Mobile charging network energy efficiency optimization system based on multiple intelligent reflecting surfaces

Abstract

The invention discloses a mobile charging network energy efficiency optimization system based on multiple intelligent reflecting surfaces, and the system comprises the steps: obtaining a signal coverage area of a base station, and determining a layout line of a sensor; and carrying out sensor preset point distribution based on the signal coverage area of the base station. According to the invention, the sensors can be visually arranged in a coverage area by using a digital twinborn technology for a worker to understand and analyze, the signal intensity of the sensor distribution position is analyzed by using a base station to send a signal, and when a monitoring value is 0, two conditions exist: one condition is that normal signal attenuation causes 0, and the other condition is that normal signal attenuation causes 0; according to the invention, the area with shielding interference can be intelligently analyzed through data, shielding removal optimization is carried out on the area, and an intelligent reflecting surface technology is also introduced, so that the transmission quality and the coverage range of wireless signals are improved; meanwhile, the overall energy consumption of the system is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of mobile charging network energy efficiency optimization, and in particular to a mobile charging network energy efficiency optimization system based on multiple intelligent reflective surfaces. Background Art

[0002] The base station is a transmitting device used in broadcasting, which can convert audio signals into electromagnetic waves and send them out through antennas to achieve the transmission of broadcast signals. However, the propagation of medium wave broadcast signals is affected by many factors.

[0003] In real life, most neural network systems are used to analyze data to determine the location of sensor points. However, this method is not intuitive and cannot allow staff to understand real-time monitoring information well. When the sensor monitoring value is zero, there is a lack of systematic analysis, and the monitoring points cannot be optimized, making it difficult to meet the needs of staff. Summary of the invention

[0004] In order to solve the above technical problems, a mobile charging network energy efficiency optimization system based on multiple intelligent reflective surfaces is provided. This technical solution solves the problem proposed in the above background technology that most of the real-life uses a neural network system to analyze data to obtain the location of sensor points, but this method is not intuitive and cannot allow staff to understand the real-time monitoring information well. When the sensor monitoring value is zero, there is a lack of systematic analysis, and the monitoring points cannot be optimized.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is: A mobile charging network energy efficiency optimization system based on multiple intelligent reflective surfaces, comprising: An acquisition module, the acquisition module is used to acquire the transmission quality and coverage of the wireless signal in the base station and determine the layout line of the sensor; A preset module, wherein the preset module sets preset sensor points in the wireless rechargeable sensor network based on the signal coverage area of ​​the base station and randomly deploys the sensor points in the two-dimensional charging space; A model building module, wherein the model building module builds a model of sensor points in the coverage area based on digital twin technology; An analysis module, wherein the analysis module analyzes the sensor point locations within the model to determine the signal strengths of the sensor point locations; The optimization module optimizes the wireless signal strength and coverage range according to the signal strength and adopts the intelligent reflective surface technology.

[0006] Preferably, the obtaining of the signal coverage area of ​​the base station and determining the layout line of the sensor specifically comprises the following steps: Determine the distance between the base station and the farthest point of signal transmission, recorded as ; With the base station as the center, Draw a circle for the radius; Divide the circle into several groups of sector-shaped areas according to the same angle; The midpoints of the arc edges of several groups of fan-shaped areas are marked, and the midpoints of the arc edges and the position points of the base stations are connected to be recorded as the sensor layout lines.

[0007] Furthermore, the distance between the base station and the farthest point of signal transmission is determined, which is recorded as In real life, the signal value is usually 0 in advance due to normal signal attenuation loss, so the maximum As a reference value, this area can better reflect the signal attenuation and help the staff to conduct analysis.

[0008] Preferably, the pre-determining of sensor points based on the signal coverage area of ​​the base station specifically comprises the following steps: Determine the distance between the sensor and the farthest point in the receiving range, denoted as ; Arrange several groups of sensors on the arrangement line; Several groups of sensors are numbered as 1, 2, 3, ... n; The location distances of several groups of sensors from the base station are:

[0009] Where n is the number of the sensor; Filter out the position points in the fan-shaped area that are not within the sensor receiving range and record them as defect points; Supplement sensors at defective points.

[0010] Further, through and , sensors can be well deployed in each sector-shaped area, and because there is a gap between the two circles, the gap is a defect point, which makes it impossible to monitor, so it is necessary to add it to ensure that each sector-shaped area can be monitored.

