Data processing method and device, storage medium and electronic equipment

Abstract

The specification discloses a data processing method and device, a storage medium, and an electronic device. The method comprises: obtaining offline transaction table data and full-amount transaction table data, then performing new scene detection based on the offline transaction table data and the full-amount transaction table data, determining new scene field information corresponding to the offline transaction table data, then determining a management task for the offline transaction table data based on the new scene field information, and processing the offline transaction table data based on the management task.

Description

Technical Field

[0001] This specification relates to the field of computer technology, and in particular to a data processing method, apparatus, storage medium, and electronic device. Background Technology

[0002] In related technologies, data timeliness is generally categorized into offline, near real-time, and real-time based on data latency. Therefore, data can be classified into offline data, near real-time data, and real-time data according to these timeliness criteria. Offline data refers to data from N days ago that is processed today. Near real-time data refers to data from N hours ago that is processed in the current hour. Real-time data refers to current data that is processed at the current moment.

[0003] Offline data processing, also known as "batch processing," involves data that is not cleaned immediately after it is generated, but rather extracted, transformed, and loaded at fixed intervals. For example, data generated the previous day might be processed after 2:00 AM each day. Currently, there are many scenarios involving offline data processing. In these scenarios, the main focus is on processing offline data (such as aggregation and cleaning), and then feeding the processed data back to the online service's storage system for use by downstream users. Summary of the Invention

[0004] This specification provides a data processing method, apparatus, storage medium, and electronic device that can promptly perform corresponding processing tasks on offline data when abnormal data is detected, preventing the abnormal data from affecting subsequent processing tasks and thus reducing the risk of offline data corruption. The technical solution is as follows:

[0005] Firstly, this specification provides a data processing method, the method comprising:

[0006] Retrieve offline transaction table data and full transaction table data;

[0007] New scenario detection is performed based on the offline transaction table data and the full transaction table data to determine the new scenario field information corresponding to the offline transaction table data;

[0008] Based on the new scenario field information, a management task is determined for the offline transaction table data, and the offline transaction table data is processed based on the management task.

[0009] Secondly, this specification provides a data processing apparatus, the apparatus comprising:

[0010] The data acquisition module is used to acquire offline transaction table data and full transaction table data;

[0011] The scene detection module is used to detect new scenes based on the offline transaction table data and the full transaction table data, and to determine the new scene field information corresponding to the offline transaction table data.

[0012] The data management module is used to determine the management tasks for the offline transaction table data based on the new scenario field information, and to process the offline transaction table data based on the management tasks.

[0013] Thirdly, this specification provides a computer storage medium having multiple instructions adapted for loading by a processor and executing the above-described method steps.

[0014] Fourthly, this specification provides a computer program product that stores at least one instruction, which is loaded by a processor and executes the above-described method steps.

[0015] Fifthly, this specification provides an electronic device that may include: a memory and a processor; wherein the memory stores a computer program adapted to be loaded by the memory and to execute the above-described method steps.

[0016] The beneficial effects of the technical solutions provided in this specification include at least the following:

[0017] In this embodiment, offline transaction table data and full transaction table data are acquired. Then, new scenario detection is performed based on the offline transaction table data and the full data table data to determine the new scenario field information corresponding to the offline transaction table data. Based on the new scenario field information, a management task is determined for the offline transaction table data, and the offline transaction table data is processed based on the management task. This embodiment can determine the corresponding management task based on the new scenario field information after detecting it, and process the offline transaction table data promptly according to the management task. Therefore, this embodiment can promptly process offline data when abnormal data is detected, avoiding the impact of abnormal data on subsequent processing tasks and reducing the risk of offline data issues. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the 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 1This is a flowchart illustrating a data processing method provided in an embodiment of this specification;

[0020] Figure 2 This is a schematic diagram illustrating the principle of scene division provided in the embodiments of this specification;

[0021] Figure 3 This is a flowchart illustrating yet another data processing method provided in the embodiments of this specification;

[0022] Figure 4 This is an example of an interaction scenario between an electronic device and a transaction management terminal provided in the embodiments of this specification;

[0023] Figure 5 This is another interaction scenario diagram between an electronic device and a transaction management terminal provided in the embodiments of this specification;

[0024] Figure 6 This is a schematic diagram of the structure of a data processing device provided in the embodiments of this specification;

[0025] Figure 7 This is a schematic diagram of the structure of an electronic device provided in the embodiments of this specification. Detailed Implementation

[0026] To make the inventive objectives, features, and advantages of the embodiments in this specification more apparent and understandable, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments in this specification, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments in this specification without creative effort are within the scope of protection of this specification.

[0027] In the description of this specification, it should be understood that the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. In the description of this specification, it should be noted that, unless otherwise expressly specified and limited, "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices. Those skilled in the art can understand the specific meaning of the above terms in this specification based on the specific circumstances. Furthermore, in the description of this specification, unless otherwise stated, "multiple" means two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship.

[0028] In related technologies, there are scenarios involving large amounts of offline data processing. Offline data processing mainly involves processing offline data before it is fed back into the online system for downstream users. Therefore, if abnormal data exists in the offline data and subsequent processing tasks are not intercepted, this abnormal data can lead to errors in the subsequent data processing results, causing serious data incidents and even significant losses. Therefore, mitigating the risks associated with offline data is a pressing technical problem that needs to be addressed.

[0029] The present specification will now be described in detail with reference to specific embodiments.

[0030] In the following method embodiments, for ease of explanation, only electronic devices are described as the subjects performing each step.

[0031] Please see Figure 1 This is a flowchart illustrating a data processing method provided in an embodiment of this specification. Figure 1 As shown, the method described in the embodiments of this specification may include the following steps:

[0032] S102, retrieve offline transaction table data and full transaction table data.

[0033] In simple terms, offline transaction table data refers to the original transaction table data from the previous day. If today is designated as day T, then offline transaction table data refers to the transaction table data from day T-1. Offline transaction table data can refer to the data stored in a data table for a specific type of transaction. Transaction types can include financial, traffic, and other types. For example, for financial transactions, offline transaction table data could refer to the data stored in the data table for a store's transaction transactions, or it could refer to the data stored in the data table for loan transactions by consumer finance institutions; for traffic-type transactions, offline transaction table data could refer to the data stored in the data table for a store's customer traffic transactions. For each transaction, its corresponding offline transaction table data can be stored in one or more data tables.

