Sensitive time resource allocation method and system for spectrum map construction

Abstract

The invention discloses a spectrum map construction-oriented sensing time resource allocation method, which belongs to the field of electromagnetic spectrum, and comprises the following steps of: establishing a time resource allocation model of a sensor; based on the timeliness index, setting an information age, and establishing a relationship between the information age and a service rate, a data arrival rate and a packet loss rate in a data transmission stage as a constraint condition of a time resource allocation model; converting a resource optimization problem in the time resource allocation model after setting the information age constraint into a convex optimization problem; solving a convex optimization problem by using a multi-strategy improved dung beetle optimization algorithm MSDBO to obtain an optimal time resource allocation scheme of three time periods of data acquisition Tc, channel detection Ts and data transmission time Tt; wherein the MSDBO improves the dung beetle optimization algorithm DBO by setting chaos pseudo-random, tangential flight search and crisscross strategies. Aiming at incoordination of communication and perception in the prior art, efficient allocation of communication time resources in a spectrum map construction process is realized.

Description

Technical Field

[0001] The present application relates to the field of electromagnetic spectrum, and more specifically, to a method and system for allocating synaesthesia time resources for spectrum map construction. Background Art

[0002] With the continuous development of wireless communication technology, electromagnetic spectrum maps have been widely used in the fields of spectrum resource management, electromagnetic pollution monitoring, and wireless communication network supervision. Electromagnetic spectrum maps are also called radio environment maps, electromagnetic environment maps, and radio frequency radio maps. It provides information about the utilization of different frequency bands, signal strength, and spectrum idle areas in a visual way, helping users to better understand and manage spectrum resources. The premise of spectrum map construction is to obtain electromagnetic spectrum data conveniently, accurately and quickly. The conventional practice is to use one or more sensors capable of spectrum monitoring to capture and analyze signals in the radio spectrum. These sensors include both dedicated spectrum monitoring sensors and non-dedicated sensors based on communication equipment. Dedicated sensors may perform well in spectrum monitoring performance, but they are usually accompanied by high costs and limited usage scenarios. In contrast, non-dedicated sensors, such as common communication devices commonly found on the market (such as smartphones, computers, battlefield wireless communication radios, etc.), are widely used in practice. Moreover, while providing communication functions, these devices often have integrated certain perception functions, which can provide electromagnetic environment perception information for the construction of spectrum maps, but the coordination between communication and perception needs to be handled well.

[0003] At present, when optimizing spectrum map sensor units, the common practice is to use intelligent optimization algorithms to optimize the location layout and data sampling of sensors. Sensors can be divided into two categories: dedicated and non-dedicated. Dedicated sensors are designed specifically for a specific task, while non-dedicated sensors have higher flexibility. In the context of integrated communication and perception, non-dedicated sensors are particularly important because they can integrate communication and perception functions, thereby making more efficient use of hardware and spectrum resources. However, the coordination between communication and perception needs to be handled well.

[0004] At present, the main perception and communication integration methods include unified waveform design, power allocation, space division and frequency division. However, these technologies face their own challenges in practical applications: unified waveform design may lead to compatibility issues with existing communication systems; power allocation methods may be inconvenient in interference control; space division technology has high requirements for the orthogonality of beams; and frequency division technology may lead to a high peak-to-average power ratio (PAPR). Therefore, in order to optimize sensors more comprehensively, save costs and increase flexibility, a sensor communication perception resource allocation method for spectrum map construction is urgently needed. Summary of the invention

[0005] 1. Technical issues to be solved

[0006] In response to the communication and perception inconsistency problems existing in the prior art, the present application provides a synaesthesia time resource allocation method and system for spectrum map construction. By establishing a time resource allocation model for sensors, introducing information age indicators as constraints, converting the resource optimization problem into a convex optimization problem, and using a multi-strategy improved dung beetle optimization algorithm to solve it, efficient allocation of synaesthesia time resources in the process of spectrum map construction can be achieved.

[0007] 2. Technical solution

[0008] The purpose of this application is achieved through the following technical solutions.

[0009] One aspect of the present application provides a method for allocating synaesthesia time resources for spectrum map construction, comprising: S1, establishing a time resource allocation model of a sensor for allocating data acquisition T c , channel detection T s and data transmission time T t ; S2, in the time resource allocation model, based on the timeliness index, the information age is set, and the relationship between the information age and the service rate, data arrival rate and packet loss rate in the data transmission stage is established, and the established relationship is used as the constraint condition of the time resource allocation model; S3, the resource optimization problem in the time resource allocation model after setting the information age constraint is transformed into a convex optimization problem, and the convex optimization model of synaesthesia time resource allocation constructed by the spectrum map is obtained; S4, the convex optimization problem of the convex optimization model is solved by using the multi-strategy improved dung beetle optimization algorithm MSDBO, and the data acquisition T c , Channel Detection T s and data transmission time T t The optimal time resource allocation scheme for three time periods; among them, MSDBO improves the dung beetle optimization algorithm DBO by setting chaotic pseudo-randomness, tangent flight search and vertical and horizontal cross strategies to avoid the algorithm falling into local optimality.

[0010] Among them, spectrum map: spectrum map is a spatial distribution representation of spectrum resource usage in a certain area. It collects and analyzes spectrum data in the area to obtain information such as occupancy status, signal strength, modulation mode, etc. of each frequency band, and presents it in a visual way. Spectrum map can help identify idle and congested areas of spectrum resources, optimize spectrum allocation and sharing schemes, and improve spectrum utilization efficiency. It is widely used in wireless communications, electronic warfare and other fields. Perceptual time resource: Perceptual time resource refers to the time resources used for tasks such as data collection, channel detection, and data transmission in wireless sensor networks for the purpose of perception. Unlike traditional communication networks, wireless sensor network nodes usually have limited energy and computing power, so it is necessary to reasonably allocate limited time resources to minimize energy consumption and delay while ensuring the quality of perception tasks. Perceptual time resource allocation is a key issue in wireless sensor networks, involving perception, communication, computing and other aspects. Time liness metric: Time liness metric measures the real-time and effectiveness of data transmission in wireless sensor networks. Since the perception data is time-sensitive, expired data may have lost its application value, so it is necessary to transmit the data from the sensor node to the aggregation node or control center as soon as possible. Common timeliness indicators include latency, deadline miss rate, data effective throughput, etc. In the synaesthesia time resource allocation, the timeliness indicators are usually modeled as constraints or objective functions of the time resource allocation model to ensure the real-time transmission of perception data. Age of Information (AoI): Age of Information is a novel timeliness indicator, which is defined as the random delay from the generation of information at the source node to the successful reception of the destination node. Unlike traditional latency, AoI depends not only on the transmission time of the data, but also on the generation time and update frequency of the data. AoI describes the freshness of information from generation to use, which is of great significance in real-time applications such as condition monitoring, process control, and autonomous driving. By minimizing the average AoI or peak AoI, the timeliness performance of the system can be optimized. In a given synaesthesia time resource allocation scheme, the information age is used to model the timeliness of the data and is introduced into the time resource allocation model as a constraint to ensure the real-time construction of the spectrum map.

