A data analysis method, device and equipment of a power transmission line and a storage medium

Abstract

The application discloses a data analysis method for a power transmission line, which comprises the following steps: in response to a data analysis operation for the power transmission line, historical monitoring data in the power transmission line is acquired; a first data analysis knowledge graph corresponding to the power transmission line is constructed according to the historical monitoring data; a first feature corresponding to the power transmission line is acquired through the first data analysis knowledge graph; a first data analysis model is constructed according to the first feature, and a data analysis operation is performed on current monitoring data through the first data analysis model. The application also discloses corresponding devices, equipment and storage media. By implementing the application, the correlation between monitoring data can be deeply analyzed, and the accuracy of data analysis results can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of big data, and in particular to a data analysis method, device and equipment for a power transmission line and a storage medium. BACKGROUND

[0002] The power transmission line is an important part of the power system, and its stable operation plays a key role in ensuring the reliable supply of electricity. Due to the influence of environmental conditions and equipment aging and other factors during the operation of the power transmission line, it is necessary to obtain and analyze monitoring data in real time to ensure the reliability and safety of the line. Through effective analysis and processing of monitoring data, abnormal conditions can be identified in a timely manner, and necessary maintenance measures can be taken to avoid the adverse effects of faults on the power grid.

[0003] At present, the analysis of monitoring data in the power transmission line usually uses a relatively simple evaluation system. In this method, the evaluation system is trained using historical monitoring data, and the trained evaluation system is used to analyze the monitoring data in the power transmission line. However, the evaluation system trained in this way cannot deeply analyze the correlation between monitoring data, especially when there is a large amount of monitoring data, the evaluation system cannot fully reflect the complex operating conditions of the power transmission line, and thus the accuracy of the data analysis result is low. SUMMARY

[0004] The technical problem to be solved by the present application is to provide a data analysis method, device and equipment for a power transmission line, which can deeply analyze the correlation between monitoring data and improve the accuracy of the data analysis result.

[0005] To solve the above technical problems, the present application provides the following technical solutions: as a first aspect of the present application, a data analysis method for a power transmission line is provided, which includes the following steps: in response to a data analysis operation for the power transmission line, obtaining historical monitoring data in the power transmission line; constructing a first data analysis knowledge graph corresponding to the power transmission line according to the historical monitoring data; obtaining a first feature corresponding to the power transmission line through the first data analysis knowledge graph, the first feature including a tower inclination feature, a conductor stress feature and a cross-span segment structure feature; constructing a first data analysis model according to the first feature, and performing a data analysis operation on the current monitoring data through the first data analysis model.

[0006] Optionally, according to historical monitoring data, a first data analysis knowledge graph corresponding to the power transmission line is constructed, specifically including: performing a classification operation on the historical monitoring data, and obtaining a feature correlation relationship between the historical monitoring data according to a classification result, the feature correlation relationship including a tower tilt relationship, a conductor tension and tower stress relationship, and a cross-span segment spatial structure relationship; taking the historical monitoring data as nodes and the feature correlation relationship as edges to construct the first data analysis knowledge graph.

[0007] Optionally, the first feature corresponding to the power transmission line is obtained through the first data analysis knowledge graph, specifically including: obtaining a target feature value corresponding to a to-be-confirmed feature through the following formula:

[0008]

[0009] wherein, K k is the target feature value, k represents a feature type corresponding to the to-be-confirmed feature, f k is an aggregation function, used to calculate the target feature value of the kth type of to-be-confirmed feature, the aggregation function including an average value function, a weighted function, and a time series analysis function, is a first set of kth type nodes, is a second set of kth type edges, v is a node attribute corresponding to any element in the first set, e is an edge attribute corresponding to any element in the second set, then represents a set of node attributes and edge attributes; it is judged whether the target feature value is greater than or equal to a preset conditional feature value corresponding to the first feature; if the target feature value is greater than or equal to the preset conditional feature value corresponding to the first feature, the to-be-confirmed feature is taken as the first feature.

[0010] Optionally, after constructing the first data analysis model according to the first feature and performing data analysis operation on the current monitoring data through the first data analysis model, the method further includes: obtaining abnormal monitoring data in the current monitoring data through the first data analysis model; correcting the abnormal monitoring data in a preset manner, and constructing a second data analysis knowledge graph through the corrected current monitoring data; obtaining a second feature corresponding to the power transmission line through the second data analysis knowledge graph, and improving the first data analysis model to a second data analysis model through the second feature; performing data analysis operation on the current monitoring data through the second data analysis model.

