions, more and more power companies have implemented anti-fraud and anti-money laundering risk control measures for electricity bills. These measures include risk assessments of electricity bills (or electricity usage data) and risk control based on the results. However, traditional risk assessment methods for electricity usage data rely on a relatively limited data source, resulting in low accuracy in risk assessments.

[0003] Therefore, how to improve the accuracy of risk assessment of electricity consumption data information has become an urgent problem to be solved. Summary of the Invention

[0004] The embodiments of the present application provide a method, apparatus, device, storage medium, and program product for risk assessment of electricity usage data information, which are conducive to improving the accuracy of risk assessment of electricity usage data information.

[0005] In a first aspect, an embodiment of the present application provides a method for risk assessment of electricity usage data information, the method comprising:

[0006] Obtaining a set of electricity usage data corresponding to high-risk users in a power grid database, and obtaining a set of transaction data corresponding to third-party blacklisted users from a third-party database; each set of electricity usage data corresponds to a first tag; the first tag is used to indicate the source of the electricity usage data;

[0007] Comparing the user identifier corresponding to each electricity usage data information in the electricity usage data information set with the user identifier corresponding to each transaction data information in the transaction data information set, and determining a second tag corresponding to each electricity usage data information based on the comparison result; the second tag is used to indicate whether the user corresponding to the electricity usage data information is a third-party blacklist user;

[0008] Invoking the target risk assessment model to determine the predicted risk level corresponding to each electricity usage data information, and determining a third tag corresponding to each electricity usage data information based on the predicted risk level corresponding to each electricity usage data information; the third tag is used to indicate the predicted risk level corresponding to the electricity usage data information;

[0009] When a selection operation for the target electricity usage data information is detected, a first tag, a second tag, and a third tag corresponding to the target electricity usage data information are output, and the first tag, the second tag, and the third tag are used to determine a risk assessment result corresponding to the target electricity usage data information.

[0010] In one embodiment, based on the comparison result, the second tag corresponding to each electricity usage data information is determined, including: for each electricity usage data information, when the comparison result indicates that the user identifier corresponding to the electricity usage data information exists in the user identifier set corresponding to the transaction data information set, determining that the second tag corresponding to the electricity usage data information is a third-party verification; wherein the user identifier set includes the user identifier corresponding to each transaction data information.

[0011] In one embodiment, the method further includes: if the comparison result indicates that the user identifier corresponding to the electricity usage data information does not exist in the user identifier set, determining that the second tag corresponding to the electricity usage data information is none.

[0012] In one embodiment, the predicted risk level includes a high risk level, a low risk level, and a no risk level; based on the predicted risk level corresponding to each electricity usage data information, the third label corresponding to each electricity usage data information is determined, including: for each electricity usage data information, when the predicted risk level corresponding to the electricity usage data information is a high risk level, determining that the third label corresponding to the electricity usage data information is risk assessment verification-high risk; when the predicted risk level corresponding to the electricity usage data information is a low risk level, determining that the third label corresponding to the electricity usage data information is risk assessment verification-low risk; when the predicted risk level corresponding to the electricity usage data information is a no risk level, determining that the third label corresponding to the electricity usage data information is risk assessment verification-no risk.

[0013] In one embodiment, the user identifier corresponding to each first data information in the electricity data information set is compared with the user identifier corresponding to each second data information in the transaction data information set, including: performing data information cleaning processing on the data information in the electricity data information set and the transaction data information set respectively to obtain a cleaned electricity data information set and a cleaned transaction data information set; performing unified format conversion processing on each data information in the cleaned electricity data information set and the cleaned transaction data information set respectively to obtain a converted electricity data information set and a converted transaction data information set; and comparing the user identifier corresponding to each first data information in the converted electricity data information set with the user identifier corresponding to each second data information in the converted transaction data information set.

[0014] In one embodiment, the method further includes: sending the first tag, the second tag, and the third tag corresponding to each electricity usage data information to the electronic device corresponding to the power grid database, so that the electronic device stores the first tag, the second tag, and the third tag corresponding to each electricity usage data information.

[0015] In a second aspect, the present application provides a risk assessment device for electricity usage data information, the device comprising:

[0016] An acquisition module is configured to acquire a set of electricity usage data corresponding to high-risk users in a power grid database, and a set of transaction data corresponding to third-party blacklisted users from a third-party database; each set of electricity usage data corresponds to a first tag; the first tag is configured to indicate the source of the electricity usage data;

[0017] a comparison and determination module, configured to compare a user identifier corresponding to each electricity usage data information in the electricity usage data information set with a user identifier corresponding to each transaction data information in the transaction data information set, and determine a second tag corresponding to each electricity usage data information based on the comparison result; the second tag is used to indicate whether the user corresponding to the electricity usage data information is a third-party blacklist user;

[0018] a determination module, configured to call a target risk assessment model, determine a predicted risk level corresponding to each electricity usage data information, and determine a third tag corresponding to each electricity usage data information based on the predicted risk level corresponding to each electricity usage data information; the third tag is used to indicate the predicted risk level corresponding to the electricity usage data information;

[0019] The output module is used to output the first label, the second label and the third label corresponding to the target electricity usage data information when a selection operation for the target electricity usage data information is detected. The first label, the second label and the third label are used to determine the risk assessment result corresponding to the target electricity usage data information.

