Intelligent operation process type training method, system, equipment and medium

Abstract

The invention relates to the technical field of operation training, in particular to an intelligent operation process type training method, system and device and a medium, and the method comprises the steps: obtaining an operation process region, a personnel operation video and personnel data information, and carrying out the construction of an operation model according to the operation process region, the personnel operation video and the personnel data information, the method comprises the steps of obtaining a work flow model of a worker, analyzing the work flow model to obtain a demonstration behavior sequence and demonstration behavior operations corresponding to the demonstration behavior sequence, and generating a demonstration dynamic model corresponding to each demonstration behavior operation based on the demonstration behavior operations, and sending the demonstration dynamic model to a preset playing device for demonstration according to the demonstration behavior sequence. According to the invention, the overall operation training efficiency and safety are improved.

Description

Technical Field

[0001] The present application relates to the technical field of job training, and in particular to an intelligent job process training method, system, equipment and medium. Background Art

[0002] At present, enterprises usually rely on traditional classroom lectures and paper teaching materials for employee safety training. Although these methods can help employees understand safety regulations to a certain extent, they lack interactivity and practical experience, making it difficult to ensure the effectiveness of training. With the development of information technology, some enterprises have begun to introduce multimedia teaching tools and virtual reality (VR) technology to enrich the form of training, which has increased the fun and participation of training. However, most existing training systems still fail to fully cover the actual operation process of the work site, especially in risk identification and emergency response in complex working environments.

[0003] In order to solve the technical problems mentioned above, common practices include using video tutorials, simulation software or physical demonstrations to conduct skill training. For example, video tutorials can show standard operating procedures, which is convenient for trainees to watch and learn repeatedly; simulation software simulates the real working environment through computers, allowing trainees to practice in a virtual environment; and physical demonstrations are to test the trainees' learning outcomes through field exercises. Although these methods have their own advantages, they also expose certain limitations. Video tutorials and simulation software lack opportunities for actual hands-on practice, and physical demonstrations are expensive and subject to site conditions.

[0004] Although the above traditional and emerging training methods can improve the quality of training to a certain extent, they cannot provide sufficient interactivity and personalized guidance, which often makes trainees unprepared when facing the real working environment. Especially in high-risk industries, the problem of frequent accidents caused by low safety awareness and technical proficiency is particularly prominent. Summary of the invention

[0005] In order to solve at least one of the above-mentioned technical problems, the present application provides an intelligent operation process training method, system, equipment and medium.

[0006] In the first aspect, the present application provides an intelligent operation process training method, which adopts the following technical solutions: Acquire the operation process area, personnel operation video and personnel data information, wherein the operation process area includes the area parameters and area images of different types of process areas, the personnel operation video includes the monitoring video of the personnel performing operations in the operation process area, and the personnel data information includes the real-time location information of the personnel in the operation process area and the physical attribute parameters within a preset time period before entering the operation process area; Constructing a work model according to the work process area, the personnel operation video and the personnel data information to obtain a work process model of the staff member; Analyze the operation process model to obtain a demonstration behavior sequence corresponding to the abnormal operation process of the staff member and a demonstration behavior operation corresponding to the demonstration behavior sequence; A demonstration dynamic model corresponding to each demonstration behavior operation is generated based on the demonstration behavior operation, and the demonstration dynamic model is sent to a preset playback device for demonstration according to the demonstration behavior sequence.

[0007] By adopting the above technical solution, the operation process area, personnel operation video and personnel data information are obtained, which provides a solid foundation for the construction of the operation model, making the constructed operation process model more accurate and close to reality, thereby improving the model's ability to reflect the staff's operation process. By analyzing the operation process model, the abnormal operation process of the staff can be accurately identified, and the demonstration behavior sequence and demonstration behavior operation are generated accordingly. The abnormal operation process can be presented intuitively, which is convenient for subsequent diagnosis and improvement. At the same time, the generation of demonstration behavior sequence and demonstration behavior operation also provides a direct basis for the construction of subsequent demonstration dynamic models. By generating demonstration dynamic models based on demonstration behavior operations, and sending these models to the preset playback device for demonstration according to the demonstration behavior sequence, the intuitive reproduction of abnormal operation processes is realized. It not only improves the timeliness of problem discovery, but also enhances the pertinence of problem solving, so that staff can quickly understand and correct abnormal operation processes, thereby improving the overall operation process training efficiency and safety.

