Internet training platform based on industrial scenarios

Abstract

The present invention discloses an Internet training platform based on industrial scenarios, which relates to the field of industrial Internet training technology. In order to solve the problem that it is impossible to fully simulate complex scenarios such as real production processes, equipment fault handling, and multi-device collaboration, and it is impossible to conduct a comprehensive and accurate evaluation of employees' performance in training; this Internet training platform based on industrial scenarios includes a training table, and the surface of the training table is respectively equipped with a control panel, a production and processing simulation platform and a monitoring sensor, and the surface of the production and processing simulation platform is respectively equipped with a processing production line, a robotic arm and a turntable flow device; the production and processing simulation platform simulates the real industrial production process, and performs practical operations in a simulated environment to improve the user's actual operation ability. The monitoring sensor can fully monitor each link in the production process to ensure the stability and safety of the processing process. At the same time, according to the user's operation data and monitoring data during the training process, intelligent learning and evaluation are carried out to improve the user's skill level and provide personalized learning suggestions.

Description

Technical Field

[0001] The present invention relates to the field of industrial Internet training technology, and in particular to an Internet training platform based on industrial scenarios. Background Art

[0002] With the continuous development of industrial Internet technology, more and more companies are beginning to pay attention to the application of the industrial Internet. However, most of the industrial Internet training platforms currently on the market have problems such as single functions and unrealistic scene simulations, which cannot meet the needs of companies for comprehensive and realistic industrial Internet skills training for their employees. Regarding Internet training platforms, Chinese patent publication number CN219758988U discloses an industrial Internet training platform, which includes a laboratory table, a vibration plate, a camera and a robot. Among them, the vibration plate, camera and robot are all arranged on the laboratory table. The vibration plate is used to place at least one material, and the vibration plate can adjust the placement of the material by vibration. The camera is used to take pictures of the material on the vibration plate. The robot can grasp it based on the camera's picture results. This industrial Internet training platform has a high degree of automation and can achieve the training purpose of disordered grasping.

[0003] However, although the platform can simulate the material grabbing process, it cannot fully simulate complex scenarios such as real production processes, equipment fault handling, and multi-device collaboration. When training employees, companies may still need to rely on traditional physical equipment and venues, which limits the flexibility and efficiency of training. It is impossible to conduct a comprehensive and accurate assessment of employees' performance in practical training, making it difficult to achieve precise and personalized teaching. Summary of the Invention

[0004] The purpose of the present invention is to provide an Internet training platform based on industrial scenarios. The real industrial production process is simulated through the processing production line, robotic arm and turntable flow device of the production and processing simulation platform. The monitoring sensors can comprehensively monitor each link in the production process. According to the user's operation data and monitoring data during the training process, intelligent learning and evaluation are carried out to improve the user's skill level and provide personalized learning suggestions to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] The Internet training platform based on industrial scenarios includes a training table with a control panel, a production and processing simulation platform and monitoring sensors installed on its surface. Control keys are set on one side of the control panel. The control panel exchanges data with the production and processing simulation platform and monitoring sensors based on the Internet of Things. The surface of the production and processing simulation platform is equipped with a processing production line, a robotic arm and a turntable flow device.

[0007] Furthermore, the processing production line consists of an assembly line conveying mechanism, a stacking mechanism and a production and processing mechanism. The robotic arm is arranged on one side of the production and processing mechanism for loading, processing, testing and unloading. The turntable flow device consists of a circular turntable, a servo motor and an intermittent mechanism reduction motor.

[0008] Furthermore, the control panel includes:

[0009] The training management unit is used to establish various industrial training plans on the production and processing simulation platform according to actual needs, and set corresponding training courses and difficulty levels based on the industrial training plans;

[0010] The industrial scenario simulation unit is used to build corresponding industrial scenarios based on industrial training plans, and monitor the user's operation data and behavior data in real time during the training process based on monitoring sensors;

[0011] The training analysis interactive unit is used to analyze the collected monitoring data, evaluate the user's skill level and existing problems, and provide corresponding learning suggestions based on the analysis results;

[0012] The training and analysis interaction unit is also used to interact with industrial scenarios in real time based on the Internet of Things, operate and control equipment in industrial scenarios, and provide real-time feedback on operation results and status information.

