A method for optimizing the deployment of spectrum sensors

Abstract

This invention provides a method and apparatus for optimizing spectrum sensor deployment, belonging to the field of spectrum mapping technology. The method includes: using a spectrum map estimated based on a model as prior knowledge to obtain global spectrum data error values ​​for an initial deployment location set; generating adjusted error uncertainty based on the global spectrum data error values; constructing an optimal location deployment equation with the goal of minimizing the difference between the error uncertainties of the initial and adjusted deployment locations; optimizing the coordinates of the sensor deployment locations using a location optimization algorithm; obtaining the coordinates of the sensor deployment locations that minimize the optimal location deployment equation, thus obtaining the target deployment location set and achieving spectrum sensor deployment optimization. This invention overcomes the limitations of existing methods. Its main idea is to utilize the interrelationship and constraints between sensor deployment and spectrum data completion. This technology can be effectively combined with spectrum data completion that combines models and data.

Description

Technical Field

[0001] This application relates to the field of spectrum mapping technology, and more specifically, to a method for optimizing the deployment of spectrum sensors. Background Technology

[0002] Spectrum mapping has wide applications in wireless communication, such as network planning, interference coordination and suppression, power control, resource allocation, multi-hop routing, and dynamic spectrum access, and has become one of the key technologies for electromagnetic environment cognition. In current research and applications, due to the limited number of spectrum monitoring sensors, the complex and variable geographical environment, and constraints on communication, computing, and storage resources, spectrum map generation faces significant challenges due to the sparse and uneven distribution of spatially sampled data. Addressing the limitation on the number of spectrum monitoring sensors, how to achieve efficient sensor deployment so that the sampled spectrum data can reflect the overall characteristics or distribution to the greatest extent possible, thereby improving the accuracy of spectrum map generation, has become a new research hotspot. This invention focuses on a method for improving the accuracy of spectrum map generation through optimized sensor deployment.

[0003] Existing research on sensor deployment mainly focuses on optimization problems in specific scenarios: one type aims to maximize the coverage of the target area under a limited number of sensors to maximize monitoring efficiency; the other aims to minimize the number of sensors used while meeting certain performance requirements to maximize economic benefits. By optimizing a limited number of sensor deployment schemes, it is possible to obtain sampled spectral data containing more spectral situational correlation information.

[0004] There are three main categories of sensor deployment optimization methods: simple deployment methods, including direct use of uniform, random, and hierarchical approaches; traditional deployment techniques, including the effective independence method and QR decomposition; and intelligent optimization algorithms, such as genetic algorithms, particle swarm optimization, and simulated annealing. However, given the real-world problems of sparse spatial sampling and uneven sampling distribution, how to combine sensor deployment with specific spectral data completion methods to maximize the total information content of the sampled data and thus generate a globally optimal spectral map has become a research hotspot. Summary of the Invention

[0005] To address at least one deficiency or improvement requirement of the prior art, the present invention provides a spectrum sensor deployment optimization method, which enables the generation of a higher-precision spectrum map by optimizing the sensor deployment location with a limited number of sensors.

[0006] In a first aspect, the present invention provides a method for optimizing the deployment of a spectrum sensor, comprising:

[0007] Obtain the global spectrum data error value for the initial deployment location set;

[0008] An adjusted error uncertainty is generated based on the global spectrum data error value; wherein, the adjusted error uncertainty is the error uncertainty obtained by updating the uncertainty of the adjusted deployment position after adjusting the initial deployment position of the sensor;

[0009] With the goal of minimizing the difference in error uncertainty between the initial deployment location and the adjusted deployment location, an optimal deployment equation is constructed to optimize the coordinates of the sensor deployment location using a location optimization algorithm.

[0010] The coordinates of the sensor deployment locations that minimize the optimal location deployment equation are obtained, resulting in a target deployment location set, thus achieving spectrum sensor deployment optimization.

