Array optimization method, device and equipment for agricultural machine positioning, medium and program product

Abstract

The invention relates to the technical field of wireless communication, and provides an array optimization method and device for agricultural machinery positioning, equipment, a medium and a program product, and the method comprises the steps: determining the position of each array element in a to-be-optimized wireless communication array, and determining a to-be-optimized group matrix based on the position of each array element; taking the maximum array aperture and the minimum array element spacing as constraint conditions, using the Cramer-Rao bound as a fitness evaluation criterion, and based on an adaptive spider bee optimization algorithm, performing sparse optimization solution on the group matrix to obtain an array element position map of a sparse optimization array; and agricultural machine positioning is carried out based on the array element position map of the sparse optimization array. According to the array optimization method and device for agricultural machine positioning, the equipment, the medium and the program product provided by the invention, the accuracy and efficiency of signal transmission are remarkably improved while the communication coverage range and quality are ensured, so that the agricultural machine positioning precision is improved.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to an array optimization method, device, equipment, medium and program product for agricultural machinery positioning. Background Art

[0002] In the agricultural field, especially in the application of Beidou positioning system for agricultural machinery, array intelligent sparse optimization technology has played a significant role. This technology significantly improves the signal reception accuracy and system positioning accuracy by optimizing the layout of the antenna array, which is particularly critical for precision agriculture.

[0003] In actual operation, agricultural machinery such as tractors and harvesters have extremely high requirements for positioning accuracy when working in the fields, which is directly related to the efficiency of the operation and the quality of crop planting.

[0004] By using sparse array technology, the Beidou positioning system for agricultural machinery can effectively reduce multipath effects and signal interference. This is because sparse arrays can enhance the directionality of antenna receiving signals, thereby improving signal clarity and positioning accuracy. The application of this technology enables agricultural machinery to navigate and operate with high precision in complex farmland environments, such as uneven terrain and different crop conditions. By precisely controlling the travel path and operating range of agricultural machinery, overlapping operations and missed areas can be significantly reduced, thereby optimizing resource utilization, such as the precise delivery of seeds, fertilizers and water resources. Sparse array technology also provides agricultural machinery with a more stable communication connection, which can maintain stable communication even in remote farmlands or farmlands with weak coverage signals. This provides technical support for the remote control operation and real-time data collection of agricultural machinery, allowing farm managers to monitor the status of agricultural machinery and field environment in real time and adjust agricultural management strategies in a timely manner.

[0005] In the field of wireless communications, the application of non-uniform arrays has gradually become a key means to improve system performance. Common types of non-uniform arrays include minimum redundant arrays, nested arrays, and coprime arrays. Their common advantages are large element spacing and high degrees of freedom, which can provide high estimation accuracy. However, these arrays also have some limitations, such as the lack of closed analytical expressions for minimum redundant arrays, and the existence of holes in nested arrays despite their dense subarray structure. These factors may affect their performance in practical applications.

[0006] In the field of wireless communications, the optimization of sparse arrays is mainly focused on improving active communication arrays, usually with beam shape control as the main limiting factor. However, in practical applications, when it is necessary to add passive monitoring functions to existing active phased array systems, a series of challenges are faced, such as layout space limitations. Therefore, a new method for array sparse optimization in passive wireless communication systems is needed under limited space layout constraints. Summary of the invention

[0007] The present invention provides an array optimization method, device, equipment, medium and program product for agricultural machinery positioning, which are used to solve the defect that the optimization of sparse arrays in the prior art has poor adaptability in passive wireless communication systems.

[0008] The present invention provides an array optimization method for agricultural machinery positioning, which is characterized by comprising: Determine the position of each array element in the wireless communication array to be optimized, and determine the group matrix to be optimized based on the position of each array element; Taking the maximum array aperture and the minimum array element spacing as constraints, using the Cramer-Rao bound as a fitness evaluation criterion, and based on the adaptive spider-bee optimization algorithm, the sparse optimization solution of the swarm matrix is ​​performed to obtain an array element position map of the sparse optimized array; Based on the array element position map of the sparse optimized array, agricultural machinery positioning is performed.