[0020] In a third aspect, the present application provides a computer device comprising 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:

[0021] Obtaining a set of electricity usage data corresponding to high-risk users in a power grid database, and obtaining a set of transaction data corresponding to third-party blacklisted users from a third-party database; each set of electricity usage data corresponds to a first tag; the first tag is used to indicate the source of the electricity usage data;

[0022] Comparing the user identifier corresponding to each electricity usage data information in the electricity usage data information set with the user identifier corresponding to each transaction data information in the transaction data information set, and determining a second tag corresponding to each electricity usage data information based on the comparison result; the second tag is used to indicate whether the user corresponding to the electricity usage data information is a third-party blacklist user;

[0023] Invoking the target risk assessment model to determine the predicted risk level corresponding to each electricity usage data information, and determining a third tag corresponding to each electricity usage data information based on the predicted risk level corresponding to each electricity usage data information; the third tag is used to indicate the predicted risk level corresponding to the electricity usage data information;

[0024] When a selection operation for the target electricity usage data information is detected, a first tag, a second tag, and a third tag corresponding to the target electricity usage data information are output, and the first tag, the second tag, and the third tag are used to determine a risk assessment result corresponding to the target electricity usage data information.

[0025] 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:

[0026] Obtaining a set of electricity usage data corresponding to high-risk users in a power grid database, and obtaining a set of transaction data corresponding to third-party blacklisted users from a third-party database; each set of electricity usage data corresponds to a first tag; the first tag is used to indicate the source of the electricity usage data;

[0027] Comparing the user identifier corresponding to each electricity usage data information in the electricity usage data information set with the user identifier corresponding to each transaction data information in the transaction data information set, and determining a second tag corresponding to each electricity usage data information based on the comparison result; the second tag is used to indicate whether the user corresponding to the electricity usage data information is a third-party blacklist user;

[0028] Invoking the target risk assessment model to determine the predicted risk level corresponding to each electricity usage data information, and determining a third tag corresponding to each electricity usage data information based on the predicted risk level corresponding to each electricity usage data information; the third tag is used to indicate the predicted risk level corresponding to the electricity usage data information;

[0029] When a selection operation for the target electricity usage data information is detected, a first tag, a second tag, and a third tag corresponding to the target electricity usage data information are output, and the first tag, the second tag, and the third tag are used to determine a risk assessment result corresponding to the target electricity usage data information.

[0030] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the following steps:

[0031] Obtaining a set of electricity usage data corresponding to high-risk users in a power grid database, and obtaining a set of transaction data corresponding to third-party blacklisted users from a third-party database; each set of electricity usage data corresponds to a first tag; the first tag is used to indicate the source of the electricity usage data;

[0032] Comparing the user identifier corresponding to each electricity usage data information in the electricity usage data information set with the user identifier corresponding to each transaction data information in the transaction data information set, and determining a second tag corresponding to each electricity usage data information based on the comparison result; the second tag is used to indicate whether the user corresponding to the electricity usage data information is a third-party blacklist user;

[0033] Invoking the target risk assessment model to determine the predicted risk level corresponding to each electricity usage data information, and determining a third tag corresponding to each electricity usage data information based on the predicted risk level corresponding to each electricity usage data information; the third tag is used to indicate the predicted risk level corresponding to the electricity usage data information;

[0034] When a selection operation for the target electricity usage data information is detected, a first tag, a second tag, and a third tag corresponding to the target electricity usage data information are output, and the first tag, the second tag, and the third tag are used to determine a risk assessment result corresponding to the target electricity usage data information.

[0035] The above-mentioned risk assessment method, device, equipment, storage medium and program product for electricity usage data information, the computer equipment can obtain an electricity usage data information set corresponding to high-risk users in the power grid database, and obtain a transaction data information set corresponding to third-party blacklist users from a third-party database; each electricity usage data information in the electricity usage data information set corresponds to a first tag; the first tag is used to indicate the source of the electricity usage data information; the user identifier corresponding to each electricity usage data information in the electricity usage data information set is compared with the user identifier corresponding to each transaction data information in the transaction data information set, and based on the comparison result, a second tag corresponding to each electricity usage data information is determined; the second tag is used to indicate whether the user corresponding to the electricity usage data information is a third-party blacklist user; calling a target risk assessment model to determine the predicted risk level corresponding to each electricity usage data information, and based on the predicted risk level corresponding to each electricity usage data information, determining a third tag corresponding to each electricity usage data information; the third tag is used to indicate the predicted risk level corresponding to the electricity usage data information; when a selection operation is detected for the target electricity usage data information, the first tag, second tag and third tag corresponding to the target electricity usage data information are output, and the first tag, second tag and third tag are used to determine the risk assessment result corresponding to the target electricity usage data information. By adopting this method, the computer device can use the user identifier and target risk assessment model of the transaction data information set corresponding to the third-party blacklist user to verify each electricity consumption data information in the electricity consumption data information set corresponding to the high-risk user in the power grid database. In this way, by using the user identifier corresponding to the data in the cross-platform database and the artificial intelligence model (i.e., the target risk assessment model) to further determine the data label of the electricity consumption data information corresponding to the high-risk user in the power grid database, the user can determine the risk assessment result corresponding to the electricity consumption data information based on the multiple labels corresponding to the electricity consumption data information, thereby helping to improve the accuracy of the risk assessment of the electricity consumption data information. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments of the present application or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying any creative work.

