Disclosure of Invention
The invention provides a report loading processing method in an industrial scene, which is characterized in that a data field mapping rule is configured through a data registration component, and the accuracy of data mapping is doubly ensured. The accurate updating of the configuration file can be ensured, and the index calculation results are consistent.
The method comprises the following steps:
s101, acquiring data source information associated with a service entity identifier, defining a physical field mapping rule corresponding to the service entity identifier based on the data source information, establishing a dynamic binding relation between service entity attributes and data source fields, and generating an index metadata template containing service entity semantic information;
S102, acquiring data source information associated with a service entity identifier through a data registration component, and configuring a data field mapping rule and a data verification logic based on the data source information;
S103, based on the service entity attribute and the data source information, configuring an index calculation formula and processing logic, and establishing a calculation mapping relation between an index field and a data source field;
s104, carrying out structured encapsulation on index metadata, data field mapping rules and calculation logic to generate a standardized configuration file containing business entity association relations;
S105, persisting the generated standardized configuration file to a database, and pushing a configuration update notification to an index engine module through a message queue;
And S106, after receiving the configuration update completion information, establishing a three-party association relation index among the index instance identifier, the service entity identifier and the data source identifier, and ensuring consistency of configuration data, association relation and index calculation results through a distributed transaction mechanism.
It should be further noted that step S101 specifically includes:
based on a preset identifier key value, carrying out business semantic analysis on an original data item in a data source, and verifying whether a target data item which is consistent with the original data item in terms of semantics exists in a target platform;
If the target data item with consistent semantics does not exist, generating an extended attribute set under a target platform according to an attribute set of a data source, wherein the extended attribute set inherits the structure of the source attribute set, matches an identifier key value through a semantic similarity algorithm, and locates candidate data items with association relation with the original data item;
And taking the candidate data item as a final data item in the target platform, and persisting the extended attribute set and the mapping relation to a metadata management module, and establishing cross-platform consistency binding of the original data item and the target data item.
It should be further noted that step S102 specifically includes:
after the data source information is acquired, analyzing the meaning of the data field, and extracting the business concept and semantic information represented by the data field;
Based on a predefined association rule library, analyzing the association between the data fields, including but not limited to the function dependency relationship between the data fields, the association relationship between the data fields and other business entity identifiers;
And integrating the semantic information and the association analysis result into the data field mapping rule as supplementary information.
It should be further noted that step S103 specifically includes:
Identifying a data source type and a data source type identifier, and loading an adaptive calculation logic template;
generating three-level computing rules of field level, table level and system level based on the data source type, the computing logic template and the business entity attribute, and configuring the three-level computing rules into a hierarchical rule configuration file;
calculating field-level statistical indexes of the hierarchical rule configuration file, and checking a calculation result to obtain check result information;
and adjusting rule parameters according to the verification result, starting a historical effective rule version when the conflict is detected, and triggering an alarm.
It should be further noted that the structured package in step S104 specifically includes:
the method comprises the steps of adopting a tree data structure to hierarchically store index metadata as a root node, adopting a data mapping rule as a child node and adopting a calculation logic as a leaf node;
Embedding field-level data source tracing information into each node, and recording a field-level data processing link;
JSON validation rules are generated based on the configuration file structure.
It should be further noted that step S105 specifically includes:
Generating a random version number of the configuration file before persistence, recording a modification time stamp, an operator identification and a change content abstract, and storing version history information by adopting a blockchain certification technology;
matching a message channel according to the system state and the type of the configuration file;
Respectively embedding field-level check nodes in a database persistence stage and a message queue pushing stage, and executing data type check, business rule check and association relation integrity verification;
when persistence or pushing fails, generating a compensation transaction log, triggering an alarm to inform operation and maintenance personnel, and backing to a latest effective configuration state according to a historical version number.
