Multi-dimensional labor load evaluation system and method

Abstract

The application discloses a multi-dimension labor load evaluation system and method, and relates to the technical field of intelligent production management. The system comprises a data acquisition module for acquiring operation data corresponding to a post type, a basic model library module for providing pre-constructed evaluation models corresponding to different dimensions, a score calculation module for calculating labor load scores of different dimensions, and a result output module for calculating labor load score results according to the labor load scores of different dimensions. The evaluation models of different dimensions comprise at least one of a body posture evaluation model, a force evaluation model and a manual carrying evaluation model, and comprise a walking distance evaluation model. The system provided by the application forms a comprehensive evaluation system covering static and dynamic elements simultaneously, and improves the comprehensiveness and accuracy of labor load evaluation.

Description

Technical Field

[0001] This application relates to the field of intelligent production management technology, and in particular to a multi-dimensional labor load evaluation system and method. Background Technology

[0002] Job intensity assessment is a crucial component of safe production and intelligent management in manufacturing. Existing ergonomic methods for evaluating workload have the following shortcomings: 1) Workload assessment tools based on single standards (such as OWAS and RULA) can only assess single dimensions like posture or force. While multi-dimensional analysis tools (such as AAWS) are more systematic, their complexity is high, focusing on musculoskeletal risk identification and lacking a direct reflection of employee workload (i.e., fatigue level); 2) Existing tools do not consider job differences in the manufacturing workshop (such as assembly and logistics positions), especially dynamic energy consumption indicators like walking distance, making it difficult to comprehensively reflect the true workload level of employees in different positions; 3) Most methods rely on manual calculations or static tables, resulting in low assessment efficiency and failing to meet the needs of rapid, dynamic analysis in an intelligent manufacturing environment. Summary of the Invention

[0003] This application aims to solve the above-mentioned technical problems, namely, to propose a multi-dimensional workload assessment system and method to improve the comprehensiveness and accuracy of workload assessment.

[0004] In a first aspect, this application provides a multi-dimensional workload assessment system, comprising:

[0005] The data acquisition module is used to acquire job data corresponding to the job type;

[0006] The basic model library module provides pre-built evaluation models corresponding to different dimensions;

[0007] The score calculation module is used to extract dimensional features from the work data obtained by the data acquisition module, match the corresponding evaluation model from the basic model library module based on the dimensional features, and calculate the workload score of the dimensional features based on the matched evaluation model to obtain multiple workload scores of different dimensions.

[0008] The result output module is used to calculate the workload score corresponding to the job type based on the workload scores of multiple different dimensions obtained by the score calculation module;

[0009] The evaluation models of different dimensions include at least one of the following: body posture evaluation model, force evaluation model, manual handling evaluation model, and walking distance evaluation model.

[0010] In some technical solutions, the walking distance evaluation model is configured to obtain the corresponding score as the labor load score of the walking distance dimension based on the walking dimension features extracted from the work data and a pre-set mapping relationship table between walking distance and labor load score.

[0011] In some technical solutions, the evaluation models of different dimensions also include influencing factor evaluation models;

[0012] The score calculation module is also used to call the influencing factor evaluation model based on the influencing factor currently selected by the user to obtain the labor load score of the influencing factor dimension;

[0013] The influencing factor evaluation model is configured to obtain the corresponding score from a pre-set influencing factor scoring table based on the influencing factors selected by the user, and use it as the labor load score for the influencing factor dimension.

[0014] In some technical solutions, the system also includes a dynamic weighting module;

[0015] The dynamic weighting module is used to provide the result output module with a first weight value and a second weight value corresponding to the job type;

[0016] The result output module is used to perform weighted calculation on the obtained workload scores of multiple different dimensions according to the first weight value and the second weight value to obtain the workload score result corresponding to the job type;

[0017] Wherein, the second weight value is used to represent the score weight of the walking distance dimension, and the first weight value is used to represent the score weight of the body dimension, which includes at least one of the body posture dimension, force dimension, manual carrying dimension, and includes the influencing factor dimension.

[0018] In some technical solutions, the dynamic weight module includes a storage unit and a weight matching unit;

[0019] The storage unit is used to store pre-set first weight values ​​and second weight values ​​corresponding to different job types;

[0020] The weight matching unit is used to obtain from the storage unit a first weight value and a second weight value corresponding to the job type or user-defined job type in the job data.

