Report loading processing method, system and equipment in industrial scene and medium

Abstract

The invention provides a report loading processing method, system and device in an industrial scene and a medium, and belongs to the technical field of report loading. Data source information associated with a service entity identifier is obtained; establishing a dynamic binding relationship between business entity attributes and data source fields, and generating an index metadata template; acquiring data source information associated with the service entity identifier through a data registration component; establishing a calculation mapping relation between the index field and the data source field; generating a standardized configuration file; persisting the generated standardized configuration file to a database; a three-party incidence relation index among an index instance identifier, a service entity identifier and a data source identifier is established, in actual industrial data processing, even if network faults and other problems occur, it can be guaranteed that configuration files are accurately updated, when service requirements change, calculation rules of corresponding levels can be rapidly positioned and adjusted, and the service requirements are met. And the flexibility and accuracy of data processing are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of report loading, and particularly relates to a method, system, device and medium for report loading processing in an industrial scenario. Background Art

[0002] In an industrial scenario, reports are the core tools connecting production data and decision-making logic, and their technical background needs to closely revolve around the real-time nature, reliability, and data diversity of the industrial environment. Industrial reports can achieve a full-link closed loop from data collection to decision execution.

[0003] In an industrial scenario, data sources are diverse and complex, including device data from different manufacturers, file data in various formats, etc. When establishing the mapping relationship between business entities and data sources, related technologies often rely on manual configuration, which is prone to mapping errors or omissions, making the subsequent report data unable to accurately reflect the actual production situation. The index calculation logic involved in industrial reports is complex, and different indexes may need to obtain data from multiple data sources and perform different calculations and processing. When the business requirements change and the index calculation method needs to be adjusted, logical conflicts are likely to occur, resulting in incorrect calculation results. Moreover, with the development of industrial operations, the configuration information related to reports is constantly increasing and changing frequently. The configuration file management method of related technologies is usually simple file storage, lacking version management and structured design. During the update process of the configuration file, it is impossible to accurately record the changed content and change history, making it difficult to trace when problems occur; at the same time, unstructured configuration files are not conducive to quickly finding and modifying specific configuration items, reducing the maintainability of the system. Summary of the Invention

[0004] The present invention provides a method for report loading processing in an industrial scenario. The method configures data field mapping rules through a data registration component to double-guarantee the accuracy of data mapping, and can ensure the accurate update of the configuration file and consistent index calculation results.

[0005] The method includes: S101: Obtain data source information associated with a business entity identifier; define a physical field mapping rule corresponding to the business entity identifier based on the data source information, establish a dynamic binding relationship between the business entity attribute and the data source field, and generate an index metadata template containing business entity semantic information; S102: Obtain data source information associated with a business entity identifier through a data registration component, and configure data field mapping rules and data verification logic based on the data source information; S103: Configure an index calculation formula and processing logic based on the business entity attribute and data source information, and establish a calculation mapping relationship between the index field and the data source field; S104: Structurally encapsulate the metric metadata, data field mapping rules, and calculation logic to generate a standardized configuration file containing the association relationships of business entities; S105: Persist the generated standardized configuration file to the database, and at the same time push a configuration update notification to the metric engine module through the message queue; S106: After receiving the configuration update completion information, establish a three-party association relationship index among the metric instance identifier, business entity identifier, and data source identifier, and ensure the consistency of the configuration data, association relationships, and metric calculation results through the distributed transaction mechanism.

[0006] Further, it should be noted that step S101 specifically includes: Based on the preset identifier key values, perform business semantic parsing on the original data items in the data source, and verify whether there are target data items in the target platform that are semantically consistent with the original data items; If there are no target data items with consistent semantics, generate an extended attribute set under the target platform according to the attribute set of the data source; the extended attribute set inherits the structure of the source attribute set, and locates candidate data items associated with the original data items by matching the identifier key values through the semantic similarity algorithm; Use the candidate data items as the final data items in the target platform, and persist the extended attribute set and the mapping relationship to the metadata management module to establish cross-platform consistency binding between the original data items and the target data items.

[0007] Further, it should be noted that step S102 specifically includes: After obtaining the data source information, parse the meaning of the data fields to extract the business concepts and semantic information represented by the data fields; Based on the predefined association rule library, analyze the association between data fields, including but not limited to the functional dependency relationship between data fields and the association relationship between data fields and other business entity identifiers; Integrate the semantic information and the results of the association analysis as supplementary information into the data field mapping rules.