It should be further noted that step S106 specifically includes creating a multi-level index based on the association relationship between the index instance identifier and the service entity identifier;
when the distributed transaction fails to execute, generating a transaction compensation log, recording an operation sequence and an intermediate state, triggering to restore to a valid state before the transaction, and notifying operation and maintenance personnel through a message queue;
Step S106 further performs data consistency verification in stages after the three-party association relationship is established:
checking the integrity of index instance metadata;
verifying the mapping relation between the service entity and the data source;
And (5) sampling and verifying the index calculation result.
The application also provides a report loading processing system in an industrial scene, which comprises an information acquisition binding module, a dynamic binding relation establishing module and a dynamic binding relation establishing module, wherein the information acquisition binding module is used for acquiring data source information associated with a business entity identifier;
the mapping rule configuration module is used for acquiring data source information associated with the service entity identifier through the data registration component and configuring a data field mapping rule and data verification logic based on the data source information;
The mapping relation establishing module is used for configuring an index calculation formula and processing logic by using the service entity attribute and the data source information, and establishing a calculation mapping relation between an index field and a data source field;
The standard file generation module is used for carrying out structural encapsulation on the index metadata, the data field mapping rule and the calculation logic to generate a standardized configuration file containing the association relation of the service entities;
The storage pushing module is used for persisting the generated standardized configuration file to the database and pushing the configuration update notification to the index engine module through the message queue;
And the index and consistency processing module is used for establishing a three-party association relation index among the index instance identifier, the service entity identifier and the data source identifier after receiving the configuration update completion information, and ensuring consistency of configuration data, association relation and index calculation results through a distributed transaction mechanism.
According to another embodiment of the present application, there is provided an electronic device including a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the steps of a report loading processing method in the industrial scenario when the processor executes the program.
According to yet another embodiment of the present application, there is also provided a storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the report loading processing method in the industrial scenario.
From the above technical scheme, the invention has the following advantages:
the report loading processing method in the industrial scene establishes a dynamic binding relation by acquiring the data source information and defining the physical field mapping rule, and configures the data field mapping rule through the data registration component to doubly ensure the accuracy of data mapping. And meanwhile, verifying the mapping relation between the service entity and the data source, and checking and correcting the mapping relation again. Therefore, various parameters of the equipment can be accurately mapped to corresponding production indexes, such as parameters of the equipment, such as the rotating speed, the temperature and the like, are accurately related to the equipment operation stability indexes, and the fact that the report data truly reflects the production conditions is ensured.
The step S103 of the application configures an index calculation formula and processing logic based on the service entity attribute and the data source information, establishes a calculation mapping relation, and adopts a hierarchical rule configuration file to divide the calculation logic into a field level, a table level and a system level for management. The calculation logic is clear, and when the service requirement changes, the calculation rule of the corresponding level can be rapidly positioned and adjusted. Step S104, adopting a tree data structure to carry out structural encapsulation on index metadata, mapping rules and calculation logic to generate a standardized configuration file, step S105, generating a dynamic version number before persistence, and adopting a blockchain certification technology to store version history information. Structured packaging makes the configuration file well-defined in hierarchy, and facilitates searching and modification. Step S105 is to embed field-level check nodes in the database persistence stage and the message queue pushing stage respectively for data type check, business rule check and association relation integrity verification.
Detailed Description
The report loading processing method in the industrial scene solves the problem that the existing report system cannot be directly used for completing report design due to the diversity of data sources in the report in the industrial scene through the report system, and simultaneously solves the problem that the industrial software cannot directly use the report data to directly guide production.
Detailed steps of a report loading processing method in an industrial scenario to which the present application relates are described in detail below, and specific details such as specific system architecture, techniques, etc., are set forth for purposes of illustration rather than limitation, in order to provide a thorough understanding of embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless expressly specified otherwise.
The statements of "one embodiment" or "some embodiments" and the like, described in this disclosure, mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present disclosure. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," and the like in the present application are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments" unless expressly specified otherwise.
