A method and system for predicting key hidden dangers based on transmission line visualization

Abstract

The present invention relates to the field of power transmission line fault monitoring technology, specifically a method and system for predicting key hidden dangers based on the visualization of power transmission lines. In the system, the hidden danger warning information feedback module of the operation and maintenance layer combines the meteorological forecast information received from the historical data, dynamically analyzes the risk thermal evolution process of the transmission line within the time period corresponding to the meteorological forecast, dynamically marks the hidden danger prediction area of ​​the transmission line, and constructs risk hidden danger warning information to be transmitted to the display end for visual presentation. The present invention continuously updates the transmission line model through the method of static data + dynamic data. The static data in the updated model remains unchanged, which effectively reduces the amount of data collection and improves the response speed of the transmission line model update. At the same time, based on the fusion analysis of multi-source data, the simulation of the risk thermal evolution process of the transmission line is realized, the hidden danger prediction area of ​​the transmission line is dynamically locked, and the accurate prediction of the key hidden dangers of the transmission line is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of transmission line fault monitoring, and in particular to a method and system for predicting key hidden dangers based on transmission line visualization. Background Art

[0002] The power system is the fundamental infrastructure of modern society. Transmission lines, as the carrier of electrical energy, have a direct impact on grid operation due to their stability and safety. However, transmission lines are constantly exposed to complex environments (such as lightning strikes, icing, strong winds, vegetation growth, wildfires, and vandalism), leading to frequent hidden dangers. If these line hazards (such as broken insulators, broken conductors, and tilted towers) are not promptly addressed, they can lead to major accidents such as short circuits, broken wires, and tower collapses, causing economic losses and even threatening public safety. Traditional methods for detecting hidden dangers rely on manual inspections (such as tower climbing and telescopic observation) or fixed sensor monitoring. These methods suffer from low efficiency, high cost, limited coverage, and poor real-time performance, making them particularly difficult to implement in remote areas or complex terrain.

[0003] Existing key hidden danger prediction systems based on transmission line visualization often use the method of constructing a transmission line topology model for visualization. However, this method collects a large amount of data, and some fixed parameters are often collected repeatedly during the data collection process. It is impossible to effectively distinguish the types of parameters and achieve effective management of the collected data, which often occupies unnecessary computing resources. Therefore, the existing technology has major defects. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for predicting key hidden dangers based on transmission line visualization to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solution: a method for predicting key hidden dangers based on transmission line visualization, the method comprising:

[0006] S1. Collect the positions of each tower in the transmission line and the sag parameters of the conductors between adjacent towers to build a static topology model of the transmission line;

[0007] S2. Acquire real-time sensor data, meteorological data, and geographic information data around the transmission line to generate multi-source data of the transmission line; perform spatiotemporal alignment and feature extraction on the multi-source data of the transmission line to construct a full-dimensional dynamic data spatiotemporal mapping model of the transmission line;

[0008] S3. Based on the geographic information data around the conductors of the transmission line and the fault records of the transmission line in the historical data, calculate the hidden danger risk assessment value corresponding to each transmission line section in the full-dimensional dynamic data spatiotemporal mapping model, and generate a risk heat map of the transmission line;

[0009] S4. Combined with the meteorological forecast information received from historical data, dynamically analyze the risk thermal evolution process of the transmission line within the time period corresponding to the meteorological forecast, dynamically mark the hidden danger prediction area of ​​the transmission line, and construct risk hidden danger warning information to be transmitted to the display end for visual presentation.

[0010] Furthermore, in the process of constructing the static topology model of the transmission line in S1, the towers included in the transmission line are numbered, and the number of towers included in the transmission line is recorded as N; the sag parameter of the conductor between the i-th tower and the i+1-th tower in the transmission line is recorded as H i→i+1 The static topology model of the transmission line includes the positions of each tower included in the transmission line, the edges formed by the conductors between two adjacent tower positions, and the sag parameters corresponding to the corresponding edges;

[0011] The conductor sag parameter represents an average value of the maximum sag distance of the conductor between two corresponding towers in the transmission line in historical data.

[0012] The construction of the static topology model in the present invention avoids the repeated acquisition of the tower positions and sag parameters of each side in the transmission line. At the same time, the static data of the transmission line reflected by the static layer topology model has a value that is not affected by time changes.

[0013] Furthermore, the sensor data in S2 includes the power transmission power and the corresponding transmission direction corresponding to the current time of the corresponding wire;

[0014] The meteorological data includes the maximum wind speed and average wind speed in a preset time period subsequent to the current time, and the ratio of the duration of the corresponding wind speed greater than or equal to the average wind speed in the corresponding preset time period to the duration of the corresponding interval in the corresponding preset time period;

[0015] Geographic information data includes the location of each obstacle within a preset radius around the edge of the conductor between the towers;

[0016] The multi-source data of the transmission line includes the summary results of the multi-source data corresponding to the edges of the conductors between each tower at the same time. The multi-source data corresponding to the edges of each conductor between towers is a summary set of real-time sensor data, meteorological data and geographic information data around the conductors corresponding to the transmission line at the same collection time.

