A Method for Searching for Maritime Unmanned Cluster Targets Based on Prior Maps

Abstract

The present application relates to the technical field of unmanned marine clusters, and particularly to a method for searching for targets of unmanned marine clusters based on a priori maps, including: comprehensively analyzing a priori information to obtain several target points for the current target search task, and all the target points form an a priori set corresponding to the current target search task; performing Gaussian modeling according to the specific positions and distribution ranges of the target points to obtain several Gaussian functions; superimposing the Gaussian functions to generate a target search probability map; expanding the a priori set based on the reliability of each target point, and optimizing the target search probability map based on the sum of the Gaussian functions of the expanded target points obtained by the expansion to obtain an optimized target search probability map; extracting several valuable search regions from the optimized target search probability map; and instructing the unmanned marine clusters to perform target search in the priority order of the valuable search regions. This method significantly improves the efficiency and accuracy of target search.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of maritime unmanned cluster technology, and particularly to a method for searching for targets of a maritime unmanned cluster based on a prior map. Background Art

[0002] Maritime rescue refers to a comprehensive operation to provide emergency assistance to targets such as distressed persons, ships, and aircraft in the sea or coastal areas. Its core objective is to ensure life safety, reduce property losses, and prevent and control marine environmental pollution. The core tasks of maritime rescue include life rescue, vehicle assistance, emergency disposal of environmental pollution, and ensuring channel safety.

[0003] Target search is a basic means for carrying out maritime rescue missions. Most traditional target search methods rely on a single unmanned system, and their search efficiency and coverage are often limited. In the complex and changeable marine environment, traditional target search methods are difficult to cope with various challenges, such as poor underwater communication, environmental interference, and dynamic changes of targets.

[0004] In recent years, the technology of maritime unmanned clusters based on cluster cooperation has gradually emerged. A maritime unmanned cluster refers to a system composed of a certain number of unmanned aerial vehicles, unmanned surface vehicles, unmanned underwater vehicles, etc., interconnected by air, surface, and underwater communication networks, and realizing collaborative tasks in a group manner. Through the collaborative operation of multiple unmanned systems, this technology can significantly improve the flexibility and efficiency of target search.

[0005] However, when facing maritime rescue missions, the target search strategy of a maritime unmanned cluster is very important. How to optimize the target search strategy to improve the efficiency and accuracy of target search is an urgent problem to be solved. Summary of the Invention

[0006] In view of this, the embodiments of the present application propose a method for searching for targets of a maritime unmanned cluster based on a prior map, which can significantly improve the efficiency and accuracy of target search, provide a more intelligent solution for target recognition in a complex marine environment, and open up a new path for maritime target search.

[0007] To achieve the above object, an embodiment of the present application proposes a method for searching for targets of a maritime unmanned cluster based on a prior map. The method includes: obtaining prior information, comprehensively analyzing the obtained prior information, and obtaining a plurality of target points corresponding to the current target search task; wherein, the prior information includes historical search data, environmental monitoring records, and expert analysis opinions, and the plurality of target points form a prior set corresponding to the current target search task; performing Gaussian modeling according to the specific positions and distribution ranges of the respective target points to obtain a plurality of Gaussian functions; using the Parzen window theory and non-parametric data-driven estimation to superimpose the respective Gaussian functions to generate a target search probability map corresponding to the current target search task; expanding the prior set based on the reliability of each target point, and optimizing the target search probability map based on the sum of the Gaussian functions of the expanded target points obtained by the expansion to obtain an optimized target search probability map; performing non-parametric data-driven estimation on the optimized target search probability map, and then analyzing each cluster to quantitatively extract a plurality of valuable search regions; instructing the maritime unmanned cluster to perform target search according to the priority order of the respective valuable search regions; wherein, the greater the target probability of the valuable search region, the higher its corresponding priority.

