Multi-sensor operation real-time parameter optimal distribution method for power transmission conductor

Abstract

The invention discloses a multi-sensor operation real-time parameter optimal distribution method for a power transmission conductor. The method comprises the following steps: firstly, sequentially establishing a sensor work task distribution model, an energy consumption index calculation model of each sensor arranged for centralized power supply of the power transmission conductor, a real-time parameter factor calculation model and a data processing information quality credibility calculation model; according to a sensor work task distribution model, an energy consumption index calculation model, a real-time parameter factor calculation model and a data processing information quality credibility calculation model, constructing an operation efficiency evaluation function of the multiple sensors for the power transmission conductor; and finally, extracting an optimal real-time parameter factor as a distribution parameter of the sensor system by utilizing a global search optimization method according to the operation efficiency evaluation function. According to the method, real-time parameter factor values of all the sensors are extracted through optimization calculation, so that the overall energy consumption, information credibility and online monitoring real-time performance of the multiple sensors are overall optimal, and the purpose of improving the operation efficiency of the multiple sensors is achieved.

Description

Technical Field

[0001] The invention relates to the technical field of transmission line sensor monitoring, and in particular to a method for optimizing and allocating real-time parameters of multi-sensor operation for transmission lines. Background Art

[0002] Transmission line faults usually cause phase-to-phase flashover discharge, wire breakage, tower collapse and other accidents, resulting in economic losses and casualties, seriously affecting the safety and reliable operation of transmission lines. By laying out sensor equipment on the transmission lines to monitor the operating conditions of the lines, real-time information on heating, insulation, leakage current and other status can be collected, and timely warning and disposal can be carried out to ensure the safe and stable operation of the transmission lines. Many sensors are uniformly laid out on the transmission lines, and centralized power supply and operation are adopted by solar energy and other methods, and the collected data is transmitted wirelessly to the background management center.

[0003] Since the power supply of these sensors is limited, the sensors usually operate in an intermittent mode, that is, they periodically wake up from sleep to collect data, process data, and transmit wirelessly, and then go back to sleep. On the one hand, the setting of the sleep cycle is a key factor affecting the real-time operation of the sensor, and it is also an important link to reduce energy consumption, but too long a sleep cycle will reduce the real-time operation of the sensor; on the other hand, due to the large energy consumption of collecting data, processing data, and wireless transmission, it is necessary to shorten the duration of these work links as much as possible, but this will lead to a decrease in real-time performance; in addition, in the data processing link, the sensor needs to perform data fusion operations such as edge computing and logical analysis, which will directly affect the information quality and credibility of sensor monitoring. A sufficiently long data processing fusion time can improve the quality and credibility of sensor information, but it will also lead to an increase in energy consumption. Therefore, there is a profound internal contradiction mechanism between the three goals of overall energy consumption of centralized power supply of transmission lines, credibility of sensor information, and real-time online monitoring. In actual applications, the importance of the three goals is also different.

[0004] In summary, only by uniformly and reasonably optimizing the allocation of data collection, data processing, wireless transmission and sleep cycle duration of many sensors on the transmission line with centralized power supply can we effectively reconcile and solve the inherent mechanism contradictions between energy consumption, reliability and real-time performance, achieve global compromise optimization, and achieve the highest efficiency of multi-sensors for transmission lines. For the above situation, the industry has not yet found a reasonable and feasible solution. Most of the existing methods are based on experience and the functional characteristics of sensors to set the duration of data collection, data processing, wireless transmission and sleep cycle, or they are too concerned about energy saving, resulting in poor real-time performance, or they bring about a large energy consumption cost in pursuit of data fusion reliability. In general, there is a general lack of means and tools to improve and optimize the operating efficiency of multi-sensors for 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 of multi-sensor operation for power transmission lines, taking the real-time characteristics reflected by the ratio of the sensor's working operation time to the sleep cycle time as the allocation parameter factor, and describing the multi-objective characteristics reflecting the overall energy consumption of the multi-sensors, the information credibility, and the real-time of online monitoring in the form of a multi-sensor operation efficiency objective function for power transmission lines, extracting the real-time parameter factors through global search, and realizing the optimal allocation of real-time parameters of the multi-sensor operation.

