Standard work efficiency digital man based on spatial layout and attitude evaluation

Abstract

The invention discloses a standard work efficiency digital man based on spatial layout and attitude evaluation, and the digital man comprises a sensing layer which is responsible for collecting various types of original data in a working scene, and carrying out the compression transmission of the data; the analysis layer is used for carrying out preliminary preprocessing on the collected original data and extracting valuable feature information; the extracted feature information is fused, analyzed and calculated; and the decision-making layer is used for comprehensively evaluating the working efficiency in the current working scene according to the personnel attitude estimation result and the spatial layout characteristic parameters transmitted by the analysis layer. And the application layer presents the final work efficiency evaluation result and the optimization strategy to a user. And a detailed work efficiency evaluation report is automatically generated according to an evaluation result. According to the method, through multi-modal data fusion, dynamic attitude modeling, attitude load grading evaluation model and personalized ergonomics optimization, digitalized and standardized analysis and optimization of personnel attitude and spatial layout ergonomics level in a working scene are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of digital humans, and particularly to a standard ergonomic digital human based on spatial layout and posture evaluation. Background Art

[0002] In the context of the rapid development of digitalization and intelligentization in today's era, various industries have an increasingly strong demand for the optimization of work scenarios, the planning of task operation modes, and the improvement of personnel work efficiency and comfort. Traditional ergonomic analysis methods often have certain limitations. For example, in the manufacturing industry, in the past, it mainly relied on manual observation and empirical judgment to evaluate the operation postures of workers and the rationality of production line layouts. This method is highly subjective and difficult to accurately quantify; in the field of logistics and warehousing, there is also a lack of accurate data collection and in-depth analysis means for the evaluation of cargo stacking and forklift driver operations; although the medical and health care industry has certain specifications for the layout of operating rooms and the operation processes of medical staff, it lacks comprehensive evaluation and real-time optimization based on multi-modal data; the placement of desks and chairs in traditional office spaces is also mostly based on fixed standards and fails to fully consider individual differences.

[0003] With the development of sensor technology, artificial intelligence algorithms, and human-computer interaction design, new opportunities have been provided to solve these problems of traditional ergonomic analysis; some existing technical solutions have begun to attempt to use image acquisition devices, depth sensors, motion sensors, etc. to obtain information in the work scenario, but these solutions often simply collect single or a few types of modal data and fail to fully integrate multi-source data for in-depth analysis and comprehensive evaluation; for example, some studies only use image data to analyze the postures of personnel, but ignore the importance of motion data and environmental space layout data; or only focus on the geometric measurement of equipment layout and do not conduct overall optimization in combination with the actual operation postures and motion behavior characteristics of personnel.

[0004] In addition, most of the previous ergonomic evaluation and optimization solutions are static and cannot be dynamically adjusted and customized according to the individual's physical characteristics, work habits, and skill levels; in terms of spatial layout optimization, there is also a lack of flexible adaptability to different task scenarios and personnel needs. Summary of the Invention

[0005] Therefore, the present invention provides a standard ergonomic digital human based on spatial layout and posture evaluation to solve the problems in the prior art.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A standard ergonomic digital human based on spatial layout and posture evaluation includes a perception layer, an analysis layer, a decision layer, and an application layer;

[0008] The perception layer is responsible for collecting various types of raw data in the work scenario and compressing and transmitting the data;

[0009] The analysis layer performs preliminary preprocessing on the collected raw data, extracts valuable feature information from the preprocessed raw data; fuses the feature information extracted from different data sources, and analyzes and calculates the load levels from two aspects: pose estimation and spatial layout based on the fused features;

[0010] The decision-making layer comprehensively evaluates the ergonomic level in the current work scenario according to the personnel pose estimation and spatial layout load evaluation result parameters transmitted by the analysis layer, combined with the pre-established ergonomic evaluation index system and model; for the problems found in the ergonomic evaluation, uses optimization algorithms to generate corresponding optimization strategies;

[0011] The application layer presents the final ergonomic level evaluation result and optimization strategy to the user in an intuitive and easy-to-understand way, provides decision-making support for the user; and automatically generates a detailed ergonomic evaluation report according to the evaluation result.

[0012] Furthermore: The various types of raw data include images, depth information, skeletal tracking data, and sensor data.

