Compressed sensing energy distribution optimization method and device based on particle swarm partitioning

Abstract

The invention discloses a compressed sensing energy distribution optimization method and device based on particle swarm partitioning, and relates to the technical field of the Internet of Things, and the method comprises the steps: obtaining the number of sensor nodes of a sensor network and an energy arrival matrix; performing node partitioning scheme optimization according to the number of the sensor nodes through a particle swarm optimization algorithm, and determining a node block energy distribution matrix of a node partitioning scheme according to the energy arrival matrix based on a compressed sensing energy distribution optimization algorithm, outputting an optimal node partitioning scheme by taking minimization of the fitness based on the node block energy distribution matrix as a target; and updating the node block energy distribution matrix of the optimal node partitioning scheme to the sensor nodes in the sensor network in an association manner. Based on the above scheme, a strategy of first partitioning and then optimization is realized through the particle swarm optimization algorithm and the compressed sensing framework, and the calculation complexity is significantly reduced while the high reconstruction precision is maintained.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of Internet of Things, and in particular to a compressed sensing energy distribution optimization method and device based on particle swarm blocking. BACKGROUND

[0002] With the continuous growth of the number of Internet of Things (IoT) devices, monitoring and communication systems based on wireless sensor networks (WSNs) are widely used in environmental monitoring, intelligent agriculture, and smart medical care. Sensor nodes in wireless sensor networks usually achieve long-term stable data collection through energy harvesting. However, the energy of nodes is limited and the acquisition of environmental energy is random. Therefore, the dynamic changes of the energy states of different nodes in this energy harvesting type wireless sensor network make the uneven energy distribution among nodes a major problem affecting the overall performance of the system. The existing energy distribution optimization scheme usually optimizes the energy matrix in the global range. When the number of nodes increases, the computational complexity increases sharply, making it difficult to meet the real-time requirements. Therefore, it is urgent to solve the problem of how to achieve low-complexity energy distribution optimization while ensuring signal reconstruction accuracy. SUMMARY

[0003] The present application provides a compressed sensing energy distribution optimization method and device based on particle swarm blocking, which solves the technical problem of high computational complexity of directly optimizing the energy matrix in the global range in the existing energy harvesting type wireless sensor network.

[0004] The first aspect of the present application provides a compressed sensing energy distribution optimization method based on particle swarm blocking, comprising:

[0005] Obtaining the number of sensor nodes and the energy arrival matrix of the sensor network;

[0006] Optimizing the node blocking scheme according to the number of sensor nodes through a particle swarm optimization algorithm, determining the node block energy distribution matrix of the node blocking scheme based on the energy arrival matrix according to a compressed sensing energy distribution optimization algorithm, and outputting the optimal node blocking scheme with the goal of minimizing the fitness based on the node block energy distribution matrix;

[0007] Updating the node block energy distribution matrix of the optimal node blocking scheme to the sensor nodes in the sensor network.

[0008] Further, the optimization of the node blocking scheme according to the number of sensor nodes through the particle swarm optimization algorithm, the determination of the node block energy distribution matrix of the node blocking scheme based on the energy arrival matrix according to the compressed sensing energy distribution optimization algorithm, and the output of the optimal node blocking scheme with the goal of minimizing the fitness based on the node block energy distribution matrix, comprise:

[0009] initialize a particle population by taking the node partition scheme of the sensor nodes as particles according to the number of sensor nodes;

[0010] determine the node block energy arrival matrix corresponding to each node block of the particle according to the energy arrival matrix;

[0011] perform energy allocation optimization on the node block energy arrival matrix of each node block by using a preset compressed sensing energy allocation optimization algorithm to determine the corresponding node block energy allocation matrix;

[0012] determine the fitness based on the node block energy allocation matrix of the particle, determine the individual optimal position and the global optimal position based on the objective of minimizing the fitness, and iteratively update the speed and position of each particle to the global optimal position meeting the iteration stop condition as the optimal node partition scheme.

[0013] Further, the calculation process of the fitness comprises:

[0014] calculate the mutual coherence value of the node block energy allocation matrix of the node partition scheme;

[0015] take the maximum mutual coherence value as the fitness of the node partition scheme.