[0034] Full transaction table data refers to the original transaction table data from the previous two days. If today is designated as day T, then full transaction table data refers to the transaction table data from day T-2. Full transaction table data can also refer to the data stored in a data table for a specific type of transaction. In the embodiments described in this specification, offline transaction table data and full transaction table data refer to the data stored in data tables for different dates of the same transaction. For each transaction, its corresponding full transaction table data can be data stored in one or more data tables.

[0035] In some embodiments, offline transaction table data and full transaction table data for the same transaction can be retrieved from the data warehouse. Specifically, offline transaction table data and full transaction table data for the same transaction can be retrieved from the data warehouse at a fixed time each day.

[0036] In some embodiments, offline transaction table data and full transaction table data for the same transaction can be obtained from an offline data acquisition device. Specifically, this can be divided into proactive data acquisition and passive data acquisition. Proactive data acquisition refers to an electronic device sending a data acquisition request to an offline data acquisition device at a fixed time each day. This data acquisition request is for obtaining the offline transaction table data and full transaction table data for a specific transaction. Based on this data acquisition request, the offline data acquisition device sends the offline transaction table data and full transaction table data for that transaction to the electronic device. Passive data acquisition refers to an electronic device receiving the offline transaction table data and full transaction table data for a specific transaction sent by the offline data acquisition device before a fixed time each day.

[0037] In a straightforward manner, for both offline transaction table data and full transaction table data, each data table used to store data can store information such as the table name, one or more fields, and one or more field values ​​corresponding to each field.

[0038] S104, perform new scenario detection based on offline transaction table data and full transaction table data, and determine the new scenario field information corresponding to the offline transaction table data.

[0039] In simple terms, new scenario field information refers to scenario field information that appears for the first time in the offline transaction table data, as well as marked scenario field information that does not appear for the first time in the offline transaction table data. Marked scenario field information can be field information where a specified field has a specified field value.

[0040] New scenario field information can refer to field information corresponding to one or more scenarios. A scenario can be determined by one key field or by multiple key fields. If a key field includes two values, 1 and 0, when the field value is 1, the field information (key field = 1) can be determined to be the field information of scenario a, and when the field value is 0, the field information (key field = 0) can be determined to be the field information of scenario b.

[0041] In some embodiments, scenario determination fields for scenario detection can be determined based on offline transaction table data and / or full transaction table data, wherein the number of scenario determination fields can be one or more; scenario detection is performed on the offline transaction table data based on the scenario determination fields to determine the first scenario field information present in the offline transaction table data; scenario detection is performed on the full transaction table data based on the scenario determination fields to determine the second scenario field information present in the full transaction table data; a third scenario field information other than the second scenario field information is detected in the first scenario field information, and it is also detected whether there is a marked scenario field information in the first scenario field information; if there is marked scenario field information, the marked scenario field information and the third scenario field information are determined as new scenario field information; if there is no marked scenario field information, the third scenario field information is determined as new scenario field information.

[0042] Specifically, determining the scene determination fields for scene detection based on offline transaction table data and / or full transaction table data can be understood as follows: First, obtain field extraction prompts and an AI generation model. Then, use the field extraction prompts and offline transaction table data as input to the AI ​​generation model to obtain the scene determination fields output by the AI ​​generation model for scene detection. Alternatively, obtain field extraction prompts and an AI generation model. Then, use the field extraction prompts and full transaction table data as input to the AI ​​generation model to obtain the scene determination fields output by the AI ​​generation model. Or, obtain field extraction prompts and an AI generation model. Then, input the field extraction prompts and full transaction table data into the AI ​​generation model to obtain a first determination field. Input the field extraction prompts and offline transaction table data into the AI ​​generation model to obtain a second determination field. Identify the common fields between the first and second determination fields, and use these common fields as the scene determination fields. The field extraction prompts can refer to prompts that guide the AI ​​generation model to extract fields from the offline or full transaction table data. The extracted fields can be used for scene detection.

[0043] Specifically, scenario detection is performed on the offline transaction table data based on the scenario determination field to determine the first scenario field information present in the offline transaction table data. This can be understood as determining the field value corresponding to the scenario determination field in the offline transaction table data, dividing the data into scenarios based on the field value, and obtaining the first scenario field information composed of field information corresponding to one or more scenarios. For example, see [link to example]. Figure 2 The diagram shown illustrates the principle of scene division. Figure 2 The table shown represents a portion of data from an offline transaction table. The data stored in the table is the offline transaction table data. The first row stores the fields, and rows two through five store the values ​​for each field. If "Field 2," "Field 4," and "Field 5" are scenario determination fields, extracting data from rows two through five yields the field values ​​for {Field 2, Field 4, Field 5} as (1,1,1), (0,1,1), (0,0,1), and (1,1,1). It is evident that the field values ​​in rows two and five are identical, resulting in three unique sets of field values: (1,1,1), (0,1,1), and (0,0,1). These three sets of field values ​​can be used to identify three different scenarios, thus obtaining... Figure 2 The scene diagram shows Scene 1, Scene 2, and Scene 3. By combining the values ​​of fields 2, 4, and 5, respectively, we can obtain three sets of scene field information.

[0044] Specifically, scenario detection is performed on the full transaction table data based on the scenario determination field to determine the second scenario field information existing in the full transaction table data. This can be understood as determining the field value corresponding to the scenario determination field in the full transaction table data, dividing the data into scenarios based on the field value, and obtaining the second scenario field information composed of field information corresponding to one or more scenarios.

[0045] S106, determine the management task for the offline transaction table data based on the new scenario field information, and process the offline transaction table data based on the management task.

[0046] Since the new scenario field information may include scenario field information appearing for the first time in the offline transaction table data, as well as marker scenario field information that does not appear for the first time, in some embodiments, when the new scenario field information includes scenario field information appearing for the first time, the interception task and notification task for the offline transaction table data can be determined as management tasks for the offline transaction table data, and the offline transaction table data can be processed according to the management tasks (i.e., the interception task and the notification task). The interception task can refer to a task that intercepts the currently running task corresponding to the offline transaction table data; the notification task can refer to a task that generates a notification message and sends the notification message to the transaction management terminal corresponding to the offline transaction table data. This notification message is used to prompt the transaction management terminal to perform data verification on the offline transaction table data. Furthermore, the electronic device can also receive the data verification result sent by the transaction management terminal based on the notification message. When the data verification result indicates that the offline transaction table data verification is successful, the electronic device can also cancel the above-mentioned interception task and allow the offline transaction table data to pass, that is, cancel the interception of the running task corresponding to the offline transaction table data. The transaction management terminal can refer to a terminal with data verification function, which refers to the function of verifying the offline transaction table data.

