Method and device for constructing object model of energy industry based on digital twinning

Abstract

The invention discloses a method and device for constructing an object model of an energy industry based on digital twinning, and the method comprises the steps: determining the use relation of energy data based on an energy data set, and enabling the energy data set to be obtained based on a target scene; the target scene is simulated based on the use relation, the use relation is optimized according to a simulation result and a domain knowledge base, and the domain knowledge base is associated with the target scene; and constructing an object model based on the use relationship of the optimized energy data. According to the construction method of the object model, the same set of model runs through the global situation through the digital twinning technology in the whole link of data production, the development period is shortened, and the delivery efficiency is improved.

Description

Technical Field

[0001] The present application relates to technical fields such as energy management and digital twins, and in particular to a method for constructing an object model for the energy industry based on digital twins. Background Art

[0002] The energy industry scenario is a complex energy application scenario with many industry characteristics and many industry needs. For example, there is a relationship between service providers and energy-consuming enterprises. Energy-consuming enterprises have various types of equipment and usage scenarios such as boilers, air compressors, HVAC, photovoltaics, and electricity. The energy industry involves a variety of equipment and production processes with great differences. There are large differences on the demand side, and there are also huge differences in the specific delivery process. After the system is running, the operation monitoring is different. This has led to the lack of unified design in many systems and a high degree of personalized customization. Because the degree of personalized customization is relatively high, the development cycle will be relatively slow, and the code reuse will be relatively low. At the delivery site, because the model is not unified, the delivery personnel need to repeatedly confirm the on-site configuration, and the delivery efficiency is not high enough. In the dimension of operation monitoring, different scenarios use different data links, and it is impossible to concentrate on solving key problems. Summary of the invention

[0003] To this end, the purpose of the implementation mode of the present application is to propose a method, device, electronic device and computer program product for constructing an object model of the energy industry based on digital twins. The object model construction method of the present invention uses digital twin technology to realize the same set of models throughout the entire link of data production, shortening the development cycle and improving delivery efficiency.

[0004] An embodiment of the present application provides a method for constructing an object model of the energy industry based on digital twins, the method comprising: determining a usage relationship of energy data based on an energy data set, wherein the energy data set is obtained based on a target scenario; simulating the target scenario based on the usage relationship, and optimizing the usage relationship according to the simulation results and a domain knowledge base, wherein the domain knowledge base is associated with the target scenario; and constructing an object model based on the usage relationship of the optimized energy data.

[0005] Exemplarily, the energy data set includes equipment data and equipment spatial relationship data, and the energy data set is obtained based on the target scenario, including: determining a target production process based on the target scenario, and determining a target production equipment corresponding to the target production process based on the target production process; determining an energy equipment connected to the target production equipment based on the target production equipment, and obtaining the equipment data and equipment spatial relationship data of the energy equipment based on the energy equipment.

[0006] Exemplarily, the object model is applied to an energy management platform, which is connected to energy equipment. The simulation of the target scenario based on the usage relationship includes: obtaining simulated IoT data of the energy equipment based on the usage relationship; and simulating the target scenario based on the simulated IoT data to meet business needs.

[0007] Exemplarily, the simulation results include algorithm simulation results, the domain knowledge base includes algorithm reference results, and the optimizing the usage relationship based on the simulation results and the domain knowledge base includes: determining the result difference based on the algorithm simulation results and the algorithm reference results; optimizing the usage relationship based on the result difference; wherein, the algorithm simulation results include the slope of the fitted heating curve, the algorithm reference results include the slope of the reference heating curve, the usage relationship includes algorithm parameters, and the optimizing the usage relationship based on the result difference includes: optimizing the algorithm parameters based on the difference between the slope of the fitted heating curve and the slope of the reference heating curve, so that the difference between the slope of the fitted heating curve and the slope of the reference heating curve is less than a preset difference.

[0008] Exemplarily, the simulation results include equipment simulation results, the domain knowledge base includes equipment reference results, and the optimizing the usage relationship based on the simulation results and the domain knowledge base includes: determining a result difference based on the equipment simulation results and the equipment reference results; optimizing the usage relationship based on the result difference, wherein the equipment simulation results include at least one of the rated power of the equipment, the startup status, and the production time of the equipment; wherein the result difference includes at least one of a processing accuracy difference, a processing speed difference, and a processing stability difference, and the usage relationship includes a target processing equipment, and the optimizing the usage relationship based on the result difference also includes: optimizing the target processing equipment based on at least one of the processing accuracy difference, the processing speed difference, and the processing stability difference, so that the result difference meets the equipment requirements.

