Workshop production safety monitoring visualization system and method based on digital twinning
By building a digital twin model and setting up protection areas, the problems of large blind spots and slow response speed in traditional production safety monitoring methods are solved, real-time and accurate monitoring and visual display of the production site are achieved, and the level of safety management is significantly improved.
Patent Information
- Application Number
- CN202411790043.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-06
- Publication Date
- 2025-04-08
AI Technical Summary
Traditional production safety monitoring methods have problems such as large monitoring blind spots, slow response speed, and insufficient data analysis capabilities. It is difficult to identify potential safety hazards in a timely and accurate manner, especially in complex and changeable production environments.
By building a digital twin model, obtain industrial data and map it into the model, monitor production dynamics in real time; set up protection areas and record the number of operators entering, displaying safety status through visual means, and achieving all-round and all-weather safety monitoring.
It realizes comprehensive, real-time and accurate monitoring and visual display of the production site, provides scientific basis to prevent safety accidents, optimize resource allocation and production efficiency.
Smart Images
Figure CN119783952A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of digital twins, and specifically relates to a workshop production safety monitoring visualization system and method based on digital twins. Background Art
[0002] In the current manufacturing workshop environment, although automation and intelligent technologies have been widely adopted to improve production efficiency, production safety issues are still one of the key factors restricting the development of the industry.
[0003] Traditional production safety monitoring methods mainly rely on manual inspections, video surveillance, and simple sensor alarms. These methods have problems such as large monitoring blind spots, slow response speeds, and insufficient data analysis capabilities. It is difficult to identify potential safety hazards in a timely and accurate manner, especially in complex and changing production environments. In particular, for situations where operators frequently enter and exit high-risk production areas (such as machine operation areas, chemical storage areas, etc.), there is a lack of effective real-time tracking and early warning mechanisms, which increases safety risks. Summary of the invention
[0004] Purpose of the invention: In order to solve the above problems, the present invention provides a workshop production safety monitoring visualization system and method based on digital twins.
[0005] Technical solution: A digital twin-based workshop production safety monitoring visualization method includes the following steps: A digital twin model is constructed in a virtual environment based on a physical workshop; wherein the physical workshop includes N devices, and M A protective area arranged around the equipment; Acquire industrial data of the production process of the physical workshop and map the industrial data into the digital twin model to show the real-time production dynamics of the physical workshop; Obtain target behavior data; determine whether the target has invaded the protection area based on the target behavior data, and obtain a judgment result: if so, count the number of times the target has invaded the protection area, and map the number to the digital twin model, and display the target intrusion information in a visual manner.
[0006] In a further embodiment, the setting of the protection area comprises the following steps: Based on the production requirements of the physical workshop, create protection constraints on the equipment; and set protection areas of different priorities based on the protection constraints; The protection constraint condition is set to set protection areas with decreasing priorities in a direction away from the device; each protection area is assigned a corresponding protection area; The protection area includes a dangerous area with the highest priority and a warning area with a lower priority than the dangerous area.
[0007] In a further embodiment, the following steps are further included: analyzing the target behavior data to obtain an analysis result, and judging whether to update the protection constraint condition based on the analysis result; the specific process is as follows: Extract target portrait information from the target behavior data, and identify the identity level corresponding to the target based on the target portrait information ; Statistics of the number of targets intruding into the danger zone during a unit production cycle The number of ; Judgement times , status level Whether the update condition is met: If so, the protection constraint condition is updated; otherwise, a warning is issued to the target.
[0008] In a further embodiment, the update condition is expressed by the following expression: ; In the formula, For identity level ; is the number of times threshold.
[0009] In a further embodiment, the setting of the identity level comprises the following steps: Will work continuously in the physical workshop for more than the predetermined number of years and master production n Key skill objectives are divided into status levels ; Get identity level The target's portrait information; Will work continuously in the physical workshop for no more than a predetermined number of years and master production m Key skill objectives are divided into status levels ; Get identity level The target's portrait information.
[0010] In a further embodiment, the following steps are also included: Count the number of times the target enters the early warning area during the unit production cycle from the target behavior data The number of ; Based on the number of Make a judgment, if there are times , then the number of markings The corresponding warning area is identified and instructions for manual inspection of the marked warning area are sent.
[0011] In a further embodiment, updating the protection constraint condition is changing the size or location of the dangerous area.
[0012] In another technical solution, a workshop production safety monitoring visualization system based on digital twin is provided, which is used to implement the workshop production safety monitoring visualization method based on digital twin as described above, including: The physical layer of the object includes at least physical devices, sensors, and data transceiver modules; A virtual simulation layer, which constructs a digital twin model based on the object physical layer; The twin data layer acquires and analyzes workshop production data and monitors the safety status of workshop production in real time; The service layer is used to realize visual real-time monitoring and data monitoring, and provide users with workshop historical working status reproduction and fault query.
