Rapid Modeling Method for the Physical Environment of Automobile Manufacturing and Its Multifunctional Application Device

Abstract

The present invention discloses a method for quickly modeling the physical environment of automobile manufacturing and its multi-functional application device, which relates to the technical field of environmental modeling. The method for quickly modeling the physical environment of automobile manufacturing obtains the workshop state data of the automobile manufacturing workshop and performs preprocessing. After the preprocessing, based on a preset parallel processing algorithm, an initial three-dimensional space model, an equipment distribution model, a logistics path model, an air flow model, and a vibration influence model of the automobile manufacturing workshop are respectively established, and fusion modeling is performed to generate a comprehensive three-dimensional space model of the automobile manufacturing workshop. By introducing a parallel processing algorithm and real-time preprocessing of workshop state data, the modeling process of the automobile manufacturing workshop can quickly respond to changes in the actual operation of the workshop. This method can obtain and process the state parameters of each device, path, air circulation, vibration, etc. in the workshop in real time, ensuring that the model always reflects the latest physical environment of the workshop.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental modeling, and particularly to a method for rapidly modeling the physical environment of automobile manufacturing and its multifunctional application device. Background Art

[0002] With the continuous acceleration of the industrialization process, especially in the automobile manufacturing industry, the production efficiency and resource utilization rate of the workshop have become increasingly important. As a highly complex environment involving various production processes, the automobile manufacturing workshop has a wide range of equipment, complex material flow, and complex air circulation and vibration. Therefore, traditional workshop design and layout methods often have difficulty in responding in a timely manner to the dynamic changes in production and the interference of various environmental factors, resulting in low production efficiency, resource waste, frequent equipment failures, and an increase in production safety hazards.

[0003] Currently, many modeling methods for automobile manufacturing workshops still rely on traditional static design drawings and empirical planning, lacking a comprehensive analysis and optimization of various complex physical factors in the workshop. These methods often fail to meet the increasingly complex requirements in the modern automobile production process, especially in the case of dynamic changes in multiple factors such as workshop equipment, logistics paths, air flow, and vibration effects. Traditional modeling methods are difficult to effectively provide real-time feedback and optimization solutions.

[0004] Based on the above solutions, it is found that the limitations of the existing technology at least include the following problems. First, existing modeling methods often only focus on the static design of workshop layout and equipment distribution, lacking real-time response to various dynamic change factors in the workshop. These methods usually rely on early data and design drawings, and fail to conduct in-depth analysis of complex factors such as equipment status, material flow, air flow, and vibration during the actual operation of the workshop, making it difficult to predict and handle actual problems that occur during the production process. This limitation is likely to lead to problems such as equipment overload, poor air flow, and vibration interference not being effectively solved in a timely manner during the workshop operation, thus easily causing equipment failures, low production efficiency, and unnecessary maintenance costs, and further affecting the overall operation efficiency of the workshop. Summary of the Invention

[0005] Aiming at the deficiencies of the existing technology, the present invention provides a method for rapidly modeling the physical environment of automobile manufacturing and its multifunctional application device, solving the problems in the existing technology that lack real-time response and comprehensive analysis of dynamic change factors in the workshop, and thus easily leading to equipment failures, low production efficiency, and increased maintenance costs.

[0006] To achieve the above object, the present invention is realized through the following technical solutions: A method for rapid physical environment modeling in automobile manufacturing, comprising the following steps: obtaining the workshop state data of the automobile manufacturing workshop and performing preprocessing, where the workshop state data includes the total volume value, the area value of each functional area, the size value of each manufacturing device, the path length value of each conveying path, the circulation area value of each air circulation area, the air velocity value in the circulation area, the temperature value in the circulation area, the number of obstacles in the circulation area, the vibration amplitude value of each vibration source, the vibration frequency value, and the vibration range value; based on a preset parallel processing algorithm, respectively establishing an initial three-dimensional space model, an equipment distribution model, a logistics path model, an air flow model, and a vibration impact model of the automobile manufacturing workshop for the preprocessed workshop state data of the automobile manufacturing workshop; generating a comprehensive three-dimensional space model of the automobile manufacturing workshop based on the initial three-dimensional space model, the equipment distribution model, the logistics path model, the air flow model, and the vibration impact model of the automobile manufacturing workshop.

[0007] Further, the specific steps of respectively establishing an initial three-dimensional space model, an equipment distribution model, a logistics path model, an air flow model, and a vibration impact model of the automobile manufacturing workshop for the preprocessed workshop state data of the automobile manufacturing workshop based on a preset parallel processing algorithm are as follows:

[0008] Obtain the number of CPU cores and the processing power index of each CPU core; for the initial three-dimensional space model, the equipment distribution model, the logistics path model, the air flow model, and the vibration impact model of the automobile manufacturing workshop, respectively analyze the three-dimensional space complexity index, the equipment distribution complexity index, the logistics path complexity index, the air flow complexity index, and the vibration impact complexity index of the automobile manufacturing workshop, and respectively combine the number of CPU cores and the processing power index of each CPU core for CPU core allocation; after CPU core allocation, parallelly establish the initial three-dimensional space model, the equipment distribution model, the logistics path model, the air flow model, and the vibration impact model of the automobile manufacturing workshop.

[0009] Further, the specific steps of analyzing the three-dimensional space complexity index of the automobile manufacturing workshop are as follows:

[0010] Obtain the regional shape complexity index of each functional area in the automobile manufacturing workshop, and perform comprehensive analysis in combination with the total volume value of the automobile manufacturing workshop and the area value of each functional area to obtain the three-dimensional space complexity index of the automobile manufacturing workshop. The specific formula is as follows: ; where is the three-dimensional space complexity index of the automobile manufacturing workshop, is the total volume value of the automobile manufacturing workshop, is the The area value of each functional area, is the index of the regional shape complexity of the nth functional area in the automobile manufacturing workshop, is the regional complexity adjustment coefficient stored in the database, is the spatial volume adjustment coefficient stored in the database, , and

[0011] is the number of functional areas in the automobile manufacturing workshop.

[0012] Furthermore, the specific steps for analyzing the equipment distribution complexity index of the automobile manufacturing workshop are as follows: For each manufacturing equipment in the automobile manufacturing workshop, respectively identify the number of adjacent manufacturing equipment within the set range, and analyze the average distance index within the set range of each manufacturing equipment; obtain the number of equipment interaction influences in the automobile manufacturing workshop, and conduct a comprehensive analysis in combination with the average distance index within the set range of each manufacturing equipment in the automobile manufacturing workshop to obtain the equipment distribution complexity index of the automobile manufacturing workshop. The specific formula is as follows: ; where is the equipment distribution complexity index of the automobile manufacturing workshop, is the size value of the nth manufacturing equipment in the automobile manufacturing workshop, is the average distance index within the set range of the nth manufacturing equipment in the automobile manufacturing workshop, is the number of equipment interaction influences in the automobile manufacturing workshop, is the size-distance adjustment coefficient stored in the database, is the equipment interaction adjustment coefficient stored in the database, , and

[0013] is the number of manufacturing equipment in the automobile manufacturing workshop.

[0014] Furthermore, the specific steps for analyzing the logistics path complexity index of the automobile manufacturing workshop are as follows: For each conveying path in the automobile manufacturing workshop, respectively obtain the path workload value, path working duration, path shape complexity index, and turning complexity index, and conduct a comprehensive analysis in combination with the path length value of the corresponding conveying path to obtain the logistics path complexity index of the automobile manufacturing workshop.

[0014] Further, the specific steps for analyzing the air flow complexity index of an automobile manufacturing workshop are as follows: Obtain the air flow resistance values and the reference temperature values of the circulation areas in the automobile manufacturing workshop, and perform comprehensive analysis by combining the circulation area values, the air flow speed values, the temperature values, and the number of obstacles in the corresponding air circulation areas respectively to obtain the air flow complexity index of the automobile manufacturing workshop.

[0015] Further, the specific steps for analyzing the vibration impact complexity index of an automobile manufacturing workshop are as follows: Obtain the number of vibration interferences in the automobile manufacturing workshop, and perform comprehensive analysis by combining the vibration amplitude values, the vibration frequency values, and the vibration range values of each vibration source to obtain the vibration impact complexity index of the automobile manufacturing workshop.