[0008] Further, it should be noted that step S103 specifically includes: Identify the data source type and data source type identifier, and load the adapted calculation logic template; Based on the data source type, calculation logic template, and business entity attributes, generate three-level calculation rules at the field level, table level, and system level, and configure them as a hierarchical rule configuration file; Calculate the field-level statistical metrics of the hierarchical rule configuration file, verify the calculation results, and obtain the verification result information; Adjust the rule parameters according to the verification result. When a conflict is detected, start the historical valid rule version and trigger an alarm.

[0009] Further, it should be noted that the structured encapsulation in step S104 specifically includes: Use a tree - shaped data structure to hierarchically store metric metadata as the root node, data mapping rules as child nodes, and calculation logics as leaf nodes; Embed field - level data source traceability information in each node to record the field - level data processing link; Generate JSON validation rules based on the configuration file structure.

[0010] Further, it should be noted that step S105 specifically includes: Generate a random version number of the configuration file before persistence, record the modification timestamp, operator identifier, and change content summary, and use blockchain evidence - storing technology to store the version history information; Match the message channel according to the system status and configuration file type; Embed field - level verification nodes respectively in the database persistence stage and the message queue push stage to perform data type verification, business rule verification, and associated relationship integrity verification; When persistence or push fails, generate a compensation transaction log, trigger an alarm to notify the operation and maintenance personnel, and roll back to the nearest valid configuration state according to the historical version number.

[0011] Further, it should be noted that step S106 specifically includes: Create a multi - level index based on the association relationship between the metric instance identifier and the business entity identifier; When the distributed transaction fails, generate a transaction compensation log, record the operation sequence and intermediate state, trigger a recovery to the valid state before the transaction, and notify the operation and maintenance personnel through the message queue; Step S106 also performs data consistency verification in stages after the establishment of the tri - partite association relationship: Metric instance metadata integrity verification; Business entity and data source mapping relationship verification; Metric calculation result sampling verification.

[0012] This application also provides a report loading and processing system in an industrial scenario. The system includes: an information acquisition and binding module, which is used to acquire data source information associated with the business entity identifier; define the physical field mapping rules corresponding to the business entity identifier based on the data source information, establish a dynamic binding relationship between the business entity attributes and the data source fields, and generate a metric metadata template containing business entity semantic information; A mapping rule configuration module, which is used to obtain data source information associated with a business entity identifier through a data registration component, and configure a data field mapping rule and data verification logic based on the data source information; A mapping relationship establishment module, which is used to configure an index calculation formula and processing logic by using business entity attributes and data source information, and establish a calculation mapping relationship between index fields and data source fields; A standard file generation module, which is used to structurally encapsulate index metadata, data field mapping rules, and calculation logic, and generate a standardized configuration file containing business entity association relationships; A storage and push module, which is used to persist the generated standardized configuration file to a database, and at the same time push a configuration update notification to an index engine module through a message queue; An index and consistency processing module, which is used to establish a tripartite association relationship index between an index instance identifier, a business entity identifier, and a data source identifier after receiving a configuration update completion message, and ensure the consistency of configuration data, association relationships, and index calculation results through a distributed transaction mechanism.

[0013] According to another embodiment of the present application, an electronic device is provided, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the steps of the report loading processing method in the industrial scenario are implemented.

[0014] According to still another embodiment of the present application, a storage medium is further provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the report loading processing method in the industrial scenario are implemented.

[0015] It can be seen from the above technical solutions that the present invention has the following advantages: The report loading processing method provided in the present application establishes a dynamic binding relationship by obtaining data source information and defining a physical field mapping rule; configures a data field mapping rule through a data registration component to double-guarantee the accuracy of data mapping. At the same time, when verifying the mapping relationship between a business entity and a data source, the mapping relationship is checked and corrected again. In this way, various parameters of the device can be accurately mapped to corresponding production indicators, such as accurately associating parameters such as the rotation speed and temperature of the device with the device operation stability indicator, ensuring that the report data truly reflects the production status.

[0016] In step S103 of the present application, based on the business entity attributes and data source information, the index calculation formula and processing logic are configured, and a calculation mapping relationship is established; at the same time, a hierarchical rule configuration file is adopted to manage the calculation logic at the field level, table level, and system level. This makes the calculation logic clear and understandable. When the business requirements change, the corresponding hierarchical calculation rules can be quickly located and adjusted. In step S104, a tree-shaped data structure is used to structurally encapsulate the index metadata, mapping rules, and calculation logic to generate a standardized configuration file; in step S105, a dynamic version number is generated before persistence, and blockchain evidence storage technology is used to store the version history information. The structural encapsulation makes the configuration file hierarchical, facilitating search and modification. In step S105, field-level verification nodes are respectively embedded in the database persistence stage and the message queue push stage to perform data type verification, business rule verification, and associated relationship integrity verification. Brief Description of the Drawings

