Energy efficiency maximization process method and system based on wireless powered communication network

Abstract

The application discloses an energy efficiency maximization processing method, system and platform based on a wireless energy supply communication network. The method creates a wireless energy supply communication network corresponding to at least one hybrid access point and at least one Internet of Things device and containing multiple sub-models. First energy supply data corresponding to the wireless energy supply communication network is analyzed and generated according to the wireless energy supply communication network. An energy efficiency maximization processing model based on the wireless energy supply communication network is created, and second energy supply data corresponding to the first energy supply data is generated. The data transmission process in the wireless energy supply communication network is optimized in real time according to the second energy supply data. The system and platform corresponding to the method realize energy efficiency maximization based on the wireless energy supply communication network, and solve the common communication system transmission problem in the wireless energy supply communication system.

Description

Technical Field

[0001] This invention belongs to the field of Internet of Things (IoT) power supply processing technology, specifically relating to a method, system, and platform for maximizing energy efficiency based on a wireless power supply communication network. Background Technology

[0002] Currently, because IoT devices are typically small in size and cannot carry sufficient battery power, they usually operate with low power consumption and have very limited battery life.

[0003] In wireless power communication systems using the "harvest-then-transmit" wireless transmission protocol, devices first collect energy from the energy transmitter during the downlink wireless energy transmission phase, and then transmit the information wirelessly to the information receiver during the uplink wireless information transmission phase. Verification has shown that wireless power transmission technology based on the "harvest-then-transmit" protocol is highly suitable for continuously powering IoT devices. It not only saves significant manpower costs by eliminating the need for manual battery replacements for IoT devices, but also greatly benefits the continuous operation of IoT devices, ensuring the sustainable development of the IoT network. However, the energy collected by IoT devices after wireless transmission from the energy transmitter largely depends on the strength of the energy signal upon arrival. Simultaneously, the signal strength when IoT devices transmit information is also closely related to the device's lifespan. Therefore, common communication system transmission problems such as severe signal attenuation, multipath effects at the receiver, and poor channel link quality severely hinder the sustainable development of wireless power communication networks and even the entire IoT network. This refers to common communication system transmission problems in wireless power communication networks, such as severe signal attenuation, multipath effects at the receiver, and poor channel link quality.

[0004] Therefore, in view of the above-mentioned technical problems and defects, there is an urgent need to design and develop an energy efficiency maximization method, system and platform based on wireless power supply communication network. Summary of the Invention

[0005] To overcome the shortcomings and difficulties of the existing technology, the purpose of this invention is to provide a method, system and platform for maximizing energy efficiency based on a wireless power supply communication network, thereby solving common communication system transmission problems in wireless power supply communication systems.

[0006] The first objective of this invention is to provide a method for maximizing energy efficiency based on a wireless power supply communication network; the second objective of this invention is to provide a system for maximizing energy efficiency based on a wireless power supply communication network; and the third objective of this invention is to provide a platform for maximizing energy efficiency based on a wireless power supply communication network.

[0007] The first objective of this invention is achieved as follows: the method comprises the following steps:

[0008] Based on at least one hybrid access point and at least one IoT device, a corresponding wireless power supply communication network containing multiple sub-models is created; wherein, the sub-models include a network model, an energy consumption model, and an energy efficiency model;

[0009] Based on the wireless power supply communication network, first power supply data corresponding to the wireless power supply communication network is generated; wherein, the first power supply data is real-time energy consumption data and energy use efficiency data;

[0010] An energy efficiency maximization processing model based on a wireless power supply communication network is created, and corresponding second power supply data is generated by combining the first power supply data; wherein, the second power supply data is the energy efficiency maximization power supply variable data of the wireless power supply communication network.

[0011] Based on the second power supply data, the data transmission process in the wireless power supply communication network is optimized in real time.

[0012] Furthermore, the creation of a corresponding wireless power supply communication network containing multiple sub-models based on at least one hybrid access point and at least one IoT device also includes:

[0013] A wireless power supply communication network model is created, and corresponding first energy data is generated based on the wireless power supply communication network model; wherein, the first energy data is the energy received in the downlink energy transmission time slot corresponding to the Internet of Things device;

[0014] The corresponding second energy data is generated based on the wireless power supply communication network model; wherein, the second energy data is the energy consumed by the entire wireless power supply communication network during the energy downlink transmission phase;

[0015] During the information uplink transmission phase, corresponding first signal-to-noise ratio (SNR) data is generated based on the wireless power supply communication network model; wherein, the first SNR data is the SNR data of the information transmitted by the IoT device;

[0016] The wireless power supply communication network model generates corresponding third energy data and first information throughput data; wherein, the third energy data is the energy consumed by the entire wireless power supply communication network during the information uplink transmission phase; and the first information throughput data is the information throughput data corresponding to the Internet of Things device.

[0017] Furthermore, the creation of a corresponding wireless power supply communication network containing multiple sub-models based on at least one hybrid access point and at least one IoT device also includes:

[0018] Calculate the first energy data, the second energy data, the first signal-to-noise ratio data, the third energy data, and the first information throughput data respectively;

[0019] The formula for calculating the first energy data is as follows:

[0020]

[0021] in, This represents the response vector of the mobile antenna of the k-th IoT device to the multipath channel it receives during the downlink power transmission phase, where T represents the matrix transpose. Let Σ0 represent the channel response matrix from the mobile antenna of the k-th IoT device to the smart reflector during the downlink power transmission phase, and let g0 represent the channel response matrix from the smart reflector to the hybrid access point during the downlink power transmission phase.

[0022] The formula for calculating the second energy data is as follows:

[0023]

[0024] Where P represents the energy transmission power at the hybrid access point in downlink energy transmission time slot τ0, P c P represents the circuit power consumption of the hybrid access point. I E represents the circuit power consumption of a single intelligent reflective unit in an intelligent reflective surface. k The energy received by the kth IoT device in the downlink energy transmission time slot τ0;

[0025] The formula for calculating the first signal-to-noise ratio data is as follows:

[0026]

[0027] Where, σ 2 This represents the noise power at the receiving antenna of the access point; This represents the response vector of the fixed antenna at the hybrid access point to the multipath channel it receives during the uplink transmission phase of information. Φ1 represents the channel response matrix from the intelligent reflector to the hybrid access point during the uplink transmission phase; Φ1 represents the phase reflection matrix of the intelligent reflector during the uplink transmission phase; Υ k This represents the channel response matrix from the mobile antenna of the k-th IoT device to the smart reflector during the uplink transmission phase. This represents the response vector of the mobile antenna of the k-th IoT device to the multipath channel it transmits during the uplink transmission phase.

[0028] The formula for calculating the third energy data is shown below:

[0029]

[0030] Where, p k This represents the uplink transmission time slot τ of the information belonging to the k-th IoT device. k The energy transmission power of the kth IoT device; P represents the power consumption of the circuit of the k-th IoT device. c P represents the circuit power consumption of the hybrid access point; I This represents the circuit power consumption of a single intelligent reflective unit in an intelligent reflective surface.