[0006] A method for optimizing the real-time parameter allocation of a transmission line using multiple sensors, the implementation steps of the method are as follows:

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

[0008] Step 2: Establish an energy consumption index calculation model for each sensor deployed with centralized power supply of transmission lines;

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

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

[0011] Step 5: Construct the operation efficiency evaluation function of the multi-sensor for the transmission line according to the sensor work task allocation model, the energy consumption index calculation model, the real-time parameter factor calculation model and the data processing information quality credibility calculation model;

[0012] Step 6: According to the operation performance evaluation function, the global search optimization method is used to extract the best real-time parameter factor as the allocation parameter for the sensor system.

[0013] Furthermore, the process of establishing the sensor work task allocation model in step 1 is as follows:

[0014] Many sensors are uniformly arranged on the transmission lines and are operated with centralized power supply. Due to the limited power supply energy of these sensors, the sensors operate in an intermittent mode, that is, they periodically wake up from sleep to collect data, process data, and transmit wirelessly, and then go back to sleep again. Therefore, the duration of the sensor's working process is divided into four steps: "collect data--process data--wireless transmission--sleep", which are executed repeatedly;

[0015] Assume that there are N sensors with centralized power supply. The power of the i-th sensor for data collection, data processing, wireless transmission, and sleep tasks is: P i1 ,P i2,P i3 ,P i4 , the corresponding execution times are t1, t2, t3, t4, and the total energy consumption is W i .

[0016] Furthermore, the process of establishing the energy consumption index calculation model of each sensor arranged with centralized power supply by transmission lines in step 2 is as follows:

[0017] Energy consumption of each sensor W i It represents the total energy consumption of the four tasks of data collection, data processing, wireless transmission, and sleep. The calculation model of the energy consumption of 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] So the following sensor energy consumption index J is constructed: i (P i ,t i ), which is used to characterize the energy consumption of the sensor per unit time. Its calculation model is:

[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 the high energy consumption index; when t4 is large, the sensor has a long sleep time and can effectively reduce energy consumption, which is reflected in the low energy consumption index.

[0025] Furthermore, the process of establishing the real-time parameter factor calculation model of each sensor arranged for centralized power supply of the transmission line in step 3 is as follows:

[0026] The energy consumption index is associated with the ratio of the sensor's operating time t1, t2, t3 to the sleep cycle t4, a parameter factor that reflects the real-time operation of the sensor: the larger the ratio, the shorter the sleep cycle, which means that the real-time operation of the sensor is higher, but it also brings greater energy consumption. A smaller ratio will lead to a decrease in real-time performance, but it can save energy better. Therefore, there is an opposing contradiction. For this reason, the real-time parameter factor K is used to reflect the core of this opposing contradiction. i (t i ) calculation model to represent:

[0027]

[0028] Furthermore, the process of establishing the data processing information quality credibility calculation model of each sensor arranged for centralized power supply of the transmission line in step 4 is as follows:

[0029] Since edge computing and logic analysis are required in the sensor data processing link, data fusion operations will directly determine the information quality and credibility of sensor monitoring. A sufficiently long data processing fusion time can improve the quality and credibility of sensor information, but it will also increase energy consumption. Therefore, the sensor data processing information quality credibility C is defined. i (t i2 )The calculation model is as follows:

[0030] C i (t2) = ft2

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

[0032] It can be seen that when t2 is relatively large, the quality of sensor information is higher and the credibility is enhanced, but it will lead to an increase in the real-time parameter factor and thus bring greater energy consumption.

[0033] Furthermore, the process of constructing the operation efficiency evaluation function of the multi-sensor for the transmission line in step 5 is as follows:

[0034] In order to minimize the energy consumption of the sensor system, the duration t1, t2, and t3 of data collection, data processing, and wireless transmission should be as short as possible, that is, the sensor energy consumption index in step two should be as low as possible; on the one hand, in order to improve the real-time performance of online monitoring, it is necessary to reduce the sleep time and perform as much collection and processing work as possible, that is, the real-time parameter factor in step three should be as large as possible; on the other hand, in order for the sensor system to have good information quality credibility, it is necessary to ensure that the data processing information quality credibility in step four is as large as possible. In order to reconcile the inherent contradictions of the three goals of the above-mentioned multi-sensor overall energy consumption, information credibility, and online monitoring real-time, 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 credibility model established in step four are respectively in inverse proportion. Direct Proportional K i , direct proportion C i The operation efficiency evaluation function of multi-sensor for transmission line is constructed by combining them in a way that their targets are consistent. as follows:

[0035]

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

[0037] Furthermore, in order to obtain the optimal set T of real-time parameters of the multi-sensor system in the performance evaluation function in step 5, op =[t 1op , t 2op , t 3op , t 4op ], extract the best real-time parameter T through global search optimization op , which is used as the optimal allocation value of the real-time parameters of the sensor system. The process of extracting the optimal real-time parameter factor as the allocation parameter of the sensor system by the global search optimization method in step 6 is as follows:

[0038] (1) Set the initial delay, end delay, delay change rate v, and 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 the system-defined value, which is initially set to

[1111] ; the end 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 multiple during the iteration process, and the chain length is the number of iterations N, so:

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

[1111] , the corresponding number of iterations is 0. After the delay decays, T(0)* = vT(0).

[0040] (2) Construct a path and calculate the probability that the ant reaches the end point and achieves the goal through 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 whether the termination criteria are met: 10 -6 <ti<10 -4 And p = p max , if it is satisfied, the current optimal solution is output and the algorithm stops, otherwise, step (3) is executed;

[0046] (5) When the algorithm stops, the optimal solution is output, which is the optimal real-time parameter T for the sensor to perform the four tasks of collecting data, processing data, wireless transmission, and sleeping. op .

[0047] Beneficial effects:

[0048] 1. The method for optimizing and allocating real-time parameters of multi-sensor operation of the present invention first establishes a sensor work task allocation model, an energy consumption index calculation model of each sensor arranged in a centralized power supply arrangement of a transmission line, a real-time parameter factor calculation model, and a data processing information quality credibility calculation model in sequence; then, an operation efficiency evaluation function of a multi-sensor for a transmission line is constructed according to the sensor work task allocation model, the energy consumption index calculation model, the real-time parameter factor calculation model, and the data processing information quality credibility calculation model; finally, according to the operation efficiency evaluation function, a global search optimization method is used to extract the best real-time parameter factor as an allocation parameter for the sensor system, thereby solving the inherent mechanism contradiction among energy consumption, credibility, and real-time performance of a multi-sensor for a transmission line, realizing global compromise optimization, and achieving the purpose of maximizing the efficiency of a multi-sensor for a transmission line.

[0049] 2. The present invention establishes for the first time an energy consumption index calculation model for each sensor arranged with centralized power supply through transmission lines, which can quantitatively reflect the overall energy consumption of the sensor's working process. It also establishes for the first time a real-time parameter factor calculation model for each sensor arranged with centralized power supply through transmission lines, which can quantitatively reflect the real-time situation of the sensor's online monitoring. At the same time, it establishes for the first time a data processing information quality credibility calculation model for each sensor arranged with centralized power supply through transmission lines, which can quantitatively reflect the credibility of the sensor's data acquisition and processing information.

[0050] 3. Aiming at the problem of inherent contradiction in the optimization orientation of the three objectives of overall energy consumption, sensor information credibility and online monitoring real-time of multi-sensor centralized power supply for transmission lines, the present invention uses the real-time characteristic reflected by the ratio of sensor working time to sleep cycle time as the allocation parameter factor, unifies the opposing conditions of reducing the real-time parameter factor to optimize energy consumption and increasing the real-time parameter factor to improve real-time and credibility, constructs a multi-sensor operation efficiency function for transmission lines, extracts the value of the real-time parameter factor of each sensor through optimization calculation, so that the overall energy consumption, information credibility and online monitoring real-time performance of the multi-sensor are optimized as a whole, so as to realize the optimal allocation of sensor operation parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 A flowchart of the steps of the method for optimizing the real-time parameter allocation of multiple sensor operations of the present invention;

[0052] Figure 2 A schematic diagram showing the decomposition of the sensor operation process duration;

[0053] Figure 3 It is a schematic diagram of the composition of the multi-sensor operation performance evaluation function;

[0054] Figure 4 Flowchart of the steps for solving the optimal real-time parameters using the ant colony algorithm. DETAILED DESCRIPTION

[0055] The present invention is described in detail below with reference to the accompanying drawings and embodiments.