[0017] To more clearly illustrate the technical solutions of the present invention, the drawings required for description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0018] Figure 1 It is a flowchart of a report loading processing method in an industrial scenario; Figure 2 It is a flowchart of an embodiment of a report loading processing method in an industrial scenario; Figure 3 It is a flowchart of another embodiment of a report loading processing method in an industrial scenario; Figure 4 It is a schematic diagram of an electronic device. Detailed Embodiments

[0019] The report loading processing method provided by the present application solves, through the report system, the problem that in an industrial scenario, due to the diverse data sources in the report, the existing report system cannot be directly used to complete the report design; at the same time, it also solves the problem that industrial software cannot directly use the report data to directly guide production.

[0020] The following will detail the steps of the report loading processing method in the industrial scenario involved in the present application. For the purpose of illustration rather than limitation, specific details such as specific system structures and technologies are proposed to thoroughly understand the embodiments of the present application. However, those skilled in the art should clearly understand that the present application can also be implemented in other embodiments without these specific details.

[0021] It should be understood that, as used in the specification of this application, the term "comprising" indicates the presence of the described features, wholes, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations. The terms "comprising", "including", "having", and their variants all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0022] Statements such as "an embodiment" or "some embodiments" described in this application mean that the specific features, structures, or characteristics described in the embodiment are included in one or more embodiments of this application. Thus, statements such as "in one embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments", etc. that appear in different places in this application do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways.

[0023] In an embodiment of the present invention, computer program code for performing the operations of the present disclosure can be written in one or more programming languages or combinations thereof. The above-mentioned programming languages include, but are not limited to, object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (exemplarily, by using an Internet service provider to connect through the Internet).

[0024] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0025] Please refer to Figure 1 The following is a flowchart of a report loading and processing method in an industrial scenario in a specific embodiment. The method includes: S101: Obtain data source information associated with a business entity identifier. The data source information includes a data source address, a data structure, and field attributes. Define a physical field mapping rule corresponding to the business entity identifier based on the data source information, establish a dynamic binding relationship between the business entity attributes and the data source fields, and generate an index metadata template containing business entity semantic information.

[0026] In some embodiments, query in the data source management module according to the business entity identifier to obtain the associated data source information. The data source information includes the data source address, which is the entry for accessing data. The data structure defines the organization form of the data. The field attributes illustrate the specific characteristics of each field, such as data type, length, etc.

[0027] Based on the obtained data source information, formulate a physical field mapping rule, that is, determine how the business entity attributes correspond to the data source fields. Through the mapping rule, establish a dynamic binding relationship between the business entity attributes and the data source fields. Finally, integrate the information to generate an index metadata template containing business entity semantic information.

[0028] As an implementation manner of this application, perform business semantic parsing on the original data items in the data source based on a preset identifier key value, and verify whether there are target data items in the target platform that are semantically consistent with the original data items. The target data items are used to represent the definition specifications of the original data items in the target platform.

[0029] If there are no target data items with semantic consistency, generate an extended attribute set under the target platform according to the attribute set of the data source. The extended attribute set inherits the structure of the source attribute set and matches the identifier key value through a semantic similarity algorithm to locate candidate data items associated with the original data items.

[0030] Take the candidate data items as the final data items in the target platform, and persist the extended attribute set and the mapping relationship to the metadata management module to establish a cross-platform consistency binding between the original data items and the target data items.

[0031] It can be seen that before generating the index metadata template, ensure the alignment of the source data with the business definitions of the target platform through semantic consistency verification to avoid mapping errors caused by semantic ambiguity. Use the business entity identifier as an index to perform matching searches in the pre-stored data source information library to find the corresponding data source information. Then, according to the data structure and field attributes, analyze the logical relationship between the business entity attributes and the data source fields, so as to define a reasonable mapping rule.

[0032] S102: Obtain data source information associated with the business entity identifier through the data registration component, and configure the data field mapping rules and data verification logic based on the data source information.

[0033] In this embodiment, after obtaining the data source information, use a preset semantic analysis model to parse the meaning of the data fields, and extract the business concepts and semantic information represented by the data fields. At the same time, based on a predefined association rule library, analyze the relevance between data fields, including but not limited to the functional dependency relationship between data fields, the association relationship between data fields and other business entity identifiers, etc. Incorporate the semantic information and the results of the relevance analysis as supplementary information into the data field mapping rules, so that the mapping relationship reflects the business logic and the association between data, thereby improving the accuracy and reliability of index calculation.

