Multi-base station cooperative wireless power supply energy efficiency evaluation optimization method and system, and electronic device

Abstract

The present application belongs to the field of transmission technology, and discloses a multi-base station cooperative wireless energy supply energy efficiency evaluation optimization method and system and an electronic device. The present application constructs a passive Internet of Things system model of multi-wireless energy supply base station cooperative energy supply passive energy device; according to the passive Internet of Things system model, the statistical average energy collection efficiency of the passive Internet of Things system is defined, and the statistical average energy collection efficiency is taken as an evaluation index of the energy efficiency of multi-base station cooperative wireless energy supply; an optimization problem of maximizing the statistical average energy collection efficiency is constructed; the optimization problem is solved to obtain power distribution information of the multi-wireless energy supply base station corresponding to the maximization of the statistical average energy collection efficiency; and the multi-base station cooperative wireless energy supply is configured and optimized according to the power distribution information. The present application can meet the demand of multi-base station energy supply for long-term reliability of energy efficiency in practical application.

Description

Technical Field

[0001] This invention belongs to the field of transmission technology, and more specifically, relates to a method and system for evaluating and optimizing the energy efficiency of multi-base station collaborative wireless power supply, as well as electronic equipment. Background Technology

[0002] Wireless Power Transfer (WPT) technology transmits energy from a transmitter to a receiver via radio frequency (RF) signals, enabling long-distance wireless power supply. It has wide applications in industrial sensing, smart homes, and wearable devices. Compared to traditional battery-powered communication methods, WPT significantly extends device lifespan and reduces manual maintenance costs, making it of significant application value.

[0003] Existing research largely focuses on single-base station power supply scenarios, where a single WPT transmitter transmits energy to energy-constrained nodes. Although this approach is simple in structure and easy to implement, the energy harvesting efficiency (EHEfficiency) under single-base station power supply is typically low due to factors such as channel fading, limited transmission directionality, and energy path loss, thus limiting the overall system performance.

[0004] To further improve energy harvesting performance, multi-base station collaborative wireless power supply has become an effective strategy. In a multi-base station WPT system, multiple independent transmitters can simultaneously send radio frequency energy to the same energy receiving node. Since multiple radio frequency signals are superimposed at the receiver to form a signal synthesis gain, and there are amplitude superposition and phase combination relationships between the signals, the effective received signal power can be effectively increased, thus significantly improving energy harvesting efficiency. Furthermore, multi-base station collaboration can also enhance power supply coverage spatially, alleviating the path loss problem in traditional single-source power supply scenarios.

[0005] However, due to factors such as channel randomness and the uncertainty of multiple source superposition involved in multi-base station power supply, the instantaneous energy harvesting performance of the system fluctuates significantly with changes in channel state. Directly optimizing based on a single channel implementation may lead to a lack of robustness in system performance, making it difficult to meet the long-term reliability requirements for energy efficiency in practical applications.

[0006] How to accurately reflect the overall performance of multi-source wireless power supply systems in actual operating environments, improve energy transmission efficiency, and thus meet the long-term reliability requirements of energy efficiency for multi-base station power supply in practical applications is a topic of concern and needs to be addressed in this field. Summary of the Invention

[0007] This invention provides a method, system, and electronic device for evaluating and optimizing the energy efficiency of multi-base station collaborative wireless power supply, thereby solving the problem that existing technologies cannot meet the long-term reliability requirements of energy efficiency for multi-base station power supply in practical applications.

[0008] In a first aspect, the present invention provides a method for evaluating and optimizing the energy efficiency of multi-base station collaborative wireless power supply, comprising the following steps:

[0009] Construct a passive Internet of Things (IoT) system model that coordinates the power supply of passive energy devices by multiple wireless power base stations;

[0010] Based on the passive IoT system model, the statistical average energy harvesting efficiency of the passive IoT system is defined, and the statistical average energy harvesting efficiency is used as an evaluation index for the energy efficiency of multi-base station collaborative wireless power supply.

[0011] An optimization problem is constructed to maximize the statistical average energy harvesting efficiency; the optimization problem is solved to obtain the power allocation information of multiple wireless power base stations corresponding to maximizing the statistical average energy harvesting efficiency;

[0012] The configuration of multi-base station collaborative wireless power supply is optimized based on the power allocation information.

