A flexible voltage management device communication enhancement method and system

Abstract

The present application relates to a kind of flexible voltage management device communication enhancement method and system, wherein the method comprises the following steps: collecting the historical carrier communication data of each flexible voltage management device is placed in original data set;With each historical carrier communication data constructs carrier communication characteristic curve, and each carrier communication characteristic curve is clustered, whether the communication of corresponding cluster result is abnormal by the carrier communication characteristic curve corresponding to the cluster center of each cluster result is determined, and communication abnormality label is added for each carrier communication characteristic curve, forming training sample set;Convolutional neural network is constructed, and the convolutional neural network is trained by training sample, and communication abnormality diagnosis model is obtained;Carrier communication data of target flexible voltage management device in preset time is obtained and is constructed as carrier communication characteristic curve, input into communication abnormality diagnosis model to obtain diagnosis result, and based on diagnosis result, according to the rule of pre-set, the communication enhancement of target flexible voltage management device is carried out.

Description

Technical Field

[0001] This invention relates to a communication enhancement method and system for a flexible voltage management device, belonging to the field of communication enhancement technology for power equipment. Background Technology

[0002] A Flexible Voltage Regulation Device (FVRD) is a device capable of dynamically adjusting voltage to improve the stability and reliability of power systems. With the continuous development of power electronics and communication technologies, FVRDs have been widely used in power systems. FVRDs require real-time acquisition and processing of parameters such as voltage, current, and power to achieve dynamic voltage adjustment. Therefore, they need good communication capabilities to meet real-time requirements.

[0003] Currently, the power system cannot determine in real time whether the communication of the flexible voltage management device is normal or whether communication enhancement is needed. This leads to untimely data transmission and delayed handling of fault events. Summary of the Invention

[0004] To address the problems existing in the prior art, this invention proposes a communication enhancement method for flexible voltage management devices.

[0005] The technical solution of the present invention is as follows:

[0006] On one hand, the present invention provides a communication enhancement method for a flexible voltage management device, characterized by comprising the following steps:

[0007] Based on the line topology of the target power network, the terminal equipment at the receiving end obtains the historical carrier communication data of each flexible voltage management device associated with the terminal equipment in the target power network and puts it into the original dataset.

[0008] Carrier communication characteristic curves are constructed using historical carrier communication data of each flexible voltage management device in the original dataset. Each carrier communication characteristic curve is clustered. The communication of the corresponding cluster result is determined by the carrier communication characteristic curve corresponding to the cluster center of each cluster result. A communication anomaly label is added to each carrier communication characteristic curve in the corresponding cluster result. A carrier communication characteristic curve and the corresponding communication anomaly label are used as a training sample and placed into the training sample set.

[0009] A convolutional neural network is constructed, with the carrier communication characteristic curve in the training samples as the input of the convolutional neural network and the communication anomaly label as the output of the convolutional neural network. The convolutional neural network is iteratively trained to obtain a trained communication anomaly diagnosis model.

[0010] The carrier communication data of the target flexible voltage management device within a preset time period is acquired and constructed into a carrier communication characteristic curve, which is then input into the communication anomaly diagnosis model to obtain the communication anomaly diagnosis result.

[0011] Based on the communication anomaly diagnosis results, the communication of the target flexible voltage management device is enhanced according to preset rules.

[0012] In a preferred embodiment, the communication anomaly tag specifically includes communication attenuation anomaly tags and communication disconnection anomaly tags of different levels, which are used to indicate that the communication signal is in a signal attenuation state and a signal loss state, respectively.

[0013] According to the line topology of the target power network, at least one relay device is set along each line, and the number of relay devices is set according to the corresponding line length; and a multi-mode communication module is set on each flexible voltage management device, the multi-mode communication module being used to communicate with terminal devices through communication methods including but not limited to WIFI, 5G, 4G, and LoRa.

[0014] In the step of enhancing the communication of the target flexible voltage management device based on the communication anomaly diagnosis results and according to preset rules, the preset rules include:

[0015] When the communication anomaly diagnosis result of the target flexible voltage management device is communication attenuation anomaly, the relay equipment on the corresponding line is activated;

[0016] When the communication anomaly diagnosis result of the target flexible voltage management device is a communication disconnection anomaly, the multi-mode communication module is activated to communicate with the terminal equipment.