[0056] The present invention provides a method for optimizing the real-time parameter allocation of multi-sensor operation of a transmission line, as shown in the attached Figure 1 As shown, the implementation steps of this method are as follows:

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

[0058] Many sensors are uniformly arranged on the transmission lines and are operated with centralized power supply. Due to the limited power supply energy of these sensors, the sensors operate in an intermittent mode, that is, they periodically wake up from sleep to collect data, process data, and transmit wirelessly, and then go back to sleep. Therefore, the duration of the sensor's working process is divided into four steps: "collect data--process data--wireless transmission--sleep", as shown in the attached figure. Figure 2 As shown, the process is executed repeatedly in a cycle.

[0059] Assume that there are N sensors with centralized power supply. The power of the i-th sensor for data collection, data processing, wireless transmission, and sleep tasks is: P i1 ,P i2 ,P i3 ,P i4 , the corresponding execution times are t1, t2, t3, t4, and the total energy consumption is W i .

[0060] Step 2: Establish an energy consumption index calculation model for each sensor deployed with centralized power supply of transmission lines;

[0061] Energy consumption of each sensor W i It represents the total energy consumption of the four tasks of data collection, data processing, wireless transmission, and sleep. The calculation model of the energy consumption of 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] So the following sensor energy consumption index J is constructed: i (P i ,t i ), which is used to characterize the energy consumption of the sensor per unit time. Its calculation model is:

[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 the high energy consumption index; when t4 is large, the sensor has a long sleep time and can effectively reduce energy consumption, which is reflected in the low energy consumption index.

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

[0070] The energy consumption index is associated with the ratio of the sensor's operating time t1, t2, t3 to the sleep cycle t4, a parameter factor that reflects the real-time operation of the sensor: the larger the ratio, the shorter the sleep cycle, which means that the real-time operation of the sensor is higher, but it also brings greater energy consumption. A smaller ratio will lead to a decrease in real-time performance, but it can save energy better. Therefore, there is an opposing contradiction. For this reason, the real-time parameter factor K is used to reflect the core of this opposing contradiction. i (t i ) calculation model to represent:

[0071]

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

[0073] Since edge computing and logic analysis are required in the sensor data processing link, data fusion operations will directly determine the information quality and credibility of sensor monitoring. A sufficiently long data processing fusion time can improve the quality and credibility of sensor information, but it will also increase energy consumption. Therefore, the sensor data processing information quality credibility C is defined. i (t i2 )The calculation model is as follows:

[0074] C i (t2) = ft2

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

[0076] It can be seen that when t2 is relatively large, the quality of sensor information is higher and the credibility is enhanced, but it will lead to an increase in the real-time parameter factor and thus bring greater energy consumption.

[0077] Step 5: Construct the operation efficiency evaluation function of the multi-sensor for the transmission line;

[0078] In order to minimize the energy consumption of the sensor system, the duration t1, t2, and t3 of data collection, data processing, and wireless transmission should be as short as possible, that is, the sensor energy consumption index in step two should be as low as possible; on the one hand, in order to improve the real-time performance of online monitoring, it is necessary to reduce the sleep time and perform as much collection and processing work as possible, that is, the real-time parameter factor in step three should be as large as possible; on the other hand, in order for the sensor system to have good information quality credibility, it is necessary to ensure that the data processing information quality credibility in step four is as large as possible. In order to reconcile the inherent contradictions of the three goals of the above-mentioned multi-sensor overall energy consumption, information credibility, and online monitoring real-time, 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 credibility model established in step four are respectively in inverse proportion. Direct Proportional K i , direct proportion C i The operation efficiency evaluation function of multi-sensor for transmission line is constructed by combining them in a way that their targets are consistent. as follows:

[0079]

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

[0081] The operating efficiency evaluation function of multiple sensors is composed as follows: Figure 3 Only by rationally optimizing the allocation of the delay parameter values ​​T = [t1, t2, t3, t4] can the three opposing conditions of multi-sensor overall energy consumption, information credibility, and online monitoring real-time performance be unified to achieve overall optimization.

