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

Abstract

The present invention relates to the field of intelligent manufacturing technology, and in particular to a method and system for teaching intelligent manufacturing factory simulation based on VR virtual technology. The method comprises: obtaining equipment operating parameters and processing technology parameters, generating a first feature based on the equipment operating parameters to obtain an equipment operating feature, generating a second feature based on the processing technology parameters to obtain a processing technology feature, generating a first graphic based on the equipment operating feature to obtain an equipment operation dynamic graph, generating a second graphic based on the processing technology feature to obtain a processing technology dynamic graph, generating a third graphic based on the equipment operation dynamic graph and the processing technology dynamic graph to obtain a factory teaching state graph, and displaying the factory teaching state graph. The method can effectively demonstrate the dynamic relationship between equipment operation and processing technology.

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 smart manufacturing, with the development of industry, enterprises are increasingly demanding efficient and intelligent production methods. As a cutting-edge immersive technology, virtual reality (VR) is increasingly being applied to the design, optimization, and teaching of smart manufacturing factories. VR allows users to intuitively observe factory layouts, production processes, and equipment operating status, improving production efficiency while reducing the risks and costs of actual production trials. Especially in teaching scenarios, VR provides a revolutionary solution for demonstrating and simulating complex processes.

[0003] Existing technologies utilize collected equipment operating parameters or process parameters to construct single-dimensional dynamic state diagrams. For example, equipment operating parameters can be used to generate dynamic curves of equipment operation, demonstrating the working status of a single piece of equipment; whereas process parameters can be used to simulate the execution path of a process flow, demonstrating the dynamic changes of a single process. However, these technologies are limited to displaying single dynamic information about equipment operating status or process flow, lacking the ability to integrate the dynamic interaction between the two, making it difficult to intuitively present the linkage and mutual influence between equipment operation and process flow.

[0004] In summary, while existing VR technologies can generate dynamic diagrams of equipment operating status or process flows, they are limited to single-dimensional displays and fail to effectively integrate the dynamic relationship between equipment operation and process flow. This limitation makes it difficult for users to fully understand the interconnected nature of equipment and processes and their impact on overall factory operations. This also limits the effectiveness of virtual scenarios for production process optimization and teaching. 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 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 operating parameters and processing 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] Generating a first graph based on the equipment operation characteristics to obtain an equipment operation dynamic graph;

[0011] Generating a second graph according to the processing technology characteristics to obtain a processing technology dynamic graph;

[0012] A third graphic is generated based on the equipment operation dynamic diagram and the processing technology dynamic diagram 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 time;

[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 based on the equipment operation parameter to obtain the equipment operation feature includes:

[0016] The load factor is calculated based on the rated power of the device and the actual power of the device to obtain the load factor. The load factor is calculated using 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 based on the rated speed of the device and the actual speed of the device to obtain the speed ratio; wherein the speed ratio is calculated using 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] An operation stability calculation is performed based on the rated frequency of the device and the actual frequency of the device to obtain the operation stability. The operation stability is calculated using 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] The consistency coefficient is calculated based on the average equipment operation interval time and the uniformity of the equipment operation interval 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, generating the second feature according to the processing parameters to obtain the processing feature includes:

[0030] The machining accuracy is calculated based on the actual machining accuracy and the target machining accuracy to obtain a machining accuracy ratio. The machining accuracy ratio is calculated using 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 based on the processing timing interval standard deviation and the processing timing interval average value to obtain the processing timing; wherein the processing timing is calculated using 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 based on the target processing temperature and the actual processing temperature to obtain the processing temperature. The processing temperature is calculated using 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] The surface roughness is calculated based on the actual workpiece surface roughness and the target workpiece surface roughness to obtain the surface roughness. The surface roughness is calculated using 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 based on the machining accuracy ratio, the machining timing, the machining temperature, and the surface roughness to obtain a machining process feature.

[0044] Preferably, generating a first graph based on 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 multidimensional 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 based on the first optimization vector to obtain a device operation dynamic diagram.