[0009] According to the array optimization method for agricultural machinery positioning provided by the present invention, the group matrix to be optimized is determined based on the positions of the respective array elements, including: Matching the positions of the array elements with the individuals in the adaptive spider bee optimization algorithm to obtain the individuals to be optimized, and encoding the individuals to be optimized; The individuals to be optimized are combined into the group matrix to be optimized.

[0010] According to the array optimization method for agricultural machinery positioning provided by the present invention, the maximum array aperture and the minimum array element spacing are used as constraints, the Cramer-Rao bound is used as a fitness evaluation standard, and the sparse optimization solution is performed on the group matrix based on the adaptive spider bee optimization algorithm to obtain the array element position map of the sparse optimized array, including: Repeat the following steps until an iteration termination condition is reached, and output an array element position map of the sparse optimization array; Determine the Cramer-Rao bound of each individual under the current array configuration based on the encoded individuals, calculate the current fitness of each individual based on the Cramer-Rao bound, and record the fitness of the best individual in this round of iteration; Based on the adaptive spider bee optimization algorithm, the current position of each individual is updated through the search and hunting behavior of each individual; Each individual builds a nest at the current location and pairs up to generate new individuals. The sparseness rate of the offspring population is adjusted to retain the best position of the previous generation to the new spider bee population. The individuals are sorted based on the current fitness to obtain the array element position map of the current sparse optimization array.

[0011] According to the array optimization method for agricultural machinery positioning provided by the present invention, the step of determining the Cramer-Rao bound of each individual under the current array configuration based on the encoded individuals includes: The encoded individual position is converted into the corresponding array coordinates based on the following formula, and the Cramer-Rao bound of each individual in the current array configuration is calculated based on the array coordinates; ; in, Indicates the antenna position in azimuth direction, Indicates the antenna position in the direction of the pitch angle. The rem function calculates the given working array element. The remainder between the total number of array elements M, Represents the position of the antenna element that is active in the array.

[0012] According to the array optimization method for agricultural machinery positioning provided by the present invention, the current fitness of each individual is calculated based on the Cramer-Rao bound, including: The current fitness of each individual is calculated based on the following formula: ; Where fit represents the current fitness, CRB ( ) is the target parameter The Cramer-Rao bound, CRB ( ) is the target parameter The Cramero boundary.

[0013] According to the array optimization method for agricultural machinery positioning provided by the present invention, the adjusting the sparseness rate of the offspring population includes: Determine the number of activated array elements and inactivated array elements of individuals in the offspring population; If the number of activated array elements of any individual is higher than the expected total number of array elements, the corresponding number of activated array elements will be adjusted to inactive array elements; If the number of activated array elements of any individual is lower than the expected total number of array elements, the corresponding number of inactivated array elements will be adjusted to activated array elements.

[0014] The present invention also provides an array optimization device for positioning agricultural machinery, comprising: A matrix determination unit, used to determine the position of each array element in the wireless communication array to be optimized, and determine the group matrix to be optimized based on the position of each array element; An optimization solving unit is used to perform sparse optimization solving on the swarm matrix based on an adaptive spider bee optimization algorithm with a maximum array aperture and a minimum array element spacing as constraints, using the Cramer-Rao bound as a fitness evaluation criterion, and obtaining an array element position diagram of a sparse optimized array; The agricultural machinery positioning unit is used to position the agricultural machinery based on the array element position diagram of the sparse optimized array.

[0015] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, an array optimization method for agricultural machinery positioning as described in any one of the above is implemented.

[0016] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the array optimization method for agricultural machinery positioning as described in any one of the above is implemented.

[0017] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the array optimization method for positioning agricultural machinery as described in any one of the above is implemented.

[0018] The array optimization method, device, equipment, medium and program product for agricultural machinery positioning provided by the present invention achieves the minimization of the array Cramer-Rao lower bound value by optimizing the optimal sparse array obtained during the configuration of the wireless communication array, taking into account the constraints of the maximum array aperture and the minimum array element spacing. Therefore, while ensuring the communication coverage and quality, the accuracy and efficiency of signal transmission are significantly improved, thereby improving the positioning accuracy of agricultural machinery. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0020] Figure 1 This is one of the flow charts of the array optimization method for agricultural machinery positioning provided by the present invention.