[0009] Exemplarily, the simulation results include production process simulation results, the domain knowledge base includes production process reference results, and the optimizing the usage relationship based on the simulation results and the domain knowledge base includes: determining result differences based on the production process simulation results and the production process reference results; optimizing the usage relationship based on the result differences, wherein the production process simulation results include at least one of equipment layout and material flow path, equipment production sequence and working time, and energy share; wherein the result differences include process cost differences, and the usage relationship includes energy structure, and the optimizing the usage relationship based on the result differences also includes: optimizing the energy structure based on the process cost differences to reduce process costs.

[0010] Exemplarily, the usage relationship of the energy data includes at least one of the structure, parameters and algorithms of the energy data, and determining the usage relationship of the energy data based on the energy data set includes: matching at least one of the structure, parameters and algorithms of the energy data based on the energy data set and the target scenario.

[0011] Exemplarily, the object model is applied to an energy management platform, and the energy management platform is connected to energy equipment. After the object model is established, the method also includes: obtaining IoT measurement point data of the energy equipment; calculating IoT indicator data based on indicator configuration information and the IoT measurement point data, wherein the indicator configuration information is obtained based on the energy management platform; and visualizing the IoT indicator data.

[0012] Another embodiment of the present application provides a device for constructing an object model of the energy industry based on digital twins, the device comprising: a determination module, used to determine the usage relationship of energy data based on an energy data set, wherein the energy data set is obtained based on a target scenario; a simulation module, used to simulate the target scenario based on the usage relationship, and optimize the usage relationship according to the simulation results and a domain knowledge base, wherein the domain knowledge base is associated with the target scenario; and a construction module, used to construct an object model of the usage relationship of the optimized energy data.

[0013] Another embodiment of the present application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method of any of the above embodiments when executing the computer program.

[0014] Another embodiment of the present application provides a computer program product, which includes instructions. When the instructions are executed by a processor of a computer device, the computer device is enabled to perform the steps of the method of any of the above embodiments.

[0015] In the above implementation, the usage relationship of energy data is determined based on the energy data set, wherein the energy data set is obtained based on the target scenario; the target scenario is simulated based on the usage relationship, and the usage relationship is optimized according to the simulation results and the domain knowledge base, wherein the domain knowledge base is associated with the target scenario; and an object model is constructed based on the usage relationship of the optimized energy data. The object model construction method of the present invention realizes the same set of models throughout the entire link of data production through digital twin technology, shortens the development cycle, and improves delivery efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram of an energy system provided for an embodiment of the present application;

[0017] Figure 2 A flowchart of a method for constructing an object model for the energy industry based on digital twins provided in an embodiment of the present application;

[0018] Figure 3 A flowchart of obtaining an energy data set based on a target scenario provided in an embodiment of the present application;

[0019] Figure 4 Schematic diagram of the energy carbon business model for the printing and dyeing industry provided for the implementation method of this application;

[0020] Figure 5 A flowchart of optimizing the usage relationship by the algorithm layer provided in the implementation mode of this application;

[0021] Figure 6 A flow chart for optimizing the usage relationship at the device layer provided in the implementation mode of the present application;

[0022] Figure 7 A flowchart for optimizing the usage relationship at the production process layer provided in the embodiment of the present application;

[0023] Figure 8 A flowchart of a visualization process provided for an embodiment of the present application;

[0024] Fig. 9 A schematic diagram showing the IoT indicator data provided by the implementation method of this application;

[0025] Fig.10 A schematic diagram of the construction process of the digital twin model provided in the implementation manner of the present application;

[0026] Fig.11 A schematic diagram of a device for constructing an object model of the energy industry based on digital twins provided in an embodiment of the present application;

[0027] Fig.12 A block diagram of an electronic device provided for an embodiment of the present application. DETAILED DESCRIPTION

[0028] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0029] In some examples, the energy industry involves a variety of equipment and production processes with great differences. There are great differences on the demand side, and there are also great differences in the specific delivery process. After the system is running, the operation monitoring is different. This has led to many systems lacking a unified design and a high degree of customization. Because the degree of customization is high, the development cycle will be slower and the code reuse will be lower. At the delivery site, because the model is not unified, the delivery personnel need to repeatedly confirm the on-site configuration, and the delivery efficiency is not high enough. In the dimension of operation monitoring, different scenarios use different data links, and it is not possible to concentrate on solving key problems.

[0030] Based on this, this application proposes a method for constructing an object model for the energy industry based on digital twins. Based on digital twin technology, the same set of models is implemented throughout the entire chain of data production through digital twin technology, shortening the development cycle and improving delivery efficiency.

