Network resource allocation method for power intelligent sensor relay back network

Abstract

The application relates to a network resource allocation method of an electric power intelligent sensor relay return network. The method comprises the following steps: for a sensor group composed of one S2S pair sharing the same channel, determining a signal interference noise ratio of the sensor group according to communication performance parameters of each sensor device in the sensor group and noise signal power of the channel; in the case that the type of interference received by a disturbed sensor in the sensor group is cumulative interference, determining an interference weight corresponding to the disturbed sensor according to the signal interference noise ratio; constructing an interference hypergraph model based on the interference weights corresponding to all the disturbed sensors; determining a maximum interference degree of the sensor network according to the interference hypergraph model; and determining a resource allocation scheme based on the maximum interference degree, so as to realize network resource allocation of the electric power sensor. The method can adaptively select the best resource allocation scheme and effectively realize resource allocation.

Description

Technical Field

[0001] This application relates to the field of power transmission line sensor network technology, and in particular to a network resource allocation method for a power intelligent sensor relay backhaul network. Background Technology

[0002] In power sensor networks, sensor devices often need to transmit information within the same frequency band, leading to overlapping communication radii and strained spectrum resources. This overlap not only exacerbates communication interference but also significantly reduces communication quality, posing a potential threat to the stable operation of the power system. Traditionally, reinforcement learning is widely used in such optimization problems. Its core mechanism involves continuously interacting with the environment to iterate and optimize decision-making strategies in order to find the optimal solution in complex or uncertain scenarios. However, this method faces numerous limitations in practical applications of power sensor networks.

[0003] Specifically, power sensor networks are typically deployed in complex and variable power environments, such as high-voltage transmission lines and substations. These environments not only have limited spectrum resources but also suffer from severe electromagnetic interference, making it difficult for reinforcement learning algorithms to make optimal decisions in real time under dynamic conditions. Furthermore, regarding energy harvesting, photovoltaic energy harvesting, due to its large size, high volatility, and high maintenance costs, is insufficient to meet the long-term stable operation requirements of sensor nodes. While electromagnetic field and vibration energy harvesting offer good stability and continuity, magnetic field energy harvesting has a limited operating range, and electric field and vibration energy harvesting have relatively low power outputs, failing to fully meet the power supply needs of sensors and self-organizing network nodes. In terms of networking, current technology primarily employs self-organizing network technology based on Orthogonal Frequency Division Multiplexing (OFDM). Although significant progress has been made in network synchronization accuracy, further improvements are needed in key performance indicators such as multi-hop transmission rate and latency.

[0004] In particular, the problem of backhauling power sensor data is especially prominent in communication-dead areas such as mountainous regions and deserts. Due to the complex geographical environment and weak communication infrastructure in these areas, it is difficult to effectively transmit sensor data back to the control center. Therefore, there is an urgent need to develop a multi-hop networking backhaul technology based on the self-powering of integrated sensors of multiple physical quantities in power transmission and the fusion of spacer bars. This technology can effectively solve the communication problems in these areas and has broad application prospects and huge market potential. Summary of the Invention

[0005] Based on this, it is necessary to provide a network resource allocation method, device, computer equipment, medium, and product for a power intelligent sensor relay backhaul network that can effectively realize resource allocation, addressing the aforementioned technical problems.

[0006] Firstly, this application provides a network resource allocation method for a power smart sensor relay backhaul network, including:

[0007] For a sensor group consisting of an S2S pair sharing the same channel, the signal-to-interference-to-noise ratio of the sensor group is determined based on the communication performance parameters of each sensor device in the sensor group and the noise signal power of the channel.

[0008] When the type of interference experienced by the interfered sensor in the sensor group is cumulative interference, the interference weight corresponding to the interfered sensor is determined according to the signal-to-interference-to-noise ratio.

[0009] An interference hypergraph model is constructed based on the interference weights corresponding to all the interfered sensors. The vertices of the interference hypergraph model represent sensor devices, and the hyperedges connecting the vertices represent a set of sensor devices that interfere with each other.

[0010] The maximum interference level of the sensor network is determined based on the interference hypergraph model, and a resource allocation scheme is determined based on the maximum interference level to realize the network resource allocation of power sensors.

[0011] In one embodiment, the process of determining the type of interference experienced by the interfered sensor in the sensor group includes:

[0012] If the signal-to-noise ratio of the sensor group is less than the maximum interference threshold, the type of interference experienced by the interfered sensor in the sensor group is determined to be strong interference.

[0013] If the signal-to-noise ratio of the sensor group is greater than or equal to the maximum interference threshold, the type of interference experienced by the interfered sensor is determined to be weak interference.

[0014] When a sensor is subjected to both strong and weak interference, the type of interference experienced by the sensor is determined to be cumulative interference.

[0015] In one embodiment, the step of determining the interference weight corresponding to the interfered sensor in the sensor group based on the signal-to-interference-to-noise ratio includes:

[0016] Based on the relationship between the signal-to-noise ratio and the maximum interference threshold, determine the upper limit of the total interference of the interfered sensors in the sensor group;

[0017] Obtain the power of the interference signal received by the interfered sensor from the interfering sensor;

[0018] The interference weight corresponding to the interfered sensor is determined based on the upper limit of the total interference and the power.

[0019] In one embodiment, the step of constructing an interference hypergraph model based on the interference weights corresponding to all interfered sensors includes:

[0020] Based on the interference weights corresponding to all the interfered sensors, an interference weight matrix is ​​constructed. Each row of the interference weight matrix represents a sensor device, and each column of the interference weight matrix represents another sensor device that interferes with the sensor device.

[0021] An interference hypergraph model is constructed based on the interference weight matrix. In the interference hypergraph model, the nodes of the hypergraph are the sensor devices in the interference weight matrix, the hyperedges are the connections between the sensor devices and other sensor devices that interfere with the sensor devices, and the weight of the hyperedge is the sum of the interference weights between the sensor devices connected by the hyperedge.