[0017] In a preferred embodiment, the historical carrier communication data includes several communication characteristic indicators, specifically including signal transmission rate, bit error rate, transmission delay, and channel capacity.

[0018] As a preferred embodiment, the method for constructing a carrier communication characteristic curve using historical carrier communication data of each flexible voltage management device in the original dataset is specifically as follows:

[0019] Normalize the communication characteristic indicators in the carrier communication data at each time point;

[0020] Each communication characteristic index is assigned a weight coefficient, and the sum of the weight coefficients of all communication characteristic indices is 1; the characteristic value of each time node is calculated by weighting the normalized value of each communication characteristic index and the weight coefficient.

[0021] The characteristics of each time point are combined to form a carrier communication characteristic curve.

[0022] In a preferred embodiment, the step of using the K-means clustering algorithm to cluster the communication characteristic curves of each carrier to obtain several clustering results is as follows:

[0023] Input the communication characteristic curves of each carrier: {x1, x2, ..., xn}, set the number of clusters K, and output the cluster partition C = {C1, C2, ..., CK};

[0024] K carrier communication characteristic curves are randomly selected as the initial K cluster centers: {μ1, μ2, ..., μk};

[0025] Calculate the distance between the i-th carrier communication characteristic curve xi and each cluster center μj:

[0026]

[0027] Where, dij is the distance between the i-th carrier communication characteristic curve and the cluster center μj; i = 1, 2, ..., n; j = 1, 2, ..., K;

[0028] Merge the i-th carrier communication characteristic curve xi into the cluster corresponding to the cluster center with the smallest distance from dij;

[0029] Recalculate the new cluster centers for all clusters after the update:

[0030]

[0031] If the new cluster center deviates from the initial cluster center by a distance greater than a preset distance, repeat the above steps until all cluster center changes are within the preset range; then output the final clustering result.

[0032] On the other hand, the present invention also provides a communication enhancement system for a flexible voltage management device, comprising:

[0033] The data acquisition module is used to acquire historical carrier communication data of each flexible voltage management device associated with the terminal device in the target power network and put it into the raw dataset, based on the line topology of the target power network.

[0034] The training set construction module is used to construct carrier communication characteristic curves from the historical carrier communication data of each flexible voltage management device in the original dataset, and to cluster each carrier communication characteristic curve. The communication of the corresponding cluster result is determined by the carrier communication characteristic curve corresponding to the cluster center of each cluster result. A communication anomaly label is added to each carrier communication characteristic curve in the corresponding cluster result. A carrier communication characteristic curve and the corresponding communication anomaly label are used as a training sample and put into the training sample set.

[0035] The model training module is used to construct a convolutional neural network. The carrier communication characteristic curve in the training samples is used as the input of the convolutional neural network, and the communication anomaly label is used as the output of the convolutional neural network. The convolutional neural network is iteratively trained to obtain a trained communication anomaly diagnosis model.

[0036] The diagnostic module is used to acquire the carrier communication data of the target flexible voltage management device within a preset time and construct it into a carrier communication characteristic curve, which is then input into the communication anomaly diagnostic model to obtain the communication anomaly diagnostic results.

[0037] The communication enhancement module enhances the communication of the target flexible voltage management device according to preset rules based on the communication anomaly diagnosis results.

[0038] In a preferred embodiment, the communication anomaly tag specifically includes communication attenuation anomaly tags and communication disconnection anomaly tags of different levels, which are used to indicate that the communication signal is in a signal attenuation state and a signal loss state, respectively.

[0039] According to the line topology of the target power network, at least one relay device is set along each line, and the number of relay devices is set according to the corresponding line length; and a multi-mode communication module is set on each flexible voltage management device, the multi-mode communication module being used to communicate with terminal devices through communication methods including but not limited to WIFI, 5G, 4G, and LoRa.

[0040] In the step of enhancing the communication of the target flexible voltage management device based on the communication anomaly diagnosis results and according to preset rules, the preset rules include:

[0041] When the communication anomaly diagnosis result of the target flexible voltage management device is communication attenuation anomaly, the relay equipment on the corresponding line is activated;

[0042] When the communication anomaly diagnosis result of the target flexible voltage management device is a communication disconnection anomaly, the multi-mode communication module is activated to communicate with the terminal equipment.

