Power transmission line health state assessment method based on multi-dimensional data fusion

Abstract

The invention provides a power transmission line health state assessment method based on multi-dimensional data fusion. Comprising the steps of collecting historical fault ledger information and machine patrol defect data, calculating a manufacturer Wilson score, calculating a basic integral of each grade risk, calculating a climate integral, calculating a hidden danger integral correction coefficient, calculating a hidden danger integral, evaluating an electric line risk grade, and performing a maintenance response strategy. In the evaluation process, through a dynamic integral mechanism, the health state and the development trend of the power transmission line can be reflected in real time, powerful data support is provided, an electric power company is helped to make a scientific maintenance plan, and finally the safe operation level of the power transmission line is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power transmission lines, and in particular to a method for evaluating the health status of power transmission lines by multi-dimensional data fusion. Background Art

[0002] In the traditional drone inspection process, inspection data is usually only used as input for system evaluation, resulting in independent management of defect and hidden danger information and lack of effective analysis and risk assessment. The data source of such systems is single, resulting in one-sided evaluation results, which are difficult to fully reflect the actual operating status of the transmission line, thus affecting the formulation of operation and maintenance and repair strategies.

[0003] With the increasing complexity of transmission lines and the diversification of potential fault hazards, traditional inspection data analysis methods can no longer meet the needs of more accurate and dynamic risk assessment. To this end, this assessment scheme proposes a health status assessment method based on the fusion of multi-dimensional data such as "historical fault records", "weather conditions", "equipment suppliers" and "machine inspection defects", and uses the "classification integration" method to quantitatively assess the risks of transmission lines.

[0004] By establishing a risk score database, the system can dynamically accumulate basic scores and hidden danger scores for each transmission line and its defects and hidden dangers, and then conduct quantitative analysis of the high, medium and low risk levels of the transmission lines. At the same time, the defects and hidden dangers are associated with the risk level and development trend of the line, and targeted operation and maintenance and repair strategies are proposed. This invention not only focuses on the number and type of inspection data, but also pays more attention to the operating status and risk level of the transmission line, thereby improving the inherent safety level of the transmission line. Summary of the invention

[0005] The purpose of the present invention is to address the deficiencies of the above-mentioned prior art and provide a transmission line health status assessment method based on multi-dimensional data fusion, which uses integrated analysis of multi-dimensional data to quantify and dynamically assess the risks of transmission lines and provide more accurate operation and maintenance and inspection strategies.

[0006] To achieve the above object, the present invention adopts the following technical solutions: The present invention aims to address the deficiencies of the above-mentioned prior art and provides a method for evaluating the health status of a transmission line by integrating multidimensional data. The method adopts integrated analysis of multidimensional data to quantify and dynamically evaluate the risks of the transmission line and provide a more accurate operation and maintenance strategy.

[0007] To achieve the above object, the present invention adopts the following technical solutions: The present invention provides a method for evaluating the health status of a power transmission line by multi-dimensional data fusion, comprising the following steps: S1. Collect historical fault record information and machine inspection defect data; S2, calculate the manufacturer Wilson score G; S3. Calculate the basic risk score for each level; S4, calculate climate integral; S5. Calculate the hidden danger integral correction coefficient; S6. Calculate hidden danger points; S7. Risk level assessment of power lines; S8. Maintenance response strategy.

