Data processing method and device applied to power transmission line, equipment and medium

Abstract

The embodiment of the invention provides a data processing method and device applied to a power transmission line, equipment and a medium. The method comprises the steps that the power transmission line in a target area is acquired; for the plurality of power transmission sections, determining a target fault type of the power transmission associated data according to the power transmission associated data and the fault type data; inputting the power transmission associated data of the power transmission section into the power transmission line operation state evaluation model, outputting a section detection value, and determining the operation state of the power transmission section based on the section detection value; determining a road section detection value according to the plurality of section detection values, and determining a trend prediction parameter of the power transmission line according to the road section detection value and a historical detection value; and according to the trend prediction parameter, presetting a risk threshold, and determining operation information of the power transmission line in the prediction duration. According to the technical scheme, the risk threshold value is preset based on the trend prediction parameter, the operation information of the power transmission line is determined, accurate monitoring of the power transmission line is achieved, and the safety of the power transmission line is improved.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the technical field of power transmission line monitoring systems, and in particular to a data processing method, device, equipment, and medium applied to power transmission lines. Background Art

[0002] With the development of science and technology, the scale of transmission lines is constantly expanding. A large number of transmission lines are exposed to the natural environment for a long time and are easily affected by various factors such as weather and equipment aging, which can cause failures to the transmission lines. For example, freezing rain may cause failures in the network such as phase-to-phase and phase-to-ground flashover of compact transmission lines. Therefore, how to monitor the operating status of transmission lines and determine the operating information of transmission lines is the key to improving the safety and operating efficiency of the power system.

[0003] The current method for determining the operation information of transmission lines is mainly to deploy sensors and monitoring equipment on the transmission lines, collect the operation data of the transmission lines in real time, calculate and analyze the collected parameters, and then judge the health status of the transmission lines. However, this method only monitors the transmission lines in real time, and there are problems such as low accuracy in risk prevention and low maintenance efficiency. Summary of the invention

[0004] The embodiments of the present disclosure provide a data processing method, device, equipment and medium applied to a transmission line, so as to preset a risk threshold based on trend prediction parameters, determine the operation information of the transmission line, realize accurate monitoring of the transmission line, and improve the safety of the transmission line.

[0005] In a first aspect, an embodiment of the present disclosure provides a data processing method applied to a power transmission line, characterized in that the method comprises:

[0006] Acquire a plurality of power transmission lines in a target area, wherein the power transmission lines are composed of a plurality of power transmission sections;

[0007] For the plurality of power transmission sections, determining a target fault type of the power transmission related data according to the power transmission related data of the power transmission sections and the fault type data corresponding to different fault types in the fault model library;

[0008] Inputting the transmission-related data of the transmission section within a first preset time length into a pre-trained transmission line operation status assessment model, outputting a section detection value of the transmission section, so as to determine the operation status of the transmission section based on the section detection value; wherein the first preset time length is a preset time length before the current moment;

[0009] Determine a section detection value of the transmission line according to a plurality of section detection values ​​of each transmission line, and determine a trend prediction parameter of the transmission line according to the section detection value and the historical detection value within the first preset time period;

[0010] A risk threshold is preset according to the trend prediction parameter to determine the operation information of the transmission line within the prediction time.

[0011] In a second aspect, an embodiment of the present invention further provides a data processing device applied to a power transmission line, characterized in that it includes:

[0012] A transmission line acquisition module, used to acquire a plurality of transmission lines in a target area, wherein the transmission line is composed of a plurality of transmission sections;

[0013] a target fault type determination module, configured to determine, for the plurality of transmission sections, a target fault type of the transmission-related data according to the transmission-related data of the transmission sections and the fault type data corresponding to different fault types in the fault model library;

[0014] An operation status determination module is used to input the transmission-related data of the transmission section within a first preset time length into a pre-trained transmission line operation status assessment model, and output a section detection value of the transmission section, so as to determine the operation status of the transmission section based on the section detection value; wherein the first preset time length is a preset time length before the current moment;

[0015] A trend prediction parameter determination module, used to determine a section detection value of each transmission line according to a plurality of section detection values ​​of the transmission line, and determine a trend prediction parameter of the transmission line according to the section detection value and a historical detection value within the first preset time period;

[0016] The operation information determination module is used to preset a risk threshold according to the trend prediction parameter and determine the operation information of the transmission line within the prediction time.

[0017] In a third aspect, an embodiment of the present invention further provides an electronic device, the electronic device comprising:

[0018] one or more processors;

[0019] a storage device for storing one or more programs,

[0020] When the one or more programs are executed by the one or more processors, the one or more processors implement the data processing method applied to a power transmission line as described in any one of the embodiments of the present invention.

[0021] In a fourth aspect, an embodiment of the present invention further provides a storage medium comprising computer executable instructions, wherein the computer executable instructions, when executed by a computer processor, are used to execute a data processing method applied to a power transmission line as described in any one of the embodiments of the present invention.