[0013] Furthermore: For the pose estimation, according to visual features and sensor features, combined with machine learning algorithms, specific pose parameters of personnel are calculated;

[0014] For the spatial layout analysis, combining personnel poses, 3D models of the work scenario, and equipment location information, uses spatial layout algorithms to evaluate the rationality of the spatial layout of the work scenario, and calculates evaluation parameters including equipment spacing, personnel activity space size, and task operation reach threshold.

[0015] Furthermore: The optimization strategies include suggestions for personnel pose adjustment and spatial layout adjustment plans.

[0016] Furthermore: The ergonomic evaluation report includes a spatial layout analysis report, a pose load evaluation report, and a comprehensive ergonomic level evaluation report; the report content includes evaluation indicators, analysis results, and optimization suggestions.

[0017] Furthermore: The perception layer includes a multi-modal data acquisition module and an output transmission module;

[0018] Among them, the multi-modal data acquisition module includes image acquisition, depth information acquisition, and motion behavior data acquisition;

[0019] The image acquisition is based on deployed high-resolution industrial cameras or intelligent monitoring cameras to capture real-time images of the work scenario in all directions;

[0020] The depth information acquisition uses a high-precision lidar or a structured light sensor to obtain the depth data of the working scenario;

[0021] The motion behavior data acquisition uses an inertial measurement unit sensor and skeleton tracking technology to monitor the joint movement angles, acceleration, and angular velocity parameters of personnel in real time;

[0022] The data transmission module uses a high-speed and stable wireless communication protocol or a fiber optic network to transmit multi-modal data from the sensing device to the edge computing node or the cloud server; during the data transmission process, data compression algorithms and encryption technologies are used.

[0023] Furthermore: The analysis layer includes a data preprocessing module, a multi-modal fusion and posture load estimation module, and a spatial layout analysis module;

[0024] The data preprocessing module can preprocess the raw data transmitted by the data transmission module, and its processing content includes:

[0025] Denoising processing, removing noise from the collected multi-source data;

[0026] Data augmentation, using geometric transformation methods to augment the image data; for depth data, new training samples are generated by adding simulated noise and local occlusion;

[0027] Data synchronization, using a time synchronization algorithm to align the multi-modal data;

[0028] The multi-modal fusion and posture load estimation module can extract and fuse features from the preprocessed raw data, and establish a posture estimation model based on the fused features;

[0029] The spatial layout analysis module constructs a three-dimensional spatial layout model of the working scenario according to the CAD drawings of the working scenario, the on-site measurement data, and the process requirements; uses graph theory algorithms and visibility calculation methods to analyze the movement accessibility of personnel between different working areas; and uses the fuzzy comprehensive evaluation method, the analytic hierarchy process, or the psychophysical method for comfort evaluation.

[0030] Furthermore: The decision-making layer includes an ergonomic level evaluation module and an optimization suggestion generation module;

[0031] The ergonomic level evaluation module establishes an ergonomic evaluation index system and constructs an ergonomic evaluation model based on international ergonomics standards and industry practices;

[0032] The optimization suggestion generation module formulates targeted spatial layout adjustment and posture correction strategies according to the ergonomic evaluation results and user requirements.

[0033] Furthermore: The application layer includes a visualization display interface and a report generation and export module;

[0034] The visualization display interface presents the optimization suggestions to the user in an intuitive three-dimensional visualization form, and displays the adjusted work scene layout and the correct postures of the personnel;

[0035] The report generation and export module generates a detailed ergonomic evaluation report.

[0036] The present invention integrates sensing technology, artificial intelligence algorithms, and human-computer interaction design concepts, and through multi-modal data fusion, dynamic posture modeling, and personalized ergonomic optimization, realizes the comprehensive analysis and optimization of the personnel postures and spatial layouts in the work scene.

[0037] Other features and advantages of the present invention will be described in the subsequent specification, and part of them will be obvious from the specification or understood by implementing the present invention. Brief Description of the Drawings

[0038] To more intuitively illustrate the prior art and the present application, exemplary drawings are given below. It should be understood that the specific shapes and structures shown in the drawings generally should not be regarded as limiting conditions when implementing the present application; for example, those skilled in the art are capable of making routine adjustments or further optimizations to the addition / deletion / attribution division of certain units (components), specific shapes, positional relationships, connection methods, dimensional proportional relationships, etc. based on the technical concept disclosed in the present application and the exemplary drawings.

