Method and device for updating digital twin of power distribution network

Abstract

The invention belongs to the technical field of power distribution network operation analysis and control, and particularly relates to a power distribution network digital twin updating method and device, and the method comprises the steps: obtaining the timestamp information of the current state of a twin of power distribution network equipment, which is established in advance, when a state request is received; judging whether the state of the twin of the power distribution network equipment needs to be updated or not by utilizing the timestamp information of the current state, and obtaining a judgment result; determining the state of the twinborn body of the power distribution network equipment at the next moment according to the judgment result; the twin of the power distribution network equipment is created by utilizing historical operation data of the power distribution network equipment. According to the technical scheme provided by the invention, accurate creation and real-time updating of the digital twinborn body of the power distribution network are realized, the operation efficiency of the power distribution system is effectively improved, the operation reliability of the system is improved, accurate and real-time auxiliary decision support is provided for power distribution network operators, and safe, reliable and efficient operation of the power distribution network is assisted.

Description

Technical Field

[0001] The present invention belongs to the technical field of distribution network operation analysis and control, and specifically relates to a method and device for updating a distribution network digital twin. Background Art

[0002] Building a new distribution system is an important part of building a new power system with new energy as the main body, and is an important measure to achieve the dual carbon goals. The optimization and regulation of traditional distribution systems are mostly based on experience, rules, and historical data, but they have problems such as poor flexibility and low accuracy when facing the complex and changing distribution network management and control needs and the growing challenges of renewable energy access. The use of digital twin technology in distribution networks can enhance the digital intelligent management and control of the system, so it has attracted much attention.

[0003] Establishing and updating the digital twin of the distribution network is one of the core tasks of modern distribution network management. With the access of a large number of distributed power sources, the morphological structure and operating characteristics of the distribution network have changed significantly, requiring grid operators to quickly and accurately understand and predict the state and behavior of the system, and to accurately, reliably and efficiently manage the distribution network. Traditional management methods can no longer meet the needs of accurate monitoring and prediction of the real-time state and behavior of the system. Therefore, the creation and updating of digital twins has become an urgent problem that needs to be solved.

[0004] There are several challenges in creating and updating the digital twin of the distribution network: First, the creation and updating of the digital twin needs to solve the real-time problem. As the amount of power system operation data increases, how to process and analyze this data under real-time requirements to ensure the timely update of the digital twin becomes a challenge; secondly, as the update frequency of the digital twin increases and the amount of data increases, the update algorithm needs to have efficient data processing capabilities and algorithm optimization capabilities, which increases the difficulty of updating the twin. Summary of the invention

[0005] In order to overcome the problems existing in the above-mentioned related technologies, the present application provides a method and device for updating a distribution network digital twin.

[0006] According to a first aspect of an embodiment of the present application, a method for updating a distribution network digital twin is provided, comprising:

[0007] When a status request is received, obtaining the timestamp information of the current status of the pre-established twin of the distribution network device;

[0008] Using the timestamp information of the current state, determining whether the state of the twin of the power distribution network device needs to be updated, and obtaining a determination result;

[0009] Determine the state of the twin of the distribution network equipment at the next moment according to the judgment result;

[0010] The twin of the distribution network equipment is created using historical operating data of the distribution network equipment.

[0011] Preferably, the process of creating the twin of the distribution network equipment includes:

[0012] Collect historical operation data of distribution network equipment;

[0013] Acquire physical characteristic parameters for constructing the twin of the distribution network equipment from a preset model library;

[0014] Constructing a twin of the distribution network equipment using the historical operation data of the distribution network equipment, the physical characteristic parameters and a preset 3D model of the distribution network equipment;

[0015] Initializing the state of the twin of the distribution network device;

[0016] The initial twin of the distribution network equipment after state initialization is mapped to the preset 3D model of the distribution network equipment to complete the creation of the twin of the distribution network equipment.

[0017] Preferably, the calculation formula of the twin of the distribution network equipment includes:

[0018] S=f(P,M,C)

[0019] In the above formula, S is the twin of the distribution network equipment, P is the historical operation data of the distribution network equipment, M is the preset 3D model of the distribution network equipment, and C is the physical characteristic parameter.

[0020] Preferably, the using the timestamp information of the current state to determine whether it is necessary to update the state of the twin of the distribution network device to obtain a determination result includes:

[0021] If the time interval from the generation time of the current state to the state request is less than the preset time, the judgment result is that the state of the twin of the power distribution network device does not need to be updated;

[0022] If the time interval from the generation time of the current state to the state request is greater than or equal to the preset time, the judgment result is that the state of the twin of the distribution network equipment needs to be updated.

[0023] Preferably, determining the state of the twin of the distribution network equipment at the next moment according to the judgment result includes:

[0024] When the judgment result is that the state of the twin of the distribution network device does not need to be updated, the current state is the state of the twin of the distribution network device at the next moment;

[0025] When the judgment result is that the state of the twin of the distribution network equipment needs to be updated, the state of the twin of the distribution network equipment is updated using the current operation data of the distribution network equipment, and the state of the twin of the distribution network equipment at the next moment is determined.

[0026] Preferably, the using current operation data of the distribution network equipment to update the state of the twin of the distribution network equipment and determine the state of the twin of the distribution network equipment at the next moment includes:

[0027] Based on the current operating data of the distribution network equipment, the state of the twin of the distribution network equipment is updated using a preset state update method to obtain an updated state, and the updated state is the state of the twin of the distribution network equipment at the next moment.

[0028] Preferably, the state of the twin of the distribution network equipment is updated by using a preset state update method based on the current operation data of the distribution network equipment, including:

[0029] If there is no historical operation data for the distribution network equipment, then based on the current operation data of the distribution network equipment, the state of the twin of the distribution network equipment is updated by using a model-driven method to obtain an updated state;

[0030] If the distribution network equipment has historical operating data, then based on the current operating data, the state of the twin of the distribution network equipment is updated in a data-driven manner to obtain an updated state.

