A quantitative assessment method for operational safety risks of densely populated power transmission channels

Abstract

The present invention discloses a method for quantitatively assessing the operational safety risk of dense power transmission channels. The method includes the following steps: determining a first candidate area affected by the dense power transmission channel; obtaining a multi-dimensional disaster portrait and a multi-dimensional portrait of the dense power transmission channel equipment in the first candidate area at time t; for any dimensional disaster portrait in the multi-dimensional disaster portrait, matching at least one dimensional equipment portrait in the multi-dimensional equipment portrait; comparing the dimensional disaster portrait with the corresponding matched at least one dimensional equipment portrait to obtain a difference portrait, and determining the operational safety risk level of the dense power transmission channel based on the difference portrait. The present invention determines the risk of equipment failure based on the difference between the disaster's attack capability on the equipment and the equipment's ability to resist the disaster attack, further determines the risk level, and thus achieves a quantitative assessment of the operational safety risk of dense power transmission channels.

Description

Technical Field

[0001] The present invention relates to the field of power safety technology, and in particular to a method for quantitatively assessing the operational safety risks of densely populated power transmission channels. Background Art

[0002] A dense transmission corridor refers to a critical transmission corridor consisting of no fewer than two UHVDC lines with a voltage level of ±800 kV or higher, with a minimum gap of no more than 100 meters between the pole conductors of two adjacent UHVDC lines. my country has vigorously developed UHV AC / DC transmission and transformation projects, and dense transmission corridors for large UHV, long-distance, and cross-regional power grids have gradually emerged. Extreme natural disasters pose an increasingly serious threat to the safe operation and production of my country's power grid, particularly the safety of dense transmission corridors. Due to the narrow corridors and complex terrain and social environments of dense transmission corridors, new technologies and methods are urgently needed to carry out disaster risk warnings for dense transmission corridors. Summary of the Invention

[0003] To address the problems of the above-mentioned existing technologies, the present invention provides a method, system, device, and computer-readable storage medium for quantitatively assessing the operational safety risks of dense power transmission channels. By determining the risk of equipment failure based on the difference between the profile of the equipment's attack capability against disasters and the profile of the equipment's ability to resist disaster attacks, the present invention further determines the risk level, thereby achieving a quantitative assessment of the operational safety risks of dense power transmission channels. The technical solution is as follows:

[0004] In the first aspect, a method for quantitatively assessing the operational safety risk of a densely populated power transmission channel is provided, comprising the following steps:

[0005] S1: Based on the occurrence area of ​​external disasters, the area affected by the dense transmission channel is determined and recorded as the first candidate area;

[0006] S2: Obtain a multi-dimensional portrait of a disaster at time t in the first candidate area and a multi-dimensional portrait of dense power transmission channel equipment at time t in the first candidate area, wherein the multi-dimensional portrait of the disaster includes disaster portraits corresponding to the disaster in multiple different dimensions that characterize the disaster attack capability, and the multi-dimensional portrait of dense power transmission channel equipment includes equipment portraits corresponding to the dense power transmission channel equipment in multiple different dimensions that characterize the equipment disaster resistance capability;

[0007] S3: For any dimensional disaster portrait in the multi-dimensional disaster portrait, match at least one dimensional device portrait in the multi-dimensional device portrait;

[0008] S4: Compare the disaster portrait of any dimension with the equipment portrait of at least one corresponding matching dimension to obtain a difference portrait, and determine the operational safety risk level of the power transmission intensive channel based on the difference portrait.

[0009] In a possible implementation, the step S1 further includes: predicting the area where the external disaster will occur at the time t to be evaluated based on a method for predicting the area where the external disaster will spread.

