A simulated annealing inversion solution method, device, equipment and medium for terrain

Abstract

The present invention discloses a method, device, equipment and medium for solving terrain through simulated annealing inversion. The present invention realizes a dual improvement in prediction accuracy and efficiency through the innovative combination of multi-dimensional starting search and genetic algorithm optimization mechanism, and has significant technological advancement and broad application prospects. The present invention includes the following beneficial effects: significant improvement in prediction performance: the integration of multi-dimensional starting search and genetic algorithm optimization mechanism enables this solution to exhibit excellent performance in complex and changeable prediction tasks, and both prediction accuracy and stability are greatly improved. Computational efficiency optimization: the dynamic mutation probability adjustment mechanism effectively reduces unnecessary computational overhead and accelerates the convergence process, so that this solution has high computational efficiency while maintaining high-precision predictions. The embodiment of the present invention can efficiently and accurately perform simulated annealing inversion solutions for terrain and can be widely used in the field of data processing technology.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a simulated annealing inversion solution method, device, equipment and medium for terrain. Background Art

[0002] The Parker-Oldenburg inversion method occupies a central position in crustal structure research, but its technical limitations and shortcomings are also significant, mainly as follows:

[0003] First, a primary flaw lies in the simplified linearization assumptions used by traditional methods when processing the key parameters of the inversion process—density difference and depth—that treat these parameters as constant. This approach ignores the complex nonlinear interactions of the subsurface medium, limiting the accuracy of the inverted seafloor topography data and making it difficult to accurately reproduce the details of the actual geological structure.

[0004] Second, when searching for optimal parameters for Parker-Oldenburg inversion, the current mainstream approach relies on genetic algorithms. However, the crossover and mutation mechanisms in these algorithms are not fully adapted to the unique nature of the inversion problem, resulting in inefficient solutions and significant fluctuations in solution accuracy under different circumstances, making it difficult to consistently achieve high-quality inversion results.

[0005] Third, existing technical solutions also face significant challenges from the perspectives of computational efficiency and automated operation. The entire computational process involves enormous amounts of data processing, and lacks an efficient, integrated execution platform to optimize and coordinate the computational process, making the automation of terrain inversion difficult. This not only increases the cost of manual operation but also weakens the accuracy and practicality of the inversion results, resulting in relatively crude terrain models that are difficult to meet the requirements of high-precision geological exploration and research. Summary of the Invention

[0006] The present invention aims to at least partially address the limitations of related technologies. To this end, the present invention provides a simulated annealing inversion solution method, apparatus, device, and medium for terrain, which can efficiently and accurately perform simulated annealing inversion solution of terrain.

[0007] In one aspect, an embodiment of the present invention provides a simulated annealing inversion method for terrain, comprising:

[0008] Initializing a vector set, a first fitness value, and an initial temperature; using the initial temperature as an annealing temperature; wherein the vector set includes a preset number of prediction vectors;

[0009] Traverse all prediction vectors in the vector set to perform inversion solution and fitness evaluation in the prediction area, and obtain the fitness function value of each prediction vector for inversion solution in the prediction area;

[0010] The result of the inversion solution is the predicted terrain data of the prediction area; the value of the fitness function is negatively correlated with the adaptability of the prediction vector for the inversion solution of the prediction area;

[0011] When the fitness function value corresponding to the prediction vector in the vector set is less than the iterative optimal threshold, the corresponding prediction vector is output as the optimal prediction vector; otherwise,

[0012] The excellence of each prediction vector is determined based on the fitness function value. Based on the excellence, a random probability selection operation is performed on the prediction vectors in the vector set. The vector set is updated according to the selection result of the selection operation. The minimum fitness function value corresponding to the vector set is then used as the second fitness value. The excellence is negatively correlated with the numerical value of the fitness function.

[0013] When the second fitness value is greater than or equal to the first fitness value, a random probability mutation operation is performed on the prediction vector in the vector set based on the annealing temperature, and the vector set is updated according to the mutation result of the mutation operation; otherwise, the first fitness value is updated according to the second fitness value;

[0014] Based on the second fitness value, performing a crossover operation with random probability on the prediction vectors in the vector set, and updating the vector set according to a crossover result of the crossover operation;

[0015] Performing temperature decay processing on the annealing temperature based on a preset temperature decay rate, and updating the annealing temperature according to the decay result of the temperature decay processing;

[0016] When the annealing temperature is greater than or equal to the preset termination temperature, the process returns to the step of traversing all prediction vectors in the vector set to perform inversion solution and fitness evaluation of the prediction area until the annealing temperature is less than the termination temperature, and the optimal prediction vector is output based on the last updated vector set.

[0017] The predicted terrain data obtained by inversion according to the optimal prediction vector is used as the terrain solution result of the prediction area.

[0018] In some embodiments, initializing a vector set comprises at least one of the following steps:

[0019] Obtaining a preset standard vector value, and randomly generating a preset number of prediction vectors near the standard vector value;

[0020] Randomly generate a preset number of prediction vectors within a target interval; the target interval represents the range of values ​​of the prediction vectors;

[0021] A preset number of prediction vectors is directly obtained in response to a manual setting instruction.

[0022] In some embodiments, traversing all prediction vectors in the vector set to perform inversion solution and fitness evaluation on the prediction area, and obtaining the fitness function value of each prediction vector for inversion solution of the prediction area, includes the following steps:

[0023] The prediction vectors in the vector set are traversed in sequence, and the predicted terrain data of the prediction area corresponding to each prediction vector is obtained by the Parker-Oldenburg inversion method;

[0024] Based on the predicted terrain data, the root mean square calculation is performed in combination with the earthquake depth matrix of the predicted area, and the root mean square value is obtained as the fitness function value of the corresponding prediction vector for the adaptability of the inversion solution in the predicted area.

[0025] In some embodiments, outputting the corresponding prediction vector as the optimal prediction vector includes the following steps:

[0026] When there is a prediction vector in the vector set whose corresponding fitness function value is less than the iterative optimal threshold, the prediction vector is directly output as the optimal prediction vector;

[0027] When there are multiple prediction vectors in the vector set whose corresponding fitness function values ​​are smaller than the iterative optimal threshold, the prediction vector corresponding to the smallest fitness function value is output as the optimal prediction vector.

[0028] In some embodiments, a random probability selection operation is performed on a prediction vector in a vector set based on the degree of excellence, including the following steps:

[0029] Normalize the excellence levels corresponding to all prediction vectors in the vector set to obtain the normalized excellence level corresponding to each prediction vector;

[0030] The expression of excellence is:

[0031]

[0032] Where, e represents the natural constant; RMS represents the fitness function value; minIterDelt represents the iterative optimal threshold; nowTemp represents the annealing temperature;

[0033] Based on the normalized excellence levels corresponding to all prediction vectors in the vector set, an excellence level array is obtained;

[0034] The value of the nth element in the excellence degree array is the cumulative sum of all normalized excellence degrees corresponding to the first prediction vector to the nth prediction vector in the vector set;

[0035] Generate a random number in the first interval as the selection probability;

[0036] Sequentially traverse the excellence array to find the first element whose probability is greater than the selection probability as the target element;

[0037] selecting a corresponding prediction vector from the vector set as an update vector according to the element number of the target element, and returning to the step of generating a random number as a selection probability within the first interval until a preset number of update vectors are selected from the vector set;

[0038] A preset number of update vectors are used as selection results of the selection operation.

[0039] In some embodiments, the prediction vectors include two types of inversion parameters: density difference and depth. Based on the annealing temperature, a random probability mutation operation is performed on the prediction vectors in the vector set, including the following steps:

[0040] Based on the annealing temperature, the mutation probability is calculated by combining the first fitness value and the second fitness value;

[0041] Among them, the expression of mutation probability is:

[0042]

[0043] Where, mutationRate represents the mutation probability; e represents the natural constant; RMS1 represents the first fitness value; RMS2 represents the second fitness value; nowTemp represents the annealing temperature;

[0044] Generate a random number in the second interval as the mutation determination probability; add 1 to the number of mutation cycles;

[0045] When the mutation probability is less than the preset mutation probability, a prediction vector is randomly selected from the vector set as the mutation prediction vector;

[0046] Perform numerical mutation on any inversion parameter in the mutated prediction vector within the target interval, and put the mutated prediction vector back into the vector set; the target interval represents the value range of the prediction vector;

[0047] Returning to the step of generating a random number within the second interval as a mutation determination probability until the number of mutation cycles equals a preset number, obtaining a mutation result of the mutation operation;

[0048] Among them, the number of mutation cycles is initialized to 0.

[0049] In some embodiments, the prediction vector includes two types of inversion parameters: density difference and depth; and based on the second fitness value, performing a random probability crossover operation on the prediction vectors in the vector set includes the following steps:

[0050] Based on the second fitness value, the number of crossovers is calculated by combining the iterative optimal threshold and the preset number;

[0051] The expression of the number of crossovers is:

[0052]

[0053] Where, crossCnt represents the number of crossovers; random(x) represents a random value generated in [0,x]; RMS2 represents the second fitness value; minIterDelt represents the iterative optimal threshold; MaxPredCnt represents the preset number;

[0054] Generate a random number in the third interval as the crossover determination probability; add 1 to the number of crossover cycles;

[0055] When the crossover determination probability is less than the preset crossover operator probability, two prediction vectors are randomly obtained from the vector set as crossover vectors; the crossover vectors include the first vector and the second vector;

[0056] Performing numerical weighted crossover on at least one type of inversion parameter in the first vector and the second vector, and placing the predicted vector after the numerical weighted crossover back into the vector set;

[0057] Returning to the step of generating a random number in the third interval as the crossover determination probability until the number of crossover cycles equals the number of crossovers, a crossover result of the crossover operation is obtained;

[0058] The number of crossover cycles is initialized to 0.