[0037] Figure 1 This is a schematic diagram of an application scenario of a risk assessment method for electricity consumption data information provided by an embodiment of the present application;

[0038] Figure 2 This is a flow chart of a risk assessment method for electricity consumption data information provided by an embodiment of the present application;

[0039] Figure 3 This is a flow chart of another method for risk assessment of electricity consumption data information provided in an embodiment of the present application;

[0040] Figure 4 This is a schematic diagram of the structure of a risk assessment device for electricity consumption data information provided in an embodiment of the present application;

[0041] Figure 5 It is a structural diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0042] 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.

[0043] The following is a brief introduction to the application scenarios of the risk assessment method for electricity consumption data information provided in the embodiments of the present application.

[0044] See Figure 1 , Figure 1 This is a schematic diagram of an application scenario of a risk assessment method for electricity consumption data information provided by an embodiment of the present application. Figure 1 As shown, it includes a computer device 101 and an electronic device 102 corresponding to the power grid database ( Figure 1 The electronic device 102 is drawn as an example), and the electronic device 103 corresponding to the third-party database ( Figure 1 The electronic device 103 is drawn as an example where the server is used).

[0045] The computer device 101 can obtain an electricity usage data information set corresponding to a high-risk user in the power grid database from an electronic device 102 corresponding to the power grid database, and obtain a transaction data information set corresponding to a third-party blacklisted user from an electronic device 103 corresponding to a third-party database; wherein each electricity usage data information in the electricity usage data information set corresponds to a first tag; the first tag is used to indicate the source of the electricity usage data information; the user identifier corresponding to each electricity usage data information in the electricity usage data information set is compared with the user identifier corresponding to each transaction data information in the transaction data information set, and based on the comparison result, a second tag corresponding to each electricity usage data information is determined; the second tag is used to indicate whether the user corresponding to the electricity usage data information is a third-party blacklisted user; calling a target risk assessment model to determine a predicted risk level corresponding to each electricity usage data information, and based on the predicted risk level corresponding to each electricity usage data information, determining a third tag corresponding to each electricity usage data information; the third tag is used to indicate the predicted risk level corresponding to the electricity usage data information; when a selection operation is detected for the target electricity usage data information, the first tag, the second tag, and the third tag corresponding to the target electricity usage data information are output, and the first tag, the second tag, and the third tag are used to determine the risk assessment result corresponding to the target electricity usage data information. By adopting this method, the computer device can use the user identifier and target risk assessment model of the transaction data information set corresponding to the third-party blacklist user to verify each electricity consumption data information in the electricity consumption data information set corresponding to the high-risk user in the power grid database. In this way, by using the user identifier corresponding to the data in the cross-platform database and the artificial intelligence model (i.e., the target risk assessment model) to further determine the data label of the electricity consumption data information corresponding to the high-risk user in the power grid database, the user can determine the risk assessment result corresponding to the electricity consumption data information based on the multiple labels corresponding to the electricity consumption data information, thereby helping to improve the accuracy of the risk assessment of the electricity consumption data information.

[0046] Optionally, computer device 101, electronic device 102, and electronic device 103 may all be terminal devices. The terminal devices referred to herein may include, but are not limited to, various personal computers, laptops, smartphones, tablet computers, IoT devices, and portable wearable devices. IoT devices may include smart speakers, smart TVs, smart car devices, and projectors. Portable wearable devices may include smart watches and smart bracelets.

[0047] Alternatively, electronic device 102 and electronic device 103 may be servers, or one may be a terminal device and the other a server. For example, electronic device 102 may be a terminal device and electronic device 103 may be a server. Another example may be electronic device 102 may be a server and electronic device 103 may be a terminal device, etc., without limitation herein. The server referred to herein may be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0048] The following describes a method for risk assessment of electricity consumption data information provided in an embodiment of the present application.

[0049] See Figure 1 , Figure 1 This is a flow chart of a method for risk assessment of electricity consumption data information provided by an embodiment of the present application. The method can be performed by a computer device (such as Figure 1 The computer device 101 in the embodiment is executed. Figure 1 As shown, the risk assessment method of the electricity consumption data information may include but is not limited to the following steps:

[0050] S101. Obtain a set of electricity usage data information corresponding to high-risk users in a power grid database, and obtain a set of transaction data information corresponding to third-party blacklist users from a third-party database; each electricity usage data information in the electricity usage data information set corresponds to a first tag; the first tag is used to indicate the source of the electricity usage data information.

[0051] Electricity usage data may include, but is not limited to, payment information, risk details, and user information. Payment information may include, but is not limited to, payment time, payment amount, number of payments, and payment channels; risk details may include, but are not limited to, risk score, risk level, risk characteristics, and time of risk discovery; and user information may include, but is not limited to, user name, user account number, user mobile number, and user address.

[0052] Optionally, the first label corresponding to each electricity usage data information may also be used to indicate the type, risk characteristics, etc. of the electricity usage data information. In this case, the first label may be determined based on the type or risk characteristics of the electricity usage data information.

[0053] The third-party database may be, for example, data from an anti-fraud center; a third-party blacklisted user may be a blacklisted user from the anti-fraud center; and each transaction data information in the transaction data information set may include, but is not limited to, user name, user account number, counterparty card number, transaction time, user mobile phone number, case type, recording time, case notes, etc. Optionally, each transaction data information in the transaction data information set may be associated with a tag, which may be determined based on information such as the source, type, or risk characteristics of the data. For example, the tag for transaction data information A may be "Anti-Fraud Center," indicating that transaction data information A is data corresponding to a third-party blacklisted user from the anti-fraud center.