[0031] Furthermore, the step of analyzing and generating first power supply data corresponding to the wireless power supply communication network based on the wireless power supply communication network also includes:

[0032] Based on the first energy supply data, target variable data to be optimized is generated, corresponding to the first energy supply data.

[0033] Furthermore, the step of creating an energy efficiency maximization processing model based on a wireless power supply communication network, and generating corresponding second power supply data by combining the first power supply data, further includes:

[0034] Based on the second power supply data and combined with the power supply constraints, a power supply optimization processing mode corresponding to the energy use efficiency of the optimized wireless power supply communication network model is generated.

[0035] Furthermore, the step of optimizing the data transmission process in the wireless power communication network in real time based on the second power supply data also includes:

[0036] Based on the power supply optimization processing mode, and combined with the separation algorithm, the phase reflection matrix of the smart reflector, the information transmission power of each IoT device, the time allocation of downlink power transmission time and uplink information transmission time in a transmission time slot, and the position of the mobile antenna at each IoT device are jointly optimized in real time.

[0037] The second objective of this invention is achieved as follows: the system is used to implement the energy efficiency maximization processing method based on a wireless power supply communication network; the system includes:

[0038] The first model creation unit is used to create a wireless power supply communication network with corresponding sub-models based on at least one hybrid access point and at least one Internet of Things device; wherein, the sub-models include a network model, an energy consumption model and an energy efficiency model;

[0039] The data analysis and generation unit is used to analyze and generate first energy supply data corresponding to the wireless energy supply communication network based on the wireless energy supply communication network; wherein, the first energy supply data is real-time energy consumption data and energy use efficiency data;

[0040] The second model creation unit is used to create an energy efficiency maximization processing model based on the wireless power supply communication network, and generate corresponding second power supply data by combining the first power supply data; wherein, the second power supply data is energy efficiency maximization power supply variable data of the wireless power supply communication network.

[0041] The power supply optimization processing unit is used to optimize the data transmission process in the wireless power supply communication network in real time based on the second power supply data.

[0042] Furthermore, the first model creation unit also includes:

[0043] The first data generation module is used to create a wireless power supply communication network model and generate corresponding first energy data based on the wireless power supply communication network model; wherein, the first energy data is the energy received in the downlink energy transmission time slot corresponding to the Internet of Things device;

[0044] The second data generation module is used to generate corresponding second energy data according to the wireless power supply communication network model; wherein, the second energy data is the energy consumed by the entire wireless power supply communication network during the energy downlink transmission phase;

[0045] The third data generation module is used in the information uplink transmission stage to generate corresponding first signal-to-noise ratio data according to the wireless power supply communication network model; wherein, the first signal-to-noise ratio data is the signal-to-noise ratio data of the information transmitted by the Internet of Things device;

[0046] The fourth data generation module is used to generate corresponding third energy data and first information throughput data according to the wireless power supply communication network model; wherein, the third energy data is the energy consumed by the entire wireless power supply communication network during the information uplink transmission phase; and the first information throughput data is the information throughput data corresponding to the Internet of Things device.

[0047] And / or, the data analysis and generation unit further includes:

[0048] The fifth data generation module is used to generate target variable data to be optimized based on the first energy supply data.

[0049] And / or, the second model creation unit further includes:

[0050] The sixth data generation module is used to generate an energy optimization processing mode corresponding to the energy usage efficiency of the optimized wireless power supply communication network model based on the second power supply data and in combination with the power supply constraints.

[0051] And / or, the power supply optimization processing unit further includes:

[0052] The first power supply optimization processing module is used to optimize the phase reflection matrix of the smart reflector, the information transmission power of each IoT device, the time allocation of downlink power transmission time and uplink information transmission time in a transmission time slot, and the position of the mobile antenna at each IoT device in real time according to the power supply optimization processing mode and in combination with the separation algorithm.

[0053] Furthermore, the first model creation unit also includes:

[0054] The first calculation module is used to calculate the first energy data, the second energy data, the first signal-to-noise ratio data, the third energy data, and the first information throughput data, respectively.

[0055] The formula for calculating the first energy data is as follows:

[0056]

[0057] in, This represents the response vector of the mobile antenna of the k-th IoT device to the multipath channel it receives during the downlink power transmission phase, where T represents the matrix transpose. Let Σ0 represent the channel response matrix from the mobile antenna of the k-th IoT device to the smart reflector during the downlink power transmission phase, and let g0 represent the channel response matrix from the smart reflector to the hybrid access point during the downlink power transmission phase.

[0058] The formula for calculating the second energy data is as follows:

[0059]

[0060] Where P represents the energy transmission power at the hybrid access point in downlink energy transmission time slot τ0, P cP represents the circuit power consumption of the hybrid access point. I E represents the circuit power consumption of a single intelligent reflective unit in an intelligent reflective surface. k The energy received by the kth IoT device in the downlink energy transmission time slot τ0;

[0061] The formula for calculating the first signal-to-noise ratio data is as follows:

[0062]

[0063] Where, σ 2 This represents the noise power at the receiving antenna of the access point; This represents the response vector of the fixed antenna at the hybrid access point to the multipath channel it receives during the uplink transmission phase of information. Φ1 represents the channel response matrix from the intelligent reflector to the hybrid access point during the uplink transmission phase; Φ1 represents the phase reflection matrix of the intelligent reflector during the uplink transmission phase; Υ k This represents the channel response matrix from the mobile antenna of the k-th IoT device to the smart reflector during the uplink transmission phase. This represents the response vector of the mobile antenna of the k-th IoT device to the multipath channel it transmits during the uplink transmission phase.

[0064] The formula for calculating the third energy data is shown below:

[0065]

[0066] Where, p k This represents the uplink transmission time slot τ of the information belonging to the k-th IoT device. k The energy transmission power of the kth IoT device; p k c P represents the power consumption of the circuit of the k-th IoT device. c P represents the circuit power consumption of the hybrid access point; I This represents the circuit power consumption of a single intelligent reflective unit in an intelligent reflective surface.

[0067] The third objective of this invention is achieved as follows: it includes a processor, a memory, and a control program for an energy efficiency maximization processing platform based on a wireless power supply communication network; wherein the control program for energy efficiency maximization processing platform based on a wireless power supply communication network is executed on the processor, the control program for energy efficiency maximization processing platform based on a wireless power supply communication network is stored in the memory, and the control program for energy efficiency maximization processing platform based on a wireless power supply communication network implements the energy efficiency maximization processing method based on a wireless power supply communication network.

[0068] This invention provides a method for creating a wireless power supply communication network based on at least one hybrid access point and at least one IoT device, comprising multiple sub-models. These sub-models include a network model, an energy consumption model, and an energy efficiency model. Based on the wireless power supply communication network, first power supply data corresponding to the network is generated. This first power supply data includes real-time energy consumption data and energy usage efficiency data. An energy efficiency maximization processing model based on the wireless power supply communication network is created, and combined with the first power supply data, corresponding second power supply data is generated. This second power supply data represents the energy efficiency maximization variable data for the wireless power supply communication network. Based on the second power supply data, the data transmission process in the wireless power supply communication network is optimized in real time, along with the corresponding system and platform, to achieve energy efficiency maximization based on the wireless power supply communication network, thus solving common communication system transmission problems in wireless power supply communication systems.