[0008] Further, the S2 includes: S201, Manufacturer Initial Score Calculation of From the start of operation, the equipment's normal operation score is calculated based on the initial value. and step length Increasing in an arithmetic progression, Equipment operation score for the year for: (1); If the device If a defect occurs in a year, the score for the year of defect will be calculated based on the score of the defect-free shipment in that year. The following year, the shipment will be deemed to have been put back into use and the initial score will be calculated again. Start growing; Equipment commissioning After the year, the equipment operation score for: (2); in, For the Devices; Supplier Equipment Operation Score for: (3); in, The total number of devices provided to manufacturers; For the number of devices Initial score of suppliers and manufacturers for: (4); S202, Wilson confidence interval correction: The manufacturer's initial score Through the sigmoid function activation, the initial score of the manufacturer is Normalized to The lower limit of the Wilson confidence interval is calculated based on the initial score of each participating supplier and the sample size. The lower limit of the Wilson confidence interval is used as the evaluation basis for the supplier. The calculation formula for the lower limit of the Wilson confidence interval is: (5); in, is the sample size, the number of devices selected for the supplier sample size; The corresponding confidence level is The statistical yield of; the confidence level is usually taken as 95%, The statistical constant of is 1.96; S203. Subjective evaluation of suppliers: Supplier subjective score Equal to the average of all subjective scores of suppliers; S204. Calculation of manufacturer Wilson score G: Set the supplier's Wilson confidence interval lower limit Linear mapping to 60-100 points to obtain objective supplier scores ; Objectively score suppliers and supplier subjective scores The subjective comprehensive score of the supplier is obtained by weighting them at a ratio of 85% and 15% respectively, with a value between 60 and 100. ; (6); in, is the maximum Wilson score of a manufacturer at a certain fineness; is the minimum Wilson score of a manufacturer at a certain fineness; (7).

[0009] Further, the S3 is specifically: Based on the analysis of line defects by hierarchical analysis method, the basic score of each transmission line is divided into high-risk basic score and high-risk basic score according to the risk level. , Medium Risk Basic Points , low risk basic points ; The high-risk basic score, medium-risk basic score and low-risk basic score are the average of the three levels of defect hidden danger basic scores on the line multiplied by the corresponding state weights. At the same time, the invisible impact caused by component manufacturers is comprehensively considered and the scores are corrected.

[0010] Furthermore, the S3 further includes: S301, average weight Calculation method: (8); in, is the state weight of the hidden dangers of defects on the line; is the number of potential defects; For the Defect hidden dangers; S302, high risk basic score Calculation method: (9); in, The number of Class III defect hazards; is the state quantity weight of Category III defect hidden danger on the line; It is the basic score of Category III defect hidden dangers on the line; Wilson score for manufacturers at the potential defect locations of Category III on the line; For the Class III defect hazards; S303, Medium Risk Basic Score Calculation method: ; in, The number of Class II defect hazards; is the state weight of Category II defect hidden dangers on the line; It is the basic integral of Category II defect hidden dangers on the line; Wilson score for manufacturers at the potential defect locations of Category II on the line; For the Class II defect hazards; S304, Low-risk basic points Calculation method: (11); in, The number of Class I defect hazards; It is the basic score of Category I defect hidden dangers on the line; is the state quantity weight of Class I defect hidden danger on the line; Wilson score for manufacturers at Category I defect risk locations on the line; For the There are Class I defect hazards.

[0011] Further, the S4 is specifically: S401, state offset calculate: (12); in, is the current status value of a certain indicator; is the appropriate value for a certain indicator; It is the maximum value that has appeared in the historical data of a certain indicator; It is the minimum value that has appeared in the historical data of a certain indicator; It is the best value that has appeared in the historical data of a certain indicator; S402, Climate Credits Calculation: (13); in, The current climate status.

[0012] Further, in S5, the hidden danger integral correction coefficient is calculated : ; in, is the bathtub curve shape factor; About time The equipment failure rate is taken as , and as the hidden danger integral correction coefficient ; are model parameters with the same shape coefficient.

[0013] Further, in S6, the hidden danger score is: (14); in, is the development integral value of Category II defect hidden dangers on the line; It is the development integral value of Class I defect hidden dangers on the line.

[0014] Further, the S7 is specifically divided into: High-risk lines: One type contains one or more Class III defect hazards, that is, The other is that there is no Class III defect hidden danger, but the hidden danger score of Class II defect hidden danger reaches 45 points or above, that is, ; Medium-risk routes: The high-risk basic score should be 0, and the medium-risk basic score should be greater than or equal to 4 points. , and the hidden danger score is less than 45 points, that is ; Low-risk routes: High-risk score should be 0, medium-risk score should be 0, and low-risk score should be greater than or equal to 1. .

