Park electric equipment supervision system and method based on digital twinning

Abstract

The invention belongs to the technical field of electric equipment supervision, and discloses a digital twinning-based park electric equipment supervision system and a digital twinning-based park electric equipment supervision method. The method comprises the following steps: acquiring physical parameters of each device, real-time operation parameters acquired by a sensor and environmental parameters of each device, inputting the data into a digital twinborn model construction platform, establishing a digital twinborn model of the device, and meanwhile, setting data updating frequency to ensure that the model can reflect the actual state change of the device in time; operation data in the model are obtained in real time, state coefficients of all devices are obtained through the operation data, the operation conditions of all the devices are analyzed according to the state coefficients, maintenance personnel are helped to rapidly position and solve problems, and then an optimal energy distribution scheme is obtained according to the operation conditions and energy consumption conditions of all the devices; and optimal utilization of energy is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of power consumption equipment supervision, and specifically relates to a supervision system and method for park power consumption equipment based on digital twin. Background Art

[0002] Under the background of the significant improvement of people's living standards, people's dependence on electricity is becoming increasingly important. Without the role of electricity, our lives will be seriously affected, and even normal life cannot be carried out. In daily life, our clothing, food, housing, and transportation are all inseparable from power consumption equipment. Therefore, the safety supervision of power consumption equipment is one of the most important links for power supply enterprises. Digital twin power grid creates a digital power grid that matches the physical entity power grid in the digital space, and reflects the state of the physical entity power grid in the real environment through holographic simulation, dynamic monitoring, real-time diagnosis, and accurate prediction.

[0003] However, in the existing technology, it is difficult to comprehensively monitor and manage the operation status of equipment in real time and accurately, and potential fault hazards cannot be detected in time, resulting in an increase in equipment downtime. At the same time, it is also impossible to dynamically adjust the power consumption distribution strategy in real time to ensure that multiple power consumption equipment in the park can operate normally. Summary of the Invention

[0004] The purpose of the present invention is to provide a supervision system and method for park power consumption equipment based on digital twin to solve the problems faced in the above background art.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] A supervision system for park power consumption equipment based on digital twin includes: a data acquisition module, a digital twin model construction module, an intelligent monitoring module, and a power consumption management module;

[0007] The data acquisition module collects the operation parameters and environmental parameters of power consumption equipment through sensors and Internet of Things devices;

[0008] The digital twin model construction module constructs a digital twin model corresponding one-to-one with the real equipment by combining the physical parameters, operation parameters, and environmental parameters of the equipment, and uses the data acquisition module to collect the operation parameters and environmental parameters of the equipment in real time, and continuously updates the digital twin model to ensure a high degree of consistency between the model and the actual equipment state;

[0009] The intelligent monitoring module analyzes the equipment operation data based on the constructed digital twin model;

[0010] The power consumption management module is used to dynamically adjust the equipment power consumption strategy according to the analysis results of the intelligent monitoring module.

[0011] As a further description of the solution of the present invention, the specific working process of the data acquisition module includes:

[0012] Number all the electrical equipment in the park to be supervised, and the numbers are 1, 2,..., y in sequence;

[0013] Real-time collect the data of the operating parameters of each electrical equipment changing with time, and the data of the environmental parameters of the environment where each electrical equipment is located changing with time, and send the collected data to the digital twin model in real time.

[0014] As a further description of the solution of the present invention, the specific working process of the digital twin model construction module includes:

[0015] First, number the electrical equipment in the park, and use 3D modeling software to construct its 3D model for different numbered equipment;

[0016] Then, collect the physical parameters of each equipment and the real-time operating parameters, load parameters and environmental parameters collected by sensors, and input these data into the digital twin model construction platform to establish the digital twin model of the equipment;

[0017] Finally, set the data update frequency to ensure that the model can reflect the actual state changes of the equipment in time.

