Adaptive energy harvesting method, system, device and medium for power Internet of Things

Abstract

The present invention provides an adaptive energy collection method, system, device and medium for an electric power Internet of Things, and relates to the field of electric power communication technology. The method includes: based on a genetic algorithm, iteratively optimizing and solving a target optimization problem with maximizing the total amount of energy collected by multiple power terminals as an objective function, and with a first communication configuration constraint corresponding to each wireless access node and a second communication configuration constraint corresponding to each power terminal as constraint conditions; according to the optimized optimal transmission state and optimal cooperation state between each power terminal and each wireless access node, as well as the optimal power splitting factor, controlling each power terminal to collect energy from the original signal sent by multiple wireless access nodes. The present invention realizes the deployment of the optimal energy strategy for each power terminal with the goal of maximizing the total amount of energy collected by the power terminal under the premise of ensuring the normal communication of the power terminal, which can effectively improve the energy collection performance of the user while meeting the communication needs.

Description

Technical Field

[0001] The present invention relates to the field of power communication technology, and in particular to an adaptive energy collection method, system, device and medium for a power Internet of Things. Background Art

[0002] With the rapid development of power Internet of Things technology, as well as the advancement of wireless communication technology and microprocessor technology, new intelligent mobile applications involving complex data analysis and processing, such as augmented reality (AR) and virtual reality (VR), have been widely used. However, due to the limitations of physical size and battery technology, the computing power and battery capacity of power terminals are limited, making it difficult to meet the fast response and high energy consumption execution requirements of emerging applications. To address this problem, the emergence of Simultaneous Wireless Information and Power Transfer (SWIPT) technology provides a new solution for saving energy, improving the battery life of equipment, and thus improving user satisfaction.

[0003] However, in actual communications, for power terminals with high battery life requirements, while improving the energy collection efficiency, they are also restricted by the communication quality. Therefore, it is difficult to effectively configure a reasonable energy collection strategy and cannot effectively guarantee the communication battery life requirements.

[0004] Therefore, in the power communication scenario, how to reasonably optimize the energy collection strategy to adaptively collect energy and effectively improve the energy collection performance of the power terminal while meeting communication needs is an important issue that the industry needs to solve urgently. Summary of the invention

[0005] The present invention provides an adaptive energy collection method, system, device and medium for an electric power Internet of Things, which are used to solve the defects in the prior art that, under the constraint of communication quality, the energy collection strategy configuration is unreasonable and it is difficult to ensure the communication endurance requirements. Under the premise of ensuring the normal communication of the power terminal, the optimal energy strategy is deployed for each power terminal with the goal of maximizing the total amount of energy collected by the power terminal, so as to effectively improve the user's energy collection performance while meeting the communication needs.

[0006] The present invention provides an adaptive energy collection method for a power Internet of Things, which is applied to a power communication system. The power communication system includes a plurality of power terminals and a plurality of wireless access nodes. The method includes:

[0007] Taking maximizing the total amount of energy collected by the plurality of power terminals from the original signals sent by the plurality of wireless access nodes as the objective function, and taking the first communication configuration constraint corresponding to each wireless access node and the second communication configuration constraint corresponding to each power terminal as constraint conditions, a target optimization problem is constructed;

[0008] Based on a genetic algorithm, the target optimization problem is iteratively optimized and solved to obtain an optimal transmission state and an optimal cooperation state between each of the power terminals and each of the wireless access nodes, as well as an optimal power division factor of each of the power terminals;

[0009] According to the optimal transmission state, the optimal cooperation state, and the optimal power division factor, each of the power terminals is controlled to collect energy from the original signals sent by the plurality of wireless access nodes.

[0010] According to an adaptive energy collection method for electric power Internet of Things provided by the present invention, the total amount of energy is determined based on the following steps:

[0011] Acquire a transmission signal received by each of the power terminals according to the original signals sent by the plurality of wireless access nodes;

[0012] Obtaining energy collected by each of the power terminals according to the transmission signal, a power division factor and an energy conversion efficiency corresponding to each of the power terminals, and noise generated by a power division circuit in each of the power terminals;

[0013] The energy collected by the multiple power terminals is aggregated to obtain the total amount of energy.

[0014] According to an adaptive energy collection method for an electric power Internet of Things provided by the present invention, the transmission signal received by each of the electric power terminals is obtained according to the original signals sent by the plurality of wireless access nodes, including:

[0015] For each power terminal, perform the following steps:

[0016] Acquire a valid signal received by the current power terminal according to the transmission state, signal gain and precoding vector between the current power terminal and each of the wireless access nodes, as well as the transmission power allocated by each of the wireless access nodes to the current power terminal and the original signal sent by each of the wireless access nodes to the current power terminal;

[0017] Acquire a first interference signal corresponding to the current power terminal according to the transmission status and precoding vector between other power terminals except the current power terminal and each of the wireless access nodes, as well as the transmission power allocated by each of the wireless access nodes to the other power terminals and the original signal sent by each of the wireless access nodes to the other power terminals;

[0018] Acquire a second interference signal corresponding to the current power terminal according to the cooperation state between the current power terminal and each of the wireless access nodes, the transmission state and precoding vector between the other power terminals and each of the wireless access nodes, the transmission power allocated by each of the wireless access nodes to the other power terminals, and the original signal sent by each of the wireless access nodes to the other power terminals;

[0019] A transmission signal currently received by the power terminal is acquired according to the effective signal, the first interference signal and the second interference signal.

[0020] According to an adaptive energy collection method for an electric power Internet of Things provided by the present invention, the first communication configuration constraint corresponding to each of the wireless access nodes includes a hybrid clustering decision constraint between the transmission service and the collaborative service corresponding to each of the wireless access nodes, a total transmission power allocation constraint, a total signal quantity transmission constraint, and a total antenna quantity configuration constraint;

[0021] The hybrid clustering decision constraint is constructed based on the constraint that the hybrid clustering decision value of each of the wireless access nodes is less than or equal to a decision threshold;

[0022] The total transmission power allocation constraint is constructed based on the constraint that the total transmission power allocated to each of the wireless access nodes is less than or equal to the maximum transmission power corresponding to each of the wireless access nodes;

[0023] The total signal quantity transmission constraint is constructed based on the constraint that the total signal quantity transmitted by each of the wireless access nodes is less than or equal to the backhaul link capacity corresponding to each of the wireless access nodes;

[0024] The total antenna quantity configuration constraint is constructed based on the constraint that the total antenna quantity allocated to each of the wireless access nodes is less than an antenna quantity configuration threshold.

[0025] According to an adaptive energy collection method for electric power Internet of Things provided by the present invention, the hybrid clustering decision constraint is formed based on the following steps:

[0026] Adding the value of the transmission service state provided by each wireless access node for each power terminal and the value of the coordination service state provided by each wireless access node for each power terminal to obtain a hybrid clustering decision value of each wireless access node;

[0027] The hybrid clustering decision constraint is constructed by taking the hybrid clustering decision value being less than or equal to the decision threshold as a constraint.