[0031] The following is a brief description of digital twin technology and the energy industry. Digital twin is a technology that creates a virtual mapping of a physical entity in the digital world. It integrates physical models, sensor data, historical data and other information to build a digital simulation model to reflect the entire life cycle of the physical entity. Digital twin technology is used in many fields such as aerospace, electricity, urban management, agriculture, construction, manufacturing, oil and gas, health care, and environmental protection. The energy industry refers to the industry involved in the entire process of transforming natural energy resources into specific energy service forms required for human production and life. It includes a series of process links and equipment such as exploration, mining, transportation, processing, distribution, conversion, storage, transmission, use and environmental protection. The focus of the energy industry is the energy system, which can be a system designed for a single building, a group of buildings or a factory to supply and distribute electricity, heat and cooling needs. The integrated energy system is an integrated system of production, supply and consumption, with the power system as the core, coupled with multiple energy subsystems such as heat, cold and natural gas, and organically coordinating and optimizing the operation of each link of "source-grid-load-storage" from the physical level during planning, construction and operation. Based on the smart grid, it realizes interconnection with various types of networks such as thermal pipelines, natural gas pipelines, and transportation networks, gives full play to the regulation capacity of flexible energy storage resources such as power storage, heat storage, cold storage, and pumped storage, effectively mobilizes the response potential of demand-side resources, and realizes horizontal multi-energy complementarity and vertical coordinated development among multiple energy systems. The efficiency of the energy system reflects the effective and reasonable use of energy in the energy system, which is affected by many factors such as nature, technology, economy, management, and society. The energy system model is a mathematical model that studies the relationship between energy demand and supply, and plays an important role in formulating energy development plans, energy conservation plans, and evaluating energy policies. In terms of digitalization and intelligence, the energy system is accelerating the application of digital technologies such as artificial intelligence, digital twins, the Internet of Things, and blockchain through integrated innovation, promoting interdisciplinary and cross-field integration, and promoting the engineering and industrialization of innovative achievements. By realizing the collection, storage, statistical analysis, energy-saving diagnosis, optimization control and comprehensive management of energy data, the energy utilization efficiency has been significantly improved and the energy use cost has been reduced, bringing economic benefits to the enterprise.

[0032] Figure 1 A schematic diagram of an energy system according to an embodiment of the present application.

[0033] like Figure 1As shown, the power grid is connected to various power source devices, including wind power generation, photovoltaic power generation, hydropower generation, thermal power generation and other types of power generation. The natural gas network is connected to the natural gas pipeline, and electricity and natural gas energy are delivered to various energy equipment, such as electric boilers, gas boilers, cogeneration, etc. The energy system includes an energy management and conversion module for managing the energy usage of various energy equipment. It should be noted that the method for constructing an object model of the energy industry based on digital twins in this application is applied to the energy management and conversion module. The energy management and conversion module is also connected to the thermal network to manage thermal loads, electrical loads, etc.

[0034] Figure 2 It is a flowchart of a method for constructing an object model for the energy industry based on digital twins according to an embodiment of the present application.

[0035] As an example, Figure 2 As shown in the figure, the method for constructing the object model of the energy industry based on digital twins includes:

[0036] S201, determining a usage relationship of energy data based on an energy data set, wherein the energy data set is obtained based on a target scenario.

[0037] S202, simulating the target scenario based on the usage relationship, and optimizing the usage relationship according to the simulation result and the domain knowledge base, wherein the domain knowledge base is associated with the target scenario.

[0038] S203: construct an object model based on the usage relationship of the optimized energy data.

[0039] Exemplarily, before constructing the object model, a comprehensive energy data set and a domain knowledge base can be constructed in advance, wherein the domain knowledge base includes knowledge associated with multiple target scenarios. For example, it includes the differences in IoT data that need to be collected in different scenarios. For example, in the air compression scenario, the focus should be on collecting information such as cumulative power consumption, cumulative flow of the mother pipe, intake temperature, ambient temperature, local atmospheric temperature at the same time, intake pressure, unit exhaust temperature, flow meter temperature, meter power, motor service factor, rated gas volume, etc.; in the boiler scenario, the focus should be on collecting information such as boiler flue gas dry basis oxygen percentage, boiler exhaust temperature, boiler operating load, boiler water level, water inlet temperature, boiler steam production, boiler rated power, etc. And build an energy data set based on the target scenario, for example, collect detailed data on energy equipment, process production equipment, and spatial structure associated with the target scenario to obtain an energy data set associated with the target scenario. Subsequently, model training, optimization, and other operations are performed based on the energy data set.

[0040] Exemplarily, the usage relationship of energy data is determined based on the collected energy data set, for example, the structure, parameters and algorithm of the data are determined. It can be understood that determining the usage relationship of energy data based on the energy data set is a preparatory construction process of the model. The target scenario is simulated based on the usage relationship, and the usage relationship is optimized according to the simulation results and the domain knowledge base, wherein the simulation of the target scenario can be simulated in an actual laboratory. The domain knowledge base may include reference simulation results, such as data such as the rated power of the equipment. The usage relationship is optimized according to the actual simulation results and the domain knowledge base, and the object model is constructed based on the optimized usage relationship of the energy data.