[0011] Preferably, the specific steps of constructing a model of sensor points in the coverage area based on digital twin technology are as follows: Use drones to collect images of base station signal coverage areas in real time; Use software to construct a three-dimensional model of the image information collected in real time; Obtaining preset point information of several groups of sensors and three-dimensional models of the sensors; The preset layout information of several groups of sensors is mapped one by one to the constructed three-dimensional model, and the model is simulated and trained to obtain the sensor layout model.

[0012] Preferably, the steps of analyzing the sensor point locations within the model and determining the signal strengths of the sensor point locations are as follows: The base station transmits signals to several groups of sensors at preset locations; Collect monitoring values ​​of sensors located on the same layout line and time information of reaching the sensors in sequence, wherein the time information corresponds to the monitoring values ​​one by one; Compare the monitoring value of the sensor with the preset threshold value; If the monitoring value of the sensor is greater than or equal to the preset threshold, the location is determined to be a strong signal area; If the monitoring value of the sensor is less than the preset threshold and greater than 0, the location is determined to be a weak signal area; If the monitoring value of the sensor is equal to 0, a secondary analysis is performed.

[0013] Preferably, the specific steps of performing the secondary analysis are as follows: Draw a curve graph of the monitoring values ​​of the sensors located on the same layout line and the time information of reaching the sensors, with the time information of reaching the sensors as the X-axis and the monitoring values ​​of the sensors as the Y-axis; Compare and analyze several groups of curve graphs to select a set of curves that conform to the law of signal attenuation; Calculate the average speed of signal attenuation based on a set of curves that conform to the law of signal attenuation; Filter out the points where the monitoring value is equal to 0; Based on the average speed of signal attenuation, the monitoring value of a sensor at that point on the same wiring line is analyzed. If it conforms to the regular attenuation, the signal is normally attenuated. If not, there is signal shielding interference in the area.

[0014] Preferably, the specific steps of calculating the average speed of signal attenuation according to the curve set that conforms to the signal attenuation law are as follows: Arrange the monitoring values ​​of the sensors in the curve that conforms to the signal attenuation law in order from small to large; Determine the detection level and determine the critical value of the kurtosis test based on the detection level; Calculate the kurtosis observation value of each monitoring value based on the kurtosis test formula; Determine whether the kurtosis observed value is greater than the critical value of the kurtosis test. If so, the point is determined to be an outlier. If not, the point is determined to be a non-outlier. Calculate the decay rate of several groups of non-outlier points on the same curve, and sum the decay rate values ​​of several groups; Count the number of curves in a curve set; Based on the sum of the speed values ​​and the number of curves, the average speed of signal attenuation is obtained; The calculation formula of the average speed of signal attenuation is:

[0015] In the formula, is the sum of several groups of decay speed values, is the number of curves.

[0016] Preferably, the calculation formula of the kurtosis observation value of the monitoring value is:

[0017] In the formula, is the observed value of kurtosis of the monitoring value; n is the number of monitoring values ​​sorted from small to large; is the average value of all monitoring values; are the monitoring values ​​before n arranged in ascending order.

[0018] Preferably, the specific steps of optimizing the wireless signal strength and coverage range according to the signal strength and using the intelligent reflective surface technology are as follows: If it is a strong signal area, no operation will be performed; If it is a weak signal area, add sensors; If it conforms to the normal attenuation law, the signal enhancement operation is performed at the position where the sensor monitoring value is 0; If the monitoring value is 0 due to signal shielding interference, the intelligent reflective surface technology will be introduced to the area.

[0019] Preferably, each reflection surface is composed of M reflection units with a side length of d, and each reflection unit can passively adjust its phase through an independent reflection coefficient.

