Transmission line status monitoring method, device, equipment and storage medium

Abstract

The present application discloses a method, apparatus, equipment and storage medium for monitoring the status of a transmission line. By acquiring multi-dimensional monitoring data of multiple tower units in the transmission line, the monitoring dimension of the transmission line is increased, thereby overcoming the limitation of a single monitoring dimension. The multi-dimensional monitoring data is then classified into status categories to obtain a target status matrix, and correlation analysis is performed on the multi-dimensional monitoring data to obtain a correlation coefficient matrix, thereby performing data fusion on the multi-dimensional data to consider the correlation between the monitoring data, making the monitoring process scientific and the monitoring results more accurate. A preset line status monitoring model is used to monitor the status of the transmission line according to the target status matrix and the correlation coefficient matrix to obtain a status monitoring result, thereby utilizing a network model to overcome the problem of increased data volume caused by multi-dimensional data and improve monitoring efficiency.

Description

Technical Field

[0001] The present application relates to the field of power grid line technology, and in particular to a method, device, equipment and storage medium for monitoring the status of a power transmission line. Background Art

[0002] Currently, traditional transmission line status monitoring focuses on single-point monitoring of individual equipment, such as generators or transformers. This monitoring approach is easy to implement, but lacks objectivity when it comes to monitoring the status of the entire transmission line.

[0003] Related technologies use a weighted approach to comprehensively evaluate the operating status of transmission lines based on the operating status of each unit within a tower segment, improving the objectivity of transmission line status monitoring results. However, these technologies rely solely on weights for evaluation, failing to consider factors such as the correlation between units and environmental characteristics. This leads to limitations such as a single monitoring dimension. Summary of the Invention

[0004] The present application provides a method, apparatus, device and storage medium for monitoring the status of a power transmission line to solve technical problems such as the limitations of the current status monitoring of power transmission lines, such as a single monitoring dimension.

[0005] In order to solve the above technical problems, in a first aspect, the present application provides a method for monitoring the status of a transmission line, comprising:

[0006] Obtain multi-dimensional monitoring data of multiple tower units in the transmission line;

[0007] Classify the multi-dimensional monitoring data and obtain the target state matrix;

[0008] Conduct correlation analysis on multi-dimensional monitoring data to obtain the correlation coefficient matrix;

[0009] Using the preset line condition monitoring model, the transmission line condition is monitored according to the target state matrix and the correlation coefficient matrix to obtain the condition monitoring results.

[0010] The present application increases the monitoring dimension of the transmission line by acquiring multi-dimensional monitoring data of multiple tower units in the transmission line, thereby solving the limitation of a single monitoring dimension; then classifies the multi-dimensional monitoring data into status categories to obtain a target state matrix, and performs correlation analysis on the multi-dimensional monitoring data to obtain a correlation coefficient matrix, thereby performing data fusion on the multi-dimensional data to consider the correlation between the monitoring data, making the monitoring process scientific and the monitoring results more accurate; utilizes a preset line state monitoring model, performs state monitoring on the transmission line according to the target state matrix and the correlation coefficient matrix, and obtains state monitoring results, so as to utilize a network model to overcome the problem of increased data volume caused by multi-dimensional data and improve monitoring efficiency.

[0011] Preferably, the multi-dimensional monitoring data includes at least one of image and video monitoring data, ambient temperature monitoring data, micro-meteorological monitoring data, tower tilt monitoring data and distributed fault location monitoring data.

[0012] As an optimal method, the multi-dimensional monitoring data is classified into state categories to obtain a target state matrix, including:

[0013] Based on the preset state quantity threshold, the multi-dimensional monitoring data is classified into state categories to obtain data vector groups corresponding to various state quantities;

[0014] Based on multiple data vector groups, a target state matrix is ​​established.

[0015] Preferably, the state quantity threshold includes a first state quantity threshold for general state quantities, a second state quantity threshold for important state quantities, and a third state quantity threshold for special state quantities.