[0048] Preferably, generating a second graphic according to the processing technology characteristics to obtain a processing technology dynamic graph includes:

[0049] Performing a second normalization process according to the processing technology characteristics to obtain a second multidimensional 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 based on the second optimization vector to obtain a machining process dynamic graph.

[0052] Preferably, generating a third graph based on the equipment operation dynamic graph and the processing technology dynamic graph to obtain a factory teaching state graph, and displaying the factory teaching state graph includes:

[0053] Performing association extraction based on the equipment operation dynamic graph and the processing technology dynamic graph to obtain an association data set;

[0054] Performing graph fusion based on 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 operating parameters and processing parameters;

[0058] A first feature module is used to generate a first feature based on the operating parameters to obtain an equipment 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 is configured to generate a first graphic based on the equipment operation characteristics to obtain a dynamic diagram of equipment operation;

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

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

[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 raw data; wherein, the factory raw data includes: equipment operating parameters and processing parameters; by using the equipment operating parameters, a first feature is generated to obtain the equipment operating feature, and by using the processing parameters, a second feature is generated to obtain the equipment operating feature,

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

[0067] Figure 1 This is a flowchart of a smart manufacturing factory simulation teaching method based on VR virtual technology provided by the first embodiment of the present invention;

[0068] Figure 2 This 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 clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

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

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

[0072] S12, generating a first feature based on 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 based on the equipment operation characteristics to obtain an equipment operation dynamic graph;

[0075] S15, generating a second graph based on 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 time;

[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's worth noting that acquiring equipment operating parameters and processing parameters in step S11 is a key step in laying the data foundation for subsequent analysis, modeling, and visualization. These parameters are derived through real-time collection of equipment operating data and precise measurement of the processing process, ensuring the accuracy, scientific nature, and usability of the data.

[0081] Equipment operating parameters include rated power, actual power, rated speed, actual speed, rated frequency, actual frequency, average operating interval, and uniformity of operating intervals. The rated power is set by the equipment manufacturer during the design phase and recorded on the equipment nameplate or technical documentation, allowing users to obtain it directly without measuring. Actual power is measured in real time using a power meter installed in the equipment's power supply line. The power meter collects current, voltage, and power factor data to calculate the actual power value of the equipment in real time. The rated speed and rated frequency of the equipment are also design parameters and can be found directly in the equipment manual. Actual speed is obtained by a speed sensor installed on the rotating component, which measures the rotational speed in real time and outputs digital data. Actual frequency is obtained in real time using a frequency meter, which calculates the current operating frequency by monitoring the periodic changes in current or voltage in the circuit.

[0082] The average interval time of equipment operation is used to measure the regularity of 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 intervals between adjacent time points, and average these intervals. The specific formula is: Where T iis the time interval 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 fluctuation of multiple interval times, which is calculated by calculating the standard deviation σ of the time interval T The ratio of the average interval time β is obtained, and the formula is This parameter describes the consistency of the equipment's operating intervals. The higher the uniformity, the more stable the equipment's operation.

[0083] Machining process parameters include actual machining accuracy, target machining accuracy, standard deviation of machining intervals, average machining intervals, target machining temperature, actual machining temperature, actual workpiece surface roughness, and target workpiece surface roughness. Actual machining accuracy is determined by measuring critical dimensions of the finished workpiece and comparing them with the target design dimensions. Target machining accuracy is set during the process design phase and directly recorded in the process specification. Standard deviation of machining intervals reflects the volatility of the time intervals between machining steps and is calculated by counting the time intervals between multiple machining steps and calculating their standard deviations. Average machining intervals are the average of multiple time intervals, describing the overall distribution of time between machining steps. Target machining temperature is set in the process design specification and can be directly referenced. Actual machining temperature is measured in real time using thermocouples or infrared sensors, reflecting actual temperature fluctuations during machining. Actual workpiece surface roughness is measured using a roughness measuring instrument, which generates roughness data by scanning the workpiece surface. Target workpiece surface roughness is set during the process design phase and can be obtained from the process documentation.

[0084] By collecting and calculating these equipment operating parameters and processing parameters, comprehensive and accurate data support is provided for subsequent analysis and model building. This data, with its reliable source and clear calculation method, provides a scientific foundation and technical support for the implementation of intelligent manufacturing factory simulation teaching.