In embodiments of the present invention, computer program code for carrying out operations of the present disclosure may be written in one or more programming languages, including but not limited to an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, a flowchart of a report loading processing method in an industrial scenario in an embodiment is shown, where the method includes:
S101, acquiring data source information associated with a service entity identifier, wherein the data source information comprises a data source address, a data structure and a field attribute, defining a physical field mapping rule corresponding to the service entity identifier based on the data source information, establishing a dynamic binding relation between the service entity attribute and the data source field, and generating an index metadata template containing service entity semantic information.
In some embodiments, the data source management module is queried according to the service entity identifier to acquire data source information associated with the service entity identifier, wherein the data source information comprises a data source address, which is an access port for accessing data, and the data structure defines the organization form of the data. The field attributes describe specific characteristics of each field, such as data type, length, etc.
And according to the acquired data source information, a physical field mapping rule is made, namely, how to correspond between the service entity attribute and the data source field is determined. And establishing a dynamic binding relation between the service entity attribute and the data source field through a mapping rule. And finally, integrating the information to generate an index metadata template containing the semantic information of the business entity.
As one implementation mode of the application, based on a preset identifier key value, business semantic analysis is carried out on the original data item in the data source, and whether the target data item which is consistent with the original data item in semantic exists in the target platform is verified. The target data item is used for representing a definition specification of the original data item by the target platform.
And if the target data items with consistent semantics do not exist, generating an extended attribute set under the target platform according to the attribute set of the data source, wherein the extended attribute set inherits the structure of the source attribute set, matches identifier key values through a semantic similarity algorithm, and locates candidate data items with association relation with the original data items.
And taking the candidate data item as a final data item in the target platform, and persisting the extended attribute set and the mapping relation to a metadata management module, and establishing cross-platform consistency binding of the original data item and the target data item.
It can be seen that before the index metadata template is generated, alignment of service definitions of the source data and the target platform is ensured through semantic consistency verification, and mapping errors caused by semantic ambiguity are avoided. And using the service entity identifier as an index, and carrying out matching search in a pre-stored data source information base to find out corresponding data source information. And then analyzing the logic relation between the business entity attribute and the data source field according to the data structure and the field attribute, thereby defining a reasonable mapping rule.
S102, acquiring data source information associated with the service entity identifier through a data registration component, and configuring a data field mapping rule and data verification logic based on the data source information.
In this embodiment, after obtaining the data source information, the meaning of the data field is resolved by using a preset semantic analysis model to extract the business concept and semantic information represented by the data field, and meanwhile, based on a predefined association rule base, the association between the data fields is analyzed, including but not limited to the function dependency relationship between the data fields, the association relationship between the data field and other business entity identifiers, and the like. And integrating the semantic information and the association analysis result into the data field mapping rule as supplementary information, so that the mapping relation reflects the association between the business logic and the data, and the accuracy and the reliability of index calculation are improved.
In some embodiments, the data registration component obtains data source information associated with a business entity identifier. After the information is acquired, mapping rules are configured for the data fields, wherein the corresponding relation between the business entity attributes and the data source fields is refined, and accurate acquisition of the data is ensured. Meanwhile, data checking logic is also configured, such as checking the integrity, accuracy, legitimacy and the like of the data, so that the quality of the data entering the system is ensured, and the stability of the system is improved.
S103, based on the service entity attribute and the data source information, configuring an index calculation formula and processing logic, and establishing a calculation mapping relation between an index field and a data source field.
In some embodiments, in combination with business entity attributes and previously acquired data source information, a calculation formula for the metrics is determined, which specifies how the required metrics are calculated from the data source fields. Meanwhile, processing logic of the data is configured, such as cleaning, converting and the like, on the data. In this way, a calculation mapping relation between the index field and the data source field is established, and the accuracy and maintainability of index calculation are improved by defining how the index is calculated from the data source.
And S104, carrying out structured encapsulation on the index metadata, the data field mapping rule and the calculation logic to generate a standardized configuration file containing the association relation of the service entity.