[0016] Further, the number of blocks in the node partition scheme is less than or equal to one fifth of the number of sensor nodes.

[0017] The second aspect of the present application provides a compressed sensing energy allocation optimization device based on particle swarm partitioning, comprising:

[0018] a data acquisition module configured to acquire the number of sensor nodes and the energy arrival matrix of a sensor network;

[0019] an energy optimization module configured to optimize the node partition scheme according to the number of sensor nodes by using a particle swarm optimization algorithm, determine the node block energy allocation matrix of the node partition scheme based on a compressed sensing energy allocation optimization algorithm according to the energy arrival matrix, and output the optimal node partition scheme as the objective of minimizing the fitness based on the node block energy allocation matrix;

[0020] an energy allocation module configured to update the node block energy allocation matrix of the optimal node partition scheme to the sensor nodes in the sensor network.

[0021] Further, the energy optimization module is specifically configured to:

[0022] initialize a particle population by taking the node partition scheme of the sensor nodes as particles according to the number of sensor nodes;

[0023] determine a node block energy arrival matrix corresponding to each node block of the particle according to the energy arrival matrix;

[0024] perform energy allocation optimization on the node block energy arrival matrix of each node block by using a preset compressed sensing energy allocation optimization algorithm, and determine a corresponding node block energy allocation matrix;

[0025] determine fitness based on the node block energy allocation matrix of the particle, determine an individual optimal position and a global optimal position based on the objective of minimizing the fitness, and iteratively update the speed and position of each particle, and take the global optimal position meeting the iteration stop condition as the optimal node block scheme.

[0026] Further, the calculation process of the fitness comprises:

[0027] calculate the mutual coherence value of the node block energy allocation matrix of the node block scheme;

[0028] take the maximum mutual coherence value as the fitness of the node block scheme.

[0029] The third aspect of the present application provides a computer device, which comprises a memory and a processor, the memory stores a computer program, and the computer program is executed by the processor to make the processor execute the steps of the compressed sensing energy allocation optimization method based on the particle swarm block as described in any one of the above aspects.

[0030] The fourth aspect of the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed to implement the compressed sensing energy allocation optimization method based on the particle swarm block as described in any one of the above aspects.

[0031] The fifth aspect of the present application provides a computer program product, which comprises computer programs / instructions, and the computer programs / instructions are executed by a processor to implement the compressed sensing energy allocation optimization method based on the particle swarm block as described in any one of the above aspects.

[0032] As can be seen from the above technical solutions, the present application has the following advantages:

[0033] The above scheme of the present application provides a compressed sensing energy distribution optimization method and device based on particle swarm block, comprising: obtaining the number of sensor nodes and energy arrival matrix of a sensor network; optimizing the node block scheme according to the number of sensor nodes through a particle swarm optimization algorithm, determining the node block energy distribution matrix of the node block scheme based on the energy arrival matrix according to a compressed sensing energy distribution optimization algorithm, and outputting the optimal node block scheme with the minimum fitness based on the node block energy distribution matrix as the target; and updating the node block energy distribution matrix of the optimal node block scheme to the sensor nodes in the sensor network. Based on the above scheme, the particle swarm optimization algorithm is used to perform energy feature clustering and adaptive block on the sensor nodes, the global optimization problem is decomposed into several low-dimensional sub-problems, and the energy distribution optimization based on compressed sensing is independently performed in each node block, so that the distributed parallel solution of the node block energy distribution matrix is realized. Through the particle swarm optimization algorithm and the compressed sensing framework, the strategy of "block first and then optimization" is realized, the high reconstruction accuracy is maintained, and the computational complexity is significantly reduced. BRIEF DESCRIPTION OF DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0035] Figure 1 A step flow chart of a compressed sensing energy distribution optimization method based on particle swarm block provided for the first embodiment of the present application;

[0036] Figure 2 An accurate recovery probability diagram at different block numbers provided for the first embodiment of the present application;

[0037] Figure 3 A normalized mean square error diagram at different block numbers provided for the first embodiment of the present application;

[0038] Figure 4 A sampling time slot diagram at different block numbers provided for the first embodiment of the present application;

[0039] Figure 5 An optimization time diagram at different block numbers provided for the first embodiment of the present application;

[0040] Figure 6 A structure block diagram of a compressed sensing energy distribution optimization device based on particle swarm block provided for the second embodiment of the present application. DETAILED DESCRIPTION

[0041] The embodiment of the present application provides a compressed sensing energy distribution optimization method and device based on particle swarm block, which is used for solving the technical problem that the existing energy collection type wireless sensor network directly optimizes the energy matrix in a global range and has high calculation complexity.