[0016] Further, the specific steps for establishing the equipment distribution model of an automobile manufacturing workshop are as follows:

[0017] Obtain the manufacturing equipment list data, the manufacturing equipment location and dimension data, and the equipment operation status data of the automobile manufacturing workshop. The equipment operation status data includes the current working load value, the current cumulative operation duration value, the historical maintenance times value, the current vibration amplitude value, the current operation temperature value, and the current operation noise value of each manufacturing equipment.

[0018] Based on the preset modeling steps, perform modeling processing on the manufacturing equipment list data, the manufacturing equipment location and dimension data, and the equipment operation status data of the automobile manufacturing workshop to obtain the equipment distribution model of the automobile manufacturing workshop.

[0019] Further, after the equipment distribution model of the automobile manufacturing workshop is established, perform fault prediction analysis on each manufacturing equipment. The specific steps are as follows:

[0020] Obtain the reference operation status data of the automobile manufacturing workshop. The reference operation status data includes the maximum reference working load value, the maximum reference operation duration value, the maximum reference maintenance times value, the maximum allowable vibration value, the maximum tolerable temperature value, and the maximum allowable noise value of each manufacturing equipment. Read the equipment operation status data of the automobile manufacturing workshop, and perform comprehensive analysis by combining the reference operation status data to obtain the fault prediction index of each manufacturing equipment in the automobile manufacturing workshop. Judge and analyze the fault prediction index of each manufacturing equipment in the automobile manufacturing workshop with the preset fault threshold interval respectively, and regard the manufacturing equipment whose fault prediction index is outside the preset fault threshold interval as a fault anomaly, and send a manufacturing equipment anomaly alarm to the relevant staff. Among them, the specific formula for calculating the fault prediction index of each manufacturing equipment in the automobile manufacturing workshop is as follows: ; where is the fault prediction index of the th manufacturing equipment in the automobile manufacturing workshop, is the current working load value of the th manufacturing equipment in the automobile manufacturing workshop, is the maximum reference value of the working load of the th manufacturing equipment in the automobile manufacturing workshop, is the working load adjustment coefficient stored in the database, is the current cumulative operation duration value of the th manufacturing equipment in the automobile manufacturing workshop, is the maximum reference value of the operation duration of the th manufacturing equipment in the automobile manufacturing workshop, is the operation duration adjustment coefficient stored in the database, is the historical maintenance times value of the th manufacturing equipment in the automobile manufacturing workshop, is the maximum reference value of the maintenance times of the th manufacturing equipment in the automobile manufacturing workshop, is the maintenance adjustment coefficient stored in the database, is the current vibration amplitude value of the th manufacturing equipment in the automobile manufacturing workshop, is the maximum allowable vibration reference value of the th manufacturing equipment in the automobile manufacturing workshop, is the vibration amplitude adjustment coefficient stored in the database, is the current operating temperature value of the th manufacturing equipment in the automobile manufacturing workshop, is the maximum temperature tolerance reference value of the th manufacturing equipment in the automobile manufacturing workshop, is the operating temperature adjustment coefficient stored in the database, is the current operating noise value of the th manufacturing equipment in the automobile manufacturing workshop, is the maximum allowable noise reference value of the th manufacturing equipment in the automobile manufacturing workshop, is the operating noise adjustment coefficient stored in the database, = 1, 2, 3, …, , is the number of manufacturing equipment in the automobile manufacturing workshop.

[0021] The rapid physical environment modeling multi-functional application device for automobile manufacturing includes: a data acquisition unit, a parallel modeling unit, a fusion modeling unit, and a fault warning unit;

[0022] The data acquisition unit is used to acquire the workshop status data of the automobile manufacturing workshop and perform preprocessing. The workshop status data includes the total volume value, the area value of each functional area, the size value of each manufacturing device, the path length value of each conveying path, the circulation area value of each air circulation area, the air velocity value in the circulation area, the temperature value in the circulation area, the number of obstacles in the circulation area, the vibration amplitude value, the vibration frequency value, and the vibration range value of each vibration source;

[0023] The parallel modeling unit is used to respectively establish an initial three-dimensional space model, an equipment distribution model, a logistics path model, an air flow model, and a vibration impact model of the automobile manufacturing workshop based on the preset parallel processing algorithm for the preprocessed workshop status data of the automobile manufacturing workshop;

[0024] The fusion modeling unit is used to generate a comprehensive three-dimensional space model of the automobile manufacturing workshop based on the initial three-dimensional space model, the equipment distribution model, the logistics path model, the air flow model, and the vibration impact model of the automobile manufacturing workshop;

[0025] The fault warning unit is used to perform fault prediction analysis on each manufacturing device after the equipment distribution model of the automobile manufacturing workshop is established.

[0026] The present invention has the following beneficial effects:

[0027] (1). In this rapid modeling method for the physical environment of automobile manufacturing, by introducing a parallel processing algorithm and real-time preprocessing of workshop status data, the modeling process of the automobile manufacturing workshop can quickly respond to changes in the actual operation of the workshop. This method can real-time acquire and process the status parameters of each device, path, air circulation, and vibration in the workshop, ensuring that the model always reflects the latest physical environment of the workshop. Different from traditional static modeling methods, this method can dynamically update the model, thereby quickly identifying potential equipment failures, logistics bottlenecks, or environmental problems during the production process and immediately adjusting production scheduling. This dynamic real-time modeling method effectively reduces the downtime during the production process and improves the production efficiency of the workshop.

[0028] (2) The rapid physical environment modeling method for automobile manufacturing can calculate the fault prediction index of equipment in real time, and evaluate the fault risk of each piece of equipment by comparing various operating state data, such as workload, running duration, vibration amplitude, temperature, etc. with the maximum allowable values. By combining the operating data of the equipment with the fault threshold, this method can predict possible faults of the equipment in advance and issue early warnings, thus helping the production line to perform equipment maintenance in a timely manner. Combining the equipment distribution model, air flow model and logistics path model, it can also optimize resource allocation and scheduling, and avoid faults caused by equipment overload and inappropriate environmental conditions. Specifically, this method can improve equipment utilization rate, reduce production interruptions caused by equipment failures, and reduce maintenance costs.

[0029] (3) The rapid physical environment modeling method for automobile manufacturing dynamically analyzes the impact of these factors on the overall production of the workshop by considering the comprehensive influence of various factors in the workshop, such as equipment distribution, logistics path, air flow, vibration influence, etc. during the modeling process. For example, by analyzing the interactive influence between equipment, it can predict possible interference between equipment in advance and optimize its layout to avoid spatial conflicts or vibration interference affecting production efficiency. For the air circulation model, it can optimize the air flow path to ensure the efficient operation of the ventilation system and prevent the influence of inappropriate environmental factors, such as temperature, humidity, etc. on equipment performance. This multi-factor analysis modeling method can help the workshop achieve global optimization, improve resource utilization rate, and thus improve the overall production efficiency and reduce waste of materials and energy.

[0030] (4) The multifunctional application device for the rapid physical environment modeling of automobile manufacturing provides an intelligent workshop management system by integrating a data acquisition unit, a parallel modeling unit, a fusion modeling unit and a fault warning unit. This device can real-time acquire and process various types of data in the workshop, dynamically generate various models of the workshop, and comprehensively analyze factors such as equipment status, logistics path, air flow, vibration influence, etc., and then generate a comprehensive three-dimensional space model. Through the fault prediction and alarm function of the equipment, this device can identify potential faults in advance and remind the operator, thus reducing downtime and production losses. Based on these data, workshop managers can make more scientific decisions on the basis of fully understanding the production environment and equipment status, optimize production plans and resource allocation, and ultimately improve the overall management efficiency and decision-making accuracy of the workshop, and promote the development of intelligent manufacturing.

[0031] Of course, it is not necessary for any product implementing the present invention to achieve all the above-mentioned advantages simultaneously. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a flowchart of the rapid physical environment modeling method for the automobile manufacturing of the present invention.

[0033] Figure 2 This is a specific step flowchart for analyzing the equipment distribution complexity index in the automotive manufacturing physical environment rapid modeling method of the present invention.

