A method for optimizing the real-time parameter allocation of multi-sensor operation for transmission lines

Abstract

This invention discloses a method for optimizing the allocation of real-time parameters for multiple sensors used in power transmission lines. First, it sequentially establishes a sensor task allocation model, an energy consumption index calculation model for each sensor deployed in a centralized power supply system along the transmission line, a real-time parameter factor calculation model, and a data processing information quality and reliability calculation model. Then, based on these models, it constructs an operational performance evaluation function for the multiple sensors used in the power transmission line. Finally, it extracts the optimal real-time parameter factors using a global search optimization method based on the operational performance evaluation function, serving as the allocation parameters for the sensor system. This invention optimizes the calculation and extraction of real-time parameter factor values ​​for each sensor, achieving overall optimal performance in terms of energy consumption, information reliability, and online monitoring real-time performance, thereby improving the operational performance of the multiple sensors.

Description

Technical Field

[0001] This invention relates to the field of power transmission line sensing and monitoring technology, specifically to a method for optimizing and allocating real-time operating parameters of multiple sensors for power transmission lines. Background Technology

[0002] Faults in power transmission lines typically cause phase-to-phase flashover, conductor strand breakage, and tower collapse, resulting in economic losses and casualties, and seriously affecting the safe and reliable operation of transmission lines. By deploying sensor equipment on transmission lines to monitor their operating conditions, real-time data on heating, insulation, leakage current, and other status information is collected, and timely warnings and responses are provided to ensure the safe and stable operation of the transmission lines. Numerous sensors are centrally deployed on the transmission lines, powered by solar energy or other methods, and the collected data is wirelessly transmitted to a central management center.

[0003] Due to limited power supply, these sensors typically operate intermittently, periodically waking from sleep mode to collect, process, and wirelessly transmit data, before returning to sleep mode. On one hand, the sleep cycle setting is a key factor affecting the sensor's real-time performance and is crucial for reducing energy consumption; however, an excessively long sleep cycle can degrade real-time performance. On the other hand, the energy consumption of data collection, processing, and wireless transmission necessitates minimizing the duration of these processes, which in turn reduces real-time performance. Furthermore, the data processing stage requires edge computing and logical analysis, directly impacting the quality and reliability of the monitored information. Sufficiently long data processing and fusion times can improve the quality and reliability of the sensor information, but also increase energy consumption. Therefore, there is a profound inherent contradiction between the overall energy consumption of centralized power supply to transmission lines, the reliability of sensor information, and the real-time performance of online monitoring. In practical applications, the importance of these three objectives varies.

[0004] In summary, only by uniformly and rationally optimizing the allocation of time for each stage of data acquisition, processing, and wireless transmission, as well as the sleep cycle time, of numerous sensors operating under centralized power supply on transmission lines can the inherent contradictions between energy consumption, reliability, and real-time performance be effectively reconciled and resolved. This will achieve a global trade-off optimization and maximize the efficiency of multi-sensor systems used in transmission lines. Currently, the industry lacks a reasonable and feasible solution to this problem. Most existing methods rely on experience combined with the sensor's functional characteristics to set the time for each stage of data acquisition, processing, and wireless transmission, as well as the sleep cycle time. These methods either prioritize energy saving, resulting in poor real-time performance, or incur significant energy costs in pursuit of data fusion reliability. In general, there is a general lack of means and tools to improve and optimize the operational efficiency of multi-sensor systems used in transmission lines. Summary of the Invention

[0005] In view of this, the present invention provides a method for optimizing the allocation of real-time parameters for multi-sensor operation of transmission lines. The method uses the real-time characteristics reflected by the ratio of sensor operating time to sleep period as the allocation parameter factor. The method describes the multi-objective characteristics reflecting the overall energy consumption, information reliability, and online monitoring real-time performance of the multi-sensor system as an objective function of the multi-sensor operation performance of transmission lines. The method extracts the real-time parameter factor through global search to achieve the optimal allocation of real-time parameters for multi-sensor operation.