[0022] The technical solution of the embodiment of the present disclosure obtains multiple transmission lines in a target area, wherein the transmission lines are composed of multiple transmission sections, and then, for the multiple transmission sections, according to the transmission-related data of the transmission sections and the fault type data corresponding to different fault types in the fault model library, the target fault type of the transmission-related data is determined, and then, the transmission-related data of the transmission sections within a first preset time length is input into a transmission line operation status evaluation model obtained by pre-training, and the section detection value of the transmission section is output, so as to determine the operation status of the transmission section based on the section detection value. state, wherein the first preset time length is the preset time length before the current moment, further, according to the multiple section detection values ​​of each transmission line, the section detection value of the transmission line is determined, and according to the section detection value and the historical detection value within the first preset time length, the trend prediction parameter of the transmission line is determined, and finally, according to the trend prediction parameter preset risk threshold, the operation information of the transmission line within the prediction time length is determined, which solves the problem that when the operation information of the transmission line is determined in the prior art, the transmission line is monitored in real time, and there is a low accuracy and low maintenance efficiency of the risk prevention of the transmission line. The embodiment of the present invention adopts a preset risk threshold based on the trend prediction parameter to determine the operation information of the transmission line, realizes accurate monitoring of the transmission line, and obtains the effect of improving the safety of the transmission line. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and the originals and elements are not necessarily drawn to scale.

[0024] Figure 1 is a flow chart of a data processing method applied to a power transmission line provided by an embodiment of the present disclosure;

[0025] Figure 2 is a flow chart of another data processing method applied to a power transmission line provided by an embodiment of the present disclosure;

[0026] Figure 3 A schematic diagram of the structure of a data processing device applied to a power transmission line provided by an embodiment of the present invention;

[0027] Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0028] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0029] Embodiment 1

[0030] Before introducing the technical solution provided by the embodiment of the present disclosure, an exemplary description of the application scenario can be given. The technical solution provided by the embodiment of the present disclosure can be applied to any scenario where a risk threshold is preset based on trend prediction parameters to determine the operation information of a transmission line within the prediction period. Optionally, the operation information may be that the operation status of the transmission line may be abnormal in the future.

[0031] Figure 1 It is a flow chart of a data processing method applied to a transmission line provided by an embodiment of the present disclosure. The embodiment of the present disclosure is applicable to situations where the operating information of a transmission line is determined within a predicted duration. The method can be executed by a data processing device applied to a transmission line. The device can be implemented in the form of software and / or hardware. The hardware can be an electronic device such as a server. The electronic device can execute the data processing method applied to a transmission line provided by the technical solution.

[0032] like Figure 1 As shown, the method includes:

[0033] S110. Acquire multiple transmission lines in the target area, wherein the transmission lines are composed of multiple transmission sections.

[0034] Among them, the target area refers to the specific geographical range or area where the transmission line data is desired to be obtained. For example, the target area can be based on the existing transmission line data, and the area that needs to be focused on is analyzed and determined. The target area can also be a province, city or county, etc. The transmission line is a power transmission system used to transmit electric energy from a power plant to a substation or directly to the user. Transmission lines can be divided into high-voltage transmission lines and low-voltage transmission lines. The transmission section refers to a specific transmission line in the power transmission system. These sections are responsible for transmitting electric energy from the power generation source to the load center or substation in the power system. It should be noted that the division of transmission sections can be based on a variety of factors, including voltage level, geographical location, transmission capacity, and system design and operation requirements.

[0035] Specifically, the specific geographical area for which the transmission line data is to be obtained is identified, and then multiple transmission lines in the target area are obtained. Each transmission line is usually composed of multiple transmission sections, and each transmission section of each transmission line can be obtained based on the starting point, end point and intermediate node of the transmission line.

[0036] S120. For multiple transmission sections, determine the target fault type of the transmission-related data according to the transmission-related data of the transmission sections and the fault type data corresponding to different fault types in the fault model library.

[0037] Among them, transmission-related data usually include line parameters, operating conditions, and line protection settings and action information of the transmission section. The fault model library refers to a model library pre-set based on the fault data of the historical transmission section or industry specifications. The fault mode library stores various known fault types and fault type data corresponding to each fault type. For example, the fault types of the fault mode library may include: single-phase ground fault, phase-to-phase short circuit fault, two-phase ground short circuit fault, and three-phase short circuit fault. Fault type data refers to the typical data feature quantity of the transmission section under the corresponding fault type. Typical data feature quantities may include a combination of current, voltage, phase flow, harmonic components, and meteorological data. The target fault type refers to the fault type determined by comparing the data collected by real-time measurement or fault recorder with the fault type data in the fault model library for similarity.

[0038] Specifically, for each transmission section, the transmission-related data of each transmission section is obtained, and then after the transmission-related data is summarized and sorted, the transmission-related data is compared with the fault type data corresponding to different fault types in the fault model library for similarity, so as to determine the fault type corresponding to the transmission section.

[0039] In this embodiment, the associated data to be used under the first field type in the power transmission associated data is obtained; the key feature data that does not meet the preset conditions in the associated data to be used is input into the fault type determination model, and the target fault type corresponding to the key feature data is output.

[0040] The transmission-related data may include data of multiple field types, and the first field type may be a data type of various data related to the operation of the transmission section and data related to the environment. The associated data to be used may include data such as the resistance of the transmission section, the reactance of the transmission section, the real-time load of the transmission section, the voltage of the transmission section, the current of the transmission section, the frequency of the transmission section, the harmonics of the transmission section, the wind speed of the transmission section, the rainfall of the transmission section, and the ice thickness of the wires in the transmission section.

