Performance evaluation method and system supporting custom

Abstract

The invention belongs to the technical field of efficiency simulation evaluation, and discloses a custom-supporting efficiency evaluation method, system, device and medium, the method comprises the following steps: recording source data and simulation information during simulation operation in real time, and updating a source database and a main database in real time according to a simulation process; the simulation information and the corresponding source data are read and preprocessed; after the preprocessing is completed, summarizing the preprocessing results, and storing the summarized preprocessing results into an independent preprocessing data file; the preprocessed source data is obtained through the source data interface service to be analyzed and processed, and evaluation result data is uploaded to a server; and the evaluation page obtains evaluation result data returned by the self-defined evaluation algorithm script from the server, and analyzes and renders the evaluation result data by using the page template. According to the method, a unified data management architecture is established, and a mode of'master library metadata + independent simulation database 'is adopted, so that data isolation is ensured, and quick retrieval is supported.

Description

A performance evaluation method and system that supports customization Technical Field

[0001] This invention relates to the field of performance simulation and evaluation technology, and in particular to a performance evaluation method and system that supports customization. Background Technology

[0002] Traditional performance evaluation systems typically employ a decentralized data recording model, storing simulation and evaluation data together without unified management. For example, some systems rely on manual recording of key parameters or save raw data using independent tools (such as MATLAB or CSV files), resulting in inconsistent data formats (e.g., a mix of binary, text, and image data), making rapid integration and analysis difficult. Metadata (such as experiment time and program number) is often maintained in Excel spreadsheet format, which is error-prone and lacks scalability.

[0003] Existing solutions largely rely on offline scripts for data processing, such as using Python scripts to parse binary logs frame by frame. However, this approach suffers from the following problems: 1. Inefficiency: The script needs to read the entire raw data each time, cannot cache intermediate results, and repeated analysis is significantly time-consuming. 2. Lack of flexibility: Statistical analysis logic (such as average and peak calculations) is fixed in the code; adding new metrics requires modifying the source code and redeploying. 3. Poor format compatibility: Raw data is often stored in proprietary binary formats (such as custom protocols), requiring dedicated parsing tools and making integration with other toolchains (such as web front-ends) difficult.

[0004] Traditional assessment results are mostly presented in static reports (PDF, Excel) or displayed through fixed template web pages, which have the following limitations: 1. Rigid visualization: Chart types (such as bar charts, line charts) and layouts are preset by the system, and users cannot customize interactive logic (such as dynamically filtering data ranges). 2. Insufficient real-time performance: Assessment conclusions can only be generated after preprocessing is completed, and key indicators (such as real-time decision-making time) cannot be displayed synchronously during simulation. 3. Poor cross-scenario adaptability: Different assessment scenarios (such as urban crackdown, logistics transportation) require separate development of independent assessment modules, resulting in high maintenance costs.

[0005] Existing systems are typically custom-developed for specific projects, resulting in high coupling between modules. For example, if a UAV strike assessment system needs to add a "target recognition accuracy" metric, it requires modification of the data acquisition, processing, and display layers of code, making it difficult to reuse in other scenarios and presenting the following technical problems: 1. Data silos: Raw data and assessment data are stored separately, lacking unified metadata management, making cross-experiment comparisons difficult. For example, multiple Excel spreadsheets need to be manually compared to analyze the performance differences of different simulation schemes. 2. Processing efficiency bottlenecks: Repeatedly parsing raw data (e.g., processing Protobuf logs from scratch for each assessment) wastes computing resources and cannot support high-frequency, multi-user concurrent analysis needs. 3. Lack of visual interaction: Fixed template display methods cannot meet users' needs for dynamic interaction (e.g., dragging a timeline to view historical data trends), limiting the ability to deeply analyze assessment results. 4. High maintenance costs: Adding new assessment metrics requires modification of multiple code stages, lacking modular design, resulting in long development cycles and high error rates.

[0006] Therefore, how to provide a performance evaluation method and system that supports customization is an urgent problem to be solved. Summary of the Invention

[0007] This invention provides a method and system for supporting customized performance evaluation, in order to solve the problems mentioned above in the prior art.