[0017] Furthermore, in the process of performing spatiotemporal alignment on the multi-source data of the transmission line in S2, the multi-source data corresponding to the edge of each inter-tower conductor at the same acquisition time are respectively bound to the corresponding edge in the static topology model of the transmission line to obtain the spatiotemporal alignment result of the multi-source data of the transmission line;

[0018] The method for extracting features from multi-source data of power transmission lines in S2 comprises the following steps:

[0019] S21. Acquire multi-source data on the transmission line; when acquiring geographic information data around each edge corresponding to the passing conductor, calculate the average value of the maximum difference between the actual wind speed corresponding to each location within the corresponding edge in the historical database and the predicted wind speed in the meteorological data at the corresponding time point, and record it as the wind speed deviation of the corresponding edge;

[0020] The sum of the average wind speed in the meteorological data based on the preset time period subsequent to the current time and the wind speed deviation of the corresponding edge is recorded as the predicted wind speed of the corresponding edge in the preset time period subsequent to the current time; the sum of the maximum wind speed in the meteorological data based on the preset time period subsequent to the current time and the wind speed deviation of the corresponding edge is recorded as the maximum predicted wind speed of the corresponding edge in the preset time period subsequent to the current time;

[0021] S22. Obtain the dancing amplitude of the conductor corresponding to each side by querying a preset table in the database, record the sag parameter of the conductor belonging to the corresponding side and the corresponding conductor dancing amplitude in the preset table in the database based on the predicted wind speed of the corresponding side in a preset time period subsequent to the current time as a first dancing amplitude of the corresponding side at time T; record the sag parameter of the conductor belonging to the corresponding side and the corresponding conductor dancing amplitude in the preset table in the database based on the maximum predicted wind speed of the corresponding side in a preset time period subsequent to the current time as a second dancing amplitude of the corresponding side at time T; the time T represents the time point corresponding to the midpoint of the preset time period subsequent to the current time; the dancing amplitude of the conductor represents the maximum horizontal displacement distance of the conductor relative to the static state during the vibration process;

[0022] S23. Construct a risk assessment array based on the wires to which the corresponding edge belongs at the current time, obtaining a first risk assessment array based on the wires to which the corresponding edge belongs at the current time, recorded as (BL1, HL1, BT1); and a second risk assessment array based on the wires to which the corresponding edge belongs at the current time, recorded as (BL2, HL2, BT2).

[0023] Among them, BL1 and BL2 both represent the ratio of the sag parameter of the conductor to which the corresponding side belongs to and the total length of the conductor to which the corresponding side belongs; HL1 represents the ratio of the first waving amplitude of the corresponding side at time T to the sag parameter of the conductor to which the corresponding side belongs; BT1 represents the ratio of the duration of the corresponding wind speed less than the average wind speed in the corresponding preset time period to the duration of the corresponding interval in the corresponding preset time period; HL2 represents the ratio of the second waving amplitude of the corresponding side at time T to the sag parameter of the conductor to which the corresponding side belongs; BT2 represents the ratio of the duration of the corresponding wind speed greater than or equal to the average wind speed in the corresponding preset time period to the duration of the corresponding interval in the corresponding preset time period;

[0024] S24. Obtain the positions of each obstacle within a preset radius around the edge corresponding to the inter-tower conductor in the geographic information data, and combine the second galloping amplitude of each edge at time T to obtain a set of distances based on each obstacle during the galloping of the corresponding edge, wherein each element in the obtained set corresponds to the shortest distance between an obstacle position within the preset radius around the edge and the corresponding edge during the second galloping amplitude at time T.

[0025] S25. Construct the feature extraction result of the multi-source data corresponding to the g-th edge in the transmission line at the current time, denoted as DH g ; the DH g It includes a first risk assessment array and a second risk assessment array based on the wire to which the g-th edge belongs at the current time, and a distance set based on each obstacle when the g-th edge is dancing;

[0026] Based on the spatiotemporal alignment results of multi-source data of the transmission line, the feature extraction results of the multi-source data corresponding to the edges of the conductors between each tower are bound to the corresponding edges in the static topology model of the transmission line to obtain a full-dimensional dynamic data spatiotemporal mapping model of the transmission line.

[0027] The full-dimensional dynamic data spatiotemporal mapping model of the transmission line of the present invention presents the environmental layer data of the transmission line. This layer of data corresponds to the transmission line data directly collected by sensors and other means (meteorological data), as well as the corresponding feature extraction results. The data of this layer will change dynamically over time, but this layer of data and the static topology model are in a spatiotemporal alignment relationship.

[0028] Furthermore, the hidden danger risk assessment value corresponding to each transmission line section in the full-dimensional dynamic data spatiotemporal mapping model is calculated in S3, and the calculation formula involved is as follows:

[0029]

