Intelligent operation and maintenance method and system based on digital twin technology and intelligent management and control platform

Abstract

The invention discloses an intelligent operation and maintenance method and system based on a digital twin technology and an intelligent management and control platform. The method comprises the following steps: receiving a job task; real-time monitoring is carried out to obtain operation data and environment data of the photovoltaic power station, and a digital twinborn model of the photovoltaic power station is constructed; performing simulation operation of the photovoltaic power station according to the operation task by using the constructed photovoltaic power station data twinborn model, and predicting the equipment fault condition in the simulation operation process; generating a plurality of operation and maintenance tasks according to the faulted equipment in the prediction simulation process, and calculating the operation and maintenance cost of each operation and maintenance task; adjusting the operation task by using each generated operation and maintenance task, performing simulation operation of the photovoltaic power station again according to the adjusted operation task, predicting the generated power generation amount, and calculating the power generation income and the equipment operation cost; and calculating the work income, judging the work income closest to the expected income, and obtaining the corresponding operation and maintenance task as the optimal operation and maintenance task output. The operation and maintenance efficiency and economic benefits of the photovoltaic power station can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent operation and maintenance, and specifically to an intelligent operation and maintenance method and system based on digital twin technology and an intelligent management and control platform. Background Art

[0002] As the global demand for clean energy continues to increase, the scale of photovoltaic power station construction has gradually expanded. This trend has not only promoted the transformation of the energy industry, but also brought huge economic benefits and social impacts. The efficient and stable operation of photovoltaic power stations is of great significance to ensuring energy supply and reducing carbon emissions. However, with the continuous expansion of the scale of power stations, the traditional operation and maintenance management model has shown many limitations and is difficult to adapt to the needs of modern energy management.

[0003] In the operation and maintenance of photovoltaic power stations, existing intelligent operation and maintenance systems generally use a variety of means to improve the speed and efficiency of fault handling. Common methods include installing various sensors and monitoring equipment to monitor the operation status of the power station in real time, and using data analysis and artificial intelligence algorithms combined with digital twin technology to conduct in-depth mining of the power station's operating data to predict potential fault risks, generate operation and maintenance strategies, and achieve fault diagnosis and prevention.

[0004] Although the above methods have been improved in some aspects, they still have shortcomings. For example, the existing intelligent operation and maintenance system ignores economic factors in the process of generating operation and maintenance strategies, and still performs poorly in terms of the rationality of resource scheduling. Specifically, there is a lack of an effective operation economic evaluation system. The existing evaluation system often only considers a single factor and fails to fully reflect the actual costs and benefits, resulting in a lack of scientific basis for the generation of operation and maintenance decisions, and it is difficult to balance the cost and economic benefits; and the resource scheduling in the operation and maintenance decision is unreasonable, because the existing system is relatively extensive in the management of work orders and spare parts, and the level of informatization is low, which easily leads to waste of resources, restricting the operation and maintenance efficiency and economic benefits of photovoltaic power stations. Summary of the invention

[0005] In order to realize intelligent operation and maintenance of photovoltaic power stations and improve the operation and maintenance efficiency and economic benefits of photovoltaic power stations, the present application provides an intelligent operation and maintenance method and system based on digital twin technology and intelligent management and control platform.

[0006] In the first aspect, the present application provides a smart operation and maintenance method based on digital twin technology and smart management and control platform, including: Receiving a work task; the work task includes work equipment, work time and expected benefits; The operation data and environmental data of the photovoltaic power station are acquired by real-time monitoring using the smart management and control platform. The digital twin model of the photovoltaic power station is constructed by combining the acquired operation data and environmental data of the photovoltaic power station using the digital twin technology. The constructed photovoltaic power station data twin model is used to simulate the operation of the photovoltaic power station according to the operation tasks, and predict the equipment failure during the simulated operation. The smart management and control platform is integrated with a photovoltaic power station inspection system. Generate multiple operation and maintenance tasks according to the equipment failure conditions in the prediction simulation process, and calculate the operation and maintenance cost of each operation and maintenance task; the operation and maintenance task includes the equipment operation and maintenance identification, equipment operation and maintenance time, and equipment operation and maintenance personnel; Use each generated operation and maintenance task to adjust the operation task, reuse the constructed PV power station data twin model to simulate the operation of the PV power station according to the adjusted operation task, predict the generated power generation, and calculate the power generation income and equipment operation cost; The operation benefits are calculated based on the power generation income, equipment operating costs and each operation and maintenance cost, the operation benefit closest to the expected benefit is determined, and the corresponding operation and maintenance task is obtained as the optimal operation and maintenance task output.

[0007] By adopting the above scheme, the constructed photovoltaic power station twin model is used to simulate the operation tasks, predict equipment failures during the execution of the operation tasks, and generate a variety of operation and maintenance tasks in advance; an economic evaluation algorithm is introduced to evaluate the benefits of different operation and maintenance tasks, determine and output the optimal operation and maintenance tasks to guide the operation and maintenance during the execution of the operation tasks and improve the overall operation and maintenance efficiency and economic benefits.