[0041] The object model construction method of this application is based on digital twin technology. Digital twin technology is used to implement the same set of models throughout the entire chain of data production, shortening the development cycle and improving delivery efficiency.

[0042] As an example, Figure 3 As shown, the energy data set includes device data and device spatial relationship data. The energy data set is obtained based on the target scenario, including:

[0043] S301, determining a target production process based on a target scenario, and determining a target production equipment corresponding to the target production process based on the target production process.

[0044] S302: determining energy equipment connected to the target production equipment based on the target production equipment, and acquiring equipment data and equipment spatial relationship data of the energy equipment based on the energy equipment.

[0045] Exemplarily, energy datasets related to the target scenario are collected, such as Figure 4 The schematic diagram of the energy carbon business model of the printing and dyeing industry is shown. The target scenario is, for example, the printing and dyeing industry scenario. The target production process is determined based on the target scenario. For example, the printing and dyeing production process includes raw cloth preparation, dyeing, desizing, cooking, bleaching, mercerizing, singeing, dehydration and other production processes. Based on the target production process, the target production equipment corresponding to the target production process is determined. For example, dyeing requires a dyeing vat and a burner. The dyeing vat and the burner are the target production equipment. Based on the target production equipment, the energy equipment connected to the target production equipment is determined. For example, the dyeing vat needs to be connected to an electric meter and an energy meter, and the burner needs to be connected to a gas meter, etc. The equipment data and equipment spatial relationship data of the energy equipment are obtained based on the energy equipment. For example, the equipment data of the energy equipment is obtained based on the electric meter, energy meter, gas meter, etc. Of course, different production processes may be deployed in different places, so that the electric meter, energy meter, and gas meter may also be deployed in different ways. When collecting data, this application also collects equipment spatial relationship data to facilitate the location of energy equipment in the event of subsequent failures.

[0046] As an example, the usage relationship of energy data includes at least one of the structure, parameters and algorithms of energy data, and determining the usage relationship of energy data based on the energy data set includes: matching at least one of the structure, parameters and algorithms of energy data based on the energy data set and the target scenario.

[0047] Exemplarily, the present application is also connected to an algorithm database, and when determining the usage relationship of energy data based on an energy data set, at least one of the structure, parameters and algorithms of the energy data required for matching the energy data set and the target scenario is used. For example, according to the focus of the business, multiple energy algorithms are automatically or manually selected and combined into a usage relationship of energy data. The algorithm database includes, for example, an outlet water temperature dynamic adjustment algorithm, a fan coil adjustment algorithm, a partition control valve control algorithm, a pump group variable pressure difference control algorithm, an equipment combination optimization algorithm, a dye vat temperature adjustment algorithm, and the like.

[0048] This application organically combines algorithms through the digital twin business model to form a usage relationship of energy data for a specific business (such as the printing and dyeing industry), providing services to industry users from a holistic perspective.

[0049] As an example, the object model is applied to an energy management platform, which is connected to energy equipment and simulates target scenarios based on usage relationships, including: obtaining simulated IoT data of energy equipment based on usage relationships; and simulating target scenarios based on simulated IoT data to meet business needs.

[0050] For example, when simulating the target scenario, the energy management platform can be connected to the energy equipment to obtain the simulated IoT data of the energy equipment. It can be understood that the object model is implemented in reality during the simulation to determine whether the object model meets the business needs. For example, simulate the printing and dyeing production process, check the energy usage, analyze the energy consumption performance, and optimize according to the results. It is also possible to simulate the entire production chain in a laboratory environment according to the production process, from energy supply to output, to check the energy usage.

[0051] As an example, Figure 5 As shown, the simulation results include algorithm simulation results, and the domain knowledge base includes algorithm reference results. The usage relationship is optimized according to the simulation results and the domain knowledge base, including:

[0052] S501, determining a result difference according to an algorithm simulation result and an algorithm reference result.

[0053] S502: Optimize the usage relationship based on the result difference.

[0054] The algorithm simulation results include the slope of the fitted heating curve, the algorithm reference results include the slope of the reference heating curve, the usage relationship includes the algorithm parameters, and the usage relationship is optimized based on the result differences, including:

[0055] The algorithm parameters are optimized based on the difference between the slope of the fitted heating curve and the slope of the reference heating curve, so that the difference between the slope of the fitted heating curve and the slope of the reference heating curve is less than a preset difference.