[0008] In a possible implementation, the operation model is constructed according to the operation process area, the personnel operation video and the personnel data information to obtain the operation process model of the staff, including: Determine a process operation node, a process operation type, and standard operation data corresponding to the process operation type according to the regional parameters; An environment model is constructed according to the regional features, process operation nodes, process operation types and the standard operation data in the regional image to obtain a demonstration operation model; Extracting process operation features in the personnel operation video to obtain personnel operation features; Constructing a character model according to the personnel operation characteristics and the physical attribute parameters to obtain a character operation model; The character operation model is combined with the demonstration operation model based on the real-time position information to obtain the operation process model of the staff.

[0009] In a possible implementation, the analysis of the work flow model to obtain a demonstration behavior sequence corresponding to the abnormal work flow of the staff member and a demonstration behavior operation corresponding to the demonstration behavior sequence includes: Monitor the operation process model to determine whether the worker has reached the process operation node position; When the worker arrives at the process operation node, the work process steps and standard behavior operations corresponding to the process operation node and the behavior operation steps and personnel behavior operations of the worker are determined based on the work process model; Matching the behavior operation steps with the operation process steps to determine the target behavior operation corresponding to the behavior operation steps; It is determined whether the personnel behavior operation is inconsistent with the target behavior operation. If so, the target behavior operation is decomposed in operation order to generate multiple demonstration behavior operations and demonstration behavior sequences.

[0010] In a possible implementation, the environmental model is constructed according to the regional features, process operation nodes, process operation types and the standard operation data in the regional image to obtain a demonstration operation model, including: Classify the regional features into categories to obtain non-device feature points and device feature points; Converting the non-device feature points, using the first two-dimensional grid obtained by the conversion as a top grid, and converting the top grid into regional spatial data; Virtualizing the characteristic points of the equipment to obtain equipment occupation data; Based on each device type in the device feature points, based on the regional spatial data and the device footprint data, and based on the distribution range of the devices corresponding to the device type in the updated vector surface data, generate multidimensional spatial data corresponding to different devices in the device footprint data; Aggregate multiple multidimensional space data to obtain an initial demonstration job model, and based on the initial demonstration job model, bind the process operation nodes, the process job types and the standard operation data to the initial demonstration job model according to the corresponding equipment to obtain a demonstration job model.

[0011] In a possible implementation, the virtualization processing of the device feature points to obtain the device occupation data includes: The device feature points are converted, and the maximum elevation value of the converted second two-dimensional grid is screened based on a preset grid function to obtain a position grid, wherein the position grid is a grid that records the maximum elevation value source of all plane spatial positions in the second two-dimensional grid in the form of integer grid data; Performing vector processing and updating on the position grid to obtain an updated position grid; Selecting grid pixels at corresponding positions in the second two-dimensional grid based on the position grid to obtain virtual device grid data composed of a single or multiple device bottom elevation grids; The virtual equipment grid data is converted to obtain equipment occupancy data.

[0012] In a possible implementation, the step of sending the demonstration dynamic model to a preset playback device for demonstration according to the demonstration behavior sequence may further include: When the sending instruction is detected, the data receiving content of the preset playback device is obtained; It is determined whether the received data content is consistent with the demonstration dynamic model corresponding to the demonstration behavior sequence. If not, a data abnormality instruction is generated to control the abnormal alarm device to respond to the output.

[0013] In a possible implementation, the step of sending the demonstration dynamic model to a preset playback device for demonstration according to the demonstration behavior sequence may further include: The real-time operation steps played in the preset playback device are obtained, and it is determined whether the behavioral operation steps completed by the staff are consistent with the real-time operation steps. If they are consistent, the real-time operation steps are updated and played according to the demonstration behavior sequence.

[0014] In the second aspect, the present application provides an intelligent operation process training system, which adopts the following technical solutions: An intelligent operation process training system, comprising: An information acquisition module is used to acquire an operation process area, a personnel operation video, and personnel data information, wherein the operation process area includes area parameters and area images of different types of process areas, the personnel operation video includes a monitoring video of the personnel performing operations in the operation process area, and the personnel data information includes the real-time position information of the personnel in the operation process area and the physical attribute parameters within a preset time period before entering the operation process area; A model building module, used to build a work model according to the work process area, the personnel operation video and the personnel data information, so as to obtain the work process model of the staff; A model analysis module, used for analyzing the operation process model to obtain a demonstration behavior sequence corresponding to the abnormal operation process of the staff member and a demonstration behavior operation corresponding to the demonstration behavior sequence; The model demonstration module is used to generate a demonstration dynamic model corresponding to each demonstration behavior operation based on the demonstration behavior operation, and send the demonstration dynamic model to a preset playback device for demonstration according to the demonstration behavior sequence.