[0041] It should be noted that the preset condition refers to the associated data to be used being within the corresponding threshold range. Specifically: based on historical data, expert knowledge or standard specifications, a reasonable threshold range is set for each associated data under the first field type in the transmission associated data; the associated data to be used under the first field type in the real-time transmission associated data is compared with the preset threshold range to detect whether the associated data to be used under the first field type in the transmission associated data exceeds the corresponding threshold range; when the associated data to be used exceeds the corresponding threshold range, it indicates that the associated data to be used does not meet the preset condition. Key feature data refers to the collection of associated data whose associated data to be used does not meet the preset condition.

[0042] It should also be noted that the fault type determination model refers to a pre-trained fault type determination model. In order to improve the accuracy of model training, key feature data corresponding to different fault types can be obtained and used as data to be trained. The sum of all data to be trained constitutes a training sample set. That is, the training sample set includes multiple data to be trained. The data to be trained is only relative and is not a specific limitation.

[0043] Among them, the model parameters in the fault type determination model are initial parameters, or a model with default parameters. The fault type is the fault type output after the current data to be trained is input into the fault type determination model to be trained. It should be noted that the model parameters in the fault type determination model to be trained do not meet the expected requirements. Therefore, there is a certain difference between the actual fault type and the theoretical fault type output based on the model parameters at this time. Therefore, the corresponding error loss value can be determined based on the actual fault type and the theoretical fault type corresponding to each set of data to be trained.

[0044] In this embodiment, the fault type determination model to be trained can be a resnet network model. It should be noted that the fault type corresponding to the data to be trained can be obtained, that is, the specific model type is not specifically limited. It should be noted that the training parameters can be set to default values ​​before training the fault type determination model to be trained. When training the fault type determination model to be trained, the training parameters in the model can be corrected based on the output results of the fault type determination model to be trained, that is, the fault type determination model can be obtained by correcting the loss function in the fault type determination model to be trained. Each set of data to be trained has a corresponding loss value, which is determined based on the actual fault type of each data to be trained.

[0045] Specifically, after the data to be trained is input into the fault type determination model to be trained, the fault type determination model to be trained can obtain the actual fault type corresponding to the data to be trained. According to the actual fault type, the loss value corresponding to the data to be trained can be determined, and the model parameters in the fault type determination model to be trained can be corrected by using the back-propagation method.

[0046] Specifically, the training error of the loss function, that is, the loss parameter, can be used as a condition for detecting whether the loss function has reached convergence, such as whether the training error is less than the preset error or whether the error change trend tends to be stable, or whether the current number of iterations is equal to the preset number. If the detection reaches the convergence condition, such as the training error of the loss function is less than the preset error or the error change tends to be stable, it indicates that the training of the fault type determination model to be trained is completed, and the iterative training can be stopped at this time. If it is detected that the convergence condition is not met at present, the data to be trained can be further obtained to train the fault type determination model to be trained until the training error of the loss function is within the preset range. When the training error of the loss function reaches convergence, the fault type determination model to be trained can be used as the fault type determination model.

[0047] Specifically, for the transmission-related data, the data under the first field type is obtained, that is, the associated data to be used is obtained. When one or more associated data to be used are monitored to exceed the threshold range, these key features are marked and these data are called key feature data. After the key feature data is obtained, the key feature data is input into the pre-trained fault type determination model, and the target fault type corresponding to the key feature data can be output.

[0048] Exemplarily, after obtaining the data of the first field type of the real-time transmission-related data, the to-be-used related data is compared with the preset threshold range, and the key characteristic data obtained are the current of the transmission section and the rainfall of the transmission section. After the key characteristic data is input into the pre-trained fault type determination model, the fault type corresponding to the key characteristic data of the current of the transmission section and the rainfall of the transmission section can be output.

[0049] S130. Input the transmission-related data of the transmission section within a first preset time length into a pre-trained transmission line operation status assessment model, and output the section detection value of the transmission section to determine the operation status of the transmission section based on the section detection value; wherein the first preset time length is a preset time length before the current moment.

[0050] Among them, in the power system, in order to evaluate the operating status of the transmission section, it is usually necessary to collect various types of data related to the transmission section within a fixed time window, that is, within a first preset time period. This time period may be one month, depending on the transmission line operating status evaluation model's requirements for data update frequency or historical data accumulation. The section detection value is a quantitative score value used to describe the operating health status of the transmission section. The larger the section monitoring value, the better the operating status of the transmission section.

[0051] It should also be noted that the transmission line operation status assessment model refers to a model obtained by pre-training to determine the section detection value of the transmission section. In order to improve the accuracy of model training, different transmission-related data can be obtained and the transmission-related data can be used as the data to be trained. The sum of all the data to be trained constitutes the training sample set. That is, the training sample set includes multiple data to be trained. The data to be trained is only relative and is not a specific limitation on it.