[0010] In a possible implementation, the step of obtaining a multi-dimensional disaster portrait of the first candidate region at time t in S2 includes:

[0011] Determining, based on the disaster category, a first attribute label corresponding to the disaster category, where the first attribute label represents different attack categories of the disaster category on different devices;

[0012] Based on the first attribute tag, obtaining disaster characteristic parameters associated with the first attribute tag;

[0013] A multi-dimensional portrait of the disaster is generated based on the associated disaster characteristic parameters corresponding to the first attribute label.

[0014] In a possible implementation, the step of obtaining a multi-dimensional portrait of dense power transmission channel equipment in the first candidate area at time t in S2 includes:

[0015] Based on the device category, determining a second attribute label corresponding to the first attribute label, wherein the second attribute label represents a disaster attack category to which the device of the category may be subjected;

[0016] Based on the second attribute tag, obtaining a device characteristic parameter associated with the second attribute tag;

[0017] Generate a multi-dimensional portrait of the device based on the associated device characteristic parameters corresponding to the second attribute tag.

[0018] In a possible implementation, acquiring, based on the second attribute tag, a device characteristic parameter associated with the second attribute tag includes:

[0019] Based on a historical equipment failure database, generating an equipment feature vector representing a failure event corresponding to a second attribute label according to preset equipment candidate feature parameters, wherein the feature vector includes component parameters representing the equipment failure state;

[0020] Clustering the device feature vectors based on the fault state component parameters of the device feature vectors;

[0021] For the device feature vectors in the same category after clustering, the parameter value distribution of the corresponding position component in the vector is analyzed;

[0022] For the parameter numerical distribution of the component at the same position in the vector, the distribution stability and the area of ​​the distribution region are determined. If the stability of the parameter numerical distribution of the component at the position and the area of ​​the distribution region meet the preset conditions, the device characteristic parameter corresponding to the component at the position is determined to be the device characteristic parameter associated with the second attribute tag.

[0023] In a possible implementation, the distribution stability is determined based on the sum of the number of distances between the same position component in each device feature vector and the same position components in other device feature vectors that are less than a preset distance threshold.

[0024] In a possible implementation, the step of obtaining a multi-dimensional portrait of dense power transmission channel equipment in the first candidate area at time t in S2 further includes:

[0025] Based on the intrinsic attribute parameters and real-time operating parameters of the dense power transmission channel equipment in the first candidate area at time t, a basic dimensional portrait of the equipment is obtained, where the basic dimensional portrait of the equipment represents the health parameters of the dense power transmission channel equipment;

[0026] In S4, the disaster portrait in any dimension is compared with the corresponding equipment portrait in at least one dimension to obtain a difference portrait, including:

[0027] Fusing the device portrait of at least one dimension corresponding to the dimensional disaster portrait and the device basic dimensional portrait to obtain a fused dimensional device portrait;

[0028] Based on the comparison between the fusion dimensional equipment portrait and the dimensional disaster portrait, a difference portrait is obtained.

[0029] Secondly, a system for quantitatively assessing the operational safety risks of densely populated power transmission channels is provided, including:

[0030] A first candidate area determination module is used to determine an area affected by dense power transmission channels based on the area where the external disaster occurs, and record it as the first candidate area;

[0031] a multi-dimensional portrait determination module, configured to obtain a multi-dimensional portrait of a disaster at time t in the first candidate area and a multi-dimensional portrait of dense power transmission channel equipment at time t in the first candidate area, wherein the multi-dimensional disaster portrait includes disaster portraits corresponding to the disaster in multiple different dimensions that characterize the disaster's attack capability, and the multi-dimensional portrait of dense power transmission channel equipment includes equipment portraits corresponding to the dense power transmission channel equipment in multiple different dimensions that characterize the equipment's disaster resistance capability;

[0032] A dimensional portrait matching module, configured to match a device portrait of at least one dimension in the multi-dimensional device portrait with a disaster portrait of any dimension in the multi-dimensional disaster portrait;

[0033] The operation safety risk analysis module is used to compare the disaster portrait of any dimension with the equipment portrait of at least one corresponding matching dimension to obtain a difference portrait, and determine the operation safety risk level of the power transmission dense channel based on the difference portrait.