[0008] To achieve the above object, an embodiment of the present application also proposes a system for searching for targets of a maritime unmanned cluster based on a prior map. The system includes: a prior analysis module, a Gaussian modeling module, a target search probability map generation module, a target search probability map optimization module, a valuable search region extraction module, and a search execution module; the prior analysis module is configured to obtain prior information, comprehensively analyze the obtained prior information, and obtain a plurality of target points corresponding to the current target search task, wherein the prior information includes historical search data, environmental monitoring records, and expert analysis opinions, and the plurality of target points form a prior set corresponding to the current target search task; the Gaussian modeling module is configured to perform Gaussian modeling according to the specific positions and distribution ranges of the respective target points to obtain a plurality of Gaussian functions; the target search probability map generation module is configured to use the Parzen window theory and non-parametric data-driven estimation to superimpose the respective Gaussian functions to generate a target search probability map corresponding to the current target search task; the target search probability map optimization module is configured to expand the prior set based on the reliability of each target point, and optimize the target search probability map based on the sum of the Gaussian functions of the expanded target points obtained by the expansion to obtain an optimized target search probability map; the valuable search region extraction module is configured to perform non-parametric data-driven estimation on the optimized target search probability map, and then analyze each cluster to quantitatively extract a plurality of valuable search regions; the search execution module is configured to instruct the maritime unmanned cluster to perform target search according to the priority order of the respective valuable search regions, wherein the greater the target probability of the valuable search region, the higher its corresponding priority.

[0009] To achieve the above object, an embodiment of the present application further provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a method for searching for targets of an unmanned marine cluster based on a priori graph as described above.

[0010] To achieve the above object, an embodiment of the present application further provides a computer-readable storage medium storing a computer program, which, when executed by a processor, can implement a method for searching for targets of an unmanned marine cluster based on a priori graph as described above.

[0011] A method for searching for targets of an unmanned marine cluster based on a priori graph proposed by the present application first comprehensively analyzes the obtained a priori information to obtain several target points corresponding to the current target search task. Subsequently, Gaussian modeling is performed according to the specific positions and distribution ranges of the target points, and the Parzen window theory and non-parametric data-driven estimation are used to superimpose the Gaussian functions obtained by modeling to generate a target search probability map corresponding to the current target search task. Then, the a priori set is expanded based on the reliability of each target point, and the target search probability map is optimized based on the sum of the Gaussian functions of the expanded target points obtained by the expansion. Non-parametric data-driven estimation is performed on the optimized target search probability map, and then each cluster is analyzed to quantitatively extract several valuable search areas. Finally, it is indicated that the unmanned marine cluster conducts target search in the order of the priorities of the valuable search areas. Such a search process can not only significantly improve the efficiency and accuracy of target search, but also provide a more intelligent solution for target recognition in complex marine environments. Through the full utilization of a priori information, the scientific construction of the target probability map, the effective application of cluster extraction, and the collaborative work of the unmanned system, the present application opens up a new path for the field of marine target search. In the future, with the continuous progress of technology, especially the development of artificial intelligence and big data analysis technologies, this method is expected to further improve its intelligent level and achieve more accurate and efficient target search. This will not only provide strong technical support for the development and protection of marine resources, but also provide an important data basis and tool for marine scientific research. The application prospect of the present application is very broad and can play an important role in multiple fields, including but not limited to marine resource exploration, environmental monitoring, marine safety and rescue, etc., and has extremely high social value and economic benefits. By continuously adjusting and optimizing in practical applications, the method for searching for targets of an unmanned marine cluster based on a priori graph proposed by the present application will become an important technical support in the future field of marine exploration and provide a strong guarantee for realizing more intelligent marine management and development.

[0012] In some alternative embodiments, the prior information further includes relevant literature and unvalidated indication reports, and there is a chance that the unvalidated indication reports contain preliminary clues of the existence of the target;

[0013] The historical search data is used to provide the frequency and location of the target appearance, providing a basis for evaluating whether there are potential targets within the search area. The environmental monitoring records are used to provide the impact of changes in ocean conditions on the target distribution. The changes in ocean conditions include, but are not limited to, tides, flow rates, and temperatures. The expert analysis opinions are used to provide a professional perspective for the interpretation of relevant literature and unvalidated indication reports.