[0013] Furthermore, the practical training management unit includes:

[0014] The user management module is used to manage the information data of training users and conduct user skill assessment based on their skill level and teaching needs based on their historical information data;

[0015] The industrial scene classification module is used to determine the categories of each industrial scene based on the scene characteristics of each industrial scene, match the corresponding industrial scene templates based on the industrial scene categories, and establish industrial scene labels based on the industrial scene templates;

[0016] The solution matching module is used to define different difficulty levels for each practical training course and clarify the professional knowledge and skill data for each level. It matches the difficulty level of the corresponding practical training course based on the user skill assessment results and the selected industrial scenario tags to generate an industrial practical training solution.

[0017] Furthermore, the industrial scene classification module establishes industrial scene labels based on the industrial scene template, specifically:

[0018] Analyze each industrial scene, determine the scene feature data, and divide the scene feature data into multiple scene blocks according to the industrial scene category;

[0019] Obtaining the type of each scene block and performing quantization processing to form a multi-dimensional feature vector, obtaining a type value for each scene block, and determining the distance value between each scene block based on the type value;

[0020] Determine the content area of ​​each scene block, extract font information and perform text recognition on the content area, and determine the scene label based on the recognition results and prior knowledge of the industrial scene;

[0021] Based on the importance and relevance factors of each scene label in the industrial scene, a queue is established and displayed. At the same time, users are allowed to provide feedback and corrections to the generated scene labels.

[0022] Furthermore, the industrial scene simulation unit includes:

[0023] The scenario modeling module is used to determine the type of industrial scenario to be simulated based on the selected industrial scenario, obtain the actual data corresponding to the industrial scenario type, and create an industrial scenario model based on the collected actual data;

[0024] A parameter adjustment module is used to adjust the equipment parameters in the industrial scenario model according to the training courses and difficulty levels of the industrial training program;

[0025] The data acquisition module is used to collect equipment operation data, user operation data and equipment status data in the industrial scene model based on monitoring sensors.

[0026] Furthermore, the practical training and analysis interactive unit includes:

[0027] Behavior analysis module, used to analyze collected user operation data and identify patterns, trends, and potential problems in user operations;

[0028] The real-time interaction module is used to receive control commands sent by users to devices in industrial scenarios, analyze the acquired device operation data and device status data, and determine whether the device operation status and command feedback are normal;

[0029] The skill assessment module is used to comprehensively evaluate the recognition results output by the behavior analysis module and the analysis results output by the real-time interaction module according to the established evaluation standards, compare the comprehensive evaluation results with the evaluation standards, and generate a skill assessment report.

[0030] Furthermore, the training and analysis interaction unit also includes:

[0031] The training progress management module is used to obtain users' training data in real time, extract the training objectives in the practical training courses, and determine the users' training progress;

[0032] The real-time feedback module is used to issue operation feedback results in real time when receiving user operation instructions and task submission instructions. When the status of equipment in the industrial scene changes, the change information is conveyed to the user in real time;

[0033] The scene synchronization module is used to update the operating status and data of each device in the industrial scene model in real time, and to visualize the user operation data of the data acquisition module and the user's training progress.

[0034] Furthermore, the scene synchronization module includes:

[0035] A first moment data real-time collection module is used to collect the change moment of user operation data in real time as the first data change moment;

[0036] A second moment data real-time collection module is used to collect the display change moment of the user's training progress in the visual display in real time as the second data change moment;

[0037] A data change time difference acquisition module, configured to acquire a data change time difference according to the first data change time and the second data change time;

[0038] The difference coefficient acquisition module is used to obtain the difference coefficient according to the difference at the data change moment, wherein the difference coefficient is obtained by the following formula:

[0039]

[0040] Where f represents the difference coefficient; n represents the number of times the user operates the data; T ci represents the difference in the data change time corresponding to the i-th data change; T e Indicates the preset data change time difference reference value; T cmax Indicates the maximum value of the difference at the time of data change; ε indicates the preset minimum parameter, which is used to prevent the denominator from being zero;

[0041] a first comparison module, configured to compare the difference coefficient with a preset coefficient threshold;

[0042] The visual operation quality judgment module is used to judge the quality of the visual response of the scene synchronization module when the difference coefficient does not exceed a preset coefficient threshold.