[0034] In a third aspect, a device for quantitatively assessing the operational safety risks of a power transmission intensive channel is provided, the device comprising:

[0035] processor;

[0036] a memory for storing processor-executable instructions;

[0037] The processor executes the executable instructions to implement the method for quantitatively assessing the operational safety risks of power transmission intensive channels as described in the first aspect above.

[0038] In a fourth aspect, a computer-readable storage medium is provided, on which computer instructions are stored. When the instructions are executed by a processor, the steps of the method for quantitatively assessing the operational safety risks of densely populated power transmission channels as described in the first aspect above are implemented.

[0039] The present invention provides a method for quantitatively assessing the operational safety risks of a power transmission intensive channel, which has the following beneficial effects:

[0040] 1. Compare the disaster profile with the equipment profile that matches the disaster profile dimension. That is, based on the difference between the disaster's attack capability against a certain type of attack on the equipment and the equipment's ability to resist the said type of attack, the risk of equipment failure is judged. When the difference is within the range of different preset levels, the different risk levels of equipment failure can be determined, thereby achieving a quantitative assessment of the safety risks of the operation of densely populated power transmission channels.

[0041] 2. In obtaining a multi-dimensional portrait of dense transmission channel equipment at time t in the first candidate area, based on the second attribute label, obtain the equipment characteristic parameters associated with the second attribute label, and generate a multi-dimensional portrait of the equipment based on the associated equipment characteristic parameters corresponding to the second attribute label. In the process of obtaining the associated equipment characteristic parameters, generate an equipment characteristic vector based on the preset equipment candidate characteristic parameters, first cluster the equipment characteristic vectors based on the fault state component parameters of the equipment characteristic vectors, and then analyze the parameter numerical distribution of the corresponding position components in the equipment characteristic vectors in the same category after clustering. Based on the stability of the parameter numerical distribution and the size of the distribution area, determine the equipment characteristic parameters associated with the second attribute label in the equipment characteristic vector, which simplifies the computational complexity of obtaining the equipment characteristic parameters associated with the second attribute label and improves the accuracy of the results of obtaining the associated equipment characteristic parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1This is a flow chart of a method for quantitatively assessing the operational safety risks of a power transmission intensive channel according to an embodiment of the present application;

[0043] Figure 2 It is a structural diagram of the transmission intensive channel operation safety risk quantitative assessment system in the embodiment of the present application. DETAILED DESCRIPTION

[0044] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting the present invention. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0045] The present invention provides a method for quantitatively assessing the operational safety risk of a power transmission channel, including the following steps:

[0046] S1: Based on the occurrence area of ​​external disasters, the area affected by the dense transmission channel is determined and recorded as the first candidate area;

[0047] S2: Obtain a multi-dimensional portrait of a disaster at time t in the first candidate area and a multi-dimensional portrait of dense power transmission channel equipment at time t in the first candidate area, wherein the multi-dimensional portrait of the disaster includes disaster portraits corresponding to the disaster in multiple different dimensions that characterize the disaster attack capability, and the multi-dimensional portrait of dense power transmission channel equipment includes equipment portraits corresponding to the dense power transmission channel equipment in multiple different dimensions that characterize the equipment disaster resistance capability;

[0048] S3: For any dimensional disaster portrait in the multi-dimensional disaster portrait, match at least one dimensional device portrait in the multi-dimensional device portrait;

[0049] S4: Compare the disaster portrait of any dimension with the equipment portrait of at least one corresponding matching dimension to obtain a difference portrait, and determine the operational safety risk level of the power transmission intensive channel based on the difference portrait.