[0052] Among them, the model parameters in the transmission line operation status assessment model are initial parameters, or models with default parameters. The section detection value of the transmission section is the section detection value output after the current data to be trained is input into the transmission line operation status assessment model to be trained. It should be noted that the model parameters in the transmission line operation status assessment model to be trained do not meet the expected requirements. Therefore, there is a certain difference between the actual section detection value and the theoretical section detection value output based on the model parameters at this time. Therefore, the corresponding error loss value can be determined based on the actual section detection value and the theoretical section detection value corresponding to each set of data to be trained.

[0053] In this embodiment, the transmission line operation status assessment model to be trained can be a resnet network model. It should be noted that it is mainly necessary to obtain the section detection value corresponding to the data to be trained, that is, the specific model type is not specifically limited. It should be noted that the training parameters can be set to default values ​​before training the transmission line operation status assessment model to be trained. When training the transmission line operation status assessment model to be trained, the training parameters in the model can be corrected based on the output results of the transmission line operation status assessment model to be trained, that is, the transmission line operation status assessment model can be obtained by correcting the loss function in the transmission line operation status assessment model to be trained. Each group of data to be trained has a corresponding loss value, which is determined based on the actual section detection value of each data to be trained.

[0054] Specifically, after the data to be trained is input into the transmission line operation status assessment model to be trained, the transmission line operation status assessment model to be trained can obtain the actual section detection value corresponding to the data to be trained. According to the actual section detection value, the loss value corresponding to the data to be trained can be determined, and the model parameters in the transmission line operation status assessment model to be trained can be corrected by using the back-transfer method.

[0055] Specifically, the training error of the loss function, that is, the loss parameter, can be used as a condition for detecting whether the loss function has reached convergence at present, such as whether the training error is less than the preset error or whether the error change trend tends to be stable, or whether the current number of iterations is equal to the preset number. If the detection reaches the convergence condition, such as the training error of the loss function is less than the preset error or the error change tends to be stable, it indicates that the training of the transmission line operation status evaluation model to be trained is completed, and the iterative training can be stopped at this time. If it is detected that the convergence condition is not reached at present, the training data to be trained can be further obtained to train the transmission line operation status evaluation model to be trained until the training error of the loss function is within the preset range. When the training error of the loss function reaches convergence, it can be considered that the training of the transmission line operation status evaluation model is completed. At this time, in order to verify the accuracy of the transmission line operation status evaluation model, a test set is obtained, and the test set contains various transmission-related data and actual section detection values. After the transmission-related data is input into the transmission line operation status assessment model, the transmission line operation status assessment model can obtain the assessment results. After all test sets have obtained the section detection values, the probability that the section detection values ​​of the transmission line operation status assessment model are correct can be obtained. When the accuracy reaches a certain score, the training of the transmission line operation status assessment model is completed and can be used as the target transmission line operation status assessment model.

[0056] Specifically, after obtaining the transmission-related data of the transmission section within the first preset time period, the transmission-related data is input into a pre-trained transmission line operation status assessment model. The input data of the model is the transmission-related data of the transmission section, and the output result of the model is the section detection value of the transmission section.

[0057] S140, determining a section detection value of the transmission line according to multiple section detection values ​​of each transmission line, and determining a trend prediction parameter of the transmission line according to the section detection value and historical detection values ​​within a first preset time period.

[0058] It should be noted that the section detection value refers to a quantitative score used to describe the health status of the entire transmission line. The larger the section detection value, the better the operating status of the transmission line. The historical detection value refers to the fault anomaly information of all transmission line sections of the transmission line within the first preset time period. The trend prediction parameter refers to the parameter that reflects the future operating trend of the transmission line.

[0059] Specifically, after obtaining multiple section detection values ​​of each transmission line, the section detection value of the transmission line is determined according to the section detection value calculation formula of the transmission line. Based on the section detection value and the fault abnormality information of all transmission line sections of the transmission line within the first preset time period, the trend prediction parameter of the transmission line is determined.

[0060] S150. Preset a risk threshold according to trend prediction parameters and determine operation information of the transmission line within the prediction period.

[0061] Specifically, the trend prediction parameter is compared with a preset abnormal risk threshold. When the trend prediction parameter is greater than the abnormal risk threshold, it is determined that the transmission line may have an abnormal operating state in the future.

[0062] Furthermore, when it is predicted that the operation status of the transmission line may be abnormal in the future, the specific fault type corresponding to the fault abnormality within the predicted time is obtained, and the fault types are sorted from large to small according to the number of times they occur within the predicted time. Based on the sorting results, combined with the common causes and treatment solutions corresponding to the fault types obtained from third-party data sources or historical experience, optimized maintenance suggestions are generated.

[0063] The technical solution of the embodiment of the present disclosure obtains multiple transmission lines in a target area, wherein the transmission lines are composed of multiple transmission sections, and then, for the multiple transmission sections, according to the transmission-related data of the transmission sections and the fault type data corresponding to different fault types in the fault model library, the target fault type of the transmission-related data is determined, and then, the transmission-related data of the transmission sections within a first preset time length is input into a transmission line operation status evaluation model obtained by pre-training, and the section detection value of the transmission section is output, so as to determine the operation status of the transmission section based on the section detection value. state, wherein the first preset time length is the preset time length before the current moment, further, according to the multiple section detection values ​​of each transmission line, the section detection value of the transmission line is determined, and according to the section detection value and the historical detection value within the first preset time length, the trend prediction parameter of the transmission line is determined, and finally, according to the trend prediction parameter preset risk threshold, the operation information of the transmission line within the prediction time length is determined, which solves the problem that when the operation information of the transmission line is determined in the prior art, the transmission line is monitored in real time, and there is a low accuracy and low maintenance efficiency of the risk prevention of the transmission line. The embodiment of the present invention adopts a preset risk threshold based on the trend prediction parameter to determine the operation information of the transmission line, realizes accurate monitoring of the transmission line, and obtains the effect of improving the safety of the transmission line.