[0008] According to a first aspect of the present invention, a performance evaluation method that supports customization is provided.

[0009] In one embodiment, the customizable performance evaluation method includes: when starting a performance simulation, initiating an independent data recording process to record source data and simulation information during the simulation run in real time, and updating the source database and main database in real time according to the simulation progress; after the performance simulation ends, automatically starting a source data preprocessing process to read the simulation information and corresponding source data for preprocessing and converting them into a preset format; and summarizing the preprocessing results after preprocessing and storing them in an independent preprocessed data file, while updating the main database status; starting a custom evaluation algorithm script on the evaluation page, obtaining the preprocessed source data through the source data interface service for analysis and processing, and uploading the evaluation result data to the server; after receiving the data, the server creates a new simulation record in the main database and stores the evaluation result data in the corresponding preprocessing database; the evaluation page obtains the evaluation result data returned by the custom evaluation algorithm script from the server, and uses a page template to parse and render the evaluation result data to generate a visualized evaluation result.

[0010] In one embodiment, the real-time recording of source data and simulation information during simulation operation, and the real-time updating of the source database and the main database according to the simulation progress, includes: real-time recording of the running data of all frames during this performance simulation as source data; synchronously recording the simulation information and simulation status during this performance simulation using the main database; real-time monitoring of the operation process of this performance simulation, saving the source data of each frame during this simulation to an independent source database file frame by frame, and updating the simulation status in the main database if the performance simulation ends.

[0011] In one embodiment, the source data includes simulation time information, simulation entity position, attitude, velocity, and acceleration; the simulation information includes experiment scheme number, start time, stop time, and simulation record ID.

[0012] In one embodiment, the process of reading simulation information and corresponding source data for preprocessing and converting them into a preset format, and then summarizing the preprocessing results and storing them in a separate preprocessed data file while updating the main database status includes: reading simulation information from the main database, searching for the source database file based on the simulation record ID, and reading the source data in the source database file corresponding to the simulation record ID frame by frame; converting the source data in the simulation process into data in a preset format, performing cumulative statistics on the accumulated data contained in the source data, and extracting the model state data and sensor data in the simulation process to achieve source data preprocessing; saving the preprocessed source data frame by frame to the preprocessed database file corresponding to the simulation record ID, and updating the main database and marking it as preprocessing complete after all frames of source data have been processed.

[0013] In one embodiment, the step of launching a custom evaluation algorithm script on the evaluation page, obtaining preprocessed source data through the source data interface service for analysis and processing, and uploading the evaluation result data to the server includes: calling the script interface of the custom evaluation algorithm script in the program on the evaluation page; according to the preset source data interface service, the user uses the custom evaluation algorithm script to call the source data interface service request; after receiving the request sent by the user, the server queries the corresponding source database file from the source database and returns the source data as the result to the user's source data interface service request; the user uses the custom evaluation algorithm script to process the source data, and after processing, calls the preset upload result data interface to upload the evaluation result data to the server; after receiving the uploaded evaluation result data, the server generates a preprocessing record, marks the preprocessing record ID, and saves the evaluation result data to a separate preprocessing database file according to the preprocessing record ID.

[0014] In one embodiment, the evaluation page obtains the evaluation result data returned by the custom evaluation algorithm script from the server, and uses a page template to parse and render the evaluation result data to generate a visualized evaluation result. This includes: the evaluation page obtaining the evaluation result data of the custom evaluation algorithm script, calling a preset evaluation data interface, and passing a preprocessed record ID to the server's evaluation data interface; the server reading the preprocessed database file through the preprocessed record ID, and returning the evaluation result data to the evaluation page; and the evaluation page rendering based on the evaluation result data to visualize and display the evaluation result.

[0015] In one embodiment, the evaluation page can be configured to display a custom page according to different evaluation result display requirements; and the evaluation page can be configured with a custom page template.

[0016] According to a second aspect of the present invention, a performance evaluation system that supports customization is provided.