[0034] In some embodiments, the data registration component obtains data source information associated with the business entity identifier. After obtaining the information, for the data fields, configure the mapping rules, which refine the corresponding relationship between the business entity attributes and the data source fields to ensure the accurate acquisition of data. At the same time, configure the data verification logic, such as checking the integrity, accuracy, and legality of the data, etc., to ensure the data quality entering the system and improve the stability of the system.

[0035] S103: Based on the business entity attributes and the data source information, configure the index calculation formula and processing logic, and establish the calculation mapping relationship between the index fields and the data source fields.

[0036] In some embodiments, combine the business entity attributes and the previously obtained data source information to determine the index calculation formula, which stipulates how to calculate the required index from the data source fields. At the same time, configure the data processing logic, such as performing operations such as data cleaning and conversion on the data. In this way, establish the calculation mapping relationship between the index fields and the data source fields, clarify how the index is calculated from the data source, and improve the accuracy and maintainability of index calculation.

[0037] S104: Structurally encapsulate the index metadata, data field mapping rules, and calculation logic to generate a standardized configuration file containing the business entity association relationship.

[0038] In some embodiments, structurally encapsulate the generated index metadata, the configured data field mapping rules, and the calculation logic. After encapsulation, generate a standardized configuration file containing the business entity association relationship. The standardized configuration file integrates all the key information of the previous steps to form a complete configuration file.

[0039] S105: Persist the generated standardized configuration file to the database, and at the same time push a configuration update notification to the index engine module through the message queue.

[0040] In some embodiments, the generated standardized configuration file is saved to a database to achieve persistent storage and ensure that the configuration information will not be lost. Meanwhile, a configuration update notification is sent to the metric engine module through a message queue, informing the metric engine module that the configuration information has changed and corresponding update operations are required.

[0041] The database provides the function of persistent data storage, and the system writes the configuration file into the database. As an asynchronous communication mechanism, the message queue sends the configuration update notification to the metric engine module, and the metric engine module triggers the corresponding update logic after receiving the notification.

[0042] S106: After receiving the configuration update completion information, establish a three-way association relationship index among the metric instance identifier, business entity identifier, and data source identifier, and ensure the consistency of the configuration data, association relationship, and metric calculation results through a distributed transaction mechanism.

[0043] In this embodiment, after receiving the configuration update completion information sent by the metric engine module, a three-way association relationship index is established among the metric instance identifier, business entity identifier, and data source identifier. Through the distributed transaction mechanism, the consistency of the configuration data, association relationship, and metric calculation results is ensured, preventing data inconsistency problems during the data processing process.

[0044] In this way, after receiving the update completion information, the three identifiers are associated to form an index table. The distributed transaction mechanism monitors and coordinates the entire data processing process to ensure that the configuration data, association relationship, and metric calculation results are consistent in any case, improving the reliability of the system and the data quality.

[0045] In an embodiment of the present invention, based on step S103, a possible embodiment will be given below to non-restrictively elaborate on its specific implementation scheme. As Figure 2 shown, step S103 specifically includes: Step S1031: Identify the data source type and the data source type identifier, and load the adapted calculation logic template.

[0046] This embodiment determines the type of the data source. For example, the data source is a relational database, a non-relational database, a file system, etc. At the same time, the identifier corresponding to the data source type is identified, and the identifier can uniquely identify the data source type. Then, according to the identified data source type, the adapted calculation logic template is loaded from the pre-stored calculation logic template library. For example, if the data source is a relational database, the calculation logic template applicable to the data processing of the relational database is loaded.

[0047] Step S1032: Generate three - level calculation rules at the field level, table level, and system level based on the data source type, calculation logic template, and business entity attributes, and configure them into a hierarchical rule configuration file.

[0048] In this embodiment, the determined data source type, loaded calculation logic template, and business entity attributes can be combined to generate calculation rules at the field level, table level, and system level respectively. Field - level calculation rules mainly calculate for individual fields in the data source, such as performing data cleaning, format conversion, etc. on a certain field. Table - level calculation rules calculate based on the entire data table, such as calculating the sum value, average value, etc. of certain fields in the table. System - level calculation rules calculate from the perspective of the entire data source system, involving association calculations between multiple data tables. After generating these rules, they are configured into a file in a hierarchical manner to form a hierarchical rule configuration file, which clearly records the calculation rules at different levels and their interrelationships.

[0049] In this way, according to the data source type and calculation logic template, analyze the data processing requirements of business entity attributes to determine the calculation rules at each level. Organize and store these rules in a hierarchical rule configuration file for easy management and use.

[0050] Step S1033: Calculate the field - level statistical indicators of the hierarchical rule configuration file, verify the calculation results, and obtain verification result information.

