Transaction processing methods, devices, electronic equipment and storage media

Abstract

This invention discloses a transaction processing method, apparatus, electronic device, and storage medium, relating to the fields of financial technology and natural language processing. The method includes: responding to a transaction request received from a front-end interface; obtaining an expected transaction result based on the transaction request; and obtaining the actual transaction result from the back-end processing system in response to the transaction request; if the expected transaction result is determined to be a successful transaction, and the actual transaction result is a failed transaction, obtaining the root cause of the transaction failure through a transaction knowledge graph based on the actual and expected transaction results; generating a root cause analysis result based on the root cause of the transaction failure and a transaction template, and displaying the actual transaction result and the root cause analysis result through the front-end interface. The technical solution of this invention not only reduces the processing cost of transaction transactions but also generates unified and standardized processing results, while improving the business clarity of the user's transaction process through root cause analysis results.

Description

Technical Field

[0001] This invention relates to the fields of financial technology and natural language processing, and in particular to a transaction processing method, apparatus, electronic device and storage medium. Background Technology

[0002] With the continuous development of the economy, the transaction volume of bank transaction systems is increasing, and the system structure is becoming more and more complex. This necessitates the promotion of the transaction processing methods supported by the transaction system to help business personnel quickly become familiar with various business operations.

[0003] In existing technologies, the promotion of transaction processing methods is usually achieved by adding on-site implementation personnel. This involves training relevant implementation personnel, providing on-site technical training and support to the areas where the business is being promoted, resolving problems encountered by business personnel on-site, or providing relevant experience guidance to business personnel by compiling experience documents, operation manuals, etc.

[0004] However, this transaction processing method not only requires high manpower and time costs, but also the dialect terminology used by business personnel in different regions is different, making it impossible to achieve a unified and standardized business processing standard. In particular, when a transaction fails, business personnel cannot provide specific reasons for the failure in a timely manner, which greatly reduces the user's business experience. Summary of the Invention

[0005] This invention provides a transaction processing method, apparatus, electronic device, and storage medium to address the problem of non-uniform and non-standardized transaction processing standards.

[0006] According to one aspect of the present invention, a transaction processing method is provided, comprising: In response to receiving a transaction request from the front-end operation interface, the system obtains the expected transaction result based on the transaction request, and obtains the actual transaction result from the back-end processing system in response to the transaction request. If the expected transaction result is determined to be a successful transaction, and the actual transaction result is a failed transaction, the root cause of the transaction failure is obtained through a transaction knowledge graph based on the actual transaction result and the expected transaction result. Root cause analysis results are generated based on the root causes of transaction failures and the transaction business template, and the actual transaction results and the root cause analysis results are displayed through the front-end operation interface.

[0007] The step of obtaining the root cause of transaction failure through a transaction knowledge graph based on the actual transaction result and the expected transaction result includes: traversing the transaction knowledge graph based on a graph traversal algorithm to obtain multiple candidate answers; and sorting each of the candidate answers through a transaction rule engine or a machine learning model to obtain the root cause of transaction failure based on the sorting result.

[0008] The step of generating root cause analysis results based on the root cause of transaction failure and the transaction business template includes: obtaining a matching transaction business template based on the answer type of the root cause of transaction failure; wherein, the answer type includes at least one of attribute value, single relation, multiple relation and Boolean question and answer; and filling the matching transaction business template according to the root cause of transaction failure to obtain the filled root cause analysis results.

[0009] After determining that the expected transaction result is a successful transaction and the actual transaction result is a failed transaction, the method further includes: resending the transaction business request to the backend processing system to obtain the target transaction result of the backend processing system for the transaction business request; if the target transaction result is determined to be a failed transaction and the transaction failure identifier of the target transaction result is different from the transaction failure identifier of the actual transaction result, it is determined that the backend processing system has an operational anomaly.

[0010] The expected transaction results include expected success results and multiple expected failure results, as well as follow-up processing suggestions matching the expected success results and transaction failure root causes matching each of the expected failure results. After obtaining the actual transaction results of the backend processing system for the transaction business request, the method further includes: if the actual transaction result is determined to be a transaction failure, selecting a target expected failure result matching the actual transaction result from the multiple expected failure results, and obtaining a target transaction failure root cause matching the target expected failure result; generating a target root cause analysis result based on the target transaction failure root cause and the transaction business template, and displaying the actual transaction result and the target root cause analysis result through the front-end operation interface.