[0028] According to an adaptive energy collection method for an electric power Internet of Things provided by the present invention, the second communication configuration constraint corresponding to each of the electric power terminals includes a power division factor constraint, an information reception signal-to-noise ratio constraint, and an energy collection amount constraint corresponding to each of the electric power terminals;

[0029] The power split factor constraint is constructed based on the power split factor of each power terminal being within a constraint range;

[0030] The information reception signal-to-noise ratio constraint is constructed based on the constraint that the information reception signal-to-noise ratio of each of the power terminals is greater than or equal to the minimum information reception signal-to-noise ratio threshold of each of the power terminals;

[0031] The energy collection amount constraint is constructed based on the constraint that the energy received by each of the power terminals is greater than or equal to the energy consumed by each of the power terminals.

[0032] According to an adaptive energy collection method for electric power Internet of Things provided by the present invention, the information reception signal-to-noise ratio constraint is formed based on the following steps:

[0033] Comparing the effective signal received by each power terminal from the original signals sent by the multiple wireless access nodes with the interference signal received by each power terminal from the original signals sent by the multiple wireless access nodes, to obtain the information reception signal-to-noise ratio of each power terminal;

[0034] The information reception signal-to-noise ratio constraint is constructed by taking the information reception signal-to-noise ratio of each of the power terminals as a constraint that the information reception signal-to-noise ratio of each of the power terminals is greater than or equal to a minimum information reception signal-to-noise ratio threshold of each of the power terminals.

[0035] The present invention also provides an adaptive energy collection system for an electric power Internet of Things, wherein the system is communicatively connected to a plurality of electric power terminals and a plurality of wireless access nodes, respectively, and the system comprises:

[0036] A model building unit, configured to build a target optimization problem by taking maximizing the total amount of energy collected by the plurality of power terminals from the original signals sent by the plurality of wireless access nodes as an objective function and taking the first communication configuration constraint corresponding to each of the wireless access nodes and the second communication configuration constraint corresponding to each of the power terminals as constraint conditions;

[0037] A strategy optimization unit, configured to perform iterative optimization and solution on the target optimization problem based on a genetic algorithm, to obtain an optimal transmission state and an optimal cooperation state between each of the power terminals and each of the wireless access nodes, and an optimal power division factor of each of the power terminals;

[0038] An energy configuration unit is used to control each of the power terminals to collect energy from original signals sent by multiple wireless access nodes according to the optimal transmission state, the optimal cooperation state, and the optimal power division factor.

[0039] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, an adaptive energy collection method for the electric power Internet of Things as described in any one of the above is implemented.

[0040] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the adaptive energy collection method for the electric power Internet of Things as described in any one of the above.

[0041] The present invention also provides a computer program product, including a computer program, which, when executed by a processor, implements the adaptive energy collection method for the electric power Internet of Things as described in any one of the above.

[0042] The adaptive energy collection method, system, device and medium of the electric power Internet of Things provided by the present invention take maximizing the total amount of energy collected by multiple power terminals from original signals sent by multiple wireless access nodes as the objective function, and construct a target optimization problem with the first communication configuration constraint corresponding to each wireless access node and the second communication configuration constraint corresponding to each power terminal as constraints, and use a genetic algorithm to optimize and solve the target optimization problem. On the premise of meeting the minimum communication requirements, the total energy collected by the power terminals in the power communication system is maximized, and the transmission channels, cooperative channels and power splitting factors of the power terminals in the power communication system are optimized. Therefore, under the premise of effectively ensuring the normal communication of the power terminals, the optimal energy strategy is deployed for each power terminal, the energy collection efficiency of the power terminals is improved, and the communication endurance and user satisfaction are further guaranteed. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 It is a flow chart of the adaptive energy collection method for the electric power Internet of Things provided by the present invention.

[0045] Figure 2 It is a structural schematic diagram of the power communication system provided by the present invention.

[0046] Figure 3 It is a schematic diagram of the flow chart of solving the target optimization problem provided by the present invention.

[0047] Figure 4 This is one of the simulation schematic diagrams of the adaptive energy collection method for the electric power Internet of Things provided by the present invention.

[0048] Figure 5 This is the second simulation schematic diagram of the adaptive energy collection method of the electric power Internet of Things provided by the present invention.

[0049] Figure 6 This is the third simulation schematic diagram of the adaptive energy collection method of the electric power Internet of Things provided by the present invention.

[0050] Figure 7 It is a structural schematic diagram of the adaptive energy collection system of the electric power Internet of Things provided by the present invention.

[0051] Figure 8 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] SWIPT is a new technology proposed for radio frequency energy harvesting. It can use the same wireless signal for information transmission and energy transfer, while improving spectrum utilization and the battery life of mobile devices and relay nodes. It provides new solutions for saving energy, improving device battery life, and improving user satisfaction.

[0054] However, in actual communications, the communication between power terminals and wireless nodes will affect the communication of other power terminals. At the same time, for power terminals with high endurance requirements, while improving the energy collection efficiency, they are also restricted by the communication quality. Therefore, it is difficult to effectively configure a reasonable energy collection strategy and cannot effectively guarantee the communication endurance requirements.

[0055] To solve the above problems, this embodiment provides an adaptive energy collection method for the power Internet of Things. In a mobile communication scenario, the method considers channel interference and energy reserve problems between power terminals (or devices or terminal devices), and while pursuing target optimization, considers the communication needs of users and the endurance needs of power terminals. A genetic algorithm is used to adaptively optimize the target optimization problem of nonlinear programming constructed with maximizing the total amount of energy collected from original signals sent by multiple power terminals from multiple wireless access nodes as the objective function and the first communication configuration constraint corresponding to each wireless access node and the second communication configuration constraint corresponding to each power terminal as the constraint conditions, so as to optimize the optimal transmission strategy, cooperation strategy and power division coefficient, optimize the energy collection of the power communication system, and then achieve the optimal energy strategy deployment for each power terminal with the goal of maximizing the total amount of energy collected by the power terminal under the premise of effectively ensuring the normal communication of the power terminal, which can effectively improve the energy collection efficiency of the power terminal and further ensure high-performance communication endurance.

[0056] Figure 1 It is a flow chart of the adaptive energy collection method of the power Internet of Things provided by the present invention; the method is applied to the power communication system; Figure 2 is a schematic diagram of the structure of the power communication system provided by the present invention; Figure 2 As shown, the power communication system includes Power terminals equipped with power splitting receivers, including but not limited to smart power terminals such as smart meters and power quality monitoring terminals; the power terminal set can be expressed as Each power terminal has the ability to collect energy and receive information. In addition, the power communication system is also equipped with wireless access nodes with multiple antennas, including but not limited to micro base stations, macro base stations, etc.; the wireless access node set can be expressed as . Wireless access node Power terminals can be connected via transmission links or cooperative links Transmit valid information or collaborate (i.e. avoid communicating with smart power terminals when transmitting information) The power communication system is also equipped with an edge cloud to coordinate with the wireless access nodes to complete the data processing of each power terminal.

