Experiment process execution method and device for mechanical parts and storage medium
By building a data model that is deeply related to the experimental data and automating the experimental process of mechanical parts, the problems of inefficiency and data accuracy in experimental data management are solved, efficient data analysis and mining are achieved, and product quality improvement is supported.
Patent Information
- Application Number
- CN202510828868.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-20
- Publication Date
- 2025-09-09
AI Technical Summary
Existing experimental data management technologies are inefficient in the experimental process of mechanical parts and lack the mining and integration of intrinsic data correlations, making it difficult to ensure data accuracy and integrity, affecting product quality analysis and fault diagnosis.
Build multiple experimental-related data models, determine the relationships between the data models, automatically execute each node of the experimental process through data model instantiation, and generate test reports.
It has achieved high efficiency, standardization and accuracy in the experimental process, improved data management efficiency, and provided a scientific basis for product quality improvement.
Smart Images
Figure CN120611956A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of experimental data management, and in particular relates to a method, device and storage medium for executing an experimental process of a mechanical component. Background Art
[0002] In modern industrial production and R&D, experimental data management is a crucial step, especially for complex mechanical products such as mechanical parts. However, current experimental data management technologies and processes face numerous challenges, severely hindering the efficiency and quality of R&D.
[0003] Traditional experimental business processes often rely on offline operations, which leads to a series of inefficiencies. Each link in the experimental process requires manual communication and coordination between different departments. This process is not only cumbersome, but also prone to time wastage due to poor communication or delayed document delivery. During the experimental planning stage, professionals need to manually formulate test plans and schedules based on past experience, which makes it difficult to fully consider all factors. Problems such as unreasonable resource allocation and improper experimental sequence are prone to occur, which in turn affects the overall progress of the experiment. During the execution of the experiment, operators operate based on paper documents, which is not only prone to errors, but also difficult to track the experimental status and progress in real time. In addition, the recording and organization of experimental data are mostly manual, and the accuracy and integrity of the data are difficult to guarantee.
[0004] In current online experimental workflows, experimental data models often simply record the experimental process and results, lacking the ability to mine and integrate the inherent connections within the data. Experimental data models are also not comprehensively and deeply designed, and unclear relationships between models make effective analysis and mining of experimental data difficult. Consequently, companies are unable to fully and accurately obtain the data they need for product quality analysis, fault diagnosis, and prediction. This makes it difficult to extract valuable information from massive amounts of data, hindering effective support for product optimization and improvement. Summary of the Invention
[0005] The purpose of the present invention is to provide a method, device and storage medium for executing the experimental process of mechanical parts. By constructing an experimental data model that is deeply related to the experimental rules and experimental data, each node and experimental element of the experimental process are defined and described, and a standardized framework support is provided for the operation of the experimental process, thereby optimizing the experimental process, improving test efficiency, and realizing in-depth analysis and mining of data.
[0006] To achieve the above object, the present invention is implemented by adopting the following technical solutions:
[0007] In a first aspect, the present invention provides a method for executing an experimental process of a mechanical component, comprising: Acquiring experimental data of mechanical parts, wherein the experimental data includes experimental sample information, experimental platform information, test items, experimental failures, and test results; Construct multiple experimental data models and determine the relationships between them. Each data model is used to describe the overall framework of the experimental process, the framework of each node in the experimental process, and the relationship between the attributes of the experimental data and the experimental data. The model parameters related to the components to be tested are input into the experimental process, and each node of the experimental process is executed in sequence according to each data model and its association relationship to generate the corresponding test report.
[0008] Optionally, constructing multiple experiment-related data models and determining the association relationship between the data models includes: According to the experimental rules, the attributes of the experimental data and the relationship between the experimental data, the structure, attributes and relationships of the data model are defined respectively; Use data modeling technology to construct each data model according to the defined content; Determine the logical relationship between nodes based on the experimental process, and determine the association between different data models based on the logical relationship; Establish association rules based on the association between data models.
[0009] Optionally, each node of the experimental process includes experiment application, experiment planning, experiment execution and result delivery; The experiment application node is used to provide key information related to the experiment; The experiment planning node is used to generate a test plan and corresponding test fees based on the information provided by the experiment application node; The experiment execution node is used to execute the test plan generated by the experiment planning node on the experimental platform and record the original data and experimental failures; The result delivery node is used to generate experimental results and experimental reports based on the execution results of the test plan.