[0022] In one embodiment, the step of determining the maximum interference level of the sensor network based on the interference hypergraph model includes:

[0023] The resource allocation matrix is ​​determined based on the interference hypergraph model; the resource allocation matrix is ​​used to characterize the resource allocation for each S2S pair.

[0024] The maximum interference of the sensor network can be obtained by solving the resource allocation matrix.

[0025] In one embodiment, the step of determining a resource allocation scheme based on the maximum interference degree includes:

[0026] Construct the state space based on the communication state parameters of all S2S pairs;

[0027] Determine the objective function based on the resource allocation goal, and establish an optimal allocation model based on the objective function;

[0028] The optimization allocation model is trained based on the state space and the objective function to obtain the objective allocation model;

[0029] Resource allocation schemes are generated based on the target allocation model, and then updated according to the maximum disturbance.

[0030] Secondly, this application also provides a network resource allocation device for a power smart sensor relay backhaul network, comprising:

[0031] The noise ratio determination module is used to determine the signal-to-interference-to-noise ratio of a sensor group consisting of an S2S pair sharing the same channel, based on the communication performance parameters of each sensor device in the sensor group and the noise signal power of the channel.

[0032] The weight determination module is used to determine the interference weight corresponding to the interfered sensor based on the signal-to-interference-to-noise ratio when the type of interference received by the interfered sensor in the sensor group is cumulative interference.

[0033] The hypergraph construction module is used to construct an interference hypergraph model based on the interference weights corresponding to all the interfered sensors. The vertices of the interference hypergraph model represent sensor devices, and the hyperedges connecting the vertices represent a set of sensor devices that interfere with each other.

[0034] The resource allocation module is used to determine the maximum interference degree of the sensor network based on the interference hypergraph model, and to determine the resource allocation scheme based on the maximum interference degree in order to realize the network resource allocation of power sensors.

[0035] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method steps of any one of the first aspects.

[0036] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method steps of any one of the first aspects.

[0037] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the method steps of any one of the first aspects.

[0038] The aforementioned network resource allocation method, device, computer equipment, medium, and product for power intelligent sensor relay backhaul networks accurately calculates the signal-to-interference-plus-noise ratio (S2S ratio) of a sensor group composed of an S2S pair sharing the same channel. When the interference type experienced by the interfered sensor in the sensor group is cumulative interference, the interference weight corresponding to the interfered sensor is determined based on the S2S ratio. Then, an interference hypergraph model is constructed, and the maximum interference degree of the sensor network is determined based on the interference hypergraph model. Thus, a resource allocation scheme is determined based on the maximum interference degree. This adaptively selects the optimal resource allocation scheme, effectively avoiding interference, improving the signal-to-interference-plus-noise ratio, and ensuring the transmission rate. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a structural diagram of a sensor network in one embodiment;

[0041] Figure 2 This is a flowchart illustrating a network resource allocation method for a power smart sensor relay backhaul network in one embodiment.

[0042] Figure 3(a) is a schematic diagram of the interference relationship between S2S and communication in one embodiment;

[0043] Figure 3(b) is a schematic diagram of the interference weight matrix in one embodiment;

[0044] Figure 4 This is a schematic diagram of an interference hypergraph model in one embodiment;

[0045] Figure 5 This is a convergence graph of a network resource management algorithm in one embodiment;

[0046] Figure 6 This is a convergence graph of the network resource management algorithm in another embodiment;

[0047] Figure 7 This is a comparison chart of network throughput in one embodiment;

[0048] Figure 8 This is a graph showing the relationship between the number of S2S links and SINR in one embodiment;

[0049] Figure 9 This is a flowchart illustrating the network resource allocation method for a power smart sensor relay backhaul network in another embodiment.

[0050] Figure 10 This is a structural block diagram of a network resource allocation device for a power smart sensor relay backhaul network in one embodiment.

[0051] Figure 11 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0053] The network resource allocation method for the power smart sensor relay backhaul network provided in this application embodiment can be applied to, for example... Figure 1 The sensor network shown is a Dense Distributed Power Sensor Network (DD-PSN). Individual sensor devices and Each base station provides spectrum resources to sensor devices via Device-to-Infrastructure (D2I) communication for data transmission. Sensor devices exchange data with each other via Server-to-Server (S2S) communication. This process includes... There are S2S pairs. Assuming sensor devices can share spectrum resources, if each sensor device uses an independent channel, it leads to a waste of spectrum resources. However, by sharing spectrum resources, multiple sensor devices can use the same sub-channel set, thereby improving spectrum utilization. That is, sensor devices share a sub-channel set. Shared spectrum resources.

[0054] In one exemplary embodiment, such as Figure 2 As shown, a network resource allocation method for a power smart sensor relay backhaul network is provided, which is applied to... Figure 1 The following steps, 202 to 208, are used as an example to illustrate the process of building a sensor network.

[0055] S202: For a sensor group consisting of an S2S pair sharing the same channel, determine the signal-to-interference-to-noise ratio of the sensor group based on the communication performance parameters of each sensor device in the sensor group and the noise signal power of the channel.

[0056] Optionally, the signal-to-interference-plus-noise ratio (SINR) is an important indicator for measuring the performance of a communication system. SINR is the ratio of the power of the received useful signal to the sum of the power of the received interference signal (including interference from other signal sources and noise within the system). The higher the SINR value, the stronger the received useful signal is relative to the interference and noise, and the better the communication quality. Conversely, a low SINR value means that the communication quality may be significantly affected.

[0057] For example, in Figure 1 In the sensor network shown, for the sensor group consisting of the m-th S2S pair sharing the same channel r (including the n-th sensor device and the m-th sensor device), the SINR of the n-th sensor device can be expressed as:

[0058]

[0059] Where, σ 2 This represents the noise signal power at the base station on subchannel r. h represents the transmission power of the nth sensor device when communicating on the rth subchannel. nThis represents the channel gain of the nth sensor device. h represents the transmission power of the m-th S2S pair on the r-th subchannel. m This indicates the interference channel gain.