[0056] Exemplarily, the domain knowledge base includes algorithm reference results. For example, in the printing and dyeing industry, when heating the dye vat, the business requirement is balanced heating, so the curve for heating the dye vat should try to meet the slope of 45°. The algorithm simulation result includes the slope of the fitted heating curve. The use relationship is optimized according to the simulation results and the domain knowledge base. For example, the slope of the fitted heating curve is as close as possible to the slope of the reference heating curve, that is, it is determined that the difference between the slope of the fitted heating curve and the slope of the reference heating curve is less than the preset difference. When the difference between the slope of the fitted heating curve and the slope of the reference heating curve is less than the preset difference, it indicates that the production process is met. If the difference between the slope of the fitted heating curve and the slope of the reference heating curve is greater than or equal to the preset difference, the use relationship can be optimized by controlling the switch of the heater of the dye vat. For example, if the temperature rises too fast, the guest adjusts the power of the heater to reduce, and if the temperature rises too slowly, the power of the adjustable heater can be increased.

[0057] For example, in the HVAC industry, the temperature must drop quickly in the summer and rise quickly in the winter, and the target value must be reached quickly and then stabilized. The algorithm should cool down or heat up as much as possible, and then reduce energy consumption after reaching the target temperature. The fan coil adjustment algorithm can be used first, and then the partition control valve control algorithm, cost-effectiveness optimization algorithm, etc. can be used.

[0058] The usage relationship optimization of this application can optimize the algorithm according to specific business scenarios and needs, mainly improving various parameters of the algorithm, and controlling the device better and faster on the basis of obtaining more IoT data, making the feedback more effective.

[0059] As an example, Figure 6 As shown, the simulation results include equipment simulation results, and the domain knowledge base includes equipment reference results. The usage relationship is optimized according to the simulation results and the domain knowledge base, including:

[0060] S601, determining a result difference according to a device simulation result and a device reference result.

[0061] S602, optimizing the usage relationship based on the result difference, wherein the equipment simulation result includes at least one of the rated power of the equipment, the startup status, and the production time of the equipment.

[0062] The result difference includes at least one of a processing accuracy difference, a processing speed difference, and a processing stability difference, the use relationship includes a target processing device, and the use relationship is optimized based on the result difference, further comprising:

[0063] The target processing equipment is optimized based on at least one of the processing accuracy difference, the processing speed difference and the processing stability difference so that the result difference meets the equipment requirements.

[0064] Exemplarily, the domain knowledge base includes equipment reference results, such as the rated power of the equipment, the startup situation, the production time of the equipment, etc. The equipment simulation results include, for example, the power, startup situation, production time, etc. of the equipment obtained from the simulation results. The result difference is determined according to the equipment simulation results and the equipment reference results, and the use relationship is optimized based on the result difference to make the result difference as small as possible. The result difference includes at least one of the processing accuracy difference, the processing speed difference, and the processing stability difference. The quality of the simulation results can be measured by the processing accuracy difference, the processing speed difference, and the processing stability difference.

[0065] Exemplarily, the optimization of the equipment layer can be combined with expert knowledge and data from the same industry in the domain knowledge base to judge the equipment situation. The model can make a big data AI judgment, or it can be judged by a delivery expert, or it can be judged by a big data AI judgment by the model first and then by a delivery expert. For example, according to the processing technology and requirements of the product, suitable processing equipment can be selected, considering processing accuracy, speed and stability. CNC processing technology can also be introduced to improve processing accuracy and stability, reduce human errors, and realize digital control and automated operation of the production process. For example, equipment performance evaluation and testing are carried out, and the stability of the equipment is evaluated and tested to ensure that the equipment can be stable and reliable during long-term operation, reducing failures and downtime. Intelligent technologies such as artificial intelligence and big data can also be introduced to optimize production scheduling and management, and improve the intelligence level and management efficiency of the production line. Real-time data collection and analysis can also be carried out to provide precise control and adjustment for the production process and other optimization operations.

[0066] As an example, Figure 7 As shown, the simulation results include the production process simulation results, and the domain knowledge base includes the production process reference results. The usage relationship is optimized according to the simulation results and the domain knowledge base, including:

[0067] S701, determining a result difference according to a production process simulation result and a production process reference result.

[0068] S702, optimizing the usage relationship based on the result difference, wherein the production process simulation result includes at least one of the equipment layout and material flow path, the production sequence and working time of the equipment, and the energy proportion.

[0069] The result difference includes the process cost difference, the usage relationship includes the energy structure, and the optimization of the usage relationship based on the result difference also includes:

[0070] The energy structure is optimized based on the differences in process costs to reduce process costs.