[0015] In a possible implementation, when the model building module builds the operation model according to the operation process area, the personnel operation video and the personnel data information to obtain the operation process model of the staff, it is specifically used to: Determine a process operation node, a process operation type, and standard operation data corresponding to the process operation type according to the regional parameters; An environment model is constructed according to the regional features, process operation nodes, process operation types and the standard operation data in the regional image to obtain a demonstration operation model; Extracting process operation features in the personnel operation video to obtain personnel operation features; Constructing a character model according to the personnel operation characteristics and the physical attribute parameters to obtain a character operation model; The character operation model is combined with the demonstration operation model based on the real-time position information to obtain the operation process model of the staff.

[0016] In another possible implementation, when the model analysis module analyzes the work process model to obtain a demonstration behavior sequence corresponding to the abnormal work process of the staff member and a demonstration behavior operation corresponding to the demonstration behavior sequence, it is specifically used to: Monitor the operation process model to determine whether the worker has reached the process operation node position; When the worker arrives at the process operation node, the work process steps and standard behavior operations corresponding to the process operation node and the behavior operation steps and personnel behavior operations of the worker are determined based on the work process model; Matching the behavior operation steps with the operation process steps to determine the target behavior operation corresponding to the behavior operation steps; It is determined whether the personnel behavior operation is inconsistent with the target behavior operation. If so, the target behavior operation is decomposed in operation order to generate multiple demonstration behavior operations and demonstration behavior sequences.

[0017] In another possible implementation, when the model building module builds the environment model according to the regional features, process operation nodes, process operation types and the standard operation data in the regional image to obtain the demonstration operation model, it is specifically used to: Classify the regional features into categories to obtain non-device feature points and device feature points; Converting the non-device feature points, using the first two-dimensional grid obtained by the conversion as a top grid, and converting the top grid into regional spatial data; Virtualizing the characteristic points of the equipment to obtain equipment occupation data; Based on each device type in the device feature points, based on the regional spatial data and the device footprint data, and based on the distribution range of the devices corresponding to the device type in the updated vector surface data, generate multidimensional spatial data corresponding to different devices in the device footprint data; Aggregate multiple multidimensional space data to obtain an initial demonstration job model, and based on the initial demonstration job model, bind the process operation nodes, the process job types and the standard operation data to the initial demonstration job model according to the corresponding equipment to obtain a demonstration job model.

[0018] In another possible implementation, when the model building module virtualizes the device feature points to obtain the device occupation data, it is specifically used to: The device feature points are converted, and the maximum elevation value of the converted second two-dimensional grid is screened based on a preset grid function to obtain a position grid, wherein the position grid is a grid that records the maximum elevation value source of all plane spatial positions in the second two-dimensional grid in the form of integer grid data; Performing vector processing and updating on the position grid to obtain an updated position grid; Selecting grid pixels at corresponding positions in the second two-dimensional grid based on the position grid to obtain virtual device grid data composed of a single or multiple device bottom elevation grids; The virtual equipment grid data is converted to obtain equipment occupancy data.

[0019] In another possible implementation, the system further includes: a content acquisition module and an abnormality alarm module, wherein: The content acquisition module is used to acquire the data received content of the preset playback device after detecting the sending instruction; The abnormal alarm module is used to determine whether the data received content is consistent with the demonstration dynamic model corresponding to the demonstration behavior sequence. If not, a data abnormality instruction is generated to control the abnormal alarm device to respond to the output.

[0020] In another possible implementation, the system further includes: a playback update module, wherein: The playback update module is used to obtain the real-time operation steps played in the preset playback device, and determine whether the behavioral operation steps completed by the staff are consistent with the real-time operation steps. If they are consistent, the real-time operation steps are updated and played according to the demonstration behavior sequence.