[0030] Among them, PHD g NF represents the hidden danger risk assessment value of the transmission line section corresponding to the g-th edge in the full-dimensional dynamic data spatiotemporal mapping model; g Indicates that in the fault records of the transmission line in the historical data, the first risk assessment array of the corresponding edge in the preset time period before the fault time satisfies DH g The number of failures in the first risk assessment array of the conductor to which the g-th edge belongs at the current time; NS g Indicates that in the fault records of the transmission line in the historical data, the second risk assessment array of the corresponding edge in the preset time period before the fault time satisfies DH gThe number of faults in the second risk assessment array of the conductor to which the g-th edge belongs at the current time is based. When it is determined that one risk assessment array satisfies another risk assessment array, then when j is a different value, the value corresponding to the j-th element in the previous risk assessment array is less than or equal to the value corresponding to the j-th element in the subsequent risk assessment array, and j∈[1,3]. NZ represents the total number of faults recorded in the fault records of the transmission line in the historical data. max{} represents the maximum value function. The distance based on the r-th obstacle when the g-th edge is dancing is recorded as LW. （g，r） NL （g，r） Indicates that in the fault records of the transmission line in the historical data, the shortest distance from the corresponding edge to the surrounding obstacles received at the time of the fault is less than LW （g，r） The total number of failures; Rg represents the total number of elements in the distance set based on each obstacle when the g-th edge dances; μ represents the preset weight coefficient;

[0031] Bind each edge in the static topology model of the transmission line with the corresponding hidden danger risk assessment value to obtain the risk heat map of the transmission line at time T.

[0032] The hidden danger layer of the present invention is mainly based on the collected data in the static layer and the environmental layer to realize the quantitative analysis of the risk hidden danger conditions in each section of the transmission line, and realize the visualization of the risk hidden danger conditions of each section of the line through the risk heat map, and provide data support for the dynamic marking of the hidden danger prediction area of ​​the transmission line in the operation and maintenance layer in the subsequent steps and the construction of risk hidden danger warning information.

[0033] Furthermore, when dynamically analyzing the risk thermal evolution process of the transmission line within the time period corresponding to the weather forecast in S4, the weather forecast information received from the historical data is combined, the current time is recorded as td, and according to the method for generating the risk thermal map of the transmission line in S3, the risk thermal maps of the transmission line corresponding to different values ​​of time t are obtained respectively, t∈[td, T];

[0034] The method for dynamically marking the hidden danger prediction area of ​​the transmission line in S4 comprises the following steps:

[0035] S41. Obtaining the risk thermal evolution process of the transmission line within the time period corresponding to the weather forecast and the sensor data at the current time;

[0036] S42, calculate the risk deviation value of the transmission line section corresponding to each edge at the current time,

[0037]

[0038] Among them, VH g Indicates the risk bias value of the transmission line section corresponding to the g-th edge at the current time; PD represents the hidden danger risk assessment value of the transmission line section corresponding to the g-th edge in the risk heat map of the transmission line at time t; g Indicates the power transmission power of the wire corresponding to the g-th edge in the sensor data at the current time;

[0039] S43: taking the summary set of edges whose corresponding risk hidden danger bias values ​​at the current time are greater than or equal to a preset threshold as the hidden danger prediction area of ​​the power transmission line at the current time;

[0040] The risk hidden danger warning information includes the hidden danger prediction area of ​​the transmission line at the current time, the multi-source data bound to each edge in the corresponding hidden danger prediction area, and the feature extraction results of the corresponding multi-source data.

[0041] A key hidden danger prediction system based on transmission line visualization, comprising: a static layer topology model construction module, an environmental layer full-dimensional spatiotemporal mapping model construction module, a hidden danger layer risk thermal analysis module, and an operation and maintenance layer hidden danger warning information feedback module;

[0042] The static layer topology model building module collects the positions of each tower included in the transmission line and the sag parameters of the conductors between adjacent towers to build a static topology model of the transmission line;

[0043] The environmental layer full-dimensional spatiotemporal mapping model construction module obtains real-time sensor data, meteorological data, and geographic information data around the transmission line to generate multi-source data of the transmission line; performs spatiotemporal alignment and feature extraction on the multi-source data of the transmission line to construct a full-dimensional dynamic data spatiotemporal mapping model of the transmission line;

[0044] The hidden danger layer risk thermal analysis module calculates the hidden danger risk assessment value corresponding to each transmission line section in the full-dimensional dynamic data spatiotemporal mapping model based on the geographic information data around the conductors of the transmission line and the fault records of the transmission line in the historical data, and generates a risk thermal map of the transmission line;

[0045] The operation and maintenance layer hidden danger warning information feedback module combines the meteorological forecast information received from historical data, dynamically analyzes the risk thermal evolution process of the transmission line within the time period corresponding to the meteorological forecast, dynamically marks the hidden danger prediction area of ​​the transmission line, and constructs risk hidden danger warning information to be transmitted to the display end for visual presentation.

[0046] Furthermore, the environment layer full-dimensional spatiotemporal mapping model construction module includes a multi-source data generation unit and a spatiotemporal mapping model construction unit;

[0047] The multi-source data generation unit acquires real-time sensor data, meteorological data and geographic information data around the transmission line, and generates multi-source data of the transmission line;

[0048] The spatiotemporal mapping model construction unit performs spatiotemporal alignment and feature extraction on multi-source data of the transmission line to construct a full-dimensional dynamic data spatiotemporal mapping model of the transmission line.

[0049] Furthermore, the hidden danger layer risk thermal analysis module includes a hidden danger risk assessment unit and a risk thermal map generation unit.