[0042] In order to make the application purpose, features and advantages of the present application more obvious and easy to understand, the technical solutions in the embodiments of the present application will be described clearly and completely below in combination with the drawings in the embodiments of the present application. Obviously, the following described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application.

[0043] Please refer to Figure 1 The embodiment one of the present application provides a compressed sensing energy distribution optimization method based on particle swarm block, which comprises the following steps.

[0044] Step 101, acquiring the number of sensor nodes of a sensor network and an energy arrival matrix.

[0045] It should be noted that in the present embodiment, for the sensor network composed of multiple sensor nodes through wireless communication mode, the number of sensor nodes in the network and the energy arrival matrix composed of the node energy arrival vector of all sensor nodes are acquired , which represents the energy arrival situation of sensor nodes in time slots, so as to serve as the data basis for subsequent energy distribution optimization.

[0046] Step 102, optimizing the node block scheme according to the number of sensor nodes through the particle swarm optimization algorithm, determining the node block energy distribution matrix of the node block scheme based on the compressed sensing energy distribution optimization algorithm according to the energy arrival matrix, and outputting the optimal node block scheme with the minimum fitness based on the node block energy distribution matrix as the target.

[0047] The particle swarm optimization algorithm (PSO) is a global optimization method based on swarm intelligence, which is inspired by the cooperative behavior of bird flocks or fish flocks. Each potential solution is regarded as a "particle", and its position represents a candidate solution of the problem. The particle updates the speed and position by tracking the individual optimal position and global optimal position, so as to realize the search process of gradually approaching the global optimum.

[0048] The node block scheme refers to the scheme of setting multiple node blocks according to the number of node blocks, and dividing the sensor nodes of the sensor network into each node block.

[0049] The node block energy distribution matrix refers to a matrix composed of energy distribution of multiple sensor nodes in a node block.

[0050] The fitness refers to an index value for evaluating the pros and cons of the node block scheme.

[0051] The compressed sensing energy distribution optimization algorithm refers to an algorithm for optimizing energy distribution of sensor nodes based on compressed sensing (CS).

[0052] In one specific implementation of the embodiment, step 102 includes the following sub-steps:

[0053] According to the number of sensor nodes, the node block scheme of the sensor nodes is taken as a particle to initialize a particle population;

[0054] According to the energy arrival matrix, a node block energy arrival matrix corresponding to each node block of the particle is determined;

[0055] A preset compressed sensing energy distribution optimization algorithm is used to optimize energy distribution of each node block energy arrival matrix to determine a corresponding node block energy distribution matrix;

[0056] The fitness is determined based on the node block energy distribution matrix of the particle, and the individual optimal position and the global optimal position are determined based on the objective of minimizing the fitness. The speed and position of each particle are iteratively updated, and the global optimal position meeting the iteration stop condition is taken as the optimal node block scheme.

[0057] It should be noted that the embodiment decomposes the global high-dimensional energy distribution problem into multiple low-dimensional sub-problems through the strategy of "block first and then optimize":

[0058] The particle population is initialized by taking the node block scheme as a particle. All sensor nodes are divided into node blocks, and the block number of each sensor node is represented by a discrete position vector: , wherein, represents a node block scheme, represents that the sensor node belongs to the node block , represents the number of blocks. For example, when and , if , it indicates that the first and second sensor nodes are divided into the first node block, the third and fifth sensor nodes are divided into the second node block, and the fourth and sixth sensor nodes are divided into the third node block.