[0014] In some alternative embodiments, after obtaining the prior information and comprehensively analyzing the obtained prior information to obtain several target points corresponding to the current target search task, the method further includes:

[0015] Define three key attributes for each target point, namely the specific location, reliability, and distribution range. Denote the specific location of the th target point as , the reliability as , and the distribution range as ;

[0016] The specific location is used to characterize the central position of the expected target and is evaluated based on the historical search data;

[0017] The reliability is used to characterize the credibility of the specific location of the target point and is a quantitatively evaluated probability of the expected target appearing at the specific location, obtained by analyzing the historical search data;

[0018] The distribution range is used to characterize the possible range of the area where the expected target exists and is set based on the characteristics of the expected target and the environmental conditions in the environmental monitoring records to ensure a comprehensive understanding of the potential distribution of the expected target.

[0019] In some alternative embodiments, Gaussian modeling is performed according to the specific locations and distribution ranges of each target point to obtain several Gaussian functions, which is achieved through the following formula:

[0020] ;

[0021] ;

[0022] where represents the Gaussian function corresponding to the th target point, represents the specific location of the th target point, represents the distribution range of the th target point, and is the total number of target points. is an independent variable, representing any position within the search area corresponding to the target search task.

[0023] In some alternative embodiments, the prior set is expanded based on the reliability of each target point, and the target search probability map is optimized based on the sum of the Gaussian functions of the expanded target points obtained by the expansion, to obtain an optimized target search probability map, including:

[0024] Based on a preset maximum expansion number and the reliability of each target point, each target point is expanded respectively to obtain a number of expanded target points. The number of expanded target points obtained by expanding different target points is different. The number of expanded target points obtained by expanding the th target point is expressed by the formula:

[0025] ;

[0026] ;

[0027] where represents the reliability of the th target point, is the preset maximum expansion number at represents the number of expanded target points obtained by expanding based on the th target point;

[0028] According to the specific positions and distribution ranges of the respective expanded target points, Gaussian modeling is performed to obtain the Gaussian functions corresponding to the respective expanded target points, and the sum of the Gaussian functions of the respective expanded target points is calculated;

[0029] Through the following formula, the target search probability map is optimized based on the sum of the Gaussian functions of the respective expanded target points to obtain an optimized target search probability map;

[0030] ;

[0031] ;

[0032] where represents the total number of expanded target points, represents the Gaussian function corresponding to the th expanded target point, represents the specific position of the th target point, represents the distribution range of the th target point, represents the unoptimized target search probability map, The value of is the target search probability at Represents the optimized target search probability map.

[0033] In some alternative embodiments, non-parametric data-driven estimation is performed on the optimized target search probability map, and then each cluster is analyzed to quantitatively extract a number of valuable search regions, including:

[0034] Through the following formula, non-parametric data-driven estimation is performed on the optimized target search probability map to obtain the non-parametric data-driven estimation result:

[0035] ;

[0036] Wherein, Represents the number of environmental changes, Is a preset probability decrease value caused by environmental changes, Represents the non-parametric data-driven estimation result;

[0037] Based on the non-parametric data-driven estimation result, each cluster is analyzed to ensure that each cluster contains adjacent points with positive values All clusters are isolated from each other, thereby quantitatively extracting a number of valuable search regions;

[0038] Wherein, as Increases, Gradually decreases, and the size of the valuable search region shrinks accordingly.