[0006] A method for optimizing and allocating real-time operating parameters of power transmission lines using multiple sensors, the implementation steps of which are as follows:

[0007] Step 1: Establish a sensor task allocation model;

[0008] Step 2: Establish a calculation model for the energy consumption index of each sensor deployed in the centralized power supply network of the transmission lines;

[0009] Step 3: Establish a calculation model for the real-time parameter factors of each sensor deployed in the centralized power supply network of the transmission lines;

[0010] Step 4: Establish a data processing information quality and reliability calculation model for each sensor deployed in the centralized power supply network of the transmission lines;

[0011] Step 5: Construct an operational performance evaluation function for multiple sensors used in power transmission lines based on the sensor task allocation model, energy consumption index calculation model, real-time parameter factor calculation model, and data processing information quality reliability calculation model;

[0012] Step 6: Based on the operational performance evaluation function, use the global search optimization method to extract the optimal real-time parameter factors as the allocation parameters for the sensor system.

[0013] Furthermore, the process of establishing the sensor task allocation model in step one is as follows:

[0014] Numerous sensors are uniformly deployed along the power transmission lines and operate under centralized power supply. Due to the limited power supply to these sensors, they operate in an intermittent mode, periodically waking up from sleep mode to collect, process, and wirelessly transmit data, before returning to sleep mode. Therefore, the sensor's operational process is broken down into four stages: "data collection – data processing – wireless transmission – sleep," which are executed cyclically.

[0015] There are N sensors deployed with a unified centralized power supply. Let P be the power consumption of the i-th sensor for data acquisition, data processing, wireless transmission, and sleep tasks, respectively. i1 ,P i2,P i3 ,P i4 The corresponding execution times are t1, t2, t3, and t4, with a total energy consumption of W. i .

[0016] Furthermore, the process of establishing the energy consumption index calculation model for each sensor in the centralized power supply deployment of the transmission line in step two is as follows:

[0017] Energy consumption W of each sensor i This represents the total energy consumption of the sensor when performing four tasks: data acquisition, data processing, wireless transmission, and sleep mode. The energy consumption calculation model for N sensors is as follows:

[0018] W1 = P 11 t 11 +P 12 t 12 +P 13 t 13 +P 14 t 14

[0019] W i =P i1 t i1 +P i2 t i2 +P i3 t i3 +P i4 t i4 ...

[0021] W N =P N1 t N1 +P N2 t N2 +P N3 t N3 +P N4 t N4

[0022] Therefore, the sensor energy consumption index J is constructed as follows. i (P i ,t i The energy consumption of the sensor per unit time is characterized by the following calculation model:

[0023]

[0024] It can be seen that when t1, t2, and t3 are large, the energy consumption of the sensor increases, which is reflected in a higher energy consumption index; when t4 is large, the sensor has a long sleep time, which can effectively reduce energy consumption, which is reflected in a lower energy consumption index.

[0025] Furthermore, the process of establishing the real-time parameter factor calculation model for each sensor in the centralized power supply deployment of the transmission line in step three is as follows:

[0026] The ratio of the sensor's operating times t1, t2, t3 to its sleep period t4, a parameter reflecting the sensor's real-time performance, is correlated with the energy consumption index. A larger ratio indicates a relatively shorter sleep period, representing higher real-time performance but also higher energy consumption. Conversely, a smaller ratio leads to decreased real-time performance but better energy savings, creating a contradictory relationship. Therefore, the core essence of this contradiction is reflected by the real-time parameter factor K. i (t i To represent this using a computational model:

[0027]

[0028] Furthermore, the process of establishing the data processing information quality reliability calculation model for each sensor in the centralized power supply deployment of the transmission line in step four is as follows:

[0029] Since data fusion operations such as edge computing and logical analysis are required in the sensor data processing stage, they directly determine the quality and reliability of the sensor monitoring information. Sufficiently long data processing and fusion time can improve the quality and reliability of sensor information, but it also leads to increased energy consumption. Therefore, a reliability index C for sensor data processing information quality is defined. i (t i2 The calculation model is as follows:

[0030] C i (t2)=ft2

[0031] Where f is a function of the data processing procedure;

[0032] It is evident that when t2 is relatively large, the quality of the sensing information is higher and the reliability is enhanced, but this will lead to an increase in the real-time parameter factor, resulting in greater energy consumption.