[0013] Preferably, the passive IoT system model includes one passive energy device and N wireless power base stations; the N wireless power base stations simultaneously broadcast radio frequency signals; the passive energy device is equipped with an energy harvesting module; the energy harvesting module is used to convert the received radio frequency signals into DC signals and power itself.

[0014] Preferably, the statistical average energy harvesting efficiency is expressed as follows:

[0015]

[0016] in, , ;

[0017] In the formula, Indicates that the input is Statistical average energy harvesting efficiency at that time. Indicates the first The transmit power of each wireless powered base station, where N represents the number of wireless powered base stations included in the passive IoT system model. Let represent the set of transmit power of N wireless power base stations. Indicates that the input is The DC charging power output by the energy harvesting module. Represents the channel path gain vector The joint probability density function, Indicates the first Channel path gain from a wireless power base station to a passive power device; Let N be the set of channel path gains, denoted as the channel path gain vector.

[0018] Preferably, the optimization problem is expressed as follows:

[0019]

[0020] In the formula, This represents the total transmit power budget for N wireless power base stations. This indicates the maximum transmit power limit for a single wireless power base station.

[0021] Preferably, the optimization problem is a non-convex optimization problem. The non-convex optimization problem is transformed into a locally convex optimization problem, and the locally convex optimization problem is solved to obtain the power allocation information.

[0022] Preferably, the local convex optimization problem is expressed as follows:

[0023]

[0024] in, , ;

[0025] In the formula, Indicates that the input is Statistical average energy harvesting efficiency at that time. This represents the statistical average energy harvesting efficiency when the logarithmic function is used. Indicates the first The reciprocal of the transmit power of a wireless power base station. This represents the set of reciprocals of the transmit power of N wireless power base stations; Indicates the number of iterations; Indicates the first In the nth continuous convex approximation iteration, regarding the th Approximate parameters of a wireless power base station, denoted as the first parameter; Indicates the first The bias term in the successive convex approximation iteration is denoted as the second parameter.

[0026] Preferably, the first parameter and the second parameter are represented as follows:

[0027]

[0028]

[0029] in, ;

[0030] In the formula, Indicates that the input is The DC charging power output by the energy harvesting module; Indicates the first In the nth continuous convex approximation iteration, the nth The value of the reciprocal of the transmit power of a wireless power base station; Indicates the first In the successive convex approximation iterations, the set of values ​​for the reciprocals of the transmit power of N wireless power base stations.

[0031] Preferably, solving the local convex optimization problem includes the following sub-steps:

[0032] S1. Determine the number N of wireless power base stations and the channel path gain vector. ;

[0033] S2. Initialize the transmit power of N wireless power base stations, and initialize the number of iterations. ;

[0034] S3, at local points calculate and ;

[0035] S4, at local points Solve the local convex optimization problem to obtain a local optimum. And the local optimal statistical average energy harvesting efficiency of the logarithmic function. The locally optimal statistical average energy harvesting efficiency is obtained through exponential transformation. ;in, , Indicates the first local optimal solution The reciprocal of the transmit power of a wireless power base station;

[0036] S5. If the result of this optimization iteration is... The result is less than the judgment threshold compared to the previous iteration. Then stop iterating and... This serves as the power allocation information for multiple wireless power base stations, corresponding to maximizing the statistical average energy harvesting efficiency; otherwise, let Return to step S3 and continue with a new round of iterative optimization.

[0037] Secondly, the present invention provides a multi-base station collaborative wireless power supply energy efficiency evaluation and optimization system, comprising:

[0038] The model building unit is used to build a passive Internet of Things system model that uses multiple wireless power base stations to collaboratively power passive energy devices.

[0039] The evaluation index determination unit is used to define the statistical average energy harvesting efficiency of the passive Internet of Things system according to the passive Internet of Things system model, and to use the statistical average energy harvesting efficiency as the evaluation index of the energy efficiency of multi-base station collaborative wireless power supply.

[0040] A power allocation optimization unit is used to construct an optimization problem that maximizes the statistical average energy harvesting efficiency, and to solve the optimization problem to obtain power allocation information of multiple wireless power supply base stations corresponding to maximizing the statistical average energy harvesting efficiency.

[0041] A configuration optimization unit is used to optimize the configuration of multi-base station collaborative wireless power supply based on the power allocation information;

[0042] The multi-base station collaborative wireless power supply efficiency evaluation and optimization system is used to perform the steps in the multi-base station collaborative wireless power supply efficiency evaluation and optimization method provided in the first aspect of the present invention.

