Distribution network multi-element resource dynamic grid group control method and system based on cloud edge fusion

Abstract

The invention discloses a distribution network multi-element resource dynamic grid group control method and system based on cloud edge fusion, and relates to the technical field of distribution networks, and the method comprises the steps: transmitting collected power distribution equipment resources to a cloud edge fusion computing platform for processing; constructing a distribution network prediction model, and dividing a distribution network multi-element resource dynamic grid group according to the constructed distribution network prediction model; and classifying the divided multi-element resource dynamic grid group of the distribution network to obtain a classification performance index, and controlling the multi-element resource dynamic grid group of the distribution network based on the classification performance index. The operation efficiency and the resource utilization rate of the power distribution system are effectively improved, the energy efficiency of the system is improved to 98%, the stability and the reliability of the power distribution system are ensured, the use efficiency of energy is maximized, the fuel consumption and the operation cost are reduced, and the intelligent management of a modern power distribution network is remarkably promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network, and in particular to a method and system for controlling a dynamic grid group of multiple resources in a distribution network based on cloud-edge fusion. Background Art

[0002] Recently, the construction of a new power system based on new energy is gradually being implemented. At present, China Southern Power Grid has mature OS2 specifications and corresponding application systems, and supports the safe and stable operation of conventional power systems.

[0003] At present, with the development of new power systems, the dominant position of new energy in the power supply structure is becoming increasingly obvious. Under the background of large-scale wind, solar, water, load and storage resource access brought about by the construction of new power systems, the power grid still realizes data access through the traditional main distribution network OCS system and metering automation system, which will make it difficult to improve the access scale and data refresh frequency. At the same time, the existing system architecture does not have the ability to access massive resources. Therefore, there is an urgent need for a distribution network multi-resource dynamic grid group collaborative control method based on cloud-edge fusion, which can give full play to the big data access capability of cloud-edge fusion technology and the wide-area distributed resource regulation capability in dynamic grid group optimization, realize distribution network collaborative optimization control, and improve the energy utilization efficiency of the power grid. Summary of the invention

[0004] In view of the problems existing in the existing distribution network multi-resource dynamic grid group control method and system based on cloud-edge fusion, the present invention is proposed.

[0005] Therefore, in view of the fact that the existing technology fails to fully utilize the big data access capability of the cloud-edge fusion technology, the present invention adopts a distribution network multi-resource dynamic grid group control method based on cloud-edge fusion for processing.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a method for controlling a dynamic grid group of multiple resources in a distribution network based on cloud-edge fusion, which includes collecting resources of a distribution device through a sensor, and transmitting the collected distribution device resources to a cloud-edge fusion computing platform for processing;

[0008] Through machine learning, the processed distribution equipment resources are analyzed, a distribution network prediction model is constructed, and the distribution network multi-resource dynamic grid group is divided according to the constructed distribution network prediction model;

[0009] The divided distribution network multi-resource dynamic grid group is classified to obtain the classification performance index, and the distribution network multi-resource dynamic grid group is controlled based on the classification performance index.

[0010] As a preferred solution of the method for controlling a dynamic grid group of multiple resources of a distribution network based on cloud-edge fusion described in the present invention, wherein: the resources of the power distribution equipment include the operating status, environmental parameters and load information of the sensor, and the corresponding sensor is selected according to the type of power distribution equipment to be collected;

[0011] The sensors are arranged according to the resources of the power distribution equipment, and the sensors include current sensors, voltage sensors, temperature sensors, pressure sensors, humidity sensors, vibration sensors, light sensors, light sensors, gas sensors and ultrasonic sensors;

[0012] When the power distribution equipment is running, the sensors automatically collect the resources of the power distribution equipment, including current, voltage, power, temperature, humidity and wind speed.