[0033] Furthermore, the process of constructing the operational performance evaluation function for multiple sensors used in transmission lines in step five is as follows:

[0034] To minimize the energy consumption of the sensor system, the durations t1, t2, and t3 of data acquisition, processing, and wireless transmission should be minimized, meaning the sensor energy consumption index in step two should be as low as possible. On the one hand, to improve the real-time performance of online monitoring, the sleep time needs to be reduced, maximizing data acquisition and processing, meaning the real-time parameter factor in step three should be as large as possible. On the other hand, to ensure good information quality reliability of the sensor system, the information quality reliability of data processing in step four should be maximized. To reconcile the inherent contradictions among these three objectives—overall energy consumption of multiple sensors, information reliability, and real-time performance of online monitoring—the energy consumption index model established in step two, the real-time parameter factor model established in step three, and the information quality reliability model established in step four are inversely proportional to each other. Direct Proportion K i Direct proportion C i By combining these sensors in a consistent manner, a performance evaluation function for multiple sensors used in power transmission lines can be constructed. as follows:

[0035]

[0036] Wherein, α, β, and γ are normalized influence factors that reflect the contribution of sensor energy consumption, information reliability, and online monitoring real-time performance to operational efficiency. They are usually determined based on specific scenario task requirements and empirical values, and α+β+γ=1.

[0037] Furthermore, to obtain the optimal set T of the real-time performance parameters of the multi-sensor system in the performance evaluation function in step five... op =[t 1op , t 2op , t 3op , t 4op The optimal real-time parameter T is extracted through a global search optimization method. op This is used as the optimal allocation value for the real-time parameters of the sensor system. The process of extracting the optimal real-time parameter factor as the allocation parameter for the sensor system in step six using the global search optimization method is as follows:

[0038] (1) Set the initial delay, the final delay, and the delay change rate v, as well as the number of iterations for each delay, i.e., the chain length L, to ensure that the ant colony algorithm has sufficient vitality in the initial stage; the initial delay represents a system-defined value, initialized to

[1111] , and the final delay represents the delay configuration value extracted by the optimization algorithm, which is [t1,t2,t3,t4]; the delay change rate represents the data growth factor during the iteration process, and the chain length is the number of iterations N, thus:

[0039] T(0) = [t1, t2, t3, t4] =

[1111] , at which point the corresponding iteration number is 0. After time delay decay, we have T(0)* = vT(0);

[0040] (2) Construct a path and calculate the probability that the ant will reach the destination and achieve its goal by taking the path [t1,t2,t3,t4].

[0041]

[0042] (3) Update information;

[0043]

[0044] ΔT(i,i+1)=1 / ∑(t1+t2+t3+t4)

[0045] (4) Determine if the termination criterion is met: 10 -6 <ti<10 -4 And p = p max If the condition is met, the current optimal solution is output and the algorithm stops; otherwise, step (3) is executed.

[0046] (5) Output the optimal solution when the algorithm stops, which is the best real-time parameter T for the sensor to perform the four tasks of data acquisition, data processing, wireless transmission, and sleep mode during operation. op .

[0047] Beneficial effects:

[0048] 1. The multi-sensor operation real-time parameter optimization allocation method of the present invention first establishes, in sequence, a sensor task allocation model, an energy consumption index calculation model for each sensor deployed in the centralized power supply of the transmission line, a real-time parameter factor calculation model, and a data processing information quality reliability calculation model; then, based on the sensor task allocation model, energy consumption index calculation model, real-time parameter factor calculation model, and data processing information quality reliability calculation model, a multi-sensor operation performance evaluation function for transmission lines is constructed; finally, based on the operation performance evaluation function, the optimal real-time parameter factor is extracted using a global search optimization method as the allocation parameter for the sensor system, thus resolving the inherent mechanism contradiction among energy consumption, reliability, and real-time performance of multi-sensors for transmission lines, achieving global trade-off optimization, and achieving the goal of maximizing the performance of multi-sensors for transmission lines.

[0049] 2. This invention establishes for the first time an energy consumption index calculation model for each sensor deployed in a centralized power supply system for transmission lines, which can quantitatively reflect the overall energy consumption during the sensor's operation. It also establishes for the first time a real-time parameter factor calculation model for each sensor deployed in a centralized power supply system for transmission lines, which can quantitatively reflect the real-time performance of the sensors' online monitoring. Furthermore, it establishes for the first time a data processing information quality reliability calculation model for each sensor deployed in a centralized power supply system for transmission lines, which can quantitatively reflect the reliability of the sensor's data acquisition and processing information.

