A multi-channel power payment exception processing method, device, equipment and medium

Abstract

This application discloses a method, apparatus, equipment, and medium for handling anomalies in multi-channel electricity payment, belonging to the field of electricity payment anomaly handling. The method comprises: acquiring first electricity payment data from several channels and standardizing it to obtain second electricity payment data; calculating an idempotent key based on the second electricity payment data to characterize the uniqueness of the same transaction across channels, and removing duplicate data according to the idempotent key to obtain third electricity payment data; acquiring multi-source receipt data corresponding to the third electricity payment data, calculating the confidence level, and determining the fourth electricity payment data with anomalies and the anomaly reason code based on the confidence level and the third electricity payment data; generating an anomaly handling plan based on the anomaly reason code and a preset rule base and executing it. Therefore, by implementing this application, the problem of low efficiency in multi-channel electricity payment anomaly handling in the prior art can be solved.

Description

Technical Field

[0001] This application relates to the field of handling abnormal electricity payment, and in particular to a method, apparatus, equipment and medium for handling abnormal electricity payment through multiple channels. Background Technology

[0002] With the expansion of electricity payment services to multiple channels such as service halls, self-service terminals, official apps, and third-party payment platforms, while greatly improving the convenience for users, the parallel operation of these channels increases the length of the underlying transaction chain. Furthermore, differences in the timing of status feedback, missing fields, and reconciliation cycles among different channel systems can easily lead to various transaction anomalies. Failure to address these anomalies promptly can not only directly result in abnormal electricity bills for users, leading to serious customer complaints and difficulties in proving disputes, but may also cause secondary errors to spread during periods of high concurrency, posing direct financial and compliance risks to both power companies and users.

[0003] Current technologies for handling multi-channel electricity payment anomalies primarily employ a decentralized processing model of "patching up by channel or by problem," meaning anomalies are often handled independently within each channel subsystem, lacking a unified transaction modeling and automated handling orchestration mechanism. This model suffers from efficiency drawbacks: anomaly identification and classification heavily rely on human experience, and the lack of cross-channel idempotent control results in extremely long processing cycles from anomaly discovery to remediation. Therefore, a technical solution is urgently needed to address the low efficiency of current multi-channel electricity payment anomaly handling technologies. Summary of the Invention

[0004] This application provides a method for handling anomalies in multi-channel electricity payment, which can solve the problem of low efficiency in handling anomalies in existing technologies.

[0005] In a first aspect, embodiments of the present invention provide a method for handling anomalies in multi-channel electricity payment, including: Acquire several first electricity payment data from several channels, and standardize each first electricity payment data to obtain each second electricity payment data; Based on each of the second electricity payment data, an idempotent key for each of the second electricity payment data is calculated to characterize the uniqueness of the same transaction across channels. Based on each of the idempotent keys, duplicate data in each of the second electricity payment data is removed to obtain several third electricity payment data. Obtain multi-source receipt data corresponding to each of the third electricity payment data, and calculate the confidence level of each of the third electricity payment data based on the multi-source receipt data. Then, based on the confidence level and each of the third electricity payment data, obtain a number of fourth electricity payment data that are abnormal and the abnormal reason code corresponding to each of the fourth electricity payment data. Based on the aforementioned anomaly reason codes and the preset rule base, an anomaly handling plan is generated for each of the fourth electricity payment data, and electricity payment anomaly handling is performed according to each of the aforementioned anomaly handling plans.

[0006] This application's embodiments standardize the first electricity payment data from multiple channels, eliminating differences in data formats and fields between different channels. This lays the data foundation for subsequent unified, batch, and automated processing across channels, avoiding the time-consuming manual adaptation and conversion for each channel. Secondly, by calculating the idempotent key representing the uniqueness of the same transaction across channels and using it to eliminate duplicate data, automated and accurate interception of duplicate transactions across channels is achieved. This effectively prevents the accumulation of redundant abnormal data and the amplification of secondary errors, avoiding the high cost of manually checking duplicate data one by one. Thirdly, the confidence level is calculated based on multi-source receipt data for screening. By mapping anomalies to a unified anomaly cause code, false anomaly reports caused by timing differences or missing fields can be effectively filtered out, and true anomalies can be identified. This avoids the invalid work order flow and backlog caused by the traditional reliance on manual experience to determine the authenticity of anomalies, and greatly improves the accuracy and efficiency of anomaly identification and triage. Finally, anomaly handling plans are automatically generated and executed based on the unified anomaly cause code and preset rule base, transforming the originally highly manual and time-consuming patchwork repair into standardized automatic or semi-automatic handling orchestration, significantly shortening the closed-loop cycle from anomaly discovery to resolution, and achieving a leap in the overall efficiency of anomaly handling for multi-channel electricity payment.

[0007] As a preferred example of the first aspect, the calculation of the idempotent key for characterizing the uniqueness of the same transaction across channels based on each of the second electricity payment data includes: Based on a number of pre-defined fields, the fields of each second electricity payment data are extracted to obtain the first field set corresponding to each second electricity payment data. Semantic normalization is performed on each of the first set of fields to obtain a second set of fields that unifies the fields across different channels; Each field in each of the second field sets is concatenated to obtain a concatenation result, and each concatenation result is hashed to obtain an idempotent key.

[0008] In this preferred example, a first set of fields is constructed through field extraction to clarify transaction identification elements and establish the basis for judging the same transaction across channels. Semantic normalization unifies fields with different expressions in different channels into standardized values, eliminating the bias of misjudging the same transaction as multiple transactions due to differences in channel encoding and format. Concatenation and hash calculation compress multi-dimensional fields into fixed-length idempotent keys, ensuring efficient calculation and storage of uniqueness determination while preventing matching failures caused by excessively long original fields or the presence of special characters. The coordinated actions of these steps achieve accurate interception of duplicate transactions across channels, avoiding the accumulation of abnormal data redundancy and the amplification of secondary errors.