[0011] According to the aforementioned spectrum sensor deployment optimization method, obtaining the global spectrum data error value of the initial deployment location set includes:

[0012] The global spectrum data error value is obtained based on the estimated global spectrum data value and the collected global spectrum data value of the initial deployment location set; wherein, the estimated global spectrum data value is the spectrum data generated by the propagation model constructed based on the prior information of the initial deployment location.

[0013] According to the aforementioned spectrum sensor deployment optimization method, the step of generating adjusted error uncertainty based on global spectrum data error values ​​includes:

[0014] The initial error uncertainty is obtained based on the initial deployment location set;

[0015] The sensor deployment locations in the initial deployment location set are adjusted to obtain the adjusted deployment location set;

[0016] The error uncertainty of the adjusted deployment location set is updated based on the initial error uncertainty to obtain the updated error uncertainty.

[0017] According to the aforementioned spectrum sensor deployment optimization method, the step of updating the error uncertainty of the adjusted deployment location set based on the initial error uncertainty to obtain the updated error uncertainty includes:

[0018] The initial error uncertainty of the initial deployment coordinate points in the adjusted deployment location set is reduced to zero.

[0019] The initial error uncertainty of the optimized deployment coordinate points in the adjusted deployment location set is updated, and the update method is as follows:

[0020] u i =(1—w)·u i

[0021] Among them, u iTo account for the uncertainty of the deployment coordinate points, w is the weight, which is obtained as follows: Wherein d(s) i s j ) represents the initial deployment coordinates s i and for s i Every point s in the d-neighborhood j The distance between them.

[0022] According to the aforementioned spectrum sensor deployment optimization method, the optimal location deployment equation is specifically as follows:

[0023]

[0024] Among them, s * To deploy coordinate points to the next optimal location, J(·) represents the sum of the reductions in error uncertainty after optimizing the sensor deployment locations in the initial deployment location set. Let s be the set of target deployment locations, where s is the deployment coordinate point.

[0025] According to the aforementioned spectrum sensor deployment optimization method, the total reduction in error uncertainty after optimizing the sensor deployment locations in the initial deployment location set is specifically as follows:

[0026]

[0027] in, Δu represents the set of indices of all discrete coordinates less than d at distance s. i (s) is the absolute reduction in the post-uncertainty of the i-th point after point s is added.

[0028] According to the aforementioned spectrum sensor deployment optimization method, a greedy algorithm is used to obtain the coordinates of the sensor deployment locations that minimize the optimal location deployment equation, thereby obtaining the target deployment location set.

[0029] According to the aforementioned spectrum sensor deployment optimization method, based on the estimated global spectrum data and the acquired global spectrum data from the initial deployment location set, a global spectrum data error value is obtained, including:

[0030] Obtain the estimated value of global spectrum data, and calculate the error based on the estimated value of global spectrum data and the collected value of global spectrum data to obtain the spectrum error value of the initial deployment location;

[0031] The spectrum error value is calculated using a spectrum data completion algorithm to obtain the global spectrum data error completion value;

[0032] The global spectrum data error completion value is normalized to obtain the global spectrum data error value.

[0033] In a second aspect, the present invention provides a spectrum sensor deployment optimization device, comprising:

[0034] The global spectrum data error value acquisition module is used to acquire the global spectrum data error value of the initial deployment location set;

[0035] The first processing module is used to optimize the sensor deployment positions in the initial deployment position set using a sequential position optimization algorithm to obtain an adjusted deployment position set, and to obtain the initial error uncertainty based on the adjusted deployment position set.

[0036] The second processing module is used to update the error uncertainty of the adjusted deployment location set based on the initial error uncertainty, obtain the updated error uncertainty, and construct the optimal location deployment equation based on the updated error uncertainty and the adjusted deployment location set.

[0037] An optimization calculation module is used to iteratively solve the optimal location deployment equation to obtain the target deployment location set, thereby achieving spectrum sensor deployment optimization.

[0038] Thirdly, the present invention provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of any of the spectrum sensor deployment optimization methods described above.

