A multi-system collaborative data processing method, system, device and medium

Abstract

This application discloses a multi-system collaborative data processing method, system, device, and medium, relating to the field of information technology. The method includes: acquiring business data streams transmitted between various production and sales systems; performing a consistency comparison on the business data streams based on a preset first verification rule to generate a data verification result; performing anomaly condition judgment on the business data streams based on a preset second verification rule to generate anomaly identification information; performing a repetitive identification operation on the business data streams based on a preset third verification rule to generate an interception control command; and controlling the corresponding production and sales systems to perform corresponding data processing operations based on the data verification result, the anomaly identification information, and the interception control command.

Description

Technical Field

[0001] This application relates to the field of information technology, and in particular to a multi-system collaborative data processing method, system, device and medium. Background Technology

[0002] Currently, in the information-based operations of modern enterprises, production and sales are typically supported by multiple independently deployed dedicated systems, such as manufacturing execution systems, cost accounting systems, and sales logistics systems. These systems engage in frequent and necessary data exchanges to ensure the consistency of production, inventory, cost, and financial information. However, due to differences in the construction period, technical architecture, and data standards of these systems, data transfer and collaborative processing between systems become crucial for ensuring the smooth operation of the overall business process. Tasks such as verifying the accuracy of data transmission results between systems, checking the compliance of business logic, identifying and intercepting duplicate or abnormal data, and synchronizing information caused by business changes primarily rely on business management personnel investing significant time in manual comparison, investigation, and processing.

[0003] However, this approach is not only inefficient and unable to handle massive amounts of data, but also highly dependent on the experience and responsibility of personnel, posing a risk of human error. This can lead to data errors being transmitted and amplified in business processes, ultimately affecting the accuracy of cost accounting and the reliability of financial statements. Summary of the Invention

[0004] The summary section introduces a series of simplified concepts, which will be further explained in detail in the detailed description section. This summary section is not intended to limit the key and essential technical features of the claimed technical solution, nor is it intended to determine the scope of protection of the claimed technical solution.

[0005] In a first aspect, embodiments of this application provide a multi-system collaborative data processing method, the method comprising: Acquire the business data streams transmitted between various production and sales systems; Based on the preset first verification rule, the business data stream is compared for consistency, and a data verification result is generated; Based on the preset second check rule, the business data stream is subjected to anomaly condition judgment, and anomaly identification information is generated; Based on the preset third check rules, the business data stream is subjected to a repeatability identification operation, and an interception control command is generated. Based on the data verification results, the anomaly identification information, and the interception control command, the corresponding production and sales system is controlled to perform the corresponding data processing operation.

[0006] In one embodiment of the present invention, the business data stream includes a first business data stream sent by a first production and sales system and a second business data stream received by a second production and sales system. The step of performing a consistency comparison on the business data stream based on a preset first verification rule to generate a data verification result includes: Extract the first total statistical information and the first inventory data from the first business data stream; Extract the second statistical total information and the second inventory data from the second business data stream; Based on robotic process automation technology, the first total statistical information and the second total statistical information are compared to generate a first comparison result; Based on robotic process automation technology, the first inventory data and the second inventory data are compared to generate a second comparison result; Based on data auditing technology, the field data of each data record in the first business data stream and the second business data stream are compared to generate a third comparison result; The data verification result is generated based on the first comparison result, the second comparison result, and the third comparison result.

[0007] In one embodiment of the present invention, the step of performing anomaly condition judgment on the business data stream based on a preset second check rule and generating anomaly identification information includes: Based on the base code, the first type of business data in the business data stream is converted into the second type of business data, and the logical relationship between each field in the second type of business data is checked to generate a logical check result. Extract quantitative inventory data from the business data stream; Based on a preset inventory allowance range, determine whether the inventory quantification data has reached a preset warning value, and generate an inventory check result; Based on a preset set of task execution prerequisites, determine whether the business data stream meets the task triggering conditions and generate a condition check result. Extract the ending data of the current accounting period and the beginning data of the next accounting period from the business data stream, and perform a consistency check on the ending data and the beginning data to obtain the consistency check result; The anomaly identification information is generated based on the logical check result, the inventory check result, the condition check result, and the consistency check result.

[0008] In one embodiment of the present invention, the step of performing a repeatability identification operation on the business data stream based on a preset third check rule and generating an interception control command includes: Extract at least one feature field from the business data stream to form a combined feature; Query the historical data record table to determine whether the combined feature exists in the historical data record table; If the combined features exist in the historical data record table, an interception control instruction is generated to reject the business data stream.

[0009] In one embodiment of the present invention, controlling the corresponding production and sales system to perform corresponding data processing operations based on the data verification result, the anomaly identification information, and the interception control command includes: When the data verification result indicates that there is a data inconsistency, the control message notification module sends a first alarm notification to the preset message receiving terminal. When the abnormal identification information indicates that there is a business logic abnormality or a state out of bounds, the control message notification module sends a second alarm notification to the message receiving end. When the interception control command is a rejection command, the control data receiving interface stops writing operations to the service data stream.