[0039] Figure 1 It is a system block diagram of a standard ergonomic digital human based on spatial layout and posture evaluation provided for an embodiment of the present application. Detailed Embodiments

[0040] The following specific embodiments illustrate the implementation manners of the present invention. Those skilled in this technology can easily understand other advantages and effects of the present invention from the content disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. It should be understood that these embodiments are only for further explaining the present invention and cannot be construed as limiting the protection scope of the present invention. Technical engineers in this field can make some non-essential improvements and adjustments to the present invention according to the above content of the invention; based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the protection scope of the present invention.

[0041] Please refer to Figure 1 , a standard ergonomic digital human based on spatial layout and posture evaluation, includes a sensing layer, an analysis layer, a decision layer, and an application layer.

[0042] The perception layer is mainly responsible for collecting various types of raw data in the working scenario and compressing and transmitting the data. The various types of raw data include images, depth information, skeleton tracking data, and sensor data, etc.

[0043] The analysis layer mainly conducts preliminary preprocessing on the collected raw data and extracts valuable feature information from the preprocessed raw data; and uses the extracted feature information for in-depth analysis and calculation. In terms of pose estimation, according to visual features and sensor features, combined with machine learning algorithms (such as neural networks), the specific pose parameters of the person are calculated, such as joint positions, pose angles, etc.; in terms of spatial layout analysis, considering information such as the person's pose, the three-dimensional model of the working scenario, and the device positions, etc., spatial layout algorithms are used to evaluate the rationality of the spatial layout of the working scenario, and evaluation parameters such as device spacing, the size of the person's activity space, and the reach threshold of task operations are calculated; the feature information from different data sources (such as images, depth sensors, sensors, etc.) is fused to obtain a more comprehensive and accurate analysis result; through data fusion, the limitations of single data source information can be made up for, and the system's understanding and analysis ability of the working scenario and the person's operation behavior can be improved.

[0044] The decision-making layer comprehensively evaluates the work efficiency under the current working scenario according to the person's pose estimation result and the spatial layout load evaluation result parameters transmitted from the analysis layer, combined with the pre-established comprehensive work efficiency evaluation index system and model; for the problems found in the work efficiency level evaluation, optimization algorithms are used to generate corresponding optimization strategies; the optimization strategies include suggestions for adjusting the person's pose, such as maintaining the correct sitting and standing postures, etc., to reduce fatigue and improve efficiency; spatial layout adjustment plans, such as repositioning the device positions, optimizing the material storage area, etc., to improve the working environment and operation process.

[0045] The application layer presents the final work efficiency level evaluation result and optimization strategy to the user in an intuitive and easy-to-understand way (such as charts, animations, text descriptions, etc.) to provide decision-making support for the user; in addition, a detailed work efficiency evaluation report will be automatically generated according to the evaluation result, including spatial layout analysis reports, pose load evaluation reports, comprehensive work efficiency level evaluation reports, etc.; the report content covers aspects such as evaluation indicators, analysis results, and optimization suggestions, providing a comprehensive decision-making basis for the user.

[0046] The perception layer includes a multi-modal data collection module and an output transmission module.

[0047] Among them, the multi-modal data collection module includes image collection, depth information collection, and motion behavior data collection.

[0048] Image acquisition: Deploy high-resolution industrial cameras or intelligent surveillance cameras to capture real-time images of the working scene in all directions, covering key information such as equipment layout, personnel operation postures, and material placement. These image data not only record the static spatial layout but also can reflect the dynamic changes in postures during the operation process through consecutive frames. For example, on an automotive assembly line, it can clearly capture the relative positions of workers, components, and assembly tools, as well as the changes in body postures, providing rich visual materials for subsequent analysis.

[0049] Depth information acquisition: Utilize high-precision lidar or structured light sensors to obtain depth data of the working scene, which helps to accurately measure three-dimensional information such as the distances between equipment and the spatial position relationships between personnel and equipment, making up for the deficiency of image data only on a two-dimensional plane. For example, in a logistics warehouse, it can accurately measure the distance between the shelves and the forklift to determine whether there is a collision risk, providing a reliable data basis for optimizing the spatial layout.

[0050] Motion behavior data acquisition: With the help of inertial measurement unit (IMU) sensors and skeleton tracking technology, real-time monitor parameters such as the joint movement angles, accelerations, and angular velocities of personnel. Taking a construction site as an example, by installing IMU sensors on workers' safety helmets, it can accurately record the body tilt angles and movement amplitudes of workers during high-altitude operations, and analyze whether their postures comply with safety regulations in combination with skeleton tracking technology, so as to timely detect potential dangerous actions and give warnings.