[0021] Figure 2 This is the second flow chart of the array optimization method for agricultural machinery positioning provided by the present invention.

[0022] Figure 3 This is the optimized one-dimensional array antenna element position diagram provided by the present invention.

[0023] Figure 4 It is a comparison diagram of the one-dimensional antenna fitness evolution curve provided by the present invention.

[0024] Figure 5 This is a comparison chart of the one-dimensional array direction finding accuracy provided by the present invention.

[0025] Figure 6 This is the optimized two-dimensional array antenna element position diagram provided by the present invention.

[0026] Figure 7 It is a comparison diagram of the two-dimensional antenna fitness evolution curve provided by the present invention.

[0027] Figure 8 This is the position diagram of the L-shaped array antenna elements.

[0028] Fig. 9 It is the position diagram of the array elements of the dual parallel antenna array.

[0029] Fig.10 This is a comparison chart of the direction finding accuracy of the two-dimensional array provided by the present invention.

[0030] Fig.11 It is a structural schematic diagram of the array optimization device for agricultural machinery positioning provided by the present invention.

[0031] Fig.12 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0033] Figure 1 This is one of the flow diagrams of the array optimization method for agricultural machinery positioning provided by the present invention, such as Figure 1 As shown, the method comprises the following steps: Step 110: determine the position of each array element in the wireless communication array to be optimized, and determine the group matrix to be optimized based on the position of each array element.

[0034] Step 120, taking the maximum array aperture and the minimum array element spacing as constraint conditions, using the Cramer-Rao bound as a fitness evaluation criterion, and based on the adaptive spider bee optimization algorithm, performing sparse optimization on the swarm matrix to obtain an array element position map of the sparse optimized array; Step 130 , positioning the agricultural machinery based on the array element position map of the sparse optimized array.

[0035] Specifically, the wireless communication array to be optimized is an array that needs to be sparsely optimized, and the wireless communication array to be optimized can be randomly generated. Then, the group matrix to be optimized is determined based on the position of each array element. Specifically, it includes: Step 111, matching the position of each array element with the individual in the adaptive spider bee optimization algorithm to obtain the individual to be optimized, and encoding the individual to be optimized; Step 112, combining the individuals to be optimized into a group matrix to be optimized.

[0036] Specifically, the position of each array element in the wireless communication array is represented as a column vector, that is, an individual in the spider bee population represents a possible array configuration. The purpose of this step is to correspond the physical array position to the individual in the optimization algorithm.

[0037] First, define the sparse optimization model of the planar array. Define the position of each array element in the wireless communication array as a vector sequence, which is represented as the entity to be optimized. ( ). Here Refers to the index position of the entity in the group to which it belongs, is the total number of elements in the array, and is the number of array elements in the horizontal and vertical directions. , it indicates that there is an array element at this position; if , it means that there is no array element at this position. Next, these entities to be sparsely optimized Combined into a group matrix to be optimized, denoted as , expressed as:

[0038] Then the encoding is performed. The individuals in the spider bee population are initially encoded, and the individual encoding expression is: ( ) (2)

[0039] Wherein the spider bee population size is denoted as NP, represents a random number that conforms to the normal distribution between [0,1]. The sparsification rate of the array element is set to s, the number of iteration cycles is marked as G, and the number of array elements after the sparsification process is defined as NL=L×s. To achieve the sparsification goal, the maximum NL spider bee positions in each member are assigned a value of 1, and the rest are set to 0 to complete the sparsification of individuals. Generation of.

[0040] On this basis, step 120 is executed, with the maximum array aperture and the minimum array element spacing as constraints, using the Cramer-Rao bound as a fitness evaluation criterion, and based on the adaptive spider bee optimization algorithm, the sparse optimization solution of the swarm matrix is ​​performed to obtain the array element position map of the sparse optimized array.

[0041] Figure 2 This is the second flow chart of the array optimization method for agricultural machinery positioning provided by the present invention, such as Figure 2 As shown, step 120 specifically includes: Repeat the following steps until an iteration termination condition is reached, and output an array element position map of the sparse optimization array; Determine the Cramer-Rao bound of each individual under the current array configuration based on the encoded individuals, calculate the current fitness of each individual based on the Cramer-Rao bound, and record the fitness of the best individual in this round of iteration; Based on the adaptive spider bee optimization algorithm, the current position of each individual is updated through the search and hunting behavior of each individual; Each individual builds a nest at the current location and pairs up to generate new individuals. The sparseness rate of the offspring population is adjusted to retain the best position of the previous generation to the new spider bee population. The individuals are sorted based on the current fitness to obtain the array element position map of the current sparse optimization array.