[0059] On the other hand, an embodiment of the present invention provides a simulated annealing inversion solution device for terrain, comprising:

[0060] The first module is configured to initialize a vector set, a first fitness value, and an initial temperature; and use the initial temperature as an annealing temperature; wherein the vector set includes a preset number of prediction vectors;

[0061] The second module is used to traverse all prediction vectors in the vector set to perform inversion solution and fitness evaluation of the prediction area, and obtain the fitness function value of each prediction vector for inversion solution of the prediction area;

[0062] The result of the inversion solution is the predicted terrain data of the prediction area; the value of the fitness function is negatively correlated with the adaptability of the prediction vector for the inversion solution of the prediction area;

[0063] The third module is used to output the corresponding prediction vector as the optimal prediction vector when the fitness function value corresponding to the prediction vector in the vector set is less than the iterative optimal threshold; otherwise,

[0064] The fourth module is configured to determine the excellence of each prediction vector based on the fitness function value, perform a random probability selection operation on the prediction vectors in the vector set based on the excellence, and update the vector set according to the selection result of the selection operation; and then use the minimum fitness function value corresponding to the vector set as the second fitness value; the excellence is negatively correlated with the numerical value of the fitness function;

[0065] a fifth module configured to, when the second fitness value is greater than or equal to the first fitness value, perform a random probability mutation operation on the prediction vectors in the vector set based on the annealing temperature, and update the vector set according to the mutation result of the mutation operation; otherwise, update the first fitness value according to the second fitness value;

[0066] a sixth module, configured to perform a crossover operation with random probability on the prediction vectors in the vector set based on the second fitness value, and update the vector set according to a crossover result of the crossover operation;

[0067] A seventh module is configured to perform temperature attenuation processing on the annealing temperature based on a preset temperature attenuation rate, and update the annealing temperature according to an attenuation result of the temperature attenuation processing;

[0068] The eighth module is configured to, when the annealing temperature is greater than or equal to the preset termination temperature, return to execute the steps of traversing all prediction vectors in the vector set to perform inversion solution and fitness evaluation of the prediction area until the annealing temperature is less than the termination temperature, and output the optimal prediction vector based on the last updated vector set;

[0069] The ninth module is used to obtain the corresponding predicted terrain data through inversion according to the optimal prediction vector as the terrain solution result of the prediction area.

[0070] On the other hand, an embodiment of the present invention provides an electronic device, comprising: a processor and a memory; the memory is used to store programs; the processor executes the program to implement the simulated annealing inversion solution method for the above-mentioned terrain.

[0071] On the other hand, an embodiment of the present invention provides a computer storage medium storing a program executable by a processor. When the program is executed by the processor, it is used to implement the simulated annealing inversion solution method for the above-mentioned terrain.

[0072] The embodiment of the present invention initializes a vector set, a first fitness value and an initial temperature; uses the initial temperature as an annealing temperature; wherein the vector set includes a preset number of prediction vectors; traverses all prediction vectors in the vector set to perform inversion solution and fitness evaluation on the prediction area, and obtains a fitness function value of each prediction vector for inversion solution of the prediction area; wherein the result of the inversion solution is the predicted terrain data of the prediction area; the numerical value of the fitness function value is negatively correlated with the adaptability of the prediction vector for inversion solution of the prediction area; when the fitness function value corresponding to the prediction vector in the vector set is less than the iterative optimal threshold, the corresponding prediction vector is output as the optimal prediction vector; otherwise, the excellence of each prediction vector is determined according to the fitness function value, and based on the excellence, a random probability selection operation is performed on the prediction vector in the vector set, and the vector set is updated according to the selection result of the selection operation; and then the minimum fitness function value corresponding to the vector set is used as the second fitness value; the optimal The degree of beauty is negatively correlated with the numerical value of the fitness function; when the second fitness value is greater than or equal to the first fitness value, a random probability mutation operation is performed on the prediction vectors in the vector set based on the annealing temperature, and the vector set is updated according to the mutation result of the mutation operation; otherwise, the first fitness value is updated by the second fitness value; based on the second fitness value, a random probability crossover operation is performed on the prediction vectors in the vector set, and the vector set is updated according to the crossover result of the crossover operation; the annealing temperature is subjected to temperature decay processing based on a preset temperature decay rate, and the annealing temperature is updated according to the decay result of the temperature decay processing; when the annealing temperature is greater than or equal to the preset termination temperature, the process returns to execute the steps of traversing all the prediction vectors in the vector set to perform inversion solution and fitness evaluation of the prediction area until the annealing temperature is less than the termination temperature, and the optimal prediction vector is output according to the last updated vector set; the corresponding predicted terrain data obtained by inversion solution based on the optimal prediction vector is used as the terrain solution result of the prediction area. The embodiment of the present invention achieves a dual improvement in prediction accuracy and efficiency through the innovative combination of multi-dimensional starting search and genetic algorithm optimization mechanism, and has significant technological advancement and broad application prospects. The present invention includes the following beneficial effects: Significantly improved prediction performance: The integration of multi-dimensional starting search and genetic algorithm optimization mechanism enables this solution to demonstrate excellent performance in complex and variable prediction tasks, significantly improving both prediction accuracy and stability. Computational efficiency optimization: The dynamic mutation probability adjustment mechanism effectively reduces unnecessary computational overhead and accelerates the convergence process, allowing this solution to maintain high-precision predictions while also having high computational efficiency. The embodiments of the present invention can efficiently and accurately perform simulated annealing inversion solutions for terrain. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] The accompanying drawings are used to provide a further understanding of the technical solution of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the technical solution of the present invention and do not constitute a limitation to the technical solution of the present invention.

[0074] Figure 1 A schematic diagram of an implementation environment for performing simulated annealing inversion solution of terrain provided by an embodiment of the present invention;

[0075] Figure 2 A schematic flow chart of a simulated annealing inversion solution method for terrain provided by an embodiment of the present invention;

[0076] Figure 3 A schematic diagram of the expansion process of the initialization vector set provided in an embodiment of the present invention;

[0077] Figure 4 A schematic diagram of the expanded process of inversion solution and fitness evaluation provided in an embodiment of the present invention;

[0078] Figure 5 A schematic diagram of the expansion process of outputting the optimal prediction vector provided by an embodiment of the present invention;

[0079] Figure 6 A schematic diagram of the expansion flow of the selection operation provided in an embodiment of the present invention;

[0080] Figure 7 A schematic diagram of the expansion process of the mutation operation provided in an embodiment of the present invention;

[0081] Figure 8 A schematic diagram of the overall process principle of the simulated annealing inversion solution method for terrain provided by an embodiment of the present invention;

[0082] Figure 9 A schematic structural diagram of a terrain simulated annealing inversion solution device provided by an embodiment of the present invention;

[0083] Figure 10 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0084] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0085] It should be noted that although the system diagrams illustrate functional module divisions and the flowcharts illustrate logical sequences, in certain circumstances, the steps shown or described may be performed in a sequence that differs from the module divisions in the system or the sequence in the flowcharts. The terms "first / S100" and "second / S200" in the specification, claims, and drawings are used to distinguish similar objects and are not necessarily used to describe a specific sequence or precedence.

[0086] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute a separate or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0087] It is understandable that the simulated annealing inversion solution method for terrain provided in the embodiment of the present invention can be applied to any computer device with data processing and computing capabilities, and this computer device can be various terminals or servers. When the computer device in the embodiment is a server, the server is an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. In some embodiments, the terminal is a smart phone, tablet computer, laptop computer, desktop computer, etc., but is not limited to this.

[0088] To facilitate understanding of the technical solutions of the present invention, the following are first explained regarding the technical features and variable parameters that may be used in the embodiments of the present invention:

[0089] The technical features are described as follows:

[0090] Seafloor topography refers to the shape of the solid Earth's surface beneath water. Due to the obscuration of seawater, the undulations of seafloor topography are difficult to observe directly. Early plumb bob depth sounding methods were time-consuming and inaccurate. Since the 1920s, ships have used echo sounders during navigation, enabling rapid measurements of seafloor depth. Combined with precise positioning, these echo sounders reveal the true nature of seafloor topography. In this context, seafloor topography refers to the depth of water at various latitudes and longitudes of the ocean.

[0091] Heuristic search: Use the heuristic information of the problem to guide the search, so as to reduce the search scope and reduce the complexity of the problem. For example, simulated annealing and genetic algorithm are classic heuristic search algorithms.

[0092] Simulated annealing, derived from solid annealing, is a probability-based algorithm that heats a solid to a sufficient temperature and then slowly cools it. During heating, the particles within the solid become disordered as the temperature rises, increasing its internal energy. However, as the temperature gradually cools, the particles gradually become ordered, reaching equilibrium at each temperature. Simulated annealing is a general optimization algorithm that theoretically exhibits probabilistic global optimization performance and has been widely used in engineering fields such as VLSI, production scheduling, and control engineering. Simulated annealing is a serial optimization algorithm that effectively avoids local minima and ultimately approaches a global optimum by imbuing the search process with a time-varying probability that eventually approaches zero.