[0064] Embodiment 2

[0065] Figure 2A flow chart of another data processing method applied to a transmission line provided in an embodiment of the present invention. On the basis of the aforementioned embodiment, "determining the section detection value of the transmission line according to the multiple section detection values ​​of each transmission line, and determining the trend prediction parameter of the transmission line according to the section detection value and the historical detection value within a first preset time period" can be further refined. The specific implementation method can be found in the detailed description of this embodiment, wherein the technical terms that are the same as or corresponding to the above-mentioned embodiments are not repeated in this embodiment.

[0066] like Figure 2 As shown, the method includes:

[0067] S210. Acquire multiple transmission lines in the target area, wherein the transmission lines are composed of multiple transmission sections.

[0068] S220. For a plurality of transmission sections, determine a target fault type of the transmission-related data according to the transmission-related data of the transmission sections and the fault type data corresponding to different fault types in the fault model library.

[0069] S230, inputting the transmission-related data of the transmission section within a first preset time length into a pre-trained transmission line operation status assessment model, outputting the section detection value of the transmission section, so as to determine the operation status of the transmission section based on the section detection value; wherein the first preset time length is a preset time length before the current moment.

[0070] S240. For each transmission line, determine a section detection value of the transmission line according to the number of sections of the transmission line, the section detection value of each transmission section, and a weight coefficient corresponding to each transmission section.

[0071] Optionally, the weight coefficient is determined based on:

[0072]

[0073] Wherein, k is a preset steepness parameter, and its value range is [1,10]; S i is the number of sections connected to the i-th transmission section, which is used to reflect the connection complexity and importance of the section in the overall transmission link; C is the preset offset parameter, and its value range is [0.3, 0.7].

[0074] It should be noted that the road section detection value is determined based on the following method:

[0075]

[0076] Among them, E totalis the section detection value of the transmission line, which is used to monitor the overall operation status of the transmission line; the larger the section detection value of the transmission line, the better the overall operation status of the transmission line; n is the total number of transmission sections of each transmission line, E i is the section detection value of the i-th transmission section, ω i is the weight coefficient of the ith transmission section.

[0077] Specifically, for a certain transmission line, the number of transmission sections included in the transmission line is calculated, the section detection value of each transmission section is calculated by the section detection value calculation formula, and the weight coefficient corresponding to each transmission section is calculated by the weight coefficient calculation formula. Then, the number of sections of the transmission line, the section detection value of each transmission section, and the weight coefficient corresponding to each transmission section are input into the section detection value calculation formula to finally determine the section detection value of the transmission line.

[0078] In this embodiment, for each transmission section, the baseline display information of the transmission section is determined according to the section detection value of the transmission section; the target display information of the transmission section is determined according to the baseline display information, the weight coefficient of the transmission section and the preset scaling factor, so as to display the transmission section based on the target display information.

[0079] It should be noted that, for each transmission section, the target display information of the transmission section refers to the color mark displayed in the visual graphical interface. The target display information of the transmission section is determined by the following formula:

[0080]

[0081] Among them, S i,Z Display information for the target of the i-th segment; S i is the reference display information of the i-th power transmission section, the reference display information indicating the reference color depth value set according to the section detection value of the power transmission section; is the preset scaling factor, the value range is [0,1], ω i is the weight coefficient of the ith transmission section.

[0082] Specifically, after obtaining the detection value of each transmission section of each transmission line, the transmission section can also be displayed based on the target display information to intuitively display the operating status of each transmission section. For each transmission section, the benchmark display information of the transmission section is determined according to the section detection value of the transmission section, and then the target display information of the transmission section is determined according to the benchmark display information, the weight coefficient of the transmission section and the preset scaling factor. The target display information of each transmission section is represented on the display interface by color marking, and the user can quickly identify the transmission section with abnormal operating status according to the color change. When the section detection value of the transmission line is lower than the set threshold, the system will issue an alarm reminder to the user. The color marking of each transmission section on the display interface is set according to the following rules: first, the corresponding relationship between the depth of the color marking of each section and the detection value of its corresponding transmission section is pre-set; the depth of the marking color of each transmission section is inversely proportional to the detection value of its transmission section. The lower the detection value of the transmission section, the darker the color, indicating that the operating status of the transmission section is worse.

[0083] S250: Determine a monitoring mean value of the transmission line according to historical detection values ​​of the transmission lines belonging to multiple transmission sections within a first preset time period.

[0084] The historical detection value refers to the detection value of each transmission section within the first preset time period. The monitoring mean value refers to the average value of the comprehensive evaluation value of the transmission line within the monitoring period.