[0043] Furthermore, the visual operation quality determination module includes:

[0044] The response duration acquisition module is used to collect the visual response duration of the scene synchronization module in real time;

[0045] The response duration coefficient acquisition module is used to obtain the response duration coefficient using the visual response duration of the scene synchronization module; wherein the response duration coefficient is obtained by the following formula:

[0046]

[0047] Among them, s represents the response time coefficient; n represents the number of times the user operates the data; T ci represents the difference in the data change time corresponding to the i-th data change; T xi Indicates the visualization response time corresponding to the i-th data change; T xe Indicates the preset reference value of visualization response time; T e Indicates the preset reference value of the difference in data change time; ε indicates the preset minimum parameter, which is used to prevent the denominator from being zero;

[0048] A difference coefficient extraction module is used to extract the difference coefficient; ε represents a preset minimum parameter, which is used to prevent the denominator from being zero;

[0049] The visualization operation quality evaluation parameter acquisition module is used to obtain the visualization operation quality evaluation parameter using the difference coefficient and the response time coefficient; wherein the visualization operation quality evaluation parameter is obtained by the following formula:

[0050]

[0051] Among them, K represents the visual operation quality evaluation parameter; f represents the difference coefficient; s represents the response time coefficient;

[0052] A second comparison module is used to compare the visual operation quality evaluation parameter with a preset evaluation parameter threshold;

[0053] The operation abnormality determination module is used to determine that the visualization operation is abnormal and issue a visualization abnormality alarm when the visualization operation quality evaluation parameter is lower than a preset evaluation parameter threshold.

[0054] Compared with the prior art, the present invention has the following beneficial effects:

[0055] The production and processing simulation platform simulates the real industrial production process through the processing production line, robotic arm and turntable flow device, which helps users to have a deep understanding of the industrial production process, perform practical operations in a simulated environment, and improve their actual operation capabilities. The monitoring sensors can comprehensively monitor each link in the production process, monitor key parameters such as temperature, vibration, pressure during the processing process, ensure the stability and safety of the processing process, and ensure that each processing node is carried out under normal conditions, helping users to discover and solve problems in a timely manner and improve the training effect. The control panel and control keys achieve an operating experience similar to that of real industrial scenarios, thereby deepening the understanding and knowledge of industrial production. At the same time, based on the user's operation data and monitoring data during the training process, intelligent learning and evaluation are carried out to improve the user's skill level and provide personalized learning suggestions. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 This is a front view of the Internet training platform based on industrial scenarios of the present invention;

[0057] Figure 2 It is a partial front view of the Internet training platform based on industrial scenarios of the present invention;

[0058] Figure 3 This is a control panel module diagram of the present invention.

[0059] In the figure: 1. Training table; 11. Control panel; 12. Production and processing simulation platform; 121. Processing production line; 122. Robotic arm; 123. Turntable flow device; 13. Monitoring sensor; 14. Control key. DETAILED DESCRIPTION

[0060] 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. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0061] To address the technical issues in existing technologies, such as the inability to fully simulate complex scenarios such as real production processes, equipment troubleshooting, and multi-device collaboration, companies may still need to rely on traditional physical equipment and venues when training employees, which limits the flexibility and efficiency of training, makes it impossible to conduct comprehensive and accurate assessments of employee performance during training, and makes it difficult to achieve precise and personalized teaching. Figure 1-3 , this embodiment provides the following technical solutions:

[0062] The Internet training platform based on industrial scenarios includes a training table 1. A control panel 11, a production and processing simulation platform 12 and a monitoring sensor 13 are installed on the surface of the training table 1. A control key 14 is set on one side of the control panel 11. The control panel 11 exchanges data with the production and processing simulation platform 12 and the monitoring sensor 13 based on the Internet of Things. A processing production line 121, a robotic arm 122 and a turntable flow device 123 are set on the surface of the production and processing simulation platform 12. The processing production line 121 consists of a pipeline conveying mechanism, a stacking mechanism and a production and processing mechanism. The robotic arm 122 is set on one side of the production and processing mechanism for loading, processing, testing and unloading. There are at least one robotic arm 122 and they are staggered on the pipeline conveying mechanism. On both sides of the feeding mechanism, according to actual needs, grippers, cutters, drill bits and detection probes are installed at the end of the robotic arm 122. The turntable flow device 123 is composed of a circular turntable, a servo motor and an intermittent mechanism reduction motor. The circular turntable rotates and stops at 90° intervals. The production and processing simulation platform 12 simulates raw material processing. Each material level is equipped with an in-place monitoring sensor to detect whether there is material, and the processing status is monitored at each processing node based on the monitoring sensor 13. The monitoring sensor 13 includes a visual detection device and an industrial sensor. Among them, the industrial sensor includes a temperature sensor, a vibration sensor, a pressure sensor, etc., as well as a three-color warning light actuator, which can provide real-time feedback on the processing status based on the monitoring data, and provide users with intuitive visual prompts.