[0051] Data transmission module: Adopt high-speed and stable wireless communication protocols or fiber optic networks to ensure that massive multi-modal data can be quickly and accurately transmitted from the sensing devices to the edge computing nodes or cloud servers. During the data transmission process, use data compression algorithms and encryption technologies to ensure the integrity and security of the data, preventing data loss or theft.

[0052] The analysis layer includes a data preprocessing module, a multi-modal fusion and posture load estimation module, and a spatial layout analysis module.

[0053] The data preprocessing module can preprocess the raw data transmitted by the data transmission module, and its processing contents include:

[0054] Denoising processing: For the noise interference problems existing in the multi-source data collected, use methods such as adaptive filtering algorithms and wavelet transforms to remove noise. For example, for salt-and-pepper noise in image data, use the median filtering algorithm for smoothing to improve the clarity of the image; for random noise in depth data, filter it through a Gaussian filter to ensure the accuracy of depth information.

[0055] Data augmentation: To increase the diversity and representativeness of data and improve the generalization ability of the model, geometric transformation methods such as rotation, translation, scaling, and flipping are used to augment image data; for depth data, new training samples are generated by adding simulated noise, local occlusion, etc.; for example, in a robot operation scenario, by performing rotation and translation transformations on robot working images taken at different angles, the model can learn the operation characteristics of the robot from various perspectives, thereby improving the accuracy of pose recognition.

[0056] Data synchronization: Since there may be differences in the acquisition frequencies and timestamps of different modality data, a time synchronization algorithm is needed to align multi-modal data; for example, according to the key frames of image data and the sampling time points of motion data, through linear interpolation or dynamic time warping (DTW) algorithm, data from different sources are mapped to a unified time axis to ensure the accuracy of subsequent fusion analysis.

[0057] The multi-modal fusion and pose load estimation module can extract and fuse features from the preprocessed raw data, and establish a pose estimation model based on the fused features.

[0058] Among them, for feature extraction and fusion, advanced deep learning algorithms such as convolutional neural network (CNN), recurrent neural network (RNN) and its variants (such as LSTM, GRU) are used to extract features from image, depth and motion data respectively; for example, CNN is used to extract texture features, object contours, etc. in images; RNN is used to mine temporal features and dependencies in motion data; the spatial features of depth data are processed by a multi-layer perceptron (MLP); then, these multi-modal features are fused by using an attention mechanism or feature concatenation method to obtain a comprehensive and compact integrated feature representation.

[0059] Pose estimation: Based on the fused features, a regression analysis method is used to establish a pose estimation model; for example, ridge regression or support vector regression (SVR) algorithm is used to predict the position information or pose angles of human joints according to the input integrated features; through a large amount of labeled data for training and verification, the model parameters are continuously optimized to improve the accuracy and real-time performance of pose estimation. In a sports training scenario, accurate pose estimation can help coaches timely discover the irregularities in athletes' movements, thereby providing personalized guidance and reducing the risk of sports injuries.

[0060] The main functions of the spatial layout analysis module are realized as follows:

[0061] (1) Layout modeling: Based on the CAD drawings of the working scenario, on-site measurement data, and process flow requirements, construct a three-dimensional spatial layout model of the working scenario. This model should include information such as the spatial positions, dimensions, and shapes of equipment, facilities, passages, working areas, etc. For example, in an electronic chip manufacturing workshop, the placement positions of chip production equipment, the directions of conveyor belts, and the layout of material storage areas are accurately presented through a CAD model, providing a detailed digital scenario for subsequent analysis.

[0062] (2) Reachability analysis: Use graph theory algorithms and visibility calculation methods to analyze the movement reachability of personnel between different working areas. For example, by constructing an undirected graph to represent the passages and connection nodes in the working scenario, calculate the shortest path lengths and travel times between nodes, and evaluate the evacuation efficiency of personnel in case of an emergency. At the same time, use ray tracing algorithms to simulate the line-of-sight range of personnel, and determine whether equipment operation buttons, control panels, etc. are within the visible area to ensure the convenience and safety of operations.

[0063] (3) Comfort evaluation: Considering factors such as the comfort of the body posture, visual fatigue, and spatial crowding of personnel in the working scenario, use fuzzy comprehensive evaluation methods, analytic hierarchy process, or psychophysical methods for comfort evaluation. For example, set the weights of different factors according to ergonomic standards, obtain the evaluation values of each factor through questionnaire surveys or expert scoring, and calculate the overall comfort score through fuzzy operations to provide a quantitative basis for the optimization of the spatial layout.

