Data transmission method and system of distribution automation terminal

Abstract

The invention discloses a data transmission method and system for a power distribution automation terminal, and belongs to the technical field of data transmission, and the method comprises the steps: obtaining monitoring time sequence data uploaded by each transmission channel of a communication unit in a preset time period, then extracting monitoring data features of the monitoring time sequence data corresponding to each transmission channel, and transmitting the monitoring data features to the communication unit; calculating feature similarity between the monitoring data features and the preset typical time sequence features; if the similarity is not smaller than a preset similarity threshold value, not uploading the monitoring time sequence data; otherwise, the compression ratio is calculated according to the similarity, and the monitoring data is compressed according to the compression ratio and then uploaded to the master station. Through the implementation of the method and the device, the problem that the efficiency and the response speed of the system are influenced by over-high data redundancy in the data uploading process due to the fact that the originally collected time sequence data are transmitted to the master station in the data transmission process in the prior art can be solved.

Description

Technical Field

[0001] This invention relates to the field of data transmission technology, and in particular to a data transmission method and system for a power distribution automation terminal. Background Technology

[0002] As modern power distribution networks become increasingly complex, power distribution automation systems face higher operational demands in diverse scenarios. Power distribution scenarios employ a wide variety of sensors, and the interconnection between power distribution terminals and communication units is crucial for reliable sensor data transmission and automated management of the power distribution system. However, the diverse types of sensors lead to significant data variations, posing challenges during data upload due to differing formats. Furthermore, ensuring the stability and efficiency of collaborative transmission across multiple devices is essential.

[0003] Existing technologies typically use traditional power line carrier communication for data transmission. During transmission, the original time-series data is often transmitted to the master station without modification, resulting in excessive data redundancy during the data upload process, which in turn reduces the efficiency of network and storage resource utilization. Summary of the Invention

[0004] This invention provides a data transmission method and system for a power distribution automation terminal, which solves the problem in the prior art where the original time-series data is transmitted to the main station without modification, resulting in excessive data redundancy during data upload and thus affecting the system's efficiency and response speed.

[0005] An embodiment of the present invention provides a data transmission method for a power distribution automation terminal, applicable to power distribution automation terminals, comprising: Acquire monitoring time-series data uploaded by each transmission channel of the communication unit within a preset time period; Extract the monitoring data features of the monitoring time sequence data corresponding to each transmission channel; Based on the above monitoring data characteristics and the corresponding preset typical time series characteristics, calculate the feature similarity between the above monitoring data characteristics and the above preset typical time series characteristics; If the above similarity is not less than the preset similarity threshold, then the above monitoring time series data will not be uploaded; Otherwise, the compression ratio is calculated based on the similarity above, and the monitoring data is compressed according to the compression ratio above before the compressed monitoring time series data is uploaded to the main station.

[0006] Furthermore, before extracting the monitoring data features of the monitoring time series data corresponding to each transmission channel as described above, the following steps are included: Based on all the above-mentioned monitoring time series data, determine the sequence length of each of the above-mentioned monitoring time series data; Based on the sequence length and monitoring time series data mentioned above, the verification value of each monitoring time series data is calculated. If the above verification value is less than the first preset verification threshold and not less than the second preset verification threshold, the above monitoring time series data is interpolated and repaired. If the above-mentioned verification value is less than the above-mentioned second preset verification threshold, a data retransmission signal is sent to the above-mentioned communication unit so that the above-mentioned communication unit re-uploads the above-mentioned monitoring time sequence data after receiving the above-mentioned data retransmission signal.

[0007] Furthermore, based on the aforementioned monitoring data characteristics and corresponding preset typical time-series characteristics, the calculation of the feature similarity between the aforementioned monitoring data characteristics and the aforementioned preset typical time-series characteristics includes: Based on the above monitoring data characteristics and the corresponding preset typical time series characteristics, the cosine similarity value is calculated; The aforementioned feature similarity is calculated based on the cosine similarity value and the similarity weight of the transmission channel corresponding to the monitoring time series data.