[0031] Preferably, the updating of the state of the twin of the distribution network equipment in a model-driven manner based on the current operation data of the distribution network equipment includes:

[0032] Based on the current operation data, a new state value of the distribution network equipment is calculated using a state equation of a preset distribution network equipment model;

[0033] The state information of the twin of the power distribution network device is updated using the new state value to obtain the updated state.

[0034] Preferably, the updating of the state of the twin of the distribution network equipment in a data-driven manner based on the current operation data includes:

[0035] The current operating data is used as input to a pre-established state update model to obtain predicted state information of the twin of the distribution network equipment, and the predicted state information is the updated state.

[0036] Preferably, the process of establishing the state update model includes:

[0037] Construct a data set using the historical operation data and historical status information of distribution network equipment;

[0038] Dividing the data set into a training set and a validation set;

[0039] Using the training set to train the machine learning model to obtain a trained machine learning model;

[0040] The trained machine learning model is verified using the verification set. If the verification is successful, the trained machine learning model is the state update model. If the verification fails, the hyperparameters of the machine learning model are adjusted and the machine learning model is retrained until the verification is successful.

[0041] According to a second aspect of an embodiment of the present application, a device for updating a distribution network digital twin is provided, the device comprising:

[0042] A first acquisition unit, configured to acquire, when receiving a status request, timestamp information of a current status of a pre-established twin of a power distribution network device;

[0043] A second acquisition unit is used to use the timestamp information of the current state to determine whether the state of the twin of the distribution network device needs to be updated to obtain a determination result;

[0044] A determination unit, configured to determine the state of the twin of the distribution network device at a next moment according to the judgment result;

[0045] The twin of the distribution network equipment is created using historical operating data of the distribution network equipment.

[0046] Preferably, the device further comprises: an establishment unit, used to create a twin of the distribution network equipment; the establishment unit comprises: a collection subunit, used to collect historical operation data of the distribution network equipment;

[0047] A first construction subunit is used to obtain physical characteristic parameters for constructing a twin of the distribution network equipment from a preset model library;

[0048] A second construction subunit is used to construct a twin of the distribution network equipment by using the historical operation data of the distribution network equipment, the physical characteristic parameters and a preset 3D model of the distribution network equipment;

[0049] An initialization subunit, used to initialize the state of the twin of the distribution network equipment;

[0050] The mapping subunit is used to map the initial twin of the distribution network equipment after state initialization with the preset 3D model of the distribution network equipment to complete the creation of the twin of the distribution network equipment.

[0051] Preferably, the calculation formula of the twin of the distribution network equipment includes:

[0052] S=f(P,M,C)

[0053] In the above formula, S is the twin of the distribution network equipment, P is the historical operation data of the distribution network equipment, M is the preset 3D model of the distribution network equipment, and C is the physical characteristic parameter.

[0054] Preferably, the second acquisition unit is specifically used to:

[0055] If the time interval from the generation time of the current state to the state request is less than the preset time, the judgment result is that the state of the twin of the power distribution network device does not need to be updated;

[0056] If the time interval from the generation time of the current state to the state request is greater than or equal to the preset time, the judgment result is that the state of the twin of the distribution network equipment needs to be updated.

[0057] Preferably, the determining unit includes:

[0058] A first determining subunit, configured to determine that when a judgment result shows that the state of the twin of the distribution network device does not need to be updated, the current state is the state of the twin of the distribution network device at the next moment;

[0059] The second determination subunit is used to update the state of the twin of the distribution network equipment using the current operation data of the distribution network equipment when the judgment result is that the state of the twin of the distribution network equipment needs to be updated, and determine the state of the twin of the distribution network equipment at the next moment.

[0060] Preferably, the second determining subunit includes:

[0061] An updating module is used to update the state of the twin of the distribution network equipment based on the current operating data of the distribution network equipment using a preset state updating device to obtain an updated state, wherein the updated state is the state of the twin of the distribution network equipment at the next moment.

[0062] Preferably, the update module includes:

[0063] A first updating submodule is used to update the state of the twin of the distribution network equipment in a model-driven manner based on the current operation data of the distribution network equipment if there is no historical operation data of the distribution network equipment to obtain an updated state;

[0064] The second updating submodule is used to update the state of the twin of the distribution network equipment in a data-driven manner based on the current operation data if there is historical operation data of the distribution network equipment to obtain an updated state.

[0065] Preferably, the first updating submodule is specifically used for:

[0066] Based on the current operation data, a new state value of the distribution network equipment is calculated using a state equation of a preset distribution network equipment model;

[0067] The state information of the twin of the power distribution network device is updated using the new state value to obtain the updated state.

[0068] Preferably, the second updating submodule is specifically used for:

[0069] The current operating data is used as input to a pre-established state update model to obtain predicted state information of the twin of the distribution network equipment, and the predicted state information is the updated state.

[0070] Preferably, the process of establishing the state update model includes:

[0071] Construct a data set using the historical operation data and historical status information of distribution network equipment;

[0072] Dividing the data set into a training set and a validation set;

[0073] Using the training set to train the machine learning model to obtain a trained machine learning model;

[0074] The trained machine learning model is verified using the verification set. If the verification is successful, the trained machine learning model is the state update model. If the verification fails, the hyperparameters of the machine learning model are adjusted and the machine learning model is retrained until the verification is successful.

[0075] According to a third aspect of an embodiment of the present application, there is provided a computer device, including: one or more processors;

[0076] The processor is used to store one or more programs;

[0077] When the one or more programs are executed by the one or more processors, the method for updating the distribution network digital twin is implemented.

[0078] According to a fourth aspect of an embodiment of the present application, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed, the method for updating the digital twin of the distribution network is implemented.