[0050] The hidden danger risk assessment unit calculates the hidden danger risk assessment value corresponding to each transmission line section in the full-dimensional dynamic data spatiotemporal mapping model based on the geographic information data around the conductors of the transmission line and the fault records of the transmission line in the historical data;

[0051] The risk heat map generating unit generates a risk heat map of the transmission line according to the analysis result of the hidden danger risk assessment unit.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] In the process of constructing the transmission line model, the present invention uses a static data + dynamic data approach to continuously update the transmission line model. As time changes, the static data (static layer) in the updated model remains unchanged, and only the dynamic data (environmental layer, hidden danger layer, and operation and maintenance layer) changes. This effectively reduces the amount of data collected and improves the response speed of the transmission line model update.

[0054] In the process of predicting key hidden dangers of transmission lines, the present invention performs fusion analysis based on multi-source data to simulate the thermal evolution process of transmission line risks, dynamically locks the hidden danger prediction area of ​​the transmission line, and realizes accurate prediction of key hidden dangers of transmission lines. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0056] Figure 1 This is a structural diagram of a key hidden danger prediction system based on transmission line visualization according to the present invention;

[0057] Figure 2 It is a flow chart of a method for predicting key hidden dangers based on visualization of transmission lines according to the present invention. DETAILED DESCRIPTION

[0058] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0059] See also Figure 1-Figure 2 , the present invention provides a technical solution: Figure 1 As shown, this embodiment provides a key hidden danger prediction system based on transmission line visualization, which includes: a static layer topology model construction module, an environmental layer full-dimensional spatiotemporal mapping model construction module, a hidden danger layer risk thermal analysis module, and an operation and maintenance layer hidden danger warning information feedback module;

[0060] The static layer topology model building module collects the positions of each tower included in the transmission line and the sag parameters of the conductors between adjacent towers to build a static topology model of the transmission line;

[0061] The environment layer full-dimensional spatiotemporal mapping model construction module includes a multi-source data generation unit and a spatiotemporal mapping model construction unit;

[0062] The multi-source data generation unit acquires real-time sensor data, meteorological data and geographic information data around the transmission line, and generates multi-source data of the transmission line;

[0063] The spatiotemporal mapping model construction unit performs spatiotemporal alignment and feature extraction on multi-source data of the transmission line to construct a full-dimensional dynamic data spatiotemporal mapping model of the transmission line;

[0064] The hidden danger layer risk thermal analysis module includes a hidden danger risk assessment unit and a risk thermal map generation unit.

[0065] The hidden danger risk assessment unit calculates the hidden danger risk assessment value corresponding to each transmission line section in the full-dimensional dynamic data spatiotemporal mapping model based on the geographic information data around the conductors of the transmission line and the fault records of the transmission line in the historical data;

[0066] The risk heat map generating unit generates a risk heat map of the transmission line according to the analysis result of the hidden danger risk assessment unit;

[0067] The operation and maintenance layer hidden danger warning information feedback module combines the meteorological forecast information received from historical data, dynamically analyzes the risk thermal evolution process of the transmission line within the time period corresponding to the meteorological forecast, dynamically marks the hidden danger prediction area of ​​the transmission line, and constructs risk hidden danger warning information to be transmitted to the display end for visual presentation.

[0068] like Figure 2As shown, this embodiment provides a method for predicting key hidden dangers based on transmission line visualization, the method comprising:

[0069] S1. Collect the positions of each tower in the transmission line and the sag parameters of the conductors between adjacent towers to build a static topology model of the transmission line;

[0070] In the process of constructing the static topology model of the transmission line in S1, the towers included in the transmission line are numbered, and the number of towers included in the transmission line is recorded as N; the sag parameter of the conductor between the i-th tower and the i+1-th tower in the transmission line is recorded as H i→i+1 The static topology model of the transmission line includes the positions of each tower included in the transmission line, the edges formed by the conductors between two adjacent tower positions, and the sag parameters corresponding to the corresponding edges;

[0071] The conductor sag parameter represents an average value of the maximum sag distance of the conductor between two corresponding towers in the transmission line in historical data.

[0072] S2. Acquire real-time sensor data, meteorological data, and geographic information data around the transmission line to generate multi-source data of the transmission line; perform spatiotemporal alignment and feature extraction on the multi-source data of the transmission line to construct a full-dimensional dynamic data spatiotemporal mapping model of the transmission line;

[0073] The sensor data in S2 includes the power transmission power and the corresponding transmission direction corresponding to the current time of the corresponding wire;

[0074] The meteorological data includes the maximum wind speed and average wind speed in a preset time period subsequent to the current time, and the ratio of the duration of the corresponding wind speed greater than or equal to the average wind speed in the corresponding preset time period to the duration of the corresponding interval in the corresponding preset time period;

[0075] Geographic information data includes the location of each obstacle within a preset radius around the edge of the conductor between the towers;

[0076] The multi-source data of the transmission line includes the summary results of the multi-source data corresponding to the edges of the conductors between each tower at the same time. The multi-source data corresponding to the edges of each conductor between towers is a summary set of real-time sensor data, meteorological data and geographic information data around the conductors corresponding to the transmission line at the same collection time.