[0050] 3. This invention addresses the inherent contradiction in optimizing the three objectives of centralized power supply for multiple sensors in transmission lines: overall energy consumption, sensor information reliability, and online monitoring real-time performance. It uses the real-time characteristic reflected by the ratio of sensor operating time to sleep cycle time as an allocation parameter factor. This unifies the opposing factors of reducing this real-time parameter factor to optimize energy consumption and increasing it to improve real-time performance and reliability. A multi-sensor operating efficiency function for transmission lines is constructed. Through optimized calculations, the values ​​of the real-time parameter factors for each sensor are extracted, achieving overall optimal performance in terms of energy consumption, information reliability, and online monitoring real-time performance, thus realizing the optimized allocation of sensor operating parameters. Attached Figure Description

[0051] Figure 1 This is a flowchart illustrating the steps of the multi-sensor operation real-time parameter optimization and allocation method of the present invention.

[0052] Figure 2 A schematic diagram showing the breakdown of the sensor's operational process duration;

[0053] Figure 3 A schematic diagram illustrating the composition of a multi-sensor operational performance evaluation function;

[0054] Figure 4 A flowchart illustrating the steps involved in using the ant colony algorithm to solve for optimal real-time performance parameters. Detailed Implementation

[0055] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0056] This invention provides a method for optimizing and allocating real-time operating parameters of multiple sensors for power transmission lines, as shown in the appendix. Figure 1 As shown, the implementation steps of this method are as follows:

[0057] Step 1: Establish a sensor task allocation model;

[0058] Numerous sensors are uniformly deployed along the power transmission lines and operate under a centralized power supply. Due to limited power resources, these sensors operate intermittently, periodically waking from sleep mode to collect, process, and wirelessly transmit data, before returning to sleep mode. Therefore, the sensor's operational process is broken down into four stages: "data collection – data processing – wireless transmission – sleep," as shown in the attached diagram. Figure 2 As shown, it executes in a cyclical manner.

[0059] There are N sensors deployed with a unified centralized power supply. Let P be the power consumption of the i-th sensor for data acquisition, data processing, wireless transmission, and sleep tasks, respectively. i1 ,P i2 ,P i3 ,P i4 The corresponding execution times are t1, t2, t3, and t4, with a total energy consumption of W. i .

[0060] Step 2: Establish a calculation model for the energy consumption index of each sensor deployed in the centralized power supply network of the transmission lines;

[0061] Energy consumption W of each sensor i This represents the total energy consumption of the sensor when performing four tasks: data acquisition, data processing, wireless transmission, and sleep mode. The energy consumption calculation model for N sensors is as follows:

[0062] W1 = P 11 t 11 +P 12 t 12 +P 13 t 13 +P 14 t 14

[0063] W i =P i1 t i1 +P i2 t i2 +P i3 t i3 +P i4 t i4 ...

[0065] W N =P N1 t N1 +P N2 t N2 +P N3 t N3 +P N4 t N4

[0066] Therefore, the sensor energy consumption index J is constructed as follows. i (P i ,t i The energy consumption of the sensor per unit time is characterized by the following calculation model:

[0067]

[0068] It can be seen that when t1, t2, and t3 are large, the energy consumption of the sensor increases, which is reflected in a higher energy consumption index; when t4 is large, the sensor has a long sleep time, which can effectively reduce energy consumption, which is reflected in a lower energy consumption index.

[0069] Step 3: Establish a calculation model for the real-time parameter factors of each sensor deployed in the centralized power supply network of the transmission lines;

[0070] The ratio of the sensor's operating times t1, t2, t3 to its sleep period t4, a parameter reflecting the sensor's real-time performance, is correlated with the energy consumption index. A larger ratio indicates a relatively shorter sleep period, representing higher real-time performance but also higher energy consumption. Conversely, a smaller ratio leads to decreased real-time performance but better energy savings, creating a contradictory relationship. Therefore, the core essence of this contradiction is reflected by the real-time parameter factor K. i (t i To represent this using a computational model:

[0071]

[0072] Step 4: Establish a data processing information quality and reliability calculation model for each sensor deployed in the centralized power supply network of the transmission lines;

[0073] Since data fusion operations such as edge computing and logical analysis are required in the sensor data processing stage, they directly determine the quality and reliability of the sensor monitoring information. Sufficiently long data processing and fusion time can improve the quality and reliability of sensor information, but it also leads to increased energy consumption. Therefore, a reliability index C for sensor data processing information quality is defined. i (t i2 The calculation model is as follows:

[0074] C i (t2)=ft2

[0075] Where f is a function of the data processing procedure;

[0076] It is evident that when t2 is relatively large, the quality of the sensing information is higher and the reliability is enhanced, but this will lead to an increase in the real-time parameter factor, resulting in greater energy consumption.