[0021] In some technical solutions, the dynamic weight module further includes a weight setting unit;

[0022] The weight setting unit is used to add or delete job types and their corresponding first weight values ​​and second weight values ​​in the storage unit according to user operations, or to modify existing job types and their corresponding first weight values ​​and second weight values ​​in the storage unit.

[0023] In some technical solutions, the result output module is also used to determine the risk level corresponding to the job type based on the obtained workload score result.

[0024] In some technical solutions, the result output module further includes an interaction unit and a statistical analysis unit;

[0025] The interaction unit is used to acquire production data input by the user; the production data includes vehicle model, output, and production line, and the production line includes multiple job types;

[0026] The statistical analysis unit is used to perform data statistics based on the production data, the labor load score results corresponding to the job type, and / or risk level to obtain statistical analysis results corresponding to the vehicle model and production line.

[0027] In some technical solutions, the interactive unit is also used to store and visualize at least one of the multiple different dimensions of workload score, workload rating result, risk level and statistical analysis result.

[0028] In a second aspect, this application provides a multi-dimensional workload evaluation method, applied to the system described in any of the above technical solutions, the method comprising:

[0029] In response to the acquired job data corresponding to the job type;

[0030] Based on the work data, dimensional features are extracted, and different dimensional evaluation models are called to calculate the workload score to obtain multiple workload scores in different dimensions.

[0031] The workload score corresponding to the job type is calculated based on the workload scores of the multiple different dimensions;

[0032] The evaluation models of different dimensions include at least one of the following: body posture evaluation model, force evaluation model, manual handling evaluation model, and walking distance evaluation model.

[0033] The above-mentioned technical solutions of this application have at least one or more of the following beneficial effects: The multi-dimensional workload evaluation system proposed in this invention can calculate multiple workload scores for different dimensions based on multiple evaluation models of different dimensions, and then obtain the workload score result corresponding to the job based on the multiple workload scores of different dimensions; the evaluation models of different dimensions include at least one of body posture evaluation model, force evaluation model, manual handling evaluation model, and walking distance evaluation model. The system provided by this invention can integrate five core dimensions (body posture, force, manual handling, influencing factors, and walking distance) to form a comprehensive evaluation system that simultaneously covers static and dynamic elements; the system provided by this invention only requires the user to input analysis data to automatically calculate the workload score, realizing the rapid generation of the total workload score for the job; the system provided by this invention also realizes the setting of differentiated weights for jobs, supports job configuration and parameter selection during system operation, so that the final evaluation result can more accurately reflect the actual workload level of various jobs. The multi-dimensional workload evaluation system and method provided by this invention improves the comprehensiveness, accuracy, and operability of workload assessment. Attached Figure Description

[0034] The preferred embodiments of the present invention are described below with reference to the accompanying drawings, in which:

[0035] Figure 1 This is a schematic diagram of the components of a multi-dimensional workload assessment system provided in this application;

[0036] Figure 2 This is a schematic diagram illustrating the construction process of the various functional components of the dimensional workload assessment system provided in this application;

[0037] Figure 3 This is a flowchart of the main steps of a multi-dimensional workload evaluation method provided in this application. Detailed Implementation

[0038] Some embodiments of this application are described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of this application and are not intended to limit the scope of protection of this application.

[0039] In the description of this application, "module" and "processor" can include hardware, software, or a combination of both. A module can include hardware circuitry, various suitable sensors, communication ports, and memory, and can also include software components, such as program code, or a combination of software and hardware. A processor can be a central processing unit, microprocessor, image processor, digital signal processor, or any other suitable processor. The processor has data and / or signal processing capabilities. The processor can be implemented in software, in hardware, or a combination of both. Non-transitory computer-readable storage media includes any suitable medium capable of storing program code, such as magnetic disks, hard disks, optical disks, flash memory, read-only memory, random access memory, etc. The term "A and / or B" means all possible combinations of A and B, such as only A, only B, or A and B. The terms "at least one A or B" or "at least one of A and B" have a similar meaning to "A and / or B" and can include only A, only B, or A and B. The singular terms "a" or "this" can also include plural forms.

[0040] The preferred embodiments of this application are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit this application. Furthermore, the embodiments and features in the embodiments of this application can be combined with each other without conflict.