[0011] Optionally, the abnormal monitoring data in the current monitoring data is obtained through the first data analysis model, specifically including: calculating a fault probability of the abnormal monitoring data in the current monitoring data through the following formula:

[0012]

[0013] Wherein, y is the failure probability, g is the failure probability evaluation function, Z(K) is the set of the first features, θ i is the weight parameter corresponding to the i-th type of the first features, m is the total number of types of the first features, z i is the feature value corresponding to the i-th type of the first features, ∈ is the model error term; if it is confirmed that the failure probability is greater than the preset failure probability, the abnormal monitoring data in the current monitoring data is obtained according to the failure probability.

[0014] Optionally, the abnormal monitoring data in the current monitoring data is obtained according to the failure probability, and specifically includes: obtaining the to-be-confirmed failure data, the to-be-confirmed failure data being any monitoring data in the current monitoring data; calculating the contribution value of the to-be-confirmed failure data to the failure probability; if it is confirmed that the contribution value is greater than the preset contribution value, the to-be-confirmed failure data is confirmed as the abnormal monitoring data.

[0015] Optionally, the abnormal monitoring data is corrected in a preset manner, and specifically includes: calculating the abnormal feature value of the abnormal monitoring data, and confirming the abnormal data type corresponding to the abnormal monitoring data according to the abnormal feature value; obtaining the correction mode corresponding to the abnormal data type in the preset correction database, the preset correction database being used to store the corresponding relationship between the abnormal data type and the correction mode, and the correction mode including an interpolation correction mode, a mean smoothing correction mode and a limiting correction mode; correcting the abnormal monitoring data according to the correction mode.

[0016] As a second aspect of the present application, a data analysis device for a power transmission line is also provided, which includes an acquisition module and a processing module, wherein,

[0017] The acquisition module is configured to, in response to a data analysis operation for the power transmission line, acquire historical monitoring data in the power transmission line; construct a first data analysis knowledge graph corresponding to the power transmission line according to the historical monitoring data; and acquire first features corresponding to the power transmission line through the first data analysis knowledge graph, the first features including tower inclination features, conductor stress features and cross-span structure features.

[0018] The processing module is configured to construct a first data analysis model according to the first features, and perform a data analysis operation on current monitoring data through the first data analysis model.

[0019] As a third aspect of the present application, an electronic device is also provided, which includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory, so that the electronic device executes the method of any one of the above.

[0020] As a fourth aspect of the present application, there is also provided a computer-readable storage medium storing a computer program for execution by a processor to perform the method of any of the above.

[0021] The embodiments of the present application have the following beneficial effects:

[0022] The present application provides a kind of data analysis of transmission line, device, equipment and storage medium, by responding to the data analysis operation for transmission line, obtain historical monitoring data in transmission line, and according to historical monitoring data, corresponding first data analysis knowledge graph of transmission line is constructed, and by first data analysis knowledge graph, corresponding first feature of transmission line is obtained, and then according to first feature, first data analysis model is constructed, and current monitoring data is carried out data analysis operation by first data analysis model, so that first data analysis model is constructed by the complex historical monitoring data correlation characteristics in first data analysis knowledge graph, and data analysis operation is carried out by first data analysis model, greatly improve the accuracy of data analysis result.

[0023] In the present application, by classifying operation to historical monitoring data, and according to the classification result, the feature correlation between historical monitoring data is obtained, so that historical monitoring data is taken as node, and feature correlation is taken as edge to construct first data analysis knowledge graph, and then data basis is provided for the construction of first data analysis model, so as to construct first data analysis model according to the complex historical monitoring data correlation characteristics in first data analysis knowledge graph, greatly improve the accuracy of first data analysis model data analysis result.

[0024] In the present application, the abnormal monitoring data in current monitoring data is obtained by first data analysis model, the abnormal monitoring data is corrected according to preset mode, and second data analysis knowledge graph is constructed by corrected current monitoring data;Second feature corresponding to transmission line is obtained by second data analysis knowledge graph, and first data analysis model is improved to second data analysis model by second feature;Current monitoring data is carried out data analysis operation by second data analysis model, so that first data analysis model is improved to second data analysis model by corrected current monitoring data, further improve the accuracy of data analysis model data analysis result. BRIEF DESCRIPTION OF DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the drawings needed in the embodiments or prior art description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings obtained according to these drawings without creative labor are still within the scope of the present application.

[0026] Figure 1 is a main flow diagram of one embodiment of the data analysis method of the power transmission line provided by the present application;

[0027] Figure 2 is a module diagram of one embodiment of the data analysis device of the power transmission line provided by the present application;

[0028] Figure 3 is a structure diagram of one embodiment of the electronic device provided by the present application.