[0011] After determining that the actual transaction result is a transaction failure, the method further includes: if no target expected failure result matching the actual transaction result is obtained among the multiple expected failure results, the transaction knowledge graph is updated according to the actual transaction result.

[0012] According to another aspect of the present invention, a transaction processing apparatus is provided, comprising: The transaction result acquisition module is used to respond to a transaction business request sent by the front-end operation interface, acquire the expected transaction result according to the transaction business request, and acquire the actual transaction result of the back-end processing system in response to the transaction business request. The failure root cause acquisition module is used to acquire the transaction failure root cause through a transaction knowledge graph based on the actual transaction result and the expected transaction result if the expected transaction result is a successful transaction and the actual transaction result is a failed transaction. The root cause result acquisition module is used to generate root cause analysis results based on the root cause of the transaction failure and the transaction business template, and to display the actual transaction results and the root cause analysis results through the front-end operation interface.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the transaction processing method described in any embodiment of the present invention.

[0014] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions, the computer instructions being configured to cause a processor to execute and implement the transaction processing method described in any embodiment of the present invention.

[0015] According to another aspect of the present invention, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the transaction processing method described in any embodiment of the present invention.

[0016] The technical solution of this invention, in response to a transaction request from the front-end operation interface, obtains the expected transaction result and the actual transaction result from the back-end processing system in response to the transaction request. If the expected transaction result is successful but the actual transaction result is unsuccessful, the root cause of the transaction failure is obtained through a transaction knowledge graph based on the actual and expected results. A root cause analysis result is generated based on the root cause and the transaction template, and the actual transaction result and the root cause analysis result are displayed on the front-end operation interface. This not only reduces the manpower and time costs of transaction processing but also generates unified and standardized business processing results, avoiding non-standard business processing results caused by the use of dialects by business personnel in different regions. Furthermore, when a transaction fails, the root cause analysis result provides a specific reason for the failure, improving the clarity of the transaction process and greatly enhancing the user experience.

[0017] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart of a transaction processing method provided in Embodiment 1 of the present invention; Figure 2 This is a flowchart of another transaction processing method provided in Embodiment 2 of the present invention; Figure 3 This is a schematic diagram of the traversal process of a transaction knowledge graph according to Embodiment 2 of the present invention; Figure 4 This is a flowchart of another transaction processing method provided in Embodiment 3 of the present invention; Figure 5 This is a schematic diagram of the structure of a transaction processing device according to Embodiment 4 of the present invention; Figure 6 This is a schematic diagram of the structure of an electronic device that implements the transaction processing method of this invention. Detailed Implementation

[0020] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0021] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0022] Example 1 Figure 1 This is a flowchart of a transaction processing method provided in Embodiment 1 of the present invention. This embodiment is applicable to situations where root cause analysis is performed when a transaction fails based on the difference between the expected and actual transaction results. This method can be executed by a transaction processing device, which can be implemented in hardware and / or software. This transaction processing device can be configured in an intelligent business system, which can be configured in electronic devices such as servers or terminal devices. Figure 1 As shown, the method includes: S101. In response to receiving a transaction business request from the front-end operation interface, obtain the expected transaction result based on the transaction business request, and obtain the actual transaction result from the back-end processing system in response to the transaction business request.

[0023] Transaction requests are requests sent by business personnel through the front-end operation page of the transaction system to process transaction business. After the business request is sent from the front-end operation page and enters the gateway device, Aspect-Oriented Programming (AOP) technology can be used to filter the message text of specific types of business (e.g., transaction business) and filter irrelevant business requests. For the specified types of business requests that are retained after filtering, they are structured and semantically analyzed.

[0024] For example, the BERT (Bidirectional Encoder Representations from Transformers) model can be used to transform the message text of a transaction request into a semantic vector. By calculating the semantic similarity between this message text and each knowledge node in the transaction knowledge graph, the knowledge node corresponding to the current transaction request can be determined. Then, the business processing text under the current knowledge node is taken as the expected transaction result of the transaction request. Here, the transaction knowledge graph is a knowledge model that represents entities, relationships, and attributes in the transaction domain through a graph structure.