In some embodiments, the generated index metadata, the configured data field mapping rule and the calculation logic are packaged in a structured manner, and a standardized configuration file containing the association relation of the service entity is generated after the packaging, wherein the standardized configuration file integrates all key information of the previous steps to form a complete configuration file.
And S105, persisting the generated standardized configuration file to a database, and pushing a configuration update notification to the index engine module through a message queue.
In some embodiments, the generated standardized configuration file is saved in a database to realize persistent storage, so as to ensure that configuration information is not lost. Meanwhile, a configuration update notification is sent to the index engine module through the message queue to inform the index engine module that the configuration information has changed and corresponding update operation is needed.
The database provides a persistent storage function for the data, and the system writes the configuration file to the database. The message queue is used as an asynchronous communication mechanism, and sends configuration update notification to the index engine module, and the index engine module triggers corresponding update logic after receiving the notification.
And S106, after receiving the configuration update completion information, establishing a three-party association relation index among the index instance identifier, the service entity identifier and the data source identifier, and ensuring consistency of configuration data, association relation and index calculation results through a distributed transaction mechanism.
After receiving the configuration update completion information sent by the index engine module, the embodiment establishes a three-way association relationship index among the index instance identifier, the service entity identifier and the data source identifier. Through a distributed transaction mechanism, consistency of configuration data, association relation and index calculation results is ensured, and the problem of inconsistent data in the data processing process is prevented.
Thus, after receiving the update completion information, the three identifiers are associated to form an index table. The distributed transaction mechanism monitors and coordinates the whole data processing process, ensures that configuration data, association relation and index calculation results are consistent under any condition, and improves the reliability and data quality of the system.
In one embodiment of the present invention, based on step S103, a possible embodiment will be given below for non-limiting illustration of its specific implementation. As shown in fig. 2, step S103 specifically includes:
Step S1031, identifying the data source type and the data source type identifier, and loading the adaptive calculation logic template.
The embodiment determines the type of the data source, such as whether the data source is a relational database, a non-relational database, a file system, and the like. And simultaneously, an identifier corresponding to the data source type is identified, and the identifier can uniquely identify the data source type. And then, loading the matched calculation logic template from a pre-stored calculation logic template library according to the identified data source type. For example, if the data source is a relational database, a computational logic template suitable for relational database data processing is loaded.
Step S1032, based on the data source type, the calculation logic template and the business entity attribute, generating three-level calculation rules of field level, table level and system level, and configuring the three-level calculation rules into a hierarchical rule configuration file.
The embodiment can respectively generate the field-level, table-level and system-level calculation rules by combining the determined data source type, the loaded calculation logic template and the business entity attribute. The field level calculation rule mainly calculates for a single field in the data source, for example, performs operations such as data cleaning, format conversion and the like on a certain field. The table level calculation rule is calculated based on the whole data table, such as calculating the total value, average value and the like of certain fields in the table. The system level computation rules are computation from the overall data source system level, involving the computation of associations between multiple data tables. After the rules are generated, the rules are configured into a file in a layered mode to form a layered rule configuration file, and the file clearly records calculation rules of different levels and correlations thereof.
In this way, according to the data source type and the calculation logic template, the processing requirements of the business entity attribute on the data are analyzed, and the calculation rule of each level is determined. The rules are organized in a hierarchical structure and stored in a hierarchical rule configuration file for convenient management and use.
And step S1033, calculating field level statistical indexes of the hierarchical rule configuration file, and checking the calculation result to obtain check result information.
According to the field level calculation rule in the hierarchical rule configuration file, the field data in the data source are calculated to obtain field level statistical indexes such as maximum value, minimum value, average value, number and the like of the fields. And then checking the calculation results in a mode of comparing with a preset threshold value, checking the integrity and consistency of data and the like. And generating verification result information according to the verification result, wherein the information records whether the calculation result accords with the expectation or not and the specific situation of not accord with the expectation.