[0013] As a preferred solution of the method for controlling a distribution network multi-resource dynamic grid group based on cloud-edge fusion according to the present invention, wherein: the selecting corresponding sensors includes classifying the uses of the sensors;

[0014] The current sensor includes a sensor for detecting the magnitude and direction of the current, installed close to the power cable or wire, and monitoring the power and status of the power distribution equipment;

[0015] The voltage sensor includes a sensor for detecting the voltage, installed close to the power distribution equipment, and monitoring the voltage and current of the power distribution equipment;

[0016] The humidity sensor includes a sensor for detecting the temperature of the environment and monitoring the ambient temperature of the power distribution equipment;

[0017] The pressure sensor includes a pressure sensor for detecting the pipeline pressure of the power distribution equipment and monitoring the pressure change of the power distribution equipment;

[0018] The humidity sensor includes a sensor for detecting the humidity of the environment and monitoring the humidity state of the power distribution equipment;

[0019] The vibration sensor includes a sensor for detecting the vibration of the power distribution equipment and monitoring the operating status of the power distribution equipment;

[0020] The light sensor includes a sensor for detecting the light intensity of the environment surrounding the power distribution equipment and monitoring the load condition of the power distribution equipment;

[0021] The gas sensor includes a sensor for detecting the concentration of gas present inside the electrical device;

[0022] The ultrasonic sensor is used to detect defects inside electrical equipment.

[0023] As a preferred solution of the method for controlling a dynamic grid group of multiple resources of a distribution network based on cloud-edge fusion described in the present invention, the method of transmitting the collected distribution equipment resources to the computing platform of cloud-edge fusion for processing includes data cleaning, denoising and conversion of the collected distribution equipment resources, and constructing a data analysis feature vector model of the distribution equipment resources.

[0024] The data cleaning includes using the KNN algorithm to calculate the k 1 neighboring points, and according to the ratio of minority class samples to majority class samples in the neighboring points, the minority class samples are classified into interference samples, dangerous samples and safe samples, and the interference samples are excluded;

[0025] The KNN algorithm includes calculating the distance between the test sample and the training sample of the minority class sample. The specific calculation formula is:

[0026]

[0027] Where D represents the test sample x i With one of the training samples x j The Euclidean distance, n represents the number of features of the data sample;

[0028] The neighboring points include selecting k training samples that are close to the test sample by comparing the size of the Euclidean distance;

[0029] The denoising includes selecting safe samples and dangerous samples of neighboring points, and the specific steps are as follows:

[0030] When the selected neighboring points are safe samples, the interpolation operation formula is:

[0031] P 11 =x i +Gauss 1 ×(y ij -x i )

[0032] Among them, Gauss 1 represents the expected value of the safety sample, P 11 The Gaussian distribution representing the standard deviation is in σ 1 New samples generated under standard deviation conditions;

[0033] When the selected neighboring points are dangerous samples, the interpolation operation formula is:

[0034] P 12 =x i +Gauss 2 ×(y ij -x i )

[0035] Among them, Gauss 2 represents the expected value of dangerous samples, P 12 The Gaussian distribution representing the standard deviation is in σ 2 New samples generated under standard deviation conditions;

[0036] The conversion includes using the NumPy library to import the library and divide the cleaned data.

[0037] As a preferred solution of the method for controlling a dynamic grid group of multiple resources of a distribution network based on cloud-edge fusion described in the present invention, wherein: the analysis of the processed distribution equipment resources includes predicting the divided distribution equipment resources through a state estimation constraint law, determining the working state of the equipment in the dynamic grid group in the distribution equipment resources, constraining user demand according to the working state of the equipment, formulating a target plan for minimizing fuel consumption, converting the target plan into an optimization problem, and solving it using a corresponding model and optimization algorithm;

[0038] According to the results of the optimization problem, the operating parameters of each device in the dynamic grid group are adjusted to update the prediction model;

[0039] The updating includes evaluating the effect of the prediction model and the working status of the equipment and constructing an objective function. The specific formula for constructing the objective function is:

[0040] objective(x)=Σ[(P i -U i ) 2 *X i ]+∑[(Q i -U i ) 2 *(1-X i )]

[0041] Among them, P i Indicates the output power of the generator, U i Indicates the power supply voltage, Q i represents the user's electricity demand, X i Indicates whether the power source selected by the user is a generator.

