Cooperative management and control method and device of plant operation equipment and computer equipment

Abstract

The application relates to a kind of plant equipment collaborative management method, device and computer equipment. Method includes: the current equipment data of each plant equipment in factory area, historical equipment data and equipment association are collected, and based on the current equipment data of each plant equipment and equipment association, the plant equipment twin model of factory area is constructed;Based on the historical equipment data of each plant equipment and plant equipment twin model, the predicted equipment cooperative operation data of each plant equipment is simulated, and the current target control scheme of each plant equipment is generated by control optimization strategy;Thus, the current running result of each plant equipment is obtained by control, and based on the current running result of each plant equipment, the current target control scheme of each plant equipment and the current equipment data of each plant equipment, the current collaborative management record of factory area is generated. Using the method can improve the collaborative management effect of each plant equipment of factory.

Description

Technical Field

[0001] This application relates to the field of intelligent factory management and control technology, and in particular to a collaborative management and control method, device and computer equipment for factory equipment. Background Technology

[0002] The Factory System (FMCS) is the public infrastructure and power supply system of a factory, also known as the Factory Monitoring and Control System. Its core function is to provide stable, safe, and compliant basic supplies and environmental conditions such as water, electricity, gas, cooling, heating, environmental protection, and security for production, ensuring continuous operation. Therefore, the effective operation of the FMCS is crucial for improving the overall operational efficiency and routine operation of the factory. Existing FMCS systems generally suffer from problems such as data silos, decentralized control, weak visualization, passive operation and maintenance, and high energy consumption. Therefore, how to effectively combine and analyze FMCS data to improve the collaborative management and control of FMCS data is a current research focus.

[0003] Traditional plant management and control technologies often rely on static displays of the operation of various plant equipment. The data for each piece of equipment is independent, and the models are not standardized. This makes it difficult for staff to intuitively understand the operational interactions between different pieces of equipment and to proactively adjust the operation of key equipment based on the displayed content. Consequently, the collaborative management and control of the plant's various equipment is ineffective. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, device, computer equipment, computer-readable storage medium, and computer program product for the collaborative management and control of plant equipment to address the above-mentioned technical problems.

[0005] Firstly, this application provides a method for collaborative management and control of plant equipment, including:

[0006] Collect current equipment data, historical equipment data, and equipment relationships of each piece of equipment in the factory area, and construct a twin model of the factory equipment in the factory area based on the current equipment data and equipment relationships of each piece of equipment.

[0007] Based on the historical equipment data of each plant equipment and the twin model of the plant equipment, the linkage and interaction process of each plant equipment is simulated to obtain the predicted equipment collaborative operation data of each plant equipment. Based on the predicted equipment collaborative operation data of each plant equipment, the current target control scheme of each plant equipment is generated through the control optimization strategy.

[0008] Based on the current target control scheme of each plant equipment, the operation of each plant equipment is controlled to obtain the current operation result of each plant equipment. Based on the current operation result of each plant equipment, the current target control scheme of each plant equipment, and the current equipment data of each plant equipment, the current collaborative management and control record of the plant area is generated.

[0009] Optionally, the step of constructing a plant equipment twin model of the plant area based on the current equipment data of each of the plant equipment and the equipment association relationships of each of the plant equipment includes:

[0010] Based on the equipment association relationships of each plant equipment, the data flow relationships and collaborative interaction relationships between each plant equipment are identified, and the current equipment operating parameters of each plant equipment are determined based on the current equipment data of each plant equipment.

[0011] Based on the current operating parameters of each of the plant equipment, a current data-driven model for each of the plant equipment is constructed. Based on the data flow relationship between each of the plant equipment, the current data-driven model of each of the plant equipment is spliced ​​to obtain the plant data flow model of the plant area.

[0012] Based on the collaborative interaction relationship between the plant equipment, the collaborative interaction logic and the collaborative interaction process between the plant equipment are identified, and based on the collaborative interaction logic and the collaborative interaction process between the plant equipment, a collaborative interaction strategy for the plant data flow model of the plant area is generated.

[0013] The plant data flow model containing the aforementioned collaborative interaction strategy will be used as a twin model of the plant equipment in the plant area.

[0014] Optionally, the step of simulating the linkage and interaction process of each of the plant equipment based on historical equipment data and the plant equipment twin model to obtain predicted equipment collaborative operation data for each of the plant equipment includes:

[0015] Based on the historical equipment data of each plant equipment, the historical operating parameters and operating tasks of each plant equipment are identified, and the operating tasks of each plant equipment are broken down into the interaction process between each plant equipment and the interaction data between each plant equipment.

[0016] The historical operating parameters of each of the plant equipment are used to replace the operating parameters of each of the plant equipment in the plant equipment twin model to obtain the target plant equipment twin model;

[0017] Based on the interaction process and interaction data between the plant equipment, the interactive operation process of each plant equipment starting from the current moment is simulated through the target plant equipment twin model, so as to obtain the predicted operation data distribution information of each plant equipment and the predicted interactive operation data distribution information between each plant equipment.

[0018] The predicted operation data distribution information of each plant equipment, as well as the predicted interactive operation data distribution information between each plant equipment, are used as the predicted equipment collaborative operation data of each plant equipment.

[0019] Optionally, the step of generating a current target control scheme for each of the plant equipment based on the predicted equipment collaborative operation data of each of the plant equipment, through a control optimization strategy, includes:

[0020] Based on the distribution information of the predicted operating data of each plant equipment, the predicted operating parameters of each plant equipment are identified. Based on the predicted operating parameters of each plant equipment and the distribution information of the predicted operating data of each plant equipment, the optimized operating parameters of each plant equipment are generated through the plant equipment twin model and the equipment control optimization strategy.

[0021] Based on the distribution information of the predicted interactive operation data between each of the plant equipment, the interaction parameters between each of the plant equipment are identified. Based on the interaction parameters between each of the plant equipment and the distribution information of the predicted interactive operation data between each of the plant equipment, the optimized interaction parameters between each of the plant equipment are generated through the plant equipment twin model and the equipment interaction optimization strategy.

[0022] Based on the optimized operating parameters of each plant equipment and the optimized interaction parameters between each plant equipment, a current target control scheme for each plant equipment is generated.

[0023] Optionally, the step of controlling the operation of each of the plant equipment based on the current target control scheme of each of the plant equipment, and obtaining the current operating result of each of the plant equipment, includes:

[0024] For each piece of plant equipment, based on the optimized operating parameters of the plant equipment, the optimized interaction parameters between the plant equipment and other plant equipment, and the current operating parameters of the plant equipment, the adjustment amount of each parameter of the plant equipment is identified;

[0025] Based on the parameter adjustment amounts of each plant equipment, parameter adjustment processing is performed on each plant equipment, and the current operating data distribution information of each plant equipment with adjusted parameters is obtained as the current operating result of each plant equipment.