[0077] Step 5: Construct an evaluation function for the operational performance of multiple sensors used in power transmission lines;

[0078] To minimize the energy consumption of the sensor system, the durations t1, t2, and t3 of data acquisition, processing, and wireless transmission should be minimized, meaning the sensor energy consumption index in step two should be as low as possible. On the one hand, to improve the real-time performance of online monitoring, the sleep time needs to be reduced, maximizing data acquisition and processing, meaning the real-time parameter factor in step three should be as large as possible. On the other hand, to ensure good information quality reliability of the sensor system, the information quality reliability of data processing in step four should be maximized. To reconcile the inherent contradictions among these three objectives—overall energy consumption of multiple sensors, information reliability, and real-time performance of online monitoring—the energy consumption index model established in step two, the real-time parameter factor model established in step three, and the information quality reliability model established in step four are inversely proportional to each other. Direct Proportion K i Direct proportion C i By combining these sensors in a consistent manner, a performance evaluation function for multiple sensors used in power transmission lines can be constructed. as follows:

[0079]

[0080] Wherein, α, β, and γ are normalized influence factors that reflect the contribution of sensor energy consumption, information reliability, and online monitoring real-time performance to operational efficiency. They are usually determined based on specific scenario task requirements and empirical values, and α+β+γ=1.

[0081] The performance evaluation function for multi-sensor operation is composed as follows: Figure 3 As shown, only by rationally optimizing the allocation of the time delay parameter T = [t1, t2, t3, t4] can the opposing factors of the three objectives of overall energy consumption of multi-sensors, information reliability, and real-time online monitoring be unified, thus achieving overall optimization.

[0082] Step Six: Global search and optimization to extract the best real-time parameter factors as allocation parameters for the sensor system. Specific steps are detailed in the appendix. Figure 4 As shown.

[0083] To obtain the optimal set T of the real-time performance parameters of the multi-sensor system in the performance evaluation function in step five. op =[t 1op , t 2op , t 3op , t 4op T is extracted using a global search optimization method. op This serves as the optimal allocation value for the real-time operating parameters of the sensor system.

[0084] The weighting factors are selected by comparing the importance (impact) of energy consumption, information credibility, and real-time performance. Based on the requirements of the scenario and task, the following factors are selected:

[0085]

[0086] The following is an example:

[0087] For example, P 11 =50,P 12 =10,P 13 =20,P 14 =2W,P 21 =30,P 22 =20,P 23 =30,P 24 =6W,

[0088] P 31 =70,P 32 =70,P 33 =15,P 34 =9W;

[0089] t 11 =t 12 =t 13 =t 14 =1t 21 =1,t 22 =2,t 23 =2,t 14 =1t 31 =t 32 =1,t 33 =t 34 =3

[0090] 1) Select a time delay rate of change v = 0.1, and choose an initial solution: T1(0) = [t 11 ,t 12 ,t 13 ,t 14 ] =

[1111] ,

[0091] T2(0)=[t 21 ,t 22 ,t 23 ,t 24 ]=

[1221] ,T3(0)=[t 31 ,t 32 ,t 33 ,t 34 ]=

[1133] , and the time delay decay is expressed as T=vT(0).

[0092] 2) Construct a path and calculate the probability that the ant will reach the destination and achieve its goal by taking the path [t1t2t3t4].

[0093]

[0094] 3) Update information,

[0095]

[0096] ΔT(i,i+1)=1 / ∑(t1+t2+t3+t4)

[0097] 4) Determine if the termination criterion is met: 10 -6 <ti<10 -4 And p = p max If the condition is met, output the current optimal solution and the algorithm stops; otherwise, proceed to step 3.

[0098] 5) When the algorithm stops, output the optimal solution as follows:

[0099] T(1)=[0.013 0.140 0.160 0.150]

[0100] T(2)=[0.043 0.064 0.086 0.065]

[0101] T(3)=[0.043 0.054 0.096 0.085]

[0102] Thus, the optimal solution [0.043 0.054 0.096 0.085] is obtained, which are the best real-time parameters for the sensor to perform the four tasks of data acquisition, data processing, wireless transmission, and sleep mode during operation.

