A mobile charging network energy efficiency optimization system based on multiple smart reflecting surfaces

Abstract

The application discloses a kind of mobile charging network energy efficiency optimization systems based on multiple intelligent reflectors, comprising: the signal coverage area of acquisition base station is determined to lay out line of sensor;Sensor preset dot is carried out based on the signal coverage area of base station.This application can intuitively lay out sensor in coverage area using digital twin technology, for staff to understand and analyze, use base station to emit signal, analyze the signal strength of sensor dot position, and there are two cases when monitoring value is 0, one is normal signal attenuation leads to 0, signal increase operation can be carried out to the area, another is that there may be shielding interference in the area, the application can analyze the area that exists shielding interference through data intelligent, and optimize it to remove shielding, also introduces intelligent reflector technology, to improve the transmission quality and coverage range of wireless signal, while reducing the overall energy consumption of system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of mobile charging network energy efficiency optimization, in particular to a mobile charging network energy efficiency optimization system based on a multi-intelligent reflecting surface. BACKGROUND

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

[0003] In real life, most of the neural network systems are used to analyze data to obtain sensor point positions, but this method does not have intuitive nature, and cannot well let the staff understand real-time monitoring information, and when the sensor monitoring value is zero, the system lacks analysis, and cannot optimize the monitoring point, which is difficult to meet the needs of staff. SUMMARY

[0004] To solve the above technical problems, a mobile charging network energy efficiency optimization system based on a multi-intelligent reflecting surface is provided, which solves the problem that in the background art, most of the neural network systems are used to analyze data to obtain sensor point positions, but this method does not have intuitive nature, and cannot well let the staff understand real-time monitoring information, and when the sensor monitoring value is zero, the system lacks analysis, and cannot optimize the monitoring point.

[0005] To achieve the above purposes, the technical scheme adopted by the present application is as follows:

[0006] A mobile charging network energy efficiency optimization system based on a multi-intelligent reflecting surface, comprising:

[0007] An acquisition module for acquiring the transmission quality and coverage range of wireless signals in the base station, and determining the layout line of the sensor;

[0008] A preset module for setting a sensor preset point in the wireless rechargeable sensor network based on the signal coverage area of the base station, and randomly deploying it in a two-dimensional charging space;

[0009] A model construction module for constructing a model of the sensor point in the coverage area based on digital twin technology;

[0010] An analysis module for analyzing the sensor point position in the model and determining the signal strength of the sensor point position;

[0011] An optimization module for optimizing the wireless signal strength and coverage range according to the signal strength and using intelligent reflecting surface technology.

[0012] Preferably, the signal coverage area of the base station is obtained, and the sensor layout line is determined specifically as follows:

[0013] The distance between the base station and the farthest point of signal transmission is determined, denoted as ;

[0014] The area is drawn as a circle with the base station as the center and as the radius;

[0015] The circle is divided into several groups of fan-shaped areas according to the same angle;

[0016] The midpoint of the arc-shaped edge of the several groups of fan-shaped areas is marked, and the midpoint and the position point of the base station are connected, denoted as the sensor layout line.

[0017] Further, the distance between the base station and the farthest point of signal transmission is determined, denoted as In real life, due to normal signal attenuation loss, the signal value is usually 0 in advance, so the maximum is taken as the reference value, so that the area can better reflect the signal decay and help the staff analyze.

[0018] Preferably, the sensor preset layout based on the signal coverage area of the base station specifically includes the following steps:

[0019] The distance between the sensor and the farthest point of the receiving range is determined, denoted as ;

[0020] Several groups of sensors are laid out on the layout line;

[0021] The several groups of sensors are numbered, denoted as 1, 2, 3,..., n;

[0022] The positions of the several groups of sensors are away from the base station by:

[0023]

[0024] In the formula, n is the number of sensors;

[0025] The position points in the fan-shaped area that do not belong to the receiving range of the sensor are screened out, denoted as defect points;

[0026] The defect points are supplemented with sensors.

[0027] Further, the distance between the base station and the farthest point of signal transmission is determined, denoted as and The sensor can be well arranged for each sector, and since there is a gap between the two circles, the gap is a defect point, which cannot be monitored, so it needs to be added to ensure that each sector can be monitored.