[0013] In another technical solution, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the above-mentioned digital twin-based workshop production safety monitoring visualization method is implemented.
[0014] In another technical solution, a computer-readable storage medium is provided, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned digital twin-based workshop production safety monitoring visualization method.
[0015] Beneficial effects: (1) The present invention realizes comprehensive, real-time, and accurate monitoring and visual display of the production site by constructing a digital twin model of workshop production; and provides a scientific basis for management personnel by setting up protection areas and recording key information such as the number of times operators enter, thereby effectively preventing the occurrence of safety accidents and optimizing resource allocation and production efficiency.
[0016] (2) The present invention collects and analyzes the behavioral data of skilled operators to adjust the area of the dangerous area to adapt to changes in the production environment and conditions; based on the real-time update of the behavioral data, the timeliness and accuracy of the dangerous area division are ensured. BRIEF DESCRIPTION OF THE DRAWINGS
[0017] Figure 1 It is the topological connection diagram of this system. DETAILED DESCRIPTION
[0018] Example 1 This embodiment provides a workshop production safety monitoring visualization method based on digital twins, including the following steps: step S1. Build a digital twin model in a virtual environment based on the physical workshop; wherein the physical workshop includes N devices, and M The protective area is set around the device.
[0019] step S 2. Obtain industrial data of the production process of the physical workshop and map the industrial data to the digital twin model to show the real-time production dynamics of the physical workshop.
[0020] step S 3. Obtain target behavior data; determine whether the target has invaded the protection area based on the target behavior data, and obtain a judgment result: if so, count the number of times the target has invaded the protection area, and map the number to the digital twin model, and display the target intrusion information in a visual manner.
[0021] By building a digital twin model of the physical workshop, setting up protection areas and recording the number of times operators enter, the safety status of the production site can be displayed in a visual way, and managers can grasp every corner of the production site in real time, including key information such as equipment operation status and personnel activity trajectory. Combined with the setting of protection areas and the recording of the number of times personnel enter, the system can automatically warn of potential safety hazards, such as illegal operations and unauthorized entry, thereby achieving all-round and all-weather monitoring of production safety and significantly improving the level of safety management.
[0022] The collected data (including the number of intrusions, time, location, etc.) is transmitted to the data processing center for statistics and analysis; and the analysis results are displayed in the form of charts through visualization technology, which is convenient for staff to view and analyze. For example, a time distribution chart of the number of intrusions and a location distribution chart are drawn to intuitively display the situation of intrusions.
[0023] Furthermore, the setting of the protection area includes the following steps: Based on the production requirements of the physical workshop, create protection constraints on the equipment; and set protection areas of different priorities based on the protection constraints; The protection constraint condition is set to set protection areas with decreasing priorities in a direction away from the device; each protection area is assigned a corresponding protection area; The protection area includes a dangerous area with the highest priority and a warning area with a lower priority than the dangerous area.
[0024] That is to say, different priority protection areas are set around different equipment. For example, the area closest to the equipment is set as a dangerous area, and the area slightly farther away from the equipment is set as a warning area. It is clearly stated that general operators are not allowed to approach the dangerous area, while the warning area will sound an alarm when general operators approach. The setting of the dangerous area effectively prevents unauthorized personnel from directly contacting the equipment, reducing the risk of equipment damage and safety accidents caused by misoperation or destruction. The existence of the warning area enables the system to immediately sound an alarm once unauthorized personnel approach key equipment, providing managers with enough time to take intervention measures to prevent safety incidents.
[0025] The setting of protection areas with different priorities allows operators to clearly know which areas are prohibited from entering and which areas require special attention, thereby enhancing the safety awareness of operators and their consciousness of complying with safety regulations. Moreover, this area division serves as a vivid material for operator safety education and training. Through simulation exercises and other methods, operators can personally experience the safety requirements of different areas and improve their ability to respond to emergencies.
[0026] Furthermore, the method further includes the following steps: analyzing the target behavior data to obtain an analysis result, and determining whether to update the protection constraint condition based on the analysis result; the specific process is as follows: Extract target portrait information from the target behavior data, and identify the identity level corresponding to the target based on the target portrait information ; Statistics of the number of targets intruding into the danger zone during a unit production cycle The number of ; Judgement times , status level Whether the update condition is met: If so, the protection constraint condition is updated; otherwise, a warning is issued to the target.