[0042] Specifically, step 120 may include the following steps: Fitness evaluation: The encoded spider bee population members will be converted into corresponding array coordinates, and the coordinate conversion formula used is: Represents the position coordinates of the antenna in azimuth and elevation respectively. For each working antenna element, its position number Determined by the activation state of the antenna element in the array, that is, when the antenna element , indicating that the element is in working state. For example, when all elements in an array are in working state , then the corresponding is 1,2,…,NP. Here, To calculate a given individual Relative to the full array number The remainder is used to determine the specific position of each active antenna element.

[0043] Considering a The regular planar array consists of array elements, where the spacing between adjacent array elements is set to , here represents the wavelength of the incident signal. The array receives far-field narrowband signals from a specific direction, which is determined by the azimuth angle and pitch angle Definition, position coordinates are used to calculate the position of value, For azimuth, For the pitch angle. In addition, it is assumed that there is no correlation between the incident signal and the background noise. When the number of data snapshots collected by the array is , and the signal-to-noise ratio is SNR, the azimuth and pitch angle The Cramer-Rao lower bound of can be quantified as follows:

[0044] In the formula,

[0045] They are the antenna positions in three directions.

[0046] The fitness value of the individual can be obtained The fitness value of each spider bee individual is then calculated and stored in the array middle.

[0047] Next, by analyzing these fitness values, the maximum fitness value of the population of this generation is calculated. , minimum fitness value And the average fitness . Special attention , that is, the fitness value of the best individual in this round of iteration. The individual corresponding to this value (that is, the most suitable array layout) is selected and saved in For subsequent use.

[0048] Search: Adopt the exploratory behavior of the adaptive spider bee algorithm to enable individuals to search for better solutions in the solution space. The position update formula during the search process of the spider bee individual is: (5)

[0049] Next, two individuals a and b are randomly selected from the spider bee population as directions The benchmark, where , is a random number between {0, 1}, and rn is also a random number, but it is generated using a normal distribution. Finally, this formula is used to update the position of the individual spider bee to simulate the search behavior during the hunting process.

[0050] Hunting: In each iteration, the spider bee individuals adaptively update their positions according to a specific formula, simulating hunting behavior to find new food sources. First, the calculation formula for determining the position change during the hunting process is:

[0051] in Represents at time t No. i The location of each spider bee individual, is a random number used to introduce randomness and simulate the uncertainty and flexibility of spider bees when chasing prey. Represents at time t , the location of a selected target or "prey". Represents the distance between the prey spider and the pursuing spider bee, multiplied by 2 and a random number Increased the dynamics and range of the search. The calculation formula for the spider's escape distance is: (7)

[0052] in Represents the coefficient of speed change, which affects the update speed of the individual position of the spider. The above formula is used to escape from the current position in the solution space to avoid falling into the local optimal solution, thereby increasing the diversity of solutions.

[0053] Nesting: The spider bee updates its position in an adaptive way to reflect the nesting behavior, a process that aims to optimize the current solution. This step ensures that the spider bee population searches thoroughly, and updates the current spider bee position in a constant motion. The formula for calculating the new position is:

[0054] Then select the best individual As a nesting reference. An adjustment factor is introduced into the update strategy, where is a randomly generated number ranging from a sine function in the range of [-1,1], which introduces changes in direction and amplitude to the position update, simulating the oscillating behavior of the spider bee when building a nest.

[0055] Pairing: Simulate the pairing process of spider bees, update the position by exchanging some genes between two spider bee individuals, and promote the diversity and innovation of solutions.

[0056] First, the probability of pairing CR is calculated according to a predefined formula; then, a pair of individuals to be paired are selected from the spider bee population. , and according to the individual length ,exist Randomly select an integer from As the crossover point, finally, the crossover operation is performed according to the pairing probability. If the pairing probability is less than , the genes of the pair of individuals are exchanged at the crossover point to generate a new individual. This process not only simulates the pairing behavior in nature, but also provides a way for the algorithm to explore new solutions.