[0028] Preferably, the specific steps of constructing a model of sensor distribution points in the coverage area based on digital twin technology are as follows:

[0029] The unmanned aerial vehicle is used to collect images of the signal coverage area of the base station in real time;

[0030] The software is used to construct a three-dimensional model based on the real-time collected image information;

[0031] Obtain the preset distribution point information of the plurality of groups of sensors and the three-dimensional model of the sensors;

[0032] Map the preset distribution point information of the plurality of groups of sensors to the constructed three-dimensional model one by one, and simulate and train the model to obtain the sensor distribution point model.

[0033] Preferably, the specific steps of analyzing the sensor distribution point position in the model to determine the signal strength of the sensor distribution point position are as follows:

[0034] The base station transmits signals to the plurality of groups of sensors at the preset distribution points;

[0035] Collect the monitoring values of the sensors located on the same distribution line and the time information of the sensors in sequence, and the time information corresponds to the monitoring values one by one;

[0036] Compare the monitoring values of the sensors with the preset threshold value;

[0037] If the monitoring value of the sensor is greater than or equal to the preset threshold value, it is determined that the position is a signal strong area;

[0038] If the monitoring value of the sensor is less than the preset threshold value and greater than 0, it is determined that the position is a signal weak area;

[0039] If the monitoring value of the sensor is equal to 0, secondary analysis is performed.

[0040] Preferably, the specific steps of the secondary analysis are as follows:

[0041] Draw a curve graph of the monitoring values of the sensors located on the same distribution line and the time information of the sensors in sequence, taking the time information of the sensors as the X-axis and the monitoring values of the sensors as the Y-axis;

[0042] Compare and analyze a plurality of curve graphs to screen out a curve set that meets the signal attenuation rule;

[0043] Calculate the average signal attenuation speed according to the curve set that meets the signal attenuation rule;

[0044] Screening out the point whose monitoring value is equal to 0;

[0045] Based on the average signal attenuation speed, the monitoring value of a sensor at the point on the same line is analyzed, if it conforms to the regular attenuation, the signal is normal attenuation, if it does not conform, the region exists signal shielding interference.

[0046] Preferably, the specific steps of calculating the average signal attenuation speed according to the curve set conforming to the signal attenuation rule are as follows:

[0047] Arranging the monitoring values of the sensors in the curve conforming to the signal attenuation rule in ascending order;

[0048] Determining the detection level and determining the critical value of kurtosis test according to the detection level;

[0049] Based on the kurtosis test formula, calculating the kurtosis observation value of each monitoring value;

[0050] Judging whether the kurtosis observation value is greater than the critical value of kurtosis test, if yes, determining that the point is an outlier, if no, determining that the point is a non-outlier;

[0051] Calculating the attenuation speed of the non-outlier points located on several groups of the same curve, and summing up the several groups of attenuation speed values;

[0052] Counting the number of curves in the curve set;

[0053] Based on the sum of speed values and the number of curves, obtaining the average signal attenuation speed;

[0054] The calculation formula of the average signal attenuation speed is:

[0055]

[0056] In the formula, is the sum of several groups of attenuation speed values, is the number of curves.

[0057] Preferably, the calculation formula of the kurtosis observation value of the monitoring value is:

[0058]

[0059] In the formula, is the kurtosis observation value of the monitoring value;

[0060] n is the number of monitoring values arranged in ascending order;

[0061] is the average value of all monitoring values;

[0062] For arranging the monitoring values before n in the order from small to large.

[0063] Preferably, the optimization of wireless signal strength and coverage range according to signal strength and using intelligent reflecting surface technology is specifically as follows:

[0064] If it is a signal strong area, no operation is performed;

[0065] If it is a signal weak area, additional sensors are added;

[0066] If it conforms to normal attenuation law, signal enhancement operation is performed on the position with a sensor monitoring value of 0;

[0067] If the monitoring value is 0 due to signal shielding interference, intelligent reflecting surface technology is introduced into the area.

[0068] Preferably, each reflecting surface is composed of M reflecting units with a side length of d, and each reflecting unit can passively adjust its phase through an independent reflecting coefficient.

