A cross-regional power dispatching method and system based on digital twin

Abstract

The present invention discloses a cross-regional power dispatching method and system based on digital twins, including: obtaining power equipment parameters, power dispatching parameters and environmental parameters in each power supply area, and establishing a digital twin model of each power supply area based on the power equipment parameters, power dispatching parameters and environmental parameters; configuring different types of cross-regional power dispatching tasks in the digital twin model; pre-training an MMOE model of a multi-task network, and using the trained MMOE model to predict parameter values ​​of different types of parameters in the digital twin model; obtaining differential data of parameters of the same type in the power supply area, and obtaining power equipment parameters, power dispatching parameters and environmental parameters in the current corresponding power supply area, inputting the differential data, power equipment parameters, power dispatching parameters and environmental parameters into the trained MMOE model, and outputting the cross-regional power dispatching tasks through the task network of the trained MMOE model.

Description

Technical Field

[0001] The present invention relates to the technical field of power dispatching, and in particular to a cross-regional power dispatching method and system based on digital twins. Background Art

[0002] At present, the traditional power dispatching method usually adopts a centralized control mode for power dispatching. For example, various power-related equipment and sensors collect relevant power data and upload the power data to the central control system. The central control system uses preset dispatching rules, which are generally preset dispatching rules to minimize power generation costs, and performs cross-regional power dispatching based on the power data. The above-mentioned existing power dispatching methods mainly have the following technical problems: 1. The relatively fixed power dispatching rules make it difficult to handle dynamic and complex collaborative tasks. 2. Power dispatching is a non-convex, high-dimensional and non-linear load distribution problem. Therefore, the power dispatching scheme of the traditional preset rule algorithm has the problem of poor scenario adaptability. 3. The existing relatively fixed power dispatching rules do not take into account the impact of the environment on power dispatching, resulting in poor prediction results. Summary of the Invention

[0003] One of the inventive purposes of the present invention is to provide a cross-regional power dispatching method and system based on digital twins. The method and system construct a digital twin model, and use the digital twin model to collect in real time parameters including but not limited to energy storage and distribution equipment parameters, power transmission parameters, monitoring and protection equipment parameters, environmental sensor parameters, communication equipment parameters and related material parameters in different regions, and construct a dynamic digital twin model based on the above different types of parameters, which is used to dynamically perceive all parameters in the power grid that may affect power dispatching, and dynamically update the digital twin model based on the parameters, wherein the digital twin model provides a cross-regional power dispatching strategy based on the above different parameters, and updates the cross-regional power dispatching strategy based on the parameters updated by the digital twin model, thereby achieving accurate and efficient dispatching under complex power dispatching tasks.

[0004] Another object of the present invention is to provide a cross-regional power dispatching method and system based on digital twins. The method and system provide an MMOE model (multi-task learning model) as the AI ​​inference and prediction model of the digital twin model. The MMOE model is used to pre-train different types of subtask networks, including but not limited to power equipment subtask networks, environmental data subtask networks, historical data subtask networks, and material properties subtask networks. After the MMOE model pre-training is completed, the corresponding physical sensor parameters of energy storage and distribution equipment in different regions, power transmission parameters, monitoring and protection equipment parameters, environmental sensor parameters, communication equipment parameters, and related material parameters are collected and input into the gated network of the trained MMOE model for routing of corresponding type parameters. The matched subtask network outputs the subtask inference and prediction results of the corresponding type parameters. Different subtask networks select the task type of the MMOE model and output weighted results of the inference and prediction corresponding to multiple subtask types. In the present invention, the inference and prediction results of the different types of subtask networks are used to update the parameters of the digital twin model in real time, and the task results of the corresponding MMOE model are executed.

[0005] Another object of the present invention is to provide a cross-regional power dispatching method and system based on digital twins. After obtaining the digital twin model parameters in different regions, the method and system further construct differential data corresponding to the same type of parameters of the digital twin models in different regions, and use the differential data to input into the MMOE model for routing processing. After analyzing the differential data according to different subtask networks, the predicted value of the corresponding type of data in the next time series is output, and the feature fusion processing is performed through the subtask network output weights assigned by the specific task network of the MMOE model to obtain the corresponding task output. The MMOE model can be used to achieve efficient and accurate dispatch of electricity between different regions, and the digital twin model has better scenario adaptability.