[0017] In one embodiment, the customizable performance evaluation system includes: a simulation data acquisition module, used to start an independent data recording process when performance simulation is started, record source data and simulation information during simulation in real time, and update the source database and main database in real time according to the simulation progress; a source data preprocessing module, used to automatically start the source data preprocessing process after the performance simulation ends, read simulation information and corresponding source data for preprocessing, convert them into a preset format; and summarize the preprocessing results after preprocessing, store them in an independent preprocessed data file, and update the main database status; a custom script processing module, used to start a custom evaluation algorithm script on the evaluation page, obtain the preprocessed source data through the source data interface service for analysis and processing, and upload the evaluation result data to the server; after receiving the data, the server creates a new simulation record in the main database and stores the evaluation result data in the corresponding preprocessing database; and a visualization result display module, used for the evaluation page to obtain the evaluation result data returned by the custom evaluation algorithm script from the server, and use the page template to parse and render the evaluation result data to generate a visualized evaluation result.

[0018] According to a third aspect of the present invention, a computer device is provided.

[0019] In some embodiments, the computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps described above for supporting a custom performance evaluation method.

[0020] According to a fourth aspect of the present invention, a computer-readable storage medium is provided.

[0021] In one embodiment, a computer program is stored on the computer-readable storage medium, which, when executed by a processor, implements the steps described above for supporting a custom performance evaluation method.

[0022] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects: 1. The present invention establishes a unified data management architecture and adopts the "master database metadata + independent simulation database" mode, which not only ensures data isolation but also supports fast retrieval (such as directly locating data by experimental scheme number); at the same time, it achieves format standardization, storing raw data (Protobuf) and preprocessed data (JSON) in dual formats, taking into account both efficient recording and ease of use, and making it easy for third-party tools to call directly.

[0023] 2. This invention designs an intelligent preprocessing mechanism that avoids redundant calculations by persistently storing preprocessed data. For example, after the initial calculation of "decision time", the results can be directly reused in subsequent analyses. Statistical rules (such as "mean" and "standard deviation") can be defined through a configurable interface, allowing the addition of new indicators without modifying the code, and supporting rapid iteration.

[0024] 3. This invention establishes a flexible visualization framework, allowing users to freely design display pages using HTML / JavaScript. It supports the integration of visualization libraries such as ECharts to achieve dynamic chart interaction. Through the WebSocket interface, the evaluation page can receive key indicators during simulation operation (such as the current altitude of the aircraft) in real time, providing a "what you see is what you get" evaluation experience.

[0025] 4. This invention is modular and reusable. The evaluation index template consists of three parts: "source data template + preprocessing template + display template". Adding new indicators only requires configuring the relevant modules without modifying the underlying code. Furthermore, the same preprocessing logic (such as trajectory analysis algorithm) can be reused in different scenarios (drone logistics vs. military strikes) with only the UI layout of the display template needing to be adjusted.

[0026] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0027] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0028] Figure 1 is a flowchart illustrating a custom performance evaluation method according to an exemplary embodiment; Figure 2 is a schematic diagram of a performance evaluation system according to an exemplary embodiment; Figure 3 is a data storage structure diagram according to an exemplary embodiment; Figure 4 is a source data management flowchart according to an exemplary embodiment; Figure 5 is a flowchart of evaluation preprocessing data management according to an exemplary embodiment; Figure 6 is an evaluation display flowchart according to an exemplary embodiment; Figure 7 is a schematic diagram of a custom display page according to an exemplary embodiment; Figure 8 is a schematic diagram of a complex preview page according to an exemplary embodiment; Figure 9 is a data processing flowchart of a custom evaluation algorithm script according to an exemplary embodiment; Figure 10 is a block diagram of a custom performance evaluation system according to an exemplary embodiment; Figure 11 is a structural schematic diagram of a computer device according to an exemplary embodiment. Detailed Implementation

[0029] The following description and accompanying drawings fully illustrate specific embodiments described herein to enable those skilled in the art to practice them. Some portions and features of certain embodiments may be included in or replace portions and features of other embodiments. The scope of the embodiments herein includes the entire scope of the claims and all available equivalents thereof. The various embodiments described herein are presented in a progressive manner, with each embodiment focusing on its differences from other embodiments; similar or identical parts between embodiments can be referred to interchangeably.