[0069] Compared with the prior art, the present application provides a mobile charging network energy efficiency optimization system based on multiple intelligent reflecting surfaces, which has the following beneficial effects:

[0070] In the present application, when the sensors are laid out, 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 layout lines are drawn in the area. The digital twin technology is a prior art, which can intuitively lay out sensors in the coverage area for workers to understand and analyze. The signal strength of the sensor layout position is analyzed by using the signal emitted by the base station. It is worth noting that the monitoring value of the sensor may be 0. When it is 0, there are two cases. One is that the normal signal attenuation causes it to be 0, and signal increase operation can be performed on the area. The other is that there may be shielding interference in the area. The present application can intelligently analyze the area with shielding interference and optimize it to remove the shielding. The intelligent reflecting surface technology is also introduced, thereby improving the transmission quality and coverage range of the wireless signal and reducing the overall energy consumption of the system. BRIEF DESCRIPTION OF DRAWINGS

[0071] Figure 1 It is a schematic diagram of the mobile charging network energy efficiency optimization method in the present application;

[0072] Figure 2 It is a schematic diagram of the method for constructing a model of sensor layout in the coverage area based on digital twin technology in the present application;

[0073] Figure 3A schematic diagram of the method for determining the signal strength of the sensor layout position in the present application;

[0074] Figure 4 A schematic diagram of the method for performing secondary analysis in the present application;

[0075] Figure 5 A schematic diagram of the method for calculating the average speed of signal attenuation in the present application;

[0076] Figure 6 A schematic diagram of the method for optimizing wireless signal strength and coverage range using intelligent reflecting surface technology in the present application;

[0077] Figure 7 A schematic diagram of the mobile charging network energy efficiency optimization system in the present application;

[0078] Figure 8 A traditional linear charging network model in real life. DETAILED DESCRIPTION

[0079] The following description is used to disclose the present application so that those skilled in the art can implement the present application. The preferred embodiments in the following description are only as examples, and other obvious modifications can be made by those skilled in the art.

[0080] Embodiment 1

[0081] Please refer to Figures 1-7 A mobile charging network energy efficiency optimization system based on multiple intelligent reflecting surfaces, comprising:

[0082] An acquisition module for acquiring the transmission quality and coverage range of wireless signals in the base station and determining the layout of the sensor;

[0083] A preset module for setting the sensor preset layout in the wireless chargeable sensor network based on the signal coverage area of the base station and randomly deploying it in the two-dimensional charging space;

[0084] A model construction module for constructing a model of the sensor layout in the coverage area based on digital twin technology;

[0085] An analysis module for analyzing the sensor layout position in the model and determining the signal strength of the sensor layout position;

[0086] An optimization module for optimizing the wireless signal strength and coverage range according to the signal strength and using intelligent reflecting surface technology.

[0087] Persons skilled in the art can understand that when the sensor is laid out, the farthest signal transmission distance of the base station is first obtained, 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 a plurality of groups of layout lines are drawn in the area, and the digital twin technology can intuitively lay out the sensor in the coverage area for the staff to understand and analyze. The signal strength of the sensor layout position is analyzed by using the signal emitted by the base station. It is worth noting that the monitoring value of the sensor may be 0, and when it is 0, there are two cases, one is that the normal signal attenuation causes it to be 0, and the signal increasing operation can be performed on the area, and the other is that there may be shielding interference in the area. The present application can intelligently analyze the area with shielding interference and optimize it to remove the shielding. The intelligent reflecting surface technology is also introduced to improve the transmission quality and coverage range of the wireless signal and reduce the overall energy consumption of the system.

[0088] The signal coverage area of the base station is obtained, and the layout line of the sensor is determined, which specifically includes the following steps:

[0089] The distance between the base station and the farthest point of signal transmission is determined, denoted as ;

[0090] The area is drawn with the base station as the center and as the radius;

[0091] The circle is divided into a plurality of groups of fan-shaped areas according to the same angle;

[0092] The midpoint of the arc-shaped edge of the plurality of groups of fan-shaped areas is marked, and the midpoint of the arc-shaped edge and the position point of the base station are connected, denoted as the layout line of the sensor.

[0093] Persons skilled in the art can understand that the distance between the base station and the farthest point of signal transmission is determined, denoted as In real life, due to normal signal attenuation loss, the signal value may be 0 in advance, so the maximum is used as a reference value, so that the area can better reflect signal decay and help the staff to analyze.

[0094] The sensor preset layout based on the signal coverage area of the base station specifically includes the following steps:

[0095] The distance between the sensor and the farthest point of the receiving range is determined, denoted as ;

[0096] A plurality of groups of sensors are laid out on the layout line;

[0097] The plurality of groups of sensors are numbered, denoted as 1, 2, 3,..., n;

[0098] The positions of the several groups of sensors are distances from the base station:

[0099]

[0100] In the formula, n is the number of the sensor;

[0101] Screen out the position points in the sector area that do not belong to the receiving range of the sensor, and mark them as defect points;

[0102] Supplement the sensor for the defect points.