[0069] In other words, by deploying intelligent reflectors between the communication links of the power transmitter and IoT devices, and between the information receiver and IoT devices, signal relay is achieved, ensuring excellent communication links even under poor direct-light communication conditions. Simultaneously, the receiving antenna of each IoT device in the wireless power supply communication network has been improved into a mobile antenna, allowing the antennas of IoT devices to receive and transmit information from better positions, reducing the impact of multipath effects. Attached Figure Description

[0070] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0071] Figure 1 This is a schematic diagram of the energy efficiency maximization processing method based on a wireless power supply communication network according to the present invention.

[0072] Figure 2 This is a schematic diagram of a specific embodiment of the energy efficiency maximization processing method based on a wireless power supply communication network according to the present invention.

[0073] Figure 3 This is a schematic diagram of an energy efficiency maximization processing system architecture based on a wireless power supply communication network according to the present invention.

[0074] Figure 4 This is a schematic diagram of an energy efficiency maximization processing platform architecture based on a wireless power supply communication network according to the present invention.

[0075] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0076] To facilitate a clearer understanding of the objectives, technical solutions, and advantages of this invention, the invention will be further described below in conjunction with the accompanying drawings and specific embodiments. Those skilled in the art can easily understand other advantages and effects of this invention from the content disclosed in this specification.

[0077] This invention can also be implemented or applied through other different specific examples, and various details in this specification can also be modified and changed based on different viewpoints and applications without departing from the spirit of this invention.

[0078] It should be noted that if the embodiments of the present invention involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of the components in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.

[0079] Furthermore, if the embodiments of this invention involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Secondly, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0080] Preferably, the energy efficiency maximization method based on a wireless power supply communication network of the present invention is applied in one or more terminals or servers. The terminal is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0081] The terminal can be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal can interact with the customer via a keyboard, mouse, remote control, touchpad, or voice control device.

[0082] This invention provides a method, system, and platform for maximizing energy efficiency based on a wireless power supply communication network.

[0083] like Figure 1 The diagram shown is a flowchart of the energy efficiency maximization processing method based on a wireless power supply communication network provided in an embodiment of the present invention.

[0084] In this embodiment, the energy efficiency maximization processing method based on the wireless power supply communication network can be applied to terminals or fixed terminals with display functions. The terminals are not limited to personal computers, smartphones, tablets, desktop computers or all-in-one computers with cameras, etc.

[0085] The energy efficiency maximization method based on a wireless power supply communication network can also be applied to a hardware environment consisting of a terminal and a server connected to the terminal via a network. The network includes, but is not limited to, a wide area network (WAN), a metropolitan area network (MAN), or a local area network (LAN). The energy efficiency maximization method based on a wireless power supply communication network in this embodiment can be executed by the server, by the terminal, or by both the server and the terminal.

[0086] For example, for terminals requiring energy efficiency maximization based on wireless power communication networks, the energy efficiency maximization function based on wireless power communication networks provided by the method of this invention can be directly integrated into the terminal, or a client for implementing the method of this invention can be installed. Alternatively, the method provided by this invention can also run on servers or other devices in the form of a Software Development Kit (SDK), providing an interface for the energy efficiency maximization function based on wireless power communication networks. Terminals or other devices can then implement the energy efficiency maximization function based on wireless power communication networks through the provided interface. The invention will be further described below with reference to the accompanying drawings.

[0087] like Figures 1-2 As shown, the present invention provides a method for maximizing energy efficiency based on a wireless power supply communication network. The method includes the following steps:

[0088] S1. Based on at least one hybrid access point and at least one IoT device, create a corresponding wireless power supply communication network containing multiple sub-models; wherein, the sub-models include a network model, an energy consumption model, and an energy efficiency model;

[0089] S2. Based on the wireless power supply communication network, analyze and generate first power supply data corresponding to the wireless power supply communication network; wherein, the first power supply data is real-time energy consumption data and energy use efficiency data;

[0090] S3. Create an energy efficiency maximization processing model based on the wireless power supply communication network, and generate corresponding second power supply data by combining the first power supply data; wherein, the second power supply data is the energy efficiency maximization power supply variable data of the wireless power supply communication network.

[0091] S4. Based on the second power supply data, optimize the data transmission process in the wireless power supply communication network in real time.

[0092] The method of creating a corresponding wireless power supply communication network containing multiple sub-models based on at least one hybrid access point and at least one IoT device also includes:

[0093] S11. Create a wireless power supply communication network model, and generate corresponding first energy data based on the wireless power supply communication network model; wherein, the first energy data is the energy received in the downlink energy transmission time slot corresponding to the Internet of Things device;

[0094] S12. Generate corresponding second energy data according to the wireless power supply communication network model; wherein, the second energy data is the energy consumed by the entire wireless power supply communication network during the energy downlink transmission phase;

[0095] S13. During the information uplink transmission stage, a corresponding first signal-to-noise ratio (SNR) data is generated according to the wireless power supply communication network model; wherein, the first SNR data is the SNR data of the information transmitted by the IoT device;

[0096] S14. Generate corresponding third energy data and first information throughput data according to the wireless power supply communication network model; wherein, the third energy data is the energy consumed by the entire wireless power supply communication network during the information uplink transmission phase; and the first information throughput data is the information throughput data corresponding to the Internet of Things device.

[0097] The method of creating a corresponding wireless power supply communication network containing multiple sub-models based on at least one hybrid access point and at least one IoT device also includes:

[0098] S15. Calculate the first energy data, the second energy data, the first signal-to-noise ratio data, the third energy data, and the first information throughput data respectively;

[0099] The formula for calculating the first energy data is as follows:

[0100] E k =ηk τ0P|f(r k 0 ) T Σ k T Φ0Σ0g0| 2 (1)

[0101] Where, f(r) k 0 ) T This represents the response vector of the mobile antenna of the k-th IoT device to the multipath channel it receives during the downlink power transmission phase, where T represents the matrix transpose, and Σ k T Let Σ0 represent the channel response matrix from the mobile antenna of the k-th IoT device to the smart reflector during the downlink power transmission phase, and let g0 represent the channel response matrix from the smart reflector to the hybrid access point during the downlink power transmission phase.

[0102] The formula for calculating the second energy data is as follows:

[0103]

[0104] Where P represents the energy transmission power at the hybrid access point in downlink energy transmission time slot τ0, P c P represents the circuit power consumption of the hybrid access point. I E represents the circuit power consumption of a single intelligent reflective unit in an intelligent reflective surface. k The energy received by the kth IoT device in the downlink energy transmission time slot τ0;

[0105] The formula for calculating the first signal-to-noise ratio data is as follows:

[0106]

[0107] Where, σ 2 This represents the noise power at the receiving antenna of the access point; This represents the response vector of the fixed antenna at the hybrid access point to the multipath channel it receives during the uplink transmission phase of information. Φ1 represents the channel response matrix from the intelligent reflector to the hybrid access point during the uplink transmission phase; Φ1 represents the phase reflection matrix of the intelligent reflector during the uplink transmission phase; Υ k This represents the channel response matrix from the mobile antenna of the k-th IoT device to the smart reflector during the uplink transmission phase. This represents the response vector of the mobile antenna of the k-th IoT device to the multipath channel it transmits during the uplink transmission phase.

