Development assistance method, device and system based on data model

Abstract

The embodiment of the invention provides a development assistance method, device and system based on a data model, and the method comprises the steps: determining development demand data which comprises demand direction data and a corresponding target data model; based on the target data model, tracing a to-be-developed target data set, and determining the to-be-developed target data set; based on the demand direction data, performing development processing on the to-be-developed target data set to obtain corresponding deployment data; and deploying the deployment data to a target environment. According to the method, the to-be-developed target is determined according to the development demand data, and the corresponding attribute parameters of the to-be-developed target are acquired, so that developers can directly perform development processing on the target according to demands without connecting businesses and customers, the judgment of the developers on the demand implementation possibility is improved, and the development efficiency is improved. The traceability period for the development target is shortened, and the system development efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent development, and in particular to a development assistance method, device, system, electronic device and storage medium thereof based on a data model. Background Art

[0002] In today's data-driven era, enterprises have an increasing demand for data. In order to improve the effective use of data, it is particularly important to develop clear demand data and accurate data sets.

[0003] Since the existing development assistance method based on data model searches for keywords in the data mall of the data cloud platform to search and verify the source table, there is a situation where possible keywords are judged based on development experience, and it is impossible to accurately search the system specified by the front end. Therefore, the existing development assistance method based on data model has the problem that developers need to trace the target data to be developed based on experience, and then develop and process the results of the traceability, and cannot directly and accurately obtain the target data for development. Summary of the invention

[0004] An embodiment of the present invention provides a development assistance method based on a data model to solve the problem that existing development assistance methods based on data models require developers to trace the target data to be developed through experience, and then develop and process the results of the tracing, and cannot directly and accurately obtain the target data for development.

[0005] In a first aspect, an embodiment of the present invention provides a development assistance method based on a data model, the method comprising the following steps: Determine development demand data, wherein the development demand data includes demand direction data and a corresponding target data model; Based on the target data model, tracing the target data set to be developed to determine the target data set to be developed; Based on the demand direction data, the target data set to be developed is developed and processed to obtain corresponding deployment data; Deploy the deployment data to the target environment.

[0006] Optionally, the determining of development requirement data includes: Confirm the process steps of the current business to be developed; Based on the process steps, determine the corresponding target business process and target business requirements; Determine a corresponding target data model based on the target business process; Corresponding demand direction data is determined based on the target business demand.

[0007] Optionally, tracing the target data set to be developed based on the target data model to determine the target data set to be developed includes: Determine the attributes, indicator types and source tables corresponding to the target data model, wherein the attributes are used to determine the target device of the target data set, the indicator types are used to determine the feasibility of functional development of the target data set, and the source table is used to trace the source of the target data set; Based on the attributes, indicator types and source tables corresponding to the target data model, data is extracted from the corresponding data source to obtain the target data set to be developed.

[0008] Optionally, the demand direction data includes a usage scenario of a target data set and a corresponding usage purpose, and based on the demand direction data, the target data set to be developed is developed and processed to obtain corresponding deployment data: Design corresponding function codes based on the usage scenarios and corresponding usage purposes of the target data set; The code corresponding to the target data set to be developed is processed based on the function code to obtain corresponding deployment data.

[0009] Optionally, deploying the deployment data to a target environment includes: Confirm the environment parameters corresponding to the target environment, including system architecture, server configuration, and network settings; Based on the system architecture, server configuration and network settings, determine a data migration tool corresponding to the deployment method for the deployed data; Based on the data migration tool, the deployment data is deployed to the target environment.

[0010] Optionally, after deploying the deployment data to the target environment, the method further includes: Performing performance optimization testing on the target environment after deployment to obtain optimization testing results; Based on the optimization detection result, determining an optimization deployment strategy for the deployment method, wherein the optimization deployment strategy includes an optimization query strategy, an optimization index strategy, and an optimization data preprocessing strategy; Based on the optimized query strategy, the optimized index strategy and the optimized data preprocessing strategy, the deployment method of the deployment data is optimized.