[0020] Compared with the prior art, the present invention provides a mobile charging network energy efficiency optimization system based on multiple intelligent reflective surfaces, which has the following beneficial effects: When deploying sensors, the present invention first obtains the farthest signal transmission distance of the base station, draws a circle with the base station as the center and the farthest distance as the radius, and the circle is the signal coverage area. Several groups of deployment lines are drawn in the area, and point deployment is performed on the several groups of deployment lines. Digital twin technology is a prior art, and sensors can be deployed intuitively in the coverage area for staff to understand and analyze. The base station sends a signal to analyze the signal strength of the sensor deployment location. It is worth noting that the monitoring value of the sensor may be 0. When it is 0, there are two situations. One is that normal signal attenuation causes it to be 0, and the signal can be increased in the area. The other is that there may be shielding interference in the area. The present invention can use data intelligence to analyze the area with shielding interference, perform de-shielding optimization, and also introduce intelligent reflection surface technology, so as to improve the transmission quality and coverage of wireless signals, while reducing the overall energy consumption of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a schematic diagram of the mobile charging network energy efficiency optimization method in the present invention; Figure 2 A schematic diagram of a method for constructing a model of sensor points in a coverage area based on digital twin technology in the present invention; Figure 3 A schematic diagram of a method for determining the signal strength of sensor placement locations in the present invention; Figure 4 A schematic diagram of a method for performing secondary analysis in the present invention; Figure 5 A schematic diagram of a method for calculating the average speed of signal attenuation in the present invention; Figure 6 A schematic diagram of a method for optimizing wireless signal strength and coverage range using intelligent reflective surface technology in the present invention; Figure 7 It is a schematic diagram of the mobile charging network energy efficiency optimization system in the present invention; Figure 8 It is a traditional wired charging network model in real life. DETAILED DESCRIPTION

[0022] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0023] Example 1 Please refer to Figure 1-Figure 7 As shown, a mobile charging network energy efficiency optimization system based on multiple intelligent reflective surfaces includes: An acquisition module, the acquisition module is used to acquire the transmission quality and coverage of the wireless signal in the base station and determine the layout line of the sensor; A preset module, wherein the preset module sets preset sensor points in the wireless rechargeable sensor network based on the signal coverage area of ​​the base station and randomly deploys the sensor points in the two-dimensional charging space; A model building module, wherein the model building module builds a model of sensor points in the coverage area based on digital twin technology; An analysis module, wherein the analysis module analyzes the sensor point locations within the model to determine the signal strengths of the sensor point locations; The optimization module optimizes the wireless signal strength and coverage range according to the signal strength and adopts the intelligent reflective surface technology.

[0024] It can be understood by those skilled in the art that when deploying sensors, the farthest signal transmission distance of the base station is first obtained, and a circle is drawn with the base station as the center and the farthest distance as the radius. The circle is the signal coverage area, and several groups of deployment lines are drawn in the area, and point deployment is performed on several groups of deployment lines. Digital twin technology is an existing technology, and sensors can be deployed intuitively in the coverage area for staff to understand and analyze. The base station sends a signal to analyze the signal strength of the sensor deployment location. It is worth noting that the monitoring value of the sensor may be 0. When it is 0, there are two situations. One is that normal signal attenuation causes it to be 0, and the signal can be increased in the area. The other is that there may be shielding interference in the area. The present invention can use data intelligence to analyze the area with shielding interference, perform de-shielding optimization, and also introduce intelligent reflection surface technology to improve the transmission quality and coverage range of wireless signals, while reducing the overall energy consumption of the system.

[0025] Obtaining the signal coverage area of ​​the base station and determining the layout of the sensor specifically includes the following steps: Determine the distance between the base station and the farthest point of signal transmission, recorded as ; With the base station as the center, Draw a circle for the radius; Divide the circle into several groups of sector-shaped areas according to the same angle; The midpoints of the arc edges of several groups of fan-shaped areas are marked, and the midpoints of the arc edges and the position points of the base stations are connected to be recorded as the sensor layout lines.

[0026] It is understood by those skilled in the art that the distance between the base station and the farthest point of signal transmission is determined as In real life, the signal value is usually 0 in advance due to normal signal attenuation loss, so the maximum As a reference value, this area can better reflect the signal attenuation and help the staff to conduct analysis.

[0027] The specific steps of pre-deploying sensors based on the signal coverage area of ​​the base station are as follows: Determine the distance between the sensor and the farthest point in the receiving range, denoted as ; Arrange several groups of sensors on the arrangement line; Several groups of sensors are numbered as 1, 2, 3, ... n; The distances between the locations of several groups of sensors and the base station are:

[0028] Where n is the number of the sensor; Filter out the position points in the fan-shaped area that are not within the sensor receiving range and record them as defect points; Supplement sensors at defective points.

[0029] It can be understood by those skilled in the art that and , sensors can be well deployed in each sector-shaped area, and because there is a gap between the two circles, the gap is a defect point, which makes it impossible to monitor, so it is necessary to add it to ensure that each sector-shaped area can be monitored.