[0043] In a preferred embodiment, the historical carrier communication data includes several communication characteristic indicators, specifically including signal transmission rate, bit error rate, transmission delay, and channel capacity.

[0044] As a preferred embodiment, the method for constructing a carrier communication characteristic curve using historical carrier communication data of each flexible voltage management device in the original dataset is specifically as follows:

[0045] Normalize the communication characteristic indicators in the carrier communication data at each time point;

[0046] Each communication characteristic index is assigned a weight coefficient, and the sum of the weight coefficients of all communication characteristic indices is 1; the characteristic value of each time node is calculated by weighting the normalized value of each communication characteristic index and the weight coefficient.

[0047] The characteristics of each time point are combined to form a carrier communication characteristic curve.

[0048] In a preferred embodiment, the clustering module uses the K-means clustering algorithm to cluster the communication characteristic curves of each carrier to obtain several clustering results. The specific steps are as follows:

[0049] Input the communication characteristic curves of each carrier: {x1, x2, ..., xn}, set the number of clusters K, and output the cluster partition C = {C1, C2, ..., CK};

[0050] K carrier communication characteristic curves are randomly selected as the initial K cluster centers: {μ1, μ2, ..., μk};

[0051] Calculate the distance between the i-th carrier communication characteristic curve xi and each cluster center μj:

[0052]

[0053] Where, dij is the distance between the i-th carrier communication characteristic curve and the cluster center μj; i = 1, 2, ..., n; j = 1, 2, ..., K;

[0054] Merge the i-th carrier communication characteristic curve xi into the cluster corresponding to the cluster center with the smallest distance from dij;

[0055] Recalculate the new cluster centers for all clusters after the update:

[0056]

[0057] If the new cluster center deviates from the initial cluster center by a distance greater than a preset distance, repeat the above steps until all cluster center changes are within the preset range; then output the final clustering result.

[0058] The present invention has the following beneficial effects:

[0059] This invention provides a communication enhancement method and system for flexible voltage regulation devices. It constructs carrier communication characteristic curves from historical carrier communication data of the flexible voltage regulation devices, forming identifiable feature data. Clustering is then used to group these carrier communication characteristic curves, accurately and objectively classifying the communication characteristics of different flexible voltage regulation devices. The cluster centers are used to identify whether communication anomalies have occurred in the corresponding clusters. Finally, a convolutional neural network is used to train a communication anomaly diagnosis model. This model can pre-diagnose whether communication anomalies have occurred in the target flexible voltage regulation device based on its historical communication data, and then perform communication enhancement processing according to pre-defined rules to ensure power system stability. Attached Figure Description

[0060] Figure 1 This is a schematic diagram of the method flow according to an embodiment of the present invention. Detailed Implementation

[0061] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0062] It should be understood that the step numbers used in the text are for ease of description only and are not intended to limit the order in which the steps are performed.

[0063] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0064] The terms “comprising” and “including” indicate the presence of the described feature, whole, step, operation, element and / or component, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0065] The term “and / or” refers to any combination of one or more of the associated listed items, as well as all possible combinations, and includes these combinations.

[0066] Example 1:

[0067] See Figure 1 This embodiment provides a communication enhancement method for a flexible voltage management device, specifically including the following steps:

[0068] S100. Based on the line topology of the target power network, the terminal equipment at the receiving end obtains the historical carrier communication data of each flexible voltage management device associated with the terminal equipment in the target power network and puts it into the original dataset.

[0069] S200. Construct carrier communication characteristic curves using historical carrier communication data of each flexible voltage management device in the original dataset, and cluster each carrier communication characteristic curve. Determine whether the communication of the corresponding cluster result is abnormal by using the carrier communication characteristic curve corresponding to the cluster center of each cluster result, and add a communication abnormality label to each carrier communication characteristic curve in the corresponding cluster result. Use a carrier communication characteristic curve and the corresponding communication abnormality label as a training sample and put it into the training sample set.

[0070] S300. Construct a convolutional neural network, using the carrier communication characteristic curve in the training samples as the input of the convolutional neural network and the communication anomaly label as the output of the convolutional neural network to iteratively train the convolutional neural network and obtain a trained communication anomaly diagnosis model.

[0071] S400: Obtain the carrier communication data of the target flexible voltage management device within a preset time and construct it into a carrier communication characteristic curve, then input it into the communication anomaly diagnosis model to obtain the communication anomaly diagnosis result.