[0011] In a second aspect, an embodiment of the present invention further provides a development assistance device based on a data model, wherein the development assistance device based on a data model comprises: A first determination module is used to determine development demand data, wherein the development demand data includes demand direction data and a corresponding target data model; A second determination module is used to trace the target data set to be developed based on the target data model, and determine the target data set to be developed; An acquisition module, used to develop and process the target data set to be developed based on the demand direction data to obtain corresponding deployment data; The deployment module is used to deploy the deployment data into the target environment.

[0012] In a third aspect, an embodiment of the present invention further provides a development assistance system based on a data model, and the development assistance system based on a data model includes: a development assistance device based on a data model, a server, and a smart terminal.

[0013] In a fourth aspect, an embodiment of the present invention provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the data model-based development assistance method provided in an embodiment of the present invention when executing the computer program.

[0014] In a fifth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the data model-based development assistance method provided in the embodiment of the invention are implemented.

[0015] In an embodiment of the present invention, development demand data is determined, and the development demand data includes demand direction data and a corresponding target data model; based on the target data model, the target data set to be developed is traced to determine the target data set to be developed; based on the demand direction data, the target data set to be developed is developed and processed to obtain corresponding deployment data; and the deployment data is deployed to the target environment. The above method determines the target to be developed according to the development demand data, and obtains the corresponding attribute parameters of the target to be developed, so that developers do not need to connect with the business and customers, and can directly develop and process the target according to the demand, thereby improving the developer's judgment on the possibility of realizing the demand, reducing the tracing cycle for the development target, and improving the efficiency of system development. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1is an architecture diagram of a development assistance system based on a data model provided by an embodiment of the present invention; Figure 2 is a flow chart of a development assistance method based on a data model provided by an embodiment of the present invention; Figure 3 is a structural diagram of a development assistance device based on a data model provided in an embodiment of the present invention; Figure 4 It is a structural schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] like Figure 1 As shown, Figure 1 It is an architecture diagram of a development assistance system 100 based on a data model provided by an embodiment of the present invention, and the development assistance system based on a data model includes: a development assistance device 300 based on a data model, a server 101, and a smart terminal 102. Among them, the above-mentioned development assistance device 300 based on a data model also includes a first determination module, which can be used to determine development demand data; a second determination module, which can be used to trace the target data set to be developed based on the target data model and determine the target data set to be developed; an acquisition module, which can be used to develop and process the target data set to be developed based on the demand direction data to obtain corresponding deployment data; a deployment module, which can be used to deploy the deployment data to the target environment.

[0020] Specifically, the above-mentioned development demand data may include but are not limited to demand direction data and the corresponding target data model. Generally speaking, the development demand data can be determined based on the docking results between business personnel and users. It can be a point for functional improvement of the current system, or it can be the user's future demand for a certain function of the system. It can also include the data or method steps required for the corresponding improved functional points. For example, in order to develop the functional requirement of adding a target person to the "communication directory", it is necessary to clarify the functional point corresponding to the "communication directory" business, that is, "add target person", and it is necessary to determine the corresponding functional area and development tools based on this functional point.

[0021] The above-mentioned demand direction data may refer to data related to a specific demand, which can help guide the development, optimization and adjustment of products or services. Specifically, it reflects the demand trends and directions of the market, users or other stakeholders for the services or functions of the current system. For example, in the above-mentioned example of developing the functional requirement of adding a target person to the "communication directory", the functional requirement of adding a target person to the "communication directory" can be used as a demand direction data, and the functional requirement of adding a target person can be used to meet the operation of the system, which can also be regarded as a demand direction data. For this demand direction data, it is also necessary to find the corresponding development tools and development steps to achieve this demand direction.

[0022] The above-mentioned target data models may include but are not limited to archival data models, collection data models, basic statistical data models and other data models that contain their own specific functional categories. All data models will have their own set of operating methods in a specific environment, that is, to collect, process, send and receive certain specific data in the above-mentioned data model-based development assistance system.

[0023] Specifically, the above-mentioned archive data model mainly stores archive data, which is consistent with the marketing management system archive. It can help development and design personnel understand the basic archive information such as users, equipment, lines, etc. involved in the business in advance, and refer to the annotations to understand information such as user category, equipment type, line entry method, etc. More specifically, the Valley archive data model can also be used to store one-to-one, one-to-many, and many-to-many relationship tables between users and terminal equipment, and terminal equipment and lines, such as the relationship between substations and lines, and the relationship between substations and lines.