[0006] In order to achieve at least one of the above-mentioned objects, the present invention further provides a cross-regional power dispatching method based on digital twins, the method comprising:

[0007] Obtaining power equipment parameters, power dispatch parameters, and environmental parameters within each power supply area, and establishing a digital twin model of each power supply area based on the power equipment parameters, power dispatch parameters, and environmental parameters;

[0008] Configuring different types of cross-regional power dispatching tasks in the digital twin model;

[0009] Pre-training a multi-task network MMOE model based on historical data including power equipment parameters, power dispatch parameters, environmental parameters, and power dispatch tasks, and using the trained MMOE model to predict parameter values ​​of different types of parameters in the digital twin model;

[0010] Obtain differential data of the same type of parameters in the power supply area, and obtain the power equipment parameters, power dispatching parameters and environmental parameters in the current corresponding power supply area, and input the differential data, power equipment parameters, power dispatching parameters and environmental parameters into the trained MMOE model. The task network output of the trained MMOE model includes cross-regional power dispatching tasks.

[0011] According to one preferred embodiment of the present invention, the method for constructing a digital twin model of a power supply area includes: constructing a data-driven formula for the digital twin model based on the power equipment parameters, power dispatch parameters, and environmental parameters, and configuring a state update formula for the digital twin model, wherein the data-driven formula is: , where f i Represents the i-th subtask model function of the MMOE model, y i represents the output of the i-th subtask model, x t is the input data at time t, θ is the model parameter corresponding to the MMOE model, the state update function of the digital twin model is defined as h(), the state variable at the current time t is s(t), and the state variable is updated according to the following formula: s(t+1)=h(s(t); d t ); where s(t+1) is the state variable at time t+1, d t is the updated data at time t.

[0012] According to another preferred embodiment of the present invention, the method includes using the digital twin model to perform model optimization according to the task type, and the optimized model formula is: \left [ {min} \right ]J(u)=\int ^{t}_{{t}_{0}} {L(s(t),u(t))}dt , where min represents the minimum constraint of the loss function of the digital twin model according to the task type; J corresponds to the model optimization result of the task type, u represents the control variable, s(t) represents the state variable at time t, L is the loss function, dt represents the differential at time t, and is defined as the input data at time t. x The MMOE model has m subtask models with output results of corresponding tasks, then , Represents the weight of the corresponding subtask i, where the loss function type is matched and set according to the task type.

[0013] According to another preferred embodiment of the present invention, the MMOE model training method includes: performing feature conversion, normalization processing and embedding processing on the multi-type historical parameters including the power equipment parameters, power dispatch parameters and environmental parameters, so that the high-dimensional multi-type parameters are mapped into a standard low-dimensional feature vector, and the low-dimensional feature vector is input into the MMOE model for training, a corresponding gating network is configured for each subtask model, the gating network is used to calculate the matching degree between the low-dimensional feature vector and the corresponding subtask model, and the routing weight of the subtask network is dynamically allocated according to the matching degree, and the corresponding type of subtask model is activated according to the routing weight, wherein each subtask model is configured with an independent loss function for calculating the independent task results of each subtask model.

[0014] According to another preferred embodiment of the present invention, the MMOE model training method includes: using an independent feedforward neural network DNN as the neural network of each subtask model, wherein the control formula of the gate network is ,in is the output weight of the gated network, k corresponds to the kth subtask, and are the weights and biases of the gating networks, and after all subtasks corresponding to the outputs of all gating networks have weighted all tasks, the outputs of all subtasks for each task are weighted summed: ,in, represents the weight of the i-th subtask of the k-th gating network, y i Output for the i-th subtask and confirm.

[0015] According to another preferred embodiment of the present invention, the method for calculating the differential data includes: defining the acquired parameter as x j , where j represents the type of parameter and the current detection time is t n , the next adjacent detection time is t n+1 , the differential data corresponding to the type parameter is: ,in It represents the difference of corresponding type parameters at different detection times, and inputs the differential data of corresponding type parameters in different regions into the MMOE model for gating processing and corresponding subtask network processing. The differential data is used to adjust the collaborative processing of power dispatching tasks including within and across regions in the time dimension.

[0016] According to another preferred embodiment of the present invention, a dynamic expansion vocabulary of different types of parameters is constructed, corresponding label data is pre-constructed based on the differential data of different types of parameters, and the label data is input into the dynamic expansion vocabulary after feature conversion, and the dynamic expansion vocabulary is used to index into a dynamic feature vector, and the dynamic feature vector of the differential data is input into the MMOE model, and the network weights of all subtasks outputted are calculated through the gating network of the MMOE model for dynamic output of collaborative tasks of power scheduling.