[0050] In an embodiment of the present application, a comparison is made between a disaster portrait and a device portrait that matches the dimension of the disaster portrait, that is, the risk of device failure is judged based on the difference between the disaster's attack capability of a certain type of attack on the device and the device's ability to resist the said type of attack. When the difference is within different preset level intervals, different risk levels of device failure can be determined. It can be understood that the attack capability of the disaster of a certain type of attack on the device represents the danger level of the disaster, and the external disasters can be various disasters such as icing disasters, lightning disasters, and fires.

[0051] It should also be noted that in step S3 above, for each dimension of the multi-dimensional disaster portrait, a device profile match needs to be performed to avoid missing out on the evaluation and analysis of attacks that may have been committed against a particular device.

[0052] Furthermore, the above step S1 also includes: predicting the area where the external disaster will occur at the time t to be evaluated based on the external disaster spread area prediction method.

[0053] Specifically, based on the real-time monitoring information and prediction information of external disasters, the real-time occurrence area and the occurrence area at future moments of the external disaster are determined. Specifically, the disaster occurrence area at the current moment and the change pattern of the disaster occurrence characteristic parameters can be determined based on the disaster monitoring information, and the occurrence area of ​​the disaster at the future evaluation time t can be predicted through the intelligent optimization algorithm.

[0054] Furthermore, the multi-dimensional disaster portrait of the first candidate area at time t is obtained in the above step S2, including:

[0055] S21: Based on the disaster category, determine a first attribute label corresponding to the disaster category, where the first attribute label represents different attack categories of the disaster category on different devices;

[0056] S22: Based on the first attribute tag, obtaining disaster characteristic parameters associated with the first attribute tag;

[0057] S23: Generate a multi-dimensional portrait of the disaster based on the associated disaster characteristic parameters corresponding to the first attribute label.

[0058] For example, the first attribute label is an ice disaster on a transmission channel. Attacks on insulators include creeping discharge, while attacks on transmission lines include line breakage and tower collapse. Different types of disasters can cause multiple attacks on different equipment. Disaster characteristic parameters associated with the first attribute label are obtained. For an attack with the first attribute label "line breakage," the disaster characteristic parameters associated with the first attribute label can include parameters representing the attack capability, such as rainfall rate, rainfall duration, and rainfall speed. Based on these disaster characteristic parameters, the thickness and weight of ice on the transmission line can be calculated, which can be used to analyze the risk probability of failure caused by the ice disaster on the transmission line.

[0059] Furthermore, the multi-dimensional portrait of dense power transmission channel equipment in the first candidate area at time t is obtained in the above step S2, including:

[0060] S24: Based on the device category, determining a second attribute label corresponding to the first attribute label, where the second attribute label represents a disaster attack category to which the device of the category may be subjected;

[0061] S25: Based on the second attribute tag, obtain device characteristic parameters associated with the second attribute tag;

[0062] S26: Generate a multi-dimensional portrait of the device based on the associated device characteristic parameters corresponding to the second attribute tag.

[0063] The second attribute tag corresponds to the first attribute tag. In step S3, for any dimensional disaster profile in the multi-dimensional disaster profile, at least one dimensional device profile in the multi-dimensional device profile is matched. This matching process includes performing a corresponding match based on the attribute tag or the category to which the attribute tag belongs. For example, if a lightning disaster causes a lightning trip attack on a transmission line between two adjacent towers, the device characteristic parameters associated with the second attribute tag may include parameters such as the average conductor height, the line's altitude, the average height of the lightning conductor, and the distance between two adjacent towers. Specifically, the characteristic parameters associated with the second attribute tag can be obtained based on a historical fault database using an association rule mining algorithm.