[0025] In addition, business entities in transaction requests can be obtained through named entity recognition models. Then, by calculating the semantic similarity between the business entity and each knowledge node, the knowledge node corresponding to the current transaction request can be determined. The named entity recognition model is built on named entity recognition (NER) technology and is used to identify named entities with specific meanings in text, such as organization names, place names, time and date, and classify them into specific entity categories, such as time, number and entity categories. Each transaction business term in the transaction request is obviously a named entity with specific meaning.

[0026] The back-end processing system refers to the business processing system corresponding to the front-end operation interface in the transaction system. It is also the back-end service cluster that supports the operation of the front-end business. Its core function is to execute business processing logic, that is, to complete the business response to the transaction business request issued by the front-end operation interface. The actual processing result refers to the transaction result after the back-end processing system executes the business processing logic for the transaction business request. After the back-end processing system verifies the transaction business request according to the transaction business rules, the transaction result may be a successful transaction, that is, the current transaction business request response is completed, or a failed transaction, that is, the current transaction business request response is not completed.

[0027] The expected transaction result can be either an assumption that the current transaction request will be successfully processed in the backend processing system, meaning the expected transaction result is always configured as a successful transaction; or it can be a prediction, after verifying the transaction request according to the transaction rules, that the transaction request will be successfully processed by the backend processing system, or that the transaction request will fail. In other words, the expected transaction result may be predicted as either a successful or a failed transaction. In particular, when querying the transaction knowledge graph based on the transaction request, the related knowledge nodes hit during the query process, as well as the business entities extracted from the transaction request, can all be stored in the expected transaction result to facilitate subsequent comparison between the expected transaction result and the actual transaction result.

[0028] S102. If the expected transaction result is determined to be a successful transaction and the actual transaction result is a failed transaction, the root cause of the transaction failure is obtained through a transaction knowledge graph based on the actual transaction result and the expected transaction result.

[0029] As described in the above technical solution, if the expected transaction result is configured as a successful transaction, or if it is predicted that the transaction request will be successfully processed by the backend system, then the subsequent processing suggestions after the current transaction request is successfully processed are obtained synchronously through the transaction knowledge graph. If the actual transaction result is also a successful transaction, then the actual transaction result and the above-mentioned subsequent processing suggestions are displayed through the front-end operation interface. In this way, while showing the transaction result to the business personnel, the subsequent processing suggestions are also provided to guide the business personnel to continue to carry out subsequent related business.

[0030] If the expected transaction result is configured as a successful transaction, but the actual transaction result is a failed transaction, it indicates that the transaction business request did not comply with the corresponding transaction business rules and was not successfully processed by the backend processing system. In this case, based on the business entity in the transaction business request recorded in the expected transaction result, the related knowledge nodes hit during the query process, and the transaction failure identifier in the actual transaction result, the corresponding transaction failure root cause is obtained through the transaction knowledge graph. Among them, the transaction failure identifier, also known as the transaction error code, indicates the error type triggered by the transaction failure.

[0031] For example, in the associated knowledge nodes, the root cause of transaction failure can be obtained directly by querying and retrieving the transaction failure identifier that matches the transaction failure identifier, or all knowledge nodes related to the transaction failure identifier can be filtered and retrieved based on the transaction failure identifier. Then, based on the semantic similarity between the business entity and each related knowledge node, the matching knowledge node can be determined, and the root cause of transaction failure can be obtained under that knowledge node. In particular, if the expected transaction result is predicted to be a transaction failure, and the actual transaction is also a transaction failure, the root cause of transaction failure can be obtained through the transaction knowledge graph based on the actual transaction result and the expected transaction result, using the same retrieval method.

[0032] Furthermore, if the expected transaction outcome is predicted to be a failure, but the actual transaction is successful, the system continues to obtain follow-up processing suggestions after the current transaction business request is successful through the transaction knowledge graph. The actual transaction result and the aforementioned follow-up processing suggestions are then displayed through the front-end operation interface. This not only shows the transaction result to the business personnel, but also provides them with follow-up processing suggestions to guide them in carrying out subsequent related business.