[0008] Preferably, it also includes: According to the equipment failure situation in the prediction simulation process, query whether there is a spare device for the faulty equipment from the spare parts management inventory. If there is a spare device, generate a spare device startup task corresponding to each generated operation and maintenance task, and calculate the operating cost of the spare device startup task; the spare device startup task includes: spare device identification and spare device operation time; Using each generated operation and maintenance task and its corresponding generated standby equipment startup task to jointly adjust the operation task in the original operation task adjustment mode; Consider the operating cost of the standby equipment startup task calculated corresponding to each operation and maintenance task, and calculate the operating income according to the power generation income, equipment operating cost, each operation and maintenance cost and the operating cost of the standby equipment startup task to replace the original operation income calculation result.

[0009] By adopting the above scheme, when a simulated device is predicted to fail, the spare parts management inventory is queried to see whether there is a spare device for the failed device. If so, the spare device is further considered, its operating cost is calculated, and it is included in the job task adjustment and job profit calculation, so as to more accurately evaluate the job profit, select the optimal operation and maintenance strategy, and improve the overall operation and maintenance efficiency and economic benefits.

[0010] Preferably, it also includes: In the process of generating each operation and maintenance task according to the equipment failure situation in the prediction simulation process, determine one by one in chronological order whether the number of equipment operation and maintenance personnel required for the same operation and maintenance time is greater than the preset number of operation and maintenance personnel; if greater, sort the operation and maintenance equipment according to the operation and maintenance urgency corresponding to the operation and maintenance equipment at the same operation and maintenance time, so that the equipment with the greater operation and maintenance urgency is prioritized; adjust the number of operation and maintenance personnel in the original operation and maintenance task of the operation and maintenance equipment at the same operation and maintenance time in reverse chronological order so that the original number of operation and maintenance personnel required for the same operation and maintenance time is equal to the preset number of operation and maintenance personnel, and extend the operation and maintenance time in the original operation and maintenance task corresponding to the operation and maintenance equipment with the adjusted number of operation and maintenance personnel; wherein, the extended running time is calculated based on the original number of operation and maintenance personnel, the adjusted operation and maintenance time corresponding to the original number of operation and maintenance personnel, and the adjusted number of operation and maintenance personnel.

[0011] By adopting the above solution, taking into account the insufficient number of equipment operation and maintenance personnel in the same time period, the operation and maintenance urgency of the faulty equipment is sorted, and the operation and maintenance personnel and time of the equipment with low operation and maintenance urgency are adjusted to ensure the operation and maintenance needs of the equipment with high operation and maintenance urgency, thereby achieving reasonable allocation of resources and maximization of operation and maintenance efficiency.

[0012] Preferably, it also includes: When querying whether there is a spare device for the faulty equipment from the spare parts management inventory according to the equipment failure situation in the prediction simulation process and querying whether there is a spare device, continue to determine whether there are multiple spare devices queried; if there are multiple spare devices, for each spare device, a spare device startup task is generated in combination with each generated operation and maintenance task, and the operating cost of the spare device startup task is calculated; compare the operating costs of multiple spare device startup tasks, and select the spare device with the lowest cost as the spare device finally queried.

[0013] By adopting the above solution, the operating costs of different backup devices are taken into consideration. By comparing the operating costs of multiple backup devices, the backup device startup tasks of the faulty devices are further optimized to ensure that the backup device startup plan with the lowest cost is selected, thereby reducing the operation and maintenance costs.

[0014] Preferably, it also includes: After generating multiple operation and maintenance tasks according to the equipment failure conditions in the prediction simulation process, the neural network algorithm is used to predict the environmental data corresponding to the operation and maintenance time in each operation and maintenance task; The environmental data and each operation and maintenance task are synchronously input into the operation and maintenance task adjustment model, the equipment operation and maintenance time adjustment value is output, the corresponding operation and maintenance task is adjusted according to the equipment operation and maintenance time adjustment value, and the adjusted operation and maintenance task is generated to replace the original target operation and maintenance task; wherein, the operation and maintenance task adjustment model adopts a neural network algorithm, and the equipment operation and maintenance time adjustment value is obtained by training and generating by subtracting historical environmental data, historical operation and maintenance personnel, historical conventional operation and maintenance time of equipment with different types of faults and historical actual operation and maintenance time of equipment with different types of faults.

[0015] By adopting the above scheme, taking into account the impact of environmental data on operation and maintenance time, the neural network model is used to predict the environmental data during the execution of the operation and maintenance task, and a more reasonable operation and maintenance time adjustment value is generated, which further optimizes the operation and maintenance cost and power generation income.

[0016] Preferably, it also includes: Before using the constructed PV power station data twin model to simulate the operation of the PV power station according to the operation task, the environmental data of the PV power station acquired through real-time monitoring on the smart management and control platform is used to predict the environmental data corresponding to the operation time; The optimal angle of the photovoltaic components in the operating equipment at the operating time is obtained according to the environmental data corresponding to the operating time and the geographical location of the photovoltaic components in the operating equipment. The optimal angle of the photovoltaic components in the operating equipment at the operating time is added to the operating task, and the supplemented operating task is output synchronously when the final optimal operation and maintenance task is output.

[0017] By adopting the above scheme, the environmental data corresponding to the operation time is predicted, and the optimal angle of the photovoltaic modules at the operation time is obtained by using the environmental data and the geographical location of the photovoltaic modules in the operation equipment. This is then added to the operation task to ensure that the photovoltaic modules work at the optimal angle, maximize power generation efficiency, and further improve the economic benefits of photovoltaic power generation.