[0008] Furthermore, it also includes: The above-mentioned monitoring data features are compared with the first preset data feature value and the second preset data feature value, respectively; If the aforementioned monitoring data feature is not less than the aforementioned first preset data feature value and not greater than the aforementioned second preset data feature value, then the fault indication function value is set to the first preset fault indication value; otherwise, the aforementioned fault indication function value is set to the second preset fault indication value; wherein, the aforementioned fault indication function value is used to characterize whether a fault has occurred in the transmission channel corresponding to each monitoring time series data. Based on all fault indication function values ​​and the corresponding fault importance weights of the transmission channels, fault determination parameter values ​​are calculated; wherein, the above fault determination parameter values ​​are used to characterize whether there is a fault risk in all transmission channels as a whole; When the similarity is not less than the preset similarity threshold, the fault determination parameter values ​​are uploaded to the main site.

[0009] Furthermore, based on all fault indication function values ​​and the corresponding fault importance weights of the transmission channels, the fault determination parameter values ​​are calculated, including: Based on the fault indication function value corresponding to each of the above transmission channels and the preset fault importance weight, the weighted fault value of each of the above transmission channels is calculated. Calculate the weighted fault value for all transmission channels to obtain the total fault value; If the total value of the above faults is greater than the preset fault judgment threshold, then the value of the above fault judgment parameter is set to the first preset fault judgment value; otherwise, the value of the above fault judgment parameter is set to the second preset fault judgment value.

[0010] Based on the above method embodiments, the present invention provides corresponding apparatus embodiments; This invention provides a data transmission system for a power distribution automation terminal, comprising: Distribution automation terminals, communication units, and master stations; The aforementioned communication unit is used to acquire monitoring time sequence data within a preset time period and upload the monitoring time sequence data to the aforementioned power distribution automation terminal along the corresponding transmission channels. The aforementioned power distribution automation terminal is used to extract the monitoring data features of the monitoring time sequence data corresponding to each transmission channel after acquiring the monitoring time sequence data uploaded from each transmission channel. Based on the above monitoring data characteristics and the corresponding preset typical time series characteristics, calculate the feature similarity between the above monitoring data characteristics and the above preset typical time series characteristics; If the above similarity is not less than the preset similarity threshold, then the above monitoring time series data will not be uploaded; Otherwise, the compression ratio is calculated based on the above similarity, and the above monitoring data is compressed according to the above compression ratio before the compressed monitoring time series data is uploaded to the main station. The aforementioned main station is used to receive the compressed monitoring time series data when the similarity is less than a preset similarity threshold.

[0011] Furthermore, the aforementioned power distribution automation terminal is also used for: Before extracting the monitoring data features of the monitoring time series data corresponding to each transmission channel, the sequence length of each of the above monitoring time series data is determined based on all the above monitoring time series data. Based on the sequence length and monitoring time series data mentioned above, the verification value of each monitoring time series data is calculated. If the above verification value is less than the first preset verification threshold and not less than the second preset verification threshold, the above monitoring time series data is interpolated and repaired. If the above-mentioned verification value is less than the above-mentioned second preset verification threshold, a data retransmission signal is sent to the above-mentioned communication unit so that the above-mentioned communication unit re-uploads the above-mentioned monitoring time sequence data after receiving the above-mentioned data retransmission signal.

[0012] Furthermore, based on the aforementioned monitoring data characteristics and corresponding preset typical time-series characteristics, the calculation of the feature similarity between the aforementioned monitoring data characteristics and the aforementioned preset typical time-series characteristics includes: Based on the above monitoring data characteristics and the corresponding preset typical time series characteristics, the cosine similarity value is calculated; The aforementioned feature similarity is calculated based on the cosine similarity value and the similarity weight of the transmission channel corresponding to the monitoring time series data.