[0082] Step 6: Global search optimization extracts the best real-time parameter factor as the allocation parameter for the sensor system. The specific steps are as follows: Figure 4 shown.

[0083] To obtain the optimal set of real-time parameters of the multi-sensor system in the performance evaluation function in step 5, op =[t 1op , t 2op , t 3op , t 4op ], extract T through global search optimization op , which is used as the optimal allocation value for the real-time operation parameters of the sensor system.

[0084] The method for selecting the weight factor is to compare the importance (influence) of energy consumption, information credibility, and real-time performance. According to the scenario task requirements, the weight factor is selected as follows:

[0085]

[0086] The following is an example demonstration:

[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 =1,t 21 =1,t 22 =2,t 23 =2,t 14 =1,t 31 =t 32 =1,t 33 =t 34 =3

[0091] 1) Select the delay change rate v = 0.1 and choose an initial solution: T1(0) = [t 11 ,t 12 ,t 13 ,t 14 ]=

[1111] ,

[0092] T2(0)=[t 21 ,t 22 ,t 23 ,t 24 ]=

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

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

[0093] 2) Construct a path and calculate the probability that the ant reaches the end point and achieves the goal through the path [t1t2t3t4]:

[0094]

[0095] 3) Update information,

[0096]

[0097] ΔT(i,i+1)=1 / ∑(t1+t2+t3+t4)

[0098] 4) Determine whether the termination criteria are met: 10 -6 <ti<10 -4 And p = p max If it is satisfied, the current optimal solution is output and the algorithm stops, otherwise, step 3 is executed.

[0099] 5) When the algorithm stops, the optimal solution is output as follows:

[0100] T(1)=[0.013 0.140 0.160 0.150]

[0101] T(2)=[0.043 0.064 0.086 0.065]

[0102] T(3)=[0.043 0.054 0.096 0.085]

[0103] Therefore, the optimal solution [0.043 0.054 0.096 0.085] is obtained, which are the best real-time parameters for the four tasks of collecting data, processing data, wireless transmission, and sleeping when the sensor is running.

[0104] In summary, the above are only preferred embodiments of the present invention and are not intended to limit the protection scope of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for optimizing the real-time parameter allocation of multi-sensor operation of a power transmission line, characterized in that: The implementation steps of this method are as follows: Step 1: Establish a sensor work task allocation model; Step 2: Establish an energy consumption index calculation model for each sensor deployed with centralized power supply of transmission lines; Step 3: Establish a real-time parameter factor calculation model for each sensor deployed in the centralized power supply of the transmission line; Step 4: Establish a credibility calculation model for the data processing information quality of each sensor deployed with centralized power supply of transmission lines; Step 5: Construct the operation efficiency evaluation function of the multi-sensor for the transmission line according to the sensor work task allocation model, the energy consumption index calculation model, the real-time parameter factor calculation model and the data processing information quality credibility calculation model; Step 6: According to the operation performance evaluation function, the global search optimization method is used to extract the best real-time parameter factor as the allocation parameter for the sensor system.

2. The method for optimizing and allocating real-time parameters of multi-sensor operation of a power transmission line according to claim 1, characterized in that: The process of establishing the sensor work task allocation model in step 1 is as follows: Many sensors are uniformly arranged on the transmission lines and are operated with centralized power supply. Due to the limited power supply energy of these sensors, the sensors operate in an intermittent mode, that is, they periodically wake up from sleep to collect data, process data, and transmit wirelessly, and then go back to sleep again. Therefore, the duration of the sensor's working process is divided into four steps: "collect data--process data--wireless transmission--sleep", which are executed repeatedly; Assume that there are N sensors with centralized power supply. The power of the i-th sensor for data collection, data processing, wireless transmission, and sleep tasks is: P i1 ,P i2 ,P i3 ,P i4 , the corresponding execution times are t1, t2, t3, t4, and the total energy consumption is W i .