[0018] Preferably, the calculation of power generation income includes: acquiring the market electricity price in real time and predicting the real-time market electricity price during the operation time, and multiplying the predicted power generation based on the predicted market electricity price.

[0019] By adopting the above scheme, the market price within the operation task time is predicted, and the power generation income is calculated using the predicted price, thereby improving economic benefits.

[0020] In the second aspect, the present application provides an intelligent operation and maintenance system based on digital twin technology and intelligent management and control platform, including: The operation task acquisition module is used to receive the operation task; the operation task includes the operation equipment, operation time and expected income; The operation task simulation operation module is used to use the smart management and control platform to monitor and obtain the operation data and environmental data of the photovoltaic power station in real time, and use the digital twin technology to build a digital twin model of the photovoltaic power station in combination with the acquired operation data and environmental data of the photovoltaic power station; use the constructed photovoltaic power station data twin model to simulate the operation of the photovoltaic power station according to the operation task, and predict equipment failures during the simulation operation; the smart management and control platform is integrated with a photovoltaic power station inspection system; An operation and maintenance task generation module is used to generate multiple operation and maintenance tasks according to the equipment failure conditions in the prediction simulation process, and calculate the operation and maintenance cost of each operation and maintenance task; the operation and maintenance task includes the equipment operation and maintenance identification, equipment operation and maintenance time, and equipment operation and maintenance personnel; The power generation prediction module is used to adjust the operation tasks using each generated operation and maintenance task, and reuse the constructed photovoltaic power station data twin model to simulate the operation of the photovoltaic power station according to the adjusted operation tasks, predict the generated power generation, and calculate the power generation income and equipment operation cost; The optimal operation and maintenance task acquisition module is used to calculate the operation income according to the power generation income, equipment operating cost and each operation and maintenance cost, determine the operation income closest to the expected income, and obtain the corresponding operation and maintenance task as the optimal operation and maintenance task output.

[0021] By adopting the above solution, the digital twin model is used to predict equipment failures during the execution of work tasks and generate a variety of operation and maintenance tasks in advance. The optimal operation and maintenance solution is selected through comprehensive economic benefit evaluation to ensure that each operation and maintenance task achieves the best effect while controlling costs.

[0022] In a third aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the method as described above.

[0023] In a fourth aspect, the present application provides a computer device, comprising a memory, a processor, and a program stored and executable on the memory, wherein the program implements the steps of the above method when executed by the processor.

[0024] In summary, this application has the following beneficial effects: 1. Use digital twin technology to build a digital twin model of the photovoltaic power station, simulate the operation tasks, predict the equipment failure during the operation task execution and generate a variety of operation and maintenance tasks in advance, so as to ensure timely maintenance during the operation task execution, shorten the fault response time, and improve the operation and maintenance efficiency; at the same time, introduce an economic evaluation algorithm, based on a variety of operation and maintenance tasks, calculate the benefits of the operation tasks separately to determine the optimal operation and maintenance plan, and improve economic benefits; 2. Combined with the management and control of spare equipment, check whether there is spare equipment, comprehensively consider the corresponding cost and operation income of starting the spare equipment, and more accurately calculate the income of the operation task, so as to select the optimal operation and maintenance plan and improve economic benefits; 3. Consider the impact of environmental factors on the operation and maintenance time in operation and maintenance tasks, use neural network technology to dynamically adjust the operation and maintenance time in multiple environments, improve the operation and maintenance plan, and thus increase economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a flow chart of the intelligent operation and maintenance method based on digital twin technology and intelligent management and control platform described in the specific embodiment; Figure 2 It is a structural diagram of the intelligent operation and maintenance system based on digital twin technology and intelligent management and control platform described in the specific embodiment. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0027] like Figure 1 As shown, the embodiment of the present application discloses a smart operation and maintenance method based on digital twin technology and smart management and control platform, and the specific steps include: S1. Receive job tasks.

[0028] Specifically, the operation task uploaded by the user is received through the interface. The operation task includes: operation area, operation equipment, operation time and operation target, such as expected income, etc. For example, the first operation task uploaded by the user is received, which specifically includes completing the power generation task and obtaining the expected income of value B between January 11 and January 13 through photovoltaic modules, inverters, transformers, protection equipment, transmission equipment, etc. in the photovoltaic power plant area A.

[0029] S2. Use the smart management and control platform and digital twin technology to build a digital twin model of the photovoltaic power station.

[0030] Specifically, the smart management and control platform is built based on advanced technologies such as cloud computing, big data, and the Internet of Things. It integrates a photovoltaic power station inspection system, a photovoltaic power station equipment management system, a photovoltaic power station personnel management system, a market price system, etc., and completes seamless connection between each system and the smart management and control platform through API interfaces and data sharing.

[0031] The smart management and control platform is used to monitor the operation data and environmental data of the photovoltaic power station in real time. The digital twin model of the photovoltaic power station is constructed using digital twin technology by combining the operation data and environmental data of the photovoltaic power station and the building data of the photovoltaic power station obtained by the smart management and control platform.

[0032] S3. Use the constructed photovoltaic power station data twin model to simulate the operation of the photovoltaic power station according to the operation tasks, and predict equipment failures during the simulated operation.