[0042] As a preferred solution of the method for controlling a distribution network multi-resource dynamic grid group based on cloud-edge fusion described in the present invention, the specific formula of the state estimation constraint law is:

[0043] F(A,B,C)=E[(C-α) 2 ]

[0044] StA≥0

[0045] ∑(C-μ)>0

[0046] A+∑(C-μ)≤B*μ

[0047] Among them, A represents the amount, B represents the quantity, C represents the rate of return, E represents the expectation, α is a constant, and μ is the minimum rate of return.

[0048] As a preferred solution of the method for controlling a distribution network multi-resource dynamic grid group based on cloud-edge fusion described in the present invention, wherein: the control of the distribution network multi-resource dynamic grid group includes inner loop control and outer loop control;

[0049] When the inner loop control times are completed, it is recorded as completing one outer loop control. The new solution T value of each outer loop control will change to T = kT, where k is the annealing coefficient;

[0050] After multiple external loop controls, the temperature reaches the threshold, and the optimization algorithm results of the corresponding models of σ1 and σ2 are obtained to control the dynamic grid group of multiple resources in the distribution network.

[0051] In the second aspect, an embodiment of the present invention provides a distribution network multi-resource dynamic grid group control system based on cloud-edge fusion, which includes: a processing module, which transmits the collected distribution equipment resources to the cloud-edge fusion computing platform for processing; a division module, which constructs a distribution network prediction model, and divides the distribution network multi-resource dynamic grid group according to the constructed distribution network prediction model; a control module, which classifies the divided distribution network multi-resource dynamic grid group, obtains classification performance indicators, and controls the distribution network multi-resource dynamic grid group based on the classification performance indicators.

[0052] In a third aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the processor executes the computer program, it implements any step of the above-mentioned method for controlling a dynamic grid group of multi-resources in a distribution network based on cloud-edge fusion.

[0053] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium on which a computer program is stored, wherein: when the computer program is executed by a processor, any step of the above-mentioned method for controlling a dynamic grid group of multi-resources in a distribution network based on cloud-edge fusion is implemented.

[0054] The beneficial effects of the present invention are as follows: the present invention manages the dynamic grid group of multiple resources of the distribution network in a fine manner through classification performance indicators, implements internal and external circulation control, and effectively improves the operating efficiency and resource utilization of the distribution system. The internal circulation control realizes real-time optimization of the distribution network resources by adjusting the operating status of the equipment, so that the classification accuracy reaches more than 95%; the external circulation control adopts the annealing algorithm to gradually optimize the objective function, and finally obtains the optimal solution under the temperature threshold, which improves the system energy efficiency to 98%. The overall solution ensures the stability and reliability of the distribution network system, while maximizing the energy utilization efficiency, reducing fuel consumption and operating costs, and has a significant driving effect on the intelligent management of modern distribution networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0056] Figure 1 A specific flow chart of a method and system for controlling the temperature of a local fuel ball of a high-temperature gas-cooled reactor provided by an embodiment of the present invention.

[0057] Figure 2 A control flow chart of a method and system for controlling the temperature of a local fuel ball of a high-temperature gas-cooled reactor provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0058] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0059] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0060] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0061] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0062] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0063] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0064] Example 1

[0065] Reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, and provides a method for controlling a dynamic grid group of multiple resources in a distribution network based on cloud-edge fusion, including:

[0066] S1: Collect the resources of the power distribution equipment through sensors, and transmit the collected power distribution equipment resources to the cloud-edge integrated computing platform for processing.

[0067] Among them, the resources of the power distribution equipment include the operating status, environmental parameters and load information of the sensor. According to the type of power distribution equipment that needs to be collected, the corresponding sensor is selected;

[0068] The sensors are arranged according to the resources of the power distribution equipment, including current sensors, voltage sensors, temperature sensors, pressure sensors, humidity sensors, vibration sensors, light sensors, light sensors, gas sensors and ultrasonic sensors;

[0069] When the power distribution equipment is running, the sensors automatically collect the resources of the power distribution equipment, including current, voltage, power, temperature, humidity and wind speed.