[0093] A genetic algorithm (GA) is a computational model that simulates biological evolution, leveraging mechanisms such as natural selection and genetic recombination to search and optimize complex problems. Its basic concept is to create a set of artificial prediction vectors by simulating the mechanisms of natural inheritance and evolution to optimize the solution. In a GA, each prediction vector represents a potential solution and has its own fitness evaluation criteria. These prediction vectors are then subjected to operations such as selection, crossover, and mutation in order to find the optimal solution. GAs have a wide range of applications, strong global search capabilities, and ease of parallel computing. They are suitable for solving optimization problems of various types, including combinatorial optimization, continuous optimization, and multi-objective optimization.

[0094] Genetic operators: Simply put, genetic operators refer to mutation, crossover, and selection operations, which are mainly used to evaluate and generate new prediction vectors or solution vectors.

[0095] The Parker-Oldenburg inversion method is a frequency domain density interface iterative inversion method proposed by Oldenburg based on the Parker formula. Since this method has the advantage of fast calculation, its application has been rapidly developed. However, there is a downward extension factor in the formula, which causes the formula to have an oscillation phenomenon of high-frequency signals during the inversion iteration process, and as the inversion depth increases, this phenomenon becomes more obvious, seriously affecting the convergence of the inversion result. To solve this situation, a low-pass filter is usually applied in the inversion process to eliminate the high-frequency oscillation phenomenon. The Parker-Oldenburg inversion method is one of the important methods for calculating seabed topography and is also the starting point of the core of the present invention. At present, the input parameters of the method (filter-related parameters, density difference, depth) and the like are designed according to constants based on experience, but in fact there is still room for optimization and excavation of these parameters.

[0096] RMS: Abbreviation for Root Mean Square, which is the result of taking the square root after dividing the sum of the squares of N terms by N, that is, the result of the root mean square.

[0097] The description of variable parameters is as follows:

[0098] The parameters mainly include two parts: the simulated annealing method driven by genetic operators and the parameters of the Parker-Oldenburg inversion method, etc.

[0099] There are a total of 2 variables searched by the simulated annealing method driven by genetic operators, which are contrast (physical meaning: density difference, unit g / cm

[0104] , <0000​​​​​​​​​​​​​​​​

[0105] Final temperature finalTemp, the value is determined according to the requirements, the default value is 1.

[0106] The temperature decay rate tempReduceRate has a value range of [0, 1] and is determined based on demand. The default value is 0.99.

[0107] MaxPredCnt, the number of prediction vectors involved in the genetic operator, is determined based on requirements. The default value is 100.

[0108] CrossoverRate is a parameter used by the crossover operator. Its value range is [0,1]. It is determined based on the requirements. The default value is 0.9.

[0109] CrossoverAlpha is a parameter used by the crossover operator. Its value range is [0,1]. It is determined based on the requirements. The default value is 0.5.

[0110] Iterate the optimal threshold minIterDelt. If a prediction vector less than or equal to minIterDelt is found, it means that the optimal solution has been found and the entire search process ends directly.

[0111] The main parameters of the Parker-Oldenburg inversion method are as follows:

[0112] Density difference contrast, unit g / cm 3 , which is one of the variables searched by the simulated annealing method driven by the improved genetic operator, and is one of the two search directions mentioned in the present invention.

[0113] Depth: unit is km. This is one of the variables searched by the simulated annealing method driven by the improved genetic operator, and is one of the two search directions mentioned in the present invention.

[0114] GravityMat: It is a matrix composed of triplets of (longitude, latitude, gravity outlier), which is generally data observed by seismic measurement, satellite measurement or other means, and is a constant in the present invention.

[0115] Seismic depth matrix SemisMat: It is a matrix composed of (longitude, latitude, depth) triplets, generally obtained from seismic measurements, satellite measurements or other observations, and is a constant in the present invention.

[0116] The iteration threshold criterio, i.e., the iteration rms threshold of the Parker-Oldenburg inversion method, is generally a constant, with a default value of 0.1, and is a constant in the present invention.

[0117] Frequency threshold WH: low-pass filter threshold, the initial value is obtained based on experience, the default value is 0.1, and it is a constant in the present invention.

[0118] Frequency threshold SH: high-pass filter threshold, the initial value is obtained based on experience, the default value is 0.12, and it is a constant in the present invention.

[0119] Window function threshold truncation: It is a signal sampling weighting function in signal processing. The initial value is obtained based on experience and is 0.1 by default. It is a constant in the present invention.

[0120] like Figure 1 FIG. 1 is a schematic diagram of an implementation environment provided by an embodiment of the invention. Figure 1 , the implementation environment includes at least one terminal 102 and a server 101. The terminal 102 and the server 101 can be connected to the network in a wireless or wired manner to complete data transmission and exchange.

[0121] Server 101 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers. It can also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), as well as big data and artificial intelligence platforms.

[0122] In addition, server 101 can also be a node server in a blockchain network. Blockchain is a new application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, and encryption algorithm.

[0123] The terminal 102 may be a smart phone, tablet computer, laptop computer, desktop computer, smart speaker, smart watch, etc., but is not limited thereto. The terminal 102 and the server 101 may be connected directly or indirectly via wired or wireless communication, which is not limited in this embodiment of the present invention.

[0124] Based on the example Figure 1 In the implementation environment shown, an embodiment of the present invention provides a simulated annealing inversion solution method for terrain. The following is an example of applying the simulated annealing inversion solution method for terrain in the server 101. It can be understood that the simulated annealing inversion solution method for terrain can also be applied to the terminal 102.

[0125] Reference Figure 2 , Figure 2The flowchart of the simulated annealing inversion solution method for terrain applied to a server provided in an embodiment of the present invention, the execution subject of the simulated annealing inversion solution method for terrain can be any of the aforementioned computer devices (including servers or terminals). Figure 2 , the method comprises the following steps:

[0126] S100, initializing a vector set, a first fitness value, and an initial temperature; using the initial temperature as the annealing temperature;

[0127] The vector set includes a preset number of prediction vectors; the first fitness value is initially set to a value large enough to ensure that it can be updated by the second fitness value in the first round of loop iteration;

[0128] It should be noted that in some embodiments, a vector set is first initialized. Each vector consists of a density difference contrast and an average seabed depth depth. The core of the initialization vector set is to generate MaxPredCnt vectors. There are three strategies, such as Figure 3 As shown, initializing the vector set may include at least one of the following steps:

[0129] S101, obtaining a preset standard vector value, and randomly generating a preset number of prediction vectors near the standard vector value;

[0130] The standard vector values ​​include the standard values ​​of density difference and depth;

[0131] For example, in some specific implementations, the generation is based on the center point (i.e., the standard vector value). If the center point is given, the generation method is as follows:

[0132] contrast=initContrast+(maxContrast-minContrast)*normal(0,1);

[0133] depth=initDepth+(maxDepth-minDepth)*normal(0,1);

[0134] S102, randomly generating a preset number of prediction vectors within a target interval; the target interval represents a value range of the prediction vector;

[0135] The target interval includes a density difference interval (determined based on a preset maximum density difference value and a preset minimum density difference value) and a seabed depth interval (determined based on a preset maximum depth value and a preset minimum depth value);

[0136] For example, in some specific implementations, random generation is performed. If no center point is given, random generation is performed within the search interval in the following manner:

[0137] contrast=minContrast+(maxContrast-minContrast)*random();

[0138] depth=minDepth+(maxDepth-minDepth)*random();

[0139] S103 : Directly obtain a preset number of prediction vectors in response to a manual setting instruction.

[0140] For example, in some embodiments, the initialization genes of all prediction vectors need to be manually specified.

[0141] Among them, initContrast and initDepth represent the center point; random() represents the random value generated in [0,1]; normal(0,1) represents a normal distribution with a mean of 0 and a standard deviation of 1. The obtained random numbers satisfy the normal distribution, thereby ensuring the fairness of gene generation.

[0142] S200, traversing all prediction vectors in the vector set to perform inversion solution and fitness evaluation on the prediction area, and obtaining the fitness function value of each prediction vector for inversion solution of the prediction area;

[0143] The result of the inversion solution is the predicted terrain data of the prediction area; the value of the fitness function is negatively correlated with the adaptability of the prediction vector for the inversion solution of the prediction area;

[0144] It should be noted that, in some embodiments, Figure 4 As shown, step S200 may include the following steps:

[0145] S201, sequentially traverse the prediction vectors in the vector set, and solve the predicted terrain data of the prediction area corresponding to each prediction vector by the Parker-Oldenburg inversion method;

[0146] S202. Based on the predicted terrain data, combined with the earthquake depth matrix of the predicted area, a root mean square calculation is performed to obtain a root mean square value as a fitness function value of the corresponding prediction vector for the adaptability of the inversion solution in the predicted area.