[0072] S500 enhances the communication of the target flexible voltage management device according to preset rules based on the communication anomaly diagnosis results.

[0073] As a preferred embodiment of this example, the communication anomaly tag specifically includes communication attenuation anomaly tags and communication disconnection anomaly tags of different levels, which are used to indicate that the communication signal is in a signal attenuation state and a signal loss state, respectively.

[0074] According to the line topology of the target power network, at least one relay device is set along each line, and the number of relay devices is set according to the corresponding line length; and a multi-mode communication module is set on each flexible voltage management device, the multi-mode communication module being used to communicate with terminal devices through communication methods including but not limited to WIFI, 5G, 4G, and LoRa.

[0075] In the step of enhancing the communication of the target flexible voltage management device based on the communication anomaly diagnosis results and according to preset rules, the preset rules include:

[0076] When the communication anomaly diagnosis result of the target flexible voltage management device is communication attenuation anomaly, the relay equipment on the corresponding line is activated;

[0077] When the communication anomaly diagnosis result of the target flexible voltage management device is a communication disconnection anomaly, the multi-mode communication module is activated to communicate with the terminal equipment.

[0078] In a preferred embodiment of this invention, in step S100, the historical carrier communication data includes several communication characteristic indicators, specifically including signal transmission rate, bit error rate, transmission delay, and channel capacity. Signal transmission rate refers to the amount of data transmitted per unit time; bit error rate refers to the probability of an error occurring during signal transmission; transmission delay refers to the time required for the signal to travel from the transmitting end to the receiving end; and channel capacity refers to the maximum information rate that a communication channel can transmit under given conditions.

[0079] As a preferred embodiment of this invention, the method for constructing a carrier communication characteristic curve using historical carrier communication data of each flexible voltage management device in the original dataset is specifically as follows:

[0080] Normalization is performed on the communication characteristic indicators in the carrier communication data at each time point. Because the carrier communication data contains a variety of different communication indicators, and there are differences in the dimensions and numerical ranges between these indicators, normalization is performed on the communication characteristic indicators in the carrier communication data at each time point to eliminate these differences and make the data on the same scale.

[0081] Each communication characteristic index is assigned a weight coefficient, and the sum of the weight coefficients of all communication characteristic indices is 1. These weight coefficients represent the importance of each communication characteristic index in the overall analysis. The weight coefficients can be assigned directly by expert experience or by using genetic algorithms or regression algorithms.

[0082] The characteristic values ​​of each time node are calculated by weighting the normalized values ​​of each communication characteristic index and the weighting coefficients. The characteristic values ​​obtained by weighting can reflect the characteristics and changing trends of network communication at different time nodes.

[0083] The characteristics of each time point are combined to form a carrier communication characteristic curve.

[0084] In a preferred embodiment of this invention, the step of using the K-means clustering algorithm to cluster the communication characteristic curves of each carrier and obtain several clustering results is as follows:

[0085] Input the communication characteristic curves of each carrier: {x1, x2, ..., xn}, set the number of clusters K, and output the cluster partition C = {C1, C2, ..., CK};

[0086] K carrier communication characteristic curves are randomly selected as the initial K cluster centers: {μ1, μ2, ..., μk};

[0087] Calculate the distance between the i-th carrier communication characteristic curve xi and each cluster center μj:

[0088]

[0089] Where, dij is the distance between the i-th carrier communication characteristic curve and the cluster center μj; i = 1, 2, ..., n; j = 1, 2, ..., K;

[0090] Merge the i-th carrier communication characteristic curve xi into the cluster corresponding to the cluster center with the smallest distance from dij;

[0091] Recalculate the new cluster centers for all clusters after the update:

[0092]

[0093] If the new cluster center deviates from the initial cluster center by a distance greater than a preset distance, repeat the above steps until all cluster center changes are within the preset range; then output the final clustering result.

[0094] Example 2:

[0095] This embodiment provides a communication enhancement system for a flexible voltage regulation device, including:

[0096] The data acquisition module is used to acquire historical carrier communication data of each flexible voltage management device associated with the terminal device in the target power network and put it into the original dataset according to the line topology of the target power network. This module is used to implement the function of step S100 in Embodiment 1, which will not be described in detail here.