[0029] Legend: 21, acquisition module; 22, processing module; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings.

[0031] As shown in Figure 1 ,

[0032] Please refer to Figure 1 , which shows a flow diagram of the data analysis method of the power transmission line provided by the embodiment of the present application, and the flow diagram mainly includes the following steps: S101 to S104.

[0033] Step S101, in response to the data analysis operation of the power transmission line, the historical monitoring data in the power transmission line is acquired.

[0034] Specifically, when the user performs a data analysis operation on the power transmission line, historical monitoring data in the power transmission line is acquired, and the historical monitoring data usually includes different kinds of data information, including but not limited to: tower inclination data, reflecting the inclination change of the tower, used to evaluate the stability and structural health of the tower; conductor stress data, including characteristic values such as tension and sag of the conductor, which are used to monitor the stress state of the conductor under temperature and load changes, and analyze whether the conductor has problems such as excessive stretching; crossing span structure data, mainly recording the distance, position and state of the conductor in the region of the power transmission line crossing other structures such as roads, railways and buildings, used to evaluate the safety of the crossing span; electrical parameter data, including voltage, current, power factor, etc., which can reflect the electrical state of the power transmission line, etc. In the embodiments provided by the present application, the historical monitoring data can be acquired in various ways, including but not limited to: the database of the real-time monitoring system, modern power transmission lines are usually equipped with online monitoring equipment, which transmits the collected data to the central database in real time, and the database can be directly accessed to call historical data by time or event; Internet of Things (IoT) device collection, sensors such as inclination sensors, stress sensors and electrical parameter sensors are installed at key positions of the power transmission line, and the data collected by these devices are collected through the Internet of Things to the data platform for subsequent calling. The present application does not limit the collection method of the historical monitoring data.

[0035] Step S102, constructing a first data analysis knowledge graph corresponding to the power transmission line according to the historical monitoring data.

[0036] Specifically, the historical monitoring data is classified, and a feature correlation relationship between the historical monitoring data is obtained according to a classification result, the feature correlation relationship including a tower inclination relationship, a conductor tension and tower stress relationship, and a cross-span segment spatial structure relationship, and the classification operation includes the following steps: first, the historical monitoring data is classified into different categories according to the content of the historical monitoring data, then, different data processing is performed on different types of data, for example, for tower data, an inclination angle and deformation data are extracted; for conductor data, tension and sag are extracted; for cross-span segment data, span distance and ground line position data are extracted; for environmental data, temperature, humidity, and wind speed data are extracted; then, the extracted data is standardized to facilitate comparison and correlation of the data in the graph, for example, numerical values such as tension and inclination angle are converted into a unified dimension; finally, the feature correlation relationship is extracted according to the correlation of different data categories, including but not limited to: a tower inclination relationship, describing the change law between tower inclination data; a conductor tension and tower stress relationship, describing the influence of conductor tension on the stress of the tower; a cross-span segment spatial structure relationship, describing the position and distance relationship between the transmission line and the cross-span structure; a first data analysis knowledge graph is constructed with the historical monitoring data as nodes and the feature correlation relationship as edges, and the steps include S21 to S24, S21, establishing nodes: defining specific features of each type of data with feature items in the historical monitoring data as nodes, including but not limited to tower inclination angle, conductor tension value, etc., and taking the specific features as nodes of the first data analysis knowledge graph, for example, establishing a “tower inclination node”, a “conductor tension node”, and an “environmental temperature node”; S22, defining the type of edge: defining the type of edge according to the aforementioned extracted feature correlation relationship, different types of data nodes have different relationship edges, including “stress relationship edges” associated with towers and conductors, “environmental influence edges” associated with towers and the environment, “spatial structure relationship edges” associated with cross-span segments and conductors, etc.; S23, constructing the correlation relationship between nodes: assigning specific weights and attributes to the nodes and edges in the first data analysis knowledge graph, for example, according to the conductor tension and tower stress features, the correlation strength is calculated and taken as the weight of the “stress relationship edge”; S24, knowledge graph structure optimization: the first data analysis knowledge graph is optimized in structure, the graph structure is simplified by removing redundant edges and merging similar nodes, to improve the efficiency of subsequent analysis, and the optimized first data analysis knowledge graph is stored to ensure that it can be accessed and updated in real time in subsequent data analysis.

[0037] In step S103, a first feature corresponding to the transmission line is obtained through the first data analysis knowledge graph, and the first feature includes a tower inclination feature, a conductor stress feature, and a cross-span segment structure feature.