[0013] Furthermore, the aforementioned power distribution automation terminal is also used for: The above-mentioned monitoring data features are compared with the first preset data feature value and the second preset data feature value, respectively; If the aforementioned monitoring data feature is not less than the aforementioned first preset data feature value and not greater than the aforementioned second preset data feature value, then the fault indication function value is set to the first preset fault indication value; otherwise, the aforementioned fault indication function value is set to the second preset fault indication value; wherein, the aforementioned fault indication function value is used to characterize whether a fault has occurred in the transmission channel corresponding to each monitoring time series data. Based on all fault indication function values ​​and the corresponding fault importance weights of the transmission channels, fault determination parameter values ​​are calculated; wherein, the above fault determination parameter values ​​are used to characterize whether there is a fault risk in all transmission channels as a whole; When the similarity is not less than the preset similarity threshold, the fault determination parameter values ​​are uploaded to the main site.

[0014] Furthermore, based on all fault indication function values ​​and the corresponding fault importance weights of the transmission channels, the fault determination parameter values ​​are calculated, including: Based on the fault indication function value corresponding to each of the above transmission channels and the preset fault importance weight, the weighted fault value of each of the above transmission channels is calculated. Calculate the weighted fault value for all transmission channels to obtain the total fault value; If the total value of the above faults is greater than the preset fault judgment threshold, then the value of the above fault judgment parameter is set to the first preset fault judgment value; otherwise, the value of the above fault judgment parameter is set to the second preset fault judgment value.

[0015] The embodiments of the present invention have the following beneficial effects: This invention provides a data transmission method and system for a power distribution automation terminal. The method includes: acquiring monitoring time-series data uploaded by each transmission channel of a communication unit within a preset time period; subsequently extracting monitoring data features corresponding to the monitoring time-series data of each transmission channel; calculating the feature similarity between the monitoring data features and the corresponding preset typical time-series features based on the monitoring data features and the corresponding preset typical time-series features; if the similarity is not less than a preset similarity threshold, then the monitoring time-series data is not uploaded; otherwise, a compression ratio is calculated based on the similarity, and the monitoring data is compressed according to the compression ratio before uploading the compressed monitoring time-series data to the main station. Therefore, this invention calculates the similarity between the monitored time-series data and the preset typical time-series features. When the similarity is not less than the preset similarity threshold, it indicates that the similarity between the monitored time-series data and the preset typical time-series features is strong, indicating that the accuracy of the data calculation by the distribution automation terminal is high. Therefore, relevant data analysis can be performed directly based on the calculation results of the distribution automation terminal, and it is not necessary to repeatedly transmit similar data to the main station. Otherwise, it indicates that the performance of the distribution automation terminal's calculation is poor, so it is still necessary to upload the original monitored time-series data to the main station for data analysis. At the same time, when uploading to the main station, data compression greatly reduces the redundancy of data upload, thus improving the efficiency of network and storage resource utilization. Attached Figure Description

[0016] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating a data transmission method for a power distribution automation terminal according to an embodiment of the present invention.

[0018] Figure 2 This is a schematic diagram of the data transmission system of a power distribution automation terminal provided in an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0021] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0022] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0023] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0024] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0025] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0026] See Figure 1To address the problem in existing technologies where raw, acquired time-series data is transmitted to the main station without modification, resulting in excessive data redundancy and impacting system efficiency and response speed, this invention provides a data transmission method for a distribution automation terminal, applicable to distribution automation terminals, comprising: Step S101: Acquire the monitoring time sequence data uploaded by each transmission channel of the communication unit within a preset time period; Specifically, the monitoring time-series data includes power data of relevant power equipment in the distribution network and environmental data, such as current data and temperature data.

[0027] Step S102: Extract the monitoring data features of the monitoring time sequence data corresponding to each transmission channel; Specifically, let the i-th monitoring time series data be... ,in For the first Given N sampling times, and a maximum value of N for each sampling time, the characteristics of the monitoring data are as follows: In the formula, This represents the monitoring data characteristics of the i-th monitoring time series data. Let be the mean of the i-th monitoring time series data. Let be the standard deviation of the i-th monitoring time series data. Let i be the maximum rate of change of the i-th monitoring time series data. Let i be the intensity of change in the i-th monitoring time series data. Let i be the first-order difference sequence of the i-th monitoring time series data. Let be the peak factor of the i-th monitoring time series data.