[0097] The training set construction module is used to construct carrier communication characteristic curves from the historical carrier communication data of each flexible voltage management device in the original dataset, and to cluster each carrier communication characteristic curve. The communication of the corresponding cluster result is determined by the carrier communication characteristic curve corresponding to the cluster center of each cluster result. A communication anomaly label is added to each carrier communication characteristic curve in the corresponding cluster result. A carrier communication characteristic curve and the corresponding communication anomaly label are used as a training sample and put into the training sample set. This module is used to implement the function of step S200 in embodiment one, and will not be described again here.

[0098] The model training module is used to construct a convolutional neural network. It uses the carrier communication characteristic curve in the training samples as the input of the convolutional neural network and the communication anomaly label as the output of the convolutional neural network to iteratively train the convolutional neural network and obtain a trained communication anomaly diagnosis model. This module is used to implement the function of step S300 in Example 1, which will not be described in detail here.

[0099] The diagnostic module is used to acquire the carrier communication data of the target flexible voltage management device within a preset time and construct it into a carrier communication characteristic curve, which is then input into the communication anomaly diagnostic model to obtain the communication anomaly diagnostic result. This module is used to implement the function of step S400 in Embodiment 1, and will not be described in detail here.

[0100] The communication enhancement module enhances the communication of the target flexible voltage management device according to preset rules based on the communication anomaly diagnosis results. This module is used to implement the function of step S500 in Embodiment 1, which will not be described in detail here.

[0101] In a preferred embodiment of this example, the communication anomaly tag specifically includes communication attenuation anomaly tags and communication disconnection anomaly tags of different levels, which are used to indicate that the communication signal is in a signal attenuation state and a signal loss state, respectively.

[0102] According to the line topology of the target power network, at least one relay device is set along each line, and the number of relay devices is set according to the corresponding line length; and a multi-mode communication module is set on each flexible voltage management device, the multi-mode communication module being used to communicate with terminal devices through communication methods including but not limited to WIFI, 5G, 4G, and LoRa.

[0103] In the step of enhancing the communication of the target flexible voltage management device based on the communication anomaly diagnosis results and according to preset rules, the preset rules include:

[0104] When the communication anomaly diagnosis result of the target flexible voltage management device is communication attenuation anomaly, the relay equipment on the corresponding line is activated;

[0105] When the communication anomaly diagnosis result of the target flexible voltage management device is a communication disconnection anomaly, the multi-mode communication module is activated to communicate with the terminal equipment.

[0106] In a preferred embodiment of this invention, the historical carrier communication data includes several communication characteristic indicators, specifically including signal transmission rate, bit error rate, transmission delay, and channel capacity.

[0107] As a preferred embodiment of this invention, the method for constructing a carrier communication characteristic curve using historical carrier communication data of each flexible voltage management device in the original dataset is specifically as follows:

[0108] Normalize the communication characteristic indicators in the carrier communication data at each time point;

[0109] Each communication characteristic index is assigned a weight coefficient, and the sum of the weight coefficients of all communication characteristic indices is 1; the characteristic value of each time node is calculated by weighting the normalized value of each communication characteristic index and the weight coefficient.

[0110] The characteristics of each time point are combined to form a carrier communication characteristic curve.

[0111] In a preferred embodiment of this invention, the clustering module uses the K-means clustering algorithm to cluster the communication characteristic curves of each carrier to obtain several clustering results. The specific steps are as follows:

[0112] Input the communication characteristic curves of each carrier: {x1, x2, ..., xn}, set the number of clusters K, and output the cluster partition C = {C1, C2, ..., CK};

[0113] K carrier communication characteristic curves are randomly selected as the initial K cluster centers: {μ1, μ2, ..., μk};

[0114] Calculate the distance between the i-th carrier communication characteristic curve xi and each cluster center μj:

[0115]

[0116] Where, dij is the distance between the i-th carrier communication characteristic curve and the cluster center μj; i = 1, 2, ..., n; j = 1, 2, ..., K;

[0117] Merge the i-th carrier communication characteristic curve xi into the cluster corresponding to the cluster center with the smallest distance from dij;

[0118] Recalculate the new cluster centers for all clusters after the update:

[0119]

[0120] If the new cluster center deviates from the initial cluster center by a distance greater than a preset distance, repeat the above steps until all cluster center changes are within the preset range; then output the final clustering result.