[0108] The formula for calculating the third energy data is shown below:

[0109]

[0110] Where, p k This represents the uplink transmission time slot τ of the information belonging to the k-th IoT device. k The energy transmission power of the kth IoT device; P represents the power consumption of the circuit of the k-th IoT device. c P represents the circuit power consumption of the hybrid access point; I This represents the circuit power consumption of a single intelligent reflective unit in an intelligent reflective surface.

[0111] The step of analyzing and generating first power supply data corresponding to the wireless power supply communication network based on the wireless power supply communication network further includes:

[0112] S21. Based on the first energy supply data, generate target variable data to be optimized, which corresponds to the first energy supply data.

[0113] The step of creating an energy efficiency maximization processing model based on a wireless power supply communication network, and generating corresponding second power supply data by combining the first power supply data, further includes:

[0114] S31. Based on the second power supply data and combined with the power supply constraints, generate a power supply optimization processing mode corresponding to the energy usage efficiency of the optimized wireless power supply communication network model.

[0115] The step of optimizing the data transmission process in the wireless power communication network in real time based on the second power supply data further includes:

[0116] S41. Based on the power supply optimization processing mode, and combined with the separation algorithm, the phase reflection matrix of the smart reflector, the information transmission power of each IoT device, the time allocation of downlink power transmission time and uplink information transmission time in a transmission time slot, and the position of the mobile antenna at each IoT device are jointly optimized in real time.

[0117] Specifically, in this embodiment of the invention, a method for maximizing energy efficiency in a wireless power supply communication system based on a mobile antenna and a smart reflector is provided. For the wireless power supply Internet of Things (IoT), a mobile antenna and a smart reflector are simultaneously deployed in the wireless power supply communication network, and a method for maximizing energy efficiency in the wireless power supply communication network is studied: The energy loss of the communication link, each device, and the energy efficiency of the entire wireless power supply communication system are modeled. An optimization problem with the energy efficiency of the wireless power supply communication system as the objective is proposed. This problem is modeled as a mathematical optimization problem, and a separation algorithm is designed to solve the established mathematical optimization problem, thereby deriving the optimal values ​​of each optimization variable in the optimization problem with the energy efficiency of the wireless power supply communication system as the objective. To achieve the objective of this invention, the invention is implemented through the following technical solution: A method for maximizing energy efficiency in a wireless power supply communication system based on a mobile antenna and a smart reflector, comprising the following three steps:

[0118] Step 1: Based on the wireless power supply communication network consisting of a hybrid access point and K IoT devices, establish a wireless power supply communication network including a network model, energy consumption model, and energy efficiency model.

[0119] Step 2: Based on the wireless power supply communication network established in Step 1, the energy efficiency maximization problem of the wireless power supply communication network was formulated, and the optimization objective function and optimization variables in the energy efficiency maximization problem were determined: the phase reflection matrix of the smart reflector, the information transmission power of each IoT device, the time allocation of downlink energy transmission time and uplink information transmission time in a transmission time slot, and the position of the mobile antenna at each IoT device.

[0120] Step 3: Based on the energy efficiency maximization problem of the wireless power supply communication network established in Step 2, an optimization method is proposed to jointly optimize several optimization variables, including the phase reflection matrix of the smart reflector, the information transmission power of each IoT device, the time allocation of downlink energy transmission time and uplink information transmission time in a transmission time slot, and the position of the mobile antenna at each IoT device. This solves the energy efficiency model maximization problem in the proposed wireless power supply communication network.

[0121] In other words, such as Figure 2 As shown, this embodiment provides a method for maximizing energy efficiency in a wireless power communication system based on a mobile antenna and a smart reflector. It includes the following steps:

[0122] Step 1: Based on the wireless power supply communication network consisting of a hybrid access point and K IoT devices, establish a wireless power supply communication network including a network model, energy consumption model, and energy efficiency model.

[0123] In step one, the wireless power supply communication network model consists of a hybrid access point, a smart reflector, and K IoT devices equipped with mobile antennas. The variable P represents the power transmission power of the hybrid access point in the downlink power transmission time slot τ0. The variable Φ0 represents the phase reflection matrix of the smart reflector during the downlink power transmission phase. and variables This represents the x and y coordinates of the mobile antenna of the k-th IoT device during the downlink power transmission phase. These two coordinates can be combined to represent... Let ηk represent the energy harvesting efficiency of the k-th IoT device. Since there are a total of K IoT devices, the value of k, which represents the IoT device number, should be in the range of 1 ≤ k ≤ K.

[0124] Therefore, the energy received by the k-th IoT device in the downlink energy transmission time slot τ0 can be expressed as:

[0125]

[0126] in This represents the response vector of the k-th IoT device's mobile antenna to the multipath channel it receives during the downlink power transmission phase. It is a vector function associated with the coordinates of the IoT device's mobile antenna, and the T in the upper right corner indicates matrix transpose. Unless otherwise specified, the T symbol in the upper right corner of the variables in this invention represents vector transpose. Let Σk represent the channel response matrix from the mobile antenna of the k-th IoT device to the smart reflector during the downlink power transmission phase. Σ0 represents the channel response matrix from the smart reflector to the hybrid access point during the downlink power transmission phase. g0 represents the response vector of the fixed antenna of the hybrid access point to the multipath channel it receives during the downlink power transmission phase.

[0127] In step one, in the wireless power supply communication network model, during the power downlink transmission phase, the energy consumed by the entire wireless power supply communication network can be expressed as:

[0128]

[0129] Wherein, variable P represents the energy transmission power at the hybrid access point in downlink energy transmission time slot τ0, P c P represents the circuit power consumption of the hybrid access point. I This represents the circuit power consumption of a single intelligent reflective unit in the intelligent reflective surface. Assuming there are N intelligent reflective units in the system, the total power consumption of the intelligent reflective surface is NP. I E kLet E be the energy received by the k-th IoT device in the downlink energy transmission time slot τ0, as shown in the formula above. Based on the above analysis, E DL This can represent the energy consumption of the wireless power supply communication system described in the energy downlink transmission phase.

[0130] In step one, in the wireless power supply communication network model, during the information uplink transmission phase, the variable p is used. k This represents the uplink transmission time slot τ of the information belonging to the k-th IoT device. k The energy transmission power of the kth IoT device, p k The relation needs to be satisfied. in Let be the power consumed by the circuit of the k-th IoT device. This formula indicates that the energy consumed by the k-th IoT device during the uplink transmission phase cannot exceed the energy it received in the previous phase. (Using variables...) and variables This represents the x and y coordinates of the mobile antenna of the k-th IoT device during the uplink transmission phase. These two coordinates can be combined to represent... Therefore, the signal-to-noise ratio formula for the information transmitted by the k-th IoT device at the hybrid access point can be expressed as:

[0131]

[0132] Where, σ 2 This represents the noise power at the receiving antenna of the access point. This represents the response vector of the fixed antenna at the hybrid access point to the multipath channel it receives during the uplink transmission phase of information. Φ represents the channel response matrix from the intelligent reflector to the hybrid access point during the uplink information transmission phase. Φ1 represents the phase reflection matrix of the intelligent reflector during the uplink information transmission phase. Υ k This represents the channel response matrix from the mobile antenna of the k-th IoT device to the smart reflector during the uplink transmission phase. This represents the response vector of the mobile antenna of the k-th IoT device to the multipath channel it transmits during the uplink transmission phase. It is a vector function associated with the coordinates of the mobile antenna of the IoT device.