[0047] In some embodiments, when the new scene field information includes scene field information appearing for the first time, the interception task and verification task for offline transaction table data can be determined as management tasks for offline transaction table data, and the offline transaction table data can be processed according to the notification tasks (i.e., the interception task and the verification task). Here, the interception task can refer to a task that intercepts the currently running task corresponding to the offline transaction table data; the verification task can refer to a task that verifies the data in the offline transaction table data. When the electronic device determines that the verification result of the offline transaction table data is a successful verification result, the electronic device can cancel the above-mentioned interception task and allow the offline transaction table data to pass through, that is, cancel the interception of the running task corresponding to the offline transaction table data.

[0048] In some embodiments, when the new scenario field information only includes the marked scenario field information, the notification task for the offline transaction table data can be determined as a management task for the offline transaction table data, and the offline transaction table data can be processed according to the management task (i.e., the notification task). Processing the offline transaction table data according to the notification task can be understood as determining the occurrence frequency of the marked scenario field information, generating a notification message containing the occurrence frequency and the marked scenario field information, and sending the notification message to the transaction management terminal corresponding to the offline transaction table data. This notification message can be used to prompt the transaction management terminal to perform corresponding processing on the marked scenario field information. In this embodiment, the marked scenario field information can be the marked scenario field information added by the transaction management terminal. Therefore, when the occurrence of the marked scenario field information is detected by the above method, the transaction management terminal can be informed of the occurrence frequency of the marked scenario field information through a notification message, so as to draw the attention of the transaction management terminal to the marked scenario field information, thereby facilitating the transaction management terminal to perform corresponding processing operations on the marked scenario field information.

[0049] In this embodiment, offline transaction table data and full transaction table data are acquired. Then, new scenario detection is performed based on the offline transaction table data and the full data table data to determine the new scenario field information corresponding to the offline transaction table data. Based on the new scenario field information, a management task is determined for the offline transaction table data, and the offline transaction table data is processed based on the management task. This embodiment can determine the corresponding management task based on the new scenario field information after detecting it, and process the offline transaction table data promptly according to the management task. Therefore, this embodiment can promptly process offline data when abnormal data is detected, avoiding the impact of abnormal data on subsequent processing tasks and reducing the risk of offline data issues.

[0050] Please see Figure 3 This is a flowchart illustrating a data processing method provided in an embodiment of this specification. Figure 3 As shown, the method described in the embodiments of this specification may include the following steps:

[0051] S302, retrieve offline transaction table data and full transaction table data.

[0052] Specifically, see Figure 1 The description of S102 in the illustrated embodiment will not be repeated here.

[0053] S304, Obtain scene recognition rules, and determine the first scene field information corresponding to the full transaction table data and the second scene field information corresponding to the offline transaction table data based on the scene recognition rules.

[0054] In simple terms, scene recognition rules refer to transaction rules or recognition models used to identify scene field information from transaction table data. Scene field information can refer to the field information corresponding to one scene, or the field information corresponding to multiple scenes. A scene can be understood as a scene categorized into different scene categories based on a key field. If a key field includes two values, 1 and 0, when the field value is 1, the field information (key field = 1) can be determined to be the field information of scene a, which belongs to scene category A. When the field value is 0, the field information (key field = 0) can be determined to be the field information of scene b, which belongs to scene category B.

[0055] In some embodiments, the scene identification rule can be a transaction rule used to identify scene field information from transaction table data. In this case, the implementation of this step can be: determining the scene identification field for offline transaction table data, determining the scene identification rule based on the scene identification field, and determining the first scene field information corresponding to the full transaction table data and the second scene field information corresponding to the offline transaction table data based on the scene identification rule.

[0056] Specifically, when determining the scene identification field for offline transaction table data, it can be as follows:

[0057] A2: Obtain the field mapping table corresponding to the reference transaction semantic type and the reference scenario identification field;

[0058] Specifically, the field mapping table can be a table that stores the mapping relationship between reference transaction semantic types and reference scenario identification fields, based on prior experience. The field mapping table can be stored in a database and can be read from the database. The reference transaction semantic type can refer to the transaction semantic type corresponding to the transaction table data. The reference scenario identification field can refer to the field in the transaction table data used to determine different scenarios.

[0059] For example, Table a is a transaction data table, specifically a table showing customer traffic data for an e-commerce store. Table a contains fields such as IP address location, product ID of viewed items, product ID of purchased items, purchase amount, and quantity purchased. The data stored in Table a can be the transaction table data in this embodiment. The reference transaction semantic type corresponding to Table a can be a traffic type, and the reference scenario identification fields in Table a can be "IP address location" and "delivery location". Therefore, in the field mapping table, one possible mapping relationship between the reference transaction semantic type and the reference scenario identification field is the mapping relationship between traffic type and "IP address location + delivery location".

[0060] A4: Determine the target transaction semantic type corresponding to the offline transaction table data, query the target scenario identification field corresponding to the target transaction semantic type in the field mapping table, and determine the target scenario identification field as the scenario identification field for the offline transaction table data.

[0061] Specifically, transaction semantic detection can be performed on offline transaction table data to obtain the target transaction semantic type corresponding to the offline transaction table data. Then, the target scene identification field corresponding to the target transaction semantic type is queried in the field mapping table. Finally, the target scene identification field is determined as the scene identification field for the offline transaction table data. In this way, the scene identification field for the offline transaction table data is determined based on the transaction semantics corresponding to the offline transaction table data, rather than using general detection rules. This achieves the effect of accurately detecting scene field information in offline data, thereby improving the accuracy of newly detected (first-time occurrences) scene field information.

[0062] Furthermore, when executing the scene recognition rule determination based on the scene recognition field, it can specifically be as follows:

[0063] When there is only one scene recognition field, the scene recognition rule is to determine the scene field information corresponding to a scene by taking the value of each field of the scene recognition field; when there are multiple scene recognition fields, the scene recognition rule is to determine the scene field information corresponding to a scene by taking the combination of the values ​​of each field of the scene recognition field.