[0015] Further, in said S8, specifically: High-risk lines: immediately arrange comprehensive maintenance, giving priority to addressing Class III defects; Medium-risk lines: Regularly inspect and monitor the development trend of Class II hidden dangers to prevent further deterioration; Low-risk lines: Continue to maintain regular inspections and adjust the maintenance frequency based on operating conditions.

[0016] The beneficial effects of the present invention are as follows: by introducing a classification and scoring mechanism, defects and hidden dangers are divided into three categories: Class I, Class II, and Class III according to the degree of equipment defects and hidden dangers, and corresponding basic points and hidden danger points are assigned. The risk points of defects and hidden dangers are dynamically adjusted using multi-dimensional data such as historical fault ledger information, manufacturer Wilson scores, equipment operation cycles, and climate impacts to form a complete transmission line health status assessment system. The risk grading rules divide the line risk levels into high risk, medium risk, and low risk according to the accumulated points of defects and hidden dangers on the line, and formulate corresponding maintenance strategies for different risk levels to achieve dynamic monitoring of line risks and gradual downgrading processing; During the design process, the multi-dimensional influencing factors in actual operation and maintenance scenarios were fully considered, especially by introducing climate points and manufacturer quality scores to improve the accuracy and universality of the evaluation results. During the evaluation process, the dynamic points mechanism can reflect the health status and development trend of the transmission line in real time, provide strong data support, help power companies formulate scientific maintenance plans, and ultimately improve the safe operation level of the transmission line; During the design process, the multi-dimensional influencing factors in the actual operation and maintenance scenarios were fully considered, especially by introducing climate points and manufacturer quality scores, which improved the accuracy and universality of the evaluation results. During the evaluation process, the dynamic integration mechanism can reflect the health status and development trend of the transmission line in real time, provide strong data support, help power companies formulate scientific maintenance plans, and ultimately improve the safe operation level of the transmission line. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a system workflow diagram of a transmission line health status assessment method based on multi-dimensional data fusion; Figure 2 Climate data for the area where the line is located; Figure 3 Insulator contamination - Class I defect; Figure 4 Missing a small number of bolts - Class II defect. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0019] See also Figure 1 , a transmission line health status assessment method based on multi-dimensional data fusion, comprising the following steps: S1. Collect historical fault record information and machine inspection defect data; Specifically, collecting historical fault data of transmission lines is an important part of power system management. By systematically recording and analyzing fault events sent in the past, it can help power companies identify the frequency and cause of faults and their impact on grid stability. These data usually include the time and location of the fault, the equipment involved, the equipment manufacturer, the type of fault, such as short circuit, line break, equipment aging, insulator damage, and the environmental conditions when the fault occurred, such as weather conditions and temperature. First, by analyzing historical fault data and machine inspection defect data, collect the following information: Historical fault records: time, location, equipment model, equipment manufacturer, fault type, such as short circuit, disconnection, aging, and environmental conditions, such as weather conditions; UAV inspection defect data: UAV inspections are used to collect equipment defect information on the line, classify and process defects, and calculate the basic score for each defect hidden danger.

[0020] S2, calculate the manufacturer Wilson score G; Specifically, in order to consider the impact of different equipment manufacturers on the failure rate, the Wilson confidence interval algorithm is used to calculate the score of each manufacturer. The Wilson confidence interval is asymmetric and will not be affected by the interval width. It can also be safely applied to small samples and biased observations, thereby correcting the basic score of each defect hidden danger to better fit the actual operation situation. When calculating the manufacturer's Wilson score G, the scores of subjective and objective factors are comprehensively considered, and the Wilson confidence interval algorithm is introduced to correct the objective score.