[0011] Further, S1, establishes a time resource allocation model for sensors, including: setting the sensors to be divided into data collection T c , Channel Detection T s and data transmission time T t Three non-overlapping time periods; data collection T c Used to obtain raw data, channel detection T sUsed to judge the channel status, data transmission time T t Used to transmit the collected data to the receiving end; establish position m i Sensors at data acquisition T c The relationship between the total amount of data and the false alarm probability and detection probability is calculated by taking the amount of collected original data as input and calculating the corresponding false alarm probability and detection probability; establishing the position m i The sensor at the data transmission time T t The relationship between the amount of data and the channel detection result is s The channel status is judged as the basis to determine the data transmission time T t The amount of data transmitted; according to data collection T c The relationship between the total data volume and the false alarm probability and detection probability, as well as the data transmission time T t The amount of data and channel detection T s The relationship between them is used to establish a time resource allocation model for sensors.

[0012] Among them, False Alarm Probability: In the field of signal detection, false alarm refers to the situation where noise is mistakenly judged as a target signal. The false alarm probability quantifies the probability of this situation occurring, and is defined as the probability of judging that a target signal (H1) exists when no target signal exists (H0 is true). The false alarm probability is usually represented by Pf, which is an important performance indicator of the signal detection system and reflects the amount of false alarms generated by the system. Detection Probability, the detection probability quantifies the probability of correctly judging that a target signal (H1) exists when the target signal does exist (H1 is true). The detection probability is usually represented by Pd, which is another important performance indicator of the signal detection system and reflects the system's ability to correctly detect the target signal.

[0013] Further, the corresponding false alarm probability and detection probability are calculated, including: calculating the position m by the following formula i Sensors at data acquisition T c The total amount of data R c : Among them, T c,i Indicates position m i The data collection time period of the sensor; Indicates position m i The channel capacity of the sensor at position m is calculated by the following formula i The false alarm probability of the sensor is: Among them, P f,i (T s,i ) indicates position T s,i The false alarm probability of the sensor at Ts,i Indicates position m i The channel detection time of the sensor at the location; Q is the complementary distribution function of the standard Gaussian function; γ i Indicates position m i The signal-to-noise ratio of the received signal of the sensor; Q -1 represents the inverse function of Q; represents the preset target detection probability; f s Represents the sampling frequency; the position m is calculated by the following formula i The detection probability of the sensor at: Among them, P d,i Indicates position m i The detection probability of the sensor at represents the target false alarm probability; τ represents the detection threshold; the amount of data transmitted during the data transmission period is determined by the following formula: R t,i =R i (T t,i ,T s )×T t,i , where R t,i Indicates position m i The amount of data from the sensor at the data transmission stage; R i Indicates position m i The transmission rate of the sensor at t,i Indicates position m i The data transmission time of the sensor at

[0014] Among them, the complementary distribution function of the standard Gaussian function (Complementary Cumulative Distribution Function of Standard Gaussian): The standard Gaussian function (also known as the standard normal distribution) is a Gaussian function with a mean of 0 and a variance of 1. Its probability density function is: f(x) = 1 / sqrt(2π)*exp(-x^2 / 2), and the complementary distribution function is defined as the probability that a random variable is greater than a certain value. For the standard Gaussian random variable Z, its complementary distribution function is: Q(x) = P(Z>x) = 1 / sqrt(2π)∫x∞exp(-t^2 / 2)dt; The Q function is widely used in the field of communication and signal detection, and is usually used to calculate performance indicators such as symbol error rate and bit error rate. In the given method, the Q function is used to calculate the false alarm probability. The Q-1 function is the inverse function of the Q function, that is, if Q(x) = y, then x = Q-1(y).

[0015] Furthermore, a time resource allocation model for sensors is established, including: c The total data volume, false alarm probability, detection probability, and data transmission time T tThe amount of data transmission is taken as the constraint condition, the time resource allocation of the three time periods is taken as the optimization variable, and the objective function is to minimize the total time of data acquisition, channel detection and data transmission. The time resource allocation model of the sensor is constructed by the following formula:

[0016] max R t,i

[0017] st

[0018] T c +T s +T t,i ≤T

[0019]

[0020] R c ≤R t

[0021] in, represents the minimum sensor acquisition time; T represents the total time; P d represents the target detection probability of the sensor; represents the set target detection probability; P(H0) represents the probability of channel idleness; 1-P f (T s ) represents the probability that the channel is idle and the sensor successfully detects that the channel is idle; R i (T t,i ) indicates position m i The transmission rate of the sensor; Indicates position m i The minimum transmission rate of the sensor at R c Represents the amount of data collected by the sensor; R t Indicates the amount of data transmitted by the sensor;

[0022] Furthermore, in the time resource allocation model, S2 sets the information age based on the timeliness index, establishes the relationship between the information age and the service rate, data arrival rate and packet loss rate in the data transmission stage, and uses the established relationship as the constraint condition of the time resource allocation model, including: for each sensor node i, for each sensor node i, set t1, t2, ..., t n is the time when the data packet is successfully uploaded, we can define Δ(t) = tu(t), where u(t) is the timestamp of the last data packet upload, t represents the current time; Δ(t) represents the information age at the current time, that is, the time elapsed since the last data packet was uploaded. Define the time interval for status update as X, and the service time of status update in the system as T; calculate the average information age Δ within the observation interval τ : Where τ represents the length of the observation interval; according to the average information age Δ τ Calculate Δ, where Δ represents the information age related to the data arrival rate and time interval; Where Λ is the data arrival rate of the system, E[X 2 ] represents the expected value of the square of the packet arrival interval; E[XT] represents the expected value of the product of the interval X and the service time T; according to the service time and service rate μ of each data packet, the sensor upload model is modeled as an M / M / 1 queue model through exponential distribution, and the average information age ΔM / M / 1 of an M / M / 1 sequence in the queue model is calculated: Where ρ represents the load factor, The packet loss rate ρ' is used to correct the average information age ΔM / M / 1 of the M / M / 1 sequence, and the information age Δ'M / M / 1 of the sensor is obtained: Based on the information age Δ'M / M / 1 of the sensor, the information age constraint is set as the constraint of the time resource allocation model: Among them, Δ'M / M / 1 represents the information age of the sensor network; Indicates the maximum allowed value of the message age.