[0064] In some embodiments, the scene recognition rule can be a recognition model used to identify scene field information from transaction table data. In this case, this step can be implemented as follows: obtain the scene recognition model, and use the scene recognition model to determine the first scene field information corresponding to the full transaction table data and the second scene field information corresponding to the offline transaction table data. The scene recognition model is trained on a machine learning model based on sample transaction data labeled with scene field information tags. Thus, using a pre-trained scene recognition model for scene detection improves the efficiency of scene detection while ensuring the accuracy of the detected scene field information.

[0065] Specifically, before executing the embodiments of this specification, a machine learning model can be trained using sample transaction data to obtain a scene recognition model. Therefore, when executing this embodiment, the scene recognition model can be directly obtained and used. The model training process of the scene recognition model is explained below:

[0066] Model creation: Create an initial scene recognition model based on the machine learning model to identify scenes using scene field information.

[0067] Sample data acquisition: Acquire a large amount of sample data. The sample data is based on a large amount of transaction table data. Data processing is performed to extract sample scenario field information to generate sample data containing sample scenario field information.

[0068] Sample data annotation: Based on the need to identify scenarios using scene field information, an expert service is introduced to manually annotate the sample data with corresponding sample labels. The sample labels include scene field information labels for each sample data.

[0069] Model training process: Input sample data into the initial scene recognition model for at least one round of model training to obtain prediction field data, which includes prediction scene field information. Based on the prediction field data (predicted scene field information) and sample data labels (scene field information labels), the model loss function is used to determine the model loss value. Based on the model loss value, the model parameters of the initial scene recognition model are adjusted until the model training termination condition is met to obtain the scene recognition model.

[0070] Optionally, the model's training termination conditions may include, for example, the loss function value being less than or equal to a preset loss function threshold, or the number of iterations reaching a preset threshold. Specific training termination conditions can be determined based on actual circumstances and are not specifically limited here.

[0071] It should be noted that the machine learning models involved in one or more embodiments of this application include, but are not limited to, fitting one or more of the following machine learning models: Convolutional Neural Network (CNN) model, Deep Neural Network (DNN) model, Recurrent Neural Networks (RNN) model, embedding model, Gradient Boosting Decision Tree (GBDT) model, Logistic Regression (LR) model, etc.

[0072] Optionally, the first scene field information and the second scene field information can also be detected simultaneously through the above two implementation methods. That is, the scene field information of offline transaction table data and full transaction table data are detected using scene recognition rules, and the scene field information of offline transaction table data and full transaction table data are detected using scene recognition models, resulting in two sets of first scene field information and two sets of second scene field information. The two sets of first scene field information are compared, and the scene field information that is the same in the two sets of first scene field information is taken as the final first scene field information. The two sets of second scene field information are compared, and the scene field information that is the same in the two sets of second scene field information is taken as the final second scene field information.

[0073] S306, Detect whether there is a marked scene field information in the second scene field information.

[0074] In a straightforward manner, the marked scenario field information can be field information where a specified field has a specified field value. For transaction table data of different transaction types, there can be marked scenario field information corresponding to the transaction table data of each transaction type.

[0075] In some embodiments, the marked scenario field information corresponding to the offline transaction table data can be obtained, and the second scenario field information and the marked scenario field information can be compared to detect whether there is field information in the second scenario field information that is exactly the same as the marked scenario field information.

[0076] S308, if the second scenario field information contains a marked scenario field information, then the marked scenario field information is determined as the new scenario field information corresponding to the offline transaction table data.

[0077] In some embodiments, if the second scenario field information contains a marked scenario field information, that is, if the second scenario field information contains a field information that is exactly the same as the marked scenario field information, then the marked scenario field information can be identified as the new scenario field information corresponding to the offline transaction table data. Thus, in addition to supporting the detection of newly appearing scenario field information, this embodiment of the specification also supports the detection of manually marked scenario field information, thereby enabling the performance of corresponding management tasks on the marked scenario field information after detection.

[0078] S310, determine the third scenario field information in the second scenario field information other than the first scenario field information, and determine the new scenario field information corresponding to the offline transaction table data based on the third scenario field information.

[0079] In some embodiments, the second scenario field information and the first scenario field information can be compared to determine the third scenario field information in the second scenario field information other than the first scenario field information. The third scenario field information can be determined as the new scenario field information corresponding to the offline transaction table data.

[0080] S312, determine the scene category corresponding to the new scene field information.

[0081] In some embodiments, since the new scene field information may include field information corresponding to one or more scenes, for each scene-related field information, the scene category of the scene corresponding to that field information can be determined. Specifically, the scene category may be the value category of the field in the scene-related field information.

[0082] In an application scenario where a key field determines the information of a new scenario field for different scenarios, this key field can have multiple values. When the key field takes any one of these values, the scenario corresponding to the scenario information containing that field value can be determined. Simultaneously, the value category corresponding to that field value can be determined as the scenario category. For example, table b is a transaction data table, specifically a data table of customer traffic for an e-commerce store. Table b contains fields such as the product ID of viewed products, the product ID of purchased products, the purchase amount, and the purchase quantity. The data stored in table b can be the transaction table data in this embodiment. The key field in table b used to determine different scenarios is "delivery region." The value of the "delivery region" field can be a province, autonomous region, or municipality in China. Therefore, the scenario information containing the "delivery region" field can correspond to 32 scenarios; that is, any province, autonomous region, or municipality in the "delivery region" field can correspond to a scenario. When the "Delivery Region" field in a certain field is set to Shanghai, the corresponding scenario for this field can be a Shanghai order scenario, and the scenario category for a Shanghai order scenario can be Shanghai.

[0083] In application scenarios where multiple key fields determine different scenarios for new scenario field information, each key field can have multiple values. For each combination of values ​​for these key fields, a scenario corresponding to the field scenario information containing that combination of values ​​can be determined. Furthermore, the value category corresponding to the combination of field values ​​can be determined as the scenario category. For example, Table A is a transaction data table, representing customer traffic data for an e-commerce store. Table A contains fields such as IP address location, product ID of viewed products, product ID of purchased products, purchase amount, and purchase quantity. The data stored in Table A can be the transaction table data in this embodiment. The reference transaction semantic type corresponding to Table A can be a traffic type, and the key fields in Table A can be "IP address location" and "delivery region." When both the "IP address location" and "delivery region" values ​​in a field are outside of China, the scenario corresponding to that field can be determined as an overseas order scenario, and the scenario category for this overseas order scenario can be the overseas category.