[0018] As a further description of the solution of the present invention, the specific process of the intelligent monitoring module includes:

[0019] Obtain the data of the operating parameters of the xth electrical equipment changing with time and the data of the environmental parameters of the environment where the electrical equipment is located changing with time based on the digital twin model;

[0020] Construct the mathematical model of the state coefficient of the xth electrical equipment, and the expression is:

[0021]

[0022] In the formula, S x is the mathematical model of the state coefficient of the xth electrical equipment, x belongs to [1, y]; S xr is the operating state coefficient of the xth electrical equipment, S xe is the environmental state coefficient of the xth electrical equipment, S xf is the fault state coefficient of the xth electrical equipment, S xL is the load state coefficient of the xth electrical equipment; n is the number of items of the operating parameters of the xth electrical equipment collected, i belongs to [0, n], S xi is the real-time value of the ith operating parameter of the xth electrical equipment, S xi,min is the minimum value of the ith operating parameter of the xth electrical equipment in the historical time period, S xi,maxis the maximum value of the i-th operating parameter of the x-th electrical device within a historical time period, k xi is the weight coefficient of the i-th operating parameter; m is the number of environmental parameter items of the x-th electrical device collected, j belongs to [0, m], S xj is the real-time value of the j-th environmental parameter of the x-th electrical device, S xj,min is the minimum value of the j-th environmental parameter of the x-th electrical device within a historical time period, S xj,max is the maximum value of the j-th environmental parameter of the x-th electrical device within a historical time period, k xj is the weight coefficient of the j-th environmental parameter; N xf is the number of faults of the x-th electrical device within a historical time period, T x is the operating duration of the x-th electrical device within a historical time period; XL c is the current load value of the x-th electrical device, XL min is the minimum load value of the x-th electrical device within a historical time period, XL max is the maximum load value of the x-th electrical device within a historical time period,

[0023] As a further description of the solution of the present invention, the

[0024] As a further description of the solution of the present invention, the specific process of the intelligent monitoring module further includes:

[0025] Compare the state coefficient S of the x-th electrical device x with the threshold interval of the state coefficient of the x-th electrical device:

[0026] If S x belongs to (-∞, S1], the device state is poor and there is a risk of failure. If S x belongs to (S1, S2], the device state is average and maintenance inspection is required. If S x belongs to (S2, S3], the device state is good and there may be potential failures. If S x belongs to (S3, +∞], the device state is excellent and the operation is normal.

[0027] As a further description of the solution of the present invention, the working process of the power consumption management module includes:

[0028] Based on the digital twin model, calculate the state coefficients of all electrical devices in the park in turn, arrange the state coefficients of all electrical devices in ascending order. The lower the state coefficient of the electrical device, the higher the energy allocation priority of the current device;

[0029] Construct a mathematical model for the energy demand of the x-th electrical device, and the expression is:

[0030]

[0031] Wherein, μ and π are weight coefficients, and E h is the energy consumption value of the previous day, and E a is the average daily energy consumption value within the historical time period;

[0032] Compare with E T . E T is the total energy supply of the park. If is less than or equal to E T , there is no need to adjust the electricity consumption strategy of the equipment;

[0033] If is greater than E T , adjust the electricity consumption strategies of each device in the order of priority, and give priority to ensuring the energy requirements of devices with higher priorities.

[0034] A method for supervising the electricity consumption equipment in a park based on digital twin, the method comprising:

[0035] Step S1: Collect the operating parameters and environmental parameters of the electricity consumption equipment through sensors and Internet of Things devices;

[0036] Step S2: Construct a digital twin model;

[0037] Step S3: Obtain the status coefficient of the electricity consumption equipment based on the digital twin model;

[0038] Step S4: Compare the status coefficient of the electricity consumption equipment with the threshold range of the status coefficient of the electricity consumption equipment to judge the operating status of each electricity consumption equipment;

[0039] Step S5: Arrange the status coefficients of each electricity consumption equipment in order to obtain the energy allocation priority of the equipment;

[0040] Step S6: Compare the total energy requirements of each electricity consumption equipment with the total energy supply of the park;

[0041] Step S7: If the total energy requirements of each electricity consumption equipment are less than or equal to the total energy supply of the park, there is no need to adjust the electricity consumption strategy of the equipment;

[0042] Step S8: If the total energy requirements of each electricity consumption equipment are greater than the total energy supply of the park, give priority to ensuring the energy requirements of equipment with higher priorities.