[0054] In an optional implementation, before step S101 , the computer device may further determine high-risk users and store electricity usage data information sets corresponding to the high-risk users in the power grid database.

[0055] Optionally, high-risk users may be users whose time between two payments is less than a preset time threshold, and / or users whose payment amount is far greater than the amount payable, and / or users whose number of payments within a preset time period is greater than a preset payment number threshold, etc., which are not limited here.

[0056] For example, assuming that user A's first payment time is April 1, 2025, the second payment time is April 2, 2025, and the third payment time is April 3, 2025, and assuming that the preset time threshold is 10 days, in this case, the computer device can determine that the time length between each two payments of user A is 1 day, which is less than the preset time threshold of 10 days. At this time, the computer device can determine that user A is a high-risk user and store user A's electricity usage data information in the power grid database.

[0057] For another example, suppose that the amount payable by user B is 100 yuan, and the amount to be paid is 100 million yuan. In this case, the computer device can determine that the amount paid by user B, 10,000, is much greater than the amount payable, 100. At this time, the computer device can determine that user B is a high-risk user and store user B's electricity usage data information in the power grid database.

[0058] For another example, assuming that the preset duration is 1 month, and the preset payment threshold within a month is 3 times, and assuming that user C paid the electricity bill 20 times within a month, in this case, the computer device can determine that the number of payments made by user C 20 times within 1 month is greater than the preset payment threshold 3 times. At this time, the computer device can determine that user C is a high-risk user and store the electricity usage data information of user C in the power grid database.

[0059] S202. Compare the user identifier corresponding to each electricity usage data information in the electricity usage data information set with the user identifier corresponding to each transaction data information in the transaction data information set, and determine the second label corresponding to each electricity usage data information based on the comparison result; the second label is used to indicate whether the user corresponding to the electricity usage data information is a third-party blacklist user.

[0060] Among them, the computer device compares the user identifier corresponding to each electricity usage data information in the electricity usage data information set with the user identifier corresponding to each transaction data information in the transaction data information set, that is, searches for whether the user identifier corresponding to each electricity usage data information in the electricity usage data information set exists in the user identifier set corresponding to the transaction data information set.

[0061] In an optional embodiment, the user identifier may include a user name and / or a user mobile phone number. In this case, the computer device compares the user identifier corresponding to each electricity usage data information in the electricity usage data information set with the user identifier corresponding to each transaction data information in the transaction data information set, which may include the following three situations:

[0062] Scenario 1: The computer device compares the user name corresponding to each electricity usage data information in the electricity usage data information set with the user name corresponding to each transaction data information in the transaction data information set;

[0063] Scenario 2: The computer device compares the user mobile phone number corresponding to each electricity consumption data information in the electricity consumption data information set with the user mobile phone number corresponding to each transaction data information in the transaction data information set.

[0064] Scenario 3: The computer device compares the user name corresponding to each electricity usage data information in the electricity usage data information set with the user name corresponding to each transaction data information in the transaction data information set, and compares the user mobile phone number corresponding to each electricity usage data information with the user mobile phone number corresponding to each transaction data information.

[0065] S203. Call the target risk assessment model to determine the predicted risk level corresponding to each electricity usage data information, and based on the predicted risk level corresponding to each electricity usage data information, determine a third label corresponding to each electricity usage data information; the third label is used to indicate the predicted risk level corresponding to the electricity usage data information.

[0066] In an optional implementation, the target risk assessment model may be a machine learning model or a deep learning model, which is not limited here.

[0067] In an optional implementation, the target risk assessment model may be pre-trained by the computer device, or may be a trained model obtained by the computer device from other database servers, which is not limited here.

[0068] S204. When a selection operation for the target electricity usage data information is detected, a first tag, a second tag, and a third tag corresponding to the target electricity usage data information are output, where the first tag, the second tag, and the third tag are used to determine a risk assessment result corresponding to the target electricity usage data information.

[0069] The target electricity consumption data information is the electricity consumption data information in the electricity consumption data information set.

[0070] In an embodiment of the present application, a computer device can obtain an electricity consumption data information set corresponding to a high-risk user in a power grid database, and obtain a transaction data information set corresponding to a third-party blacklist user from a third-party database; each electricity consumption data information in the electricity consumption data information set corresponds to a first label; the first label shown is used to indicate the source of the electricity consumption data information; the user identifier corresponding to each electricity consumption data information in the electricity consumption data information set is compared with the user identifier corresponding to each transaction data information in the transaction data information set, and based on the comparison result, the second label corresponding to each electricity consumption data information is determined; the second label is used to indicate whether the user corresponding to the electricity consumption data information is a third-party blacklist user; the target risk assessment model is called to determine the predicted risk level corresponding to each electricity consumption data information, and based on the predicted risk level corresponding to each electricity consumption data information, the third label corresponding to each electricity consumption data information is determined; the third label is used to indicate the predicted risk level corresponding to the electricity consumption data information; when a selection operation for the target electricity consumption data information is detected, the first label, the second label and the third label corresponding to the target electricity consumption data information are output, and the first label, the second label and the third label are used to determine the risk assessment result corresponding to the target electricity consumption data information. By adopting this method, the computer device can use the user identifier and target risk assessment model of the transaction data information set corresponding to the third-party blacklist user to verify each electricity consumption data information in the electricity consumption data information set corresponding to the high-risk user in the power grid database. In this way, by using the user identifier corresponding to the data in the cross-platform database and the artificial intelligence model (i.e., the target risk assessment model) to further determine the data label of the electricity consumption data information corresponding to the high-risk user in the power grid database, the user can determine the risk assessment result corresponding to the electricity consumption data information based on the multiple labels corresponding to the electricity consumption data information, thereby helping to improve the accuracy of the risk assessment of the electricity consumption data information.