[0059] The position of each particle corresponds to a node partitioning scheme, and its velocity and position update formulas are as follows:

[0060]

[0061]

[0062] In the formula, Indicates the first During the nth iteration The speed of each particle Indicates the first During the nth iteration The speed of each particle Indicates inertia weight, Represents the learning factor. It is represented as a random number that follows a uniform distribution in [0,1]. Indicates the first The sum of the individual optimal positions of each particle. Indicates the first During the nth iteration The position of each particle. Indicates the globally optimal position. Indicates the first During the nth iteration The position of each particle;

[0063] For each particle, the fitness is calculated separately: For any particle's node partitioning scheme, the node energy arrival vectors of the sensor nodes to which each node block belongs are selected from the energy arrival matrix according to the node partitioning scheme to form the node block energy arrival matrix. Each node block energy arrival matrix is ​​used as the input value of the compressed sensing energy allocation optimization algorithm to determine the node block energy allocation matrix corresponding to each node block energy arrival matrix. Then, the fitness is calculated using the node block energy allocation matrices associated with each node partitioning scheme.

[0064] Update the individual optimal position and the global optimal position based on the current fitness of each particle:

[0065]

[0066]

[0067] In the formula, Indicates the first During the nth iteration The fitness of each particle Indicates the first The fitness of an individual particle at its optimal position. The fitness value represents the global optimal position.

[0068] The above update rules embody the dynamic memory mechanism of the particle swarm: each particle records and retains the best node partitioning schemes encountered in history during the search process. If the fitness of the current node partitioning scheme is better, it is updated to the new individual optimal solution. At the same time, if the fitness of the particle is better than the historical optimal solution of the entire swarm, the global optimal position is updated synchronously. Through this two-layer optimal update mechanism, the particle swarm can continuously adjust the node partitioning scheme and the node block energy distribution matrix globally, so that the swarm gradually converges towards the direction of minimizing fitness, until the iteration stopping condition is met and the global optimal position at this time is output as the optimal node partitioning scheme. The iteration stopping condition can be set with reference to existing technologies. For example, it can be set to the maximum number of iterations or the fitness change of multiple consecutive iterations being lower than the change threshold, which indicates fitness convergence.

[0069] In a more specific implementation of this embodiment, the fitness calculation process includes:

[0070] Calculate the mutual interference value of the node block energy allocation matrix for the node block partitioning scheme;

[0071] The maximum mutual interference value is used as the fitness of the node partitioning scheme.

[0072] It should be noted that, in a preferred implementation, fitness is evaluated using mutual coherence values, and the energy allocation matrix is ​​composed of the energy allocation matrices of each node block in any node partitioning scheme. The calculation process of the mutual interference value includes:

[0073]

[0074] In the formula, Indicates mutual interference value, The first element of the energy distribution matrix represents the first element. List, The first element of the energy distribution matrix represents the first element. List, Represents L2 norm operations. Indicates transpose; The lower bound is called the Welch boundary, when When, the lower bound can be approximated as The cross-coherence value indicates the measure of the maximum similarity between matrix columns; in the energy distribution matrix Constructing the equivalent measurement matrix while satisfying the energy accumulation constraint In the formula, Represents the equivalent measurement matrix. Represents the channel coefficient matrix. represents the element-wise product (used to characterize the channel and energy coupling relationship in the signal transmission process), therefore, the mutual coherence value of the energy allocation matrix to a great extent reflects the signal recovery performance of the energy allocation matrix to the measurement matrix, because two columns are too relevant, i.e. The PSO will search for the optimal block scheme in the global search space that minimizes the maximum mutual coherence value, and then gradually approach the optimal block scheme.

[0075] In a more specific embodiment of the present embodiment, the number of blocks in the node block scheme is less than or equal to one-fifth of the number of sensor nodes.

[0076] It should be noted that the number of blocks is gradually increased from an initial value (such as 2), and a block number threshold is considered to be set, for example, the maximum is generally set to not more than to ensure that each block has a sufficient number of sensor nodes.