[0033] Optionally, in this embodiment of the invention, after determining that the expected transaction result is a successful transaction and the actual transaction result is a failed transaction, the method further includes: resending the transaction business request to the backend processing system to obtain the target transaction result of the backend processing system for the transaction business request; if it is determined that the target transaction result is a failed transaction and the transaction failure identifier of the target transaction result is different from the transaction failure identifier of the actual transaction result, it is determined that the backend processing system has an operational anomaly.

[0034] Specifically, if the expected transaction result is a successful transaction, but the actual transaction result is a failed transaction, the transaction request is sent to the backend processing system again to obtain the target transaction result for the transaction request from the backend processing system. If the target transaction result is a successful transaction, it indicates that the backend processing system experienced a temporary failure due to excessive system load or data congestion, and therefore the transaction request was not processed successfully the first time. However, the temporary failure has been resolved, so the transaction request was processed successfully the second time.

[0035] If the target transaction result is a transaction failure, and the transaction failure identifier of the target transaction result is the same as the transaction failure identifier of the actual transaction result, it indicates that the transaction business request itself does not conform to the transaction business rules and cannot pass the verification of the back-end processing system. If the transaction failure identifier of the target transaction result is different from the transaction failure identifier of the actual transaction result, it indicates that the back-end processing system failed to execute the same transaction business request twice, and failed for different reasons. In this case, it is determined that there is a system-level business failure in the back-end processing system.

[0036] Accordingly, by resending the failed transaction request to the backend processing system, the root cause of the transaction failure can be detected and obtained based on the processing results of the backend processing system for the transaction request before and after the failure. This not only achieves system-level fault detection, but also effectively distinguishes between system faults and business defects in the transaction request itself, ensuring the accurate acquisition of the root cause of the transaction failure.

[0037] S103. Generate root cause analysis results based on the root causes of transaction failure and the transaction business template, and display the actual transaction results and the root cause analysis results through the front-end operation interface.

[0038] The transaction template is a pre-configured standardized document that records general root cause descriptions. Based on the obtained root causes of transaction failures, the template is populated, and the result is a unified and standardized conclusion. The actual transaction results and root cause analysis results are then displayed through the front-end interface. This not only shows the transaction results to business personnel but also provides them with specific reasons for transaction failures, guiding them to explain the specific problems to users and thus improving the clarity of the transaction process for users.

[0039] Specifically, different templates can be configured for different types of transaction businesses, and under each type of transaction business, the template includes a transaction success template and a transaction failure template. When an actual transaction fails, the matching transaction failure template is populated according to the root cause of the transaction failure, so as to display the root cause analysis results through the populated transaction failure template. When an actual transaction succeeds, the matching transaction success template is populated according to the follow-up processing suggestions, so as to display the follow-up processing suggestions through the populated transaction success template.

[0040] The technical solution of this invention, in response to a transaction request from the front-end operation interface, obtains the expected transaction result and the actual transaction result from the back-end processing system in response to the transaction request. If the expected transaction result is successful but the actual transaction result is unsuccessful, the root cause of the transaction failure is obtained through a transaction knowledge graph based on the actual and expected results. A root cause analysis result is generated based on the root cause and the transaction template, and the actual transaction result and the root cause analysis result are displayed on the front-end operation interface. This not only reduces the manpower and time costs of transaction processing but also generates unified and standardized business processing results, avoiding non-standard business processing results caused by the use of dialects by business personnel in different regions. Furthermore, when a transaction fails, the root cause analysis result provides a specific reason for the failure, improving the clarity of the transaction process and greatly enhancing the user experience.

[0041] It should be noted that all information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for display, data used for analysis, etc.) involved in this disclosure are information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data comply with the relevant laws, regulations and standards of the relevant regions.

[0042] Example 2 Figure 2 This is a flowchart of a transaction processing method provided in Embodiment 2 of the present invention. The relationship between this embodiment and the above embodiments is that, based on a graph traversal algorithm and a candidate answer sorting mechanism, the root cause of transaction failure is determined, such as... Figure 2 As shown, the method specifically includes: S201. In response to receiving a transaction business request from the front-end operation interface, obtain the expected transaction result based on the transaction business request, and obtain the actual transaction result of the back-end processing system in response to the transaction business request.

[0043] S202. If the expected transaction result is determined to be a successful transaction and the actual transaction result is a failed transaction, based on the actual transaction result and the expected transaction result, the transaction knowledge graph is traversed using a graph traversal algorithm to obtain multiple candidate answers.