[0133] In step one, in the wireless power supply communication network model, during the information uplink transmission phase, the energy consumed by the entire wireless power supply communication network can be expressed as:

[0134]

[0135] p k This represents the uplink transmission time slot τ of the information belonging to the k-th IoT device. kThe energy transmission power of the kth IoT device Let P be the power consumption of the circuit of the k-th IoT device. c P represents the circuit power consumption of the hybrid access point. I This represents the circuit power consumption of a single intelligent reflective unit in the intelligent reflective surface. Assuming there are N intelligent reflective units in the system, the total power consumption of the intelligent reflective surface is NP. I Based on the above analysis, E UL This can represent the energy consumption of the wireless power supply communication system during the information uplink transmission phase.

[0136] In step one, the information throughput of the k-th IoT device in the wireless power supply communication network model can be expressed as:

[0137]

[0138] For the energy utilization efficiency of wireless power supply communication network models, there exists an analytical formula:

[0139]

[0140] Using this formula, the energy efficiency of the wireless power supply communication network model can be obtained as follows:

[0141]

[0142] Step Two: Based on the energy utilization efficiency of the wireless power supply communication network model analyzed in Step One, a mathematical optimization problem for maximizing the energy utilization efficiency of the wireless power supply communication network model was established:

[0143]

[0144] Wherein (1) represents the energy utilization efficiency of the wireless power supply communication network model. The purpose of this invention is to optimize this objective function, that is, to maximize the energy utilization efficiency of the wireless power supply communication network model. For this objective function, there are the following optimization variables: 1) Φ0 represents the phase reflection matrix of the intelligent reflector during the downlink energy transmission stage. 2) Φ1 represents the phase reflection matrix of the intelligent reflector during the uplink information transmission stage. 3) p represents the energy transmission power of the IoT device during the uplink information transmission stage. p=[p1,p2,...,p K ] represents the set of energy transmission power of K IoT devices. 4) τ represents the energy transmission time slot τ0 during the downlink energy transmission phase and the information transmission time slot [τ1, τ2, ..., τ] of each IoT device during the uplink information transmission phase. K ]. τ=[τ0,τ1,...,τ K[τ1,τ2,...,τ] represents the energy transmission time slot τ0 and the IoT device information transmission time slot [τ1,τ2,...,τ]. K The set of ] . 5)r 0 This indicates the coordinates of the moving antenna of an IoT device during the downlink power transmission phase. This represents the set of mobile antenna coordinates for all IoT devices during the downlink power transmission phase. 6)r 1 This indicates the coordinates of the moving antenna of an IoT device during the uplink transmission phase of information. This represents the set of mobile antenna coordinates for all IoT devices during the uplink transmission phase.

[0145] For this objective function, the following constraints exist: 1) This means that the energy consumed by an IoT device during the uplink transmission phase cannot exceed the energy it received in the previous phase. Since there are K IoT devices in the described wireless power supply communication network model, the value of k is k = 1, 2, ..., K. 2): This represents the energy transmission time slot τ0 and the IoT device information transmission time slot [τ1,τ2,...,τ] during the energy downlink transmission phase. K The sum of these values ​​should equal the specified time, which is 1 second in the described wireless power supply communication network model. 3)θ i,n ∈[0,2π), n=1,2,...N; i=0,1. This indicates that the phase of each reflection element of the intelligent reflector during the downlink energy transmission phase and the uplink information transmission phase cannot exceed the range of 0 to 2π. In the wireless power supply communication network model described above, there are a total of N reflection elements in the intelligent reflector, so n takes the values ​​n=1,2,...N. When i=0, θ i,n Represents the nth reflecting unit of the smart reflector during the downlink energy transmission phase, where θ = 1. i,n This represents the nth reflecting unit of the intelligent reflective surface during the uplink information transmission phase. 4) This represents the range of values ​​for the x and y coordinates of the mobile antenna of the IoT device during the downlink energy transmission and uplink information transmission phases. λ is the signal wavelength of the transmitted signal, defined as 0.108 m in the wireless power supply communication network model. Since there are K IoT devices in the wireless power supply communication network model, k takes values ​​from k=1,2...K. When i=0... and The x and y coordinates of the mobile antenna of the k-th IoT device during the downlink energy transmission phase, when i=1 and The x and y coordinates represent the mobile antenna of the k-th IoT device during the uplink transmission phase.

[0146] Step 3: Based on the mathematical optimization problem of maximizing the energy utilization efficiency of the wireless power supply communication network model established in Step 2, the objective function is maximized using a separation algorithm. On this basis, the original optimization variables are divided into two parts: Φ0, Φ1, p, τ and r. 0 ,r 1 Then optimize them separately.

[0147] In step three, the optimization problem with optimization variables Φ0, Φ1, p, τ can be written in the following form.

[0148]

[0149] To solve this optimization problem, we first perform variable substitution and define the variables.

[0150] b k =(Σ0g0)⊙(Σ k g0), k = 1, 2...K. Here, ⊙ represents the dot product of matrices. This variable substitution is merely for convenience during solution and does not involve complex calculations. Then, variable v0 represents the column vectorized vector after extracting the diagonal elements of the phase reflection matrix of the intelligent reflector during the downlink energy transmission phase. Variable v1 represents the column vectorized vector after extracting the diagonal elements of the phase reflection matrix of the intelligent reflector during the uplink information transmission phase. Variable E... IC =(NP I +P C () represents the total energy consumed at the hybrid access point and smart reflector throughout the entire transmission time slot. (Using variables...) This represents the product of the square root of the downlink power transmission time slot τ0 and the column vectorized vector obtained by extracting the diagonal elements of the phase reflection matrix of the smart reflector during the downlink power transmission stage. (Using variables...) p represents the energy transfer efficiency of IoT device k. k With uplink information transmission time slot τ k The reciprocal of the product. Then introduce an auxiliary variable. and

[0151] In this invention, the H symbol in the upper right corner of the variable represents the conjugate transpose of the vector. Re() represents the real number calculation of a complex number. Variable w1 represents the starting point obtained in the domain of variable v1. Variable d... k Indicates in variable c k The starting point is obtained from the domain of definition, k = 1, 2, ..., K. Variable Indicates in variable The starting point is obtained from the domain of the problem. Based on these variable substitutions, the original problem can be transformed into the following optimization problem.