[0030] The modules in the apparatus or system of this application can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0031] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0032] Figure 1 illustrates an embodiment of a customizable performance evaluation method according to the present invention.

[0033] In this optional embodiment, the customizable performance evaluation method includes: S101, when starting performance simulation, starting an independent data recording process to record source data and simulation information during simulation operation in real time, and updating the source database and main database in real time according to the simulation progress; S102, after the performance simulation ends, automatically starting the source data preprocessing process, reading simulation information and corresponding source data for preprocessing, converting them into a preset format; and summarizing the preprocessing results after preprocessing, storing them in an independent preprocessed data file, and updating the main database status at the same time; S103, starting a custom evaluation algorithm script on the evaluation page, obtaining the preprocessed source data through the source data interface service for analysis and processing, and uploading the evaluation result data to the server; after receiving the data, the server creates a new simulation record in the main database and stores the evaluation result data in the corresponding preprocessing database; S104, the evaluation page obtains the evaluation result data returned by the custom evaluation algorithm script from the server, and uses the page template to parse and render the evaluation result data to generate a visualized evaluation result.

[0034] In this optional embodiment, when the source data and simulation information during the real-time recording of the simulation run are recorded and the source database and main database are updated in real time according to the simulation progress, the running data of all frames in the current performance simulation process can be recorded in real time as source data; the simulation information and simulation status in the current performance simulation process are recorded synchronously using the main database; the running process of the current performance simulation is monitored in real time, and the source data of each frame in the current simulation process is saved to an independent source database file frame by frame; if the current performance simulation ends, the simulation status in the main database is updated.

[0035] In this optional embodiment, the source data includes simulation time information, simulation entity position, attitude, velocity, and acceleration; the simulation information includes experiment scheme number, start time, stop time, and simulation record ID.

[0036] In this optional embodiment, when the simulation information and corresponding source data are read and preprocessed to convert to a preset format; and after the preprocessing is completed, the preprocessing results are summarized and stored in an independent preprocessed data file, while the main database status is updated, the simulation information in the main database can be read, the source database file can be found according to the simulation record ID, and the source data in the source database file corresponding to the simulation record ID can be read frame by frame; the source data in the simulation process is converted into preset format data (JSON format), the cumulative data contained in the source data is accumulated and statistically analyzed, and the model state data and sensor data in the simulation process are extracted to realize the preprocessing of the source data; the preprocessed source data is saved frame by frame to the preprocessed database file corresponding to the simulation record ID, and the main database is updated and marked as preprocessing complete after all frames of source data are processed.

[0037] In this optional embodiment, when the user starts a custom evaluation algorithm script on the evaluation page, obtains preprocessed source data through the source data interface service for analysis and processing, and uploads the evaluation result data to the server, the script interface of the custom evaluation algorithm script can be called in the program on the evaluation page. According to the preset source data interface service, the user uses the custom evaluation algorithm script to call the source data interface service request. After receiving the request sent by the user, the server queries the corresponding source database file from the source database and returns the source data as the result to the user's source data interface service request. The user processes the source data using the custom evaluation algorithm script, and after processing, calls the preset upload result data interface to upload the evaluation result data to the server. After receiving the uploaded evaluation result data, the server generates a preprocessing record, marks the preprocessing record ID, and saves the evaluation result data to an independent preprocessing database file according to the preprocessing record ID.

[0038] In this optional embodiment, when the evaluation page obtains the evaluation result data returned by the custom evaluation algorithm script from the server, and uses the page template to parse and render the evaluation result data to generate a visualized evaluation result, the evaluation page obtains the evaluation result data of the custom evaluation algorithm script, calls the preset evaluation data interface, and passes the preprocessed record ID to the server's evaluation data interface; the server reads the preprocessed database file through the preprocessed record ID and returns the evaluation result data to the evaluation page, and the evaluation page renders the page according to the evaluation result data to visualize the evaluation result.

[0039] In this optional embodiment, the evaluation page can be configured to display a custom page according to different evaluation result display requirements; and the evaluation page can be configured with a custom page template.