[0057] In the power communication system, the transmission and communication relationship between the power terminal and the wireless access node, in addition to the channel gain, also includes the energy conversion rate of the power terminal. Since the distances between each power terminal and the wireless access node are different, the channel conditions are also different, which makes the power terminal more inclined to choose a wireless access node with a closer distance and a better channel. In this regard, the method provided in this embodiment considers maximizing the total energy collected by the power terminals in the power communication system under the premise of meeting the minimum communication requirements, and optimizes the configuration of the transmission channel, cooperative channel and power division factor of the power terminals in the power communication system, so as to effectively ensure the normal communication of the power terminals, deploy the optimal energy strategy for each power terminal, improve the energy collection efficiency of the power terminal, further ensure communication endurance and improve user satisfaction.

[0058] like Figure 1 As shown, the method includes step 110 , step 120 and step 130 .

[0059] Step 110, constructing a target optimization problem with maximizing the total amount of energy collected by the multiple power terminals from the original signals sent by the multiple wireless access nodes as the objective function and with the first communication configuration constraint corresponding to each wireless access node and the second communication configuration constraint corresponding to each power terminal as constraints.

[0060] Optionally, a corresponding signal transmission model and power splitting model may be created for each power terminal, and an energy collection model may be inferred based on the signal transmission model and the power splitting model. Then, based on the energy collection model, the energy collected by each power terminal from the original signals sent by multiple wireless access nodes may be obtained, and the energies collected by all power terminals may be superimposed and summed to obtain the total amount of energy collected by multiple power terminals from the original signals sent by multiple wireless access nodes.

[0061] The so-called signal transmission model is used to calculate the transmission signal received by each power terminal from the original signal sent by multiple wireless access nodes, and the so-called power partitioning model is used to calculate the signal flow for information reception obtained by each power terminal from the original signal sent by multiple wireless access nodes.

[0062] In addition, the first communication configuration constraint corresponding to each wireless access node and the second communication configuration constraint corresponding to each power terminal can also be configured according to the signal transmission model, the power splitting model, and the restriction conditions of each parameter in the energy collection model. The first communication configuration constraint corresponding to each wireless access node includes, but is not limited to, a constraint for limiting the decision state between the transmission service and the cooperative service of each wireless access node, a constraint for limiting the transmission power allocated by each wireless access node for the intelligent power terminal to transmit information, a constraint for limiting the total number of signals that can be transmitted by each wireless access node, and a constraint for limiting the number of antenna allocations of each wireless access node, so as to ensure the normal communication of each wireless access point. The second communication configuration constraint corresponding to each power terminal includes, but is not limited to, a constraint for limiting the power splitting factor of each power terminal, a constraint for limiting the information reception signal-to-noise ratio of each power terminal, and a constraint for limiting the energy collection amount of each power terminal, so as to ensure the normal communication of each terminal.

[0063] Next, the objective function is to maximize the total amount of energy collected by multiple power terminals from the original signals sent by multiple wireless access nodes, and the first communication configuration constraint corresponding to each wireless access node and the second communication configuration constraint corresponding to each power terminal are comprehensively considered as constraints to constrain the objective function. In this way, a target optimization problem is constructed with the goal of maximizing the total amount of energy collected by the power terminals, and the first communication configuration constraint corresponding to each wireless access node and the second communication configuration constraint corresponding to each power terminal as constraints. The target optimization problem constructed in this way can be solved through optimization to obtain an optimized energy collection strategy that can maximize the total amount of energy collected by the power terminals on the basis of satisfying the communication configuration constraints, and thereby optimize the energy collection configuration of each power terminal. This can enable the power terminal to deploy the optimal energy strategy for each power terminal on the premise of meeting the normal communication needs of the power terminal and each access node, thereby maximizing the energy collection efficiency of the power terminal, further ensuring communication endurance and improving user satisfaction.

[0064] Step 120, based on the genetic algorithm, iteratively optimize and solve the target optimization problem to obtain the optimal transmission state and optimal cooperation state between each of the power terminals and each of the wireless access nodes, and the optimal power division factor of each of the power terminals.

[0065] Optionally, after obtaining the target optimization problem, a genetic algorithm may be called to iteratively optimize and solve the target optimization problem, so as to achieve adaptive optimization configuration of transmission, cooperation and power splitting factors of each power terminal through the principle of survival of the fittest.

[0066] Figure 3 It is a schematic diagram of the process of solving the target optimization problem provided by the present invention; as shown in Table 1 and Figure 3 As shown, the specific optimization steps of the genetic algorithm are described as follows.

[0067] First, the connection between the power terminal and the wireless access node is initialized to obtain a pre-configured multiple different transmission state matrices. (Represents wireless access node With power terminal effective information transmission between them), collaboration status matrix (Represents wireless access node With power terminal The system can also initialize and configure other required parameters, including but not limited to the attribute information of the power terminal and the wireless access node, such as the minimum signal-to-noise ratio threshold for receiving information. , the location distribution of power terminals, energy conversion rate, signal transmission power, etc., can be initialized and configured according to actual solution requirements and are not specifically limited here.

[0068] Then, the initial group is constructed, and each individual in the group represents a possible transmission state matrix , Collaboration Status Matrix , a combination of power split factors, to calculate the optimal power cutting ratio under a given transmission matrix and cooperation matrix based on each individual in the group, and to evaluate the fitness (or benefit) of each individual based on these parameters to select the best individual from the current group, that is, the optimal transmission state matrix , optimal collaboration state matrix , optimal power division factor .

[0069] Then, for the current iterative optimization solution step, determine whether the number of current iterative optimization is less than the maximum number of iterations N or whether the iteration factor is greater than the factor threshold Or whether the benefit is stable, if the current number of iterations is less than the maximum number of iterations N or the iteration factor is greater than the factor threshold Or if the benefits are unstable, the new generation of the population is selected using the roulette wheel method based on the fitness of the individuals; among them, individuals with higher fitness are more likely to be selected.

[0070] Subsequently, the best individual selected in the previous iteration is directly added to the new population to ensure that the optimal solution is retained.

[0071] Subsequently, crossover and mutation operations are performed on the individuals in the new group to increase the diversity of the group and explore new solution spaces.

[0072] Then, according to the results of crossover and mutation, the individuals in the new group are updated, that is, the transmission state matrix is ​​updated. , Collaboration Status Matrix , power split factor; and continue to return to the group member selection step based on the new group updated by the individual, and perform the next iterative optimization solution step until the maximum number of iterations N is reached and the iteration factor is greater than or equal to the factor threshold , and the benefits are stable, the iterative optimization solution process ends.

[0073] When the iterative optimization solution process is completed, the optimal transmission state matrix is ​​output , optimal collaboration state matrix , optimal power division factor , thereby obtaining the optimal transmission state and optimal cooperation state between each power terminal and each wireless access node, as well as the optimal power division factor of each power terminal.

[0074] In summary, based on the optimization solution of the genetic algorithm, the target optimization problem can be solved more effectively and efficiently and the global optimal solution can be obtained. As a result, when the wireless access node provides channel resources for the power terminal and transmits energy, the intelligent power terminal can adaptively optimize and adjust the power division factor of the local end according to different optimized transmission states and cooperation states, thereby efficiently and adaptively realizing the energy collection optimization configuration of the power terminal.