[0063] In this embodiment, the real industrial production process is simulated through the processing production line 121, the robotic arm 122 and the turntable flow device 123 of the production and processing simulation platform 12, which helps users to have an in-depth understanding of the industrial production process, perform practical operations in a simulated environment, and improve the user's actual operation ability. The monitoring sensor 13 can comprehensively monitor each link in the production process, monitor key parameters such as temperature, vibration, and pressure in the processing process, ensure the stability and safety of the processing process, and ensure that each processing node is carried out in a normal state, helping users to discover and solve problems in a timely manner and improve the training effect. The control panel 11 and the control keys 14 achieve an operating experience similar to that of a real industrial scene, thereby deepening the understanding and knowledge of industrial production. At the same time, based on the user's operating data and monitoring data during the training process, intelligent learning and evaluation are carried out to improve the user's skill level and provide personalized learning suggestions.

[0064] In this embodiment, the control panel 11 includes:

[0065] The training management unit is used to establish various industrial training plans on the production and processing simulation platform 12 according to actual needs, and set corresponding training courses and difficulty levels based on the industrial training plans;

[0066] The industrial scenario simulation unit is used to build corresponding industrial scenarios based on the industrial training plan, including production lines, equipment, control systems, etc., and monitor the user's operation data and behavior data during the training process in real time based on the monitoring sensor 13, including:

[0067] The scenario modeling module is used to determine the type of industrial scenario to be simulated based on the selected industrial scenario, obtain the actual data corresponding to the industrial scenario type, such as the size, layout, and working principle of the equipment, and create an industrial scenario model based on the collected actual data;

[0068] A parameter adjustment module is used to adjust the equipment parameters in the industrial scenario model according to the training courses and difficulty levels of the industrial training program. The parameters include the equipment's operating speed, production capacity, failure rate, etc., to ensure that the simulation environment can meet the requirements of the training courses;

[0069] A data acquisition module is used to collect equipment operation data, user operation data and equipment status data in the industrial scene model based on the monitoring sensor 13;

[0070] The training analysis interactive unit is used to analyze the collected monitoring data, evaluate the user's skill level and existing problems, and provide corresponding learning suggestions based on the analysis results;

[0071] The training and analysis interaction unit is also used to interact with industrial scenarios in real time based on the Internet of Things, operate and control equipment in industrial scenarios, and provide real-time feedback on operation results and status information.

[0072] In this embodiment, various industrial training plans are set up according to actual needs through the training management unit, and corresponding training courses and difficulty levels are matched based on the user's skill level. The personalized training experience can better meet the learning needs of different users and improve learning efficiency and interest. The scenario modeling module constructs a highly realistic industrial scenario model based on actual data. Users conduct training under conditions close to the real working environment, which improves the practicality and effectiveness of the training. The data acquisition module can collect equipment operation data, user operation data and equipment status data in the industrial scenario model in real time. The training analysis interaction unit analyzes the collected data, evaluates the trainees' skill level, and provides trainees with personalized feedback and learning suggestions based on the evaluation results, helping trainees to quickly discover and correct problems and improve learning effects. At the same time, it allows trainees to remotely operate and control equipment in industrial scenarios, enhances the flexibility of training, and provides a richer training experience.

[0073] In this embodiment, the training management unit includes:

[0074] The user management module is used to manage the information data of training users, such as account numbers, permissions, training progress, etc., and conduct user skill assessments based on user skill levels and teaching needs based on historical user information data;

[0075] The industrial scene classification module is used to determine the categories of each industrial scene based on the scene characteristics of each industrial scene, match the corresponding industrial scene templates based on the industrial scene category, such as manufacturing, energy, and chemical industry, and establish industrial scene labels based on the industrial scene templates;

[0076] The industrial scene classification module creates industrial scene labels based on the industrial scene template, specifically:

[0077] Analyze each industrial scenario to determine scenario feature data, such as production line layout, equipment type, control system structure, etc., and divide the scenario feature data into multiple scenario blocks according to industrial scenario categories;

[0078] Obtaining the type of each scene block and performing quantization processing to form a multi-dimensional feature vector, obtaining a type value for each scene block, and determining the distance value between each scene block based on the type value;

[0079] Determine the content area of ​​each scene block, extract font information and perform text recognition on the content area, and determine the scene label based on the recognition results combined with prior knowledge of the industrial scene, such as safety operating procedures and equipment instructions.