[0033] Specifically, according to the various elements in the task, neural network technology is used to predict the operation of the simulated photovoltaic power station in real time, and based on the operation analysis and prediction, the faulty equipment and fault conditions during the operation are obtained, including: the identification of the faulty equipment, the fault degree of the faulty equipment, the fault classification of the faulty equipment, etc. For example: simulate the operation of photovoltaic modules, inverters, transformers, protection equipment, and transmission equipment in area A of the photovoltaic power plant within two days, and predict which equipment may fail and the specific fault conditions.

[0034] S4. Generate multiple operation and maintenance tasks according to the equipment failure conditions in the prediction simulation process, and calculate the operation and maintenance cost corresponding to each operation and maintenance task.

[0035] Specifically, through the historical collection and statistical analysis of various equipment failure conditions in photovoltaic power stations, the average equipment operation and maintenance time required for different equipment under different degrees of equipment failure conditions with different operation and maintenance personnel is determined, such as: a minor inverter failure can be repaired in half an hour with two operation and maintenance personnel, and in one hour with one operation and maintenance personnel; the best operation and maintenance time range for different equipment failure conditions with different degrees of failure is determined through comprehensive analysis through the best operation and maintenance time recommended by multiple experts: such as: the best operation and maintenance time for a moderate photovoltaic panel failure is within 20 minutes before the failure or within 10 minutes after the failure; under the condition of sufficient default operation and maintenance personnel, a variety of operation and maintenance tasks are generated in combination with the equipment and failure conditions in the prediction simulation process, that is, different equipment operation and maintenance personnel and operation and maintenance time are generated in combination with the actual equipment failure conditions, and the equipment operation and maintenance time is selected and determined from the best operation and maintenance time range in combination with the actual equipment failure conditions; the operation and maintenance tasks include equipment operation and maintenance identification (i.e., faulty equipment identification), equipment operation and maintenance time, and equipment operation and maintenance personnel.

[0036] For example: during the simulation of the execution of the job task, the photovoltaic panels in area A will have a minor fault at 12:30 noon on January 11, and the inverter 1 in area A will have a serious fault at 1:00 p.m. on January 11. The corresponding generated operation and maintenance task 1 includes: dispatching 1 operation and maintenance personnel to perform operation and maintenance inspection of the photovoltaic panels in area A from 12:10 to 12:30 noon on January 11, and dispatching 2 operation and maintenance personnel to perform operation and maintenance of the inverter in area A from 12:30 noon to 1:00 p.m. on January 11; or operation and maintenance task 2 includes: dispatching 1 operation and maintenance personnel to perform operation and maintenance inspection of the photovoltaic panels in area A from 12:10 to 12:30 noon on January 11, and dispatching 1 operation and maintenance personnel to perform operation and maintenance inspection of the inverter in area A from 12:00 noon to 1:00 p.m. on January 11.

[0037] The intelligent management and control platform obtains the repair material costs and labor costs for equipment with different fault levels in photovoltaic power stations in the market, and calculates the costs of different operation and maintenance tasks.

[0038] S5. Use each generated operation and maintenance task to adjust the operation task, and reuse the constructed photovoltaic power station data twin model to simulate the operation of the photovoltaic power station according to the adjusted operation task.

[0039] Specifically, during the execution of the operation and maintenance task, the execution of the corresponding job task may be closed or interrupted in advance. Specifically, the operation time in the job task is adjusted according to the execution time of the operation and maintenance task, and then the adjusted job task is generated.

[0040] For each operation and maintenance task, the corresponding operation task is adjusted, and the neural network algorithm is used to reuse the constructed photovoltaic power station data twin model to simulate the operation of the photovoltaic power station according to the adjusted operation task to predict the power generation; in this embodiment, the predicted power generation can select the neural network model using the LSTM model.

[0041] For each adjusted task operation, the power generation revenue is calculated using the predicted power generation and the market power price information, where the market power price information can be determined by the historical power price in the month of the operation time.

[0042] In order to further obtain accurate market electricity prices, the market electricity prices are obtained in real time and the real-time market electricity prices during the operation time are predicted, based on the predicted market electricity prices multiplied by the predicted power generation. In addition, the equipment operation cost needs to be calculated based on the historical unit time cost of each equipment operation and the operating time of each equipment in the adjusted operation task.

[0043] S6. Calculate the operation benefits of the simulated operation of the operation tasks adjusted according to each operation and maintenance task, compare with the expected benefits of the operation tasks, and output the optimal operation and maintenance task to guide subsequent operation and maintenance.

[0044] Specifically, the operating income is calculated separately according to the power generation income, equipment operating cost and each operation and maintenance cost, that is, power generation income - equipment operating cost - equipment operation and maintenance cost. The operating income obtained under the operation and maintenance costs corresponding to different operation and maintenance tasks is compared, and the operating income closest to the expected income (with the smallest difference with the expected income) is found. The corresponding operation and maintenance task is obtained as the optimal operation and maintenance task output, which is convenient for timely and effective operation and maintenance when actually executing the operation task, thereby improving operation and maintenance efficiency and economic benefits.

[0045] In a specific embodiment, when a faulty device is predicted, if there is a spare device, the spare device can be started accordingly, and the operating cost of the spare device startup task can be included in the calculation of the operation income, so as to achieve a more comprehensive evaluation of the economic efficiency of the operation; the method also includes: According to the equipment failure situation in the prediction simulation process, check whether there is a spare equipment in the spare parts management inventory for the faulty equipment. Specifically, the smart management and control platform can be used to obtain the spare equipment information in the spare parts management inventory.