3. The method for optimizing and allocating real-time parameters of multi-sensor operation of a power transmission line according to claim 2, characterized in that: The process of establishing the energy consumption index calculation model of each sensor arranged with centralized power supply of transmission lines in step 2 is as follows: Energy consumption of each sensor W i It represents the total energy consumption of the four tasks of data collection, data processing, wireless transmission, and sleep. The calculation model of the energy consumption of 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 So the following sensor energy consumption index J is constructed: i (P i ,t i ), which is used to characterize the energy consumption of the sensor per unit time. Its calculation model is:

4. The method for optimizing and allocating real-time parameters of multi-sensor operation of a power transmission line according to claim 3, characterized in that: The process of establishing the real-time parameter factor calculation model of each sensor arranged for centralized power supply of the transmission line in step 3 is as follows: The energy consumption index is associated with the ratio of the sensor's operating time t1, t2, t3 to the sleep cycle t4, a parameter factor that reflects the real-time operation of the sensor: the larger the ratio, the shorter the sleep cycle, which means that the real-time operation of the sensor is higher, but it also brings greater energy consumption. A smaller ratio will lead to a decrease in real-time performance, but it can save energy better. Therefore, there is an opposing contradiction. For this reason, the real-time parameter factor K is used to reflect the core of this opposing contradiction. i (t i ) calculation model to represent:

5. The method for optimizing and allocating real-time parameters of multi-sensor operation of power transmission lines according to claim 4, characterized in that: The process of establishing the data processing information quality credibility calculation model of each sensor arranged for centralized power supply of transmission lines in step 4 is as follows: Since edge computing and logic analysis are required in the sensor data processing link, data fusion operations will directly determine the information quality and credibility of sensor monitoring. A sufficiently long data processing fusion time can improve the quality and credibility of sensor information, but it will also increase energy consumption. Therefore, the sensor data processing information quality credibility C is defined. i (t i2 )The calculation model is as follows: C i (t2)=ft2 Where f is the function of the data processing process.

6. The method for optimizing and allocating real-time parameters of multi-sensor operation of a power transmission line according to claim 5, characterized in that: The process of constructing the operation efficiency evaluation function of the multi-sensor for the transmission line in step 5 is as follows: In order to minimize the energy consumption of the sensor system, the duration t1, t2, and t3 of data collection, data processing, and wireless transmission should be as short as possible, that is, the sensor energy consumption index in step two should be as low as possible; on the one hand, in order to improve the real-time performance of online monitoring, it is necessary to reduce the sleep time and perform as much collection and processing work as possible, that is, the real-time parameter factor in step three should be as large as possible; on the other hand, in order for the sensor system to have good information quality credibility, it is necessary to ensure that the data processing information quality credibility in step four is as large as possible. In order to reconcile the inherent contradictions of the three goals of the above-mentioned multi-sensor overall energy consumption, information credibility, and online monitoring real-time, 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 credibility model established in step four are respectively in inverse proportion. Direct Proportional K i , direct proportion C i The operation efficiency evaluation function of multi-sensor for transmission line is constructed by combining them in a way that their targets are consistent. as follows: Among them, α, β, and γ are normalized influencing factors that reflect the contribution of sensor energy consumption, information credibility, and online monitoring real-time performance to operational efficiency, respectively. They are usually determined based on specific scenario task requirements and empirical values, and α+β+γ=1.

7. The method for optimizing and allocating real-time parameters of multi-sensor operation of a power transmission line according to claim 6, characterized in that: The process of extracting the best real-time parameter factor as the allocation parameter of the sensor system by the global search optimization method in step 6 is as follows: (1) Set the initial delay, end delay, delay change rate v, and 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 the system-defined value, which is initially set to [1111]; the end 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 multiple during the iteration process, and the chain length is the number of iterations N, so: T(0) = [t1, t2, t3, t4] = [1111], the corresponding number of iterations is 0. After the delay decays, T(0)* = vT(0). (2) Construct a path and calculate the probability that the ant reaches the end point and achieves the goal through the path [t1, t2, t3, t4]: (3) Update information; ΔT(i,i+1)=1 / ∑(t1+t2+t3+t4) (4) Determine whether the termination criteria are met: 110 -6 <ti<10 -4 And p = p max , if it is satisfied, the current optimal solution is output and the algorithm stops, otherwise, step (3) is executed; (5) When the algorithm stops, the optimal solution is output, which is the optimal real-time parameters for the sensor to perform the four tasks of collecting data, processing data, wireless transmission, and sleeping.

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