Intelligent manufacturing factory simulation teaching method and system based on VR virtual technology

Abstract

The invention relates to the technical field of intelligent manufacturing, in particular to an intelligent manufacturing factory simulation teaching method and system based on a VR virtual technology, and the method comprises the steps: obtaining equipment operation parameters and processing technology parameters, carrying out the generation of a first feature according to the equipment operation parameters, obtaining equipment operation features, and carrying out the generation of a second feature according to the processing technology parameters, the method comprises the following steps: generating a first feature to obtain an equipment operation feature, generating a second feature to obtain a machining process feature, generating a first graph to obtain an equipment operation dynamic graph according to the equipment operation feature, generating a second graph to obtain a machining process dynamic graph according to the machining process feature, and generating a third graph according to the equipment operation dynamic graph and the machining process dynamic graph, and obtaining a factory teaching state diagram, and displaying the factory teaching state diagram. According to the method, the dynamic relationship between equipment operation and the processing technology can be effectively displayed.

Description

Technical Field

[0001] The present invention relates to the field of intelligent manufacturing technology, and in particular to an intelligent manufacturing factory simulation teaching method and system based on VR virtual technology. Background Art

[0002] In the field of intelligent manufacturing, with the development of industry, enterprises have an increasing demand for efficient and intelligent production methods. As an immersive cutting-edge technology, virtual reality (VR) technology has gradually been applied to the design, optimization and teaching of intelligent manufacturing factories. Through VR technology, users can intuitively observe the factory layout, production process and equipment operation status, which not only improves production efficiency, but also reduces the risk and cost of actual production tests. Especially in teaching scenarios, VR provides a revolutionary solution for the demonstration and simulation of complex processes.

[0003] In the prior art, the collected equipment operation parameters or processing parameters are used to construct a single-dimensional dynamic state diagram. For example, equipment operation parameters can be used to generate a dynamic curve of equipment operation to show the working status of a single device; and processing parameters can be used to simulate the execution path of the process flow to show the dynamic changes of a single processing process. However, these technologies are limited to displaying a single dynamic information of the equipment operation status or the processing process flow, lack the ability to integrate the dynamic interaction between the two, and it is difficult to intuitively present the linkage characteristics and mutual influence between equipment operation and processing technology.

[0004] In summary, although the existing technology can use VR technology to generate dynamic diagrams of equipment operation status or processing process flow, it is limited to a single-dimensional display and cannot effectively integrate the dynamic relationship between equipment operation and processing technology. This limitation makes it difficult for users to fully understand the linkage characteristics of equipment and process and its impact on the overall factory operation, and also limits the actual effect of production process optimization and teaching in virtual scenes. Summary of the invention

[0005] The present invention provides a simulation teaching method and system for an intelligent manufacturing factory based on VR virtual technology to solve the problem of single-dimensional display and even the inability to display the dynamic relationship between equipment operation and processing technology.

[0006] In the first aspect, in order to solve the above technical problems, the present invention provides a simulation teaching method for an intelligent manufacturing factory based on VR virtual technology, comprising:

[0007] Obtain equipment operation parameters and processing technology parameters;

[0008] Generating a first feature according to the equipment operation parameter to obtain an equipment operation feature;

[0009] Generating a second feature according to the processing parameters to obtain a processing feature;

[0010] According to the equipment operation characteristics, a first graph is generated to obtain an equipment operation dynamic graph;

[0011] According to the processing technology characteristics, a second graph is generated to obtain a processing technology dynamic graph;

[0012] According to the equipment operation dynamic diagram and the processing technology dynamic diagram, a third diagram is generated to obtain a factory teaching state diagram, and the factory teaching state diagram is displayed.

[0013] Preferably, the equipment operation parameters include: equipment rated power, equipment actual power, equipment rated speed, equipment actual speed, equipment rated frequency, equipment actual frequency, equipment average operation interval time and uniformity of equipment operation interval;

[0014] The processing parameters include: actual processing accuracy, target processing accuracy, processing time interval standard deviation, processing time interval average value, target processing temperature, actual processing temperature, actual workpiece surface roughness and target workpiece surface roughness.

[0015] Preferably, generating a first feature according to the equipment operation parameter to obtain the equipment operation feature includes:

[0016] The load factor is calculated according to the rated power of the device and the actual power of the device to obtain the load factor; wherein the load factor is calculated by the following formula:

[0017]

[0018] Among them, P a is the actual power of the equipment, P r is the rated power of the equipment, L is the load rate;

[0019] The speed ratio is calculated according to the rated speed of the device and the actual speed of the device to obtain the speed ratio; wherein the speed ratio is calculated by the following formula:

[0020]

[0021] Among them, R is the speed ratio, N a is the actual speed of the equipment, N r is the rated speed of the equipment;

[0022] The operation stability is calculated according to the rated frequency of the device and the actual frequency of the device to obtain the operation stability; wherein the operation stability is calculated by the following formula:

[0023]

[0024] Among them, S is the operating stability, f a is the actual operating frequency of the device, f r is the rated operating frequency of the equipment;

[0025] According to the average interval time of the equipment operation and the uniformity of the equipment operation interval, the consistency coefficient is calculated to obtain the consistency coefficient; wherein the consistency coefficient is calculated by the following formula:

[0026]

[0027] Among them, C is the consistency coefficient, β is the average interval time of equipment operation, and r is the uniformity of equipment operation interval;

[0028] A first feature extraction is performed based on the load rate, the speed ratio, the operating stability and the consistency coefficient to obtain equipment operation characteristics.