[0077] It can be understood that the compressed sensing energy allocation optimization algorithm can refer to the prior art; for example, in an implementation, the compressed sensing energy allocation optimization algorithm converts the energy allocation problem into a measurement matrix optimization problem, and minimizes the mutual coherence and column norm difference of the measurement matrix by jointly optimizing the energy allocation matrix and the channel coefficient matrix. This method uses the majorization minimization (MM) framework to iteratively solve, realizes the approximate optimal energy allocation under the energy constraint, and the iterative model of the compressed sensing energy allocation optimization algorithm includes:

[0078]

[0079] In the formula, represents the relaxation variable of the maximum mutual coherence, represents the convex approximation operator of the th iteration, represents the th sensor node in time slot allocated energy, represents the th sensor node in time slot allocated energy, represents the th column of the measurement matrix in the th iteration, represents the th column of the measurement matrix in the th iteration, represents the th sensor node in time slot allocated energy, represents the energy collected by the sensor node in the time slot energy collected by the sensor node in the time slot represents, represents the number of sensor nodes in the represents the number of sensor nodes in the energy collected by the sensor node in the time slot energy collected by the sensor node in the time slot represents the minimum column norm lower bound, represents the maximum column norm lower bound, represents the number of sensor nodes in the represents the number of time slots, represents the L2 norm operation.

[0080] Step 103, associate the node block energy allocation matrix of the optimal node block scheme to the sensor nodes in the sensor network.

[0081] It should be noted that when the optimal node block scheme is determined, the corresponding energy allocation strategy is also determined, and therefore the sensor node energy allocation reflected by the corresponding node block energy allocation matrix is transmitted to the corresponding sensor node, so as to subsequently perform signal reconstruction according to the energy allocation in each time slot.

[0082] In order to better illustrate the effect of the embodiment, simulation experiments are performed for signal recovery and performance evaluation, and the measurement matrix obtained based on each node block is used for signal reconstruction, the original sparse signal is recovered through a compressive sensing reconstruction algorithm (such as BDNP), the reconstruction performance indicators (such as normalized mean square error NMSE and accurate recovery probability PER) of each node block are counted and averaged, and the sampling time slots of each node block and the time required for optimization are summed up to verify the performance. The experimental results are shown in Figures 2 to 5 .

[0083] Figure 2 shows the change trend of PER and NMSE under each block number, Figure 3 shows the number of sampling time slots required to achieve the optimal PER and NMSE under each block number, Figure 4 shows the number of sampling time slots required to achieve the optimal PER and NMSE under each block number, Figure 5The time consumption required for optimization to achieve the optimal PER and NMSE under different block numbers is shown, wherein the block number of 1 represents no block optimization as a whole; the experimental results show that the embodiment can effectively reduce the optimization time and the number of sampling time slots under different numbers of sensor nodes, and maintain the recovery accuracy close to that of global optimization, for example, when the number of sensor nodes n=100 and the block number B=4, compared with the overall optimization, the system optimization time is reduced by more than 50%, and the number of sampling time slots is reduced by 5 compared with the global optimization (i.e., the block number is 1), therefore, without sacrificing the reconstruction accuracy, the embodiment can effectively shorten the optimization time and reduce the calculation burden, thereby meeting the demand of large-scale Internet of Things system for real-time energy distribution.

[0084] In the embodiment of the application, the particle swarm optimization algorithm is used to cluster and adaptively block the energy characteristics of the sensor nodes, to decompose the global optimization problem into a plurality of low-dimensional sub-problems, and to independently execute the energy distribution optimization based on compressed sensing in each node block, so as to realize distributed parallel solving of the node block energy distribution matrix; a double-layer collaborative optimization mechanism based on mutual coherence minimization is constructed, in each iteration, the node block energy distribution matrix of each node block is obtained based on the compressed sensing energy optimization framework, at the same time, the particle swarm optimization algorithm takes the "worst mutual coherence minimization" as the core criterion to dynamically adjust and evolution search the block structure, and the optimal scheme is updated when the new block structure can further reduce the worst mutual coherence, the collaborative optimization mechanism effectively reduces the correlation between the energy distribution matrices, and helps to significantly improve the stability and accuracy of signal reconstruction; overall, the embodiment realizes the "block first, then optimize" strategy of the particle swarm optimization algorithm and the compressed sensing framework, while maintaining high reconstruction accuracy, significantly reduces the system calculation complexity.