[0038] Specifically, by taking the nodes and edges of the first data analysis knowledge graph as input, and obtaining the first features corresponding to the transmission line according to the feature extraction model, the steps are as follows: first, the target feature value corresponding to the to-be-confirmed feature is obtained by the following formula:

[0039]

[0040] wherein K k is the target feature value, k represents the feature type corresponding to the to-be-confirmed feature, f k is a summary function, which is used to calculate the target feature value of the kth type of to-be-confirmed feature, and the summary function includes an average function, a weighted function and a time series analysis function, is the first set of the kth type of node, is the second set of the kth type of edge, v is the node attribute corresponding to any element in the first set, e is the edge attribute corresponding to any element in the second set, then represents the set of node attributes and edge attributes; it is judged whether the target feature value is greater than or equal to the preset conditional feature value corresponding to the first feature, wherein the preset conditional feature value is a specific threshold or a standard parameter, which is used to determine whether the to-be-confirmed feature meets certain performance or structural requirements, and needs to be set differently according to different feature types, for example, for the structural stability threshold: used to judge whether the tower inclination feature is within a reasonable range to ensure the safety and stability of the tower structure; for the conductor tension threshold: used to judge the conductor stress feature to ensure that the tension of the conductor is within the safe limit to prevent the conductor from being excessively loose or stretched; for the spatial distance safety threshold: used to judge the cross-span structure feature to ensure that the distance between the conductor and the crossing object is within a safe range to prevent the risk of touching or short circuit; if the target feature value is greater than or equal to the preset conditional feature value corresponding to the first feature, the to-be-confirmed feature is taken as the first feature, and the first feature includes but is not limited to: tower inclination feature, conductor stress feature and cross-span structure feature; finally, all to-be-confirmed features whose target feature values are greater than or equal to the preset conditional feature value are taken as the set of first features, for example, if there are four to-be-confirmed features K1, K2, K3 and K4, wherein K1, K3 and K4 correspond to target feature values greater than or equal to the preset conditional feature value, and K2 corresponds to a target feature value less than the preset conditional feature value, then the set of first features can be represented as Z(K)=[K1,K3,K4] wherein Z(K) is the set of first features.

[0041] In step S104, a first data analysis model is constructed according to the first feature, and the current monitoring data is subjected to data analysis operation through the first data analysis model.

[0042] Specifically, according to the type of the first feature, a suitable first data analysis model type is selected, and feature values are calculated from the set of first features, and the feature values are taken as input variables of the model to construct the first data analysis model, and by inputting the current monitoring data into the first data analysis model, a prediction result, i.e. a fault probability of the abnormal monitoring data in the monitoring data, is output, and the fault probability output by the model is analyzed to determine whether the fault probability exceeds a preset fault probability, so as to identify potential abnormal monitoring data, and then complete the data analysis operation.

[0043] In a possible implementation, the step S104 further includes: acquiring abnormal monitoring data in the current monitoring data through the first data analysis model; correcting the abnormal monitoring data in a preset manner, and constructing a second data analysis knowledge graph through the corrected current monitoring data; acquiring second features corresponding to the power transmission line through the second data analysis knowledge graph, and improving the first data analysis model to a second data analysis model through the second features; and performing a data analysis operation on the current monitoring data through the second data analysis model.

[0044] Specifically, the abnormal monitoring data in the current monitoring data is acquired through the first data analysis model, and the specific steps are as follows: the fault probability of the abnormal monitoring data in the current monitoring data is calculated through the following formula:

[0045]

[0046] wherein y is the fault probability, g is the fault probability evaluation function, Z(K) is the set of first features, θ i is the weight parameter corresponding to the i-th first feature, m is the total number of types of first features, z i is the feature value corresponding to the i-th first feature, ∈ is a model error term, which represents the error between the model prediction value and the true value, and the error term can be derived from various factors, such as data noise, simplification assumptions of the model, incompleteness of feature extraction, etc. In the embodiments of the present application, the model error term can be obtained in various ways, for example: historical data residual analysis: based on a large amount of historical monitoring data, the residual between the model prediction value and the true value is calculated, and the mean and variance of these residuals are used to estimate the error term. Specifically, assuming that the error term conforms to a certain statistical distribution (such as normal distribution), the error term can be fitted and obtained by the mean and standard deviation of the historical residual data; model cross-validation: through cross-validation technology, the data set is divided into training set and validation set, the training set is used for model training, and then the error distribution of the model is evaluated in the validation set to estimate the model error term; Bayesian estimation: through Bayesian estimation method, the model error term is taken as a random variable, and combined with prior information and observation data for estimation.