[0057] Adjusting the sparsity rate: In the process of optimizing wireless communication arrays, it is crucial to adjust the sparsity rate to ensure that the actual number of array elements remains unchanged after the search-and-hunt nesting operation. This includes: Determine the number of activated array elements and inactivated array elements of individuals in the offspring population; if the number of activated array elements of any individual is higher than the expected total number of array elements, adjust the corresponding number of activated array elements to inactivated array elements; if the number of activated array elements of any individual is lower than the expected total number of array elements, adjust the corresponding number of inactivated array elements to activated array elements.

[0058] Specifically, we first determine the number of active (1) and inactive (0) states of each member of the offspring population. Next, we calculate the number of active (1) and inactive (0) states of each member of the offspring population. Specifically, if the number of activated elements of an individual is higher than , then the excess activated array elements are randomly selected and set to inactive state (i.e., from 1 to 0); on the contrary, if the number of activated array elements of an individual is less than , then randomly select the corresponding number of inactive elements and transform them to the active state (i.e., from 0 to 1).

[0059] Optimization and screening: Sort the spider bees based on their fitness values ​​to screen out the best and worst routing paths.

[0060] Get the optimization result: determine whether the set number of iterations has been reached. If not, set the number of iterations k=k+1 and return to search, hunt and nest building again. Otherwise, derive the optimal array layout output and fitness curve. This solution ensures optimal performance while meeting specific design constraints.

[0061] The method provided in the embodiment of the present invention minimizes the Cramer-Rao lower bound of the array by optimizing the optimal sparse array obtained during the configuration of the wireless communication array, taking into account the constraints of the maximum array aperture and the minimum array element spacing. Therefore, while ensuring the communication coverage and quality, the accuracy and efficiency of signal transmission are significantly improved, thereby improving the positioning accuracy of agricultural machinery.

[0062] Compared with the prior art, the present invention has the following advantages: Aiming at the half-wavelength array range limitation and array element number limitation problems encountered in traditional antenna array design, the present invention proposes an antenna array sparse optimization method based on an adaptive spider bee algorithm, and introduces a more flexible and efficient sparse array layout strategy. The present invention uses the Cramer-Rao Bound (CRB) as the fitness evaluation criterion to optimize the antenna array through the adaptive spider bee algorithm, with the constraints of maximizing the effective aperture of the antenna array and maintaining the minimum element spacing. This method not only improves the direction finding accuracy of the antenna array, but also optimizes the stability and reliability of signal reception; Compared with traditional sparse arrays and conventional optimization methods under the same conditions, the antenna array sparse optimization method based on the adaptive spider bee algorithm in the present invention shows better direction finding performance and signal processing capability while keeping the number of array elements and the minimum array element spacing unchanged, thereby effectively improving the overall performance of the antenna system.

[0063] Based on the above embodiments, the present invention further illustrates the effect of an array intelligent sparse optimization technology for agricultural machinery positioning through a series of simulation experiments: The simulation setup for a one-dimensional sparse optimized array is as follows: Consider a far-field single source signal. When the array is fully deployed, the distance between the array elements is set to half the wavelength of the maximum signal wavelength in the signal frequency. The number of data snapshots collected is set to 200, the signal-to-noise ratio is 5dB, the spider bee population size is 400, the algorithm is iterated 250 times, and the mating rate of each spider bee is adaptively adjusted as the number of iterations increases. The number of fully deployed array elements is set to 40, while the number of array elements actually used is 16; The simulation setup for the two-dimensional sparse optimized array is as follows: Similarly, consider a far-field single source signal. When the array is fully deployed, the distance between array elements is also based on the half wavelength of the maximum signal in the signal frequency. The number of data snapshots is set to 200, the signal-to-noise ratio is 5dB, the spider bee population size is 400, and the number of genetic generations is 250. The number of fully deployed array elements is a 10*10 array, and the number of array elements actually used is 19.

