Communication enhancement method and system for flexible voltage governance device

Abstract

The invention relates to a communication enhancement method and system for flexible voltage governance devices. The method comprises the following steps: collecting historical carrier communication data of each flexible voltage governance device and putting the historical carrier communication data into an original data set; constructing a carrier communication characteristic curve according to each piece of historical carrier communication data, clustering each carrier communication characteristic curve, determining whether the communication of the corresponding clustering result is abnormal or not according to the carrier communication characteristic curve corresponding to the clustering center of each clustering result, and adding a communication abnormal label for each carrier communication characteristic curve, forming a training sample set; constructing a convolutional neural network, and training the convolutional neural network through the training sample to obtain a communication anomaly diagnosis model; and obtaining carrier communication data of the target flexible voltage treatment device in a preset time, constructing a carrier communication characteristic curve, inputting the carrier communication characteristic curve to the communication abnormality diagnosis model to obtain a diagnosis result, and performing communication enhancement of the target flexible voltage treatment device according to a preset rule based on the diagnosis result.

Description

Technical Field

[0001] The invention relates to a communication enhancement method and system for a flexible voltage management device, belonging to the technical field of communication enhancement of electric power equipment. Background Art

[0002] Flexible Voltage Regulation Device (FVRD) is a device that can dynamically adjust voltage, aiming to improve the stability and reliability of power systems. With the continuous development of power electronics and communication technologies, flexible voltage regulation devices have been widely used in power systems. Flexible voltage regulation devices need to collect and process parameters such as voltage, current, and power in real time to achieve dynamic adjustment of voltage. Therefore, it is necessary to have good communication capabilities to meet real-time requirements.

[0003] Currently, the power system is unable to determine in real time whether the communication of the flexible voltage management device is normal and whether communication enhancement is needed, which leads to untimely data transmission and delayed fault event processing. Summary of the invention

[0004] In order to solve the above problems existing in the prior art, the present invention proposes a communication enhancement method for a flexible voltage management device.

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

[0006] On the one hand, the present invention provides a method for enhancing communication of a flexible voltage management device, characterized in that it comprises the following steps:

[0007] According to the line topology of the target power network, the terminal device at the receiving end obtains the historical carrier communication data of each flexible voltage management device associated with the terminal device in the target power network and puts it into the original data set;

[0008] The carrier communication characteristic curve is constructed with the historical carrier communication data of each flexible voltage management device in the original data set, and each carrier communication characteristic curve is clustered. The carrier communication characteristic curve corresponding to the cluster center of each clustering result is used to determine whether the communication of the corresponding clustering result is abnormal, and a communication abnormality label is added to each carrier communication characteristic curve in the corresponding clustering result. A carrier communication characteristic curve and a corresponding communication abnormality label are put into the training sample set as a training sample;

[0009] Construct a convolutional neural network, use the carrier communication characteristic curve in the training sample as the input of the convolutional neural network, use the communication anomaly label as the output of the convolutional neural network, iteratively train the convolutional neural network, and obtain a trained communication anomaly diagnosis model;

[0010] Acquire carrier communication data of the target flexible voltage management device within a preset time and construct it into a carrier communication characteristic curve, and input it into a communication anomaly diagnosis model to obtain a 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] As a preferred implementation, the communication abnormality label specifically includes communication attenuation abnormality labels and communication disconnection abnormality labels 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, and the multi-mode communication module is used to communicate with the terminal device through communication methods including but not limited to WIFI, 5G, 4G, and LORA;

[0014] In the step of performing communication enhancement of the target flexible voltage management device according to preset rules based on the communication abnormality diagnosis result, the preset rules include:

[0015] When the communication abnormality diagnosis result of the target flexible voltage management device is abnormal communication attenuation, the relay device on the corresponding line is enabled;

[0016] When the target flexible voltage management device communication abnormality diagnosis result is a communication disconnection abnormality, enable the multi-mode communication module to communicate with the terminal device