[0016] Preferably, correlation analysis is performed on the multi-dimensional monitoring data to obtain a correlation coefficient matrix, including:

[0017] Standardize multi-dimensional monitoring data to obtain target monitoring data;

[0018] Based on the preset monitoring indicators, the target monitoring data is subjected to correlation analysis to obtain the correlation coefficient vector corresponding to each preset monitoring indicator;

[0019] Based on the correlation coefficient vector, a correlation coefficient matrix is ​​established.

[0020] Preferably, a preset line state monitoring model is used to monitor the state of the transmission line according to the target state matrix and the correlation coefficient matrix to obtain a state monitoring result, including:

[0021] Using the line condition monitoring model built based on the Dropout algorithm, the target state matrix and the correlation coefficient matrix are operated to obtain the condition monitoring value;

[0022] Compare the status monitoring value with the preset monitoring value interval to determine the status monitoring result of the transmission line.

[0023] Preferably, comparing the state monitoring value with a preset monitoring value interval to determine the state monitoring result of the transmission line includes:

[0024] If the state monitoring value is within the normal value sub-interval of the preset monitoring value interval, it is determined that the transmission line is in a normal state;

[0025] If the state monitoring value is within the attention value sub-interval of the preset monitoring value interval, it is determined that the transmission line is in an attention-required state;

[0026] If the state monitoring value is within the warning value sub-interval of the preset monitoring value interval, it is determined that the transmission line is in an abnormal state;

[0027] If the status monitoring value is within the warning value sub-interval of the preset monitoring value interval, it is determined that the transmission line is in a serious abnormal state.

[0028] In a second aspect, the present application provides a state monitoring device for a power transmission line, comprising:

[0029] An acquisition module is used to obtain multi-dimensional monitoring data of multiple tower units in the transmission line;

[0030] The classification module is used to classify the multi-dimensional monitoring data and obtain the target state matrix;

[0031] The analysis module is used to perform correlation analysis on multi-dimensional monitoring data to obtain a correlation coefficient matrix;

[0032] The monitoring module is used to use a preset line status monitoring model to monitor the status of the transmission line according to the target state matrix and the correlation coefficient matrix to obtain the status monitoring results.

[0033] In a third aspect, the present application provides a computer device comprising a processor and a memory, wherein the memory is used to store a computer program, and when the computer program is executed by the processor, the method for monitoring the state of a power transmission line according to the first aspect is implemented.

[0034] In a fourth aspect, the present application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method for monitoring the state of a power transmission line according to the first aspect.

[0035] It should be noted that, for the beneficial effects of the second to fourth aspects mentioned above, please refer to the relevant description of the first aspect mentioned above, and no further details will be given here. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1A schematic flow chart of a method for monitoring the state of a power transmission line according to an embodiment of the present application;

[0037] Figure 2 This is a schematic diagram of the structure of a state monitoring system for a power transmission line according to an embodiment of the present application;

[0038] Figure 3 This is a schematic structural diagram of a state monitoring device for a power transmission line according to an embodiment of the present application;

[0039] Figure 4 This is a schematic diagram of the structure of a computer device shown in an embodiment of the present application. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0041] As documented in related technologies, a weighted approach is used to comprehensively evaluate the operating status of transmission lines based on the operating status of each unit within a tower segment, improving the objectivity of transmission line status monitoring results. However, this approach uses only weights, ignoring factors such as the correlation between units and environmental characteristics, resulting in limitations such as a single monitoring dimension.

[0042] To this end, an embodiment of the present application provides a method for monitoring the status of a transmission line, which increases the monitoring dimension of the transmission line by acquiring multi-dimensional monitoring data of multiple tower units in the transmission line, thereby solving the limitation of a single monitoring dimension; then, the multi-dimensional monitoring data is classified into status categories to obtain a target status matrix, and the multi-dimensional monitoring data is subjected to correlation analysis to obtain a correlation coefficient matrix, thereby performing data fusion on the multi-dimensional data to consider the correlation between the monitoring data, making the monitoring process scientific and the monitoring results more accurate; using a preset line status monitoring model, the transmission line is monitored according to the target status matrix and the correlation coefficient matrix to obtain a status monitoring result, so as to utilize a network model to overcome the problem of increased data volume caused by multi-dimensional data and improve monitoring efficiency.