[0064] To verify the effectiveness of the present invention, a one-dimensional sparse optimization array is taken as an example. By using the method proposed by the present invention, an array element position diagram of a sparse optimization array under a one-dimensional array is generated, and a one-dimensional antenna fitness evolution curve is plotted. Figure 3 This is the optimized one-dimensional array antenna element position diagram provided by the present invention. Figure 4 It is a comparison diagram of the one-dimensional antenna fitness evolution curve provided by the present invention. Figure 5 This is a comparison chart of the direction finding accuracy of the one-dimensional array provided by the present invention. By comparing the direction finding of the optimized sparse array with the nested array and the coprime array with the same number of array elements, the results show that the present invention achieves a smaller direction finding error under the same conditions.

[0065] To verify the effectiveness of the present invention, a two-dimensional sparse optimization array is taken as an example. The present invention uses an adaptive spider bee optimization algorithm to perform sparse configuration on the wireless communication array. Through this technical solution, a coordinate diagram of the array element position of the sparse optimization array under a two-dimensional array is generated, and a two-dimensional antenna fitness evolution curve is further drawn, showing the changing trend of fitness during the array optimization process. Figure 6 This is the optimized two-dimensional array antenna element position diagram provided by the present invention. Figure 7 It is a comparison diagram of the two-dimensional antenna fitness evolution curve provided by the present invention. Figure 8 This is the position diagram of the L-shaped array antenna elements. Fig. 9 It is the position diagram of the array elements of the dual parallel antenna array. Fig.10 This is a comparison chart of the direction finding accuracy of the two-dimensional array provided by the present invention. The optimized sparse array is compared with the L-shaped array and the double parallel array with the same number of array elements. and pitch angle range The direction finding is performed under different signal-to-noise ratio conditions, and the direction finding accuracy is compared. The results show that the method proposed in the present invention can achieve lower direction finding error while keeping the number of array elements and the maximum array aperture unchanged.

[0066] The present invention introduces a sparse array optimization technology based on an adaptive spider bee optimization algorithm in the field of wireless communication array optimization technology. The technology constructs an array optimization model, encodes the array, and then uses the Cramer-Rao bound formula (CRB) as a fitness evaluation criterion to evaluate and screen the generated individuals. During the optimization process, the mating rate of each spider bee will be adaptively adjusted as the number of iterations increases to ensure the effective exploration and utilization capabilities of the algorithm. Finally, the method optimizes the sparse array layout corresponding to the optimal solution generated as the optimal configuration mode of the array elements in the wireless communication array. This method highlights the maximum array aperture and the minimum array element spacing as constraints, uses CRB as a fitness function, and ensures that the array exhibits a higher direction finding performance under the premise of the same number of array elements and the minimum array element spacing, thereby improving the accuracy and reliability of signal processing in the field of wireless communications.

[0067] The array optimization device for agricultural machinery positioning provided by the present invention is described below. The array optimization device for agricultural machinery positioning described below and the array optimization method for agricultural machinery positioning described above can be referenced to each other.

[0068] Based on any of the above embodiments, Fig.11 is a schematic diagram of the structure of the array optimization device for agricultural machinery positioning provided by the present invention, such as Fig.11 As shown, the device comprises: The matrix determination unit 1110 is used to determine the position of each array element in the wireless communication array to be optimized, and determine the group matrix to be optimized based on the position of each array element; The optimization solving unit 1120 is used to perform sparse optimization solving on the swarm matrix based on the adaptive spider bee optimization algorithm with the maximum array aperture and the minimum array element spacing as constraints, using the Cramer-Rao bound as a fitness evaluation criterion, and obtaining an array element position map of the sparse optimized array; The agricultural machinery positioning unit 1130 is used to position the agricultural machinery based on the array element position map of the sparse optimized array.

[0069] The device provided by the embodiment of the present invention achieves the minimization of the array Cramer-Rao lower bound value by optimizing the optimal sparse array obtained during the configuration of the wireless communication array, taking into account the constraints of the maximum array aperture and the minimum array element spacing. Therefore, while ensuring the communication coverage and quality, the accuracy and efficiency of signal transmission are significantly improved, thereby improving the positioning accuracy of agricultural machinery.