[0017] As a preferred implementation, 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 implementation, the method for constructing a carrier communication characteristic curve using the historical carrier communication data of each flexible voltage management device in the original data set is specifically as follows:

[0019] Normalize each communication characteristic index in the carrier communication data at each time node;

[0020] Assign a weight coefficient to each communication characteristic indicator, and the sum of the weight coefficients of each communication characteristic indicator is 1; calculate the characteristic value of each time node based on the normalized value of each communication characteristic indicator and the weight coefficient;

[0021] The characteristics of each time node are combined into a carrier communication characteristic curve.

[0022] As a preferred implementation, the step of clustering the communication characteristic curves of each carrier using the K-means clustering algorithm to obtain a number of clustering results is specifically 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] Randomly select K carrier communication characteristic curves 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] Wherein, dij is the distance between the ith 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 dij;

[0029] Recalculate new cluster centers for all updated clusters:

[0030]

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

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

[0033] The data acquisition module is used to obtain the historical carrier communication data of each flexible voltage management device associated with the terminal device in the target power network according to the line topology of the target power network, and put it into the original data set by the terminal device at the receiving end;

[0034] A training set construction module is used to construct a carrier communication characteristic curve with the historical carrier communication data of each flexible voltage management device in the original data set, and cluster each carrier communication characteristic curve, determine whether the communication of the corresponding clustering result is abnormal through the carrier communication characteristic curve corresponding to the cluster center of each clustering result, and add a communication abnormality label to each carrier communication characteristic curve in the corresponding clustering result, and put a carrier communication characteristic curve and the corresponding communication abnormality label as a training sample into the training sample set;

[0035] The model training module is used to construct a convolutional neural network, use the carrier communication characteristic curve in the training sample as the input of the convolutional neural network, use 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;

[0036] A diagnostic module, used to obtain 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 input into a communication anomaly diagnosis model to obtain a communication anomaly diagnosis result;

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

[0038] As a preferred implementation, the communication abnormality label specifically includes communication attenuation abnormality labels and communication disconnection abnormality labels 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, and the multi-mode communication module is used to communicate with the terminal device through communication methods including but not limited to WIFI, 5G, 4G, and LORA;

[0040] In the step of performing communication enhancement of the target flexible voltage management device according to preset rules based on the communication abnormality diagnosis result, the preset rules include:

[0041] When the communication abnormality diagnosis result of the target flexible voltage management device is abnormal communication attenuation, the relay device on the corresponding line is enabled;

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

[0043] As a preferred implementation, 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 implementation, the method for constructing a carrier communication characteristic curve using the historical carrier communication data of each flexible voltage management device in the original data set is specifically as follows:

[0045] Normalize each communication characteristic index in the carrier communication data at each time node;

[0046] Assign a weight coefficient to each communication characteristic indicator, and the sum of the weight coefficients of each communication characteristic indicator is 1; calculate the characteristic value of each time node based on the normalized value of each communication characteristic indicator and the weight coefficient;

[0047] The characteristics of each time node are combined into a carrier communication characteristic curve.

[0048] As a preferred implementation, the clustering module uses the K-means clustering algorithm to cluster the communication characteristic curves of each carrier, and the steps of obtaining several clustering results are specifically 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] Randomly select K carrier communication characteristic curves 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] Wherein, dij is the distance between the ith 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 dij;

[0055] Recalculate new cluster centers for all updated clusters:

[0056]

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

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

[0059] The present invention provides a communication enhancement method and system for a flexible voltage management device. The carrier communication characteristic curve is constructed through the historical carrier communication data of the flexible voltage management device to form identifiable feature data, and the carrier communication characteristic curve is clustered by clustering. The communication characteristics of different flexible voltage management devices can be accurately and objectively divided, and the corresponding clustering results can be identified according to the cluster center to determine whether communication anomalies occur. Finally, a convolutional neural network is used to train a communication anomaly diagnosis model. The model can be used to pre-diagnose whether a communication anomaly occurs in the device based on the communication data of the target flexible voltage management device at historical moments, and then communication enhancement processing is performed according to the pre-examination rules to ensure the stability of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 The figure is a schematic diagram of a method flow of an embodiment of the present invention. DETAILED DESCRIPTION

[0061] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0062] It should be understood that the step numbers used in this document are only for convenience of description and are not intended to limit the order in which the steps are executed.