[0030] The specific steps for building a model of sensor points in the coverage area based on digital twin technology are as follows: Use drones to collect images of base station signal coverage areas in real time; Use software to construct a three-dimensional model of the image information collected in real time; Obtaining preset point information of several groups of sensors and three-dimensional models of the sensors; The preset layout information of several groups of sensors is mapped one by one to the constructed three-dimensional model, and the model is simulated and trained to obtain the sensor layout model.

[0031] The specific steps for analyzing the sensor locations within the model and determining the signal strength at the sensor locations are as follows: The base station transmits signals to several groups of sensors at preset locations; Collect the monitoring values ​​of sensors on the same layout line and the time information of reaching the sensors in sequence, and the time information corresponds to the monitoring values ​​one by one; Compare the monitoring value of the sensor with the preset threshold value; If the monitoring value of the sensor is greater than or equal to the preset threshold, the location is determined to be a strong signal area; If the monitoring value of the sensor is less than the preset threshold and greater than 0, the location is determined to be a weak signal area; If the monitoring value of the sensor is equal to 0, a secondary analysis is performed.

[0032] The specific steps for secondary analysis are as follows: Draw a curve graph of the monitoring values ​​of the sensors located on the same layout line and the time information of reaching the sensors, with the time information of reaching the sensors as the X-axis and the monitoring values ​​of the sensors as the Y-axis; Compare and analyze several groups of curve graphs to select a set of curves that conform to the law of signal attenuation; Calculate the average speed of signal attenuation based on a set of curves that conform to the law of signal attenuation; Filter out the points where the monitoring value is equal to 0; Based on the average speed of signal attenuation, the monitoring value of a sensor at that point on the same wiring line is analyzed. If it conforms to the regular attenuation, the signal is normally attenuated. If not, there is signal shielding interference in the area.

[0033] The specific steps for calculating the average speed of signal attenuation based on a set of curves that conform to the signal attenuation law are as follows: Arrange the monitoring values ​​of the sensors in the curve that conforms to the signal attenuation law in order from small to large; Determine the detection level and determine the critical value of the kurtosis test based on the detection level; Calculate the kurtosis observation value of each monitoring value based on the kurtosis test formula; Determine whether the kurtosis observed value of the historical running data is greater than the critical value of the kurtosis test. If so, the point is determined to be an outlier; if not, the point is determined to be a non-outlier; Calculate the decay rate of several groups of non-outlier points on the same curve, and sum the decay rate values ​​of several groups; Count the number of curves in a curve set; Based on the sum of the speed values ​​and the number of curves, the average speed of signal attenuation is obtained; The calculation formula for the average speed of signal attenuation is:

[0034] In the formula, is the sum of several groups of decay speed values, is the number of curves.

[0035] The calculation formula for the kurtosis observation of the monitoring value is:

[0036] In the formula, is the observed value of kurtosis of the monitoring value; n is the number of monitoring values ​​sorted from small to large; is the average value of all monitoring values; are the monitoring values ​​before n arranged in ascending order.

[0037] It can be understood by those skilled in the art that by eliminating outliers and calculating the attenuation rate based on non-outlier data, the impact of interference variables in historical operating data on monitoring values ​​can be effectively reduced, thereby improving the reliability of the calculation.

[0038] According to the signal strength, the specific steps to optimize the wireless signal strength and coverage range by using intelligent reflective surface technology are as follows: If it is a strong signal area, no operation will be performed; If it is a weak signal area, add sensors; If it conforms to the normal attenuation law, the signal enhancement operation is performed at the position where the sensor monitoring value is 0; If the monitoring value is 0 due to signal shielding interference, the intelligent reflective surface technology will be introduced to the area.

[0039] In real life, in a wireless rechargeable sensor network, a known number of sensor nodes are randomly deployed in a two-dimensional space. The base station is placed at the center of the wireless rechargeable sensor network to provide energy replenishment services for mobile chargers and can sense the remaining energy and charging request information of the sensor nodes in the system. The network model is .in, Represents a sensor set of n sensor nodes, and the location is represented by ; represents a set of m mobile chargers equipped with IRS; D represents the set of Euclidean distances between any two nodes; T represents the set of time taken by the mobile charger to charge the sensor nodes that need to be charged. In the initial charging state, the mobile charger MC is located in the base station BS. When the node energy in the wireless rechargeable sensor network reaches the threshold, the mobile charger MC will start from the base station to provide charging services for the sensor nodes and return to the base station after charging is completed. The network model is as follows: Figure 8 shown.

