Automobile manufacturing physical environment rapid modeling method and multifunctional application device thereof

Abstract

The invention discloses an automobile manufacturing physical environment rapid modeling method and a multifunctional application device thereof, and relates to the technical field of environment modeling. According to the rapid modeling method for the automobile manufacturing physical environment, workshop state data of an automobile manufacturing workshop are obtained and preprocessed, and after preprocessing, 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 established based on a preset parallel processing algorithm; according to the method, the parallel processing algorithm and the real-time preprocessing of the workshop state data are introduced, so that the modeling process of the automobile manufacturing workshop can quickly respond to the change in the actual operation of the workshop; according to the method, state parameters such as equipment, paths, air circulation and vibration in the workshop can be obtained and processed in real time, and it is ensured that the model always reflects the newest physical environment of the workshop.

Description

Technical Field

[0001] The invention relates to the technical field of environmental modeling, in particular to a method for rapid modeling of a physical environment for automobile manufacturing and a multifunctional application device thereof. Background Art

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

[0003] At present, the modeling methods of many automobile manufacturing workshops still rely on traditional static design drawings and empirical planning, and lack comprehensive analysis and optimization of various complex physical factors in the workshop. These methods are often unable to meet the increasingly complex needs of modern automobile production processes, especially in the dynamic changes of workshop equipment, logistics routes, air flow, vibration effects and other factors. Traditional modeling methods are difficult to effectively provide real-time feedback and optimization solutions.

[0004] Based on the above solution, it is found that the limitations of the existing technology include at least the following problems. First, the existing modeling methods often only focus on the static workshop layout and equipment distribution design, and lack real-time response to various dynamic changing 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 in the actual operation of the workshop, which can easily lead to actual problems that are difficult to foresee and deal with during the production process. This limitation can easily lead to problems such as equipment overload, poor air flow, vibration interference, etc. that are not effectively solved in a timely manner during the operation of the workshop, which can easily lead to equipment failure, low production efficiency and unnecessary maintenance costs, thereby affecting the overall operational efficiency of the workshop. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a method for rapid modeling of the physical environment of automobile manufacturing and a multifunctional application device thereof, which solves the problem in the prior art of lacking real-time response and comprehensive analysis of dynamic changing factors in the workshop, which in turn easily leads to equipment failure, low production efficiency and increased maintenance costs.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for rapid modeling of a physical environment of automobile manufacturing, comprising the following steps: obtaining and preprocessing the workshop status data of an automobile manufacturing workshop, wherein the workshop status data includes a total volume value, an area value of each functional area, a size value of each manufacturing equipment, a path length value of each conveying path, a circulation area value of each air circulation area, a circulation area wind speed value, a circulation area temperature value, a circulation area obstacle quantity value, a vibration amplitude value, a vibration frequency value, and a vibration range value of each vibration source; 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 status data of the automobile manufacturing workshop; and 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] Furthermore, based on the preset parallel processing algorithm, the specific steps of establishing the initial three-dimensional space model, equipment distribution model, logistics path model, air flow model, and vibration impact model of the automobile manufacturing workshop are as follows: The number of processing CPU cores and the processing capacity index of each CPU core are obtained; 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, 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 are analyzed respectively, and the processing CPU cores are allocated in combination with the number of processing CPU cores and the processing capacity index of each processing CPU core; after the processing CPU cores are allocated, the initial three-dimensional space model, equipment distribution model, logistics path model, air flow model, and vibration impact model of the automobile manufacturing workshop are established in parallel.

[0008] Furthermore, the specific steps for analyzing the three-dimensional spatial complexity index of the automobile manufacturing workshop are as follows: The regional shape complexity index of each functional area in the automobile manufacturing workshop is obtained, and a comprehensive analysis is performed based on 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. The specific formula is as follows: ;in, is the three-dimensional spatial complexity index of the automobile manufacturing workshop, is the total volume of the automobile manufacturing workshop, For the first time in the automobile manufacturing workshop The area value of each functional area, For the first time in the automobile manufacturing workshop The regional shape complexity index of the functional area, is the regional complexity adjustment coefficient stored in the database, is the space volume adjustment coefficient stored in the database, =1, 2, 3, …, , It is the number of functional areas in the automobile manufacturing workshop.

[0009] Furthermore, 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, the number of adjacent manufacturing equipment within the set range is identified, and the average distance index within the set range of each manufacturing equipment is analyzed; the number of equipment interaction effects in the automobile manufacturing workshop is obtained, and combined with the average distance index within the set range of each manufacturing equipment in the automobile manufacturing workshop, a comprehensive analysis is performed to obtain the equipment distribution complexity index of the automobile manufacturing workshop, and the specific formula is as follows: ;in, is the equipment distribution complexity index of the automobile manufacturing workshop, For the first time in the automobile manufacturing workshop The size of the manufacturing equipment, For the first time in the automobile manufacturing workshop The average distance index within the set range of the manufacturing equipment, is the number of equipment interactions in the automobile 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, …, , The number of manufacturing equipment in the automobile manufacturing workshop.

[0010] Furthermore, the specific steps for analyzing the logistics path complexity index of the automobile manufacturing workshop are as follows: for each transportation path in the automobile manufacturing workshop, the path workload value, path working time, path shape complexity index and turning complexity index are obtained respectively, and the path length values ​​of the corresponding transportation paths are combined for comprehensive analysis to obtain the logistics path complexity index of the automobile manufacturing workshop.

[0011] Furthermore, the specific steps for analyzing the air flow complexity index of the automobile manufacturing workshop are as follows: obtaining the air flow resistance value and the circulation area temperature parameter value of each air circulation area in the automobile manufacturing workshop, and combining the circulation area area value, circulation area wind speed value, circulation area temperature value, and circulation area obstacle quantity value of the corresponding air circulation area for comprehensive analysis to obtain the air flow complexity index of the automobile manufacturing workshop.

[0012] Furthermore, the specific steps for analyzing the vibration influence complexity index of the automobile manufacturing workshop are as follows: obtaining the value of the vibration interference quantity in the automobile manufacturing workshop, and performing a comprehensive analysis based on the vibration amplitude value, vibration frequency value, and vibration range value of each vibration source to obtain the vibration influence complexity index of the automobile manufacturing workshop.

[0013] Furthermore, the specific steps to establish the equipment distribution model of the automobile manufacturing workshop are as follows: Obtaining manufacturing equipment list data, manufacturing equipment location and size data, and equipment operation status data of the automobile manufacturing workshop, wherein the equipment operation status data includes the current workload value, the current accumulated operation time value, the historical maintenance number value, the current vibration amplitude value, the current operation temperature value, and the current operation noise value of each manufacturing equipment; Based on the preset modeling steps, the manufacturing equipment list data, manufacturing equipment location and size data, and equipment operation status data of the automobile manufacturing workshop are modeled and processed to obtain the equipment distribution model of the automobile manufacturing workshop.