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

[0064] The terms “include” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0065] The term "and / or" means and includes any and all possible combinations of one or more of the associated listed items.

[0066] Embodiment 1:

[0067] See also Figure 1 This embodiment provides a method for enhancing communication of a flexible voltage management device, which specifically includes the following steps:

[0068] S100, according to the line topology of the target power network, the terminal device at the receiving end obtains the historical carrier communication data of each flexible voltage management device associated with the terminal device in the target power network and puts it into the original data set;

[0069] S200, constructing a carrier communication characteristic curve with the historical carrier communication data of each flexible voltage management device in the original data set, and clustering each carrier communication characteristic curve, determining whether the communication of the corresponding clustering result is abnormal through the carrier communication characteristic curve corresponding to the cluster center of each clustering result, and adding a communication abnormality label to each carrier communication characteristic curve in the corresponding clustering result, and putting a carrier communication characteristic curve and a corresponding communication abnormality label as a training sample into a training sample set;

[0070] S300, constructing a convolutional neural network, taking the carrier communication characteristic curve in the training sample as the input of the convolutional neural network, taking the communication anomaly label as the output of the convolutional neural network, and iteratively training the convolutional neural network to obtain a trained communication anomaly diagnosis model;

[0071] S400, obtaining carrier communication data of the target flexible voltage management device within a preset time and constructing it into a carrier communication characteristic curve, and inputting it into a communication abnormality diagnosis model to obtain a communication abnormality diagnosis result;

[0072] S500. Based on the communication abnormality diagnosis result, the communication of the target flexible voltage management device is enhanced according to preset rules.

[0073] As a preferred implementation of this embodiment, 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;

[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, and the multi-mode communication module is used to communicate with the terminal device through communication methods including but not limited to WIFI, 5G, 4G, and LORA;

[0075] In the step of performing communication enhancement of the target flexible voltage management device according to preset rules based on the communication abnormality diagnosis result, the preset rules include:

[0076] When the communication abnormality diagnosis result of the target flexible voltage management device is abnormal communication attenuation, the relay device on the corresponding line is enabled;

[0077] When the target flexible voltage management device communication abnormality diagnosis result is a communication disconnection abnormality, enable the multi-mode communication module to communicate with the terminal device

[0078] As a preferred implementation of this embodiment, in step S100, the historical carrier communication data includes several communication characteristic indicators, 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 error in the signal during transmission, transmission delay refers to the time required for the signal to travel from the transmitter to the receiver, and channel capacity refers to the maximum information rate that a communication channel can transmit under given conditions.

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

[0080] Each communication characteristic index in the carrier communication data at each time node is normalized; because the carrier communication data contains a variety of different communication indicators, and there are differences in dimensions and numerical ranges between these communication indicators, by normalizing each communication characteristic index in the carrier communication data at each time node, these differences are eliminated and the data are on the same scale.

[0081] A weight coefficient is assigned to each communication characteristic indicator, and the sum of the weight coefficients of each communication characteristic indicator is 1; these weight coefficients represent the importance of each communication characteristic indicator in the overall analysis, and the weight coefficients can be assigned directly through expert experience or using genetic algorithms or regression algorithms.

[0082] The characteristic value of each time node is weightedly calculated based on the normalized value of each communication characteristic indicator and the weight coefficient; the characteristic value obtained by weighted calculation can reflect the characteristics and changing trends of network communication at different time nodes.