[0010] Optionally, the data model includes: an experimental capability basic model, a sample parameter model, a test bench model, a test plan model, an original record model, an experimental failure model, a test cost model, an experimental result model, and an experimental report model; The experimental capability basic model is used to define the basic framework and rules of each node of the experimental process, and the experimental capability basic model associates key experimental elements; The sample parameter model is used to store the function and structure information of the sample; The test bench model is used to record the basic information, status and usage of the test bench; The test plan model is used to integrate experimental application information to generate a test plan; The original record model is used to standardize the original data record format; The experimental fault model is used to record fault information during the experiment execution; The test cost model is used to calculate various costs during the experiment execution; The experimental result model is used to generate experimental results; The experimental report model is used to generate an experimental report.
[0011] Optionally, the relevant data of the experiment application node includes: sample type, basic experiment information, basic sample information, test item set, standard basis set, experiment purpose and technical requirements; The sample type is selected from the preset enumeration type; Basic experimental information includes applicant, application department, and expected experimental time; The basic information of the sample includes the sample name, model, quantity, material number, and related product models; The test item set includes test items corresponding to the sample type; The standard basis set includes reference standard data corresponding to the test item set; The experimental purpose and technical requirements are text input format.
[0012] Optionally, the relevant data of the experiment planning node includes: test plan, test cost, task allocation information and experiment scheduling information; Test plans are generated based on the instantiation of the test plan model; The test fee is generated based on the instantiation of the test fee model; The task allocation information assigns the test items to the corresponding experiment execution personnel; The experiment scheduling information determines the execution time of each test item on the corresponding experiment platform.
[0013] Optionally, the relevant data of the experiment execution node includes: original data, original record set, experiment fault set, experiment bench usage information, and instrument and sensor usage information; The original data includes the collected data produced by the test bench; The original record set is obtained by instantiating and summarizing the original record model set associated with the experimental capability basic model; The experimental fault set is obtained based on the instantiation of the experimental fault model; The test bench usage information includes structured information related to the test bench usage; The instrument and sensor usage information includes structured information related to the usage of the instrument and sensor.
[0014] Optionally, the relevant data of the result delivery node includes: an experimental result set and an experimental report; The experimental result set is obtained based on the instantiation of the experimental result model; The experimental report is obtained based on the instantiation of the experimental report model.
[0015] In a second aspect, the present invention provides a device for executing an experimental process for a mechanical component, comprising: Experimental data acquisition module: used to obtain experimental data of mechanical parts, wherein the experimental data includes experimental sample information, experimental platform information, test items, experimental faults and test results; Data model construction module: used to construct multiple experimental-related data models and determine the relationship between each data model. Each data model is used to describe the overall framework of the experimental process, the framework of each node of the experimental process, and the relationship between the attributes of the experimental data and the experimental data; Experimental process execution module: used to input the model parameters related to the components to be tested into the experimental process, and execute each node of the experimental process in sequence according to each data model and its association relationship, and generate the corresponding test report.
[0016] In a third aspect, the present invention provides a computer storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for executing an experimental process of a mechanical component as described in any one of the first aspects is implemented.