[0043] The beneficial effects of the present invention:

[0044] The present invention uses three-dimensional modeling software to build three-dimensional models of different types of equipment, obtains the physical parameters of each equipment, as well as the real-time operating parameters and environmental parameters of each equipment collected by sensors, and inputs these data into the digital twin model construction platform to establish a digital twin model of the equipment. At the same time, the data update frequency is set to ensure that the model can reflect the actual status changes of the equipment in a timely manner. The digital twin model is connected to the intelligent monitoring software to obtain the operating data in the model in real time, and the status coefficient of each equipment is obtained through the operating data. The operating status of each equipment is analyzed according to the status coefficient to help maintenance personnel quickly locate and solve problems. Then, based on the operating conditions and energy consumption of all equipment, the optimal energy allocation plan is obtained. The system automatically adjusts the operating parameters of the equipment according to the plan, such as adjusting the start-up time and operating power of the equipment to achieve optimal energy utilization.

[0045] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0047] Figure 1 This is a structural diagram of the geological surveying and mapping system based on the Internet of Things of the present invention. DETAILED DESCRIPTION

[0048] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0049] See also Figure 1 As shown, a campus power equipment supervision system based on digital twin is disclosed, including: a data acquisition module, a digital twin model construction module, an intelligent monitoring module and a power management module;

[0050] The data acquisition module collects operating parameters and environmental parameters of electrical equipment through sensors and IoT devices;

[0051] The digital twin model construction module constructs a digital twin model that corresponds one-to-one with the real device by combining the physical parameters, operating parameters, and environmental parameters of the device. It uses the data acquisition module to collect the device operating parameters and environmental parameters in real time, and continuously updates the digital twin model to ensure a high degree of consistency between the model and the actual device state;

[0052] The intelligent monitoring module analyzes the device operation data based on the constructed digital twin model;

[0053] The power consumption management module is used to dynamically adjust the device power consumption strategy according to the analysis results of the intelligent monitoring module.

[0054] Through the above technical solution, the present invention provides a supervision system for park power consumption equipment based on digital twins. First, the park power consumption equipment is numbered. For different types of equipment, a 3D model is constructed using 3D modeling software, and the physical parameters of each equipment, the real-time operation parameters collected by sensors, and the environmental parameters of each equipment are obtained. These data are input into the digital twin model construction platform to establish a digital twin model of the equipment. At the same time, the data update frequency is set to ensure that the model can reflect the actual state changes of the equipment in a timely manner. The digital twin model is connected to the intelligent monitoring software to obtain the operation data in the model in real time. The status coefficients of each equipment are obtained through the operation data, and the operation conditions of each equipment are analyzed based on the status coefficients to help maintenance personnel quickly locate and solve problems. Then, according to the operation conditions and energy consumption conditions of all equipment, the optimal energy distribution plan is obtained, and the system automatically adjusts the operation parameters of the equipment according to this plan, such as adjusting the start time and operation power of the equipment, to achieve the optimal utilization of energy.

[0055] As a further description of the solution of the present invention, the specific working process of the data acquisition module includes:

[0056] Number all the power consumption equipment in the park to be supervised, and the numbers are 1, 2,..., y in sequence;

[0057] Collect the data of the operation parameters of each power consumption equipment changing with time in real time, collect the data of the environmental parameters of the environment where each power consumption equipment is located changing with time in real time, and send the collected data to the digital twin model in real time.

[0058] As a further description of the solution of the present invention, the specific working process of the digital twin model construction module includes:

[0059] First, number the park power consumption equipment, and use 3D modeling software to construct a 3D model for equipment with different numbers;

[0060] Then, collect the physical parameters of each device, as well as the real-time operation parameters, load parameters, and environmental parameters collected by sensors, and input these data into the digital twin model construction platform to establish the digital twin model of the device;

[0061] Finally, set the data update frequency to ensure that the model can reflect the actual state changes of the device in a timely manner.

[0062] As a further description of the solution of the present invention, the specific process of the intelligent monitoring module includes:

[0063] Obtain the data of the operation parameters of the xth electrical device changing with time and the data of the environmental parameters of the environment where the electrical device is located changing with time based on the digital twin model;

[0064] Construct the mathematical model of the state coefficient of the xth electrical device, and the expression is:

[0065]