[0028] In a preferred embodiment, before extracting the monitoring data features of the monitoring time series data corresponding to each transmission channel as described above, the process includes: Based on all the above-mentioned monitoring time series data, determine the sequence length of each of the above-mentioned monitoring time series data; Based on the sequence length and monitoring time series data mentioned above, the verification value of each monitoring time series data is calculated. Specifically, the edge computing capabilities of the distribution automation terminal are utilized to perform time-series consistency checks on the data uploaded by the communication unit, verifying the physical and temporal characteristics of the data. When calculating the checksum, the checksum between each pair of monitoring time-series data is first calculated. Then, for each monitoring time-series data, the checksums calculated between that data and the other monitoring time-series data are added together to obtain the corresponding checksum. Taking current and temperature monitoring time-series data as examples, where current corresponds to the i-th monitoring time-series data and temperature corresponds to the i'-th monitoring time-series data, the checksum between them is calculated using the following formula: In the formula, Let represent the checksum between the i-th and i'-th monitoring time series data points, T represent the minimum sequence length between the i-th and i'-th monitoring time series data points, and t represent the time interval. This indicates the increment / decrement symbol corresponding to the i-th monitoring time series data. This indicates the increment / decrement sign corresponding to the i'th monitoring time series data. When the increment / decrement signs corresponding to two monitoring data are the same, ... , This represents the data value of the i-th monitoring time series data at time t. This represents the data value of the i'th monitoring time series data at time t+Δt, where Δt represents the lag time between the change of the i'th monitoring time series data and the change of the i'th monitoring time series data. This represents the sequence length corresponding to the i-th monitoring time series data. This represents the sequence length corresponding to the i'th monitoring time series data.

[0029] Subsequently, the above verification values ​​corresponding to all monitoring time series data are calculated using the following formula: In the formula, Let represent the check value corresponding to the i-th monitoring time series data, and I represent the sequence set composed of all monitoring time series data.

[0030] If the above verification value is less than the first preset verification threshold and not less than the second preset verification threshold, the above monitoring time series data is interpolated and repaired. Specifically, the interpolation repair process is an existing technology and will not be described in detail here.

[0031] For illustration purposes, the first preset verification threshold can be set to 0.9, and the second preset verification threshold can be set to 0.2.

[0032] If the above-mentioned verification value is less than the above-mentioned second preset verification threshold, a data retransmission signal is sent to the above-mentioned communication unit so that the above-mentioned communication unit re-uploads the above-mentioned monitoring time sequence data after receiving the above-mentioned data retransmission signal.

[0033] Specifically, the comparison process between the verification value and the first preset verification threshold and the second preset verification threshold is represented by the following formula: In the formula, This represents the data corresponding to the actual calculated feature similarity and fault determination parameter values ​​for the i-th monitoring time series data. This indicates that the i-th monitoring time series data corresponds to the data after being re-uploaded. This represents the i-th monitoring time series data obtained in step S101. This indicates the second preset verification threshold. This indicates the first preset verification threshold. This represents the i-th monitoring time series data after interpolation and repair.

[0034] Specifically, if the above verification value is not less than the first preset verification threshold, then the i-th monitoring time series data originally acquired will be directly used. This data is used to calculate feature similarity and fault determination parameter values.

[0035] Preferably, the edge computing capabilities of the power distribution automation terminal are utilized to perform timing consistency checks on the data uploaded by the communication unit based on physical and timing characteristics, ensuring timing consistency between different transmission channels. Secondly, a first preset verification threshold and a second preset verification threshold with different values ​​are set. Based on the timing consistency check results, retransmission or interpolation repair is automatically triggered to ensure data accuracy and consistency, significantly improving data accuracy and communication efficiency.

[0036] In this preferred embodiment, the verification value of each monitoring data is obtained by calculating the verification value of the timing consistency between pairs of monitoring time series data.

[0037] Step S103: Based on the above monitoring data characteristics and the corresponding preset typical time series characteristics, calculate the feature similarity between the above monitoring data characteristics and the above preset typical time series characteristics; Specifically, the aforementioned detection data features are mean, standard deviation, maximum rate of change, intensity of change, and peak factor. The preset typical time series features also specifically include the aforementioned detection data features, except that the preset typical time series features are preset feature values.