[0014] Furthermore, after the equipment distribution model of the automobile manufacturing workshop is established, the fault prediction analysis is performed on each manufacturing equipment. The specific steps are as follows: The operating status parameter data of the automobile manufacturing workshop is obtained, and the operating status parameter data includes the maximum parameter value of the workload, the maximum parameter value of the operating time, the maximum parameter value of the maintenance times, the maximum parameter value of the allowable vibration, the maximum parameter value of the withstand temperature, and the maximum parameter value of the allowable noise of each manufacturing equipment; the equipment operating status data of the automobile manufacturing workshop is read, and a comprehensive analysis is performed in combination with the operating status parameter data to obtain the fault prediction index of each manufacturing equipment in the automobile manufacturing workshop; the fault prediction index of each manufacturing equipment in the automobile manufacturing workshop is judged and analyzed with the preset fault threshold interval respectively, and the manufacturing equipment whose fault prediction index is outside the preset fault threshold interval is regarded as a fault abnormality, and a manufacturing equipment abnormality alarm is sent to relevant staff; wherein, the specific formula for calculating the fault prediction index of each manufacturing equipment in the automobile manufacturing workshop is as follows: ;in, For the first time in the automobile manufacturing workshop Failure prediction index of manufacturing equipment, For the first time in the automobile manufacturing workshop The current workload value of the manufacturing equipment, For the first time in the automobile manufacturing workshop The maximum parameter value of the workload of a manufacturing equipment, is the workload adjustment factor stored in the database, For the first time in the automobile manufacturing workshop The current cumulative operating time value of the manufacturing equipment, For the first time in the automobile manufacturing workshop The maximum operating time of a manufacturing device. is the runtime adjustment factor stored in the database. For the first time in the automobile manufacturing workshop The historical maintenance times of the manufacturing equipment, For the first time in the automobile manufacturing workshop The maximum maintenance parameter value of a manufacturing equipment. is the maintenance adjustment factor stored in the database, For the first time in the automobile manufacturing workshop The current vibration amplitude value of a manufacturing device, For the first time in the automobile manufacturing workshop The maximum permissible vibration parameter value of a manufacturing equipment, is the vibration amplitude adjustment coefficient stored in the database, For the first time in the automobile manufacturing workshop The current operating temperature value of the manufacturing equipment, For the first time in the automobile manufacturing workshop The maximum temperature parameter value of the manufacturing equipment is is the operating temperature adjustment coefficient stored in the database, For the first time in the automobile manufacturing workshop The current operating noise value of the manufacturing equipment, For the first time in the automobile manufacturing workshop The maximum permissible noise parameter value of a manufacturing device, is the operating noise adjustment coefficient stored in the database, =1, 2, 3, …, , The number of manufacturing equipment in the automobile manufacturing workshop.

[0015] A multifunctional application device for rapid modeling of the physical environment of 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, wherein the workshop status data includes a total volume value, an area value of each functional area, a size value of each manufacturing equipment, a path length value of each conveying path, a circulation area value of each air circulation area, a circulation area wind speed value, a circulation area temperature value, a circulation area obstacle quantity value, a vibration amplitude value, a vibration frequency value, and a vibration range value of each vibration source; The parallel modeling unit is used to 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 pre-processed workshop state data of the automobile manufacturing workshop based on 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 of the automobile manufacturing workshop, the equipment distribution model, the logistics path model, the air flow model, and the vibration impact model; The fault early 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.

[0016] The present invention has the following beneficial effects: (1) This method for rapid modeling of the physical environment of automobile manufacturing introduces parallel processing algorithms and real-time preprocessing of workshop status data, so that 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 status parameters of each device, path, air circulation and vibration in the workshop in real time, ensuring that the model always reflects the latest physical environment of the workshop. Unlike 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 adjusting production scheduling in real time. This dynamic and real-time modeling method effectively reduces the downtime in the production process and improves the production efficiency of the workshop.

[0017] (2) The rapid modeling method for the physical environment of automobile manufacturing can calculate the equipment's failure prediction index in real time and evaluate the failure risk of each device based on the comparison of various operating status data, such as workload, operating time, vibration amplitude, temperature, etc., with the maximum allowable value. By combining the equipment's operating data with the failure threshold, the method can predict the possible failure of the equipment in advance and issue an early warning, thereby helping the production line to perform equipment maintenance in a timely manner. Combined with the equipment distribution model, air flow model and logistics path model, it can also optimize resource allocation and scheduling to avoid failures caused by equipment overload and inappropriate environmental conditions. Specifically, the method can improve equipment utilization, reduce production interruptions caused by equipment failures, and reduce maintenance costs.

[0018] (3) The rapid modeling method of the physical environment of automobile manufacturing considers the comprehensive impact of multiple factors in the workshop, such as equipment distribution, logistics path, air flow, vibration impact, etc., and dynamically analyzes the impact of these factors on the overall production of the workshop during the modeling process. For example, by analyzing the interaction between equipment, it can predict possible interference between equipment and optimize their 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 environmental factors such as temperature and humidity from affecting equipment performance. This multi-factor analysis modeling method can help the workshop achieve global optimization and improve resource utilization, thereby improving overall production efficiency and reducing material and energy waste.

[0019] (4) The multifunctional application device for rapid modeling of the physical environment 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. The device can acquire and process various types of data in the workshop in real time, dynamically generate various models of the workshop, and comprehensively analyze factors such as equipment status, logistics path, air flow, vibration impact, etc., and then generate a comprehensive integrated three-dimensional space model. Through the fault prediction and alarm function of the equipment, the device can identify potential faults in advance and remind operators, thereby reducing downtime and production losses. Based on these data, workshop managers can make more scientific decisions based on a comprehensive understanding of 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.

[0020] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a flow chart of the method for rapid modeling of the physical environment of automobile manufacturing according to the present invention.

[0022] Figure 2 The present invention is a flowchart of the specific steps for analyzing the equipment distribution complexity index of an automobile manufacturing workshop in the automobile manufacturing physical environment rapid modeling method of the present invention.

[0023] Figure 3 A block diagram of a multifunctional application device for rapid modeling of the physical environment of automobile manufacturing according to the present invention. DETAILED DESCRIPTION

[0024] See also Figure 1The embodiment of the present invention provides a technical solution: a method for rapid modeling of a physical environment of automobile manufacturing, comprising the following steps: obtaining and preprocessing the workshop status data of an automobile manufacturing workshop, wherein the workshop status data includes a total volume value, an area value of each functional area, a size value of each manufacturing equipment, a path length value of each conveying path, a circulation area value of each air circulation area, a circulation area wind speed value, a circulation area temperature value, a circulation area obstacle quantity value (i.e., buildings or equipment higher than a set height are regarded as obstacles), a vibration amplitude value, a vibration frequency value, and a vibration range value (i.e., the area where the vibration range is located) of each vibration source; 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 status data of the automobile manufacturing workshop; and 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.

