Simulation data acquisition and storage method and system, server and medium

Abstract

The invention relates to a simulation data acquisition and storage method and system, a server and a medium. The simulation data acquisition and storage method comprises the following steps: when a simulation task is created, generating a task identifier of the simulation task; in the simulation task execution process, simulation data of all simulation stages are asynchronously collected, and the simulation stages comprise the pre-processing stage, the solving stage and the post-processing stage; and taking the task identifier as a primary key, and storing the simulation data in series to obtain a data link. By implementing the simulation data acquisition and storage method provided by the invention, the problems of simulation parameter missing and scattered file storage in the prior art can be improved, and subsequent data summarization and comparative analysis are facilitated.

Description

Technical Field

[0001] The present application relates to the field of automobile technology, and in particular to a simulation data acquisition and storage method, system, server and medium. Background Art

[0002] Existing technologies have automated simulation tasks and can improve simulation efficiency. In order to support more simulation disciplines and achieve higher computing efficiency, distributed deployment can be used to deploy each simulation service independently on different nodes to disperse the computing load of the simulation process.

[0003] However, this method has problems such as missing simulation parameters and scattered file storage, which makes it inconvenient to aggregate and compare simulation data or use it as sample data for AI training. Summary of the invention

[0004] Based on this, the present application provides a simulation data acquisition and storage method, system, server and medium, which can improve the problems of missing simulation parameters and scattered file storage in the prior art.

[0005] In a first aspect, the present application provides a simulation data collection and storage method, which includes: generating a task identifier of the simulation task when the simulation task is created; asynchronously collecting simulation data of each simulation stage during the execution of the simulation task, wherein the simulation stage includes a pre-processing stage, a solution stage, and a post-processing stage; using the task identifier as the primary key, and storing the simulation data in series to obtain a data link.

[0006] In combination with the first aspect, in a first possible implementation mode of the first aspect, during the execution of the simulation task, the step of asynchronously collecting simulation data at each simulation stage includes: in the pre-processing stage, asynchronously collecting the initial model, configuration parameters and the pre-processing model; in the solution stage, asynchronously collecting the submitted parameters and the calculation status; in the post-processing stage, asynchronously collecting the process files, result files and the simulation report.

[0007] In combination with the first aspect, in a second possible implementation method of the first aspect, the task identifier is used as the primary key, and the simulation data of each stage of the simulation task are stored in series, including: storing the simulation data in a non-relational database and obtaining the corresponding storage path; generating a data identifier for the stored simulation data, and using the task identifier as the primary key, associating the recorded data identifier with the storage path in the relational database to obtain a simulation data link.

[0008] In combination with the first aspect, in a third possible implementation of the first aspect, after using the task identifier as the primary key and storing the simulation data in series to obtain a data link, the method also includes: obtaining task information of each simulation task, wherein the task information is used to indicate task attributes; extracting analysis items of the task information, and determining multiple target simulation tasks with consistent analysis items; generating a simulation data table with a unified format based on the data link of each target simulation task; and performing a horizontal comparative analysis of multiple target simulation tasks based on the simulation data table.

[0009] In combination with the third possible implementation method of the first aspect, in a fourth possible implementation method of the first aspect, performing a horizontal comparative analysis of multiple target simulation tasks based on a simulation data table includes: in the simulation data table, comparing the corresponding data between each target simulation task in turn; if data items with consistent data are compared, the data items with consistent data are displayed in grayscale; if data items with inconsistent data are compared, the data items with inconsistent data are highlighted.

[0010] In the second aspect, the present application also provides a simulation data acquisition and storage system, which includes: a data acquisition server, used to execute a simulation data acquisition and storage method such as the first aspect or any one of the implementation modes of the first aspect; a network storage server, used to mount to a task management server, a pre-processing server, a solution server, a post-processing server and a data acquisition server to store all simulation data.

[0011] In combination with the second aspect, in a first feasible implementation of the second aspect, the network storage server is specifically used to: after the simulation task is created, create a directory as a working path for the simulation task; after the initial model is uploaded, upload the initial model to the working path; when the simulation task is executed, store all simulation data in the working path.