[0038] In a preferred embodiment, calculating the feature similarity between the monitoring data features and the corresponding preset typical time-series features based on the monitoring data features and the corresponding preset typical time-series features includes: Based on the above monitoring data characteristics and the corresponding preset typical time series characteristics, the cosine similarity value is calculated; Specifically, the calculation of cosine similarity is a current technique and will not be elaborated upon here. Feature similarity is calculated using the following formula: In the formula, S represents the feature similarity. This represents the preset data channel similarity weight of the transmission channel corresponding to the i-th monitoring time series data. This represents the cosine similarity corresponding to the i-th monitoring time series data. This represents the monitoring data characteristics corresponding to the i-th monitoring time series data. This represents the preset typical time series characteristics corresponding to the i-th monitoring time series data.

[0039] The aforementioned feature similarity is calculated based on the cosine similarity value and the similarity weight of the transmission channel corresponding to the monitoring time series data.

[0040] In this preferred embodiment, feature similarity is calculated by monitoring data features and corresponding preset typical time-series features.

[0041] Step S104: If the above similarity is not less than the preset similarity threshold, then the above monitoring time series data will not be uploaded; otherwise, the compression rate is calculated based on the above similarity, and the above monitoring data is compressed according to the above compression rate, and then the compressed monitoring time series data is uploaded to the main station.

[0042] Specifically, the compression ratio is calculated using the following formula: In the formula, represents the compression ratio, and 'b' represents a positive constant used to control the sensitivity of the compression ratio to changes in similarity. This indicates the preset similarity threshold.

[0043] Preferably, the compression ratio is inversely proportional to the similarity, in order to reduce the uploading of redundant data with high similarity.

[0044] Preferably, within the distribution automation terminal, compressed monitoring time-series data is uploaded only when the similarity is relatively low. This reduces the uploading of redundant data, improves the utilization efficiency of network and storage resources, and achieves efficient interconnection between the distribution automation terminal and the master station.

[0045] In a preferred embodiment, it further includes: The above-mentioned monitoring data features are compared with the first preset data feature value and the second preset data feature value, respectively; If the aforementioned monitoring data feature is not less than the aforementioned first preset data feature value and not greater than the aforementioned second preset data feature value, then the fault indication function value is set to the first preset fault indication value; otherwise, the aforementioned fault indication function value is set to the second preset fault indication value; wherein, the aforementioned fault indication function value is used to characterize whether a fault has occurred in the transmission channel corresponding to each monitoring time series data. Specifically, the first preset fault indication value can be set to 0, and the second preset fault indication value can be set to 1. Therefore, the fault indication function value is calculated using the following formula: In the formula, This represents the fault indication function value corresponding to the i-th monitoring time series data. This represents the first preset data feature value. This represents the second preset data feature value.

[0046] For illustration, if the final value of the fault indication function is 1, it indicates that the corresponding transmission channel has fault characteristics; if the final value of the fault indication function is 0, it indicates that the corresponding transmission channel does not have fault characteristics.

[0047] Based on all fault indication function values ​​and the corresponding fault importance weights of the transmission channels, fault determination parameter values ​​are calculated; wherein, the above fault determination parameter values ​​are used to characterize whether there is a fault risk in all transmission channels as a whole; Specifically, the aforementioned fault importance weights are preset values, determined based on the importance of the corresponding transmission channel and the reliability of historical data.

[0048] When the similarity is not less than the preset similarity threshold, the fault determination parameter values ​​are uploaded to the main site.

[0049] Specifically, if the similarity is not less than the preset similarity threshold, it means that the monitoring data characteristics of the transmitted monitoring time series data are highly similar to the preset typical time series characteristics, and only the fault judgment parameter value F needs to be uploaded to the main station; otherwise, it means that the similarity is weak, and at the same time as uploading the fault judgment parameter value F, it is also necessary to compress the original monitoring time series data before uploading it to the main station.

[0050] Preferably, based on the fault indication function value and the fault judgment parameter value, fault assessment and other services can be performed on the power equipment of the distribution network.