[0085] In step S12, a first feature is generated based on the equipment operation parameters to obtain the equipment operation feature, including: calculating the load rate using 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 based on the rated speed of the device and the actual speed of the device to obtain the speed ratio; wherein the speed ratio is calculated using 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] An operation stability calculation is performed based on the rated frequency of the device and the actual frequency of the device to obtain the operation stability. The operation stability is calculated using 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] The consistency coefficient is calculated based on the average equipment operation interval time and the uniformity of the equipment operation interval 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 during the extraction of equipment operating characteristics, the load factor, speed ratio, operational stability, and consistency coefficient need to be integrated from multiple dimensions to accurately reflect the equipment's operating status. First, data preprocessing is a critical step. The load factor, speed ratio, operational stability, and consistency coefficient reflect the equipment's power, speed, frequency, and time interval stability, respectively. These parameters have different dimensions and numerical ranges during the calculation process, so they must be normalized. The purpose of normalization is to adjust the value of each parameter to a uniform range so that the impact of each parameter on the final result remains consistent during subsequent feature integration. Normalization is achieved by mapping the value of each parameter to a range between 0 and 1, eliminating dimensional differences between different parameters. The normalization process relies on the equipment's design specifications or the maximum and minimum values ​​of real-time operating data to ensure that the calculation process for each feature conforms to actual operating conditions.

[0099] After data preprocessing, the next step is weighting. Equipment operating characteristics must comprehensively reflect the equipment's operating efficiency, stability, and consistency. Therefore, when integrating parameters, each parameter must be assigned an appropriate weight based on the application scenario. The load factor reflects the equipment's operating efficiency and is a key indicator for assessing whether the equipment is operating under a reasonable load. The load factor is weighted at 40% because the load at which the equipment operates directly affects its stability and energy efficiency. Under high load conditions, when the load factor approaches or exceeds the rated power, the equipment risks overheating or reduced energy efficiency. The speed ratio is weighted at 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 can significantly impact product quality and equipment life. Operational stability is weighted at 20%. Operational stability is assessed by measuring the deviation between the equipment's actual frequency and the rated frequency. Large frequency fluctuations reduce equipment stability, potentially leading to failure or performance degradation. The consistency coefficient is weighted at 10%. It reflects the consistency of the equipment's operating time intervals. In automated production lines or scenarios with high production efficiency requirements, a higher consistency coefficient indicates greater equipment stability and production efficiency.

[0100] After completing 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 the results are added to obtain the comprehensive operating characteristic value of the equipment. Through this weighted summation, the comprehensive characteristic value can accurately describe the overall operating status of the equipment, providing strong data support for subsequent equipment monitoring, performance evaluation and optimization.

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

[0102] Through these steps, load factor, speed ratio, operational stability, and consistency coefficient are precisely integrated into a comprehensive equipment operating profile, fully reflecting the equipment's operating status. This entire process does not rely on vague empirical assumptions, but rather through concrete and feasible steps such as standardization, weight allocation, feature integration, and verification, ensuring the final result is highly reliable and operational.

[0103] In step S13, a second feature is generated based on the machining process parameters to obtain a machining process feature, including: performing machining accuracy calculation based on the actual machining accuracy and the target machining accuracy to obtain a machining accuracy ratio; wherein the machining accuracy ratio is calculated using 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 based on the processing timing interval standard deviation and the processing timing interval average value to obtain the processing timing; wherein the processing timing is calculated using 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 based on the target processing temperature and the actual processing temperature to obtain the processing temperature. The processing temperature is calculated using 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] The surface roughness is calculated based on the actual workpiece surface roughness and the target workpiece surface roughness to obtain the surface roughness. The surface roughness is calculated using 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 based on the machining accuracy ratio, the machining timing, the machining temperature, and the surface roughness to obtain a machining process feature.

[0116] It is worth noting that in the process of extracting machining process features, it is first necessary to perform detailed calculations on the four key parameters: machining accuracy ratio, machining timing, machining temperature, and surface roughness. Each parameter reflects a specific aspect of the machining process and is of great significance, so it needs to be derived through a clear calculation formula. The machining accuracy ratio is used to evaluate whether the machining has met 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 dimensions of the workpiece, while 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.