[0027] In the actual production process, the pre-set size of the dangerous area may be imperfect due to various factors, such as equipment layout adjustment, production process changes, or newly discovered safety hazards. In other words, as production activities proceed, the original dangerous area division may not fully cover all potential sources of danger, or may no longer be applicable due to changes in the production environment. At this time, if you only rely on static rules and preset data, it is difficult to achieve accurate division of dangerous areas. Therefore, it is necessary to introduce a dynamic adjustment mechanism, that is, to update the protection constraints to adapt to changes in the production site. In this embodiment, the behavioral data of skilled operators is used to improve the area or location setting of the dangerous area.
[0028] The above-mentioned identity level settings adopt the following rules: Will work continuously in the physical workshop for more than the predetermined number of years and master productionn Key skill objectives are divided into status levels ; Get identity level The target's portrait information; Will work continuously in the physical workshop for no more than a predetermined number of years and master production m Key skill objectives are divided into status levels ; Get identity level The target's portrait information.
[0029] The above-mentioned predetermined period ranges from 3 to 5 years. n ≥3; m <3. Obtaining status level The target (i.e., operator) has extensive work experience and can quickly respond to equipment layout adjustments, production process changes, or newly discovered safety hazards. The operators have shorter working years and less professional knowledge. When classifying the levels, the facial information of each operator is collected so that in the subsequent inspection process, the facial information can be obtained to match the corresponding identity level.
[0030] Furthermore, the update condition is expressed by the following expression: ; In the formula, For identity level ; is the number of times threshold.
[0031] The following conditions must be met at the same time to meet the update conditions: The target's identity level is , and the number of times it enters the danger zone is greater than or equal to the threshold ;Threshold The range is 3~10, and the specific value is based on actual production.
[0032] During the unit production cycle, the identity level When operators go to inspect the physical workshop, their identity level The operators have rich professional knowledge and a clear understanding of the production environment. Therefore, the number of times they come into contact with the dangerous area can be used to judge whether the area of the dangerous area is appropriate. If the operator repeatedly invades the danger zone, it means that the danger zone is too large or the location of the danger zone is unreasonable, then the area of the danger zone should be reduced or the location of the danger zone should be changed.
[0033] Status level When operators go to inspect the physical workshop, their identity level Operators are not allowed to enter the danger zone to protect their personal safety; so when they enter the danger zone, they need to be warned and told to comply with the workshop safety production regulations.
[0034] Furthermore, the method further comprises the following steps: Count the number of times the target enters the early warning area during the unit production cycle from the target behavior data The number of ; Based on the number of Make a judgment, if there are times , then the number of markings The corresponding warning area is identified and instructions for manual inspection of the marked warning area are sent.
[0035] Since there are multiple devices in the physical workshop, each device has a danger zone and a protection zone. During the unit production cycle, each device needs to be inspected. Therefore, when analyzing the target behavior data, if there is a warning area around a device that no one inspects, the device is ignored and needs to be marked, and an instruction to inspect the device is issued to the operator to ensure that each device is inspected.
[0036] Example 2 like Figure 1 As shown, this embodiment provides a workshop production safety monitoring visualization system based on digital twins, which is used to implement the workshop production safety monitoring visualization method based on digital twins as described in Example 1, including: The physical layer of the object includes at least physical devices, sensors, and data transceiver modules; A virtual simulation layer, which constructs a digital twin model based on the object physical layer; The twin data layer acquires and analyzes workshop production data and monitors the safety status of workshop production in real time; The service layer is used to realize visual real-time monitoring and data monitoring, and provide users with workshop historical working status reproduction and fault query.
[0037] This embodiment takes the production of box packaging machines as an example to further illustrate the above system. The physical entity layer includes physical devices such as robots, material conveyor belts, laser scanners, safety light curtains, and devices composed of sensors, data transceiver modules, etc. The physical entity layer is the foundation of the digital twin system, and subsequent work is carried out on this basis.
[0038] The virtual model layer includes geometric information such as the shape and size of the box packaging machine production line, as well as physical information such as mechanical material properties, constraints of the driving and dumping vehicles, and motion coupling. The virtual model is a mirror image of the physical entity in the virtual space, with high fidelity, reflecting the real state of the physical entity. The construction of a digital twin model for safe production should meet the requirements of good organizational hierarchy, conformity to real physical and behavioral characteristics, strong interactivity, and a balance between effect and efficiency.
[0039] Data is the core of the smooth operation of the digital twin system. In order to achieve safe production in the workshop and prevent and reduce the occurrence of safety accidents, it is necessary to collect and analyze various data in the workshop production process and monitor the safety status of the workshop production in real time. Through timely warning and visual display of unsafe conditions, staff can intuitively understand the production status of the workshop, discover and locate safety problems in time, and make corresponding decisions to ensure the safe production of the workshop.