[0034] Figure 3 This is a block diagram of a multi-functional application device for the automotive manufacturing physical environment rapid modeling of the present invention. Specific embodiments

[0035] Please refer to Figure 1 , an embodiment of the present invention provides a technical solution: an automotive manufacturing physical environment rapid modeling method, including the following steps: obtaining the workshop state data of the automotive manufacturing workshop and performing preprocessing, where the workshop state data includes the total volume value, the area value of each functional area, the size value of each manufacturing device, the path length value of each conveying path, the circulation area area value of each air circulation area, the circulation area wind speed value, the circulation area temperature value, the number of obstacles in the circulation area (i.e., buildings or devices higher than the set height are regarded as obstacles), the vibration amplitude value of each vibration source, the vibration frequency value, and the vibration range value (i.e., the area where the vibration range is located); based on a preset parallel processing algorithm, respectively establish an initial three-dimensional space model, an equipment distribution model, a logistics path model, an air flow model, and a vibration influence model of the automotive manufacturing workshop for the preprocessed workshop state data of the automotive manufacturing workshop; generate a comprehensive three-dimensional space model of the automotive manufacturing workshop based on the initial three-dimensional space model, the equipment distribution model, the logistics path model, the air flow model, and the vibration influence model of the automotive manufacturing workshop.

[0036] Specifically, the specific steps for separately establishing the initial three-dimensional space model, equipment distribution model, logistics path model, air flow model, and vibration impact model of an automobile manufacturing workshop based on the pre-set parallel processing algorithm for the pre-processed workshop state data of the automobile manufacturing workshop are as follows: Obtain the number of CPU cores for processing and the processing power index of each CPU core; for the initial three-dimensional space model, equipment distribution model, logistics path model, air flow model, and vibration impact model of the automobile manufacturing workshop, analyze the three-dimensional space complexity index, equipment distribution complexity index, logistics path complexity index, air flow complexity index, and vibration impact complexity index of the automobile manufacturing workshop respectively, and respectively combine the number of CPU cores for processing and the processing power index of each CPU core for processing to perform CPU core allocation for processing. The specific steps are as follows: Read the processing power index of each CPU core for processing and the complexity index of each model; calculate the resource allocation coefficient of each CPU core for processing for each model, and perform a summation analysis on the resource allocation coefficients to obtain the number of CPU cores required for each model. And if the CPU core value for processing is a decimal, round it down. Among them, the resource allocation coefficient of each CPU core for processing for each model is specifically the ratio result of each CPU core for processing and the complexity index of each model.

[0037] Among them, the specific steps for obtaining the processing power index of the CPU core for processing are as follows: First, consider the basic computing power of each CPU core for processing. Usually, it can be estimated by its clock frequency (such as GHz), the number of instructions that can be executed per clock cycle (such as IPC, Instructions Per Cycle), and the cache size of the core. Second, combined with the hardware configuration, manufacturing process, and performance optimization of the core (such as multi-threading support, acceleration technology, etc.), the actual processing power of each core can be quantified through benchmark tests (such as SPECint, Geekbench, etc.) to generate a processing power index to measure the performance of the core in actual tasks.

[0038] After the CPU core allocation for processing, parallelly establish the initial three-dimensional space model, equipment distribution model, logistics path model, air flow model, and vibration impact model of the automobile manufacturing workshop.

[0039] Among them, the initial three-dimensional space model of the automobile manufacturing workshop includes:

[0040] Total area and volume of the workshop: including the overall space of the workshop and the space of each functional area (such as production line, storage area, office area, etc.).

[0041] Spatial layout: showing the layout of each area in the workshop and the relative positions between different functional areas.

[0042] Physical constraints: Include physical elements such as building structures, walls, doors, and windows to ensure that the workshop space meets the actual design requirements.

[0043] The specific establishment steps are as follows:

[0044] Obtain the workshop design drawing or CAD drawing: Obtain the floor plan of the workshop as the basic data for the space model.

[0045] Space division: Divide the workshop space into different areas according to the functional area division, such as the production area, storage area, office, etc.

[0046] Space volume calculation: Calculate the volume of each area in the workshop according to its size.

[0047] 3D modeling: Use modeling software (such as AutoCAD, Revit, or SolidWorks) to establish a three-dimensional model, considering the structures, walls, ceilings, etc. of different areas.

[0048] Modeling accuracy verification: Ensure that the three-dimensional model accurately reflects the actual physical environment of the workshop and accurately represents all building elements (such as doors, windows, walls, etc.).

[0049] Among them, the logistics path model of the automobile manufacturing workshop includes:

[0050] Conveyor path: The path of materials in the workshop from one location to another, including conveyor belts, forklift transportation routes, manual handling channels, etc.

[0051] Path length and shape: The length, turning angle, and path shape (such as straight or curved) of each path.

[0052] Path working load and passing capacity: The transportation capacity of each path and the amount of materials that can be transported per unit time.

[0053] The specific establishment steps are as follows:

[0054] Obtain path planning data: Obtain the path planning data for material transportation in the workshop, including path types and connection methods between paths.

[0055] Calculate the path length and shape complexity: Calculate the length and degree of curvature of each path and evaluate the complexity of the path.

[0056] Logistics path allocation: Reasonably allocate the load of the path according to the production task volume and equipment requirements to avoid path overload.

[0057] Path model establishment: Use modeling software (such as CAD, Arena) to establish a three-dimensional model of the logistics path to accurately reflect the path layout.

[0058] Among them, the air flow model of an automobile manufacturing workshop includes:

[0059] Air flow path: The path and direction of air flow, including air conditioners, ventilation systems, etc.

[0060] Circulation area: The area of the air flow region and the wind speed distribution.

[0061] Temperature and humidity distribution: The temperature distribution in the air flow region, especially the influence of heat sources and cold sources.

[0062] The specific establishment steps are as follows:

[0063] Obtain workshop ventilation system data: Obtain data of the workshop air conditioners, ventilation equipment, and air flow systems.

[0064] Design the flow path: According to the workshop layout, design a reasonable air flow path to ensure that the circulation area can cover all key working areas.

[0065] Simulate temperature and humidity: Use CFD (Computational Fluid Dynamics) software to simulate the temperature and humidity changes during the air flow process.

[0066] 3D modeling: Combine with the three-dimensional space model of the workshop to construct an air flow model to ensure the adaptation of air flow to equipment and worker areas.

[0067] Among them, the vibration influence model of an automobile manufacturing workshop includes:

[0068] Vibration source position and amplitude: The position, vibration amplitude, vibration frequency, etc. of the equipment vibration source.

[0069] Vibration propagation path: The path and propagation range of vibration propagation.

[0070] Vibration interference influence: The vibration interference between different equipment affects the accuracy, stability, etc. of the equipment.

[0071] The specific establishment steps are as follows:

[0072] Obtain equipment vibration data: Obtain data such as the vibration amplitude and frequency of each manufacturing equipment.

[0073] Model vibration propagation: Use methods such as finite element analysis (FEA) to simulate the vibration propagation path and influence range in the workshop.

[0074] Analyze vibration interference: According to the spatial positions and vibration data between equipment, analyze the possible vibration interference between equipment.

[0075] 3D modeling and vibration source calibration: Combine with the three-dimensional model of the workshop to calibrate the vibration source and the affected area to generate a vibration influence model.

[0076] In this implementation scheme, through the comprehensive analysis based on the parallel processing algorithm and workshop status data, the modeling efficiency and accuracy of the automobile manufacturing workshop are significantly improved. First, by obtaining the workshop status data and performing preprocessing, the real-time monitoring of various dynamic factors in the workshop can be realized, including spatial layout, equipment distribution, logistics path, air flow, vibration impact, etc. After being preprocessed, these data can help quickly establish the initial three-dimensional space model and other functional models, providing comprehensive support for subsequent modeling and fault prediction. The workshop status data is simultaneously applied to the establishment of multiple models to ensure the completion of the modeling task of the complex workshop environment in a short time. Especially on the basis of efficiently allocating CPU core resources, the computing resources can be reasonably allocated according to the complexity index of each model to ensure the efficient processing of the computing tasks of each model, reducing the modeling time and enhancing the system response ability.

[0077] Specifically, the specific steps for analyzing the three-dimensional space complexity index of the automobile manufacturing workshop are as follows: Obtain the regional shape complexity index of each functional area in the automobile manufacturing workshop, and conduct comprehensive analysis in combination with the total volume value of the automobile manufacturing workshop and the regional area value of each functional area to obtain the three-dimensional space complexity index of the automobile manufacturing workshop; among them, the regional shape complexity index is calculated through ratio analysis based on the boundary length and area of the region. The boundary length refers to the total length of the surrounding edges of the region. For example, if the region is a rectangle or polygon, the boundary is the sum of all the sides of this shape, and this value can be obtained through direct measurement or through the calculation of the regional graph.