[0026] Optionally, generating the current collaborative management and control record of the plant area based on the current operating results of each of the plant equipment, the current target control scheme of each of the plant equipment, and the current equipment data of each of the plant equipment includes:

[0027] Based on the current operating results of each plant equipment and the predicted collaborative operating data of each plant equipment, the prediction deviation data of each plant equipment is identified, and based on the prediction deviation data of each plant equipment, the simulation parameters of the target plant equipment twin model are adjusted to obtain a new target plant equipment twin model.

[0028] Based on the current operating results of each of the plant equipment, the fault monitoring strategy identifies the operating fault information of each of the plant equipment, and based on the operating fault information of each of the plant equipment, generates a visual fault display information of the plant area;

[0029] Based on the current target control scheme of each plant equipment and the current operating results of each plant equipment, equipment collaborative management and control traceability information of the plant area is generated, and the new target plant equipment twin model, the visualized fault display information of the plant area, and the equipment collaborative management and control traceability information of the plant area are used as the current collaborative management and control record of the plant area.

[0030] Secondly, this application also provides a collaborative control device for plant equipment, comprising:

[0031] The data acquisition module is used to collect the current equipment data of each piece of equipment in the plant area, the historical equipment data of each piece of equipment, and the equipment association relationship of each piece of equipment. Based on the current equipment data of each piece of equipment and the equipment association relationship of each piece of equipment, a twin model of the plant equipment in the plant area is constructed.

[0032] The simulation module is used to simulate the linkage and interaction process of each plant equipment based on the historical equipment data and the plant equipment twin model, to obtain the predicted equipment collaborative operation data of each plant equipment, and to generate the current target control scheme of each plant equipment based on the predicted equipment collaborative operation data of each plant equipment through the control optimization strategy.

[0033] The generation module is used to control the operation of each plant equipment based on the current target control scheme of each plant equipment, obtain the current operation results of each plant equipment, and generate the current collaborative management and control record of the plant area based on the current operation results of each plant equipment, the current target control scheme of each plant equipment, and the current equipment data of each plant equipment.

[0034] Optionally, the acquisition module is specifically used for:

[0035] Based on the equipment association relationships of each plant equipment, the data flow relationships and collaborative interaction relationships between each plant equipment are identified, and the current equipment operating parameters of each plant equipment are determined based on the current equipment data of each plant equipment.

[0036] Based on the current operating parameters of each of the plant equipment, a current data-driven model for each of the plant equipment is constructed. Based on the data flow relationship between each of the plant equipment, the current data-driven model of each of the plant equipment is spliced ​​to obtain the plant data flow model of the plant area.

[0037] Based on the collaborative interaction relationship between the plant equipment, the collaborative interaction logic and the collaborative interaction process between the plant equipment are identified, and based on the collaborative interaction logic and the collaborative interaction process between the plant equipment, a collaborative interaction strategy for the plant data flow model of the plant area is generated.

[0038] The plant data flow model containing the aforementioned collaborative interaction strategy will be used as a twin model of the plant equipment in the plant area.

[0039] Optionally, the simulation module is specifically used for:

[0040] Based on the historical equipment data of each plant equipment, the historical operating parameters and operating tasks of each plant equipment are identified, and the operating tasks of each plant equipment are broken down into the interaction process between each plant equipment and the interaction data between each plant equipment.

[0041] The historical operating parameters of each of the plant equipment are used to replace the operating parameters of each of the plant equipment in the plant equipment twin model to obtain the target plant equipment twin model;

[0042] Based on the interaction process and interaction data between the plant equipment, the interactive operation process of each plant equipment starting from the current moment is simulated through the target plant equipment twin model, so as to obtain the predicted operation data distribution information of each plant equipment and the predicted interactive operation data distribution information between each plant equipment.

[0043] The predicted operation data distribution information of each plant equipment, as well as the predicted interactive operation data distribution information between each plant equipment, are used as the predicted equipment collaborative operation data of each plant equipment.

[0044] Optionally, the simulation module is specifically used for:

[0045] Based on the distribution information of the predicted operating data of each plant equipment, the predicted operating parameters of each plant equipment are identified. Based on the predicted operating parameters of each plant equipment and the distribution information of the predicted operating data of each plant equipment, the optimized operating parameters of each plant equipment are generated through the plant equipment twin model and the equipment control optimization strategy.

[0046] Based on the distribution information of the predicted interactive operation data between each of the plant equipment, the interaction parameters between each of the plant equipment are identified. Based on the interaction parameters between each of the plant equipment and the distribution information of the predicted interactive operation data between each of the plant equipment, the optimized interaction parameters between each of the plant equipment are generated through the plant equipment twin model and the equipment interaction optimization strategy.

[0047] Based on the optimized operating parameters of each plant equipment and the optimized interaction parameters between each plant equipment, a current target control scheme for each plant equipment is generated.

[0048] Optionally, the generation module is specifically used for:

[0049] For each piece of plant equipment, based on the optimized operating parameters of the plant equipment, the optimized interaction parameters between the plant equipment and other plant equipment, and the current operating parameters of the plant equipment, the adjustment amount of each parameter of the plant equipment is identified;

[0050] Based on the parameter adjustment amounts of each plant equipment, parameter adjustment processing is performed on each plant equipment, and the current operating data distribution information of each plant equipment with adjusted parameters is obtained as the current operating result of each plant equipment.

[0051] Optionally, the generation module is specifically used for:

[0052] Based on the current operating results of each plant equipment and the predicted collaborative operating data of each plant equipment, the prediction deviation data of each plant equipment is identified, and based on the prediction deviation data of each plant equipment, the simulation parameters of the target plant equipment twin model are adjusted to obtain a new target plant equipment twin model.

[0053] Based on the current operating results of each of the plant equipment, the fault monitoring strategy identifies the operating fault information of each of the plant equipment, and based on the operating fault information of each of the plant equipment, generates a visual fault display information of the plant area;

[0054] Based on the current target control scheme of each plant equipment and the current operating results of each plant equipment, equipment collaborative management and control traceability information of the plant area is generated, and the new target plant equipment twin model, the visualized fault display information of the plant area, and the equipment collaborative management and control traceability information of the plant area are used as the current collaborative management and control record of the plant area.

[0055] Thirdly, this application provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the method described in any one of the first aspects.

[0056] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the method described in any one of the first aspects.

[0057] Fifthly, this application provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the steps of the method described in any one of the first aspects.