[0024] The above-mentioned acquisition data model can be used to store various data automatically collected by the equipment terminal, such as the voltage and current data of the operating electric energy meter and the power data of the operating electric energy meter.

[0025] The above basic statistical data model can be used to store lightly summarized data based on equipment and collected data, such as data collection completeness rate, meter reading success rate, and work order processing rate.

[0026] In a possible embodiment, the above-mentioned data model-based development assistance system can select a data model suitable for the development of corresponding functional requirements according to the content corresponding to the above-mentioned demand direction data, and quickly and further confirm and judge the content of the data source according to the data model specifications that the data model can provide.

[0027] The target data set to be developed can be a data set that has not yet been constructed or improved to achieve a specific goal or requirement, that is, data content that needs to be developed and processed. These data sets usually contain data that need to be collected, processed and analyzed in the process of project development, product design, market analysis, etc., such as code corresponding to a certain functional requirement, case table data, etc. It is understandable that since the functional points corresponding to different functional requirements may involve overlapping target data sets, the overlapping target data sets can be used as key data sets, that is, the target data sets to be developed that are focused on in the development process.

[0028] In another possible embodiment, the data model-based development assistance system can trace the target data set to be developed through log management tools (Splunk, ELK) or generation management systems (ERP, MES) and other tools according to the specification content of the target data model, and find the corresponding data source content, such as the business process, steps, functional points corresponding to the data source, and the feasibility judgment of the corresponding functional points to be developed.

[0029] The above-mentioned deployment data can be a complete data set for realizing a certain function or requirement according to the content of the above-mentioned demand direction data, that is, the data content that needs to have been developed and processed. These data sets usually contain data that meet the above process content goals during project development, product design, market analysis, etc., such as the completed code corresponding to a certain functional requirement, complete case table data, etc.

[0030] The target environment can be the production environment corresponding to the product, that is, the environment that users actually come into contact with when using the product. For example, as a web page developer, there is a UAT test environment and a production environment. When the code or functional requirements developed according to the demand direction data are tested in the UAT test environment and passed, they can be deployed in the corresponding production environment, so that users can use the code or functional requirements developed according to the demand direction data in the production environment.

[0031] In a possible embodiment, the above-mentioned data model-based development assistance system determines the target to be developed according to the development requirement data through the above-mentioned method, and obtains the corresponding attribute parameters of the target to be developed, so that developers do not need to connect with the business and customers, and can directly develop and process the target according to the needs, thereby improving developers' judgment on the possibility of realizing the needs, reducing the tracing cycle for development goals, and improving the efficiency of system development.

[0032] like Figure 2 As shown, Figure 2 1 is a flow chart of a development assistance method based on a data model provided by an embodiment of the present invention. The development assistance method based on a data model comprises the following steps: 201. Determine development requirement data.

[0033] In an embodiment of the present invention, the above-mentioned data model-based development assistance method can be applied to the above-mentioned data model-based development assistance system. The above-mentioned data model-based development assistance system has functions such as data model data processing, data model data transmission and reception, and data model data memory storage, and can be constructed based on a server or a server cluster. The above-mentioned server or server cluster can be an electronic device with data model data processing capabilities.

[0034] The above-mentioned development demand data may include but are not limited to demand direction data and the corresponding target data model. Generally speaking, the development demand data can be determined based on the docking results between business personnel and users. It can be a point for functional improvement of the current system, or it can be the user's future demand for a certain function of the system. It can also include the data or method steps required for the corresponding improved functional points. For example, in order to develop the functional requirement of adding a target person to the "communication directory", it is necessary to clarify the functional point corresponding to the "communication directory" business, that is, "add target person", and it is necessary to determine the corresponding functional area and development tools based on this functional point.

[0035] The above-mentioned demand direction data may refer to data related to a specific demand, which can help guide the development, optimization and adjustment of products or services. Specifically, it reflects the demand trends and directions of the market, users or other stakeholders for the services or functions of the current system. For example, in the above-mentioned example of developing the functional requirement of adding a target person to the "communication directory", the functional requirement of adding a target person to the "communication directory" can be used as a demand direction data, and the functional requirement of adding a target person can be used to meet the operation of the system, which can also be regarded as a demand direction data. For this demand direction data, it is also necessary to find the corresponding development tools and development steps to achieve this demand direction.