[0041] Existing ergonomic assessment tools and standards mostly focus on musculoskeletal disease risk identification, making it difficult to comprehensively and intuitively reflect the workload of a job. Traditional workload assessment methods are cumbersome to calculate, relying on manual operation or complex forms, making them difficult to promote in manufacturing workshops. Especially in the automotive manufacturing industry, different positions (such as rework positions, front-line assembly positions, logistics handling positions, and inspection positions) have significantly different labor characteristics, and existing assessment methods lack job-specificity. To address this, this application proposes a data-driven, multi-dimensional workload assessment system and method that can specifically evaluate and analyze the fatigue level of different positions and provide quantitative analysis results in conjunction with the production line. This achieves automation and standardization of workload assessment, solves the problems of low efficiency and inconsistent results of manual calculation, and provides a digital solution for assessing the labor intensity of manufacturing positions.

[0042] like Figure 1 The diagram shown is a block diagram of the multi-dimensional workload assessment system provided in this application embodiment. The system mainly includes an acquisition module 101, a basic model library module 102, a score calculation module 103, and a result output module 104. The functions of each module are described in detail below: Data acquisition module 101 is used to acquire job data corresponding to the job type; In this embodiment, the data acquisition module 101 is configured to support acquiring job data corresponding to the job type through data import and data entry. Further, the data acquisition module 101 may specifically include a processing unit and a verification unit, wherein: The processing unit is used to perform information completion operations and / or data cleaning operations on the acquired job data; The verification unit is used to perform data structure verification on the data processed by the processing unit to ensure that structured job data is provided to the score calculation module.

[0043] In this embodiment, the structured job data may include the following fields: job description, code, and duration.

[0044] The basic model library module 102 is used to provide pre-built evaluation models corresponding to different dimensions; In this embodiment, the evaluation models of different dimensions include at least one of a body posture evaluation model, a force evaluation model, and a manual handling evaluation model, as well as a walking distance evaluation model. The body posture evaluation model, the force evaluation model, and the manual handling evaluation model can be established based on existing human ergonomics analysis methods in the industry; that is, they can be directly configured using existing scoring logic and score conversion rules. This embodiment does not impose specific limitations on this. For example, in this embodiment, the body posture evaluation model is configured to output a workload score for the body posture dimension based on the job description. Further, the body posture evaluation model can be configured to extract body posture features based on the input job description, and obtain the workload score for the body posture dimension based on the body posture features and pre-set scoring logic and score conversion rules.

[0045] In this embodiment of the application, the walking distance evaluation model is configured to obtain the corresponding score from a pre-set walking distance and labor load score mapping table as the labor load score of the walking distance dimension based on the walking dimension features extracted from the work data.

[0046] For example, the mapping table between walking distance and workload score provided in this embodiment is as follows:

[0047] In some embodiments, the evaluation models for different dimensions may further include an influencing factor evaluation model; wherein, the influencing factor evaluation model is configured to obtain corresponding scores from a pre-set influencing factor scoring table as the workload score for the influencing factor dimension based on the influencing factors selected by the user. In this application, the influencing factors (or adverse factors) can be understood as various factors that negatively affect the workload evaluation, such as factors affecting operational efficiency, safety and health, or the quality of task completion, and can be specifically defined according to the actual situation.

[0048] For example, the influencing factor scoring table provided in this embodiment is as follows:

[0049] It should be understood that the basic model library module can be selectively configured to provide evaluation models with multiple dimensions according to specific application scenarios. For example, the basic model library module can be configured to include evaluation models with the following five dimensions: body posture evaluation model, force evaluation model, manual handling evaluation model, influencing factor evaluation model, and walking distance evaluation model.

[0050] The score calculation module 103 is used to extract dimensional features from the work data acquired by the data acquisition module 101, match the corresponding evaluation model from the basic model library module 102 based on the dimensional features, and calculate the workload score of the dimensional features based on the matched evaluation model to obtain multiple workload scores of different dimensions. For example, suppose a vehicle manufacturer operates at a production rate of 60 JPH, working 8 hours a day, producing a total of 480 vehicles per day. An employee at a certain position spends 60 seconds working on each vehicle. Using video capture, force gauges, stopwatches, and wearable devices, the following data can be quantified: "Standing posture: 50 seconds; Bending posture: 10 seconds; Installing a cap with thumb: 50N force, installation time: 10 seconds; Moving a part: 20 seconds, part weight: 20kg, ground flat, body upright during movement; Head raised for 10 seconds while pressing the cap; Walking a total of 12 meters in 60 seconds." After inputting this data into the system, the system's data acquisition module can obtain the following structured operational data:

[0051] In this embodiment of the application, the structured work data can be understood as the data format that is standardized from the original collected data to meet the requirements of the existing ergonomic evaluation system standards.