[0023] Among them, the time interval of status update (Status Update Interval): During the data transmission process, the sensor node periodically sends the latest data it collects to the receiving end. The time interval between two adjacent data updates is called the time interval of status update, which is represented by Y. The status update interval reflects the frequency of data update. The smaller the interval, the more frequent the data update, and the higher the timeliness of the data. The service time of status update in the system (Service Time of Status Update): When the sensor node sends the latest data to the receiving end, the data experiences a certain delay in the transmission channel. This delay is called the service time of status update in the system, which is represented by T. The service time depends on the size of the data packet and the rate of the transmission link, reflecting the delay of the data during the transmission process. Queueing model (Queueing Model): The queueing model is used to describe the process of customers arriving to receive service, waiting for service if the service is not available immediately, and leaving the system after receiving the service if the service is waiting. In the field of data transmission, data packets can be regarded as customers, and the transmission channel can be regarded as servers. The process of data packets waiting for transmission at the sending end and waiting for processing at the receiving end can be characterized by the queueing model. Common queue models include M / M / 1, M / G / 1, G / M / 1, etc., where M represents Poisson arrival (Memoryless), G represents general distribution (General), and 1 represents a single server. In the given method, the sensor upload model is modeled as an M / M / 1 queue, that is, the packet arrival follows Poisson distribution, the service time follows exponential distribution, and there is only one transmission channel.

[0024] Further, S3, converting the resource optimization problem in the time resource allocation model after setting the information age constraint into a convex optimization problem, and obtaining the convex optimization model of synaesthesia time resource allocation constructed by the spectrum map, including: taking the established time resource allocation model as the objective function, taking the information age constraint as the constraint condition, and constructing the resource optimization problem of time resource allocation; performing convexity analysis on the resource optimization problem; according to the result of the convexity analysis, if the objective function is a convex function and the constraint condition is a convex set, converting the resource optimization problem into a convex optimization problem; according to the converted convex optimization problem, establishing the convex optimization model of synaesthesia time resource allocation constructed by the spectrum map;

[0025] Among them, convexity analysis is a method used to determine whether an optimization problem is a convex optimization problem. In the field of optimization, convex optimization problems are a class of problems with good properties, with global optimal solutions, and can be solved by algorithms with polynomial time complexity. Therefore, converting general optimization problems into convex optimization problems can greatly simplify the solution of the problem. An optimization problem is called a convex optimization problem, and it needs to meet the following conditions: the objective function is a convex function. For a function f defined on a convex set, if for any two points x1, x2 and 0≤θ≤1, there is f(θx1+(1-θ)x2)≤θf(x1)+(1-θ)f(x2), then f is called a convex function. Intuitively speaking, a convex function is above the line connecting any two points. The constraint set is a convex set. If for any two points x1, x2 and 0≤θ≤1 in the set S, the point θx1+(1-θ)x2 is also in the set S, then S is called a convex set. Intuitively speaking, the line connecting any two points inside a convex set is still inside the set. The process of convexity analysis is to verify whether the objective function and constraints of the optimization problem satisfy the properties of convex functions and convex sets. Commonly used methods include: Direct method: According to the definition of convex functions and convex sets, directly verify whether the objective function and constraint set satisfy the conditions. First-order condition: For a differentiable function, if its gradient satisfies Then the function is a convex function. Second-order condition: For a quadratic differentiable function, if its Hessian matrix is ​​semi-positive definite, then the function is a convex function. Convexity-preserving operations: Common functions such as polynomial functions and exponential functions are still convex functions after linear operations, maximum values, summations, and other operations. In this application, convexity analysis is used to determine whether the established resource optimization model can be converted into a convex optimization model. If the objective function (time resource allocation model) is a convex function with respect to the decision variables, and the constraints (information age constraints) constitute a convex set, the original problem can be equivalently converted into a convex optimization problem for solution. There are many ready-made solution algorithms for convex optimization problems, such as the interior point method, subgradient method, ellipsoid method, etc., which will greatly reduce the difficulty of solving the time resource allocation problem and improve the efficiency of spectrum map construction.

[0026] Furthermore, the expression of the convex optimization model is as follows:

[0027]

[0028] st

[0029] T c +T s +T t,i ≤T

[0030]

[0031] R c ≤R t

[0032] in, α i represents the coefficient based on the detection probability; γ i represents the signal-to-noise ratio (SNR); β represents the time parameter T s The coefficient, Q -1 Represents the inverse cumulative distribution function of the standard normal distribution.

[0033] Furthermore, S4 uses the multi-strategy improved dung beetle optimization algorithm MSDBO to solve the convex optimization problem of the convex optimization model and obtains the data collection T c , Channel Detection T s and data transmission time T t The optimal time resource allocation scheme for three time periods includes: using a chaotic pseudo-random strategy to perform chaotic pseudo-random updates on the positions of individuals in MSDBO; using a tangent flight search strategy to update the positions of individuals in the algorithm processed by the chaotic pseudo-random strategy; wherein the update includes concentrated search and exploratory search; using a vertical and horizontal cross strategy to optimize the positions of individuals in the optimization algorithm processed by the tangent flight search strategy; using the convex optimization model of synaesthesia time resource allocation constructed by the spectrum map as the optimization target of the multi-strategy improved dung beetle optimization algorithm MSDBO, obtaining the optimal solution of the convex optimization model through multiple iterations, and obtaining the optimal time resource allocation scheme t for the three time periods of data acquisition, channel detection and data transmission. i,c , t i,s , t i,t , as the final solution for synaesthesia time resource allocation for spectrum map construction. The chaotic pseudo-random strategy is adopted to update the position of individuals. The formula is as follows:

[0034]

[0035] Among them, X i,d (t+1) represents the position of the individual at time t+1; X i,d (t) represents the position of individual i at time t; η and μ represent chaotic coefficients; mod represents modulus division (remainder) operation; when η and μ are between 0 and 1, the system is in a chaotic state, and r is a random number between 0 and 1;

[0036] For pre-selection, η=0.4 and μ=0.3 are taken.

[0037] Among them, Chaotic Pseudo-random Strategy: Chaos is a nonlinear dynamic phenomenon with the characteristics of sensitivity to initial values, randomness, and ergodicity. Chaotic pseudo-random strategy refers to the use of chaotic systems to generate pseudo-random number sequences for updating individual positions in optimization algorithms. Compared with traditional random number generation methods, chaotic pseudo-random numbers have better randomness and ergodicity, which can enhance the global search ability of optimization algorithms and avoid premature convergence. Commonly used chaotic systems include Logistic mapping, Tent mapping, Kent mapping, etc.

[0038] Tangent Flight Search Strategy: Tangent flight search is a heuristic search strategy that simulates the search behavior of animals such as birds and insects. In tangent flight search, individuals conduct local searches along the tangent direction in the neighborhood of the current optimal solution (concentrated search), and also jump to farther areas with a certain probability (exploratory search) to find possible better solutions. By balancing concentrated search and exploratory search, the tangent flight search strategy can improve the convergence speed and global optimization ability of the optimization algorithm.

[0039] Vertical-Horizontal Crossover Strategy: Crossover is a common operation in evolutionary algorithms. New offspring individuals are generated by exchanging some genes of two parent individuals. Vertical-Horizontal Crossover Strategy is a special crossover method. First, two parent individuals are crossovered vertically (in the dimension direction), and then the generated intermediate individuals are crossovered horizontally (within the dimension) to finally obtain offspring individuals. Compared with traditional single-point crossover, multi-point crossover and other methods, vertical-horizontal crossover can more fully inherit the excellent characteristics of parent individuals, and also introduce new gene combinations, which helps to maintain the diversity of the population.