[0051] In this embodiment, based on the field - level calculation rules in the hierarchical rule configuration file, calculate the field data in the data source to obtain field - level statistical indicators, such as the maximum value, minimum value, average value, quantity, etc. of the field. Then verify these calculation results. The verification method can be to compare with a preset threshold, check the integrity and consistency of the data, etc. According to the verification results, generate verification result information, which records whether the calculation results meet the expectations and the specific situations where they do not meet the expectations.

[0052] Step S1034: Adjust the rule parameters according to the verification results. When a conflict is detected, start the historical valid rule version and trigger an alarm.

[0053] It should be noted that, according to the verification result information, adjust the rule parameters in the hierarchical rule configuration file. If the verification result shows that a certain calculation result does not meet the expectations, it may be necessary to adjust the parameters of the relevant rules, such as adjusting the calculation weights, thresholds, etc., to make the calculation results more accurate. When a conflict is detected between rules during the rule adjustment or calculation process, the system starts the historical valid rule version, that is, uses the previously normally running rule version to process the data. At the same time, the system triggers an alarm to notify relevant personnel that a rule conflict has occurred and needs to be processed.

[0054] In this way, the system analyzes the rule parameters that need to be adjusted according to the verification result information, and modifies the hierarchical rule configuration file. During the rule adjustment and calculation process, it monitors in real time whether there are conflicts between rules. When a conflict is detected, it selects a valid rule version from the historical rule version library for replacement, and notifies relevant personnel through an alarm mechanism.

[0055] Furthermore, as a refinement and extension of the specific implementation manner of the above step S104, in order to fully illustrate the specific implementation process in step S104, as Figure 3 shown, the structured encapsulation of step S104 specifically includes: Step S1041: Use a tree data structure to hierarchically store metric metadata as the root node, data mapping rules as child nodes, and calculation logic as leaf nodes.

[0056] This embodiment uses a tree data structure to store relevant information. The metric metadata is used as the root node, and the metric metadata contains key information such as the basic definition and business semantics of the metric. The data mapping rules are connected to the root node as child nodes. The data mapping rules define the correspondence between business entity attributes and data source fields, indicating where the data comes from and how it is associated with the metric. In this way, the tree data structure itself has the characteristics of clear hierarchy, easy organization, and management. Starting from the metric metadata, the data mapping rules and calculation logic are organized in an orderly manner through the parent-child node relationship.

[0057] Step S1042: Embed field-level data source traceability information in each node to record the field-level data processing link.

[0058] In each node of the tree structure, field-level data source traceability information is embedded. The information records from which specific data source each field's data is obtained, including the address, table name, field name, etc. of the data source. At the same time, the field-level data processing link is recorded, that is, what operations the field data has experienced during the entire processing process.

[0059] Step S1043: Generate JSON verification rules based on the configuration file structure.

[0060] In this embodiment, according to the structural characteristics of the tree data structure configuration file constructed previously, the system generates JSON verification rules. These verification rules are used to verify whether the subsequent input or output JSON format data meets the requirements of the configuration file. Analyze the information of each node in the tree data structure configuration file, including metric metadata, data mapping rules, and calculation logic, etc., and extract the key constraints and specifications therein. According to the information, generate corresponding JSON verification rules, which can be implemented using tools such as JSONSchema.

[0061] In an embodiment of the present invention, based on step S105, a possible embodiment will be given below to non - restrictively elaborate on its specific implementation. Step S105 specifically includes: Step S1051: Generate a random version number of the configuration file before persistence, record the modification timestamp, operator identification, and change content summary, and use blockchain evidence - storage technology to store the version history information.

[0062] In this embodiment, before persisting the configuration file to the database, a random version number is generated for the configuration file. The random version number is unique and changes with each modification, and is used to identify different versions of the configuration file. At the same time, the timestamp of this modification is recorded, accurate to the specific moment when the modification occurs. The identification of the person performing the operation is recorded to clarify who made the modification; the change content summary is also recorded to describe the main content of this modification. The blockchain evidence - storage technology is used to store these version history information, and the characteristics of the blockchain ensure that the information is tamper - proof and traceable.

[0063] Step S1052: Match the message channel according to the system state and configuration file type.

[0064] This embodiment monitors its own state in real - time. At the same time, it identifies the type of the configuration file, such as the metric configuration file, data mapping rule configuration file, etc. According to the identification information, a suitable message channel is matched from the pre - defined list of message channels. Different system states and configuration file types may correspond to different message channels.

[0065] Step S1053: Embed field - level verification nodes respectively in the database persistence stage and the message queue push stage to perform data type verification, business rule verification, and associated relationship integrity verification.