[0147] In some embodiments, the root mean square calculation specifically includes the following steps: obtaining the earthquake depth matrix of the predicted area; traversing the first terrain values ​​of all data points of the predicted terrain data corresponding to the predicted vector, and collecting the second terrain values ​​of the corresponding data points from the earthquake depth matrix; obtaining the third terrain value of the corresponding data point based on the square of the difference between the first terrain value and the second terrain value; and performing square root processing based on the average value of the third terrain values ​​of all data points to obtain the root mean square value corresponding to the corresponding predicted vector.

[0148] For example, in some specific implementations, the corresponding fitness function value can be calculated based on the set of prediction vectors. This is specifically implemented as follows: For the current set of vectors, the contrast and depth of each vector are traversed one by one, and the corresponding seafloor topography data is calculated using the Parker-Oldenburg inversion method. The corresponding RMS is then calculated in conjunction with the seismic depth matrix SemisMat. Each prediction vector will have its own RMS calculated, ultimately resulting in an array of size MaxPredCnt.

[0149] S300, when there is a prediction vector in the vector set whose corresponding fitness function value is less than the iterative optimal threshold, the corresponding prediction vector is output as the optimal prediction vector, and the process goes to step S900; otherwise, the process goes to the next step;

[0150] It should be noted that, in some embodiments, Figure 5 As shown, outputting the corresponding prediction vector as the optimal prediction vector may include the following steps: S301, when there is a prediction vector in the vector set whose corresponding fitness function value is less than the iterative optimal threshold, directly outputting the prediction vector as the optimal prediction vector; S302, when there are multiple prediction vectors in the vector set whose corresponding fitness function values ​​are less than the iterative optimal threshold, outputting the prediction vector corresponding to the smallest fitness function value as the optimal prediction vector.

[0151] For example, in some specific implementations, all population prediction vectors are traversed to determine whether there is an RMS smaller than the iterative optimal threshold minIterDelt. If so, go to step S900; otherwise, execute the next step.

[0152] S400: Determine the excellence of each prediction vector based on the fitness function value, perform a random probability selection operation on the prediction vectors in the vector set based on the excellence, and update the vector set based on the selection result of the selection operation; and then use the minimum fitness function value corresponding to the vector set as the second fitness value;

[0153] Among them, the degree of excellence is negatively correlated with the numerical value of the fitness function;

[0154] It should be noted that, in some embodiments, Figure 6 As shown, based on the degree of excellence, the random probability selection operation of the prediction vector in the vector set may include the following steps:

[0155] S401, normalizing the excellence levels corresponding to all prediction vectors in the vector set to obtain a normalized excellence level corresponding to each prediction vector;

[0156] The expression of excellence is:

[0157]

[0158] Where, e represents the natural constant; RMS represents the fitness function value; minIterDelt represents the iterative optimal threshold; nowTemp represents the annealing temperature;

[0159] S402, sorting out the normalized excellence levels corresponding to all prediction vectors in the vector set to obtain an excellence level array;

[0160] The value of the nth element in the excellence degree array is the cumulative sum of all normalized excellence degrees corresponding to the first prediction vector to the nth prediction vector in the vector set;

[0161] S403, generating a random number within the first interval as a selection probability;

[0162] S404, sequentially traverse the excellence array to find the first element whose probability is greater than the selection probability as the target element;

[0163] S405: Select a corresponding prediction vector from the vector set according to the element number of the target element as an update vector, and return to the step of generating a random number as a selection probability within the first interval until a preset number of update vectors are selected from the vector set;

[0164] S406: Use the preset number of update vectors as the selection result of the selection operation.

[0165] For example, in some specific implementations, applying a selection operator to a prediction vector based on probability can be implemented as follows:

[0166] The selection operator is mainly based on probability to select a better prediction vector. The smaller the RMS, the better the prediction vector, and the larger the RMS, the worse the prediction vector. At the same time, the influence of temperature is introduced. The higher the temperature, the better the prediction vector, and the smaller the temperature, the worse the prediction vector. The present invention uses To express the excellence of the prediction vector, A larger value indicates a better prediction vector. Considering that solid particles tend to become disordered at high temperatures, meaning that the search variable is more likely to undergo drastic changes at high temperatures, we incorporate the current temperature into the evaluation system. This value is also used in the mutation operator: the higher the temperature, the greater the mutation probability.

[0167] Formula (1) is used to solve for the sum of the priorities of all prediction vectors; Formula (2) calculates the normalized prediction vector priorities; referring to Formula (3), the sum of all prediction vector priorities is 1; Formula (4) is used to calculate the array of cumulative priority sums. How to select an excellent prediction vector? Calculate a random number R0 in the range [0, 1] and traverse the accRMS array to find the first prediction vector greater than R0. This prediction vector is then selected.

[0168] With this selection operator, the larger the averRMS, the greater the probability of being selected. At the same time, prediction vectors with a smaller averRMS still have a chance of being selected. This design is because currently, excellent prediction vectors indicate excellent gene expression and are worthy of selection. However, considering that a prediction vector with a lower averRMS may be due to slower gene expression, a certain probability is still maintained for selection.

[0169]

[0170] In general, the number of prediction vectors is greater than MaxPredCnt. Here, by running the selection operator MaxPredCnt times, we finally get MaxPredCnt excellent prediction vectors. Note that the same prediction vector may be selected multiple times.

[0171] S500: When the second fitness value is greater than or equal to the first fitness value, performing a random probability mutation operation on the prediction vectors in the vector set based on the annealing temperature, and updating the vector set according to the mutation result of the mutation operation; otherwise, updating the first fitness value by using the second fitness value;

[0172] It should be noted that, in some embodiments, Figure 7 As shown, the prediction vector includes two types of inversion parameters: density difference and depth. Based on the annealing temperature, a random probability mutation operation is performed on the prediction vector in the vector set, which may include the following steps:

[0173] S501, based on the annealing temperature, combining the first fitness value and the second fitness value to calculate the mutation probability;

[0174] Among them, the expression of mutation probability is:

[0175]

[0176] Where, mutationRate represents the mutation probability; e represents the natural constant; RMS1 represents the first fitness value; RMS2 represents the second fitness value; nowTemp represents the annealing temperature;

[0177] S502: Generate a random number in the second interval as the mutation determination probability; add 1 to the number of mutation cycles;

[0178] S503: When the mutation determination probability is less than the preset mutation probability, a prediction vector is randomly selected from the vector set as a mutation prediction vector;

[0179] S504, performing numerical mutation on any inversion parameter in the mutated prediction vector within a target interval, and placing the prediction vector after the numerical mutation back into the vector set; the target interval represents the value range of the prediction vector;

[0180] S505: Return to the step of generating a random number as a mutation determination probability within the second interval until the number of mutation cycles equals a preset number, thereby obtaining a mutation result of the mutation operation;

[0181] Among them, the number of mutation cycles is initialized to 0.

[0182] For example, in some specific implementations, it is assumed that RMS1 represents the optimal evaluation value currently searched globally, and RMS2 represents the minimum evaluation value among all current prediction vectors. If RMS2 is less than RMS1, it means that there is a prediction vector that is better than the current one, and go to step S600, otherwise the mutation operation is performed and the first fitness value is updated. It is important to note here that step S600 is a crossover operator process, and step S500 involves a mutation operator process. The crossover operator is used in each iteration, but the mutation operator is not necessarily used. This design is different from the genetic algorithm. Because the core point of the mutation operator is to jump out of the local optimum, only when a better solution is not found, it means that it is trapped in the local optimum, and only then is a mutation operator needed.

[0183] Specifically, the specific process of the mutation operation can be implemented as follows:

[0184] First, the mutation probability is calculated based on the current temperature. The probability calculation of the mutation operator refers to formula (5).

[0185]

[0186] Taking into account that solid particles tend to become disordered at high temperatures, that is, the search variable is more likely to undergo drastic changes at high temperatures, the current temperature is introduced into the mutation operator probability calculation formula. The higher the temperature, the greater the mutation probability, and the lower the temperature, the smaller the mutation probability. At the same time, the closer RMS2 is to RMS1, the greater the mutation probability, and the farther RMS2 is from RMS1, the smaller the mutation probability. In the critical state, that is, when RMS2=RMS1, the mutation probability is 1, that is, mutation is bound to occur. In this case, it means that the search may have fallen into a local optimum, and the mutation probability needs to be increased to jump out of the local optimum. Based on this, the present invention realizes the dynamic adjustment of the mutation probability.

[0187] Then, the mutation operator is applied to the prediction vector based on probability. The mutation operator randomly selects a single prediction vector and then selects a gene for mutation. The maximum number of mutation operations is MaxPredCnt. A random number R1 in the range [0, 1] is calculated. If R1 is less than mutationRate, a random prediction vector is selected for mutation. The mutation operator then randomly selects one of Case 1 and Case 2 for the mutation operation.

[0188] Case 1: Density difference mutation, only density difference mutation operation is performed. The mutation formula is as follows:

[0189] A1[contrast]=A1[contrast]+(maxContrast-minContrast)*normal(0,1);

[0190] Case 2: Deep mutation, only deep mutation operation is performed. The mutation formula is as follows:

[0191] A1[depth]=A1[depth]+(maxDepth-minDepth)*normal(0,1);

[0192] Here, normal(0,1) represents a normal distribution with a mean of 0 and a standard deviation of 1.