[0078] The specific formula for calculating the three-dimensional space complexity index is as follows: ; where is the three-dimensional space complexity index of the automobile manufacturing workshop, is the total volume value of the automobile manufacturing workshop, is the regional area value of the th functional area in the automobile manufacturing workshop, is the th functional area in the automobile manufacturing workshop, is the regional complexity adjustment coefficient stored in the database, is the space volume adjustment coefficient stored in the database, = 1, 2, 3, …, , is the number of functional areas in the automobile manufacturing workshop.

[0079] It should be noted that the regional complexity adjustment coefficient The specific acquisition steps are as follows: First, according to the actual functional area division of the workshop, such as the production area, storage area, office, etc., calculate the ratio of the boundary length to the area of each area to determine the shape complexity of each area. Then, by comparing the historical data and the area usage in the actual operation of the workshop, and combining the design specifications and experience rules, optimize and adjust these coefficients to better reflect the actual impact of the area complexity. Finally, the area complexity adjustment coefficients stored in the database will be updated according to these calculation results.

[0080] The space volume adjustment coefficient stored in the database The specific acquisition steps are as follows: First, collect the overall volume data of the workshop and analyze the functions and usage frequencies of different areas in the workshop. Then, by comparing the designed space and the actual used space of the workshop, and combining the changes in production tasks and the adjustment of equipment layout, determine the actual usage efficiency of the workshop space. Finally, the space volume adjustment coefficient is updated according to these analysis results and stored in the database to reflect the specific impact of different workshop layouts on the space complexity.

[0081] In this implementation plan, by calculating the three-dimensional space complexity index of the automobile manufacturing workshop, this method can effectively evaluate the complexity of the workshop space layout and provide a scientific basis for space optimization and resource allocation. First, use the area shape complexity index to measure the shape complexity of each functional area. This index is calculated through the ratio analysis of the boundary length and area of the area. The boundary length reflects the complexity of the space shape and can reveal the physical connection and space usage efficiency between different functional areas. Combining the overall volume of the workshop and the area data of each functional area, finally obtain the three-dimensional space complexity index to provide a quantitative evaluation of the workshop space layout. This method not only considers the actual space occupancy of each functional area but also combines the complexity of the area shape, and can comprehensively reflect the usage of the workshop space. Through the stored area complexity adjustment coefficient and space volume adjustment coefficient, the three-dimensional space complexity index can be flexibly adjusted according to the actual needs and design requirements of different workshops. This process provides data support for the functional area division and equipment layout of the workshop, helps to discover potential space waste and unreasonable layout problems, and thus optimize the workshop design and improve production efficiency and space utilization rate.

[0082] Specifically, such as Figure 2As shown in the figure, the specific steps for analyzing the equipment distribution complexity index of an automobile manufacturing workshop are as follows: For each manufacturing equipment in the automobile manufacturing workshop, respectively identify the number of adjacent manufacturing equipment within the set range, and analyze the average distance index within the set range of each manufacturing equipment (that is, obtain the average value by analyzing the distance values between the manufacturing equipment and each adjacent manufacturing equipment); Obtain the number of equipment interaction effects in the automobile manufacturing workshop, and conduct a comprehensive analysis in combination with the average distance index within the set range of each manufacturing equipment in the automobile manufacturing workshop to obtain the equipment distribution complexity index of the automobile manufacturing workshop; Among them, the number of equipment interaction effects in the automobile manufacturing workshop refers to the situation in the workshop where due to the functions, work processes, physical spaces, or other operation requirements of the equipment, some mutual dependencies or influences are generated between different equipment. The interaction effects can be manifested at multiple levels, such as functional dependencies between equipment, shared spaces of equipment, or physical interferences (such as vibrations, noises, airflows, etc.) that may occur during the operation process between equipment.

[0083] The number of equipment interaction effects includes:

[0084] Number of functional dependencies: Some equipment may need to cooperate with each other during the work process. For example, for the equipment on the production line, some equipment must wait until the previous equipment completes a certain operation before it can start running. In this case, there are functional interaction effects between these equipment. The specific acquisition steps are as follows: First, it is necessary to understand the functions of each manufacturing equipment. For example, some equipment may depend on the working status of other equipment (for example, the welding equipment needs to be used after the stamping equipment completes its work). Construct a dependency matrix between equipment to identify which equipment is functionally dependent. For example, if equipment A must run first and equipment B cannot run simultaneously, then the functional dependency relationship between them needs to be marked. Determine this relationship by analyzing the work process and production scheduling information of the equipment.

[0085] Number of shared spaces: Multiple equipment may be located in the same area of the workshop. In a narrow area, interference between equipment is likely to occur, and problems such as equipment collisions and aisle congestion may occur. The specific acquisition steps are as follows: Obtain the equipment layout (equipment layout drawings or actual measurement data), which is the basis for determining the equipment location. The coordinates, occupied area of each equipment, and its relative position to other equipment need to be obtained. By analyzing the physical distance between the equipment, determine whether they are in the same area. For equipment with a relatively short distance, there may be a risk of spatial interference or collision.

[0086] Number of physical interferences: Vibrations, noises, heat, or airflows generated during the operation of a device may affect surrounding devices. For example, the high-frequency vibrations of a certain machine may affect nearby precision equipment, or the airflow of an air conditioner may affect the working environment of surrounding devices. Obtain the physical parameters of the device, such as vibration, noise, airflow, etc. These can be obtained through device manuals, sensor data during the production process, or environmental monitoring systems. Use finite element analysis (FEA) or other physical modeling methods to calculate the propagation range and impact degree of vibrations, heat, or airflow. If the vibration range of device A overlaps with the working precision range of device B, there is a physical interference between these two devices.

[0087] The specific formula for calculating the device distribution complexity index is as follows: ; where is the device distribution complexity index of the automotive manufacturing workshop, is the size value of the th manufacturing device in the automotive manufacturing workshop, is the average distance index within the set range of the th manufacturing device in the automotive manufacturing workshop, is the number of device interaction impacts in the automotive manufacturing workshop, is the size-distance adjustment coefficient stored in the database, is the device interaction adjustment coefficient stored in the database, = 1, 2, 3, …, , is the number of manufacturing devices in the automotive manufacturing workshop.

[0088] It should be noted that the specific steps for obtaining the size-distance adjustment coefficient stored in the database are as follows: First, it is necessary to collect the actual size, floor area of the devices, and the relative position data between them. By combining the requirements of the actual workshop space, calculate the reasonable spacing between the devices, and adjust the distance between the devices based on the size and working requirements of the devices. Usually, the acquisition of the size-distance adjustment coefficient also takes into account the working load of the devices, the working space requirements, and the production tasks of the workshop to ensure that the devices can be reasonably arranged without affecting production efficiency, thereby optimizing space utilization and device operation efficiency.

[0089] The device interaction adjustment coefficient The specific acquisition steps are as follows: By analyzing the workshop layout diagram and the operating status of equipment, the mutual dependencies between equipment and potential sources of interference can be identified. For example, some equipment may affect each other during operation. For instance, the vibration of one equipment may affect the precision of another equipment, or multiple equipment sharing a narrow area may cause spatial interference. Based on these analyses, the equipment interaction adjustment coefficient can be adjusted to maximize the collaborative working efficiency between equipment and reduce potential interference.

[0090] In this implementation plan, by calculating the equipment distribution complexity index of the automobile manufacturing workshop, this method can accurately evaluate the rationality of the equipment layout in the workshop and its potential influencing factors. First, this method comprehensively reflects the interaction relationship between equipment by analyzing the average distance between each equipment and its adjacent equipment, and combining the functional dependencies, space sharing, and physical interference of the equipment. This process helps to identify the mutual dependencies between equipment, especially the equipment interference that may be caused by functional dependencies and space sharing, and then provides data support for optimizing the equipment layout and resource allocation. By calculating the number of equipment interaction impacts, this method can identify the physical interferences that may be caused during the operation of the equipment, such as vibration, noise, air flow, etc. These factors often affect the working precision and production efficiency of the equipment. Using this information, the workshop manager can adjust the position of the equipment in advance or optimize the operation scheduling of the equipment to reduce unnecessary interference and conflicts, and improve the stability of the equipment and the overall efficiency of the production line. In addition, in the plan, by introducing the size distance adjustment coefficient and the equipment interaction adjustment coefficient, the complexity index can be flexibly adjusted according to the specific requirements of the workshop, so that the equipment layout can better adapt to the requirements of different production tasks, further improving the production efficiency and reducing potential space conflicts or equipment failures. This comprehensive evaluation method provides solid data support for the efficient operation of the workshop, reducing equipment failures, and enhancing productivity.