[0009] As a preferred example of the first aspect, the step of performing hash calculations on each of the concatenation results to obtain each of the idempotent keys includes: According to the preset hash function, each of the concatenation results is hashed to obtain each initial idempotent key; Based on each of the second field sets, generate a semantic fingerprint corresponding to each of the initial idempotent keys; Each semantic fingerprint is combined with its corresponding initial idempotent key to obtain each idempotent key.

[0010] In this preferred example, the concatenation result is compressed into a fixed-length initial idempotent key through hash calculation, achieving efficient retrieval and compact storage of cross-channel transaction uniqueness; semantic fingerprints are generated based on a normalized field set to capture the semantic features of transaction content, making up for the deficiency of simple hashing in terms of sensitivity to synonymous and heterogeneous fields; the combination of the two forms a composite idempotent key, enabling the system to quickly determine strict matches and identify duplicate transactions with the same semantics due to differences in channel descriptions, thereby improving the robustness and accuracy of cross-channel deduplication while taking into account processing efficiency.

[0011] As a preferred example of the first aspect, the step of removing duplicate data from each of the second electricity payment data according to each of the idempotent keys to obtain a plurality of third electricity payment data includes: Based on the idempotent keys described, an idempotent ledger is constructed. Iterate through each of the idempotent keys, and query the idempotent ledger according to the currently traversed idempotent key. If the idempotent key does not exist, write the currently traversed idempotent key into the idempotent ledger; if the idempotent key already exists, determine the second electricity payment data corresponding to the currently traversed idempotent key as duplicate data and remove it. All remaining second electricity payment data will be used as the third electricity payment data.

[0012] In this preferred example, an idempotent ledger is established to provide a unified deduplication record carrier for cross-channel transactions, enabling data from each channel to have a traceable comparison benchmark; traversal querying verifies the existence of identical idempotent keys in the ledger to accurately identify duplicate payment data, avoiding redundant accumulation caused by channel concurrency or retries; existing records are directly removed to ensure that subsequent processing is based on only a single transaction, blocking duplicate deductions and cancellation conflicts from the source, and ensuring the data purity and accuracy of anomaly analysis.

[0013] As a preferred example of the first aspect, the step of calculating the confidence level of each of the third electricity payment data based on each of the multi-source receipt data includes: For each of the multi-source receipt data, a verifiable factor and a dynamic calibration factor are extracted from the multi-source receipt data, and the event source credibility score corresponding to the multi-source receipt data is calculated based on the verifiable factor and the dynamic calibration factor. Based on the multi-source receipt data and the preset internal accounting and cancellation events, a status candidate satisfaction score is determined, and a timeline consistency score is determined based on the event timeline corresponding to the multi-source receipt data. Then, based on preset weights, the event source credibility score, the status candidate satisfaction score, and the timeline consistency score are weighted and calculated to obtain the credibility level confidence.

[0014] In this preferred example, verifiable factors and dynamic calibration factors are extracted from the receipt data to quantify the objective reliability and historical performance of the source, avoiding static trust bias. The state candidate satisfaction score reflects the degree of matching between the receipt status and internal accounting, capturing unilateral state missingness. The timeline consistency score verifies the causal order of events, eliminating interference from temporal discrepancies. The three factors are weighted and fused to obtain the confidence level, comprehensively evaluating the degree of acceptance of anomaly criteria, and effectively filtering out false anomalies and misreports caused by unreliable sources or temporal differences.

[0015] As a preferred example of the first aspect, the step of obtaining a number of abnormal fourth electricity payment data and an anomaly reason code corresponding to each of the third electricity payment data based on the confidence level of each of the aforementioned confidence levels and the third electricity payment data includes: For each of the aforementioned third electricity payment data, anomaly candidate results are determined through preset anomaly identification rules, and it is determined whether the confidence level is greater than or equal to the first preset anomaly judgment threshold. If the confidence level is greater than or equal to the first preset anomaly determination threshold, the third electricity payment data is regarded as the fourth electricity payment data with anomalies, and an anomaly reason code corresponding to the fourth electricity payment data is generated according to the anomaly candidate results.

[0016] In this preferred example, data is initially screened using anomaly identification rules to quickly identify suspicious candidate results for transactions. A confidence level threshold is introduced as a secondary judgment checkpoint; only when the confidence level of multi-source evidence fusion meets the threshold is a candidate confirmed as a true anomaly. This mechanism filters out false alarms caused by missing data or unreliable sources, ensuring that a unified reason code is generated only for high-confidence anomalies. This reduces the burden of invalid work order processing and manual review, improving the accuracy and efficiency of anomaly triage.

[0017] As a preferred example of the first aspect, the step of generating an anomaly handling plan corresponding to each of the fourth electricity payment data based on each of the anomaly reason codes and a preset rule base includes: For each of the aforementioned exception reason codes, access control conditions are generated based on the exception reason code and a preset rule base to characterize whether automatic processing is allowed. If the access control result meets the first preset condition, then the anomaly handling plan for the fourth electricity payment data corresponding to the anomaly reason code is set as an automatic handling plan. If the access control result meets the second preset condition, then the anomaly handling plan for the fourth electricity payment data corresponding to the anomaly reason code is set to a semi-automatic handling plan. If the access control result meets the third preset condition, then the anomaly handling plan for the fourth electricity payment data corresponding to the anomaly reason code will be set to a manual handling plan.