[0071] Exemplarily, the domain knowledge base also includes production process reference results, and the simulation results include production process simulation results. The result difference is determined based on the production process simulation results and the production process reference results. The usage relationship is optimized based on the result difference between the production process simulation results and the production process reference results. For example, the result difference includes process cost difference. The energy structure is optimized based on the process cost difference to reduce the process cost. For example, the energy structure is adjusted based on the energy price, such as using medium-pressure steam or natural gas for the heating end.

[0072] Exemplarily, the optimization of the production process layer also includes: re-planning the equipment layout and material flow path according to the difference between the production process simulation results and the production process reference results to reduce the material transportation time and waiting time and improve the coordination efficiency between equipment. For example, the production sequence and working time of the equipment are reasonably arranged according to the difference between the production process simulation results and the production process reference results to maximize the equipment utilization rate and reduce the idle time. For example, data and experience in the production process are collected, data analysis and evaluation are carried out, and the operating parameters and efficiency of the production line are continuously optimized. For example, the network upgrade of industrial equipment, the comprehensive use of industrial bus, industrial Ethernet, 5G and other technologies, accelerates the network intelligent transformation and upgrading of industrial equipment, instrumentation, and automatic reading equipment on the industrial site. For example, the use of multi-energy combination applications, the transformation of production processes, the reduction of energy costs, and the improvement of process results.

[0073] After the usage relationship is constructed and optimized according to the above method, an object model is established based on the optimized usage relationship, and then the object model is applied to the actual equipment or structure, and continuous monitoring and adjustment are performed.

[0074] As an example, the object model is applied to the energy management platform, which is connected to the energy equipment. After the object model is established, Figure 8 As shown, the method for constructing the object model of the energy industry based on digital twins also includes:

[0075] S801, obtaining IoT measurement point data of energy equipment.

[0076] S802, calculating IoT indicator data based on indicator configuration information and IoT measurement point data, wherein the indicator configuration information is obtained based on an energy management platform.

[0077] S803: Visualize the IoT indicator data.

[0078] For example, after the object model is established according to the above method, the object model is applied to the actual device or structure to obtain the IoT measurement point data of the energy device. The IoT measurement point data can be obtained through meter collection, such as various types of electric meters. IoT indicator data is calculated based on the indicator configuration information and IoT measurement point data, such as Fig. 9 As shown, the indicator configuration information can be completed in the enterprise management and configuration of the operation center. The indicator configuration information may include formula or relationship configuration, and the indicator calculation formula is generated according to the configured relationship. The IoT layer obtains IoT measurement point data, such as electricity, gas usage, etc. IoT indicator data is calculated based on the indicator configuration information and IoT measurement point data. IoT indicator data includes, for example, lost electricity, actual power generation, system efficiency, theoretical photovoltaic power generation, electricity transaction amount, loss ratio, etc. IoT indicator data includes single-station indicator calculation and multi-station indicator calculation. Finally, the indicators are summarized and stored in the data layer. The data layer is connected to the business display layer through the data interface service layer, and the IoT indicator data is visualized through the data interface. Of course, the visualized data is not limited to IoT indicator data, but can also be other business data.

[0079] This application uses real-time collection technology for basic IoT data, such as Flink, kafka and other real-time computing technologies, TDengine, mySql as storage, and finally stores the business data in a professional database through real-time computing, ensuring the real-time and accuracy of the data.

[0080] The construction process of the object model of this application is to design model standards in the development state, conduct on-site IoT and configuration of equipment based on standards in the delivery state, realize presentation based on the collected model instances for operation monitoring, and realize corresponding positioning control and execution based on the model implementation for operation control. The on-site topology and real-time operation data are linked with cloud measurement through real-time streaming computing technology to realize visual monitoring and control. The data collected and reported by the meter are used in the digital model established by digital twin technology to realize the binding of reality and digital, and the actual collected data is associated with the twin model in real time. Then, the visualization ability of the twin is used to present the real IoT operation data in the digital product, and finally various energy sources, process energy flows, energy consumption, and carbon emission models are precipitated and standardized. From the development state, delivery state, operation monitoring state, and operation control state, the same set of models are realized through the whole chain through digital twin technology.

[0081] Fig.10 This is the process of constructing a digital twin model of an embodiment of the present application.

[0082] like Fig.10As shown in the figure, first, build a data set and domain knowledge base for the integrated energy system. Secondly, collect data: collect detailed data on relevant energy equipment, process production equipment, and spatial structure. Preliminary construction of the model: Based on the collected data, build the energy usage relationship, including determining the structure, parameters, and algorithms of the data. For example, through the business model of the digital twin, the required algorithms are organically combined to form an object model for a specific business, providing services to industry users from an overall perspective. Simulation and optimization: simulate the above usage relationship, analyze the energy consumption performance, and optimize according to the results. In a laboratory environment, according to the production process, simulate the entire production chain, from energy supply to output, to see the energy usage. The optimization process may include algorithm layer optimization, equipment layer optimization, and production process layer optimization. Based on the result set of the Internet of Things and the strategy evaluation results of the previous step, build a data twin model of the energy system, that is, build an object model based on the optimized usage relationship. Apply the optimized model to the actual equipment or structure, and perform continuous detection and adjustment. The model is also evaluated and iterated according to the implementation and monitoring structure to obtain a better object model. The construction of the object model of this application is a general modeling technology for energy flow and energy consumption under different energy equipment, process production equipment, and spatial structures.