[0010] In one embodiment of the present invention, the method further includes: If new master data is added in the business data stream, the new master data is converted into the format corresponding to the target system based on the preset data conversion rules. The control data synchronization interface pushes the newly added master data, after format conversion, to the target system.

[0011] In one embodiment of the present invention, the method further includes: Monitor the task execution status of the data transmission interface between the production and sales systems; When a task is detected as failing, the corresponding data transmission task is re-invoked.

[0012] In one embodiment of the present invention, the method further includes: Identify negative output records in the business data stream; Based on the preset accounting object mapping rules, a replacement accounting object is determined for the negative output record; Determine whether there is a positive output record under the current output category, and compare the quantified value of the original accounting object corresponding to the negative output with the quantified value of the replacement accounting object; If it is determined that there is no positive output record or the quantization value of the replacement accounting object is less than the quantization value of the original accounting object, a third alarm notification is generated. Based on preset record conversion rules, a single negative output record is converted into multiple production adjustment records and inventory adjustment records.

[0013] Secondly, this application proposes a multi-system collaborative data processing system, the system comprising: a data acquisition module, a data verification module, and a data processing module; The data acquisition module is configured to acquire business data streams transmitted between various production and sales systems. The data verification module is configured to: perform consistency comparison on the business data stream based on a preset first verification rule and generate a data verification result; perform anomaly condition judgment on the business data stream based on a preset second verification rule and generate anomaly identification information; and perform repetitive identification operation on the business data stream based on a preset third verification rule and generate an interception control command. The data processing module is configured to control the corresponding production and sales system to perform corresponding data processing operations based on the data verification results, the anomaly identification information, and the interception control instructions.

[0014] Thirdly, an electronic device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program stored in the memory to implement the steps of a multi-system collaborative data processing method as described in any of the first aspects above.

[0015] Fourthly, this application also proposes a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, it implements the steps of a multi-system collaborative data processing method according to any one of the first aspects.

[0016] In summary, the multi-system collaborative data processing method of this application acquires the business data streams transmitted between various production and sales systems, and automatically performs consistency comparison, anomaly judgment, and duplicate identification sequentially based on preset multi-type verification rules. Finally, it automatically controls the relevant systems to perform precise data processing operations based on the comprehensive verification results. This integrates the originally scattered, repetitive data verification and intervention processes between systems into a standardized, automated closed-loop processing logic. As a result, the integrity of data transmission between systems, the compliance of business logic, and the uniqueness of the data itself are reliably guaranteed in a fully automated, multi-faceted manner, significantly reducing the risk of data errors and business interruptions caused by human error or processing delays.

[0017] The multi-system collaborative data processing method proposed in this application, along with other advantages, objectives, and features of this application, will be partly apparent from the following description and partly understood by those skilled in the art through research and practice of this application. Attached Figure Description

[0018] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit this specification. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 This is a flowchart illustrating a multi-system collaborative data processing method provided in an embodiment of this application. Figure 2 A schematic diagram of a multi-system collaborative data processing system structure is provided in an embodiment of this application; Figure 3 This is a schematic diagram of a multi-system collaborative data processing electronic device provided in an embodiment of this application. Detailed Implementation

[0019] To better understand the technical solutions provided in the embodiments of this specification, the technical solutions of the embodiments of this specification will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of this specification and the specific features in the embodiments are detailed descriptions of the technical solutions of the embodiments of this specification, rather than limitations on the technical solutions of this specification. In the absence of conflict, the embodiments of this specification and the technical features in the embodiments can be combined with each other.

[0020] In this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, without necessarily requiring or implying any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. The term "two or more" includes two or more cases.

[0021] Please see Figure 1 This is a flowchart illustrating a multi-system collaborative data processing method provided in an embodiment of this application, specifically including: S110. Obtain the business data streams transmitted between various production and sales systems; For example, first collect all production and sales-related systems, such as cost accounting systems, manufacturing management systems, material feeding PES (Process Execution System), and sales logistics systems, and collect the business data that is exchanged between them. This data may include production records, inventory information, weight difference data, or cost accounting-related data. It is equivalent to capturing the data raw materials flowing between multiple systems in advance to prepare for subsequent processing.

[0022] S120. Based on the preset first verification rule, perform a consistency comparison on the business data stream and generate a data verification result; For example, a rule for determining whether data is consistent can be set in advance, namely the first check rule. Then, this first check rule is used to compare the data streams transmitted between different systems to see if they match, and finally, the data verification result of whether the data is consistent or inconsistent is obtained.

[0023] S130. Based on the preset second check rule, perform anomaly condition judgment on the business data stream and generate anomaly identification information; For example, rules for identifying data anomalies are pre-defined, namely the second check rules. Based on the second check rules, the data stream is checked for illogical or abnormal situations that exceed the normal range. If such situations exist, they are marked to form anomaly identification information. It is also necessary to clarify the type and location of the anomaly.