[0043] Thirdly, the present invention provides an electronic device, including the multi-base station collaborative wireless power supply energy efficiency evaluation and optimization system as described in the second aspect of the present invention.

[0044] One or more technical solutions provided in this invention have at least the following technical effects or advantages:

[0045] This invention constructs a passive IoT system model for collaborative power supply of passive energy devices by multiple wireless power base stations. Based on this model, it defines the statistical average energy harvesting efficiency (SAP) of the passive IoT system and uses it as an evaluation index for the energy efficiency of collaborative wireless power supply by multiple base stations. It then constructs an optimization problem to maximize the SAP. Solving this problem yields power allocation information for the multiple wireless power base stations corresponding to maximizing the SAP. Based on this power allocation information, the collaborative wireless power supply configuration is optimized. To accurately reflect the overall performance of the multi-source wireless power supply system in a real-world operating environment, this invention introduces SAP as an evaluation index and maximizes it as the optimization objective. By calculating the expected energy harvesting performance under channel distribution, it can more comprehensively evaluate the system's power supply capability, thereby guiding the design of power allocation and resource management strategies. This invention can improve energy transmission efficiency (i.e., maximizing the statistical average wireless power transmission per unit power through reasonable transmission power allocation), meet the long-term reliability requirements of multi-base station power supply in practical applications, and enhance the system's energy efficiency and robustness during long-term operation. Attached Figure Description

[0046] Figure 1 This is an overall flowchart of a multi-base station collaborative wireless power supply energy efficiency evaluation and optimization method provided in Embodiment 1 of the present invention;

[0047] Figure 2 This is a schematic diagram of the passive Internet of Things system model constructed in the multi-base station collaborative wireless power supply energy efficiency evaluation and optimization method provided in Embodiment 1 of the present invention. Detailed Implementation

[0048] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0049] Example 1:

[0050] Example 1 provides a method for evaluating and optimizing the energy efficiency of multi-base station collaborative wireless power supply. See [link to example]. Figure 1 This includes the following steps:

[0051] Construct a passive Internet of Things (IoT) system model that coordinates the power supply of passive energy devices by multiple wireless power base stations;

[0052] Based on the passive IoT system model, the statistical average energy harvesting efficiency of the passive IoT system is defined, and the statistical average energy harvesting efficiency is used as an evaluation index for the energy efficiency of multi-base station collaborative wireless power supply.

[0053] An optimization problem is constructed to maximize the statistical average energy harvesting efficiency; the optimization problem is solved to obtain the power allocation information of multiple wireless power base stations corresponding to maximizing the statistical average energy harvesting efficiency;

[0054] The configuration of multi-base station collaborative wireless power supply is optimized based on the power allocation information.

[0055] Among them, see Figure 2 The passive IoT system model includes a passive energy device (i.e., an energy receiving node, such as a passive sensor) and N wireless power base stations; the N wireless power base stations simultaneously broadcast radio frequency signals; the passive energy device is equipped with an energy harvesting module; the energy harvesting module is used to convert the received radio frequency signals into DC signals and power itself.

[0056] Specifically, the statistical average energy harvesting efficiency is expressed as follows:

[0057]

[0058] in, , ;

[0059] In the formula, Indicates that the input is Statistical average energy harvesting efficiency at that time. Indicates the first The transmit power of each wireless powered base station, where N represents the number of wireless powered base stations included in the passive IoT system model. Let represent the set of transmit power of N wireless power base stations. Indicates that the input is The DC charging power output by the energy harvesting module. Represents the channel path gain vector The joint probability density function, Indicates the first Channel path gain from a wireless power base station to a passive power device; Let N be the set of channel path gains (i.e., the random variables representing each independent channel path gain), denoted as the channel path gain vector.

[0060] The statistical average energy harvesting efficiency is explained in detail below.

[0061] This invention defines the statistical average energy harvesting efficiency of a passive IoT system based on the aforementioned passive IoT system model, and uses this statistical average energy harvesting efficiency as an evaluation index for the energy efficiency of multi-base station collaborative wireless power supply. Specifically, this invention defines a system statistical average energy harvesting efficiency index based on a multi-base station collaborative wireless power supply architecture and a nonlinear energy harvesting model. Specifically, it includes:

[0062] (1) During wireless power transmission, the energy harvesting power of the passive energy device is:

[0063]

[0064] In the formula, This indicates the charging power of passive energy devices.