[0058] The aforementioned collaborative management and control method, device, and computer equipment for plant equipment collect current equipment data, historical equipment data, and equipment relationships of each piece of equipment in the plant area. Based on the current equipment data and equipment relationships of each piece of equipment, a twin model of the plant equipment in the plant area is constructed. Based on the historical equipment data and the twin model of the plant equipment, the linkage and interaction process of each piece of equipment is simulated to obtain predicted collaborative operation data of each piece of equipment. Based on the predicted collaborative operation data of each piece of equipment, a current target control scheme for each piece of equipment is generated through a control optimization strategy. Based on the current target control scheme, the operation of each piece of equipment is controlled to obtain the current operation result of each piece of equipment. Based on the current operation result, the current target control scheme, and the current equipment data of each piece of equipment, a current collaborative management and control record of the plant area is generated. This application constructs digital twin models of various plant equipment within a factory area. This allows for comprehensive monitoring and control of the individual equipment's operation, as well as the interaction and collaborative control between different pieces of equipment, enhancing the overall comprehensiveness of equipment monitoring. Furthermore, by simulating the interconnected interaction processes of each piece of equipment using these digital twin models, the application generates predictive collaborative operation data. This not only demonstrates the collaborative interaction process of each piece of equipment using digital twin technology but also predicts its collaborative operation, improving the comprehensiveness of equipment operation and development identification. Finally, using the predicted operational data and optimization techniques, the application intelligently regulates the operation of each piece of equipment, ensuring better interactive operation and effectively improving the collaborative control of the factory's various equipment. Attached Figure Description

[0059] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0060] Figure 1 This is a flowchart illustrating a collaborative management and control method for plant equipment in one embodiment;

[0061] Figure 2 This is a flowchart illustrating an example of collaborative management and control of plant equipment in one embodiment;

[0062] Figure 3 This is a structural block diagram of a collaborative control device for plant equipment in one embodiment;

[0063] Figure 4 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0064] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0065] The collaborative management and control method for plant equipment provided in this application can be applied to a collaborative management and control system for plant equipment. This system can be applied to a terminal, which can be, but is not limited to, various personal computers, laptops, mid-range computers, etc. Specifically, the terminal constructs a plant equipment twin model for each piece of equipment within the plant area. This allows for comprehensive monitoring and control of the individual operation and processes of each piece of equipment, as well as simulation, identification, and monitoring of the interactive operation and collaborative control between different pieces of equipment, thus improving the comprehensiveness of monitoring each piece of equipment. Furthermore, by constructing the plant equipment twin model, this application simulates the linkage and interaction process of each piece of equipment, thereby generating predictive collaborative operation data for each piece of equipment. This not only realizes the digital twin technology's demonstration of the collaborative interaction process of each piece of equipment but also enables prediction of the collaborative operation process of each piece of equipment, improving the comprehensiveness of identifying the operational development of each piece of equipment. Furthermore, this application uses predicted operational data and optimization methods to intelligently regulate the operation of each plant's equipment, thereby ensuring better interactive operation of each plant's equipment and effectively improving the collaborative management and control of the plant's equipment.

[0066] In one exemplary embodiment, such as Figure 1 As shown, a collaborative management and control method for plant equipment is provided. Taking the application of this method to a terminal as an example, the method includes the following steps S101 to S103. Wherein:

[0067] Step S101: Collect the current equipment data, historical equipment data, and equipment relationships of each piece of equipment in the plant area, and construct a twin model of the plant equipment based on the current equipment data and equipment relationships of each piece of equipment.

[0068] In this embodiment, the terminal, based on an edge gateway, collects real-time data such as temperature, humidity, pressure, flow rate, operating status, and fault codes of various plant equipment via protocols such as Modbus, BACnet, OPC UA, and PLC. It then converts the collected real-time data into a unified data format to obtain the current equipment data for each plant device, thereby achieving standardized access to all plant equipment. These plant devices may include, but are not limited to, equipment in the power and electrical systems, pure and ultrapure water systems, process gas systems, vacuum systems, process cooling and temperature control systems, cleanroom and environmental control systems, waste gas treatment systems, wastewater treatment systems, chemical supply systems, fire protection and security systems, and other auxiliary plant equipment. Then, in response to the information upload operation by the staff, the terminal retrieves the different equipment data of various plant equipment and the interactive relationships between them. For example, there is an interaction of water data between pure water and ultrapure water equipment, and between pure water and wastewater treatment equipment; an interaction of gas data between process gas equipment and waste gas treatment equipment; an interaction of energy data between power and electrical equipment and other plant equipment; and an interaction of gas data between vacuum equipment and waste gas treatment equipment, etc. The terminal then builds three-dimensional twin models according to the hierarchy of plant area, system, plant equipment, and component, thus forming a plant equipment twin model that can display and simulate the operation process of each piece of plant equipment itself, as well as the interactive and collaborative operation process between various pieces of plant equipment.

[0069] Step S102: Based on the historical equipment data of each plant equipment and the plant equipment twin model, simulate the linkage and interaction process of each plant equipment to obtain the predicted equipment collaborative operation data of each plant equipment. Based on the predicted equipment collaborative operation data of each plant equipment, generate the current target control scheme of each plant equipment through the control optimization strategy.

[0070] In this embodiment, the terminal simulates the linkage and interaction process of each plant's equipment based on historical equipment data and plant equipment twin models, obtaining predicted equipment collaborative operation data for each plant. Based on this data, the terminal generates the current target control scheme for each plant through a control optimization strategy. Specifically, the terminal performs load forecasting, energy efficiency simulation, and control strategy optimization based on twin historical data, environmental conditions, production load, and electricity price, generating optimal operating parameters and linkage logic. The specific simulation process and the generation process of the optimal operating parameters and linkage logic will be described in detail later. The predicted equipment collaborative operation data is generated by simulating the operation process of each plant's equipment from the current moment to the future, using historical equipment data as input. This generates predicted operation data distribution information for each plant's equipment and predicted interaction operation data distribution information between the plant's equipment. Then, based on the predicted operational data distribution information of each plant's equipment and the predicted interactive operational data distribution information between the equipment, the terminal optimizes the parameters of the equipment operation and interactive operation process through optimization strategies for each individual plant device and for the interaction between them. This ensures efficient operation and interaction of the equipment, and avoids operational anomalies. The specific execution process of the optimization strategies for each individual plant device and for the interaction between them will be explained in detail later. The current target control scheme for each plant device includes start-stop times, operational processes, operating modes, and data reception / transfer directions.

[0071] Step S103: Based on the current target control scheme of each plant equipment, control the operation of each plant equipment, obtain the current operation results of each plant equipment, and generate the current collaborative management and control record of the plant area based on the current operation results of each plant equipment, the current target control scheme of each plant equipment, and the current equipment data of each plant equipment.