[0103] Those skilled in the art can understand that, by and , the sensor layout for each sector area can be well performed, and since there is a gap between the two circles, the gap is a defect point, which leads to the inability to monitor, so it is necessary to add it to ensure that each sector area can be monitored.

[0104] The specific steps of constructing the model of the sensor layout points in the coverage area based on the digital twin technology are as follows:

[0105] Real-time image acquisition of the signal coverage area of the base station is performed by using a UAV;

[0106] The real-time collected image information is used to construct a three-dimensional model by using software;

[0107] The preset layout information of the several groups of sensors and the three-dimensional model of the sensor are obtained;

[0108] The preset layout information of the several groups of sensors is mapped one by one into the constructed three-dimensional model, and the model is simulated and trained to obtain the model of the sensor layout points.

[0109] The specific steps of analyzing the sensor layout point positions in the model to determine the signal strength of the sensor layout point positions are as follows:

[0110] The base station transmits signals to the several groups of sensors at the preset layout points;

[0111] The monitoring values of the sensors located on the same layout line and the time information of the sensors reached in turn are collected, and the time information and the monitoring values are one-to-one corresponding;

[0112] The monitoring values of the sensors are compared with the preset threshold value;

[0113] If the monitoring value of the sensor is greater than or equal to the preset threshold value, it is determined that the position is a signal strong area;

[0114] If the monitoring value of the sensor is less than the preset threshold value and greater than 0, it is determined that the position is a signal weak area;

[0115] If the monitoring value of the sensor is equal to 0, secondary analysis is performed.

[0116] The specific steps of the secondary analysis are as follows:

[0117] The monitoring values of the sensors located on the same layout line and the time information of reaching the sensors are plotted into a curve graph, taking the time information of reaching the sensors as the X axis and the monitoring values of the sensors as the Y axis.

[0118] A comparison analysis is performed on a plurality of sets of curve graphs, and a curve set meeting the signal attenuation rule is screened out.

[0119] The signal attenuation average speed is calculated according to the curve set meeting the signal attenuation rule.

[0120] Points with a monitoring value equal to 0 are screened out.

[0121] Based on the signal attenuation average speed, the monitoring value of a sensor on the same layout line at the point is analyzed, and if it meets the regular attenuation, the signal is normal attenuation, and if it does not meet the regular attenuation, there is signal shielding interference in the region.

[0122] The specific steps of calculating the signal attenuation average speed according to the curve set meeting the signal attenuation rule are as follows:

[0123] The monitoring values of the sensors in the curve meeting the signal attenuation rule are arranged in ascending order.

[0124] The detection level is determined, and the critical value of kurtosis test is determined according to the detection level.

[0125] The kurtosis observation value of each monitoring value is calculated based on the kurtosis test formula.

[0126] It is judged whether the kurtosis observation value of the historical operation data is greater than the critical value of the kurtosis test, if yes, the point is determined as an outlier, and if no, the point is determined as a non-outlier.

[0127] The attenuation speed of the non-outlier located on a plurality of sets of the same curve is calculated, and a plurality of sets of attenuation speed values are summed up.

[0128] The number of curves in the curve set is counted.

[0129] Based on the sum of the speed values and the number of curves, the signal attenuation average speed is obtained.

[0130] The calculation formula of the signal attenuation average speed is:

[0131]

[0132] In the formula, is the sum of a plurality of sets of attenuation speed values, The number of curves.

[0133] The formula for calculating the kurtosis observation value of the monitoring value is:

[0134]

[0135] In the formula, is the kurtosis observation value of the monitoring value;

[0136] n is the number of monitoring values in ascending order;

[0137] is the average of all monitoring values;

[0138] is the monitoring value arranged in ascending order before n.

[0139] As can be understood by those skilled in the art, by removing outliers and calculating the decay rate according to non-outlier data, the influence of interference variables existing in historical operation data on the monitoring value can be effectively reduced, and the reliability of the calculation is improved.

[0140] According to the signal strength, the wireless signal strength and coverage range are optimized by using the intelligent reflecting surface technology. The specific steps are as follows:

[0141] If it is a signal strong area, no operation is performed;

[0142] If it is a signal weak area, additional sensors are added;

[0143] If it conforms to the normal attenuation law, signal enhancement operation is taken for the position where the sensor monitoring value is 0;

[0144] If the monitoring value is 0 due to signal shielding interference, intelligent reflecting surface technology is introduced to the area.