[0021] S3. Calculate the basic risk score for each level; S4, calculate climate integral; The present invention mainly focuses on air temperature, ground wind speed, air humidity and precipitation; wind factor: the suitable wind force is 0m / s. A windless environment does not interfere with the power transmission environment. If there is not enough data on the maximum wind speed, the standard wind speed of 10m / s shall be used. Standard wind speed: the annual average maximum wind speed at a standard height, according to the specified time interval and recurrence period at 10m on the ground or sea surface. Precipitation factor: the suitable value is 0mm, and the maximum precipitation is the highest value of precipitation in local history. Air humidity factor: the suitable air humidity is 38.8%, and the highest and lowest values ​​that have appeared in historical data are taken. Temperature factor: the suitable temperature is 28.9°C, and the highest and lowest values ​​that have appeared in historical data are taken. The optimal value has not appeared in history. When taking the most suitable value, the value closest to the most suitable value is taken, and the most suitable value is taken at other times.

[0022] By analyzing historical fault information, it is found that the occurrence of some major defects is strongly correlated with severe weather. The induction of severe weather is a necessary condition for the occurrence of major defects in transmission lines. Therefore, when calculating the development integral, combining weather factor analysis can improve the scientificity and reliability of the prediction results. The climate integral is calculated here to predict the development trend of the health status of the future transmission line.

[0023] S5. Calculate the hidden danger integral correction coefficient; S6. Calculate hidden danger points; S7. Risk level assessment of power lines; S8. Maintenance response strategy.

[0024] The S2 includes: S201, Manufacturer Initial Score Calculation of From the start of operation, the equipment's normal operation score is calculated based on the initial value. and step length Increasing in an arithmetic progression, Equipment operation score for the year for: (1); If the device If a defect occurs in a year, the score for the year of defect will be calculated based on the score of the defect-free shipment in that year. The following year, the shipment will be deemed to have been put back into use and the initial score will be calculated again. Start growing; Equipment commissioning After the year, the equipment operation score for: (2); in, For the Devices; Supplier Equipment Operation Score for: (3); in, The total number of devices provided to manufacturers; For the number of devices Initial score of suppliers and manufacturers for: (4); S202, Wilson confidence interval correction: The manufacturer's initial score Through the sigmoid function activation, the initial score of the manufacturer is Normalized to The lower limit of the Wilson confidence interval is calculated based on the initial score of each participating supplier and the sample size. The lower limit of the Wilson confidence interval is used as the evaluation basis for the supplier. The calculation formula for the lower limit of the Wilson confidence interval is: (5); in, is the sample size, the number of devices selected for the supplier sample size; The corresponding confidence level is The statistical yield of; the confidence level is usually taken as 95%, The statistical constant of is 1.96; S203. Subjective evaluation of suppliers: Supplier subjective score Equal to the average of all subjective scores of suppliers; Specifically, a subjective evaluation mechanism of "one city, one vote" was introduced. Based on the principle of "whoever uses it, evaluates it", and on the basis of the previous objective evaluation, the system's 337 municipal companies, 27 companies and 4 management offices of the operating companies gave subjective evaluation scores to the equipment manufacturers within their jurisdiction.

[0025] S204. Calculation of manufacturer Wilson score G: Set the supplier's Wilson confidence interval lower limit Linear mapping to 60-100 points to obtain objective supplier scores ; Objectively score suppliers and supplier subjective scores The subjective comprehensive score of the supplier is obtained by weighting them at a ratio of 85% and 15% respectively, with a value between 60 and 100. ; (6); in, is the maximum Wilson score of a manufacturer at a certain fineness; is the minimum Wilson score of a manufacturer at a certain fineness; (7).

[0026] The S3 is specifically: Based on the analysis of line defects by hierarchical analysis method, the basic score of each transmission line is divided into high-risk basic score and high-risk basic score according to the risk level. , Medium Risk Basic Points , low risk basic points ; The high-risk basic score, medium-risk basic score and low-risk basic score are the average of the three levels of defect hidden danger basic scores on the line multiplied by the corresponding state weights. At the same time, the invisible impact caused by component manufacturers is comprehensively considered and the scores are corrected.

