Energy efficiency optimization method for low-power-consumption electric power ad hoc network

Abstract

The invention relates to an energy efficiency optimization method of a low-power-consumption electric power ad hoc network, an electric power system, computer equipment, a computer readable storage medium and a computer program product. Comprising the following steps: constructing an energy collection model and a signal-to-noise ratio model based on a power intelligent sensor network; according to the energy collection model and the signal-to-noise ratio model, constructing a target function by taking maximization of network energy efficiency as a target, and setting constraint conditions of the target function; constructing a polyhedral model, learning the polyhedral model by using the sample data of the independent identically distributed imperfect channel state, and outputting channel gain information; and according to the channel gain information, based on the constraint condition of the target function, solving the target function, determining the allocation power of each pair of secondary sensors on different sub-channels and the binary selection information of each pair of secondary sensors on the sub-channels, and obtaining a resource allocation scheme. By adopting the method, the influence of imperfect CSI can be reduced, the network throughput can be maximized, and the energy efficiency and the data transmission quality can be ensured.

Description

Technical Field

[0001] The present application relates to the technical field of power grid optimization, and in particular to an energy efficiency optimization method for a low-power self-organizing power network, a power system, a computer device, a computer-readable storage medium, and a computer program product. Background Art

[0002] In recent years, my country has attached great importance to digital development and has started to vigorously promote the construction of "Digital China", which has put forward higher requirements for the application of digital technologies including intelligent sensing in the power industry. Based on this, the grand vision of the next generation of smart grids - "transparent grids" has been proposed: the future of the power grid is a transparent grid, which effectively integrates sensor technology, information technology, data communication technology, electronic control technology, artificial intelligence, etc. in the power system, and can realize the transparency of equipment status, operation status, and transaction status.

[0003] At present, the main online monitoring systems use many discrete and single sensors to monitor the operation status of the power system, which cannot achieve effective integration and information sharing at the sensor level, resulting in slow development of technologies such as multi-parameter information fusion sharing and comprehensive diagnosis, and cannot meet the development requirements of smart grids. Multi-integrated sensors use miniaturized integration, multi-physical quantity synchronous detection, full-scale measurement, and on-demand analog / digital format technology solutions to integrate sensitive components, signal conditioning circuits, microprocessors, and communication interface modules, and have the intelligent trend of multiple environmental physical quantity perception and sensor information fusion. The intelligence and informatization of smart grids require the configuration of a large number of sensors and data acquisition modules. How to organically integrate sensors and corresponding data acquisition and transmission systems to form a multi-physical quantity integrated sensor array, and use multi-dimensional information comprehensive analysis technology to perform efficient status analysis and fault diagnosis on equipment is a hot research direction in the current digital grid construction.

[0004] By 2020, the total mileage of China's power transmission lines will increase from 1.15 million kilometers in 2014 to more than 1.59 million kilometers, and the mileage of power transmission lines continues to increase. The contradiction between the rapid growth of the mileage of power grid operation and maintenance lines and the relatively insufficient number of power grid operation and maintenance personnel has gradually emerged. With the advancement of smart grid construction and the development of artificial intelligence technology, the inspection of power transmission lines has transitioned from the traditional "human patrol" to the "human patrol + drone patrol" stage, but the flight capability of drones is limited by weather and battery life. At the same time, there are also problems such as limited monitoring volume to images, high technical requirements for operators, short patrol distances, and the impact of no-fly zones. It fails to cover the monitoring needs of power transmission lines in an all-round and real-time manner; there is also the problem of how to realize the data fusion of multiple integrated sensors and long-distance transmission back to the server.

[0005] Therefore, in order to further improve the observability of power grid operation and enhance the information perception capability of transmission lines, it is necessary to study multi-sensor integration technology to realize all-round and multi-angle monitoring of transmission lines. By studying multi-physical quantity integrated sensor technology, massive deployment of integrated sensor elements can be realized; multi-physical quantity integrated sensor distributed self-organizing network data communication technology can be studied to complete the networking communication between multiple multi-physical quantity integrated sensor devices; multi-physical quantity data analysis, feature extraction technology and auxiliary decision-making technology of integrated sensors can be studied, multi-physical quantity data fusion application system can be developed, and dynamic capacity expansion monitoring software modules for transmission lines can be developed to promote the application of artificial intelligence technology in massive monitoring data of transmission lines, solve the actual needs of operation and maintenance of transmission lines, realize high-efficiency and low-latency real-time transmission of measurement data to the server for processing and real-time monitoring of the working status of transmission lines, so as to achieve the goal of automation, informatization and digitization of intelligent monitoring of transmission lines, and ultimately achieve the goal of transparent perception of power grids.

[0006] The multi-physical quantity integrated sensor for transmission lines (multi-split, supporting AC lines and spacers with more than four splits) is a transmission line intelligent steward used for panoramic real-time monitoring and intelligent early warning of overhead transmission lines. It adopts wide-range inductive self-powering technology to ensure continuous and reliable operation of the device, and provides continuous and reliable power supply to the sensor through distributed wide-range inductive power supply modules, realizing real-time online intelligent monitoring of low-load lines, and has the advantages of easy expansion and high reliability.

[0007] The multi-split version can be directly installed on 500kV transmission lines by integrating with the spacer bar; the double-split and single-conductor version adopts an expandable lightweight integrated design and can be directly installed on 110 / 220kV transmission lines. The power measurement gripper is suitable for various types of split conductors. It adopts an independent measurement power gripper structure, which is connected to the monitoring host through a flexible connection line, integrates the sub-conductor current and temperature acquisition module, and ensures that the device and the conductor are reliably equipotential. The monitoring host modularly integrates visible light / ultraviolet / infrared cameras, environmental temperature and humidity sensors, pressure sensors, acceleration sensors, attitude sensors, high-precision Beidou positioning modules, etc. The device supports access to the Internet of Things platform, file server, and video server, and has the online fusion perception and abnormal intelligent recognition alarm functions of physical quantities such as conductor temperature / current / sag / dancing, environmental temperature / humidity / pressure / altitude, channel visible light image / video, ultraviolet / infrared (optional), etc.