[0079] The technical solution provided by the present invention has the following beneficial effects:

[0080] The present invention provides a method and device for updating a digital twin of a distribution network. The method and device create a twin of the distribution network equipment by utilizing the historical operation data of the distribution network equipment. When a status request is received, the timestamp information of the current status of the twin of the distribution network equipment established in advance is obtained. The timestamp information of the current status is used to determine whether the status of the twin of the distribution network equipment needs to be updated, and a judgment result is obtained. The status of the twin of the distribution network equipment at the next moment is determined according to the judgment result. The method and device realize accurate creation and real-time updating of the digital twin of the distribution network, effectively improve the operating efficiency of the distribution system, enhance the system operation reliability, provide accurate and real-time auxiliary decision support for distribution network operators, and help realize safe, reliable and efficient operation of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. 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 paying creative work.

[0082] Figure 1 is a flow chart of a method for updating a distribution network digital twin provided by an embodiment of the present invention;

[0083] Figure 2 is a flowchart of twin creation provided by an embodiment of the present invention;

[0084] Figure 3 is a twin state acquisition process diagram provided by an embodiment of the present invention;

[0085] Figure 4 It is a diagram of the internal process of twin state update provided by an embodiment of the present invention;

[0086] Figure 5 It is a structural block diagram of an updating device for a distribution network digital twin provided by an embodiment of the present invention;

[0087] Figure 6 It is a structural block diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0088] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the following embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0089] Embodiment 1

[0090] The present invention provides a method for updating a distribution network digital twin, such as Figure 1 As shown, the following steps are included:

[0091] Step 11: When a status request is received, obtain the timestamp information of the current status of the pre-established twin of the distribution network device;

[0092] Step 12: Using the timestamp information of the current state, determine whether the state of the twin of the distribution network equipment needs to be updated, and obtain a determination result;

[0093] Step 13: Determine the state of the twin of the distribution network equipment at the next moment according to the judgment result;

[0094] Among them, the twin of the distribution network equipment is created using the historical operation data of the distribution network equipment.

[0095] The present invention dynamically updates the state of the distribution network equipment through real-time data integration to reflect the current situation of the distribution network (including real-time monitoring of key parameters such as voltage, current, and power), so as to use digital twins for fault simulation and diagnosis, and quickly identify and locate fault points in the power grid. Through interaction with the twins, it is possible to more accurately analyze the cause of the fault, predict the fault trend, and formulate corresponding repair and optimization plans.

[0096] Furthermore, the method further includes: Step 10: creating a twin of the distribution network equipment; Step 10 includes:

[0097] Step 101: Collect historical operation data of distribution network equipment;

[0098] Step 102: Acquire physical characteristic parameters of the twin of the distribution network equipment from a preset model library;

[0099] Step 103: constructing a twin of the distribution network equipment using historical operating parameters, physical characteristic parameters and a preset 3D model of the distribution network equipment;

[0100] Step 104: Initializing the state of the twin of the distribution network equipment;

[0101] Step 105: Mapping the initial twin of the distribution network equipment after state initialization with the preset 3D model of the distribution network equipment to complete the creation of the twin of the distribution network equipment.

[0102] In some embodiments, the collected historical operating data of the distribution network equipment is stored in a historical database.

[0103] It should be noted that the method of "mapping the initial twin of the distribution network device after state initialization with the preset 3D model of the distribution network device" involved in the embodiments of the present invention is well known to those skilled in the art, and therefore, its specific implementation method will not be described in detail. In some embodiments, mapping the twin of the distribution network device after state initialization with the preset 3D model of the distribution network device includes:

[0104] The data and state of the twin of the distribution network equipment after state initialization are applied to the preset 3D model of the distribution network equipment.

[0105] Furthermore, the calculation formula of the twin of the distribution network equipment includes:

[0106] S=f(P,M,C)

[0107] In the above formula, S is the twin of the distribution network equipment, P is the historical operating parameters of the distribution network equipment, M is the preset 3D model of the distribution network equipment, and C is the physical characteristic parameter.

[0108] It can be understood that a virtual twin instance is constructed based on the historical operating parameters and physical characteristic parameters of the distribution network equipment, and the twin instance includes all information corresponding to the distribution network equipment, and the twin is set to an initial state to match the current state of the distribution network equipment to ensure the accuracy of subsequent simulations and predictions; the created twin object is associated with the corresponding 3D model previously found, and the twin's data and state are applied to the 3D model to visualize and simulate the behavior of the twin in a simulation environment; after completing the configuration and association of the twin, the successfully created twin is returned, which contains all the information and state corresponding to the actual distribution network equipment and can be used in subsequent applications.

[0109] Further, step 12 includes:

[0110] Step 121: If the time interval from the generation time of the current state to the state request is less than the preset time, the judgment result is that the state of the twin of the distribution network device does not need to be updated;

[0111] Step 122: If the time interval from the generation time of the current state to the state request is greater than or equal to the preset time, the judgment result is that the state of the twin of the distribution network equipment needs to be updated.

[0112] Further, step 13 includes:

[0113] Step 131: when the judgment result is that the state of the twin of the distribution network device does not need to be updated, the current state is the state of the twin of the distribution network device at the next moment;

[0114] Step 132: When the judgment result is that the state of the twin of the distribution network equipment needs to be updated, the state of the twin of the distribution network equipment is updated using the current operation data of the distribution network equipment, and the state of the twin of the distribution network equipment at the next moment is determined.

[0115] It should be noted that if the time interval from the generation time of the current state to the state request is less than the preset time, it means that the current state is a newly generated state, and the current state is the state of the twin of the distribution network equipment at the next moment; if the time interval from the generation time of the current state to the state request is greater than or equal to the preset time, it means that the current state is not a newly generated state, and the state of the twin of the distribution network equipment needs to be updated.

[0116] Further, step 132 includes:

[0117] Step 1321: Based on the current operating data of the distribution network equipment, the status of the twin of the distribution network equipment is updated using a preset status update method to obtain an updated status, which is the status of the twin of the distribution network equipment at the next moment.