[0051] Preferably, after receiving F, the master station performs data analysis based on F and the original monitoring time series data, such as extracting more typical data time series features, strengthening the training of the algorithm model, and then distributing it to the edge through lightweight processing to realize the update of the edge lightweight algorithm model and typical data time series features.

[0052] In this preferred embodiment, the fault indication function value is determined by comparing the monitoring data features, the first preset data feature value, and the second preset data feature value. Furthermore, the fault judgment parameter value is calculated by combining the fault importance weight of the transmission channel. When the similarity is not less than the preset similarity threshold, the fault judgment parameter value is uploaded to the main station.

[0053] In another preferred embodiment, the fault determination parameter value calculated based on all fault indication function values ​​and the corresponding fault importance weights of the transmission channels includes: Based on the fault indication function value corresponding to each of the above transmission channels and the preset fault importance weight, the weighted fault value of each of the above transmission channels is calculated. Calculate the weighted fault value for all transmission channels to obtain the total fault value; If the total value of the above faults is greater than the preset fault judgment threshold, then the value of the above fault judgment parameter is set to the first preset fault judgment value; otherwise, the value of the above fault judgment parameter is set to the second preset fault judgment value.

[0054] Specifically, the first preset fault determination value can be set to 1, and the second preset fault determination value can be set to 0. Therefore, the fault determination parameter value is calculated according to the following formula: In the formula, F represents the fault determination parameter value. This represents the preset fault importance weight corresponding to the i-th monitoring time series data. This indicates the preset fault determination threshold.

[0055] In this preferred embodiment, the fault determination parameter value is calculated using the fault indication function value and the preset fault importance weight.

[0056] Based on the above method embodiments, the present invention provides corresponding apparatus embodiments; like Figure 2 As shown, an embodiment of the present invention provides a data transmission system for a power distribution automation terminal, comprising: Distribution automation terminals, communication units, and master stations; The aforementioned communication unit is used to acquire monitoring time sequence data within a preset time period and upload the monitoring time sequence data to the aforementioned power distribution automation terminal along the corresponding transmission channels. The aforementioned power distribution automation terminal is used to extract the monitoring data features of the monitoring time sequence data corresponding to each transmission channel after acquiring the monitoring time sequence data uploaded from each transmission channel. Based on the above monitoring data characteristics and the corresponding preset typical time series characteristics, calculate the feature similarity between the above monitoring data characteristics and the above preset typical time series characteristics; If the above similarity is not less than the preset similarity threshold, then the above monitoring time series data will not be uploaded; Otherwise, the compression ratio is calculated based on the above similarity, and the above monitoring data is compressed according to the above compression ratio before the compressed monitoring time series data is uploaded to the main station. The aforementioned main station is used to receive the compressed monitoring time series data when the similarity is less than a preset similarity threshold.

[0057] Specifically, the aforementioned communication unit is a southbound communication unit.

[0058] Furthermore, the aforementioned power distribution automation terminal is also used for: Before extracting the monitoring data features of the monitoring time series data corresponding to each transmission channel, the sequence length of each of the above monitoring time series data is determined based on all the above monitoring time series data. Based on the sequence length and monitoring time series data mentioned above, the verification value of each monitoring time series data is calculated. If the above verification value is less than the first preset verification threshold and not less than the second preset verification threshold, the above monitoring time series data is interpolated and repaired. If the above-mentioned verification value is less than the above-mentioned second preset verification threshold, a data retransmission signal is sent to the above-mentioned communication unit so that the above-mentioned communication unit re-uploads the above-mentioned monitoring time sequence data after receiving the above-mentioned data retransmission signal.

[0059] Furthermore, based on the aforementioned monitoring data characteristics and corresponding preset typical time-series characteristics, the calculation of the feature similarity between the aforementioned monitoring data characteristics and the aforementioned preset typical time-series characteristics includes: Based on the above monitoring data characteristics and the corresponding preset typical time series characteristics, the cosine similarity value is calculated; The aforementioned feature similarity is calculated based on the cosine similarity value and the similarity weight of the transmission channel corresponding to the monitoring time series data.