[0117] Process timing evaluates the uniformity of the time intervals between processing steps, reflecting the temporal consistency of the manufacturing process. This metric is calculated by calculating the ratio of the standard deviation of the processing time intervals to the mean. A smaller standard deviation indicates more uniform time intervals between processing steps and more stable process control. Process timing is important because it reflects the sequence and timing management of each process step in the production process. Large fluctuations can lead to process inefficiency or discontinuous production.

[0118] Processing temperature calculations assess the difference between actual and target processing temperatures. Temperature stability significantly impacts material properties and quality during processing. By quantifying the difference between actual and target processing temperatures, we can assess the accuracy of process control. The closer the processing temperature is to the target, the more stable the process and the better the product quality.

[0119] Surface roughness is a core indicator for measuring the surface quality of a machined workpiece. It assesses machining quality by measuring the roughness of the workpiece's surface microstructure. Lower surface roughness values ​​indicate a smooth, high-quality surface; higher values ​​indicate significant machining marks, impacting product performance and appearance.

[0120] After obtaining the calculated results for these four parameters, the next step is to standardize them. Because the four parameters—machining accuracy ratio, machining timing, machining temperature, and surface roughness—have different dimensions and numerical ranges, standardization is necessary to bring them into the same numerical range. This involves mapping the value of each parameter to a range between 0 and 1. This eliminates dimensional differences and ensures that each parameter has the same influence in subsequent analysis.

[0121] After standardization is completed, the weight distribution link begins. Since the machining accuracy ratio, machining timing, machining temperature and surface roughness reflect different dimensions of the machining process, their influence on the final machining results is also different. Therefore, before feature integration, different weights must be assigned to these parameters. The weight distribution 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 precision of the product. In high-speed machining or batch production processes, 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 together 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 characteristic. This value can fully reflect the overall performance of the processing technology.

[0123] The resulting composite characteristic value is a dynamic variable that will be used to generate dynamic graphs in subsequent steps. This composite characteristic value represents the performance of the machining process across various process parameters and provides the underlying data for the subsequent dynamic graphs. In subsequent steps, this characteristic value will be dynamically updated based on real-time data changes and converted into a dynamic graph, visually demonstrating 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 steps such as standardization, weight assignment, and feature integration, ensuring that the generated machining process features accurately reflect the overall performance of the process. This also provides an operational, data-supported basis for subsequent dynamic graph generation.

[0125] In step S14, a first graph is generated based on 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 multidimensional 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 based on the first optimization vector to obtain a device operation dynamic diagram.

[0129] It's worth noting that the device's operating characteristics include load factor, speed ratio, operating stability, and consistency coefficient. Each characteristic value represents the device's operating status in different dimensions. For example, the load factor reflects the device's workload, the speed ratio indicates the device's speed efficiency, operating stability reflects the device's frequency stability, and the consistency coefficient reflects the device's operating regularity. The next goal is to generate a dynamic graph based on these characteristic values ​​to more intuitively demonstrate the dynamic changes in the device during operation.

[0130] First, these eigenvalues ​​need to be standardized. Because equipment operating characteristics involve different parameters with varying dimensions and numerical ranges, standardization is essential. Through standardization, load factor, speed ratio, operational stability, and consistency coefficients can be adjusted to a uniform numerical range, mapping them to the interval 0 to 1. Standardized eigenvalues ​​eliminate dimensional differences between parameters, ensuring they have equal influence in subsequent integration and graph generation.

[0131] After standardization, these eigenvalues ​​enter the feature integration stage. Each feature is combined with other features using a weighting coefficient to form a comprehensive dynamic feature. The calculation method for this comprehensive feature has been described in the previous step. It reflects the overall operating status of the equipment. For example, the load factor is given a higher weight, while the speed ratio and operating stability are appropriately weighted based on the equipment's operating requirements. The integrated eigenvalue is not just a number but a dynamic variable that fluctuates with changes in the equipment's operating status. This comprehensive eigenvalue becomes the basis for the dynamic graph.