[0039] The spectrum sensor deployment optimization method and apparatus provided by this invention uses a spectrum map estimated by a model as prior knowledge to obtain the global spectrum data error value of the initial deployment location set. Based on the global spectrum data error value, an adjusted error uncertainty is generated. With the goal of minimizing the difference between the error uncertainties of the initial deployment location and the adjusted deployment location, an optimal location deployment equation is constructed. A location optimization algorithm is then used to optimize the coordinates of the sensor deployment locations. The coordinates of the sensor deployment locations that minimize the optimal location deployment equation are obtained, resulting in the target deployment location set, thus achieving spectrum sensor deployment optimization. This method overcomes the limitations of existing methods. Its main idea is to utilize the interrelationship and constraints between sensor deployment and spectrum data completion. This technology can be effectively combined with spectrum data completion that combines models and data.

[0040] The proposed spectrum sensor deployment optimization method can generate a higher-precision spectrum map by optimizing the deployment locations of sensors with a limited number of sensors. Furthermore, under the constraint of spectrum map accuracy, it determines the minimum required number of sensors and their location distribution, thereby improving spectrum sampling efficiency or saving economic costs. This method incorporates error considerations in practical deployment scenarios and utilizes mathematical models and algorithm optimization techniques to improve the overall performance and efficiency of spectrum sensor networks. It is of great significance for spectrum management and the effective utilization of wireless communication resources. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0042] Figure 1 This is a flowchart illustrating the spectrum sensor deployment optimization method provided by the present invention;

[0043] Figure 2 This is a schematic diagram of the structure of the spectrum sensor deployment optimization device provided by the present invention;

[0044] Figure 3 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0046] It should be noted that, in the description of the embodiments of the present invention, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0047] The following is combined Figures 1-3This invention describes the spectrum sensor deployment optimization method and apparatus provided in embodiments of the present invention.

[0048] Figure 1 This is a flowchart illustrating the spectrum sensor deployment optimization method provided by the present invention, as shown below. Figure 1 As shown, including but not limited to the following steps (STEP, abbreviated as S):

[0049] S102: Obtain the global spectrum data error value of the initial deployment location set.

[0050] The initial deployment location set refers to the initial layout of the sensors within the spectrum monitoring area. The global spectrum data error value refers to the quantified difference between the spectrum data collected by the sensors and the actual spectrum environment under this initial layout.

[0051] Optionally, the method for optimizing the deployment of spectrum sensors provided by the present invention includes obtaining the global spectrum data error value of the initial deployment location set, comprising:

[0052] The global spectrum data error value is obtained based on the estimated global spectrum data and the acquired global spectrum data from the initial deployment location set.

[0053] The estimated global spectrum data is generated from a propagation model constructed based on prior information about the initial deployment location. For example, mean squared error (MSE) or mean absolute error (MAE) can be used to measure the error value of the global spectrum data.

[0054] S104: Generate adjusted error uncertainty based on global spectrum data error values.

[0055] The adjusted error uncertainty refers to the error uncertainty obtained by updating the uncertainty of the adjusted deployment position after adjusting the initial deployment position of the sensor.

[0056] For example, Bayesian theory can be used to dynamically update error uncertainty by combining prior knowledge and new observation data, and the uncertainty of error can be quantified by calculating the entropy value of error information; or Kalman filtering can be used to update error uncertainty recursively.

[0057] Optionally, the generation of adjusted error uncertainty based on global spectrum data error values ​​in the spectrum sensor deployment optimization method provided by the present invention includes:

[0058] The initial error uncertainty is obtained from the initial deployment location set; the sensor deployment locations in the initial deployment location set are adjusted to obtain the adjusted deployment location set; the error uncertainty of the adjusted deployment location set is updated based on the initial error uncertainty to obtain the updated error uncertainty.

[0059] In an optional embodiment, updating the error uncertainty of the adjusted deployment location set based on the initial error uncertainty to obtain the updated error uncertainty includes:

[0060] The initial error uncertainty of the initial deployment coordinate points in the adjusted deployment location set is reduced to zero.