[0152]

[0153] According to the definition of a convex optimization problem, this is a standard convex optimization problem, which can be solved using existing convex optimization algorithms. Specifically, it can be solved using the CVX convex optimization function package in the mathematical software Matlab. The specific algorithm language is as follows:

[0154]

[0155]

[0156] Executing the above statements in the mathematical software Matlab with the CVX environment configured will yield the optimal solution to the optimization problem.

[0157] In step three, for the optimization variable r 0 ,r 1 The optimization problem can be written in the following form:

[0158]

[0159] This mathematical problem is a multimodal univariate optimization problem. It can be solved using the genetic algorithm toolbox included in the mathematical software Matlab. The specific algorithm language is as follows:

[0160] [Count,Result,BestMember]=Genetic1(12,100,@(x)(abs([exp(1i*a(k,1)*x),exp(1i*a(k,2)*x),exp(1i*a(k,3)*x)]*

[0161] E(:,k)))^4,-4*wave,4*wave,SCA_Sum,0.4,100);

[0162] [Count,Result,BestMember]=Genetic1(12,100,@(y)(abs([exp(1i*g(k,1)*y),exp(1i*g(k,2)*y),exp(1i*g(k,3)*y)]*

[0163] H(:,k)))^4,-4*wave,4*wave,Xreal_Sum,0.4,100);

[0164] By executing the above algorithm, the optimization variable r can be obtained. 0 ,r 1 The optimal value.

[0165] In step three, the optimized variables Φ0, Φ1, p, τ and r obtained after separation are... 0 ,r 1 By combining these methods, we can obtain the optimal optimization variables for the mathematical optimization problem of maximizing the energy utilization efficiency of the wireless power supply communication network model.

[0166] In summary, the information aging performance of the optimal adaptive scheduling scheme proposed in this invention is consistently superior to other transmission schemes, providing a useful approach for practical system design.

[0167] To achieve the above objectives, the present invention also provides an energy efficiency maximization processing system based on a wireless power supply communication network, such as... Figure 3 As shown, the system is used to implement the energy efficiency maximization processing method based on a wireless power supply communication network; the system specifically includes:

[0168] The first model creation unit is used to create a wireless power supply communication network with corresponding sub-models based on at least one hybrid access point and at least one Internet of Things device; wherein, the sub-models include a network model, an energy consumption model and an energy efficiency model;

[0169] The data analysis and generation unit is used to analyze and generate first energy supply data corresponding to the wireless energy supply communication network based on the wireless energy supply communication network; wherein, the first energy supply data is real-time energy consumption data and energy use efficiency data;

[0170] The second model creation unit is used to create an energy efficiency maximization processing model based on the wireless power supply communication network, and generate corresponding second power supply data by combining the first power supply data; wherein, the second power supply data is energy efficiency maximization power supply variable data of the wireless power supply communication network.

[0171] The power supply optimization processing unit is used to optimize the data transmission process in the wireless power supply communication network in real time based on the second power supply data.

[0172] The first model creation unit further includes: a first data generation module, used to create a wireless power supply communication network model and generate corresponding first energy data based on the wireless power supply communication network model; wherein, the first energy data is the energy received in the downlink power transmission time slot corresponding to the IoT device; a second data generation module, used to generate corresponding second energy data based on the wireless power supply communication network model; wherein, the second energy data is the energy consumed by the entire wireless power supply communication network during the downlink power transmission phase; and a third data generation module, used to generate corresponding first signal-to-noise ratio data based on the wireless power supply communication network model during the uplink information transmission phase; wherein, the first signal-to-noise ratio data is the signal-to-noise ratio data of the information transmitted from the IoT device.

[0173] The fourth data generation module is used to generate corresponding third energy data and first information throughput data according to the wireless power supply communication network model; wherein, the third energy data is the energy consumed by the entire wireless power supply communication network during the information uplink transmission phase; and the first information throughput data is the information throughput data corresponding to the Internet of Things device.

[0174] And / or, the data analysis and generation unit further includes: a fifth data generation module, used to generate target variable data to be optimized based on the first energy supply data;

[0175] And / or, the second model creation unit further includes: a sixth data generation module, used to generate an energy supply optimization processing mode corresponding to the energy use efficiency of the optimized wireless power supply communication network model based on the second power supply data and in combination with the power supply constraints;

[0176] And / or, the power supply optimization processing unit further includes: a first power supply optimization processing module, used to jointly optimize the phase reflection matrix of the smart reflector, the information transmission power of each IoT device, the time allocation of downlink energy transmission time and uplink information transmission time in a transmission time slot, and the position of the mobile antenna at each IoT device in real time according to the power supply optimization processing mode and in combination with the separation algorithm.

[0177] The first model creation unit further includes: a first calculation module, used to calculate the first energy data, the second energy data, the first signal-to-noise ratio data, the third energy data, and the first information throughput data respectively;

[0178] The formula for calculating the first energy data is as follows:

[0179]

[0180] in, This represents the response vector of the mobile antenna of the k-th IoT device to the multipath channel it receives during the downlink power transmission phase, where T represents the matrix transpose. Let Σ0 represent the channel response matrix from the mobile antenna of the k-th IoT device to the smart reflector during the downlink power transmission phase, and let g0 represent the channel response matrix from the smart reflector to the hybrid access point during the downlink power transmission phase.

[0181] The formula for calculating the second energy data is as follows:

[0182]

[0183] Where P represents the energy transmission power at the hybrid access point in downlink energy transmission time slot τ0, P c P represents the circuit power consumption of the hybrid access point. I E represents the circuit power consumption of a single intelligent reflective unit in an intelligent reflective surface. k The energy received by the kth IoT device in the downlink energy transmission time slot τ0;

[0184] The formula for calculating the first signal-to-noise ratio data is as follows:

[0185]

[0186] Where, σ 2 This represents the noise power at the receiving antenna of the access point; This represents the response vector of the fixed antenna at the hybrid access point to the multipath channel it receives during the uplink transmission phase of information. Φ1 represents the channel response matrix from the intelligent reflector to the hybrid access point during the uplink transmission phase; Φ1 represents the phase reflection matrix of the intelligent reflector during the uplink transmission phase; Υ k This represents the channel response matrix from the mobile antenna of the k-th IoT device to the smart reflector during the uplink transmission phase. This represents the response vector of the mobile antenna of the k-th IoT device to the multipath channel it transmits during the uplink transmission phase.

[0187] The formula for calculating the third energy data is shown below:

[0188]

[0189] Where, p k This represents the uplink transmission time slot τ of the information belonging to the k-th IoT device. k The energy transmission power of the kth IoT device; P represents the power consumption of the circuit of the k-th IoT device.c P represents the circuit power consumption of the hybrid access point; I This represents the circuit power consumption of a single intelligent reflective unit in an intelligent reflective surface.

[0190] In the system solution embodiment of the present invention, the specific details of the method steps involved in the energy efficiency maximization processing based on the wireless power supply communication network have been described above. That is to say, the functional modules in the system are used to implement the steps or sub-steps in the above method embodiment, and will not be repeated here.