[0066] In the process of persisting the configuration file to the database and in the process of pushing the configuration update notification to the message queue in this embodiment, field - level verification nodes are respectively embedded in the corresponding stages. At the verification node, data type verification is performed on each field in the configuration file to ensure that the data type of the field meets the expectation. In the processes of database persistence and message queue push, verification nodes are set. When the data of the configuration file flows through these nodes, the verification module checks each field according to the pre - defined data type rules, business rules, and associated relationship rules. If a situation that does not conform to the rules is found, error information is recorded and corresponding handling measures are taken.

[0067] Step S1054: When the persistence or push fails, generate a compensation transaction log, trigger an alarm to notify the operation and maintenance personnel, and roll back to the most recent valid configuration state according to the historical version number.

[0068] In this embodiment, during the process of database persistence or message queue pushing, if a failure occurs, a compensation transaction log is immediately generated. The log records the specific situation of the failure, including the time, location, possible reasons, etc. of the failure. At the same time, the alarm mechanism is triggered to notify the operation and maintenance personnel by means of emails, text messages or alarm messages within the system. And, according to the historical version number recorded previously, roll back to the nearest valid configuration state to ensure that the system can resume normal operation.

[0069] In some specific embodiments, step S106 specifically includes: creating a multi-level index based on the association relationship between the metric instance identifier and the business entity identifier. When the distributed transaction fails, generate a transaction compensation log, record the operation sequence and intermediate state, trigger the restoration to the valid state before the transaction, and notify the operation and maintenance personnel through the message queue.

[0070] Specifically, the association relationship between the metric instance identifier and the business entity identifier is clarified and determined based on the metric metadata, data mapping rules, etc. established in the previous steps. Create a multi-level index according to the association relationship.

[0071] It can be seen that according to the association information between the metric instance identifier and the business entity identifier, analyze the characteristics and query requirements of the data, and determine the layering method and index rules of the multi-level index. Then store and organize these associated data according to the index rules to form a multi-level index structure. When querying data, the relevant data can be quickly located according to the index.

[0072] During the execution of the distributed transaction, if a failure occurs, a transaction compensation log is immediately generated. The log records the operation sequence during the transaction execution, that is, the sequence and specific content of each operation; at the same time, record the intermediate state during the transaction execution, such as the temporary modification of some data, etc. Then the system triggers the recovery mechanism to restore the system state to the valid state before the transaction execution to ensure the consistency and integrity of the data. Send a notification to the operation and maintenance personnel through the message queue to inform them of the failure of the transaction execution.

[0073] After the three-party association relationship is established, step S106 also performs data consistency verification in stages: verifying the integrity of the metric instance metadata; verifying the mapping relationship between the business entity and the data source; and sampling verification of the metric calculation results.

[0074] In some embodiments, after establishing the tripartite association relationship among the metric instance identifier, business entity identifier, and data source identifier, integrity verification is performed on the metric instance metadata. This includes checking whether all the information in the metric instance metadata is complete, such as whether the metric name, metric definition, calculation method, etc. are accurately recorded; and checking whether the association information in the metadata is correct, such as whether the association between the metric instance and the business entity and data source matches. According to the predefined metadata specifications and association rules, each item of the metric instance metadata is checked item by item. By comparing with the standard data in the database, the integrity and accuracy of the metadata are verified.

[0075] Then, according to the previously configured data mapping rules, the mapping relationship between the business entity and the data source is checked. By comparing the definitions and association information of the business entity attributes and data source fields, the correctness of the mapping relationship is verified.

[0076] The system also selects samples from the metric calculation results according to the sampling rules. Then, the samples are recalculated or verified using the same calculation method or other verification means, and the results are compared with the original calculation results. Timely detection of calculation errors can avoid misleading business decisions with incorrect results and improve the reliability and accuracy of metric calculations.

[0077] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not imply the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0078] The following are embodiments of the report loading processing system in an industrial scenario provided by the embodiments of the present disclosure. This system and the industrial scenario report loading processing method of the above embodiments belong to the same inventive concept. For the details not described in detail in the embodiments of the industrial scenario report loading processing system, reference can be made to the embodiments of the industrial scenario report loading processing method above.

[0079] The system includes: an information acquisition and binding module, configured to acquire data source information associated with the business entity identifier; define physical field mapping rules corresponding to the business entity identifier based on the data source information, establish a dynamic binding relationship between the business entity attributes and the data source fields, and generate a metric metadata template containing business entity semantic information.

[0080] A mapping rule configuration module, configured to acquire data source information associated with the business entity identifier through a data registration component, and configure data field mapping rules and data verification logic based on the data source information.