[0061] The initial error uncertainty of the optimized deployment coordinate points in the adjusted deployment location set is updated, and the update method is as follows:

[0062] u i =(1-w)·u i

[0063] Where ui represents the uncertainty of the deployment coordinates, and w is the weight, which is obtained as follows: Wherein d(s) i s j ) represents the initial deployment coordinates s i and for s i Every point s in the d-neighborhood j The distance between them.

[0064] S106: Construct the optimal deployment equation with the goal of minimizing the difference in error uncertainty between the initial deployment location and the adjusted deployment location.

[0065] The optimal deployment equation describes the relationship between sensor location and spectral data error. Its objective is to minimize the difference in error uncertainty between the initial and adjusted deployment locations. The objective function is defined as minimizing this difference in error uncertainty, and constraints are set based on the physical limitations of the sensors (such as deployment area and number of sensors). For example:

[0066]

[0067] Among them, s * To deploy coordinate points to the next optimal location, J(·) represents the sum of the reductions in error uncertainty after optimizing the sensor deployment locations in the initial deployment location set. Let s be the set of target deployment locations, where s is the deployment coordinate point.

[0068] Optionally, the total reduction in error uncertainty after optimizing the sensor deployment locations in the initial deployment location set in the spectrum sensor deployment optimization method provided by the present invention specifically includes:

[0069]

[0070] Where, Δu i (s) is the absolute reduction in the post-uncertainty of the i-th point after point s is added.

[0071] S108: Optimize the coordinates of the sensor deployment location using location optimization algorithms. Optimization algorithms such as sequential location optimization, gradient descent, or greedy algorithms are used to optimize the coordinates of the sensor deployment location.

[0072] By iteratively solving the optimal location deployment equation, we find the set of sensor deployment location coordinates that minimizes the objective function, i.e., the target deployment location set. For example, we can set the number of iterations or an error threshold, and stop iterating when the conditions are met.

[0073] This invention combines dynamic adjustment, error uncertainty updating, and optimization algorithms, which can effectively reduce spectrum data errors, improve the accuracy and reliability of sensor deployment, and is suitable for sensor layout optimization tasks in complex spectrum environments.

[0074] Based on the above embodiments, as an optional embodiment, the position optimization algorithm provided by the present invention is a sequential position optimization technique. The application of this algorithm will be described below with reference to a specific embodiment.

[0075] Based on prior information about the target area, a propagation model is established to estimate the global spectrum data.

[0076] Define M as geographic environmental information such as buildings and elevation of the target area, and P T L T Given prior information about signal propagation, such as transmitter power and position, and using methods like propagation models or ray tracing, estimated global spectrum data can be calculated. Model-based methods can estimate global spectrum data.

[0077]

[0078] Let ρ be the proportion of presampling sensors, and let ρN be the number of presampling sensors. Through presampling, sampled spectral data can be obtained. Presampling, for example, yields a small amount of spectral data based on deployed sensor measurements.

[0079] use Represents the spectral error at the sampling location, where e nThis represents the global spectrum data acquisition value at the nth sampling position and... The deviation of the corresponding estimated value. The error between the global spectrum data acquired at the sampling location and the estimated global spectrum data based on the model is calculated using the following formula:

[0080]

[0081] Using E = (e1, e2, ..., e L The ) represents the global spectral error. The spectral error value at each sampling point is then calculated using the spectral data completion algorithm ψ. After completing the fill-in to all positions, the global spectral error E is obtained, and its calculation formula is as follows:

[0082]

[0083] Considering that the obtained spectral error value E is a predicted value of the actual error and reflects an absolute error value, it is normalized to map it to the range [0,1]. The normalization calculation formula is as follows:

[0084]

[0085] Where E min and E max Let E represent the minimum and maximum values, respectively.

[0086] Phase 2: Location Selection for Uncertainty Perception

[0087] Use U=(u1,u2,…,u L The value represents the error uncertainty between the estimated spectrum map of the global spectrum data and the true spectrum map of the global spectrum data acquisition. This value reflects the degree of error uncertainty, and a larger uncertainty indicates a higher expected error value.