[0025] Specifically, based on the preset parallel processing algorithm, the workshop status data of the automobile manufacturing workshop is preprocessed, and the specific steps of establishing the initial three-dimensional space model, equipment distribution model, logistics path model, air flow model, and vibration impact model of the automobile manufacturing workshop are as follows: obtain the number of processing CPU cores and the processing capacity 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, vibration impact index of the automobile manufacturing workshop The complexity index is calculated, and the number of processing CPU cores and the processing capacity index of each processing CPU core are combined to allocate the processing CPU cores. The specific steps are: reading the processing capacity index of each processing CPU core and the complexity index of each model; calculating the resource allocation coefficient of each processing CPU core for each model, and summing and analyzing the resource allocation coefficients to obtain the number of processing CPU cores required for each model, and if the processing CPU core value is a decimal, it will be rounded down, where the resource allocation coefficient of each processing CPU core for each model is specifically the ratio of each processing CPU core to the complexity index of each model.

[0026] The specific steps for obtaining the processing power index of the CPU core are as follows: first, consider the basic computing power of each CPU core, which can usually 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 core's cache size; secondly, combined with the core's hardware configuration, manufacturing process and performance optimization (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.

[0027] After processing the CPU core allocation, the initial three-dimensional space model of the automobile manufacturing workshop, equipment distribution model, logistics path model, air flow model, and vibration impact model are established in parallel.

[0028] Among them, the initial three-dimensional space model of the automobile manufacturing workshop includes: Total area and volume of the workshop: includes the overall space of the workshop, as well as the space of each functional area (such as production line, storage area, office area, etc.).

[0029] Spatial layout: Display the layout of each area in the workshop and the relative positions between different functional areas.

[0030] Physical constraints: including physical elements such as building structure, walls, doors and windows, to ensure that the workshop space meets the actual design requirements.

[0031] The specific steps to establish it are as follows: Obtain workshop design drawings or CAD drawings: Obtain the floor plan of the workshop as the basic data for the space model.

[0032] Space division: According to the division of functional areas, the workshop space is divided into different areas, such as production area, storage area, office, etc.

[0033] Space volume calculation: Calculate the volume of each area of ​​the workshop based on its dimensions.

[0034] 3D Modeling: Use modeling software (such as AutoCAD, Revit or SolidWorks) to create a three-dimensional model, taking into account the structure, walls, ceilings, etc. in different areas.

[0035] Modeling accuracy verification: Ensure that the 3D model accurately reflects the actual physical environment of the workshop and accurately represents all building elements (such as doors, windows, walls, etc.).

[0036] Among them, the logistics path model of the automobile manufacturing workshop includes: Conveying path: The path of materials from one location to another in the workshop, including conveyor belts, forklift transport routes, manual handling channels, etc.

[0037] Path length and shape: The length of each path, the angle of turns, and the shape of the path (such as straight or curved).

[0038] Path workload and capacity: the transportation capacity of each path and the amount of materials that can be transported per unit time.

[0039] The specific steps to establish it are as follows: Obtain path planning data: Obtain path planning data for material transportation within the workshop, including path types and connection methods between paths.

[0040] Calculate path length and shape complexity: Calculate the length and curvature of each path and evaluate the complexity of the path.

[0041] Logistics path allocation: According to the production task volume and equipment requirements, the path load is reasonably allocated to avoid path overload.

[0042] Path model establishment: Use modeling software (such as CAD, Arena) to establish a three-dimensional model of the logistics path to accurately reflect the layout of the path.

[0043] Among them, the air flow model of the automobile manufacturing workshop includes: Air flow path: the path and direction of air flow, including air conditioning, ventilation systems, etc.

[0044] Circulation area: area of ​​air flow and wind speed distribution.

[0045] Temperature and humidity distribution: The temperature distribution in the air flow area, especially the influence of heat and cold sources.

[0046] The specific steps to establish it are as follows: Get data on workshop ventilation systems: Get data on workshop air conditioning, ventilation equipment, and air flow systems.

[0047] Flow path design: Design a reasonable air flow path based on the workshop layout to ensure that the circulation area can cover all key working areas.

[0048] Temperature and humidity simulation: Use CFD (computational fluid dynamics) software to simulate the changes in temperature and humidity during air flow.

[0049] 3D modeling: Combine the three-dimensional spatial model of the workshop to build an air flow model to ensure that the air flow is compatible with the equipment and worker areas.

[0050] Among them, the vibration impact model of the automobile manufacturing workshop includes: Vibration source location and amplitude: location of equipment vibration source, vibration amplitude, vibration frequency, etc.

[0051] Vibration propagation path: the path and range of vibration propagation.

[0052] Impact of vibration interference: Vibration interference between different devices affects the accuracy and stability of the equipment.

[0053] The specific steps to establish it are as follows: Obtain equipment vibration data: Obtain data such as vibration amplitude and frequency for each piece of manufacturing equipment.

[0054] Vibration propagation modeling: Use finite element analysis (FEA) and other methods to simulate the propagation path and impact range of vibration in the workshop.

[0055] Vibration interference analysis: Analyze the possible vibration interference between devices based on the spatial position and vibration data between the devices.

[0056] 3D modeling and vibration source calibration: Combine the 3D model of the workshop, calibrate the vibration source and the affected area, and generate a vibration impact model.

[0057] In this implementation scheme, the modeling efficiency and accuracy of the automobile manufacturing workshop are significantly improved through comprehensive analysis based on parallel processing algorithms and workshop status data. First, by obtaining the workshop status data and preprocessing it, real-time monitoring of various dynamic factors in the workshop can be achieved, including spatial layout, equipment distribution, logistics path, air flow, vibration impact, etc. After preprocessing, these data can help to 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 used to establish multiple models to ensure that the modeling task of the complex workshop environment is completed in a short time. In particular, on the basis of efficient allocation of CPU core resources, it can reasonably allocate computing resources according to the complexity index of each model to ensure that the computing tasks of each model are efficiently processed, reducing the modeling time and improving the system responsiveness.

[0058] Specifically, the specific steps for analyzing the three-dimensional space complexity index of an automobile manufacturing workshop are as follows: obtain the regional shape complexity index of each functional area in the automobile manufacturing workshop, and conduct a comprehensive analysis based on 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; wherein, the regional shape complexity index is calculated based on a ratio analysis of the boundary length and area of ​​the region, and the boundary length refers to the total length of the edges around the region. For example, if the region is a rectangle or a polygon, the boundary is the sum of all sides of the shape, and this value can be obtained by direct measurement or by calculation of the regional graphics.

[0059] The specific formula for calculating the three-dimensional space complexity index is as follows: ;in, is the three-dimensional spatial complexity index of the automobile manufacturing workshop, is the total volume of the automobile manufacturing workshop, For the first time in the automobile manufacturing workshop The area value of each functional area, For the first time in the automobile manufacturing workshop The regional shape complexity index of the functional area, is the regional complexity adjustment coefficient stored in the database, is the space volume adjustment coefficient stored in the database, =1, 2, 3, …, , It is the number of functional areas in the automobile manufacturing workshop.