[0044] As described in the above technical solution, in the process of obtaining the expected transaction result according to the transaction business request, the transaction business entity of the transaction business request has been extracted and recorded in the expected transaction result. At this time, based on the transaction business entity in the expected transaction result and the first failure identifier of the actual transaction result, each knowledge node in the transaction knowledge graph is traversed according to the graph traversal method to obtain related knowledge nodes with similar semantics, and the node information under each related knowledge node is used to form a candidate answer set.

[0045] S203. The candidate answers are sorted using a transaction rule engine or machine learning model to obtain the root cause of transaction failure based on the sorting results.

[0046] The trading rule engine is a rule model that implements real-time trading decisions through predefined trading logic rules; the machine learning model is a data-based prediction function that learns historical patterns through algorithms and outputs a mathematical model for decisions; through the trading rule engine or machine learning model, reasoning methods such as symbolic reasoning, statistical reasoning, and temporal reasoning are used to score each candidate answer in the candidate answer set. The score reflects the degree of matching between the candidate answer and the actual trading result. The higher the score, the greater the degree of matching.

[0047] Symbolic reasoning refers to a reasoning method that derives new things based on ontological logic; statistical reasoning refers to graph neural networks predicting missing relationships; and temporal reasoning refers to the process of filtering facts within a validity period by assigning timestamps when selecting information from a knowledge graph. Based on the scoring results, each candidate answer is ranked, and the candidate answer with the highest score is used as the root cause of the transaction failure. Thus, multiple candidate answers obtained based on graph traversal algorithms ensure the complete acquisition of related knowledge nodes, avoiding the loss of answers. Simultaneously, the root cause of the transaction failure obtained based on the ranking results of each candidate answer ensures the accuracy of the root cause results.

[0048] S204. Generate root cause analysis results based on the root causes of transaction failure and the transaction business template, and display the actual transaction results and the root cause analysis results through the front-end operation interface.

[0049] Optionally, in this embodiment of the invention, generating root cause analysis results based on the root cause of transaction failure and the transaction business template includes: obtaining a matching transaction business template based on the answer type of the root cause of transaction failure; wherein, the answer type includes at least one of attribute value, single relation, multiple relation, and Boolean question and answer; and filling the matching transaction business template according to the root cause of transaction failure to obtain a filled root cause analysis result.

[0050] Specifically, since different types of answers often have different forms of expression, different types of answers require different response mechanisms with different forms of expression and complexity. That is, different types of transaction business templates need to be obtained for different types of answers. Attribute value means that the current answer answers a specific numerical value or state. Single relation means that the current answer only contains a direct relationship between two entities. Multiple relation means that the current answer involves multiple entities and complex causal networks. Boolean question answering means that the current answer is a yes or no judgment result.

[0051] Taking Boolean question and answer as an example, the corresponding transaction template can be "Hello, the account name (XX) you entered does not match the actual account name. Please confirm again, thank you!". When the root cause of the transaction failure is determined to be "account name 12345 does not match the actual account name", the account name "12345" is filled into the transaction template. The root cause analysis result generated accordingly is "Hello, the account name 12345 you entered does not match the actual account name. Please confirm again, thank you!". Thus, based on the answer type of the root cause of the transaction failure, a matching transaction template is obtained, ensuring the standardization of the root cause result expression. At the same time, the filling of the transaction template based on the root cause of the transaction failure ensures the accuracy and completeness of the root cause result expression.

[0052] by Figure 3For example, when the actual transaction result is a failed transaction, and the actual transaction result reflects that the "Agreement Number" is non-compliant, the probabilities of hitting the knowledge nodes "APPID" (Application Identifier), "contractID" (Contract Agreement Identifier), "Binding," and "Approval" in the transaction knowledge graph based on the "Agreement Number" are 0.2, 0.5, 0.2, and 0.1, respectively; while under the knowledge node "APPID," the probabilities of hitting the sub-nodes "Open" and "Search" are 0.2 and 0.8, respectively; in the knowledge node... Under “contractID”, the probabilities of hitting the child nodes “sign” and “query” are 0.7 and 0.3 respectively; under the knowledge node “bind”, the probabilities of hitting the child nodes “operation page” and “failure” are 0.6 and 0.4 respectively; under the knowledge node “approval”, the probabilities of hitting the child nodes “process” and “operation page” are 0.8 and 0.2 respectively; at this time, the child node with the highest probability of hitting is “sign” = 0.5 × 0.7 = 0.35. Therefore, the processing text information under “sign” is taken as the root cause of transaction failure.