[0066] In the formula, S x is the mathematical model of the state coefficient of the xth electrical device, and x belongs to [1, y]; S xr is the operating state coefficient of the xth electrical device, S xe is the environmental state coefficient of the xth electrical device, S xf is the fault state coefficient of the xth electrical device, S xL is the load state coefficient of the xth electrical device; n is the number of items of the operation parameters of the xth electrical device collected, and i belongs to [0, n], S xi is the real-time value of the ith operation parameter of the xth electrical device, S xi,min is the minimum value of the ith operation parameter of the xth electrical device within the historical time period, S xi,max is the maximum value of the ith operation parameter of the xth electrical device within the historical time period, k xi is the weight coefficient of the ith operation parameter; m is the number of items of the environmental parameters of the xth electrical device collected, and j belongs to [0, m], S xj is the real-time value of the jth environmental parameter of the xth electrical device, S xj,min is the minimum value of the jth environmental parameter of the xth electrical device within the historical time period, S xj,max is the maximum value of the jth environmental parameter of the xth electrical device within the historical time period, k xj is the weight coefficient of the jth environmental parameter; N xf is the number of faults of the xth electrical device within the historical time period, T x is the operating duration of the xth electrical device within the historical time period; XL c is the current load value of the xth electrical device, XL minis the minimum load value of the xth electrical device within the historical time period, XL max is the maximum load value of the xth electrical device within the historical time period,

[0067] As a further description of the solution of the present invention, the

[0068] As a further description of the solution of the present invention, the specific process of the intelligent monitoring module further includes::

[0069] Compare the state coefficient S of the xth electrical device x with the threshold interval of the state coefficient of the xth electrical device:

[0070] If S x belongs to (-∞, S1], the device state is poor and there is a risk of failure. If S x belongs to (S1, S2], the device state is average and maintenance inspection is required. If S x belongs to (S2, S3], the device state is good and there may be potential failures. If S x belongs to (S3, +∞], the device state is excellent and it is operating normally.

[0071] Through the above technical solution, this embodiment provides a method for monitoring the state of a device based on a digital twin model. First, obtain the real-time operation data and historical data of the device (such as current, voltage, vibration frequency, temperature, etc.), select different monitoring parameters according to different devices, and then through the formula calculate the operation state coefficient of the device, obtain the real-time data and historical technical bureau of the environment where the device is located, and then through the formula calculate the environment state coefficient of the device. Then, obtain the failure parameters and usage duration of the device and calculate the failure state coefficient of the device through the formula Finally, based on the load data of the device and the physical parameters of the device, calculate the load state coefficient of the device through the formula Finally, through the formula S x = α * S xr + β * S xe + γ * S xf + δ * S xL calculate the state coefficient of the device by weighted summation.

[0072] Example

[0073] α is the weight for scoring operation parameters, usually with a relatively high value. β is the weight for scoring environmental parameters, usually with a medium value. γ is the weight for scoring historical fault parameters, usually with a relatively low value. δ is the weight for scoring load parameters, usually with the lowest value. It is required that α + β + γ + δ = 1. The operation status coefficient S of a certain x device xr = 0.7, the environmental status coefficient S xe = 0.8, the fault status coefficient S xf = 0.5, the load status coefficient S xL = 0.9, α = 0.4, β = 0.3, γ = 0.2, δ = 0.1;

[0074] Substituting into the mathematical model, we get S x = 0.7 * 0.4 + 0.8 * 0.3 + 0.5 * 0.3 + 0.9 * 0.2 = 0.85. Then compare 0.85 with the threshold interval set by the system to judge the operation status of the device.

[0075] As a further description of the solution of the present invention, the working process of the power consumption management module includes:

[0076] Based on the digital twin model, calculate the status coefficients of all power-consuming devices in the park in turn, arrange the status coefficients of all power-consuming devices in ascending order. The lower the status coefficient of the power-consuming device, the higher the priority of the current device's energy allocation;

[0077] Construct the energy demand mathematical model of the xth power-consuming device, and the expression is:

[0078]

[0079] In the formula, μ and π are weight coefficients, and E h is the energy consumption value of the previous day, and E a is the average daily energy consumption value within the historical time period;

[0080] Compare with E T , and E T is the total energy supply of the park. If is less than or equal to E T , there is no need to adjust the power consumption strategy of the device;

[0081] If is greater than E T , adjust the power consumption strategies of each device in the order of priority, and give priority to ensuring the energy demand of devices with high priority.