[0018] In this preferred example, access control conditions are generated based on the anomaly cause code and the rule base, providing a unified and quantifiable basis for determining whether automatic handling is permitted. By matching the access control conditions with tiered preset conditions, anomalies are automatically triaged into automatic, semi-automatic, or manual handling plans, achieving precise matching of handling strategies with risk levels. This mechanism maximizes the scope of automated handling while ensuring that high-risk anomalies are under control, significantly shortening the processing cycle and reducing the cost of manual intervention.

[0019] Secondly, the present invention provides a multi-channel electricity payment anomaly handling device, comprising: a data acquisition module, a first processing module, a second processing module and a third processing module; The data acquisition module is used to acquire several first electricity payment data from several channels, and to standardize each first electricity payment data to obtain each second electricity payment data. The first processing module is used to calculate an idempotent key for each of the second electricity payment data to characterize the uniqueness of the same transaction across channels based on each of the second electricity payment data, and to remove duplicate data in each of the second electricity payment data according to each of the idempotent keys to obtain a number of third electricity payment data. The second processing module is used to obtain multi-source receipt data corresponding to each of the third electricity payment data, and calculate the confidence level of each of the third electricity payment data based on each of the multi-source receipt data, and then obtain a number of fourth electricity payment data that are abnormal and the abnormal reason code corresponding to each of the third electricity payment data based on each of the confidence levels and each of the third electricity payment data. The third processing module is used to generate an anomaly handling plan corresponding to each of the fourth electricity payment data according to each anomaly reason code and a preset rule base, and to perform electricity payment anomaly handling according to each anomaly handling plan.

[0020] As a preferred example of the second aspect, the calculation of the idempotent key for characterizing the uniqueness of the same transaction across channels based on each of the second electricity payment data includes: Based on a number of pre-defined fields, the fields of each second electricity payment data are extracted to obtain the first field set corresponding to each second electricity payment data. Semantic normalization is performed on each of the first set of fields to obtain a second set of fields that unifies the fields across different channels; Each field in each of the second field sets is concatenated to obtain a concatenation result, and each concatenation result is hashed to obtain an idempotent key.

[0021] As a preferred example of the second aspect, the step of performing hash calculations on each of the concatenation results to obtain each of the idempotent keys includes: According to the preset hash function, each of the concatenation results is hashed to obtain each initial idempotent key; Based on each of the second field sets, generate a semantic fingerprint corresponding to each of the initial idempotent keys; Each semantic fingerprint is combined with its corresponding initial idempotent key to obtain each idempotent key.

[0022] As a preferred example of the second aspect, the step of removing duplicate data from each of the second electricity payment data according to each of the idempotent keys to obtain a plurality of third electricity payment data includes: Based on the idempotent keys described, an idempotent ledger is constructed. Iterate through each of the idempotent keys, and query the idempotent ledger according to the currently traversed idempotent key. If the idempotent key does not exist, write the currently traversed idempotent key into the idempotent ledger; if the idempotent key already exists, determine the second electricity payment data corresponding to the currently traversed idempotent key as duplicate data and remove it. All remaining second electricity payment data will be used as the third electricity payment data.

[0023] As a preferred example of the second aspect, the step of calculating the confidence level of each of the third electricity payment data based on each of the multi-source receipt data includes: For each of the multi-source receipt data, a verifiable factor and a dynamic calibration factor are extracted from the multi-source receipt data, and the event source credibility score corresponding to the multi-source receipt data is calculated based on the verifiable factor and the dynamic calibration factor. Based on the multi-source receipt data and the preset internal accounting and cancellation events, a status candidate satisfaction score is determined, and a timeline consistency score is determined based on the event timeline corresponding to the multi-source receipt data. Then, based on preset weights, the event source credibility score, the status candidate satisfaction score, and the timeline consistency score are weighted and calculated to obtain the credibility level confidence.

[0024] As a preferred example of the second aspect, the step of obtaining a number of abnormal fourth electricity payment data and an anomaly reason code corresponding to each of the third electricity payment data based on the confidence level of each of the aforementioned confidence levels and the third electricity payment data includes: For each of the aforementioned third electricity payment data, anomaly candidate results are determined through preset anomaly identification rules, and it is determined whether the confidence level is greater than or equal to the first preset anomaly judgment threshold. If the confidence level is greater than or equal to the first preset anomaly determination threshold, the third electricity payment data is regarded as the fourth electricity payment data with anomalies, and an anomaly reason code corresponding to the fourth electricity payment data is generated according to the anomaly candidate results.

[0025] As a preferred example of the second aspect, the step of generating an anomaly handling plan corresponding to each of the fourth electricity payment data based on each of the anomaly reason codes and a preset rule base includes: For each of the aforementioned exception reason codes, an access control result is generated based on the exception reason code and a preset rule base to characterize whether automatic processing is allowed; If the access control result meets the first preset condition, then the anomaly handling plan for the fourth electricity payment data corresponding to the anomaly reason code is set as an automatic handling plan. If the access control result meets the second preset condition, then the anomaly handling plan for the fourth electricity payment data corresponding to the anomaly reason code is set to a semi-automatic handling plan. If the access control result meets the third preset condition, then the anomaly handling plan for the fourth electricity payment data corresponding to the anomaly reason code will be set to a manual handling plan.