[0040] Figure 10 illustrates an embodiment of a customizable performance evaluation system according to the present invention.

[0041] In this optional embodiment, the customizable performance evaluation system includes: a simulation data acquisition module 201, used to start an independent data recording process when performance simulation is started, record source data and simulation information during simulation operation in real time, and update the source database and main database in real time according to the simulation progress; a source data preprocessing module 202, used to automatically start the source data preprocessing process after the performance simulation ends, read simulation information and corresponding source data for preprocessing, convert them into a preset format; and summarize the preprocessing results after preprocessing, store them in an independent preprocessed data file, and update the main database status at the same time; a custom script processing module 203, used to start a custom evaluation algorithm script on the evaluation page, obtain the preprocessed source data through the source data interface service for analysis and processing, and upload the evaluation result data to the server; after receiving the data, the server creates a new simulation record in the main database and stores the evaluation result data in the corresponding preprocessing database; and a visualization result display module 204, used for the evaluation page to obtain the evaluation result data returned by the custom evaluation algorithm script from the server, and use the page template to parse and render the evaluation result data to generate a visualized evaluation result.

[0042] To facilitate understanding of the above technical solutions of the present invention, the following further explains the technical solutions of the present invention from the perspective of architecture and principle, as follows: The present invention adopts a pre-built performance evaluation system architecture, as shown in Figure 2. The performance evaluation system architecture includes evaluation source data management, evaluation preprocessing data management, evaluation display template management, and evaluation indicator template management. The underlying layer adopts a hierarchical data storage architecture of business master database + data sub-database. Each evaluation indicator template consists of a source data template + preprocessing data template + display template. The display is divided into source data display, preprocessing data display, and overall evaluation conclusion display page.

[0043] As shown in Figure 3, the hierarchical database includes a business data layer (as the main database), a source data layer (which can be called the source database), and a preprocessed data layer (which can be called the preprocessed database).

[0044] The business data layer in the database serves as the main database, employing MySQL (an open-source relational database management system) to record simulation information (including experiment scheme number, timestamp, associated data ID, etc.) to support efficient querying and access control. In the source data layer, each raw data record is stored in an independent file using SQLite (a lightweight embedded database). Simulation record data (including record ID, simulation start time, and end time) is recorded in the main database to decouple different record data and improve the performance of single-record data queries. In the preprocessing data layer, the preprocessing results of each simulation record are stored in an independent preprocessing database file (SQLite) to decouple different preprocessed record data and improve storage and query performance.

[0045] As shown in Figure 4, the management of evaluation source data includes the following aspects: 1. The running data of each frame during the system simulation process is the evaluation source data. The source data records all the running data of all frames in the simulation process, including simulation time information, simulation entity position, attitude, velocity, acceleration and other running data.

[0046] 2. After starting the simulation, start an independent data recording process.

[0047] 3. The main database records the simulation information for this experiment (experiment scheme number, start time, stop time, and corresponding simulation record ID), which corresponds to the simulation information recorded in Figure 4; the simulation record data is a record of the simulation information, which corresponds to the simulation information record data in Figure 3.

[0048] 4. Save the source data (including simulation time information, simulation entity position, attitude, velocity, acceleration and other running data) of each frame in this simulation process to an independent sqlite database file (source data database).

[0049] For the evaluation of preprocessing data management, as shown in Figure 5, after preprocessing begins, the simulation information in the main database is read, and the independent database of the simulation record (source data sqlite database file) is found according to the simulation record ID. The data is read frame by frame and converted into a structured data format. Currently, the original data of the system is in protobuf format. After preprocessing, all data is processed into JSON format. At the same time, some statistical analysis and summary processing are performed, including the following aspects: 1. After preprocessing begins, a preprocessing process is started, and the simulation record ID of this preprocessing is passed in when starting.

[0050] 2. Read simulation information (simulation start time, end time, simulation status) from the MySQL master database.