[0080] Based on the importance and relevance of each scene label in the industrial scenario, a queue is established and displayed. At the same time, users are allowed to provide feedback and modify the generated scene labels.

[0081] The solution matching module is used to define different difficulty levels for each practical training course, such as elementary, intermediate, and advanced, and to clarify the professional knowledge and skill data for each level. It matches the difficulty level of the corresponding practical training course based on the user's skill assessment results and the selected industrial scenario tags to generate an industrial training solution.

[0082] In this embodiment, the user management module manages the information data of the training users and combines the users' historical information data to perform skill assessment, providing a personalized learning experience based on each user's skill level and teaching needs, which helps to improve the user's learning efficiency and satisfaction. The industrial scene classification module uses a scene classification method based on multidimensional feature vectors to accurately reflect the characteristics and needs of industrial scenes, providing strong support for the matching of subsequent training plans. When generating scene labels, not only the characteristics and needs of industrial scenes are considered, but also user feedback and corrections are combined to ensure the accuracy and practicality of the labels. Through the display of the queuing queue, the user can clearly understand the importance and relevance of each scene label, providing a reference for the subsequent selection of training plans. The solution matching module matches the corresponding difficulty level for each training course based on the user's skill assessment results and the selected industrial scene label, ensuring that the content of the training course matches the user's skill level and teaching needs, and improving the pertinence and effectiveness of the training.

[0083] In this embodiment, the training analysis interaction unit includes:

[0084] Behavior analysis module, used to analyze collected user operation data and identify patterns, trends, and potential problems in user operations;

[0085] The real-time interaction module is used to receive control commands sent by users to devices in industrial scenarios, analyze the acquired device operation data and device status data, and determine whether the device operation status and command feedback are normal;

[0086] The skill assessment module is used to comprehensively evaluate the recognition results output by the behavior analysis module and the analysis results output by the real-time interaction module based on the established evaluation standards, such as operation accuracy, efficiency, and safety, and compare the comprehensive evaluation results with the evaluation standards to generate a skill assessment report;

[0087] In this embodiment, the behavior analysis module analyzes user operation data to accurately identify patterns, trends, and potential problems in user operations, helping to promptly discover and correct user errors. The real-time interaction module enables users to control equipment in industrial scenarios in real time and immediately obtain equipment operation data and status feedback, enhancing the user's interactive experience with the training system. It monitors equipment operation status and instruction feedback in real time to ensure the safety and reliability of the training process. The skill assessment module comprehensively evaluates user skills based on established evaluation criteria and comprehensively considers the output results of the behavior analysis module and the real-time interaction module.

[0088] The training progress management module is used to obtain users' training data in real time, including their operation records and task completion status, extract training objectives in the practical training courses, such as the time and accuracy of completing specific tasks, and determine the user's training progress;

[0089] The real-time feedback module is used to issue real-time feedback on the results of operations when receiving user operation instructions and task submission instructions, such as whether the task was successfully completed and whether the operation was correct. When the status of equipment in the industrial scene changes, such as equipment failure or task progress update, the change information is conveyed to the user in real time;

[0090] The scene synchronization module is used to update the operating status and data of each device in the industrial scene model in real time, visualize the user operation data of the data acquisition module and the user's training progress, and generate operation guidance and suggestions based on the user operation data and the user's training progress data to help users improve their operating skills.

[0091] In this embodiment, the training progress management module enables users to understand their learning situation at any time, adjust learning strategies in a timely manner, and improve learning efficiency. The real-time feedback module can give feedback results immediately after the user operates or submits a task, so that users can promptly understand whether their operations are correct and whether the task is completed. At the same time, operation instructions and suggestions are generated based on user operation data and training progress data to help users improve their operating skills and improve training effects. The visual display and operation instructions of the scene synchronization module enable users to more intuitively understand the equipment operation status and data in the industrial scene, deepen their understanding of the training content, and help improve users' learning interest and enthusiasm.