[0118] Further, step 1321 includes:

[0119] Step 13211: If there is no historical operation data for the distribution network equipment, then based on the current operation data of the distribution network equipment, the twin of the distribution network equipment is updated in a model-driven manner to obtain an updated state;

[0120] Step 13212: If there is historical operating data for the distribution network equipment, based on the current operating data, the status of the twin of the distribution network equipment is updated using a data-driven approach to obtain an updated status.

[0121] Further, step 13211 includes:

[0122] Based on the current operating data, the state equation of the preset distribution network equipment model is used to calculate the new state value of the distribution network equipment;

[0123] The state information of the twin of the distribution network equipment is updated using the new state value to obtain the updated state.

[0124] It should be noted that the model-driven approach mainly updates the state of the twin through the existing distribution network equipment model and its state equation. It focuses on using the physical characteristics and mathematical models of distribution network equipment to make accurate state predictions. Taking the photovoltaic power generation system as an example, the update process is as follows:

[0125] 1. Build or obtain a distribution network equipment model: First, it is necessary to build or obtain a mathematical model that describes the state changes of distribution network equipment. The electrical behavior of a photovoltaic module can be described by a single diode model, which uses a diode and a current source to approximate the photovoltaic module.

[0126] 2. Define state variables and equations: Based on the device model, define key variables that describe the device state (such as voltage, current, power, etc.). Define the output current of the photovoltaic module as I and the output voltage as V, and establish the corresponding state equation:

[0127]

[0128] Among them, I L is the photocurrent generated under illumination, I O is the reverse saturation current, V is the output voltage of the photovoltaic module, R S is the series resistance of the photovoltaic module, n is the ideality factor of the diode, V T is the thermal voltage, R SH is the parallel resistance of the photovoltaic module. These equations can reflect the relationship between state variables and their changes over time.

[0129] It is understood that the state equation of a photovoltaic power generation system generally describes the electrical behavior of a photovoltaic module, including light conditions, module temperature, and current-voltage relationship. When deriving the state equation of a photovoltaic module, the present invention uses a simplified equivalent circuit model, called a single diode model. This model assumes that a photovoltaic module can be approximated by a diode and a current source. This is done to more conveniently describe the electrical behavior of a photovoltaic module. In this equivalent circuit model, there are the following elements: a photocurrent source I L : Represents the photocurrent generated under illumination conditions, which is directly converted from light energy; a reverse saturation current I O : Indicates the small leakage current of the photovoltaic module under open circuit conditions; a series resistor R S : Represents the internal series resistance of the photovoltaic module, which affects the current and voltage characteristics of the photovoltaic module; an ideal diode: composed of the ideal factor n of the diode and the thermal voltage V T Control is used to simulate the nonlinear characteristics of photovoltaic modules. When light shines on photovoltaic modules (solar cells), light energy is converted into electrical energy to generate current. The photovoltaic state equation is a mathematical expression used to describe the relationship between the current and voltage of photovoltaic modules.

[0130] 3. Input real-time data: obtain real-time operating data of photovoltaic equipment through sensors, monitoring equipment, etc., including lighting conditions, component temperature, etc.

[0131] 4. Calculate the new state: Substitute the real-time data into the state equation for calculation. According to the real-time lighting conditions, component temperature and output voltage, the output current of the photovoltaic component can be calculated.

[0132] 5. Update the twin state: assign the calculated new state value (i.e. the output current of the PV module) to the twin and update its state information. This ensures that the twin can reflect the actual operating status of the distribution network equipment in real time.

[0133] Further, step 13212 includes:

[0134] The current operating data is used as the input of the pre-established state update model to obtain the predicted state information of the twin of the distribution network equipment, and the predicted state information is the updated state.

[0135] Furthermore, the process of establishing the state update model includes:

[0136] Construct a data set using the historical operation data and historical status information of distribution network equipment;

[0137] Divide the dataset into training and validation sets;

[0138] Using the training set to train the machine learning model to obtain a trained machine learning model;

[0139] The trained machine learning model is verified using the verification set. If the verification is successful, the trained machine learning model is used as the state update model. If the verification fails, the hyperparameters of the machine learning model are adjusted and the machine learning model is retrained until the verification is successful.

[0140] It should be noted that the methods of "training the machine learning model using the training set to obtain the trained machine learning model", "validating the trained machine learning model using the validation set" and "adjusting the hyperparameters of the machine learning model" involved in the embodiments of the present invention are well known to those skilled in the art, and therefore, their specific implementation methods are not described in detail. In some embodiments, training the machine learning model using the training set to obtain the trained machine learning model includes:

[0141] The historical operation data of the distribution network equipment in the training set is used as the input layer training samples of the machine learning model, and the historical status information of the distribution network equipment in the training set is used as the output layer training samples of the machine learning model. The machine learning model is trained to obtain a trained machine learning model.

[0142] Use the validation set to validate the trained machine learning model, including:

[0143] The historical operation data of the distribution network equipment in the verification set is used as the input of the trained machine learning model, and the predicted status information of the distribution network equipment corresponding to the historical operation data is output;

[0144] Determining the accuracy of the prediction based on the predicted status information of the distribution network equipment corresponding to the historical operation data and the historical status information of the distribution network equipment;

[0145] If the accuracy is greater than or equal to the accuracy threshold, the verification is successful; if the accuracy is less than the accuracy threshold, the verification fails.

[0146] It should be noted that the method of "determining the prediction accuracy based on the predicted status information of the distribution network equipment corresponding to the historical operation data and the historical status information of the distribution network equipment" involved in the embodiment of the present invention is well known to those skilled in the art, and therefore, its specific implementation method will not be described in detail.

[0147] The data-driven approach updates the state of the twin based on a large amount of historical operating data and machine learning algorithms. This approach focuses on mining the operating rules and trends of distribution network equipment from historical data to predict and update the state. To provide a specific example, the update process is as follows:

[0148] 1. Collect historical data: Collect the operating status data of the target distribution network equipment from the historical records of the distribution network, including parameters such as voltage, current, and power at different time points.