[0029] Preferably, the generating of the second feature according to the processing parameters to obtain the processing feature includes:

[0030] According to the actual machining accuracy and the target machining accuracy, a machining accuracy calculation is performed to obtain a machining accuracy ratio; wherein the machining accuracy ratio is calculated by the following formula:

[0031]

[0032] Among them, P is the machining accuracy ratio, p a is the actual machining accuracy, p t The target machining accuracy;

[0033] The processing timing is calculated according to the processing timing interval standard deviation and the processing timing interval average value to obtain the processing timing; wherein the processing timing is calculated by the following formula:

[0034]

[0035] Among them, S t is the processing sequence, σ t For processing

[0036] Standard deviation of time interval, t a is the average value of the processing time interval;

[0037] The processing temperature is calculated according to the target processing temperature and the actual processing temperature to obtain the processing temperature; wherein the processing temperature is calculated by the following formula:

[0038]

[0039] Among them, T c is the processing temperature, t a is the actual processing temperature, t t is the target processing temperature;

[0040] According to the actual workpiece surface roughness and the target workpiece surface roughness, the surface roughness is calculated to obtain the surface roughness; wherein the surface roughness is calculated by the following formula:

[0041]

[0042] Among them, R c is the surface roughness, r a is the actual surface roughness, r t is the target surface roughness;

[0043] A second feature extraction is performed according to the machining accuracy ratio, the machining sequence, the machining temperature and the surface roughness to obtain a machining process feature.

[0044] Preferably, generating a first graph according to the equipment operation characteristics to obtain an equipment operation dynamic graph includes:

[0045] Performing a first normalization process according to the equipment operation characteristics to obtain a first multi-dimensional vector;

[0046] Performing a first feature weighted combination according to the first multidimensional vector to obtain a first optimized vector;

[0047] A first three-dimensional dynamic mapping is performed according to the first optimization vector to obtain a dynamic diagram of equipment operation.

[0048] Preferably, the generating of the second graph according to the processing technology feature to obtain the processing technology dynamic graph includes:

[0049] Performing a second normalization process according to the processing technology characteristics to obtain a second multi-dimensional vector;

[0050] Performing a second feature weighted combination according to the second multidimensional vector to obtain a second optimized vector;

[0051] A second three-dimensional dynamic mapping is performed according to the second optimization vector to obtain a machining process dynamic diagram.

[0052] Preferably, the third graph is generated according to the equipment operation dynamic graph and the processing technology dynamic graph to obtain a factory teaching state graph, and the factory teaching state graph is displayed, including:

[0053] According to the equipment operation dynamic graph and the processing technology dynamic graph, correlation extraction is performed to obtain a correlation data set;

[0054] Performing graph fusion according to the associated data set to obtain a collaborative operation state graph;

[0055] According to the collaborative operation status diagram, layout integration is performed to obtain a factory teaching status diagram.

[0056] In a second aspect, the present invention provides an intelligent manufacturing factory simulation teaching system based on VR virtual technology, comprising:

[0057] Data acquisition module, used to obtain equipment operation parameters and processing parameters;

[0058] A first feature module, used to generate a first feature according to the operating parameters to obtain a device operating feature;

[0059] A second feature module is used to generate a second feature according to the processing parameters to obtain a processing feature;

[0060] A first graphics module, used to generate a first graphics according to the equipment operation characteristics to obtain a dynamic diagram of equipment operation;

[0061] A second graphics module is used to generate a second graphics according to the processing technology characteristics to obtain a processing technology dynamic diagram;

[0062] The factory teaching module is used to generate a third graph based on the equipment operation dynamic graph and the processing technology dynamic graph, obtain a factory teaching state graph, and display the factory teaching state graph.

[0063] In a third aspect, the present invention also provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the intelligent manufacturing factory simulation teaching method based on VR virtual technology as described above is implemented.

[0064] In a fourth aspect, the present invention also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned intelligent manufacturing factory simulation teaching methods based on VR virtual technology.

[0065] Compared with the prior art, the present invention has the following beneficial effects: the method obtains factory original data; wherein the factory original data includes: equipment operation parameters and processing technology parameters; by using the equipment operation parameters, the first feature is generated to obtain the equipment operation feature, and by using the processing technology parameters, the second feature is generated to obtain the equipment operation feature,

[0066] The first graph is generated through the equipment operation characteristics to obtain the equipment operation dynamic graph, the second graph is generated through the processing technology characteristics to obtain the processing technology dynamic graph, the third graph is generated through the equipment operation dynamic graph and the processing technology dynamic graph to obtain the factory teaching state graph, and the factory teaching state graph is displayed. The method generates dynamic graphs and factory teaching state graphs through multi-parameter calculation and feature extraction, effectively displays the dynamic relationship between equipment operation and processing technology, realizes the accurate and intuitive presentation of complex production processes, and improves the interactivity, immersion and efficiency of teaching. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 It is a flow chart of a simulation teaching method for an intelligent manufacturing factory based on VR virtual technology provided by the first embodiment of the present invention;

[0068] Figure 2 It is a schematic diagram of an intelligent manufacturing factory simulation teaching system based on VR virtual technology provided by the second embodiment of the present invention. DETAILED DESCRIPTION

[0069] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0070] Reference Figure 1 The first embodiment of the present invention provides a simulation teaching method for an intelligent manufacturing factory based on VR virtual technology, comprising the following steps:

[0071] S11, obtaining equipment operation parameters and processing parameters;

[0072] S12, generating a first feature according to the equipment operation parameter to obtain an equipment operation feature;

[0073] S13, generating a second feature according to the processing parameters to obtain a processing feature;

[0074] S14, generating a first graph according to the equipment operation characteristics to obtain an equipment operation dynamic graph;

[0075] S15, generating a second graph according to the processing technology characteristics to obtain a processing technology dynamic graph;

[0076] S16, according to the equipment operation dynamic diagram

[0077] And the processing technology dynamic diagram, generate the third graphic, obtain the factory teaching status diagram, and display the factory teaching status diagram.