[0191] To achieve the above objectives, the present invention also provides an energy efficiency maximization processing platform based on a wireless power supply communication network, such as... Figure 4 As shown, it includes a processor, a memory, and a control program for an energy efficiency maximization processing platform based on a wireless power supply communication network. The processor executes the control program, which is stored in the memory. This control program implements the steps of the energy efficiency maximization processing method based on the wireless power supply communication network. For example:

[0192] S1. Based on at least one hybrid access point and at least one IoT device, create a corresponding wireless power supply communication network containing multiple sub-models; wherein the sub-models include a network model, an energy consumption model, and an energy efficiency model; S2. Based on the wireless power supply communication network, analyze and generate first power supply data corresponding to the wireless power supply communication network; wherein the first power supply data is real-time energy consumption data and energy usage efficiency data; S3. Create an energy efficiency maximization processing model based on the wireless power supply communication network, and combine it with the first power supply data to generate corresponding second power supply data; wherein the second power supply data is energy efficiency maximization power supply variable data of the wireless power supply communication network; S4. Based on the second power supply data, optimize the data transmission process in the wireless power supply communication network in real time.

[0193] The specific details of the steps have been explained above and will not be repeated here.

[0194] In this embodiment of the invention, the built-in processor of the energy efficiency maximization processing platform based on the wireless power supply communication network can be composed of integrated circuits. For example, it can be composed of a single packaged integrated circuit, or it can be composed of multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor connects to various components using various interfaces and lines, and executes programs or units stored in the memory, and calls data stored in the memory, to perform various functions and process data for energy efficiency maximization based on the wireless power supply communication network.

[0195] The memory is used to store program code and various data. It is installed in the energy efficiency maximization processing platform based on the wireless power supply communication network and enables high-speed and automatic access to programs or data during operation.

[0196] The memory includes read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0197] This invention provides a method for creating a wireless power supply communication network based on at least one hybrid access point and at least one IoT device, comprising multiple sub-models. These sub-models include a network model, an energy consumption model, and an energy efficiency model. Based on the wireless power supply communication network, first power supply data corresponding to the network is generated. This first power supply data includes real-time energy consumption data and energy usage efficiency data. An energy efficiency maximization processing model based on the wireless power supply communication network is created, and combined with the first power supply data, corresponding second power supply data is generated. This second power supply data represents the energy efficiency maximization variable data for the wireless power supply communication network. Based on the second power supply data, the data transmission process in the wireless power supply communication network is optimized in real time, along with the corresponding system and platform, to achieve energy efficiency maximization based on the wireless power supply communication network, thus solving common communication system transmission problems in wireless power supply communication systems.

[0198] In other words, by deploying intelligent reflectors between the communication links of the power transmitter and IoT devices, and between the information receiver and IoT devices, signal relay is achieved, ensuring excellent communication links even under poor direct-light communication conditions. Simultaneously, the receiving antenna of each IoT device in the wireless power supply communication network has been improved into a mobile antenna, allowing the antennas of IoT devices to receive and transmit information from better positions, reducing the impact of multipath effects.

[0199] In other words, this invention proposes a method for maximizing energy efficiency in a wireless power supply communication system based on mobile antennas and smart reflectors. Targeting the wireless power supply Internet of Things (IoT), mobile antennas and smart reflectors are simultaneously deployed in the wireless power supply communication network, and a method for maximizing energy efficiency in the wireless power supply communication network is studied. This involves modeling the communication links in the wireless power supply communication system, the energy losses of each device, and the energy efficiency of the entire wireless power supply communication system. An optimization problem with the energy efficiency of the wireless power supply communication system as the objective is proposed. This problem is modeled as a mathematical optimization problem, and a separation algorithm is designed to solve the established mathematical optimization problem, thereby deriving the optimal values ​​of each optimization variable in the optimization problem with the energy efficiency of the wireless power supply communication system as the objective.

[0200] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A method for maximizing energy efficiency based on a wireless power supply communication network, characterized in that, The method includes the following steps: Based on at least one hybrid access point and at least one IoT device, a corresponding wireless power supply communication network containing multiple sub-models is created; wherein, the sub-models include a network model, an energy consumption model, and an energy efficiency model; Based on the wireless power supply communication network, first power supply data corresponding to the wireless power supply communication network is generated; wherein, the first power supply data is real-time energy consumption data and energy use efficiency data; An energy efficiency maximization processing model based on a wireless power supply communication network is created, and corresponding second power supply data is generated by combining the first power supply data; wherein, the second power supply data is the energy efficiency maximization power supply variable data of the wireless power supply communication network. Based on the second power supply data, the data transmission process in the wireless power supply communication network is optimized in real time.

2. The energy efficiency maximization processing method based on a wireless power supply communication network according to claim 1, characterized in that, The method of creating a corresponding wireless power supply communication network containing multiple sub-models based on at least one hybrid access point and at least one IoT device also includes: A wireless power supply communication network model is created, and corresponding first energy data is generated based on the wireless power supply communication network model; wherein, the first energy data is the energy received in the downlink energy transmission time slot corresponding to the Internet of Things device; The corresponding second energy data is generated based on the wireless power supply communication network model; wherein, the second energy data is the energy consumed by the entire wireless power supply communication network during the energy downlink transmission phase; During the information uplink transmission phase, a corresponding first signal-to-noise ratio (SNR) data is generated based on the wireless power supply communication network model; wherein, the first SNR data is the SNR data of the information transmitted by the IoT device; The wireless power supply communication network model generates corresponding third energy data and first information throughput data; wherein, the third energy data is the energy consumed by the entire wireless power supply communication network during the information uplink transmission phase; and the first information throughput data is the information throughput data corresponding to the Internet of Things device.

3. A method for maximizing energy efficiency based on a wireless power supply communication network according to claim 1 or 2, characterized in that, The method of creating a corresponding wireless power supply communication network containing multiple sub-models based on at least one hybrid access point and at least one IoT device also includes: Calculate the first energy data, the second energy data, the first signal-to-noise ratio data, the third energy data, and the first information throughput data respectively; The formula for calculating the first energy data is as follows: in, This represents the response vector of the mobile antenna of the k-th IoT device to the multipath channel it receives during the downlink power transmission phase, where T represents the matrix transpose. Let Σ0 represent the channel response matrix from the mobile antenna of the k-th IoT device to the smart reflector during the downlink power transmission phase, and let g0 represent the channel response matrix from the smart reflector to the hybrid access point during the downlink power transmission phase. The formula for calculating the second energy data is as follows: Where P represents the energy transmission power at the hybrid access point in downlink energy transmission time slot τ0, P c P represents the circuit power consumption of the hybrid access point. I E represents the circuit power consumption of a single intelligent reflective unit in an intelligent reflective surface. k The energy received by the k-th IoT device in downlink energy transmission time slot τ0; The formula for calculating the first signal-to-noise ratio data is as follows: Where, σ 2 This represents the noise power at the receiving antenna of the access point; This represents the response vector of the fixed antenna at the hybrid access point to the multipath channel it receives during the uplink transmission phase of information. Φ1 represents the channel response matrix from the intelligent reflector to the hybrid access point during the uplink transmission phase; Φ1 represents the phase reflection matrix of the intelligent reflector during the uplink transmission phase; Υ k This represents the channel response matrix from the mobile antenna of the k-th IoT device to the smart reflector during the uplink transmission phase. This represents the response vector of the mobile antenna of the k-th IoT device to the multipath channel it transmits during the uplink transmission phase. The formula for calculating the third energy data is shown below: Where, p k This represents the uplink transmission time slot τ of the information belonging to the k-th IoT device. k The energy transmission power of the kth IoT device; P represents the power consumption of the circuit of the k-th IoT device. c P represents the circuit power consumption of the hybrid access point; I This represents the circuit power consumption of a single intelligent reflective unit in an intelligent reflective surface.