[0071] In an optional embodiment, Figure 1In the risk assessment method for electricity usage data information shown, the computer device determines the second label corresponding to each electricity usage data information based on the comparison result, which may include: for each electricity usage data information, when the comparison result indicates that the user identifier corresponding to the electricity usage data information exists in the user identifier set corresponding to the transaction data information set, determining that the second label corresponding to the electricity usage data information is a third-party verification; wherein the user identifier set includes the user identifier corresponding to each transaction data information.

[0072] Optionally, the comparison result may include a hit or a miss. A hit indicates that the user identifier corresponding to the electricity usage data exists in the set of user identifiers corresponding to the transaction data set; a miss indicates that the user identifier corresponding to the electricity usage data does not exist in the set of user identifiers corresponding to the transaction data set.

[0073] For example, assuming that the user identification set corresponding to the transaction data information set includes four user names, namely Diandiandian, Lala La, Hahaha, and Xixixi, and assuming that the user identification corresponding to the electricity usage data information 1 is the user name Diandiandian, in this case, the computer device compares the user name Diandiandian corresponding to the electricity usage data information 1 with the user names in the user identification set corresponding to the transaction data information set (i.e., Diandiandian, Lala La, Hahaha, and Xixixi), and the comparison result obtained is a hit. At this time, the computer device can determine that the second tag corresponding to the electricity usage data information 1 is third-party verification.

[0074] Optionally, when the comparison result indicates that the user identifier corresponding to the electricity usage data information does not exist in the user identifier set, it is determined that the second tag corresponding to the electricity usage data information is none.

[0075] Continuing with the above example, assuming that the user identifier corresponding to the electricity usage data information 2 is the user name Lehehe, in this case, the computer device compares the user name Lehehe corresponding to the electricity usage data information 2 with the user names in the user identifier set corresponding to the transaction data information set (i.e., Diandiandian, Lala La, Hahaha, Xixixi), and the comparison result is a miss. At this time, the computer device can determine that the second label corresponding to the electricity usage data information 2 is none.

[0076] By using the embodiment of the present application, the computer device verifies each electricity usage data information in the electricity usage data information set corresponding to the high-risk users in the power grid database by utilizing the user identifier corresponding to the data in the cross-platform database, that is, the user identifier of the transaction data information set corresponding to the third-party blacklist user. In this way, the data label (second label) of the electricity usage data information corresponding to the high-risk users in the power grid database can be further determined, thereby helping to improve the accuracy of the risk assessment of the electricity usage data information.

[0077] In an optional embodiment, Figure 1 In the risk assessment method for electricity usage data information shown, the predicted risk level includes a high risk level, a low risk level, and a no risk level; the computer device determines the third label corresponding to each electricity usage data information based on the predicted risk level corresponding to each electricity usage data information, which may include: for each electricity usage data information, when the predicted risk level corresponding to the electricity usage data information is a high risk level, determining that the third label corresponding to the electricity usage data information is risk assessment verification-high risk; when the predicted risk level corresponding to the electricity usage data information is a low risk level, determining that the third label corresponding to the electricity usage data information is risk assessment verification-low risk; when the predicted risk level corresponding to the electricity usage data information is a no risk level, determining that the third label corresponding to the electricity usage data information is risk assessment verification-no risk.

[0078] For example, assuming that the computer device calls the target risk assessment model and determines that the predicted risk level corresponding to the electricity usage data information 1 is high risk, the computer device can determine that the third label corresponding to the electricity usage data information 1 is risk assessment verification-high risk; assuming that the computer device calls the target risk assessment model and determines that the predicted risk level corresponding to the electricity usage data information 2 is low risk, the computer device can determine that the third label corresponding to the electricity usage data information 1 is risk assessment verification-low risk; assuming that the computer device calls the target risk assessment model and determines that the predicted risk level corresponding to the electricity usage data information 1 is no risk, the computer device can determine that the third label corresponding to the electricity usage data information 1 is risk assessment verification-no risk.

[0079] By adopting this embodiment, the computer device can further determine the data label (third label) of the electricity usage data information corresponding to high-risk users in the power grid database by predicting the risk level corresponding to the electricity usage data information based on the target risk identification model, thereby helping to improve the accuracy of risk assessment of electricity usage data information.

[0080] See Figure 3 , Figure 3 This is a flow chart of another method for risk assessment of electricity consumption data information provided by an embodiment of the present application. Figure 2 Compared with the risk assessment method of electricity consumption data information shown, Figure 3 The method shown also describes how the computer device compares the user identification corresponding to each first data information in the electricity data information set with the user identification corresponding to each second data information in the transaction data information set. Figure 3 As shown, the risk assessment method of the electricity consumption data information may include but is not limited to the following steps:

[0081] S301. Obtain a set of electricity usage data information corresponding to high-risk users in the power grid database, and obtain a set of transaction data information corresponding to third-party blacklist users from a third-party database; each electricity usage data information in the electricity usage data information set corresponds to a first tag; the first tag shown is used to indicate the source of the electricity usage data information.

[0082] In an optional implementation, the relevant description of step S301 can be found in the description of the aforementioned step S201, and will not be repeated here.