[0027] The S3 further includes: S301, average weight Calculation method: (8); in, is the state weight of the hidden dangers of defects on the line; is the number of potential defects; For the Defect hidden dangers; S302, high risk basic score Calculation method: (9); in, The number of Class III defect hazards; is the state quantity weight of Category III defect hidden danger on the line; It is the basic score of Category III defect hidden dangers on the line; Wilson score for manufacturers at the potential defect locations of Category III on the line; For the Class III defect hazards; S303, Medium Risk Basic Score Calculation method: ; in, The number of Class II defect hazards; is the state weight of Category II defect hidden dangers on the line; It is the basic integral of Category II defect hidden dangers on the line; Wilson score for manufacturers at the potential defect locations of Category II on the line; For the Class II defect hazards; S304, Low-risk basic points Calculation method: (11); in, The number of Class I defect hazards; It is the basic score of Category I defect hidden dangers on the line; is the state quantity weight of Class I defect hidden danger on the line; Wilson score for manufacturers at Category I defect risk locations on the line; For the There are Class I defect hazards.

[0028] The S4 is specifically: S401, state offset calculate: (12); in, is the current status value of a certain indicator; is the appropriate value for a certain indicator; It is the maximum value that has appeared in the historical data of a certain indicator; It is the minimum value that has appeared in the historical data of a certain indicator; It is the best value that has appeared in the historical data of a certain indicator; S402, Climate Credits Calculation: (13); in, The current climate status.

[0029] In S5, the hidden danger integral correction coefficient is calculated : ; in, is the bathtub curve shape factor; About time The equipment failure rate is taken as , and as the hidden danger integral correction coefficient ; The same shape coefficient is used as the model parameter. , 0.3), ( )、( ) is substituted into the fitted bathtub curve and the failure rate value is used as the correction coefficient of the hidden danger integral.

[0030] Based on historical fault record data and manual inspection, the boundary years of the three stages of the bathtub curve, namely the premature death period, the stable period, and the aging period, were finally determined to be 5 and 30 years, and the maximum operation period was set to 50 years. The failure rate of the line when it was initially put into operation was set to 40%, and the failure rate of the line at each critical point was set to 30%, 60%, and 80%. The critical points are 5, 30, and 50.

[0031] In S6, When calculating the hidden danger points, the hidden danger points of Class II and Class I defects on the line multiplied by the state weight is used as the basic score of the hidden danger points. The state weight here is taken from the specification, and the influence of climate factors on the development of line defects is introduced, and the hidden danger points correction coefficient is used for correction.

[0032] The hidden danger points are: (14); in, is the development integral value of Category II defect hidden dangers on the line; It is the development integral value of Class I defect hidden dangers on the line.

[0033] The S7 is specifically divided into: High-risk lines: One type contains one or more Class III defect hazards, that is, The other is that there is no Class III defect hidden danger, but the hidden danger score of Class II defect hidden danger reaches 45 points or above, that is, ; Medium-risk routes: The high-risk basic score should be 0, and the medium-risk basic score should be greater than or equal to 4 points. , and the hidden danger score is less than 45 points, that is ; Low-risk routes: High-risk score should be 0, medium-risk score should be 0, and low-risk score should be greater than or equal to 1. .

[0034] In the S8, specifically: High-risk lines: immediately arrange comprehensive maintenance, giving priority to addressing Class III defects; Medium-risk lines: Regularly inspect and monitor the development trend of Class II hidden dangers to prevent further deterioration; Low-risk lines: Continue to maintain regular inspections and adjust the maintenance frequency based on operating conditions.

[0035] Embodiment 1: See also Figures 2 to 4 , Hubei Province 500 kV XX line, with a total of more than 130 towers. The terrain of the line area is mostly plains, and a small part is located in small hills, and the terrain is relatively flat. The line inspection time is July 15, 2018. As of the inspection date, it has been in operation for 13 years, two months and 5 days. The climate data on that day was: temperature 30.6℃, humidity 77.6%, precipitation 0.017mm / h, and ground wind speed 3.08m. The historical climate data of the area is: temperature: maximum 39.6℃, minimum 24.7℃, humidity: maximum 93.4%, minimum 77.6%, precipitation: maximum 9.39mm / h, ground wind speed: maximum 5.2m / s. According to the inspection results, there were 2 Type III defects and hidden dangers on the line, including broken sub-conductors of #12-#13 and 6 self-explosion of glass insulators of phase A of #15 tower; 3 Type II defects and hidden dangers, including 4 self-explosion of glass insulators of phase B of #25 tower, a tree barrier with a clearance of 6.8 meters under the #71-#72 line, and a tree barrier with a clearance of 7.5 meters under the #92-#93 line; 2 Type I defects and hidden dangers, including slippage of the anti-vibration hammer of #81 tower and a tree barrier with a clearance of 8.5 meters under the #94-#95 line.