[0036] 202. Based on the target data model, trace the target data set to be developed to determine the target data set to be developed.

[0037] The above-mentioned target data models may include but are not limited to archival data models, collection data models, basic statistical data models and other data models that contain their own specific functional categories. All data models will have their own set of operating methods in a specific environment, that is, to collect, process, send and receive certain specific data in the above-mentioned data model-based development assistance system.

[0038] Specifically, the above-mentioned archive data model mainly stores archive data, which is consistent with the marketing management system archive. It can help development and design personnel understand the basic archive information such as users, equipment, lines, etc. involved in the business in advance, and refer to the annotations to understand information such as user category, equipment type, line entry method, etc. More specifically, the Valley archive data model can also be used to store one-to-one, one-to-many, and many-to-many relationship tables between users and terminal equipment, and terminal equipment and lines, such as the relationship between substations and lines, and the relationship between substations and lines.

[0039] The above-mentioned acquisition data model can be used to store various data automatically collected by the equipment terminal, such as the voltage and current data of the operating electric energy meter and the power data of the operating electric energy meter.

[0040] The above basic statistical data model can be used to store lightly summarized data based on equipment and collected data, such as data collection completeness rate, meter reading success rate, and work order processing rate.

[0041] In a possible embodiment, the above-mentioned data model-based development assistance system can select a data model suitable for the development of corresponding functional requirements according to the content corresponding to the above-mentioned demand direction data, and quickly and further confirm and judge the content of the data source according to the data model specifications that the data model can provide.

[0042] The above method improves data management efficiency and accuracy through flexible model configuration, ensures consistency between different data sources, and can quickly switch to appropriate data models, thereby automatically collecting, processing and transmitting data, reducing human intervention and errors.

[0043] 203. Based on the demand direction data, the target data set to be developed is developed and processed to obtain corresponding deployment data.

[0044] In a possible embodiment, the above-mentioned data model-based development assistance system analyzes the target data set to be developed through demand direction data, identifies key data model requirements, such as archive, collection or basic statistical data models, and then selects corresponding data processing solutions according to different requirements, automatically collects and organizes data, and generates corresponding deployment data.

[0045] Through this data-driven development and processing approach, the target data set can be accurately configured and processed according to the required data to ensure data consistency and accuracy.

[0046] 204. Deploy the deployment data to the target environment.

[0047] The above-mentioned deployment data can be a complete data set for realizing a certain function or requirement according to the content of the above-mentioned demand direction data, that is, the data content that needs to have been developed and processed. These data sets usually contain data that meet the above process content goals during project development, product design, market analysis, etc., such as the completed code corresponding to a certain functional requirement, complete case table data, etc.

[0048] The target environment can be the production environment corresponding to the product, that is, the environment that users actually come into contact with when using the product. For example, as a web page developer, there is a UAT test environment and a production environment. When the code or functional requirements developed according to the demand direction data are tested in the UAT test environment and passed, they can be deployed in the corresponding production environment, so that users can use the code or functional requirements developed according to the demand direction data in the production environment.

[0049] In a possible embodiment, the above-mentioned data model-based development assistance system determines the target to be developed according to the development requirement data through the above-mentioned method, and obtains the corresponding attribute parameters of the target to be developed, so that developers do not need to connect with the business and customers, and can directly develop and process the target according to the needs, thereby improving developers' judgment on the possibility of realizing the needs, reducing the tracing cycle for development goals, and improving the efficiency of system development.

[0050] In an embodiment of the present invention, development demand data is determined, and the development demand data includes demand direction data and a corresponding target data model; based on the target data model, the target data set to be developed is traced to determine the target data set to be developed; based on the demand direction data, the target data set to be developed is developed and processed to obtain corresponding deployment data; and the deployment data is deployed to the target environment. The above method determines the target to be developed according to the development demand data, and obtains the corresponding attribute parameters of the target to be developed, so that developers do not need to connect with the business and customers, and can directly develop and process the target according to the demand, thereby improving the developer's judgment on the possibility of realizing the demand, reducing the tracing cycle for the development target, and improving the efficiency of system development.