[0021] In a third aspect, the present application provides an electronic device, which adopts the following technical solution: at least one processor; Memory; At least one application, wherein at least one application is stored in a memory and configured to be executed by at least one processor, and at least one application is configured to: execute an intelligent operation process training method as described in any one of the first aspects.

[0022] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium stores a computer program, which, when executed in a computer, causes the computer to execute any intelligent operation process training method as described in the first aspect.

[0023] In summary, the present application includes at least one of the following beneficial technical effects: By adopting the above technical solution, the operation process area, personnel operation video and personnel data information are obtained, which provides a solid foundation for the construction of the operation model, making the constructed operation process model more accurate and close to reality, thereby improving the model's ability to reflect the staff's operation process. By analyzing the operation process model, the abnormal operation process of the staff can be accurately identified, and the demonstration behavior sequence and demonstration behavior operation are generated accordingly. The abnormal operation process can be presented intuitively, which is convenient for subsequent diagnosis and improvement. At the same time, the generation of demonstration behavior sequence and demonstration behavior operation also provides a direct basis for the construction of subsequent demonstration dynamic models. By generating demonstration dynamic models based on demonstration behavior operations, and sending these models to the preset playback device for demonstration according to the demonstration behavior sequence, the intuitive reproduction of abnormal operation processes is realized. It not only improves the timeliness of problem discovery, but also enhances the pertinence of problem solving, so that staff can quickly understand and correct abnormal operation processes, thereby improving the overall operation process training efficiency and safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A flowchart of an intelligent operation process training method provided in an embodiment of the present application.

[0025] Figure 2 A schematic diagram of the structure of an intelligent operation process training system provided in an embodiment of the present application.

[0026] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0027] The following is combined with Figure 1-3 This application is described in further detail.

[0028] This specific embodiment is merely an explanation of the present application and is not a limitation of the present application. After reading this specification, a person skilled in the art may make non-creative modifications to the present embodiment as needed, but such modifications are protected by the patent law as long as they are within the scope of the present application.

[0029] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0030] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article, unless otherwise specified, generally means that the associated objects before and after are in an "or" relationship.

[0031] The embodiments of the present application are further described in detail below in conjunction with the drawings in the specification.

[0032] The embodiment of the present application provides a method for intelligent operation process training method, which is executed by an electronic device, wherein the electronic device can be an independent physical electronic device, or an electronic device cluster or distributed system composed of multiple physical electronic devices, or a cloud electronic device that provides cloud computing services. The embodiment of the present application is not limited here, such as Figure 1 As shown, the method includes: Step S10: Acquire the operation process area, personnel operation video and personnel data information.

[0033] Among them, the operation process area includes the area parameters and area images of different types of process areas, the personnel operation video includes the monitoring video of the staff performing operations in the operation process area, and the personnel data information is the real-time location information of the staff in the operation process area and the physical attribute parameters within the preset time period before entering the operation process area.

[0034] For the embodiment of the present application, the operation process area represents a specific work area, which is divided into different types, such as production lines, inspection areas, etc. Each type has its own unique area parameters and corresponding area images (which can be floor plans, actual photos taken). In the present application, the area parameters include process operation nodes, process operation types, and standard operation data corresponding to process operation types. These area parameters and area images jointly define the specific characteristics and scope of the operation process area. Personnel operation video refers to video data recorded to monitor the operation activities of staff in the operation process area. These video materials can record the operation process, behavioral performance, etc. of the staff in real time, providing a basis for subsequent evaluation and analysis. Personnel data information is used to represent the real-time location information of the staff in the operation process area (such as the current area, movement trajectory, etc.) and the physical attribute parameters (such as body temperature, heart rate, blood pressure, height, etc., as well as fatigue level, emotional state, etc.) within a preset time period before entering the operation process area.

[0035] Specifically, the sensors and cameras installed in the work process area collect the area image and personnel operation video in real time. For the area parameters, the process operation nodes, operation types and standard operation data corresponding to the process operation types of the work process are stored in the database in advance, and the index related to the area image is established. After the area image is obtained, the area parameters corresponding to the area image can be collected. Then, the real-time location information of the staff is obtained by using positioning technology (such as RFID, Bluetooth, etc.), and the physical attribute parameters of the staff before entering the work process area are obtained by connecting with the personnel monitoring system.

[0036] Step S11: construct an operation model according to the operation process area, personnel operation video and personnel data information to obtain the operation process model of the staff.