[0064] The decision-making layer includes an ergonomic level evaluation module and an optimization suggestion generation module.

[0065] The ergonomic level evaluation module can establish a comprehensive set of ergonomic evaluation index systems and construct an ergonomic evaluation model based on international ergonomic standards and industry best practices.

[0066] Among them, for the construction of the index system, based on international ergonomic standards (such as ISO 11228 series standards) and industry best practices, establish a comprehensive set of ergonomic evaluation index systems. This index system should cover multiple dimensions such as production efficiency, product quality, personnel fatigue, error rate, etc. For example, production efficiency can be measured by the product output or task completion volume per unit time; product quality can be evaluated according to indicators such as the defective rate and rework rate; personnel fatigue is determined by combining subjective questionnaire surveys and physiological data analysis (such as heart rate variability, muscle activity electrical signals, etc.); the error rate counts the number of mistakes or the frequency of violation operations during the operation process.

[0067] Establish an evaluation model, and use methods such as the Analytic Hierarchy Process (AHP), Fuzzy Comprehensive Evaluation Method, or Data Envelopment Analysis (DEA) to construct an ergonomic evaluation model. Taking AHP as an example, first divide the ergonomic evaluation index system into an objective layer, a criterion layer, and a scheme layer. Determine the weights of each criterion layer relative to the objective layer and the weights of each scheme layer relative to the criterion layer through expert scoring. Finally, calculate the comprehensive weight scores of each scheme to achieve the ergonomic evaluation and ranking of different work scenarios or design schemes.

[0068] For example, when comparing two different office layout schemes, through the AHP model calculation, the comprehensive ergonomic score of Scheme A is 0.85, and that of Scheme B is 0.75, indicating that Scheme A is superior to Scheme B in terms of overall ergonomic performance.

[0069] The optimization suggestion generation module formulates targeted space layout adjustment and posture correction strategies according to the ergonomic evaluation results and user requirements.

[0070] For space layout optimization, propose specific measures such as repositioning of equipment, widening or adjusting of channels, and improvement of work area division; for posture correction, provide personalized operation posture guidance, improvement suggestions for work processes, and design schemes for auxiliary tools. For example, if the ergonomic evaluation finds that workers on a factory production line frequently bend over to operate, resulting in fatigue, the optimization suggestions may include adjusting the equipment height to fit the optimal height range for human standing operation, or designing a liftable work platform to facilitate worker operation.

[0071] The application layer includes a visual display interface and a report generation and export module.

[0072] The visual display interface presents the optimization suggestions to the user in an intuitive three-dimensional visual form, and shows the adjusted work scenario layout and correct postures of personnel through virtual tours, animation demonstrations, etc.

[0073] The report generation and export module generates a detailed ergonomic evaluation report, including evaluation index data, comparative analysis results, effect prediction before and after optimization, etc., providing decision-making support and implementation guidance for users.

[0074] For example, in the optimization project of airport ground handling operation scenarios, through visual display, it can be seen that the driving path of the baggage handling vehicle is smoother after the new layout, and the posture of personnel operating the baggage is more labor-saving. The data comparison in the report shows that it is expected to increase the ground handling operation efficiency by more than 20% and reduce the personnel fatigue degree by 30%.

[0075] The present invention integrates sensing technology, artificial intelligence algorithms, and the concept of human-computer interaction design, and innovatively proposes a digital ergonomic evaluation and spatial layout optimization method based on standard evaluation; through the application of key technologies such as multi-modal data fusion, dynamic posture modeling, and personalized ergonomic optimization, it realizes a comprehensive analysis and optimization of the personnel postures and spatial layouts in the working scenario.

[0076] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A standard ergonomic digital human based on spatial layout and posture evaluation, comprising a perception layer, an analysis layer, a decision-making layer and an application layer, characterized in that the perception layer is responsible for collecting various types of raw data in the work scenario and compressing and transmitting the data; the analysis layer conducts preliminary preprocessing on the collected raw data, extracts valuable feature information from the preprocessed raw data; fuses the feature information extracted from different data sources, and analyzes and calculates the load levels from two aspects of posture estimation and spatial layout based on the fused feature information; the decision-making layer comprehensively evaluates the ergonomic level in the current work scenario according to the personnel posture estimation and spatial layout load evaluation result parameters transmitted by the analysis layer, in combination with the pre-established comprehensive evaluation index system and model of ergonomic level; for the problems found in the ergonomic evaluation, uses an optimization algorithm to generate corresponding optimization strategies; the application layer directly presents the final ergonomic level evaluation result and optimization strategy to the user to provide decision support for the user; and automatically generates a detailed ergonomic evaluation report according to the evaluation result.