[0075] Table 1 Algorithm table of adaptive energy harvesting method for power Internet of Things

[0076]

[0077] Step 130: Control each of the power terminals to collect energy from original signals sent by the plurality of wireless access nodes according to the optimal transmission state, the optimal cooperation state, and the optimal power division factor.

[0078] Optionally, after obtaining the optimal transmission state and the optimal cooperation state, each power terminal can receive signals from the original signals sent by multiple wireless access nodes in the optimal transmission state and the optimal cooperation state to obtain the transmission signal received by each power terminal, and cut part of the signal from the transmission signal received by each power terminal with the optimal power division factor for energy collection, thereby realizing energy collection with the optimal transmission state, the optimal cooperation state, and the optimal power division factor, effectively utilizing the wireless resources of each wireless access node, reducing interval interference, and ensuring the communication quality. At the same time, energy can be collected from the received transmission signal more efficiently, effectively extending the battery life and service life of the power terminal.

[0079] The method provided in this embodiment constructs a target optimization problem by taking maximizing the total amount of energy collected by multiple power terminals from original signals sent by multiple wireless access nodes as the objective function, taking the first communication configuration constraint corresponding to each wireless access node and the second communication configuration constraint corresponding to each power terminal as constraints, and combines a genetic algorithm to optimize and solve the target optimization problem, so as to optimize the configuration of transmission channels, cooperative channels and power splitting factors of power terminals in the power communication system with the goal of maximizing the total energy collected by power terminals in the power communication system under the premise of meeting the minimum communication requirements, and thereby effectively ensuring the normal communication of the power terminals, deploying the optimal energy strategy for each power terminal, improving the energy collection efficiency of the power terminals, further ensuring communication endurance and improving user satisfaction.

[0080] In some embodiments, the total amount of energy is determined based on the following steps: obtaining a transmission signal received by each of the power terminals based on original signals sent by multiple wireless access nodes; obtaining the energy collected by each of the power terminals based on the transmission signal, the power division factor and energy conversion efficiency corresponding to each of the power terminals, and the noise generated by the power division circuit in each of the power terminals; and aggregating the energy collected by multiple power terminals to obtain the total amount of energy.

[0081] Optionally, for the calculation of the total amount of energy, the original signals sent by multiple wireless access nodes can be input into a signal transmission model, so that the signal transmission model can calculate the effective signal and interference signal of each power terminal based on the original signal, and then output the corresponding transmission signal based on the effective signal and the interference signal to obtain the transmission signal received by each power terminal.

[0082] In some embodiments, acquiring the transmission signal received by each of the power terminals according to the original signals sent by the plurality of wireless access nodes includes: performing the following steps for each power terminal:

[0083] According to the transmission state, signal gain and precoding vector between the current power terminal and each of the wireless access nodes, as well as the transmission power allocated by each of the wireless access nodes to the current power terminal and the original signal sent by each of the wireless access nodes to the current power terminal, the effective signal received by the current power terminal is obtained; according to the transmission state and precoding vector between other power terminals other than the current power terminal and each of the wireless access nodes, as well as the transmission power allocated by each of the wireless access nodes to the other power terminals and the original signal sent by each of the wireless access nodes to the other power terminals, the first interference signal corresponding to the current power terminal is obtained; according to the cooperation state between the current power terminal and each of the wireless access nodes, the transmission state and precoding vector between the other power terminals and each of the wireless access nodes, as well as the transmission power allocated by each of the wireless access nodes to the other power terminals and the original signal sent by each of the wireless access nodes to the other power terminals, the second interference signal corresponding to the current power terminal is obtained; according to the effective signal, the first interference signal and the second interference signal, the transmission signal received by the current power terminal is obtained.

[0084] Optionally, in the process of creating the signal transmission model, in order to make full use of the diversity gain and anti-interference capability among multiple wireless access nodes to improve the information reception signal-to-noise ratio of the power terminal, the transmission state matrix can be introduced = , and the collaboration status matrix = ,in Indicates wireless access node For power terminals Valid information is transmitted, otherwise it is 0. Indicates wireless access node For power terminals to collaborate (i.e. avoid transmitting information to power terminals interference occurs), otherwise it is 0.

[0085] Therefore, for each power terminal , the received transmission signal Specifically, it can be calculated by the following calculation formula of the signal transmission model:

[0086] ;

[0087] Alternatively, it can be calculated using the following signal transmission model calculation formula:

[0088] ;

[0089] in, For power terminals Received valid signal; and Power Terminal Wireless access node Transmission status and signal gain between; For power terminals At the wireless access node A precoding vector of unit norm; Wireless access node For power terminals the allocated transmission power; Wireless access node To power terminal The data symbol formed by encoding the original signal sent; For power terminals The corresponding first interference signal, that is, the transmission between the wireless access point and other power terminals to the power terminal The interference caused, and Other power terminals Wireless access node The transmission state and precoding vector between For power terminals Wireless access node The signal gain between Wireless access node For other power terminals The allocated transmission power, Wireless access node To other power terminals The data symbol formed by encoding the original signal sent; For power terminals The corresponding second interference signal, that is, the interference signal caused by the cooperation between the wireless access point and the current power terminal to the power terminal Reduced interference, Wireless access node With power terminal The state of collaboration between them; For power terminals The corresponding additive white Gaussian noise.

[0090] Optionally, after the transmission signal of each power terminal is obtained based on the above signal transmission model calculation, the power division factor corresponding to each power terminal introduced in the power division model, the energy conversion efficiency corresponding to each power terminal, and the noise generated by the power division circuit in each power terminal can be input into the energy collection model, so that the energy collection model can perform energy calculation to obtain the energy collected by each power terminal.

[0091] Among them, the energy collection model can calculate the energy of the signal flow used for energy collection in the transmission signal received by the power terminal. The specific calculation formula is as follows:

[0092] ;

[0093] in, For power terminals Energy collected; For power terminals The corresponding energy conversion efficiency is For power terminals The corresponding power division factor is, For power terminals The variance of the noise generated within the power-dividing circuit.

[0094] After the energy collected by each power terminal is calculated through the above energy collection model, the energy collected by multiple power terminals can be superimposed and summarized to obtain the total energy .

[0095] The method provided in this embodiment can more accurately calculate the energy that each power terminal can actually collect by considering the original signal sent by the wireless access node, the power division factor of the power terminal, the energy conversion efficiency, and the noise generated by the power division circuit. This refined calculation method helps to optimize the energy collection process, reduce energy loss, and thus improve the overall energy collection efficiency; and considers the transmission interference and collaborative interference between the wireless access point and the power terminal for energy calculation, so as to help reduce the impact of interference on the energy collection optimization process, and further improve the signal quality and the stability of energy collection.