[0103] In summary, the above are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for optimizing and allocating real-time operating parameters of a transmission line using multiple sensors, characterized in that, The implementation steps of this method are as follows: Step 1: Establish a sensor task allocation model; Step 2: Establish a calculation model for the energy consumption index of each sensor deployed in the centralized power supply network of the transmission lines; Step 3: Establish a calculation model for the real-time parameter factors of each sensor deployed in the centralized power supply network of the transmission lines; Step 4: Establish a data processing information quality and reliability calculation model for each sensor deployed in the centralized power supply network of the transmission lines; Step 5: Construct an operational performance evaluation function for multiple sensors used in power transmission lines based on the sensor task allocation model, energy consumption index calculation model, real-time parameter factor calculation model, and data processing information quality reliability calculation model; Step 6: Based on the operational performance evaluation function, use the global search optimization method to extract the optimal real-time parameter factors as the allocation parameters for the sensor system; The real-time parameter factor is the ratio of the sum of the time for sensor to acquire data, process data, and transmit wirelessly to the sensor's sleep time.

2. The method for optimizing and allocating real-time operating parameters of multi-sensor transmission lines as described in claim 1, characterized in that, The process of establishing the sensor task allocation model in step one is as follows: Numerous sensors are uniformly deployed on the power transmission line and operate under centralized power supply. Due to the limited power supply energy for these sensors, they operate in an intermittent mode, that is, periodically waking up from sleep mode to carry out the workflow of data acquisition, data processing, and wireless transmission, and then returning to sleep mode. Thus, the working process of the sensors is broken down into four stages: "data acquisition - data processing - wireless transmission - sleep mode", which are executed in a cyclical manner. There are N sensors deployed with a unified centralized power supply. Let P be the power consumption of the i-th sensor for data acquisition, data processing, wireless transmission, and sleep tasks, respectively. i1 ,P i2 ,P i3 ,P i4 The corresponding execution times are t. i1 ,t i2 ,t i3 ,t i4 Total energy consumption is W i .

3. The method for optimizing and allocating real-time operating parameters of multi-sensor transmission lines as described in claim 2, characterized in that, The process of establishing the energy consumption index calculation model for each sensor deployed in the centralized power supply network of the transmission line in step two is as follows: Energy consumption W of each sensor i This represents the total energy consumption of the sensor when performing four tasks: data acquisition, data processing, wireless transmission, and sleep mode. The energy consumption calculation model for N sensors is as follows: W1=P 11 t 11 +P 12 t 12 +P 13 t 13 +P 14 t 14 W i =P i1 t i1 +P i2 t i2 +P i3 t i3 +P i4 t i4 ... W N =P N1 t N1 +P N2 t N2 +P N3 t N3 +P N4 t N4 Therefore, the sensor energy consumption index J is constructed as follows. i (P i ,t i The energy consumption of the sensor per unit time is characterized by the following calculation model:

4. The method for optimizing and allocating real-time operating parameters of multi-sensor transmission lines as described in claim 3, characterized in that, In step three, the real-time parameter factor K i (t i The calculation model is as follows:

5. The method for optimizing and allocating real-time operating parameters of multi-sensor transmission lines as described in claim 4, characterized in that, In step four, the reliability of sensor data processing information quality C i (t i The calculation model is as follows: C i (t i )=f(t i2 ) Where f is a function of the data processing procedure.

6. The method for optimizing and allocating real-time operating parameters of multi-sensor transmission lines as described in claim 5, characterized in that, The process of constructing the multi-sensor operation performance evaluation function for transmission lines in step five is as follows: The energy consumption index model established in step two, the real-time parameter factor model established in step three, and the data processing information quality reliability model established in step four are respectively inversely proportional. Direct Proportion K i (t i ), direct proportion C i (t i By combining them in a consistent manner, a multi-sensor operational performance evaluation function for power transmission lines can be constructed. as follows: Wherein, α, β, and γ are normalized influence factors that reflect the contribution of sensor energy consumption, online monitoring real-time performance, and information reliability to operational efficiency, respectively. They are usually determined based on specific scenario task requirements and empirical values, and α+β+γ=1.

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