[0085] Specifically, after obtaining historical detection values ​​of the transmission lines belonging to multiple transmission sections within a first preset time period, a monitoring mean value of the transmission lines is determined.

[0086] S260. Determine trend prediction parameters of the transmission line within the prediction period according to the monitoring mean and the objective function.

[0087] Optionally, the objective function is:

[0088]

[0089] Among them, P total is the trend prediction parameter; E avg,total is the monitoring mean value of the transmission line within the first preset time period; Q is the weighted value of the fault severity within the first preset time period; δ is a preset convergence coefficient for converging Q, which is a positive number;

[0090]

[0091] Wherein, m is the number of times the monitoring mean value is determined within the first preset time period; C j It is the total number of fault anomalies in the transmission line in the jth time interval; j is the number of the time interval, and the larger j is, the closer the corresponding time interval is to the current time.

[0092] Specifically, according to the monitoring mean and the objective function formula, the trend prediction parameters of the transmission line within the prediction period can be calculated.

[0093] S270. Preset a risk threshold according to trend prediction parameters and determine operation information of the transmission line within the prediction period.

[0094] The technical solution of the embodiment of the present disclosure, after outputting the section detection value of the transmission section, for each transmission line, according to the number of sections of the transmission line, the section detection value of each transmission section and the weight coefficient corresponding to each transmission section, the section detection value of the transmission line is determined, and then, according to the historical detection values ​​of the transmission lines belonging to multiple transmission sections within the first preset time length, the monitoring mean of the transmission line is determined, and further, according to the detection mean and the objective function, the trend prediction parameters of the transmission line within the prediction time length are determined, and finally, according to the risk threshold preset according to the trend prediction parameters, the operation information of the transmission line within the prediction time length is determined. The technical solution provided by the embodiment of the present invention can achieve accurate monitoring of the transmission line by determining the trend prediction parameters of the transmission line within the prediction time length according to the detection mean and the objective function, and then according to the risk threshold preset according to the trend prediction parameters, the operation information of the transmission line within the prediction time length is determined, thereby improving the safety and operation efficiency of the transmission line.

[0095] Embodiment 3

[0096] Figure 3 It is a structural schematic diagram of a data processing device applied to a transmission line provided by an embodiment of the present disclosure. As shown in the figure, the device includes: a transmission line acquisition module 310, a target fault type determination module 320, an operation status determination module 330, a trend prediction parameter determination module 340 and an operation information determination module 350.

[0097] The transmission line acquisition module 310 is used to acquire multiple transmission lines in the target area, wherein the transmission lines are composed of multiple transmission sections; the target fault type determination module 320 is used to determine the target fault type of the transmission-related data for the multiple transmission sections according to the transmission-related data of the transmission sections and the fault type data corresponding to different fault types in the fault model library; the operation state determination module 330 is used to input the transmission-related data of the transmission section within a first preset time length into the transmission line operation state evaluation model obtained by pre-training, and output the transmission line operation state evaluation model; The section detection value of the power transmission section is used to determine the operating status of the power transmission section based on the section detection value; wherein the first preset time period is the preset time period before the current moment; a trend prediction parameter determination module 340 is used to determine the section detection value of the transmission line according to multiple section detection values ​​of each transmission line, and determine the trend prediction parameter of the transmission line according to the section detection value and the historical detection value within the first preset time period; an operation information determination module 350 is used to preset a risk threshold according to the trend prediction parameter, and determine the operation information of the transmission line within the prediction time period.

[0098] The technical solution of the embodiment of the present disclosure, the technical solution of the embodiment of the present disclosure, obtains multiple transmission lines in the target area, wherein the transmission lines are composed of multiple transmission sections, then, for the multiple transmission sections, according to the transmission-related data of the transmission sections and the fault type data corresponding to different fault types in the fault model library, determines the target fault type of the transmission-related data, then, inputs the transmission-related data of the transmission sections within a first preset time length into a transmission line operation status evaluation model obtained by pre-training, outputs the section detection value of the transmission section, so as to determine the target fault type of the transmission section based on the section detection value. The operation status of the section, wherein the first preset time length is the preset time length before the current moment, further, according to the multiple section detection values ​​of each transmission line, the section detection value of the transmission line is determined, and according to the section detection value and the historical detection value within the first preset time length, the trend prediction parameter of the transmission line is determined, and finally, according to the trend prediction parameter preset risk threshold, the operation information of the transmission line within the prediction time length is determined, which solves the problem that when the operation information of the transmission line is determined in the prior art, the transmission line is monitored in real time, and there is a low accuracy and maintenance efficiency of the risk prevention of the transmission line. The embodiment of the present invention adopts a preset risk threshold based on the trend prediction parameter to determine the operation information of the transmission line, realizes accurate monitoring of the transmission line, and obtains the effect of improving the safety of the transmission line.

[0099] On the basis of the above technical solutions, the target fault type determination module 320 further includes: a to-be-used associated data acquisition submodule and a target fault type output submodule.

[0100] A to-be-used associated data acquisition submodule, used to acquire the to-be-used associated data of the first field type in the power transmission associated data;

[0101] The target fault type output submodule is used to input the key feature data that does not meet the preset conditions in the to-be-used associated data into the fault type determination model, and output the target fault type corresponding to the key feature data.