[0024] S140. Based on the preset third check rule, perform a repeatability identification operation on the business data stream and generate an interception control command; For example, a rule for determining whether data is duplicated is set up, namely the third check rule. The third check rule filters out duplicate data in the data stream, and then an interception control instruction to reject the duplicate data is generated to prevent duplicate data from entering the system.

[0025] S150. Based on the data verification results, the anomaly identification information, and the interception control command, control the corresponding production and sales system to perform the corresponding data processing operation.

[0026] For example, based on the results of whether the previously obtained data is consistent, whether there are any anomalies and abnormal situations, and whether it is necessary to intercept duplicate data, the corresponding production and sales system can respond, such as issuing an alarm when the data is inconsistent, notifying relevant personnel when there are anomalies, and refusing to receive duplicate data.

[0027] In summary, the multi-system collaborative data processing method proposed in this application acquires the business data streams transmitted between various production and sales systems, and automatically performs consistency comparison, anomaly judgment, and duplicate identification sequentially based on preset multi-type verification rules. Finally, it automatically controls relevant systems to perform precise data processing operations based on the comprehensive verification results. This integrates the originally scattered, repetitive data verification and intervention processes between systems into a standardized, automated closed-loop processing logic. As a result, the integrity of data transmission between systems, the compliance of business logic, and the uniqueness of the data itself are reliably guaranteed in a fully automated, multi-faceted manner, significantly reducing the risk of data errors and business interruptions caused by human error or processing delays.

[0028] In some examples, the business data stream includes a first business data stream sent by a first production and sales system and a second business data stream received by a second production and sales system. The step of performing a consistency comparison on the business data stream based on a preset first verification rule to generate a data verification result includes: Extract the first total statistical information and the first inventory data from the first business data stream; Extract the second statistical total information and the second inventory data from the second business data stream; Based on robotic process automation technology, the first total statistical information and the second total statistical information are compared to generate a first comparison result; Based on robotic process automation technology, the first inventory data and the second inventory data are compared to generate a second comparison result; Based on data auditing technology, the field data of each data record in the first business data stream and the second business data stream are compared to generate a third comparison result; The data verification result is generated based on the first comparison result, the second comparison result, and the third comparison result.

[0029] For example, the operation objects are first clearly defined as the first business data stream sent by the first production and sales system and the second business data stream received by the second production and sales system. Specifically, the first statistical total information and the first inventory data are extracted from the first business data stream, and the corresponding second statistical total information and the second inventory data are extracted from the second business data stream. Subsequently, based on robotic process automation (RPA) technology, two independent automated comparison operations are performed: first, the first statistical total information and the second statistical total information are compared to generate a first comparison result reflecting the consistency at the data total level; second, the first inventory data and the second inventory data are compared to generate a second comparison result reflecting the consistency at the key business status level. This is used to achieve automatic verification of coarse-dimensional data (total number of transmitted data entries, daily inventory data) between the cost accounting system and systems such as manufacturing management, material input PES, and sales logistics.

[0030] Building upon this foundation, further utilizing data auditing technology, a meticulous, item-by-item comparison is performed on every field of each data record in both the first and second business data streams, generating a third comparison result reflecting the consistency of data records at the micro-level. Finally, a comprehensive analysis and judgment are conducted based on the first, second, and third comparison results to generate the final data verification result, thus completing a comprehensive consistency verification from macro-statistics to micro-fields. This enables automatic data verification at a fine-dimensional level (down to each field of every data record) between the cost accounting system and systems such as manufacturing management, material input PES, and sales logistics.

[0031] The advantages of setting up two verification modes are: first, the two types of tasks can supervise each other, ensuring that the daily automatic verification process runs on time; second, the verification results can be reflected from different dimensions, which helps managers quickly pinpoint the problem and improve problem-solving efficiency. Take the data transfer between the manufacturing management system and the cost accounting system as an example. Each account set uploads approximately 1 million data entries per month, each containing 148 fields. If only coarse-dimensional verification tasks are set up using RPA (Robotic Process Automation), it is time-consuming to identify which specific field is inconsistent when discrepancies occur. Therefore, by using data governance (auditing) methods to implement fine-dimensional (field-dimensional) automatic verification between the cost accounting system and the manufacturing management system, the efficiency of anomaly data investigation is greatly improved.

[0032] In some examples, the step of performing anomaly condition judgment on the business data stream based on a preset second check rule and generating anomaly identification information includes: Based on the base code, the first type of business data in the business data stream is converted into the second type of business data, and the logical relationship between each field in the second type of business data is checked to generate a logical check result. Extract quantitative inventory data from the business data stream; Based on a preset inventory allowance range, determine whether the inventory quantification data has reached a preset warning value, and generate an inventory check result; Based on a preset set of task execution prerequisites, determine whether the business data stream meets the task triggering conditions and generate a condition check result. Extract the ending data of the current accounting period and the beginning data of the next accounting period from the business data stream, and perform a consistency check on the ending data and the beginning data to obtain the consistency check result; The anomaly identification information is generated based on the logical check result, the inventory check result, the condition check result, and the consistency check result.