[0060] Similarly, the SINR of the m′-th S2S pair in the r-th sub-channel can be calculated as:

[0061]

[0062] in, h represents the transmission power of the m′-th S2S pair in the r-th sub-channel. m′ Let represent the channel gain of the m′-th S2S pair.

[0063] S204: When the type of interference experienced by the interfered sensor in the sensor group is cumulative interference, the interference weight corresponding to the interfered sensor is determined according to the signal-to-interference-to-noise ratio.

[0064] Optionally, to address the problem of compound overlapping interference in power sensor networks, it is necessary to classify the interference sources for targeted solutions. Cumulative interference refers to the simultaneous presence of interference sources of varying intensities within a sensor system, which interact and gradually interfere with each other, ultimately negatively impacting system performance. For cases of cumulative interference, an appropriate interference weight is determined based on the signal-to-interference-to-noise ratio (SNR) of each affected sensor device. This interference weight represents the intensity of interference experienced by the affected sensor.

[0065] S206: Construct an interference hypergraph model based on the interference weights corresponding to all the interfered sensors; the vertices of the interference hypergraph model represent sensor devices, and the hyperedges connecting the vertices represent a set of sensor devices that interfere with each other.

[0066] Optionally, to visually represent interference relationships in a sensor network, a hypergraph model can be used. In this interference hypergraph model, sensor devices are vertices, and the hyperedges connecting these vertices represent a set of sensor devices that interfere with each other. This model is constructed by analyzing and quantifying the interference relationships between the sensor devices in the sensor network. For example, if multiple sensor devices communicate on the same frequency band or are in close physical locations, they may interfere with each other; these devices can be connected by a hyperedge.

[0067] S208: Determine the maximum interference degree of the sensor network based on the interference hypergraph model, and determine the resource allocation scheme based on the maximum interference degree to realize the network resource allocation of power sensors.

[0068] Optionally, based on the interference hypergraph model, the maximum interference level of the sensor network can be calculated. The maximum interference level quantifies the overall network interference level; when the maximum interference level is zero, it indicates no interference conflicts within the network; when the maximum interference level is not zero, it indicates the presence of interference. The maximum interference level reflects the severity of interference in the sensor network, and the rationality of resource allocation directly affects the magnitude of interference. For example, unreasonable allocation of resources such as channel resources and transmit power may lead to increased interference between sensor devices. By analyzing the maximum interference level, it is possible to identify which areas or sensor devices are experiencing more severe interference, thereby enabling targeted resource adjustments.

[0069] Furthermore, based on the maximum interference level, corresponding resource allocation schemes are formulated. For example, channel allocation for sensor devices can be adjusted, assigning devices with higher interference levels to different channels to reduce mutual interference; transmission power can be adjusted to reduce the transmission power of interference sources to minimize interference to other devices; or the network topology can be optimized to increase the distance between sensor devices, reducing interference propagation. By adjusting resource allocation, interference in the sensor network can be reduced, communication quality and network performance can be improved, ensuring that power sensors can transmit data accurately and stably.

[0070] In the aforementioned network resource allocation method for the power smart sensor relay backhaul network, the signal-to-interference-plus-noise ratio (S2S ratio) of a sensor group consisting of an S2S pair sharing the same channel is accurately calculated. When the interference type experienced by the interfered sensor in the sensor group is cumulative interference, the interference weight corresponding to the interfered sensor is determined based on the S2S ratio. Then, an interference hypergraph model is constructed, and the maximum interference degree of the sensor network is determined based on the interference hypergraph model. Thus, the resource allocation scheme is determined based on the maximum interference degree. This method can adaptively select the optimal resource allocation scheme, effectively avoid interference, improve the signal-to-interference-plus-noise ratio, and ensure the transmission rate.

[0071] In an exemplary embodiment, the process of determining the type of interference experienced by the interfered sensor in the sensor group includes: determining that the type of interference experienced by the interfered sensor in the sensor group is strong interference when the signal-to-interference-to-noise ratio of the sensor group is less than the maximum interference threshold; determining that the type of interference experienced by the interfered sensor is weak interference when the signal-to-interference-to-noise ratio of the sensor group is greater than or equal to the maximum interference threshold; and determining that the type of interference experienced by the interfered sensor is cumulative interference when the interfered sensor is simultaneously subjected to strong interference and weak interference.

[0072] Optionally, the interference experienced by the sensor device can be categorized into three types: strong interference, weak interference, and cumulative interference. Cumulative interference consists of multiple strong or weak interferences. Different interference types can be represented by the relationship between SINR and the maximum interference threshold. For example, when... Exceeding the maximum interference threshold γ th When the first S2S pair can communicate normally, strong interference can be caused by... This indicates that weak interference can be caused by express.

[0073] Specifically, when multiple sensor devices operate on the same spectrum, there is a risk of severe interference between them. For example, if multiple sensor devices transmit on similar frequencies, they may interfere with each other, leading to signal quality degradation or even complete communication interruption. This type of interference is considered strong interference. Weak interference refers to interference in the sensor system that is relatively minor, potentially affecting communication quality but not to the point of interruption or severely impacting communication effectiveness. Cumulative interference refers to the simultaneous presence of interference sources of varying intensities in the sensor system. These sources interact and gradually interfere with each other, ultimately negatively impacting system performance. This type of interference consists of both strong and weak interference and affects the normal operation of the DD-PSN network. S2S pairs subjected to strong interference cannot share resources, while S2S pairs with weak interference have minimal impact on each other, allowing normal communication and sharing of the same resources. Therefore, to determine the operational status of the DD-PSN network, it is necessary to accurately assess cumulative interference to determine whether it has reached a level that severely interferes with normal communication, thereby preventing resource waste.

[0074] In this embodiment, by classifying the interference received by the sensor device into three types—strong interference, weak interference, and cumulative interference—different management strategies can be adopted for different types of interference. Through precise interference management, the impact of interference on sensor performance can be reduced, and the transmission rate can be guaranteed.