[0017] According to another preferred embodiment of the present invention, the method includes: after obtaining differential data of multiple corresponding type parameters, further using the K-Means clustering method to map the differential data labels of the same similar categories into the same word embedding, and using the feature labels of the center cluster as feature words in the dynamic expansion vocabulary to reduce the complexity of feature vector processing, so that the MMOE model has dynamic adaptability to new labels.

[0018] In order to achieve at least one of the above-mentioned purposes of the invention, the present invention further provides a cross-regional power dispatching system based on digital twins, which executes the above-mentioned cross-regional power dispatching method based on digital twins.

[0019] The present invention further provides a computer-readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the above-mentioned cross-regional power dispatching method based on digital twins. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 Shown is a flow chart of a cross-regional power dispatching method based on digital twins according to the present invention. DETAILED DESCRIPTION

[0021] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are for illustrative purposes only, and those skilled in the art will readily appreciate other obvious variations. The basic principles of the present invention defined in the following description may be applied to other embodiments, variations, improvements, equivalents, and other technical solutions that do not depart from the spirit and scope of the present invention.

[0022] It is to be understood that the term "one" should be understood as "at least one" or "one or more", that is, in one embodiment, the number of an element may be one, while in another embodiment, the number of the elements may be multiple, and the term "one" should not be understood as a limitation on the quantity.

[0023] Please combine Figure 1The present invention discloses a cross-regional power dispatching method and system based on digital twins. The method comprises: first, the present invention requires constructing a digital twin model related to power dispatching, particularly a digital twin model for different cross-regional power dispatching scenarios. It should be noted that the digital twin model in the present invention requires collecting historical data including, but not limited to, power equipment parameters, power dispatching parameters, and environmental parameters, and establishing a relational digital twin model based on the parameter types of the collected historical data. The digital twin model can be implemented using system modeling tools including, but not limited to, MATLAB / Simulink. MATLAB / Simulink is used to establish a hierarchical and classified digital twin model. The digital twin model has parameters related to power dispatching, such as, but not limited to, the rated voltage, rated current, rated capacity, transformation ratio, temperature rise data, efficiency value, no-load current, loss, and impedance voltage of the transformer. The equipment types included in the power equipment parameters in the present invention include, but are not limited to, transformers, power dispatching control chips, energy storage batteries, mutual inductors, relays, generators, and circuit breakers. Each of the above power equipment has its own related power equipment parameters, which will not be described in detail in the present invention.

[0024] Specifically, the present invention establishes digital twin models of their respective physical relationships based on the performance parameters of the aforementioned different devices. For example, a model including SOC and SOH can be established based on the type and performance of energy storage batteries, a transformer dynamic performance model can be established based on the power transmission performance of transformers, or a safety model including but not limited to power transmission can be established based on the electrical isolation performance of relays. These models of various electrical devices are all related to the performance of cross-regional power dispatch, and the digital twin models of these power devices conform to the power characteristics of the corresponding devices, thus being universal and non-specific. Therefore, the present invention can integrate the relevant models of different power devices to generate digital twin models including but not limited to power transmission safety models, power transmission efficiency models, power transmission balance models, and power transmission stability models. It should be noted that due to the different tasks performed by digital twin models, the present invention does not specifically limit how to construct a digital twin model for cross-regional power dispatch. The present invention pre-trains a MMOE model (multi-task model) based on the digital twin model for cross-regional power dispatch. The MMOE model includes multiple subtask models, each designed to address a different category of technical problems within the digital twin model. For example, the task types of the subtask model include but are not limited to regression prediction tasks, similarity detection classification tasks, etc. According to the power dispatching task, n subtask types are preset, and n subtask models of the MMOE model are constructed according to the n subtask types, and each subtask model is configured with an independent gating network. The present invention uses the historical data of the power equipment parameters, power dispatching parameters and environmental parameters to train each subtask model of the MMOE model separately, and applies an independent loss function to train each subtask model, so that each subtask model has the ability to independently solve specific power dispatching technical problems. For example, the subtask model includes a power safety transmission task, wherein the power safety transmission includes transformer safety-related parameters, circuit breaker safety parameters, and energy storage battery safety parameters, wherein the above-mentioned safety parameters include specific temperature thresholds, current thresholds or voltage thresholds, etc. Therefore, after obtaining the safety parameters of power transmission of specific temperature data, overvoltage data and overcurrent data, the above safety parameters can be input into the MMOE model, routed through the gating network of the MMOE model, and input into subtask models including but not limited to the transformer safe operation subtask model and the energy storage battery safe operation subtask model for processing, and the weighted processing results of the corresponding subtask models are output according to the gating network of the MMOE model.