[0037] Specifically, the process operation nodes, process job types and standard operation data corresponding to the process operation types are determined according to the regional parameters, the environment model is constructed according to the regional features, process operation nodes, process job types and standard operation data in the regional image to obtain a demonstration operation model, the process operation features in the personnel operation video are extracted to obtain the personnel operation features, the character model is constructed according to the personnel operation features and physical attribute parameters to obtain a character operation model, and the character operation model is combined with the demonstration operation model based on the real-time location information to obtain the staff's operation process model.

[0038] For the embodiment of the present application, when the environmental model is constructed according to the regional features, process operation nodes, process job types and standard operation data in the regional image to obtain the demonstration operation model, it includes: categorizing the regional features to obtain non-equipment feature points and equipment feature points, converting the non-equipment feature points, and using the first two-dimensional grid obtained by the conversion as the top grid, converting the top grid into regional spatial data, virtualizing the equipment feature points to obtain equipment footprint data, based on each equipment type in the equipment feature points, based on the regional spatial data and the equipment footprint data, and based on the distribution range of the equipment corresponding to the equipment type in the updated vector surface data as a condition, generating multidimensional spatial data corresponding to different equipment in the equipment footprint data, aggregating multiple multidimensional spatial data to obtain an initial demonstration operation model, and based on the initial demonstration operation model, data binding the process operation nodes, process job types and standard operation data with the initial demonstration operation model according to the corresponding equipment to obtain a demonstration operation model.

[0039] For the embodiment of the present application, non-equipment feature points refer to spatial environment feature points in the regional space, such as spatial height feature points, spatial width feature points, etc. Equipment feature points refer to equipment feature points existing in the regional space, such as operating speed features, size features, etc.

[0040] In an embodiment of the present application, a specific method for constructing a character operation model includes: determining the two-dimensional coordinates of key points of the human body based on body attribute parameters and personnel operation characteristics of the staff in the personnel operation model, determining the coordinate system of the human body model according to the human body model in the human body model library, inputting the two-dimensional coordinates of the key points of the human body into the deep learning model for perspective transformation, obtaining the three-dimensional coordinates of the key points of the human body corresponding to the two-dimensional coordinates of the key points of the human body, mapping the three-dimensional coordinates of the key points of the human body to the coordinate system of the human body model, and obtaining the character operation model.

[0041] Step S12: Analyze the work process model to obtain a demonstration behavior sequence corresponding to the abnormal work process of the staff and a demonstration behavior operation corresponding to the demonstration behavior sequence.

[0042] Specifically, the operation process model is monitored to determine whether the staff has reached the process operation node position. When the staff has reached the process operation node position, the operation process steps and standard behavior operations corresponding to the process operation nodes as well as the staff's behavior operation steps and personnel behavior operations are determined based on the operation process model. The behavior operation steps are matched with the operation process steps, and the target behavior operations corresponding to the behavior operation steps are determined. It is judged whether the personnel behavior operations do not match the target behavior operations. If so, the target behavior operations are decomposed in operation sequence to generate multiple demonstration behavior operations and demonstration behavior sequences.

[0043] Step S13: Generate a demonstration dynamic model corresponding to each demonstration behavior operation based on the demonstration behavior operation, and send the demonstration dynamic model to a preset playback device for demonstration according to the demonstration behavior sequence.

[0044] In an embodiment of the present application, after the demonstration dynamic model is sent to the preset playback device, the system will update and play the demonstration dynamic model in the preset playback device according to the demonstration behavior sequence based on the personnel operation steps. For example: when the personnel operation steps change from the first step to the second step, it means that the user has completed all operations of the first step. At this time, the system will update the demonstration dynamic model played by the preset playback device, and adjust and update the demonstration dynamic model from the first step animation stage to the second step animation stage, so that the staff can timely view the demonstration operation steps in real time.

[0045] In the embodiment of the present application, the operation process area, the personnel operation video and the personnel data information are obtained, which provides a solid foundation for the construction of the operation model, making the constructed operation process model more accurate and close to reality, thereby improving the model's ability to reflect the staff's operation process. By analyzing the operation process model, the abnormal operation process of the staff can be accurately identified, and the demonstration behavior sequence and the demonstration behavior operation are generated accordingly. The abnormal operation process is intuitively presented, which is convenient for subsequent diagnosis and improvement. At the same time, the generation of the demonstration behavior sequence and the demonstration behavior operation also provides a direct basis for the construction of the subsequent demonstration dynamic model. By generating a demonstration dynamic model based on the demonstration behavior operation, and sending these models to the preset playback device for demonstration according to the demonstration behavior sequence, the intuitive reproduction of the abnormal operation process is realized. Not only the timeliness of problem discovery is improved, but also the pertinence of problem solving is enhanced, so that the staff can quickly understand and correct the abnormal operation process, thereby improving the overall operation process training efficiency and safety.