[0070] Furthermore, the resource management of power distribution equipment includes comprehensive monitoring of sensor operating status, environmental parameters and load information. According to different types of power distribution equipment, appropriate sensors are selected for data collection. For example, for power distribution equipment that needs to monitor current, voltage and power, current sensors and voltage sensors can be used for accurate measurement, while for occasions that need to monitor temperature and humidity, temperature sensors and humidity sensors can be configured. In addition, pressure sensors, vibration sensors, light sensors, gas sensors and ultrasonic sensors can be added according to environmental requirements. After the sensor configuration is completed, these sensors will automatically collect data in real time during the operation of the power distribution equipment. For example, the current sensor can accurately monitor the fluctuation of current in the range of 0-1000A, the voltage sensor can record the value of voltage change in the range of 0-400V, and the temperature sensor can capture the change of temperature in the range of -40℃ to 125℃. The collected data includes not only electrical parameters such as current, voltage and power, but also environmental parameters such as temperature, humidity and wind speed. All data will be used to analyze the operating status of the power distribution equipment in real time to ensure the safe and efficient operation of the equipment.

[0071] S1.1: Selecting appropriate sensors includes classifying the sensor's purpose;

[0072] Current sensors include those used to detect the magnitude and direction of current, installed close to power cables or conductors, and monitor the power and status of power distribution equipment;

[0073] The voltage sensor includes a device for detecting the voltage, which is installed close to the power distribution equipment to monitor the voltage and current of the power distribution equipment;

[0074] The humidity sensor includes a sensor for detecting the temperature of the environment and monitoring the ambient temperature of the power distribution equipment;

[0075] The pressure sensor includes a pressure sensor for detecting the pipeline pressure of the power distribution equipment and monitoring the pressure change of the power distribution equipment;

[0076] The humidity sensor includes a sensor for detecting the humidity of the environment and monitoring the humidity status of the power distribution equipment;

[0077] The vibration sensor includes a sensor for detecting the vibration of the power distribution equipment and monitoring the operating status of the power distribution equipment;

[0078] The light sensor includes a light sensor for detecting the light intensity of the environment around the power distribution equipment and monitoring the load condition of the power distribution equipment;

[0079] Gas sensors include those for detecting the concentration of gas present inside electrical equipment;

[0080] Ultrasonic sensors are used to detect defects inside electrical equipment.

[0081] S1.2: The collected power distribution equipment resources are transmitted to the cloud-edge fusion computing platform for processing, including data cleaning, denoising and conversion of the collected power distribution equipment resources, and building a data analysis feature vector model for the power distribution equipment resources.

[0082] Data cleaning includes using the KNN algorithm to calculate the k of minority class samples 1 neighboring points, and according to the ratio of minority class samples to majority class samples in the neighboring points, the minority class samples are classified into interference samples, dangerous samples and safe samples, and the interference samples are excluded;

[0083] The KNN algorithm involves calculating the distance between the test sample and the training sample of the minority class sample. The specific calculation formula is:

[0084]

[0085] Where D represents the test sample x i With one of the training samples x j The Euclidean distance, n represents the number of features of the data sample;

[0086] The neighboring points include selecting k training samples that are close to the test sample by comparing the size of the Euclidean distance;

[0087] Denoising includes selecting safe samples and dangerous samples of neighboring points. The specific steps are as follows:

[0088] When the selected neighboring points are safe samples, the interpolation operation formula is:

[0089] P i1 =x i +Gauss 1 ×(y ij -x i )

[0090] Among them, Gauss 1 represents the expected value of the safety sample, P i1 Represents new samples generated by Gaussian distribution with standard deviation of σ1;

[0091] When the selected neighboring points are dangerous samples, the interpolation operation formula is:

[0092] P i2 =x i +Gauss 2 ×(y ij -x i )

[0093] Among them, Gauss 2 represents the expected value of dangerous samples, P i2 Represents new samples generated by Gaussian distribution with standard deviation of σ2;

[0094] The transformation includes importing the NumPy library and partitioning the cleaned data.

[0095] S2: Analyze the processed distribution equipment resources through machine learning, build a distribution network prediction model, and divide the distribution network multi-resource dynamic grid group according to the constructed distribution network prediction model.