[0065] in, Defined as:

[0066]

[0067] In the formula, constant Defined as , This represents the load impedance, and n represents the ideality factor. Indicates the thermistor voltage; The Lambert W function is... The inverse function; Indicates the reverse bias saturation current; It is denoted as the third parameter.

[0068] in, Defined as:

[0069]

[0070] In the formula, Represents the cutoff constant; Indicates the first The parameters in the power exponent of each wireless power base station are positive integers not greater than the truncation constant. The order of the combination term is a positive integer not greater than the cutoff constant. For any satisfying and for A sequence of non-negative integers, i.e., satisfying sequence; Let it be the fourth parameter. It is denoted as the fifth parameter.

[0071] For example, when the number of wireless power base stations is 2 and the order of the combination term is 2, that is... Possible combinations include: .

[0072] in, , Let it be the sixth parameter. , This represents the matched antenna impedance. The mathematical symbol ! represents factorial.

[0073] in, Defined as:

[0074]

[0075] In the formula, Indicates the first In a nonlinear energy harvesting (EH) model, the order of a radio frequency (RF) signal channel is... The waveform coefficient of the unit power at that time.

[0076] (2) The statistical average energy harvesting efficiency index of the system is defined as the energy harvesting power of the passive energy devices divided by the total transmit power of the base stations, expressed as:

[0077]

[0078] This is the system statistical average energy harvesting efficiency index defined in this invention.

[0079] After the evaluation indicators are determined, this invention constructs a power allocation optimization problem for multiple wireless power base stations aimed at maximizing the statistical average energy harvesting efficiency of the system.

[0080] Specifically, the optimization problem (denoted as P1) is expressed as follows:

[0081]

[0082] In the formula, This represents the total transmit power budget for N wireless power base stations. This indicates the maximum transmit power limit for a single wireless power base station.

[0083] The optimization problem (P1) constructed in this invention is a non-convex optimization problem, which is difficult to solve directly using traditional convex optimization methods. Therefore, this invention transforms the non-convex optimization problem into a locally convex optimization problem, solves the locally convex optimization problem, and obtains the power allocation information.

[0084] The local convex optimization problem (denoted as P2) is expressed as follows:

[0085]

[0086] in, , ;

[0087] In the formula, Indicates that the input is Statistical average energy harvesting efficiency at that time. This represents the statistical average energy harvesting efficiency when the logarithmic function is used. Indicates the first The reciprocal of the transmit power of a wireless power base station. This represents the set of reciprocals of the transmit power of N wireless power base stations; Indicates the number of iterations; Indicates the first In the nth continuous convex approximation iteration, regarding the th Approximate parameters of a wireless power base station, denoted as the first parameter; Indicates the first The bias term in the successive convex approximation iteration is denoted as the second parameter.

[0088] The first parameter and the second parameter are represented as follows:

[0089]

[0090]

[0091] in, ;

[0092] In the formula, Indicates that the input is The DC charging power output by the energy harvesting module; Indicates the first In the nth continuous convex approximation iteration, the nth The value of the reciprocal of the transmit power of a wireless power base station; Indicates the first In the successive convex approximation iterations, the set of values ​​for the reciprocals of the transmit power of N wireless power base stations.

[0093] This invention constructs a locally convex optimization problem through mathematical operations such as variable substitution, continuous convex approximation, and taking the logarithm. The following provides a detailed explanation of how to construct the locally convex optimization problem (i.e., P2).

[0094] (1) Perform variable substitution and define ,Right now Since these are the substituted variables, the charging power of passive energy devices can therefore be redefined as:

[0095]

[0096] In the formula, This represents the charging power of the passive energy device after variable substitution.

[0097] (2) Due to For about The joint convex function can be approximated as follows based on the properties of convex functions:

[0098]

[0099] Based on this continuous convex approximation step, the objective function becomes:

[0100]

[0101] (3) Based on the continuous convex approximation, the objective function of statistical average energy collection efficiency is logarithmically transformed to transform the original non-convex optimization problem into a standard convex optimization problem.

[0102] Specifically, the objective function after introducing the logarithmic transformation takes the following form:

[0103]

[0104] Furthermore, the original non-convex optimization problem (i.e., P1) is transformed into a locally convex problem (i.e., P2).