[0040] Dung Beetle Optimization Algorithm (DBOA): The Dung Beetle Optimization Algorithm is a new type of swarm intelligence optimization algorithm that simulates the behavior of dung beetles pushing balls to feed in nature. In DBOA, each dung beetle individual represents a candidate solution. By simulating the dung beetle's rolling, attacking, searching and other behaviors, the individual position is continuously updated to eventually find the global optimal solution. Compared with other swarm intelligence algorithms, DBOA has stronger development and local search capabilities and has been applied in many fields.

[0041] Improved Dung Beetle Optimization Algorithm with Multiple Strategies (MSDBOA): Based on the standard DBOA, the algorithm performance can be further enhanced and the optimization efficiency and accuracy can be improved by introducing multiple strategies such as chaos pseudo-random, tangent flight search, and vertical and horizontal crossover. Among them, the chaos pseudo-random strategy is used to initialize the population and perturb the individual position; the tangent flight search strategy is used to balance local development and global exploration; the vertical and horizontal crossover strategy is used to generate new offspring individuals. Through the coordination of multiple strategies, MSDBOA can solve complex optimization problems more efficiently.

[0042] Furthermore, the focused search is performed by the following formula: in represents the position of the individual at the next iteration, is the position of the individual at the current iteration, step is the step length, tan(θ) is the tangent function, and θ is a random angle, whose value is usually between 0 and between, is the location of the currently found optimal solution.

[0043] Further, the exploration search is performed through the following formula: The randn() function generates a standard normal distribution random number. The vertical and horizontal cross strategy is used to optimize the position of the individual in the dung beetle optimization algorithm MSDBO after the tangent flight search strategy is processed, including: i and individual X j , the next generation individual position generated by horizontal crossover in the dth dimension is calculated by the following formula:

[0044]

[0045] in, and are the positions of the new offspring generated by the previous generation of individuals, and are the positions of the individuals of the previous generation in the nth dimension, r1 and r2 are random numbers in the interval [0, 1], c1 and c2 are random numbers in the interval [-1, 1]. The generated offspring are compared with the individuals of the previous generation, and the better individuals are retained;

[0046] For individual X i , k≠d is a different dimension, and the position of the next generation of individuals generated by vertical crossover is calculated by the following formula:

[0047]

[0048] in, Indicates the position of the next generation of individuals; The position of the current generation of individuals represented by dimension n1; Indicates the position of the current generation of individuals in dimension n2.

[0049] Another aspect of the present application further provides a synaesthesia time resource allocation system for spectrum map construction, which is used to execute a synaesthesia time resource allocation method for spectrum map construction of the present application.

[0050] 3. Beneficial effects

[0051] Compared with the prior art, the advantages of this application are:

[0052] By establishing a time resource allocation model for sensors, taking data collection, channel detection and data transmission as three non-overlapping time periods, and constructing the relationship between parameters such as data volume, false alarm probability, and detection probability in each time period, we can achieve refined allocation of sensor resources in the time dimension, improve the utilization efficiency of time resources, and avoid the incoordination problem between communication and perception.

[0053] Introducing the information age indicator as a constraint condition of the time resource allocation model and establishing the relationship between the information age and the service rate, data arrival rate and packet loss rate in the data transmission stage can quantify the timeliness of the data, ensure the real-time and effectiveness of the data, and improve the accuracy and reliability of spectrum map construction.

[0054] The resource optimization problem in the time resource allocation model after introducing the information age constraint is transformed into a convex optimization problem. The feasibility of the transformation is verified through convexity analysis, which can simplify the difficulty of solving the optimization problem, improve the computational efficiency of resource allocation, and realize the efficient allocation of synaesthesia time resources in the process of spectrum map construction.

[0055] A multi-strategy improved dung beetle optimization algorithm was designed. The randomness and diversity of the algorithm were enhanced through the chaotic pseudo-random strategy, the centralized search and exploratory search capabilities of the algorithm were balanced through the tangent flight search strategy, and the local search capability of the algorithm was enhanced through the vertical and horizontal cross strategy. This can improve the convergence speed and global optimization capability of the algorithm, avoid falling into the local optimum, and obtain a better time resource allocation plan.

[0056] The constructed convex optimization model is solved using the multi-strategy improved dung beetle optimization algorithm, and the optimal resource allocation scheme for the three time periods of data acquisition, channel detection and data transmission is obtained through iterative optimization. This can maximize the utilization efficiency of spectrum resources while ensuring the timeliness of data transmission, balance the performance requirements of communication and perception, and provide the optimal time resource allocation scheme for the efficient construction of spectrum maps. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 This is a schematic diagram of the overall structure of this application;

[0058] Figure 2 is the example scene model;

[0059] Figure 3 Time resource allocation diagram for sensors;

[0060] Figure 4 The initial population distribution diagram is generated by chaotic pseudo-random;

[0061] Figure 5 It is the step length diagram of concentrated search and exploratory search obtained by simulating 500 iterations;

[0062] Figure 6 The influence of the time allocation ratio of data acquisition, channel detection and data transmission on the information age;

[0063] Figure 7 This paper is a comparative experiment to compare the performance of the multi-strategy improved dung beetle optimization algorithm (MSDBO) with the original dung beetle optimization algorithm (DBO), the improved dung beetle optimization algorithm (GODBO), the peacock optimization algorithm (POA) and the artificial rabbit algorithm (ARO).

[0064] Figure 8 Another comparative experiment of the performance of the multi-strategy improved dung beetle optimization algorithm (MSDBO) with the original dung beetle optimization algorithm (DBO), the improved dung beetle optimization algorithm (GODBO), the peacock optimization algorithm (POA) and the artificial rabbit algorithm (ARO);

[0065] Fig. 9 This is a comparison chart of the time resource allocation algorithm based on MSDBO, the commonly used optimization algorithm and the random allocation method. DETAILED DESCRIPTION

[0066] The present application is described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0067] Figure 1 The following is a schematic diagram of the overall structure of the present application. In one embodiment, the problem scenario provided by the present application is as follows:

[0068] In the area under consideration, multiple radiation sources with unknown positions and uncertain powers and a group of sensors are arranged. The main task of the sensor is to collect the signals emitted by the radiation source and realize the effective return of data. Figure 2 The emission power of the radiation source can be obtained by the collection Indicates that the position of the radiation source can be represented by the set where m is the number of radiation sources. The signal strength of the radiation source received by the sensors arranged in the area is represented by P(m i ) indicates that m i is the location of the sensor.

[0069] Location i The received signal power of the sensor at is modeled as: Where K is the free space path loss factor, ε is the path loss exponent, and m j represents the location of the radiation source, is at position m i The shadow loss obeys the log-normal distribution with a mean of 0 and a standard deviation of σ.