[0012] On the third aspect, the present application also provides a data acquisition server, which includes: a creation unit, which is used to generate a task identifier of a simulation task when the simulation task is created; a collection unit, which is used to asynchronously collect simulation data of each simulation stage during the execution of the simulation task, wherein the simulation stage includes a pre-processing stage, a solution stage and a post-processing stage; a storage unit, which is used to use the task identifier as a primary key to store the simulation data in series to obtain a data link.

[0013] In a fourth aspect, the present application also provides a data acquisition server, which includes a processor and a memory, and the processor and the memory are connected via a bus; the processor is used to execute multiple instructions; the memory is used to store multiple instructions, and the instructions are suitable for being loaded by the processor and executed by the simulation data acquisition storage method such as the first aspect or any one embodiment of the first aspect.

[0014] In a fifth aspect, the present application further provides a computer-readable storage medium storing a plurality of instructions, wherein the instructions are suitable for being loaded by a processor and executing a simulation data acquisition and storage method such as the first aspect or any one embodiment of the first aspect.

[0015] In summary, the present application provides a simulation data acquisition and storage method, system, server and medium, wherein the data acquisition server asynchronously collects simulation data of each simulation stage, uses the task identifier as the primary key, and stores the simulation data in series, so that when the simulation task is completed, all simulation data of the simulation task can be quickly obtained according to the task identifier of the simulation task, thereby improving the problems of missing simulation parameters and scattered file storage in the prior art, and facilitating data aggregation and comparative analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram of a flow chart of a simulation data acquisition and storage method in an embodiment of the present application;

[0017] Figure 2 A schematic diagram of a flow chart of a simulation data acquisition and storage method in another embodiment of the present application;

[0018] Figure 3 A simulation data table of a simulation data collection and storage method in an embodiment of the present application;

[0019] Figure 4 The figure is a schematic diagram of the structure of a simulation data acquisition and storage system in one embodiment of the present application. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0021] At present, a distributed deployment method is usually adopted to deploy the scripts of each stage of the simulation process to different computing nodes to improve the computational efficiency of the simulation. However, due to the dispersion of the simulation process, simulation data is missing and file storage is scattered, which is inconvenient for subsequent data analysis.

[0022] In this regard, the present application provides a simulation data collection and storage method, which uses the task identifier as the primary key to store the simulation data of each simulation stage collected during the simulation process in series to obtain a data link, thereby improving the problems of missing simulation data and scattered file storage in the prior art.

[0023] It should be noted that the data acquisition server proposed in this application can be any type of server with computing capabilities, and this application does not limit this. In addition, the data acquisition server can interact with other servers or terminal devices, and execute the method proposed in this application.

[0024] In order to better understand the simulation data acquisition and storage method proposed in this application, this application also provides an embodiment of the simulation data acquisition and storage method, such as Figure 1 As shown, next, this application uses the data acquisition server as the execution subject to illustrate the simulation data acquisition and storage method:

[0025] 110: When a simulation task is created, a task identifier of the simulation task is generated;

[0026] 120: During the execution of the simulation task, asynchronously collect simulation data of each simulation stage;

[0027] 130: Use the task ID as the primary key, store the simulation data in series, and obtain the data link.

[0028] Among them, the task identification (ID) is used to uniquely identify the simulation task. Due to the uniqueness of the task ID, the data acquisition server uses the task ID as the primary key (Primary Key) to collect and serially store the simulation data of each simulation stage of the simulation task according to the simulation stages of the simulation task, including the pre-processing stage, the solution stage and the post-processing stage, so that the simulation data are correlated and logical. Subsequently, all simulation data of the simulation task can be found based on the task ID, which is convenient for subsequent summary, comparison and analysis, etc., improving the problems of missing simulation parameters and scattered file storage in the prior art. Moreover, the present application asynchronously collects the simulation data of each simulation stage, that is, the collection service of the data collection server is triggered by an asynchronous thread call. On the one hand, even if the collection service fails, it will not affect the execution process of the simulation task. On the other hand, the real-time performance of the collection and storage is improved. When the simulation task is completed, the data collection and storage is basically completed.