[0046] Furthermore, the demonstration dynamic model is sent to a preset playback device for demonstration according to the demonstration behavior sequence, and then it also includes: obtaining the real-time operation steps played in the preset playback device, and determining whether the behavioral operation steps completed by the staff are consistent with the real-time operation steps. If consistent, the real-time operation steps are updated and played according to the demonstration behavior sequence.

[0047] The following is an introduction to an intelligent operation process training system provided by an embodiment of the present application. The intelligent operation process training system described below and the intelligent operation process training method described above can be referred to each other. Please refer to Figure 2 , Figure 2 : is a structural diagram of an intelligent operation process training system 20 provided in an embodiment of the present application, including: The information acquisition module 21 is used to acquire the operation process area, personnel operation video and personnel data information. The operation process area includes the regional parameters and regional images of different types of process areas. The personnel operation video includes the monitoring video of the personnel performing operations in the operation process area. The personnel data information is the real-time position information of the personnel in the operation process area and the physical attribute parameters within a preset time period before entering the operation process area. The model building module 22 is used to build a work model according to the work process area, the personnel operation video and the personnel data information to obtain the work process model of the staff; The model analysis module 23 is used to analyze the operation process model to obtain a demonstration behavior sequence corresponding to the abnormal operation process of the staff member and a demonstration behavior operation corresponding to the demonstration behavior sequence; The model demonstration module 24 is used to generate a demonstration dynamic model corresponding to each demonstration behavior operation based on the demonstration behavior operation, and send the demonstration dynamic model to a preset playback device for demonstration according to the demonstration behavior sequence.

[0048] In a possible implementation of the embodiment of the present application, when the model building module 22 builds the operation model according to the operation process area, the personnel operation video and the personnel data information to obtain the operation process model of the staff, it is specifically used to: Determine the process operation node, the process operation type and the standard operation data corresponding to the process operation type according to the regional parameters; An environmental model is constructed according to regional features, process operation nodes, process operation types and standard operation data in the regional image to obtain a demonstration operation model; Extract process operation features from personnel operation videos to obtain personnel operation features; A character model is constructed according to the operating characteristics and physical attribute parameters of the personnel to obtain a character operating model; Based on the real-time location information, the character operation model is combined with the demonstration operation model to obtain the staff's operation process model.

[0049] In another possible implementation of the embodiment of the present application, when the model analysis module 23 analyzes the work process model to obtain a demonstration behavior sequence corresponding to the abnormal work process of the staff member and a demonstration behavior operation corresponding to the demonstration behavior sequence, it is specifically used to: Monitor the operation process model to determine whether the staff has reached the process operation node location; When the staff arrives at the process operation node, the work process steps and standard behavior operations corresponding to the process operation node and the staff behavior operation steps and staff behavior operations are determined based on the work process model; Match the behavior operation steps with the work process steps to determine the target behavior operation corresponding to the behavior operation steps; Determine whether the personnel behavior operation is inconsistent with the target behavior operation. If so, decompose the target behavior operation in operation order to generate multiple demonstration behavior operations and demonstration behavior sequences.

[0050] In another possible implementation of the embodiment of the present application, when the model building module 22 builds the environment model according to the regional features, process operation nodes, process operation types and standard operation data in the regional image to obtain the demonstration operation model, it is specifically used to: Classify the regional features to obtain non-device feature points and device feature points; The non-device feature points are converted, and the first two-dimensional grid obtained by the conversion is used as the top grid, and the top grid is converted into regional spatial data; Virtualize the equipment feature points to obtain the equipment footprint data; Based on each device type in the device feature points, based on the regional spatial data and the device footprint data, and based on the distribution range of the devices corresponding to the device type in the updated vector surface data, generate multi-dimensional spatial data corresponding to different devices in the device footprint data; An initial demonstration operation model is obtained by aggregating multiple multidimensional spatial data, and based on the initial demonstration operation model, process operation nodes, process operation types and standard operation data are data-bound with the initial demonstration operation model according to corresponding equipment to obtain a demonstration operation model.