[0070] The work of data acquisition sensors is clearly divided into three main stages: electromagnetic data acquisition, channel detection and data communication. Figure 3 As shown in Figure 1, time T is divided into three time periods: electromagnetic data collection, channel detection, and data transmission. These three phases do not overlap with each other. For a single sensor, it includes an electromagnetic data collection time T c , a channel detection time T s and a data communication time T t Based on this, in this embodiment, if Figure 3 As shown, the present application proposes a communication perception time resource allocation method for spectrum map construction, comprising the following steps: A communication perception time resource allocation method for spectrum map construction, comprising: establishing a time resource allocation model of a sensor for allocating time resources for data collection, channel detection and data transmission, specifically, setting the electromagnetic data collection time T c , a channel detection time T s and data transmission time T t ; Set the sensor to be divided into three non-overlapping time periods: data acquisition, channel detection, and data transmission; among them, the data acquisition time period is used to obtain raw data, the channel detection time period is used to determine the channel status, and the data transmission time period is used to transmit the collected data to the receiving end.

[0071] Establish position m i The relationship between the total amount of data collected by the sensor at position m in the data collection period and the false alarm probability and detection probability is calculated by taking the collected original data as input and calculating the corresponding false alarm probability and detection probability. i The relationship between the amount of data of the sensor in the data transmission time period and the channel detection result is determined based on the channel state judged during the channel detection time to determine the amount of data transmitted in the data transmission time period; according to the relationship between the total amount of data in the data collection time period and the false alarm probability and detection probability, as well as the relationship between the amount of data in the data transmission time period and the channel detection result, a time resource allocation model for the sensor is established. The corresponding false alarm probability and detection probability are calculated, including:

[0072] The position m is calculated by the following formula iThe total amount of data R of the sensor in the data collection period c : Among them, T c,i Indicates the data collection period; Indicates position m i The channel capacity of the sensor at position m is calculated by the following formula i The false alarm probability of the sensor is: Among them, P f,i (T s,i ) indicates position T s,i The false alarm probability of the sensor at position m i The channel detection time of the sensor at the location; Q is the complementary distribution function of the standard Gaussian function; γ i Indicates position m i The signal-to-noise ratio of the received signal of the sensor; Q -1 represents the inverse function of Q; represents the preset target detection probability; represents the sampling frequency; the position m is calculated by the following formula i The detection probability of the sensor at: Among them, P d,i Indicates position m i The detection probability of the sensor at T s,i is the position m i The channel detection time of the sensor at i is the position m i The signal-to-noise ratio of the received signal of the sensor at s is the sampling frequency, Q(*) is the complementary distribution function of the standard Gaussian function; represents the target false alarm probability; τ represents the detection threshold; the amount of data transmitted during the data transmission period is determined by the following formula: R t,i =R i (T t,i ,T s )×T t,i , where R t,i Indicates position m i The amount of data from the sensor at the data transmission stage; R i Indicates position m i The transmission rate of the sensor at t,i Indicates position m i The data transmission time of the sensor at s Indicates the channel detection time.

[0073] The time resource allocation model of the sensor is established, including: taking the total data volume, false alarm probability, detection probability of the data collection time period, and the transmission data volume of the data transmission time period as constraints, taking the time resource allocation of the three time periods as optimization variables, and the objective function is to minimize the total time of data collection, channel detection and data transmission. The time resource allocation model of the sensor is constructed by the following formula:

[0074] max R t,i

[0075] st

[0076] T c +T s +T t,i ≤T

[0077]

[0078] R c ≤R t

[0079] Among them, T c Indicates the sensor data collection time; represents the minimum sensor acquisition time; T represents the total time; P d represents the target detection probability of the sensor; represents the set target detection probability; P(H0) represents the probability of channel idleness; 1-P f (T s ) represents the probability that the channel is idle and the sensor successfully detects that the channel is idle; R i (T t,i ) indicates position m i The transmission rate of the sensor; Indicates position m i The minimum transmission rate of the sensor at R c Indicates the amount of data collected by the sensor; Indicates the amount of data transmitted by the sensor.

[0080] In the time resource allocation model, based on the timeliness index, the information age is set, and the relationship between the information age and the service rate, data arrival rate and packet loss rate in the data transmission stage is established, and the established relationship is used as the constraint condition of the time resource allocation model. Specifically, for each sensor node, for each sensor node, set t1, t2, ..., t n is the time when the data packet is successfully uploaded, we can define Δ(t) = tu(t), where u(t) is the timestamp of the last data packet upload. Define the time interval of state update as X, and the service time of state update in the system as T. Then the average information age in the observation interval (0, τ) is expressed as: can be converted to: Where Λ is the data arrival rate of the system. The sensor upload model in this paper is modeled as a typical M / M / 1 queue model, assuming that the service time of each data packet is exponentially distributed with the service rate μ, and the load rate is Then the average information age of an M / M / 1 sequence can be expressed as: In actual sensor network operations, data packets may be lost due to various reasons (such as signal interference, poor channel quality, overload, etc.). The information age of the sensor is obtained by correcting the average value of the information age of the M / M / 1 sequence ΔM / M / 1 using the packet loss rate ρ': Set the constraint formula: Among them, Δ'M / M / 1 represents the information age of the sensor network; Indicates the maximum allowed value of the message age.

[0081] The resource optimization problem in the time resource allocation model after setting the information age constraint is transformed into a convex optimization problem, and the convex optimization model of synaesthesia time resource allocation constructed by the spectrum map is obtained; first, the established time resource allocation model is used as the objective function, and the information age constraint is used as the constraint condition to construct the resource optimization problem of time resource allocation. Specifically, on the basis of the time resource allocation model, the information age constraint is introduced to construct the resource optimization problem of time resource allocation; then, the convexity analysis of the resource optimization problem is performed. By analyzing the convexity of the objective function and the constraint condition, it is judged whether the resource optimization problem is a convex optimization problem. Specifically, the second-order derivative of the objective function is calculated. If its Hessian matrix is ​​semi-positive definite in the domain of definition, the objective function is a convex function; for the constraint conditions, if they define a convex set, that is, they satisfy the definition of a convex set, then the constraint conditions are convex sets.