[0040] The present invention introduces multiple intelligent reflective surface technologies, each of which is composed of M reflective units with a side length of d. Each reflective unit can passively adjust its phase through an independent reflection coefficient, thereby improving the transmission quality and coverage of wireless signals and reducing the overall energy consumption of the system. The location of the IRS is indicated by The position of sensor node k is represented by express.

[0041] During the energy transmission process, the channels of MC-IRS, IRS-sensor k, and MC-sensor k are expressed as , and The distance between the charger MC and its equipped IRS is fixed, so the channel gain of the link between MC and IRS can be expressed as:

[0042] in represents the channel power gain at a reference distance of 1 m, Indicates that the IRS is Line and The reflection unit is composed of a column, , Indicates the carrier frequency, represents the speed of light, . , denote the cosine and sine of the horizontal arrival angle of the signal at the IRS, respectively. Represents the sine of the vertical angle of arrival of the signal at the IRS.

[0043] Channel gain from the IRS equipped with the mobile charger to the kth sensor node Given by:

[0044] in , They represent the cosine and sine of the horizontal deviation angle of the signal to the kth sensor node respectively; Represents the sine value of the vertical deviation angle of the signal to the kth sensor node; The channel gain achieved at the kth sensor node can be expressed as:

[0045] represents the channel gain of the direct connection between the charger and the kth sensor device, represents the distance from MC to sensor k, represents the phase shift matrix of IRS, where The phase value range of each reflection unit.

[0046] Ignoring the noise power, the charging power at the kth sensor node is expressed as :

[0047] in is the efficiency of energy harvesting at the sensor node.

[0048] To sum up, when deploying sensors, the present invention first obtains the farthest signal transmission distance of the base station, draws a circle with the base station as the center and the farthest distance as the radius, and the circle is the signal coverage area. Several groups of deployment lines are drawn in the area, and point deployment is performed on several groups of deployment lines. Digital twin technology is an existing technology, which can intuitively deploy sensors in the coverage area for staff to understand and analyze. The base station sends a signal to analyze the signal strength of the sensor deployment location. It is worth noting that the monitoring value of the sensor may be 0. When it is 0, there are two situations. One is that normal signal attenuation causes it to be 0, and the signal can be increased in the area. The other is that there may be shielding interference in the area. The present invention can use data intelligence to analyze the area with shielding interference, perform de-shielding optimization, and also introduce intelligent reflection surface technology to improve the transmission quality and coverage range of wireless signals, while reducing the overall energy consumption of the system.

[0049] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A mobile charging network energy efficiency optimization system based on multiple intelligent reflective surfaces, characterized in that: include: An acquisition module, the acquisition module is used to acquire the transmission quality and coverage of the wireless signal in the base station and determine the layout line of the sensor; A preset module, wherein the preset module sets preset sensor points in the wireless rechargeable sensor network based on the signal coverage area of ​​the base station and randomly deploys the sensor points in the two-dimensional charging space; A model building module, wherein the model building module builds a model of sensor points in the coverage area based on digital twin technology; An analysis module, wherein the analysis module analyzes the sensor point locations within the model to determine the signal strengths of the sensor point locations; The optimization module optimizes the wireless signal strength and coverage range according to the signal strength and adopts the intelligent reflective surface technology.

2. According to claim 1, a mobile charging network energy efficiency optimization system based on multiple intelligent reflective surfaces is characterized in that: The obtaining of the signal coverage area of ​​the base station and determining the layout of the sensor specifically includes the following steps: Determine the distance between the base station and the farthest point of signal transmission, recorded as ; With the base station as the center, Draw a circle for the radius; Divide the circle into several groups of sector-shaped areas according to the same angle; The midpoints of the arc edges of several groups of fan-shaped areas are marked, and the midpoints of the arc edges and the location points of the base stations are connected to be recorded as the sensor layout lines.

3. According to claim 2, a mobile charging network energy efficiency optimization system based on multiple intelligent reflective surfaces is characterized in that: The sensor pre-positioning based on the signal coverage area of ​​the base station specifically includes the following steps: Determine the distance between the sensor and the farthest point in the receiving range, denoted as ; Arrange several groups of sensors on the arrangement line; Several groups of sensors are numbered as 1, 2, 3, ... n; The location distances of several groups of sensors from the base station are: Where n is the number of the sensor; Filter out the position points in the fan-shaped area that are not within the sensor receiving range and record them as defect points; Supplement sensors at defective points.