[0033] For example, based on a series of information conversion rules, such as the conversion of base codes to cost accounts, the first type of business data in the business data stream, originating from a manufacturing management system (i.e., production information), is automatically converted into the second type of business data required by the cost accounting system (i.e., cost information). Subsequently, based on preset logical relationship verification rules between fields, the correspondence of key fields in the converted second type of business data is automatically verified. These logical relationship verification rules include, but are not limited to: whether the mapping relationships between cost centers and output products, cost centers and input products, output products and input products, and production units and cost centers are correct, and whether the product categories under the same event association number and the comprehensive judgment codes under the same history sequence number are consistent. This process verifies the accuracy of the conversion from production information to cost information and also checks the inherent quality of the data uploaded by the front-end system, thereby generating logical verification results.

[0034] Specifically, taking data transfer and conversion between the manufacturing management system and the cost accounting system as an example: Establish information conversion rules: Since the manufacturing management system uploads production information, while the cost accounting process requires corresponding cost information, the interface needs to complete the conversion of base code to cost account, production transaction time to accounting period, production process to cost center code, production information to product code, steel mark or grade to cost accounting code, transaction information to accounting code, and production weight to cost weight during data reception, so as to realize the conversion of production information to cost information.

[0035] Establish logical relationship verification rules between key fields: Information converted by the cost accounting system needs to have its logical relationships automatically verified. This includes verifying the correct correspondence between cost centers and output products, cost centers and input products, output products and input products, production units and cost centers, output units and upstream units and products, material types, production line types, production events, transaction codes, function codes, material status and accounting codes, as well as verifying the consistency of product categories under the same event association number and comprehensive judgment codes under the same history sequence number. This allows for both verification of the data quality uploaded to the manufacturing management system and validation of the results of converting production information into cost information.

[0036] Next, an inventory status check is performed: Quantitative inventory data is extracted from the business data stream, such as the material data in the cost accounting system; based on preset inventory allowance ranges for different material categories (e.g., upper and lower limits for ironmaking products, and differentiated warning values ​​for steelmaking products by material number), the system automatically determines whether the quantitative inventory data has reached or exceeded the preset warning values, generating inventory check results. Specifically: for ironmaking products, upper and lower limits for inventory are added; for steelmaking products (products produced and managed by block or coil), an automatic weight check function based on material number (slab number or hot-rolled coil number) is added, setting different warning values ​​for different product categories such as slabs and hot-rolled coils, flat plates and pickled plates, and usable materials; the inventory warning function is combined with the daily automatic monthly closing steps of the system. If the weight exceeds the range, the system issues an alarm, and simultaneously, abnormal data is automatically pushed through WeChat.

[0037] Secondly, precondition checks are performed for task execution: Before triggering critical business operations (such as the cost accounting system sending an accounting task to the SAP (Systems, Applications, and Products in Data Processing) system), the system automatically determines whether the current business data flow meets all task triggering conditions based on a preset set of preconditions. If the conditions are not met, a corresponding condition check result is generated to prevent task execution. Specifically, taking the cost accounting system sending an accounting task to the SAP system as an example: To ensure that the accounting data sent by the cost accounting system meets the requirements and to prevent erroneous data from being sent to the SAP system, the cost accounting system's "Account Sending" button has been enhanced with an automatic data check function before sending the accounting data. If any conditions are not met during the check, the system automatically stops sending the accounting data and issues an alarm. This function reduces the process of manual item-by-item checking and confirmation after the cost accounting system's month-end closing, improving operational efficiency; it effectively avoids the risk of business personnel forgetting to check, prevents abnormal data from being sent to the SAP system, and avoids subsequent red-inking operations from the source, thus improving the company's monthly financial closing efficiency.

[0038] Finally, for critical business operations (such as the cost accounting system executing closing tasks), the system extracts the ending data of the current accounting period and the beginning data of the next accounting period from the business data stream. It then performs automatic consistency checks on quantity and amount dimensions according to material type to obtain consistency verification results. Specifically, the system has an automatic verification function for the ending data of the current accounting period and the beginning data of the next accounting period, set up according to material type. Each data type involves two dimensions: quantity and amount (including standard amount and actual amount), ensuring data consistency before and after task execution. Finally, based on the combined results of the logical check, inventory check, conditional check, and consistency verification, the system generates anomaly identification information that accurately identifies the type and location of anomalies.

[0039] In some examples, the process of performing a repeatability identification operation on the business data stream based on a preset third check rule and generating an interception control command includes: Extract at least one feature field from the business data stream to form a combined feature; Query the historical data record table to determine whether the combined feature exists in the historical data record table; If the combined features exist in the historical data record table, an interception control instruction is generated to reject the business data stream.