[0047] In the process, the prior distribution of the error term and the likelihood function can be combined to calculate the optimal estimate value of the error term through the posterior distribution. If it is confirmed that the fault probability is greater than the preset fault probability, the abnormal monitoring data in the current monitoring data is obtained according to the fault probability, then the to-be-confirmed fault data is obtained, the to-be-confirmed fault data is any monitoring data in the current monitoring data, and the contribution value of the to-be-confirmed fault data to the fault probability is calculated. If it is confirmed that the contribution value is greater than the preset contribution value, the to-be-confirmed fault data is confirmed as abnormal monitoring data; the abnormal monitoring data is corrected according to the preset mode, and the second data analysis knowledge graph is constructed through the corrected current monitoring data. The steps of correcting the abnormal monitoring data according to the preset mode are as follows: the abnormal feature value of the abnormal monitoring data is calculated, and the abnormal data type corresponding to the abnormal monitoring data is confirmed according to the abnormal feature value; the correction mode corresponding to the abnormal data type is obtained in the preset correction database. The preset correction database is used to store the corresponding relationship between the abnormal data type and the correction mode. The correction mode includes the interpolation correction mode, the mean value smoothing correction mode and the amplitude limiting correction mode. The abnormal monitoring data is corrected according to the correction mode. For example, when the abnormal feature value is missing or significantly deviates from the historical mean value, the interpolation calculation is performed through the feature values of adjacent time points to correct the abnormal feature value. When the abnormal feature value fluctuates sharply, the mean value of the feature values of adjacent time points is taken to smooth the fluctuation. When the abnormal feature value exceeds the reasonable range, it is corrected to the preset maximum or minimum limit value to ensure that the feature value meets the reasonable interval. Then, the second data analysis knowledge graph is constructed according to the method described in step S102 through the corrected current monitoring data, and the second feature corresponding to the transmission line is obtained through the second data analysis knowledge graph. The first data analysis model is improved to the second data analysis model through the second feature, so that the data analysis operation is performed according to the improved second data analysis model, and the accuracy of the model analysis is improved.

[0048] By adopting the above method, in response to the data analysis operation of the transmission line, the historical monitoring data in the transmission line is obtained, and the first data analysis knowledge graph corresponding to the transmission line is constructed according to the historical monitoring data. The first feature corresponding to the transmission line is obtained through the first data analysis knowledge graph, and then the first data analysis model is constructed according to the first feature, and the data analysis operation is performed on the current monitoring data through the first data analysis model. Therefore, the first data analysis model is constructed through the complex historical monitoring data correlation characteristics in the first data analysis knowledge graph, and the data analysis operation is performed through the first data analysis model, which greatly improves the accuracy of the data analysis result.

[0049] Please refer to Figure 2 which shows a module schematic diagram of a data analysis device for a transmission line provided by an embodiment of the present application. The device comprises an acquisition module 21 and a processing module 22.

[0050] The acquisition module 21 is configured to acquire historical monitoring data in the power transmission line in response to a data analysis operation on the power transmission line; construct a first data analysis knowledge graph corresponding to the power transmission line according to the historical monitoring data; and acquire a first feature corresponding to the power transmission line through the first data analysis knowledge graph, wherein the first feature includes a tower inclination feature, a conductor stress feature, and a cross-span segment structure feature.

[0051] The processing module 22 is configured to construct a first data analysis model according to the first feature, and perform a data analysis operation on the current monitoring data through the first data analysis model.

[0052] In a possible implementation, the acquisition module 21 is configured to construct a first data analysis knowledge graph corresponding to the power transmission line according to the historical monitoring data, and specifically includes: performing a classification operation on the historical monitoring data, and acquiring a feature association relationship between the historical monitoring data according to a classification result, wherein the feature association relationship includes a tower inclination relationship, a conductor tension and tower stress relationship, and a cross-span segment spatial structure relationship; and constructing the first data analysis knowledge graph by taking the historical monitoring data as nodes and the feature association relationship as edges.

[0053] In a possible implementation, the acquisition module 21 is configured to acquire a first feature corresponding to the power transmission line through the first data analysis knowledge graph, and specifically includes: acquiring a target feature value corresponding to a to-be-confirmed feature through the following formula:

[0054]

[0055] wherein K k is a target feature value, k represents a feature type corresponding to the to-be-confirmed feature, f k is an aggregation function used to calculate the target feature value of the kth type of to-be-confirmed feature, the aggregation function includes an average value function, a weighted function, and a time series analysis function, is a first set of kth type nodes, is a second set of kth type edges, v is a node attribute corresponding to any element in the first set, e is an edge attribute corresponding to any element in the second set, and then represents a set of node attributes and edge attributes; it is judged whether the target feature value is greater than or equal to a preset condition feature value corresponding to the first feature; if the target feature value is greater than or equal to the preset condition feature value corresponding to the first feature, the to-be-confirmed feature is taken as the first feature.