[0145] In real life, in a wireless rechargeable sensor network, a known number of sensor nodes are randomly deployed in a two-dimensional space, and a base station is placed at the center of the wireless rechargeable sensor network to provide energy supplement services for the mobile charger, and can sense the residual energy and charging request information of the sensor nodes in the system. The network model is . Among them, represents a sensor set of n sensor nodes, and the position is represented as ; denotes the set of m IRS-equipped mobile chargers; D denotes the set of Euclidean distances between any two nodes; T denotes the set of time taken by the mobile charger to charge the sensor nodes in need of charging. In the initial state of charging, the mobile chargers MC are all located in the base station BS, when the energy of the nodes in the wireless rechargeable sensor network reaches the threshold value, the mobile charger MC will start from the base station to provide charging service for the sensor nodes, and return to the base station after the charging is completed, and the network model is as shown in Figure 8 .

[0146] The present application introduces a plurality of intelligent reflecting surface technologies, each reflecting surface is composed of M reflecting units with side length d, each reflecting unit can passively adjust its phase through independent reflection coefficient, so as to improve the transmission quality and coverage range of wireless signal, and reduce the overall energy consumption of the system. In the nth time slot, the position of the charger is denoted by , the position of the IRS is denoted by , and the position of the sensor node k is denoted by .

[0147] During the energy transmission process, the channels of MC-IRS, IRS-sensor k and MC-sensor k are denoted by , 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 represented as:

[0148]

[0149] wherein denotes the channel power gain at the reference distance of 1 m, denotes that the IRS is composed of rows and columns of reflecting units, , denotes the carrier frequency, denotes the speed of light, . , denote the cosine value and the sine value of the horizontal angle of arrival of the signal at the IRS respectively, denotes the sine value of the vertical angle of arrival of the signal at the IRS.

[0150] The channel gain of the IRS equipped by the mobile charger to the kth sensor node is given by the following formula:

[0151]

[0152] wherein ,​ respectively represent the cosine and sine values of the horizontal deviation angle of the signal to the kth sensor node; represents the sine value of the vertical deviation angle of the signal to the kth sensor node;

[0153] The channel gain achieved at the kth sensor node can be represented as:

[0154]

[0155] represents the channel gain of the direct connection between the charger and the kth sensor device, represents the distance from the MC to the sensor k, represents the phase shift matrix of the IRS, where is the phase value range of each reflecting element.

[0156] Ignoring the noise power, the charging power at the kth sensor node is represented as :

[0157]

[0158] where is the efficiency of energy harvesting of the sensor node.

[0159] In summary, when the sensor is laid out, 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 layout lines are drawn in the area. The digital twin technology can intuitively lay out sensors in the coverage area for staff to understand and analyze. The signal strength of the sensor point position is analyzed by using the signal sent by the base station. It is worth noting that the monitoring value of the sensor may be 0. When it is 0, there are two cases. One is that the normal signal attenuation causes it to be 0, and signal increasing operation can be performed on the area. The other is that there may be shielding interference in the area. The present application can intelligently analyze the area with shielding interference and optimize it to remove the shielding. The intelligent reflecting surface technology is also introduced to improve the transmission quality and coverage range of the wireless signal and reduce the overall energy consumption of the system.

[0160] The basic principles, main features and advantages of the present application are shown and described above. Those skilled in the art should understand that the present application is not limited by the above examples, and the above examples and descriptions in the specification are only the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the claimed present application. The scope of protection required by the present application is defined by the appended claims and their equivalents.