[0060] It should be explained that the regional complexity adjustment coefficient stored in the database The specific steps for obtaining are: first, according to the actual functional area division of the workshop, such as production area, storage area, office, etc., calculate the ratio of the boundary length to the area of ​​each area, and determine the shape complexity of each area. Then, by comparing the historical data and the area usage in the actual operation of the workshop, combined with the design specifications and empirical rules, optimize and adjust these coefficients to better reflect the actual impact of regional complexity. Finally, the regional complexity adjustment coefficients stored in the database will be updated according to these calculation results.

[0061] Space volume adjustment factors stored in the database The specific steps for obtaining are: first, collect the total volume data of the workshop and analyze the functions and usage frequencies of different areas in the workshop; then, by comparing the designed space of the workshop with the actual used space, combined with the changes in production tasks and the adjustments to the equipment layout, determine the actual utilization 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 space complexity.

[0062] In this implementation scheme, 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, the regional shape complexity index is used to measure the shape complexity of each functional area. The index is calculated by analyzing the ratio of the boundary length and area of ​​the area. The boundary length reflects the complexity of the spatial shape and can reveal the physical connection and space utilization efficiency between different functional areas. Combined with the total volume of the workshop and the area data of each functional area, the three-dimensional space complexity index is finally obtained to provide a quantitative evaluation for the spatial layout of the workshop. This method not only takes into account the actual space occupancy of each functional area, but also combines the complexity of the regional shape, which can comprehensively reflect the use of the workshop space. Through the stored regional 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, which helps to discover potential space waste and unreasonable layout problems, thereby optimizing the workshop design and improving production efficiency and space utilization.

[0063] Specifically, Figure 2 As shown in FIG. 1 , 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, the number of adjacent manufacturing equipment within a set range is identified respectively, and the average distance index within the set range of each manufacturing equipment is analyzed (that is, the distance value between the manufacturing equipment and each adjacent manufacturing equipment is obtained by performing a mean analysis); the number of equipment interaction effects in the automobile manufacturing workshop is obtained, and a comprehensive analysis is performed 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; wherein, the number of equipment interaction effects in the automobile manufacturing workshop refers to the situation in which different equipment in the workshop has a certain degree of mutual dependence or influence due to the function, workflow, physical space or other operation requirements of the equipment. The interaction effect can be manifested at multiple levels, such as functional dependence between equipment, equipment shared space, or physical interference (such as vibration, noise, airflow, etc.) that may be generated between equipment during operation.

[0064] The number of device interactions that affect the number of devices includes: Functional dependency number: Some equipment may need to cooperate with each other during the work process, such as equipment on a production line. Some equipment must wait until the previous equipment completes a certain operation before it can start running. In this case, there is a functional interaction between these equipment. The specific steps to obtain it are: First, the function of each manufacturing equipment must be understood. For example, some equipment may depend on the working status of other equipment (for example, welding equipment can only be used after the stamping equipment completes its work). Build 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 at the same time, the functional dependency between them needs to be marked. This relationship is determined by analyzing the equipment's workflow and production scheduling information.

[0065] Number of shared spaces: Multiple devices may be located in the same area of ​​a workshop. Interference between devices is likely to occur in a small area, and there may be problems such as collisions between devices and channel congestion. The specific steps for obtaining the layout are as follows: Obtain the equipment layout (equipment layout drawings or actual measurement data), which is the basis for determining the location of the equipment. The coordinates, occupied area and relative position of each device with other devices need to be obtained. By analyzing the physical distance between the devices, it is determined whether they are in the same area. For devices that are close to each other, there may be a risk of spatial interference or collision.

[0066] Number of physical interferences: The vibration, noise, heat or airflow generated by the equipment when it is working may affect the surrounding equipment. For example, the high-frequency vibration of a machine may affect the precision equipment nearby, or the airflow of an air conditioner may affect the working environment of the surrounding equipment. The physical parameters of the equipment such as vibration, noise, airflow and other parameters can be obtained through the equipment manual, sensor data in the production process or environmental monitoring system. Finite element analysis (FEA) or other physical modeling methods are used to calculate the propagation range and impact of vibration, heat or airflow. If the vibration range of device A overlaps with the working accuracy range of device B, there is physical interference between the two devices.

[0067] The specific formula for calculating the device distribution complexity index is as follows: ;in, is the equipment distribution complexity index of the automobile manufacturing workshop, For the first time in the automobile manufacturing workshop The size of the manufacturing equipment, For the first time in the automobile manufacturing workshop The average distance index within the set range of the manufacturing equipment, is the number of equipment interactions in the automobile 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, …, , The number of manufacturing equipment in the automobile manufacturing workshop.

[0068] It should be explained that the size distance adjustment coefficient stored in the database The specific steps for obtaining the coefficient are as follows: first, the actual size, floor space and relative position data of the equipment need to be collected; by combining the actual space requirements of the workshop, the reasonable spacing between the equipment is calculated; and the distance between the equipment is adjusted based on the size and working requirements of the equipment. Usually, the acquisition of the size distance adjustment coefficient also takes into account the equipment's workload, workspace requirements and production tasks of the workshop to ensure that the equipment can be reasonably laid out without affecting production efficiency, thereby optimizing space utilization and equipment operating efficiency.

[0069] Device interaction adjustment coefficients stored in the database The specific steps for obtaining the information are as follows: by analyzing the workshop layout and equipment operating status, the interdependencies between equipment and possible sources of interference can be identified. For example, some equipment may affect each other during operation, such as the vibration of one device may affect the accuracy of another device, or multiple devices occupying a small area together 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.

[0070] In this implementation scheme, 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 analyzes the average distance between each device and the adjacent equipment, and combines the functional dependence, space sharing and physical interference of the equipment to fully reflect the interaction relationship between the equipment. This process helps to identify the interdependence between the equipment, especially the equipment interference that may be caused by functional dependence and space sharing, and then provides data support for optimizing equipment layout and resource allocation. By calculating the number of equipment interaction effects, this method can identify the physical interference that may be caused during the operation of the equipment, such as vibration, noise, airflow, etc. These factors often affect the working accuracy 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, reduce unnecessary interference and conflict, and improve the stability of the equipment and the overall efficiency of the production line. In addition, by introducing the size distance adjustment coefficient and the equipment interaction adjustment coefficient in the scheme, the complexity index can be flexibly adjusted according to the specific needs of the workshop, so that the equipment layout can better adapt to the requirements of different production tasks, further improve production efficiency and reduce 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 improving productivity.

[0071] Specifically, the specific steps for analyzing the logistics path complexity index of an automobile manufacturing workshop are as follows: for each transportation path in the automobile manufacturing workshop, the path workload value, path working time, path shape complexity index and turning complexity index are obtained respectively, and the path length values ​​of the corresponding transportation paths are combined for comprehensive analysis to obtain the logistics path complexity index of the automobile manufacturing workshop; wherein, the path workload value represents the amount of materials or transportation tasks undertaken by the path in unit time.

[0072] The path working time indicates the total time required for path delivery, taking into account the workload period.