[0193] In general, given a selected MaxPredCnt prediction vector, the maximum number of mutations is MaxPredCnt. Then, based on the mutationRate probability, whether a mutation is necessary is determined. If a mutation is indicated, individuals are randomly selected, and the gene to be mutated is chosen. The new prediction vector is obtained after the mutation operator completes.

[0194] S600, performing a crossover operation with random probability on the prediction vectors in the vector set based on the second fitness value, and updating the vector set according to the crossover result of the crossover operation;

[0195] It should be noted that in some embodiments, the prediction vector includes two types of inversion parameters: density difference and depth. Based on the second fitness value, performing a random probability crossover operation on the prediction vectors in the vector set may include the following steps:

[0196] Based on the second fitness value, the number of crossovers is calculated by combining the iterative optimal threshold and the preset number;

[0197] The expression of the number of crossovers is:

[0198]

[0199] Where, crossCnt represents the number of crossovers; random(x) represents a random value generated in [0,x]; RMS2 represents the second fitness value; minIterDelt represents the iterative optimal threshold; MaxPredCnt represents the preset number;

[0200] Generate a random number in the third interval as the crossover determination probability; add 1 to the number of crossover cycles;

[0201] When the crossover determination probability is less than the preset crossover operator probability, two prediction vectors are randomly obtained from the vector set as crossover vectors; the crossover vectors include the first vector and the second vector;

[0202] Performing numerical weighted crossover on at least one type of inversion parameter in the first vector and the second vector, and placing the predicted vector after the numerical weighted crossover back into the vector set;

[0203] Returning to the step of generating a random number in the third interval as the crossover determination probability until the number of crossover cycles equals the number of crossovers, a crossover result of the crossover operation is obtained;

[0204] The number of crossover cycles is initialized to 0.

[0205] For example, in some specific implementations, applying a crossover operator to a prediction vector based on probability can be implemented as follows:

[0206] The crossover operator selects two prediction vectors and then performs a gene crossover. Referring to formula (6), RMS2 represents the minimum evaluation value among all current prediction vectors, round() indicates rounding up, and crossCnt represents the number of crossovers. The advantage of this design is that when RMS2 is larger, it indicates a greater distance from the global optimal solution, and the crossover operator performs more operations; when RMS2 is smaller, it indicates a closer distance from the global optimal solution, and the crossover operator can guarantee at least MaxPredCnt operations.

[0207]

[0208] For a single crossover operation, based on the excellent prediction vector obtained by the selection operator, two prediction vectors A1 and A2 are randomly selected; then a random number R2 in [0,1] is calculated. If R2 is less than crossoverRate, it is determined that a crossover is performed on these two prediction vectors A1 and A2; then one is randomly selected from Case1, Case2 and Case3 for the crossover operator operation; then a single crossover operator operation is completed; this is repeated, and crossCnt crossover operations are performed, and finally the crossover operation of the entire population is completed.

[0209] Case 1: Density difference crossover, only density difference crossover is performed. The crossover formula is as follows:

[0210] t1=A1[contrast];

[0211] t2=A2[contrast];

[0212] A1[contrast]=t1*crossoverAlpha+t2*(1-crossoverAlpha);

[0213] A2[contrast]=t2*crossoverAlpha+t1*(1-crossoverAlpha);

[0214] Case 2: Depth crossover, only depth crossover is performed. The crossover formula is as follows:

[0215] t1=A1[depth];

[0216] t2=A2[depth];

[0217] A1[depth]=t1*crossoverAlpha+t2*(1-crossoverAlpha);

[0218] A2[depth]=t2*crossoverAlpha+t1*(1-crossoverAlpha);

[0219] Case 3: Density difference crossover and depth crossover, all genes are crossovered, the operation formula is as above.

[0220] In general, given a selected MaxPredCnt prediction vector, the total number of crossovers is calculated using RMS1. A random prediction vector is then randomly selected to participate in the crossover based on probability. For each prediction vector participating in the crossover, genes are selected based on probability. The result of the crossover operator is a new prediction vector.

[0221] S700, performing temperature attenuation processing on the annealing temperature based on a preset temperature attenuation rate, and updating the annealing temperature according to the attenuation result of the temperature attenuation processing;

[0222] For example, in some specific implementations, after the operations of steps S400 to S600, a round of temperature iteration is completed, and a new vector is obtained. After the random iteration of the current temperature is completed, temperature decay is required, where the decay formula is: tNowTemp = tNowTemp * tempReduceRate, where the initial value of tNowTemp (i.e., the annealing temperature) is the initial temperature initTemp.

[0223] S800, when the annealing temperature is greater than or equal to the preset termination temperature, returning to the step of traversing all prediction vectors in the vector set to perform inversion solution and fitness evaluation of the prediction region until the annealing temperature is less than the termination temperature, and outputting the optimal prediction vector based on the last updated vector set;

[0224] It should be noted that, in some embodiments, outputting the optimal prediction vector based on the last updated vector set may include the following steps: obtaining the minimum value of the fitness function values ​​corresponding to all prediction vectors in the last updated vector set, and outputting the prediction vector corresponding to the minimum value as the optimal prediction vector.

[0225] For example, in some specific implementations, steps S200 to S700 are executed in a loop iterative manner. Each time the steps are executed, the temperature decays once. It is determined whether tNowTemp is less than the final temperature finalTemp. If so, it indicates that the iterative process ends and the process goes to step S900. Otherwise, the process goes to step S200.

[0226] S900 , obtaining corresponding predicted terrain data through inversion according to the optimal prediction vector as a terrain solution result for the prediction area.

[0227] For example, in some specific implementations, this step yields the optimal solution for the entire search process. There are only two possible solutions: an optimal vector that satisfies the iterative optimality threshold minIterDelt, or an optimal vector obtained by searching the entire space. At this point, the search process concludes, yielding not only the optimal solution but also the corresponding seafloor topography data.

[0228] In order to explain the principle of the technical solution of the present invention in detail, the overall process of the present invention is described below in combination with some specific embodiments. It is easy to understand that the following is an explanation of the technical principle of the present invention and cannot be regarded as a limitation of the present invention.

[0229] First of all, it should be noted that the core idea of ​​the current technical solution is to search for the optimal value by simulated annealing, then confirm the optimal value by the Parker-Oldenburg inversion method, and finally obtain the final result by adaptively updating the filter matrix parameters. A large number of experiments have shown that although the existing technical solution can well solve the optimal density difference contrast and depth depth, it has problems such as low solution efficiency, unstable solution accuracy, and easy to fall into local optimality. Disadvantages of existing technology:

[0230] 1) Low solution efficiency. Existing solutions generate density contrast and depth through purely random methods. This means that a large number of random tests are required to obtain the optimal solution, resulting in extremely low solution efficiency. For example, sometimes it takes 10 hours to get the optimal solution.

[0231] 2) Average solution accuracy is unstable. Existing solutions generate density contrast and depth through purely random methods. Extensive testing has found that for all optimal solutions, 40% of the results have good accuracy, while the rest have poor accuracy, resulting in unstable average solution accuracy.

[0232] 3) It's easy to get stuck in a local optimum and fail to find the optimal solution. When the iteration parameters are set low, such as a temperature setting of 50 degrees and a temperature decay rate of 0.6, it's easy to fail to find the optimal solution. Through extensive testing, we found that the optimal solution was found in 50% of cases, but not in the rest.

[0233] In view of this, the present invention proposes a genetic operator driven simulated annealing method and a Parker-Oldenburg inversion method for solving seabed topography. The genetic operator driven simulated annealing method is used to find the optimal parameters, and the Parker-Oldenburg inversion method is used to obtain a more accurate seabed topography. Figure 8 As shown, the implementation process of the present invention can be as follows:

[0234] Step 1: Parameter initialization. The parameters mainly include the genetic operator-driven simulated annealing method and the Parker-Oldenburg inversion method parameters (for details, please refer to the variable parameter description above and will not be repeated here).

[0235] Step 2: Initialize the vector set. Each vector consists of the density difference contrast and the average seabed depth depth. The core of the initialization vector set is to generate MaxPredCnt vectors. There are three strategies:

[0236] Case 1: Generate based on the center point. If the center point is given, the generation method is as follows:

[0237] contrast=initContrast+(maxContrast-minContrast)*normal(0,1);

[0238] depth=initDepth+(maxDepth-minDepth)*normal(0,1);

[0239] Case 2: Random generation. If no center point is given, it is randomly generated within the search interval. The generation method is as follows:

[0240] contrast=minContrast+(maxContrast-minContrast)*random();

[0241] depth=minDepth+(maxDepth-minDepth)*random();

[0242] Case 3: Manually specified. In this case, the initialization genes of all prediction vectors need to be manually specified.

[0243] In this example, initContrast and initDepth represent the center point; random() generates a random value in the range [0,1]; and normal(0,1) represents a normal distribution with a mean of 0 and a standard deviation of 1. The resulting random numbers adhere to a normal distribution, ensuring fairness in gene generation. Go to step 3.

[0244] Step 3: Calculate the corresponding fitness function value based on the set of predicted vectors. For the current set of vectors, iterate through the contrast and depth of each vector one by one, and then calculate the corresponding seafloor topography data using the Parker-Oldenburg inversion method. Then, combined with the seismic depth matrix SemisMat, calculate the corresponding RMS. Each predicted vector has its own RMS calculated, ultimately resulting in an array of size MaxPredCnt. Go to Step 4.