[0046] If there is a standby device, a standby device startup task is generated in combination with each generated operation and maintenance task, and the standby device startup task includes: standby device identification and standby device operation time. Specifically, in order to avoid equipment failure affecting operation requirements, the standby device start time can be set during the operation and maintenance time, and the standby device shutdown time can be set after the operation and maintenance time ends, that is, the standby device startup task is determined according to the operation and maintenance equipment and operation and maintenance time in each operation and maintenance task.

[0047] The operating cost of executing the standby equipment startup task is calculated based on the unit operating cost of the historical standby equipment startup.

[0048] Use each generated operation and maintenance task and its corresponding generated standby equipment startup task to jointly adjust the operation task in the original operation task adjustment method, and then complete the simulation operation of the subsequent adjusted operation task; specifically, adjust the operation time of the corresponding equipment in the original operation task according to the operation and maintenance equipment identification and operation and maintenance time in the operation and maintenance task, and add the standby equipment identification and standby equipment operation time in the standby equipment task to the original operation task.

[0049] Consider the operating cost of the standby equipment startup task calculated corresponding to each operation and maintenance task, and calculate the operating income according to the power generation income, equipment operating cost, each operation and maintenance cost and the operating cost of the standby equipment startup task to replace the original operation income calculation result.

[0050] When considering starting the backup equipment, there may be a situation where the expected profit is exceeded. In this case, the maximum operating profit that exceeds the expected profit is found instead of the operating profit that is closest to the expected profit, and the corresponding operation and maintenance task is obtained as the optimal operation and maintenance task output.

[0051] In addition, based on this embodiment, considering that the number of the queried backup devices may be more than one, and different backup devices may have different corresponding operating costs, in order to improve economic benefits, the above method further includes: When querying whether there is a spare device for the faulty equipment from the spare parts management inventory according to the equipment failure situation in the prediction simulation process and querying whether there is a spare device, continue to determine whether the number of spare devices queried is multiple; if there is multiple, for each spare device, generate a spare device startup task in combination with each generated operation and maintenance task, and calculate the operating cost of the spare device startup task; compare the operating costs of multiple spare device startup tasks, and select the spare device with the lowest cost as the spare device finally queried.

[0052] In a specific embodiment, considering that there is a shortage of operators during the same operation and maintenance time, the operation and maintenance time needs to be further adjusted, and the method further includes: In the process of generating each operation and maintenance task according to the equipment failure situation in the prediction simulation process, determine one by one in chronological order whether the number of equipment operation and maintenance personnel required at the same operation and maintenance time is greater than the preset number of operation and maintenance personnel; for example: in the first operation and maintenance task, the operation and maintenance time of equipment A in operation and maintenance task 1 is 12:00-1:00, and the number of operation and maintenance personnel is 2; the operation and maintenance time of equipment B is 12:30-1:00, and the number of operation and maintenance personnel is 1; determine in chronological order that the current number of operation and maintenance personnel between the same operation and maintenance time of 12:30 and 1:00 is 3, assuming that the preset number of operation and maintenance personnel is 2.

[0053] If it is greater than , it indicates that there are insufficient operators during the current operation and maintenance period, and the operation and maintenance tasks need to be further adjusted; The operation and maintenance equipment are sorted according to the operation and maintenance urgency corresponding to the operation and maintenance equipment at the same operation and maintenance time, so that the equipment with the greater operation and maintenance urgency is prioritized; the operation and maintenance urgency is weightedly calculated based on the predicted failure degree index of the operation and maintenance equipment and the time interval index from the operation and maintenance start time to the predicted failure of the operation and maintenance equipment; wherein, the greater the predicted failure degree of the operation and maintenance equipment, the larger the corresponding index number; the smaller the time interval from the operation and maintenance start time to the predicted failure of the operation and maintenance equipment, the larger the corresponding index number; such as: the operation and maintenance equipment at the same operation and maintenance time of 12:30 to 1:00 in the first operation and maintenance task include A and B, the failure degree of device A in operation and maintenance task 1 is mild, and the time interval between the operation and maintenance start time 12:00 and the predicted failure 1:00 is 1 hour, the failure degree of device B in operation and maintenance task 2 is severe, and the time interval between the operation and maintenance start time 12:30 and the predicted failure 1:00 is half an hour, then the urgency of device B is greater, and device A is correspondingly arranged after device B.

[0054] Adjust the number of operators in the original operation and maintenance tasks of the operation and maintenance equipment with the same operation and maintenance time in reverse order, so that the number of operators required for the equipment at the same operation and maintenance time is equal to the preset number of operators, and extend the operation and maintenance time of the original operation and maintenance tasks corresponding to the operation and maintenance equipment with the adjusted number of operators. For example, adjust the number of operators from 12:30 to 1:00 in the operation and maintenance task corresponding to equipment A to 1 person, and extend the operation and maintenance time of equipment A accordingly. The calculation standard for the corresponding extended operation time T is: the adjusted number of operators (1) * T1 = the original number of operators (2) * the adjusted operation and maintenance time corresponding to the original number of operators (30 minutes), T = T1 - the adjusted operation and maintenance time corresponding to the original number of operators.