[0073] The path shape complexity index is obtained by analyzing the ratio of the path boundary length to the effective area of ​​the path. The boundary length and effective area are obtained through path planning tools or workshop design drawings. If the shape of the path is complex, this value is higher, indicating that the path is tortuous.

[0074] The turning complexity index refers to the sum of the angle values ​​of each turn in the path. Each turning angle in the path is calculated through the geometric design of the path, or the curve information in the path is obtained through sensor data.

[0075] The specific formula for calculating the logistics path complexity index is as follows: ;in, is the logistics path complexity index of the automobile manufacturing workshop, For the first time in the automobile manufacturing workshop The path length value of the conveying path, For the first time in the automobile manufacturing workshop The path shape complexity index of the conveying path, For the first time in the automobile manufacturing workshop The turning complexity index of the conveying path, For the first time in the automobile manufacturing workshop The path workload value of the transport path, For the first time in the automobile manufacturing workshop The working time of each conveying path, The path work adjustment factor stored in the database, is the logistics path complexity adjustment coefficient stored in the database, =1, 2, 3, …, , It is the number of conveying paths in the automobile manufacturing workshop.

[0076] It should be explained that the path work adjustment coefficient stored in the database The specific steps for obtaining the coefficient are as follows: The acquisition of this coefficient requires a detailed analysis of the logistics system of the workshop, including the specific workload, workload and working hours of each transportation path. Through the analysis of historical data, changes in production workload and actual path usage, the path work adjustment coefficient can be flexibly adjusted according to the workload of different paths. The specific steps for obtaining the coefficient include collecting the usage data of each path, and obtaining the optimized coefficient by comparing and analyzing the historical workload and actual usage, so that each path can reasonably distribute the load according to its actual working status to ensure the smooth operation of the logistics system.

[0077] Logistics path complexity adjustment coefficient stored in the database The specific steps for obtaining are: first, all logistics paths in the workshop need to be measured, and the path length, tortuosity, turning angle and other parameters need to be calculated to obtain the complexity of the path. Then, by analyzing the logistics needs of the workshop, the equipment distribution and the actual situation of the transportation task, the specific adjustment coefficient of each path can be obtained. These adjustment coefficients will be flexibly adjusted according to the path shape, complexity and other actual production conditions, so as to better reflect the actual complexity of the path. Finally, through the storage of the database, the complexity adjustment coefficient of the logistics path can be updated and dynamically optimized in real time, which helps to improve the efficiency of material transportation and reduce unnecessary material handling time.

[0078] In this implementation scheme, by calculating the logistics path complexity index of the automobile manufacturing workshop, the method can comprehensively evaluate the complexity of the logistics path in the workshop, and then optimize material transportation and production efficiency. First, the method provides a comprehensive evaluation of the path by analyzing the workload, working time, path shape complexity and turning complexity of each conveying path. The path workload value and working time can reflect the density and time requirements of material transportation on the path, thereby helping to determine the workload and running time of the path, and then reasonably allocate resources to avoid path overload and resource waste. The path shape complexity index and the 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 the production line with more complex 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 logistics path complexity index after comprehensive analysis provides data support for the material flow of the workshop, helps to optimize the path layout, reduce transportation time, improve overall production efficiency, and reduce production costs.

[0079] 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 flow area temperature parameter value of each air flow area in the automobile manufacturing workshop, and respectively combine the flow area area value, flow area wind speed value, flow area temperature value, and flow area obstacle quantity value of the corresponding air flow area for comprehensive analysis 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: ;in, is the air flow complexity index of the automobile manufacturing workshop, For the first time in the automobile manufacturing workshop The area value of the air circulation area is For the first time in the automobile manufacturing workshop The wind speed value of the air circulation area in each air circulation area, For the first time in the automobile manufacturing workshop The air flow resistance value of each air circulation area, is the wind speed adjustment coefficient stored in the database, For the first time in the automobile manufacturing workshop The number of obstacles in the air circulation area of ​​each air circulation area, is the obstacle adjustment coefficient stored in the database, For the first time in the automobile manufacturing workshop The circulation area temperature value of each air circulation area, For the first time in the automobile manufacturing workshop The circulation area temperature parameter value of each air circulation area, is the temperature adjustment coefficient stored in the database, =1, 2, 3, …, , It is the number of air circulation areas in the automobile manufacturing workshop.

[0080] It should be explained that the wind speed adjustment factor stored in the database The specific steps to obtain it are: collect wind speed data from each air circulation area in the workshop, and adjust it 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, equipment layout and production needs of the workshop. By analyzing the air flow conditions, equipment heat generation and ventilation needs in different areas, the appropriate wind speed adjustment coefficient is obtained. Usually, the wind speed adjustment coefficient also takes into account factors such as seasonal changes, workshop space utilization rate and air flow interference between areas.

[0081] Obstacle adjustment factors stored in the database The specific steps for obtaining are: first, analyze the layout of each air circulation area in the workshop, including the distribution of equipment and structures; then, simulate or measure the number and type of obstacles in the workshop (such as walls, equipment, shelves, etc.) that hinder the air flow, and make adjustments based on the actual ventilation needs of the workshop. Usually, the obstacle adjustment coefficient is dynamically calculated based on the number and arrangement of equipment and the degree of their impact on air flow, thereby improving air flow efficiency and reducing energy waste.

[0082] Temperature adjustment factors stored in the database The specific acquisition steps are: first, collect the temperature data of each air circulation area in the workshop, and analyze the impact of temperature fluctuations on the production environment; secondly, calculate the temperature distribution of each area based on factors such as the heat generated by the equipment and the ventilation system in the workshop, 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, so as to maintain a suitable working temperature in the workshop and ensure the comfort and safety of equipment and personnel.

[0083] In this implementation scheme, by calculating the air flow complexity index of the automobile manufacturing workshop, the method can optimize the design of the air circulation system in the workshop and improve the air quality and equipment operation efficiency. First, the method comprehensively considers multiple factors of 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 be at risk 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 of poor air quality on equipment and worker health. By introducing wind speed adjustment coefficients, obstacle adjustment coefficients and temperature adjustment coefficients, the scheme can flexibly respond to the needs of different workshop environments and optimize for different environmental conditions. These coefficients are adjusted according to the design and operating conditions of the specific workshop to ensure that the model is more in line with the actual situation and improve the adaptability and accuracy of air flow.

[0084] Specifically, the specific steps for analyzing the vibration influence complexity index of an automobile manufacturing workshop are as follows: obtain the number of vibration interference values ​​in the automobile manufacturing workshop, and conduct a comprehensive analysis based on the vibration amplitude value, vibration frequency value, and vibration range value of each vibration source to obtain the vibration influence complexity index of the automobile manufacturing workshop.

[0085] The vibration interference value refers to the total amount of interference between vibration sources in the workshop. It measures the mutual influence of multiple vibration sources and is usually related to the following factors: Frequency interference: When the frequencies of multiple vibration sources are close, they may produce resonance or superposition effects, enhancing the vibration impact of each other. This interference is the most common impact between vibration sources.