[0132] Next, a dynamic graph is generated based on this dynamic eigenvalue. 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 changes in the operating characteristics of the equipment, each frame of the graph will be updated based on real-time collected data. With each update, the characteristic values ​​are adjusted according to the current state of the equipment and mapped to new coordinate points. This process is driven by real-time data streams, ensuring 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 dots to reflect the degree of fluctuation in the consistency coefficient. Color changes can be used to indicate the load level of the equipment, with dark colors indicating high loads and light colors indicating low loads. The size of the dots can indicate the stability of the equipment operation, with larger dots representing higher stability and smaller dots indicating fluctuating or unstable operating conditions. Ultimately, the generated dynamic graph can demonstrate the overall performance of the equipment operation.

[0134] In step S15, a second graph is generated based on 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 multidimensional 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 based on the second optimization vector to obtain a machining process dynamic graph.

[0138] It is worth noting that in the process of generating dynamic graphics based on machining process characteristics, 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. This 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 is mapped to the Y-axis, the machining timing is mapped to the Z-axis, and the machining temperature is represented by the color change of the point. Each characteristic value represents a machining state of a specific dimension. 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 based on changes in the timeline and convert it into coordinate points in the dynamic graph. The processing process eigenvalue at each time point corresponds to a coordinate position in 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 fluctuations in 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 time consistency issues during the processing; and changes in processing temperature are represented by changes in the color of the graph, with darker colors indicating rising temperatures and lighter colors indicating falling temperatures.

[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, generating 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] Furthermore, by continuously rendering and updating each frame of the graphics, the position and color of the dynamic feature values ​​in three-dimensional space change as the machining process progresses, reflecting the real-time status of machining 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, allowing the fluctuations of each feature to be intuitively displayed, thus providing an intuitive and dynamic learning tool for understanding and teaching machining processes. Ultimately, the generated dynamic graph can demonstrate how the various process parameters affect each other during the machining process and show the overall performance of the process.

[0142] In step S16, a third graph is generated based on 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] Performing association extraction based on the equipment operation dynamic graph and the processing technology dynamic graph to obtain an association data set;

[0144] Performing graph fusion based on 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's worth noting that the generation of the factory teaching state diagram is based on the integrated analysis and visualization of equipment operation dynamic diagrams and process dynamic diagrams. Its core goal is to fuse equipment operating status with process characteristics to create an intuitive teaching tool that comprehensively reflects the dynamic operation of the factory. The following is a detailed explanation of this step, including the specific processes of data extraction, graphic fusion, and VR display.

[0147] First, it is necessary to extract a related system dataset from the equipment operation dynamic graph and the processing process dynamic graph. The equipment operation dynamic graph and the processing process dynamic graph contain the dynamic operation characteristics of the equipment and the processing process characteristics, respectively. They are synchronized through time and spatial association to extract key data from both. The extracted key data includes equipment operating parameters at the time node, such as load rate and speed ratio, and processing process parameters, such as processing accuracy and surface roughness. This data constitutes the related system dataset. To ensure the accuracy and reliability of the extracted data, the two types of dynamic graphs need to be aligned using unified timestamps and spatial coordinates. Data filtering and statistical methods are used to eliminate outliers or redundant data, ultimately forming a high-quality dataset for integration.

[0148] Next, the linked system data sets are graphically fused to generate a collaborative operation status diagram. At this stage, the extracted equipment operation data and process data are combined through dynamic mapping. Equipment operation characteristics and process characteristics are assigned to different levels in three-dimensional space and intuitively presented through color, shape, and dynamic effects. For example, equipment load rate can be displayed through node size, surface roughness can be expressed through 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 linked system data to generate a collaborative operation status diagram that reflects both local details and overall collaborative relationships. After the graphical fusion is completed, the collaborative operation status diagram not only dynamically displays the overall picture of factory operations, but also helps users intuitively understand the linkage relationship between equipment and processes with high readability.