[0072] In this embodiment, the terminal controls the operation of each plant facility based on the current target control scheme of each plant facility, obtains the current operation results of each plant facility, and generates the current collaborative management and control record of the plant area based on the current operation results of each plant facility, the current target control scheme of each plant facility, and the current device data of each plant facility. Among them, the control method is to directly adjust the operation parameters and interactive operation parameters of each plant facility with the current target control scheme of each plant facility generated in step S102, so as to achieve the direct control process of each plant facility. In this current collaborative management and control record, it includes the new target plant facility twin model, the visual fault display information of the plant area, and the device collaborative management and control traceability information of the plant area. Among them, the new target plant facility twin model is a new model obtained by adjusting the simulation parameters of the plant facility twin model based on the deviation value between the current operation results of each plant facility and the predicted device collaborative operation data of each predicted plant facility. In this visual fault display information, it includes the fault points, fault causes, and specific fault details reflected by the current operation results of each plant facility, the display of the position in the three-dimensional structure model of the plant facility twin model, and the information displayed by the fault information identifier. The device collaborative management and control traceability information is the process of the terminal controlling each plant facility to adjust the operation parameters and interactive operation parameters (the record information of the terminal controlling each plant facility to operate based on the current target control scheme of each plant facility and obtaining the current operation results of each plant facility), the reason for parameter adjustment (the current target control scheme of each plant facility), and the result of parameter adjustment (the current operation results of each plant facility). The specific generation process will be described in detail later.

[0073] Based on the above solution, by constructing the plant facility twin model of each plant facility in the plant area, in addition to comprehensively monitoring the operation conditions and operation processes of each plant facility in the plant area and controlling the operation process, it is also possible to simulate, identify, and monitor the interactive operation and collaborative control of each plant facility, improving the comprehensiveness of monitoring of each plant facility. Then, this application constructs a plant facility twin model to simulate the linkage interaction process of each plant facility, thereby generating the predicted device collaborative operation data of each plant facility. Thus, not only does it realize the digital twin technology to display the collaborative interaction process of each plant facility, but it can also predict the operation process of each plant facility during collaborative operation, improving the comprehensiveness of identifying the operation development of each plant facility. Furthermore, this application uses the predicted operation data to intelligently regulate the operation process of each plant facility through the method of control optimization, so as to ensure better interactive operation effects of each plant facility, thereby effectively improving the collaborative management and control effect of each plant facility in the factory.

[0074] Optionally, based on the current equipment data of each plant's equipment and the equipment relationships between them, a plant equipment twin model is constructed, including: identifying the data flow relationships and collaborative interaction relationships between the plant's equipment based on their equipment relationships, and determining the current operating parameters of each plant's equipment based on their current equipment data; constructing a current data-driven model for each plant's equipment based on its current operating parameters, and performing data splicing processing on the current data-driven models based on the data flow relationships between them to obtain the plant's data flow model; identifying the collaborative interaction logic and process between the plant's equipment based on their collaborative interaction relationships, and generating a collaborative interaction strategy for the plant's data flow model based on this logic and process; and using the data flow model containing the collaborative interaction strategy as the plant's equipment twin model.

[0075] In this embodiment, the terminal identifies the data flow relationships and collaborative interaction relationships between various plant equipment based on their equipment association relationships, and determines the current operating parameters of each plant equipment based on its current equipment data. The data flow relationships include data transmission, data flow direction, and data conversion relationships between the plant equipment, and also include the ownership relationships between the plant equipment, which characterize the systems to which different plant equipment belongs and the various components of the plant equipment.

[0076] Based on the current operating parameters of each plant's equipment, a current data-driven model for each plant's equipment is constructed. Then, based on the data flow relationships between the plant's equipment, the current data-driven models of each plant's equipment are spliced ​​together to obtain the plant's operational data flow model. Specifically, for each plant's equipment, the terminal identifies the system to which the equipment belongs, the component parameters of each component, and the component splicing method based on the equipment's current operating parameters. Then, the terminal constructs a component model for each component using finite element modeling technology based on the component parameters. Next, based on the component splicing method of each component, the terminal performs structural splicing processing on each component to obtain the equipment model of that plant's equipment. Finally, the terminal obtains the system structure connection parameters of each system and, based on these parameters, splices the equipment models of each plant's equipment within each system to obtain the plant's operational data flow model. Among them, the system structure connection parameters of each system are preset by the staff on the terminal to guide the plant equipment that has connection relationship or data flow relationship between the systems, as well as the structural connection relationship of the above-mentioned plant equipment.

[0077] Then, based on the collaborative interaction relationships between various plant equipment, the terminal identifies the collaborative interaction logic and process between these equipment. Based on this logic and process, it generates a collaborative interaction strategy for the plant's data flow model. The collaborative interaction relationships between the equipment represent the data flow and transformation logic related to collaborative interaction during task execution. The collaborative interaction process represents the order of data interaction between the equipment during task execution. The collaborative interaction strategy of this data flow model uses the collaborative interaction process as the task execution flow and the collaborative interaction logic as the interaction method to execute the collaborative interaction tasks. Specifically, task A requires data processing via device b in system B, then data processing via device c, then data analysis via device d in system C, and finally data packaging via device f in system E to obtain the execution result G of task A. Therefore, the collaborative interaction strategy of this plant data flow model is the interaction process between the above systems.

[0078] Finally, the terminal will include a plant data flow model with collaborative interaction strategies, serving as a twin model of plant equipment in the plant area.

[0079] Based on the above scheme, by constructing a multi-architecture data flow model between the system, plant equipment, and components, it is possible to simulate the collaborative interaction process of various plant equipment, ensuring the simulation authenticity and accuracy of the data interaction process of each plant equipment.

[0080] Optionally, based on the historical equipment data of each plant's equipment and the plant's equipment twin model, the linkage and interaction process of each plant's equipment is simulated to obtain the predicted equipment collaborative operation data of each plant's equipment. This includes: based on the historical equipment data of each plant's equipment, identifying the historical operating parameters and operating tasks of each plant's equipment, and breaking down the operating tasks of each plant's equipment into the interaction process and interaction data between each plant's equipment; replacing the operating parameters of each plant's equipment in the plant's equipment twin model with the historical operating parameters of each plant's equipment to obtain the target plant's equipment twin model; based on the interaction process and interaction data between each plant's equipment, simulating the interaction operation process of each plant's equipment from the current moment through the target plant's equipment twin model to obtain the predicted operation data distribution information of each plant's equipment and the predicted interaction operation data distribution information between each plant's equipment; and using the predicted operation data distribution information of each plant's equipment and the predicted interaction operation data distribution information between each plant's equipment as the predicted equipment collaborative operation data of each plant's equipment.

[0081] In this embodiment, the terminal identifies the historical operating parameters and operational tasks of each plant's equipment based on historical equipment data. It then breaks down the operational tasks into interaction flows and data between the equipment. The terminal replaces the operating parameters of each plant's equipment in the plant equipment twin model with the historical operating parameters, resulting in the target plant equipment twin model. During the replacement, the terminal first identifies the parameter identification information in each model parameter of the plant equipment twin model. When constructing the model, the terminal personnel add parameter identification information to each model parameter to identify the equipment and its operational parameters that are simulated by each parameter in the model. Then, based on the parameter identification information in each model parameter, the terminal identifies the historical operating parameters corresponding to each model parameter. Finally, according to the above correspondence, the terminal replaces the historical operating parameters of each plant's equipment in the plant equipment twin model, resulting in the target plant equipment twin model.