[0075] In an exemplary embodiment, the step of determining the interference weight corresponding to the interfered sensor in the sensor group based on the signal-to-interference-noise ratio includes: determining the upper limit of the total interference of the interfered sensors in the sensor group based on the relationship expression between the signal-to-interference-noise ratio and the maximum interference threshold; obtaining the power of the interference signal received by the interfered sensor from the interfering sensor; and determining the interference weight corresponding to the interfered sensor based on the upper limit of the total interference and the power.

[0076] Optionally, firstly, based on the relationship between SINR and the maximum interference threshold, a threshold can be set. This threshold represents the maximum interference level that each sensor in the sensor group can withstand. Using this threshold, the upper limit of the total interference from the interfered sensors in the sensor group can be calculated; that is, the sum of all interference signals must not exceed this upper limit to ensure the normal operation of the sensors. Next, the power of the interference signals received by the interfered sensor from the interfering sensor needs to be obtained. Then, based on the upper limit of the total interference and the power of each interference signal, the interference weight corresponding to the interfered sensor is calculated. The interference weight reflects the degree of influence of each interference signal on the interfered sensor.

[0077] For example, when the m-th S2S pair can communicate normally, the relationship between its SINR and the maximum interference threshold is expressed as follows:

[0078]

[0079] in, h represents the transmission power of the m′-th S2S pair in the r-th sub-channel. m′ p represents the channel gain of the m′-th S2S pair. r i h represents the interference signal power from other sensor device i. i The channel gain σ of sensor device i represents 2 Indicates noise power.

[0080] Furthermore, it can be deduced that:

[0081]

[0082] Therefore, interference weight The expression for this value is derived from the ratio of the power received by the interfered sensor from the interfering sensor to the upper limit of the total interference.

[0083]

[0084] Among them, P s This is an expression representing the power received by the interfered sensor device from the interfering sensor device, with the denominator being the upper limit of the total interference.

[0085] In this embodiment, by determining the upper limit of the total interference of the interfered sensors in the sensor group according to the relationship expression between the signal interference-to-noise ratio and the maximum interference threshold, the power of the interference signal received by the interfered sensor from the interfering sensor is obtained. Based on the upper limit of the total interference and the power, the interference weight corresponding to the interfered sensor is determined. This allows for a more accurate understanding of the interference between various sensors in the sensor network, thereby optimizing the network layout and parameter settings and improving the overall performance of the network.

[0086] In an exemplary embodiment, the step of constructing an interference hypergraph model based on the interference weights corresponding to all interfered sensors includes: constructing an interference weight matrix according to the interference weights corresponding to all interfered sensors; each row of the interference weight matrix represents a sensor device, and each column of the interference weight matrix represents an other sensor device that interferes with the sensor device; constructing an interference hypergraph model according to the interference weight matrix; in the interference hypergraph model, the nodes of the hypergraph are the sensor devices in the interference weight matrix, the hyperedges are the connections between the sensor devices and other sensor devices that interfere with the sensor devices, and the weight of the hyperedge is the sum of the interference weights of the sensor devices connected by the hyperedge.

[0087] Optionally, an interference weight matrix is ​​constructed based on the interference weights corresponding to all interfered sensors. Each row of this matrix represents a sensor device, and each column represents another sensor device that interferes with that sensor device. The elements in the matrix (i.e., interference weights) reflect the degree of interference between sensor devices. Then, based on the interference weight matrix, an interference hypergraph model is constructed. In this model, the nodes of the hypergraph correspond to the sensor devices in the interference weight matrix, and the hyperedges represent the connections between a sensor device and other sensor devices that interfere with it. The weight of a hyperedge is the sum of the interference weights between the sensor devices connected by the hyperedge, reflecting the strength of the interference relationship represented by that hyperedge.

[0088] For example, Figure 3(a) is a schematic diagram of the S2S interference relationship in communication. The arrows depicted in Figure 3(a) indicate potential interference caused by communication with the indicated sensor devices. Figure 3(b) is an interference weight matrix recorded based on the interference relationship diagram in Figure 3(a). Each row represents a different sensor device experiencing interference, and each column within a row represents interference caused by other sensor devices. For example, in row S3, columns S4, S6, and S7... 10 and S 11 The interference weights for S3 are 0.2, 0.3, 0.4, and 0.4, respectively. Then, the 16th row of the matrix represents the interference from S. 15 Independent interference exceeds S 16 The maximum interference threshold is determined, thus requiring the allocation of different resources. In the matrix, each row is marked by a color to represent different conditions; for example, green indicates that the interference remains within the maximum interference threshold, thus eliminating the need to allocate additional resources to the corresponding sensor device, red indicates resource allocation requirements, and yellow indicates the sensor device causing the interference.

[0089] For example, a hypergraph can be represented as Where ε=(e1, e2, ..., e J) is a hypergraph edge set. It is the vertex set of a hypergraph. The relationships between vertices and hyperedges in a hypergraph can be represented by an incidence matrix M of size J×K. J×K This means that the elements in the matrix can be represented as:

[0090]

[0091] Among them, h (j，k) This represents the value in the j-th row and k-th column of matrix M.

[0092] Furthermore, when analyzing only cumulative interference, the cumulative interference can be represented by an interference hypergraph model. Based on the cumulative interference shown in Figure 3(b), the receiver is regarded as a hyperedge in the hypergraph, and the transmitter interfering with the receiver is regarded as a point in the hypergraph, thus establishing an interference hypergraph model.