[0149] 2. Data preprocessing: Perform preprocessing operations such as cleaning, sorting and standardization on the collected historical data to eliminate problems such as noise, outliers and dimensional differences.

[0150] 3. Select a suitable machine learning algorithm: According to the characteristics and forecasting requirements of the distribution network equipment, select a suitable algorithm such as LSTM, GBDT or Lasso to learn the operating rules of the equipment from the data and perform status prediction.

[0151] 4. Training model: Use preprocessed historical data to train the machine learning model. By adjusting the parameters and structure of the model, it can accurately predict the state changes of the equipment.

[0152] 5. Real-time prediction and update: When new real-time data arrives, it is input into the trained model for real-time prediction. Based on the prediction results, the status information of the twin is updated so that it can reflect the latest status of the distribution network equipment.

[0153] The present invention updates the status of the distribution network equipment twin by utilizing a model-driven approach and a data-driven approach, thereby solving the problem in the prior art that as the update frequency of the digital twin increases and the amount of data increases, the update algorithm needs to have efficient data processing and algorithm optimization capabilities, thereby ensuring the efficiency and real-time performance of the update process.

[0154] In some embodiments, after the state of the distribution network equipment twin is updated, the state update model needs to be updated, including: first, collecting and preprocessing the real-time operation data and state information of the distribution network equipment to ensure the accuracy and reliability of the data. Subsequently, the performance of the state update model is evaluated, and the deficiencies in the model are identified by comparing the predicted state information with the actual state information. Next, the optimization technology in the machine learning algorithm is used to fine-tune the model parameters to improve the prediction accuracy and stability of the model. After the parameter adjustment is completed, the new model is verified by a validation set or cross-validation method to ensure that it has good performance in practical applications. Finally, the adjusted model parameters are applied to the twin to update the model to better reflect the current operating status and characteristics of the distribution network equipment. The update of the fitting model can make the prediction and simulation results of the twin more accurate and reliable.

[0155] Adjusting the state update model parameters includes: first, clarifying the target and optimization direction of parameter adjustment according to the actual performance and business needs of the state update model. Then, using the parameter search algorithm, automatically explore different parameter combinations in the parameter space to find the parameter value that optimizes the objective function. During the search process, the performance of each set of parameters is evaluated, and the effects of different parameter combinations are compared by calculating the value of the objective function. Based on the evaluation results, the optimal parameter combination is selected and applied to the data-driven model of the twin (i.e., the state update model). By continuously optimizing the parameters, the prediction accuracy and stability of the model can be improved, making it better adapted to the state changes of the distribution network equipment. At the same time, the performance of the model is continuously monitored, and real-time adjustments are made based on new data feedback to ensure that the model is highly consistent with the actual equipment status.

[0156] The present invention provides a method for updating a digital twin of a distribution network. The method creates a twin of a distribution network device by utilizing historical operating data of the distribution network device. When a status request is received, the method obtains the timestamp information of the current status of the twin of the distribution network device established in advance. The method uses the timestamp information of the current status to determine whether the status of the twin of the distribution network device needs to be updated, and obtains a judgment result. The method determines the status of the twin of the distribution network device at the next moment according to the judgment result, thereby realizing accurate creation and real-time updating of the digital twin of the distribution network, effectively improving the operating efficiency of the distribution system, improving the operating reliability of the distribution system, providing accurate and real-time auxiliary decision support for distribution network operators, and helping to achieve safe, reliable and efficient operation of the distribution network.

[0157] The present invention takes the distribution network equipment transformer as an example to further illustrate the above-mentioned distribution network digital twin update method, which includes the following steps:

[0158] Step 1: Data collection and storage

[0159] (1) Extracting data from physical devices

[0160] The real-time operation data of the transformer is obtained through sensors and monitoring equipment, including current, voltage, temperature, etc. The device data set can be expressed as D t ={d1,d2,…,d i ,…,d n}, where i∈[1,n], n is the total number of data points collected, D t is the device data set extracted at time point t, d1 is the first data point, d2 is the second data point, and d i is the i-th data point, d n is the nth data point, which is the real-time running data.

[0161] (2) Storing new data and historical data

[0162] The data in the above-obtained device data set is stored and added to the historical data set to establish a historical data record. The historical data set can be expressed as:

[0163] H={D1,D2,…,D t} (1)

[0164] In the above formula, H is the historical data set, D1 is the device data set extracted at the first time point, D2 is the device data set extracted at the second time point, and D t is the set of equipment data extracted at time point t, recorded as a time series.

[0165] Step 2: Create a twin of the distribution network equipment based on the historical data set

[0166] like Figure 2 As shown in the figure, taking the transformer as an example, the twin creation process is explained. Assume that the transformer twin is S1, the transformer historical operating parameters are P1, the physical characteristics are C1, and the twin state is S m , the 3D model is M1, then the following steps are included:

[0167] (1) Find relevant model libraries:

[0168] Before the twin creation process begins, first search for relevant models in the preset model library. Through the preset model library, you can find the physical characteristic parameters C1 required to build the twin of the transformer.

[0169] (2) Create a twin object of the transformer:

[0170] After finding an appropriate model, create the transformer's twin object as follows:

[0171] S1=f(P1,M1,C1) (2)

[0172] In the above formula, S1 is the twin of the transformer of the distribution network equipment, P1 is the historical operating parameters of the transformer, M1 is the preset 3D model of the distribution network equipment, C1 is the physical characteristic parameter, and f is the creation function.

[0173] The process involves building a virtual twin instance based on the transformer's historical operating parameters and characteristics, including all information corresponding to the actual transformer.

[0174] (3) Initialize the twin state:

[0175] After creating the twin object, its state needs to be initialized. This includes setting the twin to an initial state that matches the current state of the actual transformer. By initializing the twin state, the accuracy of subsequent simulations and predictions is ensured.