[0017] Compared with the existing technology, the beneficial effects achieved by the present invention are: by constructing a series of interrelated data models that are deeply related to experimental data and experimental rules, each link and element of the experiment is described comprehensively and meticulously, providing a clear and standardized framework support for the experimental process, and combining the instantiation of the data model to run each link of the experimental process, it can realize the automatic generation of test plans, experimental failures, experimental results and test reports, minimize manual intervention to the greatest extent, and improve efficiency and accuracy; based on the structured storage of data from various experimental models and utilizing the relationship network between data models, it can realize multi-dimensional and multi-level aggregation and mining of data, deeply analyze data, and provide a scientific data basis for improving the quality of mechanical parts products. BRIEF DESCRIPTION OF THE DRAWINGS
[0018] Figure 1 Shown is a flow chart of a method for executing an experiment process for a mechanical component in one embodiment of the present invention;
[0019] Figure 2 Shown is a flowchart of an experimental application node in an embodiment of the present invention;
[0020] Figure 3 Shown is a flowchart of an experiment planning node in an embodiment of the present invention;
[0021] Figure 4 Shown is a flowchart of an experiment execution node in an embodiment of the present invention;
[0022] Figure 5 Shown is a result delivery node flow chart in an embodiment of the present invention;
[0023] Figure 6 FIG2 is a schematic diagram showing the association structure of the experimental capability basic model in one embodiment of the present invention;
[0024] Figure 7 Shown is a schematic diagram of the associated structure of a test bench model in one embodiment of the present invention;
[0025] Figure 8 FIG2 is a schematic diagram of the association structure of a sample parameter model in an embodiment of the present invention;
[0026] Figure 9 FIG2 is a schematic diagram showing the associated structure of a test solution model in an embodiment of the present invention;
[0027] Figure 10 FIG2 is a schematic diagram of the association structure of the original record model in one embodiment of the present invention;
[0028] Figure 11 Shown is a schematic diagram of the associated structure of an experimental fault model in an embodiment of the present invention;
[0029] Figure 12 FIG2 is a schematic diagram showing the associated structure of a test fee model in an embodiment of the present invention;
[0030] Figure 13 FIG2 is a schematic diagram showing the correlation structure of an experimental result model in an embodiment of the present invention;
[0031] Figure 14 Shown is a schematic diagram of the associated structure of the experimental report model in an embodiment of the present invention. DETAILED DESCRIPTION
[0032] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and are not intended to limit the scope of protection of the present invention.
[0033] Example 1
[0034] like Figure 1 As shown, this embodiment provides a method for executing an experimental process of a mechanical component, including: Acquiring experimental data of mechanical parts, wherein the experimental data includes experimental sample information, experimental platform information, test items, experimental failures, and test results; Construct multiple experimental data models and determine the relationships between them. Each data model is used to describe the overall framework of the experimental process, the framework of each node in the experimental process, and the relationship between the attributes of the experimental data and the experimental data. The model parameters related to the components to be tested are input into the experimental process, and each node of the experimental process is executed in sequence according to each data model and its association relationship to generate the corresponding test report.
[0035] Through the structured storage and multi-dimensional analysis of experimental data through the experimental data model, a standardized framework is provided for the execution of the experimental process. The instantiation of each data model is used to automatically execute each node of the experimental process, achieving efficient, standardized and accurate experimental process, significantly shortening the experimental cycle, reducing labor costs, and greatly improving data management efficiency, providing strong support for product development and quality control.
[0036] Example 2
[0037] Based on Example 1, this example also makes the following design.
[0038] This embodiment constructs multiple data models that are interrelated and related to the experiment, comprehensively and meticulously describing each link and element of the experiment, and providing a clear and standardized framework support for the experimental process. Based on various models of experimental data, model parameter data is input into the experimental process, and after automated processing by the system, the automatic generation of structured data such as original data records, test plans, experimental failures, test costs, bench data, experimental results, and test reports is achieved, minimizing manual intervention and improving efficiency and accuracy. At the same time, the relationship network between models is utilized to achieve multi-dimensional and multi-level aggregation and mining of data, deeply analyze data, and provide a strong basis for product quality improvement.
[0039] In this embodiment, the key experimental data models are the experimental capability basic model, the experimental platform model, the sample parameter model, the test plan model, the original record model, the experimental failure model, the test cost model, the experimental result model, and the experimental report model.
[0040] 1. Basic model of experimental capability
[0041] like Figure 6 As shown in the figure, the experimental capability basic model is the core of the system process and model. It defines the basic framework and rules of the experiment, determines the corresponding relationships between the sample parameter model, sample type, test bench, test item, original record model, key parameter model, test cost model, experimental method, reference standard, instrument and sensor, technical indicator model, and report use case model, and establishes a data model relationship network.
[0042] The relationship between sample type and sample parameter model: one-to-one correspondence.
[0043] The sample type and the test item name set of the laboratory bench construct the test item set: the laboratory bench contains a test item name set, which is independent of the sample type; for each sample type S, all the test item names that can be executed by all laboratory benches T1-Tn are selected to form the test item set that can be executed on all laboratory benches of this sample type, namely test items S-T1.1, S-T1.2, S-T1.3~S-T1.n, S-T2.1, S-T2.2, S-T2.3~S-T2.n, S-Tn.1, S-Tn.2, S-Tn.3~S-Tn.n. Each test item is associated with a unique original record model, key parameter model, test cost model, experimental method, reference standard, instrument and sensor, technical indicator model and report use case model.