[0083] The characteristics of each time node are combined into a carrier communication characteristic curve.

[0084] As a preferred implementation of this embodiment, the steps of clustering the communication characteristic curves of each carrier using the K-means clustering algorithm to obtain several clustering results are specifically 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] Randomly select K carrier communication characteristic curves 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] Wherein, dij is the distance between the ith 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 dij;

[0091] Recalculate new cluster centers for all updated clusters:

[0092]

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

[0094] Embodiment 2:

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

[0096] A data acquisition module is used to obtain the historical carrier communication data of each flexible voltage management device associated with the terminal device in the target power network according to the line topology of the target power network, and put it into the original data set by the terminal device at the receiving end; this module is used to implement the function of step S100 in the first embodiment, and will not be repeated here;

[0097] A training set construction module is used to construct a carrier communication characteristic curve with the historical carrier communication data of each flexible voltage management device in the original data set, and cluster each carrier communication characteristic curve, determine whether the communication of the corresponding clustering result is abnormal through the carrier communication characteristic curve corresponding to the cluster center of each clustering result, and add a communication abnormality label to each carrier communication characteristic curve in the corresponding clustering result, and put a carrier communication characteristic curve and a corresponding communication abnormality label as a training sample into the training sample set; this module is used to implement the function of step S200 in embodiment 1, and will not be repeated here;

[0098] A model training module is used to construct a convolutional neural network, using the carrier communication characteristic curve in the training sample as the input of the convolutional neural network, and using the communication anomaly label as the output of the convolutional neural network to iteratively train the convolutional neural network to obtain a trained communication anomaly diagnosis model; this module is used to implement the function of step S300 in embodiment 1, and will not be repeated here;

[0099] A diagnostic module, used to obtain carrier communication data of the target flexible voltage management device within a preset time and construct it into a carrier communication characteristic curve, and input it into the communication abnormality diagnosis model to obtain a communication abnormality diagnosis result; this module is used to implement the function of step S400 in the first embodiment, and will not be repeated here;

[0100] The communication enhancement module performs communication enhancement of the target flexible voltage management device based on the communication anomaly diagnosis result and according to preset rules; this module is used to implement the function of step S500 in embodiment 1 and will not be repeated here.

[0101] As a preferred implementation of this embodiment, the communication abnormality label specifically includes communication attenuation abnormality labels and communication disconnection abnormality labels 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, and the multi-mode communication module is used to communicate with the terminal device through communication methods including but not limited to WIFI, 5G, 4G, and LORA;

[0103] In the step of performing communication enhancement of the target flexible voltage management device according to preset rules based on the communication abnormality diagnosis result, the preset rules include:

[0104] When the communication abnormality diagnosis result of the target flexible voltage management device is abnormal communication attenuation, the relay device on the corresponding line is enabled;

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

[0106] As a preferred implementation of this embodiment, 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 implementation of this embodiment, the method for constructing a carrier communication characteristic curve using the historical carrier communication data of each flexible voltage management device in the original data set is specifically as follows:

[0108] Normalize each communication characteristic index in the carrier communication data at each time node;

[0109] Assign a weight coefficient to each communication characteristic indicator, and the sum of the weight coefficients of each communication characteristic indicator is 1; calculate the characteristic value of each time node based on the normalized value of each communication characteristic indicator and the weight coefficient;

[0110] The characteristics of each time node are combined into a carrier communication characteristic curve.

[0111] As a preferred implementation of this embodiment, the clustering module uses the K-means clustering algorithm to cluster the communication characteristic curves of each carrier, and the steps of obtaining several clustering results are specifically 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] Randomly select K carrier communication characteristic curves 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] Wherein, dij is the distance between the ith 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 dij;

[0118] Recalculate new cluster centers for all updated clusters:

[0119]

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

[0121] In the embodiments of the present application, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Among them, A and B can be singular or plural. The character " / " generally indicates that the previous and next associated 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 single or plural items. For example, at least one of a, b and c can be represented by: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, c can be single or multiple.