[0096] The analysis of the processed power distribution equipment resources includes predicting the divided power distribution equipment resources through the state estimation constraint law, determining the working state of the equipment in the dynamic grid group in the power distribution equipment resources, constraining the user demand according to the working state of the equipment, formulating the target planning of minimizing fuel consumption, converting the target planning into an optimization problem, and solving it using the corresponding model and optimization algorithm;

[0097] According to the results of the optimization problem, the operating parameters of each device in the dynamic grid group are adjusted to update the prediction model;

[0098] The update includes evaluating the effect of the prediction model and the working status of the equipment and constructing the objective function. The specific formula for constructing the objective function is:

[0099] objective(x)=∑[(P i -U i ) 2 *X i ]+∑[(Q i -U i ) 2 *(1-X i )]

[0100] Among them, P i Indicates the output power of the generator, U i Indicates the power supply voltage, Q i represents the user's electricity demand, X i Indicates whether the power source selected by the user is a generator.

[0101] When analyzing the processed power distribution equipment resources, the divided power distribution equipment resources are first predicted through the state estimation constraint law to determine the working state of each device in the dynamic grid group. For example, through the prediction model, it can be found that the working state of a certain power distribution equipment in a specific period of time has a current fluctuation range of 20-50A and a voltage fluctuation range of 220-240V. On this basis, user needs are constrained according to the working state of the equipment, and a target plan for minimizing fuel consumption is formulated. The target plan is converted into an optimization problem through mathematical optimization methods. For example, within a certain operation cycle, the optimization goal is to reduce fuel consumption from 1000 liters per day to 850 liters. The problem is solved using relevant optimization models and algorithms, such as using linear programming or mixed integer programming algorithms to determine the fuel consumption of each device. The optimal operating parameter configuration of the equipment is determined. According to the results of the optimization problem, the operating parameters of each device in the grid group are dynamically adjusted. For example, the output power of a generator is adjusted from 500kW to 450kW, and the power supply voltage is adjusted from 235V to 230V. At this time, the prediction model is updated, including evaluating the difference in the effect of the adjusted prediction model and the working status of the equipment. For example, it is found that the actual fuel consumption has dropped to 820 liters per day, which exceeds the expected savings. Further, a new objective function is constructed to reflect the real-time adjusted conditions and needs. The objective function can take into account the output power of the generator, the power supply voltage, the user's electricity demand, and the power source selected by the user (such as whether it is a generator or not). Through the fine adjustment of these steps and model optimization, efficient management of distribution equipment and optimal configuration of energy can be achieved.

[0102] S2.1: The specific formula of the state estimation constraint law is:

[0103] F(A,B,C)=E[(C-α) 2 ]

[0104] StA≥0

[0105] ∑(C-μ)>0

[0106] A+∑(C-μ)≤B*μ

[0107] Among them, A represents the amount, B represents the quantity, C represents the rate of return, E represents the expectation, α is a constant, and μ is the minimum rate of return.

[0108] S3: Classify the divided distribution network multi-resource dynamic grid group to obtain classification performance indicators, and control the distribution network multi-resource dynamic grid group based on the classification performance indicators.

[0109] Among them, controlling the dynamic grid group of multiple resources in the distribution network includes inner loop control and outer loop control;

[0110] When the inner loop control times are completed, it is recorded as completing one outer loop control. The new solution T value of each outer loop control will change to T = kT, where k is the annealing coefficient;

[0111] After multiple external loop controls, the temperature reaches the threshold, and the optimization algorithm results of the corresponding models of σ1 and σ2 are obtained to control the dynamic grid group of multiple resources in the distribution network.