4. The mobile charging network energy efficiency optimization system based on multiple intelligent reflective surfaces according to claim 3 is characterized in that: The specific steps of constructing a model of sensor points in the coverage area based on digital twin technology are as follows: Use drones to collect images of base station signal coverage areas in real time; Use software to construct a three-dimensional model of the image information collected in real time; Obtaining preset point information of several groups of sensors and three-dimensional models of the sensors; The preset layout information of several groups of sensors is mapped one by one to the constructed three-dimensional model, and the model is simulated and trained to obtain the sensor layout model.

5. The mobile charging network energy efficiency optimization system based on multiple intelligent reflective surfaces according to claim 4 is characterized in that: The specific steps of analyzing the sensor point locations within the model and determining the signal strength of the sensor point locations are as follows: The base station transmits signals to several groups of sensors at preset locations; Collect monitoring values ​​of sensors located on the same layout line and time information of reaching the sensors in sequence, wherein the time information corresponds to the monitoring values ​​one by one; Compare the monitoring value of the sensor with the preset threshold value; If the monitoring value of the sensor is greater than or equal to the preset threshold, the location is determined to be a strong signal area; If the monitoring value of the sensor is less than the preset threshold and greater than 0, the location is determined to be a weak signal area; If the monitoring value of the sensor is equal to 0, a secondary analysis is performed.

6. The mobile charging network energy efficiency optimization system based on multiple intelligent reflective surfaces according to claim 5 is characterized in that: The specific steps of the secondary analysis are as follows: Draw a curve graph of the monitoring values ​​of the sensors located on the same layout line and the time information of reaching the sensors, with the time information of reaching the sensors as the X-axis and the monitoring values ​​of the sensors as the Y-axis; Compare and analyze several groups of curve graphs to select a set of curves that conform to the signal attenuation law; Calculate the average signal attenuation speed based on a set of curves that conform to the signal attenuation law; Filter out the points where the monitoring value is equal to 0; Based on the average speed of signal attenuation, the monitoring value of a sensor at that point on the same wiring line is analyzed. If it conforms to the regular attenuation, the signal is normally attenuated. If not, there is signal shielding interference in the area.

7. The mobile charging network energy efficiency optimization system based on multiple intelligent reflective surfaces according to claim 6 is characterized in that: The specific steps of calculating the average speed of signal attenuation based on a set of curves that conform to the signal attenuation law are as follows: Arrange the monitoring values ​​of the sensors in the curve that conforms to the signal attenuation law in order from small to large; Determine the detection level and determine the critical value of the kurtosis test based on the detection level; Calculate the kurtosis observation value of each monitoring value based on the kurtosis test formula; Determine whether the kurtosis observed value is greater than the critical value of the kurtosis test. If so, the point is determined to be an outlier. If not, the point is determined to be a non-outlier. Calculate the decay rate of several groups of non-outlier points on the same curve, and sum the decay rate values ​​of several groups; Count the number of curves in a curve set; Based on the sum of the speed values ​​and the number of curves, the average speed of signal attenuation is obtained; The calculation formula of the average speed of signal attenuation is: In the formula, is the sum of several groups of decay speed values, is the number of curves.

8. The mobile charging network energy efficiency optimization system based on multiple intelligent reflective surfaces according to claim 7 is characterized in that: The calculation formula of the kurtosis observation value of the monitoring value is: In the formula, is the observed value of kurtosis of the monitoring value; n is the number of monitoring values ​​sorted from small to large; is the average value of all monitoring values; are the monitoring values ​​before n arranged in ascending order.

9. The mobile charging network energy efficiency optimization system based on multiple intelligent reflective surfaces according to claim 8, characterized in that: The specific steps of optimizing wireless signal strength and coverage range based on signal strength and using intelligent reflective surface technology are as follows: If it is a strong signal area, no operation will be performed; If it is a weak signal area, add sensors; If it conforms to the normal attenuation law, the signal enhancement operation is performed at the position where the sensor monitoring value is 0; If the monitoring value is 0 due to signal shielding interference, the intelligent reflective surface technology will be introduced to the area.

10. The mobile charging network energy efficiency optimization system based on multiple intelligent reflective surfaces according to claim 9, characterized in that: Each reflection surface is composed of M reflection units with a side length of d, and each reflection unit can passively adjust its phase through an independent reflection coefficient.

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