[0096] In some embodiments, the first communication configuration constraint corresponding to each of the wireless access nodes includes a hybrid clustering decision constraint between the transmission service and the collaborative service corresponding to each of the wireless access nodes, a total transmission power allocation constraint, a total signal quantity transmission constraint, and a total antenna quantity configuration constraint; the hybrid clustering decision constraint is constructed based on the constraint that the hybrid clustering decision value of each of the wireless access nodes is less than or equal to the decision threshold; the total transmission power allocation constraint is constructed based on the constraint that the total transmission power allocated by each of the wireless access nodes is less than or equal to the maximum transmission power corresponding to each of the wireless access nodes; the total signal quantity transmission constraint is constructed based on the constraint that the total number of signals transmitted by each of the wireless access nodes is less than or equal to the backhaul link capacity corresponding to each of the wireless access nodes; the total antenna quantity configuration constraint is constructed based on the constraint that the total number of antennas allocated by each of the wireless access nodes is less than the antenna quantity configuration threshold.

[0097] Optionally, by limiting the same wireless access node from providing transmission services and collaborative services to the same power terminal at the same time, resource conflicts can be avoided, service quality can be guaranteed, and the energy collection effect can be improved; reasonable power allocation can ensure that as much power as possible is used for energy transmission while meeting the transmission quality, thereby increasing the energy collection amount of the power terminal; by limiting the number of transmitted signals, signal congestion and mutual interference can be avoided, ensuring that each signal can be effectively transmitted, thereby improving resource utilization; through reasonable antenna configuration, the coverage range and transmission performance of the wireless access node can be optimized, thereby improving the energy collection efficiency of the entire network. Therefore, the first communication configuration constraint corresponding to each wireless access node in the method provided in this embodiment includes at least a hybrid clustering decision constraint for limiting the same wireless access node from simultaneously providing transmission services and collaborative services to the same power terminal, a total transmission power allocation constraint for ensuring the rationality of power allocation, a total signal number transmission constraint for limiting the number of transmitted signals, and a total antenna number configuration constraint for ensuring the rationality of antenna configuration. Through these constraints, it is ensured that the wireless access network maintains a stable operating state in a complex and changeable environment, reduces system failures caused by problems such as resource conflicts, signal interference or power overload, reduces the risk of interruption in the energy collection process of the power terminal, and thereby improves the energy collection efficiency.

[0098] Among them, the hybrid clustering decision constraint is used to restrict the same wireless access node from providing transmission service and collaboration service to the same power terminal at the same time. For example, it can be achieved by restricting the sum of the value of the transmission service state and the value of the collaboration service state provided by the same wireless access node to the same power terminal to be no greater than the decision threshold, or restricting the Boolean state between the value of the transmission service state and the value of the collaboration service state provided by the same wireless access node to the same power terminal to not be true at the same time, etc. This embodiment does not make specific limitations on this.

[0099] In some embodiments, the hybrid clustering decision constraint is formed based on the following steps: adding the value of the transmission service state provided by each of the wireless access nodes for each of the power terminals and the value of the collaborative service state provided by each of the wireless access nodes for each of the power terminals to obtain a hybrid clustering decision value of each of the wireless access nodes; and constructing the hybrid clustering decision constraint with the constraint that the hybrid clustering decision value is less than or equal to the decision threshold.

[0100] Optionally, the hybrid clustering decision constraint may be calculated using the following formula:

[0101] ;

[0102] in, is the decision threshold, which can be set within a range greater than or equal to 1 and less than 2. For example, it can be set to 1. The specific configuration can be based on actual needs. Wireless access node With power terminal The state of collaboration between them.

[0103] Here, the total transmission power allocation constraint is used to limit the total amount of transmission power that the wireless access node can allocate to the power terminal. Since the transmission power that any wireless access node can supply is limited, that is, the total transmission power allocated by the wireless access node for the transmission information of the power terminal is subject to the following constraints:

[0104] ;

[0105] in, and Wireless access nodes For power terminals Allocated transmission power and wireless access nodes The corresponding maximum transmission power is also the upper limit of power supply.

[0106] Here, the total signal transmission quantity constraint is used to limit the total number of signals transmitted by the wireless access node to a limited number. Limited backhaul link capacity to the cloud , that is, the total number of signals that can be transmitted by the wireless access node is limited, so the total number of signal transmission constraints are subject to the following constraints:

[0107] .

[0108] Here, the total number of antennas is used to limit the total number of antennas allocated to the wireless access node. The number of antennas required for transmission and collaboration cannot exceed the configured antenna number threshold. Therefore, the number of wireless access node antennas is limited, and there are the following total antenna number configuration constraints:

[0109] .

[0110] In some embodiments, the second communication configuration constraint corresponding to each of the power terminals includes a power split factor constraint, an information reception signal-to-noise ratio constraint and an energy collection amount constraint corresponding to each of the power terminals; the power split factor constraint is constructed based on the constraint that the power split factor of each of the power terminals is within the constraint range; the information reception signal-to-noise ratio constraint is constructed based on the constraint that the information reception signal-to-noise ratio of each of the power terminals is greater than or equal to the minimum information reception signal-to-noise ratio threshold of each of the power terminals; the energy collection amount constraint is constructed based on the constraint that the energy reception amount of each of the power terminals is greater than or equal to the energy consumption amount of each of the power terminals.

[0111] Optionally, since it is difficult to provide wired power supply for some small smart power terminals in real scenarios, and it is difficult to frequently replace batteries, the smart power terminals have short battery life and are difficult to achieve long-term uninterrupted new power Internet of Things response. In this regard, in the method provided in this embodiment, it is considered to equip each power terminal with a power splitting receiver. In the power splitter, the low-power power terminal can split the received RF signal into two signal streams with different power levels, one of which is sent to the rectifying antenna circuit for energy collection, and the other signal stream is converted into a baseband signal for signal decoding to realize information transmission; and a reasonable power splitting factor constraint can ensure that as much power as possible is used for energy transmission under the premise of meeting the transmission quality, thereby increasing the energy collection amount of the power terminal. In addition, the information reception signal-to-noise ratio constraint ensures that the power terminal can maintain a sufficient signal strength to noise strength ratio when receiving information, thereby improving the reliability and stability of information transmission. This helps to reduce problems such as signal interference, loss or erroneous reception caused by too low a signal-to-noise ratio; in addition, a reasonable energy collection amount constraint can ensure that the power terminal will not interrupt service due to energy exhaustion during long-term operation. Therefore, the second communication configuration constraints corresponding to each power terminal in the method provided in this embodiment include at least a power division factor constraint for limiting the reasonable configuration of the power division factor, an information reception signal-to-noise ratio constraint for limiting the information reception signal-to-noise ratio, and an energy collection amount constraint for ensuring that the power terminal will not interrupt service due to energy depletion during long-term operation. Through these constraints, it is ensured that the energy collection efficiency is improved, the information transmission quality is guaranteed, and the balance between energy and information transmission performance is achieved.

[0112] The power split factor constraint indicates that the proportion of the signal used for information reception in the transmission signal is within a reasonable constraint range, which can be specifically expressed as:

[0113] ;

[0114] in, For power terminals The power division factor.

[0115] Therefore, the power terminal Signal flow for information reception It can be expressed as:

[0116] ;

[0117] in, Represents intelligent power terminal The noise generated by the power division circuit within the distribution meets the , is the variance, For power terminals Received transmission signal.

[0118] Here, the information reception signal-to-noise ratio is calculated based on the power or amplitude ratio between the effective signal and the interference signal.