[0078] In step S11, equipment operation parameters and processing parameters are obtained, wherein the equipment operation parameters include: equipment rated power, equipment actual power, equipment rated speed, equipment actual speed, equipment rated frequency, equipment actual frequency, equipment average operation interval time and uniformity of equipment operation interval;

[0079] The processing parameters include: actual processing accuracy, target processing accuracy, processing time interval standard deviation, processing time interval average value, target processing temperature, actual processing temperature, actual workpiece surface roughness and target workpiece surface roughness.

[0080] It is worth noting that in step S11, obtaining equipment operation parameters and processing parameters is the core link to lay the data foundation for subsequent analysis, modeling and visualization. These parameters are obtained through real-time collection of equipment operation data and precise measurement of the processing process, ensuring the accuracy, scientificity and availability of the data.

[0081] Equipment operation parameters include equipment rated power, equipment actual power, equipment rated speed, equipment actual speed, equipment rated frequency, equipment actual frequency, average equipment operation interval time and uniformity of equipment operation interval. Among them, the equipment rated power is set by the equipment manufacturer during the design phase and recorded in the equipment nameplate or technical documents. Users can directly obtain it without measurement; the equipment actual power is measured in real time by a power meter installed on the equipment power supply line. The power meter calculates the actual power value of the equipment in real time by collecting data on current, voltage and power factor. The rated speed and rated frequency of the equipment are also design parameters, which can be directly consulted in the equipment manual; the actual speed of the equipment is obtained by a speed sensor installed on the rotating part, which collects the rotation speed in real time and outputs digital data. The actual frequency of the equipment is obtained in real time by a frequency meter, which calculates the current operating frequency by monitoring the periodic changes of current or voltage in the circuit.

[0082] The average interval time of equipment operation is used to measure the time interval between multiple operations and stops of the equipment. The calculation method is to record the time points of each equipment operation and stop, calculate the time interval between adjacent time points, and average these intervals. The specific formula is: Where T iis the interval time between each operation, and N is the number of recorded operations. The time recording module in the equipment control system accurately collects these time points to ensure the integrity and accuracy of the data. The uniformity of the equipment operation interval reflects the volatility of multiple interval times, which is calculated by calculating the standard deviation of the time interval σ T The ratio of β to the average interval time is obtained as follows: This parameter describes the consistency of the equipment's operating intervals. The higher the uniformity, the more stable the equipment's operation.

[0083] The machining process parameters include actual machining accuracy, target machining accuracy, machining time interval standard deviation, machining time interval average value, target machining temperature, actual machining temperature, actual workpiece surface roughness and target workpiece surface roughness. The actual machining accuracy is obtained by measuring the key dimensions of the workpiece after machining and comparing them with the target design dimensions; the target machining accuracy is set in the process design stage and directly recorded in the process specification. The machining time interval standard deviation reflects the volatility of the time interval of the machining steps, which is obtained by counting the time intervals of multiple machining steps and calculating their standard deviations. The machining time interval average value is the result of averaging multiple time intervals, which is used to describe the overall law of the time distribution between machining steps. The target machining temperature is set by the process design specification and can be directly consulted; the actual machining temperature is measured in real time by thermocouples or infrared sensors to reflect the actual temperature changes during the machining process. The actual workpiece surface roughness is detected by a roughness measuring instrument, and the instrument generates roughness data by scanning the workpiece surface; the target workpiece surface roughness is set in the process design stage and can be obtained from the process file.

[0084] By collecting and calculating the operating parameters and processing parameters of the above equipment, comprehensive and accurate data support is provided for subsequent analysis and model building. These data sources are reliable and the calculation methods are clear, providing a scientific basis and technical guarantee for the realization of simulation teaching in intelligent manufacturing factories.

[0085] In step S12, a first feature is generated according to the equipment operation parameter to obtain the equipment operation feature, including: calculating the load rate by the following formula:

[0086]

[0087] Among them, P a is the actual power of the equipment, P r is the rated power of the equipment, L is the load rate;

[0088] The speed ratio is calculated according to the rated speed of the device and the actual speed of the device to obtain the speed ratio; wherein the speed ratio is calculated by the following formula:

[0089]

[0090] Among them, R is the speed ratio, N a is the actual speed of the equipment, N r is the rated speed of the equipment;

[0091] The operation stability is calculated according to the rated frequency of the device and the actual frequency of the device to obtain the operation stability; wherein the operation stability is calculated by the following formula:

[0092]

[0093] Among them, S is the operating stability, f a is the actual operating frequency of the device, f r is the rated operating frequency of the equipment;

[0094] According to the average interval time of the equipment operation and the uniformity of the equipment operation interval, the consistency coefficient is calculated to obtain the consistency coefficient; wherein the consistency coefficient is calculated by the following formula:

[0095]

[0096] Among them, C is the consistency coefficient, β is the average interval time of equipment operation, and r is the uniformity of equipment operation interval;

[0097] A first feature extraction is performed based on the load rate, the speed ratio, the operating stability and the consistency coefficient to obtain equipment operation characteristics.

[0098] It is worth noting that in the process of extracting equipment operation characteristics, load rate, speed ratio, operation stability and consistency coefficient need to be integrated from multiple dimensions to accurately reflect the operating status of the equipment. First of all, data preprocessing is a key step. Load rate, speed ratio, operation stability and consistency coefficient reflect the power, speed, frequency and time interval stability of the equipment respectively. These parameters have different dimensions and value ranges during the calculation process, so they must be standardized. The purpose of standardization is to adjust the value of each parameter to a unified range so that the impact of each parameter on the final result remains consistent during subsequent feature integration. The standardization operation is achieved by mapping the value of each parameter to the interval of 0 to 1, which can eliminate the dimensional differences between different parameters. The standardization process relies on the maximum and minimum values ​​of the equipment's design specifications or real-time operating data to ensure that the calculation process of each feature meets the actual operating conditions.