[0085] Please refer to Figure 6 The embodiment two of the application provides a compressed sensing energy distribution optimization device based on particle swarm block, which comprises:

[0086] The data acquisition module 601 is used for acquiring the number of sensor nodes and the energy arrival matrix of the sensor network.

[0087] The energy optimization module 602 is used for optimizing the node block scheme according to the number of sensor nodes through the particle swarm optimization algorithm, and determining the node block energy distribution matrix of the node block scheme according to the energy arrival matrix based on the compressed sensing energy distribution optimization algorithm, so as to minimize the fitness based on the node block energy distribution matrix and output the optimal node block scheme.

[0088] The energy distribution module 603 is used for associating and updating the node block energy distribution matrix of the optimal node block scheme to the sensor nodes in the sensor network.

[0089] In one specific implementation of the embodiment, the energy optimization module 602 is specifically configured to:

[0090] The particle population is initialized according to the node block scheme of the sensor nodes with the number of sensor nodes as a particle;

[0091] The node block energy arrival matrix corresponding to each node block of the particle is determined according to the energy arrival matrix;

[0092] The energy distribution optimization of each node block energy arrival matrix is performed by using a preset compressed sensing energy distribution optimization algorithm to determine the corresponding node block energy distribution matrix;

[0093] The fitness is determined based on the node block energy distribution matrix of the particle, and the individual optimal position and the global optimal position are determined based on the minimization of the fitness as a target, and the speed and position of each particle are iteratively updated, and the global optimal position meeting the iteration stop condition is taken as the optimal node block scheme.

[0094] In a more specific implementation of the embodiment, the calculation process of the fitness includes:

[0095] The mutual coherence value of the node block energy distribution matrix of the node block scheme is calculated;

[0096] The maximum mutual coherence value is taken as the fitness of the node block scheme.

[0097] In a more specific implementation of the embodiment, the number of blocks in the node block scheme is less than or equal to one fifth of the number of sensor nodes.

[0098] Embodiment three of the present application also provides a computer device including a memory and a processor, and the memory stores a computer program; the computer program is executed by the processor to make the processor execute the steps of the compressed sensing energy distribution optimization method based on particle swarm block as described in the above embodiment one of the present application.

[0099] Embodiment four of the present application also provides a computer readable storage medium, which stores a computer program / instruction, and the computer program / instruction is executed by the processor to implement the steps of the compressed sensing energy distribution optimization method based on particle swarm block as described in the above embodiment one of the present application.

[0100] Embodiment five of the present application also provides a computer program product, which includes a computer program / instruction, and the computer program / instruction is executed by the processor to implement the steps of the compressed sensing energy distribution optimization method based on particle swarm block as described in the above embodiment one of the present application.

[0101] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working process of the above-described apparatus and modules can refer to the corresponding process in the foregoing method embodiments, and will not be described here.

[0102] In several embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. For example, the apparatus embodiments described above are only schematic. The division of the units is only a logical function division. In actual implementation, another division mode can be used. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or other forms.

[0103] The modules described as separate components can or can not be physically separated, and the components shown as modules can or can not be physical modules, i.e. can be located in one place or distributed to a plurality of network modules. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiment.

[0104] In addition, the functional modules in each embodiment of the present application can be integrated into a processing module, or each module can exist physically, or two or more modules can be integrated into one module. The integrated module can be realized in the form of hardware or in the form of a software functional module.

[0105] The integrated module, if realized in the form of a software functional module and sold or used as an independent product, can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The foregoing storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various program code storage media.

[0106] The above-described embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit the present application; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that the technical solutions recorded in the foregoing embodiments can still be modified, or some technical features can be replaced by equivalent replacements; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for optimization of energy allocation in compressive sensing based on particle swarm block partitioning, characterized in that, include: Obtain the number of sensor nodes and the energy arrival matrix of the sensor network; The node partitioning scheme is optimized according to the number of sensor nodes using the particle swarm optimization algorithm. The node block energy allocation matrix of the node partitioning scheme is determined based on the energy arrival matrix using the compressed sensing energy allocation optimization algorithm. The optimal node partitioning scheme is output with the goal of minimizing the fitness of the node block energy allocation matrix. The node block energy allocation matrix of the optimal node partitioning scheme is updated in association with the sensor nodes in the sensor network.