[0149] Finally, the collaborative operation state diagram completes the generation of the factory teaching state diagram through layout integration. During this stage, the various modules within the collaborative operation state diagram are adjusted based on the factory layout and logical relationships, combining dynamic elements with static layouts. By adjusting the viewing angle, scale, and interactive functions, the diagram's readability and user experience are further optimized. The resulting factory teaching state diagram incorporates complete factory operation logic and provides real-time dynamic updates to reflect the impact of parameter adjustments on overall factory operations during the teaching process.

[0150] VR technology provides a more immersive way to present factory state diagrams. Users can wear VR devices, such as head-mounted displays, to gain a panoramic view of the virtual factory. In the virtual factory, the factory state diagram is presented as a three-dimensional scene. Users can adjust the viewing angle by moving their heads and use handles to select and zoom in on specific areas for detailed analysis. The VR environment supports multiple levels of interactive functions, such as clicking on a device node to view its specific operating parameters, or adjusting process parameters through dynamic control to observe the impact on the global state. VR presentations not only make learning more intuitive and interesting, but also help users gain a deeper understanding of the overall logic and local details of factory operations through real-time feedback.

[0151] Through the above steps, the generation of factory teaching state diagrams and VR displays integrate dynamic information about equipment and processes from multiple dimensions, achieving intuitive, realistic, and efficient teaching objectives. The entire process relies on data collection, analysis, integration, and visualization technologies, providing an innovative approach to teaching and training in intelligent manufacturing.

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

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

[0154] A first feature module is used to generate a first feature based on the operating parameters to obtain an equipment 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 is configured to generate a first graphic based on the equipment operation characteristics to obtain a dynamic diagram of equipment operation;

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

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

[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] An embodiment of the present invention further 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 VR-based intelligent manufacturing factory simulation teaching program. When the processor executes the computer program, the steps in the above-mentioned embodiments of the VR-based intelligent manufacturing factory simulation teaching method 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, and the instruction segments 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, notebook, PDA, or smart tablet. The electronic device may include, but is not limited to, a processor and memory. Those skilled in the art will appreciate that the aforementioned components are merely examples of electronic devices and do not constitute a limitation of the electronic device. The electronic device may include more or fewer components than those described above, or a combination of certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, and the like.

[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, connecting various parts of the entire electronic device using various interfaces and lines.

[0164] The memory can be used to store the computer programs and / or modules, and the processor realizes various functions of the electronic device by running or executing the computer programs and / or modules 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, at least one application required for a function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created based on the use of the mobile phone (such as audio data, a phone book, 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 device.

[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 this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by a computer program to instruct the relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, it can implement the steps of each of the above-mentioned method embodiments. 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, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), 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 illustrative, 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 across multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the 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 can understand and implement the present invention without inventive effort.

[0167] The specific embodiments described above further illustrate the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A simulation teaching method for intelligent manufacturing factory based on VR virtual technology, characterized by: include: Obtain equipment operating parameters and processing 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; Generating a first graph based on the equipment operation characteristics to obtain an equipment operation dynamic graph; Generating a second graph according to the processing technology characteristics to obtain a processing technology dynamic graph; Generating a third graph based on the equipment operation dynamic graph and the processing technology dynamic graph to obtain a factory teaching state graph, and displaying the factory teaching state graph; The generating of a first graph based on the equipment operation characteristics to obtain an equipment operation dynamic graph includes: performing a first normalization process based on the equipment operation characteristics to obtain a first multidimensional vector; performing a first feature weighted combination based on the first multidimensional vector to obtain a first optimized vector; and performing a first three-dimensional dynamic mapping based on the first optimized vector to obtain the equipment operation dynamic graph. The step of generating a second graph based on the processing technology characteristics to obtain a processing technology dynamic graph includes: performing a second normalization process based on the processing technology characteristics to obtain a second multi-dimensional vector; Performing a second feature weighted combination based on the second multidimensional vector to obtain a second optimized vector; performing a second three-dimensional dynamic mapping based on the second optimized vector to obtain a machining process dynamic graph; The generating of a third graph based on the equipment operation dynamic graph and the processing technology dynamic graph to obtain a factory teaching state graph and displaying the factory teaching state graph includes: Performing association extraction based on the equipment operation dynamic graph and the processing technology dynamic graph to obtain an association system data set; performing graphic fusion based on the association system data set to obtain a collaborative operation state graph; performing layout integration based on the collaborative operation state graph to obtain a factory teaching state 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 time axis and spatial association, and key data including equipment operation parameters and processing technology parameters at time nodes are extracted to form a correlation system data set. The extracted equipment operating parameters and processing technology parameters are combined through a dynamic mapping method. The equipment operating characteristics and processing technology characteristics are assigned to different levels in the three-dimensional space and intuitively presented through color, shape and dynamic effects. Among them, the equipment load rate is displayed through the size of the node, the surface roughness is expressed through color changes, and the timeline shows the synchronization process of different process stages through dynamic animation. During the fusion process, the related system data sets are layered and dynamically combined to generate a collaborative operation status diagram that can reflect both local details and the overall collaborative relationship.