[0051] Optionally, the step of determining development requirement data also includes confirming the process steps of the current business to be developed; based on the process steps, determining the corresponding target business process and target business requirements; based on the target business process, determining the corresponding target data model; and based on the target business requirements, determining the corresponding demand direction data.

[0052] In an embodiment of the present invention, the process steps of the above-mentioned current business to be developed may be business operations, decision-making processes and analysis requirements on the product. For example, in the metering business, voltage and current monitoring, line loss management, abnormal user behavior, etc. are all important target business processes. It is understandable that how to enable users to obtain accurate voltage and current monitoring, line loss management, abnormal user behavior and other data more clearly or simply is the corresponding target business requirement.

[0053] The determination of the above-mentioned target data model can be completed according to the corresponding target business process. Specifically, the parameter data that may be involved or touched in the target business process can be determined. For example, in the metering business, the case table or corresponding code data for voltage and current monitoring can be traced back in the data model within the corresponding specification range based on these data, which can greatly reduce the traceability process during development.

[0054] In a possible embodiment, the above-mentioned data model-based development assistance system determines the target business process, such as equipment management, energy efficiency optimization and user interaction, according to the business process steps, and then clarifies the target business needs, such as real-time equipment status monitoring, data report generation and other functions. Furthermore, based on the demand direction data, the collection data (such as equipment operation data), archival data (such as user configuration files) and basic statistical data (such as equipment health status analysis) are determined to support the system functions.

[0055] This method ensures that every link in business development has a basis and clear process, avoids demand deviation and data inconsistency problems. By accurately matching business requirements with data models, the system can efficiently process business data, improve development efficiency and system stability, and provide a solid data foundation for subsequent optimization and functional expansion.

[0056] Optionally, in the step of tracing the target data set to be developed based on the target data model and determining the target data set to be developed, it also includes determining the attributes, indicator types and source tables corresponding to the target data model; based on the attributes, indicator types and source tables corresponding to the target data model, data is extracted from the corresponding data source to obtain the target data set to be developed.

[0057] In the embodiment of the present invention, the attributes corresponding to the above-mentioned target data model can also be determined according to the dimensions in the requirements and the content of the corresponding data model. Specifically, it can be a key attribute that can describe the fact. For example, in the electricity user dimension table, it should include the customer's basic information (such as user name, electricity address, electricity category, etc.), contact information (such as telephone, email, etc.), purchase history and other attributes. It can be understood that these attribute contents can also be determined according to the covered data in the corresponding model data during the development process.

[0058] The above indicator types can be used to describe the role and type of the corresponding data model.

[0059] The above-mentioned source table may be an example table in a table list in a system database, which is used to trace the source of a target data set to be developed or to determine the data source content.

[0060] The above attributes can also be used to determine the target device of the target data set, the above indicator type can also be used to determine the feasibility of functional development of the target data set, and the above source table can also be used to trace the source of the target data set.

[0061] In another possible embodiment, the data model-based development assistance system can trace the target data set to be developed through log management tools (Splunk, ELK) or generation management systems (ERP, MES) and other tools according to the specification content of the target data model, and find the corresponding data source content, such as the business process, steps, functional points corresponding to the data source, and the feasibility judgment of the corresponding functional points to be developed.

[0062] Optionally, in the step where the demand direction data includes usage scenarios and corresponding usage purposes of the target data set, and based on the demand direction data, the target data set to be developed is developed and processed to obtain a corresponding deployment number, the step also includes designing a corresponding function code based on the usage scenarios and corresponding usage purposes of the target data set; and processing the code corresponding to the target data set to be developed based on the function code to obtain corresponding deployment data.

[0063] In the embodiment of the present invention, the above-mentioned usage scenario may refer to the usage environment of the product that can be driven by the target data set to be developed, for example, in the search service, the usage scenario of the search for data such as power and power consumption driven by the target data set to be developed, or in the registration service, the usage scenario of the input data corresponding to the target data set to be developed, etc. The above-mentioned corresponding usage purpose may be the functional requirements or corresponding functional points that need to be realized according to the above-mentioned demand direction data.