[0056] In a possible implementation, the processing module 22 is configured to obtain abnormal monitoring data in the current monitoring data through the first data analysis model after constructing the first data analysis model according to the first features and performing the data analysis operation on the current monitoring data through the first data analysis model; correct the abnormal monitoring data in a preset manner, and construct a second data analysis knowledge graph through the corrected current monitoring data; obtain the second features corresponding to the power transmission line through the second data analysis knowledge graph, and improve the first data analysis model to a second data analysis model through the second features; and perform the data analysis operation on the current monitoring data through the second data analysis model.

[0057] In a possible implementation, the processing module 22 is configured to obtain abnormal monitoring data in the current monitoring data through the first data analysis model, and specifically includes: calculating the fault probability of the abnormal monitoring data in the current monitoring data through the following formula:

[0058]

[0059] wherein y is the fault probability, g is the fault probability evaluation function, Z(K) is the set of the first features, θ i is the weight parameter corresponding to the i-th type of first features, m is the total number of types of the first features, z i is the feature value corresponding to the i-th type of first features, and ∈ is the model error term; if it is confirmed that the fault probability is greater than a preset fault probability, the abnormal monitoring data in the current monitoring data is obtained according to the fault probability.

[0060] In a possible implementation, the processing module 22 is configured to obtain abnormal monitoring data in the current monitoring data according to the fault probability, and specifically includes: obtaining to-be-confirmed fault data, the to-be-confirmed fault data being any monitoring data in the current monitoring data; calculating the contribution value of the to-be-confirmed fault data to the fault probability; and if it is confirmed that the contribution value is greater than a preset contribution value, the to-be-confirmed fault data is confirmed as the abnormal monitoring data.

[0061] In a possible implementation, the processing module 22 is configured to correct the abnormal monitoring data in a preset manner, and specifically includes: calculating the abnormal feature value of the abnormal monitoring data, and confirming the abnormal data type corresponding to the abnormal monitoring data according to the abnormal feature value; obtaining the correction manner corresponding to the abnormal data type in a preset correction database, the preset correction database being configured to store the corresponding relationship between the abnormal data type and the correction manner, and the correction manner including an interpolation correction manner, a mean smoothing correction manner and a limiting correction manner; and correcting the abnormal monitoring data according to the correction manner.

[0062] It should be noted that the device provided in the above embodiment is only used as an example to divide the above functional modules to achieve its function, and in actual application, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the above described functions. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is described in detail in the method embodiments Figure 1 The method embodiments are described here, and details are not repeated.

[0063] The application further provides an electronic device. Referring to Figure 3 , Figure 3 is a structural schematic diagram of an electronic device provided by an embodiment of the application. The electronic device can include at least one processor 301, at least one communication bus 302, a user interface 303, at least one network interface 304, and a memory 305.

[0064] The communication bus 302 is used to realize the connection and communication between the components.

[0065] The user interface 303 can include a display screen (Display), a camera (Camera), and optionally the user interface 303 can further include a standard wired interface and a wireless interface.

[0066] The network interface 304 can optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).

[0067] The processor 301 can include one or more processing cores. The processor 301 connects various parts within the server through various interfaces and lines, performs various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Alternatively, the processor 301 can be implemented in at least one of a hardware form of a digital signal processing (DSP), a field-programmable gate array (FPGA), and a programmable logic array (PLA). The processor 301 can integrate a combination of one or more of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. Among them, the CPU mainly processes operating systems, user interfaces, and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; and the modem is used for processing wireless communication. It can be understood that the above-mentioned modem can also not be integrated into the processor 301, but can be realized by a separate chip.

[0068] The memory 305 can include a random access memory (RAM) and can also include a read-only memory (ROM). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 can include a program storage area and a data storage area, wherein the program storage area can store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area can store data involved in the above-mentioned various method embodiments, etc. The memory 305 can also be at least one storage device located away from the aforementioned processor 301. Referring to Figure 3 The memory 305 as a computer storage medium can include an operating system, a network communication module, a user interface module, and a data analysis application program of a power transmission line.