[0060] Furthermore, the aforementioned power distribution automation terminal is also used for: The above-mentioned monitoring data features are compared with the first preset data feature value and the second preset data feature value, respectively; If the aforementioned monitoring data feature is not less than the aforementioned first preset data feature value and not greater than the aforementioned second preset data feature value, then the fault indication function value is set to the first preset fault indication value; otherwise, the aforementioned fault indication function value is set to the second preset fault indication value; wherein, the aforementioned fault indication function value is used to characterize whether a fault has occurred in the transmission channel corresponding to each monitoring time series data. Based on all fault indication function values ​​and the corresponding fault importance weights of the transmission channels, fault determination parameter values ​​are calculated; wherein, the above fault determination parameter values ​​are used to characterize whether there is a fault risk in all transmission channels as a whole; When the similarity is not less than the preset similarity threshold, the fault determination parameter values ​​are uploaded to the main site.

[0061] Furthermore, based on all fault indication function values ​​and the corresponding fault importance weights of the transmission channels, the fault determination parameter values ​​are calculated, including: Based on the fault indication function value corresponding to each of the above transmission channels and the preset fault importance weight, the weighted fault value of each of the above transmission channels is calculated. Calculate the weighted fault value for all transmission channels to obtain the total fault value; If the total value of the above faults is greater than the preset fault judgment threshold, then the value of the above fault judgment parameter is set to the first preset fault judgment value; otherwise, the value of the above fault judgment parameter is set to the second preset fault judgment value.

[0062] It should be noted that the device embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationship between modules indicates that they have a communication connection, which can be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without creative effort. The above schematic diagrams are merely examples of a data transmission system for a power distribution automation terminal and do not constitute a limitation on a data transmission system for a power distribution automation terminal. It may include more or fewer components than illustrated, or combine certain components, or use different components.

[0063] The above are preferred embodiments of the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A data transmission method for a power distribution automation terminal, characterized by, The application is suitable for power distribution automation terminal, comprising: obtaining monitoring time sequence data transmitted by each transmission channel of the communication unit within a preset period; extracting monitoring data features of the monitoring time sequence data corresponding to each transmission channel; calculating feature similarity between the monitoring data features and preset typical time sequence features according to the monitoring data features and the corresponding preset typical time sequence features; if the similarity is not less than a preset similarity threshold, the monitoring time sequence data is not uploaded; otherwise, a compression rate is calculated according to the similarity, the monitoring data is compressed according to the compression rate, and the compressed monitoring time sequence data is uploaded to the master station.

2. The data transmission method of a power distribution automation terminal according to claim 1, wherein, Before extracting the monitoring data features of the monitoring time sequence data corresponding to each transmission channel, comprising: determining the sequence length of each monitoring time sequence data according to all the monitoring time sequence data; calculating a check value of each monitoring time sequence data according to the sequence length and the monitoring time sequence data; in the case that the check value is less than a first preset check threshold and not less than a second preset check threshold, interpolating and repairing the monitoring time sequence data; in the case that the check value is less than the second preset check threshold, sending a data retransmission signal to the communication unit, so that the communication unit reuploads the monitoring time sequence data after receiving the data retransmission signal.

3. The data transmission method of a power distribution automation terminal according to claim 2, wherein, The calculation of the feature similarity between the monitoring data features and the preset typical time sequence features according to the monitoring data features and the corresponding preset typical time sequence features comprises: calculating a cosine similarity value according to the monitoring data features and the corresponding preset typical time sequence features; calculating the feature similarity according to the cosine similarity value and the similarity weight of the transmission channel corresponding to the monitoring time sequence data.

4. The data transmission method of a power distribution automation terminal according to claim 3, wherein, Further comprising: comparing the monitoring data features with a first preset data feature value and a second preset data feature value respectively; if the monitoring data feature is not less than the first preset data feature value and not greater than the second preset data feature value, setting a fault indication function value as a first preset fault indication value; otherwise, setting the fault indication function value as a second preset fault indication value; wherein the fault indication function value is used to represent whether the transmission channel corresponding to each monitoring time sequence data has a fault; calculating a fault determination parameter value according to all the fault indication function values and the fault importance weight of the corresponding transmission channel; wherein the fault determination parameter value is used to represent whether there is a fault risk in all transmission channels as a whole; uploading the fault determination parameter value to the master station when the similarity is not less than the preset similarity threshold.