[0055] In a specific embodiment, considering the influence of environmental conditions on the operation and maintenance duration, for example, under complex environmental conditions, the operation and maintenance duration will be longer than that under simple conditions, because it is necessary to comprehensively consider environmental data, further adjust the generated operation and maintenance tasks, the method also includes: After generating multiple operation and maintenance tasks according to the equipment failure conditions in the prediction simulation process, the neural network algorithm is used to predict the environmental data corresponding to the operation and maintenance time in each operation and maintenance task; The environmental data and each operation and maintenance task are synchronously input into the operation and maintenance task adjustment model, the equipment operation and maintenance time adjustment value is output, the corresponding operation and maintenance task is adjusted according to the equipment operation and maintenance time adjustment value, and the adjusted operation and maintenance task is generated to replace the original target operation and maintenance task; Among them, the operation and maintenance task adjustment model adopts a neural network algorithm, and the equipment operation and maintenance time adjustment value is obtained by training and generating by subtracting historical environmental data, historical operation and maintenance personnel, historical conventional operation and maintenance time of different types of faulty equipment and historical actual operation and maintenance time of different types of faulty equipment.

[0056] In a specific embodiment, in order to further improve economic benefits, the operation tasks can be further adjusted according to environmental factors, and the method further includes: Before using the constructed PV power station data twin model to simulate the operation of the PV power station according to the operation task, the intelligent management and control platform is used to monitor the environmental data of the PV power station in real time and predict the environmental data corresponding to the operation time; The environmental data corresponding to the operation time and the geographical location of the photovoltaic components in the operation equipment are input into the angle acquisition model to obtain the optimal angle of the photovoltaic components in the operation equipment at the operation time, and the optimal angle of the photovoltaic components in the operation equipment at the operation time is added to the operation task. The angle acquisition model adopts a neural network algorithm, which is generated by training the historical environmental data, the geographical location of the historical photovoltaic components, and the optimal angle corresponding to the maximum power generation of the historical photovoltaic components; the supplemented operation tasks are output synchronously when the final optimal operation and maintenance tasks are output.

[0057] like Figure 2As shown, the embodiment of the present application provides a smart operation and maintenance system based on digital twin technology and smart management and control platform, which specifically includes: The operation task acquisition module 101 is used to receive the operation task; the operation task includes operation equipment, operation time and expected income; The operation task simulation operation module 102 is used to use the smart management and control platform to monitor and obtain the operation data and environmental data of the photovoltaic power station in real time, and use the digital twin technology to build a digital twin model of the photovoltaic power station in combination with the acquired operation data and environmental data of the photovoltaic power station; use the constructed photovoltaic power station data twin model to simulate the operation of the photovoltaic power station according to the operation task, and predict the equipment failure during the simulation operation; the smart management and control platform is integrated with a photovoltaic power station inspection system; The operation and maintenance task generation module 103 is used to generate multiple operation and maintenance tasks according to the equipment failure conditions in the prediction simulation process, and calculate the operation and maintenance cost of each operation and maintenance task; the operation and maintenance task includes the equipment operation and maintenance identification, equipment operation and maintenance time, and equipment operation and maintenance personnel; The power generation prediction module 104 is used to adjust the operation tasks using each generated operation and maintenance task, and reuse the constructed photovoltaic power station data twin model to simulate the operation of the photovoltaic power station according to the adjusted operation tasks, predict the generated power generation, and calculate the power generation income and equipment operation cost; The optimal operation and maintenance task acquisition module 105 is used to calculate the operation income according to the power generation income, equipment operation cost and each operation and maintenance cost, determine the operation income closest to the expected income, and obtain the corresponding operation and maintenance task as the optimal operation and maintenance task output.

[0058] In a specific embodiment, the power generation prediction module 104 in the system is also used to query whether there is a spare device for the faulty device in the spare parts management inventory according to the equipment failure situation in the prediction simulation process. If there is a spare device, a spare device startup task is generated in combination with each generated operation and maintenance task, and the operation cost of the spare device startup task is calculated; the spare device startup task includes: a spare device identifier and a spare device operation time; each generated operation and maintenance task and its corresponding generated spare device startup task are used to jointly adjust the operation task in the original operation task adjustment mode; The optimal operation and maintenance task acquisition module 105 is also used to consider the operating cost of the standby equipment startup task calculated corresponding to each operation and maintenance task, so as to calculate the operation profit according to the power generation income, equipment operating cost, each operation and maintenance cost and the operating cost of the standby equipment startup task to replace the original operation profit calculation result.

[0059] The system further comprises: The operation task adjustment module 106 is used to use the constructed photovoltaic power station data twin model to simulate the operation of the photovoltaic power station according to the operation task, and use the environmental data of the photovoltaic power station obtained by real-time monitoring on the smart management and control platform to predict the environmental data corresponding to the operation time; according to the environmental data corresponding to the operation time and the geographical location of the photovoltaic components in the operation equipment, the optimal angle of the photovoltaic components in the operation equipment at the operation time is obtained, and the optimal angle of the photovoltaic components in the operation equipment at the operation time is added to the operation task. The optimal operation and maintenance task acquisition module 105 is also used to synchronously output the supplementary operation tasks when outputting the final optimal operation and maintenance tasks.