[0077] In the process of performing spatiotemporal alignment on the multi-source data of the transmission line in S2, the multi-source data corresponding to the edge of each inter-tower conductor at the same acquisition time are respectively bound to the corresponding edge in the static topology model of the transmission line to obtain the spatiotemporal alignment result of the multi-source data of the transmission line;

[0078] The method for extracting features from multi-source data of power transmission lines in S2 comprises the following steps:

[0079] S21. Acquire multi-source data on the transmission line; when acquiring geographic information data around each edge corresponding to the passing conductor, calculate the average value of the maximum difference between the actual wind speed corresponding to each location within the corresponding edge in the historical database and the predicted wind speed in the meteorological data at the corresponding time point, and record it as the wind speed deviation of the corresponding edge;

[0080] The sum of the average wind speed in the meteorological data based on the preset time period subsequent to the current time and the wind speed deviation of the corresponding edge is recorded as the predicted wind speed of the corresponding edge in the preset time period subsequent to the current time; the sum of the maximum wind speed in the meteorological data based on the preset time period subsequent to the current time and the wind speed deviation of the corresponding edge is recorded as the maximum predicted wind speed of the corresponding edge in the preset time period subsequent to the current time;

[0081] The wind speed deviation is introduced in this embodiment because, in practice, there are differences in the topography and landforms around the transmission lines, which leads to a certain degree of deviation between the actual wind speed around the transmission lines and the weather forecast data. Therefore, the introduction of the wind speed deviation can achieve accurate prediction of the wind speed conditions around the transmission lines.

[0082] S22. Obtain the dancing amplitude of the conductor corresponding to each side by querying a preset table in the database, record the sag parameter of the conductor belonging to the corresponding side and the corresponding conductor dancing amplitude in the preset table in the database based on the predicted wind speed of the corresponding side in a preset time period subsequent to the current time as a first dancing amplitude of the corresponding side at time T; record the sag parameter of the conductor belonging to the corresponding side and the corresponding conductor dancing amplitude in the preset table in the database based on the maximum predicted wind speed of the corresponding side in a preset time period subsequent to the current time as a second dancing amplitude of the corresponding side at time T; the time T represents the time point corresponding to the midpoint of the preset time period subsequent to the current time; the dancing amplitude of the conductor represents the maximum horizontal displacement distance of the conductor relative to the static state during the vibration process;

[0083] S23. Construct a risk assessment array based on the wires to which the corresponding edge belongs at the current time, obtaining a first risk assessment array based on the wires to which the corresponding edge belongs at the current time, recorded as (BL1, HL1, BT1); and a second risk assessment array based on the wires to which the corresponding edge belongs at the current time, recorded as (BL2, HL2, BT2).

[0084] Among them, BL1 and BL2 both represent the ratio of the sag parameter of the conductor to which the corresponding side belongs to and the total length of the conductor to which the corresponding side belongs; HL1 represents the ratio of the first waving amplitude of the corresponding side at time T to the sag parameter of the conductor to which the corresponding side belongs; BT1 represents the ratio of the duration of the corresponding wind speed less than the average wind speed in the corresponding preset time period to the duration of the corresponding interval in the corresponding preset time period; HL2 represents the ratio of the second waving amplitude of the corresponding side at time T to the sag parameter of the conductor to which the corresponding side belongs; BT2 represents the ratio of the duration of the corresponding wind speed greater than or equal to the average wind speed in the corresponding preset time period to the duration of the corresponding interval in the corresponding preset time period;

[0085] S24. Obtain the positions of each obstacle within a preset radius around the edge corresponding to the inter-tower conductor in the geographic information data, and combine the second galloping amplitude of each edge at time T to obtain a set of distances based on each obstacle during the galloping of the corresponding edge, wherein each element in the obtained set corresponds to the shortest distance between an obstacle position within the preset radius around the edge and the corresponding edge during the second galloping amplitude at time T.

[0086] S25. Construct the feature extraction result of the multi-source data corresponding to the g-th edge in the transmission line at the current time, denoted as DH g ; the DH g It includes a first risk assessment array and a second risk assessment array based on the wire to which the g-th edge belongs at the current time, and a distance set based on each obstacle when the g-th edge is dancing;

[0087] Based on the spatiotemporal alignment results of multi-source data of the transmission line, the feature extraction results of the multi-source data corresponding to the edges of the conductors between each tower are bound to the corresponding edges in the static topology model of the transmission line to obtain a full-dimensional dynamic data spatiotemporal mapping model of the transmission line.

[0088] S3. Based on the geographic information data around the conductors of the transmission line and the fault records of the transmission line in the historical data, calculate the hidden danger risk assessment value corresponding to each transmission line section in the full-dimensional dynamic data spatiotemporal mapping model, and generate a risk heat map of the transmission line;

[0089] In S3, the hidden danger risk assessment value corresponding to each transmission line section in the full-dimensional dynamic data spatiotemporal mapping model is calculated, and the calculation formula involved is as follows:

[0090]

[0091] Among them, PHD g NF represents the hidden danger risk assessment value of the transmission line section corresponding to the g-th edge in the full-dimensional dynamic data spatiotemporal mapping model; g Indicates that in the fault records of the transmission line in the historical data, the first risk assessment array of the corresponding edge in the preset time period before the fault time satisfies DHg The number of failures in the first risk assessment array of the conductor to which the g-th edge belongs at the current time; NS g Indicates that in the fault records of the transmission line in the historical data, the second risk assessment array of the corresponding edge in the preset time period before the fault time satisfies DH g The number of faults in the second risk assessment array of the conductor to which the g-th edge belongs at the current time; when it is determined that one risk assessment array satisfies another risk assessment array, then when j is a different value, the value corresponding to the j-th element in the previous risk assessment array is less than or equal to the value corresponding to the j-th element in the next risk assessment array, and j∈[1,3];