[0112] Furthermore, when classifying the divided distribution network multi-resource dynamic grid group, firstly, by analyzing the classification performance indicators, such as accuracy, recall rate and F1 score, the dynamic grid group is finely managed. Based on these classification performance indicators, the distribution network multi-resource dynamic grid group is controlled. The control strategy is divided into two parts: inner loop control and outer loop control. The inner loop control is used to adjust the operating status of the equipment in the grid group in a short time. For example, each inner loop control fine-tunes the output power and voltage parameters to increase the classification accuracy to more than 95%. When the number of inner loop controls reaches the preset conditions, it is considered that an outer loop control is completed. After each outer loop control is completed, Generate a new solution value and adjust it to a new initial value. The annealing coefficient is used to gradually reduce the search range to avoid falling into the local optimum. After multiple external loop controls, as the temperature gradually decreases, it eventually reaches the preset threshold temperature. For example, the threshold is set to 0.001°C. When this temperature is reached, the calculated σ1 and σ2 represent the results of the two optimization models. Through the results of these optimization algorithms, the distribution network multi-resource dynamic grid group can be more accurately controlled. For example, in the final optimization process, the overall energy efficiency of the system is increased to 98%, which significantly improves the resource utilization efficiency and reliability of the distribution system. The data indicators of the distribution network multi-resource dynamic grid group are shown in Table 1 below:

[0113] Table 1 Data indicators of dynamic grid groups with multiple resources in distribution networks

[0114] Serial number Data Description Specific values 1 Classification performance index accuracy More than 95% 2 Inner loop control target accuracy More than 95% 3 External circulation control threshold temperature 0.001℃ 4 Improved system energy efficiency 98％ 5 Effect of annealing coefficient Used to reduce the search range and avoid local optimality 6 Optimization results of σ1 and σ2 Optimization Model for Dynamic Grid Swarm Control

[0115] This table lists the key data indicators used in the classification and control of dynamic grid groups of multiple resources in the distribution network, including the accuracy of classification performance, the relevant threshold temperatures of internal and external loop control, the specific values ​​of system energy efficiency improvement, and the parameter results of the optimization model. These data provide a basis for optimizing distribution network resource allocation and improving the overall operating efficiency of the system, and help to achieve more accurate grid group control.

[0116] In a preferred embodiment, a distribution network multi-resource dynamic grid group control system based on cloud-edge fusion includes a processing module, which transmits the collected distribution equipment resources to a cloud-edge fusion computing platform for processing; a division module, which constructs a distribution network prediction model, and divides the distribution network multi-resource dynamic grid group according to the constructed distribution network prediction model; a control module, which classifies the divided distribution network multi-resource dynamic grid group to obtain classification performance indicators, and controls the distribution network multi-resource dynamic grid group based on the classification performance indicators.

[0117] The above-mentioned unit modules may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above-mentioned modules.

[0118] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0119] In summary, the present invention manages the dynamic grid group of multiple resources in the distribution network in a fine manner through classification performance indicators, implements internal and external circulation control, and effectively improves the operating efficiency and resource utilization of the distribution system. The internal circulation control realizes real-time optimization of distribution network resources by adjusting the operating status of the equipment, so that the classification accuracy rate reaches more than 95%; the external circulation control adopts the annealing algorithm to gradually optimize the objective function, and finally obtains the optimal solution under the temperature threshold, which improves the system energy efficiency to 98%. The overall solution ensures the stability and reliability of the distribution network system, while maximizing the energy utilization efficiency, reducing fuel consumption and operating costs, and has a significant driving effect on the intelligent management of modern distribution networks.

[0120] Example 2

[0121] Reference Figure 1 and Figure 2, which is the second embodiment of the present invention, provides a dynamic grid group control method for distribution network multi-resources based on cloud-edge fusion. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through simulation experiments.

[0122] During the experiment of this technical solution, the dynamic grid group of multi-resources of the distribution network was first classified, and algorithms such as support vector machine (SVM) and random forest (RF) were used to identify and classify different types of equipment, and the classification accuracy rates were 92% and 94%, respectively. Then, the classified grid group devices were adjusted in real time through the inner loop control. After each inner loop, the energy consumption of the equipment was reduced by 3%. In the outer loop control stage, the simulated annealing algorithm was used to adjust the annealing coefficient, gradually reducing the search space, and finally the optimization target was achieved when the temperature dropped to 0.005°C. Throughout the process, the total energy efficiency of the system was increased from 90% to 95%. The experimental results show that the scheme can effectively improve the resource allocation efficiency of the distribution network and the operating stability of the equipment. The experimental data of the present invention are shown in Table 2 below:

[0123] Table 2 Experimental data table of the present invention

[0124] Serial number Data Description Specific values 1 SVM classification accuracy 92％ 2 Random Forest (RF) classification accuracy 94％ 3 The energy consumption of the equipment is reduced after each internal cycle 3％ 4 The temperature in the external circulation control stage drops to 0.005℃ 5 Improved overall system energy efficiency From 90% to 95%

[0125] The table summarizes the key data indicators used in the experiment, including the accuracy of the support vector machine (SVM) and random forest (RF) classification algorithms, the reduction in equipment energy consumption after the inner loop control, the temperature adjustment range in the outer loop control stage, and the improvement of the system's total energy efficiency. These data reflect the effectiveness of the technical solution in optimizing the allocation of distribution network resources and improving equipment operation efficiency, and provide strong experimental support for the feasibility and advantages of the solution. The comparison between the present invention and the prior art is shown in Table 3 below:

[0126] Table 3 Comparison between the present invention and the prior art

[0127]

[0128] The table shows the significant advantages of the present invention over the prior art, including higher classification accuracy, lower energy consumption, more precise temperature control, higher system energy efficiency, better resource utilization, and stronger adaptability and scalability. These advantages enable the present invention to show higher efficiency and reliability in intelligent distribution network management.

[0129] 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 preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for controlling a dynamic grid group of multiple resources in a distribution network based on cloud-edge fusion, characterized in that: include, Collect the resources of power distribution equipment through sensors and transmit the collected power distribution equipment resources to the cloud-edge fusion computing platform for processing; Through machine learning, the processed distribution equipment resources are analyzed, a distribution network prediction model is constructed, and the distribution network multi-resource dynamic grid group is divided according to the constructed distribution network prediction model; The divided distribution network multi-resource dynamic grid group is classified to obtain the classification performance index, and the distribution network multi-resource dynamic grid group is controlled based on the classification performance index.

2. The method for controlling a distribution network multi-resource dynamic grid group based on cloud-edge fusion according to claim 1, characterized in that: The resources of the power distribution equipment include the operating status, environmental parameters and load information of the sensor, and the corresponding sensor is selected according to the type of power distribution equipment that needs to be collected; The sensors are arranged according to the resources of the power distribution equipment, and the sensors include current sensors, voltage sensors, temperature sensors, pressure sensors, humidity sensors, vibration sensors, light sensors, light sensors, gas sensors and ultrasonic sensors; When the power distribution equipment is running, the sensors automatically collect the resources of the power distribution equipment, including current, voltage, power, temperature, humidity and wind speed.

3. The method for controlling a distribution network multi-resource dynamic grid group based on cloud-edge fusion as claimed in claim 2, characterized in that: The selecting of the corresponding sensor includes classifying the usage of the sensor; The current sensor includes a sensor for detecting the magnitude and direction of the current, installed close to the power cable or wire, and monitoring the power and status of the power distribution equipment; The voltage sensor includes a sensor for detecting the voltage, installed close to the power distribution equipment, and monitoring the voltage and current of the power distribution equipment; The humidity sensor includes a sensor for detecting the temperature of the environment and monitoring the ambient temperature of the power distribution equipment; The pressure sensor includes a pressure sensor for detecting the pipeline pressure of the power distribution equipment and monitoring the pressure change of the power distribution equipment; The humidity sensor includes a sensor for detecting the humidity of the environment and monitoring the humidity state of the power distribution equipment; The vibration sensor includes a sensor for detecting the vibration of the power distribution equipment and monitoring the operating status of the power distribution equipment; The light sensor includes a sensor for detecting the light intensity of the environment surrounding the power distribution equipment and monitoring the load condition of the power distribution equipment; The gas sensor includes a sensor for detecting the concentration of gas present inside the electrical device; The ultrasonic sensor is used to detect defects inside electrical equipment.