[0036] Table 1 List of risk defects of 500 kV XX line equipment

[0037] Compare the Wilson scores of each supplier in Table 2 and Table 3 to calculate the high-risk basic score of the line 18.482, medium risk basic score 13.752 points, low risk basic score is 1.271 points, and the running time is Substituting into the bathtub curve, we can get the hidden danger integral correction coefficient , combined with climate data, the climate score C is 5.6884, and the hidden danger score D is 10.1154 points. The score is 18.482 ≥ 8 points, so the line is judged as a high-risk line. Compared with the status of the manual inspection line, the results are consistent, and the calculation model is feasible.

[0038] Table 2 Risk score assessment and treatment strategies for defects and hidden dangers of drone inspection equipment (partial)

[0039] Table 3. Wilson score sheet for component suppliers (partial)

[0040] Replace or re-crimp #12-#13 sub-conductors within one week, and replace the 6 glass insulators on phase A of tower #15 that exploded on their own; Replace the 4 self-exploding glass insulators of phase B of tower #25 within two months, clear the 6.8-meter tree barrier under line #71-#72, and clear the 7.5-meter tree barrier under line #92-#93 within three months; Within one year, the anti-vibration hammer slip defect of #81 tower was resolved and the 8.5-meter tree obstacle under the #94-#95 line was cleared.

[0041] The above-mentioned embodiments only express the implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention should be based on the attached claims.

Claims

1. A method for evaluating the health status of a power transmission line based on multi-dimensional data fusion, characterized in that: The following steps are involved: S1. Collect historical fault record information and machine inspection defect data; S2, calculate the manufacturer Wilson score G; S3. Calculate the basic risk score for each level; S4, calculate climate integral; S5. Calculate the hidden danger integral correction coefficient; S6. Calculate hidden danger points; S7. Risk level assessment of power lines; S8, maintenance response strategy; The S2 includes: S201, Manufacturer Initial Score Calculation of From the start of operation, the equipment's normal operation score is calculated based on the initial value. and step length Increasing in an arithmetic progression, Equipment operation score for the year for: （1）； If the device If a defect occurs in a year, the score for the year of defect will be calculated based on the score of the defect-free shipment in that year. The following year, the shipment will be deemed to have been put back into use and the initial score will be calculated again. Start growing; Equipment commissioning After the year, the equipment operation score for: （2）； in, For the Devices; Supplier Equipment Operation Score for: （3）； in, The total number of devices provided to manufacturers; For the number of devices Initial score of suppliers and manufacturers for: （4）； S202, Wilson confidence interval correction: The manufacturer's initial score Through the sigmoid function activation, the initial score of the manufacturer is Normalized to The lower limit of the Wilson confidence interval is calculated based on the initial score of each participating supplier and the sample size. The lower limit of the Wilson confidence interval is used as the evaluation basis for the supplier. The calculation formula for the lower limit of the Wilson confidence interval is: （5）； in, is the sample size, the number of devices selected for the supplier sample size; The corresponding confidence level is The statistical yield of; the confidence level is usually 95%, The statistical constant of is 1.96; S203. Subjective evaluation of suppliers: Supplier subjective score Equal to the average of all subjective scores of suppliers; S204. Calculation of manufacturer Wilson score G: Set the lower limit of the Wilson confidence interval for the supplier Linear mapping to 60-100 points to obtain objective supplier scores ; Objectively score suppliers and supplier subjective scores The subjective comprehensive score of the supplier is obtained by weighting them at a ratio of 85% and 15% respectively, with a value between 60 and 100. ; （6）； in, is the maximum Wilson score of a manufacturer at a certain fineness; is the minimum Wilson score of a manufacturer at a certain fineness; （7）； The S3 is specifically: Based on the analysis of line defects by hierarchical analysis method, the basic score of each transmission line is divided into high-risk basic score and high-risk basic score according to the risk level. , Medium Risk Basic Points , low risk basic points ; The high-risk basic score, medium-risk basic score and low-risk basic score are the average of the three levels of defect hidden danger basic scores on the line multiplied by the corresponding state weights. At the same time, the invisible impact caused by component manufacturers is comprehensively considered and the scores are corrected.