[0008] In recent years, with the continuous improvement of the level of urban intelligence, various sensor devices and automatic control systems in smart grids have been widely used. Smart grids achieve efficient operation and management of power systems by integrating advanced sensing, communication and control technologies. Power smart sensor networks that support wireless communications can provide diversified monitoring and management services for urban power systems. However, with the surge in diversified services, power smart sensor networks face the problem of scarce spectrum resources. Cognitive Radio (CR) based on power smart sensor networks is considered to be a key technology to alleviate the problem of spectrum scarcity. CR allows unauthorized secondary user sensors to share spectrum with authorized primary user sensors, thereby meeting the spectrum requirements of power smart sensor networks.

[0009] At the same time, the CR-based power intelligent sensor network system requires efficient data exchange between different sensor nodes, so it is necessary to improve the network flexibility of the power intelligent sensor network. Device to Device (D2D) communication technology is a key technology for building a highly flexible network, which realizes direct business communication without data forwarding through base stations, greatly improving the efficiency of flexible networking.

[0010] With the high-speed communication of D2D technology in power smart sensor networks, business traffic will increase rapidly and the energy consumption of sensor nodes will also increase, making energy efficiency an important research topic in smart grid scenarios. In order to extend the working time of sensor nodes, energy harvesting (EH) technology is widely used in power smart sensor networks. EH technology allows sensor nodes to obtain energy from the environment, such as solar energy, wind energy, vibration energy, etc., thereby reducing dependence on external power sources. By combining energy harvesting technology, power smart sensor networks can maximize network energy efficiency (EE) while ensuring the quality of data transmission.

[0011] At present, there are two aspects in the research of power smart sensor networks that combine D2D technology, energy harvesting technology and spectrum sharing: one is to focus on the impact of energy on transmission efficiency; the other is to focus on the quality of data transmission. In order to improve transmission efficiency, traditional research mostly considers the energy problem of D2D technology, but pays less attention to the quality of data transmission, which may lead to reduced reliability of data transmission. There are also some studies that only focus on the data transmission quality of D2D, but pay less attention to energy efficiency, which may increase energy consumption and reduce the working time of sensor nodes.

[0012] In addition, the existence of imperfect channel state information (CSI) caused by the complexity of the environment affects the optimization effect of the smart grid. Currently, few studies have focused on the problem of channel information uncertainty in power smart sensor network systems. Therefore, the main challenge facing power smart sensor networks that use D2D technology, energy harvesting technology, and spectrum sharing is how to ensure energy efficiency and data transmission quality under the condition of channel information uncertainty. Summary of the invention

[0013] Based on this, it is necessary to provide an energy efficiency optimization method, power system, computer equipment, computer readable storage medium and computer program product for a low-power power ad hoc network to address the above-mentioned technical problems, which can reduce the impact of imperfect CSI, maximize network throughput, and ensure energy efficiency and data transmission quality.

[0014] In a first aspect, the present application provides a method for optimizing energy efficiency of a low-power self-organizing power network, comprising:

[0015] Based on the power smart sensor network, an energy collection model and a signal-to-noise ratio model are constructed; the power smart sensor network includes a primary sensor network and a secondary user network, the primary sensor network includes a base station and M primary sensors, and the secondary user network is composed of N pairs of secondary sensors for device-to-device communication; the energy collection model is used to characterize the energy collected at the nth pair of secondary sensors; the signal-to-noise ratio model is used to characterize the signal-to-noise ratio of the nth pair of secondary sensors;

[0016] According to the energy harvesting model and the signal-to-noise ratio model, an objective function is constructed with the goal of maximizing network energy efficiency, and constraints of the objective function are set;

[0017] Construct a polyhedron model, use the sample data of independent and identically distributed imperfect channel states to learn the polyhedron model, and output channel gain information;

[0018] According to the channel gain information and based on the constraint condition of the objective function, the objective function is solved to determine the allocated power of each pair of secondary sensors on different sub-channels and the binary selection information of each pair of secondary sensors on the sub-channels, and obtain a resource allocation scheme.

[0019] In one embodiment, the energy harvesting model is expressed by the following formula (1):

[0020]

[0021] In formula (1), P M , α and β are all constants, P M It represents the maximum harvested power when the actual energy harvesting circuit reaches saturation. α and β are determined by the characteristics of the actual energy harvesting circuit. represents the input power of the nth pair of sensors, which is expressed by the following formula (2):

[0022]

[0023] In formula (2), θ represents the power allocation factor, represents the transmission power of the nth pair of secondary sensors on the subchannel occupied by the mth primary sensor, represents the channel gain of the mth main sensor on the sub-channel, represents the channel gain from the nth pair of secondary sensors to the mth primary sensor, Indicates the channel gain of the main sensor.

[0024] In one embodiment, the signal-to-noise ratio model is expressed by the following formula (3):

[0025]

[0026] In formula (3), represents the transmission power of the nth pair of secondary sensors on the subchannel occupied by the mth primary sensor, represents the channel gain of the mth main sensor on the sub-channel, represents the channel gain from the nth pair of secondary sensors to the mth primary sensor, represents the channel gain of the main sensor; α n，m is the user association factor between the nth pair of secondary sensors and the mth primary sensor, when α n，m =1, it means that the nth pair of secondary sensors occupies the channel of the mth primary sensor. n，m =0, it means that the nth pair of secondary sensors does not occupy the channel of the mth primary sensor; σ 2 Represents the noise power.