[0082] Through the above technical solution, the present invention provides a power distribution strategy for power-consuming devices. First, based on the load parameters and historical energy consumption parameters of each device, through the formula The mathematical model of the energy demand of each electrical device is calculated. Then, the status coefficients of all electrical devices are arranged in ascending order. The lower the status coefficient of an electrical device, the higher the priority of the current device's energy allocation. Finally, is compared with E T . E T is the total energy supply of the park. If is less than or equal to E T , there is no need to adjust the power consumption strategy of the device; if is greater than E T , adjust the power consumption strategy of each device in the order of priority, and give priority to ensuring the energy demand of devices with high priority.

[0083] Example

[0084] The allocable power in the park is 950 KW, and there are 5 electrical devices, namely A, B, C, D, and E. S A = 0.3, S B = 0.5, S C = 0.6, S D = 0.8, and S E = 0.9. Therefore, the priorities of A, B, C, D, and E are A > B > C > D > E. The energy demands of A, B, C, D, and E are 100 KW, 150 KW, 200 KW, 250 KW, and 300 KW respectively. The power consumption demands of A, B, C, and D are preferentially guaranteed and 100 KW, 150 KW, 200 KW, and 250 KW are allocated respectively. The remaining 250 KW of energy is allocated to E, and the operating power or operating time of device E is adjusted so that E can operate stably under the energy supply of 250 KW.

[0085] It should be noted that the minimum supply energy of each device needs to be obtained in advance. Under the principle of preferential allocation, it is also necessary to ensure that the energy allocated to each device is higher than the minimum supply energy.

[0086] A method for supervising electrical devices in a park based on digital twins, the method includes:

[0087] Step S1: Collect the operating parameters and environmental parameters of electrical devices through sensors and Internet of Things devices;

[0088] Step S2: Build a digital twin model;

[0089] Step S3: Obtain the status coefficients of electrical devices based on the digital twin model;

[0090] Step S4: Compare the status coefficients of electrical devices with the threshold interval of the status coefficients of electrical devices to judge the operating status of each electrical device;

[0091] Step S5: Arrange the state coefficients of each electrical equipment in sequence to obtain the energy distribution priority of the equipment;

[0092] Step S6: Compare the total energy demand of each electrical equipment with the total energy supply of the park;

[0093] Step S7: If the total energy demand of each electrical equipment is less than or equal to the total energy supply of the park, there is no need to adjust the electrical equipment usage strategy;

[0094] Step S8: If the total energy demand of each electrical equipment is greater than the total energy supply of the park, prioritize ensuring the energy demand of equipment with a higher priority.

[0095] It should be noted that the thresholds, threshold intervals, and coefficients set in this application are all empirical values, and all calculations in this application are dimensionless calculations, which will not be elaborated here.

[0096] The above content is only an example and explanation of the concept of the present invention. Those skilled in the art of this technology can make various modifications, supplements, or use similar methods to replace the specific embodiments described, as long as they do not deviate from the concept of the invention or exceed the scope defined by this claim book, they should fall within the protection scope of the present invention.

Claims

1. A supervision system for park electrical equipment based on digital twin, characterized in that include: Data acquisition module, digital twin model construction module, intelligent monitoring module and power management module; The data acquisition module collects operating parameters and environmental parameters of electrical equipment through sensors and IoT devices; The digital twin model construction module combines the physical parameters, operating parameters and environmental parameters of the device to build a digital twin model that corresponds to the real device one by one. The data acquisition module collects the device operating parameters and environmental parameters in real time and continuously updates the digital twin model to ensure high consistency between the model and the actual device status. The intelligent monitoring module analyzes the equipment operation data based on the constructed digital twin model; The power management module is used to dynamically adjust the power usage strategy of the equipment according to the analysis results of the intelligent monitoring module.

2. The supervision system for park electrical equipment based on digital twin according to claim 1, characterized in that, The specific working process of the data acquisition module includes: Number all electrical equipment in the park to be supervised, with the numbers being 1, 2, ..., y; Collect data on changes in the operating parameters of each electrical device over time in real time, collect data on changes in the environmental parameters of the environment in which each electrical device is located over time in real time, and send the collected data to the digital twin model in real time.

3. The supervision system for park electrical equipment based on digital twin according to claim 2, wherein, The specific working process of the digital twin model construction module includes: First, the electrical equipment in the park is numbered, and for each numbered device, a 3D model is constructed using 3D modeling software. Then, the physical parameters of each device, as well as the real-time operating parameters, load parameters, and environmental parameters collected by sensors, are collected and input into the digital twin model construction platform to build a digital twin model of the device. Finally, set the data update frequency to ensure that the model can reflect the actual status changes of the equipment in a timely manner.