[0084] S314, If the scenario category does not belong to the scenario category set, then determine the interception task and the first notification task for the offline transaction table data.

[0085] As is easily understood, the scene category set may include multiple historical scene categories. A historical scene category refers to the scene category corresponding to the scene field information in the historical transaction table data. In the embodiments of this specification, historical transaction table data may refer to the full transaction table data, and historical scene categories may refer to the scene category corresponding to the scene field information in the full transaction table data. The method for dividing historical scene categories can be found in the description of S312. Historical scene categories can be determined before executing S314, specifically before executing the embodiments described in this specification, and stored in the scene category set.

[0086] In some embodiments, when a scene category does not belong to the scene category set, it indicates that the scene category did not appear before day T-1, and it is unclear whether the new scene field information is risky data. Therefore, an interception task and a first notification task targeting the offline transaction table data can be determined. The interception task can be a task that intercepts and processes the running task of the offline transaction table data. The first notification task can be a task that generates a first notification message corresponding to the offline transaction table data.

[0087] S316, based on the interception task, intercepts and processes the running tasks corresponding to the offline transaction table data.

[0088] In some embodiments, Data Quality Center (DQC) technology can be used to intercept the running tasks corresponding to offline transaction table data. When using DQC technology for interception, the interception level corresponding to the offline transaction table data can be set to a high level, that is, the running tasks corresponding to the offline transaction table data are strongly intercepted. Specifically, the task can be set to a failed state, and its downstream tasks will not be executed.

[0089] S318, Generate a first notification message corresponding to the offline transaction table data based on the first notification task, and send the first notification message to the transaction management terminal.

[0090] In simple terms, a transaction management terminal can refer to a terminal that has the function of verifying transaction table data.

[0091] The first notification message is used to instruct the transaction management terminal to verify the offline transaction table data.

[0092] In some embodiments, DQC technology can also be used to generate a first notification message and send it to the transaction management terminal. When generating the first notification message, a first notification message containing new scenario field information and data verification indication information can be generated.

[0093] Optionally, the first notification message can be sent via an instant messaging application, such as DingTalk, SMS, or email.

[0094] S320: Obtain the data verification result sent by the transaction management terminal based on the first notification message, and perform recovery control processing on the offline transaction table data based on the data verification result.

[0095] In some embodiments, after receiving the first notification message, the transaction management terminal can perform data verification on the new scenario field information contained in the first notification message. Data verification may refer to verifying whether the new scenario field information is abnormal data. If the new scenario field information is normal data, the transaction management terminal can generate a data verification result of type "verification successful". If the new scenario field information is abnormal data, it may be necessary to modify the new scenario field information to obtain the correct offline transaction table data, and a data verification result of type "verification failed" can be generated. Further, the transaction management terminal can send the data verification result to the electronic device based on the first notification message, and the electronic device can obtain the data verification result. The electronic device performs recovery control processing on the offline transaction table data based on the data verification result. Specifically, if the data verification result is of type "verification successful", the task of intercepting the offline transaction table data is canceled; if the data verification result is of type "verification failed", the task of intercepting the corresponding offline data table data is maintained for a preset time.

[0096] When the data validation result is a successful validation result, it indicates that the new scenario field information is normal data. Therefore, the running task corresponding to the offline transaction table data no longer needs to be intercepted. DQC technology can be used to set the task to a successful state and enable its downstream tasks to be executed.

[0097] If the data validation result is "validation failed," it indicates that the new scenario field information contains abnormal data. This means the new scenario field information may contain erroneous data, and subsequent tasks can only be executed after the erroneous data is corrected. Since it's unclear when the erroneous data will be corrected, the running tasks corresponding to the offline data table can be intercepted for a preset time. This prevents subsequent tasks from executing if the abnormal offline transaction table data is not corrected, which could lead to errors in the data processing results. The preset time can be set based on prior experience.

[0098] Optionally, when the transaction management terminal performs data verification on the new scenario field information contained in the first notification message, in one feasible implementation, it may refer to... Figure 4 The diagram illustrates an interaction scenario between an electronic device and a transaction management terminal. In this scenario, the transaction management terminal automatically verifies the new scenario field information contained in the first notification message and automatically sends the verification result to the electronic device. In another feasible implementation, see... Figure 5The diagram shows another interaction scenario between an electronic device and a transaction management terminal. Alternatively, the transaction management terminal can output a first notification message to the corresponding transaction management personnel. The transaction management personnel then control the transaction management terminal to perform data verification on the new scenario field information contained in the first notification message. After the verification is completed, the transaction management personnel determine the data verification result and control the transaction management terminal to send the data verification result to the electronic device.

[0099] S322, if the scenario category belongs to the scenario category set, then determine the second notification task for the offline transaction table data.

[0100] In some embodiments, when the scene category belongs to the scene category set, it indicates that the scene category appeared before day T-1. That is, the new scene field information is the marked scene field information, indicating that the new scene field information is not risky data. Therefore, a second notification task for the offline transaction table data can be determined. The second notification task can be a task that generates a second notification message corresponding to the offline transaction table data.

[0101] S324, Based on the second notification task, determine the number of times the marker appears in the marker scene field information, and generate a second notification message containing the number of times the marker appears and the marker scene field information.

[0102] In some embodiments, the total number of times the marked scenario field information appears from the marked date to day T-1 can be determined, and this number can be recorded as the mark occurrence count. Further, a second notification message containing the mark occurrence count and the marked scenario field information can be generated.

[0103] S326, send the second notification message to the transaction management terminal.

[0104] As is easily understood, the second notification message is used to prompt the transaction management terminal to perform scenario reminder processing on the marked scenario field information. For example, scenario reminder processing can refer to the subsequent processing performed by the transaction management terminal on the marked scenario field information based on the prompt.

[0105] In some embodiments, a second notification message may be sent to a transaction management terminal, so that after reading the second notification message, the transaction management terminal can know the number of times the marker corresponding to the marker scene field information appears, and perform corresponding processing operations on the marker scene field information.