[0027] In one embodiment, the objective function is expressed by the following formula (4):

[0028]

[0029] In formula (4), represents the transmit power of the nth pair of secondary sensors on the subchannel occupied by the mth primary sensor; represents the data rate in the secondary user network, which is expressed by the following formula (5):

[0030]

[0031] In formula (5), The signal-to-noise ratio of the nth pair of sensors;

[0032] The constraints of the objective function include signal-to-noise ratio constraints, energy collection constraints, sub-sensor power constraints, and sub-channel constraints; among which:

[0033] The signal-to-noise ratio constraint is expressed by the following formula (6):

[0034]

[0035] In formula (6), γ th To set the threshold;

[0036] The energy harvesting constraints are expressed by the following formula (7):

[0037] E min ≤E eh (7);

[0038] In formula (7), E min is the minimum energy required by the secondary sensor;

[0039] The secondary sensor power limitation condition is expressed by the following formula (8):

[0040]

[0041] In formula (8), P max is the power limit value of the secondary sensor;

[0042] The subchannel constraint is expressed by the following formula (9):

[0043]

[0044] In formula (9), α n，m is the user association factor between the nth pair of secondary sensors and the mth primary sensor.

[0045] In one embodiment, the polyhedron model is represented by the following formula (10):

[0046]

[0047] In formula (10), represents the center of the polyhedron; λ>0, represents the size of the polyhedron, represents the uncertain channel state region learned from sample data of independent and identically distributed imperfect channel states; the channel gain information output by the polyhedron model is and represents the channel gain from the nth pair of secondary sensors to the mth primary sensor; Indicates the channel gain of the main sensor.

[0048] In one embodiment, the polyhedron model is learned using sample data of independent and identically distributed imperfect channel states, and the channel gain information is output, including:

[0049] Dividing the sample data of the independent and identically distributed imperfect channel state into two parts to obtain first sample data and second sample data;

[0050] The shape of the high probability region is approximated by the first sample data, and the center of the polyhedron is set by the sample average of the first sample data to determine Parameter values ​​and Parameter value;

[0051] Using the second sample data to calibrate the size of the polyhedron, and determine the λ parameter value;

[0052] The determined parameter value is used in the polyhedron model to determine and output the channel gain information and

[0053] In a second aspect, the present application further provides a power system, in which a power intelligent sensor network is provided, the power intelligent sensor network includes a primary sensor network and a secondary user network, the primary sensor network includes a base station and M primary sensors, and the secondary user network is composed of N pairs of secondary sensors for device-to-device communication;

[0054] The secondary sensor performs resource configuration based on the resource allocation scheme determined by the method of the first aspect.

[0055] In a third aspect, the present application further provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method of the first aspect when executing the computer program.

[0056] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method of the first aspect above.

[0057] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, which implements the steps of the method of the first aspect above when executed by a processor.

[0058] The energy efficiency optimization method, power system, computer device, computer readable storage medium and computer program product of the low-power power ad hoc network are based on a power intelligent sensor network to construct an energy collection model and a signal-to-noise ratio model; the power intelligent sensor network includes a main sensor network and a secondary user network, the main sensor network includes a base station and M main sensors, and the secondary user network is composed of N pairs of secondary sensors for device-to-device communication; the energy collection model is used to characterize the energy collected at the nth pair of secondary sensors; the signal-to-noise ratio model is used to characterize the signal-to-noise ratio of the nth pair of secondary sensors; according to the energy collection model and the signal-to-noise ratio model, an objective function is constructed with the goal of maximizing network energy efficiency, and constraints of the objective function are set; a polyhedron model is constructed, and the polyhedron model is learned using sample data of independent and identically distributed imperfect channel states to output channel gain information; according to the channel gain information, based on the constraints of the objective function, the objective function is solved to determine the allocated power of each pair of secondary sensors on different sub-channels and the binary selection information of each pair of secondary sensors on the sub-channels, and a resource allocation plan is obtained. Through the above method, the energy collection process of the secondary sensor is modeled, and an energy collection optimization model under non-perfect CSI conditions is established to maximize the energy efficiency of the network; in order to solve the problem that imperfect CSI makes the objective function difficult to solve, a polyhedron model is constructed and sample data is learned to reduce the impact of imperfect CSI, output a better resource allocation plan, maximize network throughput, and ensure energy efficiency and data transmission quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0060] Figure 1 A schematic diagram of a flow chart of a method for optimizing energy efficiency of a low-power self-organizing power network in one embodiment;

[0061] Figure 2 A schematic diagram of the composition of a power intelligent sensor network in one embodiment;

[0062] Figure 3 is a schematic diagram of the relationship between noise power and total throughput of a secondary network in one embodiment;

[0063] Figure 4 A schematic diagram of the relationship between noise power and secondary network energy efficiency in one embodiment;

[0064] Figure 5A schematic diagram of the relationship between the SINR threshold of the secondary sensor and the secondary network throughput in one embodiment;

[0065] Figure 6 A schematic diagram of the relationship between the SINR threshold of the secondary sensor and the secondary network energy efficiency in one embodiment;

[0066] Figure 7 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0067] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0068] In an exemplary embodiment, Figure 1 As shown, a method for optimizing energy efficiency of a low-power self-organizing power network is provided, comprising:

[0069] Step 102, constructing an energy collection model and a signal-to-noise ratio model based on the power intelligent sensor network; the power intelligent sensor network includes a primary sensor network and a secondary user network, the primary sensor network includes a base station and M primary sensors, and the secondary user network is composed of N pairs of secondary sensors for device-to-device communication; the energy collection model is used to characterize the energy collected at the nth pair of secondary sensors; the signal-to-noise ratio model is used to characterize the signal-to-noise ratio of the nth pair of secondary sensors.