[0069] In Figure 3The user interface 303 is mainly used to provide an interface for the user to input, and obtain data input by the user. The processor 301 can be used to call the data analysis application stored in the memory 305, and when executed by one or more processors 301, the electronic device performs the method described in one or more of the above embodiments. It should be noted that, for the above-mentioned method embodiments, in order to simply describe, they are all expressed as a combination of a series of actions, but those skilled in the art should know that the present application is not limited by the order of the described actions, because according to the present application, certain steps can be performed in other order or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification all belong to preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0070] The present application also provides a computer-readable storage medium, which stores instructions. When executed by one or more processors, the electronic device performs the method described in one or more of the above embodiments.

[0071] In the above embodiments, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0072] In several embodiments provided by the present application, it should be understood that the disclosed device can be implemented in other manners. For example, the described device embodiments are merely schematic. For example, the division of the units is only a logical function division. There can be another division manner for the actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections can be indirect couplings or communication connections through some interfaces, devices or units, and can be electrical, mechanical or in other forms.

[0073] The units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, i.e. they can be located in one place, or distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0074] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically, or two or more units can be integrated into one unit. The integrated unit can be implemented in the form of hardware or software functional units.

[0075] If the integrated unit is implemented in the form of a software function unit and sold or used as an independent product, it can be stored in a computer readable memory. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product, which is stored in a memory and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method of each embodiment of the present application. The aforementioned memory includes: a U disk, a mobile hard disk, a magnetic disk or an optical disk and various program code storage media.

[0076] The embodiments of the present application have the following beneficial effects:

[0077] The present application provides a kind of data analysis of transmission line, device, equipment and storage medium, by responding to the data analysis operation for transmission line, obtain historical monitoring data in transmission line, and according to historical monitoring data, corresponding first data analysis knowledge graph of transmission line is constructed, and by first data analysis knowledge graph, corresponding first feature of transmission line is obtained, and then according to first feature, first data analysis model is constructed, and current monitoring data is carried out data analysis operation by first data analysis model, so that first data analysis model is constructed by the complex historical monitoring data correlation characteristics in first data analysis knowledge graph, and data analysis operation is carried out by first data analysis model, greatly improve the accuracy of data analysis result.

[0078] In the present application, by classifying operation to historical monitoring data, and according to the classification result, the feature correlation between historical monitoring data is obtained, so that the first data analysis knowledge graph is constructed with historical monitoring data as node and with feature correlation as edge, and then data basis is provided for the construction of first data analysis model, so as to construct first data analysis model according to the complex historical monitoring data correlation characteristics in first data analysis knowledge graph, greatly improve the accuracy of first data analysis model data analysis result.

[0079] In the present application, the abnormal monitoring data in current monitoring data is obtained by first data analysis model, the abnormal monitoring data is corrected according to preset mode, and second data analysis knowledge graph is constructed by corrected current monitoring data;Second feature corresponding to transmission line is obtained through second data analysis knowledge graph, and first data analysis model is improved to second data analysis model through second feature;Current monitoring data is carried out data analysis operation by second data analysis model, so that first data analysis model is improved to second data analysis model through corrected current monitoring data, further improve the accuracy of data analysis model data analysis result.

[0080] The above merely provides the preferred embodiment of the application, and cannot allude the scope of the application therefore; any equivalent changes made according to the claims of the application shall still fall into the scope of the application.

Claims

1. A method of data analysis of a power transmission line, characterized in that, The method comprises: in response to a data analysis operation for a power transmission line, obtaining historical monitoring data in the power transmission line; according to the historical monitoring data, constructing a first data analysis knowledge graph corresponding to the power transmission line; obtaining a first feature corresponding to the power transmission line through the first data analysis knowledge graph, the first feature including tower tilt feature, conductor stress feature, and cross-span segment structure feature; constructing a first data analysis model according to the first feature, and performing the data analysis operation on current monitoring data through the first data analysis model; wherein, according to the historical monitoring data, constructing a first data analysis knowledge graph corresponding to the power transmission line, specifically comprising: classifying the historical monitoring data, and obtaining feature association relationships between the historical monitoring data according to the classification results, the feature association relationships including tower tilt relationship, conductor tension and tower stress relationship, and cross-span segment spatial structure relationship; constructing the first data analysis knowledge graph with the historical monitoring data as nodes and the feature association relationships as edges; wherein, obtaining a first feature corresponding to the power transmission line through the first data analysis knowledge graph, specifically comprising: obtaining a target feature value corresponding to a to-be-confirmed feature through the following formula: in, The target feature value, This indicates the feature type corresponding to the feature to be confirmed. This is a summary function used to calculate the first... The target feature value of the feature to be confirmed, as described above, and the summarization function includes an average function, a weighting function, and a time series analysis function. For the first The first set of nodes described in the class, For the first The second set of edges described in the class, For any element in the first set, the node attribute is... Let be the edge attribute corresponding to any element in the second set, then This represents the set of node attributes and edge attributes; determining whether the target feature value is greater than or equal to a preset condition feature value corresponding to the first feature; if the target feature value is greater than or equal to the preset condition feature value corresponding to the first feature, the to-be-confirmed feature is taken as the first feature.