[0083] S302 : Perform data information cleaning on the data information in the electricity usage data information set and the transaction data information set to obtain a cleansed electricity usage data information set and a cleansed transaction data information set.

[0084] Optionally, the computer device performs data cleaning processing on the data information in the electricity data information set and the transaction data information set respectively to obtain a cleaned electricity data information set and a cleaned transaction data information set. The computer device may perform deduplication, fill in missing values, and correct erroneous data on the electricity data information in the electricity data information set and the transaction data information in the transaction data information set respectively to obtain a cleaned electricity data information set and a cleaned transaction data information set.

[0085] For example, assuming that the transaction information data in the transaction information data set is recorded according to the number of payments, and assuming that some third-party blacklist users correspond to multiple transaction data information, the computer device can eliminate the multiple transaction data information based on the user identification of the third-party blacklist user and retain the one transaction data information corresponding to the third-party blacklist user.

[0086] S303 , performing unified format conversion processing on each data information in the cleaned electricity usage data information set and the cleaned transaction data information set, respectively, to obtain a converted electricity usage data information set and a converted transaction data information set.

[0087] For example, assuming that the data format of each transaction data information in the cleaned transaction data information set is format 1, and the data format of each electricity consumption data information in the cleaned electricity consumption data information set is format 2, the computer device can convert the data format of each transaction data information in the cleaned transaction data information set from format 1 to format 2.

[0088] S304: Compare the user identifier corresponding to each first data information in the converted electricity consumption data information set with the user identifier corresponding to each second data information in the converted transaction data information set to obtain a comparison result.

[0089] S305: Based on the comparison result, determine a second tag corresponding to each electricity usage data information; the second tag is used to indicate whether the user corresponding to the electricity usage data information is a third-party blacklist user.

[0090] In an optional embodiment, the computer device determines the second tag corresponding to each electricity usage data information based on the comparison result, which may include: for each electricity usage data information, when the comparison result indicates that the user identifier corresponding to the electricity usage data information exists in the user identifier set corresponding to the transaction data information set, determining that the second tag corresponding to the electricity usage data information is third-party verification; wherein the user identifier set includes the user identifier corresponding to each transaction data information; when the comparison result indicates that the user identifier corresponding to the electricity usage data information does not exist in the user identifier set corresponding to the transaction data information set, determining that the second tag corresponding to the electricity usage data information is none.

[0091] S306. Call the target risk assessment model to determine the predicted risk level corresponding to each electricity usage data information, and based on the predicted risk level corresponding to each electricity usage data information, determine a third label corresponding to each electricity usage data information; the third label is used to indicate the predicted risk level corresponding to the electricity usage data information.

[0092] In an optional embodiment, the predicted risk level includes a high risk level, a low risk level and a no risk level; the computer device determines the third label corresponding to each electricity usage data information based on the predicted risk level corresponding to each electricity usage data information, which may include: for each electricity usage data information, when the predicted risk level corresponding to the electricity usage data information is a high risk level, determining that the third label corresponding to the electricity usage data information is risk assessment verification-high risk; when the predicted risk level corresponding to the electricity usage data information is a low risk level, determining that the third label corresponding to the electricity usage data information is risk assessment verification-low risk; when the predicted risk level corresponding to the electricity usage data information is a no risk level, determining that the third label corresponding to the electricity usage data information is risk assessment verification-no risk.

[0093] S307. When a selection operation for the target electricity usage data information is detected, a first tag, a second tag, and a third tag corresponding to the target electricity usage data information are output, where the first tag, the second tag, and the third tag are used to determine a risk assessment result corresponding to the target electricity usage data information.

[0094] For example, assuming that the target electricity consumption data information is electricity consumption data information 1, the first label corresponding to the electricity user number information 1 is high-risk user verification in the power grid database, the second label is third-party blacklist verification, and the third label is risk assessment model verification-high risk, then when the computer device detects a selection operation for electricity consumption data information 1, the following Table 1 can be displayed on the user interface.

[0095] Table 1

[0096]

[0097] After the computer device displays Table 1 on the user interface, relevant staff comprehensively determine the risk assessment results corresponding to the electricity consumption data information 1 based on the content in Table 1, and determine prevention and control measures based on the risk assessment results.

[0098] In an optional embodiment, after step S307, the computer device may further transmit the first tag, second tag, and third tag corresponding to each electricity usage data message to the electronic device corresponding to the power grid database, so that the electronic device stores the first tag, second tag, and third tag corresponding to each electricity usage data message. This facilitates the subsequent computer device to directly obtain the multiple tags corresponding to each electricity usage data message from the electronic device corresponding to the power grid database, and directly output the first tag, second tag, and third tag corresponding to the electricity usage data message, thereby improving the efficiency of risk assessment of the electricity usage data message.

[0099] In an embodiment of the present application, a computer device performs data information cleaning processing on the data information in the electricity data information set and the transaction data information set, respectively, to obtain a cleaned electricity data information set and a cleaned transaction data information set; performs unified format conversion processing on each data information in the cleaned electricity data information set and the cleaned transaction data information set, respectively, to obtain a converted electricity data information set and a converted transaction data information set; and compares the user identifier corresponding to each first data information in the converted electricity data information set with the user identifier corresponding to each second data information in the converted transaction data information set to obtain a comparison result. In this way, the accuracy of the comparison result can be improved, and thus, based on the comparison result, the second label corresponding to each electricity data information can be accurately determined, which is conducive to further improving the accuracy of the risk assessment of the electricity data information.