[0106] Optionally, the second notification message can be sent via instant messaging applications, such as DingTalk, SMS, email, etc.

[0107] It should be noted that, since new scenario field information can include both first-time occurrences of scenario field information and non-first-time occurrences of marked scenario field information, the scenario category corresponding to the new scenario field information can include a first scenario category that does not belong to the scenario category set, and it can also include a second scenario category that belongs to the scenario category set. Therefore, for new scenario field information detected in an offline transaction table, S314-S320 and S322-S326 can be executed simultaneously. For scenarios where the new scenario field information only includes first-time occurrences of scenario field information, S314-S320 can be executed, but S322-S326 cannot. For scenarios where the new scenario field information only includes non-first-time occurrences of marked scenario field information, S322-S326 can be executed, but S314-S320 cannot.

[0108] In the embodiments of this specification, the first scene field information corresponding to the full transaction table data and the second scene field information corresponding to the offline transaction table data are determined according to scene recognition rules. Then, it is detected whether there is marked scene field information in the second scene field information. If marked scene field information exists in the second scene field information, the third scene field information other than the first scene field information and the marked scene field information in the second scene field information are determined as new scene field information. If the scene category corresponding to the new scene field information does not belong to the scene category set, an interception task and a first notification task for the offline transaction table data are determined, and the offline transaction table data is processed based on the interception task and the first notification task. If the scene category corresponding to the new scene field information belongs to the scene category set, a second notification task for the offline transaction table data is determined, and the offline transaction table data is processed based on the second notification task. Thus, not only is the detection of the first occurrence of scene field information realized, but also the detection of marked scene field information with custom marking. After detecting the first occurrence of scene field information, by intercepting the running task corresponding to the offline transaction table data, the risk can be blocked within the domain, avoiding the impact of abnormal data in the offline transaction table data on the subsequent data processing results, thereby reducing the risk of offline transaction table data.

[0109] Please see Figure 6 This is a schematic diagram of the structure of a data processing device provided in an embodiment of this specification. The data processing device 600 can be implemented as all or part of a terminal through software, hardware, or a combination of both. The device 600 includes:

[0110] Data acquisition module 610 is used to acquire offline transaction table data and full transaction table data;

[0111] Scene detection module 620 is used to perform new scene detection based on the offline transaction table data and the full transaction table data, and determine the new scene field information corresponding to the offline transaction table data;

[0112] The data management module 630 is used to determine the management task for the offline transaction table data based on the new scenario field information, and to process the offline transaction table data based on the management task.

[0113] Optionally, the scene detection module 620 includes:

[0114] An information recognition unit is used to acquire scene recognition rules and determine, based on the scene recognition rules, the first scene field information corresponding to the full transaction table data and the second scene field information corresponding to the offline transaction table data;

[0115] The information determination unit is used to determine the third scenario field information in the second scenario field information other than the first scenario field information, and to determine the new scenario field information corresponding to the offline transaction table data based on the third scenario field information.

[0116] Optionally, the scene detection module 620 is also used for:

[0117] Detect whether there is a marked scene field information in the second scene field information;

[0118] If the marked scenario field information exists in the second scenario field information, then the marked scenario field information is determined as the new scenario field information corresponding to the offline transaction table data.

[0119] Optionally, the information recognition unit includes:

[0120] A first identification unit is configured to determine a scene identification field for the offline transaction table data, determine a scene identification rule based on the scene identification field, and determine, based on the scene identification rule, first scene field information corresponding to the full transaction table data and second scene field information corresponding to the offline transaction table data; and / or

[0121] The second identification unit is used to acquire a scene identification model and use the scene identification model to determine the first scene field information corresponding to the full transaction table data and the second scene field information corresponding to the offline transaction table data. The scene identification model is obtained by training a machine learning model based on sample transaction data with labeled scene field information tags.

[0122] Optionally, the first identification unit is used for:

[0123] Obtain the field mapping table corresponding to the reference transaction semantic type and the reference scenario identification field;

[0124] Determine the target transaction semantic type corresponding to the offline transaction table data, query the target scene identification field corresponding to the target transaction semantic type in the field mapping table, and determine the target scene identification field as the scene identification field for the offline transaction table data.

[0125] Optionally, the data management module 630 includes:

[0126] A category determination unit is used to determine the scene category corresponding to the new scene field information;

[0127] The first task determination unit is used to determine an interception task and a first notification task for the offline transaction table data if the scenario category does not belong to the scenario category set.

[0128] The second task determination unit is used to determine a second notification task for the offline transaction table data if the scenario category belongs to the scenario category set.

[0129] Optionally, the data management module 630 includes:

[0130] The first processing unit is used to intercept and process the running task corresponding to the offline transaction table data based on the interception task;

[0131] The second processing unit is configured to generate a first notification message corresponding to the offline transaction table data based on the first notification task, and send the first notification message to the transaction management terminal. The first notification message is used to instruct the transaction management terminal to verify the offline transaction table data.

[0132] The third processing unit is used to obtain the data verification result sent by the transaction management terminal based on the first notification message, and to perform recovery control processing on the offline transaction table data based on the data verification result.

[0133] Optionally, the third processing unit is used for:

[0134] If the data verification result is of type "verification successful", then cancel the task of intercepting the offline transaction table data.

[0135] If the data verification result is of the verification failure type, the running task corresponding to the offline data table data will be blocked for a preset time.

[0136] Optionally, the data management module 630 includes:

[0137] The fourth processing unit is used to determine the number of times the marker appears in the marker scene field information based on the second notification task, and generate a second notification message containing the number of times the marker appears and the marker scene field information;

[0138] The fifth processing unit is used to send the second notification message to the transaction management terminal, wherein the second notification message is used to prompt the transaction management terminal to perform scene reminder processing on the marked scene field information.

[0139] Please refer to Figure 7 This diagram illustrates the structure of an electronic device provided in an exemplary embodiment of this specification. The electronic device in this specification may include one or more components such as a processor 110, a memory 120, an input device 130, an output device 140, and a bus 150. The processor 110, memory 120, input device 130, and output device 140 may be connected via the bus 150.