[0064] In one embodiment, the above step S25, based on the second attribute tag, obtaining the device characteristic parameter associated with the second attribute tag, includes:

[0065] S251: Based on a historical equipment failure database, generating a device feature vector representing a failure event corresponding to a second attribute label according to preset device candidate feature parameters, wherein the feature vector includes component parameters representing the equipment failure state;

[0066] S252: Clustering the device feature vector based on the fault state component parameters of the device feature vector;

[0067] S253: for the device feature vectors in the same category after clustering, analyzing the parameter value distribution of the corresponding position component in the vector;

[0068] S254: Determine the distribution stability and the area of ​​the distribution region for the parameter numerical distribution of the component at the same position in the vector. If the stability of the parameter numerical distribution and the area of ​​the distribution region of the component at the position meet the preset conditions, determine that the device characteristic parameter corresponding to the component at the position is the device characteristic parameter associated with the second attribute tag.

[0069] In an embodiment of the present application, for the disaster attack category corresponding to the second attribute label, a type of equipment failure corresponding to the second attribute label is determined, and in the equipment failure database, fault data of this type of failure is obtained, and multi-dimensional information when the equipment occurs this type of failure is obtained, that is, preset equipment candidate feature parameters.

[0070] Based on the historical fault database, a device feature vector including multiple components is obtained to form a device feature vector set. The device feature vectors in the set are analyzed for similarity and difference based on the component parameters representing the device fault state to obtain a clustering result. The device fault states represented by the device feature vectors in each cluster class are similar. The component parameters representing the device fault state in the device feature vector are recorded as the first component parameter segment. The non-first component parameter segments in the device feature vectors in each cluster class are analyzed for similarity and difference, that is, for the device feature vectors in the same category after clustering, the parameter value distribution of the corresponding position component in the vector is analyzed. If the stability of the parameter value distribution of the same position component is within the preset first range and the area of ​​the distribution area is within the second preset range, it indicates that the same position components of the device feature vector are similar, that is, the device characteristic parameter corresponding to the position component of the device feature vector is associated with the second attribute label, wherein the stability of the parameter value distribution of the same position component represents the density of the parameter value distribution, and the area of ​​the distribution area of ​​the parameter value of the same position component represents the discreteness of the parameter value distribution. When the stability of the parameter value distribution is greater and the area of ​​the distribution area is smaller, it represents that the distribution of the parameter value of the same position component is more consistent. Further, The stability parameter can be determined based on the sum of the number of distances between the same position component in each device feature vector and the same position component in other device feature vectors that are less than a preset distance threshold. For example, if the distances between the first position component in device feature vector A, the first position component in device feature vector B, and the first position component in device feature vector C are all less than the preset distance threshold, then the number of distances between the first position component in device feature vector A and the first position component in other device feature vectors that are less than the preset distance threshold is 2. Similarly, the number of distances between the same position component in each device feature vector and the same position component in other device feature vectors that are less than the preset distance threshold is obtained, and then the sum of the numbers is obtained.

[0071] Furthermore, the distribution stability in the above step S254 is determined based on the total number of distances between the same position component in each device feature vector and the same position components in other device feature vectors that are less than a preset distance threshold.

[0072] Furthermore, the step S2 of obtaining a multi-dimensional portrait of dense power transmission channel equipment in the first candidate area at time t further includes:

[0073] Based on the intrinsic attribute parameters and real-time operating parameters of the dense power transmission channel equipment in the first candidate area at time t, a basic dimensional portrait of the equipment is obtained, where the basic dimensional portrait of the equipment represents the health parameters of the dense power transmission channel equipment;

[0074] In S4, the disaster portrait in any dimension is compared with the corresponding equipment portrait in at least one dimension to obtain a difference portrait, including:

[0075] Fusing the device portrait of at least one dimension corresponding to the dimensional disaster portrait and the device basic dimensional portrait to obtain a fused dimensional device portrait;

[0076] Based on the comparison between the fusion dimensional equipment portrait and the dimensional disaster portrait, a difference portrait is obtained.