[0121] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, A and B simultaneously, or B alone. A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one of a, b, and c can represent: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, and c can be single or multiple.

[0122] Those skilled in the art will recognize that the units and algorithm steps described in the embodiments disclosed herein can be implemented using electronic hardware, computer software, or a combination of electronic hardware and software. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0123] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0124] In the several embodiments provided in this application, any function, if implemented as a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0125] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A flexible voltage management device communication enhancement method, characterized in that, The method comprises the following steps: According to the line topology of the target power network, the terminal equipment at the receiving end obtains the historical carrier communication data of each flexible voltage management device associated with the terminal equipment in the target power network and puts it into a raw data set; The historical carrier communication data of each flexible voltage management device in the raw data set is used to construct a carrier communication characteristic curve, and each carrier communication characteristic curve is clustered. Whether the communication of the corresponding cluster result is abnormal is determined by the carrier communication characteristic curve corresponding to the cluster center of each cluster result, and a communication abnormality label is added to each carrier communication characteristic curve in the corresponding cluster result. A carrier communication characteristic curve and the corresponding communication abnormality label are put into a training sample set as a training sample; A convolutional neural network is constructed, and the carrier communication characteristic curves in the training samples are used as the input of the convolutional neural network, and the communication abnormality labels are used as the output of the convolutional neural network. The convolutional neural network is iteratively trained to obtain a trained communication abnormality diagnosis model; The carrier communication data of the target flexible voltage management device within a preset time is obtained and constructed as a carrier communication characteristic curve, which is input into the communication abnormality diagnosis model to obtain a communication abnormality diagnosis result; Based on the communication abnormality diagnosis result, the communication of the target flexible voltage management device is enhanced according to a preset rule; The method of constructing a carrier communication characteristic curve based on the historical carrier communication data of each flexible voltage management device in the raw data set is as follows: The carrier communication data of each time node is normalized; Each communication characteristic index is assigned a weight coefficient, and the sum of the weight coefficients of each communication characteristic index is 1. The characteristic value of each time node is calculated based on the normalized value of each communication characteristic index and the weight coefficient; The characteristics of each time node are combined to form a carrier communication characteristic curve.

2. The method of claim 1, wherein, The communication abnormality label specifically includes different levels of communication attenuation abnormality labels and communication disconnection abnormality labels, which are used to indicate that the communication signal is in a signal attenuation state and a signal loss state, respectively; According to the line topology of the target power network, at least one relay device is arranged along each line, and the number of relay devices is set according to the length of the corresponding line; and a multi-mode communication module is arranged on each flexible voltage management device, which is used to communicate with the terminal equipment through WIFI, 5G, 4G, and LORA communication modes; In the step of enhancing the communication of the target flexible voltage management device based on the communication abnormality diagnosis result according to the preset rule, the preset rule includes: When the communication abnormality diagnosis result of the target flexible voltage management device is communication attenuation abnormality, the relay device on the corresponding line is enabled; When the communication abnormality diagnosis result of the target flexible voltage management device is communication disconnection abnormality, the multi-mode communication module is enabled to communicate with the terminal equipment.

3. The method of claim 1, wherein, The historical carrier communication data includes several communication characteristic indexes, specifically including signal transmission rate, error rate, transmission delay, and channel capacity.

4. The method of claim 1, wherein, In the step of clustering each carrier communication characteristic curve, the K-means clustering algorithm is used to cluster each carrier communication characteristic curve, and the steps are as follows: Input each carrier communication characteristic curve: { x1, x2, …, x n}, set the cluster number K of clustering, output cluster division C = { C1, C2, …, C K}; K carrier communication characteristic curves are randomly selected as the initial K cluster centers: { 1, 2, ..., k }; Computing the i-th carrier communication characteristic curve x i and the distance to the respective cluster center μ j : ； where d ij is the distance between the ith carrier communication characteristic curve and the cluster center μ j ; i = 1, 2, …, n; j = 1, 2, …, K. The i-th carrier communication characteristic curve x i Merging to distance d ij The cluster corresponding to the smallest cluster center The new cluster centers are recalculated for all updated clusters: ； If the new cluster center deviates from the initial cluster center by more than a preset distance, the above steps are repeated until the change of all cluster centers is within the preset range; and the final clustering result is output.