[0092] Specifically, the scene synchronization module includes:

[0093] A first moment data real-time collection module is used to collect the change moment of user operation data in real time as the first data change moment;

[0094] A second moment data real-time collection module is used to collect the display change moment of the user's training progress in the visual display in real time as the second data change moment;

[0095] A data change time difference acquisition module, configured to acquire a data change time difference according to the first data change time and the second data change time;

[0096] The difference coefficient acquisition module is used to obtain the difference coefficient according to the difference at the data change moment, wherein the difference coefficient is obtained by the following formula:

[0097]

[0098] Where f represents the difference coefficient; n represents the number of times the user operates the data; T ci represents the difference in the data change time corresponding to the i-th data change; T e Indicates the preset data change time difference reference value; Tcmax Indicates the maximum value of the difference at the time of data change; ε indicates the preset minimum parameter, which is used to prevent the denominator from being zero;

[0099] a first comparison module, configured to compare the difference coefficient with a preset coefficient threshold;

[0100] The visual operation quality judgment module is used to judge the quality of the visual response of the scene synchronization module when the difference coefficient does not exceed a preset coefficient threshold.

[0101] The technical solution described above achieves the following: The first-moment real-time data acquisition module and the second-moment real-time data acquisition module can capture changes in user operation data and the instant at which these changes are displayed in the visual display in real time. This real-time performance ensures that the system can quickly respond to user operations, improving the fluidity and immediacy of user interaction.

[0102] The Data Change Time Difference Acquisition Module and the Difference Coefficient Acquisition Module work together to quantitatively assess the system's ability to synchronize and respond to user operations by calculating the time difference between user operations and visual display (i.e., the data change time difference) and calculating the difference coefficient based on this difference. This helps identify and resolve potential synchronization delay issues and optimize the user experience.

[0103] The calculation of the difference coefficient takes into account the number of data changes (n), the difference between each data change (Tci), a preset reference value for the difference between data changes (Te), the maximum difference between data changes (Tcmax), and a preset minimum parameter (ε) to prevent the denominator from reaching zero. This design enables the system to intelligently adjust its response strategy based on operation frequency and latency, enhancing its adaptability and stability.

[0104] The first comparison module and the visualization quality assessment module together constitute the system's quality monitoring mechanism. By comparing the difference coefficient with a preset coefficient threshold, the system automatically determines whether the visualization response quality meets the standard. If not, appropriate optimization or adjustments can be made to improve the visualization effect and user satisfaction.

[0105] Overall, this technical solution significantly improves the visual response quality and user experience of the scene synchronization module through real-time acquisition, synchronization optimization, intelligent adjustment, and quality monitoring. Users experience smoother and more immediate operational feedback and more accurate and synchronized visual displays, which enhances their trust and satisfaction with the system.

[0106] Specifically, the visual operation quality determination module includes:

[0107] The response duration acquisition module is used to collect the visual response duration of the scene synchronization module in real time;

[0108] The response duration coefficient acquisition module is used to obtain the response duration coefficient using the visual response duration of the scene synchronization module; wherein the response duration coefficient is obtained by the following formula:

[0109]

[0110] Among them, s represents the response time coefficient; n represents the number of times the user operates the data; T ci represents the difference in the data change time corresponding to the i-th data change; T xi Indicates the visualization response time corresponding to the i-th data change; T xe Indicates the preset reference value of visualization response time; T e Indicates the preset reference value of the difference in data change time; ε indicates the preset minimum parameter, which is used to prevent the denominator from being zero;

[0111] A difference coefficient extraction module is used to extract the difference coefficient; ε represents a preset minimum parameter, which is used to prevent the denominator from being zero;

[0112] The visualization operation quality evaluation parameter acquisition module is used to obtain the visualization operation quality evaluation parameter using the difference coefficient and the response time coefficient; wherein the visualization operation quality evaluation parameter is obtained by the following formula:

[0113]

[0114] Among them, K represents the visual operation quality evaluation parameter; f represents the difference coefficient; s represents the response time coefficient;

[0115] A second comparison module is used to compare the visual operation quality evaluation parameter with a preset evaluation parameter threshold;

[0116] The operation abnormality determination module is used to determine that the visualization operation is abnormal and issue a visualization abnormality alarm when the visualization operation quality evaluation parameter is lower than a preset evaluation parameter threshold.