[0064] In a possible embodiment, the function code corresponding to the target data set to be developed is designed and edited by selecting code compilation and deployment software suitable for the data model, and the original function code is replaced, and the corresponding deployment data is obtained after compilation. It can be understood that the deployment data is the code data that can meet the functional requirements or function points corresponding to the demand direction data.

[0065] Optionally, the step of deploying the deployment data to the target environment also includes confirming the environmental parameters corresponding to the target environment; determining the data migration tool corresponding to the deployment method of the deployment data based on the system architecture, server configuration and network settings; and deploying the deployment data to the target environment based on the data migration tool.

[0066] In an embodiment of the present invention, the above-mentioned environmental parameters may include but are not limited to system architecture, server configuration, and network settings. Specifically, during the development process, the quality of the fact table also needs to be considered, that is, it must accurately reflect the actual data in the business process, without data errors or deviations. When acquiring data, strict data cleaning and verification must be performed to ensure the accuracy of the fact data. More specifically, it can be determined by selecting an appropriate fact type. The above-mentioned fact data can be divided into additive facts, semi-additive facts, and non-additive facts. Among them, additive facts (such as power supply, power consumption, etc.) can be summarized in different dimensions; semi-additive facts (such as settlement households, etc.) are meaningless in certain dimensions; non-additive facts (such as line loss rate, etc.) usually need to be obtained by calculation.

[0067] The above data migration tools can ensure that the deployment data after development is complete and adaptable to the target environment of the current deployment.

[0068] In a possible embodiment, the data model-based development assistance system selects a suitable server configuration by confirming the parameters of the target environment, including the server operating system, hardware configuration, storage capacity, and network bandwidth, and determines the best data migration solution based on the network settings, and selects a dedicated data migration tool, such as an ETL tool and a database replication tool, based on the content of the required direction data to transfer deployment data between different environments. Using these tools, the deployment data including user information, transaction records, and financial data are smoothly migrated to the production environment to ensure that the target system can run efficiently and stably.

[0069] By confirming the target environment parameters and selecting appropriate data migration tools, the above-mentioned data model-based development assistance system can complete the data migration work efficiently and smoothly, reducing the risk of data loss or migration failure due to environment mismatch.

[0070] Optionally, the steps after deploying the deployment data to the target environment also include performing performance optimization testing on the deployed target environment to obtain optimization testing results; determining an optimized deployment strategy for the deployment method based on the optimization testing results; and optimizing the deployment method of the deployment data based on an optimized query strategy, an optimized index strategy, and an optimized data preprocessing strategy.

[0071] In the embodiment of the present invention, the above optimization deployment strategy may include but is not limited to the optimization query strategy, the optimization index strategy and the optimization data preprocessing strategy. Specifically, in order to optimize the query performance, the following strategies may be adopted: Use the appropriate mode: Select the star mode, snowflake mode or constellation mode according to the power grid business needs and data volume. The star mode has better query performance and is suitable for simple query needs; the snowflake mode can reduce data redundancy, but the query complexity is higher; the constellation mode is suitable for situations where multiple related fact tables share dimensions.

[0072] Reasonable use of indexes: Create indexes on dimension tables and fact tables to improve data query speed. For example, create indexes on the primary key of the dimension table, and create indexes on the foreign key and commonly used query columns of the fact table.

[0073] Data preprocessing and precomputation: For some commonly used aggregated data, you can preprocess and precompute them, and store the results in the data result table to improve query performance. For example, precompute the abnormal users of each month, the abnormal devices of each county and district bureau, etc.

[0074] More specifically, after optimization according to the above strategy, the above data model-based development assistance system can re-test the query performance according to the optimized strategy, and compare the query performance before optimization. If the query performance is improved compared with the query performance before optimization, the target data set to be developed in the above development process and other contents are queried according to the optimized query strategy, and deployed.

[0075] like Figure 3 As shown, the embodiment of the present invention further provides a development assistance device 300 based on a data model, and the development assistance device based on a data model includes: A first determination module 301 is used to determine development demand data, wherein the development demand data includes demand direction data and a corresponding target data model; A second determination module 302 is used to trace the target data set to be developed based on the target data model to determine the target data set to be developed; The acquisition module 303 is used to develop and process the target data set to be developed based on the demand direction data to obtain corresponding deployment data; The deployment module 304 is used to deploy the deployment data to the target environment.