[0083] This application also proposes a device for constructing an object model of the energy industry based on digital twins.

[0084] As an example, Fig.11 As shown, a device for constructing an object model of the energy industry based on digital twins includes: a determination module 1101, used to determine the usage relationship of energy data based on an energy data set, wherein the energy data set is obtained based on a target scenario; a simulation module 1102, used to simulate the target scenario based on the usage relationship, and optimize the usage relationship according to the simulation results and a domain knowledge base, wherein the domain knowledge base is associated with the target scenario; a construction module 1103, used to construct an object model of the usage relationship of the optimized energy data.

[0085] The application also proposes a computer-readable storage medium.

[0086] In this embodiment, a computer program is stored on a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the above-mentioned train turnaround control method are implemented.

[0087] Fig.12 A block diagram of an electronic device provided for an embodiment of the present application.

[0088] An embodiment of the present application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the above-mentioned train turnaround control method when executing the computer program.

[0089] like Fig.12 As shown, for ease of understanding, the embodiment of the present application shows a specific electronic device.

[0090] Electronic devices are intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. Electronic devices may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0091] like Fig.12 As shown, the device includes a computing unit 1201, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 1202 or a computer program loaded from a storage unit 1208 into a random access memory (RAM) 1203. In RAM 1203, various programs and data required for the operation of the electronic device can also be stored. The computing unit 1201, ROM 1202, and RAM 1203 are connected to each other via a bus 1204. An input / output (I / O) interface 1205 is also connected to the bus 1204.

[0092] Multiple components in the electronic device are connected to the I / O interface 1205, including: an input unit 1206, such as a keyboard, a mouse, etc.; an output unit 1207, such as various types of displays, speakers, etc.; a storage unit 1208, such as a disk, an optical disk, etc.; and a communication unit 1209, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 1209 allows the electronic device to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0093] The computing unit 1201 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 1201 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 1201 executes the various methods described above, such as the control method of the train turnaround end. For example, in some embodiments, the control method of the train turnaround end may be implemented as a computer software program, which is tangibly included in a machine-readable medium, such as a storage unit 1208. In some embodiments, part or all of the computer program may be loaded and / or installed on an electronic device via ROM 1202 and / or a communication unit 1209. When the computer program is loaded into RAM 1203 and executed by the computing unit 1201, the control method of the train turnaround end described above may be executed. Alternatively, in other embodiments, the computing unit 1201 may be configured to execute the train turnaround control method in any other appropriate manner (eg, by means of firmware).

[0094] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this application, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection portion with one or more wirings (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or otherwise processing in a suitable manner if necessary, and then stored in a computer memory.

[0095] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0096] In the description of the present application, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In the present application, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0097] In the description of the present application, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0098] In addition, the terms "first", "second", etc. used in the embodiments of the present application are only used for descriptive purposes and should not be understood as indicating or implying relative importance, or implicitly indicating the number of technical features indicated in the present embodiment. Therefore, the features defined by the terms "first", "second", etc. in the embodiments of the present application can explicitly or implicitly indicate that at least one of the features is included in the embodiment. In the description of the present application, the word "multiple" means at least two or two or more, such as two, three, four, etc., unless otherwise clearly and specifically defined in the embodiments.

[0099] In this application, unless otherwise clearly specified or limited in the embodiments, the terms "installed", "connected", "connected" and "fixed" etc. appearing in the embodiments should be understood in a broad sense. For example, the connection can be a fixed connection, a detachable connection, or an integrated connection. It can be understood that it can also be a mechanical connection, an electrical connection, etc.; of course, it can also be a direct connection, or an indirect connection through an intermediate medium, or it can be the internal connection of two elements, or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to the specific implementation situation.

[0100] In the present application, unless otherwise clearly specified and limited, a first feature being “above” or “below” a second feature may mean that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. Moreover, a first feature being “above”, “above”, and “above” a second feature may mean that the first feature is directly above or obliquely above the second feature, or simply means that the first feature is higher in level than the second feature. A first feature being “below”, “below”, and “below” a second feature may mean that the first feature is directly below or obliquely below the second feature, or simply means that the first feature is lower in level than the second feature.