[0077] In the embodiment of the present application, a basic dimensional portrait of the equipment that characterizes the health parameters of dense transmission channel equipment is used as the basic data for the equipment's ability to resist different disasters. On this basis, the specific device characteristic parameters of the equipment for a specific type of attack, that is, the fusion dimensional equipment portrait, is combined to comprehensively analyze the equipment's disaster resistance ability for a specific type of attack. The basic dimensional portrait of the equipment characterizes the device characteristic parameters that are implicitly related to a specific type of attack in the device characteristic parameters. The device portrait of at least one dimension that corresponds to the dimensional disaster portrait characterizes the device characteristic parameters that are relatively explicitly associated with a specific type of attack. Based on the difference portrait obtained by comparing the fusion dimensional device portrait with the dimensional disaster portrait, the risk parameters and risk level of equipment failure are more accurately analyzed and determined.

[0078] The present application also provides a system for quantitatively assessing the operational safety risks of a power transmission intensive channel, including:

[0079] A first candidate area determination module is used to determine an area affected by dense power transmission channels based on the area where the external disaster occurs, and record it as the first candidate area;

[0080] a multi-dimensional portrait determination module, configured to obtain a multi-dimensional portrait of a disaster at time t in the first candidate area and a multi-dimensional portrait of dense power transmission channel equipment at time t in the first candidate area, wherein the multi-dimensional disaster portrait includes disaster portraits corresponding to the disaster in multiple different dimensions that characterize the disaster's attack capability, and the multi-dimensional portrait of dense power transmission channel equipment includes equipment portraits corresponding to the dense power transmission channel equipment in multiple different dimensions that characterize the equipment's disaster resistance capability;

[0081] A dimensional portrait matching module, configured to match a device portrait of at least one dimension in the multi-dimensional device portrait with a disaster portrait of any dimension in the multi-dimensional disaster portrait;

[0082] The operation safety risk analysis module is used to compare the disaster portrait of any dimension with the equipment portrait of at least one corresponding matching dimension to obtain a difference portrait, and determine the operation safety risk level of the power transmission dense channel based on the difference portrait.

[0083] It should be noted that the power transmission channel operation safety risk quantitative assessment system provided in this embodiment only uses the division of the above-mentioned functional modules as an example when performing safety risk quantitative assessment. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the operation safety risk quantitative assessment system can be divided into different functional modules to complete all or part of the functions described above. In addition, the power transmission channel operation safety risk quantitative assessment system provided in this embodiment and the power transmission channel operation safety risk quantitative assessment method embodiment provided in the above embodiment are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0084] The present application also provides a device for quantitatively assessing the operational safety risks of a power transmission intensive channel, the device comprising:

[0085] processor;

[0086] a memory for storing processor-executable instructions;

[0087] The processor executes the executable instructions to implement the method for quantitatively assessing the operational safety risks of power transmission-intensive channels in the above embodiment.

[0088] Of course, the device may also have components such as a wired or wireless network interface, a keyboard, and an input / output interface for input and output. The computer device may also include other components for realizing the functions of the device, which will not be described in detail here.

[0089] An embodiment of the present application also provides a computer-readable storage medium having computer instructions stored thereon, characterized in that when the instructions are executed by a processor, the steps of the method for quantitatively assessing the operational safety risks of dense power transmission channels in the above-mentioned embodiment are implemented. The computer-readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), a magnetic tape, a floppy disk, an optical data storage node, etc.

[0090] The present invention is not limited to the above-mentioned specific implementation methods. Various changes made by ordinary technicians in this field based on the above-mentioned concept without creative work are all within the scope of protection of the present invention.