[0029] With respect to step 120, in one practicable manner, the data acquisition server may asynchronously acquire all data input and output at each simulation stage during the execution of the entire simulation task, and use all data input and output at each simulation stage as simulation data.

[0030] Preferably, the data acquisition server uses data embedding to embed important parameters in the simulation data to reduce the omission of important simulation data. The key to this implementation lies in the accuracy of embedding, because accurate data embedding is the key to reducing the omission of important simulation data. The specific content of data embedding in different technical fields is different. For example, in the field of task simulation, the aforementioned important parameters especially refer to the process parameters of the simulation task, such as configuration parameters, submission parameters and calculation status.

[0031] Based on this, the present application also provides another implementable method for step 120. The present application embeds the simulation data to be collected at each stage of the simulation process in advance through code embedding and other methods, so that the data acquisition server can automatically and accurately asynchronously collect important simulation data at each simulation stage, reduce the collection of useless data, and generate data links for important simulation data, and subsequently realize more valuable data analysis. Specifically, the aforementioned steps of asynchronously collecting simulation data at each simulation stage during the execution of the simulation task include: in the pre-processing stage, asynchronously collecting the initial model, configuration parameters and pre-processing model; in the solution stage, asynchronously collecting submission parameters and calculation status; in the post-processing stage, asynchronously collecting process files, result files and simulation reports.

[0032] For example, taking a distributed task simulation system including a task management server, a pre-processing server, a solution server, and a post-processing server as an example, in the process of executing an automated simulation task flow in the distributed task simulation system:

[0033] In the pre-processing stage, the data acquisition server asynchronously acquires the model input to the pre-processing server by the task management server as the initial model, as well as the configuration parameters input to the pre-processing server by the user through the task management server. The configuration parameters include the computing resource type, the number of CPU cores required for the calculation, the number of nodes, the solving software, etc., and asynchronously acquires the new model output by the pre-processing server after processing the initial model as the pre-processing model;

[0034] In the solving stage, the data collection server asynchronously collects the submission parameters input by the user to the solving server through the task management server and the calculation status generated by the solving server during the solving process. The submission parameters include the pre-processing script call command, the Rest interface request content and the post-processing script call command. The Rest interface request content includes the submitted calculation task name, task submission time, etc. The calculation status includes the task status, task end time and end status, etc.

[0035] In the post-processing stage, the data acquisition server asynchronously collects the process files and result files output by the solution server, as well as the simulation report output by the post-processing server.

[0036] With respect to step 130, in order to improve the efficiency of data query and the scalability of data, the present application provides a specific and implementable method, which realizes the serial storage of simulation data by combining the relational database and the non-relational database. Specifically, the aforementioned step 130 includes: storing the simulation data in the non-relational database and obtaining the corresponding storage path; generating a data identifier of the stored simulation data, and using the task identifier as the primary key, associating the record data identifier and the storage path in the relational database to obtain a simulation data link.

[0037] Among them, the non-relational database is used to store parameter information, that is, specific data content, while the relational database is used to store path metadata, that is, to represent data name, data ID, storage path and other contents. When the data identifier and storage path are associated in the relational database, the data identifier and storage path of the simulation data are recorded in a data table with the task identifier as the primary key, and an association relationship is established between the data identifier and the storage path, so that when querying the simulation data, the simulation data can be queried in the non-relational database by querying the storage path associated with the data identifier of the simulation data in the relational database. The relational database associates and stores the data identifier and storage path, and can quickly access and retrieve data through the primary key, thereby improving the query efficiency of data query; the non-relational database stores simulation data, which can improve the scalability of data and reduce the pressure on the relational database by processing high-concurrency read and write requests.