[0086] Spatial overlap: If the influence ranges of multiple vibration sources overlap, the vibration intensity of the entire area may be increased, resulting in increased interference.

[0087] Relative position and working status: The working status and relative position of devices will affect the interference between them. For example, if two devices are in the same area of ​​the workshop or are physically close to each other, they may produce stronger interference.

[0088] The calculation steps to obtain the vibration disturbance quantity are: Determine the area of ​​influence of each vibration source, which can be estimated based on the power, frequency and environmental characteristics of the vibration source.

[0089] The affected area can be estimated by a propagation model of the vibration source, for example using finite element analysis (FEA) or other simulation methods to calculate the vibration propagation range.

[0090] All vibration sources are grouped according to spatial position and frequency, and pairs of vibration sources that may interfere with each other are identified.

[0091] For each pair of potentially interfering vibration sources, if their frequencies overlap or their influence areas overlap, then interference is considered to exist between them.

[0092] If the impact areas of a pair of interference sources overlap and their frequencies are close, their degree of interference can be calculated. This value is usually calculated based on the vibration amplitude, frequency difference and physical location.

[0093] The specific formula for calculating the vibration impact complexity index is as follows: ;in, is the vibration impact complexity index of the automobile manufacturing workshop, For the first time in the automobile manufacturing workshop The vibration amplitude value of each vibration source, For the first time in the automobile manufacturing workshop The vibration frequency value of a vibration source, For the first time in the automobile manufacturing workshop The vibration range value of each vibration source, is the value of vibration disturbance in automobile manufacturing workshop, is the vibration interference adjustment coefficient stored in the database, is the vibration impact adjustment coefficient stored in the database, =1, 2, 3, …, , is the number of vibration sources in the automobile manufacturing workshop.

[0094] It should be explained that the vibration interference adjustment coefficient stored in the database 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 source; then, calculate the possible vibration interference intensity by measuring the mutual interference between equipment and combining the distance, relative position and working status between equipment; further, use finite element analysis (FEA) and other methods to evaluate the vibration propagation path and impact range, and obtain the vibration interference coefficient between equipment or areas; finally, adjust these coefficients to reflect the actual interference situation, so as to ensure that the impact of vibration in the workshop on equipment and production environment is minimized.

[0095] Vibration effect adjustment factors stored in the database The specific steps for obtaining are: first, analyze the vibration sensitivity of each equipment in the workshop, including the equipment's operating frequency range, vibration tolerance and environmental conditions; then, measure the equipment's vibration response through simulation or experiment to understand the equipment's response characteristics to different vibration intensities; further, calculate the vibration tolerance of each device based on the equipment's working status, location and vibration source in the workshop, and obtain the corresponding vibration impact adjustment coefficient; finally, these coefficients are stored in the database for dynamic adjustment and optimization of the anti-vibration design and operating status of the equipment in the workshop.

[0096] In this implementation scheme, by calculating the vibration impact complexity index of the automobile manufacturing workshop, the method can effectively evaluate the impact of the vibration source in the workshop on the equipment and production environment, and then optimize the workshop layout and equipment configuration. First, the method comprehensively considers multiple factors such as the number of vibration interferences, vibration amplitude, frequency, range, etc., and comprehensively analyzes the mutual interference between the vibration sources. This makes it possible to identify the vibration sources that may cause resonance or superposition effects, adjust the equipment position or working state in time, reduce interference between equipment, and improve working accuracy and stability. By analyzing the impact area of ​​the vibration source, the equipment within the vibration propagation range can be effectively identified, and the vibration propagation path can be simulated using tools such as finite element analysis (FEA), so as to accurately predict the impact of vibration on the equipment. For vibration sources that may interfere with each other, by calculating their interference level, it can provide strong support for workshop design and avoid equipment failure or production interruption caused by vibration interference.

[0097] Specifically, the specific steps for establishing an equipment distribution model for an automobile manufacturing workshop are as follows: obtaining the manufacturing equipment inventory 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), manufacturing equipment location and size data (including the actual floor space, size, and relative position of each manufacturing equipment with other equipment, which can be determined through workshop layout drawings or actual measurement data), and equipment operating status data. The equipment operating status data includes the current workload value, current cumulative operating time value, historical maintenance count value, current vibration amplitude value, current operating temperature value, and current operating noise value of each manufacturing equipment. These values ​​can be obtained through a real-time monitoring system, sensors, or equipment management software.

[0098] Based on the preset modeling steps, the manufacturing equipment list data, manufacturing equipment location and size data, and equipment operation status data of the automobile manufacturing workshop are modeled and processed to obtain the equipment distribution model of the automobile manufacturing workshop, including: Equipment position calibration: Task: According to the workshop floor plan and equipment list, determine the specific position of each manufacturing equipment in three-dimensional space, the relative position relationship of each manufacturing equipment, the distance between equipment, and interdependent equipment.

[0099] Tools: Use CAD tools, BIM (Building Information Modeling) software or 3D modeling software (such as SolidWorks, AutoCAD) for spatial modeling.

[0100] Equipment footprint and space optimization: Task: Calculate the occupied area of ​​each manufacturing equipment based on the equipment's size data, and reasonably allocate equipment locations to avoid equipment being too dense or overlapping, ensuring the smoothness of the production line and the movement of personnel and materials.

[0101] Tools: Use space layout optimization tools and material flow analysis software to optimize the design.

[0102] Analysis of the interaction relationship between devices: Task: Analyze the interdependencies between equipment, including space sharing, functional dependencies, etc., especially equipment on a production line, which may need to run in a specific order.

[0103] Tool: Generate a dependency matrix between devices through production process analysis to ensure reasonable spatial distribution between devices.

[0104] In this implementation scheme, by establishing an equipment distribution model for an automobile manufacturing workshop, this method can effectively optimize the layout of equipment in the workshop, improve production efficiency and space utilization. First, the calibration of equipment positions and the acquisition of dimensional data enable each device to be accurately positioned in three-dimensional space, and clearly display the relative positions and interdependencies between devices. This process ensures that the location of each device in the workshop meets production requirements and avoids excessive density or conflict between equipment. Equipment footprint and space optimization analysis further ensure the reasonable layout of equipment, avoid overlap or space waste between equipment, and thus optimize the efficiency of material flow and personnel passage. By using space layout optimization tools and material flow analysis software, possible bottlenecks and conflicts can be foreseen in the early stages of design, and the layout plan can be adjusted to maximize the smoothness and flexibility of the production line.