[0088] Through the presampling, error completion, and normalization processes described above, a normalized error is obtained. Then, the initial value of the error uncertainty is calculated using the normalized error obtained from the presampling.

[0089] Considering that a certain number of sensors have already been deployed in the pre-sampling process, and that the error uncertainty at the corresponding locations changes as the actual spectrum data is acquired, we propose the following method for updating the error uncertainty:

[0090] (1) For the deployed location s i The uncertainty is reduced to 0, i.e., u i =0.

[0091] (2) For si Every point s in the d-neighborhood j Uncertainty through u i =(1-w)·u i The update is performed, and the weight w is determined by the weight formula of inverse distance weighted (IDW).

[0092]

[0093] Where d(s) i s j ) represents s i and s j The distance between them.

[0094] To simplify the expression, the function η(·) is used to represent the update of the above error uncertainty. It should be noted that in the process of gradually adding sensors by adjusting their initial deployment positions, each additional sensor means acquiring new sampled spectral data, which affects the attenuation uncertainty. Therefore, for each newly deployed sensor, the error uncertainty must be updated. The update function for the newly added sampling location point s can be expressed as:

[0095] U = n(U, s).

[0096] By updating the presampled location set From all the s, we obtain the initial error uncertainty U0.

[0097] Sequential location optimization is employed to optimize sensor deployment. Sensor locations are selected after obtaining the initial error uncertainty U0, and the uncertainty is updated in real time, aiming to minimize the error uncertainty.

[0098] The function J(·) is introduced to represent the sum of the reductions in error uncertainty after optimizing the sensor deployment locations in the initial deployment location set. As more sensors are deployed, the error uncertainty decreases; therefore, this formula is used to calculate the reduction in error uncertainty. Its expression can be formally defined as follows:

[0099]

[0100] in, Δu represents the set of indices of all discrete coordinates less than d at distance s. i (s) is the absolute reduction in the post-uncertainty of the i-th point after point s is added.

[0101] The objective function for sensor deployment optimization is transformed into the uncertainty-aware sensor deployment location optimization given by the following equation, formally expressed as:

[0102]

[0103] The objective function given in the above formula needs to be derived from the set of all grid locations. The optimal deployment location set, which is also the target deployment location set, is obtained from this process. This problem is a combinatorial optimization problem, which is NP-hard and cannot be solved optimally. Therefore, this embodiment uses a greedy algorithm to find an approximate solution for the optimal deployment location set. The main idea of ​​the greedy algorithm is as follows:

[0104] First, the presampled location set Add to sample set In the process, the error uncertainty U is initialized to U0. The error uncertainty after adding the pre-sampled location points is updated using a formula.

[0105] Subsequently, the next optimal position that minimizes error uncertainty can be found using the following formula:

[0106]

[0107] Where \ and ∪ represent the relative complement and union operations on the set, respectively.

[0108] The current optimal deployment location s * Add to Continue searching for the next optimal position until the set contains N sampling points.

[0109] This invention can be applied to spectrum plotting by using sampled real spectrum data to correct the estimated results based on model-based estimation, thereby achieving online updates.

[0110] This invention is also applicable to improving the accuracy of spectrum map construction by optimizing sensor deployment based on uncertainty awareness, under conditions where prior information and the number of sensors are limited.

[0111] In application, the above methods can be effectively combined with spectrum data completion methods that combine models and data to obtain the optimal spectrum map, or they can be corrected based on the results of specific model estimations using sampled real spectrum data to achieve online updates of the spectrum map.

[0112] The present invention also provides a method and apparatus for optimizing the deployment of a spectrum sensor, the apparatus comprising:

[0113] The planning model construction module is used to obtain the starting and ending positions of the path to be planned, and to construct the path planning model of the unmanned surface vessel based on the sea area data and the structure data of the unmanned surface vessel.