2. The method of claim 1, wherein, After constructing a first data analysis model according to the first feature and performing the data analysis operation on current monitoring data through the first data analysis model, the method further comprises: obtaining abnormal monitoring data in the current monitoring data through the first data analysis model; correcting the abnormal monitoring data in a preset manner, and constructing a second data analysis knowledge graph through the corrected current monitoring data; obtaining a second feature corresponding to the power transmission line through the second data analysis knowledge graph, and improving the first data analysis model to a second data analysis model through the second feature; performing the data analysis operation on the current monitoring data through the second data analysis model.

3. The method of claim 2, wherein, The method further comprises: calculating a fault probability of the abnormal monitoring data existing in the current monitoring data through the following formula: ； wherein, is the failure probability, is a failure probability evaluation function, is the set of the first features, is the first feature, is a weight parameter corresponding to the first feature, is the total number of types of the first features, is the first feature, is a feature value corresponding to the first feature, is a model error term; if it is confirmed that the fault probability is greater than a preset fault probability, obtaining the abnormal monitoring data in the current monitoring data according to the fault probability.

4. The method of claim 3, wherein, The method further comprises: obtaining to-be-confirmed fault data, the to-be-confirmed fault data being any monitoring data in the current monitoring data; calculating a contribution value of the to-be-confirmed fault data to the fault probability; if it is confirmed that the contribution value is greater than a preset contribution value, the to-be-confirmed fault data is confirmed as the abnormal monitoring data.

5. The method of claim 4, wherein, The abnormal monitoring data is corrected in a preset manner, and the correction specifically includes: calculating an abnormal feature value of the abnormal monitoring data, and confirming an abnormal data type corresponding to the abnormal monitoring data according to the abnormal feature value; obtaining a correction manner corresponding to the abnormal data type in a preset correction database, the preset correction database being used to store a corresponding relationship between the abnormal data type and the correction manner, and the correction manner including an interpolation correction manner, a mean value smoothing correction manner, and a limiting amplitude correction manner; correcting the abnormal monitoring data according to the correction manner.

6. A data analysis device for a power transmission line, characterized by The device includes an obtaining module and a processing module, wherein: The obtaining module is configured to, in response to a data analysis operation on a power transmission line, obtain historical monitoring data in the power transmission line, construct a first data analysis knowledge graph corresponding to the power transmission line according to the historical monitoring data, and obtain a first feature corresponding to the power transmission line through the first data analysis knowledge graph, the first feature including a tower inclination feature, a conductor stress feature, and a cross-span segment structure feature. The processing module is configured to construct a first data analysis model according to the first feature, and perform the data analysis operation on current monitoring data through the first data analysis model. In the obtaining module, the first data analysis knowledge graph corresponding to the power transmission line is constructed according to the historical monitoring data in the following manner: The historical monitoring data is classified, and a feature association relationship between the historical monitoring data is obtained according to a classification result, the feature association relationship including a tower inclination relationship, a conductor tension and tower stress relationship, and a cross-span segment spatial structure relationship. The first data analysis knowledge graph is constructed with the historical monitoring data as nodes and the feature association relationship as edges. In the obtaining module, the first feature corresponding to the power transmission line is obtained through the first data analysis knowledge graph in the following manner: A target feature value corresponding to a to-be-confirmed feature is obtained through the following formula: in, The target feature value, This indicates the feature type corresponding to the feature to be confirmed. This is a summary function used to calculate the first... The target feature value of the feature to be confirmed, as described above, and the summarization function includes an average function, a weighting function, and a time series analysis function. For the first The first set of nodes described in the class, For the first The second set of edges described in the class, For any element in the first set, the node attribute is... Let be the edge attribute corresponding to any element in the second set, then This represents the set of node attributes and edge attributes; It is determined whether the target feature value is greater than or equal to a preset condition feature value corresponding to the first feature. If the target feature value is greater than or equal to the preset condition feature value corresponding to the first feature, the to-be-confirmed feature is taken as the first feature.

7. An electronic device, comprising: The electronic device includes a processor, a communication bus, a user interface, a network interface, and a memory, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to enable the electronic device to perform the method of any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions, and when the instructions are executed, the method of any one of claims 1 to 5 is performed.

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