[0105] Specifically, after the equipment distribution model of the automobile manufacturing workshop is established, a fault prediction analysis is performed on each manufacturing equipment, and the specific steps are as follows: the operating status parameter data of the automobile manufacturing workshop is obtained, and the operating status parameter data include the maximum parameter value of the workload, the maximum parameter value of the operating time, the maximum parameter value of the maintenance times (referring to the maximum value within the normal maintenance frequency range of the equipment), the maximum parameter value of the allowable vibration, the maximum parameter value of the temperature, and the maximum parameter value of the allowable noise of each manufacturing equipment; the equipment operating status data of the automobile manufacturing workshop is read, and a comprehensive analysis is performed in combination with the operating status parameter data to obtain the fault prediction index of each manufacturing equipment in the automobile manufacturing workshop; the fault prediction index of each manufacturing equipment in the automobile manufacturing workshop is judged and analyzed with the preset fault threshold range, and the manufacturing equipment whose fault prediction index is outside the preset fault threshold range is regarded as a fault abnormality, and a manufacturing equipment abnormality alarm is sent to relevant staff.

[0106] Among them, the specific formula for calculating the failure prediction index of each manufacturing equipment in the automobile manufacturing workshop is as follows: ;in, For the first time in the automobile manufacturing workshop Failure prediction index of manufacturing equipment, For the first time in the automobile manufacturing workshop The current workload value of the manufacturing equipment, For the first time in the automobile manufacturing workshop The maximum parameter value of the workload of a manufacturing equipment, is the workload adjustment factor stored in the database, For the first time in the automobile manufacturing workshop The current cumulative operating time value of the manufacturing equipment, For the first time in the automobile manufacturing workshop The maximum operating time of a manufacturing device. is the runtime adjustment factor stored in the database. For the first time in the automobile manufacturing workshop The historical maintenance times of the manufacturing equipment, For the first time in the automobile manufacturing workshop The maximum maintenance parameter value of a manufacturing equipment. is the maintenance adjustment factor stored in the database, For the first time in the automobile manufacturing workshop The current vibration amplitude value of a manufacturing device, For the first time in the automobile manufacturing workshop The maximum permissible vibration parameter value of a manufacturing equipment, is the vibration amplitude adjustment coefficient stored in the database, For the first time in the automobile manufacturing workshop The current operating temperature value of the manufacturing equipment, For the first time in the automobile manufacturing workshop The maximum temperature parameter value of the manufacturing equipment is is the operating temperature adjustment coefficient stored in the database, For the first time in the automobile manufacturing workshop The current operating noise value of the manufacturing equipment, For the first time in the automobile manufacturing workshop The maximum permissible noise parameter value of a manufacturing device, is the operating noise adjustment coefficient stored in the database, =1, 2, 3, …, , The number of manufacturing equipment in the automobile manufacturing workshop.

[0107] It should be explained that the workload adjustment factor stored in the database , Run time adjustment factor , Maintenance adjustment factor The specific acquisition steps are as follows: first, collect the workload data of each device during its life cycle, and estimate the workload adjustment coefficient by comparing the changes in equipment performance under different loads. Secondly, the operating time adjustment coefficient can be derived through the correlation between the cumulative operating 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 maintenance frequency of the equipment, and examining the impact of the frequency of equipment maintenance on its performance and failure prediction.

[0108] Vibration amplitude adjustment factors stored in the database , Operating temperature adjustment coefficient , Operation noise adjustment coefficient The specific steps for obtaining are as follows: the vibration amplitude adjustment coefficient is obtained by monitoring the vibration level of the equipment during operation, combining factors such as the equipment type and operating environment, and evaluating its impact on the equipment performance. The operating temperature adjustment coefficient is calculated by continuously monitoring the operating temperature changes of the equipment, combined with the temperature tolerance and working efficiency of the equipment. The operating noise adjustment coefficient is determined by analyzing the operating conditions of the equipment under different noise levels, determining the potential impact of noise on equipment performance, and optimizing the equipment's operation management based on the noise level adjustment coefficient.

[0109] In this implementation scheme, by performing fault prediction analysis on the equipment distribution model of 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, combined with the equipment's operating status parameter data (such as maximum workload, maximum operating time, maintenance times, allowable vibration, temperature tolerance and allowable noise, etc.), it can comprehensively evaluate the gap between the operating status of each device and the preset standard, thereby obtaining the fault prediction index of each device. This quantitative fault prediction model can identify the possible failure risks of the equipment in advance and provide a basis for timely maintenance and scheduling. By comparing and analyzing the equipment's fault prediction index with the preset fault threshold interval, it can automatically identify high-risk equipment and immediately issue a fault warning to remind relevant staff to perform maintenance. This intelligent warning mechanism can effectively reduce unexpected equipment shutdowns and sudden failures, and ensure the continuity and stability of the production line.

[0110] See also Figure 3The embodiment of the present invention provides a technical solution: a multifunctional application device for rapid modeling of a physical environment of automobile manufacturing, comprising: 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 including 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 planning map or design drawing of the workshop), the size value of each manufacturing equipment (which can be obtained from the technical documentation 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 map or design drawing of the workshop), the circulation area area value of each air circulation area (which can be obtained from the ventilation system design drawing of the workshop), the wind speed value of the circulation area (which can be obtained from the air conditioning system of the workshop or the data of the ventilation system), the temperature value of the circulation area (which can be obtained from the air conditioning system of the workshop), the number of obstacles in the circulation area The value (that is, buildings or equipment higher than the set height are regarded as obstacles, which can be obtained from the workshop layout diagram), the vibration amplitude value of each vibration source (can be obtained through a vibration analyzer), the vibration frequency value (can be obtained through a vibration analyzer), and the vibration range value (that is, the area where the vibration range is located, which can be simulated by finite element analysis software. The propagation range of vibration in the workshop and the area of ​​the affected area are calculated); a parallel modeling unit is used to 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 based on the pre-processed workshop status data of the automobile manufacturing workshop based on a preset parallel processing algorithm; a 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, equipment distribution model, logistics path model, air flow model, and vibration impact model of the automobile manufacturing workshop; a 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.

[0111] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0112] 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 equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A rapid modeling method for automobile manufacturing physical environment, characterized in that: The following steps are involved: Acquire the workshop status data of the automobile manufacturing workshop and perform preprocessing, wherein the workshop status data includes the total volume value, the area value of each functional area, the size value of each manufacturing equipment, the path length value of each conveying path, the circulation area value of each air circulation area, the circulation area wind speed value, the circulation area temperature value, the circulation area obstacle quantity value, the vibration amplitude value, the vibration frequency value, and the vibration range value of each vibration source; Based on the preset parallel processing algorithm, the initial three-dimensional space model, equipment distribution model, logistics path model, air flow model and vibration impact model of the automobile manufacturing workshop are established for the pre-processed workshop status data of the automobile manufacturing workshop; Based on the initial three-dimensional space model of the automobile manufacturing workshop, the equipment distribution model, the logistics path model, the air flow model, and the vibration impact model, a comprehensive three-dimensional space model of the automobile manufacturing workshop is generated.

2. The method for rapid modeling of automobile manufacturing physical environment according to claim 1 is characterized in that: Based on the preset parallel processing algorithm, the specific steps of establishing the initial three-dimensional space model, equipment distribution model, logistics path model, air flow model, and vibration impact model of the automobile manufacturing workshop are as follows: Get the number of 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, 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 are analyzed respectively, and the number of processing CPU cores and the processing capacity index of each processing CPU core are combined to perform processing CPU core allocation; After processing the CPU core allocation, the initial three-dimensional space model of the automobile manufacturing workshop, equipment distribution model, logistics path model, air flow model, and vibration impact model are established in parallel.