[0082] According to the results of convexity analysis, if the objective function is a convex function and the constraints are convex sets, the resource optimization problem is transformed into a convex optimization problem. The convex optimization problem has a global optimal solution and can be efficiently solved using the existing convex optimization algorithm. Finally, based on the transformed convex optimization problem, a convex optimization model for synaesthesia time resource allocation constructed by spectrum map is established; the expression of the convex optimization model is as follows:

[0083]

[0084] st

[0085] T c +T s +T t,i ≤T

[0086]

[0087] R c ≤R t

[0088] in:

[0089]

[0090] The convex optimization model of synaesthesia time resource allocation constructed by spectrum map is solved by using multi-strategy improved dung beetle optimization algorithm (MSDBO). A chaotic pseudo-random strategy is used to update the positions of individuals in MSDBO in a chaotic pseudo-random manner. Specifically, a chaotic mapping is introduced to generate pseudo-random numbers through a chaotic sequence, and the positions of individuals in the algorithm are disturbed to increase the randomness and diversity of the search and avoid the algorithm from falling into the local optimum. The tangent flight search strategy is used to update the positions of individuals in the algorithm processed by the chaotic pseudo-random strategy. The tangent flight search strategy includes two stages: concentrated search and exploratory search. By balancing local search and global search, the search efficiency and optimization ability of the algorithm are improved; among them, the update includes concentrated search and exploratory search; the concentrated search is achieved through the following formula: in represents the position of the individual at the next iteration, is the position of the individual at the current iteration, step is the step length, tan(θ) is the tangent function, and θ is a random angle, whose value is usually between 0 and between, is the location of the currently found optimal solution. The exploration search is performed using the following formula:

[0091] Here, randn(*) generates a standard normally distributed random number.

[0092] The vertical and horizontal cross strategy is used to optimize the position of individuals in the optimization algorithm after the tangent flight search strategy. The vertical and horizontal cross strategy enhances the local search ability of the algorithm and accelerates the convergence speed through information exchange between individuals. i and individual X j , the next generation individual position generated by horizontal crossover in the dth dimension is calculated by the following formula:

[0093]

[0094] in, and are the positions of the new offspring generated by the previous generation of individuals, and are the positions of the individuals of the previous generation in the nth dimension, r1 and r2 are random numbers in the interval [0, 1], c1 and c2 are random numbers in the interval [-1, 1]. The generated offspring are compared with the individuals of the previous generation, and the better individuals are retained. i , k≠d is a different dimension, and the position of the next generation of individuals generated by vertical crossover is calculated by the following formula: in, Indicates the position of the next generation of individuals; The position of the current generation of individuals represented by dimension n1; Represented as the position of the current generation individual of dimension n2.

[0095] A chaotic pseudo-random strategy is used to update the position of the individual in a chaotic pseudo-random manner. The formula is as follows:

[0096]

[0097] Among them, X i,d (t+1) represents the position of individual i at time t+1, X i,d (t) represents the position of individual i at time t; η and μ represent chaotic coefficients; mod represents modulus division (remainder) operation; when η and μ are between 0 and 1, the system is in a chaotic state, and r is a random number between 0 and 1; here η=0.4, μ=0.3.

[0098] The convex optimization model of synaesthesia time resource allocation constructed by the spectrum map is used as the optimization target of MSDBO, and the optimal solution of the convex optimization model is obtained through multiple iterations. Specifically, the objective function of the convex optimization model is used as the fitness function of MSDBO, and the individual positions are updated and optimized through the chaotic pseudo-random strategy, tangent flight search strategy and vertical and horizontal cross strategy. It is continuously iterated until the termination condition is met, and the optimal time resource allocation scheme is obtained. The optimal time resource allocation scheme t for the three time periods of data acquisition, channel detection and data transmission is obtained. i,c , t i,s , t i,t , as the final solution for synaesthesia time resource allocation for spectrum map construction. Through the above steps, the convex optimization model of synaesthesia time resource allocation for spectrum map construction is solved by using the multi-strategy improved dung beetle optimization algorithm MSDBO, and the optimal time resource allocation scheme for the three time periods of data acquisition, channel detection and data transmission is obtained. MSDBO improves the algorithm's search efficiency, optimization ability and convergence speed by introducing chaotic pseudo-randomness, tangent flight search and vertical and horizontal cross strategies, and provides an optimized time resource allocation scheme for the efficient construction of spectrum maps.

[0099] In this example, in order to verify the performance of the proposed time resource allocation algorithm, it is compared with other different algorithms. In order to make the experimental results fair and objective, the scale of all algorithms is set to 30, the maximum number of iterations is 200, and in order to exclude the influence of randomness, all algorithms are independently run 50 times to take the average value. The relevant simulation parameters are shown in the following table:

[0100]

[0101]

[0102] Figure 4 is the initial population distribution diagram generated by chaotic pseudo-random, where the horizontal axis represents the position of the individual and the vertical axis represents the fitness value of the individual. Figure 4 It can be seen that the initialization population generated by the chaotic pseudo-random strategy is more evenly distributed in the search space, and the proportion of high-quality solutions (individuals with higher fitness values) is higher. This distribution characteristic helps the algorithm to quickly locate potential good areas in the early stages of optimization, accelerate the convergence speed of the algorithm, and improve the optimization efficiency. Compared with random initialization, chaotic pseudo-random initialization can provide a better starting point for the algorithm and increase the probability of finding the global optimal solution.

[0103] Figure 5 This is a graph of the step size changes of the concentrated search and exploratory search obtained by simulating 500 iterations, where the horizontal axis represents the number of iterations and the vertical axis represents the size of the search step size. Figure 5 The following characteristics can be observed in the figure: In the early stage of iteration (such as the first 100 iterations), the change of the concentrated search step size is large, showing obvious volatility. This shows that in the initial stage of optimization, the algorithm actively explores the optimization space through large step size changes in order to quickly find potential good areas. This behavior helps the algorithm identify promising search directions in the early stage and provides a good foundation for subsequent optimization. As the number of iterations increases, the change of the concentrated search step size gradually becomes gentler and the fluctuation amplitude decreases. This shows that as the optimization proceeds, the algorithm begins to focus on the best area currently found and conducts fine local search by reducing the step size. This strategy can help the algorithm further explore in the promising area in order to find a better solution. The exploratory search step size shows large volatility throughout the iteration process, and the change amplitude is relatively strong. This reflects the behavioral characteristics of the algorithm in global search, that is, exploring the entire search space through large step size changes to find new possible areas. This global exploration helps the algorithm escape from the local optimal solution and find a better solution.

[0104] The step sizes of concentrated search and exploratory search change alternately throughout the iteration process, reflecting the algorithm's characteristic of seeking a balance between exploration and utilization. By reasonably adjusting the step size changes of the two search strategies, the algorithm can find a balance between expanding the search range and focusing on excellent areas, improving the optimization efficiency and the quality of the solution. It can be seen that the initial population generated by chaotic pseudo-random distribution is more evenly distributed and has a higher proportion of high-quality solutions, which helps the algorithm to quickly find potential excellent areas. The step size changes of concentrated search and exploratory search reflect the behavioral characteristics of the algorithm during the optimization process. By balancing exploration and utilization, the algorithm's optimization ability and convergence speed are improved. These characteristics enable the MSDBO algorithm to efficiently solve the synaesthesia time resource allocation problem constructed by the spectrum map and obtain an optimized time resource allocation plan.