[0040] For example, based on a pre-defined deduplication logic for a specific business scenario, one or more key feature fields are extracted from the currently transmitted business data stream, and these fields are combined according to pre-defined rules to form a combination feature for uniqueness determination. The pre-defined deduplication logic is determined based on the analysis of historical duplicate data patterns. For instance, in a scenario where a sales logistics system transmits weight difference data to a cost accounting system, the pre-defined combination of the three fields "code number," "material number," and "weight difference" is used as a deduplication condition. Subsequently, the system queries a pre-established and continuously updated historical data record table, which stores the combination features corresponding to successfully processed business data. By comparing and determining whether the combination features of the current business data stream already exist in the historical data record table, it is possible to identify whether the data is duplicate data transmitted due to system restarts or message retransmissions.

[0041] If, after evaluation, it is determined that the combined features already exist in the historical data record table, it indicates that the current business data stream is duplicate data. The system then generates a clear rejection instruction as the interception control instruction. This instruction is sent to the corresponding data receiving interface, controlling the interface to immediately stop receiving and writing operations on the current business data stream, thereby intercepting it from the data entry point. Furthermore, to address the issue of duplicate primary keys that may occur during data transmission due to front-end system anomalies, the third check rule also includes primary key duplication check logic. That is, a uniqueness check rule for the primary key field of the data record is preset at the interface. When it is detected that the primary key of the incoming data is duplicated with the primary key of an existing record, a rejection interception control instruction is also generated and executed to prevent primary key conflicts from causing abnormalities in subsequent processing logic.

[0042] Specifically, taking the transmission of weight difference data between the sales logistics system and the cost accounting system as an example: When the sales logistics system receives PES (Product Order System) codes from each process, it sends a message (i.e., weight difference data) to the cost accounting system. If the sales logistics system has not yet completed processing the code message, but the message center or server experiences a problem, the code message processing will fail. When the message center or server restarts, the system will re-recall the code message and send the weight difference information to the cost accounting system again, resulting in duplicate weight difference data in the cost accounting system. Data transmission and conversion tables related to this interface table will automatically generate two sets of duplicate data. To deduplicate the data, a "code number + material number + weight difference" combination field is set in the cost accounting system backend. When all of these fields are completely duplicated, the cost accounting system will reject the message information.

[0043] Taking data transfer between the material feeding PES system and the cost accounting system as an example: During problem processing, the front-end system may cause duplicate primary keys. To prevent duplicate primary key data from entering subsequent cost accounting stages and causing anomalies, an automatic primary key check and interception function should be set up at the interface between the cost accounting system and the material feeding PES system.

[0044] In some examples, controlling the corresponding production and sales system to perform corresponding data processing operations based on the data verification results, the anomaly identification information, and the interception control instructions includes: When the data verification result indicates that there is a data inconsistency, the control message notification module sends a first alarm notification to the preset message receiving terminal. When the abnormal identification information indicates that there is a business logic abnormality or a state out of bounds, the control message notification module sends a second alarm notification to the message receiving end. When the interception control command is a rejection command, the control data receiving interface stops writing operations to the service data stream.

[0045] For example, when the data verification result indicates that there is a data inconsistency, it means that the cross-system consistency comparison performed by the first verification rule has found a mismatch at the macro statistical summary or micro field level. The system then calls and controls the message notification module to send a first alarm notification carrying details of the inconsistent data (such as the difference system, difference type, and difference value) to a pre-configured enterprise WeChat account, email, or system monitoring console, according to the preset communication protocol and message template, so as to prompt the management personnel to intervene.

[0046] When the anomaly identification information indicates that there is a business logic anomaly or a state out of bounds, it means that the second check rule has identified specific business rule violations such as logical relationship contradictions, inventory exceeding limits, task conditions not being met, or data inconsistencies before and after closing the account. The system also controls the message notification module to send a more specific second alarm notification to the message receiving end. This notification clearly includes the anomaly type, triggering rule, occurrence time, and the identifier of the data record involved, so as to achieve accurate alarm.

[0047] When the interception control command is a rejection command, it indicates that the current business data stream has been identified as completely duplicated data or duplicate primary key data through the third verification rule. The system will directly control the data receiving interface located at the front end of the target production and sales system (such as a cost accounting system), causing it to immediately stop the subsequent operations such as parsing, verifying, and writing to the database of the current business data stream, thus physically preventing abnormal data from flowing into the system and completing automatic interception at the data entry level. In addition to the above alarm and interception operations, the system will also execute the default release and processing logic when all verification results are normal (i.e., the data verification results are consistent, no abnormal identification information is generated, and no interception control command is generated), controlling the corresponding interfaces and business modules to receive, store, and process the data stream normally.

[0048] This system enables rapid feedback and precise handling of data processing results, significantly improving usability and problem-solving efficiency. Firstly, sending alarm notifications to pre-defined receivers via the message notification module allows relevant personnel to be immediately aware of data inconsistencies and anomalies, preventing problem backlog and allowing more time for timely resolution. Previously, administrators needed to periodically check system logs to discover issues; now, proactive notifications reduce manual monitoring costs. Secondly, alarm notifications clearly identify the problem type and related information, allowing administrators to quickly pinpoint the root cause without sifting through massive amounts of data, improving the targeted nature of problem handling. Finally, halting write operations at the data receiving interface intercepts duplicate data at the source, preventing it from impacting subsequent business operations and ensuring the accuracy and purity of system data. Simultaneously, business personnel can receive all notifications through WeChat Work, eliminating the need to log into multiple systems, enabling effective control over the system's operation and results, demonstrating the system's automation and intelligence.