[0052] Based on the structured work data described above, the extracted dimensional features include body posture features (i.e., feature data in column 5 of the table), force features (i.e., feature data in column 6 of the table), manual handling features (i.e., feature data in column 7 of the table), and walking distance features (i.e., feature data in column 8 of the table). Accordingly, by applying the extracted dimensional features to the body posture evaluation model, force evaluation model, manual handling evaluation model, and walking distance evaluation model respectively to calculate the workload score, the workload score for the body posture dimension, the workload score for the force dimension, the workload score for the manual handling dimension, and the workload score for the walking distance dimension can be obtained.

[0053] Furthermore, when the evaluation model provided by the basic model library module also includes an influencing factor evaluation model, the score calculation module can also be used to call the influencing factor evaluation model based on the influencing factor currently selected by the user to obtain the labor load score for the influencing factor dimension. It should be understood that in practical applications, the score calculation module can be configured to obtain the influencing factor currently selected by the user by providing an influencing factor selection interface.

[0054] The result output module 104 is used to calculate the workload score corresponding to the job type based on the workload scores of multiple different dimensions obtained by the score calculation module 103. In this embodiment, the result output module can be specifically used to perform weighted calculations on the workload scores of multiple different dimensions according to pre-set weight values ​​corresponding to different dimensions to obtain the workload score result. For example, the workload score result Q is calculated as follows: ,in, This represents the workload score for dimension i. This represents the weight value corresponding to dimension i. For example, dimension 1 represents the body posture dimension, dimension 2 represents the force dimension, dimension 3 represents the manual handling dimension, dimension 4 represents the walking distance dimension, and dimension 5 represents the influencing factor dimension.

[0055] In some embodiments, the system proposed in this application may further include a dynamic weighting module, which is used to provide the result output module with a first weight value and a second weight value corresponding to the job type. Accordingly, the result output module is used to perform a weighted calculation on the obtained workload scores of multiple different dimensions based on the first weight value and the second weight value to obtain a workload score result corresponding to the job type. The second weight value represents the score weight of the walking distance dimension, and the first weight value represents the score weight of the body dimension. The body dimension includes at least one of body posture dimension, force dimension, and manual handling dimension, as well as a dimension including influencing factors. For example, the workload score result Q is calculated as follows: Q = (Q1+Q2+Q3+Q4)×ω1 + Q5×ω2, where ω1 represents the first weight value, ω2 represents the second weight value, and Q1, Q2, Q3, Q4, and Q5 represent the labor load scores for each dimension, including body posture, force, manual handling, influencing factors, and walking distance, respectively.

[0056] In some embodiments, the dynamic weighting module may specifically include a storage unit and a weight matching unit; the storage unit is used to store pre-set first weight values ​​and second weight values ​​corresponding to different job types respectively; the weight matching unit is used to obtain the first weight value and second weight value corresponding to the job type of the job data or the job type set by the user from the storage unit.

[0057] For example, the first weight value and the second weight value corresponding to different job types stored in the storage unit are shown in the table below:

[0058] In some embodiments, the dynamic weight module further includes a weight setting unit; the weight setting unit is used to add or delete job types and their corresponding first weight values ​​and second weight values ​​in the storage unit according to user operations, or to modify existing job types and their corresponding first weight values ​​and second weights in the storage unit.

[0059] In some embodiments, the result output module described in this application can also be used to determine the risk level corresponding to the job type based on the obtained workload score result. One specific implementation is as follows: when the obtained workload score result is higher than a first threshold, the risk level of the current job is determined to be high workload; when the obtained workload score result is not higher than the first threshold, the risk level of the current job is determined to be low workload. Alternatively, the risk level of the job can be divided into multiple levels based on multiple set thresholds. For example, a first threshold and a second threshold are set. If the workload score result is higher than the first threshold, the corresponding risk level is high workload; if the workload score result is lower than the second threshold, the corresponding risk level is low workload; if the workload score result is between the first threshold and the second threshold, the corresponding risk level is medium workload. For example, the total workload (i.e., the workload score result) can be divided into three levels: low workload (0–25 points, including 25 points), medium workload (>25–50 points, including 50 points), and high workload (>50 points).

[0060] It should be noted that the evaluation models of different dimensions provided in the basic model library module of this application embodiment can also be configured to determine the risk level corresponding to the labor load score after calculating the labor load score. Similarly, the risk level can be determined according to the correspondence between the calculated labor load score and the preset threshold.