[0060] In a specific embodiment, the operation and maintenance task generation module 103 in the system is also used to generate each operation and maintenance task according to the equipment failure situation in the prediction simulation process, and determine one by one in chronological order whether the number of equipment operation and maintenance personnel required for the same operation and maintenance time is greater than the preset number of operation and maintenance personnel; if greater, the operation and maintenance equipment is sorted according to the operation and maintenance urgency corresponding to the operation and maintenance equipment at the same operation and maintenance time, so that the equipment with greater operation and maintenance urgency is prioritized; the number of operation and maintenance personnel in the original operation and maintenance task of the operation and maintenance equipment at the same operation and maintenance time is adjusted in reverse order so that the original number of equipment operation and maintenance personnel required for the same operation and maintenance time is equal to the preset number of operation and maintenance personnel, and the operation and maintenance time in the original operation and maintenance task corresponding to the operation and maintenance equipment with the adjusted number of operation and maintenance personnel is extended.

[0061] In a specific embodiment, the power generation prediction module 104 in the system is also used to query whether there is a spare device for the faulty equipment from the spare parts management inventory according to the equipment failure situation in the prediction simulation process, and when querying whether there is a spare device, continue to determine whether there are multiple spare devices queried; if there are multiple spare devices, for each spare device, a spare device startup task is generated in combination with each generated operation and maintenance task, and the operating cost of the spare device startup task is calculated; the operating costs of multiple spare device startup tasks are compared, and the spare device with the lowest cost is selected as the spare device finally queried.

[0062] In a specific embodiment, the power generation prediction module 104 in the system is also used to generate multiple operation and maintenance tasks according to the equipment failure conditions in the prediction simulation process, and then use a neural network algorithm to predict the environmental data corresponding to the operation and maintenance time in each operation and maintenance task; the environmental data and each operation and maintenance task are synchronously input into the operation and maintenance task adjustment model, and the equipment operation and maintenance time adjustment value is output, and the corresponding operation and maintenance task is adjusted according to the equipment operation and maintenance time adjustment value, and the adjusted operation and maintenance task is generated to replace the original target operation and maintenance task; wherein, the operation and maintenance task adjustment model adopts a neural network algorithm, and the equipment operation and maintenance time adjustment value is obtained by subtracting historical environmental data, historical operation and maintenance personnel, historical conventional operation and maintenance time of equipment with different types of faults from the actual operation and maintenance time of equipment with different types of faults in history.

[0063] The embodiment of the present application also discloses a computer-readable storage medium.

[0064] Specifically, the computer-readable storage medium stores a computer program that can be loaded by a processor and executed such as the above-mentioned intelligent operation and maintenance method based on digital twin technology and intelligent management and control platform. The computer-readable storage medium includes, for example: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk and other media that can store program codes.

[0065] The embodiment of the present application also discloses a computer device.

[0066] Specifically, the computer device includes a memory and a processor, and the memory stores a computer program that can be loaded by the processor and execute the above-mentioned intelligent operation and maintenance method based on digital twin technology and intelligent management and control platform.

[0067] The above are all preferred embodiments of the present application, and are not intended to limit the protection scope of the present application. Any feature disclosed in this specification (including the abstract and drawings), unless otherwise stated, can be replaced by other equivalent or alternative features with similar purposes. That is, unless otherwise stated, each feature is only an example of a series of equivalent or similar features.

Claims

1. A smart operation and maintenance method based on digital twin technology and smart management and control platform, characterized in that: include: Receiving a work task; the work task includes work equipment, work time and expected benefits; The operation data and environmental data of the photovoltaic power station are acquired by real-time monitoring using the smart management and control platform. The digital twin model of the photovoltaic power station is constructed by combining the acquired operation data and environmental data of the photovoltaic power station using the digital twin technology. The constructed photovoltaic power station data twin model is used to simulate the operation of the photovoltaic power station according to the operation tasks, and predict the equipment failure during the simulated operation. The smart management and control platform is integrated with a photovoltaic power station inspection system. Generate multiple operation and maintenance tasks according to the equipment failure conditions in the prediction simulation process, and calculate the operation and maintenance cost of each operation and maintenance task; the operation and maintenance task includes the equipment operation and maintenance identification, equipment operation and maintenance time, and equipment operation and maintenance personnel; Use each generated operation and maintenance task to adjust the operation task, reuse the constructed PV power station data twin model to simulate the operation of the PV power station according to the adjusted operation task, predict the generated power generation, and calculate the power generation income and equipment operation cost; The operation benefits are calculated based on the power generation income, equipment operating costs and each operation and maintenance cost, the operation benefit closest to the expected benefit is determined, and the corresponding operation and maintenance task is obtained as the optimal operation and maintenance task output.

2. The intelligent operation and maintenance method based on digital twin technology and intelligent management and control platform according to claim 1 is characterized in that: Also includes: According to the equipment failure situation in the prediction simulation process, query whether there is a spare device for the faulty equipment from the spare parts management inventory. If there is a spare device, generate a spare device startup task corresponding to each generated operation and maintenance task, and calculate the operating cost of the spare device startup task; the spare device startup task includes: spare device identification and spare device operation time; Using each generated operation and maintenance task and its corresponding generated standby equipment startup task to jointly adjust the operation task in the original operation task adjustment mode; Consider the operating cost of the standby equipment startup task calculated corresponding to each operation and maintenance task, and calculate the operating income according to the power generation income, equipment operating cost, each operation and maintenance cost and the operating cost of the standby equipment startup task to replace the original operation income calculation result.