[0105] Figure 6 The effect of the time allocation ratio of data acquisition, channel detection, and data transmission on the information age is shown. The horizontal and vertical axes represent the data acquisition time ratio and the data transmission time ratio, respectively. The colors in the figure represent the information age under the corresponding time allocation ratio. Figure 6 The following characteristics can be observed: The blue area indicates a lower information age, which usually appears in areas where the data collection time ratio and data transmission time ratio are both small. This shows that when the data collection and transmission time are both short, the timeliness of the information is high, and the system can quickly acquire and update data, thereby maintaining a low information age and improving the real-time performance of the system. The yellow area indicates a higher information age, which usually appears in areas where the data collection time ratio or data transmission time ratio is large. This shows that when the data collection or transmission time is long, the timeliness of the information decreases, the data update speed slows down, resulting in a significant increase in the information age and a decrease in the real-time performance of the system. Therefore, in order to ensure the efficient operation of the system and the timeliness of the data, it is necessary to reasonably adjust the data collection and transmission time and find the optimal time allocation ratio. By balancing the time overhead of data collection and transmission, it is necessary to ensure both the sufficiency and accuracy of data collection and the timeliness and reliability of data transmission, thereby reducing the information age and improving the overall performance of the system.

[0106] Figure 7 and Figure 8 The performance comparison results of the MSDBO algorithm and other optimization algorithms are shown, where the horizontal axis represents the number of iterations and the vertical axis represents the objective function value. Figure 7 and Figure 8It can be seen that the final objective function value of the MSDBO algorithm converges best, and the convergence curve almost shows a horizontal trend after 20 iterations. This shows that the MSDBO algorithm can quickly find a solution close to the optimal one, and has a strong optimization ability and convergence speed. Compared with other algorithms, the MSDBO algorithm has significantly improved in convergence speed. Compared with the fastest converging ARO algorithm, the convergence speed of MSDBO increased by about 33.33%; compared with the slowest converging POA algorithm, the convergence speed of MSDBO increased by about 73.68%. This shows that the MSDBO algorithm effectively speeds up the convergence speed of the algorithm and shortens the optimization time by introducing multiple optimization strategies.

[0107] Fig. 9 The performance comparison results of the MSDBO-based time resource allocation algorithm and other optimization algorithms and random allocation methods are shown. Fig. 9 It can be seen that the MSDBO algorithm has improved the optimization target by about 12.82% compared with the random time allocation method. This shows that the MSDBO algorithm can effectively optimize the allocation of time resources and obtain better system performance. It can be seen that the time allocation ratio of data acquisition, channel detection, and data transmission has a significant impact on the information age. It is necessary to reasonably adjust the time allocation to reduce the information age and improve system performance. The MSDBO algorithm shows excellent optimization ability and convergence speed in the problem of time resource allocation optimization. It can effectively optimize the allocation of time resources and obtain better system performance, providing strong support for the efficient construction of spectrum maps.

[0108] This application proposes a sensor communication perception time resource allocation model for spectrum map construction, which realizes the trade-off between data collection, channel detection and data transmission of nodes. Simulation results show that compared with other commonly used optimization algorithms, the proposed algorithm significantly accelerates the convergence speed on the basis of performance improvement, can quickly realize spectrum map reconstruction, and improves the accuracy of spectrum map construction.

[0109] The invention of the present application and its implementation methods are described schematically above. The description is not restrictive. Without departing from the spirit or basic features of the present application, the present application can be implemented in other specific forms. What is shown in the accompanying drawings is only one of the implementation methods of the invention of the present application. The actual structure is not limited to this. Any figure mark in the claims should not limit the claims involved. Therefore, if a person of ordinary skill in the art is inspired by it, without departing from the purpose of the present invention, a structural method and an embodiment similar to the technical solution are designed without creativity, which should all fall within the scope of protection of this patent. In addition, the word "including" does not exclude other elements or steps, and the word "one" before the element does not exclude the inclusion of "multiple" elements. The multiple elements stated in the product claim can also be implemented by one element through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any specific order.

Claims

1. A synaesthesia time resource allocation method for spectrum map construction, comprising: S1, establish the sensor time resource allocation model to allocate data collection T c , Channel Detection T s and data transmission time T t ; S2, in the time resource allocation model, based on the timeliness index, the information age is set, and the relationship between the information age and the service rate, data arrival rate and packet loss rate in the data transmission stage is established, and the established relationship is used as the constraint condition of the time resource allocation model; S3, transforming the resource optimization problem in the time resource allocation model after setting the information age constraint into a convex optimization problem, and obtaining a convex optimization model of synaesthesia time resource allocation constructed by the spectrum map; S4, using the multi-strategy improved dung beetle optimization algorithm MSDBO, solve the convex optimization problem of the convex optimization model and obtain the data collection T c , Channel Detection T s and data transmission time T t The optimal time resource allocation scheme for three time periods; among them, MSDBO improves the dung beetle optimization algorithm DBO by setting chaotic pseudo-randomness, tangent flight search and vertical and horizontal cross strategies to avoid the algorithm falling into local optimality.

2. The method for allocating synaesthesia time resources for spectrum map construction according to claim 1, characterized in that: S1, establish the sensor time resource allocation model, including: Set the sensor to be divided into data acquisition time T c , Channel Detection T s and data transmission time T t Three non-overlapping time periods; data collection T c Used to obtain raw data, channel detection T s Used to judge the channel status, data transmission time T t Used to transmit the collected data to the receiving end; Establish position m i Sensors at data acquisition T c The relationship between the total amount of data and the false alarm probability and detection probability is calculated by taking the collected original data amount as input and calculating the corresponding false alarm probability and detection probability; Establish position m i The sensor at the data transmission time T t The relationship between the amount of data and the channel detection result is s The channel status is judged as the basis to determine the data transmission time T t The amount of data transmitted; According to data collection T c The relationship between the total data volume and the false alarm probability and detection probability, as well as the data transmission time T t The amount of data and channel detection T s The relationship between them is used to establish a time resource allocation model for sensors.