[0051] 3. Based on the simulation record ID, read the source data SQLite database file corresponding to the simulation record ID frame by frame for source data preprocessing. The source data preprocessing process includes the following: 3.1 First, convert the format of the data in the simulation process, converting the binary data of protobuf into the common JSON format data; 3.2 Then, perform cumulative statistics on the accumulated data in the simulation process (such as UAV flight mileage, detection coverage, power consumption, etc.); 3.3 Extract the model status data (damage status, operation status, network status, etc.) in the simulation process; 3.4 Extract the sensor data in the simulation process.

[0052] 4. Save the preprocessed data frame by frame to the preprocessed data sqlite database file corresponding to the simulation record ID.

[0053] 5. After all frame data has been processed, update the processing status in the MySQL master database to: Preprocessing complete.

[0054] As shown in Figure 6, after the user opens the evaluation page, they need to call the evaluation data preprocessing interface and the preprocessing result data acquisition interface to obtain the display input data of the evaluation page, and then customize the evaluation display page.

[0055] The evaluation page can be customized to display different evaluation results, as shown in Figure 7; a more complex preview page is shown in Figure 8. Additionally, the evaluation page can be configured with a custom HTML + JavaScript template. A simple example of a custom page is shown below:<!DOCTYPE html> <meta charset="UTF-8"> <meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1"> <title>Urban Target Strike< / title> <style>.show {padding: 10px;box-sizing: border-box;}.content {font-size: 16px;color: #fff;}.city-bg {background-image: linear-gradient(rgba(0, 0, 0, 0.5), rgba(0,0, 0, 0.5)), url( / Plugins / images / pg.jpg);margin: 0 auto;width: 100%;min-height: 80vh;background-repeat: no-repeat;background-size: 100% 100%;padding: 0rem 10px;padding-bottom: 30px;}.city-title {text-align: center;padding-top: 20px;padding-bottom: 20px;color: #fff;font-size: 24px;font-weight: bold;}.topIn {border: 0.16rem solid rgba(14, 253, 255, 0.5);width: 32%;min-height: 60px;height: 14vh;position: relative;padding: 1.6rem 1rem;margin-bottom: 20px;}.chartIn {border: 0.16rem solid rgba(14, 253, 255, 0.5);width: 100%;min-height: 60px;height: 50vh;position: relative;padding: 1.6rem 1rem;}.border_bg_leftTop {background: #323a5e url( / Plugins / images / title_left_bg.png)no-repeat;background-size: cover;}.topIn .shu,.chartIn .shu {font-size: 2.4rem;color: rgba(14, 253, 255, 1);font-family: dig;margin-bottom: 0.4rem;}.topIn .shu i,.chartIn .shu i {font-size: 0.8rem;margin-left: 1.2rem;font-style: normal;}.topIn .zi,.chartIn .zi {font-size: 0.8rem;color: #fff;position: relative;z-index: 10;}.topIn .zi .span1,.chartIn .zi .span1 {margin-right: 2rem;}.city-box {display: flex;flex-wrap: wrap;justify-content: space-between;}.city-pie {padding-top: 10px;width: 50%;height: 40vh;border: 1px solid rgba(0, 255, 132, 0.5);}.city-line { / padding-top: 10px; / width: 100%;height: 50vh;}.city-bar {padding-top: 10px;width: 50%;height: 40vh;border: 1px solid rgba(0, 255, 132, 0.5);}.city-time {padding-top: 10px;width: 50%;height: 40vh;border: 1px solid rgba(0, 255, 132, 0.5);}< / style> <section class="city-bg"> Urban Target Strike <h3> Operator decision-making time< / h3> <span class="shu1" ; <h3> Task execution time< / h3> <span class="shu1" ; <h3> Number of drones consumed< / h3> <span class="shu1" ; <h3> Full health< / h3> <span class="shu1" ; <h3> Number of severe injuries< / h3> <span class="shu1" ; <h3> Number of moderate injuries< / h3> <span class="shu1" ; <h3> Number of minor injuries< / h3> <span class="shu1" ; <h3> Total distance traveled by all drones< / h3> <span class="shu1" ; <h3> Weighted average score of indicators< / h3> <span class="shu1" ; < / section> <script type="text / javascript">const url = window.location.href;const recordID = url.split("?")[1].split("&")[0].split("=")[1];const FANM = url.split("?")[1].split("&")[1].split("=")[1];let charts2;sendPostRequest(window.config.webServerUrl + " / evaluate / getEvaluateResultData",{ FANM: FANM, recordID: recordID},(error, response) => {if (error){console.log(error);return;}console.log(response,"response");let data= JSON.parse(response.data);console.log(data);setlabelValue(data)});function setlabelValue(data) {let obj = data.target_damage_statistic || {}document.querySelector('#make_policy_time').innerHTML =data.make_policy_time || 0;document.querySelector('#destroy_num').innerHTML = data.destroy_num ||0;document.querySelector('#task_time').innerHTML = data.task_time|| 0;document.querySelector('#cmp_path_length').innerHTML = data.cmp_path_length || 0;document.querySelector('#score').innerHTML = data.score || 0;document.querySelector('#no_damage').innerHTML = obj.no_damage || 0;document.querySelector('#severe_damage').innerHTML = obj.severe_damage || 0;document.querySelector('#moderate_damage').innerHTML =obj.moderate_damage || 0;document.querySelector('#mild_damage').innerHTML = obj.mild_damage || 0;}< / script> This invention utilizes a Python script to customize the processing of evaluation data, as shown in Figure 9. The process includes the following aspects: 1. After opening the evaluation page, the user calls the user-defined evaluation algorithm script interface within the evaluation page's program. 2. The system provides a source data interface service, allowing users to request the source data interface service within their customized evaluation algorithm script. 3. Upon receiving the user's source data request, the system service queries the corresponding simulation source database file from the source database and returns the source data as the result to the user's interface request. 4. The user processes the source data in their customized evaluation data processing algorithm. 5. After processing the data, the user calls the system service's upload result data interface to upload the evaluation processing result data, and the system returns the uploaded result. 6. After receiving the user-uploaded evaluation processing result data, the system records a record for this preprocessing in the simulation record, and then saves the preprocessed data to a separate evaluation preprocessing database file based on the preprocessing record ID. 7. After obtaining the return result from the user-defined evaluation algorithm, the evaluation page calls the system service's interface for obtaining evaluation data, passing the preprocessing record ID to the server's evaluation data interface. 8. The system service reads the evaluation preprocessing database file through the preprocessing record ID and returns the evaluation preprocessing result data to the evaluation page. 9. The evaluation page renders the page based on the evaluation preprocessing result data to display the evaluation results.