[0044] 2. Experimental bench model
[0045] like Figure 7 As shown, the structured data recorded by the test bench model include: basic information, current status, usage information, maintenance information, scheduling information, and test item name collection.
[0046] Basic information: Contains basic structured data such as the test bench name, test bench number, responsible person, storage date, manufacturer, etc.
[0047] Maintenance information: includes structured data such as maintenance date, maintenance unit, maintenance description, maintenance date, maintenance cycle, maintenance personnel, maintenance content, calibration date, calibration unit, and calibration cycle.
[0048] Usage information: Usage information data comes from the experimental platform usage information of the experiment execution node, including current usage information and historical usage information.
[0049] Current status: The current status of the test bench is automatically determined by the system based on the current usage information and maintenance information of the test bench. It is generally an enumeration value, usually including unused, in use, under repair, under maintenance, and calibrated.
[0050] Scheduling information: Scheduling information data comes from the experiment scheduling information of the experiment planning node, including current scheduling information and historical scheduling information.
[0051] Test item name set: The test item name is a plain text input, and all the test items that can be performed on the corresponding test bench constitute the test item name set.
[0052] 3. Sample parameter model
[0053] like Figure 8As shown, the structured data stored in the sample parameter model include: functional information and structural information.
[0054] Functional information: quantitative information related to the sample's capability characteristics, such as rated power, operating voltage, sensitivity, displacement, flow rate, etc. Different samples have different functional information data sets.
[0055] Structural information: quantitative or qualitative information related to the structural characteristics of the sample, such as length, width, height, material, quality, color, etc. Different samples have different sets of structural information data.
[0056] 4. Test plan model
[0057] like Figure 9 As shown in the figure, the test plan model contains structured data such as sample type, sample basic information, sample parameters, test item set, standard basis set, experimental environment information set, technical indicator set, test bench set, instrument and sensor set, experimental method set, and attachments. Based on the test plan model and the corresponding model input, the test plan document can be automatically generated, which greatly improves the efficiency of document preparation.
[0058] Sample type, sample basic information, sample parameters, test item set, standard basis set: The data comes from the structured data corresponding to the experiment application node.
[0059] Experimental environment information set: Based on the test item set, a structured input configuration is performed on the experimental environment of each test item to form an experimental environment information set.
[0060] Technical Indicator Set: There are two input methods for the technical indicator set. One is to manually identify the corresponding technical indicators based on the technical requirements of the experiment application node to form a technical indicator set. The other is to input each test item indicator into a structured form based on the one-to-one correspondence between the test item indicator model and the test item in the experimental capability basic model.
[0061] Experimental bench set, instrument and sensor set, experimental method set: Based on the test item set and the experimental capability basic model, the corresponding experimental bench set, instrument and sensor set, and experimental method set can be determined.
[0062] Attachment: Usually the corresponding picture attachment.
[0063] 5. Original Record Model
[0064] like Figure 10 As shown, the original record model contains important structured data: test items, sample types, experimental environment information, experimental bench, instrument and sensor collection, structured cleaning data, and structured key parameters.
[0065] Test items: The data comes from the corresponding test items in the test item set of the experiment application node.
[0066] Sample type: The data comes from the sample type of the experiment application node.
[0067] Experimental environment information, experimental bench, instruments and sensors: According to the test items and based on the experimental capability basic model, the corresponding experimental environment information, experimental bench, instruments and sensors can be determined.
[0068] Structured cleaned data: Based on the data cleaning algorithm library, the raw data of the test bench is cleaned to generate structured cleaned data. The raw data of the test bench for each test item has a corresponding cleaning algorithm. If the structure of the raw data of the test bench changes, the cleaning algorithm must also be changed accordingly to ensure the correctness of the structured cleaned data.
[0069] Structured key parameters: For some key data in the experimental process, which cannot be obtained through cleaning and processing of the original data of the experimental bench, structured input configuration is required based on the key parameter model.