[0043] Please refer to Figure 1 , Figure 1The following is a flow chart of a method for monitoring the status of a power transmission line according to an embodiment of the present application. The method for monitoring the status of a power transmission line according to an embodiment of the present application can be applied to computer devices, including but not limited to smartphones, laptops, tablet computers, desktop computers, physical servers, and cloud servers.

[0044] Optionally, Figure 2 Figure 2 shows a schematic diagram of a transmission line condition monitoring system. Figure 2 As shown, the computer device is in communication with the edge gateway, which is used to forward the multi-dimensional data collected by the tower unit. The computer device is used to receive the multi-dimensional data forwarded by the edge gateway, perform data fusion on the multi-dimensional data, and perform status monitoring in combination with the Dropout algorithm.

[0045] like Figure 1 As shown, the state monitoring method of the power transmission line of this embodiment includes steps S101 to S104, which are described in detail as follows:

[0046] Step S101: Acquire multi-dimensional monitoring data of multiple tower units in a transmission line.

[0047] In this step, the transmission line includes multiple tower units, each equipped with multiple sensors that collect monitoring data. This multi-dimensional monitoring data includes, but is not limited to, image and video monitoring data, ambient temperature monitoring data, micro-meteorological monitoring data, tower tilt monitoring data, and distributed fault location monitoring data.

[0048] Optionally, sensors on each tower unit collect monitoring data and upload the monitoring data to an edge gateway, which then forwards the monitoring data to a computer device for processing.

[0049] Optionally, the multi-dimensional monitoring data of all tower units are classified according to multiple dimensions to create data groups: y1…y n is the data vector corresponding to each dimension.

[0050] Optionally, the multi-dimensional monitoring data includes real-time monitoring data and historical monitoring data.

[0051] Step S102: classify the multi-dimensional monitoring data into status categories to obtain a target status matrix.

[0052] In this step, based on the state thresholds corresponding to the various data states, the multi-dimensional monitoring data and the state thresholds are compared to determine the data states of the monitoring data, and a target state matrix is ​​established.

[0053] In one embodiment, step S102 includes:

[0054] Based on a preset state quantity threshold, the multi-dimensional monitoring data is state-classified to obtain data vector groups corresponding to multiple state quantities;

[0055] The target state matrix is ​​established based on a plurality of the data vector groups.

[0056] In this optional embodiment, the state quantity thresholds include a first state quantity threshold for general state quantities, a second state quantity threshold for important state quantities, and a third state quantity threshold for special state quantities. General state quantities are state quantities that have relatively little impact on the performance and safe operation of the transmission line; important state quantities are state quantities that have a significant impact on the performance and safe operation of the transmission line; and special state quantities are a general term for parameters, technical indicators, and experimental data reflecting aspects such as the design strength, service life, and disaster prevention performance of the transmission line.

[0057] Optionally, the target state matrix is:

[0058]

[0059] Where x1, x2, and x3 represent the monitoring status data vectors of the transmission line tower unit, and U1, U2, and U3 represent the interval ranges of the first, second, and third state quantity thresholds. Based on the state quantity thresholds, the state quantity threshold interval to which x belongs is determined to achieve the purpose of state classification.

[0060] Step S103: performing correlation analysis on the multi-dimensional monitoring data to obtain a correlation coefficient matrix.

[0061] In this step, the operating status of the tower unit has different correlations. For example, when the tower sinks, it will also tilt to a certain extent, and it will also be accompanied by changes in micro-meteorology, such as storms and heavy rains. Therefore, correlation analysis is performed on the multi-dimensional monitoring data to improve the accuracy of the top-level monitoring.

[0062] In one embodiment, step S103 includes:

[0063] Standardizing the multi-dimensional monitoring data to obtain target monitoring data;

[0064] Based on the preset monitoring indicators, the target monitoring data is subjected to correlation analysis to obtain the correlation coefficient vector corresponding to each preset monitoring indicator;

[0065] The correlation coefficient matrix is ​​established based on the correlation coefficient vector.