[0119] In some embodiments, the information reception signal-to-noise ratio constraint is formed based on the following steps: comparing the effective signal received by each power terminal from the original signal sent by the multiple wireless access nodes with the interference signal received by each power terminal from the original signal sent by the multiple wireless access nodes to obtain the information reception signal-to-noise ratio of each power terminal; taking the information reception signal-to-noise ratio of each power terminal as a constraint that is greater than or equal to the minimum information reception signal-to-noise ratio threshold of each power terminal, constructing the information reception signal-to-noise ratio constraint.

[0120] Optionally, the power terminal The signal-to-noise ratio of the received information It can be expressed as the following formula:

[0121] ;

[0122] in, For power terminals The valid signal received in the information reception, For power terminals The interference signal received during the information reception.

[0123] In order to ensure the real-time information reception of the power terminal, it is necessary to ensure the minimum information reception signal-to-noise ratio requirement of the power terminal (minimum information reception signal-to-noise ratio threshold) , therefore, there are the following information reception signal-to-noise ratio constraints:

[0124] .

[0125] Here, the energy collection constraint can ensure that the power terminal will not be interrupted due to energy exhaustion during long-term operation. In order to extend its battery life and ensure uninterrupted smart IoT services, the energy obtained from the RF signal (Radio Frequency Signal, RF signal) should be more than the power terminal Energy consumption , that is, there are the following energy collection constraints:

[0126] .

[0127] In summary, according to the energy collection models and constraints of the above-mentioned power terminals, the total energy collection efficiency is maximized while satisfying various constraints through the design of the calculation configuration algorithm. Therefore, the calculation formula of the target optimization problem P1 is as follows:

[0128] ;

[0129] , ;

[0130] ;

[0131] ;

[0132] ;

[0133] ;

[0134] ;

[0135] ;

[0136] in, For power terminals The energy collected. Transmission state matrix = Collaboration Status Matrix = , represents the state set of the hybrid clustering scheduling scheme.

[0137] It represents the transmission power set allocated by the wireless access node for the transmission information of the power terminal.

[0138] Represents the set of power division factors for the power terminal.

[0139] In the above objective optimization problem, the C1 constraint ensures the feasibility of the hybrid clustering decision, the C2 constraint represents the limited power supply of the wireless access node, the C3-C4 constraints represent the limited transmission capacity and number of antennas of the access node, the C5 constraint ensures the effectiveness of the power allocation factor, and the C6-C7 constraints represent the minimum signal-to-noise ratio and energy collection requirements of the smart power terminal respectively.

[0140] In summary, the method provided in this embodiment takes into account the channel capacity of the wireless access node, the number of antennas, the communication interference between the power terminals, and the signal-to-noise ratio requirements of the communication between the power terminal and the wireless access node in mobile communication; and, in order to truly reflect the differentiated needs of users, the energy collection efficiency, minimum communication requirements, endurance requirements, and sensitivity to communication quality of the power terminal are digitized, and parameters such as the equipment energy conversion rate and signal transmission power are alienated. Furthermore, on the premise of ensuring the normal communication of the power terminal, the energy collected by the power terminal is used as the main consideration of the power terminal, and a target optimization problem is constructed. The target optimization problem is optimized and solved using a genetic algorithm to optimize the connection between the power terminal and the wireless access node and the power division factor, and then the energy collection strategy deployment is implemented based on the optimized parameters to improve the energy collection speed and thereby improve user satisfaction.

[0141] Furthermore, in order to verify that the method provided in this embodiment can achieve the effect of improving the overall energy collection efficiency while ensuring the normal communication and basic endurance requirements of the user, a specific simulation embodiment is used for specific description below.

[0142] The model parameters of the simulation example are set as follows: Energy conversion efficiency The value range is , the variance of the noise is The radius of the simulation area is .

[0143] Figure 4 It is one of the simulation schematic diagrams of the adaptive energy collection method of the power Internet of Things provided by the present invention; Figure 4As shown, the adaptive energy harvesting strategy based on genetic algorithm (AEHGA) of the power Internet of Things provided in this embodiment, when the number of wireless access nodes is 30 and the number of power terminals is 50, as the number of iterations increases, the energy collected by the power communication system gradually increases; specifically, in the iterative process, through selection, crossover and mutation, the power terminal continuously changes the energy collection strategy decision, and optimizes the group according to the energy collection situation corresponding to different decisions until the system is stable. At the same time, this embodiment adopts the best strategy retention method, which retains the best decision of the previous iteration in each iteration, effectively improving the stability of the energy collected by the power terminal in the power communication system during the iteration process.

[0144] Figure 5 This is the second simulation schematic diagram of the adaptive energy collection method of the power Internet of Things provided by the present invention; Figure 5 As shown, it shows how the system noise interference changes with the transmission power. As the signal transmission power of the wireless access node increases, the sum of the system's signal noise and interference also increases. Compared with the non-cooperative adaptive energy harvesting strategy based on genetic algorithm (NCAEHGA for short), the AEHGA provided in this embodiment takes into account the cooperative relationship between the wireless access node and the user. Therefore, the system has lower noise interference during energy collection and signal transmission, that is, the system's anti-interference ability is stronger.

[0145] Figure 6 This is the third simulation schematic diagram of the adaptive energy collection method of the power Internet of Things provided by the present invention; Figure 6 As shown, it is shown that the antennas of wireless access nodes are limited. When the number is small, it cannot meet the communication and energy collection needs of users. As the number of wireless access nodes increases, the needs of users are gradually met. Compared with the random transmission of signals (abbreviated as Rand) and the non-cooperative adaptive energy collection strategy based on genetic algorithm, the AEHGA provided in this embodiment takes into account the state of the channel, the signal transmission power of the wireless access node and the power cutting ratio of the power terminal, and can better collect energy.

[0146] To summarize, the method provided in this embodiment is based on the downlink transmission scenario of wireless energy-carrying communication, and solves the problems of power cutting and signal transmission and cooperative relationship matching of heterogeneous multi-power terminals in the SWIPT system. The AEHGA algorithm is used to solve the constructed nonlinear programming problem. The system energy is optimized by optimizing the system's transmission strategy, cooperative strategy and power allocation coefficient, and compared with the non-cooperative adaptive energy collection strategy and random communication. The simulation results show that the algorithm provided in this embodiment has better energy collection speed, anti-interference ability and stability, and can effectively maximize the energy collection efficiency while ensuring user communication and endurance requirements.

[0147] The adaptive energy collection system of the electric power Internet of Things provided by the present invention is described below. The adaptive energy collection system of the electric power Internet of Things described below and the adaptive energy collection method of the electric power Internet of Things described above can be referenced to each other.

[0148] Figure 7 Schematic diagram of the structure of the adaptive energy collection system of the power Internet of Things provided by the present invention. Figure 7 As shown, the system is communicatively connected with a plurality of power terminals and a plurality of wireless access nodes respectively, and the system includes a model building unit 710 , a strategy optimization unit 720 and an energy configuration unit 730 .

[0149] The model building unit 710 is used to construct a target optimization problem with maximizing the total amount of energy collected by multiple power terminals from the original signals sent by multiple wireless access nodes as the objective function, and with the first communication configuration constraint corresponding to each wireless access node and the second communication configuration constraint corresponding to each power terminal as constraints.