[0082] Then, based on the interaction flow and data between various plant equipment, the terminal simulates the interactive operation process of each plant equipment from the current moment using a twin model of the target plant equipment. This yields the predicted operational data distribution information for each plant equipment, as well as the predicted interactive operational data distribution information between the plant equipment. Specifically, the predicted operational data distribution information for each plant equipment represents the data change distribution information of its own operational data over time, while the predicted interactive operational data distribution information between the plant equipment represents the data change distribution information of each interactive data over time during data interaction, data flow, and data transformation between the plant equipment. Finally, the terminal uses the predicted operational data distribution information for each plant equipment and the predicted interactive operational data distribution information between the plant equipment as the predicted collaborative operation data for each plant equipment.

[0083] Based on the above scheme, by adjusting the historical operating parameters of each plant equipment to construct the plant equipment twin model in this application, the plant equipment twin model can more accurately simulate the current operating process of each plant equipment, thereby improving the simulation accuracy.

[0084] Optionally, based on the predicted collaborative operation data of each plant's equipment, a current target control scheme for each plant's equipment is generated through a control optimization strategy. This includes: identifying the predicted operating parameters of each plant's equipment based on the distribution information of the predicted operating data; generating optimized operating parameters for each plant's equipment based on the predicted operating parameters and the distribution information of the predicted operating data of each plant's equipment, using a plant equipment twin model and an equipment control optimization strategy; identifying the interaction parameters between each plant's equipment based on the distribution information of the predicted interactive operating data between each plant's equipment; generating optimized interaction parameters between each plant's equipment based on the interaction parameters and the distribution information of the predicted interactive operating data between each plant's equipment, using a plant equipment twin model and an equipment interaction optimization strategy; and generating a current target control scheme for each plant's equipment based on the optimized operating parameters of each plant's equipment and the optimized interaction parameters between each plant's equipment.

[0085] In this embodiment, the terminal identifies the predicted operating parameters of each plant's equipment based on the distribution information of predicted operating data. Then, based on these predicted operating parameters and the distribution information of predicted operating data, it generates optimized operating parameters for each plant's equipment through a plant equipment twin model and an equipment control optimization strategy. This equipment control optimization strategy is based on a multi-objective genetic algorithm, which balances conflicting objectives such as energy consumption, production capacity, equipment lifespan, and safety to generate a Pareto optimal parameter combination. During control optimization, the weight values ​​of each objective are manually defined in the spot area, and then parameter optimization is performed, thereby achieving a plant equipment operating parameter optimization effect that allows for autonomous adjustment of the emphasis direction.

[0086] Specifically, based on the predicted operating parameters and predicted operating data distribution information of each plant equipment obtained above, the terminal performs parameter optimization through a multi-objective genetic algorithm to obtain new operating parameters for each plant equipment. Then, the terminal replaces the twin model of each plant equipment with the new operating parameters to obtain a new twin model. The terminal then replaces the target plant equipment twin model with the new twin model and re-executes the interaction process and interaction data between each plant equipment. Through the target plant equipment twin model, the terminal simulates the interaction operation process of each plant equipment starting from the current moment to obtain the predicted operating data distribution information of each plant equipment and the predicted interaction operating data distribution information between each plant equipment. The terminal iterates this process until the deviation between the optimized new operating parameters and the predicted operating parameters generated after simulation is lower than the deviation threshold preset by the terminal. At this point, the terminal uses the new operating parameters obtained in the last iteration as the optimized operating parameters for each target plant equipment.

[0087] Then, based on the distribution information of the predicted interactive operation data between each plant equipment, the terminal identifies the interaction parameters between each plant equipment, and based on the interaction parameters between each plant equipment and the distribution information of the predicted interactive operation data between each plant equipment, it generates optimized interaction parameters between each plant equipment through plant equipment twin models and equipment interaction optimization strategies. The device interaction optimization strategy is the dynamic load adaptive optimization strategy constructed in this application. Specifically, the terminal identifies the upstream and downstream devices of each plant device according to the execution order of each plant device. Then, the terminal adjusts the predicted operating parameters of the plant device based on the input / output data of the upstream and downstream devices in each plant device to obtain new operating parameters. Then, the terminal replaces the target plant device twin model with the new plant device twin model, and then re-executes the interaction process and interaction data between each plant device. Through the target plant device twin model, the terminal simulates the interaction operation process of each plant device from the current moment to obtain the predicted operating data distribution information of each plant device and the predicted interaction operating data distribution information between each plant device. The terminal iterates this process until the deviation between the optimized new operating parameters and the predicted operating parameters generated after simulation is lower than the deviation threshold preset in the terminal. Then, the terminal uses the new operating parameters obtained in the last iteration as the optimized interaction parameters between the target plant devices.

[0088] Finally, based on the optimized operating parameters of each plant equipment and the optimized interaction parameters between each plant equipment, the current target control scheme for each plant equipment is generated.

[0089] Based on the above scheme, after twin simulation, each operating parameter is iteratively optimized based on the interaction parameters, thereby generating optimized operating parameters for each plant equipment and optimized interaction parameters between each plant equipment. This not only improves the operational optimization effect of each plant equipment, but also enhances the interactive operation effect between each plant equipment.

[0090] Optionally, based on the current target control scheme of each plant equipment, the operation of each plant equipment is controlled to obtain the current operating results of each plant equipment, including: for each plant equipment, based on the optimized operating parameters of the plant equipment, the optimized interaction parameters between the plant equipment and other plant equipment, and the current equipment operating parameters of the plant equipment, the adjustment amount of each parameter of the plant equipment is identified; based on the adjustment amount of each parameter of each plant equipment, parameter tuning processing is performed on each plant equipment, and the current operating data distribution information of each plant equipment with adjusted parameters is obtained as the current operating result of each plant equipment.

[0091] In this embodiment, the terminal identifies the adjustment amount of each parameter for each plant equipment based on the optimized operating parameters of the plant equipment, the optimized interaction parameters between the plant equipment and other plant equipment, and the current operating parameters of the plant equipment. Specifically, each parameter adjustment amount represents the deviation between the optimized operating parameters of each plant equipment and its current operating parameters, as well as the deviation between the optimized interaction parameters between each plant equipment and its current interaction parameters with other plant equipment.

[0092] Then, the terminal performs parameter adjustment processing on each plant equipment based on the parameter adjustment amount of each plant equipment, and obtains the current operating data distribution information of each plant equipment with adjusted parameters as the current operating result of each plant equipment.