[0176] (4) Associated with the corresponding 3D model

[0177] The created twin object is associated with the corresponding 3D model found previously, that is, the data and state of the twin are applied to the 3D model so as to visualize and simulate the behavior of the twin in the simulation environment.

[0178] (5) Return the successfully created twin

[0179] After completing the configuration and association of the transformer's twin, the successfully created twin is returned, which contains all the information and status corresponding to the actual transformer and can be used in subsequent applications.

[0180] Step 3: Get the running status of the digital twin

[0181] like Figure 3 As shown in the figure, the process of obtaining the state of the digital twin is as follows:

[0182] (1) Get status request

[0183] The process of obtaining and updating the twin state begins with the arrival of a state request. When an external system or user initiates a state request, the twin will respond to the request and prepare to start the state acquisition and update process.

[0184] (2) Check the timestamp of the current status

[0185] After receiving a status request, the twin checks the timestamp of the current status. The timestamp is used to track the time information of the state data and ensure the timing accuracy of the data.

[0186] (3) Determine whether the current state is a new state

[0187] Determine whether the current state is a newly generated state based on the timestamp information. If the time interval from the generation time of the current state to the state request is less than the preset time, it means that the current state is the latest and within the error range, that is, the newly generated state. At this time, the twin directly returns the current state, that is, the current state is the state of the twin at the next moment.

[0188] (4) Select the update method based on the preset status method

[0189] If the time interval from the generation time of the current state to the state request is greater than or equal to the preset time, it means that the current state is not the latest, and the system needs to select an appropriate update method based on the preset state update method. Depending on whether the distribution network equipment has historical operation data, the twin can choose a model-driven or data-driven approach to update the state. If the model-driven approach is selected, the twin will use the existing state model to calculate and return the new state to the requester based on the characteristics and parameters of the current state. If the data-driven approach is selected, the twin will use the existing historical data and generate new data based on changes in the data. The system will then apply these new data to the twin, update the current state, and return the updated state to the requester.

[0190] Step 4: Update the digital twin operation status

[0191] The specific internal process of twin state update is as follows Figure 4 As shown, when the bottom layer receives new data, the status is updated. The detailed steps are as follows:

[0192] (1) Save the current status and data reception

[0193] When the twin is in the current state, the underlying system first saves the current state information, including various parameters, characteristics, and state equations such as voltage, current, and power. At the same time, when the underlying system receives new data, it passes these new data to the twin for state update.

[0194] (2) Update the current status

[0195] After receiving new data, the twin will use this data to update the current status. This update process can involve various aspects of information such as the operating status of the equipment, fault conditions, and risk assessment. Through the input of data, the twin can more accurately reflect the latest status of the equipment.

[0196] The present invention proposes a method for updating the digital twin of the distribution network for high-proportion distributed power sources connected to the distribution network. First, the real-time operation data of typical distribution network equipment or systems such as transformers are obtained and stored through measuring equipment such as sensors to obtain historical operation data; then, a typical distribution network equipment twin is created based on the historical operation data, and the twin is associated with the corresponding 3D model to visualize and simulate the behavior of the twin in a simulation environment; finally, the operation status of the digital twin is obtained, and the update method is selected according to the preset status update method, so as to update its operation status at the next moment, support the operation analysis of the distribution network, effectively improve the operating efficiency of the distribution system, improve the system operation reliability, provide accurate and real-time auxiliary decision support for distribution network operators, and help realize the safe, reliable and efficient operation of the distribution network.

[0197] The present invention proposes a method for updating a digital twin of a distribution network, which effectively improves the operating efficiency and power supply reliability of the distribution network. The method accurately simulates or predicts the operating status of the distribution network based on the real-time operating data of the distribution network equipment, improves the reliability of the distribution system, and meets the needs of practical applications. Moreover, in practical applications, the status of the twin can be visualized, thereby providing a more intuitive management interface for the operation and maintenance personnel of the distribution system.

[0198] Embodiment 2

[0199] The present invention also provides a device for updating a digital twin of a distribution network, such as Figure 5 As shown, the device comprises:

[0200] A first acquisition unit, configured to acquire, when receiving a status request, timestamp information of a current status of a pre-established twin of a power distribution network device;

[0201] A second acquisition unit is used to use the timestamp information of the current state to determine whether the state of the twin of the distribution network equipment needs to be updated to obtain a determination result;

[0202] A determination unit, used to determine the state of the twin of the distribution network equipment at the next moment according to the judgment result;

[0203] The twin of the distribution network equipment is created using the historical operating data of the distribution network equipment.

[0204] Further, the device also includes: an establishment unit for creating a twin of the distribution network equipment; the establishment unit includes: a collection subunit for collecting historical operation data of the distribution network equipment;

[0205] A first construction subunit is used to obtain physical characteristic parameters of a twin of a distribution network device from a preset model library;

[0206] The second construction subunit is used to construct a twin of the distribution network equipment by using the historical operation data, physical characteristic parameters and the preset 3D model of the distribution network equipment;

[0207] An initialization subunit, used to initialize the state of the twin of the distribution network equipment;

[0208] The mapping subunit is used to map the initial twin of the distribution network equipment after state initialization with the preset 3D model of the distribution network equipment to complete the creation of the twin of the distribution network equipment.

[0209] Furthermore, the calculation formula of the twin of the distribution network equipment includes:

[0210] S=f(P,M,C)

[0211] In the above formula, S is the twin of the distribution network equipment, P is the historical operation data of the distribution network equipment, M is the preset 3D model of the distribution network equipment, and C is the physical characteristic parameter.

[0212] Furthermore, the second acquisition unit is specifically used for:

[0213] If the time interval between the generation time of the current state and the state request is less than the preset time, the judgment result is that the state of the twin of the distribution network equipment does not need to be updated;

[0214] If the time interval from the generation time of the current state to the state request is greater than or equal to the preset time, the judgment result is that the state of the twin of the distribution network equipment needs to be updated.