3. The intelligent operation and maintenance method based on digital twin technology and intelligent management and control platform according to claim 1 is characterized in that: Also includes: In the process of generating each operation and maintenance task according to the equipment failure situation in the prediction simulation process, determine one by one in chronological order whether the number of equipment operation and maintenance personnel required for the same operation and maintenance time is greater than the preset number of operation and maintenance personnel; if greater, sort the operation and maintenance equipment according to the operation and maintenance urgency corresponding to the operation and maintenance equipment at the same operation and maintenance time, so that the equipment with the greater operation and maintenance urgency is prioritized; adjust the number of operation and maintenance personnel in the original operation and maintenance task of the operation and maintenance equipment at the same operation and maintenance time in reverse chronological order so that the original number of operation and maintenance personnel required for the same operation and maintenance time is equal to the preset number of operation and maintenance personnel, and extend the operation and maintenance time in the original operation and maintenance task corresponding to the operation and maintenance equipment with the adjusted number of operation and maintenance personnel; wherein, the extended running time is calculated based on the original number of operation and maintenance personnel, the adjusted operation and maintenance time corresponding to the original number of operation and maintenance personnel, and the adjusted number of operation and maintenance personnel.

4. The intelligent operation and maintenance method based on digital twin technology and intelligent management and control platform according to claim 2 is characterized in that: Also includes: When querying whether there is a spare device for the faulty equipment from the spare parts management inventory according to the equipment failure situation in the prediction simulation process and querying whether there is a spare device, continue to determine whether there are multiple spare devices queried; if there are multiple spare devices, for each spare device, a spare device startup task is generated in combination with each generated operation and maintenance task, and the operating cost of the spare device startup task is calculated; compare the operating costs of multiple spare device startup tasks, and select the spare device with the lowest cost as the spare device finally queried.

5. The intelligent operation and maintenance method based on digital twin technology and intelligent management and control platform according to claim 1 is characterized in that: Also includes: After generating multiple operation and maintenance tasks according to the equipment failure conditions in the prediction simulation process, the neural network algorithm is used to predict the environmental data corresponding to the operation and maintenance time in each operation and maintenance task; The environmental data and each operation and maintenance task are synchronously input into the operation and maintenance task adjustment model, the equipment operation and maintenance time adjustment value is output, the corresponding operation and maintenance task is adjusted according to the equipment operation and maintenance time adjustment value, and the adjusted operation and maintenance task is generated to replace the original target operation and maintenance task; wherein, the operation and maintenance task adjustment model adopts a neural network algorithm, and the equipment operation and maintenance time adjustment value is obtained by training and generating by subtracting historical environmental data, historical operation and maintenance personnel, historical conventional operation and maintenance time of equipment with different types of faults and historical actual operation and maintenance time of equipment with different types of faults.

6. The intelligent operation and maintenance method based on digital twin technology and intelligent management and control platform according to claim 1 is characterized in that: Also includes: Before using the constructed PV power station data twin model to simulate the operation of the PV power station according to the operation task, the environmental data of the PV power station acquired through real-time monitoring on the smart management and control platform is used to predict the environmental data corresponding to the operation time; The optimal angle of the photovoltaic components in the operating equipment at the operating time is obtained according to the environmental data corresponding to the operating time and the geographical location of the photovoltaic components in the operating equipment. The optimal angle of the photovoltaic components in the operating equipment at the operating time is added to the operating task, and the supplemented operating task is output synchronously when the final optimal operation and maintenance task is output.

7. The intelligent operation and maintenance method based on digital twin technology and intelligent management and control platform according to claim 1 is characterized in that: The calculation of power generation income includes: obtaining the market electricity price in real time and predicting the real-time market electricity price during the operation time, and multiplying the predicted power generation based on the predicted market electricity price.

8. An intelligent operation and maintenance system based on digital twin technology and intelligent management and control platform, characterized in that: include: The operation task acquisition module is used to receive the operation task; the operation task includes the operation equipment, operation time and expected income; The operation task simulation operation module is used to use the smart management and control platform to monitor and obtain the operation data and environmental data of the photovoltaic power station in real time, and use the digital twin technology to build a digital twin model of the photovoltaic power station in combination with the acquired operation data and environmental data of the photovoltaic power station; use the constructed photovoltaic power station data twin model to simulate the operation of the photovoltaic power station according to the operation task, and predict equipment failures during the simulation operation; the smart management and control platform is integrated with a photovoltaic power station inspection system; An operation and maintenance task generation module is used to generate multiple operation and maintenance tasks according to the equipment failure conditions in the prediction simulation process, and calculate the operation and maintenance cost of each operation and maintenance task; the operation and maintenance task includes the equipment operation and maintenance identification, equipment operation and maintenance time, and equipment operation and maintenance personnel; The power generation prediction module is used to adjust the operation tasks using each generated operation and maintenance task, and reuse the constructed photovoltaic power station data twin model to simulate the operation of the photovoltaic power station according to the adjusted operation tasks, predict the generated power generation, and calculate the power generation income and equipment operation cost; The optimal operation and maintenance task acquisition module is used to calculate the operation income according to the power generation income, equipment operating cost and each operation and maintenance cost, determine the operation income closest to the expected income, and obtain the corresponding operation and maintenance task as the optimal operation and maintenance task output.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the method according to any one of claims 1 to 7.

10. A computer device, characterized in that: The computer device comprises a memory, a processor and a program stored and executable on the memory, and the program implements the steps of the method according to any one of claims 1 to 7 when executed by the processor.

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