2. The method for evaluating the health status of a power transmission line based on multi-dimensional data fusion according to claim 1, characterized in that: The S3 further includes: S301, average weight Calculation method: （8）； in, is the state weight of the hidden dangers of defects on the line; is the number of potential defects; For the Defect hidden dangers; S302, High-risk basic score Calculation method: （9）； in, The number of Class III defect hazards; is the state quantity weight of Category III defect hidden danger on the line; It is the basic score of Category III defect hidden dangers on the line; Wilson score for manufacturers at the potential defect locations of Category III on the line; For the Class III defect hazards; S303, Medium Risk Basic Score Calculation method: ； in, The number of Class II defect hazards; is the state weight of Category II defect hidden dangers on the line; It is the basic integral of Category II defect hidden dangers on the line; Wilson score for manufacturers at the potential defect locations of Category II on the line; For the Class II defect hazards; S304, Low-risk basic points Calculation method: （11）； in, is the number of Class I defect hazards; It is the basic score of Category I defect hidden dangers on the line; is the state quantity weight of Class I defect hidden danger on the line; Wilson score for manufacturers at Category I defect risk locations on the line; For the There are Class I defect hazards.

3. The method for evaluating the health status of a power transmission line based on multi-dimensional data fusion according to claim 2, characterized in that: The S4 is specifically: S401, state offset calculate: （12）； in, is the current status value of a certain indicator; is the appropriate value for a certain indicator; It is the maximum value that has appeared in the historical data of a certain indicator; It is the minimum value that has appeared in the historical data of a certain indicator; It is the best value that has appeared in the historical data of a certain indicator; S402, Climate Credits Calculation: （13）； in, The current climate status.

4. The method for evaluating the health status of a power transmission line based on multi-dimensional data fusion according to claim 3, characterized in that: In S5, the hidden danger integral correction coefficient is calculated : ； in, is the bathtub curve shape factor; About time The equipment failure rate is taken as , and as the hidden danger integral correction coefficient ; are model parameters with the same shape coefficient.

5. The method for evaluating the health status of a power transmission line based on multi-dimensional data fusion according to claim 4, characterized in that: In S6, the hidden danger score is: （14）； in, is the development integral value of Category II defect hidden dangers on the line; It is the development integral value of Class I defect hidden dangers on the line.

6. A method for evaluating the health status of a power transmission line using multi-dimensional data fusion according to claim 5, characterized in that: The S7 is specifically divided into: High-risk lines: One type contains one or more Class III defect hazards, that is, The other is that there is no Class III defect hidden danger, but the hidden danger score of Class II defect hidden danger reaches 45 points or above, that is, ; Medium-risk routes: The high-risk basic score should be 0, and the medium-risk basic score should be greater than or equal to 4 points. , and the hidden danger score is less than 45 points, that is ; Low-risk routes: High-risk score should be 0, medium-risk score should be 0, and low-risk score should be greater than or equal to 1. .

7. A method for evaluating the health status of a power transmission line using multi-dimensional data fusion according to claim 6, characterized in that: In the S8, specifically: High-risk lines: immediately arrange comprehensive maintenance, giving priority to addressing Class III defects; Medium-risk lines: Regularly inspect and monitor the development trend of Class II hidden dangers to prevent further deterioration; Low-risk lines: Continue to maintain regular inspections and adjust the maintenance frequency based on operating conditions.

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