And step S1034, adjusting rule parameters according to the verification result, starting a historical valid rule version when the conflict is detected, and triggering an alarm.
It should be noted that, according to the verification result information, the rule parameters in the hierarchical rule configuration file are adjusted. If the verification result shows that a certain calculation result does not meet the expectations, parameters of the correlation rule may need to be adjusted, such as adjusting the weight, the threshold value and the like of calculation, so that the calculation result is more accurate. When a conflict is detected between rules in the process of adjusting the rules or calculating, the system starts the historical effective rule version, namely, the rule version which is normally operated before is used for data processing. Meanwhile, the system triggers an alarm to inform related personnel that the rule conflict occurs and needs to be processed.
In this way, the system analyzes rule parameters to be adjusted according to the verification result information, and modifies the hierarchical rule configuration file. In the rule adjustment and calculation process, whether the rules have conflict or not is monitored in real time, when the conflict is detected, an effective rule version is selected from a historical rule version library for replacement, and relevant personnel are notified through an alarm mechanism.
Further, as a refinement and extension of the foregoing embodiment of the step S104, for a complete description of the implementation process in the step S104, as shown in fig. 3, the structured package of the step S104 specifically includes:
Step S1041, hierarchically storing index metadata by adopting a tree data structure as a root node, taking a data mapping rule as a child node and taking a calculation logic as a leaf node.
The present embodiment uses a tree data structure to store the related information. And taking the index metadata as a root node, wherein the index metadata comprises key information such as basic definition of indexes, business semantics and the like. The data mapping rule is connected with the root node as a child node, and the data mapping rule defines the corresponding relation between the service entity attribute and the data source field, and indicates where and how the data is related to the index. Thus, the tree data structure has the characteristics of distinct hierarchy and easiness in organization and management. Starting with index metadata, the data mapping rules and the calculation logic are orderly organized through the relationship of parent-child nodes.
Step S1042, embedding field level data source tracing information in each node, recording field level data processing link.
In each node of the tree structure, field-level data source trace back information is embedded. The information records from which specific data source the data of each field was obtained, including the address of the data source, table name, field name, etc. At the same time, the field level data processing link is recorded, i.e., which operations the field data undergoes throughout the process.
Step S1043, generating JSON verification rules based on the configuration file structure.
According to the structural characteristics of the tree data structure configuration file constructed in the embodiment, the system generates a JSON verification rule. These validation rules are used to verify whether the subsequently input or output JSON formatted data meets the requirements of the configuration file. And analyzing information of each node in the tree data structure configuration file, including index metadata, data mapping rules, calculation logic and the like, and extracting key constraint and specification. Based on the information, the corresponding JSON validation rules are generated, which can be implemented using JSONSchema or other tools.
In one embodiment of the present invention, based on step S105, a possible embodiment will be given below for non-limiting illustration of its specific implementation. The step S105 specifically includes:
Step S1051, generating a random version number of the configuration file before persistence, recording a modification time stamp, an operator identification and a change content abstract, and storing version history information by adopting a blockchain certification technology.
This embodiment generates a random version number for the configuration file before persisting the configuration file to the database. The random version number is unique and varies with each modification to identify different versions of the configuration file. And simultaneously, recording the time stamp of the modification until the specific time when the modification occurs. Record the personnel identification of the operation, and the exact who has been modified, and record the abstract of the modified content, and describe the main content of the modification. The version history information is stored by adopting a blockchain certification technology, and the characteristics of the blockchain ensure that the information is not tamperable and traceable.
Step S1052, according to the system state and the configuration file type, the message channel is matched.
The embodiment monitors the state of the device in real time. At the same time, the type of the configuration file, such as an index configuration file, a data mapping rule configuration file, etc., is identified. And matching proper message channels from a predefined message channel list according to the identification information. Different system states and profile types may correspond to different message channels.
Step S1053, respectively embedding field-level check nodes in a database persistence stage and a message queue pushing stage, and executing data type check, business rule check and association relation integrity verification.