[0070] It is understandable that the deployment of power intelligent sensor networks in the power system will form a system with the ability to perceive transmission line information. Power intelligent sensors can collect information such as electrical quantities, physical quantities, environmental quantities, state quantities, and behavioral quantities in all aspects of power production, building a bridge from the physical power system to the cyber-physical fusion system.

[0071] Among them, refer to Figure 2 ,exist Figure 2 In the communication scenario shown, the power smart sensor network consists of two main communication networks: the primary sensor network and the secondary user network. The primary sensor network consists of a base station (BS) and a primary sensor (PS), which obtains fixed spectrum resources through a spectrum license. The secondary user network consists of secondary sensors (DS) for device-to-device (D2D) communication. Since the secondary user network lacks a licensed frequency band, it relies on sharing the resources of the primary sensor network. Definition and Represents the number of PS and D2D pairs respectively. PS, BS and D2D pairs will cause mutual interference during communication. Figure 2In this embodiment, the interference between D2D pairs is mainly focused on the primary user (PU) interference link and the auxiliary user (SU) interference link. Each DS as a D2D communication transmitter is equipped with an EH (Energy Harvesting) rectifier circuit. The tasks of DS include the EH stage and the information transmission stage. In the EH stage, the transmitter of each D2D pair captures the radio frequency (RF) energy during the PS information transmission. In the information transmission stage, DS uses the collected energy to transmit information.

[0072] Optionally, a linear energy harvesting model is constructed, where the harvested energy is modeled as the input power A linear function of Where 0≤η≤1 represents the EH conversion efficiency. Specifically, based on the input power The expression of and the linear relationship between the collected energy and input power are used to construct an energy harvesting model.

[0073] Optionally, since EH circuits usually exhibit nonlinear EH characteristics, a sub-linear energy harvesting model is constructed, and the harvested energy is modeled as the input power Specifically, based on the input power The expression of energy harvesting and the nonlinear relationship between the collected energy and input power are used to construct an energy harvesting model.

[0074] Optionally, a signal-to-noise ratio model is constructed by taking into account the co-channel interference (ie, interference between D2D pairs) generated when the DS occupies the PS channel.

[0075] Step 104 , constructing an objective function based on the energy harvesting model and the signal-to-noise ratio model with the goal of maximizing network energy efficiency, and setting constraints of the objective function.

[0076] The system performance is optimized by maximizing the network energy efficiency (EE) while satisfying various types of constraints. Optionally, an optimization model is established with the goal of improving the network energy efficiency by comprehensively considering constraints such as signal-to-noise ratio constraints, energy collection requirements, and transmission power limits.

[0077] Step 106: construct a polyhedron model, use the sample data of independent and identically distributed imperfect channel states to learn the polyhedron model, and output channel gain information.

[0078] Among them, a high probability region (HPR) is used to represent uncertainty, and the HPR is used to optimize system performance. In order to determine the HPR, by collecting samples of independent and identically distributed imperfect CSI channels, it is helpful to characterize the uncertainty set and derive the HPR of CSI errors. Encapsulating and modeling the uncertainty associated with CSI through polyhedral integration can effectively simulate the uncertainty of channel status.

[0079] Step 108, solving the objective function based on the constraint conditions of the objective function according to the channel gain information, determining the allocated power of each pair of secondary sensors on different sub-channels and the binary selection information of each pair of secondary sensors on the sub-channels, and obtaining a resource allocation scheme.

[0080] Among them, by considering the channel gain information learned from imperfect CSI, based on various constraints, the objective function is solved to determine the resource allocation scheme that can maximize the network energy efficiency under the constraints, including the allocation power of each pair of sensors on different sub-channels. (that is, the optimal transmit power of the transmitter in the secondary sensor pair) and the binary selection information α of each pair of secondary sensor pairs n，m , the binary selection information is used to determine which sub-channel is selected by the main sensor.

[0081] In the energy efficiency optimization method of the above-mentioned low-power self-organizing network, the secondary sensor energy collection process is modeled, and an energy collection optimization model under non-perfect CSI conditions is established to maximize the energy efficiency of the network; in order to solve the problem that imperfect CSI makes the objective function difficult to solve, a polyhedron model is constructed and sample data is learned to reduce the impact of imperfect CSI, output a better resource allocation plan, maximize network throughput, and ensure energy efficiency and data transmission quality.

[0082] In an exemplary embodiment, the energy harvesting model is expressed by the following formula (1):

[0083]

[0084] In formula (1), P M , α and β are all constants, P M It represents the maximum harvested power when the actual energy harvesting circuit reaches saturation. α and β are determined by the characteristics of the actual energy harvesting circuit. represents the input power of the nth pair of sensors, which is expressed by the following formula (2):

[0085]

[0086] In formula (2), θ represents the power allocation factor, represents the transmission power of the nth pair of secondary sensors on the subchannel occupied by the mth primary sensor, represents the channel gain of the mth main sensor on the sub-channel, represents the channel gain from the nth pair of secondary sensors to the mth primary sensor, Indicates the channel gain of the main sensor.

[0087] Compared with the linear energy collection model, the energy collection model constructed in this embodiment can express the nonlinear EH characteristics of the actual EH circuit, so that the optimization process is more in line with the actual situation, thereby improving the optimization effect of the smart grid. In the specific implementation, setting a suitable power allocation factor is selected to find a balance between energy collection and information transmission to maximize the energy efficiency (EE) and information transmission efficiency of the network.