[0092] In this embodiment, if there are two risk assessment arrays, they are recorded as (BL3, HL3, BT3) and (BL4, HL4, BT4);

[0093] If it is determined that (BL3, HL3, BT3) satisfies (BL4, HL4, BT4),

[0094] Then BL3≤BL4 and HL3≤HL4 and BT3≤BT4;

[0095] On the contrary, if 0, 1, or two of the three conditions BL3≤BL4, HL3≤HL4, and BT3≤BT4 are met at the same time, then it cannot be determined that (BL3, HL3, BT3) meets (BL4, HL4, BT4);

[0096] NZ represents the total number of faults recorded in the fault records of the transmission line in the historical data; max{} represents the maximum value function; the distance based on the rth obstacle when the gth edge is dancing is recorded as LW （g，r） NL （g，r） Indicates that in the fault records of the transmission line in the historical data, the shortest distance from the corresponding edge to the surrounding obstacles received at the time of the fault is less than LW （g，r） The total number of failures; Rg represents the total number of elements in the distance set based on each obstacle when the g-th edge dances; μ represents the preset weight coefficient;

[0097] Bind each edge in the static topology model of the transmission line with the corresponding hidden danger risk assessment value to obtain the risk heat map of the transmission line at time T.

[0098] S4. Combined with the weather forecast information received from historical data, dynamically analyze the risk thermal evolution process of the transmission line within the time period corresponding to the weather forecast, dynamically mark the hidden danger prediction area of ​​the transmission line, and construct risk hidden danger warning information to transmit to the display end for visual presentation;

[0099] When dynamically analyzing the risk thermal evolution process of the transmission line within the time period corresponding to the weather forecast in S4, the weather forecast information received from the historical data is combined, the current time is recorded as td, and according to the method for generating the risk thermal map of the transmission line in S3, the risk thermal maps of the transmission line corresponding to different values ​​of time t are obtained, t∈[td, T];

[0100] The method for dynamically marking the hidden danger prediction area of ​​the transmission line in S4 comprises the following steps:

[0101] S41. Obtaining the risk thermal evolution process of the transmission line within the time period corresponding to the weather forecast and the sensor data at the current time;

[0102] S42, calculate the risk deviation value of the transmission line section corresponding to each edge at the current time,

[0103]

[0104] Among them, VH g Indicates the risk bias value of the transmission line section corresponding to the g-th edge at the current time; PD represents the hidden danger risk assessment value of the transmission line section corresponding to the g-th edge in the risk heat map of the transmission line at time t; g Indicates the power transmission power of the wire corresponding to the g-th edge in the sensor data at the current time;

[0105] S43: taking the summary set of edges whose corresponding risk hidden danger bias values ​​at the current time are greater than or equal to a preset threshold as the hidden danger prediction area of ​​the power transmission line at the current time;

[0106] The risk hidden danger warning information includes the hidden danger prediction area of ​​the transmission line at the current time, the multi-source data bound to each edge in the corresponding hidden danger prediction area, and the feature extraction results of the corresponding multi-source data.