[0026] In summary, this application's embodiments, by standardizing the first electricity payment data from multiple channels, eliminate the differences in data formats and fields between different channels, laying a data foundation for subsequent unified and batch automated processing across channels, and avoiding the time-consuming manual adaptation and conversion for each channel. Secondly, by calculating the idempotent key representing the uniqueness of the same transaction across channels and using it to eliminate duplicate data, automated and accurate interception of duplicate transactions across channels is achieved, effectively preventing the redundant accumulation of abnormal data and the amplification of secondary errors, and avoiding the high cost of manually checking duplicate data one by one. Thirdly, by calculating the confidence level based on multi-source receipt data to screen... By selecting anomalies and mapping them to a unified anomaly cause code, false anomaly reports caused by timing differences or missing fields can be effectively filtered out, and true anomalies can be identified. This avoids the invalid work order flow and backlog caused by the traditional reliance on manual experience to determine the authenticity of anomalies, and greatly improves the accuracy and efficiency of anomaly identification and triage. Finally, anomaly handling plans are automatically generated and executed based on the unified anomaly cause code and preset rule base, transforming the originally highly manual and time-consuming patchwork repair into standardized automatic or semi-automatic handling orchestration, which significantly shortens the closed-loop cycle from anomaly discovery to resolution, and achieves a leap in the efficiency of anomaly handling for multi-channel electricity payment.

[0027] Another embodiment of this application also provides a terminal device, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the steps of the multi-channel electricity payment anomaly handling method of this application.

[0028] Another embodiment of this application also provides a computer-readable storage medium item, including: a stored computer program, which, when the computer program is running, controls the device where the computer-readable storage medium is located to perform the steps of the multi-channel electricity payment anomaly handling method of this application. Attached Figure Description

[0029] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0030] Figure 1 This is a flowchart illustrating an embodiment of a multi-channel electricity payment anomaly handling method provided by the present invention; Figure 2 This is a module structure diagram of one embodiment of a multi-channel electricity payment anomaly handling device provided by the present invention. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0032] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0033] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0034] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0035] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0036] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0037] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0038] Example 1 Please refer to Figure 1 To address the problem of low efficiency in handling anomalies in multi-channel electricity payment in existing technologies, this application provides a method for handling anomalies in multi-channel electricity payment, comprising: S1. Obtain several first electricity payment data from several channels, and standardize each first electricity payment data to obtain each second electricity payment data. Specifically, in electricity payment services, channels include electricity service halls, self-service terminals, official apps, mini-programs, WeChat official accounts, third-party payment platforms, bank direct debit, and aggregated payment methods. The system first receives payment requests from any of these channels. The initial electricity payment data carried in this request includes at least the following raw fields: channel transaction number, user identity information, request time, payment amount, billing period, payment method, device information, network information, channel error code, and status code.

[0039] Upon receiving this initial electricity payment data, it is not used directly but instead enters a standardization process. The first step in standardization is to establish a payment session (PaySession) and a channel context (ChannelContext). The PaySession records the SessionID, UserID, ChannelID, request time, and device information for subsequent event association and traceability. The ChannelContext records the channel type, channel transaction details, channel error / status codes, callback address, and retry policy, providing verifiable evidence for determining missing or timed-out callbacks. Simultaneously, the system creates a multi-key association index with the SessionID, channel transaction details, third-party order numbers, and internal order numbers to ensure traceability during subsequent normalization and reconciliation.

[0040] Next, the ChannelContext is mapped to a unified transaction envelope (TxnEnvelope), a process that itself constitutes a deep normalization of the first electricity payment data. Specifically, this embodiment performs semantic normalization (canonicalization) on key fields in the TxnEnvelope: unifying the monetary unit and precision, unifying the billing period format, enumerating and unifying payment methods from different channels, and validating and supplementing account numbers or customer numbers. Simultaneously, this embodiment retains the source path of each normalized field, recording which path of the original field from which the field originates, making the normalization process traceable.

[0041] Furthermore, this embodiment also prepares the normalized fields for semantic fingerprint generation. For example, fields such as account number, billing period, amount, payment method, and channel transaction history are concatenated in a preset order to form a standardized string that can be used for subsequent idempotent key calculations. After this entire process is completed, the output is a second electricity payment data with a unified format, unified semantics, and cross-channel comparability, which is the standard transaction data after cleaning, normalization, verification, and completion.

[0042] S2. Based on each of the second electricity payment data, calculate the idempotent key of each of the second electricity payment data to characterize the uniqueness of the same transaction across channels, and according to each of the idempotent keys, remove duplicate data in each of the second electricity payment data to obtain several third electricity payment data. In some embodiments, calculating the idempotent key for each of the second electricity payment data to characterize the uniqueness of the same transaction across channels, based on each of the second electricity payment data, includes: Based on a number of pre-defined fields, the fields of each second electricity payment data are extracted to obtain the first field set corresponding to each second electricity payment data. Semantic normalization is performed on each of the first set of fields to obtain a second set of fields that unifies the fields across different channels; Each field in each of the second field sets is concatenated to obtain a concatenation result, and each concatenation result is hashed to obtain an idempotent key.

[0043] Specifically, after standardizing the second electricity payment data in this embodiment, several pre-defined fields are extracted from each second electricity payment data. These fields typically include: account number (meterId), billing period (billPeriod), payment amount (amount), payment method (payMethod), and the transaction channel (channelTxnId). In this embodiment, these field values ​​are combined into a first field set, which serves as the raw material for generating idempotent keys.

[0044] Next, this embodiment performs semantic normalization processing on the first field set to eliminate differences in field expressions for the same business meaning across different channels. After the above processing, the fields that were originally scattered across different channels, with inconsistent naming and different formats, are transformed into a second field set with unified semantics.

[0045] Then, in this embodiment, the fields in the second field set are concatenated into a single string in a preset order, which is the concatenation result. To prevent collisions caused by delimiters between different field combinations, a specific connector is usually used during concatenation, or concatenation is performed directly without a delimiter. Finally, the system uses a hash function (such as SHA-256 or MD5) to calculate the concatenation result, and the generated hash value is the idempotency key of the transaction. This idempotency key can uniquely identify a cross-channel electricity payment transaction and is subsequently used for idempotency verification during the data entry and execution phases to prevent duplicate deductions and cancellations.