[0076] Optionally, the first determining module 301 includes: The first determination submodule is used to confirm the process steps of the current business to be developed; A second determination submodule is used to determine the corresponding target business process and target business requirements based on the process steps; A third determination submodule, configured to determine a corresponding target data model based on the target business process; The fourth determination submodule is used to determine corresponding demand direction data based on the target business demand.

[0077] Optionally, the second determining module 302 includes: A fifth determination submodule is used to determine the attributes, indicator types and source tables corresponding to the target data model, wherein the attributes are used to determine the target device of the target data set, the indicator types are used to determine the feasibility of the functional development of the target data set, and the source table is used to trace the source of the target data set; The sixth determination submodule is used to extract data from the corresponding data source based on the attributes, indicator types and source tables corresponding to the target data model to obtain the target data set to be developed.

[0078] Optionally, the acquisition module 303 includes: A design submodule, used to design corresponding function codes based on the usage scenario and corresponding usage purpose of the target data set; The processing submodule is used to process the code corresponding to the target data set to be developed based on the function code to obtain corresponding deployment data.

[0079] Optionally, the deployment module 304 includes: A seventh determination submodule is used to confirm the environment parameters corresponding to the target environment, wherein the environment parameters include system architecture, server configuration and network settings; An eighth determination submodule, for determining a data migration tool corresponding to the deployment method for the deployment data based on the system architecture, server configuration, and network settings; The deployment submodule is used to deploy the deployment data into the target environment based on the data migration tool.

[0080] Optionally, the above device further comprises: An optimization module is used to perform performance optimization detection on the target environment after deployment to obtain optimization detection results; A ninth determination submodule, configured to determine an optimized deployment strategy for the deployment method based on the optimized detection result, wherein the optimized deployment strategy includes an optimized query strategy, an optimized index strategy, and an optimized data preprocessing strategy; The optimization submodule is used to optimize the deployment method of the deployment data based on the optimized query strategy, the optimized index strategy and the optimized data preprocessing strategy.

[0081] like Figure 4As shown, an embodiment of the present invention further provides an electronic device 400, including a processor, and the processor can execute any one of the above-mentioned development assistance methods based on the data model.

[0082] Specifically, it includes a processor 401 and a memory 402, and a computer program for executing a development assistance method based on a data model, which is stored in the memory 402 and can be run on the processor 401, wherein: The processor 401 runs the computer program of the development assistance method based on the data model stored in the memory 402 to perform the following steps: Determine development demand data, wherein the development demand data includes demand direction data and a corresponding target data model; Based on the target data model, tracing the target data set to be developed to determine the target data set to be developed; Based on the demand direction data, the target data set to be developed is developed and processed to obtain corresponding deployment data; Deploy the deployment data to the target environment.

[0083] Optionally, the processor 401 executes the determining of development requirement data, including: Confirm the process steps of the current business to be developed; Based on the process steps, determine the corresponding target business process and target business requirements; Determine a corresponding target data model based on the target business process; Corresponding demand direction data is determined based on the target business demand.

[0084] Optionally, the processor 401 performs the tracing of the target data set to be developed based on the target data model to determine the target data set to be developed, including: Determine the attributes, indicator types and source tables corresponding to the target data model, wherein the attributes are used to determine the target device of the target data set, the indicator types are used to determine the feasibility of functional development of the target data set, and the source table is used to trace the source of the target data set; Based on the attributes, indicator types and source tables corresponding to the target data model, data is extracted from the corresponding data source to obtain the target data set to be developed.

[0085] Optionally, the processor 401 executes the demand direction data including the usage scenario and the corresponding usage purpose of the target data set, and based on the demand direction data, performs development processing on the target data set to be developed to obtain corresponding deployment data: Design corresponding function codes based on the usage scenarios and corresponding usage purposes of the target data set; The code corresponding to the target data set to be developed is processed based on the function code to obtain corresponding deployment data.

[0086] Optionally, the processor 401 executes the step of deploying the deployment data to the target environment, including: Confirm the environment parameters corresponding to the target environment, including system architecture, server configuration, and network settings; Based on the system architecture, server configuration and network settings, determine a data migration tool corresponding to the deployment method for the deployed data; Based on the data migration tool, the deployment data is deployed to the target environment.