[0061] In some embodiments, the result output module described in this application may further include an interaction unit and a statistical analysis unit. The interaction unit is used to acquire production data input by the user; the production data includes vehicle model, output, and production line, and the production line includes multiple job types; it should be understood that the interaction unit can acquire the user-input production data by providing a user interface. The statistical analysis unit is used to perform data statistics based on the production data, the labor load score results corresponding to the job type, and / or risk level to obtain statistical analysis results corresponding to the vehicle model and production line, wherein the output corresponding to the vehicle model can be used to weight the labor load score results of each job in the production line to obtain more accurate statistical analysis results.

[0062] Furthermore, the interactive unit is also used to store and visualize at least one of the multiple different dimensions of workload scores, workload rating results, risk levels, and statistical analysis results. For example, the statistical analysis results can be presented to the user intuitively through charts or other forms. Furthermore, risk level changes can be presented by combining color grading with interactive charts, such as green for low workload, orange for medium workload, and red for high workload.

[0063] The multi-dimensional workload evaluation system provided in this application adds two dimensions of evaluation—walking distance and / or influencing factors—to the existing human ergonomics analysis methods in the industry. It also realizes the automation and standardization of workload evaluation by using a unified database structure to import process data, configure weights, perform hierarchical calculations, and visualize the results. This solves the problems of low efficiency and inconsistent results in manual calculations and provides a digital solution for assessing the labor intensity of manufacturing jobs.

[0064] like Figure 2 The diagram shown illustrates the construction process of a multi-dimensional workload evaluation system provided in this application embodiment. It mainly includes the following functional components: establishment of a basic model library, information input, automatic calculation of module scores, dynamic weight evaluation, and data export and display. The establishment of the basic model library includes the establishment of body posture evaluation models, force evaluation models, manual handling evaluation models, adverse factor splicing models, and walking distance evaluation models; Information entry includes creating new projects, importing process document data, data cleaning and supplementation, and data structuring and analysis. The information entry supports importing external process documents or manually entering data, supports batch import and "whether to overwrite existing data" option, supports data structure verification through field comparison to ensure consistency and integrity, and supports the "one-click synchronization" function to synchronize manually modified parameters to the database to maintain real-time data consistency. The automatic calculation of module scores includes body posture score calculation, force score calculation, manual handling score calculation, adverse factor score calculation, and walking distance score calculation; Dynamic weight assessment includes setting weight parameters for each job position, dynamic weighting and calculation of the total NLE score (i.e., workload score result), and quantitative classification of job risk. Data export and display includes exporting result data, storing and visualizing results, generating and exporting reports, and displaying job list sorting results. The job list sorting feature allows users to rank all jobs based on indicators such as total workload score, risk level, or vehicle model weighted score. Users simply need to input production data for multiple vehicle models and select the production line; the system will then generate the job list sorting results in real time. This function helps managers quickly identify high-load jobs and develop targeted improvement plans or resource allocation schemes. The data export and display feature supports exporting raw data or calculation results from the current project and automatically generating standardized template files. For example, exported files can be named using the "Factory – Production Line – Job" naming convention for easy archiving and traceability.

[0065] The multi-dimensional workload assessment system provided in this application, through the coordinated operation of the above-mentioned functional modules, realizes the automation, standardization and digitization of the workload assessment process, effectively improves the assessment efficiency and consistency of results, and also realizes the risk classification and quantitative assessment of job workload. It can be widely applied to the workload assessment of jobs involving manual handling, assembly operations and a certain range of walking movement.

[0066] like Figure 3 The diagram shown is a flowchart illustrating a multi-dimensional workload assessment method implemented by the multi-dimensional workload assessment system based on any of the above embodiments, as provided in this application. The method includes the following steps 201 to 203: Step 201: In response to the acquired job data corresponding to the job type; Step 202: Extract dimensional features from the work data, and use different dimensional evaluation models to calculate the workload score based on the dimensional features to obtain multiple workload scores in different dimensions; The evaluation models of different dimensions include at least one of body posture evaluation model, force evaluation model, and manual handling evaluation model, as well as a walking distance evaluation model. Step 203: Calculate the workload score corresponding to the job type based on the workload scores of the multiple different dimensions.

[0067] The specific implementation methods of steps 201 to 203 above can be referred to the relevant descriptions of each component module or functional module of the multi-dimensional workload evaluation system provided in this application.

[0068] It should be noted that although the steps in the above embodiments are described in a specific order, those skilled in the art will understand that in order to achieve the effect of this application, different steps do not necessarily have to be executed in such an order. They can be executed simultaneously (in parallel) or in other orders. These adjusted solutions are equivalent to the technical solutions described in this application and therefore will also fall within the protection scope of this application.