[0100] 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.

[0101] Based on the same inventive concept, embodiments of the present application also provide a device for risk assessment of electricity usage data information for implementing the aforementioned method for risk assessment of electricity usage data information. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more embodiments of the device for risk assessment of electricity usage data information provided below can be found in the limitations of the method for risk assessment of electricity usage data information described above and will not be further elaborated here.

[0102] See Figure 4 , Figure 4 This is a schematic diagram of the structure of a risk assessment device for electricity consumption data information provided by an embodiment of the present application. Figure 4 As shown, the risk assessment device for the electricity consumption data information may include but is not limited to:

[0103] Acquisition module 401 is used to obtain a set of electricity usage data corresponding to high-risk users from a power grid database, and a set of transaction data corresponding to third-party blacklisted users from a third-party database; each electricity usage data in the set of electricity usage data corresponds to a first tag; the first tag is used to indicate the source of the electricity usage data;

[0104] The comparison and determination module 402 is configured to compare the user identifier corresponding to each electricity usage data information in the electricity usage data information set with the user identifier corresponding to each transaction data information in the transaction data information set, and determine a second tag corresponding to each electricity usage data information based on the comparison result; the second tag is used to indicate whether the user corresponding to the electricity usage data information is a third-party blacklist user;

[0105] Determination module 403 is used to call the target risk assessment model to determine the predicted risk level corresponding to each electricity usage data information, and based on the predicted risk level corresponding to each electricity usage data information, determine a third tag corresponding to each electricity usage data information; the third tag is used to indicate the predicted risk level corresponding to the electricity usage data information;

[0106] The output module 404 is used to output the first label, the second label and the third label corresponding to the target electricity usage data information when a selection operation for the target electricity usage data information is detected. The first label, the second label and the third label are used to determine the risk assessment result corresponding to the target electricity usage data information.

[0107] In one embodiment, when the comparison and determination module 402 is used to determine the second tag corresponding to each electricity usage data information based on the comparison result, it is specifically used to: for each electricity usage data information, when the comparison result indicates that the user identifier corresponding to the electricity usage data information exists in the user identifier set corresponding to the transaction data information set, determine that the second tag corresponding to the electricity usage data information is third-party verification; wherein the user identifier set includes the user identifier corresponding to each transaction data information.

[0108] In one embodiment, the comparison and determination module 402 is further configured to: if the comparison result indicates that the user identifier corresponding to the electricity usage data information does not exist in the user identifier set, determine that the second tag corresponding to the electricity usage data information is none.

[0109] In one embodiment, the predicted risk level includes a high risk level, a low risk level and a no risk level; when the determination module 403 is used to determine the third label corresponding to each electricity usage data information based on the predicted risk level corresponding to each electricity usage data information, it is specifically used to: for each electricity usage data information, when the predicted risk level corresponding to the electricity usage data information is a high risk level, determine that the third label corresponding to the electricity usage data information is risk assessment verification-high risk; when the predicted risk level corresponding to the electricity usage data information is a low risk level, determine that the third label corresponding to the electricity usage data information is risk assessment verification-low risk; when the predicted risk level corresponding to the electricity usage data information is a no risk level, determine that the third label corresponding to the electricity usage data information is risk assessment verification-no risk.

[0110] In one embodiment, when the comparison and determination module 402 is used to compare the user identifier corresponding to each first data information in the electricity data information set with the user identifier corresponding to each second data information in the transaction data information set, it is specifically used to: perform data information cleaning processing on the data information in the electricity data information set and the transaction data information set, respectively, to obtain a cleaned electricity data information set and a cleaned transaction data information set; perform unified format conversion processing on each data information in the cleaned electricity data information set and the cleaned transaction data information set, respectively, to obtain a converted electricity data information set and a converted transaction data information set; compare the user identifier corresponding to each first data information in the converted electricity data information set with the user identifier corresponding to each second data information in the converted transaction data information set.

[0111] In one embodiment, the apparatus may further include a sending module configured to send the first tag, the second tag, and the third tag corresponding to each electricity usage data information to the electronic device corresponding to the power grid database, so that the electronic device stores the first tag, the second tag, and the third tag corresponding to each electricity usage data information.

[0112] Each module in the aforementioned power consumption data risk assessment device can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a terminal device in hardware form, or can be stored in a memory in the terminal device in software form, so that the processor can call and execute the corresponding operations of each module.

[0113] In an exemplary embodiment, the present application provides a computer device, the internal structure of which can be as follows: Figure 4 As shown. The computer device includes a processor, memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are 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 internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals via wired or wireless means, and the wireless means can be implemented via Wi-Fi, a mobile cellular network, near-field communication (NFC), or other technologies. When executed by the processor, the computer program implements a risk assessment method for electricity usage data information. The display unit of the computer device is used to form a visually visible image, and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse.

[0114] Those skilled in the art will understand that Figure 4 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.

[0115] In an exemplary embodiment, the present application provides a computer device including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the steps in the risk assessment method of the above-mentioned electricity consumption data information are implemented.

[0116] In an exemplary embodiment, the present application provides a computer-readable storage medium having a computer program stored thereon, which implements the steps in the above-mentioned risk assessment method for electricity usage data information when the computer program is executed by a processor.

[0117] In an exemplary embodiment, the present application provides a computer program product, including a computer program, which implements the steps in the above-mentioned risk assessment method for electricity usage data information when executed by a processor.