[0091] Specifically, the specific steps for analyzing the logistics path complexity index of the automobile manufacturing workshop are as follows: For each conveying path in the automobile manufacturing workshop, obtain the path workload value, path working duration, path shape complexity index, and turning complexity index respectively, and conduct a comprehensive analysis in combination with the path length value of the corresponding conveying path to obtain the logistics path complexity index of the automobile manufacturing workshop; among them, the path workload value represents the amount of materials or transportation tasks borne by this path per unit time.

[0092] The path working duration represents the total duration required for path conveying, considering the time period of the workload.

[0093] The path shape complexity index is obtained by analyzing the ratio of the boundary length of the path to the effective area of the path. The boundary length and the effective area are obtained through path planning tools or workshop design drawings. If the shape of the path is complex, this value is relatively high, indicating a large degree of tortuosity of the path.

[0094] The turning complexity index refers to the sum of the turning angle values at each turn in the path. It calculates each turning angle in the path through the geometric design of the path, or obtains the curve information in the path through sensor data.

[0095] The specific formula for calculating the logistics path complexity index is as follows: ; where is the logistics path complexity index of the automotive manufacturing workshop, is the path length value of the th conveying path in the automotive manufacturing workshop, is the path shape complexity index of the th conveying path in the automotive manufacturing workshop, is the turning complexity index of the th conveying path in the automotive manufacturing workshop, is the path workload value of the th conveying path in the automotive manufacturing workshop, is the path working duration of the th conveying path in the automotive manufacturing workshop, is the path work adjustment coefficient stored in the database, is the logistics path complexity adjustment coefficient stored in the database, = 1, 2, 3, …, , is the number of conveying paths in the automotive manufacturing workshop.

[0096] It should be explained that the specific acquisition steps of the path work adjustment coefficient stored in the database are as follows: The acquisition of this coefficient requires a detailed analysis of the logistics system of the workshop, including the specific workload, work load, and working duration of each conveying path. Through the analysis of historical data, changes in production task volume, and the actual usage of the path, the path work adjustment coefficient can be flexibly adjusted according to the work load of different paths. The specific acquisition steps include collecting the usage data of each path and obtaining the optimized coefficient through comparative analysis of historical work load and actual usage, so that each path can reasonably allocate the load according to its actual working state and ensure the stable operation of the logistics system.

[0097] The logistics path complexity adjustment coefficient The specific acquisition steps are as follows: First, it is necessary to measure all the logistics paths in the workshop, calculate parameters such as the length, tortuosity, and turning angle of the paths, so as to obtain the complexity of the paths. Then, by analyzing the logistics requirements, equipment distribution, and actual situation of transportation tasks in the workshop, the specific adjustment coefficients for each path are obtained. These adjustment coefficients will be flexibly adjusted according to the path shape, complexity, and other actual production situations to better reflect the actual complexity of the paths. Finally, through the storage in the database, the complexity adjustment coefficients of the logistics paths can be updated in real time and dynamically optimized, helping to improve the efficiency of material transportation and reduce unnecessary material handling time.

[0098] In this implementation plan, by calculating the logistics path complexity index of the automobile manufacturing workshop, this method can comprehensively evaluate the complexity of the logistics paths in the workshop, and then optimize the material transportation and production efficiency. First, this method provides a comprehensive evaluation of the paths by analyzing the workload, working duration, path shape complexity, and turning complexity of each conveying path. The path workload value and working duration can reflect the density and time requirements of material transportation on the path, thus helping to determine the workload and running duration of the path, and then reasonably allocate resources to avoid path overload and resource waste. The path shape complexity index and turning complexity index can reveal the complexity of the path by analyzing the tortuosity of the path shape and the turning angle in the path. Especially in a more complex production line design, these factors directly affect the material transportation efficiency. By optimizing the design of these paths, the transportation time can be reduced, the work efficiency can be improved, and unnecessary obstacles can be avoided. Finally, by introducing the path work adjustment coefficient and the logistics path complexity adjustment coefficient, the complexity index can be flexibly adjusted according to the specific requirements of the workshop to meet the needs of different production tasks. The comprehensively analyzed logistics path complexity index provides data support for the material flow in the workshop, helps to optimize the path layout, reduce the transportation time, improve the overall production efficiency, and reduce the production cost.

[0099] Specifically, the specific steps for analyzing the air flow complexity index of the automobile manufacturing workshop are as follows: Obtain the air flow resistance value and the reference temperature value of the circulation area for each air circulation area in the automobile manufacturing workshop, and conduct a comprehensive analysis by combining the circulation area value, circulation area wind speed value, circulation area temperature value, and the number of obstacles in the circulation area of the corresponding air circulation area respectively to obtain the air flow complexity index of the automobile manufacturing workshop; The specific formula for calculating the air flow complexity index is as follows: ; where is the air flow complexity index of the automobile manufacturing workshop, is the circulation area value of the th air circulation area in the automobile manufacturing workshop, is the The air velocity value of the nth air circulation area in the automobile manufacturing workshop; The wind speed adjustment coefficient stored in the database; The number of obstacles in the nth air circulation area in the automobile manufacturing workshop; The obstacle adjustment coefficient stored in the database; The temperature value of the nth air circulation area in the automobile manufacturing workshop; The reference temperature value of the nth air circulation area in the automobile manufacturing workshop; The temperature adjustment coefficient stored in the database; n = 1, 2, 3, … , where n is the number of air circulation areas in the automobile manufacturing workshop.

[0100] It should be noted that the specific steps for obtaining the wind speed adjustment coefficient stored in the database are as follows: collect the wind speed data of each air circulation area in the workshop, and adjust according to the ventilation requirements and actual wind speed conditions of different areas. These coefficients need to be dynamically optimized in combination with the ventilation system design of the workshop, the layout of equipment, and the production requirements of the workshop. By analyzing the air flow conditions, equipment heat generation, and ventilation requirements of different areas, the appropriate wind speed adjustment coefficient can be obtained. Usually, the wind speed adjustment coefficient also takes into account factors such as seasonal changes, the utilization rate of the workshop space, and air flow interference between areas.

[0101] The specific steps for obtaining the obstacle adjustment coefficient stored in the database are as follows: first, analyze the layout of each air circulation area in the workshop, including the distribution of equipment and structures. Then, simulate or measure the obstruction effect of the number and type of obstacles (such as walls, equipment, shelves, etc.) in the workshop on air flow, and adjust according to the actual ventilation requirements of the workshop. Usually, the obstacle adjustment coefficient is dynamically calculated based on the number of equipment, their arrangement, and their influence on air flow, so as to improve the efficiency of air flow and reduce energy waste.

[0102] The specific steps for obtaining the temperature adjustment coefficient The specific acquisition steps are as follows: First, collect the temperature data of each air circulation area in the workshop and analyze the impact of temperature fluctuations on the production environment. Second, combine factors such as the heat generated by the equipment and the ventilation system in the workshop to calculate the temperature distribution of each area, and then optimize the matching of air circulation and temperature. Usually, the temperature adjustment coefficient will be adjusted according to the specific production requirements and environmental conditions of the workshop to maintain a suitable working temperature in the workshop and ensure the comfort and safety of the equipment and personnel.