[0099] After completing the data preprocessing, the next step is weight assignment. The equipment operation characteristics need to comprehensively reflect the equipment's operating efficiency, stability and consistency. Therefore, when integrating parameters, each parameter must be assigned an appropriate weight according to the application scenario. The load rate reflects the equipment's operating efficiency and is the core indicator for evaluating whether the equipment is operating under a reasonable load. The weight of the load rate is set to 40%, because the load of the equipment during operation directly affects the stability and energy efficiency of the equipment. Under high load conditions, when the load rate is close to or exceeds the rated power, the equipment is at risk of overheating or reduced energy efficiency. The weight of the speed ratio is set to 30%, because the speed ratio directly affects the equipment's operating efficiency and precision. In applications with strict speed requirements, such as high-precision machining or high-speed operation, changes in the speed ratio will significantly affect product quality and equipment life. The weight of operating stability is 20%, and operating stability is evaluated by the deviation between the actual frequency of the equipment and the rated frequency. When the frequency fluctuates greatly, the stability of the equipment is reduced, which is prone to failure or performance degradation. The weight of the consistency coefficient is set to 10%, which reflects the consistency of the equipment's operating time interval. In automated production lines or scenarios with high production efficiency requirements, a higher consistency coefficient means higher equipment stability and production efficiency.

[0100] After completing the standardization and weight assignment, the next step is feature integration. The purpose of feature integration is to form a characteristic value that comprehensively reflects the operating status of the equipment by weighted summation of various parameters. Specifically, the load rate, speed ratio, operating stability and consistency coefficient will be weighted and summed according to their weights to obtain a comprehensive characteristic value. For example, the standardized value of the load rate is 0.75, the speed ratio is 0.8, the operating stability is 0.9, and the consistency coefficient is 0.7. According to the weights, they are multiplied by 0.40, 0.30, 0.20 and 0.10 respectively, and then the results are added to obtain the comprehensive operation characteristic value of the equipment. Through this weighted summation, the comprehensive characteristic value can accurately describe the overall operating status of the equipment and provide strong data support for subsequent equipment monitoring, performance evaluation and optimization.

[0101] Finally, the generated equipment operation characteristics need to be verified by comparing with the actual equipment operation data to ensure their accuracy and rationality. Verification is performed based on real-time collected operation data. First, the calculated characteristic value is compared with the actual operating status of the equipment to check whether it exceeds the set safety range. The thresholds for load rate, speed ratio, operation stability and consistency coefficient are set according to the design specifications or actual working conditions of the equipment. If a characteristic value exceeds the threshold, it means that the equipment is abnormal. For example, if the load rate exceeds 80%, the equipment is in an overloaded state. When the speed ratio exceeds ±5% of the rated speed, the equipment is unstable. When the frequency deviation is too large, it indicates that the equipment is unstable. A low consistency coefficient indicates that the equipment operation time is uneven, which will lead to failures or reduced efficiency. By monitoring these characteristic values ​​in real time, abnormal conditions of the equipment can be discovered in time, and adjustments and optimizations can be made.

[0102] Through the above steps, the load rate, speed ratio, operating stability and consistency coefficient are accurately integrated into a comprehensive equipment operation feature, which fully reflects the operating status of the equipment. The whole process does not rely on vague empirical settings, but through specific and feasible steps such as standardization, weight allocation, feature integration and verification, to ensure that the final result is highly reliable and operable.

[0103] In step S13, a second feature is generated according to the machining process parameters to obtain a machining process feature, including: performing machining accuracy calculation according to the actual machining accuracy and the target machining accuracy to obtain a machining accuracy ratio; wherein the machining accuracy ratio is calculated by the following formula:

[0104]

[0105] Among them, P is the machining accuracy ratio, p a is the actual machining accuracy, p t The target machining accuracy;

[0106] The processing timing is calculated according to the processing timing interval standard deviation and the processing timing interval average value to obtain the processing timing; wherein the processing timing is calculated by the following formula:

[0107]

[0108] Among them, S t is the processing sequence, σ t is the standard deviation of the processing time interval, t a is the average value of the processing time interval;

[0109] The processing temperature is calculated according to the target processing temperature and the actual processing temperature to obtain the processing temperature; wherein the processing temperature is calculated by the following formula:

[0110]

[0111] Among them, T c is the processing temperature, t a is the actual processing temperature, t t is the target processing temperature;

[0112] According to the actual workpiece surface roughness and the target workpiece surface roughness, the surface roughness is calculated to obtain the surface roughness; wherein the surface roughness is calculated by the following formula:

[0113]

[0114] Among them, R c is the surface roughness, r a is the actual surface roughness, r t is the target surface roughness;

[0115] A second feature extraction is performed according to the machining accuracy ratio, the machining sequence, the machining temperature and the surface roughness to obtain a machining process feature.

[0116] It is worth mentioning that in the process of extracting machining process features, the four key parameters of machining accuracy ratio, machining timing, machining temperature and surface roughness need to be calculated in detail first. Each parameter reflects a specific aspect of the machining process and is of great significance, so it needs to be obtained through a clear calculation formula. The machining accuracy ratio is to evaluate whether the machining meets the expected quality requirements by comparing the actual machining accuracy with the target machining accuracy. The actual machining accuracy is obtained by measuring the actual size of the workpiece, and the target machining accuracy is the target value set according to the process requirements. The machining accuracy ratio is calculated by dividing the difference between the actual machining accuracy and the target machining accuracy by the target machining accuracy, and normalizing the result to obtain a proportional value reflecting the machining accuracy. The smaller this value is, the closer the machining accuracy is to the target requirement and the higher the accuracy is.