[0093] Based on the above scheme, the operating parameters of each plant equipment are adjusted in real time by optimizing the parameters, thus ensuring that the operation optimization effect of each plant equipment is improved in real time.

[0094] Optionally, based on the current operating results of each plant's equipment, the current target control scheme for each plant's equipment, and the current equipment data of each plant's equipment, a current collaborative management and control record for the plant area is generated. This includes: identifying the prediction deviation data of each plant's equipment based on the current operating results of each plant's equipment and the predicted collaborative operation data of each plant's equipment; adjusting the simulation parameters of the target plant's equipment twin model based on the prediction deviation data of each plant's equipment to obtain a new target plant's equipment twin model; identifying the operational fault information of each plant's equipment based on the current operating results of each plant's equipment through a fault monitoring strategy; generating visualized fault display information for the plant area based on the operational fault information of each plant's equipment; and generating equipment collaborative management and control traceability information for the plant area based on the current target control scheme for each plant's equipment and the current operating results of each plant's equipment. The new target plant's equipment twin model, the visualized fault display information for the plant area, and the equipment collaborative management and control traceability information for the plant area are used as the current collaborative management and control record for the plant area.

[0095] In this embodiment, the terminal identifies the prediction deviation data of each plant's equipment based on the current operating results and the predicted collaborative operation data of each plant's equipment. Based on this prediction deviation data, the terminal adjusts the simulation parameters of the target plant's equipment twin model to obtain a new target plant's equipment twin model. Specifically, this step involves real-time updates to the target plant's equipment twin model. After acquiring the predicted collaborative operation data, the terminal also collects the actual operating results of each plant's equipment during the prediction period (i.e., the current operating results). The terminal then calculates the deviation between these actual operating results and the predicted collaborative operation data to obtain the prediction deviation data. The terminal identifies the plant's equipment to which the prediction deviation data belongs and the corresponding generation parameters. Following a pre-set equipment parameter adjustment strategy for each plant's equipment on the terminal, the terminal adjusts the generation parameters of that plant's equipment in the target plant's equipment twin model to obtain the new target plant's equipment twin model. This method allows for real-time updates to the target plant equipment twin model, ensuring that the target plant equipment twin model can more accurately simulate the operation of each plant piece of equipment.

[0096] Then, based on the current operating results of each plant's equipment, the terminal identifies operational fault information of each plant's equipment through a fault monitoring strategy, and generates visualized fault display information for the plant area based on this information. The fault monitoring strategy is preset within the terminal's operational data range corresponding to each fault. The terminal queries the current operating results for sub-current operating results belonging to this range, identifies the plant's equipment to which the current operating result belongs, and identifies the structural components of that equipment. Then, the terminal marks the sub-current operating result and the structural components of the equipment in the 3D structural model of the target plant's equipment twin model to obtain the visualized fault display information for the plant area.

[0097] Based on the current target control schemes and current operating results of each plant's equipment, the terminal generates equipment collaborative management and traceability information for the plant area. It then uses the new target plant equipment twin model, the plant's visualized fault display information, and the plant's equipment collaborative management and traceability information as the plant's current collaborative management and control record. Specifically, the terminal summarizes and arranges the current target control schemes and current operating results of each plant's equipment according to the order of control first, then result generation, and the data interaction order between the plant's equipment, thus obtaining the plant's equipment collaborative management and traceability information.

[0098] Based on the above scheme, by tracing and summarizing the control information, and then recording it in combination with the visualized fault display information, the current collaborative control record of the plant area is generated, which improves the accuracy of fault display, the intuitiveness of fault display, and the intuitive traceability of control process and data generation results.

[0099] This application also provides an example of collaborative management and control of plant equipment, such as... Figure 2 As shown, the specific processing procedure includes the following steps:

[0100] Step S201: Collect the current equipment data, historical equipment data, and equipment relationships of each piece of equipment in the plant area.

[0101] Step S202: Based on the equipment association relationship of each plant equipment, identify the data flow relationship between each plant equipment and the collaborative interaction relationship between each plant equipment, and determine the current equipment operating parameters of each plant equipment based on the current equipment data of each plant equipment.

[0102] Step S203: Based on the current operating parameters of each plant equipment, construct the current data-driven model of each plant equipment, and based on the data flow relationship between each plant equipment, perform data splicing processing on the current data-driven model of each plant equipment to obtain the plant data flow model of the plant area.

[0103] Step S204: Based on the collaborative interaction relationship between various plant equipment, identify the collaborative interaction logic and process between various plant equipment, and generate a collaborative interaction strategy for the plant data flow model based on the collaborative interaction logic and process between various plant equipment.

[0104] Step S205: The plant data flow model containing the collaborative interaction strategy is used as the plant equipment twin model of the plant area.

[0105] Step S206: Based on the historical equipment data of each plant equipment, identify the historical operating parameters and operating tasks of each plant equipment, and break down the operating tasks of each plant equipment into the interaction process between each plant equipment and the interaction data between each plant equipment.

[0106] Step S207: Use the historical operating parameters of each plant equipment to replace the operating parameters of each plant equipment in the plant equipment twin model to obtain the target plant equipment twin model.

[0107] Step S208: Based on the interaction process and interaction data between various plant equipment, simulate the interactive operation process of each plant equipment from the current moment through the target plant equipment twin model, and obtain the predicted operation data distribution information of each plant equipment and the predicted interactive operation data distribution information between each plant equipment.

[0108] Step S209: The predicted operation data distribution information of each plant equipment and the predicted interactive operation data distribution information between each plant equipment are used as the predicted equipment collaborative operation data of each plant equipment.

[0109] Step S210: Based on the distribution information of the predicted operating data of each plant equipment, identify the predicted operating parameters of each plant equipment, and based on the predicted operating parameters of each plant equipment and the distribution information of the predicted operating data of each plant equipment, generate the optimized operating parameters of each plant equipment through the plant equipment twin model and the equipment control optimization strategy.

[0110] Step S211: Based on the distribution information of predicted interactive operation data between various plant equipment, identify the interaction parameters between various plant equipment, and based on the interaction parameters between various plant equipment and the distribution information of predicted interactive operation data between various plant equipment, generate optimized interaction parameters between various plant equipment through plant equipment twin models and equipment interaction optimization strategies.

[0111] Step S212: Based on the optimized operating parameters of each plant equipment and the optimized interaction parameters between each plant equipment, generate the current target control scheme for each plant equipment.

[0112] Step S213: For each plant equipment, based on the optimized operating parameters of the plant equipment, the optimized interaction parameters between the plant equipment and other plant equipment, and the current operating parameters of the plant equipment, identify the adjustment amount of each parameter of the plant equipment.