[0025] It is worth mentioning that the present invention utilizes the subtask model of the MMOE model to construct the data-driven model of the digital twin model and configures the state update formula of the digital twin model, where the data-driven formula is: , where fi Represents the i-th subtask model function of the MMOE model, and the subtask model is not the only model. It configures multiple subtask types according to the task type and quantity. The present invention will not go into details about this. In the above data-driven formula, y i represents the output result of the i-th subtask model, which can be the predicted value of the corresponding type parameter or the power dispatch strategy or safety protection strategy output by the corresponding subtask model, etc. t is the input data at time t, θ is the model parameter corresponding to the MMOE model, the state update function of the digital twin model is defined as h(), the state variable at the current time t is s(t), and the state variable is updated according to the following formula: s(t+1)=h(s(t); d t ); where s(t+1) is the state variable at time t+1, d t is the updated data at time t. It should be noted that the state variables can be data after function mapping based on the state parameters of different electrical devices. For example, the state variables can be safety state, stability state, energy-saving state, etc. Different state variables need to call the state parameters of different devices and be obtained through data fusion calculation. The data fusion calculation is an algorithm that can be referenced by those skilled in the art, and the present invention will not elaborate on this in detail.

[0026] Furthermore, the present invention provides a training method for the MMOE model subtask model, including: performing feature conversion, normalization processing, and embedding processing on multiple types of historical parameters including the power equipment parameters, power dispatch parameters, and environmental parameters, so that the high-dimensional multi-type parameters are mapped to standard low-dimensional feature vectors, wherein the environmental parameters include temperature, weather conditions, and altitude in different power supply areas. The environmental parameters can be obtained by obtaining relevant sensors or directly calling real-time weather network data in the local area. The environmental parameters are generally label data of specific vocabulary, so the labels need to be uniformly preprocessed with data including the above-mentioned feature conversion, normalization processing, and embedding processing, and the low-dimensional feature vector is input into the MMOE model for training, a corresponding gating network is configured for each subtask model, the gating network is used to calculate the matching degree between the low-dimensional feature vector and the corresponding subtask model, and the routing weight of the subtask network is dynamically allocated according to the matching degree, and the subtask model of the corresponding type is activated according to the routing weight, wherein each subtask model is configured with an independent loss function for calculating the independent task results of each subtask model. Therefore, when the input data is related parameters of different devices, standard feature data can be obtained through feature conversion, normalization processing and embedding processing and input into the MMOE model for routing processing. Therefore, the present invention can take into account the diversity of data input content, and at the same time solve the targeted output of data content, thereby reducing the output of irrelevant content and increasing the logic and relevance of the output content.

[0027] It is worth mentioning that the construction method of the gated network includes: using an independent feedforward neural network DNN as the neural network of each subtask model. Since the DNN model has the function of layer-by-layer feature transformation processing, the DNN model can process high-dimensional data and low-dimensional data at the same time, especially for image data, audio data and text data. It has a good processing effect. Therefore, the present invention can use the DNN network to realize the processing of multimodal data and integrate the output of the corresponding task results. The control formula of the gated network is ,in is the output weight of the gated network, k corresponds to the kth subtask, and are the weight matrix and bias of the gating network respectively. After all subtasks corresponding to the outputs of all gating networks have weighted all tasks, the outputs of all subtasks for each task are weighted summed: ,in, represents the weight of the i-th subtask of the k-th gating network, y i It should be noted that each gated network is connected to a subtask network, and the weights and biases of the corresponding task k in the corresponding subtask network are and To determine the output weight of the corresponding subtask network. In the present invention, it is necessary to set the lower limit threshold G of the subtask output weight s , calculate the gate output weights in all subtasks ≥G s The subtask model output result y i (x), and filter out the corresponding subtask model output result y i (x) Perform weighted summation processing.

[0028] It is worth mentioning that in order to improve the collaborative processing effect of the multi-task model, the present invention also uses the digital twin model to optimize the model according to the task type. The optimized model formula is: \left [ {min} \right ]J(u)=\int ^{t}_{{t}_{0}} {L(s(t),u(t))}dt , where min represents the minimum constraint of the loss function of the digital twin model according to the task type; J corresponds to the model optimization result of the task type, u represents the control variable, s(t) represents the state variable at time t, L is the loss function, dt represents the differential at time t, and is defined as the input data at time t. x The MMOE model has m subtask models with output results of corresponding tasks, then , Represents the weight of the corresponding subtask i, where the loss function class is matched and set according to the task type. For example, for the regression prediction task, a loss function that minimizes the mean square error can be set, etc. The present invention does not limit this in detail.