[0107] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0108] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art will be able to modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A method for predicting key hidden dangers based on transmission line visualization, characterized in that: The method comprises: S1. Collect the positions of each tower in the transmission line and the sag parameters of the conductors between adjacent towers to build a static topology model of the transmission line; S2. Acquire real-time sensor data, meteorological data, and geographic information data around the conductors of the transmission line to generate multi-source data of the transmission line; perform spatiotemporal alignment and feature extraction on the multi-source data of the transmission line to construct a full-dimensional dynamic data spatiotemporal mapping model of the transmission line; in the process of performing spatiotemporal alignment on the multi-source data of the transmission line in S2, bind the multi-source data corresponding to the edge of each conductor between towers at the same acquisition time to the corresponding edge in the static topology model of the transmission line to obtain the spatiotemporal alignment result of the multi-source data of the transmission line; The method for extracting features from multi-source data of power transmission lines in S2 comprises the following steps: S21. Acquire multi-source data on the transmission line; when acquiring geographic information data around each edge corresponding to the passing conductor, calculate the average value of the maximum difference between the actual wind speed corresponding to each location within the corresponding edge in the historical database and the predicted wind speed in the meteorological data at the corresponding time point, and record it as the wind speed deviation of the corresponding edge; The sum of the average wind speed in the meteorological data based on the preset time period subsequent to the current time and the wind speed deviation of the corresponding edge is recorded as the predicted wind speed of the corresponding edge in the preset time period subsequent to the current time; the sum of the maximum wind speed in the meteorological data based on the preset time period subsequent to the current time and the wind speed deviation of the corresponding edge is recorded as the maximum predicted wind speed of the corresponding edge in the preset time period subsequent to the current time; S22. Obtain the dancing amplitude of the conductor corresponding to each side by querying a preset table in the database, record the sag parameter of the conductor belonging to the corresponding side and the corresponding conductor dancing amplitude in the preset table in the database based on the predicted wind speed of the corresponding side in a preset time period subsequent to the current time as a first dancing amplitude of the corresponding side at time T; record the sag parameter of the conductor belonging to the corresponding side and the corresponding conductor dancing amplitude in the preset table in the database based on the maximum predicted wind speed of the corresponding side in a preset time period subsequent to the current time as a second dancing amplitude of the corresponding side at time T; the time T represents the time point corresponding to the midpoint of the preset time period subsequent to the current time; the dancing amplitude of the conductor represents the maximum horizontal displacement distance of the conductor relative to the static state during the vibration process; S23. Construct a risk assessment array based on the wires to which the corresponding edge belongs at the current time, obtaining a first risk assessment array based on the wires to which the corresponding edge belongs at the current time, recorded as (BL1, HL1, BT1); and a second risk assessment array based on the wires to which the corresponding edge belongs at the current time, recorded as (BL2, HL2, BT2). Among them, BL1 and BL2 both represent the ratio of the sag parameter of the conductor to which the corresponding side belongs to and the total length of the conductor to which the corresponding side belongs; HL1 represents the ratio of the first waving amplitude of the corresponding side at time T to the sag parameter of the conductor to which the corresponding side belongs; BT1 represents the ratio of the duration of the corresponding wind speed less than the average wind speed in the corresponding preset time period to the duration of the corresponding interval in the corresponding preset time period; HL2 represents the ratio of the second waving amplitude of the corresponding side at time T to the sag parameter of the conductor to which the corresponding side belongs; BT2 represents the ratio of the duration of the corresponding wind speed greater than or equal to the average wind speed in the corresponding preset time period to the duration of the corresponding interval in the corresponding preset time period; S24. Obtain the positions of each obstacle within a preset radius around the edge corresponding to the inter-tower conductor in the geographic information data, and combine the second galloping amplitude of each edge at time T to obtain a set of distances based on each obstacle during the galloping of the corresponding edge, wherein each element in the obtained set corresponds to the shortest distance between an obstacle position within the preset radius around the edge and the corresponding edge during the second galloping amplitude at time T. S25. Construct the feature extraction result of the multi-source data corresponding to the g-th edge in the transmission line at the current time, denoted as DH g ; the DH g It includes a first risk assessment array and a second risk assessment array based on the wire to which the g-th edge belongs at the current time, and a distance set based on each obstacle when the g-th edge is dancing; Based on the spatiotemporal alignment results of multi-source data of the transmission line, the feature extraction results of the multi-source data corresponding to the edge of each inter-tower conductor are respectively bound to the corresponding edge in the static topology model of the transmission line to obtain a full-dimensional dynamic data spatiotemporal mapping model of the transmission line; S3. Based on the geographic information data around the conductors of the transmission line and the fault records of the transmission line in the historical data, calculate the hidden danger risk assessment value corresponding to each transmission line section in the full-dimensional dynamic data spatiotemporal mapping model, and generate a risk heat map of the transmission line; S4. Combined with the meteorological forecast information received from historical data, dynamically analyze the risk thermal evolution process of the transmission line within the time period corresponding to the meteorological forecast, dynamically mark the hidden danger prediction area of ​​the transmission line, and construct risk hidden danger warning information to be transmitted to the display end for visual presentation.

2. The method for predicting key hidden dangers based on transmission line visualization according to claim 1, characterized in that: In the process of constructing the static topology model of the transmission line in S1, the towers included in the transmission line are numbered, and the number of towers included in the transmission line is recorded as N; the sag parameter of the conductor between the i-th tower and the i+1-th tower in the transmission line is recorded as H i→i+1 The static topology model of the transmission line includes the positions of each tower included in the transmission line, the edges formed by the conductors between two adjacent tower positions, and the sag parameters corresponding to the corresponding edges; The conductor sag parameter represents an average value of the maximum sag distance of the conductor between two corresponding towers in the transmission line in historical data.

3. The method for predicting key hidden dangers based on transmission line visualization according to claim 1, characterized in that: The sensor data in S2 includes the power transmission power and the corresponding transmission direction corresponding to the current time of the corresponding wire; The meteorological data includes the maximum wind speed and average wind speed in a preset time period subsequent to the current time, and the ratio of the duration of the corresponding wind speed greater than or equal to the average wind speed in the corresponding preset time period to the duration of the corresponding interval in the corresponding preset time period; Geographic information data includes the location of each obstacle within a preset radius around the edge of the conductor between the towers; The multi-source data of the transmission line includes the summary results of the multi-source data corresponding to the edges of the conductors between each tower at the same time. The multi-source data corresponding to the edges of each conductor between towers is a summary set of real-time sensor data, meteorological data and geographic information data around the conductors corresponding to the transmission line at the same collection time.