[0117] The processing sequence evaluates the uniformity of the time intervals between processing steps and reflects the time consistency of the processing process. This indicator is obtained by calculating the ratio of the standard deviation of the processing time interval to the mean value. The smaller the standard deviation, the more uniform the time intervals between processing steps and the more stable the process control. The importance of processing sequence lies in that it reflects the order and time management of each process in the production process. Large fluctuations will lead to inefficient processes or discontinuous production.

[0118] The calculation of processing temperature is to evaluate the difference between the actual processing temperature and the target processing temperature. The stability of temperature has an important impact on the material properties and processing quality during the processing. By quantifying the difference between the actual processing temperature and the target temperature, we can understand whether the process control is accurate. The closer the processing temperature is to the target temperature, the higher the stability of the processing process and the better the quality of the product.

[0119] Surface roughness is the core indicator for measuring the surface quality of workpieces after processing. It evaluates the processing quality by measuring the roughness of the microstructure of the workpiece surface. A lower surface roughness value indicates a smooth surface with higher quality; while a higher surface roughness value indicates that there are larger processing marks on the surface, which affects the performance and appearance quality of the product.

[0120] After obtaining the calculation results of these four parameters, the next step is to standardize them. Since the dimensions and value ranges of the four parameters of machining accuracy ratio, machining timing, machining temperature and surface roughness are different, they need to be adjusted to the same value range through standardization, that is, mapping the value of each parameter to the interval of 0 to 1, so that the dimension difference can be eliminated, so that each parameter has the same influence in the subsequent analysis.

[0121] After completing the standardization, the weight allocation phase begins. Since the machining accuracy ratio, machining timing, machining temperature, and surface roughness reflect different dimensions of the machining process, they have different degrees of influence on the final machining results. Therefore, before feature integration, different weights must be assigned to these parameters. The weight allocation process is determined according to the specific application scenario. In the scenario of precision machining, the machining accuracy ratio and surface roughness have higher weights because they are directly related to the quality and accuracy of the product. In the process of high-speed machining or batch production, the influence of machining timing and machining temperature is more critical because they directly affect production efficiency and process stability. In this case, the weight of the machining accuracy ratio is set to 40%, the weight of the surface roughness is 20%, and the weights of the machining timing and machining temperature are each set to 20%.

[0122] Next, the four parameters after standardization and weight assignment will enter the feature integration stage. The goal of feature integration is to generate a comprehensive processing technology characteristic value by weighted summation of the information of these four dimensions. In this process, parameters such as load rate, speed ratio, operating stability and consistency coefficient will be multiplied according to their weights, and all weighted values ​​will be added to obtain the final comprehensive characteristic value. Through weighted summation, the contribution of each parameter will be quantified and taken into account in the comprehensive characteristics. For example, the standardized value of the processing accuracy ratio is multiplied by its weight, and the result is summed with the calculation results of the processing timing, processing temperature and surface roughness to form a comprehensive processing technology feature. This value can fully reflect the overall performance of the processing technology.

[0123] The generated comprehensive eigenvalue is a dynamic variable, which will be used to generate dynamic graphs in the subsequent steps. This comprehensive eigenvalue represents the performance of the machining process on different process parameters and provides basic data for the subsequent dynamic graphs. In the subsequent steps, this eigenvalue will be dynamically updated according to the changes in real-time data and converted into a dynamic graph to intuitively display the dynamic evolution of the machining process and the changing trends of various parameters during the machining process.

[0124] The entire feature extraction process goes through standardization, weight assignment, feature integration and other steps to ensure that the generated machining process features accurately reflect the overall performance of the process, while also providing an operational, data-supported basis for subsequent dynamic graph generation.

[0125] In step S14, a first graph is generated according to the equipment operation characteristics to obtain an equipment operation dynamic graph, including:

[0126] Performing a first normalization process according to the equipment operation characteristics to obtain a first multi-dimensional vector;

[0127] Performing a first feature weighted combination according to the first multidimensional vector to obtain a first optimized vector;

[0128] A first three-dimensional dynamic mapping is performed according to the first optimization vector to obtain a dynamic diagram of equipment operation.

[0129] It is worth mentioning that the equipment operation characteristics include load rate, speed ratio, operation stability and consistency coefficient. Each characteristic value represents the operation status of the equipment in different dimensions. For example, the load rate reflects the workload of the equipment, the speed ratio represents the speed efficiency of the equipment, the operation stability reflects the frequency stability of the equipment, and the consistency coefficient reflects the operation regularity of the equipment. The next goal is to generate a dynamic graph based on these characteristic values, so as to more intuitively show the dynamic changes of the equipment during operation.

[0130] First, these eigenvalues ​​need to be standardized. Since the equipment operation characteristics involve different parameters with different dimensions and numerical ranges, standardization is a necessary step. Through standardization, the load rate, speed ratio, operating stability and consistency coefficient can be adjusted to a unified numerical range, that is, mapped to the interval of 0 to 1. The standardized eigenvalues ​​can eliminate the dimensional differences between the parameters, so that they have the same influence in subsequent integration and graphics generation.

[0131] After standardization, these eigenvalues ​​will enter the feature integration stage. Each feature is combined with other features through weighting coefficients to form a comprehensive dynamic feature. The calculation method of this comprehensive feature has been mentioned in the previous step, and it reflects the overall operating status of the equipment. For example, the weight of the load rate is large, and the weights of the speed ratio and operating stability are also reasonably allocated according to the working requirements of the equipment. The integrated eigenvalue is not just a number, but a dynamic variable that fluctuates as the operating status of the equipment changes. This comprehensive eigenvalue will become the basis of the dynamic graph.