Claims

1. A method for quantitatively assessing the operational safety risk of a densely populated power transmission channel, characterized in that: include: S1: Determine the area affected by the transmission-intensive corridor based on the area where the external disaster occurs, and record it as the first candidate area; S2: Obtain a multi-dimensional portrait of a disaster at time t in the first candidate area and a multi-dimensional portrait of power transmission channel equipment at time t in the first candidate area, wherein the multi-dimensional portrait of the disaster includes disaster portraits corresponding to the disaster in multiple different dimensions that characterize the disaster attack capability, and the multi-dimensional portrait of power transmission channel equipment includes equipment portraits corresponding to the power transmission channel equipment in multiple different dimensions that characterize the equipment's disaster resistance capability; S3: For any dimensional disaster portrait in the multi-dimensional disaster portrait, match at least one dimensional device portrait in the multi-dimensional device portrait; S4: Compare the disaster profile in any dimension with the corresponding equipment profile in at least one dimension to obtain a difference profile, and determine the operational safety risk level of the power transmission channel based on the difference profile; The step S2 of obtaining a multi-dimensional disaster portrait of the first candidate region at time t includes: Determining, based on the disaster category, a first attribute label corresponding to the disaster category, where the first attribute label represents different attack categories of the disaster category on different devices; Based on the first attribute tag, obtaining disaster characteristic parameters associated with the first attribute tag; Generate a multi-dimensional disaster portrait based on the associated disaster characteristic parameters corresponding to the first attribute label; The step S2 of obtaining a multi-dimensional portrait of the power transmission dense channel equipment in the first candidate area at time t includes: Based on the device category, determining a second attribute label corresponding to the first attribute label, wherein the second attribute label represents a disaster attack category to which the device of the category may be subjected; Based on the second attribute tag, obtaining a device characteristic parameter associated with the second attribute tag; Generate a multi-dimensional portrait of the device based on the associated device characteristic parameters corresponding to the second attribute tag; The acquiring, based on the second attribute tag, a device characteristic parameter associated with the second attribute tag includes: Based on a historical equipment failure database, generating an equipment feature vector representing a failure event corresponding to a second attribute label according to preset equipment candidate feature parameters, wherein the feature vector includes component parameters representing the equipment failure state; Clustering the device feature vectors based on the fault state component parameters of the device feature vectors; For the device feature vectors in the same category after clustering, the parameter value distribution of the corresponding position component in the vector is analyzed; Determining the distribution stability and the area of ​​the distribution region for the parameter value distribution of the component at the same position in the vector; if the stability of the parameter value distribution and the area of ​​the distribution region of the component at the position meet preset conditions, determining that the device characteristic parameter corresponding to the component at the position is the device characteristic parameter associated with the second attribute tag; The distribution stability is determined based on the sum of the number of distances between the same position component in each device feature vector and the same position component in other device feature vectors that are less than a preset distance threshold; The step S2 of obtaining a multi-dimensional portrait of the power transmission dense channel equipment in the first candidate area at time t also includes: Based on the intrinsic attribute parameters and real-time operating parameters of the power transmission channel equipment in the first candidate area at time t, a basic dimensional portrait of the equipment is obtained, where the basic dimensional portrait of the equipment represents the health parameters of the power transmission channel equipment; In S4, the disaster portrait in any dimension is compared with the equipment portrait in at least one corresponding matching dimension to obtain a difference portrait, including: Fusing the device portrait of at least one dimension corresponding to the dimensional disaster portrait and the device basic dimensional portrait to obtain a fused dimensional device portrait; Based on the comparison between the fusion dimensional equipment portrait and the dimensional disaster portrait, a difference portrait is obtained.

2. A method for quantitatively assessing the operational safety risk of a power transmission intensive channel according to claim 1, characterized in that: Said S1 also includes: predicting the occurrence area of ​​the external disaster at the time t to be evaluated based on the external disaster spread area prediction method.