[0066] In this optional embodiment, in order to more intuitively demonstrate the strength of the correlation between multiple monitoring data, multivariate statistical analysis is used to standardize the multi-dimensional monitoring data. Then, the autocorrelation matrix of R can be calculated using Matlab to obtain the correlation coefficient vector r1…r of each monitoring indicator. n , and establish the correlation coefficient matrix: R(r)=[r1 … r n ].

[0067] Step S104 : using a preset line state monitoring model, and according to the target state matrix and the correlation coefficient matrix, performing state monitoring on the transmission line to obtain a state monitoring result.

[0068] In this step, the status monitoring results include normal status, caution status, abnormal status and severe abnormal status. The normal status indicates that the status quantities of the line (unit) are stable and within the warning value and caution value specified in the regulations, and can operate normally. The caution status indicates that some status quantities of the line are trending towards the standard limit, but have not exceeded the standard limit, and can continue to operate. The monitoring during operation should be strengthened or maintenance should be arranged according to the actual situation. The abnormal status indicates that some important status quantities of the line are close to or slightly exceed the standard limit. The operation should be monitored and maintenance should be arranged in a timely manner. The severe abnormal status indicates that some important status quantities of the line have seriously exceeded the standard limit, and maintenance needs to be arranged as soon as possible.

[0069] Optionally, the target state matrix and the correlation coefficient matrix are input into a line state monitoring model, and the line state monitoring model outputs a state monitoring result.

[0070] In one embodiment, the step S104 includes:

[0071] Using the line state monitoring model constructed based on the Dropout algorithm, the target state matrix and the correlation coefficient matrix are operated to obtain a state monitoring value;

[0072] The state monitoring value is compared with a preset monitoring value interval to determine the state monitoring result of the transmission line.

[0073] In this optional embodiment, Dropout can be used as a trick for training deep neural networks. In each training batch, by ignoring half of the feature detectors (letting half of the hidden layer node values ​​be 0), the overfitting phenomenon can be significantly reduced. This method can reduce the interaction between feature detectors (hidden layer nodes). Detector interaction means that some detectors rely on other detectors to function. During forward propagation, the activation value of a certain neuron stops working with a certain probability p, so that the model generalizes more strongly and does not rely too much on certain local features. In this way, the interference of environmental factors can be ignored to highlight the real hidden danger features. This embodiment adopts the Dropout model to achieve comprehensive self-evaluation of the status of the transmission line, and to a certain extent overcomes the overfitting caused by training with a large amount of data, while reducing the model training time and improving efficiency.

[0074] Optionally, the state of the transmission line is determined according to the sub-interval range of the preset monitoring value interval into which the state monitoring value falls:

[0075] If the state monitoring value is within the normal value sub-interval of the preset monitoring value interval, determining that the transmission line is in a normal state;

[0076] If the state monitoring value is within the attention value sub-interval of the preset monitoring value interval, determining that the transmission line is in an attention-required state;

[0077] If the state monitoring value is within the warning value sub-interval of the preset monitoring value interval, determining that the power transmission line is in an abnormal state;

[0078] If the state monitoring value is within the warning value sub-interval of the preset monitoring value interval, it is determined that the power transmission line is in a serious abnormal state.

[0079] In this optional embodiment, the status monitoring value Among them, t1...t4 represent the monitoring values ​​represented by the above four states respectively, R1...R4 represent the sub-intervals of the four states respectively. When the monitoring values ​​are in different intervals, they represent different states of the transmission line.

[0080] In order to implement the state monitoring method of the power transmission line corresponding to the above method embodiment, to achieve the corresponding functions and technical effects. Figure 3 , Figure 3 The following is a structural block diagram of a state monitoring device for a power transmission line provided in an embodiment of the present application. For ease of explanation, only the parts related to this embodiment are shown. The state monitoring device for a power transmission line provided in an embodiment of the present application includes:

[0081] An acquisition module 301 is used to acquire multi-dimensional monitoring data of multiple tower units in a transmission line;

[0082] A classification module 302 is used to classify the multi-dimensional monitoring data to obtain a target state matrix;

[0083] An analysis module 303 is configured to perform correlation analysis on the multi-dimensional monitoring data to obtain a correlation coefficient matrix;

[0084] The monitoring module 304 is configured to utilize a preset line state monitoring model to perform state monitoring on the transmission line according to the target state matrix and the correlation coefficient matrix to obtain a state monitoring result.