[0049] In some examples, the method further includes: If new master data is added in the business data stream, the new master data is converted into the format corresponding to the target system based on the preset data conversion rules. The control data synchronization interface pushes the newly added master data, after format conversion, to the target system.

[0050] For example, when a source production and sales system (such as a sales management system) generates new master data (e.g., a new product brand or delivery type) and enters the business data stream, the system first identifies this new master data from the business data stream based on preset new data identification rules. After identification, the system calls a preset interface communication protocol to automatically transmit the new master data from the source system to the target system (such as a cost accounting system) as an intermediate processing node. In the target system, there is a pre-set set of structured data transformation rules, which defines how to convert the raw data format of the source system into a standardized format that can be recognized and processed within the target system. For example, the transformation rules include the mapping relationship between brand information and product code, the association logic between delivery type and budget factor calculation rules, etc. Based on these rules, the system performs automatic parsing and matching operations on the received raw new master data to generate standardized data records that conform to the target system's data model.

[0051] Subsequently, the target system triggers the subsequent cross-system synchronization process based on preset data synchronization logic. This synchronization logic defines the target of data push (such as the financial system), the triggering conditions for push, and the data encapsulation format. The system controls the data synchronization interface to automatically package and push the newly added master data (which now includes the mapped product code, associated budget factors, and other derived information) that has undergone format conversion to downstream related systems, according to the predetermined communication protocol and data format. For example, the "product code-brand" information set with established correspondence is pushed to the financial system, thereby completing the end-to-end closed-loop processing of new master data from the origin system, through intermediate system conversion, to all relevant business systems.

[0052] Specifically, when new data is added to the front-end system, the back-end system needs to synchronously configure the corresponding data or rules, which requires relatively complex configuration and conversion work. Taking the addition of a brand name or delivery form in the sales management system as an example (brand name information needs to be pushed to the cost accounting system; the cost accounting system matches product codes, establishes budget factor rules, and pushes the product code-brand name mapping relationship to the financial system). When a new brand name is added to the sales management system, the interface enables automatic uploading of brand name information from the sales management system to the cost accounting system; the cost accounting system establishes a rule maintenance table (including delivery form group, product code, standard unit price, budget factor, and coefficient information); the cost accounting system backend sets the logic for converting to a product code-brand name mapping relationship and budget factor; and the cost accounting system sets the logic for automatically pushing product code and brand name information to the financial system.

[0053] Taking the addition of identical master data between the cost accounting system and the business decision-making system as an example. (The business decision-making system extracts data from the cost accounting system for data analysis to support company-level business decisions.) After adding master data (products and brands, cost items, etc.) to the cost accounting system, the system automatically synchronizes it to the business decision-making system, avoiding multiple maintenance of identical master data and improving work efficiency.

[0054] This system enables end-to-end automated synchronization and transformation of new and modified master data across systems. Traditionally, such operations required business personnel to manually input data across multiple systems, repeatedly communicate and verify it, resulting in low efficiency and a high risk of errors. Through pre-defined identification, transformation, and synchronization rules, new master data is automatically identified, accurately transformed according to business logic, and pushed to all relevant systems in real time. This ensures consistency, accuracy, and real-time performance of master data across multiple systems, completely eliminating omissions, mismatches, and delays that can occur with manual transmission, significantly improving system collaboration efficiency and overall data governance.

[0055] In some examples, the method further includes: Monitor the task execution status of the data transmission interface between the production and sales systems; When a task is detected as failing, the corresponding data transmission task is re-invoked.

[0056] For example, the system continuously monitors the operational status of the data transmission interface between the production and sales systems, tracking whether each data transmission task is successfully completed. If a data transmission task is detected as failed, for example due to network interruption, temporary server malfunction, or temporary data format anomaly, the system automatically re-initiates the failed task without manual intervention. The receiving system automatically requests the task until the task succeeds or the preset retry limit is reached. By leveraging RPA, the system automatically checks and invokes the interface daily, improving the accuracy of data transmission results when a data reception failure is detected.

[0057] Automatic retries after data transmission failures have been implemented, greatly improving the reliability and stability of system data transmission. Firstly, it avoids data transmission interruptions caused by unexpected events such as network failures and server problems, ensuring complete and timely data transmission between multiple systems and guaranteeing the normal operation of subsequent business processes. For example, the cost accounting system can obtain complete production data and weight difference data, ensuring the accuracy of cost accounting results. Secondly, automatic retries without manual intervention reduce the workload of business personnel. Previously, business personnel needed to periodically check the data transmission status and manually re-initiate tasks after failures; now, the process is fully automated, saving labor costs.