4. The energy efficiency maximization processing method based on a wireless power supply communication network according to claim 1, characterized in that, The step of analyzing and generating first power supply data corresponding to the wireless power supply communication network based on the wireless power supply communication network further includes: Based on the first energy supply data, target variable data to be optimized is generated, corresponding to the first energy supply data.

5. The energy efficiency maximization processing method based on a wireless power supply communication network according to claim 1, characterized in that, The step of creating an energy efficiency maximization processing model based on a wireless power supply communication network, and generating corresponding second power supply data by combining the first power supply data, further includes: Based on the second power supply data and combined with the power supply constraints, a power supply optimization processing mode corresponding to the energy use efficiency of the optimized wireless power supply communication network model is generated.

6. The energy efficiency maximization processing method based on a wireless power supply communication network according to claim 1, characterized in that, The step of optimizing the data transmission process in the wireless power communication network in real time based on the second power supply data further includes: Based on the power supply optimization processing mode, and combined with the separation algorithm, the phase reflection matrix of the smart reflector, the information transmission power of each IoT device, the time allocation of downlink power transmission time and uplink information transmission time in a transmission time slot, and the position of the mobile antenna at each IoT device are jointly optimized in real time.

7. An energy efficiency maximization processing system based on a wireless power supply communication network, characterized in that, The system is used to implement the energy efficiency maximization processing method based on a wireless power supply communication network as described in any one of claims 1 to 6; the system includes: The first model creation unit is used to create a wireless power supply communication network with corresponding sub-models based on at least one hybrid access point and at least one Internet of Things device; wherein, the sub-models include a network model, an energy consumption model and an energy efficiency model; The data analysis and generation unit is used to analyze and generate first energy supply data corresponding to the wireless energy supply communication network based on the wireless energy supply communication network; wherein, the first energy supply data is real-time energy consumption data and energy use efficiency data; The second model creation unit is used to create an energy efficiency maximization processing model based on the wireless power supply communication network, and generate corresponding second power supply data by combining the first power supply data; wherein, the second power supply data is energy efficiency maximization power supply variable data of the wireless power supply communication network. The power supply optimization processing unit is used to optimize the data transmission process in the wireless power supply communication network in real time based on the second power supply data.

8. The energy efficiency maximization processing system based on a wireless power supply communication network according to claim 7, characterized in that, The first model creation unit further includes: The first data generation module is used to create a wireless power supply communication network model and generate corresponding first energy data based on the wireless power supply communication network model; wherein, the first energy data is the energy received in the downlink energy transmission time slot corresponding to the Internet of Things device; The second data generation module is used to generate corresponding second energy data according to the wireless power supply communication network model; wherein, the second energy data is the energy consumed by the entire wireless power supply communication network during the energy downlink transmission phase; The third data generation module is used in the information uplink transmission stage to generate corresponding first signal-to-noise ratio data according to the wireless power supply communication network model; wherein, the first signal-to-noise ratio data is the signal-to-noise ratio data of the information transmitted by the Internet of Things device; The fourth data generation module is used to generate corresponding third energy data and first information throughput data according to the wireless power supply communication network model; wherein, the third energy data is the energy consumed by the entire wireless power supply communication network during the information uplink transmission phase; and the first information throughput data is the information throughput data corresponding to the Internet of Things device. And / or, the data analysis and generation unit further includes: The fifth data generation module is used to generate target variable data to be optimized based on the first energy supply data. And / or, the second model creation unit further includes: The sixth data generation module is used to generate an energy optimization processing mode corresponding to the energy usage efficiency of the optimized wireless power supply communication network model based on the second power supply data and in combination with the power supply constraints. And / or, the power supply optimization processing unit further includes: The first power supply optimization processing module is used to optimize the phase reflection matrix of the smart reflector, the information transmission power of each IoT device, the time allocation of downlink power transmission time and uplink information transmission time in a transmission time slot, and the position of the mobile antenna at each IoT device in real time according to the power supply optimization processing mode and in combination with the separation algorithm.

9. An energy efficiency maximization processing system based on a wireless power supply communication network according to claim 7 or 8, characterized in that, The first model creation unit further includes: The first calculation module is used to calculate the first energy data, the second energy data, the first signal-to-noise ratio data, the third energy data, and the first information throughput data, respectively. The formula for calculating the first energy data is as follows: in, This represents the response vector of the mobile antenna of the k-th IoT device to the multipath channel it receives during the downlink power transmission phase, where T represents the matrix transpose. Let Σ0 represent the channel response matrix from the mobile antenna of the k-th IoT device to the smart reflector during the downlink power transmission phase, and let g0 represent the channel response matrix from the smart reflector to the hybrid access point during the downlink power transmission phase. The formula for calculating the second energy data is as follows: Where P represents the energy transmission power at the hybrid access point in downlink energy transmission time slot τ0, P c P represents the circuit power consumption of the hybrid access point. I E represents the circuit power consumption of a single intelligent reflective unit in an intelligent reflective surface. k The energy received by the kth IoT device in the downlink energy transmission time slot τ0; The formula for calculating the first signal-to-noise ratio data is as follows: Where, σ 2 This represents the noise power at the receiving antenna of the access point; This represents the response vector of the fixed antenna at the hybrid access point to the multipath channel it receives during the uplink transmission phase of information. Φ1 represents the channel response matrix from the intelligent reflector to the hybrid access point during the uplink transmission phase; Φ1 represents the phase reflection matrix of the intelligent reflector during the uplink transmission phase; Υ k This represents the channel response matrix from the mobile antenna of the k-th IoT device to the smart reflector during the uplink transmission phase. This represents the response vector of the mobile antenna of the k-th IoT device to the multipath channel it transmits during the uplink transmission phase. The formula for calculating the third energy data is shown below: Where, p k This represents the uplink transmission time slot τ of the information belonging to the k-th IoT device. k The energy transmission power of the kth IoT device; P represents the power consumption of the circuit of the k-th IoT device. c P represents the circuit power consumption of the hybrid access point; I This represents the circuit power consumption of a single intelligent reflective unit in an intelligent reflective surface.

10. An energy efficiency maximization processing platform based on a wireless power supply communication network, characterized in that, The system includes a processor, a memory, and a control program for an energy efficiency maximization processing platform based on a wireless power supply communication network. The processor executes the control program, which is stored in the memory. The control program implements the energy efficiency maximization processing method based on a wireless power supply communication network as described in any one of claims 1 to 6.

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