[0046] In addition, as an optional enhancement, the system generates a semantic fingerprint based on the semantically normalized key fields while generating idempotent keys. This fingerprint is used to identify the same transaction across channels through semantic comparison in the event of missing or altered channel transaction records. The semantic fingerprint and idempotent keys are stored together in the idempotent ledger, forming the basis for anti-duplicate control.

[0047] In some embodiments, performing hash calculations on each of the concatenation results to obtain each of the idempotent keys includes: According to the preset hash function, each of the concatenation results is hashed to obtain each initial idempotent key; Based on each of the second field sets, generate a semantic fingerprint corresponding to each of the initial idempotent keys; Each semantic fingerprint is combined with its corresponding initial idempotent key to obtain each idempotent key.

[0048] In some embodiments, the step of removing duplicate data from each of the second electricity payment data according to each of the idempotent keys to obtain a plurality of third electricity payment data includes: Based on the idempotent keys described, an idempotent ledger is constructed. Iterate through each of the idempotent keys, and query the idempotent ledger according to the currently traversed idempotent key. If the idempotent key does not exist, write the currently traversed idempotent key into the idempotent ledger; if the idempotent key already exists, determine the second electricity payment data corresponding to the currently traversed idempotent key as duplicate data and remove it. All remaining second electricity payment data will be used as the third electricity payment data.

[0049] S3. Obtain the multi-source receipt data corresponding to each of the third electricity payment data, and calculate the confidence level of each of the third electricity payment data based on the multi-source receipt data. Then, based on the confidence level and each of the third electricity payment data, obtain a number of fourth electricity payment data that are abnormal and the abnormal reason code corresponding to each of the fourth electricity payment data. In some embodiments, calculating the confidence level of each third electricity payment data based on each of the multi-source receipt data includes: For each of the multi-source receipt data, a verifiable factor and a dynamic calibration factor are extracted from the multi-source receipt data, and the event source credibility score corresponding to the multi-source receipt data is calculated based on the verifiable factor and the dynamic calibration factor. Based on the multi-source receipt data and the preset internal accounting and cancellation events, a status candidate satisfaction score is determined, and a timeline consistency score is determined based on the event timeline corresponding to the multi-source receipt data. Then, based on preset weights, the event source credibility score, the status candidate satisfaction score, and the timeline consistency score are weighted and calculated to obtain the credibility level confidence.

[0050] Specifically, upon receiving channel receipts, third-party callbacks, or bank statements, they are first normalized into a unified receipt event (ReceiptEvent), and a status candidate set (StatusCandidate) is constructed. When calculating the confidence level of each multi-source receipt data, this embodiment extracts two types of factors from the receipt data: verifiable factors and dynamic calibration factors.

[0051] Verifiable factors are directly generated from objective verification and mainly include: whether the signature verification passed (SignatureVerified, i.e., whether the callback signature or certificate chain was successfully verified); ChannelAuthLevel (e.g., whether the source is a controlled dedicated line, a whitelisted callback domain name, or a fixed IP range); and PayloadIntegrity (whether key fields are complete, whether the range is valid, and whether the SourcePath is traceable). This embodiment calculates the initial trust score of the event source based on the degree to which these factors are satisfied.

[0052] The dynamic calibration factor is derived from the feedback of historical quality indicators of the system, including: the proportion of times the source was "correctly judged" in historical reconciliations (SourceHistoricalHitRate); the occurrence rate of missed or timed-out callbacks (SourceTimeoutRate / MissingRate); and the proportion associated with complaints and work order disputes (SourceDisputeRate). These indicators, derived from historical data, are used to periodically update the source weights, enabling the trust level to have continuous self-correction capabilities. This embodiment combines the initial trust score with the dynamic calibration factor to obtain the final event source trust score (SourceTrustScore).

[0053] Finally, the system performs a weighted sum of the three scores based on preset weights (e.g., source credibility score weight 0.4, state candidate satisfaction score weight 0.35, and timeline consistency score weight 0.25) to obtain the confidence level (ConfidenceValue) corresponding to the receipt data. This confidence value is used for threshold judgment in subsequent anomaly identification: if the confidence value is higher than the preset threshold, the anomaly is directly identified and a reason code is mapped.

[0054] In some embodiments, obtaining a plurality of abnormal fourth electricity payment data and an anomaly reason code corresponding to each of the third electricity payment data based on the confidence level of each of the aforementioned confidence levels and the third electricity payment data includes: For each of the aforementioned third electricity payment data, anomaly candidate results are determined through preset anomaly identification rules, and it is determined whether the confidence level is greater than or equal to the first preset anomaly judgment threshold. If the confidence level is greater than or equal to the first preset anomaly determination threshold, the third electricity payment data is regarded as the fourth electricity payment data with anomalies, and an anomaly reason code corresponding to the fourth electricity payment data is generated according to the anomaly candidate results.

[0055] S4. Based on the aforementioned anomaly reason codes and the preset rule base, generate an anomaly handling plan corresponding to each of the fourth electricity payment data, and perform electricity payment anomaly handling according to each of the aforementioned anomaly handling plans.