[0039] In some alternative embodiments, after instructing the unmanned marine cluster to perform target search according to the priority order of each valuable search region, the method further includes:

[0040] Based on the estimated return, sailing time, and coverage time of each valuable search region, a comprehensive evaluation is performed to obtain the comprehensive evaluation value of each valuable search region respectively. Denote the comprehensive evaluation value of the th valuable search region as , The calculation process of is expressed by the formula:

[0041] ;

[0042] Wherein, Represents the estimated return of the th valuable search region, Based on the probability integral of each point in the th valuable search region, Represents the sailing time of the th valuable search region, Represents the Coverage time of a valuable search area. Description of the Drawings

[0043] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the related art, the following will briefly introduce the drawings required for the description of the embodiments of the present application or the related art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0044] Figure 1 is a flowchart of a method for searching for maritime unmanned cluster targets based on a prior map provided in an embodiment of the present application;

[0045] Figure 2 is a target search probability map provided in an embodiment of the present application;

[0046] Figure 3 is an optimized target search probability map provided in an embodiment of the present application;

[0047] Figure 4 is a schematic diagram of cluster analysis provided in an embodiment of the present application;

[0048] Figure 5 is a schematic diagram of extracting a valuable search area provided in an embodiment of the present application;

[0049] Figure 6 is a schematic diagram showing the change of search accuracy over time provided in an embodiment of the present application;

[0050] Figure 7 is a schematic diagram of a cumulative reward curve provided in an embodiment of the present application;

[0051] Figure 8 is a schematic structural diagram of a system for searching for maritime unmanned cluster targets based on a prior map provided in another embodiment of the present application;

[0052] Figure 9 is a schematic structural diagram of an electronic device provided in another embodiment of the present application. Detailed Embodiments

[0053] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the following will elaborate on each embodiment of this application in conjunction with the accompanying drawings. However, those of ordinary skill in the art can understand that in each embodiment of this application, many technical details are presented to help readers better understand this application. However, even without these technical details and various changes and modifications based on the following embodiments, the technical solutions claimed in this application can still be implemented. The division of the following embodiments is for convenience of description and should not constitute any limitation on the specific implementation of this application. The various embodiments can be combined and cross-referenced with each other on the premise of not being contradictory.

[0054] To address the problem of the low efficiency and accuracy of traditional target search strategies, an embodiment of this application proposes a method for searching for targets by an unmanned maritime cluster based on a prior graph, which is applied to an electronic device. The electronic device can be a terminal or a server. In this embodiment and the following embodiments, the server is taken as an example for illustration. The following specifically describes the implementation details of a method for searching for targets by an unmanned maritime cluster based on a prior graph. The following content is only implementation details provided for convenience of understanding and is not necessary for implementing this solution.

[0055] The specific process of a method for searching for targets by an unmanned maritime cluster based on a prior graph proposed in this embodiment can be as Figure 1 shown and includes:

[0056] Step 101: Obtain prior information, comprehensively analyze the obtained prior information, and obtain several target points corresponding to the current target search task. All the target points form the prior set corresponding to the current target search task.

[0057] In specific implementation, the server first needs to obtain prior information, and then comprehensively analyze the obtained prior information to obtain several target points corresponding to the current target search task. All the target points form the prior set corresponding to the current target search task. The obtained prior information includes, but is not limited to, historical search data, environmental monitoring records, and expert analysis opinions.

[0058] In an example, when implementing the method for searching for targets by an unmanned maritime cluster based on a prior graph, the primary task is to systematically collect prior information related to target search. This prior information includes historical search data, environmental monitoring records, expert analysis opinions, relevant literature, and unvalidated indication reports, etc. The collection of prior information is crucial because high-quality prior information can significantly improve the accuracy and effectiveness of subsequent modeling.

[0059] In one example, historical search data is used to provide the frequency and location of the target appearance, providing a basis for evaluating whether there are potential targets in the search area. Environmental monitoring records are used to provide the impact of changes in ocean conditions on the target distribution. Changes in ocean conditions include, but are not limited to, tides, flow rates, and temperatures. There is a chance that relevant literature and unvalidated indication reports contain preliminary clues about the existence of the target. Expert analysis opinions can provide a professional perspective for the interpretation of relevant literature and unvalidated indication reports.