Claims

1. A multi-intelligent-reflective-surface-based mobile charging network energy efficiency optimization system, characterized in that, The application relates to a wireless sensor network signal optimization method and device. The method comprises the following steps: An acquisition module is used to acquire the transmission quality and coverage range of a wireless signal in a base station, and to determine the layout line of a sensor; A preset module is used to preset the sensor layout points in a wireless rechargeable sensor network based on the signal coverage area of the base station, and to randomly deploy the sensor layout points in a two-dimensional charging space; A model construction module is used to construct a model of the sensor layout points in the coverage area based on a digital twin technology; An analysis module is used to analyze the sensor layout point positions in the model, and to determine the signal strength of the sensor layout point positions; An optimization module is used to optimize the wireless signal strength and coverage range according to the signal strength and by adopting an intelligent reflecting surface technology. The specific steps of constructing the model of the sensor layout points in the coverage area based on the digital twin technology are as follows: An unmanned aerial vehicle is used to collect images of the signal coverage area of the base station in real time; A software is used to construct a three-dimensional model based on the collected image information; The preset layout point information of a plurality of groups of sensors and the three-dimensional model of the sensors are acquired; The preset layout point information of the plurality of groups of sensors is mapped into the constructed three-dimensional model one by one, and the model is simulated and trained to acquire the model of the sensor layout points. The specific steps of analyzing the sensor layout point positions in the model and determining the signal strength of the sensor layout point positions are as follows: The base station transmits signals to the plurality of groups of sensors at the preset layout points; The monitoring values of the sensors located on the same layout line and the time information of the sensors in sequence are collected, and the time information corresponds to the monitoring values one by one; The monitoring values of the sensors are compared with a preset threshold value; If the monitoring value of the sensor is greater than or equal to the preset threshold value, the position is determined as a signal strong area; If the monitoring value of the sensor is less than the preset threshold value and greater than 0, the position is determined as a signal weak area; If the monitoring value of the sensor is equal to 0, secondary analysis is performed. The specific steps of the secondary analysis are as follows: A curve graph is drawn for the monitoring values of the sensors located on the same layout line and the time information of the sensors in sequence, taking the time information of the sensors in sequence as the X-axis and the monitoring values of the sensors as the Y-axis; A plurality of curve graphs are compared and analyzed, and a curve set meeting the signal attenuation law is screened out; The signal attenuation average speed is calculated according to the curve set meeting the signal attenuation law; Points with the monitoring value equal to 0 are screened out; Based on the signal attenuation average speed, the monitoring value of a sensor on the same layout line is analyzed, and if the signal attenuation law is met, the signal is normally attenuated, and if the signal attenuation law is not met, the area exists signal shielding interference. The specific steps of calculating the signal attenuation average speed according to the curve set meeting the signal attenuation law are as follows: The monitoring values of the sensors in the curve meeting the signal attenuation law are arranged in ascending order; A detection level is determined, and a critical value of a kurtosis test is determined according to the detection level; The kurtosis observation value of each monitoring value is calculated based on a kurtosis test formula; It is judged whether the kurtosis observation value is greater than the critical value of the kurtosis test, if yes, the point is determined as an outlier, and if no, the point is determined as a non-outlier. The attenuation speed of non-outlier points on several groups of the same curve is calculated, and several groups of attenuation speed values are summed up; The number of curves in the statistical curve set is counted; Based on the sum of the speed values and the number of curves, the average signal attenuation speed is obtained; The calculation formula of the average signal attenuation speed is: ； wherein is a sum value for a number of sets of decay rate values, is the number of curves; The calculation formula of the kurtosis observation value of the monitoring value is: ； wherein is the kurtosis observation of the monitoring value; n is the number of the monitoring value in ascending order; Average of all monitored values; to arrange the monitoring values in ascending order up to n; The specific steps of optimizing the wireless signal strength and coverage range by using the intelligent reflecting surface technology according to the signal strength are as follows: If it is a signal strong area, no operation is performed; If it is a signal weak area, additional sensors are added; If it conforms to the normal attenuation law, signal enhancement operation is performed on the position where the sensor monitoring value is 0; If the monitoring value is 0 due to signal shielding interference, the intelligent reflecting surface technology is introduced into the area; Each reflecting surface is composed of M reflecting units with a side length of d, and each reflecting unit passively adjusts its phase through an independent reflection coefficient.

2. The multi-intelligent reflecting surface-based mobile charging network energy efficiency optimization system of claim 1, wherein, The specific steps of obtaining the signal coverage area of the base station and determining the sensor layout line are as follows: determining a distance between the base station and the farthest point of signal transmission, denoted as ; Draw a circle with the base station as the center and a radius of ; The circle is divided into several groups of fan-shaped areas according to the same angle; The midpoint of the arc-shaped edge of the several groups of fan-shaped areas is marked, and the midpoint and the position point of the base station are connected, which is recorded as the sensor layout line.

3. The multi-intelligent reflecting surface-based mobile charging network energy efficiency optimization system of claim 2, wherein, The specific steps of pre-setting the sensor points based on the signal coverage area of the base station are as follows: determining a distance between the sensor and the farthest point of the reception range, denoted as ; Several groups of sensors are laid out on the layout line; The several groups of sensors are numbered, which are recorded as 1, 2, 3,..., n; The distance between the positions of the several groups of sensors and the base station is: ； In the formula, n is the number of the sensor; The position points in the fan-shaped area that do not belong to the receiving range of the sensor are screened out, which are recorded as defect points; The sensor is supplemented to the defect points.

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