[0056] In one embodiment, a computer device, which may be a server, is provided, and its internal structure is shown in Figure 11. The computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores static and dynamic information data. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements the steps in the above method embodiment.

[0057] Those skilled in the art will understand that the structure shown in FIG11 is merely a block diagram of a portion of the structure related to the present invention and does not constitute a limitation on the computer device to which the present invention is applied. A specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0058] In addition, the present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0059] In addition, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0060] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0061] This invention is not limited to the structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this invention is limited only by the appended claims.

Claims

1. A performance evaluation method that supports customization, characterized in that, include: When performance simulation is started, an independent data recording process is initiated to record source data and simulation information during the simulation run in real time, and to update the source database and main database in real time according to the simulation progress. After the performance simulation is completed, the source data preprocessing process is automatically started to read the simulation information and the corresponding source data for preprocessing and converting them into a preset format; After preprocessing is completed, the preprocessing results are summarized and stored in a separate preprocessed data file, while the status of the main database is updated. On the evaluation page, a custom evaluation algorithm script is launched to obtain preprocessed source data through the source data interface service for analysis and processing, and the evaluation result data is uploaded to the server. After receiving the data, the server creates a new simulation record in the main database and stores the evaluation result data in the corresponding preprocessing database. The evaluation page retrieves the evaluation result data returned by the custom evaluation algorithm script from the server, and uses the page template to parse and render the evaluation result data to generate a visual evaluation result.