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 time; 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: Generating a first feature according to the equipment operation parameter to obtain the equipment operation feature includes: The load factor is calculated based on the rated power of the device and the actual power of the device to obtain the load factor. The load factor is calculated using the following formula: ； in, is the actual power of the device, is the rated power of the equipment, is the load factor; The speed ratio is calculated based on the rated speed of the device and the actual speed of the device to obtain the speed ratio; wherein the speed ratio is calculated using the following formula: ； in, is the speed ratio, is the actual speed of the device, is the rated speed of the equipment; An operation stability calculation is performed based on the rated frequency of the device and the actual frequency of the device to obtain the operation stability. The operation stability is calculated using the following formula: ； in, For operational stability, is the actual operating frequency of the device, is the rated operating frequency of the equipment; The consistency coefficient is calculated based on the average equipment operation interval time and the uniformity of the equipment operation interval to obtain the consistency coefficient; wherein the consistency coefficient is calculated by the following formula: ； in, is the consistency coefficient, is the average interval time between equipment operations, The uniformity of the equipment operation interval reflects the fluctuation of the equipment's multiple operation intervals. The uniformity is calculated by and the average interval between equipment operations The ratio is worth getting; 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: Generating a second feature according to the processing parameters to obtain a processing feature includes: The machining accuracy is calculated based on the actual machining accuracy and the target machining accuracy to obtain a machining accuracy ratio. The machining accuracy ratio is calculated using the following formula: ； in, is the machining accuracy ratio, is the actual processing accuracy, The target machining accuracy; The processing timing is calculated based on the processing timing interval standard deviation and the processing timing interval average value to obtain the processing timing; wherein the processing timing is calculated using the following formula: ； in, For the processing sequence, is the standard deviation of the processing time interval, is the average value of the processing time interval; The processing temperature is calculated based on the target processing temperature and the actual processing temperature to obtain the processing temperature. The processing temperature is calculated using the following formula: ； in, is the processing temperature, is the actual processing temperature, is the target processing temperature; The surface roughness is calculated based on the actual workpiece surface roughness and the target workpiece surface roughness to obtain the surface roughness. The surface roughness is calculated using the following formula: ； in, is the surface roughness, is the actual surface roughness, is the target surface roughness; A second feature extraction is performed based on the machining accuracy ratio, the machining timing, the machining temperature, and the surface roughness to obtain a machining process feature.

5. A VR-based intelligent manufacturing factory simulation teaching system, characterized by: A method for implementing a VR-based intelligent manufacturing factory simulation teaching method as claimed in any one of claims 1 to 4, comprising: Data acquisition module, used to obtain equipment operating parameters and processing parameters; A first feature module is used to generate a first feature based on the operating parameters to obtain an equipment 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 is configured to generate a first graphic based on the equipment operation characteristics to obtain a dynamic diagram of equipment operation; A second graphics module is used to generate a second graphic according to the processing technology characteristics to obtain a processing technology dynamic graph; The factory teaching module is used to generate a third graphic based on the equipment operation dynamic diagram and the processing technology dynamic diagram, obtain a factory teaching status diagram, and display the factory teaching status diagram.

6. 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 4.

7. 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 4.

Images