[0113] Step S214: Based on the parameter adjustment amount of each plant equipment, perform parameter adjustment processing on each plant equipment, and obtain the current operating data distribution information of each plant equipment with adjusted parameters as the current operating result of each plant equipment.

[0114] Step S215: Based on the current operating results of each plant equipment and the predicted collaborative operation data of each plant equipment, identify the prediction deviation data of each plant equipment, and adjust the simulation parameters of the target plant equipment twin model based on the prediction deviation data of each plant equipment to obtain a new target plant equipment twin model.

[0115] Step S216: Based on the current operating results of each plant equipment, identify the operating fault information of each plant equipment through the fault monitoring strategy, and generate a visual fault display information of the plant area based on the operating fault information of each plant equipment.

[0116] Step S217: Based on the current target control scheme of each plant equipment and the current operating results of each plant equipment, generate equipment collaborative management and control traceability information for the plant area, and use the new target plant equipment twin model, the visualized fault display information of the plant area, and the equipment collaborative management and control traceability information of the plant area as the current collaborative management and control record of the plant area.

[0117] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0118] Based on the same inventive concept, this application also provides a collaborative management and control device for plant equipment to implement the collaborative management and control method for plant equipment as described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more embodiments of the collaborative management and control device for plant equipment provided below can be found in the limitations of the collaborative management and control method for plant equipment described above, and will not be repeated here.

[0119] In one exemplary embodiment, such as Figure 3 As shown, a collaborative control device for plant equipment is provided, comprising: a data acquisition module 310, a simulation module 320, and a generation module 330, wherein:

[0120] The data acquisition module 310 is used to collect the current equipment data of each piece of equipment in the factory area, the historical equipment data of each piece of equipment, and the equipment association relationship of each piece of equipment, and to construct a twin model of the factory equipment in the factory area based on the current equipment data of each piece of equipment and the equipment association relationship of each piece of equipment.

[0121] The simulation module 320 is used to simulate the linkage and interaction process of each of the plant equipment based on the historical equipment data and the twin model of each plant equipment, to obtain the predicted equipment collaborative operation data of each of the plant equipment, and to generate the current target control scheme of each of the plant equipment based on the predicted equipment collaborative operation data of each of the plant equipment through a control optimization strategy.

[0122] The generation module 330 is used to control the operation of each plant equipment based on the current target control scheme of each plant equipment, obtain the current operation result of each plant equipment, and generate the current collaborative management and control record of the plant area based on the current operation result of each plant equipment, the current target control scheme of each plant equipment, and the current equipment data of each plant equipment.

[0123] Optionally, the acquisition module 310 is specifically used for:

[0124] Based on the equipment association relationships of each plant equipment, the data flow relationships and collaborative interaction relationships between each plant equipment are identified, and the current equipment operating parameters of each plant equipment are determined based on the current equipment data of each plant equipment.

[0125] Based on the current operating parameters of each of the plant equipment, a current data-driven model for each of the plant equipment is constructed. Based on the data flow relationship between each of the plant equipment, the current data-driven model of each of the plant equipment is spliced ​​to obtain the plant data flow model of the plant area.

[0126] Based on the collaborative interaction relationship between the plant equipment, the collaborative interaction logic and the collaborative interaction process between the plant equipment are identified, and based on the collaborative interaction logic and the collaborative interaction process between the plant equipment, a collaborative interaction strategy for the plant data flow model of the plant area is generated.

[0127] The plant data flow model containing the aforementioned collaborative interaction strategy will be used as a twin model of the plant equipment in the plant area.

[0128] Optionally, the simulation module 320 is specifically used for:

[0129] Based on the historical equipment data of each plant equipment, the historical operating parameters and operating tasks of each plant equipment are identified, and the operating tasks of each plant equipment are broken down into the interaction process between each plant equipment and the interaction data between each plant equipment.

[0130] The historical operating parameters of each of the plant equipment are used to replace the operating parameters of each of the plant equipment in the plant equipment twin model to obtain the target plant equipment twin model;

[0131] Based on the interaction process and interaction data between the plant equipment, the interactive operation process of each plant equipment starting from the current moment is simulated through the target plant equipment twin model, so as to obtain the predicted operation data distribution information of each plant equipment and the predicted interactive operation data distribution information between each plant equipment.

[0132] The predicted operation data distribution information of each plant equipment, as well as the predicted interactive operation data distribution information between each plant equipment, are used as the predicted equipment collaborative operation data of each plant equipment.

[0133] Optionally, the simulation module 320 is specifically used for:

[0134] Based on the distribution information of the predicted operating data of each plant equipment, the predicted operating parameters of each plant equipment are identified. Based on the predicted operating parameters of each plant equipment and the distribution information of the predicted operating data of each plant equipment, the optimized operating parameters of each plant equipment are generated through the plant equipment twin model and the equipment control optimization strategy.

[0135] Based on the distribution information of the predicted interactive operation data between each of the plant equipment, the interaction parameters between each of the plant equipment are identified. Based on the interaction parameters between each of the plant equipment and the distribution information of the predicted interactive operation data between each of the plant equipment, the optimized interaction parameters between each of the plant equipment are generated through the plant equipment twin model and the equipment interaction optimization strategy.

[0136] Based on the optimized operating parameters of each plant equipment and the optimized interaction parameters between each plant equipment, a current target control scheme for each plant equipment is generated.

[0137] Optionally, the generation module 330 is specifically used for:

[0138] For each piece of plant equipment, based on the optimized operating parameters of the plant equipment, the optimized interaction parameters between the plant equipment and other plant equipment, and the current operating parameters of the plant equipment, the adjustment amount of each parameter of the plant equipment is identified;

[0139] Based on the parameter adjustment amounts of each plant equipment, parameter adjustment processing is performed on each plant equipment, and the current operating data distribution information of each plant equipment with adjusted parameters is obtained as the current operating result of each plant equipment.

[0140] Optionally, the generation module 330 is specifically used for:

[0141] Based on the current operating results of each plant equipment and the predicted collaborative operating data of each plant equipment, the prediction deviation data of each plant equipment is identified, and based on the prediction deviation data of each plant equipment, the simulation parameters of the target plant equipment twin model are adjusted to obtain a new target plant equipment twin model.

[0142] Based on the current operating results of each of the plant equipment, the fault monitoring strategy identifies the operating fault information of each of the plant equipment, and based on the operating fault information of each of the plant equipment, generates a visual fault display information of the plant area;

[0143] Based on the current target control scheme of each plant equipment and the current operating results of each plant equipment, equipment collaborative management and control traceability information of the plant area is generated, and the new target plant equipment twin model, the visualized fault display information of the plant area, and the equipment collaborative management and control traceability information of the plant area are used as the current collaborative management and control record of the plant area.

[0144] Each module in the aforementioned collaborative control device for plant equipment can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0145] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 4As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a collaborative management method for plant equipment. The display unit is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0146] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0147] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of a collaborative control method for plant equipment.