[0053] The technical solution of this invention involves traversing a transaction knowledge graph using a graph traversal algorithm based on the actual and expected transaction results to obtain multiple candidate answers. These candidate answers are then sorted using a transaction rule engine or machine learning model to determine the root cause of transaction failure. This method ensures the complete acquisition of related knowledge nodes by using multiple candidate answers, preventing answer loss. Furthermore, the root cause of transaction failure obtained based on the sorting of each candidate answer ensures the accuracy of the root cause determination.

[0054] Example 3 Figure 4 This is a flowchart of a transaction processing method provided in Embodiment 3 of the present invention. The relationship between this embodiment and the above embodiments is that, based on the transaction request, multiple possible transaction results are obtained, such as... Figure 4 As shown, the method specifically includes: S301. In response to receiving a transaction business request from the front-end operation interface, obtain the expected transaction result based on the transaction business request, and obtain the actual transaction result of the back-end processing system for the transaction business request; wherein, the expected transaction result includes an expected success result and multiple expected failure results, as well as follow-up processing suggestions matching the expected success result, and transaction failure root causes matching each of the expected failure results.

[0055] After receiving the transaction request from the front-end interface, we can list all the possibilities of the transaction request, namely the expected success result when the transaction is successful, and the various failure possibilities when the transaction fails, namely multiple expected failure results. At the same time, when obtaining the expected success result, we can simultaneously obtain subsequent processing suggestions; when obtaining various expected failure results, we can obtain the matching root cause of the transaction failure.

[0056] S302. If the actual transaction result is determined to be a transaction failure, select the target expected failure result that matches the actual transaction result from the plurality of expected failure results, and obtain the target transaction failure root cause that matches the target expected failure result.

[0057] The expected transaction results include various expected failure results, which involve different aspects of the processing results where the transaction business request does not comply with the transaction business rules and is rejected by the backend processing system. Therefore, if the actual transaction result is determined to be a transaction failure, the target expected failure result that matches the actual transaction result is selected from among the many expected failure results. Then, the transaction failure root cause corresponding to the target expected failure result, i.e. the target transaction failure root cause, is taken as the root cause corresponding to the current transaction failure.

[0058] S303. Generate target root cause analysis results based on the target transaction failure root cause and transaction business template, and display the actual transaction results and the target root cause analysis results through the front-end operation interface.

[0059] Based on the obtained root causes of the target transaction failure, the transaction business template is populated. The populated result is a unified and standardized conclusion. At this time, the actual transaction result and the above-mentioned root cause analysis results are displayed through the front-end operation interface. This not only shows the transaction results to the business personnel and provides specific reasons for the transaction failure, but also guides the business personnel to explain the specific problems to the users. At the same time, it improves the efficiency of obtaining root cause analysis results when the transaction fails, thereby improving the response speed of transaction business requests.

[0060] Optionally, in this embodiment of the invention, after determining that the actual transaction result is a transaction failure, the method further includes: if no target expected failure result matching the actual transaction result is obtained among the plurality of expected failure results, updating the transaction knowledge graph according to the actual transaction result.

[0061] Specifically, if no target expected failure result matching the actual transaction result is obtained among multiple expected failure results, it indicates that the transaction business rules recorded in the transaction system may have changed, and this change has not been reflected in the transaction knowledge graph. This means that newly added transaction failure types in the transaction system have not been updated in the transaction knowledge graph. Therefore, the transaction knowledge graph is updated according to the actual transaction results to update the newly added transaction failure types in the transaction knowledge graph, thereby ensuring the timely update of newly added transaction failure types in the transaction knowledge graph and improving the knowledge completeness and accuracy of the transaction knowledge graph.