In this embodiment, field-level check nodes are embedded in the corresponding stages respectively during the process of persisting the configuration file to the database and during the process of pushing the configuration update notification to the message queue. At the check node, a data type check is performed on each field in the configuration file, ensuring that the data type of the field meets expectations. And setting check nodes in the processes of database persistence and message queue pushing. When the data of the configuration file flows through the nodes, the verification module checks each field according to the predefined data type rule, the predefined business rule and the predefined association relation rule. If the condition of not conforming to the rule is found, error information is recorded and corresponding processing measures are taken.
Step S1054, when the persistence or pushing fails, generating a compensation transaction log, triggering an alarm to notify operation and maintenance personnel, and backing to the latest valid configuration state according to the historical version number.
In the process of database persistence or message queue pushing, the embodiment immediately generates a compensation transaction log if a failure condition occurs. Specific cases of failure are logged, including time, location, possible reasons for failure, etc. Meanwhile, an alarm mechanism is triggered, and operation and maintenance personnel are notified through mails, short messages or alarm messages in the system. And the system is returned to the latest effective configuration state according to the history version number recorded before, so that the system can be ensured to be restored to the normal operation state.
In some specific embodiments, step S106 specifically includes creating a multi-level index based on the association of the index instance identifier with the business entity identifier. When the distributed transaction fails to execute, a transaction compensation log is generated, an operation sequence and an intermediate state are recorded, the effective state before the transaction is triggered and restored, and an operation and maintenance personnel is notified through a message queue.
Specifically, the association relationship between the specific index instance identifier and the service entity identifier is determined based on the index metadata, the data mapping rule and other information established in the previous step. And creating a multi-level index according to the association relation.
It can be seen that the hierarchical manner and index rules of the multi-level index are determined by analyzing the characteristics of the data and the query requirements according to the association information between the index instance identifier and the business entity identifier. And then storing and organizing the associated data according to an index rule to form a multi-level index structure. When data inquiry is carried out, relevant data can be quickly positioned according to the index.
During distributed transaction execution, if a failure occurs, a transaction compensation log is immediately generated. The log records the operation sequence in the transaction execution process, namely the sequence and specific content of each operation, and simultaneously records the intermediate state in the transaction execution process, such as the temporary modification condition of certain data. And then the system triggers a recovery mechanism to recover the system state to an effective state before the transaction is executed, so that the consistency and the integrity of the data are ensured. And sending a notification to the operation and maintenance personnel through the message queue to inform the situation of failed execution of the transaction.
Step S106 is to execute data consistency verification in stages after the three-party association relation is established, namely index instance metadata integrity verification, service entity and data source mapping relation verification and index calculation result sampling verification.
In some embodiments, the integrity check is performed on the index instance metadata after the three-way association between the index instance identifier, the business entity identifier, and the data source identifier is established. The method comprises the steps of checking whether various information in index instance metadata is complete, such as whether index names, index definitions, calculation methods and the like have accurate records, and checking whether associated information in the metadata is correct, such as whether associations between index instances and business entities, data sources are matched or not. And checking index instance metadata item by item according to the predefined metadata specification and association rules. And verifying the integrity and accuracy of the metadata by comparing the metadata with standard data in a database.
And then checking the mapping relation between the service entity and the data source according to the previously configured data mapping rule. And verifying the correctness of the mapping relation by comparing the definition and the association information of the service entity attribute and the data source field.
The system also selects samples from the index calculation results according to the sampling rules. And then the same calculation method or other verification means are used for re-calculating or verifying the sample, the result is compared with the original calculation result, and the calculation error can be found out in time, so that the error result can avoid misleading the service decision, and the reliability and the accuracy of index calculation are improved.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present invention.