[0088] In an exemplary embodiment, the signal-to-noise ratio model is expressed by the following formula (3):

[0089]

[0090] In formula (3), represents the transmission power of the nth pair of secondary sensors on the subchannel occupied by the mth primary sensor, represents the channel gain of the mth main sensor on the sub-channel, represents the channel gain from the nth pair of secondary sensors to the mth primary sensor, represents the channel gain of the main sensor; α n，m is the user association factor between the nth pair of secondary sensors and the mth primary sensor, when α n，m =1, it means that the nth pair of secondary sensors occupies the channel of the mth primary sensor. n，m =0, it means that the nth pair of secondary sensors does not occupy the channel of the mth primary sensor; σ 2 Represents the noise power.

[0091] In the information transmission stage, DS uses the collected energy to perform information transmission; considering the co-channel interference generated when DS occupies the PS channel, a signal-to-noise ratio model is constructed as shown in formula (3). represents the transmission power of the i-th pair of secondary sensors on the subchannel occupied by the m-th primary sensor, α i，m represents the user association factor between the i-th pair of secondary sensors and the m-th primary sensor, represents the channel gain from the i-th pair of secondary sensors to the m-th primary sensor; the i-th pair of secondary sensors are other D2D pairs among the N pairs of secondary sensors except the n-th pair of secondary sensors.

[0092] In an exemplary embodiment, the objective function is expressed by the following formula (4):

[0093]

[0094] In formula (4), represents the transmit power of the nth pair of secondary sensors on the subchannel occupied by the mth primary sensor; represents the data rate in the secondary user network, which is expressed by the following formula (5):

[0095]

[0096] In formula (5), The signal-to-noise ratio of the nth pair of sensors;

[0097] The constraints of the objective function include signal-to-noise ratio constraints, energy collection constraints, sub-sensor power constraints, and sub-channel constraints; among which:

[0098] The signal-to-noise ratio constraint is expressed by the following formula (6):

[0099]

[0100] In formula (6), γ th To set the threshold;

[0101] The energy harvesting constraints are expressed by the following formula (7):

[0102] E min ≤E eh (7);

[0103] In formula (7), E min is the minimum energy required by the secondary sensor;

[0104] The secondary sensor power limitation condition is expressed by the following formula (8):

[0105]

[0106] In formula (8), P max is the power limit value of the secondary sensor;

[0107] The subchannel constraint is expressed by the following formula (9):

[0108]

[0109] In formula (9), α n，m is the user association factor between the nth pair of secondary sensors and the mth primary sensor.

[0110] Among them, by setting the signal-to-noise ratio constraint condition, it is ensured that the SINR of DS exceeds the set threshold and the data transmission quality of DS is ensured. By setting the energy collection constraint condition, the collected energy is greater than the consumed energy to ensure that DS can continue to operate and complete the communication task. By setting the secondary sensor power constraint condition, the transmission power of DS is limited so that it does not exceed the power limit value to meet the actual operation requirements. By setting the sub-channel constraint condition, the nth pair of secondary sensors only occupies a sub-channel of the main sensor.

[0111] In an exemplary embodiment, the polyhedron model is represented by the following formula (10):

[0112]

[0113] In formula (10), represents the center of the polyhedron; λ>0, represents the size of the polyhedron, represents the uncertain channel state region learned from sample data of independent and identically distributed imperfect channel states; the channel gain information output by the polyhedron model is and represents the channel gain from the nth pair of secondary sensors to the mth primary sensor; Indicates the channel gain of the main sensor.

[0114] The problem of solving the objective function is a non-convex optimization problem. Since the objective function is in a fractional form, that is, the ratio of the total data rate of all DSs to the total transmission power, and the constraints involve nonlinear factors such as SINR, EH, and resource allocation, it is difficult to solve directly. Based on this, the high probability region (HPR) is used in this embodiment to represent uncertainty, and the HPR is used to optimize system performance. In order to determine the HPR, samples of the independent and identically distributed imperfect CSI channel are collected. (Among them, a m ∈R 2 ), which helps to characterize the uncertainty set and derive the HPR of CSI error. Encapsulating and modeling the uncertainty associated with CSI through polyhedral ensemble can effectively simulate the uncertainty of channel state. Polyhedral ensemble modeling provides a general framework that can be combined with various optimization tools and techniques to enhance the flexibility and robustness of system optimization.

[0115] In an exemplary embodiment, the polyhedron model is learned using sample data of independent and identically distributed imperfect channel states to output channel gain information, including: dividing the sample data of independent and identically distributed imperfect channel states into two parts to obtain first sample data and second sample data; using the first sample data to approximate the shape of the high probability area, and setting the center of the polyhedron by the sample average of the first sample data to determine Parameter values ​​and Parameter value; using the second sample data to calibrate the size of the polyhedron, determine the λ parameter value; determine and output the channel gain information in the polyhedron model of the determined parameter value and

[0116] In order to solve the problem that the objective function is difficult to solve, this embodiment provides a polyhedron-based learning robust algorithm (hereinafter referred to as PLRA algorithm) to deal with the imperfect CSI problem. First, the user channel set is modeled to construct a polyhedron model as shown in formula (10); then, the various parameters of the polyhedron are determined by a specific learning algorithm, which specifically includes two steps: shape learning and size calibration. By dividing the sample data into two parts, namely and use to approximate the shape of the HPR, and use To calibrate the size of the uncertainty set so that it satisfies the optimization model and has coverage.

[0117] In an optional implementation, shape learning includes the following steps: setting and As the origin of the polyhedron uncertainty set, according to the following formula (11), and is calculated as the sample mean:

[0118]

[0119] It is understandable that the polyhedron is selected in this embodiment because it is easy to handle, thereby producing robust optimization. After performing the shape learning step of the polyhedron uncertainty set, in order to ensure that the obtained HPR parameters have good adaptability and generalizability, a size calibration step is performed next.