[0058] In some examples, the method further includes: Identify negative output records in the business data stream; Based on the preset accounting object mapping rules, a replacement accounting object is determined for the negative output record; Determine whether there is a positive output record under the current output category, and compare the quantified value of the original accounting object corresponding to the negative output with the quantified value of the replacement accounting object; If it is determined that there is no positive output record or the quantization value of the replacement accounting object is less than the quantization value of the original accounting object, a third alarm notification is generated. Based on preset record conversion rules, a single negative output record is converted into multiple production adjustment records and inventory adjustment records.

[0059] For example, the system identifies negative output records (such as records with negative output due to production returns, scrap, weight corrections, etc.) from the business data flow. Then, according to the pre-set accounting object mapping rules, it matches a suitable replacement accounting object (i.e., a new auxiliary accounting object) for this negative output record. Next, it determines whether there is a positive output record (i.e., a record with positive output) under the current output category corresponding to the negative output record, and compares the quantitative values ​​(such as weight, quantity, etc.) of the original accounting object and the replacement accounting object corresponding to the negative output record. If the determination result is that there is no positive output record under the current output category or the quantitative value of the replacement accounting object is less than the quantitative value of the original accounting object, a third alarm notification is generated to inform relevant personnel. Finally, according to the preset record conversion rules, this single negative output record is converted into multiple production adjustment records and inventory adjustment records (such as adjustment records corresponding to multiple fields such as cost center, product code, auxiliary accounting object, etc.) to ensure that the inventory data can accurately reflect the results of the negative output adjustment.

[0060] Specifically, in accordance with company management needs, an automatic negative production processing function was added. First, a new interface was developed, clarifying the logical relationships between fields involved in negative production, enabling automatic acquisition of key field information such as product code, cost center, original auxiliary accounting object, and weight. Second, matching rules for the "new auxiliary accounting object" were determined in collaboration with the manufacturing department, enabling automatic configuration of the "new auxiliary accounting object" field. Third, the production volume of other auxiliary accounting objects under the negative production product was assessed, and the weight of the "original auxiliary accounting object" was compared with the weight of the "new auxiliary accounting object." If there was no positive production volume for the current product in the current month, or if the weight of the "new auxiliary accounting object" was less than the weight of the "original auxiliary accounting object," the system would issue an alarm. Finally, negative production data processing logic was added to the backend, enabling the conversion of one negative production adjustment data point into four production and inventory adjustment data points, including automatic conversion and matching of multiple fields such as cost center, product code, auxiliary accounting object, accounting code, and event association number.

[0061] By improving the functions of the cost accounting system itself and using RPA platforms, the monthly closing steps are automatically executed, and the monthly closing process data is automatically checked, based on tasks such as "automatic verification of data transfer results between systems, automatic investigation of abnormal data, automatic interception of duplicate data, automatic invocation of failed tasks, and automatic handling of negative output". The execution results are then automatically pushed through WeChat.

[0062] like Figure 2 As shown, this application proposes a multi-system collaborative data processing system, which includes: a data acquisition module 21, a data verification module 22, and a data processing module 23. The data acquisition module 21 is configured to acquire business data streams transmitted between various production and sales systems; The data verification module 22 is configured to: perform consistency comparison on the business data stream based on a preset first verification rule and generate a data verification result; perform anomaly condition judgment on the business data stream based on a preset second verification rule and generate anomaly identification information; and perform repetitive identification operation on the business data stream based on a preset third verification rule and generate an interception control command. The data processing module 23 is configured to control the corresponding production and sales system to perform corresponding data processing operations based on the data verification results, the anomaly identification information, and the interception control command.

[0063] The effects of applying the aforementioned method in the above system can be found in the description of the aforementioned method embodiments, and will not be repeated here.

[0064] like Figure 3 As shown, this application embodiment also provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 310 and executable on the processor. When the processor 320 executes the computer program 311, it implements the steps of any of the above-mentioned multi-system collaborative data processing methods.

[0065] Since the electronic device described in this embodiment is a device used to implement a multi-system collaborative data processing device in the embodiments of this application, those skilled in the art can understand the specific implementation method and various variations of the electronic device in this embodiment based on the method described in the embodiments of this application. Therefore, how the electronic device implements the method in the embodiments of this application will not be described in detail here. Any device used by those skilled in the art to implement the method in the embodiments of this application falls within the scope of protection of this application.

[0066] In practical implementation, when the computer program 311 is executed by the processor, it can achieve the following: Figure 1 Any of the corresponding implementation methods in the embodiments.

[0067] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0068] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-readable program code.

[0069] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0070] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0071] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0072] This application also provides a computer program product, which includes computer software instructions that, when executed on a processing device, cause the processing device to execute the LDPC decoding method of a solid-state drive controller.

[0073] A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0074] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0075] In the several embodiments provided in this application, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between devices or units, and may be electrical, mechanical, or other forms.

[0076] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0077] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0078] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0079] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

[0080] Although preferred embodiments have been described in this specification, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this specification.

[0081] Obviously, those skilled in the art can make various modifications and variations to this specification without departing from its spirit and scope. Therefore, if such modifications and variations fall within the scope of the claims and their equivalents, this specification is also intended to include such modifications and variations.