[0105] The optimal solution to the locally convex problem (i.e., P2) can be obtained using the traditional interior point method.

[0106] This invention is based on an iterative algorithm to obtain the global suboptimal statistical average energy collection efficiency and the corresponding power allocation results of multiple wireless power base stations.

[0107] Specifically, solving the local convex optimization problem to obtain the power allocation information includes the following sub-steps:

[0108] S1. Determine the number N of wireless power base stations and the channel path gain vector. ;

[0109] S2. Initialize the transmit power of N wireless power base stations, and initialize the number of iterations. ;

[0110] S3, at local points calculate and ;

[0111] S4, at local points Solve the local convex optimization problem (P2) to obtain the local optimum. And the local optimal statistical average energy harvesting efficiency of the logarithmic function. The locally optimal statistical average energy harvesting efficiency is obtained through exponential transformation. ;in, , Indicates the first local optimal solution The reciprocal of the transmit power of a wireless power base station;

[0112] S5. If the result of this optimization iteration is... The result is less than the judgment threshold compared to the previous iteration. Then stop iterating and... This serves as the power allocation information for multiple wireless power base stations, corresponding to maximizing the statistical average energy harvesting efficiency; otherwise, let Return to step S3 and continue with a new round of iterative optimization.

[0113] After obtaining the power allocation information, the present invention optimizes the configuration of multi-base station collaborative wireless power supply based on the power allocation information.

[0114] Example 2:

[0115] Example 2 provides a multi-base station collaborative wireless power supply energy efficiency evaluation and optimization system, including:

[0116] The model building unit is used to build a passive Internet of Things system model that uses multiple wireless power base stations to collaboratively power passive energy devices.

[0117] The evaluation index determination unit is used to define the statistical average energy harvesting efficiency of the passive Internet of Things system according to the passive Internet of Things system model, and to use the statistical average energy harvesting efficiency as the evaluation index of the energy efficiency of multi-base station collaborative wireless power supply.

[0118] A power allocation optimization unit is used to construct an optimization problem that maximizes the statistical average energy harvesting efficiency, and to solve the optimization problem to obtain power allocation information of multiple wireless power supply base stations corresponding to maximizing the statistical average energy harvesting efficiency.

[0119] A configuration optimization unit is used to optimize the configuration of multi-base station collaborative wireless power supply based on the power allocation information;

[0120] The multi-base station collaborative wireless power supply energy efficiency evaluation and optimization system provided in Example 2 is used to execute the steps in the multi-base station collaborative wireless power supply energy efficiency evaluation and optimization method as described in Example 1.

[0121] Since the functions of each unit in the multi-base station collaborative wireless power supply energy efficiency evaluation and optimization system provided in Embodiment 2 correspond to the steps in the multi-base station collaborative wireless power supply energy efficiency evaluation and optimization method provided in Embodiment 1, Embodiment 2 can be understood by referring to the description of Embodiment 1, and will not be repeated here.

[0122] Example 3:

[0123] Example 3 provides an electronic device, including the multi-base station collaborative wireless power supply energy efficiency evaluation and optimization system as described in Example 2.

[0124] Finally, it should be noted that the above specific embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to examples, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for evaluating and optimizing the energy efficiency of multi-base station collaborative wireless power supply, characterized in that, Includes the following steps: Construct a passive Internet of Things (IoT) system model that coordinates the power supply of passive energy devices by multiple wireless power base stations; Based on the passive IoT system model, the statistical average energy harvesting efficiency of the passive IoT system is defined, and the statistical average energy harvesting efficiency is used as an evaluation index for the energy efficiency of multi-base station collaborative wireless power supply. Construct an optimization problem that maximizes the statistical average energy harvesting efficiency; Solving the optimization problem yields power allocation information for multiple wireless power base stations corresponding to maximizing the statistical average energy harvesting efficiency. The configuration of multi-base station collaborative wireless power supply is optimized based on the power allocation information. The passive IoT system model includes one passive energy device and N wireless power base stations; the N wireless power base stations simultaneously broadcast radio frequency signals; the passive energy device is equipped with an energy harvesting module; the energy harvesting module is used to convert the received radio frequency signals into DC signals and power itself. The statistical average energy harvesting efficiency is expressed as follows: in, , ; In the formula, Indicates that the input is Statistical average energy harvesting efficiency at that time. Indicates the first The transmit power of each wireless powered base station, where N represents the number of wireless powered base stations included in the passive IoT system model. Let represent the set of transmit power of N wireless power base stations. Indicates that the input is The DC charging power output by the energy harvesting module. Represents the channel path gain vector The joint probability density function, Indicates the first Channel path gain from a wireless power base station to a passive power device; Let N be the set of channel path gains, denoted as the channel path gain vector.