[0029] The method for calculating the differential data in the present invention includes: defining the acquired parameter as x j , where j represents the type of parameter and the current detection time is t n , the next adjacent detection time is t n+1 , the differential data corresponding to the type parameter is: ,in The differential data represents the difference between parameters of the corresponding type at different detection times. The differential data for parameters of the corresponding type in different regions is input into the MMOE model for gating and corresponding subtask network processing. This differential data is used to coordinate the coordinated processing of power dispatch tasks, including those within and across regions, over time. It should be noted that this differential data is used to calculate the rate of change of the corresponding parameter type. In a specific subtask, this rate of change reflects the stability of the parameter characteristics, facilitating the dynamic adjustment of the corresponding digital twin model.

[0030] In order to adapt to the corresponding dynamically changing parameters related to power dispatching, the present invention needs to construct a dynamic expansion vocabulary of different types of parameters, pre-construct corresponding label data based on the differential data of different types of parameters, and input the label data into the dynamic expansion vocabulary after feature conversion. For example, the label data can be labels such as overcurrent, overvoltage, high temperature, etc., and a mapping relationship between labels is established through the relevant threshold range. When the numerical value of the corresponding type of parameter is collected, the corresponding parameter can be compared within a specific threshold range to extract the corresponding label parameter. The present invention further utilizes the dynamic expansion vocabulary to index the dynamic feature vector, inputs the dynamic feature vector of the differential data into the MMOE model, and calculates the output network weights of all subtasks through the gated network of the MMOE model, which is used to dynamically output the collaborative task of power dispatching.

[0031] After obtaining differential data for multiple corresponding type parameters, K-Means clustering is further used to map differential data labels of similar categories to the same word embedding. The feature labels of the central cluster are used as feature words in the dynamic expansion vocabulary to reduce the complexity of feature vector processing and enable the MMOE model to dynamically adapt to new labels. In other preferred embodiments of the present invention, other clustering algorithms can also be used to obtain feature words from the central cluster for use in acquiring the dynamic vocabulary. This will not be discussed in detail in the present invention.

[0032] In the embodiments disclosed in the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. The embodiments disclosed in the present invention include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication part, and / or installed from a removable medium. When the computer program is executed by a central processing unit (CPU), the above-mentioned functions defined in the method of the present application are executed. It should be noted that the computer-readable medium mentioned above in the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more wire segments, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take a variety of forms, including, but not limited to, an electromagnetic signal, an optical signal, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transfer a program for use by or in conjunction with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, electrical wire, optical fiber cable, RF, etc., or any suitable combination thereof.

[0033] The flow charts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the system, method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0034] Those skilled in the art should understand that the embodiments of the present invention described above and shown in the accompanying drawings are only examples and do not limit the present invention. The objectives of the present invention have been fully and effectively achieved. The functional and structural principles of the present invention have been demonstrated and explained in the embodiments. Without departing from the principles, the implementation methods of the present invention may be subject to any deformation or modification.

Claims

1. A cross-regional power dispatching method based on digital twins, characterized in that: The method comprises: Obtaining power equipment parameters, power dispatch parameters, and environmental parameters within each power supply area, and establishing a digital twin model of each power supply area based on the power equipment parameters, power dispatch parameters, and environmental parameters; configuring different types of cross-regional power dispatch tasks in the digital twin model; Pre-training a multi-task network MMOE model based on historical data including power equipment parameters, power dispatch parameters, environmental parameters, and power dispatch tasks, and using the trained MMOE model to predict parameter values ​​of different types of parameters in the digital twin model; Obtain differential data of the same type of parameters in the power supply area, and obtain the power equipment parameters, power dispatching parameters and environmental parameters in the current corresponding power supply area, and input the differential data, power equipment parameters, power dispatching parameters and environmental parameters into the trained MMOE model. The task network output of the trained MMOE model includes cross-regional power dispatching tasks.