[0140] Processor 110 may include one or more processing cores. Processor 110 connects to various parts of the terminal using various interfaces and lines, and performs various functions and processes data of terminal 100 by running or executing instructions, programs, code sets, or instruction sets stored in memory 120, and by calling data stored in memory 120. Optionally, processor 110 may be implemented using at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), or programmable logic array (PLA). Processor 110 may integrate one or more of a central processing unit (CPU), graphics processing unit (GPU), and modem. The CPU mainly handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the displayed content; and the modem is used for wireless communication. It is understood that the modem may also not be integrated into processor 110, but implemented separately through a communication chip.

[0141] The memory 120 may include random access memory (RAM) or read-only memory (ROM). Optionally, the memory 120 may include a non-transitory computer-readable storage medium. The memory 120 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 120 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (e.g., touch function, sound playback function, image playback function, etc.), instructions for implementing the various method embodiments described below, etc. The operating system may be the Android system, including systems deeply developed based on the Android system, the iOS system developed by Apple Inc., including systems deeply developed based on the iOS system, or other systems.

[0142] In order for the operating system to distinguish the specific application scenarios of third-party applications, it is necessary to establish data communication between the third-party applications and the operating system. This would allow the operating system to obtain the current scenario information of the third-party applications at any time, and then perform targeted system resource adaptation based on the current scenario.

[0143] The input device 130 is used to receive input instructions or data, and includes, but is not limited to, a keyboard, mouse, camera, microphone, or touch device. The output device 140 is used to output instructions or data, and includes, but is not limited to, a display device and a speaker. In one example, the input device 130 and the output device 140 can be combined, and the input device 130 and the output device 140 can be a touch display screen.

[0144] The touch display screen can be designed as a full-screen, curved screen, or irregularly shaped screen. It can also be designed as a combination of a full-screen and a curved screen, or a combination of an irregularly shaped screen and a curved screen; however, this specification does not limit the specific design of the embodiments described herein.

[0145] In addition, those skilled in the art will understand that the structure of the electronic device shown in the above figures does not constitute a limitation on the electronic device. The electronic device may include more or fewer components than shown, or combine certain components, or have different component arrangements. For example, the electronic device may also include radio frequency circuits, input units, sensors, audio circuits, Wireless Fidelity (WiFi) modules, power supplies, Bluetooth modules, etc., which will not be described in detail here.

[0146] exist Figure 7In the illustrated electronic device, the processor 110 can be used to call a program of a data processing method stored in the memory 120, and specifically perform the following operations:

[0147] Retrieve offline transaction table data and full transaction table data;

[0148] New scenario detection is performed based on the offline transaction table data and the full transaction table data to determine the new scenario field information corresponding to the offline transaction table data;

[0149] Based on the new scenario field information, a management task is determined for the offline transaction table data, and the offline transaction table data is processed based on the management task.

[0150] In one embodiment, when the processor 110 performs new scenario detection based on the offline transaction table data and the full transaction table data, and determines the new scenario field information corresponding to the offline transaction table data, it specifically performs the following operations:

[0151] Obtain scene recognition rules, and determine the first scene field information corresponding to the full transaction table data and the second scene field information corresponding to the offline transaction table data based on the scene recognition rules;

[0152] Determine the third scenario field information in the second scenario field information, excluding the first scenario field information, and determine the new scenario field information corresponding to the offline transaction table data based on the third scenario field information.

[0153] In one embodiment, the processor 110 also performs the following operations:

[0154] Detect whether there is a marked scene field information in the second scene field information;

[0155] If the marked scenario field information exists in the second scenario field information, then the marked scenario field information is determined as the new scenario field information corresponding to the offline transaction table data.

[0156] In one embodiment, when the processor 110 executes the scene identification rule and determines the first scene field information corresponding to the full transaction table data and the second scene field information corresponding to the offline transaction table data based on the scene identification rule, it specifically performs the following operations:

[0157] Determine the scene identification field for the offline transaction table data; determine the scene identification rule based on the scene identification field; and determine the first scene field information corresponding to the full transaction table data and the second scene field information corresponding to the offline transaction table data based on the scene identification rule; and / or,

[0158] A scene recognition model is obtained, and the scene recognition model is used to determine the first scene field information corresponding to the full transaction table data and the second scene field information corresponding to the offline transaction table data. The scene recognition model is obtained by training a machine learning model based on sample transaction data with labeled scene field information.

[0159] In one embodiment, when the processor 110 executes the process of determining the scenario identification field for the offline transaction table data, it specifically performs the following operations:

[0160] Obtain the field mapping table corresponding to the reference transaction semantic type and the reference scenario identification field;

[0161] Determine the target transaction semantic type corresponding to the offline transaction table data, query the target scene identification field corresponding to the target transaction semantic type in the field mapping table, and determine the target scene identification field as the scene identification field for the offline transaction table data.

[0162] In one embodiment, when the processor 110 executes the management task for determining the offline transaction table data based on the new scenario field information, it specifically performs the following operations:

[0163] Determine the scene category corresponding to the new scene field information;

[0164] If the scenario category does not belong to the scenario category set, then an interception task and a first notification task for the offline transaction table data are determined.

[0165] If the scenario category belongs to the scenario category set, then a second notification task is determined for the offline transaction table data.

[0166] In one embodiment, when the processor 110 processes the offline transaction table data based on the management task, it specifically performs the following operations:

[0167] Based on the interception task, the running task corresponding to the offline transaction table data is intercepted and processed;

[0168] Based on the first notification task, a first notification message corresponding to the offline transaction table data is generated, and the first notification message is sent to the transaction management terminal. The first notification message is used to instruct the transaction management terminal to verify the offline transaction table data.

[0169] Obtain the data verification result sent by the transaction management terminal based on the first notification message, and perform recovery control processing on the offline transaction table data based on the data verification result.

[0170] In one embodiment, when the processor 110 performs the recovery control processing on the offline transaction table data based on the data verification result, it specifically performs the following operations:

[0171] If the data verification result is of type "verification successful", then cancel the task of intercepting the offline transaction table data.

[0172] If the data verification result is of the verification failure type, the running task corresponding to the offline data table data will be blocked for a preset time.

[0173] In one embodiment, when the processor 110 processes the offline transaction table data based on the management task, it specifically performs the following operations:

[0174] Based on the second notification task, determine the number of times the tag appears in the tag scene field information, and generate a second notification message containing the number of times the tag appears and the tag scene field information;

[0175] The second notification message is sent to the transaction management terminal, and the second notification message is used to prompt the transaction management terminal to perform scene reminder processing on the marked scene field information.