[0103] In this implementation plan, by calculating the air flow complexity index of the automobile manufacturing workshop, this method can optimize the design of the air circulation system in the workshop, improve the air quality and the operation efficiency of the equipment. First, this method comprehensively considers multiple factors in each air circulation area, including wind speed, temperature, air flow resistance, and the number of obstacles. By analyzing parameters such as air flow resistance, temperature, and wind speed, the efficiency of air flow in different areas can be comprehensively evaluated. This comprehensive analysis helps to determine which areas may have risks of poor air flow, and then optimize the design of the ventilation system to avoid uneven temperature and humidity caused by poor air circulation, and reduce the negative impact on the health of the equipment and workers due to poor air quality. By introducing a wind speed adjustment coefficient, an obstacle adjustment coefficient, and a temperature adjustment coefficient, the plan can flexibly meet the needs of different workshop environments and optimize for different environmental conditions. These coefficients are adjusted according to the specific design and operating conditions of the workshop to ensure that the model is more in line with the actual situation and improve the adaptability and accuracy of air flow.

[0104] Specifically, the specific steps for analyzing the vibration impact complexity index of the automobile manufacturing workshop are as follows: Obtain the vibration interference quantity value in the automobile manufacturing workshop, and comprehensively analyze it in combination with the vibration amplitude value, vibration frequency value, and vibration range value of each vibration source to obtain the vibration impact complexity index of the automobile manufacturing workshop.

[0105] Among them, the vibration interference quantity value refers to the total number of interferences generated between vibration sources in the workshop, which measures the mutual influence of multiple vibration sources and is usually related to the following factors:

[0106] Frequency interference: When the frequencies of multiple vibration sources are close, resonance or superposition effects may occur between them, enhancing each other's vibration influence. This kind of interference is the most common influence between vibration sources.

[0107] Spatial overlap: If the influence ranges of multiple vibration sources overlap, it may increase the vibration intensity of the entire area, resulting in increased interference.

[0108] Relative position and working state: The working state and relative position of the equipment will affect the interference between them. For example, if two devices are in the same area of the workshop or their physical positions are close to each other, they may generate stronger interference.

[0109] The calculation steps for obtaining the number of vibration interferences are as follows:

[0110] Determine the influence area of each vibration source, which can be estimated by the power, frequency of the vibration source and the characteristics of the environment.

[0111] The influence area can be estimated through the propagation model of the vibration source. For example, finite element analysis (FEA) or other simulation methods can be used to calculate the vibration propagation range.

[0112] Group all the vibration sources according to their spatial positions and frequencies, and identify the pairs of vibration sources that may interfere with each other.

[0113] For each pair of vibration sources that may interfere, if their frequencies overlap or their influence areas overlap, it is considered that there is interference between them.

[0114] If the influence areas of a pair of interfering sources overlap and their frequencies are close, the degree of interference between them can be calculated, and this value is usually calculated based on the vibration amplitude, frequency difference and physical position.

[0115] The specific formula for calculating the vibration influence complexity index is as follows: ; where is the vibration influence complexity index of the automobile manufacturing workshop, is the vibration amplitude value of the th vibration source in the automobile manufacturing workshop, is the vibration frequency value of the th vibration source in the automobile manufacturing workshop, is the vibration range value of the th vibration source in the automobile manufacturing workshop, is the value of the number of vibration interferences in the automobile manufacturing workshop, is the vibration interference adjustment coefficient stored in the database, is the vibration influence adjustment coefficient stored in the database, = 1, 2, 3, …, , is the number of vibration sources in the automobile manufacturing workshop.

[0116] It should be explained that the vibration interference adjustment coefficient The specific acquisition steps are as follows: First, collect the vibration data of all vibration sources in the workshop, including the location, frequency, amplitude, etc. of the vibration sources. Then, by measuring the mutual interference between the devices, combined with the distance, relative position and working state between the devices, calculate the possible vibration interference intensity. Further, use methods such as finite element analysis (FEA) to evaluate the vibration propagation path and influence range, and obtain the vibration interference coefficient between devices or regions. Finally, adjust these coefficients to reflect the actual interference situation, so as to ensure that the impact of vibration in the workshop on the devices and production environment is minimized.

[0117] Vibration impact adjustment coefficients stored in the database The specific acquisition steps are as follows: First, analyze the vibration sensitivity of each device in the workshop, including the working frequency range, vibration tolerance and environmental conditions of the device. Then, through simulation or experimental measurement of the vibration response of the device, understand the response characteristics of the device to different vibration intensities. Further, combined with the working state, location of the device and the vibration sources in the workshop, calculate the vibration tolerance of each device, and obtain the corresponding vibration impact adjustment coefficients. Finally, these coefficients are stored in the database for dynamically adjusting and optimizing the anti-vibration design and operating state of the devices in the workshop.

[0118] In this implementation plan, by calculating the vibration impact complexity index of the automobile manufacturing workshop, this method can effectively evaluate the impact of vibration sources in the workshop on the devices and production environment, and then optimize the workshop layout and device configuration. First, this method comprehensively considers multiple factors such as the number of vibration interferences, vibration amplitude, frequency, range, etc., and comprehensively analyzes the mutual interference between each vibration source, which enables the identification of vibration sources that may cause resonance or superposition effects, timely adjustment of the device position or working state, reduction of interference between devices, and improvement of working accuracy and stability. By analyzing the influence area of the vibration source, the devices within the vibration propagation range can be effectively identified, and tools such as finite element analysis (FEA) can be used to simulate the vibration propagation path, so as to accurately predict the impact of vibration on the devices. For vibration sources that may interfere with each other, by calculating their interference degree, it can provide strong support for the workshop design and avoid equipment failures or production interruptions caused by vibration interference.

[0119] Specifically, the specific steps to establish the equipment distribution model of an automobile manufacturing workshop are as follows: Obtain the manufacturing equipment list data of the automobile manufacturing workshop (including basic information such as the type, model, working parameters, service life, production tasks, etc. of all equipment in the workshop, which can be obtained through an equipment management system such as CMMS), the manufacturing equipment location and size data (including the actual floor area, size, and relative position between each manufacturing equipment and other equipment, and the location and size of the equipment can be determined through the workshop layout drawings or actual measurement data), and the equipment operation status data. The equipment operation status data includes the current working load value, the current cumulative operation duration value, the historical maintenance times value, the current vibration amplitude value, the current operation temperature value, and the current operation noise value of each manufacturing equipment, and these values can be obtained through a real-time monitoring system, sensors, or equipment management software.

[0120] Based on the preset modeling steps, perform modeling processing on the manufacturing equipment list data, manufacturing equipment location and size data, and equipment operation status data of the automobile manufacturing workshop to obtain the equipment distribution model of the automobile manufacturing workshop, including:

[0121] Equipment location calibration:

[0122] Task: According to the floor plan of the workshop and the equipment list, determine the specific location of each manufacturing equipment in three-dimensional space, the relative position relationship between each manufacturing equipment, the spacing between equipment, and the interdependent equipment, etc.

[0123] Tool: Use CAD tools, BIM (Building Information Modeling) software, or 3D modeling software (such as SolidWorks, AutoCAD) for spatial modeling.

[0124] Equipment floor area and space optimization:

[0125] Task: According to the size data of the equipment, calculate the occupied area of each manufacturing equipment, and reasonably allocate the equipment location to avoid overcrowding or overlapping of equipment, and ensure the smooth flow of the production line and the movement of personnel and materials.

[0126] Tool: Use spatial layout optimization tools and material flow analysis software for optimization design.

[0127] Analysis of the interaction relationship between equipment:

[0128] Task: Analyze the interdependent relationship between equipment, including space sharing, functional dependence, etc., especially the equipment on the production line, which may need to operate in a specific order.

[0129] Tool: Generate a dependence matrix between equipment through production process analysis to ensure a reasonable spatial distribution between equipment.

[0130] In this implementation plan, by establishing an equipment distribution model for the automobile manufacturing workshop, this method can effectively optimize the layout of the equipment in the workshop, improve production efficiency and space utilization rate. First, the calibration of the equipment position and the acquisition of size data enable each piece of equipment to be accurately positioned in the three-dimensional space, and clearly display the relative positions and interdependent relationships between the equipment. This process ensures that the position of each piece of equipment in the workshop meets the production requirements and avoids overcrowding or conflicts between the equipment. The analysis of the equipment floor area and space optimization further ensures the reasonable layout of the equipment, avoiding overlap or space waste between the equipment, thereby optimizing the efficiency of material flow and personnel passage. By using spatial layout optimization tools and material flow analysis software, possible bottlenecks and conflicts can be foreseen in the initial design stage, and the layout plan can be adjusted to maximize the smoothness and flexibility of the production line.