[0038] In an practicable manner, non-relational databases include MongoDB, Redis or HBase, etc., and relational databases include MySQL, Oracle and DB2, etc. The non-relational database of the present application preferably adopts MongoDB database, which has the advantages of high performance, scalability, powerful query function, high availability and reliability, and rich tools and ecosystem. The relational database of the present application preferably adopts MySQL database, which has the advantages of open source and free, high performance, easy to use, cross-platform, reliability, stability, flexibility and scalability.

[0039] For example, combining Figure 2 , taking the distributed task simulation system including the task management server, pre-processing server, solution server and post-processing server, the non-relational database is the MongoDB database, and the relational database is the MySQL database as an example, in the process of the distributed task simulation system executing the automated simulation task process:

[0040] In the pre-processing stage, after the initial model is uploaded to the task management server and the data acquisition server asynchronously acquires the initial model, the data acquisition server uses the task ID as the primary key to associate and record the initial model ID and storage path in the MySQL database; after the user enters the configuration parameters, the data acquisition server stores the acquired configuration parameters in the MongoDB database, and uses the task ID as the primary key to associate and record the configuration parameter ID and storage path in the MySQL database; the pre-processing server submits the pre-processing model to the solution server, and after the data acquisition server asynchronously acquires the pre-processing model, the data acquisition server uses the task ID as the primary key to associate and record the pre-processing model ID and storage path in the MySQL database;

[0041] In the solution phase, the user submits parameters to the solution server through the task management server, so that the simulation task is submitted to the solution server for calculation. The solution server can be a high-performance computing server (HPC) server. After the data acquisition server asynchronously collects the submitted parameters, it stores the collected submitted parameters in the MongoDB database, and uses the task ID as the primary key to associate and record the submitted parameter ID and the storage path in the MySQL database; the data acquisition server tracks the calculation status of the simulation task, such as the task status and time consumption. After asynchronously collecting the task status and time consumption, the data acquisition server stores the collected task status and time consumption in the MongoDB database, and uses the task ID as the primary key to record the task status and time consumption in the MySQL database;

[0042] In the post-processing stage, the solver server outputs process files and result files, submits the calculation results to the post-processing server, and the post-processing server outputs a simulation report. After the data acquisition server asynchronously collects the process files, result files, and simulation reports, it stores the collected process files, result files, and simulation reports in the MongoDB database, and uses the task ID as the primary key to associate and record the process file ID, result file ID, simulation report ID, and storage path in the MySQL database.

[0043] In one feasible method, after collecting the process files, result files and simulation reports, the data acquisition server can also extract specific process parameters, process parameters and process parameters from the process files, result files and simulation reports respectively, and store the process parameters, process parameters and process parameters in the MongoDB database, and use the task ID as the primary key to associate and record the process parameter ID, process parameter ID, process parameter ID and storage path in the MySQL database to facilitate subsequent data analysis and comparison.

[0044] In one feasible manner, the initial model and the pre-processing model are stored in a working path specially opened for the simulation task, which can be specifically stored in NFS. In this case, the data acquisition service associates and records the model ID and the storage path of the model in NFS.

[0045] For example, after storing all simulation data in series, a data link is obtained. Taking MySQL database as an example, the data link is shown in the following Table 1:

[0046]

[0047]

[0048] Table 1

[0049] Furthermore, after the simulation data is stored in series to obtain the data link, the data acquisition server can also perform data analysis and application based on the data link. Specifically, a simulation data table with a unified format is generated based on the data link of each simulation task, for example Figure 2 As shown, a simulation data table with a unified format of "submitted parameters|initial model|preprocessing model|simulation results|task status|process file|result file|simulation report" is generated according to the data links of simulation tasks A, simulation task B, simulation task C and simulation task D, and then a horizontal comparative analysis is performed on the data of the target simulation tasks, wherein the simulation results include process files, result files and simulation reports.