[0245] Step 4: Is there a vector smaller than the threshold? Traverse all population prediction vectors and determine whether there is a vector with an RMS smaller than the iterative optimal threshold minIterDelt. If so, go to step 13, indicating that the optimal prediction vector (optimal solution) has been found. Otherwise, go to step 5.

[0246] Step 5: Apply the selection operator to the prediction vector based on probability. The selection operator mainly selects the better prediction vector based on probability. The smaller the RMS, the better the prediction vector, and the larger the RMS, the worse the prediction vector. At the same time, the influence of temperature is introduced. The higher the temperature, the better the prediction vector, and the lower the temperature, the worse the prediction vector. The present invention uses To express the excellence of the prediction vector, A larger value indicates a better prediction vector. Considering that solid particles tend to become disordered at high temperatures, meaning that the search variable is more likely to undergo drastic changes at high temperatures, we incorporate the current temperature into the evaluation system. This value is also used in the mutation operator: the higher the temperature, the greater the mutation probability.

[0247] Formula (1) is used to solve for the sum of the priorities of all prediction vectors; Formula (2) calculates the normalized prediction vector priorities; referring to Formula (3), the sum of all prediction vector priorities is 1; Formula (4) is used to calculate the array of cumulative priority sums. How to select an excellent prediction vector? Calculate a random number R0 in the range [0, 1] and traverse the accRMS array to find the first prediction vector greater than R0. This prediction vector is then selected.

[0248] With this selection operator, the larger the averRMS, the greater the probability of being selected. At the same time, prediction vectors with a smaller averRMS still have a chance of being selected. This design is because currently, excellent prediction vectors indicate excellent gene expression and are worthy of selection. However, considering that a prediction vector with a lower averRMS may be due to slower gene expression, a certain probability is still maintained for selection.

[0249]

[0250]

[0251] For example, if minIterDelt is 1.5 and nowTemp is 1.0, the original RMS array is [4.5, 8.5, 2.5, 10.5], that is, there are four prediction vectors with RMS of 4.5, 8.5, 2.5, and 10.5 respectively.

[0252] According to formula (1), we can get

[0253] The averRMS array is obtained by formula (2): The calculated value is averRMS = [0.1189, 0.0022, 0.8786, 0.0003]. Formula (3) verifies that the sum of the averRMS array is 1.

[0254] Calculate the accRMS array through formula (4).

[0255] accRMS[0] = 0.1189;

[0256] accRMS[1] = 0.1189 + 0.0022 = 0.1211;

[0257] accRMS[2] = 0.1189 + 0.0022 + 0.8786 = 0.9997;

[0258] accRMS[3] = 0.1189 + 0.0022 + 0.8786 + 0.0003 = 1.0000;

[0259] Then calculate the random value R1. If R1 < accRMS[0], then accRMS[0] is selected; if R1 >= accRMS[0] and R1 < accRMS[1], then accRMS[1] is selected; if R1 >= accRMS[1] and R1 < accRMS[2], then accRMS[2] is selected; if R1 >= accRMS[2] and R1 < accRMS[3], then accRMS[3] is selected; if R1 >= accRMS[3], then accRMS[3] is selected, and this is the critical case.

[0260] Generally, the number of prediction vectors is greater than MaxPredCnt. Here, the selection operator is run MaxPredCnt times, and finally MaxPredCnt excellent prediction vectors are obtained. Note that the same prediction vector may be selected twice (this is the difference from simulated annealing). Go to step 6.

[0261] Step 6: Whether there is a vector better than the current one. Assume that RMS1 represents the currently globally searched best evaluation value, and RMS2 represents the minimum evaluation value among all current prediction vectors. If RMS2 is less than RMS1, it means there is a prediction vector better than the current one, go to step 9; otherwise, go to step 7.

[0262] Here, it should be emphasized that step 9 is the crossover operator process, and steps 7 and 8 are the mutation operator processes. The crossover operator is used in each iteration, but the mutation operator may not be used. This design is different from the genetic algorithm. Because the core point of the mutation operator is to jump out of the local optimum. Only when no better solution is found does it mean that it has fallen into the local optimum, and at this time, the mutation operator is needed.

[0263] Step 7: Calculate the mutation probability based on the current temperature. The probability calculation of the mutation operator refers to formula (5).

[0264]

[0265] Taking into account that solid particles tend to become disordered at high temperatures, that is, the search variable is more likely to undergo drastic changes at high temperatures, the current temperature is introduced into the mutation operator probability calculation formula. The higher the temperature, the greater the mutation probability, and the lower the temperature, the smaller the mutation probability. At the same time, the closer RMS2 is to RMS1, the greater the mutation probability, and the farther RMS2 is from RMS1, the smaller the mutation probability. In the critical state, that is, when RMS2=RMS1, the mutation probability is 1, that is, mutation is bound to occur. In this case, it means that the search may have fallen into a local optimum, and the mutation probability needs to be increased to jump out of the local optimum. Based on this, the present invention implements dynamic adjustment of the mutation probability. Go to step 8.

[0266] Step 8: Apply the mutation operator to the prediction vector based on probability. The mutation operator randomly selects a single prediction vector and then selects a gene for mutation. The maximum number of mutation operations is MaxPredCnt. Then, a random number R1 in the range [0, 1] is calculated. If R1 is less than mutationRate, a random prediction vector is selected for mutation. The mutation operator then randomly selects one of Case 1 and Case 2 for the mutation operation.

[0267] Case 1: Density difference mutation, only density difference mutation operation is performed. The mutation formula is as follows:

[0268] A1[contrast]=A1[contrast]+(maxContrast-minContrast)*normal(0,1);

[0269] Case 2: Deep mutation, only deep mutation operation is performed. The mutation formula is as follows:

[0270] A1[depth]=A1[depth]+(maxDepth-minDepth)*normal(0,1);

[0271] Here, normal(0,1) represents a normal distribution with a mean of 0 and a standard deviation of 1.

[0272] In general, given a selected MaxPredCnt prediction vector, the maximum number of mutations is MaxPredCnt. Then, based on the mutationRate probability, whether mutation is necessary is determined. If a mutation is indicated, individuals are randomly selected and the gene to be mutated is determined. After the mutation operator completes, the new prediction vector is obtained. Go to step 10.

[0273] Step 9: Apply the crossover operator to the prediction vector based on the probability. The crossover operator selects two prediction vectors and then performs a gene crossover. Referring to formula (6), RMS2 represents the minimum evaluation value of all current prediction vectors, round() represents rounding up, and crossCnt represents the number of crossovers. The advantage of this design is that when RMS2 is larger, it indicates that the solution is farther from the global optimal solution, and the crossover operator operates more frequently; when RMS2 is smaller, it indicates that the solution is closer to the global optimal solution, and the crossover operator can guarantee at least MaxPredCnt times.

[0274]

[0275] For a single crossover operation, based on the excellent prediction vector obtained by the selection operator, two prediction vectors A1 and A2 are randomly selected; then a random number R2 in [0,1] is calculated. If R2 is less than crossoverRate, it is determined that a crossover is performed on these two prediction vectors A1 and A2; then one is randomly selected from Case1, Case2 and Case3 for the crossover operator operation; then a single crossover operator operation is completed; this is repeated, and crossCnt crossover operations are performed, and finally the crossover operation of the entire population is completed.

[0276] Case 1: Density difference crossover, only density difference crossover is performed. The crossover formula is as follows:

[0277] t1=A1[contrast];

[0278] t2=A2[contrast];

[0279] A1[contrast]=t1*crossoverAlpha+t2*(1-crossoverAlpha);

[0280] A2[contrast]=t2*crossoverAlpha+t1*(1-crossoverAlpha);

[0281] Case 2: Depth crossover, only depth crossover is performed. The crossover formula is as follows:

[0282] t1=A1[depth];

[0283] t2=A2[depth];

[0284] A1[depth]=t1*crossoverAlpha+t2*(1-crossoverAlpha);

[0285] A2[depth]=t2*crossoverAlpha+t1*(1-crossoverAlpha);

[0286] Case 3: Density difference crossover and depth crossover, all genes are crossovered, the operation formula is as above.

[0287] In general, given a selected MaxPredCnt prediction vector, the total number of crossovers is calculated using RMS1. A random prediction vector is then randomly selected to participate in the crossover based on probability. For each prediction vector participating in the crossover, genes are selected based on probability. The result of the crossover operator is a new prediction vector.

[0288] Here's a specific example: if minRMS is 1.2, minIterDelt is 1, sizePop is 4, and crossoverAlpha is 0.8, then corssCnt = round(1.2 / 1)*4 = 8, meaning a maximum of 8 pairs of individuals can participate in the crossover operation. Here, we use the crossover of a pair of individuals as an example. Assume that the density difference of individual A1 is A1[contrast] = 0.5, and the depth of individual A1 is A1[depth] = 20; the density difference of individual A2 is A2[contrast] = 0.3, and the depth of individual A2 is A2[depth] = 10. If the random number R2 is less than the crossoverRate, then A1 and A2 are definitely crossed.