[0117] The technical effect of the above technical solution is that it comprehensively evaluates the quality of visualization operation by introducing two dimensions: the response time coefficient and the difference coefficient. This not only reflects the system's ability to synchronously respond to user operations (via the difference coefficient), but also measures the system's immediate response speed to user operations (via the response time coefficient). This multi-dimensional evaluation method is more comprehensive and accurate.

[0118] The calculation formulas for the response time coefficient and the difference coefficient take into account multiple variables, such as the number of data changes during user operations, the time difference between individual data changes, and the visual response time. This design enables the system to make fine adjustments based on specific operational conditions and response performance to optimize the user experience.

[0119] By acquiring visual operation quality evaluation parameters and comparing them with preset evaluation parameter thresholds, the system can intelligently determine whether the visual operation quality meets the standards. This intelligent judgment mechanism can promptly detect and report potential operational anomalies, thus preventing problems from escalating.

[0120] Real-time Alarm and Response: When the visual operation quality evaluation parameters fall below the preset evaluation parameter threshold, the system will determine that the visual operation is abnormal and issue a visual abnormality alarm. This real-time alarm and response mechanism ensures that the problem is handled promptly, avoiding unnecessary trouble and impact to users.

[0121] Through multi-dimensional assessment, refined adjustments, intelligent judgment, and real-time alarm and response, this technical solution can significantly improve the visual operation quality of the scene synchronization module. Users will be able to enjoy a smoother, more immediate, and more accurate interactive experience, thereby increasing their satisfaction and trust in the system.

[0122] By monitoring and evaluating visual operational quality in real time, the system can promptly identify and resolve potential issues, thereby enhancing system stability and reliability. This helps reduce user complaints and losses caused by system failures or performance degradation.

[0123] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. An Internet training platform based on industrial scenarios, comprising a training platform (1), characterized by: A control panel (11), a production and processing simulation platform (12), and a monitoring sensor (13) are respectively installed on the surface of the training platform (1); a control key (14) is provided on one side of the control panel (11); the control panel (11) performs data interaction with the production and processing simulation platform (12) and the monitoring sensor (13) based on the Internet of Things; and a processing production line (121), a mechanical arm (122), and a turntable circulation device (123) are respectively provided on the surface of the production and processing simulation platform (12); The control panel (11) comprises: A training management unit is used to establish various industrial training programs on the production and processing simulation platform (12) according to actual needs, and to set corresponding training courses and difficulty levels based on the industrial training programs; An industrial scene simulation unit is used to construct a corresponding industrial scene based on an industrial training plan, and to monitor the user's operation data and behavior data during the training process in real time based on a monitoring sensor (13); The training analysis interactive unit is used to analyze the collected monitoring data, evaluate the user's skill level and existing problems, and provide corresponding learning suggestions based on the analysis results; The training and analysis interaction unit is also used to interact with industrial scenarios in real time based on the Internet of Things, operate and control equipment in industrial scenarios, and provide real-time feedback on operation results and status information; Practical training and analysis interactive unit, including: The training progress management module is used to obtain users' training data in real time, extract the training objectives in the practical training courses, and determine the users' training progress; The real-time feedback module is used to issue operation feedback results in real time when receiving user operation instructions and task submission instructions. When the status of equipment in the industrial scene changes, the change information is conveyed to the user in real time; The scene synchronization module is used to update the operating status and data of each device in the industrial scene model in real time, and to visualize the user operation data of the data acquisition module and the user's training progress; Scene synchronization module, including: A first moment data real-time collection module is used to collect the change moment of user operation data in real time as the first data change moment; A second moment data real-time collection module is used to collect the display change moment of the user's training progress in the visual display in real time as the second data change moment; A data change time difference acquisition module, configured to acquire a data change time difference according to the first data change time and the second data change time; The difference coefficient acquisition module is used to obtain the difference coefficient according to the difference at the data change moment, wherein the difference coefficient is obtained by the following formula: Where f represents the difference coefficient; n represents the number of times the user operates the data; T ci represents the difference in the data change time corresponding to the i-th data change; T e Indicates the preset data change time difference reference value; T cmax Indicates the maximum value of the difference at the time of data change; ε indicates the preset minimum parameter, which is used to prevent the denominator from being zero; a first comparison module, configured to compare the difference coefficient with a preset coefficient threshold; A visual operation quality determination module, configured to determine the quality of the visual response of the scene synchronization module when the difference coefficient does not exceed a preset coefficient threshold; The visual operation quality determination module includes: The response duration acquisition module is used to collect the visual response duration of the scene synchronization module in real time; The response duration coefficient acquisition module is used to obtain the response duration coefficient using the visual response duration of the scene synchronization module; wherein the response duration coefficient is obtained by the following formula: Among them, s represents the response time coefficient; n represents the number of times the user operates the data; T ci represents the difference in the data change time corresponding to the i-th data change; T xi Indicates the visualization response time corresponding to the i-th data change; T xe Indicates the preset reference value of visualization response time; T e Indicates the preset reference value of the difference in data change time; ε indicates the preset minimum parameter, which is used to prevent the denominator from being zero; A difference coefficient extraction module is used to extract the difference coefficient; ε represents a preset minimum parameter, which is used to prevent the denominator from being zero; The visualization operation quality evaluation parameter acquisition module is used to obtain the visualization operation quality evaluation parameter using the difference coefficient and the response time coefficient; wherein the visualization operation quality evaluation parameter is obtained by the following formula: Among them, K represents the visual operation quality evaluation parameter; f represents the difference coefficient; s represents the response time coefficient; A second comparison module is used to compare the visual operation quality evaluation parameter with a preset evaluation parameter threshold; The operation abnormality determination module is used to determine that the visualization operation is abnormal and issue a visualization abnormality alarm when the visualization operation quality evaluation parameter is lower than a preset evaluation parameter threshold.