[0093] For example, interference hypergraph models such as Figure 4 As shown, in Figure 4 There are six vertices (S1, S4, S5, S8, S...). 11 S 12 ) and five superedges (R2, R3, R5, R7, R 10 The vertices represent six different interference sources and five cumulative interferences, respectively. The hyperedges connecting the vertices represent the interference relationships between these sensor devices; that is, sensor devices within the same hyperedge interfere with each other. A single sensor device may be located in multiple hyperedges, resulting in multiple cumulative interferences. According to... Figure 4 The interference hypergraph model in the image provides an intuitive understanding of the interference relationships between S2S pairs. Figure 4 The correlation matrix in the data can be represented as:

[0094]

[0095] Furthermore, when the cumulative interference weight exceeds 1, it signifies a significant impact on communication, requiring different resource allocation strategies to mitigate the interference. When the sum of the weights is less than 1, the same resources can continue to be shared. To address the interference avoidance problem in resource allocation, for example, in row R2 of the correlation matrix A, S6, S... 10 and S 12 If the cumulative interference weight of any two sensor devices in R2 exceeds 1, then different resources must be allocated to these three sensor devices to prevent interference. Based on this, the interference avoidance problem in the interference hypergraph can be transformed into a point coloring problem to be solved.

[0096] In this embodiment, by constructing an interference weight matrix based on the interference weights corresponding to all interfered sensors, and then constructing an interference hypergraph model based on the interference weight matrix, the interference situation in the network can be analyzed more accurately, potential interference sources and interference paths can be identified, and the best resource allocation scheme can be selected in a targeted manner to effectively avoid interference.

[0097] In an exemplary embodiment, the step of determining the maximum interference degree of a sensor network based on an interference hypergraph model includes: determining a resource allocation matrix based on the interference hypergraph model; the resource allocation matrix being used to characterize the resource allocation for each S2S pair; and solving for the maximum interference degree of the sensor network based on the resource allocation matrix.

[0098] Optionally, based on the constructed interference hypergraph model, a resource allocation matrix is ​​determined. This matrix characterizes the resource allocation among sensor-to-sensor (S2S) pairs in the sensor network. Each element of the resource allocation matrix represents the amount of resources or resource identifier allocated to each S2S pair. Reasonable resource allocation can reduce interference between sensors and improve network communication efficiency. Subsequently, the maximum interference degree of the sensor network is calculated based on the resource allocation matrix. The maximum interference degree is an indicator of the interference intensity in the network, reflecting the most severe interference situation under a given resource allocation strategy.

[0099] For example, to determine the interference between S2S pairs in a DD-PSN, the maximum interference degree Θ based on the correlation matrix of the interference hypergraph model is used. M To quantify the overall interference situation of DD-PSN. Among them, the maximum interference degree Θ M It can be represented as:

[0100] Θ M =MAX[max(M J×K H K×C ),1]-1,

[0101] Among them, H K×C This represents a resource allocation matrix, specifying how C resources are allocated to K S2S pairs. `max(·)` represents the maximum value in the matrix, and `MAX[·, 1]` means comparing each element with 1 and selecting the larger of the two values. When Θ M =0 indicates the maximum interference level Θ when there are no interference collisions within the network. M It can quantify the overall network interference level, while Θ M If ≠0, it indicates the presence of interference.

[0102] In this embodiment, the resource allocation matrix is ​​determined based on the interference hypergraph model. The resource allocation matrix is ​​used to characterize the resource allocation for each S2S pair. The maximum interference degree of the sensor network is obtained by solving the resource allocation matrix. The optimal resource allocation scheme can be adaptively selected, which can effectively avoid interference, improve the signal-to-interference-plus-noise ratio, and ensure the transmission rate.

[0103] In an exemplary embodiment, the step of determining a resource allocation scheme based on the maximum interference includes: constructing a state space based on the communication state parameters of all S2S pairs; determining an objective function based on the resource allocation objective, and establishing an optimized allocation model based on the objective function; training the optimized allocation model based on the state space and the objective function to obtain a target allocation model; generating a resource allocation scheme based on the target allocation model, and updating the resource allocation scheme based on the maximum interference.

[0104] Optionally, a state space is constructed by acquiring the communication state parameters of all S2S pairs in the sensor network. This state space is a multi-dimensional space, where each dimension represents a communication state parameter, and each point in the state space represents a specific communication state. The purpose of constructing the state space is to provide input data for the subsequent optimization allocation model and to help the model understand changes in the communication states within the network. Then, based on the resource allocation objective, such as maximizing network throughput, minimizing transmission delay, or maximizing resource utilization, an objective function is determined. This function will be used to measure the effectiveness of the resource allocation scheme. Finally, based on the objective function and the state space, an optimization allocation model is established to find the optimal resource allocation scheme, that is, to achieve the optimal value of the objective function while satisfying the constraints.

[0105] Furthermore, the collected communication state parameter data is used to train the optimization allocation model, enabling the model to learn the relationship between the state space and the objective function, thereby predicting the objective function values ​​under different resource allocation schemes. During training, the optimal resource allocation scheme is found through optimization algorithms, and the scheme is updated based on the maximum interference. For example, the interference situation under the current scheme is evaluated by the maximum interference, and the scheme is adjusted based on the evaluation results to reduce interference and improve network performance.

[0106] For example, in a DD-PSN scenario, the joint channel and power resource allocation problem is modeled as a Markov Decision Process (MDP). Since the dynamics of the environment in a wireless network are often unknown, Deep Reinforcement Learning (DRL) is used to learn the optimal policy through extensive training on the current state, which may transition to other states with incompletely unknown transition probabilities. The MDP model mainly consists of a state space, an action space, a reward function, and a value function, as shown below:

[0107] 1) State Space: In time slot t, for the m-th link, the DRL agent observes the communication status and collects the following parameters that constitute the link state:

[0108] C m : Represents the set of channels selected for all S2S links in a dense DD-PSN;

[0109] C s : Represents the set of remaining available channels in a dense DD-PSN;

[0110] make Representing the state space, therefore, the system state at time slot t Defined as:

[0111] 2) Operation space: The optimal action space A contains all possible channel allocation decisions. and power allocation decisions Therefore, the action of time slot t can be expressed as:

[0112]

[0113] Where, N c This represents the number of available channels. In the MDP model, power resources are designed to be divided into N... p Therefore, the action space has N dimensions. c ×N p Each action corresponds to a specific combination of spectrum subband and power selection.