[0289] If we only do density intersection:

[0290] A1[contrast]=0.5*0.8+(1-0.8)*0.3=0.46,

[0291] A2[contrast]=0.3*0.8+(1-0.8)*0.5=0.34;

[0292] If only depth intersection is done, then:

[0293] A1[depth]=20*0.8+(1-0.8)*10=18,

[0294] A2[depth]=10*0.8+(1-0.8)*20=12;

[0295] If you do the intersection of density difference and depth at the same time:

[0296] A1[contrast]=0.5*0.8+(1-0.8)*0.3=0.46,

[0297] A2[contrast]=0.3*0.8+(1-0.8)*0.5=0.34;

[0298] A1[depth]=20*0.8+(1-0.8)*10=18,

[0299] A2[depth]=10*0.8+(1-0.8)*20=12;

[0300] Go to step 10.

[0301] Step 10: Obtain a new set of vectors based on the genetic operator. After steps 5 through 9, a round of temperature iteration is complete, resulting in a new set of vectors. Using the survival of the fittest and natural selection of the genetic operator, determine whether the global optimal solution has been found based on the new population. Go to step 11.

[0302] Step 11: Reduce the temperature. After the random iteration of the current temperature is complete, the temperature needs to be attenuated. The attenuation formula is: tNowTemp = tNowTemp * tempReduceRate, where the initial value of tNowTemp is the initial temperature initTemp. Go to step 12.

[0303] Step 12: Check whether the minimum temperature threshold has been reached. Steps 3 to 11 are executed iteratively. Each time the temperature is executed, the temperature decays. It is determined whether tNowTemp is less than the final temperature, finalTemp. If so, the iteration ends and the process goes to step 13. Otherwise, the process goes to step 3.

[0304] Step 13: Find the optimal solution. This step yields the optimal solution for the entire search process. There are only two possible solutions: an optimal vector that satisfies the iterative optimality threshold, minIterDelt, or an optimal vector obtained by searching the entire space. At this point, the search process is complete, and not only has the optimal solution been found, but also the corresponding seafloor topography data.

[0305] In summary, the present invention proposes a method for solving seabed topography by combining genetic operator-driven simulated annealing and Parker-Oldenburg inversion. The method uses the genetic operator-driven simulated annealing method to find the optimal parameters, and then uses the Parker-Oldenburg inversion method to obtain a more accurate seabed topography. The technical features of the present invention include but are not limited to:

[0306] 1) A genetic operator-driven simulated annealing method is proposed to search for the optimal density difference contrast and depth. Real number coding has advantages in continuous parameter optimization and continuous gradient problems, ultimately improving the accuracy of Parker-Oldenburg inversion of seafloor topography.

[0307] 2) We propose applying optimized genetic operators to simulated annealing vector generation. The improved genetic operator's mutation probability dynamically adjusts with temperature and search progress, enhancing the ability to escape local optima. This ensures a more efficient simulated annealing search process and improves data solution accuracy and efficiency.

[0308] 3) The genetic operator-driven simulated annealing method and Parker-Oldenburg inversion are combined to ensure the accuracy of the seabed topography and improve computational efficiency.

[0309] It is important to note that, in response to the common problem of improving the Parker-Oldenburg inversion method, unlike existing work, this invention proposes a genetic operator-driven simulated annealing search strategy, which improves the efficiency and stability of the solution accuracy. A small number of steps are consistent with existing work, while the core steps are unique to this invention. Through extensive testing, the experimental results of this invention meet expectations. In summary, compared with existing simulated annealing related work, the core points of this invention are:

[0310] 1) Based on the initial search of multiple prediction vectors, existing work is a single initial search variable;

[0311] 2) Introduce selection, mutation, and crossover operators to predict new vectors. The predicted vectors obtained by mutation and crossover operators are added to the existing vectors, and the selection operator is used to eliminate redundant vectors.

[0312] 3) Dynamic changes in mutation probability based on temperature and search progress.

[0313] Compared with existing genetic algorithm related work, the core points of this invention are:

[0314] 1) The mutation probability changes dynamically based on temperature and search progress, while the mutation probability of existing work is a fixed value set manually;

[0315] 2) The calculation formulas for introducing selection, mutation and crossover operators are considered from different perspectives.

[0316] 3) The processes of mutation and crossover operators are different. In genetic algorithms, mutation and crossover operators may act on individuals at the same time, but the present invention will not. For example, for individuals A1 and A2, based on the present invention, A1 and A2 cross to obtain A3 and A4, A1 mutates to obtain A5, and finally obtain A1, A2, A3, A4, A5. Mutation and crossover will not act on the newly obtained prediction vector at the same time. By selecting the operator to eliminate the redundant vectors, what may be obtained in the end is A3 and A5. In the existing genetic algorithm work, A1 and A2 cross to obtain A3 and A4, A4 mutates to obtain A5, and finally obtain A3 and A5, among which A3 has undergone the crossover operator, and A5 has undergone the crossover and mutation operators.

[0317] On the other hand, Figure 9 As shown, an embodiment of the present invention provides a simulated annealing inversion solution device 900 for terrain, which may include:

[0318] The first module 901 is used to initialize a vector set, a first fitness value, and an initial temperature; the initial temperature is used as an annealing temperature; wherein the vector set includes a preset number of prediction vectors;

[0319] The second module 902 is used to traverse all prediction vectors in the vector set to perform inversion solution and fitness evaluation on the prediction area, and obtain the fitness function value of each prediction vector for inversion solution of the prediction area;

[0320] The result of the inversion solution is the predicted terrain data of the prediction area; the value of the fitness function is negatively correlated with the adaptability of the prediction vector for the inversion solution of the prediction area;

[0321] The third module 903 is used to output the corresponding prediction vector as the optimal prediction vector when the fitness function value corresponding to the prediction vector in the vector set is less than the iterative optimal threshold; otherwise,

[0322] The fourth module 904 is configured to determine the excellence of each prediction vector based on the fitness function value, perform a random probability selection operation on the prediction vectors in the vector set based on the excellence, and update the vector set according to the selection result of the selection operation; and then use the minimum fitness function value corresponding to the vector set as the second fitness value; the excellence is negatively correlated with the numerical value of the fitness function;

[0323] The fifth module 905 is configured to perform a random probability mutation operation on the prediction vectors in the vector set based on the annealing temperature when the second fitness value is greater than or equal to the first fitness value, and update the vector set according to the mutation result of the mutation operation; otherwise, update the first fitness value according to the second fitness value;

[0324] A sixth module 906 is configured to perform a crossover operation with a random probability on the prediction vectors in the vector set based on the second fitness value, and update the vector set according to a crossover result of the crossover operation;

[0325] The seventh module 907 is configured to perform temperature decay processing on the annealing temperature based on a preset temperature decay rate, and update the annealing temperature according to the decay result of the temperature decay processing;

[0326] The eighth module 908 is configured to, when the annealing temperature is greater than or equal to the preset termination temperature, return to the step of traversing all prediction vectors in the vector set to perform inversion solution and fitness evaluation on the prediction region until the annealing temperature is less than the termination temperature, and output the optimal prediction vector based on the last updated vector set;

[0327] The ninth module 909 is used to obtain the corresponding predicted terrain data obtained by inversion according to the optimal prediction vector as the terrain solution result of the prediction area.

[0328] The contents of the method embodiments of the present invention are all applicable to the device embodiments. The functions specifically implemented by the device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0329] In another aspect, an embodiment of the present invention further provides an electronic device comprising a memory and a processor. The memory stores a computer program, and the processor implements the above-described method for predicting the bottom boundary of the hydrate stability region when executing the computer program. The electronic device can be any smart terminal, including a tablet computer and an in-vehicle computer.

[0330] It can be understood that the contents of the above method embodiments are applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0331] like Figure 10 As shown, Figure 10 The hardware structure of an electronic device 1000 according to another embodiment is shown. The electronic device 1000 includes:

[0332] The processor 1001 may be implemented as a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is configured to execute relevant programs to implement the technical solutions provided by the embodiments of the present invention.

[0333] The memory 1002 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 1002 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1002 and is called by the processor 1001 to execute the network node population optimization method of the embodiment of the present invention.

[0334] Input / output interface 1003, used to implement information input and output;

[0335] Communication interface 1004, used to implement communication interaction between this device and other devices, which can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WiFi, Bluetooth, etc.);

[0336] Bus 1005 , which transmits information between various components of the device (e.g., processor 1001 , memory 1002 , input / output interface 1003 , and communication interface 1004 );

[0337] The processor 1001 , the memory 1002 , the input / output interface 1003 and the communication interface 1004 are connected to each other in communication within the device via the bus 1005 .

[0338] The electronic device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, i.e., they may be located in one location or distributed across multiple network units. Some or all of these modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0339] The contents of the method embodiments of the present invention are all applicable to the electronic device embodiments. The functions specifically implemented by the electronic device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0340] Another aspect of an embodiment of the present invention further provides a computer-readable storage medium, wherein the storage medium stores a program, and the program is executed by a processor to implement the above method.

[0341] The contents of the method embodiments of the present invention are all applicable to the computer-readable storage medium embodiments. The functions specifically implemented by the computer-readable storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0342] The present invention also discloses a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium and execute the computer instructions, causing the computer device to perform the above method.

[0343] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the claims and their equivalents.

[0344] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the embodiments. Those skilled in the art can make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of the present invention.