The following is an embodiment of a report loading processing system in an industrial scenario provided by the embodiments of the present disclosure, where the system and the report loading processing method in an industrial scenario of the embodiments belong to the same inventive concept, and details of the report loading processing system in an industrial scenario, which are not described in detail in the embodiment of the report loading processing system in an industrial scenario, may refer to the embodiment of the report loading processing method in an industrial scenario.
The system comprises an information acquisition binding module, a dynamic binding relation generation module and a data source module, wherein the information acquisition binding module is used for acquiring data source information associated with a service entity identifier, defining a physical field mapping rule corresponding to the service entity identifier based on the data source information, establishing a dynamic binding relation between a service entity attribute and a data source field, and generating an index metadata template containing service entity semantic information.
And the mapping rule configuration module is used for acquiring the data source information associated with the service entity identifier through the data registration component and configuring the data field mapping rule and the data verification logic based on the data source information.
The mapping relation establishing module is used for configuring an index calculation formula and processing logic by using the service entity attribute and the data source information and establishing a calculation mapping relation between the index field and the data source field.
And the standard file generation module is used for carrying out structural encapsulation on the index metadata, the data field mapping rule and the calculation logic to generate a standardized configuration file containing the association relation of the service entity.
And the storage pushing module is used for persisting the generated standardized configuration file to the database and pushing the configuration update notification to the index engine module through the message queue.
And the index and consistency processing module is used for establishing a three-party association relation index among the index instance identifier, the service entity identifier and the data source identifier after receiving the configuration update completion information, and ensuring consistency of configuration data, association relation and index calculation results through a distributed transaction mechanism.
Besides the modules, the system can also configure the index by inquiring the data metadata information in the metadata management module for calculation by an index calculation engine. And acquiring index configuration information through an index configuration management module. The metadata management module can receive the data sources and the data information sent by the report integrating module and can also manually maintain the data sources and the data information of the three parties for the index configuration module and the data engine module to use. The data engine module has three main functions, namely, one is to provide basic data for the index calculation engine module to provide indexes, one is to provide industrial software to inquire the index data through the report form integration module, and the last is to store the index data into an index library after the index calculation engine module completes calculation. The report integrating module is integrated into industrial software needing a report system, and comprises three components, namely a data registering component, an index configuration component and an index inquiring component, wherein the data registering component registers data information (comprising data source information and metadata) in the industrial software into the metadata management module for the use of the data engine module and can inquire the data information in the metadata management module, the index configuration component can establish connection between index configuration and business entities in the industrial software, and the index inquiring component can inquire index data through the index configuration information. Therefore, the problem that the existing report system cannot be directly used for completing report design due to the diversification of data sources in the report in an industrial scene is solved, and meanwhile, the problem that industrial software cannot directly use report data to directly guide production is solved.
As shown in fig. 4, the present application further provides an electronic device, which includes a display module 103, a memory 102, a processor 101, and a computer program stored in the memory and capable of running on the processor 101, where the processor 101 implements steps of a report loading processing method in an industrial scenario when executing the program.
In embodiments of the present application, electronic devices include, but are not limited to, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. The electronic device may also represent various forms of mobile modules, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing modules. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the embodiments of the application described and/or claimed herein.
In embodiments of the application, processor 101 may be implemented 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, an electronic unit designed to perform the functions described herein, and in some cases such an implementation may be implemented in a controller. For a software implementation, an embodiment such as a process or function may be implemented with a separate software module that allows for performing at least one function or operation. The software codes may be implemented by a software application (or program) written in any suitable programming language, which may be stored in memory and executed by a controller.
The display module 103 is used to display information input by a user or information provided to the user. The display module 103 may include a display panel, which may be configured in the form of a liquid crystal display, an organic light emitting diode, or the like.
The memory 102 may be used to store software programs as well as various data. The memory 102 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device.
The application also provides a storage medium on which a computer program is stored, which when being executed by a processor, implements the steps of the report loading processing method in the industrial scenario.
A storage medium may employ 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 can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, module, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of a 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
In a storage medium, a readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, module, or device.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.