[0114] 3) Reward function: used to evaluate the quality of learning decision-making strategies.

[0115] 4) Value function: The value function is used to evaluate the long-term benefits of taking an action in a given state.

[0116] Furthermore, when solving the MDP model, the Multi-Agent Deep Dual-Q Network (MADDQN) algorithm can be used. This algorithm utilizes deep neural networks to approximate the Q-value function and finds the optimal resource allocation strategy through multi-agent collaboration. In constructing the DDQN-based power sensor network training framework, each S2S link acts as an intelligent agent. The agent observes the current state and selects actions to interact with the environment. The environment updates its state based on the actions of multiple agents and provides feedback (such as updated state and rewards). Two independent neural networks (training network and target network) are used to select actions and calculate Q-values ​​respectively, reducing estimation bias. The parameters of the training network are updated according to the loss function, and the parameters of the target network are updated using a soft update strategy. This distributed decision-making framework enables each sensor device to autonomously learn and adjust its decisions based on local information, thereby enhancing the network's scalability, adaptability, and overall robustness.

[0117] For example, using a decaying ε-greedy strategy for action selection, the action selection is represented as:

[0118]

[0119] ε t =ε min +(ε max -ε min )·e -βt ,

[0120] Where, ε t ε represents the probability of randomly selecting an action. max ε represents the initial exploration probability. min Let ε represent the minimum exploration probability, and β represent the decay exponent. t Greedy strategies can introduce randomness to better explore the environment and learn more useful information. Initially, when ε t When ε is relatively high, the agent has less knowledge of the environment, which can help the agent more likely to choose exploratory actions randomly. Over time, as the agent's understanding of the environment increases, ε... t The decrease enables the agent to support the high-value actions it chooses.

[0121] The loss function of DDQN can be expressed as:

[0122]

[0123] in, Q(s) represents the target value output by the target network. t ,a t ;θ) represents the estimated value obtained by training the network, θ, θ -Here, are the parameters for the training and target networks, respectively; γ represents the discount factor used to balance current and future rewards; and a′ represents the action in the action set that yields the maximum Q-value. The neural network is trained by updating the loss function using stochastic gradient descent until the loss function converges.

[0124] The soft update strategy is represented as: θ - ←ηθ+(1-η)θ - Where θ represents the soft update parameter, the larger θ is, the higher the synchronization rate of the target network.

[0125] Since a single DDQN network model is trained using random samples from its local experience replay region, the complexity of local updates is O(T). s ×N lr This depends on the target Q-network complexity T. s and the number of local training points N lr The complexity of model aggregation is O(k) because it increases linearly with the number of agents. The total complexity of the MADDQN algorithm is... Therefore, the more agents there are, the faster the MADDQN algorithm can be trained.

[0126] In this embodiment, a state space is constructed based on the communication state parameters of all S2S pairs, an objective function is determined based on the resource allocation objective, an optimized allocation model is established based on the objective function, the optimized allocation model is trained based on the state space and the objective function to obtain the target allocation model, a resource allocation scheme is generated based on the target allocation model, and the resource allocation scheme is updated based on the maximum interference. This can adaptively select the best resource allocation scheme, effectively avoid interference, improve the signal-to-interference-plus-noise ratio, and ensure the transmission rate.

[0127] In one exemplary embodiment, to verify the effectiveness of the MADDQN algorithm, it was compared with the SADQN, SADDQN, and BASELINE algorithms through simulation. The simulation parameter settings are shown in Table 1.

[0128] Table 1

[0129]

[0130] For example, such as Figure 5 As shown, Figure 5This paper presents the convergence behavior of the FedAvg-DQN-based network resource management algorithm at various learning rates. Throughout the training phase, with a fixed number of S2S links of 20, the reward value stabilizes as training progresses. A significantly improved convergence performance is observed when the learning rate is set to 0.001. Excessively high learning rates lead to unstable parameter updates, hindering stable convergence. Conversely, excessively low learning rates may cause the algorithm to get trapped in local optima. Therefore, a learning rate of 0.001 is used for subsequent experiments.

[0131] Furthermore, such as Figure 6 As shown, Figure 6 This paper presents the convergence behavior of the MADDQN-based network resource management algorithm under various discount factors. Throughout the training phase, with a fixed S2S connection count of 20 and a learning rate of 0.0001, the reward value stabilizes with training. The improved convergence performance is significantly demonstrated when the discount factor is set to 0.001. An excessively high discount factor leads to unstable parameter updates, hindering stable convergence. Conversely, an excessively low discount factor may cause the algorithm to get trapped in local optima. Therefore, a discount factor of 0.95 is used for subsequent experiments. Figure 6 and Figure 5 It can be seen that the MADQN algorithm can achieve network resource management more effectively than the FedAvg-DQN algorithm.

[0132] Furthermore, when evaluating the performance of power sensor networks, the main evaluation metrics include network throughput and SINR.

[0133] For example, such as Figure 7 As shown, Figure 7 In power sensor network environments, network throughput increases with the number of D2D connections, as various algorithms are implemented. This can be attributed to enhanced transmission links, which enable the simultaneous transmission of larger amounts of data, thereby improving overall network throughput. Figure 7 It is evident that the MADDQN algorithm is significantly superior to other algorithms.

[0134] For example, such as Figure 8 As shown, Figure 8 The relationship between the number of S2S links and SINR is shown. A higher SINR value means that the network has lower interference. Compared with the other three algorithms, the MADDQN algorithm has a higher signal-to-interference-plus-noise ratio.

[0135] In one exemplary embodiment, such as Figure 9 As shown, a network resource allocation method for a power smart sensor relay backhaul network is provided, which includes the following steps:

[0136] S902: For a sensor group consisting of an S2S pair sharing the same channel, determine the signal-to-interference-to-noise ratio of the sensor group based on the communication performance parameters of each sensor device in the sensor group and the noise signal power of the channel.