3. The method for rapid modeling of automobile manufacturing physical environment according to claim 2 is characterized in that: The specific steps for analyzing the three-dimensional spatial complexity index of an automobile manufacturing workshop are as follows: The regional shape complexity index of each functional area in the automobile manufacturing workshop is obtained, and a comprehensive analysis is performed based on 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. The specific formula is as follows: ； in, , They are the three-dimensional space complexity index and total volume value of the automobile manufacturing workshop, , The first The area value and regional shape complexity index of each functional area, , They are the regional complexity adjustment coefficient and the space volume adjustment coefficient stored in the database, =1, 2, 3, ..., , It is the number of functional areas in the automobile manufacturing workshop.

4. The method for rapid modeling of automobile manufacturing physical environment according to claim 2, characterized in that: The specific steps to analyze the equipment distribution complexity index of an automobile manufacturing workshop are as follows: For each manufacturing equipment in the automobile manufacturing workshop, the number of adjacent manufacturing equipment within the set range is identified, and the average distance index within the set range of each manufacturing equipment is analyzed; The number of equipment interaction effects in the automobile manufacturing workshop is obtained, and a comprehensive analysis is performed based on 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: ； in, is the equipment distribution complexity index of the automobile manufacturing workshop, , The first The size value of each manufacturing device, the average distance index within the set range, is the number of equipment interactions in the automobile manufacturing workshop, , They are the size distance adjustment coefficient and the device interaction adjustment coefficient stored in the database. =1, 2, 3, ..., , The number of manufacturing equipment in the automobile manufacturing workshop.

5. The method for rapid modeling of automobile manufacturing physical environment according to claim 2, characterized in that: The specific steps for analyzing the logistics path complexity index of an automobile manufacturing workshop are as follows: for each transportation path in the automobile manufacturing workshop, the path workload value, path working time, path shape complexity index and turning complexity index are obtained respectively, and the path length value of the corresponding transportation path is combined for comprehensive analysis to obtain the logistics path complexity index of the automobile manufacturing workshop.

6. The method for rapid modeling of automobile manufacturing physical environment according to claim 2, characterized in that: The specific steps for analyzing the air flow complexity index of an automobile manufacturing workshop are as follows: obtaining the air flow resistance value and the temperature parameter value of each air circulation area in the automobile manufacturing workshop, and performing a comprehensive analysis based on the air circulation area area value, the air circulation area wind speed value, the air circulation area temperature value, and the number of obstacles in the air circulation area of ​​the corresponding air circulation area to obtain the air flow complexity index of the automobile manufacturing workshop.

7. The method for rapid modeling of automobile manufacturing physical environment according to claim 2, characterized in that: The specific steps for analyzing the vibration influence complexity index of an automobile manufacturing workshop are as follows: obtaining the number of vibration interference values ​​in the automobile manufacturing workshop, and performing a comprehensive analysis based on the vibration amplitude value, vibration frequency value, and vibration range value of each vibration source to obtain the vibration influence complexity index of the automobile manufacturing workshop.

8. The method for rapid modeling of automobile manufacturing physical environment according to claim 2, characterized in that: The specific steps to establish the equipment distribution model of an automobile manufacturing workshop are as follows: Obtaining manufacturing equipment list data, manufacturing equipment location and size data, and equipment operation status data of the automobile manufacturing workshop, wherein the equipment operation status data includes the current workload value, the current accumulated operation time value, the historical maintenance number value, the current vibration amplitude value, the current operation temperature value, and the current operation noise value of each manufacturing equipment; Based on the preset modeling steps, the manufacturing equipment list data, manufacturing equipment location and size data, and equipment operation status data of the automobile manufacturing workshop are modeled and processed to obtain the equipment distribution model of the automobile manufacturing workshop.

9. The method for rapid modeling of automobile manufacturing physical environment according to claim 8, characterized in that: After the equipment distribution model of the automobile manufacturing workshop is established, the fault prediction analysis is carried out on each manufacturing equipment. The specific steps are as follows: Acquire the operating status parameter data of the automobile manufacturing workshop, wherein the operating status parameter data includes the maximum parameter value of the workload of each manufacturing equipment, the maximum parameter value of the operating time, the maximum parameter value of the number of maintenance times, the maximum parameter value of the allowable vibration, the maximum parameter value of the withstand temperature, and the maximum parameter value of the allowable noise; Read the equipment operation status data of the automobile manufacturing workshop, and conduct a comprehensive analysis based on the operation status parameter data to obtain the fault prediction index of each manufacturing equipment in the automobile manufacturing workshop; The fault prediction index of each manufacturing equipment in the automobile manufacturing workshop is judged and analyzed with the preset fault threshold range, and the manufacturing equipment whose fault prediction index is outside the preset fault threshold range is regarded as abnormal fault, and the abnormal alarm of the manufacturing equipment is sent to the relevant staff; Among them, the specific formula for calculating the failure prediction index of each manufacturing equipment in the automobile manufacturing workshop is as follows: ； in, , , , , , , , , , , , , The first The failure prediction index of the manufacturing equipment is the first in the automobile manufacturing workshop. The current workload value of the manufacturing equipment, the maximum workload parameter value, the current cumulative operating time value, the maximum operating time parameter value, the historical maintenance times value, the maximum maintenance times parameter value, the current vibration amplitude value, the maximum allowable vibration parameter value, the current operating temperature value, the maximum withstand temperature parameter value, the current operating noise value, the maximum allowable noise parameter value, , , , , , The following are the workload adjustment factor, operation time adjustment factor, maintenance adjustment factor, vibration amplitude adjustment factor, operation temperature adjustment factor, and operation noise adjustment factor stored in the database. =1, 2, 3, ..., , The number of manufacturing equipment in the automobile manufacturing workshop.

10. A multifunctional application device for rapid modeling of a physical environment for automobile manufacturing, applying the method for rapid modeling of a physical environment for automobile manufacturing as claimed in any one of claims 1 to 9, characterized in that: include: Data acquisition unit, parallel modeling unit, fusion modeling unit, fault warning unit; The data acquisition unit is used to acquire the workshop status data of the automobile manufacturing workshop and perform preprocessing, wherein the workshop status data includes a total volume value, an area value of each functional area, a size value of each manufacturing equipment, a path length value of each conveying path, a circulation area value of each air circulation area, a circulation area wind speed value, a circulation area temperature value, a circulation area obstacle quantity value, a vibration amplitude value, a vibration frequency value, and a vibration range value of each vibration source; The parallel modeling unit is used to 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 pre-processed workshop state data of the automobile manufacturing workshop based on 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 of the automobile manufacturing workshop, the equipment distribution model, the logistics path model, the air flow model, and the vibration impact model; The fault early 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.

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