[0150] The strategy optimization unit 720 is used to iteratively optimize and solve the target optimization problem based on a genetic algorithm to obtain the optimal transmission state and optimal cooperation state between each of the power terminals and each of the wireless access nodes, as well as the optimal power division factor of each of the power terminals.

[0151] The energy configuration unit 730 is used to control each of the power terminals to collect energy from the original signals sent by the multiple wireless access nodes according to the optimal transmission state, the optimal cooperation state, and the optimal power division factor.

[0152] The system provided in this embodiment constructs a target optimization problem by taking maximizing the total amount of energy collected by multiple power terminals from original signals sent by multiple wireless access nodes as the objective function, taking the first communication configuration constraint corresponding to each wireless access node and the second communication configuration constraint corresponding to each power terminal as constraints, and combines a genetic algorithm to optimize and solve the target optimization problem, so as to optimize the configuration of transmission channels, cooperative channels and power splitting factors of power terminals in the power communication system with the goal of maximizing the total energy collected by power terminals in the power communication system under the premise of meeting the minimum communication requirements, and thereby effectively ensuring the normal communication of the power terminals, deploying the optimal energy strategy for each power terminal, improving the energy collection efficiency of the power terminals, further ensuring communication endurance and improving user satisfaction.

[0153] Figure 8 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 8 As shown, the electronic device may include: a processor 810, a communication interface 820, a memory 830 and a communication bus 840, wherein the processor 810, the communication interface 820 and the memory 830 communicate with each other through the communication bus 840. The processor 810 may call the logic instructions in the memory 830 to execute the adaptive energy collection method of the power Internet of Things, the method comprising: taking the total amount of energy collected by the multiple power terminals from the original signals sent by the multiple wireless access nodes as the objective function, taking the first communication configuration constraint corresponding to each wireless access node and the second communication configuration constraint corresponding to each power terminal as the constraint conditions, constructing a target optimization problem; based on a genetic algorithm, iteratively optimizing and solving the target optimization problem, obtaining the optimal transmission state and the optimal cooperation state between each power terminal and each wireless access node, and the optimal power splitting factor of each power terminal; according to the optimal transmission state, the optimal cooperation state, and the optimal power splitting factor, controlling each power terminal to collect energy from the original signals sent by the multiple wireless access nodes.

[0154] In addition, the logic instructions in the above-mentioned memory 830 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0155] On the other hand, the present invention also provides a computer program product, which includes a computer program, and the computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the adaptive energy collection method of the power Internet of Things provided by the above-mentioned methods, and the method includes: taking maximizing the total amount of energy collected by multiple power terminals from the original signals sent by multiple wireless access nodes as the objective function, and taking the first communication configuration constraint corresponding to each wireless access node and the second communication configuration constraint corresponding to each power terminal as constraints, to construct a target optimization problem; based on a genetic algorithm, iteratively optimizing and solving the target optimization problem to obtain the optimal transmission state and optimal cooperation state between each power terminal and each wireless access node, as well as the optimal power splitting factor of each power terminal; according to the optimal transmission state and the optimal cooperation state, as well as the optimal power splitting factor, controlling each power terminal to collect energy from the original signals sent by multiple wireless access nodes.

[0156] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements an adaptive energy collection method for the electric power Internet of Things provided by the above-mentioned methods, the method comprising: taking maximizing the total amount of energy collected by multiple power terminals from the original signals sent by multiple wireless access nodes as the objective function, and taking the first communication configuration constraint corresponding to each wireless access node and the second communication configuration constraint corresponding to each power terminal as constraints, constructing a target optimization problem; based on a genetic algorithm, iteratively optimizing and solving the target optimization problem to obtain the optimal transmission state and the optimal cooperation state between each power terminal and each wireless access node, as well as the optimal power splitting factor of each power terminal; according to the optimal transmission state and the optimal cooperation state, as well as the optimal power splitting factor, controlling each power terminal to collect energy from the original signals sent by the multiple wireless access nodes.

[0157] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0158] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0159] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An adaptive energy collection method for power Internet of Things, characterized in that: Applied to a power communication system, the power communication system includes a plurality of power terminals and a plurality of wireless access nodes, the method includes: Taking maximizing the total amount of energy collected by the multiple power terminals from the original signals sent by the multiple wireless access nodes as the objective function, and taking the first communication configuration constraint corresponding to each wireless access node and the second communication configuration constraint corresponding to each power terminal as constraint conditions, a target optimization problem is constructed; Based on a genetic algorithm, the target optimization problem is iteratively optimized and solved to obtain an optimal transmission state and an optimal cooperation state between each of the power terminals and each of the wireless access nodes, as well as an optimal power division factor of each of the power terminals; According to the optimal transmission state, the optimal cooperation state, and the optimal power splitting factor, control each of the power terminals to collect energy from the original signals sent by the plurality of wireless access nodes; The target optimization problem is iteratively optimized and solved based on the genetic algorithm to obtain the optimal transmission state and the optimal cooperation state between each of the power terminals and each of the wireless access nodes, as well as the optimal power division factor of each of the power terminals, including: Acquire a plurality of different pre-configured transmission state matrices, cooperation state matrices, and power split factors; wherein the transmission state matrix is ​​used to represent the effective information transmission between each of the wireless access nodes and each of the power terminals, the cooperation state matrix is ​​used to represent the cooperation between each of the wireless access nodes and each of the power terminals, and the power split factor is used to split the signal power received by each of the power terminals into two parts, one of which is used for signal decoding and the other is used for energy collection; Constructing an initial group; wherein each individual in the group represents a combination of a transmission state matrix, a cooperation state matrix and a power splitting factor; For the current iterative optimization solution step, determine whether the number of current iterative optimization is less than the maximum number of iterations, whether the iteration factor is greater than the factor threshold, or whether the fitness of each individual is stable. If the number of current iterative optimization is less than the maximum number of iterations, or the iteration factor is greater than the factor threshold, or the fitness of each individual is unstable, select a new generation of population according to the fitness of the individual using the roulette method; Add the best individual selected in the previous iteration directly to the new group; Perform crossover and mutation operations on individuals in the new group; According to the results of crossover and mutation, the individuals of the new group are updated; and based on the new group updated with the individuals, the group member selection step is returned to perform the next iterative optimization solution step, until the maximum number of iterations is reached, the iteration factor is greater than or equal to the factor threshold, and the fitness of each individual is stable, then the iterative optimization solution process is terminated; When the iterative optimization solution process is completed, the optimal transmission state matrix, the optimal cooperation state matrix and the optimal power division factor are output to obtain the optimal transmission state and the optimal cooperation state between each power terminal and each wireless access node, as well as the optimal power division factor of each power terminal.

2. The adaptive energy collection method of the electric power Internet of Things according to claim 1 is characterized in that: The total amount of energy is determined based on the following steps: Acquire a transmission signal received by each of the power terminals according to the original signals sent by the plurality of wireless access nodes; Obtaining energy collected by each of the power terminals according to the transmission signal, a power division factor and an energy conversion efficiency corresponding to each of the power terminals, and noise generated by a power division circuit in each of the power terminals; The energy collected by the multiple power terminals is aggregated to obtain the total amount of energy.