[0081] The mapping relationship establishment module is used to configure the index calculation formula and processing logic by using business entity attributes and data source information, and establish the calculation mapping relationship between index fields and data source fields.

[0082] The standard file generation module is used to structurally encapsulate index metadata, data field mapping rules, and calculation logic, and generate a standardized configuration file containing business entity association relationships.

[0083] The storage and push module is used to persist the generated standardized configuration file to the database, and at the same time push configuration update notifications to the index engine module through the message queue.

[0084] The index and consistency processing module is used to establish a tripartite association relationship index between index instance identifiers, business entity identifiers, and data source identifiers after receiving the configuration update completion information, and ensure the consistency of configuration data, association relationships, and index calculation results through a distributed transaction mechanism.

[0085] In addition to the above modules, the system of the present application can also configure indexes by querying data metadata information in the metadata management module for use in index calculation by the index calculation engine. Obtain index configuration information through the index configuration management module. The metadata management module can receive the data source and data information sent in the report integration module and can also manually maintain the data source and data information of the three parties for use by the index configuration module and the data engine module. The data engine module mainly has three functions: one is to provide basic data for calculating indexes by the index calculation engine module, one is to provide industrial software to query index data through the report integration module, and the last one is to save the index data to the index library after the index calculation engine module completes the calculation. The report integration module is integrated into the industrial software that needs to use the report system. The report integration module includes three components: a data registration component, an index configuration component, and an index query component: the data registration component registers the data information (including data source information and metadata) in the industrial software into the metadata management module for use by the data engine module, and can query the data information in the metadata management module; the index configuration component can establish a connection between the index configuration and the business entity in the industrial software; the index query component can query index data through the index configuration information. In this way, it solves the problem that in the industrial scenario, due to the diversification of data sources in the report, the existing report system cannot be directly used to complete report design; at the same time, it also solves the problem that industrial software cannot directly use report data to directly guide production.

[0086] As Figure 4 shown, the present application also provides an electronic device, including a display module 103, a memory 102, a processor 101, and a computer program stored on the memory and executable on the processor 101. When the processor 101 executes the program, it implements the steps of the report loading processing method in the industrial scenario.

[0087] In embodiments of the present invention, the electronic device includes, but is not limited to, a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device may also represent various forms of mobile modules, such as, a personal digital processor, a cellular phone, a smart phone, a wearable device, and other similar computing modules. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the embodiments of the present application described herein and / or claimed.

[0088] In embodiments of the present application, the processor 101 may be implemented by using at least one of an application specific integrated circuit, a programmable logic module, a field programmable gate array, a processor, a controller, a microcontroller, a microprocessor, and an electronic unit designed to execute the functions described herein. In some cases, such an implementation may be implemented in a controller. For a software implementation, an implementation of a process or function may be implemented with a separate software module that permits execution of at least one function or operation. The software code may be implemented by a software application (or program) written in any suitable programming language. The software code may be stored in a memory and executed by a controller.

[0089] The display module 103 is configured to display information input by a user or information provided to the user. The display module 103 may include a display panel, and the display panel may be configured in the form of a liquid crystal display, an organic light emitting diode, or the like.

[0090] The memory 102 may be used to store software programs and various data. The memory 102 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other volatile solid state storage devices.

[0091] The present application also provides a storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for processing report loading in the industrial scenario are implemented.

[0092] The storage medium may adopt any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0093] In the storage medium, the readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which the readable program code is carried. Such a propagated data signal may take various forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The readable signal medium may also be any readable medium other than the readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device.

[0094] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for processing report loading in an industrial scenario, characterized in that, The method includes: S101: Obtain data source information associated with a business entity identifier; Define a physical field mapping rule corresponding to the business entity identifier based on the data source information, establish a dynamic binding relationship between the business entity attributes and the data source fields, and generate an index metadata template containing business entity semantic information; S102: Obtain data source information associated with the business entity identifier through a data registration component, and configure a data field mapping rule and data verification logic based on the data source information; S103: Configure an index calculation formula and processing logic based on the business entity attributes and data source information, and establish a calculation mapping relationship between the index fields and the data source fields; S104: Structurally encapsulate the index metadata, data field mapping rule, and calculation logic to generate a standardized configuration file containing business entity association relationships; S105: Persist the generated standardized configuration file to a database, and at the same time push a configuration update notification to the index engine module through a message queue; S106: After receiving the configuration update completion information, establish a tripartite association relationship index between the index instance identifier, business entity identifier, and data source identifier, and ensure the consistency of the configuration data, association relationship, and index calculation result through a distributed transaction mechanism.