[0118] It should be noted that the data involved in this application (including but not limited to electricity consumption data information sets, transaction data information sets, the first label, second label and third label corresponding to each electricity consumption data information, user identification, etc.) 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.

[0119] Those skilled in the art will understand 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 embodiments of the above-mentioned methods. 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 memory 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, blockchain-based distributed databases. 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), quantum computing-based data processing logic devices, artificial intelligence (AI) processors, and the like.

[0120] The technical features of the above embodiments can be combined arbitrarily. In order 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 application.

[0121] 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 risk assessment method for electricity consumption data information, characterized in that: The method comprises: Obtaining a set of electricity usage data corresponding to high-risk users in a power grid database, and obtaining a set of transaction data corresponding to third-party blacklisted users from a third-party database; each electricity usage data in the electricity usage data set corresponds to a first tag; the first tag is used to indicate the source of the electricity usage data; comparing the user identifier corresponding to each piece of electricity usage data information in the electricity usage data information set with the user identifier corresponding to each piece of transaction data information in the transaction data information set, and determining a second tag corresponding to each piece of electricity usage data information based on the comparison result; the second tag is used to indicate whether the user corresponding to the electricity usage data information is a user on the third-party blacklist; Invoking a target risk assessment model to determine a predicted risk level corresponding to each piece of electricity usage data information, and determining a third tag corresponding to each piece of electricity usage data information based on the predicted risk level corresponding to each piece of electricity usage data information; the third tag is used to indicate the predicted risk level corresponding to the electricity usage data information; When a selection operation for target electricity usage data information is detected, the first tag, the second tag, and the third tag corresponding to the target electricity usage data information are output, and the first tag, the second tag, and the third tag are used to determine a risk assessment result corresponding to the target electricity usage data information.

2. The method according to claim 1, characterized in that The determining, based on the comparison result, a second tag corresponding to each piece of electricity usage data information includes: For each piece of electricity usage data information, if the comparison result indicates that the user identifier corresponding to the electricity usage data information exists in the user identifier set corresponding to the transaction data information set, determining that the second tag corresponding to the electricity usage data information is third-party verification; The user identification set includes a user identification corresponding to each transaction data information.

3. The method according to claim 2, characterized in that The method further comprises: If the comparison result indicates that the user identifier corresponding to the electricity usage data information does not exist in the user identifier set, it is determined that the second tag corresponding to the electricity usage data information is none.

4. The method according to claim 1, wherein The predicted risk levels include high risk level, low risk level and no risk level; The determining, based on the predicted risk level corresponding to each piece of electricity usage data, a third tag corresponding to each piece of electricity usage data, includes: For each piece of electricity usage data information, when the predicted risk level corresponding to the electricity usage data information is the high risk level, determining that the third label corresponding to the electricity usage data information is risk assessment verification-high risk; When the predicted risk level corresponding to the electricity usage data information is the low risk level, determining that the third label corresponding to the electricity usage data information is risk assessment verification-low risk; When the predicted risk level corresponding to the electricity usage data information is the risk-free level, the third label corresponding to the electricity usage data information is determined to be risk assessment verification-risk-free.

5. The method according to claim 1, wherein The comparing the user identifier corresponding to each first data information in the electricity usage data information set with the user identifier corresponding to each second data information in the transaction data information set includes: performing data information cleaning processing on the electricity usage data information set and the transaction data information set respectively to obtain a cleaned electricity usage data information set and a cleaned transaction data information set; Performing unified format conversion processing on each data information in the cleaned electricity usage data information set and the cleaned transaction data information set to obtain a converted electricity usage data information set and a converted transaction data information set; The user identifier corresponding to each first data information in the converted electricity consumption data information set is compared with the user identifier corresponding to each second data information in the converted transaction data information set.

6. The method according to any one of claims 1 to 5, characterized in that The method further comprises: The first tag, the second tag, and the third tag corresponding to each piece of electricity usage data are sent to an electronic device corresponding to the power grid database, so that the electronic device stores the first tag, the second tag, and the third tag corresponding to each piece of electricity usage data.

7. A risk assessment device for electricity consumption data information, characterized in that: The device comprises: An acquisition module is configured to acquire a set of electricity usage data corresponding to high-risk users from a power grid database, and a set of transaction data corresponding to third-party blacklisted users from a third-party database; each set of electricity usage data corresponds to a first tag; the first tag is configured to indicate the source of the electricity usage data; a comparison and determination module, configured to compare a user identifier corresponding to each piece of electricity usage data information in the electricity usage data information set with a user identifier corresponding to each piece of transaction data information in the transaction data information set, and determine, based on the comparison result, a second tag corresponding to each piece of electricity usage data information; the second tag is configured to indicate whether the user corresponding to the electricity usage data information is a user on the third-party blacklist; a determination module, configured to call a target risk assessment model, determine a predicted risk level corresponding to each piece of electricity usage data information, and determine a third tag corresponding to each piece of electricity usage data information based on the predicted risk level corresponding to each piece of electricity usage data information; the third tag is used to indicate the predicted risk level corresponding to the electricity usage data information; An output module is used to output the first label, the second label and the third label corresponding to the target electricity usage data information when a selection operation for the target electricity usage data information is detected, and the first label, the second label and the third label are used to determine the risk assessment result corresponding to the target electricity usage data information.

8. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method according to any one of claims 1 to 6 when executing the computer program.

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.

Images