[0050] Based on this, the present application also provides an implementation method of data application. After using the task identifier as the primary key and storing the simulation data in series to obtain a data link, the method also includes: obtaining task information of each simulation task, wherein the task information is used to indicate task attributes; extracting analysis items of the task information, and determining multiple target simulation tasks with consistent analysis items; generating a simulation data table with a unified format based on the data link of each target simulation task; and performing horizontal comparative analysis of multiple target simulation tasks based on the simulation data table.

[0051] For example, if the analysis items of simulation task A and simulation task B are "front hood torsional stiffness analysis", simulation task A and simulation task B are used as target simulation tasks, and a simulation data table with a unified format is generated according to the data link of simulation task A and simulation task B, for example Figure 2 As shown, a horizontal comparative analysis of multiple target simulation tasks is then performed based on the simulation data table, for example Figure 3 shown.

[0052] In an implementable manner, the data link table also stores task information. For example, taking a MySQL database as an example, the task information in the data link is shown in Table 2:

[0053]

[0054]

[0055] Table 2

[0056] In a specific implementation, in order to improve the intuitiveness of the data and facilitate users to see the difference data at a glance, thereby achieving more in-depth data analysis, this implementation performs color differentiation on the data items that are consistent and the data items that are inconsistent. Specifically, the aforementioned horizontal comparative analysis of multiple target simulation tasks based on the simulation data table includes: in the simulation data table, comparing the corresponding data between each target simulation task in turn; if the data items with consistent data are compared, the data items with consistent data are displayed in grayscale; if the data items with inconsistent data are compared, the data items with inconsistent data are highlighted.

[0057] Among them, based on the simulation data table, the data acquisition server can perform horizontal comparison of analysis items of different times, different models, different submission parameters, and different results. During the comparative analysis, the simulation results are compared with the simulation data table as the dimension. For string type data, determine whether the strings are the same; for model files, determine whether the model parameters are the same; for PPT, process screenshots and other files, compare whether the md5 codes are consistent. If the data items with consistent data are compared, the data items with consistent data are displayed in grayscale; if the data items with inconsistent data are compared, the data items with inconsistent data are highlighted. At the same time, this application also supports horizontal comparison of multiple simulation data.

[0058] like Figure 4 As shown, the present application also provides a simulation data processing system, which reduces data transmission bandwidth consumption by mounting NFS through task management service, pre-processing service, solver service, and post-processing service. Specifically, the data acquisition server is used to execute the method of any of the foregoing embodiments or implementation methods; the network storage server is used to mount to the task management server, pre-processing server, solver server, post-processing server and data acquisition server to store all simulation data.

[0059] It should be noted that mounting the network storage server to each server is to further improve the acquisition and storage efficiency and improve the possibility of missing simulation data. After mounting, the read data and write data of each server are written to the network storage server, which enables the servers to quickly read and write each other's data, realize data interaction, and reduce the file transfer bandwidth consumption between different servers, thereby improving the acquisition and storage efficiency and further reducing the possibility of missing simulation data.

[0060] In an practicable manner, the network storage server includes a network file system (NFS), Ceph, and network attached storage (Network Attached Storage), etc. The present application preferably uses NFS because, compared with other network storage servers, NFS has the advantages of being simple and easy to use, saving space, improving efficiency, and supporting multiple platforms. It is particularly suitable for distributed scenarios such as task simulation that have the characteristics of multi-server collaboration and large data transmission volume.

[0061] In a specific implementable manner, when a simulation task is created, a network storage server creates a directory as a working path for the simulation task, so that when a simulation task model is uploaded, the task management service stores the model file in the task working path. Specifically, the network storage server is used to: create a directory as a working path for the simulation task after the simulation task is created; upload the initial model to the working path after the initial model is uploaded; and store all simulation data in the working path when the simulation task is executed.

[0062] The present application also provides a data acquisition server. The embodiment of the present application can divide the functional modules of the device according to the above method example. For example, each functional module can be divided corresponding to each function, or two or more functions can be integrated into one module. The above integrated module can be implemented in the form of hardware or in the form of software functional modules. It should be noted that the division of modules in the embodiment of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation. Specifically, the data acquisition server includes: a creation unit, which is used to generate a task identifier of a simulation task when a simulation task is created; a collection unit, which is used to asynchronously collect simulation data of each simulation stage during the execution of the simulation task, wherein the simulation stage includes a pre-processing stage, a solution stage, and a post-processing stage; a storage unit, which is used to use the task identifier as a primary key to store the simulation data in series to obtain a data link.