4. The supervision system for park electrical equipment based on digital twin according to claim 3, wherein, The specific process of the intelligent monitoring module includes: Based on the digital twin model, obtain the time-varying data of the operating parameters of the x-th electrical equipment and the time-varying data of the environmental parameters of the environment in which the electrical equipment is located; Construct the mathematical model of the x-th electrical equipment state coefficient, the expression is: Wherein, S x is the mathematical model of the state coefficient of the xth electrical equipment, and x belongs to [1, y]; S xr is the operating state coefficient of the xth electrical equipment, S xe is the environmental state coefficient of the xth electrical equipment, S xf is the fault state coefficient of the xth electrical equipment, S xL is the load state coefficient of the xth electrical equipment; n is the number of operating parameter items of the xth electrical equipment collected, and i belongs to [0, n], S xi is the real-time value of the ith operating parameter of the xth electrical equipment, S xi,min is the minimum value of the ith operating parameter of the xth electrical equipment within the historical time period, S xi,max is the maximum value of the ith operating parameter of the xth electrical equipment within the historical time period, k xi is the weight coefficient of the ith operating parameter; m is the number of environmental parameter items of the xth electrical equipment collected, and j belongs to [0, m], S xj is the real-time value of the jth environmental parameter of the xth electrical equipment, S xj,min is the minimum value of the jth environmental parameter of the xth electrical equipment within the historical time period, S xj,max is the maximum value of the jth environmental parameter of the xth electrical equipment within the historical time period, k xj is the weight coefficient of the jth environmental parameter; N xf is the number of faults of the xth electrical equipment within the historical time period, T x is the operating duration of the xth electrical equipment within the historical time period; XL c is the current load value of the xth electrical equipment, XL min is the minimum load value of the xth electrical equipment within the historical time period, XL max is the maximum load value of the xth electrical equipment within the historical time period, 5. The supervision system for park electrical equipment based on digital twin according to claim 4, characterized in that, The described 6. A digital twin-based campus electrical equipment monitoring system according to claim 4, characterized in that: The specific process of the intelligent monitoring module also includes: Compare the x-th electrical equipment status coefficient S x with the threshold range of the x-th electrical equipment status coefficient: If S x belongs to (-∞, S1], the device status is poor and there is a risk of failure. If S x belongs to (S1, S2], the device status is average and maintenance inspection is required. If S x belongs to (S2, S3], the device status is good and there may be potential failures. If S x belongs to (S3, +∞), the device status is excellent and it is operating normally.

7. The supervision system for park electrical equipment based on digital twin according to claim 4, characterized in that The working process of the power management module includes: Based on the digital twin model, the status coefficients of all electrical equipment in the park are calculated in turn, and the status coefficients of all electrical equipment are arranged in order from small to large. The lower the status coefficient of the electrical equipment, the higher the energy allocation priority of the current equipment; Construct a mathematical model of the energy demand of the x-th electrical equipment, expressed as: where μ and π are weight coefficients, E h is the energy consumption value of the previous day, and E a is the average daily energy consumption value within the historical time period; Compare with E T . If E T is the total energy supply of the park, and if is less than or equal to E T , there is no need to adjust the equipment power consumption strategy; If is greater than E T , adjust the power consumption strategies of each device according to the priority order, and give priority to ensuring the energy requirements of devices with higher priorities.

8. A supervision method for park electrical equipment based on digital twin, characterized in that, The method comprises: Step S1: Collect operating parameters and environmental parameters of electrical equipment through sensors and IoT devices; Step S2: building a digital twin model; Step S3: Obtaining the state coefficient of the electrical equipment based on the digital twin model; Step S4: Compare the electrical equipment state coefficient with the electrical equipment state coefficient threshold range to determine the operating state of each electrical equipment; Step S5: Arrange the status coefficients of the various electrical devices in order to obtain the energy allocation priority of the devices; Step S6: Compare the total energy demand of each electrical device with the total energy supply of the park; Step S7: If the total energy demand of each power-consuming device is less than or equal to the total energy supply of the park, there is no need to adjust the device power consumption strategy; Step S8: If the total energy demand of each electrical device is greater than the total energy supply of the park, prioritize ensuring the energy demand of devices with a higher priority.

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