[0215] Further, the determination unit includes:

[0216] A first determining subunit is used for, when the judgment result is that the state of the twin of the distribution network equipment does not need to be updated, the current state is the state of the twin of the distribution network equipment at the next moment;

[0217] The second determination subunit is used to update the state of the twin of the distribution network equipment using the current operation data of the distribution network equipment when the judgment result is that the state of the twin of the distribution network equipment needs to be updated, and determine the state of the twin of the distribution network equipment at the next moment.

[0218] Furthermore, the second determining subunit includes:

[0219] The update module is used to update the state of the twin of the distribution network equipment based on the current operating data of the distribution network equipment using a preset state update device to obtain an updated state, which is the state of the twin of the distribution network equipment at the next moment.

[0220] Further, update modules, including:

[0221] The first updating submodule is used to update the state of the twin of the distribution network equipment by using a model-driven method based on the current operation data of the distribution network equipment if there is no historical operation data of the distribution network equipment to obtain an updated state;

[0222] The second updating submodule is used to update the status of the twin of the distribution network equipment in a data-driven manner based on the current operating data if there is historical operating data for the distribution network equipment to obtain an updated status.

[0223] Furthermore, the first updating submodule is specifically used for:

[0224] Based on the current operating data, the state equation of the preset distribution network equipment model is used to calculate the new state value of the distribution network equipment;

[0225] The state information of the twin of the distribution network equipment is updated using the new state value to obtain the updated state.

[0226] Furthermore, the second updating submodule is specifically used for:

[0227] The current operating data is used as the input of the pre-established state update model to obtain the predicted state information of the twin of the distribution network equipment, and the predicted state information is the updated state.

[0228] Furthermore, the process of establishing the state update model includes:

[0229] Construct a data set using the historical operation data and historical status information of distribution network equipment;

[0230] Divide the dataset into training and validation sets;

[0231] Using the training set to train the machine learning model to obtain a trained machine learning model;

[0232] The trained machine learning model is verified using the verification set. If the verification is successful, the trained machine learning model is used as the state update model. If the verification fails, the hyperparameters of the machine learning model are adjusted and the machine learning model is retrained until the verification is successful.

[0233] It can be understood that the device embodiment provided above corresponds to the method embodiment described above, and the corresponding specific contents can be referenced to each other and will not be repeated here.

[0234] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0235] Embodiment 3

[0236] like Figure 6 As shown, the present invention also provides an electronic device, which may be a computer device, a single-chip device, an intelligent mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, the processor, and the transceiver component are connected via a bus; the memory may be used to store an execution program, and an exemplary execution program may include instructions; the processor is used to execute the instructions stored in the memory. The memory may also be used to store data, which may be called and / or modified when the instructions are executed.

[0237] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in a storage medium to implement the corresponding method flow or corresponding functions, so as to implement the steps of a method for updating a distribution network digital twin in the above embodiment.

[0238] Embodiment 4

[0239] Based on the same inventive concept, the present invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory), which is a memory device in an electronic device for storing programs and data. It can be understood that the storage medium here can include both built-in storage media in electronic devices and, of course, extended storage media supported by electronic devices. The storage medium provides a storage space that stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more execution programs (including program codes). It should be noted that the storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk storage. The processor loads and executes one or more instructions stored in the storage medium, which can implement the steps of a method for updating a digital twin of a distribution network in the above embodiment.

[0240] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0241] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0242] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0243] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0244] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for updating a distribution network digital twin, characterized in that: include: When a status request is received, obtaining the timestamp information of the current status of the pre-established twin of the distribution network device; Using the timestamp information of the current state, determining whether the state of the twin of the power distribution network device needs to be updated, and obtaining a determination result; Determine the state of the twin of the distribution network equipment at the next moment according to the judgment result; The twin of the distribution network equipment is created using historical operating data of the distribution network equipment.

2. The method according to claim 1, characterized in that The process of creating the twin of the distribution network equipment includes: Collect historical operation data of distribution network equipment; Acquire physical characteristic parameters for constructing the twin of the distribution network equipment from a preset model library; Constructing a twin of the distribution network equipment using the historical operation data of the distribution network equipment, the physical characteristic parameters and a preset 3D model of the distribution network equipment; Initializing the state of the twin of the distribution network device; The initial twin of the distribution network equipment after state initialization is mapped to the preset 3D model of the distribution network equipment to complete the creation of the twin of the distribution network equipment.

3. The method according to claim 2, characterized in that The calculation formula of the twin of the distribution network equipment includes: S=f(P,M,C) In the above formula, S is the twin of the distribution network equipment, P is the historical operation data of the distribution network equipment, M is the preset 3D model of the distribution network equipment, and C is the physical characteristic parameter.

4. The method according to claim 1, characterized in that The using the timestamp information of the current state to determine whether it is necessary to update the state of the twin of the power distribution network device, and obtaining a determination result, includes: If the time interval from the generation time of the current state to the state request is less than the preset time, the judgment result is that the state of the twin of the power distribution network device does not need to be updated; If the time interval from the generation time of the current state to the state request is greater than or equal to the preset time, the judgment result is that the state of the twin of the distribution network equipment needs to be updated.

5. The method according to claim 1, characterized in that Determining the state of the twin of the distribution network equipment at the next moment according to the judgment result includes: When the judgment result is that the state of the twin of the distribution network device does not need to be updated, the current state is the state of the twin of the distribution network device at the next moment; When the judgment result is that the state of the twin of the distribution network equipment needs to be updated, the state of the twin of the distribution network equipment is updated using the current operation data of the distribution network equipment, and the state of the twin of the distribution network equipment at the next moment is determined.

6. The method according to claim 5, characterized in that The method of updating the state of the twin of the distribution network equipment by using the current operation data of the distribution network equipment and determining the state of the twin of the distribution network equipment at the next moment includes: Based on the current operating data of the distribution network equipment, the state of the twin of the distribution network equipment is updated using a preset state update method to obtain an updated state, and the updated state is the state of the twin of the distribution network equipment at the next moment.