[0051] In another possible implementation of the embodiment of the present application, when the model building module 22 virtualizes the equipment feature points to obtain the equipment occupation data, it is specifically used to: The device feature points are converted, and the maximum elevation value of the converted second two-dimensional grid is screened based on a preset grid function to obtain a position grid, where the position grid is a grid that records the maximum elevation value source of all plane spatial positions in the second two-dimensional grid in the form of integer grid data; Performing vector processing and updating on the position grid to obtain an updated position grid; Selecting grid pixels at corresponding positions in the second two-dimensional grid based on the position grid, and obtaining virtual device grid data composed of a single or multiple device bottom elevation grids; The virtual equipment raster data is converted to obtain the equipment occupancy data.

[0052] In another possible implementation of the embodiment of the present application, the system 20 further includes: a content acquisition module and an abnormality alarm module, wherein: A content acquisition module, used to acquire data reception content of a preset playback device after detecting a sending instruction; The abnormal alarm module is used to determine whether the data received content is consistent with the demonstration dynamic model corresponding to the demonstration behavior sequence. If not, a data abnormality instruction is generated to control the abnormal alarm device response output.

[0053] In another possible implementation of the embodiment of the present application, the system 20 further includes: a playback update module, wherein: The playback update module is used to update and play the demonstration dynamic model in the preset playback device according to the demonstration behavior sequence based on the personnel operation steps.

[0054] The present application embodiment provides an electronic device, such as Figure 3 As shown, Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application, Figure 3 The electronic device 300 shown includes: a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, such as through a bus 302. Optionally, the electronic device 300 may also include a transceiver 304. It should be noted that in actual applications, the transceiver 304 is not limited to one, and the structure of the electronic device 300 does not constitute a limitation on the embodiments of the present application.

[0055] The processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of the embodiments of the present application. The processor 301 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like.

[0056] The bus 302 may include a path to transmit information between the above components. The bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 302 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0057] The memory 303 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0058] The memory 303 is used to store application code for executing the solution of the embodiment of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the content shown in the above method embodiment.

[0059] Among them, electronic devices include but are not limited to: mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 3 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0060] A computer-readable storage medium provided in an embodiment of the present application is introduced below. The computer-readable storage medium described below and the method described above can be referenced to each other.

[0061] An embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned intelligent operation process training method are implemented.

[0062] Since the embodiments of the computer-readable storage medium part and the embodiments of the method part correspond to each other, the embodiments of the computer-readable storage medium part refer to the description of the embodiments of the method part.

[0063] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0064] The above are only some implementation methods of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. An intelligent operation process training method, characterized in that: include: Acquire the operation process area, personnel operation video and personnel data information, wherein the operation process area includes the area parameters and area images of different types of process areas, the personnel operation video includes the monitoring video of the personnel performing operations in the operation process area, and the personnel data information includes the real-time location information of the personnel in the operation process area and the physical attribute parameters within a preset time period before entering the operation process area; Constructing a work model according to the work process area, the personnel operation video and the personnel data information to obtain a work process model of the staff member; Analyze the operation process model to obtain a demonstration behavior sequence corresponding to the abnormal operation process of the staff member and a demonstration behavior operation corresponding to the demonstration behavior sequence; A demonstration dynamic model corresponding to each demonstration behavior operation is generated based on the demonstration behavior operation, and the demonstration dynamic model is sent to a preset playback device for demonstration according to the demonstration behavior sequence.

2. The intelligent operation process training method according to claim 1, characterized in that: The step of constructing a work model according to the work process area, the personnel operation video and the personnel data information to obtain the work process model of the staff member includes: Determine a process operation node, a process operation type, and standard operation data corresponding to the process operation type according to the regional parameters; An environment model is constructed according to the regional features, process operation nodes, process operation types and the standard operation data in the regional image to obtain a demonstration operation model; Extracting process operation features in the personnel operation video to obtain personnel operation features; Constructing a character model according to the personnel operation characteristics and the physical attribute parameters to obtain a character operation model; The character operation model is combined with the demonstration operation model based on the real-time position information to obtain the operation process model of the staff.