[0120] In an optional implementation, the size calibration includes the following steps: using a data set To calibrate the polyhedral uncertainty sets so that they contain the confidence level The core method involves estimating the quantile of the transformed data sample. Specifically, a transformation function is defined according to the following formula (12):

[0121]

[0122] Among them, t p (a) The random vector space Map to This transform quantizes the components of a to the reference value and deviation.

[0123] Using the sample dataset The derived transformation t p (a) Estimated Quantiles are used to determine the size of the HPR. The size can be set based on the sample dataset t p (a) The distribution of To formalize it, we define it according to the following formula (13): Quantile:

[0124]

[0125] Using this definition, The 1-K confidence limit of the quantile can be derived based on the confidence interval estimation method. In addition, in order to achieve the desired confidence level, the minimum sample number D-D1 must satisfy the constraints shown in the following formula (13):

[0126]

[0127] calculate The t of each sample in p (a) function values, and arrange these values ​​in ascending order as t p (1) ≤…≤t p (D) . Take the first values, which can be considered as t p (a) is the upper limit, where According to the given upper limit, the set can be set according to the following formula (14): Size:

[0128]

[0129] In determining the polyhedral uncertainty set After the size of the channel gain, the channel gain can be obtained by the above formula (10): and

[0130] It can be understood that after obtaining more accurate channel gains through learning the polyhedron model, in order to maximize the network efficiency under the model, we use a polyhedron-based learning resource algorithm to solve the model. The optimization problem is nonlinear because the objective function in the optimization model is affected by random parameters. In order to find the optimal solution, convex optimization tools can be used to simplify the problem and solve the maximum network energy efficiency. After solving the objective function, a set of parameter values ​​can be obtained as a resource allocation scheme, including the allocated power of each node n on different sub-channels, and the binary selection of each node n for sub-channels, aiming to maximize the network energy efficiency while satisfying the signal-to-noise ratio threshold, energy demand limit, and power upper and lower limit constraints.

[0131] In an exemplary embodiment, referring to Table 1, Table 1 shows a pseudo code for robust optimization of resource allocation for polyhedral learning. The specific steps are as follows: collect independent and identically distributed samples under incomplete channel state information, divide the samples into two parts, one for shape learning and the other for size calibration. Secondly, set the size of HPR, and perform shape learning and size calibration steps to learn channel gain parameters. Finally, use the CVX tool in Python to solve the model to obtain the optimal transmit power of the user.

[0132] Table 1:

[0133]

[0134] In an exemplary embodiment, numerical and simulation results are provided to illustrate the performance of the PLRA algorithm provided in the embodiment of the present application, and compared with the ellipsoid-based learning algorithm (ELRA), the robust optimization algorithm (RA) and the baseline algorithm (hereinafter, the algorithm compared with the ELRA algorithm is referred to as the "comparison algorithm"). The coverage range of the BS is a circle with a radius of 600m, the BS bandwidth B is 5MHz, the number of DUs (distributed units) is 40, and the number of data sets is 1000. The remaining simulation parameter settings refer to Table 2.

[0135] Table 2:

[0136]

[0137]

[0138] Reference Figure 3 , Figure 3 It shows that the noise power σ 2 The relationship between DS and the total rate. Figure 3 It can be seen that as the noise power σ 2 As the total rate of DS increases, it is obvious that the PLRA algorithm shows superior performance over the comparison algorithm. In the power smart sensor network, the DS group needs to share the same frequency band under limited spectrum resources, resulting in increased competition for spectrum resources and noise. Due to the imperfect CSI in the network, this may cause errors during transmission and communication. The PLRA algorithm provided in this application can effectively solve the imperfect CSI problem and improve the reliability and efficiency of the system through the modeled channel.

[0139] Reference Figure 4 , Figure 4 Denotes the noise power σ 2 The relationship between DS and energy efficiency EE. As the noise power increases, the total data rate of DS tends to decrease. Figure 4It can be clearly seen that the PLRA algorithm significantly outperforms the comparison algorithms in maintaining higher data rates under different noise conditions. Due to the degradation of signal quality, more retransmissions and error correction processes will occur in the network, increasing the overall energy consumption. As more resources are consumed, in order to achieve reliable information transmission, the EE of the network gradually decreases. Figure 4 It is shown that PLRA effectively mitigates these adverse effects and ensures better performance optimization in terms of energy efficiency compared to other algorithms despite the higher noise power, highlighting the effectiveness of the PLRA algorithm in optimizing network performance under challenging conditions.

[0140] Reference Figure 5 , Figure 5 The SINR threshold γ of DS in power smart sensor network is shown th The relationship between the throughput of the secondary network. Figure 5 As can be seen in Figure 2, the PLRA algorithm is significantly better than the other algorithms. As the SINR threshold increases, the total data rate of the DS tends to rise. This is because a higher SINR threshold requires a stronger signal relative to the interference, thereby reducing the impact of interference on signal quality. As a result, the system can more efficiently utilize the available spectrum and bandwidth. This optimized spectrum utilization directly contributes to improving the overall throughput of the network. By setting a higher SINR threshold, the system can better manage network resources and adopt more efficient information transmission technology, ultimately increasing the data rate achieved by the DS, thereby improving the robustness of the communication link, reducing the error rate, and improving the overall performance of information transmission.

[0141] Reference Figure 6 , Figure 6 The SINR threshold γ of DS in power smart sensor network is shown th The relationship between the energy efficiency and the secondary network EE. Figure 6 It can be seen that when the SINR threshold γ th When increases, the EE of DS increases, and the EE of the PLRA algorithm is greater than the EE of the comparison algorithm, proving that the proposed PLRA algorithm outperforms the comparison algorithm. In power smart sensor networks with imperfect CSI, the increase in SINR threshold leads to an increase in signal quality requirements, which reduces the impact of interference on the signal. A higher SINR threshold allows the system to transmit data under higher signal quality conditions, which helps reduce the probability of transmission errors and data loss and improves the overall performance of the network. Since the PLRA algorithm can optimize resource allocation and signal processing more effectively, it can still optimize higher network energy efficiency even in the presence of imperfect CSI information, indicating that it has significant advantages in improving network performance and optimizing energy usage. This result further verifies the effectiveness of the PLRA algorithm in handling optimization tasks in complex network environments.