[0062] The technical solution of this invention, after obtaining the actual transaction result of the transaction business request from the backend processing system, if it is determined that the actual transaction result is a transaction failure, selects a target expected failure result that matches the actual transaction result from multiple expected failure results, and obtains the target transaction failure root cause that matches the target expected failure result; generates a target root cause analysis result based on the target transaction failure root cause and the transaction business template, and displays the actual transaction result and the target root cause analysis result through the front-end operation interface. This improves the efficiency of obtaining root cause analysis results when a transaction fails, thereby increasing the response speed of transaction business requests, while simultaneously showing the transaction result and providing specific reasons for the transaction failure to business personnel to guide them in explaining the specific problem to users.

[0063] Example 4 Figure 5 This is a structural block diagram of a transaction processing device provided in Embodiment 4 of the present invention. The device specifically includes: The transaction result acquisition module 501 is used to respond to a transaction business request sent by the front-end operation interface, acquire the expected transaction result according to the transaction business request, and acquire the actual transaction result of the back-end processing system in response to the transaction business request. The failure root cause acquisition module 502 is used to acquire the transaction failure root cause through a transaction knowledge graph based on the actual transaction result and the expected transaction result if it is determined that the expected transaction result is a successful transaction and the actual transaction result is a failed transaction. The root cause result acquisition module 503 is used to generate root cause analysis results based on the root cause of the transaction failure and the transaction business template, and to display the actual transaction results and the root cause analysis results through the front-end operation interface.

[0064] The technical solution of this invention, in response to a transaction request from the front-end operation interface, obtains the expected transaction result and the actual transaction result from the back-end processing system in response to the transaction request. If the expected transaction result is successful but the actual transaction result is unsuccessful, the root cause of the transaction failure is obtained through a transaction knowledge graph based on the actual and expected results. A root cause analysis result is generated based on the root cause and the transaction template, and the actual transaction result and the root cause analysis result are displayed on the front-end operation interface. This not only reduces the manpower and time costs of transaction processing but also generates unified and standardized business processing results, avoiding non-standard business processing results caused by the use of dialects by business personnel in different regions. Furthermore, when a transaction fails, the root cause analysis result provides a specific reason for the failure, improving the clarity of the transaction process and greatly enhancing the user experience.

[0065] Optionally, the failure root cause acquisition module 502 is specifically used to traverse the transaction knowledge graph based on the actual transaction result and the expected transaction result to obtain multiple candidate answers; and to sort each of the candidate answers through a transaction rule engine or machine learning model to obtain the transaction failure root cause based on the sorting result.

[0066] Optionally, the root cause result acquisition module 503 is specifically used to acquire a matching transaction business template based on the answer type of the transaction failure root cause; wherein, the answer type includes at least one of attribute value, single relation, multiple relation and Boolean question and answer; and to fill the matching transaction business template according to the transaction failure root cause to obtain the completed root cause analysis result.

[0067] Optionally, the transaction processing device is further configured to resend the transaction request to the back-end processing system to obtain the target transaction result of the back-end processing system for the transaction request; if it is determined that the target transaction result is a transaction failure, and the transaction failure identifier of the target transaction result is different from the transaction failure identifier of the actual transaction result, it is determined that the back-end processing system has an operational abnormality.

[0068] Optionally, the expected transaction results include expected success results and multiple expected failure results, as well as follow-up processing suggestions matching the expected success results, and transaction failure root causes matching each of the expected failure results; The transaction processing device is further configured to, if the actual transaction result is determined to be a transaction failure, select a target expected failure result that matches the actual transaction result from the plurality of expected failure results, and obtain a target transaction failure root cause that matches the target expected failure result; generate a target root cause analysis result based on the target transaction failure root cause and the transaction business template, and display the actual transaction result and the target root cause analysis result through the front-end operation interface.

[0069] Optionally, the transaction processing device is further configured to update the transaction knowledge graph based on the actual transaction result if no target expected failure result matching the actual transaction result is obtained among the plurality of expected failure results.

[0070] The above-described apparatus can execute the transaction processing method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method. Technical details not described in detail in this embodiment can be found in the transaction processing method provided in any embodiment of the present invention.

[0071] Example 5 Figure 6 A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, electronic devices, blade electronic devices, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0072] like Figure 6 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0073] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0074] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as transaction processing methods.