[0070] Based on any of the above embodiments, the matrix determination unit is specifically used for: Matching the positions of the array elements with the individuals in the adaptive spider bee optimization algorithm to obtain the individuals to be optimized, and encoding the individuals to be optimized; The individuals to be optimized are combined into the group matrix to be optimized.

[0071] Based on any of the above embodiments, the optimization solution unit is specifically used for: Repeat the following steps until an iteration termination condition is reached, and output an array element position map of the sparse optimization array; Determine the Cramer-Rao bound of each individual under the current array configuration based on the encoded individuals, calculate the current fitness of each individual based on the Cramer-Rao bound, and record the fitness of the best individual in this round of iteration; Based on the adaptive spider bee optimization algorithm, the current position of each individual is updated through the search and hunting behavior of each individual; Each individual builds a nest at the current location and pairs up to generate new individuals. The sparseness rate of the offspring population is adjusted to retain the best position of the previous generation to the new spider bee population. The individuals are sorted based on the current fitness to obtain the array element position map of the current sparse optimization array.

[0072] Based on any of the above embodiments, the optimization solution unit is specifically used for: The encoded individual position is converted into the corresponding array coordinates based on the following formula, and the Cramer-Rao bound of each individual in the current array configuration is calculated based on the array coordinates; ; in, Indicates the antenna position in azimuth direction, Indicates the antenna position in the direction of the pitch angle. The rem function calculates the given working array element. The remainder between the total number of array elements M, Indicates that it is active in the array ( )’s corresponding antenna element position.

[0073] Based on any of the above embodiments, the optimization solution unit is specifically used for: The current fitness of each individual is calculated based on the following formula: ; Where fit represents the current fitness, CRB ( ) is the target parameter The Cramer-Rao bound, CRB ( ) is the target parameter The Cramero boundary.

[0074] Based on any of the above embodiments, the optimization solution unit is specifically used for: Determine the number of activated array elements and inactivated array elements of individuals in the offspring population; If the number of activated array elements of any individual is higher than the expected total number of array elements, the corresponding number of activated array elements will be adjusted to inactive array elements; If the number of activated array elements of any individual is lower than the expected total number of array elements, the corresponding number of inactivated array elements will be adjusted to activated array elements.

[0075] Fig.12 An example of a physical structure diagram of an electronic device is shown in FIG. Fig.12As shown, the electronic device may include: a processor 1210, a communication interface 1220, a memory 1230 and a communication bus 1240, wherein the processor 1210, the communication interface 1220 and the memory 1230 communicate with each other through the communication bus 1240. The processor 1210 may call the logic instructions in the memory 1230 to execute an array optimization method for positioning agricultural machinery, the method comprising: determining the position of each array element in the wireless communication array to be optimized, and determining the group matrix to be optimized based on the position of each array element; taking the maximum array aperture and the minimum array element spacing as constraints, using the Cramer-Rao bound as a fitness evaluation criterion, and based on the adaptive spider bee optimization algorithm, performing sparse optimization on the group matrix to obtain an array element position map of the sparse optimized array; and positioning agricultural machinery based on the array element position map of the sparse optimized array.

[0076] In addition, the logic instructions in the above-mentioned memory 1230 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0077] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the array optimization method for agricultural machinery positioning provided by the above methods, the method including: determining the position of each array element in the wireless communication array to be optimized, and determining the group matrix to be optimized based on the position of each array element; using the maximum array aperture and the minimum array element spacing as constraints, using the Cramer-Rao bound as a fitness evaluation criterion, based on the adaptive spider bee optimization algorithm, sparsely optimizing and solving the group matrix to obtain an array element position map of the sparse optimized array; and positioning the agricultural machinery based on the array element position map of the sparse optimized array.

[0078] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the array optimization method for agricultural machinery positioning provided by the above-mentioned methods, the method comprising: determining the position of each array element in the wireless communication array to be optimized, and determining the group matrix to be optimized based on the position of each array element; using the maximum array aperture and the minimum array element spacing as constraints, using the Cramer-Rao bound as a fitness evaluation criterion, and based on an adaptive spider bee optimization algorithm, performing sparse optimization solution on the group matrix to obtain an array element position map of the sparse optimized array; and performing agricultural machinery positioning based on the array element position map of the sparse optimized array.