[0142] It can be understood that the energy efficiency optimization method for low-power self-organizing power networks provided in the embodiments of the present application is intended to achieve efficient EE in power intelligent sensor networks under EH scenarios, and can effectively improve the network throughput of power intelligent sensor networks, which is better than the comparison algorithm. First, the energy collection of sensor nodes is modeled; on this basis, combined with power control and rate requirement constraints, a robust optimization model with imperfect CSI is established, and this model aims to maximize network energy efficiency; and a robust learning algorithm based on polyhedron is proposed, which reduces the impact of imperfect CSI by modeling and learning the channel, and finally obtains a more accurate solution. The simulation results verify that this method can effectively improve the performance of the network.

[0143] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0144] In an exemplary embodiment, based on the same inventive concept, the embodiment of the present application further provides a power system, in which a power intelligent sensor network is provided, the power intelligent sensor network includes a primary sensor network and a secondary user network, the primary sensor network includes a base station and M primary sensors, and the secondary user network is composed of N pairs of secondary sensors for device-to-device communication;

[0145] The secondary sensor performs resource configuration based on the resource allocation scheme determined by the energy efficiency optimization method for low-power self-organizing power networks provided in the embodiment of the present application.

[0146] In an optional implementation, a computer device arranged in the power system or a computer device arranged at a remote end performs optimization analysis based on the energy efficiency optimization method of the low-power power ad hoc network provided in the embodiment of the present application, outputs a resource allocation plan, and provides it to each pair of secondary sensors in the power intelligent sensor network through the communication network. The transmitter in the secondary sensor pair captures energy according to the resource allocation plan and transmits information based on the collected energy.

[0147] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 7 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for optimizing energy efficiency of a low-power self-organizing power network is implemented.

[0148] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0149] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the following steps when executing the computer program: constructing an energy collection model and a signal-to-noise ratio model based on an electric power intelligent sensor network; the electric power intelligent sensor network includes a primary sensor network and a secondary user network, the primary sensor network includes a base station and M primary sensors, and the secondary user network is composed of N pairs of secondary sensors for device-to-device communication; the energy collection model is used to characterize the energy collected at the nth pair of secondary sensors; the signal-to-noise ratio model is used to characterize the signal-to-noise ratio of the nth pair of secondary sensors; based on the energy collection model and the signal-to-noise ratio model, constructing an objective function with the goal of maximizing network energy efficiency, and setting constraints of the objective function; constructing a polyhedron model, using sample data of independent and identically distributed imperfect channel states to learn the polyhedron model, and outputting channel gain information; solving the objective function based on the channel gain information and the constraints of the objective function, determining the allocated power of each pair of secondary sensors on different sub-channels and the binary selection information of each pair of secondary sensors on the sub-channels, and obtaining a resource allocation plan.

[0150] In one embodiment, when the processor executes the computer program, the following steps are further implemented: dividing the sample data of the independent and identically distributed imperfect channel state into two parts to obtain first sample data and second sample data; using the first sample data to approximate the shape of the high probability area, and setting the center of the polyhedron by the sample average value of the first sample data to determine Parameter values ​​and Parameter value; using the second sample data to calibrate the size of the polyhedron, determine the λ parameter value; determine and output the channel gain information in the polyhedron model of the determined parameter value and

[0151] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: based on an electric power intelligent sensor network, an energy collection model and a signal-to-noise ratio model are constructed; the electric power intelligent sensor network includes a primary sensor network and a secondary user network, the primary sensor network includes a base station and M primary sensors, and the secondary user network is composed of N pairs of secondary sensors for device-to-device communication; the energy collection model is used to characterize the energy collected at the nth pair of secondary sensors; the signal-to-noise ratio model is used to characterize the signal-to-noise ratio of the nth pair of secondary sensors; according to the energy collection model and the signal-to-noise ratio model, an objective function is constructed with the goal of maximizing network energy efficiency, and constraints of the objective function are set; a polyhedron model is constructed, and the polyhedron model is learned using sample data of independent and identically distributed imperfect channel states, and channel gain information is output; according to the channel gain information, based on the constraints of the objective function, the objective function is solved to determine the allocated power of each pair of secondary sensors on different sub-channels and the binary selection information of each pair of secondary sensors on the sub-channels, and a resource allocation scheme is obtained.

[0152] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: dividing the sample data of the independent and identically distributed imperfect channel state into two parts to obtain first sample data and second sample data; using the first sample data to approximate the shape of the high probability area, and setting the center of the polyhedron by the sample average of the first sample data to determine Parameter values ​​and Parameter value; using the second sample data to calibrate the size of the polyhedron, determine the λ parameter value; determine and output the channel gain information in the polyhedron model of the determined parameter value and

[0153] In one embodiment, a computer program product is provided, including a computer program, which implements the following steps when executed by a processor: constructing an energy collection model and a signal-to-noise ratio model based on an electric power smart sensor network; the electric power smart sensor network includes a primary sensor network and a secondary user network, the primary sensor network includes a base station and M primary sensors, and the secondary user network is composed of N pairs of secondary sensors for device-to-device communication; the energy collection model is used to characterize the energy collected at the nth pair of secondary sensors; the signal-to-noise ratio model is used to characterize the signal-to-noise ratio of the nth pair of secondary sensors; based on the energy collection model and the signal-to-noise ratio model, constructing an objective function with the goal of maximizing network energy efficiency, and setting constraints on the objective function; constructing a polyhedron model, using sample data of independent and identically distributed imperfect channel states to learn the polyhedron model, and outputting channel gain information; according to the channel gain information, based on the constraints of the objective function, solving the objective function, determining the allocated power of each pair of secondary sensors on different sub-channels and the binary selection information of each pair of secondary sensors on the sub-channels, and obtaining a resource allocation plan.