[0040] The twin data layer includes data on the operation of the box packaging production line, data collected by sensors such as the number of people intruding into the safety protection area and the number of illegal operations, as well as relevant data generated by the operation of the production line virtual model. The original data of the physical space is pre-processed by the IoT gateway to drive the virtual model and stored in the database. The twin data layer updates and analyzes the data in real time, allowing the virtual model to fully reflect the various characteristics of the physical entity.
[0041] The service layer is a collection of technical support, user terminals, and functions required in the digital twin system. The monitoring system drives the virtual body of the information space through twin data to link with the physical body of the box packaging production line, realizes visual real-time monitoring and data monitoring, and provides users with functional services such as historical working status reproduction and fault query.
[0042] The above systems use a variety of modeling software and Unity 3 D The engine builds a virtual model and a visual interactive interface; it uses data acquisition and transmission technology to ensure the smooth interaction between the virtual and real in the digital twin system. The feasibility of the system is verified by monitoring and testing personnel intrusion and illegal operations on the actual production line, achieving virtual-real synchronization and real-time monitoring, improving the visualization level of safety protection of mechanical production lines, and thus improving the safety and stability of the production line.
[0043] Example 3 This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the digital twin-based workshop production safety monitoring visualization method as described in Example 1 is implemented.
[0044] Example 4 This embodiment provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the digital twin-based workshop production safety monitoring visualization method as described in Example 1.
Claims
1. A digital twin-based workshop production safety monitoring visualization method, characterized in that: The following steps are involved: A digital twin model is constructed in a virtual environment based on a physical workshop; wherein the physical workshop includes N devices, and M A protective area arranged around the equipment; Acquire industrial data of the production process of the physical workshop and map the industrial data into the digital twin model to show the real-time production dynamics of the physical workshop; Obtain target behavior data; determine whether the target has invaded the protection area based on the target behavior data, and obtain a judgment result: if so, count the number of times the target has invaded the protection area, and map the number to the digital twin model, and display the target intrusion information in a visual manner.
2. The method for visualizing workshop production safety monitoring based on digital twins according to claim 1, characterized in that: The setting of the protection area includes the following steps: Based on the production requirements of the physical workshop, create protection constraints on the equipment; and set protection areas of different priorities based on the protection constraints; The protection constraint condition is set to set protection areas with decreasing priorities in a direction away from the device; each protection area is assigned a corresponding protection area; The protection area includes a dangerous area with the highest priority and a warning area with a lower priority than the dangerous area.
3. The method for visualizing workshop production safety monitoring based on digital twins according to claim 2 is characterized in that: The following steps are also included: Analyze the target behavior data to obtain analysis results, and determine whether to update the protection constraint conditions based on the analysis results; the specific process is as follows: Extract target portrait information from the target behavior data, and identify the identity level corresponding to the target based on the target portrait information ; Statistics of the number of targets intruding into the danger zone during a unit production cycle The number of ; Judgement times , Identity level Whether the update conditions are met: If yes, update the protection constraints; Otherwise, the target is warned.
4. The method for visualizing workshop production safety monitoring based on digital twins according to claim 3 is characterized in that: The update condition is expressed by the following expression: ； In the formula, For identity level ; is the number of times threshold.
5. The method for visualizing workshop production safety monitoring based on digital twins according to claim 4 is characterized in that: The setting of the identity level includes the following steps: Will work continuously in the physical workshop for more than the predetermined number of years and master production n Key skill objectives are divided into status levels ; Get identity level The target's portrait information; Will work continuously in the physical workshop for no more than a predetermined number of years and master production m Key skill objectives are divided into status levels ; Get identity level The target's portrait information.
6. The method for visualizing workshop production safety monitoring based on digital twins according to claim 3 is characterized in that: The following steps are also included: Count the number of times the target enters the early warning area during the unit production cycle from the target behavior data The number of ; Based on the number of Make a judgment, if there are times , then the number of markings The corresponding warning area is identified and instructions for manual inspection of the marked warning area are sent.
7. The method for visualizing workshop production safety monitoring based on digital twins according to claim 3 is characterized in that: Updating protection constraints means changing the size or location of the hazardous area.
8. A workshop production safety monitoring visualization system based on digital twins, used to implement a workshop production safety monitoring visualization method based on digital twins as claimed in any one of claims 1 to 7, characterized in that: include: The physical layer of the object includes at least physical devices, sensors, and data transceiver modules; A virtual simulation layer, which constructs a digital twin model based on the object physical layer; The twin data layer acquires and analyzes workshop production data and monitors the safety status of workshop production in real time; The service layer is used to realize visual real-time monitoring and data monitoring, and provide users with workshop historical working status reproduction and fault query.
9. A computer device, characterized in that: It includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the digital twin-based workshop production safety monitoring visualization method as described in any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, which, when executed by a processor, implements the digital twin-based workshop production safety monitoring visualization method as described in any one of claims 1 to 7.