[0079] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0080] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0081] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An array optimization method for agricultural machinery positioning, characterized in that: include: Determine the position of each array element in the wireless communication array to be optimized, and determine the group matrix to be optimized based on the position of each array element; Taking the maximum array aperture and the minimum array element spacing as constraints, using the Cramer-Rao bound as a fitness evaluation criterion, and based on the adaptive spider-bee optimization algorithm, the sparse optimization solution of the swarm matrix is ​​performed to obtain an array element position map of the sparse optimized array; Based on the array element position map of the sparse optimized array, agricultural machinery positioning is performed.

2. The array optimization method for agricultural machinery positioning according to claim 1, characterized in that: The determining of the group matrix to be optimized based on the positions of the respective array elements comprises: Matching the positions of the respective array elements with the individuals in the adaptive spider bee optimization algorithm to obtain the individuals to be optimized, and encoding the individuals to be optimized; The individuals to be optimized are combined into the group matrix to be optimized.

3. The array optimization method for agricultural machinery positioning according to claim 2, characterized in that: The maximum array aperture and the minimum array element spacing are used as constraint conditions, the Cramer-Rao bound is used as a fitness evaluation standard, and based on the adaptive spider bee optimization algorithm, the sparse optimization solution of the group matrix is ​​performed to obtain the array element position map of the sparse optimization array, including: Repeat the following steps until an iteration termination condition is reached, and output an array element position map of the sparse optimization array; Determine the Cramer-Rao bound of each individual under the current array configuration based on the encoded individuals, calculate the current fitness of each individual based on the Cramer-Rao bound, and record the fitness of the best individual in this round of iteration; Based on the adaptive spider bee optimization algorithm, the current position of each individual is updated through the search and hunting behavior of each individual; Each individual builds a nest at the current location and pairs up to generate new individuals. The sparseness rate of the offspring population is adjusted to retain the best position of the previous generation to the new spider bee population. The individuals are sorted based on the current fitness to obtain the array element position map of the current sparse optimization array.

4. The array optimization method for agricultural machinery positioning according to claim 3, characterized in that: The step of determining the Cramer-Rao bound of each individual under the current array configuration based on the encoded individuals includes: The encoded individual position is converted into the corresponding array coordinates based on the following formula, and the Cramer-Rao bound of each individual in the current array configuration is calculated based on the array coordinates; ； in, Indicates the antenna position in azimuth direction, Indicates the antenna position in the direction of the pitch angle. The rem function calculates the given working array element. The remainder between the total number of array elements M, Represents the position of the antenna element that is active in the array.

5. The array optimization method for agricultural machinery positioning according to claim 3, characterized in that: The current fitness of each individual is calculated based on the Cramer-Rao bound, including: The current fitness of each individual is calculated based on the following formula: ； Where fit represents the current fitness, CRB ( ) is the target parameter The Cramer-Rao bound, CRB ( ) is the target parameter The Cramero boundary.

6. The array optimization method for agricultural machinery positioning according to claim 3, characterized in that: The adjusting the sparseness rate of the offspring population comprises: Determine the number of activated array elements and inactivated array elements of individuals in the offspring population; If the number of activated array elements of any individual is higher than the expected total number of array elements, the corresponding number of activated array elements will be adjusted to inactive array elements; If the number of activated array elements of any individual is lower than the expected total number of array elements, the corresponding number of inactivated array elements will be adjusted to activated array elements.

7. An array optimization device for agricultural machinery positioning, characterized in that: include: A matrix determination unit, used to determine the position of each array element in the wireless communication array to be optimized, and determine the group matrix to be optimized based on the position of each array element; An optimization solving unit is used to perform sparse optimization solving on the swarm matrix based on an adaptive spider bee optimization algorithm with a maximum array aperture and a minimum array element spacing as constraints, using the Cramer-Rao bound as a fitness evaluation criterion, and obtaining an array element position diagram of a sparse optimized array; The agricultural machinery positioning unit is used to position the agricultural machinery based on the array element position diagram of the sparse optimized array.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the array optimization method for agricultural machinery positioning as described in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the array optimization method for agricultural machinery positioning as described in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the array optimization method for agricultural machinery positioning as described in any one of claims 1 to 6 is implemented.

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