3. The method for allocating synaesthesia time resources for spectrum map construction according to claim 2, characterized in that: Calculate the corresponding false alarm probability and detection probability, including: The position m is calculated by the following formula i Sensors at data acquisition T c The total amount of data R c : Among them, T c,i Indicates position m i The data collection time period of the sensor; Indicates position m i The channel capacity of the sensor at The position m is calculated by the following formula i The false alarm probability of the sensor is: Among them, P f,i (T s,i ) indicates position T s,i The false alarm probability of the sensor at T s,i Indicates position m i The channel detection time of the sensor at the location; Q is the complementary distribution function of the standard Gaussian function; γ i Indicates position m i The signal-to-noise ratio of the received signal of the sensor; Q -1 represents the inverse function of Q; represents the preset target detection probability; f s Indicates the sampling frequency; The position m is calculated by the following formula i The detection probability of the sensor at: Among them, P d,i Indicates position m i The detection probability of the sensor at represents the target false alarm probability; τ represents the detection threshold; The amount of data transmitted during a data transmission period is determined by the following formula: R t,i =R i (T t,i ,T s )×T t,i Among them, R t,i Indicates position m i The amount of data from the sensor at the data transmission stage; R i Indicates position m i The transmission rate of the sensor at t,i Indicates position m i The data transmission time of the sensor at 4. The method for allocating synaesthesia time resources for spectrum map construction according to claim 3 is characterized by: Establish a sensor time resource allocation model, including: The data collection T c The total data volume, false alarm probability, detection probability, and data transmission time T t The amount of data transmission is taken as the constraint condition, the time resource allocation of the three time periods is taken as the optimization variable, and the objective function is to minimize the total time of data acquisition, channel detection and data transmission. The time resource allocation model of the sensor is constructed by the following formula: max R t,i T c +T s +T t,i ≤T R c ≤R t in, represents the minimum sensor acquisition time; T represents the total time; P d represents the target detection probability of the sensor; represents the set target detection probability; P(H0) represents the probability of channel idleness; 1-P f (T s ) represents the probability that the channel is idle and the sensor successfully detects that the channel is idle; R i (T t,i ) indicates position m i The transmission rate of the sensor; Indicates position m i The minimum transmission rate of the sensor at R c Represents the amount of data collected by the sensor; R t Indicates the amount of data transmitted by the sensor.

5. The method for allocating synaesthesia time resources for spectrum map construction according to any one of claims 2 to 4, characterized in that: S2, the constraints for establishing the time resource allocation model, including: For each sensor node i, let t1,t2,...,t n is the time when the data packet is successfully uploaded, and Δ(t)=tu(t) is defined, where u(t) is the timestamp of the last data packet upload, t represents the current time, and represents the age of the information at the current time, that is, the time elapsed since the last data packet was uploaded; the time interval for status update is defined as X, and the service time of status update in the system is T; Calculate the average information age Δ in the observation interval (0,τ) τ : Where τ represents the length of the observation interval; According to the average age of information Δ τ Calculate Δ, where Δ represents the information age related to the data arrival rate and time interval; Where Λ is the data arrival rate of the system, E[X 2 ] represents the expected value of the square of the packet arrival interval; E[XT] represents the expected value of the product of the interval X and the service time T; According to the service time and service rate μ of each data packet, the sensor upload model is modeled as an M / M / 1 queue model through exponential distribution, and the average information age ΔM / M / 1 of an M / M / 1 sequence in the queue model is calculated: Where ρ represents the load factor, The packet loss rate ρ' is used to correct the average information age ΔM / M / 1 of the M / M / 1 sequence, and the information age Δ'M / M / 1 of the sensor is obtained: Based on the information age Δ'M / M / 1 of the sensor, the information age constraint is set as the constraint of the time resource allocation model: Among them, Δ'M / M / 1 represents the information age of the sensor network; Indicates the maximum allowed value of the message age.

6. The method for allocating synaesthesia time resources for spectrum map construction according to claim 5, characterized in that: S3, transforming the resource optimization problem in the time resource allocation model after setting the information age constraint into a convex optimization problem, and obtaining a convex optimization model of synaesthesia time resource allocation constructed by the spectrum map, including: The established time resource allocation model is used as the objective function and the information age constraint is used as the constraint condition to construct the resource optimization problem of time resource allocation; Conduct convexity analysis on resource optimization problems; According to the results of convexity analysis, if the objective function is a convex function and the constraints are convex sets, the resource optimization problem is transformed into a convex optimization problem; According to the transformed convex optimization problem, a convex optimization model for synaesthesia time resource allocation constructed by spectrum map is established.

7. The method for allocating synaesthesia time resources for spectrum map construction according to claim 6, characterized in that: The expression of the convex optimization model is as follows: T c +T s +T t,i ≤T R c ≤R t in, α i represents the coefficient based on the detection probability; γ i represents the signal-to-noise ratio (SNR); β represents the time parameter T s The coefficient, Q -1 Represents the inverse cumulative distribution function of the standard normal distribution.

8. The method for allocating synaesthesia time resources for spectrum map construction according to claim 7, characterized in that: S4, using the multi-strategy improved dung beetle optimization algorithm MSDBO, solve the convex optimization problem of the convex optimization model and obtain the data collection T c , Channel Detection T s and data transmission time T t The optimal time resource allocation scheme for three time periods includes: Adopting chaotic pseudo-random strategy, the positions of individuals in MSDBO are updated in a chaotic pseudo-random manner; The tangent flight search strategy is adopted to update the position of individuals in the algorithm after being processed by the chaotic pseudo-random strategy; the update includes concentrated search and exploration search; The vertical and horizontal cross strategy is adopted to optimize the position of individuals in the optimization algorithm after being processed by the tangent flight search strategy; The convex optimization model of synaesthesia time resource allocation constructed by spectrum map is used as the optimization target of multi-strategy improved dung beetle optimization algorithm MSDBO. The optimal solution of the convex optimization model is obtained through multiple iterations, and the optimal time resource allocation scheme t for the three time periods of data acquisition, channel detection and data transmission is obtained. i,c , t i,s , t i,t ,The final scheme of synaesthesia time resource allocation as spectrum map construction; A chaotic pseudo-random strategy is used to update the position of the individual in a chaotic pseudo-random manner. The formula is as follows: Among them, X i,d (t+1) represents the position of the individual at time t+1; X i,d (t) represents the position of individual i at time t; η and μ represent chaotic coefficients; mod represents modular division operation; when η and μ are between 0 and 1, the system is in a chaotic state, and r is a random number between 0 and 1.

9. The method for allocating synaesthesia time resources for spectrum map construction according to claim 8, characterized in that: The concentrated search is done by the following formula: in represents the position of the individual at the next iteration, is the position of the individual at the current iteration, step is the step size, tan(θ) is the tangent function, and θ is a random angle, which is usually between 0 and between, is the location of the optimal solution currently found; The exploration search is done through the following formula: The random number generated by randn() is a standard normal distribution. The vertical and horizontal cross strategy is used to optimize the position of the individuals in the dung beetle optimization algorithm MSDBO after the tangent flight search strategy is processed, including: For individual X i and individual X j , the next generation individual position generated by horizontal crossover in the dth dimension is calculated by the following formula: in, and are the positions of the new offspring generated by the previous generation of individuals, and are the positions of the individuals of the previous generation in the nth dimension, r1 and r2 are random numbers in the interval [0, 1], c1 and c2 are random numbers in the interval [-1, 1]. The generated offspring are compared with the individuals of the previous generation, and the better individuals are retained; For individual X i , k≠d is a different dimension, and the position of the next generation of individuals generated by vertical crossover is calculated by the following formula: in, Indicates the position of the next generation of individuals; The position of the current generation of individuals represented by dimension n1; Indicates the position of the current generation of individuals in dimension n2.

10. A synaesthesia time resource allocation system for spectrum map construction, characterized in that: include: At least one processing unit PE; used to execute instructions to implement the synaesthesia time resource allocation method for spectrum map construction according to any one of claims 1 to 9.

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