2. The standard ergonomic digital human based on spatial layout and posture evaluation according to claim 1, characterized in that, The various types of raw data include images, depth information, skeleton tracking data and sensor data.

3. The standard ergonomic digital human based on spatial layout and posture assessment according to claim 1, characterized in that, For the posture estimation, according to visual features and sensor features, combined with machine learning algorithms, specific posture parameters of the personnel are calculated; For the spatial layout analysis, by integrating personnel postures, 3D models of the work scenario and equipment position information, uses spatial layout algorithms to evaluate the rationality of the spatial layout of the work scenario, and calculates evaluation parameters including equipment spacing, personnel activity space size, task operation reach threshold, etc.

4. The standard ergonomic digital human based on spatial layout and posture evaluation according to claim 1, characterized in that, The optimization strategies include personnel posture adjustment suggestions and spatial layout adjustment plans.

5. A standard ergonomic digital human based on spatial layout and posture assessment according to claim 1, characterized in that, The ergonomic evaluation report includes a spatial layout analysis report, a posture load evaluation report, and a comprehensive ergonomic level evaluation report; the report content includes evaluation indicators, analysis results, and optimization suggestions.

6. The standard ergonomic digital human based on spatial layout and pose evaluation according to claim 1, characterized in that, The perception layer includes a multimodal data acquisition module and an output transmission module; wherein, the multimodal data acquisition module includes image acquisition, depth information acquisition and motion behavior data acquisition; the image acquisition is based on deployed high-resolution industrial cameras or intelligent monitoring cameras to capture real-time images of the work scenario in all directions; the depth information acquisition uses high-precision lidar or structured light sensors to obtain depth data of the work scenario; the motion behavior data acquisition uses inertial measurement unit sensors and skeleton tracking technology to monitor the joint movement angles, accelerations, and angular velocity parameters of personnel in real time; the data transmission module uses a high-speed and stable wireless communication protocol or fiber optic network to transmit multimodal data from the perception device to the edge computing node or cloud server; during the data transmission process, data compression algorithms and encryption technologies are used.

7. A standard ergonomic digital human based on spatial layout and posture evaluation according to claim 1, characterized in that, The analysis layer includes a data preprocessing module, a multimodal fusion and posture load estimation module, and a spatial layout analysis module; the data preprocessing module can preprocess the raw data transmitted by the data transmission module, and its processing content includes: (1) Denoising processing, removing noise from the collected multi-source data; (2) Data augmentation, using geometric transformation methods to augment image data; for depth data, generating new training samples by adding simulated noise and local occlusion; (3) Data synchronization, using a time synchronization algorithm to align multi-modal data; The multi-modal fusion and pose load estimation module can extract and fuse features from the preprocessed raw data, and establish a pose estimation model based on the fused features; The spatial layout analysis module constructs a three-dimensional spatial layout model of the work scenario according to the CAD drawings of the work scenario, field measurement data, and process flow requirements; uses graph theory algorithms and visibility calculation methods to analyze the movement accessibility of personnel between different work areas; and conducts comfort evaluation using the fuzzy comprehensive evaluation method, analytic hierarchy process, or psychophysical method.

8. The standard ergonomic digital human based on spatial layout and pose evaluation according to claim 1, characterized in that The decision-making layer includes an ergonomic level evaluation module and an optimization suggestion generation module; The ergonomic level evaluation module establishes an ergonomic level evaluation index system and constructs an ergonomic level evaluation model based on international ergonomics standards and industry practices; The optimization suggestion generation module formulates targeted spatial layout adjustment and pose correction strategies according to the ergonomic evaluation results and user requirements.

9. The standard ergonomic digital human based on spatial layout and posture evaluation according to claim 1, characterized in that, The application layer includes a visualization display interface and a report generation and export module; The visualization display interface presents the optimization suggestions to the user in an intuitive three-dimensional visualization form, and displays the adjusted work scenario layout and the correct poses of personnel; The report generation and export module generates a detailed ergonomic evaluation report.

Images