2. The Internet training platform based on industrial scenarios according to claim 1 is characterized in that: The processing production line (121) is composed of an assembly line conveying mechanism, a stacking mechanism and a production and processing mechanism. The mechanical arm (122) is arranged on one side of the production and processing mechanism for loading, processing, testing and unloading. The turntable flow device (123) is composed of a circular turntable, a servo motor and an intermittent mechanism reduction motor.

3. The Internet training platform based on industrial scenarios according to claim 2 is characterized in that: Practical training management unit, including: The user management module is used to manage the information data of training users and conduct user skill assessment based on their skill level and teaching needs based on their historical information data; The industrial scene classification module is used to determine the categories of each industrial scene based on the scene characteristics of each industrial scene, match the corresponding industrial scene templates based on the industrial scene categories, and establish industrial scene labels based on the industrial scene templates; The solution matching module is used to define different difficulty levels for each practical training course and clarify the professional knowledge and skill data for each level. It matches the difficulty level of the corresponding practical training course based on the user skill assessment results and the selected industrial scenario tags to generate an industrial practical training solution.

4. The Internet training platform based on industrial scenarios according to claim 3 is characterized in that: The industrial scene classification module creates industrial scene labels based on the industrial scene template, specifically: Analyze each industrial scene, determine the scene feature data, and divide the scene feature data into multiple scene blocks according to the industrial scene category; Obtaining the type of each scene block and performing quantization processing to form a multi-dimensional feature vector, obtaining a type value for each scene block, and determining the distance value between each scene block based on the type value; Determine the content area of ​​each scene block, extract font information and perform text recognition on the content area, and determine the scene label based on the recognition results and prior knowledge of the industrial scene; Based on the importance and relevance factors of each scene label in the industrial scene, a queue is established and displayed. At the same time, users are allowed to provide feedback and corrections to the generated scene labels.

5. The Internet training platform based on industrial scenarios according to claim 4 is characterized in that: Industrial scene simulation unit, including: The scenario modeling module is used to determine the type of industrial scenario to be simulated based on the selected industrial scenario, obtain the actual data corresponding to the industrial scenario type, and create an industrial scenario model based on the collected actual data; A parameter adjustment module is used to adjust the equipment parameters in the industrial scenario model according to the training courses and difficulty levels of the industrial training program; The data acquisition module is used to collect equipment operation data, user operation data and equipment status data in the industrial scene model based on the monitoring sensor (13).

6. The Internet training platform based on industrial scenarios according to claim 5 is characterized in that: The practical training and analysis interactive unit also includes: Behavior analysis module, used to analyze collected user operation data and identify patterns, trends, and potential problems in user operations; The real-time interaction module is used to receive control commands sent by users to devices in industrial scenarios, analyze the acquired device operation data and device status data, and determine whether the device operation status and command feedback are normal; The skill assessment module is used to comprehensively evaluate the recognition results output by the behavior analysis module and the analysis results output by the real-time interaction module according to the established evaluation standards, compare the comprehensive evaluation results with the evaluation standards, and generate a skill assessment report.

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