Claims

1. A multi-system collaborative data processing method, characterized in that, The method includes: Acquire the business data streams transmitted between various production and sales systems; Based on the preset first verification rule, the business data stream is compared for consistency, and a data verification result is generated; Based on the preset second check rule, the business data stream is subjected to anomaly condition judgment, and anomaly identification information is generated; Based on the preset third check rules, the business data stream is subjected to a repeatability identification operation, and an interception control command is generated. Based on the data verification results, the anomaly identification information, and the interception control command, the corresponding production and sales system is controlled to perform the corresponding data processing operation.

2. The multi-system collaborative data processing method according to claim 1, characterized in that, The business data stream includes a first business data stream sent by a first production and sales system and a second business data stream received by a second production and sales system. The step of performing a consistency comparison on the business data stream based on a preset first verification rule to generate a data verification result includes: Extract the first total statistical information and the first inventory data from the first business data stream; Extract the second statistical total information and the second inventory data from the second business data stream; Based on robotic process automation technology, the first total statistical information and the second total statistical information are compared to generate a first comparison result; Based on robotic process automation technology, the first inventory data and the second inventory data are compared to generate a second comparison result; Based on data auditing technology, the field data of each data record in the first business data stream and the second business data stream are compared to generate a third comparison result; The data verification result is generated based on the first comparison result, the second comparison result, and the third comparison result.

3. The multi-system collaborative data processing method according to claim 1, characterized in that, The method based on a preset second check rule performs anomaly condition judgment on the business data stream and generates anomaly identification information, including: Based on the base code, the first type of business data in the business data stream is converted into the second type of business data, and the logical relationship between each field in the second type of business data is checked to generate a logical check result. Extract quantitative inventory data from the business data stream; Based on a preset inventory allowance range, determine whether the inventory quantification data has reached a preset warning value, and generate an inventory check result; Based on a preset set of task execution prerequisites, determine whether the business data stream meets the task triggering conditions and generate a condition check result. Extract the ending data of the current accounting period and the beginning data of the next accounting period from the business data stream, and perform a consistency check on the ending data and the beginning data to obtain the consistency check result; The anomaly identification information is generated based on the logical check result, the inventory check result, the condition check result, and the consistency check result.

4. The multi-system collaborative data processing method according to claim 1, characterized in that, The process of performing a repeatability identification operation on the business data stream based on a preset third check rule and generating an interception control command includes: Extract at least one feature field from the business data stream to form a combined feature; Query the historical data record table to determine whether the combined feature exists in the historical data record table; If the combined features exist in the historical data record table, an interception control instruction is generated to reject the business data stream.

5. The multi-system collaborative data processing method according to claim 1, characterized in that, The step of controlling the corresponding production and sales system to perform corresponding data processing operations based on the data verification results, the anomaly identification information, and the interception control command includes: When the data verification result indicates that there is a data inconsistency, the control message notification module sends a first alarm notification to the preset message receiving terminal. When the abnormal identification information indicates that there is a business logic abnormality or a state out of bounds, the control message notification module sends a second alarm notification to the message receiving end. When the interception control command is a rejection command, the control data receiving interface stops writing operations to the service data stream.

6. The multi-system collaborative data processing method according to claim 1, characterized in that, The method further includes: If new master data is added in the business data stream, the new master data is converted into the format corresponding to the target system based on the preset data conversion rules. The control data synchronization interface pushes the newly added master data, after format conversion, to the target system.

7. The multi-system collaborative data processing method according to claim 1, characterized in that, The method further includes: Monitor the task execution status of the data transmission interface between the production and sales systems; When a task is detected as failing, the corresponding data transmission task is re-invoked.

8. The multi-system collaborative data processing method according to claim 1, characterized in that, The method further includes: Identify negative output records in the business data stream; Based on the preset accounting object mapping rules, a replacement accounting object is determined for the negative output record; Determine whether there is a positive output record under the current output category, and compare the quantified value of the original accounting object corresponding to the negative output with the quantified value of the replacement accounting object; If it is determined that there is no positive output record or the quantization value of the replacement accounting object is less than the quantization value of the original accounting object, a third alarm notification is generated. Based on preset record conversion rules, a single negative output record is converted into multiple production adjustment records and inventory adjustment records.

9. A multi-system collaborative data processing system, characterized in that, The system includes: a data acquisition module, a data verification module, and a data processing module; The data acquisition module is configured to acquire business data streams transmitted between various production and sales systems. The data verification module is configured to: perform consistency comparison on the business data stream based on a preset first verification rule and generate a data verification result; perform anomaly condition judgment on the business data stream based on a preset second verification rule and generate anomaly identification information; and perform repetitive identification operation on the business data stream based on a preset third verification rule and generate an interception control command. The data processing module is configured to control the corresponding production and sales system to perform corresponding data processing operations based on the data verification results, the anomaly identification information, and the interception control instructions.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of a multi-system collaborative data processing method as described in any one of claims 1-8.

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