[0075] In some embodiments, the transaction processing method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and / or installed on a heterogeneous hardware accelerator via ROM and / or a communication unit. When the computer program is loaded into RAM and executed by a processor, one or more steps of the transaction processing method described above may be performed. Alternatively, in other embodiments, the processor may be configured to perform the transaction processing method by any other suitable means (e.g., by means of firmware).

[0076] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0077] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0078] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0079] To provide user interaction, the systems and techniques described herein can be implemented on a heterogeneous hardware accelerator, which includes: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the heterogeneous hardware accelerator. Other types of devices can also be used to provide user interaction; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or haptic feedback); and input from the user can be received in any form (including sound input, voice input, or haptic input).

[0080] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0081] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0082] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0083] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A transaction service processing method characterized by comprising: include: In response to receiving a transaction request from the front-end operation interface, the system obtains the expected transaction result based on the transaction request, and obtains the actual transaction result from the back-end processing system in response to the transaction request. If the expected transaction result is determined to be a successful transaction, and the actual transaction result is a failed transaction, the root cause of the transaction failure is obtained through a transaction knowledge graph based on the actual transaction result and the expected transaction result. Root cause analysis results are generated based on the root causes of transaction failures and the transaction business template, and the actual transaction results and the root cause analysis results are displayed through the front-end operation interface.

2. The transaction processing method of claim 1, wherein, The step of obtaining the root cause of transaction failure through a transaction knowledge graph based on the actual transaction result and the expected transaction result includes: Based on the actual transaction results and the expected transaction results, the transaction knowledge graph is traversed using a graph traversal algorithm to obtain multiple candidate answers; The candidate answers are sorted using a transaction rules engine or machine learning model to obtain the root cause of transaction failure based on the sorting results.

3. The transaction processing method according to claim 1 or 2, characterized by, The generation of root cause analysis results based on the root causes of transaction failures and the transaction business template includes: Based on the answer type of the root cause of the transaction failure, a matching transaction business template is obtained; wherein, the answer type includes at least one of attribute value, single relation, multiple relation, and Boolean question and answer; The matching transaction business template is populated based on the root cause of the transaction failure to obtain the completed root cause analysis results.

4. The transaction processing method of claim 1, wherein After determining that the expected transaction result is a successful transaction and the actual transaction result is a failed transaction, the process further includes: The transaction request is sent again to the back-end processing system to obtain the target transaction result of the back-end processing system for the transaction request. If the target transaction result is determined to be a transaction failure, and the transaction failure identifier of the target transaction result is different from the transaction failure identifier of the actual transaction result, it is determined that the back-end processing system has an operational anomaly.

5. The transaction processing method of claim 1, wherein The expected transaction results include expected success results and multiple expected failure results, as well as follow-up processing suggestions matching the expected success results and transaction failure root causes matching each of the expected failure results; After obtaining the actual transaction result from the backend processing system in response to the transaction request, the process also includes: If the actual transaction result is determined to be a transaction failure, select the target expected failure result that matches the actual transaction result from the plurality of expected failure results, and obtain the target transaction failure root cause that matches the target expected failure result; Based on the root causes of the target transaction failure and the transaction business template, a target root cause analysis result is generated, and the actual transaction result and the target root cause analysis result are displayed through the front-end operation interface.

6. The transaction processing method of claim 5, wherein, After determining that the actual transaction result is a transaction failure, the process also includes: If no target expected failure result matching the actual transaction result is obtained among the multiple expected failure results, the transaction knowledge graph is updated according to the actual transaction result.

7. A transaction processing device, characterized in that, include: The transaction result acquisition module is used to respond to a transaction business request sent by the front-end operation interface, acquire the expected transaction result according to the transaction business request, and acquire the actual transaction result of the back-end processing system in response to the transaction business request. The failure root cause acquisition module is used to acquire the transaction failure root cause through a transaction knowledge graph based on the actual transaction result and the expected transaction result if the expected transaction result is a successful transaction and the actual transaction result is a failed transaction. The root cause result acquisition module is used to generate root cause analysis results based on the root cause of the transaction failure and the transaction business template, and to display the actual transaction results and the root cause analysis results through the front-end operation interface.

8. An electronic device, comprising: The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the transaction processing method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the transaction processing method according to any one of claims 1-6.

10. A computer program product comprising a computer program that, when executed by a processor, implements the transaction processing method according to any one of claims 1-6.

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