[0132] Next, based on this dynamic eigenvalue, a dynamic graph is generated. First, the integrated eigenvalue is mapped to a three-dimensional coordinate system, where the X-axis, Y-axis, and Z-axis represent different dimensions such as load rate, speed ratio, and operating stability, respectively. At each point in time, the operating status of the equipment is represented by the position of a point in the three-dimensional coordinate system. Over time, these eigenvalues ​​will change as the equipment status changes, and the corresponding points will also shift in three-dimensional space. Specifically, as the load on the equipment increases, the load rate will increase, causing the points in the graph to move along the X-axis; if the speed ratio changes, the coordinates of the points on the Y-axis will also change; fluctuations in operating stability will affect the position on the Z-axis.

[0133] To ensure that the dynamic graph accurately reflects the changes in the operating characteristics of the equipment, each frame of the graph will be updated based on the data collected in real time. At each update, the characteristic value will be adjusted according to the current state of the equipment and mapped to the new coordinate point. This process is driven by a real-time data stream to ensure that the graph reflects the real-time operation of the equipment. During the graphics rendering process, different visual elements can also be set, such as the depth of color to indicate the stability of the equipment operation, or the size of the point to reflect the degree of fluctuation of the consistency coefficient. The change of color can be used to indicate the load level of the equipment, dark colors indicate high loads, and light colors indicate low loads. The size of the point can indicate the stability of the equipment operation. Larger points represent higher stability, and smaller points indicate that the equipment is fluctuating or unstable. Finally, the generated dynamic graph can show the overall performance of the equipment operation.

[0134] In step S15, a second graph is generated according to the processing technology characteristics to obtain a processing technology dynamic graph, including:

[0135] Performing a second normalization process according to the processing technology characteristics to obtain a second multi-dimensional vector;

[0136] Performing a second feature weighted combination according to the second multidimensional vector to obtain a second optimized vector;

[0137] A second three-dimensional dynamic mapping is performed according to the second optimization vector to obtain a machining process dynamic diagram.

[0138] It is worth noting that in the process of generating dynamic graphs based on machining process features, it is first necessary to integrate the characteristic values ​​of machining accuracy ratio, machining timing, machining temperature and surface roughness. After the previous standardization process, these characteristic values ​​have been adjusted to a unified numerical range between 0 and 1, which can eliminate the dimensional differences between the various features and lay the foundation for subsequent graphics generation. Next, these standardized features will be mapped to a three-dimensional coordinate system, and each characteristic value will correspond to a coordinate axis in the three-dimensional space. The machining accuracy ratio can be mapped to the X-axis, the surface roughness to the Y-axis, the machining timing to the Z-axis, and the machining temperature is represented by the color change of the point. Each characteristic value represents a specific dimension of the machining state, and when these features change as the machining process progresses, they will be dynamically reflected in the graph.

[0139] After the eigenvalues ​​are mapped to the three-dimensional coordinate system, the next step is to update each eigenvalue in real time according to the changes in the time axis and convert it into coordinate points in the dynamic graph. The process eigenvalue at each time point will correspond to a coordinate position in the three-dimensional space. As time goes by, these coordinate points will continue to change, reflecting the dynamic state of the processing process. For example, during the processing process, if the processing accuracy ratio changes, the points in the graph will shift along the X-axis, indicating the fluctuation of the processing accuracy; if the time interval of the processing sequence is unstable, the points in the graph will shift in the Z-axis direction, reflecting the time consistency problem in the processing process; and the change of processing temperature is represented by the color change of the graph, the darker the color, the higher the temperature, and the lighter the color, the lower the temperature.

[0140] Each frame of the dynamic graph updates the position and color of the points in the graph based on the latest eigenvalues. The points in the graph move along the corresponding coordinate axis according to the changes in the eigenvalues, and the changes in color can intuitively show the fluctuations in the processing temperature. This process is dynamic, which means that the data changes at each moment will update the graph in real time to generate a continuously changing dynamic graph. In this way, the generated dynamic graph can not only show the changes in different dimensions of the processing technology, but also clearly reflect the relationship between the various eigenvalues, helping viewers understand how different process parameters change and interact with each other as the processing progresses.

[0141] In addition, by continuously rendering and updating each frame of graphics, the position and color of the dynamic feature value in three-dimensional space will change as the processing technology progresses, reflecting the real-time status of processing accuracy, temperature stability, time consistency and surface quality. The generation of the entire dynamic graph is a dynamic process based on real-time feature changes, so that the fluctuation of each feature can be intuitively displayed, thus providing an intuitive and dynamic learning tool for understanding and teaching the processing technology. Ultimately, the generated dynamic graph can show how the various process parameters in the processing process affect each other and show the overall performance of the process.

[0142] In step S16, a third graph is generated according to the equipment operation dynamic graph and the processing technology dynamic graph to obtain a factory teaching state graph, and the factory teaching state graph is displayed, including:

[0143] According to the equipment operation dynamic graph and the processing technology dynamic graph, correlation extraction is performed to obtain a correlation data set;

[0144] Performing graph fusion according to the associated data set to obtain a collaborative operation state graph;

[0145] According to the collaborative operation status diagram, layout integration is performed to obtain a factory teaching status diagram.

[0146] It is worth noting that the generation of the factory teaching state diagram is based on the integrated analysis and visual display of the equipment operation dynamic diagram and the processing technology dynamic diagram. Its core goal is to integrate the equipment operation status with the processing technology characteristics to generate an intuitive teaching tool that fully reflects the dynamic operation of the factory. The following is a detailed explanation of this step, including the specific process of data extraction, graphic fusion and VR display.