5. The data transmission method of a power distribution automation terminal according to claim 4, wherein, The calculation of the fault determination parameter value according to all the fault indication function values and the fault importance weight of the corresponding transmission channel comprises: calculating a weighted fault value of each transmission channel according to the fault indication function value corresponding to each transmission channel and a preset fault importance weight; calculating the weighted fault value of all transmission channels to obtain a total fault value; If the total fault value is greater than a preset fault determination threshold, the fault determination parameter value is set as a first preset fault determination value; otherwise, the fault determination parameter value is set as a second preset fault determination value.

6. A data transfer system for a power distribution automation terminal, characterized by The method comprises the steps of: The power distribution automation terminal, the communication unit, and the master station; The communication unit is configured to acquire monitoring time series data within a preset time period and upload the monitoring time series data to the power distribution automation terminal along corresponding transmission channels respectively; The power distribution automation terminal is configured to extract monitoring data features of the monitoring time series data corresponding to each transmission channel after acquiring the monitoring time series data uploaded by each transmission channel; According to the monitoring data features and corresponding preset typical time series features, a feature similarity between the monitoring data features and the preset typical time series features is calculated; If the similarity is not less than a preset similarity threshold, the monitoring time series data is not uploaded; Otherwise, a compression rate is calculated according to the similarity, the monitoring data is compressed according to the compression rate, and the compressed monitoring time series data is uploaded to the master station; The master station is configured to receive the compressed monitoring time series data when the similarity is less than the preset similarity threshold.

7. A data transmission system for a power distribution automation terminal according to claim 6, characterized in that, The power distribution automation terminal is further configured to: Before extracting the monitoring data features of the monitoring time series data corresponding to each transmission channel, the sequence length of each monitoring time series data is determined according to all the monitoring time series data; According to the sequence length and the monitoring time series data, a check value of each monitoring time series data is calculated; In the case that the check value is less than a first preset check threshold and not less than a second preset check threshold, the monitoring time series data is interpolated and repaired; In the case that the check value is less than the second preset check threshold, a data retransmission signal is sent to the communication unit, so that the communication unit reuploads the monitoring time series data after receiving the data retransmission signal.

8. The data transmission system of an electric power distribution automation terminal according to claim 7, wherein The calculation of the feature similarity between the monitoring data features and the preset typical time series features according to the monitoring data features and the corresponding preset typical time series features comprises: According to the monitoring data features and the corresponding preset typical time series features, a cosine similarity value is calculated; According to the cosine similarity value and the similarity weight of the transmission channel corresponding to the monitoring time series data, the feature similarity is calculated.

9. The data transmission system of an electric power distribution automation terminal according to claim 8, wherein, The power distribution automation terminal is further configured to: The monitoring data features are compared with a first preset data feature value and a second preset data feature value respectively; If the monitoring data features are not less than the first preset data feature value and not greater than the second preset data feature value, a fault indication function value is set as a first preset fault indication value; otherwise, the fault indication function value is set as a second preset fault indication value; wherein the fault indication function value is used to represent whether a fault occurs in the transmission channel corresponding to each monitoring time series data; According to all the fault indication function values and the fault importance weight of the corresponding transmission channel, a fault determination parameter value is calculated; wherein the fault determination parameter value is used to represent whether there is a fault risk in all the transmission channels as a whole. When the similarity is not less than a preset similarity threshold, the fault determination parameter value is uploaded to a master station.

10. The data transmission system of an electric power distribution automation terminal according to claim 9, wherein, The fault determination parameter value is calculated according to all the fault indication function values and the fault importance weights of the corresponding transmission channels, and includes: A weighted fault value of each transmission channel is calculated according to the fault indication function value of the corresponding transmission channel and a preset fault importance weight. The weighted fault values of all the transmission channels are calculated to obtain a fault total value. If the fault total value is greater than a preset fault determination threshold, the fault determination parameter value is a first preset fault determination value; otherwise, the fault determination parameter value is a second preset fault determination value.

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