4. The method for predicting key hidden dangers based on transmission line visualization according to claim 1, characterized in that: In S3, the hidden danger risk assessment value corresponding to each transmission line section in the full-dimensional dynamic data spatiotemporal mapping model is calculated, and the calculation formula involved is as follows: Among them, PHD g NF represents the hidden danger risk assessment value of the transmission line section corresponding to the g-th edge in the full-dimensional dynamic data spatiotemporal mapping model; g Indicates that in the fault records of the transmission line in the historical data, the first risk assessment array of the corresponding edge in the preset time period before the fault time satisfies DH g The number of failures in the first risk assessment array of the conductor to which the g-th edge belongs at the current time; NS g Indicates that in the fault records of the transmission line in the historical data, the second risk assessment array of the corresponding edge in the preset time period before the fault time satisfies DH g The number of faults in the second risk assessment array of the conductor to which the g-th edge belongs at the current time is based. When it is determined that one risk assessment array satisfies another risk assessment array, then when j is a different value, the value corresponding to the j-th element in the previous risk assessment array is less than or equal to the value corresponding to the j-th element in the subsequent risk assessment array, and j∈[1,3]. NZ represents the total number of faults recorded in the fault records of the transmission line in the historical data. max{} represents the maximum value function. The distance based on the r-th obstacle when the g-th edge is dancing is recorded as LW. (g，r) NL (g，r) Indicates that in the fault records of the transmission line in the historical data, the shortest distance from the corresponding edge to the surrounding obstacles received at the time of the fault is less than LW (g，r) The total number of failures; Rg represents the total number of elements in the distance set based on each obstacle when the g-th edge dances; μ represents the preset weight coefficient; Bind each edge in the static topology model of the transmission line with the corresponding hidden danger risk assessment value to obtain the risk heat map of the transmission line at time T.

5. The method for predicting key hidden dangers based on transmission line visualization according to claim 4 is characterized in that: When dynamically analyzing the risk thermal evolution process of the transmission line within the time period corresponding to the weather forecast in S4, the weather forecast information received from the historical data is combined, the current time is recorded as td, and according to the method for generating the risk thermal map of the transmission line in S3, the risk thermal maps of the transmission line corresponding to different values ​​of time t are obtained, t∈[td, T]; The method for dynamically marking the hidden danger prediction area of ​​the transmission line in S4 comprises the following steps: S41. Obtaining the risk thermal evolution process of the transmission line within the time period corresponding to the weather forecast and the sensor data at the current time; S42, calculate the risk deviation value of the transmission line section corresponding to each edge at the current time, Among them, VH g Indicates the risk bias value of the transmission line section corresponding to the g-th edge at the current time; PD represents the hidden danger risk assessment value of the transmission line section corresponding to the g-th edge in the risk heat map of the transmission line at time t; g Indicates the power transmission power of the wire corresponding to the g-th edge in the sensor data at the current time; S43: taking the summary set of edges whose corresponding risk hidden danger bias values ​​at the current time are greater than or equal to a preset threshold as the hidden danger prediction area of ​​the power transmission line at the current time; The risk hidden danger warning information includes the hidden danger prediction area of ​​the transmission line at the current time, the multi-source data bound to each edge in the corresponding hidden danger prediction area, and the feature extraction results of the corresponding multi-source data.

6. A key hidden danger prediction system based on transmission line visualization, applying a key hidden danger prediction method based on transmission line visualization according to any one of claims 1 to 5, characterized in that: The system includes: a static layer topology model construction module, an environmental layer full-dimensional spatiotemporal mapping model construction module, a hidden danger layer risk thermal analysis module and an operation and maintenance layer hidden danger warning information feedback module; The static layer topology model building module collects the positions of each tower included in the transmission line and the sag parameters of the conductors between adjacent towers to build a static topology model of the transmission line; The environmental layer full-dimensional spatiotemporal mapping model construction module obtains real-time sensor data, meteorological data, and geographic information data around the transmission line to generate multi-source data of the transmission line; performs spatiotemporal alignment and feature extraction on the multi-source data of the transmission line to construct a full-dimensional dynamic data spatiotemporal mapping model of the transmission line; The hidden danger layer risk thermal analysis module calculates the hidden danger risk assessment value corresponding to each transmission line section in the full-dimensional dynamic data spatiotemporal mapping model based on the geographic information data around the conductors of the transmission line and the fault records of the transmission line in the historical data, and generates a risk thermal map of the transmission line; The operation and maintenance layer hidden danger warning information feedback module combines the meteorological forecast information received from historical data, dynamically analyzes the risk thermal evolution process of the transmission line within the time period corresponding to the meteorological forecast, dynamically marks the hidden danger prediction area of ​​the transmission line, and constructs risk hidden danger warning information to be transmitted to the display end for visual presentation.

7. The key hidden danger prediction system based on transmission line visualization according to claim 6 is characterized by: The environment layer full-dimensional spatiotemporal mapping model construction module includes a multi-source data generation unit and a spatiotemporal mapping model construction unit; The multi-source data generation unit acquires real-time sensor data, meteorological data and geographic information data around the transmission line, and generates multi-source data of the transmission line; The spatiotemporal mapping model construction unit performs spatiotemporal alignment and feature extraction on multi-source data of the transmission line to construct a full-dimensional dynamic data spatiotemporal mapping model of the transmission line.

8. The key hidden danger prediction system based on transmission line visualization according to claim 6 is characterized by: The hidden danger layer risk thermal analysis module includes a hidden danger risk assessment unit and a risk thermal map generation unit. The hidden danger risk assessment unit calculates the hidden danger risk assessment value corresponding to each transmission line section in the full-dimensional dynamic data spatiotemporal mapping model based on the geographic information data around the conductors of the transmission line and the fault records of the transmission line in the historical data; The risk heat map generating unit generates a risk heat map of the transmission line according to the analysis result of the hidden danger risk assessment unit.

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