[0147] First, it is necessary to extract the associated system data set from the equipment operation dynamic graph and the processing technology dynamic graph. The equipment operation dynamic graph and the processing technology dynamic graph contain the equipment dynamic operation characteristics and processing technology characteristics respectively. They are synchronized through the time axis and spatial association to extract the key data of both. The extracted key data include equipment operation parameters at the time node such as load rate, speed ratio, etc. and processing technology parameters such as processing accuracy, surface roughness, etc., which constitute the associated system data set. In order to ensure that the extracted data is accurate and reliable, it is necessary to align the two types of dynamic graphs through unified timestamps and spatial coordinates, and use data screening and statistical methods to eliminate outliers or redundant data, and finally form a high-quality data set for integration.

[0148] Next, the associated system data sets are graphically fused to generate a collaborative operation status diagram. In this stage, the extracted equipment operation data and processing technology data are combined through dynamic mapping. The equipment operation characteristics and processing technology characteristics are assigned to different levels of the three-dimensional space and are intuitively presented through colors, shapes, and dynamic effects. For example, the equipment load rate can be displayed by the size of the node, the surface roughness can be expressed by color changes, and the timeline can display the synchronization process of different process stages through dynamic animation. During the fusion process, data mapping algorithms and visualization tools are used to hierarchically process and dynamically combine the associated system data to generate a collaborative operation status diagram that can reflect both local details and the overall collaborative relationship. After the graphic fusion is completed, the collaborative operation status diagram can not only dynamically display the overall picture of the factory operation, but also help users intuitively understand the linkage relationship between equipment and process with high readability.

[0149] Finally, the collaborative operation state diagram completes the generation of the factory teaching state diagram through layout integration. At this stage, the various modules in the collaborative operation state diagram are adjusted according to the factory layout and logical relationship, combining dynamic elements with static layout. By setting the observation perspective, scale and interactive function, the readability and user experience of the graphics are further optimized. The final generated factory teaching state diagram has a complete factory operation logic and can provide real-time dynamic update function to reflect the impact of different parameter adjustments on the overall operation of the factory during the teaching process.

[0150] VR technology provides a more immersive display method for factory teaching status diagrams. Users can wear VR devices, such as head-mounted displays, to enter a panoramic view of the virtual factory. In the virtual factory, the factory teaching status diagram is presented in the form of a three-dimensional scene. Users can adjust the viewing angle by moving their heads, and select and zoom in on specific areas for detailed analysis through the handle. The VR environment supports multi-level interactive functions, such as clicking on a certain equipment node to view its specific operating parameters, or adjusting process parameters through dynamic control to observe the impact on the global state. VR display not only improves the intuitiveness and fun of learning, but also helps users to more deeply understand the overall logic and local details of factory operation through real-time feedback.

[0151] Through the above steps, the generation of factory teaching state diagrams and VR display integrates dynamic information of equipment and processes from multiple dimensions, achieving intuitive, realistic and efficient teaching goals. The entire process relies on data collection, analysis, integration and visualization technology, providing innovative means for teaching and training of intelligent manufacturing.

[0152] Reference Figure 2 The second embodiment of the present invention provides a simulation teaching system for an intelligent manufacturing factory based on VR virtual technology, including:

[0153] Data acquisition module, used to obtain equipment operation parameters and processing parameters;

[0154] A first feature module, used to generate a first feature according to the operating parameters to obtain a device operating feature;

[0155] A second feature module is used to generate a second feature according to the processing parameters to obtain a processing feature;

[0156] A first graphics module, used to generate a first graphics according to the equipment operation characteristics to obtain a dynamic diagram of equipment operation;

[0157] A second graphics module is used to generate a second graphics according to the processing technology characteristics to obtain a processing technology dynamic diagram;

[0158] The factory teaching module is used to generate a third graph based on the equipment operation dynamic graph and the processing technology dynamic graph, obtain a factory teaching state graph, and display the factory teaching state graph.

[0159] It should be noted that the intelligent manufacturing factory simulation teaching system based on VR virtual technology provided in an embodiment of the present invention is used to execute all the process steps of the intelligent manufacturing factory simulation teaching method based on VR virtual technology in the above embodiment. The working principles and beneficial effects of the two correspond one to one, so they will not be repeated here.

[0160] The embodiment of the present invention also provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a simulation teaching program for an intelligent manufacturing factory based on VR virtual technology. When the processor executes the computer program, the steps in the above-mentioned embodiments of the simulation teaching method for an intelligent manufacturing factory based on VR virtual technology are implemented, such as Figure 1 Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are realized, such as the factory teaching module.

[0161] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the electronic device.

[0162] The electronic device may be a computing device such as a desktop computer, a notebook, a PDA, and a smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. The electronic device may include more or fewer components than the above components, or may combine certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0163] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the electronic device, and uses various interfaces and lines to connect various parts of the entire electronic device.

[0164] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the electronic device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0165] Wherein, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0166] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0167] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A simulation teaching method for intelligent manufacturing factory based on VR virtual technology, characterized in that: include: Obtain equipment operation parameters and processing technology parameters; Generating a first feature according to the equipment operation parameter to obtain an equipment operation feature; Generating a second feature according to the processing parameters to obtain a processing feature; According to the equipment operation characteristics, a first graph is generated to obtain an equipment operation dynamic graph; According to the processing technology characteristics, a second graph is generated to obtain a processing technology dynamic graph; According to the equipment operation dynamic diagram and the processing technology dynamic diagram, a third diagram is generated to obtain a factory teaching state diagram, and the factory teaching state diagram is displayed.