[0085] Preferably, the multi-dimensional monitoring data includes at least one of image and video monitoring data, ambient temperature monitoring data, micro-meteorological monitoring data, tower tilt monitoring data and distributed fault location monitoring data.

[0086] In one embodiment, Figure 3 Based on the embodiment shown, the classification module 302 is specifically configured to:

[0087] Based on a preset state quantity threshold, the multi-dimensional monitoring data is state-classified to obtain data vector groups corresponding to multiple state quantities;

[0088] The target state matrix is ​​established based on a plurality of the data vector groups.

[0089] Optionally, the state quantity threshold includes a first state quantity threshold for a general state quantity, a second state quantity threshold for an important state quantity, and a third state quantity threshold for a special state quantity.

[0090] In one embodiment, Figure 3 Based on the embodiment shown, the analysis module 303 is specifically configured to:

[0091] Standardizing the multi-dimensional monitoring data to obtain target monitoring data;

[0092] Based on the preset monitoring indicators, the target monitoring data is subjected to correlation analysis to obtain the correlation coefficient vector corresponding to each preset monitoring indicator;

[0093] The correlation coefficient matrix is ​​established based on the correlation coefficient vector.

[0094] In one embodiment, Figure 3 Based on the embodiment shown, the monitoring module 304 includes:

[0095] an operation unit, configured to operate the target state matrix and the correlation coefficient matrix using the line state monitoring model constructed based on the Dropout algorithm to obtain a state monitoring value;

[0096] The comparison unit is used to compare the state monitoring value with a preset monitoring value interval to determine the state monitoring result of the transmission line.

[0097] Optionally, the comparison unit is specifically used to:

[0098] If the state monitoring value is within the normal value sub-interval of the preset monitoring value interval, determining that the transmission line is in a normal state;

[0099] If the state monitoring value is within the attention value sub-interval of the preset monitoring value interval, determining that the transmission line is in an attention-required state;

[0100] If the state monitoring value is within the warning value sub-interval of the preset monitoring value interval, determining that the power transmission line is in an abnormal state;

[0101] If the state monitoring value is within the warning value sub-interval of the preset monitoring value interval, it is determined that the power transmission line is in a serious abnormal state.

[0102] The above-mentioned transmission line status monitoring device can implement the transmission line status monitoring method of the above-mentioned method embodiment. The optional options in the above-mentioned method embodiment are also applicable to this embodiment and will not be described in detail here. The remaining contents of the embodiment of this application can refer to the contents of the above-mentioned method embodiment and will not be repeated in this embodiment.

[0103] Figure 4 This is a schematic diagram of the structure of a computer device provided in one embodiment of the present application. Figure 4 As shown, the computer device 4 of this embodiment includes: at least one processor 40 ( Figure 4 Only one is shown in the figure) a processor, a memory 41, and a computer program 42 stored in the memory 41 and executable on the at least one processor 40, wherein the processor 40 implements the steps of any of the above method embodiments when executing the computer program 42.

[0104] The computer device 4 may be a computing device such as a smart phone, a tablet computer, a desktop computer, or a cloud server. The computer device may include but is not limited to a processor 40 and a memory 41. It will be understood by those skilled in the art that Figure 4 This is merely an example of the computer device 4 and does not constitute a limitation on the computer device 4 . The computer device 4 may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device 4 may also include input and output devices, network access devices, etc.

[0105] The processor 40 may be a central processing unit (CPU), or other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. A general-purpose processor may be a microprocessor or any conventional processor.

[0106] In some embodiments, the memory 41 may be an internal storage unit of the computer device 4, such as a hard disk or memory of the computer device 4. In other embodiments, the memory 41 may also be an external storage device of the computer device 4, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the computer device 4. Furthermore, the memory 41 may include both an internal storage unit of the computer device 4 and an external storage device. The memory 41 is used to store an operating system, application programs, a boot loader, data, and other programs, such as the program code of the computer program. The memory 41 may also be used to temporarily store data that has been output or is about to be output.