[0063] In another implementable manner, the acquisition unit is specifically used for: asynchronously acquiring the initial model, configuration parameters and pre-processing model in the pre-processing stage; asynchronously acquiring submission parameters and calculation status in the solution stage; and asynchronously acquiring process files, result files and simulation reports in the post-processing stage.

[0064] In another implementable method, the storage unit is specifically used to: use the task identifier as the primary key to store the simulation data of each stage of the simulation task in series, including: storing the simulation data in a non-relational database and obtaining the corresponding storage path; generating a data identifier for the stored simulation data, and using the task identifier as the primary key to associate the recorded data identifier with the storage path in the relational database to obtain a simulation data link.

[0065] In another implementable manner, the data acquisition server also includes an analysis unit, which is used to: obtain task information of each simulation task, wherein the task information is used to indicate task attributes; extract analysis items of the task information, and determine multiple target simulation tasks with consistent analysis items; generate a simulation data table with a unified format based on the data link of each target simulation task; and perform horizontal comparative analysis on multiple target simulation tasks based on the simulation data table.

[0066] In another implementable manner, the analysis unit is specifically used to: in the simulation data table, sequentially compare the corresponding data between each target simulation task; if consistent data items are found out through comparison, the data items with consistent data are displayed in grayscale; if inconsistent data items are found out through comparison, the inconsistent data items are highlighted.

[0067] The present application also provides a data acquisition server, which may include: a processor and a memory. The processor and the memory are connected via a bus. The processor is used to execute multiple instructions; the memory is used to store multiple instructions, which are suitable for being loaded by the processor and executed as in the simulation data acquisition storage method in the above embodiment.

[0068] Among them, the processor can be an electronic control unit (ECU), a central processing unit (CPU), a general processor, a coprocessor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. The processor can also be a combination that realizes computing functions, such as a combination of one or more microprocessors, a combination of 5SP and a microprocessor, etc. In this embodiment, the processor can adopt a single-chip microcomputer, and various control functions can be realized by programming the single-chip microcomputer. The processor has the advantages of strong computing power and fast processing. Specifically, the processor is used to execute the function of the creation unit, which is used to generate the task identifier of the simulation task when the simulation task is created; it is also used to execute the function of the acquisition unit, which is used to asynchronously collect simulation data of each simulation stage during the execution of the simulation task, wherein the simulation stage includes a pre-processing stage, a solution stage and a post-processing stage; it is also used to execute the function of the storage unit, which is used to use the task identifier as the primary key to store the simulation data in series to obtain a data link.

[0069] In another implementable manner, the processor is specifically used to: asynchronously collect the initial model, configuration parameters and pre-processing model in the pre-processing stage; asynchronously collect submission parameters and calculation status in the solution stage; and asynchronously collect process files, result files and simulation reports in the post-processing stage.

[0070] In another implementable method, the processor is specifically used to: use the task identifier as the primary key to store the simulation data of each stage of the simulation task in series, including: storing the simulation data in a non-relational database and obtaining the corresponding storage path; generating a data identifier for the stored simulation data, and using the task identifier as the primary key, associating the recorded data identifier with the storage path in the relational database to obtain a simulation data link.

[0071] In another implementable manner, the processor is also used to execute the functions of the analysis unit, for obtaining task information of each simulation task, wherein the task information is used to indicate task attributes; extracting analysis items of the task information, and determining multiple target simulation tasks with consistent analysis items; generating a simulation data table with a unified format based on the data link of each target simulation task; and performing horizontal comparative analysis of multiple target simulation tasks based on the simulation data table.