[0101] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for constructing an object model of the energy industry based on digital twins, characterized in that: The method comprises: Determining a usage relationship of energy data based on an energy data set, wherein the energy data set is obtained based on a target scenario; Simulating the target scenario based on the usage relationship, and optimizing the usage relationship according to the simulation result and a domain knowledge base, wherein the domain knowledge base is associated with the target scenario; Build an object model based on the usage relationship of optimized energy data.

2. The method for constructing an object model of the energy industry based on digital twins according to claim 1 is characterized in that: The energy data set includes device data and device spatial relationship data. The energy data set is obtained based on the target scenario, including: Determine a target production process based on a target scenario, and determine a target production equipment corresponding to the target production process based on the target production process; An energy device connected to the target production device is determined based on the target production device, and device data and device spatial relationship data of the energy device are acquired based on the energy device.

3. The method for constructing an object model of the energy industry based on digital twins according to claim 1 is characterized in that: The object model is applied to an energy management platform, the energy management platform is connected to energy equipment, and the target scenario is simulated based on the usage relationship, including: Acquire simulated IoT data of the energy device based on the usage relationship; The target scenario is simulated based on the simulated IoT data to meet business needs.

4. The method for constructing an object model of the energy industry based on digital twins according to claim 1 is characterized in that: The simulation result includes an algorithm simulation result, the domain knowledge base includes an algorithm reference result, and the optimizing the usage relationship according to the simulation result and the domain knowledge base includes: Determining a result difference according to the algorithm simulation result and the algorithm reference result; Optimizing the usage relationship based on the result difference; The algorithm simulation result includes a slope of a fitted heating curve, the algorithm reference result includes a slope of a reference heating curve, the usage relationship includes an algorithm parameter, and the optimization of the usage relationship based on the result difference includes: The algorithm parameters are optimized based on the difference between the slope of the fitting heating curve and the slope of the reference heating curve, so that the difference between the slope of the fitting heating curve and the slope of the reference heating curve is less than a preset difference.

5. The method for constructing an object model of the energy industry based on digital twins according to claim 1 is characterized in that: The simulation result includes a device simulation result, the domain knowledge base includes a device reference result, and the optimizing the usage relationship according to the simulation result and the domain knowledge base includes: determining a result difference according to the device simulation result and the device reference result; Optimizing the usage relationship based on the result difference, wherein the equipment simulation result includes at least one of the rated power, startup condition, and production time of the equipment; The result difference includes at least one of a processing accuracy difference, a processing speed difference, and a processing stability difference, the usage relationship includes a target processing device, and the optimizing the usage relationship based on the result difference further includes: The target processing equipment is optimized based on at least one of the processing accuracy difference, the processing speed difference and the processing stability difference, so that the result difference meets the equipment requirement.

6. The method for constructing an object model of the energy industry based on digital twins according to claim 1 is characterized in that: The simulation result includes a production process simulation result, the domain knowledge base includes a production process reference result, and the optimization of the usage relationship according to the simulation result and the domain knowledge base includes: Determining a result difference according to the production process simulation result and the production process reference result; Optimizing the usage relationship based on the result difference, wherein the production process simulation result includes at least one of equipment layout and material flow path, equipment production sequence and working time, and energy proportion; The result difference includes a process cost difference, the usage relationship includes an energy structure, and the optimizing the usage relationship based on the result difference further includes: The energy structure is optimized based on the process cost difference to reduce the process cost.

7. The method for constructing an object model of the energy industry based on digital twins according to claim 1 is characterized in that: The usage relationship of the energy data includes at least one of a structure, a parameter, and an algorithm of the energy data. The determining the usage relationship of the energy data based on the energy data set includes: At least one of a structure, a parameter, and an algorithm of the energy data is matched based on the energy data set and the target scenario.

8. The method for constructing an object model of the energy industry based on digital twins according to claim 1, characterized in that: The object model is applied to an energy management platform, and the energy management platform is connected to energy equipment. After the object model is established, the method further includes: Obtaining IoT measurement point data of the energy device; Calculating IoT indicator data based on indicator configuration information and the IoT measurement point data, wherein the indicator configuration information is obtained based on the energy management platform; The IoT indicator data is visualized.

9. A device for constructing an object model of the energy industry based on digital twins, characterized in that: The device comprises: A determination module, configured to determine a usage relationship of energy data based on an energy data set, wherein the energy data set is obtained based on a target scenario; A simulation module, configured to simulate the target scenario based on the usage relationship, and optimize the usage relationship according to the simulation result and a domain knowledge base, wherein the domain knowledge base is associated with the target scenario; A building module is used to build an object model based on the usage relationship of the optimized energy data.

10. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and wherein the processor implements the steps of the method described in any one of claims 1 to 8 when executing the computer program.

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