2. The method for evaluating and optimizing the energy efficiency of multi-base station collaborative wireless power supply according to claim 1, characterized in that, The optimization problem is expressed as follows: In the formula, This represents the total transmit power budget for N wireless power base stations. This indicates the maximum transmit power limit for a single wireless power base station.

3. The method for evaluating and optimizing the energy efficiency of multi-base station collaborative wireless power supply according to claim 2, characterized in that, The optimization problem is a non-convex optimization problem. The non-convex optimization problem is transformed into a locally convex optimization problem. The locally convex optimization problem is solved to obtain the power allocation information.

4. The method for evaluating and optimizing the energy efficiency of multi-base station collaborative wireless power supply according to claim 3, characterized in that, The local convex optimization problem is expressed as follows: in, , ; In the formula, Indicates that the input is Statistical average energy harvesting efficiency at that time. This represents the statistical average energy harvesting efficiency when the logarithmic function is used. Indicates the first The reciprocal of the transmit power of a wireless power base station. This represents the set of reciprocals of the transmit power of N wireless power base stations; Indicates the number of iterations; Indicates the first In the nth continuous convex approximation iteration, regarding the th Approximate parameters of a wireless power base station, denoted as the first parameter; Indicates the first The bias term in the successive convex approximation iteration is denoted as the second parameter.

5. The method for evaluating and optimizing the energy efficiency of multi-base station collaborative wireless power supply according to claim 4, characterized in that, The first parameter and the second parameter are represented as follows: in, ; In the formula, Indicates that the input is The DC charging power output by the energy harvesting module; Indicates the first In the nth continuous convex approximation iteration, the nth The value of the reciprocal of the transmit power of a wireless power base station; Indicates the first In the successive convex approximation iterations, the set of values ​​for the reciprocals of the transmit power of N wireless power base stations.

6. The method for evaluating and optimizing the energy efficiency of multi-base station collaborative wireless power supply according to claim 5, characterized in that, Solving the local convex optimization problem includes the following sub-steps: S1. Determine the number N of wireless power base stations and the channel path gain vector. ; S2. Initialize the transmit power of N wireless power base stations, and initialize the number of iterations. ; S3, at local points calculate and ; S4, at local points Solve the local convex optimization problem to obtain a local optimum. And the local optimal statistical average energy harvesting efficiency of the logarithmic function. ; The local optimal statistical average energy harvesting efficiency is obtained through exponential transformation. ;in, , Indicates the first local optimal solution The reciprocal of the transmit power of a wireless power base station; S5. If the result of this optimization iteration is... The result is less than the judgment threshold compared to the previous iteration. Then stop iterating and... This serves as the power allocation information for multiple wireless power base stations, corresponding to maximizing the statistical average energy harvesting efficiency; otherwise, let Return to step S3 and continue with a new round of iterative optimization.

7. A multi-base station collaborative wireless power supply energy efficiency evaluation and optimization system, characterized in that, include: The model building unit is used to build a passive Internet of Things system model that uses multiple wireless power base stations to collaboratively power passive energy devices. The evaluation index determination unit is used to define the statistical average energy harvesting efficiency of the passive Internet of Things system according to the passive Internet of Things system model, and to use the statistical average energy harvesting efficiency as the evaluation index of the energy efficiency of multi-base station collaborative wireless power supply. A power allocation optimization unit is used to construct an optimization problem that maximizes the statistical average energy harvesting efficiency, and to solve the optimization problem to obtain power allocation information of multiple wireless power supply base stations corresponding to maximizing the statistical average energy harvesting efficiency. A configuration optimization unit is used to optimize the configuration of multi-base station collaborative wireless power supply based on the power allocation information; The multi-base station collaborative wireless power supply energy efficiency evaluation and optimization system is used to perform the steps in the multi-base station collaborative wireless power supply energy efficiency evaluation and optimization method as described in any one of claims 1-6.

8. An electronic device, characterized in that, This includes the multi-base station collaborative wireless power supply energy efficiency evaluation and optimization system as described in claim 7.

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