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

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

[0124] In several embodiments provided in the present application, if any function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory; hereinafter referred to as: ROM), random access memory (Random Access Memory; hereinafter referred to as: RAM), disk or optical disk, and other media that can store program codes.

[0125] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for enhancing communication of a flexible voltage management device, characterized in that: The following steps are involved: According to the line topology of the target power network, the terminal device at the receiving end obtains the historical carrier communication data of each flexible voltage management device associated with the terminal device in the target power network and puts it into the original data set; The carrier communication characteristic curve is constructed with the historical carrier communication data of each flexible voltage management device in the original data set, and each carrier communication characteristic curve is clustered. The carrier communication characteristic curve corresponding to the cluster center of each clustering result is used to determine whether the communication of the corresponding clustering result is abnormal, and a communication abnormality label is added to each carrier communication characteristic curve in the corresponding clustering result. A carrier communication characteristic curve and a corresponding communication abnormality label are put into the training sample set as a training sample; Construct a convolutional neural network, use the carrier communication characteristic curve in the training sample as the input of the convolutional neural network, use the communication anomaly label as the output of the convolutional neural network, iteratively train the convolutional neural network, and obtain a trained communication anomaly diagnosis model; Acquire carrier communication data of the target flexible voltage management device within a preset time and construct it into a carrier communication characteristic curve, and input it into a communication anomaly diagnosis model to obtain a communication anomaly diagnosis result; Based on the communication anomaly diagnosis results, the communication of the target flexible voltage management device is enhanced according to preset rules.

2. According to claim 1, a method for enhancing communication of a flexible voltage management device is characterized in that: The communication abnormality labels specifically include communication attenuation abnormality labels and communication disconnection abnormality labels of different levels, 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 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, and the multi-mode communication module is used to communicate with the terminal device through communication methods including but not limited to WIFI, 5G, 4G, and LORA; In the step of performing communication enhancement of the target flexible voltage management device according to preset rules based on the communication abnormality diagnosis result, the preset rules include: When the communication abnormality diagnosis result of the target flexible voltage management device is abnormal communication attenuation, the relay device on the corresponding line is enabled; When the communication abnormality diagnosis result of the target flexible voltage management device is a communication disconnection abnormality, the multi-mode communication module is enabled to communicate with the terminal device.

3. According to claim 1, a method for enhancing communication of a flexible voltage management device is characterized in that: The historical carrier communication data includes several communication characteristic indicators, specifically including signal transmission rate, bit error rate, transmission delay and channel capacity.

4. A method for enhancing communication of a flexible voltage management device according to claim 3, characterized in that: The method for constructing a carrier communication characteristic curve using the historical carrier communication data of each flexible voltage management device in the original data set is specifically as follows: Normalize each communication characteristic index in the carrier communication data at each time node; A weight coefficient is assigned to each communication characteristic indicator, and the sum of the weight coefficients of each communication characteristic indicator is 1; The characteristic value of each time node is calculated based on the normalized value of each communication characteristic indicator and the weight coefficient; The characteristics of each time node are combined into a carrier communication characteristic curve.

5. According to claim 1, a method for enhancing communication of a flexible voltage management device is characterized in that: In the step of clustering the communication characteristic curves of each carrier, the K-means clustering algorithm is specifically used to cluster the communication characteristic curves of each carrier, and the steps are specifically as follows: 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}; Randomly select K carrier communication characteristic curves as the initial K cluster centers: {μ1, μ2, …, μk}; Calculate the distance between the i-th carrier communication characteristic curve xi and each cluster center μj: Wherein, dij is the distance between the ith carrier communication characteristic curve and the cluster center μj; i = 1, 2, ..., n; j = 1, 2, ..., K; Merge the i-th carrier communication characteristic curve xi into the cluster corresponding to the cluster center with the smallest distance dij; Recalculate new cluster centers for all updated clusters: If the new cluster center deviates from the initial cluster center by a distance greater than the preset distance, the above steps are repeated until the changes of all cluster centers are within the preset range; and the final clustering result is output.