3. A system for quantitatively assessing the operational safety risks of densely populated power transmission channels, characterized in that: include: A first candidate area determination module is used to determine the area affected by the power transmission dense channel based on the area where the external disaster occurs, and record it as the first candidate area; a multi-dimensional portrait determination module, configured to obtain a multi-dimensional portrait of a disaster at time t in the first candidate area and a multi-dimensional portrait of power transmission channel equipment at time t in the first candidate area, wherein the multi-dimensional disaster portrait includes disaster portraits corresponding to the disaster in multiple different dimensions that characterize the disaster's attack capability, and the multi-dimensional power transmission channel equipment portraits include equipment portraits corresponding to the power transmission channel equipment in multiple different dimensions that characterize the equipment's disaster resistance capability; A dimensional portrait matching module, configured to match a device portrait of at least one dimension in the multi-dimensional device portrait with respect to any dimensional disaster portrait in the multi-dimensional disaster portrait; An operation safety risk analysis module is used to compare the disaster profile in any dimension with the equipment profile in at least one corresponding dimension to obtain a difference profile, and determine the operation safety risk level of the power transmission dense channel based on the difference profile; The multi-dimensional portrait determination module obtains a multi-dimensional portrait of a disaster at the first candidate area at time t, including: Determining, based on the disaster category, a first attribute label corresponding to the disaster category, where the first attribute label represents different attack categories of the disaster category on different devices; Based on the first attribute tag, obtaining disaster characteristic parameters associated with the first attribute tag; Generate a multi-dimensional disaster portrait based on the associated disaster characteristic parameters corresponding to the first attribute label; The multi-dimensional portrait determination module obtains a multi-dimensional portrait of the power transmission dense channel equipment in the first candidate area at time t, including: Based on the device category, determining a second attribute label corresponding to the first attribute label, wherein the second attribute label represents a disaster attack category to which the device of the category may be subjected; Based on the second attribute tag, obtaining a device characteristic parameter associated with the second attribute tag; Generate a multi-dimensional portrait of the device based on the associated device characteristic parameters corresponding to the second attribute tag; The acquiring, based on the second attribute tag, a device characteristic parameter associated with the second attribute tag includes: Based on a historical equipment failure database, generating an equipment feature vector representing a failure event corresponding to a second attribute label according to preset equipment candidate feature parameters, wherein the feature vector includes component parameters representing the equipment failure state; Clustering the device feature vectors based on the fault state component parameters of the device feature vectors; For the device feature vectors in the same category after clustering, the parameter value distribution of the corresponding position component in the vector is analyzed; Determining the distribution stability and the area of ​​the distribution region for the parameter value distribution of the component at the same position in the vector; if the stability of the parameter value distribution and the area of ​​the distribution region of the component at the position meet preset conditions, determining that the device characteristic parameter corresponding to the component at the position is the device characteristic parameter associated with the second attribute tag; The distribution stability is determined based on the sum of the number of distances between the same position component in each device feature vector and the same position component in other device feature vectors that are less than a preset distance threshold; The step of obtaining a multi-dimensional portrait of power transmission dense channel equipment in the first candidate area at time t also includes: Based on the intrinsic attribute parameters and real-time operating parameters of the power transmission channel equipment in the first candidate area at time t, a basic dimensional portrait of the equipment is obtained, where the basic dimensional portrait of the equipment represents the health parameters of the power transmission channel equipment; The step of comparing the disaster portrait in any dimension with the equipment portrait in at least one corresponding dimension to obtain a difference portrait includes: Fusing the device portrait of at least one dimension corresponding to the dimensional disaster portrait and the device basic dimensional portrait to obtain a fused dimensional device portrait; Based on the comparison between the fusion dimensional equipment portrait and the dimensional disaster portrait, a difference portrait is obtained.

4. A device for quantitatively assessing the operational safety risks of dense power transmission channels, characterized in that: The device comprises: processor; a memory for storing processor-executable instructions; The processor executes the executable instructions to implement the method for quantitatively assessing the operational safety risk of a power transmission intensive channel as described in any one of claims 1-2.

5. A computer-readable storage medium having computer instructions stored thereon, characterized in that: When the instructions are executed by the processor, the steps of the method for quantitatively assessing the operational safety risks of a power transmission intensive channel according to any one of claims 1 to 2 are implemented.

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