[0176] This specification also provides a computer program product that stores at least one instruction, which is loaded and executed by the processor to implement the rare character processing method described in the above embodiments.

[0177] Those skilled in the art will recognize that the functions described in the embodiments of this specification in one or more of the above examples can be implemented using hardware, software, firmware, or any combination thereof. When implemented in software, these functions can be stored in a computer-readable medium or transmitted as one or more instructions or code on a computer-readable medium. Computer-readable media include computer storage media and communication media, wherein communication media include any medium that facilitates the transfer of a computer program from one place to another. Storage media can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0178] The above description is merely an optional embodiment of this specification and is not intended to limit this specification. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this specification shall be included within the protection scope of this specification.

[0179] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

Claims

1. A data processing method, the method comprising: obtaining offline transaction table data and full-amount transaction table data of a same transaction; performing new scene detection based on the offline transaction table data and the full-amount transaction table data to determine new scene field information corresponding to the offline transaction table data; wherein the performing new scene detection based on the offline transaction table data and the full-amount transaction table data to determine new scene field information corresponding to the offline transaction table data comprises: determining a scene identification field for the offline transaction table data, determining a scene identification rule based on the scene identification field, determining first scene field information corresponding to the full-amount transaction table data and second scene field information corresponding to the offline transaction table data based on the scene identification rule, the scene identification rule being a rule for identifying scene field information from transaction table data; obtaining a scene identification model, and determining the first scene field information corresponding to the full-amount transaction table data and the second scene field information corresponding to the offline transaction table data using the scene identification model, wherein the scene identification model is obtained by training a machine learning model based on sample transaction data with labeled scene field information tags; determining identical first scene field information in the two groups of first scene field information, determining identical second scene field information in the two groups of second scene field information, determining third scene field information in the identical second scene field information other than the identical first scene field information, and determining new scene field information corresponding to the offline transaction table data based on the third scene field information, the new scene field information including scene field information that first appears in offline transaction table data and marked scene field information that does not first appear in offline transaction table data, the marked scene field information being field information in which a specified field has a specified field value; determining a management task for the offline transaction table data based on the new scene field information, and processing the offline transaction table data based on the management task.

2. The method of claim 1, further comprising: detecting whether there is marked scene field information in the second scene field information; if there is the marked scene field information in the second scene field information, determining the marked scene field information as the new scene field information corresponding to the offline transaction table data.

3. The method of claim 1, wherein the determining a scene identification field for the offline transaction table data comprises: obtaining a field mapping table corresponding to a reference transaction semantic type and a reference scene identification field; determining a target transaction semantic type corresponding to the offline transaction table data, querying a target scene identification field corresponding to the target transaction semantic type in the field mapping table, and determining the target scene identification field as the scene identification field for the offline transaction table data.

4. The method of claim 1, wherein the determining a management task for the offline transaction table data based on the new scene field information comprises: determining a scene category corresponding to the new scene field information. if the scenario category does not belong to the set of scenario categories, determining an interception task and a first notification task for the offline transaction table data; if the scenario category belongs to the set of scenario categories, determining a second notification task for the offline transaction table data.

5. The method of claim 4, wherein the processing the offline transaction table data based on the management task comprises: intercepting a running task corresponding to the offline transaction table data based on the interception task; generating a first notification message corresponding to the offline transaction table data based on the first notification task, and sending the first notification message to a transaction management terminal, the first notification message being used to instruct the transaction management terminal to check the offline transaction table data; obtaining a data check result sent by the transaction management terminal based on the first notification message, and performing recovery control processing on the offline transaction table data based on the data check result.

6. The method of claim 5, wherein the performing recovery control processing on the offline transaction table data based on the data check result comprises: if a type of the data check result is a check success type, canceling interception of the running task of the offline transaction table data; if the type of the data check result is a check failure type, maintaining interception of the running task corresponding to the offline transaction table data within a preset time.

7. The method of claim 4, wherein the processing the offline transaction table data based on the management task comprises: determining a number of occurrences of a mark based on the second notification task, the mark being used to mark scenario field information, and generating a second notification message containing the number of occurrences of the mark and the mark scenario field information; sending the second notification message to a transaction management terminal, the second notification message being used to prompt the transaction management terminal to perform scenario reminding processing on the mark scenario field information.

8. A data processing apparatus, the apparatus comprising: a data obtaining module configured to obtain offline transaction table data and full-amount transaction table data of a same transaction; The scene detection module is configured to perform new scene detection based on the offline transaction table data and the full-amount transaction table data, and determine new scene field information corresponding to the offline transaction table data. The new scene detection based on the offline transaction table data and the full-amount transaction table data, and the determination of the new scene field information corresponding to the offline transaction table data include: determining a scene identification field for the offline transaction table data, determining a scene identification rule based on the scene identification field, determining first scene field information corresponding to the full-amount transaction table data and second scene field information corresponding to the offline transaction table data based on the scene identification rule, the scene identification rule being a rule for identifying scene field information from transaction table data; obtaining a scene identification model, and determining the first scene field information corresponding to the full-amount transaction table data and the second scene field information corresponding to the offline transaction table data by using the scene identification model, wherein the scene identification model is obtained by training a machine learning model based on sample transaction data with labeled scene field information tags; determining identical first scene field information in the two groups of first scene field information, determining identical second scene field information in the two groups of second scene field information, determining third scene field information in the identical second scene field information except for the identical first scene field information, determining new scene field information corresponding to the offline transaction table data based on the third scene field information, the new scene field information including scene field information that first appears in offline transaction table data and marked scene field information that does not first appear in offline transaction table data, the marked scene field information being field information in which a specified field has a specified field value; The data management module is configured to determine a management task for the offline transaction table data based on the new scene field information, and perform processing on the offline transaction table data based on the management task.

9. A computer storage medium storing a plurality of instructions, the instructions being adapted to be loaded and executed by a processor to perform the method steps of any one of claims 1-7.

10. A computer program product storing at least one instruction, the at least one instruction being loaded and executed by a processor to perform the method steps of any one of claims 1-7.

11. An electronic device comprising: A processor and a memory; wherein the memory stores a computer program, and the computer program is adapted to be loaded and executed by the processor to perform the method steps of any one of claims 1-7.

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