[0131] Specifically, after the equipment distribution model of the automobile manufacturing workshop is established, a failure prediction analysis is carried out on each manufacturing equipment, and the specific steps are as follows: Obtain the operation status reference data of the automobile manufacturing workshop. The operation status reference data includes the maximum reference value of the working load of each manufacturing equipment, the maximum reference value of the operation duration, the maximum reference value of the maintenance times (referring to the maximum value within the normal maintenance frequency range of the equipment), the maximum reference value of the allowable vibration, the maximum reference value of the bearing temperature, and the maximum reference value of the allowable noise; Read the equipment operation status data of the automobile manufacturing workshop, and conduct a comprehensive analysis in combination with the operation status reference data to obtain the failure prediction index of each manufacturing equipment in the automobile manufacturing workshop; Respectively judge and analyze the failure prediction index of each manufacturing equipment in the automobile manufacturing workshop with the preset failure threshold interval, and regard the manufacturing equipment whose failure prediction index is outside the preset failure threshold interval as a failure anomaly, and send an abnormal alarm of the manufacturing equipment to the relevant staff.

[0132] Among them, the specific formula for calculating the failure prediction index of each manufacturing equipment in the automobile manufacturing workshop is as follows: ; where is the failure prediction index of the th manufacturing equipment in the automobile manufacturing workshop, is the current working load value of the th manufacturing equipment in the automobile manufacturing workshop, is the maximum reference value of the working load of the th manufacturing equipment in the automobile manufacturing workshop, is the working load adjustment coefficient stored in the database, is the current cumulative operation duration value of the th manufacturing equipment in the automobile manufacturing workshop, is the maximum reference value of the operation duration of the th manufacturing equipment in the automobile manufacturing workshop, is the operation duration adjustment coefficient stored in the database, is the historical maintenance times value of the th manufacturing equipment in the automobile manufacturing workshop, is the maximum reference value of the maintenance times of the th manufacturing equipment in the automobile manufacturing workshop, is the maintenance adjustment coefficient stored in the database, is the current vibration amplitude value of the th manufacturing equipment in the automobile manufacturing workshop, is the maximum reference value of the allowable vibration of the th manufacturing equipment in the automobile manufacturing workshop, is the vibration amplitude adjustment coefficient stored in the database, is the current operating temperature value of the th manufacturing equipment in the automobile manufacturing workshop, is the maximum reference value of the temperature tolerance of the th manufacturing equipment in the automobile manufacturing workshop, is the operating temperature adjustment coefficient stored in the database, is the current operating noise value of the th manufacturing equipment in the automobile manufacturing workshop, is the maximum reference value of the allowable noise of the th manufacturing equipment in the automobile manufacturing workshop, is the operating noise adjustment coefficient stored in the database, = 1, 2, 3, …, , is the number of manufacturing equipment in the automobile manufacturing workshop.

[0133] It should be noted that the specific acquisition steps of the workload adjustment coefficient, operation duration adjustment coefficient, maintenance adjustment coefficient stored in the database are as follows: First, collect the workload data of each equipment during its life cycle, and estimate the workload adjustment coefficient by comparing the changes in equipment performance under different workloads. Secondly, the operation duration adjustment coefficient can be deduced from the correlation between the cumulative operation time of the equipment and its performance, and obtained by analyzing the efficiency and failure conditions of the equipment under different working hours. Finally, the maintenance adjustment coefficient is obtained by analyzing the maintenance records and repair frequencies of the equipment, and examining the impact of the frequency of equipment repair on its performance and failure prediction.

[0134] The vibration amplitude adjustment coefficient, operation temperature adjustment coefficient, operation noise adjustment coefficient The specific acquisition steps are as follows: The vibration amplitude adjustment coefficient is obtained by monitoring the vibration level during equipment operation and evaluating its impact on equipment performance in combination with factors such as equipment type and operating environment. The operating temperature adjustment coefficient is deduced by continuously monitoring the change of the equipment's working temperature and combining the equipment's temperature tolerance and working efficiency. The operating noise adjustment coefficient is obtained by analyzing the operation status of the equipment under different noise levels, determining the potential impact of noise on equipment performance, and optimizing the operation management of the equipment based on the noise level adjustment coefficient.

[0135] In this implementation plan, through the fault prediction analysis of the equipment distribution model in the automobile manufacturing workshop, this method can greatly improve the operation and maintenance management efficiency of the workshop equipment, prevent equipment failures and reduce production downtime. First, by combining the operating state parameter data of the equipment (such as maximum working load, maximum operating duration, maintenance times, allowable vibration, bearing temperature, and allowable noise, etc.), the gap between the operating state of each equipment and the preset standard can be comprehensively evaluated, so as to obtain the fault prediction index of each equipment. This quantitative fault prediction model can identify the possible fault risks of the equipment in advance and provide a basis for timely maintenance and scheduling. By comparing and analyzing the fault prediction index of the equipment with the preset fault threshold interval, high-risk equipment can be automatically identified and a fault warning can be issued immediately to remind the relevant staff to carry out maintenance. This intelligent warning mechanism can effectively reduce equipment unexpected shutdowns and sudden failures and ensure the continuity and stability of the production line.

[0136] Please refer to Figure 3, an embodiment of the present invention provides a technical solution: a multifunctional application device for rapid physical environment modeling in automobile manufacturing, including: a data acquisition unit, a parallel modeling unit, a fusion modeling unit, and a fault warning unit; the data acquisition unit is used to acquire the workshop status data of the automobile manufacturing workshop and perform preprocessing. The workshop status data includes the total volume value (which can be obtained from the architectural design drawing of the workshop), the area value of each functional area (which can be obtained from the functional area division plan or design drawing of the workshop), the size value of each manufacturing equipment (which can be obtained from the technical document of the equipment, the equipment manual or the specification book provided by the equipment supplier), the path length value of each conveying path (which can be obtained from the logistics layout drawing or design drawing of the workshop), the circulation area value of each air circulation area (which can be obtained from the ventilation system design drawing of the workshop), the wind speed value in the circulation area (which can be obtained from the data of the air conditioning system or ventilation system in the workshop), the temperature value in the circulation area (which can be obtained from the air conditioning system in the workshop), the number of obstacles in the circulation area (that is, buildings or equipment higher than the set height are regarded as obstacles, which can be obtained from the workshop layout drawing), the vibration amplitude value of each vibration source (which can be obtained through a vibration analyzer), the vibration frequency value (which can be obtained through a vibration analyzer), and the vibration range value (that is, the area where the vibration range is located. The propagation range of the vibration in the workshop can be simulated through finite element analysis software, and the area of the affected area can be calculated); the parallel modeling unit is used to respectively establish an initial three-dimensional space model, an equipment distribution model, a logistics path model, an air flow model, and a vibration impact model of the automobile manufacturing workshop based on the preset parallel processing algorithm for the preprocessed workshop status data of the automobile manufacturing workshop; the fusion modeling unit is used to generate a comprehensive three-dimensional space model of the automobile manufacturing workshop based on the initial three-dimensional space model, the equipment distribution model, the logistics path model, the air flow model, and the vibration impact model of the automobile manufacturing workshop; the fault warning unit is used to perform fault prediction analysis on each manufacturing equipment after the equipment distribution model of the automobile manufacturing workshop is established.

[0137] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they know the basic creative concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments and all changes and modifications falling within the scope of the present invention.

[0138] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention also intends to include these modifications and variations.