[0102] On the basis of the above technical solutions, the operation status determination module 330 further includes: a section detection value determination submodule.

[0103] The section detection value determination submodule is used to input the key feature data of the transmission section that meets the preset conditions within the first preset time period into the transmission line operation status evaluation model, and output the section detection value of the transmission section within the predicted time period.

[0104] On the basis of the above-mentioned technical solutions, the trend prediction parameter determination module 340 further includes: a road section detection value determination submodule, a monitoring mean value determination submodule and a trend prediction parameter determination submodule.

[0105] A section detection value determination submodule, configured to determine, for each of the transmission lines, a section detection value of the transmission line according to the number of sections of the transmission line, the section detection value of each transmission section, and a weight coefficient corresponding to each transmission section;

[0106] A monitoring mean value determination submodule, configured to determine a monitoring mean value of the transmission line according to historical detection values ​​of the transmission lines belonging to a plurality of transmission sections within the first preset time period;

[0107] The trend prediction parameter determination submodule is used to determine the trend prediction parameters of the transmission line within the prediction time period according to the detection mean and the objective function.

[0108] On the basis of the above technical solutions, the road section detection value determination submodule further includes: a weight coefficient determination unit.

[0109] Weight coefficient determination unit, used for

[0110]

[0111] Wherein, k is a preset steepness parameter, and its value range is [1,10]; S i is the number of sections connected to the i-th transmission section, which is used to reflect the connection complexity and importance of the section in the overall transmission link; C is the preset offset parameter, and its value range is [0.3, 0.7].

[0112] On the basis of the above-mentioned technical solutions, the weight coefficient determination unit also includes: for each transmission section, determining the baseline display information of the transmission section according to the section detection value of the transmission section; determining the target display information of the transmission section according to the baseline display information, the weight coefficient of the transmission section and the preset scaling factor, so as to display the transmission section based on the target display information.

[0113] On the basis of the above technical solutions, the trend prediction parameter determination submodule further includes: an objective function determination unit.

[0114] Objective function determination unit, used for

[0115]

[0116] Among them, P total is the trend prediction parameter; E avg,total is the monitoring mean value of the transmission line within the first preset time period; Q is the weighted value of the fault severity within the first preset time period; δ is a preset convergence coefficient for converging Q, which is a positive number;

[0117]

[0118] Wherein, m is the number of times the monitoring mean value is determined within the first preset time period; C j It is the total number of fault anomalies in the transmission line in the jth time interval; j is the number of the time interval, and the larger j is, the closer the corresponding time interval is to the current time.

[0119] The data processing device applied to the transmission line provided in the embodiment of the present disclosure can execute the data processing method applied to the transmission line provided in any embodiment of the present disclosure, and has the corresponding functional modules and beneficial effects of the execution method.

[0120] It is worth noting that the various units and modules included in the above-mentioned device are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the protection scope of the embodiments of the present disclosure.

[0121] Embodiment 4

[0122] Figure 4 Schematic diagram of the structure of an electronic device provided by an embodiment of the present disclosure. Figure 4 , which shows an electronic device (eg, Figure 4The terminal device in the embodiment of the present disclosure may include but is not limited to mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 4 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0123] like Figure 4 As shown, the electronic device 500 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage device 508 into a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the electronic device 500 are also stored. The processing device 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An edit / output (I / O) interface 505 is also connected to the bus 504.

[0124] Typically, the following devices may be connected to the I / O interface 505: an input device 506 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 507 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 508 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 509. The communication device 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 4 The electronic device 500 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead.

[0125] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device 509, or installed from a storage device 508, or installed from a ROM 502. When the computer program is executed by the processing device 501, the above-mentioned functions defined in the method of the embodiment of the present disclosure are executed.

[0126] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0127] The electronic device provided in the embodiment of the present disclosure and the data processing method applied to the power transmission line provided in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0128] Embodiment 5

[0129] The embodiments of the present disclosure provide a computer storage medium on which a computer program is stored. When the program is executed by a processor, the data processing method applied to a power transmission line provided in the above embodiments is implemented.

[0130] It should be noted that the computer-readable medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0131] In some embodiments, the server can communicate using any currently known or future developed network protocol such as HTTP (HyperTextTransferProtocol), and can be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0132] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0133] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device:

[0134] Acquire a plurality of power transmission lines in a target area, wherein the power transmission lines are composed of a plurality of power transmission sections;

[0135] For the plurality of power transmission sections, determining a target fault type of the power transmission related data according to the power transmission related data of the power transmission sections and the fault type data corresponding to different fault types in the fault model library;

[0136] Inputting the transmission-related data of the transmission section within a first preset time length into a pre-trained transmission line operation status assessment model, outputting a section detection value of the transmission section, so as to determine the operation status of the transmission section based on the section detection value; wherein the first preset time length is a preset time length before the current moment;

[0137] Determine a section detection value of the transmission line according to a plurality of section detection values ​​of each transmission line, and determine a trend prediction parameter of the transmission line according to the section detection value and the historical detection value within the first preset time period;

[0138] A risk threshold is preset according to the trend prediction parameter to determine the operation information of the transmission line within the prediction time.