[0114] The cost function construction module is used to construct a dynamic weighting function based on the unmanned surface vessel path planning model to calculate the comprehensive cost of each trajectory;

[0115] The path planning module is used to obtain the comprehensive cost of the trajectory where the dynamic weighting function reaches its minimum value, so as to realize the path planning of the unmanned surface vessel.

[0116] It should be noted that the spectrum sensor deployment optimization method and apparatus provided in this embodiment of the invention can execute the spectrum sensor deployment optimization method described in any of the above embodiments during specific operation, and this embodiment will not elaborate on this.

[0117] Figure 3 This is a schematic diagram of the structure of the electronic device provided by the present invention, such as... Figure 3 As shown, the electronic device may include: a processor 310, a communication interface 320, a memory 330, and a communication bus 340, wherein the processor 310, the communication interface 320, and the memory 330 communicate with each other via the communication bus 340. The processor 310 can call logical instructions in the memory 330 to execute a spectrum sensor deployment optimization method, which includes:

[0118] S1: Obtain the global spectrum data error value of the initial deployment location set;

[0119] S2: Generate an adjusted error uncertainty based on the global spectrum data error value; wherein, the adjusted error uncertainty is the error uncertainty obtained by updating the uncertainty of the adjusted deployment position after adjusting the initial deployment position of the sensor;

[0120] S3: With the goal of minimizing the difference in error uncertainty between the initial deployment location and the adjusted deployment location, construct the optimal location deployment equation, and use the location optimization algorithm to optimize the coordinates of the sensor deployment location;

[0121] S4: Obtain the coordinates of the sensor deployment locations that minimize the optimal location deployment equation, obtain the target deployment location set, and realize spectrum sensor deployment optimization.

[0122] Furthermore, the logical instructions in the aforementioned memory 330 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0123] On the other hand, the present invention also provides a computer program product, the computer program product including a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, when the program instructions are executed by a computer, the computer is able to execute the spectrum sensor deployment optimization method provided in the above embodiments, the method including: obtaining global spectrum data error values ​​of an initial deployment location set; generating an adjusted error uncertainty based on the global spectrum data error values; wherein, the adjusted error uncertainty is the error uncertainty obtained by adjusting the initial deployment location of the sensor and updating the uncertainty of the adjusted deployment location; constructing an optimal location deployment equation with the goal of minimizing the difference between the error uncertainties of the initial deployment location and the adjusted deployment location, so as to optimize the coordinates of the sensor deployment location using a location optimization algorithm; obtaining the coordinates of the sensor deployment location that minimizes the optimal location deployment equation, obtaining a target deployment location set, and realizing spectrum sensor deployment optimization.

[0124] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements the spectrum sensor deployment optimization method provided in the above embodiments. The method includes: obtaining global spectrum data error values ​​of an initial deployment location set; generating an adjusted error uncertainty based on the global spectrum data error values; wherein the adjusted error uncertainty is the error uncertainty obtained by adjusting the initial deployment location of the sensor and updating the uncertainty of the adjusted deployment location; constructing an optimal location deployment equation with the goal of minimizing the difference between the error uncertainties of the initial deployment location and the adjusted deployment location, and optimizing the coordinates of the sensor deployment locations using a location optimization algorithm; obtaining the coordinates of the sensor deployment locations that minimize the optimal location deployment equation, thereby obtaining a target deployment location set and realizing spectrum sensor deployment optimization.

[0125] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0126] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0127] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing the deployment of a spectrum sensor, characterized in that, include: A global spectrum data error value is obtained based on the estimated global spectrum data and the acquired global spectrum data of the initial deployment location set. The estimated global spectrum data is generated from the spectrum data of a propagation model constructed based on prior information about the initial deployment locations. The process of obtaining the global spectrum data error value based on the estimated global spectrum data and the acquired global spectrum data includes: acquiring the estimated global spectrum data; calculating the error based on the estimated global spectrum data and the acquired global spectrum data to obtain the spectrum error value for the initial deployment location; performing a spectrum data completion algorithm on the spectrum error value to obtain the completed global spectrum data error value; and normalizing the completed global spectrum data error value to obtain the final global spectrum data error value. An adjusted error uncertainty is generated based on the global spectrum data error value; wherein, the adjusted error uncertainty is the error uncertainty obtained by updating the uncertainty of the adjusted deployment position after adjusting the initial deployment position of the sensor; With the goal of minimizing the difference in error uncertainty between the initial deployment location and the adjusted deployment location, an optimal deployment equation is constructed to optimize the coordinates of the sensor deployment location using a location optimization algorithm. The coordinates of the sensor deployment locations that minimize the optimal location deployment equation are obtained, resulting in a target deployment location set, thus achieving spectrum sensor deployment optimization.