3. The adaptive energy collection method of the electric power Internet of Things according to claim 2 is characterized in that: The acquiring, according to the original signals sent by the plurality of wireless access nodes, the transmission signal received by each of the power terminals comprises: For each power terminal, perform the following steps: Acquire a valid signal received by the current power terminal according to the transmission state, signal gain and precoding vector between the current power terminal and each of the wireless access nodes, as well as the transmission power allocated by each of the wireless access nodes to the current power terminal and the original signal sent by each of the wireless access nodes to the current power terminal; Acquire a first interference signal corresponding to the current power terminal according to the transmission status and precoding vector between other power terminals except the current power terminal and each of the wireless access nodes, as well as the transmission power allocated by each of the wireless access nodes to the other power terminals and the original signal sent by each of the wireless access nodes to the other power terminals; Acquire a second interference signal corresponding to the current power terminal according to the cooperation state between the current power terminal and each of the wireless access nodes, the transmission state and precoding vector between the other power terminals and each of the wireless access nodes, the transmission power allocated by each of the wireless access nodes to the other power terminals, and the original signal sent by each of the wireless access nodes to the other power terminals; A transmission signal currently received by the power terminal is acquired according to the effective signal, the first interference signal and the second interference signal.

4. The adaptive energy collection method of the electric power Internet of Things according to any one of claims 1 to 3, characterized in that: The first communication configuration constraints corresponding to each of the wireless access nodes include a hybrid clustering decision constraint between a transmission service and a cooperative service corresponding to each of the wireless access nodes, a total transmission power allocation constraint, a total signal quantity transmission constraint, and a total antenna quantity configuration constraint; The hybrid clustering decision constraint is constructed based on the constraint that the hybrid clustering decision value of each of the wireless access nodes is less than or equal to a decision threshold; The total transmission power allocation constraint is constructed based on the constraint that the total transmission power allocated to each of the wireless access nodes is less than or equal to the maximum transmission power corresponding to each of the wireless access nodes; The total signal quantity transmission constraint is constructed based on the constraint that the total signal quantity transmitted by each of the wireless access nodes is less than or equal to the backhaul link capacity corresponding to each of the wireless access nodes; The total antenna quantity configuration constraint is constructed based on the constraint that the total antenna quantity allocated to each of the wireless access nodes is less than an antenna quantity configuration threshold.

5. The adaptive energy collection method of the electric power Internet of Things according to claim 4 is characterized in that: The hybrid clustering decision constraints are formed based on the following steps: Adding the value of the transmission service state provided by each wireless access node for each power terminal and the value of the coordination service state provided by each wireless access node for each power terminal to obtain a hybrid clustering decision value of each wireless access node; The hybrid clustering decision constraint is constructed by taking the hybrid clustering decision value being less than or equal to the decision threshold as a constraint.

6. The adaptive energy collection method of the electric power Internet of Things according to any one of claims 1 to 3, characterized in that: The second communication configuration constraints corresponding to each of the power terminals include a power division factor constraint, an information reception signal-to-noise ratio constraint, and an energy collection amount constraint corresponding to each of the power terminals; The power split factor constraint is constructed based on the power split factor of each power terminal being within a constraint range; The information reception signal-to-noise ratio constraint is constructed based on the constraint that the information reception signal-to-noise ratio of each of the power terminals is greater than or equal to the minimum information reception signal-to-noise ratio threshold of each of the power terminals; The energy collection amount constraint is constructed based on the constraint that the energy received by each of the power terminals is greater than or equal to the energy consumed by each of the power terminals.

7. The adaptive energy collection method of the electric power Internet of Things according to claim 6 is characterized in that: The information reception signal-to-noise ratio constraint is formed based on the following steps: Comparing the effective signal received by each power terminal from the original signals sent by the multiple wireless access nodes with the interference signal received by each power terminal from the original signals sent by the multiple wireless access nodes, to obtain the information reception signal-to-noise ratio of each power terminal; The information reception signal-to-noise ratio constraint is constructed by taking the information reception signal-to-noise ratio of each of the power terminals as a constraint that the information reception signal-to-noise ratio of each of the power terminals is greater than or equal to a minimum information reception signal-to-noise ratio threshold of each of the power terminals.

8. An adaptive energy harvesting system for power Internet of Things, characterized in that: The system is respectively connected to multiple power terminals and multiple wireless access nodes for communication, and the system includes: A model building unit, configured to build a target optimization problem by taking maximizing the total amount of energy collected by the plurality of power terminals from the original signals sent by the plurality of wireless access nodes as an objective function and taking the first communication configuration constraint corresponding to each of the wireless access nodes and the second communication configuration constraint corresponding to each of the power terminals as constraint conditions; A strategy optimization unit, configured to perform iterative optimization and solution on the target optimization problem based on a genetic algorithm, to obtain an optimal transmission state and an optimal cooperation state between each of the power terminals and each of the wireless access nodes, and an optimal power division factor of each of the power terminals; An energy configuration unit, configured to control each of the power terminals to collect energy from original signals sent by the plurality of wireless access nodes according to the optimal transmission state, the optimal cooperation state, and the optimal power splitting factor; The strategy optimization unit is specifically used for: Acquire a plurality of different pre-configured transmission state matrices, cooperation state matrices, and power split factors; wherein the transmission state matrix is ​​used to represent the effective information transmission between each of the wireless access nodes and each of the power terminals, the cooperation state matrix is ​​used to represent the cooperation between each of the wireless access nodes and each of the power terminals, and the power split factor is used to split the signal power received by each of the power terminals into two parts, one of which is used for signal decoding and the other is used for energy collection; Constructing an initial group; wherein each individual in the group represents a combination of a transmission state matrix, a cooperation state matrix and a power splitting factor; For the current iterative optimization solution step, determine whether the number of current iterative optimization is less than the maximum number of iterations, whether the iteration factor is greater than the factor threshold, or whether the fitness of each individual is stable. If the number of current iterative optimization is less than the maximum number of iterations, or the iteration factor is greater than the factor threshold, or the fitness of each individual is unstable, select a new generation of population according to the fitness of the individual using the roulette method; Add the best individual selected in the previous iteration directly to the new group; Perform crossover and mutation operations on individuals in the new group; According to the results of crossover and mutation, the individuals of the new group are updated; and based on the new group updated with the individuals, the group member selection step is returned to perform the next iterative optimization solution step, until the maximum number of iterations is reached, the iteration factor is greater than or equal to the factor threshold, and the fitness of each individual is stable, then the iterative optimization solution process is terminated; When the iterative optimization solution process is completed, the optimal transmission state matrix, the optimal cooperation state matrix and the optimal power division factor are output to obtain the optimal transmission state and the optimal cooperation state between each power terminal and each wireless access node, as well as the optimal power division factor of each power terminal.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, it implements the adaptive energy collection method of the electric power Internet of Things as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the adaptive energy collection method of the electric power Internet of Things as described in any one of claims 1 to 7 is implemented.

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