[0070] 6. Experimental Fault Model
[0071] like Figure 11 As shown in Figure 1, the structured data contained in the experimental fault model include: sample type, fault model, fault description, and associated product model.
[0072] Sample type, related product model: The data comes from the relevant data of the experimental application.
[0073] Failure mode: Based on the failure mode library, manually select the corresponding failure mode.
[0074] Fault description: Plain text input.
[0075] 7. Test Cost Model
[0076] like Figure 12 As shown, the test cost model contains structured data including energy consumption cost, site cost, labor cost, depreciation cost, and equipment maintenance cost, which can be input in a structured manner during instantiation.
[0077] 8. Experimental results model
[0078] like Figure 13 As shown, the structured data contained in the experimental result model is: a set of test item results.
[0079] The original records of the test items come from the original record set of the experiment execution node, and the test item indicators come from the technical indicator set of the test plan of the experiment planning node. The original records of the test items correspond to the test item indicators one by one.
[0080] Each test item's original record and its corresponding test item technical indicators are processed by the corresponding processing algorithm in the result processing algorithm library to generate the corresponding test item test results. If the test item's original record model or test item technical indicator model changes, the corresponding result processing algorithm must also be changed to ensure the accuracy of the test item's experimental results.
[0081] If the test project results cannot be automatically generated by the system, they can also be processed through manual input.
[0082] 9. Experimental Report Model
[0083] like Figure 14 As shown, the experimental report model contains structured data such as sample type, sample basic information, sample parameters, technical requirements, experimental purpose, standard basis set, experimental environment information set, technical indicator set, report case set, experimental result set, experimental conclusion, and attachments. Based on the test report model and the corresponding model input, the test report document can be automatically generated, which greatly improves the efficiency of document preparation.
[0084] Sample type, basic sample information, sample parameters, technical requirements, and experimental purpose: The data comes from the corresponding data of the experiment application node.
[0085] Experimental environment information set and technical indicator set: The data comes from the corresponding data of the test plan in the experiment planning node.
[0086] Report use case set: The report use case model comes from the report use case model corresponding to the corresponding test item in the test item set of the experiment application node; the test item original record comes from the corresponding test item original record in the original record set of the experiment execution node; the report use case model of the test item corresponds to the test item original record one by one; the report use case model of each test item and its corresponding test item original record, after being processed by the corresponding processing algorithm of the report use case algorithm library, can generate the corresponding test item report use case; if the test item original record model or the test item technical indicator model has changed, the corresponding report use case algorithm must also be changed accordingly to ensure the correctness of the test item report use case; if the test item report use case cannot be automatically generated by the system, it can also be input manually.
[0087] Experimental result collection and experimental conclusions: The data comes from the result delivery related data.
[0088] In this embodiment, the experimental process consists of four nodes, namely, experiment application, experiment planning, experiment execution, and result delivery. The execution order of the experimental process is experiment application → experiment planning → experiment execution → result delivery.
[0089] 1. Experimental Application
[0090] The experimental application is the starting point of the entire experimental process. Applicants must fill in and submit key information related to the experiment in detail. Figure 2 As shown in the figure, the main structured data included in the experiment application stage are: sample type, basic experimental information, basic sample information, test item set, standard basis set, experimental purpose, and technical requirements.
[0091] Experimental purpose and technical requirements: Plain text input.
[0092] Sample type: It is an enumeration type. When submitting an experiment application, an enumeration value is manually selected.
[0093] Basic experimental information: includes some non-important structured business data such as the experiment applicant, application department, and expected experiment time.
[0094] Sample basic information: contains structured data about sample description, such as sample name, model, quantity, material number, and related product model. Figure 8 Corresponding to the sample structure information in the sample parameter model shown.
[0095] Test item collection: such as Figure 6 In the experimental capability basic model shown, a specific sample type corresponds to a series of test items. When applying for an experiment, the applicant manually selects the required test items from this series of test items to form a test item set.
[0096] Standard basis set: For example, in the experimental capability basic model, each test item contains the corresponding reference standard. Based on the selected test item set, the standard basis set is automatically bound.
[0097] 2. Experimental planning
[0098] During the experiment planning stage, the system will automatically generate the test plan and test fee based on the information submitted during the experiment application stage. Figure 3 As shown in Figure 2, the main structured data included in the experiment planning stage include: test plan, test cost, task allocation information, and experiment scheduling information.