[0087] Optionally, after the processor 401 further executes the step of deploying the deployment data to the target environment, the method further includes: Performing performance optimization testing on the target environment after deployment to obtain optimization testing results; Based on the optimization detection result, determining an optimization deployment strategy for the deployment method, wherein the optimization deployment strategy includes an optimization query strategy, an optimization index strategy, and an optimization data preprocessing strategy; Based on the optimized query strategy, the optimized index strategy and the optimized data preprocessing strategy, the deployment method of the deployment data is optimized.

[0088] An embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the various processes of the development assistance method based on a data model or the development assistance method based on a data model on the application side provided by the embodiment of the present invention, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0089] Those skilled in the art can understand that the implementation of all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM).

[0090] The above disclosure is only the preferred embodiment of the present invention, which certainly cannot be used to limit the scope of the present invention. Therefore, equivalent changes made according to the claims of the present invention are still within the scope of the present invention.

Claims

1. A development assistance method based on a data model, characterized in that: include: Determine development demand data, wherein the development demand data includes demand direction data and a corresponding target data model; Based on the target data model, tracing the target data set to be developed to determine the target data set to be developed; Based on the demand direction data, the target data set to be developed is developed and processed to obtain corresponding deployment data; Deploy the deployment data to the target environment.

2. The data model-based development assistance method according to claim 1, characterized in that: The determining of development requirement data includes: Confirm the process steps of the current business to be developed; Based on the process steps, determine the corresponding target business process and target business requirements; Determine a corresponding target data model based on the target business process; Corresponding demand direction data is determined based on the target business demand.

3. The data model-based development assistance method according to claim 1, characterized in that: The tracing of the target data set to be developed based on the target data model to determine the target data set to be developed includes: Determine the attributes, indicator types and source tables corresponding to the target data model, wherein the attributes are used to determine the target device of the target data set, the indicator types are used to determine the feasibility of functional development of the target data set, and the source table is used to trace the source of the target data set; Based on the attributes, indicator types and source tables corresponding to the target data model, data is extracted from the corresponding data source to obtain the target data set to be developed.

4. The data model-based development assistance method according to claim 1, characterized in that: The demand direction data includes the usage scenario and the corresponding usage purpose of the target data set. Based on the demand direction data, the target data set to be developed is developed and processed to obtain corresponding deployment data: Design corresponding function codes based on the usage scenarios and corresponding usage purposes of the target data set; The code corresponding to the target data set to be developed is processed based on the function code to obtain corresponding deployment data.

5. The data model-based development assistance method according to claim 1, characterized in that: The deploying the deployment data to the target environment includes: Confirm the environment parameters corresponding to the target environment, including system architecture, server configuration, and network settings; Based on the system architecture, server configuration and network settings, determine a data migration tool corresponding to the deployment method for the deployed data; Based on the data migration tool, the deployment data is deployed to the target environment.

6. The data model-based development assistance method according to claim 5, characterized in that: After deploying the deployment data to the target environment, the method further includes: Performing performance optimization testing on the target environment after deployment to obtain optimization testing results; Based on the optimization detection result, determining an optimization deployment strategy for the deployment method, wherein the optimization deployment strategy includes an optimization query strategy, an optimization index strategy, and an optimization data preprocessing strategy; Based on the optimized query strategy, the optimized index strategy and the optimized data preprocessing strategy, the deployment method of the deployment data is optimized.

7. A development assistance device based on a data model, characterized in that: include: A first determination module is used to determine development demand data, wherein the development demand data includes demand direction data and a corresponding target data model; A second determination module is used to trace the target data set to be developed based on the target data model, and determine the target data set to be developed; An acquisition module, used to develop and process the target data set to be developed based on the demand direction data to obtain corresponding deployment data; The deployment module is used to deploy the deployment data into the target environment.

8. A development assistance system based on a data model, characterized in that: The data model-based development assistance system includes: a data model-based development assistance device; The data model-based development assistance device is determined by a data model-based development assistance method shown in claims 1-6.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the data model-based development assistance method as described in any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the data model-based development assistance method according to any one of claims 1 to 6 are implemented.

Images