[0056] Specifically, this embodiment internally maintains a strategy matrix composed of cause codes, user risk levels, amount ranges, and the scope of anomaly impact. It also presets several access control conditions based on confidence vectors and evidence completeness. For each anomaly, the system first determines the priority handling direction from the strategy matrix, starting with the anomaly cause code. Then, this embodiment calls the confidence vector associated with the anomaly case, which includes at least the event source credibility score, timeline consistency score, ledger consistency score, replay conflict score, and geographic consistency score generated through forward geocoding; these scores provide quantitative basis for triage decisions. Next, this embodiment verifies each access control condition, including: whether the source credibility score reaches a first preset threshold, whether the ledger consistency score reaches a second preset threshold, whether the current time is within the effective time limit for reversible operations, whether the idempotent ledger allows entry into the compensation state, and whether the processing frequency of the same user or account number exceeds the limit within a short period. This embodiment allows the handling plan for an anomaly to be set as an automatic handling plan only when all access control conditions are met. If some conditions are not met but can be resolved by supplementing evidence or user confirmation, it is set as a semi-automatic handling plan. If access control conditions are severely lacking or the risk is too high, it is forced to enter a manual handling plan. For the automatic handling plan, this embodiment further instantiates the plan as a compensation transaction arrangement containing a specific sequence of steps. For example, for the "successful deduction but not cleared" anomaly, the clearing action is executed first and the retry is performed according to the channel avoidance strategy. After exceeding the threshold, it is automatically upgraded to semi-automatic. For the "successful clearing but not deducted" anomaly, the subsequent clearing diffusion is frozen first, the reconciliation and supplementary evidence are initiated, and then the clearing is revoked or transferred to manual verification according to the channel rules. For the "duplicate deduction" anomaly, the fund flow is frozen first and then the reversal or refund is initiated. For the "missing or timed-out callback" anomaly, the third-party reconciliation statement is actively retrieved and the callback is attempted to be reissued. Throughout the entire process, this embodiment continuously monitors the anomaly rate and reconciliation failure rate of each channel. Once a channel's indicator exceeds the circuit breaker threshold, the automatic processing capability of that channel is automatically downgraded to semi-automatic or manual, and the reason for the downgrade and the time window are recorded in the evidence package and audit log. Finally, the system drives the compensation state machine to sequentially execute the request, execution, receipt, and verification according to the generated anomaly handling plan until completion or rollback, thereby realizing the entire process from anomaly cause code to standardized processing closed loop.

[0057] In some embodiments, generating an anomaly handling plan corresponding to each of the fourth electricity payment data based on each anomaly reason code and a preset rule base includes: For each of the aforementioned exception reason codes, an access control result is generated based on the exception reason code and a preset rule base to characterize whether automatic processing is allowed; If the access control result meets the first preset condition, then the anomaly handling plan for the fourth electricity payment data corresponding to the anomaly reason code is set as an automatic handling plan. If the access control result meets the second preset condition, then the anomaly handling plan for the fourth electricity payment data corresponding to the anomaly reason code is set to a semi-automatic handling plan. If the access control result meets the third preset condition, then the anomaly handling plan for the fourth electricity payment data corresponding to the anomaly reason code will be set to a manual handling plan.

[0058] In summary, this application's embodiments, by standardizing the first electricity payment data from multiple channels, eliminate the differences in data formats and fields between different channels, laying a data foundation for subsequent unified and batch automated processing across channels, and avoiding the time-consuming manual adaptation and conversion for each channel. Secondly, by calculating the idempotent key representing the uniqueness of the same transaction across channels and using it to eliminate duplicate data, automated and accurate interception of duplicate transactions across channels is achieved, effectively preventing the redundant accumulation of abnormal data and the amplification of secondary errors, and avoiding the high cost of manually checking duplicate data one by one. Thirdly, by calculating the confidence level based on multi-source receipt data to screen... By selecting anomalies and mapping them to a unified anomaly cause code, false anomaly reports caused by timing differences or missing fields can be effectively filtered out, and true anomalies can be identified. This avoids the invalid work order flow and backlog caused by the traditional reliance on manual experience to determine the authenticity of anomalies, and greatly improves the accuracy and efficiency of anomaly identification and triage. Finally, anomaly handling plans are automatically generated and executed based on the unified anomaly cause code and preset rule base, transforming the originally highly manual and time-consuming patchwork repair into standardized automatic or semi-automatic handling orchestration, which significantly shortens the closed-loop cycle from anomaly discovery to resolution, and achieves a leap in the efficiency of anomaly handling for multi-channel electricity payment.

[0059] Example 2 like Figure 2 As shown, based on the above method embodiments, corresponding device embodiments are provided; An embodiment of the present invention provides a multi-channel electricity payment anomaly handling device, comprising: a data acquisition module 21, a first processing module 22, a second processing module 23, and a third processing module 24; The data acquisition module 21 is used to acquire several first electricity payment data from several channels, and to standardize each first electricity payment data to obtain each second electricity payment data. The first processing module 22 is used to calculate an idempotent key for each of the second electricity payment data to characterize the uniqueness of the same transaction across channels based on each of the second electricity payment data, and to remove duplicate data in each of the second electricity payment data according to each of the idempotent keys to obtain a number of third electricity payment data. The second processing module 23 is used to obtain multi-source receipt data corresponding to each of the third electricity payment data, and calculate the confidence level of each of the third electricity payment data according to each of the multi-source receipt data, and then obtain a number of fourth electricity payment data with anomalies and the anomaly reason code corresponding to each of the third electricity payment data according to each of the confidence levels and each of the third electricity payment data. The third processing module 24 is used to generate an anomaly handling plan corresponding to each of the fourth electricity payment data according to each of the anomaly reason codes and the preset rule base, and to perform electricity payment anomaly handling according to each of the anomaly handling plans.

[0060] In some embodiments, calculating the idempotent key for each of the second electricity payment data to characterize the uniqueness of the same transaction across channels, based on each of the second electricity payment data, includes: Based on a number of pre-defined fields, the fields of each second electricity payment data are extracted to obtain the first field set corresponding to each second electricity payment data. Semantic normalization is performed on each of the first set of fields to obtain a second set of fields that unifies the fields across different channels; Each field in each of the second field sets is concatenated to obtain a concatenation result, and each concatenation result is hashed to obtain an idempotent key.