2. A cross-regional power dispatching method based on digital twins according to claim 1, characterized in that: The method for constructing a digital twin model of a power supply area includes: constructing a data-driven formula for the digital twin model based on the power equipment parameters, power dispatch parameters, and environmental parameters, and configuring a state update formula for the digital twin model, wherein the data-driven formula is: , where f i Represents the i-th subtask model function of the MMOE model, y i represents the output of the i-th subtask model, x t is the input data at time t, θ is the model parameter corresponding to the MMOE model, the state update function of the digital twin model is defined as h(), the state variable at the current time t is s(t), and the state variable is updated according to the following formula: s(t+1)=h(s(t); d t ); where s(t+1) is the state variable at time t+1, d t is the updated data at time t.

3. A cross-regional power dispatching method based on digital twins according to claim 2, characterized in that: The method includes using the digital twin model to perform model optimization according to the task type, and the optimized model formula is: , where min represents the minimum constraint of the loss function of the digital twin model according to the task type; J corresponds to the model optimization result of the task type, u represents the control variable, s(t) represents the state variable at time t, L is the loss function, dt represents the differential at time t, and it is defined that at time t, the MMOE model corresponding to the input data x has m subtask models with the output results of the corresponding tasks, then ,λ i Represents the weight of the corresponding subtask i, where the loss function type is matched and set according to the task type.

4. A cross-regional power dispatching method based on digital twins according to claim 1, characterized in that: The MMOE model training method includes: performing normalization processing and embedding processing on multi-type historical parameters including the power equipment parameters, power dispatch parameters, and environmental parameters, so that the high-dimensional multi-type parameters are mapped into standard low-dimensional feature vectors, and inputting the low-dimensional feature vectors into the MMOE model for training, configuring a corresponding gating network for each subtask model, using the gating network to calculate the matching degree between the low-dimensional feature vector and the corresponding subtask model, and dynamically allocating the routing weight of the subtask network according to the matching degree, and activating the corresponding type of subtask model according to the routing weight, wherein each subtask model is configured with an independent loss function for calculating the independent task results of each subtask model.

5. A cross-regional power dispatching method based on digital twins according to claim 4, characterized in that: The MMOE model training method includes: using an independent feedforward neural network DNN as the neural network of each subtask model, wherein the control formula of the gate network is: ,in is the output weight of the gated network, k corresponds to the kth task, and are the weights and biases of the gating networks, and after all subtasks corresponding to the outputs of all gating networks have weighted all tasks, the outputs of all subtasks for each task are weighted summed: ,in, represents the weight of the i-th subtask of the k-th gating network, y i Output of the ith subtask; set the lower limit threshold G of the subtask output weight s , calculate the gate output weights in all subtasks ≥G s The subtask model output result y i (x), and filter out the corresponding subtask model output result y i (x) Perform weighted summation processing.

6. A cross-regional power dispatching method based on digital twins according to claim 1, characterized in that: The method for calculating the differential data includes: defining the acquired parameter as x j , where j represents the type of parameter and the current detection time is t n , the next adjacent detection time is t n+1 , the differential data corresponding to the type parameter is: = ,in It represents the difference of corresponding type parameters at different detection times, and inputs the differential data of corresponding type parameters in different regions into the MMOE model for gating processing and corresponding subtask network processing. The differential data is used to adjust the collaborative processing of power dispatching tasks including within and across regions in the time dimension.

7. The cross-regional power dispatching method based on digital twin according to claim 1 is characterized in that: A dynamic extended vocabulary of different types of parameters is constructed, and corresponding label data is pre-constructed based on the differential data of different types of parameters. The label data is input into the dynamic extended vocabulary after feature conversion, and the dynamic extended vocabulary is used to index into a dynamic feature vector. The dynamic feature vector of the differential data is input into the MMOE model, and the network weights of all subtasks outputted are calculated through the gating network of the MMOE model for the collaborative task of dynamically outputting power scheduling.

8. A cross-regional power dispatching method based on digital twins according to claim 7, characterized in that: The method includes: after obtaining differential data of multiple corresponding type parameters, further using the K-Means clustering method to map the differential data labels of the same similar categories into the same word embedding, and using the feature labels of the center cluster as the feature words in the dynamic expansion vocabulary to reduce the complexity of feature vector processing, so that the MMOE model has dynamic adaptability to new labels.

9. A cross-regional power dispatching system based on digital twins, characterized in that: The system executes a cross-regional power dispatching method based on digital twins as described in any one of claims 1 to 8.

10. A computer-readable storage medium storing a computer program, wherein the computer program is executed by a processor to implement a cross-regional power dispatching method based on digital twins as described in any one of claims 1 to 8.

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