[0137] S904: When the signal-to-noise ratio of the sensor group is less than the maximum interference threshold, the type of interference experienced by the interfered sensor in the sensor group is determined to be strong interference; when the signal-to-noise ratio of the sensor group is greater than or equal to the maximum interference threshold, the type of interference experienced by the interfered sensor is determined to be weak interference; when the interfered sensor is simultaneously subjected to strong interference and weak interference, the type of interference experienced by the interfered sensor is determined to be cumulative interference.

[0138] S906: When the type of interference experienced by the interfered sensor in the sensor group is cumulative interference, determine the upper limit of the total interference of the interfered sensor in the sensor group according to the relationship expression between the signal-to-interference-to-noise ratio and the maximum interference threshold; obtain the power of the interference signal received by the interfered sensor from the interfering sensor; and determine the interference weight corresponding to the interfered sensor according to the upper limit of the total interference and the power.

[0139] S908: Construct an interference weight matrix based on the interference weights corresponding to all interfered sensors; each row of the interference weight matrix represents a sensor device, and each column of the interference weight matrix represents another sensor device that interferes with the sensor device; construct an interference hypergraph model based on the interference weight matrix; in the interference hypergraph model, the nodes of the hypergraph are the sensor devices in the interference weight matrix, the hyperedges are the connections between the sensor devices and other sensor devices that interfere with the sensor devices, and the weight of the hyperedge is the sum of the interference weights of the sensor devices connected by the hyperedge.

[0140] S910: Determine the resource allocation matrix based on the interference hypergraph model; the resource allocation matrix is ​​used to characterize the resource allocation for each S2S pair; the maximum interference degree of the sensor network is obtained by solving the resource allocation matrix.

[0141] S912: Construct a state space based on the communication state parameters of all S2S pairs; determine the objective function based on the resource allocation objective, and establish an optimized allocation model based on the objective function; train the optimized allocation model based on the state space and the objective function to obtain the target allocation model; generate a resource allocation scheme based on the target allocation model, and update the resource allocation scheme based on the maximum interference.

[0142] In this embodiment, by accurately calculating the signal-to-interference-plus-noise ratio (S2S ratio) of a sensor group composed of an S2S pair sharing the same channel, and when the interference type experienced by the interfered sensor in the sensor group is cumulative interference, the interference weight corresponding to the interfered sensor is determined based on the S2S ratio. Then, an interference hypergraph model is constructed, and the maximum interference degree of the sensor network is determined based on the interference hypergraph model. Thus, a resource allocation scheme is determined based on the maximum interference degree. This allows for adaptive selection of the optimal resource allocation scheme, effectively avoiding interference, improving the signal-to-interference-plus-noise ratio, and ensuring the transmission rate.

[0143] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0144] Based on the same inventive concept, this application also provides a network resource allocation device for a power smart sensor relay backhaul network, used to implement the network resource allocation method for the power smart sensor relay backhaul network described above. The solution provided by this device is similar to the implementation described in the above method. Therefore, the specific limitations in one or more embodiments of the network resource allocation device for a power smart sensor relay backhaul network provided below can be found in the limitations of the network resource allocation method for the power smart sensor relay backhaul network described above, and will not be repeated here.

[0145] In one exemplary embodiment, such as Figure 10 As shown, a network resource allocation device for a power smart sensor relay backhaul network is provided, comprising: a noise ratio determination module 10, a weight determination module 20, a hypergraph construction module 30, and a resource allocation module 40, wherein:

[0146] The noise ratio determination module 10 is used to determine the signal-to-interference-to-noise ratio of a sensor group consisting of an S2S pair sharing the same channel, based on the communication performance parameters of each sensor device in the sensor group and the noise signal power of the channel.

[0147] The weight determination module 20 is used to determine the interference weight corresponding to the interfered sensor based on the signal-to-interference-to-noise ratio when the type of interference received by the interfered sensor in the sensor group is cumulative interference.

[0148] The hypergraph construction module 30 is used to construct an interference hypergraph model based on the interference weights corresponding to all the interfered sensors. The vertices of the interference hypergraph model represent sensor devices, and the hyperedges connecting the vertices represent a set of sensor devices that interfere with each other.

[0149] The resource allocation module 40 is used to determine the maximum interference degree of the sensor network according to the interference hypergraph model, and to determine the resource allocation scheme based on the maximum interference degree, so as to realize the network resource allocation of the power sensor.

[0150] In an exemplary embodiment, the weight determination module 20 is further configured to determine that the type of interference received by the interfered sensor in the sensor group is strong interference when the signal-to-interference-to-noise ratio of the sensor group is less than the maximum interference threshold; determine that the type of interference received by the interfered sensor is weak interference when the signal-to-interference-to-noise ratio of the sensor group is greater than or equal to the maximum interference threshold; and determine that the type of interference received by the interfered sensor is cumulative interference when the interfered sensor is simultaneously subjected to strong interference and weak interference.

[0151] In an exemplary embodiment, the weight determination module 20 is further configured to determine the upper limit of the total interference of the interfered sensors in the sensor group according to the relationship expression between the signal interference noise ratio and the maximum interference threshold; obtain the power of the interference signal received by the interfered sensor from the interfering sensor; and determine the interference weight corresponding to the interfered sensor according to the upper limit of the total interference and the power.

[0152] In an exemplary embodiment, the hypergraph construction module 30 is further configured to construct an interference weight matrix based on the interference weights corresponding to all the interfered sensors; each row of the interference weight matrix represents a sensor device, and each column of the interference weight matrix represents another sensor device that interferes with the sensor device; based on the interference weight matrix, an interference hypergraph model is constructed; in the interference hypergraph model, the nodes of the hypergraph are the sensor devices in the interference weight matrix, the hyperedges are the connections between the sensor devices and other sensor devices that interfere with the sensor devices, and the weight of the hyperedge is the sum of the interference weights of the sensor devices connected by the hyperedge.