[0099] Test plan: Based on Figure 9 The test plan model shown is instantiated to automatically generate a test plan.
[0100] Test fee: Based on Figure 12The test cost model shown is instantiated to automatically generate test costs Task assignment information: Based on the test item set in the experiment application, each test item is manually assigned to the corresponding experiment executor.
[0101] Experiment Scheduling Information: There are two scheduling methods for experiments: manual scheduling and automatic scheduling. Manual scheduling manually schedules the execution time of each test item on the corresponding laboratory bench based on the current status of the relevant laboratory bench and existing scheduling information. Automatic scheduling automatically schedules the execution time of each test item on the corresponding laboratory bench based on the current status of the relevant laboratory bench and existing scheduling information.
[0102] 3. Experimental Execution
[0103] During the experiment execution phase, actual operations are performed on the test bench according to the test plan generated during the experiment planning phase. The operator strictly follows the test plan to perform the experiment, and records the original data and experimental failure information. Figure 4 As shown in Figure 2, the main data included in the experiment execution phase include: original data, original record set, experimental fault set, laboratory bench usage information, instrument and sensor usage information.
[0104] Raw data: Data collected by the test bench, generally has a specific data format.
[0105] Original record set: Based on the experimental capability basic model, the test item set under the experimental application can be associated with the original record model set. Figure 10 The original record model shown is instantiated and aggregated to obtain the original record set.
[0106] Experimental fault collection: Figure 11 The experimental fault model shown is instantiated to obtain experimental faults, and the accumulation of faults forms an experimental fault set.
[0107] Test bench usage information: simple structured usage information related to the user and usage cycle of the test bench, etc. Figure 7 The test bench model shown corresponds to the usage information.
[0108] Instrument and sensor usage information: simple structured usage information related to the instrument and sensor users, usage cycles, etc.
[0109] 4. Results delivery
[0110] The delivery of results marks the completion of the experiment, e.g. Figure 5 As shown in Figure 2, the main data included in the result delivery stage are: experimental result set, experimental conclusion set, and experimental report.
[0111] Experimental results collection: Figure 13 The experimental result model shown is instantiated to obtain a set of experimental results.
[0112] Experimental conclusion collection: The experimental conclusion is input in plain text.
[0113] Experimental report: Figure 14 Instantiate the experimental report model shown to obtain the experimental report.
[0114] Example 3
[0115] This embodiment provides a device for executing an experimental process for a mechanical component, including: Experimental data acquisition module: used to obtain experimental data of mechanical parts, wherein the experimental data includes experimental sample information, experimental platform information, test items, experimental faults and test results; Data model construction module: used to construct multiple experimental-related data models and determine the relationship between each data model. Each data model is used to describe the overall framework of the experimental process, the framework of each node of the experimental process, and the relationship between the attributes of the experimental data and the experimental data; Experimental process execution module: used to input the model parameters related to the components to be tested into the experimental process, and execute each node of the experimental process in sequence according to each data model and its association relationship, and generate the corresponding test report.
[0116] Example 4
[0117] This embodiment provides a computer storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for executing the experimental process of mechanical parts as described in any step of Example 1 or Example 2 is implemented.
[0118] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0119] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 2 a process or multiple processes and / or boxes Figure 2 A device that provides the functions specified in a block or multiple blocks.
[0120] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 2 a process or multiple processes and / or boxes Figure 2 The function specified in one or more boxes.
[0121] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 2 a process or multiple processes and / or boxes Figure 2 A step that specifies a function in one or more boxes.
[0122] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which are all protected by the present invention.
Claims
1. A method for executing an experimental process of a mechanical component, characterized in that: include: Acquiring experimental data of mechanical parts, wherein the experimental data includes experimental sample information, experimental platform information, test items, experimental failures, and test results; Construct multiple experimental data models and determine the relationships between them. Each data model is used to describe the overall framework of the experimental process, the framework of each node in the experimental process, and the relationship between the attributes of the experimental data and the experimental data. The model parameters related to the components to be tested are input into the experimental process, and each node of the experimental process is executed in sequence according to each data model and its association relationship to generate the corresponding test report.