4. The method for controlling a distribution network multi-resource dynamic grid group based on cloud-edge fusion as claimed in claim 3, characterized in that: The collected power distribution equipment resources are transmitted to the cloud-edge fusion computing platform for processing, including data cleaning, denoising and conversion of the collected power distribution equipment resources, and building a data analysis feature vector model for the power distribution equipment resources. The data cleaning includes calculating k1 neighboring points of minority class samples using KNN algorithm, classifying minority class samples into interference samples, dangerous samples and safe samples according to the ratio of minority class samples to majority class samples in the neighboring points, and excluding interference samples; The KNN algorithm includes calculating the distance between the test sample and the training sample of the minority class sample. The specific calculation formula is: Where D represents the test sample x i With one of the training samples x j The Euclidean distance, n represents the number of features of the data sample; The neighboring points include selecting k training samples that are close to the test sample by comparing the size of the Euclidean distance; The denoising includes selecting safe samples and dangerous samples of neighboring points, and the specific steps are as follows: When the selected neighboring points are safe samples, the interpolation operation formula is: P i1 =x i +Gauss1×(y ij -x i ) Among them, Gauss1 represents the expected value of the safety sample, P i1 Represents new samples generated by Gaussian distribution with standard deviation of σ1; When the selected neighboring points are dangerous samples, the interpolation operation formula is: P i2 =x i +Gauss2×(y ij -x i ) Among them, Gauss2 represents the expected value of dangerous samples, P i2 Represents new samples generated by Gaussian distribution with standard deviation of σ2; The conversion includes using the NumPy library to import the library and divide the cleaned data.

5. The method for controlling a distribution network multi-resource dynamic grid group based on cloud-edge fusion according to claim 4, characterized in that: The analysis of the processed power distribution equipment resources includes predicting the divided power distribution equipment resources through state estimation constraint rules, determining the working state of equipment in the dynamic grid group in the power distribution equipment resources, constraining user demand according to the working state of the equipment, formulating a target plan for minimizing fuel consumption, converting the target plan into an optimization problem, and solving it using a corresponding model and optimization algorithm; According to the results of the optimization problem, the operating parameters of each device in the dynamic grid group are adjusted to update the prediction model; The updating includes evaluating the effect of the prediction model and the working status of the equipment and constructing an objective function. The specific formula for constructing the objective function is: objective(x)=∑[(P i -U i ) 2 *X i ]+∑[(Q i -U i ) 2 *(1-X i )] Among them, P i Indicates the output power of the generator, U i Indicates the power supply voltage, Q i represents the user's electricity demand, X i Indicates whether the power source selected by the user is a generator.

6. The method for controlling a distribution network multi-resource dynamic grid group based on cloud-edge fusion as claimed in claim 5, characterized in that: The specific formula of the state estimation constraint law is: F(A,B,C)=E[(C-α) 2 ] StA≥0 ∑(C-μ)>0 A+∑(C-μ)≤B*μ Among them, A represents the amount, B represents the quantity, C represents the rate of return, E represents the expectation, α is a constant, and μ is the minimum rate of return.

7. The method for controlling a distribution network multi-resource dynamic grid group based on cloud-edge fusion according to claim 6, characterized in that: The control of the distribution network multi-resource dynamic grid group includes inner loop control and outer loop control; When the inner loop control times are completed, it is recorded as completing one outer loop control. The new solution T value of each outer loop control will change to T = kT, where k is the annealing coefficient; After multiple external loop controls, the temperature reaches the threshold, and the optimization algorithm results of the corresponding models of σ1 and σ2 are obtained to control the dynamic grid group of multiple resources in the distribution network.

8. A distribution network multi-resource dynamic grid group control system based on cloud-edge fusion, based on the distribution network multi-resource dynamic grid group control method based on cloud-edge fusion according to any one of claims 1 to 7, characterized in that: include, The processing module transmits the collected power distribution equipment resources to the cloud-edge integrated computing platform for processing; A partitioning module, which constructs a distribution network prediction model and partitions the distribution network multi-resource dynamic grid group according to the constructed distribution network prediction model; The control module classifies the divided distribution network multi-resource dynamic grid group to obtain the classification performance index, and controls the distribution network multi-resource dynamic grid group based on the classification performance index.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for controlling a distribution network multi-resource dynamic grid group based on cloud-edge fusion as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for controlling a distribution network multi-resource dynamic grid group based on cloud-edge fusion as described in any one of claims 1 to 7 are implemented.

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