7. The method according to claim 6, characterized in that The method of updating the state of the twin of the distribution network device based on the current operation data of the distribution network device using a preset state update method includes: If there is no historical operation data for the distribution network equipment, then based on the current operation data of the distribution network equipment, the state of the twin of the distribution network equipment is updated by using a model-driven method to obtain an updated state; If the distribution network equipment has historical operating data, then based on the current operating data, the state of the twin of the distribution network equipment is updated in a data-driven manner to obtain an updated state.

8. The method according to claim 7, characterized in that The updating of the state of the twin of the distribution network device in a model-driven manner based on the current operation data of the distribution network device includes: Based on the current operation data, a new state value of the distribution network equipment is calculated using a state equation of a preset distribution network equipment model; The state information of the twin of the power distribution network device is updated using the new state value to obtain the updated state.

9. The method according to claim 7, characterized in that: The updating of the state of the twin of the power distribution network equipment in a data-driven manner based on the current operation data includes: The current operating data is used as input to a pre-established state update model to obtain predicted state information of the twin of the distribution network equipment, and the predicted state information is the updated state.

10. The method according to claim 9, characterized in that The process of establishing the state update model includes: Construct a data set using the historical operation data and historical status information of distribution network equipment; Dividing the data set into a training set and a validation set; Using the training set to train the machine learning model to obtain a trained machine learning model; The trained machine learning model is verified using the verification set. If the verification is successful, the trained machine learning model is the state update model. If the verification fails, the hyperparameters of the machine learning model are adjusted and the machine learning model is retrained until the verification is successful.

11. A device for updating a digital twin of a distribution network, characterized in that: The device comprises: A first acquisition unit, configured to acquire, when receiving a status request, timestamp information of a current status of a pre-established twin of a power distribution network device; A second acquisition unit is used to use the timestamp information of the current state to determine whether the state of the twin of the distribution network device needs to be updated to obtain a determination result; A determination unit, configured to determine the state of the twin of the distribution network device at a next moment according to the judgment result; The twin of the distribution network equipment is created using historical operating data of the distribution network equipment.

12. The device according to claim 11, characterized in that It also includes: an establishment unit, which is used to create a twin of the distribution network equipment; the establishment unit includes: a collection subunit, which is used to collect historical operation data of the distribution network equipment; A first construction subunit is used to obtain physical characteristic parameters for constructing a twin of the distribution network equipment from a preset model library; A second construction subunit is used to construct a twin of the distribution network equipment by using the historical operation data of the distribution network equipment, the physical characteristic parameters and a preset 3D model of the distribution network equipment; An initialization subunit, used to initialize the state of the twin of the distribution network equipment; The mapping subunit is used to map the initial twin of the distribution network equipment after state initialization with the preset 3D model of the distribution network equipment to complete the creation of the twin of the distribution network equipment.

13. The device according to claim 12, characterized in that The calculation formula of the twin of the distribution network equipment includes: S=f(P,M,C) In the above formula, S is the twin of the distribution network equipment, P is the historical operation data of the distribution network equipment, M is the preset 3D model of the distribution network equipment, and C is the physical characteristic parameter.

14. The device according to claim 11, characterized in that The second acquisition unit is specifically used to: If the time interval from the generation time of the current state to the state request is less than the preset time, the judgment result is that the state of the twin of the power distribution network device does not need to be updated; If the time interval from the generation time of the current state to the state request is greater than or equal to the preset time, the judgment result is that the state of the twin of the distribution network equipment needs to be updated.

15. The device according to claim 11, characterized in that The determining unit comprises: A first determining subunit, configured to determine that when a judgment result shows that the state of the twin of the distribution network device does not need to be updated, the current state is the state of the twin of the distribution network device at the next moment; The second determination subunit is used to update the state of the twin of the distribution network equipment using the current operation data of the distribution network equipment when the judgment result is that the state of the twin of the distribution network equipment needs to be updated, and determine the state of the twin of the distribution network equipment at the next moment.

16. The device according to claim 15, characterized in that The second determining subunit includes: An updating module is used to update the state of the twin of the distribution network equipment based on the current operating data of the distribution network equipment using a preset state updating device to obtain an updated state, wherein the updated state is the state of the twin of the distribution network equipment at the next moment.

17. The device according to claim 16, characterized in that The update module comprises: A first updating submodule is used to update the state of the twin of the distribution network equipment in a model-driven manner based on the current operation data of the distribution network equipment if there is no historical operation data of the distribution network equipment to obtain an updated state; The second updating submodule is used to update the state of the twin of the distribution network equipment in a data-driven manner based on the current operation data if there is historical operation data of the distribution network equipment to obtain an updated state.

18. The device according to claim 17, characterized in that The first updating submodule is specifically used for: Based on the current operation data, a new state value of the distribution network equipment is calculated using a state equation of a preset distribution network equipment model; The state information of the twin of the power distribution network device is updated using the new state value to obtain the updated state.

19. The device according to claim 17, characterized in that The second updating submodule is specifically used for: The current operating data is used as input to a pre-established state update model to obtain predicted state information of the twin of the distribution network equipment, and the predicted state information is the updated state.

20. The device according to claim 19, characterized in that The process of establishing the state update model includes: Construct a data set using the historical operation data and historical status information of distribution network equipment; Dividing the data set into a training set and a validation set; Using the training set to train the machine learning model to obtain a trained machine learning model; The trained machine learning model is verified using the verification set. If the verification is successful, the trained machine learning model is the state update model. If the verification fails, the hyperparameters of the machine learning model are adjusted and the machine learning model is retrained until the verification is successful.

21. An electronic device, characterized in that: include: at least one processor and memory; The memory and the processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, the method for updating the distribution network digital twin according to any one of claims 1 to 10 is implemented.

22. A readable storage medium, characterized in that: An execution program is stored thereon, and when the execution program is executed, the updating method of the distribution network digital twin as described in any one of claims 1 to 10 is implemented.

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