2. The performance evaluation method supporting customization according to claim 1, characterized in that, The real-time recording of source data and simulation information during the simulation run, and the real-time updating of the source database and the main database according to the simulation progress, includes: real-time recording of the running data of all frames during this performance simulation as source data; synchronously recording the simulation information and simulation status during this performance simulation using the main database; real-time monitoring of the running process of this performance simulation, saving the source data of each frame during this simulation to an independent source database file, and updating the simulation status in the main database if the performance simulation ends.

3. The performance evaluation method supporting customization according to claim 1, characterized in that, The source data includes simulation time information, simulation entity position, attitude, velocity, and acceleration; the simulation information includes experiment scheme number, start time, stop time, and simulation record ID.

4. The performance evaluation method supporting customization according to claim 1, characterized in that, The simulation information and corresponding source data are read and preprocessed, and converted into a preset format; After preprocessing, the preprocessing results are summarized and stored in a separate preprocessed data file. Simultaneously, the main database status is updated, including: reading simulation information from the main database; locating the source database file based on the simulation record ID; reading the source data frame by frame from the source database file corresponding to the simulation record ID; converting the source data during the simulation process into a preset format; summarizing and statistically analyzing the cumulative data contained in the source data; and extracting model state data and sensor data during the simulation process to achieve source data preprocessing; saving the preprocessed source data frame by frame to the preprocessed database file corresponding to the simulation record ID; and updating the main database and marking it as preprocessing complete after all frames of source data have been processed.

5. The performance evaluation method supporting customization according to claim 1, characterized in that, The process of launching a custom evaluation algorithm script on the evaluation page, obtaining preprocessed source data through the source data interface service for analysis and processing, and uploading the evaluation result data to the server includes: calling the script interface of the custom evaluation algorithm script in the program on the evaluation page; according to the preset source data interface service, the user uses the custom evaluation algorithm script to call the source data interface service request; after receiving the request sent by the user, the server queries the corresponding source database file from the source database and returns the source data as the result to the user's source data interface service request; the user uses the custom evaluation algorithm script to process the source data, and after processing, calls the preset upload result data interface to upload the evaluation result data to the server; after receiving the uploaded evaluation result data, the server generates a preprocessing record, marks the preprocessing record ID, and saves the evaluation result data to a separate preprocessing database file according to the preprocessing record ID.

6. The performance evaluation method supporting customization according to claim 1, characterized in that, The evaluation page retrieves the evaluation result data returned by the custom evaluation algorithm script from the server, and uses a page template to parse and render the evaluation result data to generate a visualized evaluation result. This includes: the evaluation page retrieves the evaluation result data from the custom evaluation algorithm script, calls a preset evaluation data interface, and passes a preprocessed record ID to the server's evaluation data interface; the server reads the preprocessed database file through the preprocessed record ID and returns the evaluation result data to the evaluation page; the evaluation page renders the page based on the evaluation result data and visualizes the evaluation result.

7. The performance evaluation method supporting customization according to claim 1, characterized in that, The evaluation page can be configured to display custom pages according to different evaluation result display requirements; and the evaluation page can be configured with custom page templates.

8. A performance evaluation system that supports customization, characterized in that, include: The simulation data acquisition module is used to start an independent data recording process when performance simulation is started, to record source data and simulation information in real time during the simulation run, and to update the source database and main database in real time according to the simulation progress. The source data preprocessing module is used to automatically start the source data preprocessing process after the performance simulation ends, read the simulation information and the corresponding source data for preprocessing, and convert them into a preset format; After preprocessing is completed, the preprocessing results are summarized and stored in a separate preprocessed data file, while the status of the main database is updated. The custom script processing module is used to launch a custom evaluation algorithm script on the evaluation page, obtain preprocessed source data through the source data interface service for analysis and processing, and upload the evaluation result data to the server. After receiving the data, the server creates a new simulation record in the main database and stores the evaluation result data in the corresponding preprocessing database. The visualization results display module is used to evaluate the evaluation results data returned by the custom evaluation algorithm script from the server, and to parse and render the evaluation results data using the page template to generate visualized evaluation results.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of supporting a custom performance evaluation method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of supporting a custom performance evaluation method as described in any one of claims 1 to 7.

Images