[0072] In another implementable manner, the processor is specifically used to: in the simulation data table, sequentially compare the corresponding data between each target simulation task; if consistent data items are found out through comparison, the data items with consistent data are displayed in grayscale; if inconsistent data items are found out through comparison, the inconsistent data items are highlighted.

[0073] In one embodiment, the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a plurality of instructions, which are suitable for being loaded by a processor and executing the method in any of the foregoing embodiments. The processor is used to execute the plurality of instructions; the memory is used to store the plurality of instructions, which are loaded by the processor and executed as in the simulation data acquisition and storage method in the foregoing embodiments.

[0074] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0075] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A simulation data collection and storage method, characterized in that: The method comprises: When a simulation task is created, a task identifier of the simulation task is generated; During the execution of the simulation task, asynchronously collect simulation data of each simulation stage, wherein the simulation stage includes a pre-processing stage, a solution stage and a post-processing stage; The task identifier is used as a primary key, and the simulation data is stored in series to obtain a data link.

2. The method according to claim 1, characterized in that: The step of asynchronously collecting simulation data of each simulation stage during the execution of the simulation task includes: In the pre-processing stage, the initial model, configuration parameters and pre-processing model are collected asynchronously; In the solution phase, asynchronous collection and submission of parameters and calculation status; In the post-processing stage, process files, result files and simulation reports are collected asynchronously.

3. The method according to claim 1, characterized in that The step of using the task identifier as a primary key, storing the simulation data in series, and obtaining a data link comprises: Storing the simulation data in a non-relational database and obtaining a corresponding storage path; A data identifier of the stored simulation data is generated, and the task identifier is used as a primary key, and the data identifier and the storage path are associated and recorded in a relational database to obtain a simulation data link.

4. The method according to claim 1, characterized in that: After the task identifier is used as a primary key and the simulation data is stored in series to obtain a data link, the method further includes: Acquire task information of each simulation task, wherein the task information is used to represent task attributes; Extracting analysis items from the task information, and acquiring a plurality of target simulation tasks with consistent analysis items; Generate a simulation data table with a unified format according to the data link of each target simulation task; A horizontal comparative analysis is performed on multiple target simulation tasks according to the simulation data table.

5. The method according to claim 4, characterized in that The performing horizontal comparative analysis on multiple target simulation tasks according to the simulation data table comprises: In the simulation data table, each data corresponding to each target simulation task is compared in turn; If the data items with consistent data are compared, the data items with consistent data are displayed in grayscale; If inconsistent data items are found during the comparison, the inconsistent data items are highlighted.

6. A simulation data acquisition and storage system, characterized in that: include: A data acquisition server, configured to execute the method according to any one of claims 1 to 5; The network storage server is used to be mounted on the task management server, the pre-processing server, the solution server, the post-processing server and the data acquisition server to store all simulation data.

7. The system according to claim 6, characterized in that The network storage server is specifically used for: After the simulation task is created, a directory is created as a working path of the simulation task; After the initial model is uploaded, uploading the initial model to the working path; When the simulation task is executed, all simulation data are stored in the working path.

8. A data acquisition server, characterized in that: The data acquisition server comprises: A creation unit, used to generate a task identifier of a simulation task when the simulation task is created; A collection unit, used for asynchronously collecting simulation data of each simulation stage during the execution of the simulation task, wherein the simulation stage includes a pre-processing stage, a solution stage and a post-processing stage; The storage unit is used to store the simulation data in series by taking the task identifier as a primary key to obtain a data link.

9. A data collection server, characterized in that: The acquisition, storage and processing device includes a processor and a memory, and the processor and the memory are connected via a bus; the processor is used to execute multiple instructions; the memory is used to store the multiple instructions, and the instructions are suitable for being loaded by the processor and executing the simulation data acquisition and storage method described in any one of claims 1 to 5.

10. A computer-readable storage medium, characterized in that a plurality of instructions are stored in the computer-readable storage medium, and the instructions are suitable for being loaded by a processor and executing the simulation data acquisition and storage method according to any one of claims 1 to 5.

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