6. A communication enhancement system for a flexible voltage management device, characterized in that: include: The data acquisition module is used to obtain the historical carrier communication data of each flexible voltage management device associated with the terminal device in the target power network according to the line topology of the target power network, and put it into the original data set by the terminal device at the receiving end; A training set construction module is used to construct a carrier communication characteristic curve with the historical carrier communication data of each flexible voltage management device in the original data set, and cluster each carrier communication characteristic curve, determine whether the communication of the corresponding clustering result is abnormal through the carrier communication characteristic curve corresponding to the cluster center of each clustering result, and add a communication abnormality label to each carrier communication characteristic curve in the corresponding clustering result, and put a carrier communication characteristic curve and the corresponding communication abnormality label as a training sample into the training sample set; The model training module is used to construct a convolutional neural network, use the carrier communication characteristic curve in the training sample as the input of the convolutional neural network, use 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; A diagnostic module, used to obtain 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 input into a communication anomaly diagnosis model to obtain a communication anomaly diagnosis result; The communication enhancement module performs communication enhancement of the target flexible voltage management device according to preset rules based on the communication anomaly diagnosis results.

7. A communication enhancement system for a flexible voltage management device according to claim 6, characterized in that: The communication abnormality labels specifically include communication attenuation abnormality labels and communication disconnection abnormality labels of different levels, 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 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, and the multi-mode communication module is used to communicate with the terminal device through communication methods including but not limited to WIFI, 5G, 4G, and LORA; In the step of performing communication enhancement of the target flexible voltage management device according to preset rules based on the communication abnormality diagnosis result, the preset rules include: When the communication abnormality diagnosis result of the target flexible voltage management device is abnormal communication attenuation, the relay device on the corresponding line is enabled; When the communication abnormality diagnosis result of the target flexible voltage management device is a communication disconnection abnormality, the multi-mode communication module is enabled to communicate with the terminal device.

8. A communication enhancement system for a flexible voltage management device according to claim 6, characterized in that: The historical carrier communication data includes several communication characteristic indicators, specifically including signal transmission rate, bit error rate, transmission delay and channel capacity.

9. A communication enhancement system for a flexible voltage management device according to claim 8, characterized in that: The method for constructing a carrier communication characteristic curve using the historical carrier communication data of each flexible voltage management device in the original data set is specifically as follows: Normalize each communication characteristic index in the carrier communication data at each time node; A weight coefficient is assigned to each communication characteristic indicator, and the sum of the weight coefficients of each communication characteristic indicator is 1; The characteristic value of each time node is calculated based on the normalized value of each communication characteristic indicator and the weight coefficient; The characteristics of each time node are combined into a carrier communication characteristic curve.

10. A communication enhancement system for a flexible voltage management device according to claim 6, characterized in that: The clustering module uses the K-means clustering algorithm to cluster the communication characteristic curves of each carrier, and the steps of obtaining several clustering results are specifically as follows: 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}; Randomly select K carrier communication characteristic curves as the initial K cluster centers: {μ1, μ2, …, μk}; Calculate the distance between the i-th carrier communication characteristic curve xi and each cluster center μj: Wherein, dij is the distance between the ith carrier communication characteristic curve and the cluster center μj; i = 1, 2, ..., n; j = 1, 2, ..., K; Merge the i-th carrier communication characteristic curve xi into the cluster corresponding to the cluster center with the smallest distance dij; Recalculate new cluster centers for all updated clusters: If the new cluster center deviates from the initial cluster center by a distance greater than the preset distance, the above steps are repeated until the changes of all cluster centers are within the preset range; and the final clustering result is output.

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