2. The spectrum sensor deployment optimization method according to claim 1, characterized in that, The step of generating the adjusted error uncertainty based on the global spectrum data error value includes: The initial error uncertainty is obtained based on the initial deployment location set; The sensor deployment locations in the initial deployment location set are adjusted to obtain the adjusted deployment location set; The error uncertainty of the adjusted deployment location set is updated based on the initial error uncertainty to obtain the updated error uncertainty.

3. The spectrum sensor deployment optimization method according to claim 2, characterized in that, The process of updating the error uncertainty of the adjusted deployment location set based on the initial error uncertainty to obtain the updated error uncertainty includes: The initial error uncertainty of the initial deployment coordinate points in the adjusted deployment location set is reduced to zero. The initial error uncertainty of the optimized deployment coordinate points in the adjusted deployment location set is updated, and the update method is as follows: in, To address the uncertainty of coordinate points, This is the weight, and the method for obtaining this weight is... in, Indicates the initial deployment coordinates. and for of Every point in the neighborhood The distance between them.

4. The spectrum sensor deployment optimization method according to claim 1, characterized in that, The optimal location deployment equation is as follows: in, To deploy coordinate points for the next optimal location, the This represents the sum of the reductions in error uncertainty after optimizing the sensor deployment locations within the initial deployment location set. Deploy a set of locations for the target. To deploy coordinate points.

5. The spectrum sensor deployment optimization method according to claim 4, characterized in that, The total reduction in error uncertainty after optimizing the sensor deployment locations in the initial deployment location set is specifically as follows: in, Indicates distance point Less than The set of indices of all discrete coordinates. It is a point After adding the first The absolute reduction in post-uncertainty at each point.

6. The method for optimizing the deployment of spectrum sensors according to claim 1, characterized in that, A greedy algorithm is used to obtain the coordinates of the sensor deployment locations that minimize the optimal deployment equation, thus obtaining the target deployment location set.

7. A spectrum sensor deployment optimization device, characterized in that, include: A global spectrum data error value acquisition module is used to obtain a global spectrum data error value based on the estimated global spectrum data value and the acquired global spectrum data value of the initial deployment location set. The estimated global spectrum data value is the spectrum data generated by a propagation model constructed based on prior information of the initial deployment location. The process of obtaining the global spectrum data error value based on the estimated global spectrum data value and the acquired global spectrum data value includes: acquiring the estimated global spectrum data value; performing error calculation based on the estimated global spectrum data value and the acquired global spectrum data value to obtain the spectrum error value of the initial deployment location; performing a spectrum data completion algorithm on the spectrum error value to obtain the completed global spectrum data error value; and normalizing the completed global spectrum data error value to obtain the global spectrum data error value. The first processing module is used to optimize the sensor deployment positions in the initial deployment position set using a sequential position optimization algorithm to obtain an adjusted deployment position set, and to obtain the initial error uncertainty based on the adjusted deployment position set. The second processing module is used to update the error uncertainty of the adjusted deployment location set based on the initial error uncertainty, obtain the updated error uncertainty, and construct the optimal location deployment equation based on the updated error uncertainty and the adjusted deployment location set. An optimization calculation module is used to iteratively solve the optimal location deployment equation to obtain the target deployment location set, thereby achieving spectrum sensor deployment optimization.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the spectrum sensor deployment optimization method as described in any one of claims 1 to 6.

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