2. The method for executing the experimental process of mechanical parts according to claim 1, characterized in that: The step of constructing multiple experimentally related data models and determining the relationships between the data models includes: According to the experimental rules, the attributes of the experimental data and the relationship between the experimental data, the structure, attributes and relationships of the data model are defined respectively; Use data modeling technology to construct each data model according to the defined content; Determine the logical relationship between nodes based on the experimental process, and determine the association between different data models based on the logical relationship; Establish association rules based on the association between data models.
3. The method for executing the experimental process of mechanical parts according to claim 1, characterized in that: Each node of the experimental process includes experimental application, experimental planning, experimental execution and result delivery; The experiment application node is used to provide key information related to the experiment; The experiment planning node is used to generate a test plan and corresponding test fees based on the information provided by the experiment application node; The experiment execution node is used to execute the test plan generated by the experiment planning node on the experimental platform and record the original data and experimental failures; The result delivery node is used to generate experimental results and experimental reports based on the execution results of the test plan.
4. The method for executing the experimental process of mechanical parts according to claim 3, characterized in that: The data model includes: experimental capability basic model, sample parameter model, experimental platform model, test plan model, original record model, experimental failure model, test cost model, experimental result model and experimental report model; The experimental capability basic model is used to define the basic framework and rules of each node of the experimental process, and the experimental capability basic model associates key experimental elements; The sample parameter model is used to store the function and structure information of the sample; The test bench model is used to record the basic information, status and usage of the test bench; The test plan model is used to integrate experimental application information to generate a test plan; The original record model is used to standardize the original data record format; The experimental fault model is used to record fault information during the experiment execution; The test cost model is used to calculate various costs during the experiment execution; The experimental result model is used to generate experimental results; The experimental report model is used to generate an experimental report.
5. The method for executing the experimental process of mechanical parts according to claim 4, characterized in that: The relevant data of the experiment application node include: sample type, basic experiment information, sample basic information, test item set, standard basis set, experiment purpose and technical requirements; The sample type is selected from the preset enumeration type; Basic experimental information includes applicant, application department, and expected experimental time; The basic information of the sample includes the sample name, model, quantity, material number, and related product models; The test item set includes test items corresponding to the sample type; The standard basis set includes reference standard data corresponding to the test item set; The experimental purpose and technical requirements are text input format.
6. The method for executing the experimental process of mechanical parts according to claim 4, characterized in that: The relevant data of the experiment planning node includes: test plan, test cost, task allocation information and experiment scheduling information; Test plans are generated based on the instantiation of the test plan model; The test fee is generated based on the instantiation of the test fee model; The task allocation information assigns the test items to the corresponding experiment execution personnel; The experiment scheduling information determines the execution time of each test item on the corresponding experiment platform.
7. The method for executing the experiment process of mechanical parts according to claim 4, characterized in that: The relevant data of the experiment execution node includes: original data, original record set, experiment fault set, experiment table usage information, and instrument and sensor usage information; The original data includes the collected data produced by the test bench; The original record set is obtained by instantiating and summarizing the original record model set associated with the experimental capability basic model; The experimental fault set is obtained based on the instantiation of the experimental fault model; The test bench usage information includes structured information related to the test bench usage; The instrument and sensor usage information includes structured information related to the usage of the instrument and sensor.
8. The method for executing the experiment process of mechanical parts according to claim 4, characterized in that: The relevant data of the result delivery node includes: experimental result set and experimental report; The experimental result set is obtained based on the instantiation of the experimental result model; The experimental report is obtained based on the instantiation of the experimental report model.
9. A device for executing an experimental process of a mechanical component, characterized in that: include: Experimental data acquisition module: used to obtain experimental data of mechanical parts, wherein the experimental data includes experimental sample information, experimental platform information, test items, experimental faults and test results; Data model construction module: used to construct multiple experimental-related data models and determine the relationship between each data model. Each data model is used to describe the overall framework of the experimental process, the framework of each node of the experimental process, and the relationship between the attributes of the experimental data and the experimental data; Experimental process execution module: used to input the model parameters related to the components to be tested into the experimental process, and execute each node of the experimental process in sequence according to each data model and its association relationship, and generate the corresponding test report.
10. A computer storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for executing an experimental process of a mechanical component according to any one of claims 1 to 8 is implemented.