[0107] In addition, an embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0108] An embodiment of the present application provides a computer program product. When the computer program product is run on a computer device, the computer device implements the steps in the above-mentioned various method embodiments when executing the computer program product.

[0109] In several embodiments provided in the present application, it is understood that each box in the flow chart or block diagram can represent a part of a module, program segment or code, and the part of the module, program segment or code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which depends on the functions involved.

[0110] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks or optical disks.

[0111] The specific embodiments described above further illustrate the objectives, technical solutions, and beneficial effects of this application. It should be understood that the above descriptions are merely specific embodiments of this application and are not intended to limit the scope of protection of this application. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of this application by those skilled in the art should be included within the scope of protection of this application.

Claims

1. A method for monitoring the state of a transmission line, characterized in that: include: Obtain multi-dimensional monitoring data of multiple tower units in the transmission line; Based on a preset state quantity threshold, the multi-dimensional monitoring data is classified into state categories to obtain data vector groups corresponding to the multiple state quantities, and the target state matrix is ​​established based on the multiple data vector groups; the multi-dimensional monitoring data is standardized to obtain target monitoring data, and based on preset monitoring indicators, the target monitoring data is subjected to correlation analysis to obtain correlation coefficient vectors corresponding to each preset monitoring indicator, and then the correlation coefficient matrix is ​​established based on the correlation coefficient vectors; using the line state monitoring model constructed based on the Dropout algorithm, the target state matrix and the correlation coefficient matrix are operated to obtain state monitoring values, and the state monitoring values ​​are compared with the preset monitoring value interval to determine the state monitoring result of the transmission line.

2. The method for monitoring the state of a power transmission line according to claim 1, wherein: The multi-dimensional monitoring data includes at least one of image and video monitoring data, ambient temperature monitoring data, micro-meteorological monitoring data, tower tilt monitoring data, and distributed fault location monitoring data.

3. The method for monitoring the state of a power transmission line according to claim 1, wherein: The state quantity thresholds include a first state quantity threshold for a general state quantity, a second state quantity threshold for an important state quantity, and a third state quantity threshold for a special state quantity.

4. The method for monitoring the state of a power transmission line according to claim 1, wherein: The comparing the state monitoring value with a preset monitoring value interval to determine the state monitoring result of the transmission line includes: If the state monitoring value is within the normal value sub-interval of the preset monitoring value interval, determining that the transmission line is in a normal state; If the state monitoring value is within the attention value sub-interval of the preset monitoring value interval, determining that the transmission line is in an attention-required state; If the state monitoring value is within the warning value sub-interval of the preset monitoring value interval, determining that the power transmission line is in an abnormal state; If the state monitoring value is within the warning value sub-interval of the preset monitoring value interval, it is determined that the power transmission line is in a serious abnormal state.

5. A state monitoring device for a power transmission line, characterized in that: include: An acquisition module is used to obtain multi-dimensional monitoring data of multiple tower units in the transmission line; a classification module, configured to classify the multi-dimensional monitoring data based on a preset state quantity threshold, obtain data vector groups corresponding to a plurality of state quantities, and establish the target state matrix based on a plurality of the data vector groups; an analysis module, configured to standardize the multi-dimensional monitoring data to obtain target monitoring data, and perform correlation analysis on the target monitoring data based on preset monitoring indicators to obtain correlation coefficient vectors corresponding to various preset monitoring indicators, and then establish the correlation coefficient matrix based on the correlation coefficient vectors; A monitoring module is used to use the line state monitoring model constructed based on the Dropout algorithm to operate on the target state matrix and the correlation coefficient matrix to obtain a state monitoring value, and compare the state monitoring value with a preset monitoring value interval to determine the state monitoring result of the transmission line.

6. A computer device, characterized in that: The method comprises a processor and a memory, wherein the memory is used to store a computer program, and when the computer program is executed by the processor, the method for monitoring the state of a power transmission line according to any one of claims 1 to 4 is implemented.

7. A computer-readable storage medium, characterized in that It stores a computer program, which, when executed by a processor, implements the method for monitoring the state of a power transmission line according to any one of claims 1 to 4.

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