2. The method for processing report loading in an industrial scenario according to claim 1, wherein Step S101 specifically includes: Perform business semantic parsing on the original data items in the data source based on a preset identifier key value, and verify whether there are target data items in the target platform that are semantically consistent with the original data items; If there are no semantically consistent target data items, generate an extended attribute set under the target platform according to the attribute set of the data source; the extended attribute set inherits the structure of the source attribute set, and locates candidate data items associated with the original data items by matching the identifier key value through a semantic similarity algorithm; Use the candidate data items as the final data items in the target platform, and persist the extended attribute set and the mapping relationship to the metadata management module to establish cross-platform consistent binding between the original data items and the target data items.

3. The method for processing report loading in an industrial scenario according to claim 1, wherein, Step S102 specifically includes: After obtaining the data source information, parse the meaning of the data fields, and extract the business concepts and semantic information represented by the data fields; Based on a predefined association rule library, analyze the association between data fields, including but not limited to the functional dependency relationship between data fields and the association relationship between data fields and other business entity identifiers; Integrate the semantic information and the result of the association analysis as supplementary information into the data field mapping rule.

4. The method for processing report loading in an industrial scenario according to claim 1, wherein Step S103 specifically includes: Identify the data source type and data source type identifier, and load an adapted calculation logic template; Generate three-level calculation rules at the field level, table level, and system level based on the data source type, calculation logic template, and business entity attributes, and configure them as a hierarchical rule configuration file; Calculate the field-level statistical indicators of the hierarchical rule configuration file, verify the calculation results, and obtain verification result information; Adjust the rule parameters according to the verification results. When a conflict is detected, start the historical valid rule version and trigger an alarm.

5. The method for processing report loading in an industrial scenario according to claim 1, wherein The structural encapsulation of step S104 specifically includes: The hierarchical storage of metric metadata is adopted using a tree - shaped data structure as the root node, based on data mapping rules as the child nodes, and based on calculation logic as the leaf nodes; Field - level data source traceability information is embedded in each node to record the field - level data processing link; JSON validation rules are generated based on the configuration file structure.

6. The method for processing report loading in an industrial scenario according to claim 1, wherein Step S105 specifically includes: Before persistence, a random version number of the configuration file is generated, the modification timestamp, operator identifier, and change content summary are recorded, and blockchain evidence - storage technology is used to store the version history information; According to the system status and configuration file type, the message channel is matched; Field - level verification nodes are respectively embedded in the database persistence stage and the message queue push stage to perform data type verification, business rule verification, and associated relationship integrity verification; When persistence or push fails, a compensation transaction log is generated, an alarm is triggered to notify the operation and maintenance personnel, and the system is rolled back to the most recent valid configuration state according to the historical version number.

7. The method for processing report loading in an industrial scenario according to claim 1, wherein Step S106 specifically includes: Based on the association relationship between the metric instance identifier and the business entity identifier, a multi - level index is created; When a distributed transaction fails, a transaction compensation log is generated, the operation sequence and intermediate state are recorded, the system is triggered to recover to the valid state before the transaction, and the operation and maintenance personnel are notified through the message queue; Step S106 also performs data consistency verification in stages after the establishment of the tripartite association relationship: Metric instance metadata integrity verification; Business entity and data source mapping relationship verification; Sampling verification of metric calculation results.

8. A report loading and processing system in an industrial scenario, characterized in that, The system is used to implement the report loading and processing method in the industrial scenario described in any one of claims 1 to 7; The system includes: An information acquisition and binding module, which is used to acquire data source information associated with the business entity identifier; based on the data source information, define the physical field mapping rules corresponding to the business entity identifier, establish a dynamic binding relationship between the business entity attributes and the data source fields, and generate a metric metadata template containing business entity semantic information; A mapping rule configuration module, which is used to acquire data source information associated with the business entity identifier through a data registration component, and configure data field mapping rules and data verification logic based on the data source information; A mapping relationship establishment module, which is used to use business entity attributes and data source information to configure metric calculation formulas and processing logic, and establish a calculation mapping relationship between metric fields and data source fields; A standard file generation module, which is used to structurally encapsulate metric metadata, data field mapping rules, and calculation logic, and generate a standardized configuration file containing business entity association relationships; A storage and push module, which is used to persist the generated standardized configuration file to the database, and at the same time push a configuration update notice to the metric engine module through the message queue; An index and consistency processing module, which is used to establish a tripartite association relationship index between the metric instance identifier, the business entity identifier, and the data source identifier after receiving the configuration update completion information, and ensure the consistency of configuration data, association relationships, and metric calculation results through a distributed transaction mechanism.

9. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the report loading and processing method in the industrial scenario described in any one of claims 1 to 7.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the report loading processing method in the industrial scenario according to any one of claims 1 to 7.

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