[0069] Those skilled in the art will understand that all or part of the processes in the method of the above-described embodiment can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable storage medium can include any entity or device capable of carrying the computer program code, a medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory, a random access memory, an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0070] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A multi-dimensional workload evaluation system, characterized in that, include: The data acquisition module is used to acquire job data corresponding to the job type; The basic model library module provides pre-built evaluation models corresponding to different dimensions; The score calculation module is used to extract dimensional features from the work data obtained by the data acquisition module, match the corresponding evaluation model from the basic model library module based on the dimensional features, and calculate the workload score of the dimensional features based on the matched evaluation model to obtain multiple workload scores of different dimensions. The result output module is used to calculate the workload score corresponding to the job type based on the workload scores of multiple different dimensions obtained by the score calculation module; The evaluation models of different dimensions include at least one of the following: body posture evaluation model, force evaluation model, manual handling evaluation model, and walking distance evaluation model.

2. The multi-dimensional workload evaluation system according to claim 1, characterized in that, The walking distance evaluation model is configured to obtain the corresponding score as the labor load score of the walking distance dimension based on the walking dimension features extracted from the work data and from a pre-set mapping table of walking distance and labor load score.

3. The multi-dimensional workload evaluation system according to claim 1, characterized in that, The evaluation models of different dimensions also include the evaluation model of influencing factors; The score calculation module is also used to call the influencing factor evaluation model based on the influencing factor currently selected by the user to obtain the labor load score of the influencing factor dimension; The influencing factor evaluation model is configured to obtain the corresponding score from a pre-set influencing factor scoring table based on the influencing factors selected by the user, and use it as the labor load score for the influencing factor dimension.

4. The multi-dimensional workload evaluation system according to claim 3, characterized in that, The system also includes a dynamic weighting module; The dynamic weighting module is used to provide the result output module with a first weight value and a second weight value corresponding to the job type; The result output module is used to perform weighted calculation on the obtained workload scores of multiple different dimensions according to the first weight value and the second weight value to obtain the workload score result corresponding to the job type; Wherein, the second weight value is used to represent the score weight of the walking distance dimension, and the first weight value is used to represent the score weight of the body dimension, which includes at least one of the body posture dimension, force dimension, manual carrying dimension, and includes the influencing factor dimension.

5. The multi-dimensional workload evaluation system according to claim 4, characterized in that, The dynamic weighting module includes a storage unit and a weight matching unit; The storage unit is used to store pre-set first weight values ​​and second weight values ​​corresponding to different job types; The weight matching unit is used to obtain from the storage unit a first weight value and a second weight value corresponding to the job type or user-defined job type in the job data.

6. The multi-dimensional workload evaluation system according to claim 4, characterized in that, The dynamic weight module also includes a weight setting unit; The weight setting unit is used to add or delete job types and their corresponding first weight values ​​and second weight values ​​in the storage unit according to user operations, or to modify existing job types and their corresponding first weight values ​​and second weight values ​​in the storage unit.

7. The multi-dimensional workload evaluation system according to claim 1, characterized in that, The result output module is also used to determine the risk level corresponding to the job type based on the obtained workload score results.

8. The multi-dimensional workload evaluation system according to claim 7, characterized in that, The result output module also includes an interactive unit and a statistical analysis unit; The interaction unit is used to acquire production data input by the user; the production data includes vehicle model, output, and production line, and the production line includes multiple job types; The statistical analysis unit is used to perform data statistics based on the production data, the labor load score results corresponding to the job type, and / or risk level to obtain statistical analysis results corresponding to the vehicle model and production line.

9. The multi-dimensional workload evaluation system according to claim 8, characterized in that, The interactive unit is also used to store and visualize at least one of the multiple different dimensions of workload score, workload rating result, risk level and statistical analysis result.

10. A multi-dimensional workload evaluation method, characterized in that, Applied to the system according to any one of claims 1-9, the method comprises: In response to the acquired job data corresponding to the job type; Based on the work data, dimensional features are extracted, and different dimensional evaluation models are called to calculate the workload score to obtain multiple workload scores in different dimensions. The workload score corresponding to the job type is calculated based on the workload scores of the multiple different dimensions; The evaluation models of different dimensions include at least one of the following: body posture evaluation model, force evaluation model, manual handling evaluation model, and walking distance evaluation model.

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