[0061] In some embodiments, performing hash calculations on each of the concatenation results to obtain each of the idempotent keys includes: According to the preset hash function, each of the concatenation results is hashed to obtain each initial idempotent key; Based on each of the second field sets, generate a semantic fingerprint corresponding to each of the initial idempotent keys; Each semantic fingerprint is combined with its corresponding initial idempotent key to obtain each idempotent key.

[0062] In some embodiments, the step of removing duplicate data from each of the second electricity payment data according to each of the idempotent keys to obtain a plurality of third electricity payment data includes: Based on the idempotent keys described, an idempotent ledger is constructed. Iterate through each of the idempotent keys, and query the idempotent ledger according to the currently traversed idempotent key. If the idempotent key does not exist, write the currently traversed idempotent key into the idempotent ledger; if the idempotent key already exists, determine the second electricity payment data corresponding to the currently traversed idempotent key as duplicate data and remove it. All remaining second electricity payment data will be used as the third electricity payment data.

[0063] In some embodiments, calculating the confidence level of each third electricity payment data based on each of the multi-source receipt data includes: For each of the multi-source receipt data, a verifiable factor and a dynamic calibration factor are extracted from the multi-source receipt data, and the event source credibility score corresponding to the multi-source receipt data is calculated based on the verifiable factor and the dynamic calibration factor. Based on the multi-source receipt data and the preset internal accounting and cancellation events, a status candidate satisfaction score is determined, and a timeline consistency score is determined based on the event timeline corresponding to the multi-source receipt data. Then, based on preset weights, the event source credibility score, the status candidate satisfaction score, and the timeline consistency score are weighted and calculated to obtain the credibility level confidence.

[0064] In some embodiments, obtaining a plurality of abnormal fourth electricity payment data and an anomaly reason code corresponding to each of the third electricity payment data based on the confidence level of each of the aforementioned confidence levels and the third electricity payment data includes: For each of the aforementioned third electricity payment data, anomaly candidate results are determined through preset anomaly identification rules, and it is determined whether the confidence level is greater than or equal to the first preset anomaly judgment threshold. If the confidence level is greater than or equal to the first preset anomaly determination threshold, the third electricity payment data is regarded as the fourth electricity payment data with anomalies, and an anomaly reason code corresponding to the fourth electricity payment data is generated according to the anomaly candidate results.

[0065] In some embodiments, generating an anomaly handling plan corresponding to each of the fourth electricity payment data based on each anomaly reason code and a preset rule base includes: For each of the aforementioned exception reason codes, an access control result is generated based on the exception reason code and a preset rule base to characterize whether automatic processing is allowed; If the access control result meets the first preset condition, then the anomaly handling plan for the fourth electricity payment data corresponding to the anomaly reason code is set as an automatic handling plan. If the access control result meets the second preset condition, then the anomaly handling plan for the fourth electricity payment data corresponding to the anomaly reason code is set to a semi-automatic handling plan. If the access control result meets the third preset condition, then the anomaly handling plan for the fourth electricity payment data corresponding to the anomaly reason code will be set to a manual handling plan.

[0066] For more detailed steps and working principles of this embodiment, please refer to the relevant description in Embodiment 1, but not limited to these descriptions.

[0067] In summary, this application's embodiments, by standardizing the first electricity payment data from multiple channels, eliminate the differences in data formats and fields between different channels, laying a data foundation for subsequent unified and batch automated processing across channels, and avoiding the time-consuming manual adaptation and conversion for each channel. Secondly, by calculating the idempotent key representing the uniqueness of the same transaction across channels and using it to eliminate duplicate data, automated and accurate interception of duplicate transactions across channels is achieved, effectively preventing the redundant accumulation of abnormal data and the amplification of secondary errors, and avoiding the high cost of manually checking duplicate data one by one. Thirdly, by calculating the confidence level based on multi-source receipt data to screen... By selecting anomalies and mapping them to a unified anomaly cause code, false anomaly reports caused by timing differences or missing fields can be effectively filtered out, and true anomalies can be identified. This avoids the invalid work order flow and backlog caused by the traditional reliance on manual experience to determine the authenticity of anomalies, and greatly improves the accuracy and efficiency of anomaly identification and triage. Finally, anomaly handling plans are automatically generated and executed based on the unified anomaly cause code and preset rule base, transforming the originally highly manual and time-consuming patchwork repair into standardized automatic or semi-automatic handling orchestration, which significantly shortens the closed-loop cycle from anomaly discovery to resolution, and achieves a leap in the efficiency of anomaly handling for multi-channel electricity payment.

[0068] It is understood that the above-described device embodiments correspond to the method embodiments of this application, and can implement the multi-channel electricity payment anomaly handling method provided by any of the above-described method embodiments of this application.

[0069] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided in this application, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0070] Example 3 Based on the above embodiments of the multi-channel electricity payment anomaly handling method, another embodiment of this application provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the multi-channel electricity payment anomaly handling method of any embodiment of this application.

[0071] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete this application. The one or more module units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0072] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0073] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0074] Example 4 Based on the above-described method embodiments, another embodiment of this application provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the multi-channel electricity payment anomaly handling method described in any of the above-described method embodiments of this application.

[0075] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above-described embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

Claims

1. A method for handling anomalies in multi-channel electricity payment, characterized in that, include: Acquire several first electricity payment data from several channels, and standardize each first electricity payment data to obtain each second electricity payment data; Based on each of the second electricity payment data, an idempotent key for each of the second electricity payment data is calculated to characterize the uniqueness of the same transaction across channels. Based on each of the idempotent keys, duplicate data in each of the second electricity payment data is removed to obtain several third electricity payment data. Obtain multi-source receipt data corresponding to each of the third electricity payment data, and calculate the confidence level of each of the third electricity payment data based on the multi-source receipt data. Then, based on the confidence level and each of the third electricity payment data, obtain a number of fourth electricity payment data that are abnormal and the abnormal reason code corresponding to each of the fourth electricity payment data. Based on the aforementioned anomaly reason codes and the preset rule base, an anomaly handling plan is generated for each of the fourth electricity payment data, and electricity payment anomaly handling is performed according to each of the aforementioned anomaly handling plans.