Claims

1. A simulated annealing inversion method for terrain, characterized in that: The following steps are involved: Initializing a vector set, a first fitness value, and an initial temperature; using the initial temperature as an annealing temperature; wherein the vector set includes a preset number of prediction vectors, and the prediction vectors include two types of inversion parameters: density difference and depth; Traversing all the prediction vectors in the vector set to perform inversion solution and fitness evaluation on the prediction area, and obtaining a fitness function value of each prediction vector for performing the inversion solution on the prediction area; The result of the inversion solution is the predicted terrain data of the prediction area; the value of the fitness function is negatively correlated with the adaptability of the prediction vector for performing the inversion solution in the prediction area; When the fitness function value corresponding to the prediction vector in the vector set is smaller than the iterative optimal threshold, the corresponding prediction vector is output as the optimal prediction vector; otherwise, Determining the excellence of each prediction vector according to the fitness function value, performing a random probability selection operation on the prediction vectors in the vector set based on the excellence, and updating the vector set according to the selection result of the selection operation; and then using the minimum fitness function value corresponding to the vector set as a second fitness value; the excellence is negatively correlated with the numerical value of the fitness function value; When the second fitness value is greater than or equal to the first fitness value, performing a random probability mutation operation on the prediction vector in the vector set based on the annealing temperature, and updating the vector set according to the mutation result of the mutation operation; otherwise, updating the first fitness value by using the second fitness value; performing a crossover operation with random probability on the prediction vectors in the vector set based on the second fitness value, and updating the vector set according to a crossover result of the crossover operation; Performing temperature decay processing on the annealing temperature based on a preset temperature decay rate, and updating the annealing temperature according to a decay result of the temperature decay processing; When the annealing temperature is greater than or equal to the preset termination temperature, returning to the step of traversing all the prediction vectors in the vector set to perform inversion solution and fitness evaluation of the prediction area, until the annealing temperature is less than the termination temperature, outputting the optimal prediction vector according to the last updated vector set; The predicted terrain data obtained by the inversion solution according to the optimal prediction vector is used as the terrain solution result of the prediction area.

2. The simulated annealing inversion solution method for terrain according to claim 1, characterized in that: The initialization vector set includes at least one of the following steps: Obtaining a preset standard vector value, and randomly generating a preset number of prediction vectors near the standard vector value; Randomly generating the preset number of prediction vectors within a target interval; the target interval represents a value range of the prediction vectors; The preset number of prediction vectors is directly obtained in response to a manual setting instruction.

3. The simulated annealing inversion solution method for terrain according to claim 1, characterized in that: The step of traversing all the prediction vectors in the vector set to perform inversion solution and fitness evaluation on the prediction area, and obtaining a fitness function value of each prediction vector for performing the inversion solution on the prediction area, comprises the following steps: The prediction vectors in the vector set are sequentially traversed, and the predicted terrain data of the prediction area obtained by inverting each prediction vector using the Parker-Oldenburg inversion method; Based on the predicted terrain data, combined with the earthquake depth matrix of the predicted area, a root mean square calculation is performed to obtain a root mean square value as the fitness function value of the adaptability of the corresponding prediction vector for performing the inversion solution in the predicted area.

4. The simulated annealing inversion solution method for terrain according to claim 1, characterized in that: Outputting the corresponding prediction vector as the optimal prediction vector comprises the following steps: When there is a prediction vector in the vector set whose corresponding fitness function value is smaller than the iterative optimal threshold, directly outputting the prediction vector as the optimal prediction vector; When there are multiple prediction vectors in the vector set whose corresponding fitness function values ​​are smaller than the iterative optimal threshold, the prediction vector corresponding to the smallest fitness function value is output as the optimal prediction vector.

5. The simulated annealing inversion solution method for terrain according to claim 1, characterized in that: The step of performing a random probability selection operation on the prediction vector in the vector set based on the excellence level comprises the following steps: Normalizing the excellence levels corresponding to all the prediction vectors in the vector set to obtain a normalized excellence level corresponding to each prediction vector; The expression of the excellence degree is: ; Where, represents a natural constant; represents the fitness function value; represents the iterative optimal threshold; represents the annealing temperature; Arranging the normalized excellence levels corresponding to all the prediction vectors in the vector set to obtain an excellence level array; The value of the nth element in the excellence degree array is the cumulative sum of all the normalized excellence degrees corresponding to the first prediction vector to the nth prediction vector in the vector set; Generate a random number in the first interval as the selection probability; Sequentially traverse the excellence array to find the first element whose probability is greater than the selection probability as the target element; selecting the corresponding prediction vector from the vector set as an update vector according to the element number of the target element, and returning to the step of generating a random number as the selection probability within the first interval until the preset number of update vectors are selected from the vector set; The preset number of update vectors is used as the selection result of the selection operation.

6. The simulated annealing inversion solution method for terrain according to claim 1, characterized in that: The step of performing a random probability mutation operation on the prediction vector in the vector set based on the annealing temperature comprises the following steps: Based on the annealing temperature, the mutation probability is calculated in combination with the first fitness value and the second fitness value; The expression of the mutation probability is: ; Where, represents the probability of mutation; represents a natural constant; represents the first fitness value; represents the second fitness value; represents the annealing temperature; Generate a random number in the second interval as the mutation determination probability; add 1 to the number of mutation cycles; When the mutation determination probability is less than the preset mutation probability, randomly taking one of the prediction vectors from the vector set as a mutation prediction vector; Performing a numerical variation on any of the inversion parameters in the variation prediction vector within a target interval, and placing the prediction vector after the numerical variation back into the vector set; the target interval represents a value range of the prediction vector; Returning to the step of generating a random number within the second interval as a mutation determination probability until the number of mutation cycles equals the preset number, obtaining the mutation result of the mutation operation; The number of mutation cycles is initialized to 0.

7. The simulated annealing inversion solution method for terrain according to claim 1, characterized in that: The step of performing a random probability crossover operation on the prediction vectors in the vector set based on the second fitness value comprises the following steps: Based on the second fitness value, the number of crossovers is calculated in combination with the iterative optimal threshold and the preset number; The expression of the crossover number is: MaxPredCnt; Where, Indicates the number of crossovers; Indicates rounding x upwards; represents the second fitness value; Represents the iterative optimal threshold; MaxPredCnt represents the preset number; Generate a random number in the third interval as the crossover determination probability; add 1 to the number of crossover cycles; When the crossover determination probability is less than a preset crossover operator probability, randomly obtaining two of the prediction vectors from the vector set as crossover vectors; the crossover vectors include a first vector and a second vector; Performing numerical weighted crossover on at least one type of the inversion parameters in the first vector and the second vector, and placing the predicted vector after the numerical weighted crossover back into the vector set; Returning to the step of generating a random number as a crossover determination probability within the third interval until the number of crossover cycles is equal to the number of crossovers, and obtaining the crossover result of the crossover operation; The number of crossover cycles is initialized to 0.

8. A simulated annealing inversion solution device for terrain, characterized in that: include: The first module is used to initialize the vector set, the first fitness value and the initial temperature; The initial temperature is used as the annealing temperature; wherein the vector set includes a preset number of prediction vectors, and the prediction vectors include two types of inversion parameters: density difference and depth; The second module is used to traverse all the prediction vectors in the vector set to perform inversion solution and fitness evaluation on the prediction area, and obtain the fitness function value of each prediction vector for performing the inversion solution on the prediction area; The result of the inversion solution is the predicted terrain data of the prediction area; the value of the fitness function is negatively correlated with the adaptability of the prediction vector for performing the inversion solution in the prediction area; The third module is configured to output the corresponding prediction vector as the optimal prediction vector when the fitness function value corresponding to the prediction vector in the vector set is less than the iterative optimal threshold; otherwise, a fourth module, configured to determine an excellence level of each of the prediction vectors based on the fitness function value, perform a random probability selection operation on the prediction vectors in the vector set based on the excellence level, and update the vector set according to the selection result of the selection operation; and then use the minimum fitness function value corresponding to the vector set as the second fitness value; the excellence level is negatively correlated with the numerical value of the fitness function value; a fifth module, configured to, when the second fitness value is greater than or equal to the first fitness value, perform a random probability mutation operation on the prediction vector in the vector set based on the annealing temperature, and update the vector set according to the mutation result of the mutation operation; otherwise, update the first fitness value by using the second fitness value; a sixth module, configured to perform a crossover operation with random probability on the prediction vectors in the vector set based on the second fitness value, and update the vector set according to a crossover result of the crossover operation; A seventh module is configured to perform temperature attenuation processing on the annealing temperature based on a preset temperature attenuation rate, and update the annealing temperature according to an attenuation result of the temperature attenuation processing; an eighth module, configured to, when the annealing temperature is greater than or equal to a preset termination temperature, return to executing the steps of traversing all the prediction vectors in the vector set to perform inversion solution and fitness evaluation of the prediction region, until the annealing temperature is less than the termination temperature, and output the optimal prediction vector based on the last updated vector set; A ninth module is configured to use the predicted terrain data obtained by the inversion solution according to the optimal prediction vector as a terrain solution result for the prediction area.

9. An electronic device, characterized in that: including a processor and a memory; The memory is used to store programs; The processor executes the program to implement the method according to any one of claims 1 to 7.

10. A computer storage medium storing a program executable by a processor, characterized in that: The program executable by the processor is used to implement the method according to any one of claims 1 to 7 when executed by the processor.

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