[0154] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: dividing the sample data of the independent and identically distributed imperfect channel state into two parts to obtain first sample data and second sample data; using the first sample data to approximate the shape of the high probability area, and setting the center of the polyhedron by the sample average of the first sample data to determine Parameter values ​​and Parameter value; using the second sample data to calibrate the size of the polyhedron, determine the λ parameter value; determine and output the channel gain information in the polyhedron model of the determined parameter value and

[0155] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0156] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0157] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0158] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for optimizing energy efficiency of a low-power self-organizing power network, characterized in that: The method comprises: Based on the power intelligent sensor network, an energy collection model and a signal-to-noise ratio model are constructed; the power intelligent sensor network includes a primary sensor network and a secondary user network, the primary sensor network includes a base station and M primary sensors, and the secondary user network is composed of N pairs of secondary sensors for device-to-device communication; the energy collection model is used to characterize the energy collected at the nth pair of secondary sensors; the signal-to-noise ratio model is used to characterize the signal-to-noise ratio of the nth pair of secondary sensors; According to the energy collection model and the signal-to-noise ratio model, an objective function is constructed with the goal of maximizing network energy efficiency, and constraints of the objective function are set; Constructing a polyhedron model, learning the polyhedron model using sample data of independent and identically distributed imperfect channel states, and outputting channel gain information; According to the channel gain information and based on the constraint conditions of the objective function, the objective function is solved to determine the allocated power of each pair of secondary sensors on different sub-channels and the binary selection information of each pair of secondary sensors on the sub-channels, and obtain a resource allocation scheme.

2. The method according to claim 1, characterized in that The energy harvesting model is expressed by the following formula (1): In formula (1), P M , α and β are all constants, P M It represents the maximum harvested power when the actual energy harvesting circuit reaches saturation. α and β are determined by the characteristics of the actual energy harvesting circuit. represents the input power of the nth pair of sensors, which is expressed by the following formula (2): In formula (2), θ represents the power allocation factor, represents the transmission power of the nth pair of secondary sensors on the subchannel occupied by the mth primary sensor, represents the channel gain of the mth main sensor on the sub-channel, represents the channel gain from the nth pair of secondary sensors to the mth primary sensor, Indicates the channel gain of the main sensor.

3. The method according to claim 2, characterized in that The signal-to-noise ratio model is expressed by the following formula (3): In formula (3), represents the transmission power of the nth pair of secondary sensors on the subchannel occupied by the mth primary sensor, represents the channel gain of the mth main sensor on the sub-channel, represents the channel gain from the nth pair of secondary sensors to the mth primary sensor, represents the channel gain of the main sensor; α n，m is the user association factor between the nth pair of secondary sensors and the mth primary sensor, when α n，m =1, it means that the nth pair of secondary sensors occupies the channel of the mth primary sensor. n，m =0, it means that the nth pair of secondary sensors does not occupy the channel of the mth primary sensor; σ 2 Represents the noise power.

4. The method according to claim 3, characterized in that The objective function is expressed by the following formula (4): In formula (4), represents the transmit power of the nth pair of secondary sensors on the subchannel occupied by the mth primary sensor; represents the data rate in the secondary user network, which is expressed by the following formula (5): In formula (5), The signal-to-noise ratio of the nth pair of sensors; The constraints of the objective function include signal-to-noise ratio constraints, energy collection constraints, secondary sensor power constraints, and sub-channel constraints; wherein: The signal-to-noise ratio constraint is expressed by the following formula (6): In formula (6), γ th To set the threshold; The energy harvesting constraint is expressed by the following formula (7): AND min ≤E eh (7); In formula (7), E min is the minimum energy required by the secondary sensor; The secondary sensor power limitation condition is expressed by the following formula (8): In formula (8), P max is the power limit value of the secondary sensor; The subchannel constraint is expressed by the following formula (9): In formula (9), α n，m is the user association factor between the nth pair of secondary sensors and the mth primary sensor.

5. The method according to claim 1, characterized in that The polyhedron model is expressed by the following formula (10): In formula (10), represents the center of the polyhedron; λ>0, represents the size of the polyhedron, represents the uncertain channel state region learned from sample data of independent and identically distributed imperfect channel states; the channel gain information output by the polyhedron model is and represents the channel gain from the nth pair of secondary sensors to the mth primary sensor; Indicates the channel gain of the main sensor.

6. The method according to claim 5, characterized in that The method of learning the polyhedron model by using the sample data of the independent and identically distributed imperfect channel state and outputting the channel gain information includes: Dividing the sample data of the independent and identically distributed imperfect channel state into two parts to obtain first sample data and second sample data; The shape of the high probability region is approximated by using the first sample data, and the center of the polyhedron is set by the sample average value of the first sample data to determine Parameter values ​​and Parameter value; Using the second sample data to calibrate the size of the polyhedron, and determine the λ parameter value; The determined parameter values ​​are applied to the polyhedral model to determine and output channel gain information and 7. A power system, characterized in that: The power system is provided with a power intelligent sensor network, the power intelligent sensor network includes a primary sensor network and a secondary user network, the primary sensor network includes a base station and M primary sensors, and the secondary user network is composed of N pairs of secondary sensors for device-to-device communication; The secondary sensor performs resource configuration based on the resource allocation scheme determined by the method according to any one of claims 1 to 6.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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