[0153] In an exemplary embodiment, the resource allocation module 40 is further configured to determine a resource allocation matrix based on the interference hypergraph model; the resource allocation matrix is ​​used to characterize the resource allocation for each S2S pair; and the maximum interference degree of the sensor network is obtained by solving the resource allocation matrix.

[0154] In an exemplary embodiment, the resource allocation module 40 is further configured to construct a state space based on the communication state parameters of all S2S pairs; determine an objective function based on the resource allocation objective; establish an optimized allocation model based on the objective function; train the optimized allocation model based on the state space and the objective function to obtain a target allocation model; generate a resource allocation scheme based on the target allocation model; and update the resource allocation scheme based on the maximum interference.

[0155] Each module in the network resource allocation device of the aforementioned power intelligent sensor relay backhaul network can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0156] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 11 As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a network resource allocation method for a power smart sensor relay backhaul network. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0157] Those skilled in the art will understand that Figure 11The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0158] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0159] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0160] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0161] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this 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. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0162] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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.

[0163] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for allocating network resources of a power intelligent sensor relay backhaul network, characterized in that, The method is applied to a sensor network composed of multiple sensors using S2S communication, and the method comprises the following steps: For a sensor group composed of one S2S pair sharing the same channel, the signal-to-interference-and-noise ratio of the sensor group is determined according to the communication performance parameters of each sensor device in the sensor group and the noise signal power of the channel; In the case that the type of interference received by the interfered sensor in the sensor group is cumulative interference, the upper limit of the total interference of the interfered sensor in the sensor group is determined according to the relationship expression between the signal-to-interference-and-noise ratio and the maximum interference threshold; The power of the interference signal received by the interfered sensor from the interfering sensor is obtained; According to the total interference upper limit and the power, the interference weight corresponding to the interfered sensor is determined; Based on the interference weights corresponding to all interfered sensors, an interference hypergraph model is constructed; the vertices of the interference hypergraph model represent sensor devices, and the hyperedges connecting the vertices represent a set of sensor devices that interfere with each other; According to the maximum interference degree of the sensor network determined by the interference hypergraph model, a resource allocation scheme is determined based on the maximum interference degree to realize network resource allocation of power sensors.

2. The method of claim 1, wherein, The process of determining the type of interference received by the interfered sensor in the sensor group comprises the following steps: In the case that the signal-to-interference-and-noise ratio of the sensor group is less than the maximum interference threshold, it is determined that the type of interference received by the interfered sensor in the sensor group is strong interference; In the case that the signal-to-interference-and-noise ratio of the sensor group is greater than or equal to the maximum interference threshold, it is determined that the type of interference received by the interfered sensor is weak interference; In the case that the interfered sensor is simultaneously subjected to strong interference and weak interference, it is determined that the type of interference received by the interfered sensor is cumulative interference.

3. The method of claim 1, wherein, The process of constructing the interference hypergraph model based on the interference weights corresponding to all interfered sensors comprises the following steps: According to the interference weights corresponding to all interfered sensors, an interference weight matrix is constructed; each row of the interference weight matrix represents a sensor device, and each column of the interference weight matrix represents another sensor device that interferes with the sensor device; According to the interference weight matrix, an interference hypergraph model is constructed; in the interference hypergraph model, the nodes of the hypergraph are the sensor devices in the interference weight matrix, the hyperedges are the connections between the sensor devices and the other sensor devices that interfere with the sensor devices, and the weights of the hyperedges are the sum of the interference weights between the sensor devices connected by the hyperedges.

4. The method of claim 1, wherein, The process of determining the maximum interference degree of the sensor network according to the interference hypergraph model comprises the following steps: According to the interference hypergraph model, a resource allocation matrix is determined; the resource allocation matrix is used to represent the resource allocation of each S2S pair; The maximum interference degree of the sensor network is obtained by solving the resource allocation matrix.

5. The method of claim 1, wherein, The process of determining the resource allocation scheme based on the maximum interference degree comprises the following steps: According to the communication state parameters of all S2S pairs, a state space is constructed; According to the resource allocation target, a target function is determined, and an optimization allocation model is established according to the target function; training the optimization allocation model based on the state space and the target function to obtain a target allocation model; generating a resource allocation scheme based on the target allocation model, and updating the resource allocation scheme according to the maximum interference degree.

6. A network resource allocation device for a power intelligent sensor relay backhaul network, characterized in that, The apparatus comprises: a noise ratio determination module configured to determine, for a sensor group composed of one S2S pair sharing the same channel, a signal-to-interference-and-noise ratio of the sensor group according to a communication performance parameter of each sensor device in the sensor group and a noise signal power of the channel; a weight determination module configured to, in a case where the type of interference received by a disturbed sensor in the sensor group is cumulative interference, determine an upper limit of an interference sum of the disturbed sensor in the sensor group according to a relationship expression between the signal-to-interference-and-noise ratio and a maximum interference threshold, acquire a power of an interference signal received by the disturbed sensor from a disturbing sensor, and determine an interference weight corresponding to the disturbed sensor according to the upper limit of the interference sum and the power; a hypergraph construction module configured to construct a hypergraph model of interference based on the interference weights corresponding to all disturbed sensors, wherein a vertex of the hypergraph model represents a sensor device, and a hyperedge connecting the vertices represents a set of sensor devices that interfere with each other; a resource allocation module configured to determine a maximum interference degree of a sensor network according to the hypergraph model of interference, and determine a resource allocation scheme based on the maximum interference degree to realize network resource allocation of power sensors. 7.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-6 when the computer program is executed by the processor. The processor implements the steps of the method of any one of claims 1 to 5 when executing the computer program.

8. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program, when executed by the processor, implements the steps of the method of any one of claims 1 to 5.

9. A computer program product comprising a computer program, characterized in that, The computer program, when executed by the processor, implements the steps of the method of any one of claims 1 to 5. The computer program, when executed by the processor, implements the steps of the method of any one of claims 1 to 5.

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