[0148] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program being executed by a processor to implement the steps of a method for the coordinated management and control of plant equipment.

[0149] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of a method for the coordinated management and control of plant equipment.

[0150] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0151] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0152] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0153] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for collaborative management and control of plant equipment, characterized in that, The method includes: Collect current equipment data, historical equipment data, and equipment relationships of each piece of equipment in the factory area, and construct a twin model of the factory equipment in the factory area based on the current equipment data and equipment relationships of each piece of equipment. Based on the historical equipment data of each plant equipment and the twin model of the plant equipment, the linkage and interaction process of each plant equipment is simulated to obtain the predicted equipment collaborative operation data of each plant equipment. Based on the predicted equipment collaborative operation data of each plant equipment, the current target control scheme of each plant equipment is generated through the control optimization strategy. Based on the current target control scheme of each plant equipment, the operation of each plant equipment is controlled to obtain the current operation result of each plant equipment. Based on the current operation result of each plant equipment, the current target control scheme of each plant equipment, and the current equipment data of each plant equipment, the current collaborative management and control record of the plant area is generated.

2. The method according to claim 1, characterized in that, The process of constructing a twin model of the plant's equipment based on the current equipment data of each piece of equipment and the equipment relationships among them includes: Based on the equipment association relationships of each plant equipment, the data flow relationships and collaborative interaction relationships between each plant equipment are identified, and the current equipment operating parameters of each plant equipment are determined based on the current equipment data of each plant equipment. Based on the current operating parameters of each of the plant equipment, a current data-driven model for each of the plant equipment is constructed. Based on the data flow relationship between each of the plant equipment, the current data-driven model of each of the plant equipment is spliced ​​to obtain the plant data flow model of the plant area. Based on the collaborative interaction relationship between the plant equipment, the collaborative interaction logic and the collaborative interaction process between the plant equipment are identified, and based on the collaborative interaction logic and the collaborative interaction process between the plant equipment, a collaborative interaction strategy for the plant data flow model of the plant area is generated. The plant data flow model containing the aforementioned collaborative interaction strategy will be used as a twin model of the plant equipment in the plant area.

3. The method according to claim 2, characterized in that, The process of simulating the linkage and interaction of each piece of plant equipment based on historical equipment data and the plant equipment twin model is used to obtain predicted equipment collaborative operation data for each piece of plant equipment, including: Based on the historical equipment data of each plant equipment, the historical operating parameters and operating tasks of each plant equipment are identified, and the operating tasks of each plant equipment are broken down into the interaction process between each plant equipment and the interaction data between each plant equipment. The historical operating parameters of each of the plant equipment are used to replace the operating parameters of each of the plant equipment in the plant equipment twin model to obtain the target plant equipment twin model; Based on the interaction process and interaction data between the plant equipment, the interactive operation process of each plant equipment starting from the current moment is simulated through the target plant equipment twin model, so as to obtain the predicted operation data distribution information of each plant equipment and the predicted interactive operation data distribution information between each plant equipment. The predicted operation data distribution information of each plant equipment, as well as the predicted interactive operation data distribution information between each plant equipment, are used as the predicted equipment collaborative operation data of each plant equipment.

4. The method according to claim 3, characterized in that, The method of generating a current target control scheme for each of the plant equipment based on the predicted equipment collaborative operation data of each of the plant equipment through a control optimization strategy includes: Based on the distribution information of the predicted operating data of each plant equipment, the predicted operating parameters of each plant equipment are identified. Based on the predicted operating parameters of each plant equipment and the distribution information of the predicted operating data of each plant equipment, the optimized operating parameters of each plant equipment are generated through the plant equipment twin model and the equipment control optimization strategy. Based on the distribution information of the predicted interactive operation data between each of the plant equipment, the interaction parameters between each of the plant equipment are identified. Based on the interaction parameters between each of the plant equipment and the distribution information of the predicted interactive operation data between each of the plant equipment, the optimized interaction parameters between each of the plant equipment are generated through the plant equipment twin model and the equipment interaction optimization strategy. Based on the optimized operating parameters of each plant equipment and the optimized interaction parameters between each plant equipment, a current target control scheme for each plant equipment is generated.

5. The method according to claim 4, characterized in that, The current target control scheme based on each of the plant equipment controls the operation of each of the plant equipment to obtain the current operating results of each of the plant equipment, including: For each piece of plant equipment, based on the optimized operating parameters of the plant equipment, the optimized interaction parameters between the plant equipment and other plant equipment, and the current operating parameters of the plant equipment, the adjustment amount of each parameter of the plant equipment is identified; Based on the parameter adjustment amounts of each plant equipment, parameter adjustment processing is performed on each plant equipment, and the current operating data distribution information of each plant equipment with adjusted parameters is obtained as the current operating result of each plant equipment.

6. The method according to claim 3, characterized in that, The process of generating a current collaborative management and control record for the plant area based on the current operating results of each piece of plant equipment, the current target control scheme for each piece of plant equipment, and the current equipment data of each piece of plant equipment includes: Based on the current operating results of each plant equipment and the predicted collaborative operating data of each plant equipment, the prediction deviation data of each plant equipment is identified, and based on the prediction deviation data of each plant equipment, the simulation parameters of the target plant equipment twin model are adjusted to obtain a new target plant equipment twin model. Based on the current operating results of each of the plant equipment, the fault monitoring strategy identifies the operating fault information of each of the plant equipment, and based on the operating fault information of each of the plant equipment, generates a visual fault display information of the plant area; Based on the current target control scheme of each plant equipment and the current operating results of each plant equipment, equipment collaborative management and control traceability information of the plant area is generated, and the new target plant equipment twin model, the visualized fault display information of the plant area, and the equipment collaborative management and control traceability information of the plant area are used as the current collaborative management and control record of the plant area.

7. A collaborative control device for factory equipment, characterized in that, The device includes: The data acquisition module is used to collect the current equipment data of each piece of equipment in the plant area, the historical equipment data of each piece of equipment, and the equipment association relationship of each piece of equipment. Based on the current equipment data of each piece of equipment and the equipment association relationship of each piece of equipment, a twin model of the plant equipment in the plant area is constructed. The simulation module is used to simulate the linkage and interaction process of each plant equipment based on the historical equipment data and the plant equipment twin model, to obtain the predicted equipment collaborative operation data of each plant equipment, and to generate the current target control scheme of each plant equipment based on the predicted equipment collaborative operation data of each plant equipment through the control optimization strategy. The generation module is used to control the operation of each plant equipment based on the current target control scheme of each plant equipment, obtain the current operation results of each plant equipment, and generate the current collaborative management and control record of the plant area based on the current operation results of each plant equipment, the current target control scheme of each plant equipment, and the current equipment data of each plant equipment.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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