2. The intelligent manufacturing factory simulation teaching method based on VR virtual technology according to claim 1 is characterized in that: The equipment operation parameters include: equipment rated power, equipment actual power, equipment rated speed, equipment actual speed, equipment rated frequency, equipment actual frequency, equipment average operation interval time and uniformity of equipment operation interval; The processing parameters include: actual processing accuracy, target processing accuracy, processing time interval standard deviation, processing time interval average value, target processing temperature, actual processing temperature, actual workpiece surface roughness and target workpiece surface roughness.

3. The intelligent manufacturing factory simulation teaching method based on VR virtual technology according to claim 2 is characterized in that: The step of generating a first feature according to the equipment operation parameter to obtain the equipment operation feature includes: The load factor is calculated according to the rated power of the device and the actual power of the device to obtain the load factor; wherein the load factor is calculated by the following formula: Among them, P a is the actual power of the equipment, P r is the rated power of the equipment, L is the load rate; The speed ratio is calculated according to the rated speed of the device and the actual speed of the device to obtain the speed ratio; wherein the speed ratio is calculated by the following formula: Among them, R is the speed ratio, N a is the actual speed of the equipment, N r is the rated speed of the equipment; The operation stability is calculated according to the rated frequency of the device and the actual frequency of the device to obtain the operation stability; wherein the operation stability is calculated by the following formula: Among them, S is the operating stability, f a is the actual operating frequency of the device, f r is the rated operating frequency of the equipment; According to the average interval time of the equipment operation and the uniformity of the equipment operation interval, the consistency coefficient is calculated to obtain the consistency coefficient; wherein the consistency coefficient is calculated by the following formula: Among them, C is the consistency coefficient, β is the average interval time of equipment operation, and r is the uniformity of equipment operation interval; A first feature extraction is performed based on the load rate, the speed ratio, the operating stability and the consistency coefficient to obtain equipment operation characteristics.

4. The intelligent manufacturing factory simulation teaching method based on VR virtual technology according to claim 2 is characterized in that: The step of generating a second feature according to the processing parameters to obtain a processing feature includes: According to the actual machining accuracy and the target machining accuracy, a machining accuracy calculation is performed to obtain a machining accuracy ratio; wherein the machining accuracy ratio is calculated by the following formula: Among them, P is the machining accuracy ratio, p a is the actual machining accuracy, p t The target machining accuracy; The processing timing is calculated according to the processing timing interval standard deviation and the processing timing interval average value to obtain the processing timing; wherein the processing timing is calculated by the following formula: Among them, S t is the processing sequence, σ t is the standard deviation of the processing time interval, t a is the average value of the processing time interval; The processing temperature is calculated according to the target processing temperature and the actual processing temperature to obtain the processing temperature; wherein the processing temperature is calculated by the following formula: Among them, T c is the processing temperature, t a is the actual processing temperature, t t is the target processing temperature; According to the actual workpiece surface roughness and the target workpiece surface roughness, the surface roughness is calculated to obtain the surface roughness; wherein the surface roughness is calculated by the following formula: Among them, R c is the surface roughness, r a is the actual surface roughness, r t is the target surface roughness; A second feature extraction is performed according to the machining accuracy ratio, the machining sequence, the machining temperature and the surface roughness to obtain a machining process feature.

5. The intelligent manufacturing factory simulation teaching method based on VR virtual technology according to claim 1 is characterized in that: The step of generating a first graph according to the equipment operation characteristics to obtain an equipment operation dynamic graph includes: Performing a first normalization process according to the equipment operation characteristics to obtain a first multi-dimensional vector; Performing a first feature weighted combination according to the first multidimensional vector to obtain a first optimized vector; A first three-dimensional dynamic mapping is performed according to the first optimization vector to obtain a dynamic diagram of equipment operation.

6. The intelligent manufacturing factory simulation teaching method based on VR virtual technology according to claim 1 is characterized in that: The step of generating a second graph according to the processing technology feature to obtain a processing technology dynamic graph includes: Performing a second normalization process according to the processing technology characteristics to obtain a second multi-dimensional vector; Performing a second feature weighted combination according to the second multidimensional vector to obtain a second optimized vector; A second three-dimensional dynamic mapping is performed according to the second optimization vector to obtain a machining process dynamic diagram.

7. The intelligent manufacturing factory simulation teaching method based on VR virtual technology according to claim 1 is characterized in that: The third graph is generated according to the equipment operation dynamic graph and the processing technology dynamic graph to obtain a factory teaching state graph, and the factory teaching state graph is displayed, including: According to the equipment operation dynamic graph and the processing technology dynamic graph, correlation extraction is performed to obtain a correlation data set; Performing graph fusion according to the associated data set to obtain a collaborative operation state graph; According to the collaborative operation status diagram, layout integration is performed to obtain a factory teaching status diagram.

8. A simulation teaching system for intelligent manufacturing factory based on VR virtual technology, characterized in that: include: Data acquisition module, used to obtain equipment operation parameters and processing parameters; A first feature module, used to generate a first feature according to the operating parameters to obtain a device operating feature; A second feature module is used to generate a second feature according to the processing parameters to obtain a processing feature; A first graphics module, used to generate a first graphics according to the equipment operation characteristics to obtain a dynamic diagram of equipment operation; A second graphics module is used to generate a second graphics according to the processing technology characteristics to obtain a processing technology dynamic diagram; The factory teaching module is used to generate a third graph based on the equipment operation dynamic graph and the processing technology dynamic graph, obtain a factory teaching state graph, and display the factory teaching state graph.

9. An electronic device, characterized in that: It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the intelligent manufacturing factory simulation teaching method based on VR virtual technology as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the intelligent manufacturing factory simulation teaching method based on VR virtual technology as described in any one of claims 1 to 7.

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