5. A flexible voltage management device communication enhancement system, characterized by, The method comprises the following steps: The data acquisition module is configured to: according to the line topology of the target power network, acquire, by a terminal device at a receiving end, historical carrier communication data of each flexible voltage regulation device associated with the terminal device in the target power network, and place the historical carrier communication data in an original data set; The training set construction module is configured to: construct a carrier communication characteristic curve based on the historical carrier communication data of each flexible voltage regulation device in the original data set, cluster the carrier communication characteristic curves, determine whether communication of a corresponding cluster result is abnormal based on a carrier communication characteristic curve corresponding to a cluster center of the cluster result, and add a communication abnormality label to each carrier communication characteristic curve in the corresponding cluster result, so as to place a carrier communication characteristic curve and a corresponding communication abnormality label in a training sample set as a training sample; The model training module is configured to: construct a convolutional neural network, use the carrier communication characteristic curves in the training samples as inputs of the convolutional neural network, and use the communication abnormality labels as outputs of the convolutional neural network to iteratively train the convolutional neural network, so as to obtain a trained communication abnormality diagnosis model; The diagnosis module is configured to: acquire carrier communication data of a target flexible voltage regulation device within a preset time and construct the carrier communication data into a carrier communication characteristic curve, and input the carrier communication characteristic curve into the communication abnormality diagnosis model to obtain a communication abnormality diagnosis result; The communication enhancement module is configured to: based on the communication abnormality diagnosis result, perform communication enhancement of the target flexible voltage regulation device according to a preset rule. The method of constructing the carrier communication characteristic curve based on the historical carrier communication data of each flexible voltage regulation device in the original data set comprises the following steps: normalizing each communication characteristic index in the carrier communication data of each time node; assigning a weight coefficient to each communication characteristic index, and the sum of the weight coefficients of the communication characteristic indices is 1; and calculating a feature value of each time node based on the normalized value of each communication characteristic index and the weight coefficient; combining the features of the time nodes to form the carrier communication characteristic curve.

6. A flexible voltage management device communication enhancement system according to claim 5, wherein, The communication abnormality label specifically comprises different levels of communication attenuation abnormality labels and communication disconnection abnormality labels, which are respectively used to indicate that the communication signal is in a signal attenuation state and a signal loss state. According to the line topology of the target power network, at least one relay device is arranged along each line, the number of relay devices is set according to the length of the corresponding line, and a multi-mode communication module is arranged on each flexible voltage regulation device, the multi-mode communication module being configured to communicate with the terminal device through WIFI, 5G, 4G, and LORA communication modes; In the step of performing communication enhancement of the target flexible voltage regulation device according to a preset rule based on the communication abnormality diagnosis result, the preset rule comprises: when the communication abnormality diagnosis result of the target flexible voltage regulation device is a communication attenuation abnormality, the relay device on the corresponding line is enabled; when the communication abnormality diagnosis result of the target flexible voltage regulation device is a communication disconnection abnormality, the multi-mode communication module is enabled to communicate with the terminal device.

7. A flexible voltage management device communication enhancement system according to claim 5, wherein, The historical carrier communication data comprises several communication characteristic indexes, specifically including signal transmission rate, error code rate, transmission delay and channel capacity.

8. A flexible voltage management device communication enhancement system according to claim 5, wherein, The step of adopting the K-means clustering algorithm to cluster the carrier communication characteristic curves to obtain several clustering results is specifically as follows: Input each carrier communication characteristic curve: { x1, x2, …, x n}, set the cluster number K of clustering, output cluster division C = { C1, C2, …, C K}; K carrier communication characteristic curves are randomly selected as the initial K cluster centers: { 1, 2, ..., k }; Computing the i-th carrier communication characteristic curve x i and the distance to the respective cluster center μ j : ； where d ij is the distance between the ith carrier communication characteristic curve and the cluster center μ j ; i = 1, 2, …, n; j = 1, 2, …, K. The i-th carrier communication characteristic curve x i Merging to distance d ij The cluster corresponding to the smallest cluster center The new clustering centers are recalculated for all the updated clusters: ； If the new clustering center has a deviation greater than a preset distance from the position of the initial clustering center, the above steps are repeated until the change of all the clustering centers is within the preset range; and the final clustering result is output.

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