Claims

1. A method for rapidly modeling the physical environment of automobile manufacturing, characterized in that It includes the following steps: Obtain the workshop status data of the automobile manufacturing workshop and perform preprocessing. The workshop status data includes the total volume value, the area value of each functional area, the size value of each manufacturing device, the path length value of each conveying path, the circulation area value of each air circulation area, the air velocity value in the circulation area, the temperature value in the circulation area, the number of obstacles in the circulation area, the vibration amplitude value of each vibration source, the vibration frequency value, and the vibration range value; Based on a preset parallel processing algorithm, respectively establish an initial three-dimensional space model, an equipment distribution model, a logistics path model, an air flow model, and a vibration impact model of the automobile manufacturing workshop for the preprocessed workshop status data of the automobile manufacturing workshop; Generate a comprehensive three-dimensional space model of the automobile manufacturing workshop based on the initial three-dimensional space model, equipment distribution model, logistics path model, air flow model, and vibration impact model of the automobile manufacturing workshop; The specific steps of respectively establishing an initial three-dimensional space model, an equipment distribution model, a logistics path model, an air flow model, and a vibration impact model of the automobile manufacturing workshop based on a preset parallel processing algorithm for the preprocessed workshop status data of the automobile manufacturing workshop are as follows: Obtain the number of processing CPU cores and the processing power index of each CPU core; For the initial three-dimensional space model, equipment distribution model, logistics path model, air flow model, and vibration impact model of the automobile manufacturing workshop, respectively analyze the three-dimensional space complexity index, equipment distribution complexity index, logistics path complexity index, air flow complexity index, and vibration impact complexity index of the automobile manufacturing workshop, and respectively combine the number of processing CPU cores and the processing power index of each processing CPU core to perform processing CPU core allocation; After the processing CPU core allocation, parallelly establish an initial three-dimensional space model, an equipment distribution model, a logistics path model, an air flow model, and a vibration impact model of the automobile manufacturing workshop; The specific steps of analyzing the three-dimensional space complexity index of the automobile manufacturing workshop are as follows: Obtain the regional shape complexity index of each functional area in the automobile manufacturing workshop, and perform comprehensive analysis in combination with the total volume value of the automobile manufacturing workshop and the area value of each functional area to obtain the three-dimensional space complexity index of the automobile manufacturing workshop. The specific formula is as follows: Among them, SwF and CjT are the three-dimensional space complexity index and the total volume value of the automobile manufacturing workshop, respectively, and QyM i , XzF i are the area value and the area shape complexity index of the i-th functional area in the automobile manufacturing workshop, respectively. α and ω are the area complexity adjustment coefficient and the space volume adjustment coefficient stored in the database, respectively. i = 1, 2, 3, …, i0, where i0 is the number of functional areas in the automobile manufacturing workshop; The specific steps of analyzing the equipment distribution complexity index of the automobile manufacturing workshop are as follows: For each manufacturing device in the automobile manufacturing workshop, respectively identify the number of adjacent manufacturing devices within a set range, and analyze the average distance index within the set range of each manufacturing device; Obtain the number of equipment interaction impacts in the automobile manufacturing workshop, and perform comprehensive analysis in combination with the average distance index within the set range of each manufacturing device in the automobile manufacturing workshop to obtain the equipment distribution complexity index of the automobile manufacturing workshop. The specific formula is as follows: Among them, SfB is the equipment distribution complexity index of the automobile manufacturing workshop, CcZ u , PjJ u are the size value of the u-th manufacturing equipment in the automobile manufacturing workshop and the average distance index within the set range respectively. SbJ is the number of equipment interaction influences in the automobile manufacturing workshop. μ and δ are the size-distance adjustment coefficient and equipment interaction adjustment coefficient stored in the database respectively. u = 1, 2, 3,..., u0, where u0 is the number of manufacturing equipment in the automobile manufacturing workshop; The specific steps for analyzing the logistics path complexity index of an automobile manufacturing workshop are as follows: For each conveying path in the automobile manufacturing workshop, obtain the path workload value, path working duration, path shape complexity index, and turning complexity index respectively, and conduct comprehensive analysis in combination with the path length value of the corresponding conveying path to obtain the logistics path complexity index of the automobile manufacturing workshop; The specific steps for analyzing the air flow complexity index of an automobile manufacturing workshop are as follows: Obtain the air flow resistance value and the reference temperature value of the circulation area for each air circulation area in the automobile manufacturing workshop, and conduct comprehensive analysis in combination with the circulation area area value, circulation area wind speed value, circulation area temperature value, and the number of obstacles in the circulation area of the corresponding air circulation area to obtain the air flow complexity index of the automobile manufacturing workshop; The specific steps for analyzing the vibration impact complexity index of an automobile manufacturing workshop are as follows: Obtain the number of vibration interferences in the automobile manufacturing workshop, and conduct comprehensive analysis in combination with the vibration amplitude value, vibration frequency value, and vibration range value of each vibration source to obtain the vibration impact complexity index of the automobile manufacturing workshop.

2. The rapid physical environment modeling method for automobile manufacturing according to claim 1, characterized in that The specific steps for establishing the equipment distribution model of an automobile manufacturing workshop are as follows: Obtain the manufacturing equipment list data, manufacturing equipment location and size data, and equipment operation status data of the automobile manufacturing workshop. The equipment operation status data includes the current workload value, current cumulative operation duration value, historical maintenance times value, current vibration amplitude value, current operation temperature value, and current operation noise value of each manufacturing equipment; Based on the preset modeling steps, conduct modeling processing on the manufacturing equipment list data, manufacturing equipment location and size data, and equipment operation status data of the automobile manufacturing workshop to obtain the equipment distribution model of the automobile manufacturing workshop.

3. The rapid physical environment modeling method for automobile manufacturing according to claim 2, wherein After the equipment distribution model of the automobile manufacturing workshop is established, conduct fault prediction analysis on each manufacturing equipment. The specific steps are as follows: Obtain the operation status reference data of the automobile manufacturing workshop. The operation status reference data includes the maximum reference value of the workload, the maximum reference value of the operation duration, the maximum reference value of the maintenance times, the maximum reference value of the allowable vibration, the maximum reference value of the tolerable temperature, and the maximum reference value of the allowable noise of each manufacturing equipment; Read the equipment operation status data of the automobile manufacturing workshop, and conduct comprehensive analysis in combination with the operation status reference data to obtain the fault prediction index of each manufacturing equipment in the automobile manufacturing workshop; Respectively judge and analyze the fault prediction index of each manufacturing equipment in the automobile manufacturing workshop with the preset fault threshold interval, and regard the manufacturing equipment whose fault prediction index is outside the preset fault threshold interval as a fault anomaly, and send an abnormal alarm of the manufacturing equipment to the relevant staff; Among them, the specific formula for calculating the fault prediction index of each manufacturing equipment in the automobile manufacturing workshop is as follows: Among them, GzY u , Gz u , Gc u , Ls u , Lc u , Ws u , Wc u , Zf u , Zc u , Yw u , Yc u , Zs u , Sc u are successively the fault prediction indexes of the u-th manufacturing equipment in the automobile manufacturing workshop, the current working load value, the maximum reference value of the working load, the current cumulative operation duration value, the maximum reference value of the operation duration, the historical maintenance times value, the maximum reference value of the maintenance times, the current vibration amplitude value, the maximum allowable vibration reference value, the current operation temperature value, the maximum allowable temperature reference value, the current operation noise value, and the maximum allowable noise reference value of the u-th manufacturing equipment in the automobile manufacturing workshop. ε1, ε2, ε3, are successively the working load adjustment coefficient, the operation duration adjustment coefficient, the maintenance adjustment coefficient, the vibration amplitude adjustment coefficient, the operation temperature adjustment coefficient, and the operation noise adjustment coefficient stored in the database. u = 1, 2, 3,..., u0, where u0 is the number of manufacturing equipment in the automobile manufacturing workshop.

4. A multi-functional application device for rapid physical environment modeling in automobile manufacturing, applying the rapid physical environment modeling method for automobile manufacturing according to any one of claims 1-3, characterized in that, Including: A data acquisition unit, a parallel modeling unit, a fusion modeling unit, and a fault warning unit; The data acquisition unit is used to acquire the workshop status data of the automobile manufacturing workshop and perform preprocessing. The workshop status data includes the total volume value, the area value of each functional area, the size value of each manufacturing device, the path length value of each conveying path, the circulation area value of each air circulation area, the air velocity value in the circulation area, the temperature value in the circulation area, the number of obstacles in the circulation area, the vibration amplitude value of each vibration source, the vibration frequency value, and the vibration range value; The parallel modeling unit is used to respectively establish an initial three-dimensional space model, an equipment distribution model, a logistics path model, an air flow model, and a vibration influence model of the automobile manufacturing workshop based on the preprocessed workshop status data of the automobile manufacturing workshop by using a preset parallel processing algorithm; The fusion modeling unit is used to generate a comprehensive three-dimensional space model of the automobile manufacturing workshop based on the initial three-dimensional space model, the equipment distribution model, the logistics path model, the air flow model, and the vibration influence model of the automobile manufacturing workshop; The fault warning unit is used to perform fault prediction and analysis on each manufacturing device after the equipment distribution model of the automobile manufacturing workshop is established.

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