3. The intelligent operation process training method according to claim 1 is characterized in that: The analyzing the operation process model to obtain a demonstration behavior sequence corresponding to the abnormal operation process of the staff member and a demonstration behavior operation corresponding to the demonstration behavior sequence includes: Monitor the operation process model to determine whether the worker has reached the process operation node position; When the worker arrives at the process operation node, the work process steps and standard behavior operations corresponding to the process operation node and the behavior operation steps and personnel behavior operations of the worker are determined based on the work process model; Matching the behavior operation steps with the operation process steps to determine the target behavior operation corresponding to the behavior operation steps; It is determined whether the personnel behavior operation is inconsistent with the target behavior operation. If so, the target behavior operation is decomposed in operation order to generate multiple demonstration behavior operations and demonstration behavior sequences.

4. The intelligent operation process training method according to claim 2 is characterized in that: The step of constructing an environment model according to the regional features, process operation nodes, process operation types and the standard operation data in the regional image to obtain a demonstration operation model includes: Classify the regional features into categories to obtain non-device feature points and device feature points; Converting the non-device feature points, using the first two-dimensional grid obtained by the conversion as a top grid, and converting the top grid into regional spatial data; Virtualizing the characteristic points of the equipment to obtain equipment occupation data; Based on each device type in the device feature points, based on the regional spatial data and the device footprint data, and based on the distribution range of the devices corresponding to the device type in the updated vector surface data, generate multidimensional spatial data corresponding to different devices in the device footprint data; Aggregate multiple multidimensional space data to obtain an initial demonstration job model, and based on the initial demonstration job model, bind the process operation nodes, the process job types and the standard operation data to the initial demonstration job model according to the corresponding equipment to obtain a demonstration job model.

5. The intelligent operation process training method according to claim 4 is characterized in that: The virtualization processing of the equipment feature points to obtain equipment occupation data includes: The device feature points are converted, and the maximum elevation value of the converted second two-dimensional grid is screened based on a preset grid function to obtain a position grid, wherein the position grid is a grid that records the maximum elevation value source of all plane spatial positions in the second two-dimensional grid in the form of integer grid data; Performing vector processing and updating on the position grid to obtain an updated position grid; Selecting grid pixels at corresponding positions in the second two-dimensional grid based on the position grid to obtain virtual device grid data composed of a single or multiple device bottom elevation grids; The virtual equipment grid data is converted to obtain equipment occupancy data.

6. The intelligent operation process training method according to claim 1, characterized in that: The step of sending the demonstration dynamic model to a preset playback device for demonstration according to the demonstration behavior sequence further includes: When the sending instruction is detected, the data receiving content of the preset playback device is obtained; It is determined whether the received data content is consistent with the demonstration dynamic model corresponding to the demonstration behavior sequence. If not, a data abnormality instruction is generated to control the abnormal alarm device to respond to the output.

7. The intelligent operation process training method according to claim 1, characterized in that: The step of sending the demonstration dynamic model to a preset playback device for demonstration according to the demonstration behavior sequence further includes: The real-time operation steps played in the preset playback device are obtained, and it is determined whether the behavioral operation steps completed by the staff are consistent with the real-time operation steps. If they are consistent, the real-time operation steps are updated and played according to the demonstration behavior sequence.

8. An intelligent operation process training system, characterized in that: include: An information acquisition module is used to acquire an operation process area, a personnel operation video, and personnel data information, wherein the operation process area includes area parameters and area images of different types of process areas, the personnel operation video includes a monitoring video of the personnel performing operations in the operation process area, and the personnel data information includes the real-time position information of the personnel in the operation process area and the physical attribute parameters within a preset time period before entering the operation process area; A model building module, used to build a work model according to the work process area, the personnel operation video and the personnel data information, so as to obtain the work process model of the staff; A model analysis module, used for analyzing the operation process model to obtain a demonstration behavior sequence corresponding to the abnormal operation process of the staff member and a demonstration behavior operation corresponding to the demonstration behavior sequence; The model demonstration module is used to generate a demonstration dynamic model corresponding to each demonstration behavior operation based on the demonstration behavior operation, and send the demonstration dynamic model to a preset playback device for demonstration according to the demonstration behavior sequence.

9. An electronic device, characterized in that: The electronic device includes: at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute an intelligent operation process training method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: include: A computer program is stored which can be loaded by a processor and execute an intelligent operation process training method as described in any one of claims 1-7.

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