[0139] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including, but not limited to, object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0140] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0141] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware, wherein the name of a unit does not, in some cases, limit the unit itself.

[0142] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0143] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0144] The above description is only a preferred embodiment of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present disclosure (but not limited to) by each other to form a technical solution.

[0145] In addition, although each operation is described in a specific order, this should not be understood as requiring these operations to be performed in the specific order shown or in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

[0146] Although the subject matter has been described in language specific to structural features and / or methodological logical actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions described above are merely example forms of implementing the claims.

Claims

1. A data processing method applied to a power transmission line, characterized in that: include: Acquire a plurality of power transmission lines in a target area, wherein the power transmission lines are composed of a plurality of power transmission sections; For the plurality of power transmission sections, determining a target fault type of the power transmission related data according to the power transmission related data of the power transmission sections and the fault type data corresponding to different fault types in the fault model library; Inputting the transmission-related data of the transmission section within a first preset time length into a pre-trained transmission line operation status assessment model, outputting a section detection value of the transmission section, so as to determine the operation status of the transmission section based on the section detection value; wherein the first preset time length is a preset time length before the current moment; Determine a section detection value of the transmission line according to a plurality of section detection values ​​of each transmission line, and determine a trend prediction parameter of the transmission line according to the section detection value and the historical detection value within the first preset time period; A risk threshold is preset according to the trend prediction parameter to determine the operation information of the transmission line within the prediction time.

2. The method according to claim 1, characterized in that The step of determining a target fault type of the transmission-related data based on the transmission-related data of the transmission section and the fault type data corresponding to different fault types in the fault model library comprises: Acquire the to-be-used associated data under the first field type in the power transmission associated data; The key feature data that does not meet the preset conditions in the associated data to be used are input into the fault type determination model, and the target fault type corresponding to the key feature data is output.

3. The method according to claim 1, characterized in that Determining the section detection value of the transmission line according to the multiple section detection values ​​of each transmission line includes: For each of the transmission lines, the section detection value of the transmission line is determined according to the number of sections of the transmission line, the section detection value of each transmission section, and the weight coefficient corresponding to each transmission section.

4. The method according to claim 3, characterized in that The weight coefficient is determined based on the following method: Wherein, k is a preset steepness parameter, and its value range is [1,10]; S i is the number of sections connected to the i-th transmission section, which is used to reflect the connection complexity and importance of the section in the overall transmission link; C is the preset offset parameter, and its value range is [0.3, 0.7].

5. The method according to claim 4, characterized in that Also includes: For each power transmission section, determining the reference display information of the power transmission section according to the section detection value of the power transmission section; According to the reference display information, the weight coefficient of the power transmission section and a preset zoom factor, target display information of the power transmission section is determined, so as to display the power transmission section based on the target display information.

6. The method according to claim 1, characterized in that The step of determining the trend prediction parameter of the power transmission line according to the section detection value and the historical detection value within the first preset time period includes: Determine a monitoring mean value of the transmission line according to historical detection values ​​of the transmission lines belonging to a plurality of transmission sections within the first preset time period; According to the detection mean and the objective function, the trend prediction parameters of the power transmission line within the prediction time period are determined.

7. The method according to claim 6, characterized in that The objective function is: Among them, P total is the trend prediction parameter; E avg,total is the monitoring mean value of the transmission line within the first preset time period; Q is the weighted value of the fault severity within the first preset time period; δ is a preset convergence coefficient for converging Q, which is a positive number; Wherein, m is the number of times the monitoring mean value is determined within the first preset time period; C j It is the total number of fault anomalies in the transmission line in the jth time interval; j is the number of the time interval, and the larger j is, the closer the corresponding time interval is to the current time.

8. A data processing device applied to a power transmission line, characterized in that: include: A transmission line acquisition module, used to acquire a plurality of transmission lines in a target area, wherein the transmission line is composed of a plurality of transmission sections; a target fault type determination module, configured to determine, for the plurality of transmission sections, a target fault type of the transmission-related data according to the transmission-related data of the transmission sections and the fault type data corresponding to different fault types in the fault model library; An operation status determination module is used to input the transmission-related data of the transmission section within a first preset time length into a pre-trained transmission line operation status assessment model, and output a section detection value of the transmission section, so as to determine the operation status of the transmission section based on the section detection value; wherein the first preset time length is a preset time length before the current moment; A trend prediction parameter determination module, used to determine a section detection value of each transmission line according to a plurality of section detection values ​​of the transmission line, and determine a trend prediction parameter of the transmission line according to the section detection value and a historical detection value within the first preset time period; The operation information determination module is used to preset a risk threshold according to the trend prediction parameter and determine the operation information of the transmission line within the prediction time.

9. An electronic device, characterized in that: The electronic device comprises: one or more processors; a storage device for storing one or more programs, When one or more programs are executed by one or more processors, the one or more processors implement the data processing method applied to a power transmission line as claimed in any one of claims 1 to 7.

10. A storage medium containing computer executable instructions, characterized in that: When the computer executable instructions are executed by a computer processor, they are used to execute the data processing method applied to a power transmission line according to any one of claims 1 to 7.

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