2. The method for handling abnormal electricity payment through multiple channels as described in claim 1, characterized in that, The step of calculating the idempotent key for each of the second electricity payment data to characterize the uniqueness of the same transaction across channels, based on each of the second electricity payment data, includes: Based on a number of pre-defined fields, the fields of each second electricity payment data are extracted to obtain the first field set corresponding to each second electricity payment data. Semantic normalization is performed on each of the first set of fields to obtain a second set of fields that unifies the fields across different channels; Each field in each of the second field sets is concatenated to obtain a concatenation result, and each concatenation result is hashed to obtain an idempotent key.

3. The method for handling abnormal electricity payment through multiple channels as described in claim 2, characterized in that, The step of performing hash calculations on each of the concatenation results to obtain each of the idempotent keys includes: According to the preset hash function, each of the concatenation results is hashed to obtain each initial idempotent key; Based on each of the second field sets, generate a semantic fingerprint corresponding to each of the initial idempotent keys; Each semantic fingerprint is combined with its corresponding initial idempotent key to obtain each idempotent key.

4. The method for handling abnormal electricity payment through multiple channels as described in claim 1, characterized in that, The step of removing duplicate data from each of the second electricity payment data based on each of the idempotent keys yields several third electricity payment data sets, including: Establish an idempotent ledger based on the idempotent keys described above; Iterate through each of the idempotent keys, and query the idempotent ledger according to the currently traversed idempotent key. If the idempotent key does not exist, write the currently traversed idempotent key into the idempotent ledger; if the idempotent key already exists, determine the second electricity payment data corresponding to the currently traversed idempotent key as duplicate data and remove it. All remaining second electricity payment data will be used as the third electricity payment data.

5. The method for handling abnormal electricity payment through multiple channels as described in claim 1, characterized in that, The step of calculating the confidence level of each third electricity payment data according to each of the multi-source receipt data includes: For each of the multi-source receipt data, a verifiable factor and a dynamic calibration factor are extracted from the multi-source receipt data, and the event source credibility score corresponding to the multi-source receipt data is calculated based on the verifiable factor and the dynamic calibration factor. Based on the multi-source receipt data and the preset internal accounting and cancellation events, a status candidate satisfaction score is determined, and a timeline consistency score is determined based on the event timeline corresponding to the multi-source receipt data. Then, based on preset weights, the event source credibility score, the status candidate satisfaction score, and the timeline consistency score are weighted and calculated to obtain the credibility level confidence.

6. The method for handling abnormal electricity payment through multiple channels as described in claim 1, characterized in that, The step of obtaining several abnormal fourth electricity payment data and corresponding abnormality reason codes for each of the aforementioned confidence levels and third electricity payment data, based on the confidence levels of each of the aforementioned confidence levels and third electricity payment data, includes: For each of the aforementioned third electricity payment data, anomaly candidate results are determined through preset anomaly identification rules, and it is determined whether the confidence level is greater than or equal to the first preset anomaly judgment threshold. If the confidence level is greater than or equal to the first preset anomaly determination threshold, the third electricity payment data is regarded as the fourth electricity payment data with anomalies, and an anomaly reason code corresponding to the fourth electricity payment data is generated according to the anomaly candidate results.

7. The method for handling abnormal electricity payment through multiple channels as described in claim 1, characterized in that, The step of generating an anomaly handling plan corresponding to each of the fourth electricity payment data based on each anomaly reason code and a preset rule base includes: For each of the aforementioned exception reason codes, an access control result is generated based on the exception reason code and a preset rule base to characterize whether automatic processing is allowed; If the access control result meets the first preset condition, then the anomaly handling plan for the fourth electricity payment data corresponding to the anomaly reason code is set as an automatic handling plan. If the access control result meets the second preset condition, then the anomaly handling plan for the fourth electricity payment data corresponding to the anomaly reason code is set to a semi-automatic handling plan. If the access control result meets the third preset condition, then the anomaly handling plan for the fourth electricity payment data corresponding to the anomaly reason code will be set to a manual handling plan.

8. A multi-channel electricity payment anomaly handling device, characterized in that, include: The system comprises a data acquisition module, a first processing module, a second processing module, and a third processing module. The data acquisition module is used to acquire several first electricity payment data from several channels, and to standardize each first electricity payment data to obtain each second electricity payment data. The first processing module is used to calculate an idempotent key for each of the second electricity payment data to characterize the uniqueness of the same transaction across channels based on each of the second electricity payment data, and to remove duplicate data in each of the second electricity payment data according to each of the idempotent keys to obtain a number of third electricity payment data. The second processing module is used to obtain multi-source receipt data corresponding to each of the third electricity payment data, and calculate the confidence level of each of the third electricity payment data according to each of the multi-source receipt data, and then obtain a number of fourth electricity payment data with anomalies and the anomaly reason code corresponding to each of the third electricity payment data according to each of the confidence levels and each of the third electricity payment data. The third processing module is used to generate an anomaly handling plan corresponding to each of the fourth electricity payment data according to each anomaly reason code and a preset rule base, and to perform electricity payment anomaly handling according to each anomaly handling plan.

9. A terminal device, characterized in that, The device includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement a multi-channel electricity payment anomaly handling method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform a multi-channel electricity payment anomaly handling method as described in any one of claims 1 to 7.

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