2. The compressed sensing energy allocation optimization method based on particle swarm optimization according to claim 1, characterized in that, The process involves optimizing the node partitioning scheme using a particle swarm optimization algorithm based on the number of sensor nodes, determining the node block energy allocation matrix of the node partitioning scheme based on the energy arrival matrix using a compressed sensing energy allocation optimization algorithm, and outputting the optimal node partitioning scheme with the objective of minimizing the fitness based on the node block energy allocation matrix. This includes: Based on the number of sensor nodes and the node partitioning scheme of the sensor nodes, a particle population is initialized and constructed. The node block energy arrival matrix corresponding to each node block of the particle is determined based on the energy arrival matrix. A pre-defined compressed sensing energy allocation optimization algorithm is used to optimize the energy arrival matrix of each node block and determine the corresponding node block energy allocation matrix. Fitness is determined based on the node block energy allocation matrix of the particles, and individual optimal position and global optimal position are determined with the goal of minimizing fitness. The velocity and position of each particle are iteratively updated, and the global optimal position that satisfies the iteration stopping condition is taken as the optimal node block scheme.

3. The compressed sensing energy allocation optimization method based on particle swarm optimization according to claim 1 or 2, characterized in that, The fitness calculation process includes: Calculate the mutual interference value of the node block energy allocation matrix for the node block partitioning scheme; The maximum mutual interference value is used as the fitness of the node partitioning scheme.

4. The compressed sensing energy allocation optimization method based on particle swarm optimization according to claim 1 or 2, characterized in that, The number of blocks in the node partitioning scheme is less than or equal to one-fifth of the number of sensor nodes.

5. A compressed sensing energy allocation optimization device based on particle swarm optimization, characterized in that, include: The data acquisition module is used to acquire the number of sensor nodes and the energy arrival matrix of the sensor network; The energy optimization module is used to optimize the node partitioning scheme according to the number of sensor nodes using the particle swarm optimization algorithm, and to determine the node block energy allocation matrix of the node partitioning scheme based on the energy arrival matrix using the compressed sensing energy allocation optimization algorithm, with the goal of minimizing the fitness of the node block energy allocation matrix and outputting the optimal node partitioning scheme. The energy allocation module is used to associate and update the node block energy allocation matrix of the optimal node partitioning scheme to the sensor nodes in the sensor network.

6. The compressed sensing energy allocation optimization method based on particle swarm optimization according to claim 5, characterized in that, The energy optimization module is specifically used for: Based on the number of sensor nodes and the node partitioning scheme of the sensor nodes, a particle population is initialized and constructed. The node block energy arrival matrix corresponding to each node block of the particle is determined based on the energy arrival matrix. A pre-defined compressed sensing energy allocation optimization algorithm is used to optimize the energy arrival matrix of each node block and determine the corresponding node block energy allocation matrix. Fitness is determined based on the node block energy allocation matrix of the particles, and individual optimal position and global optimal position are determined with the goal of minimizing fitness. The velocity and position of each particle are iteratively updated, and the global optimal position that satisfies the iteration stopping condition is taken as the optimal node block scheme.

7. The compressed sensing energy allocation optimization method based on particle swarm optimization according to claim 5 or 6, characterized in that, The fitness calculation process includes: Calculate the mutual interference value of the node block energy allocation matrix for the node block partitioning scheme; The maximum mutual interference value is used as the fitness of the node partitioning scheme.

8. A computer device, characterized in that, The system includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor causes the processor to perform the steps of the compressed sensing energy allocation optimization method based on particle swarm optimization as described in any one of claims 1-4.

9. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the compressed sensing energy allocation optimization method based on particle swarm optimization as described in any one of claims 1-4.

10. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the compressed sensing energy allocation optimization method based on particle swarm optimization as described in any one of claims 1-4.

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