[0060] In one example, after the server obtains several target points corresponding to the current target search task, it is also necessary to define three key attributes for each target point, namely specific location, reliability, and distribution range. The server records the specific location of the th target point as , the reliability as , and the distribution range as . The specific location is used to represent the central location of the expected target and is evaluated based on historical search data. The reliability is used to represent the credibility of the specific location of the target point and is the probability of the expected target appearing at the specific location through a quantitative evaluation, which can be obtained by analyzing historical search data. The distribution range is used to represent the possible range of the area where the expected target exists and is set based on the characteristics of the expected target and the environmental conditions in the environmental monitoring records to ensure a comprehensive understanding of the potential distribution of the expected target.

[0061] Step 102: Perform Gaussian modeling based on the specific locations and distribution ranges of each target point to obtain several Gaussian functions.

[0062] In a specific implementation, in order to more effectively express the uncertainty of the target location, the server needs to perform Gaussian modeling based on the specific locations and distribution ranges of each target point, thereby obtaining several Gaussian functions. The mean of each Gaussian function is the specific location of the target point, and the standard deviation is the distribution range of the target point. This modeling method can effectively generate the probability distribution of the target point, providing a reliable basis for subsequent search strategies. The choice of the Gaussian distribution is reasonable because it can effectively describe the distribution characteristics of many natural phenomena. By applying the Gaussian function to each target point, the probability of the target point existing in space can be reflected, making the target search more scientific and controllable.

[0063] In one example, the server performs Gaussian modeling based on the specific locations and distribution ranges of each target point to obtain several Gaussian functions, which can be achieved through the following formula:

[0064] ;

[0065] ;

[0066] where represents the Gaussian function corresponding to the th target point, represents the specific position of the th target point, represents the distribution range of the th target point, is the total number of target points, is the independent variable, representing any position within the search area corresponding to the target search task.

[0067] Step 103: Using the Parzen window theory and non-parametric data-driven estimation, superimpose each Gaussian function to generate the target search probability map corresponding to this target search task.

[0068] In the specific implementation, the server uses the Parzen window theory to superimpose multiple Gaussian functions to construct a comprehensive target search probability map. The target search probability map intuitively shows the target existence probability of each area, providing a clear direction for subsequent target search. The construction of the target search probability map not only improves the search efficiency but also enables the unmanned system to make more accurate decisions in complex environments. The information reflected in the target search probability map is extremely rich, not only showing the high-probability areas of potential targets but also marking the low-probability areas, enabling the reasonable allocation of search resources. By analyzing the target search probability map, the unmanned system can concentrate its efforts on searching the high-probability areas first, greatly improving the overall search efficiency.

[0069] In one example, the target search probability map can be as Figure 2 shown. Figure 2 The X-axis in represents the abscissa of the search area corresponding to the target search task, the Y-axis represents the ordinate of the search area corresponding to the target search task, and the Z-axis represents the target search probability corresponding to any position

[0070] Step 104: Expand the prior set based on the reliability of each target point, and optimize the target search probability map based on the sum of the Gaussian functions of the expanded target points obtained by the expansion to obtain the optimized target search probability map.

[0071] In the specific implementation, there are only target points in the prior set, which results in the target search probability map still being not precise enough. Therefore, the server needs to expand the prior set based on the reliability of each target point and optimize the target search probability map based on the sum of the Gaussian functions of the expanded target points obtained by the expansion to obtain the optimized target search probability map.

[0072] In one example, the server first needs to expand each target point based on a preset maximum expansion number and the reliability of each target point, obtaining a number of expanded target points. The number of expanded target points obtained by expanding different target points is different. The number of expanded target points obtained by expanding the th target point is expressed by the formula:

[0073] ;

[0074] ;

[0075] where, represents the reliability of the th target point, is the preset maximum expansion number at , represents the number of expanded target points obtained by expanding based on the th target point.

[0076] Subsequently, the server performs Gaussian modeling based on the specific positions and distribution ranges of the expanded target points, obtains the Gaussian functions corresponding to the expanded target points, and calculates the sum of the Gaussian functions of the expanded target points.

[0077] Finally, the server optimizes the target search probability map based on the sum of the Gaussian functions of the expanded target points through the following formula, obtaining an optimized target search probability map;

[0078] ;

[0079] ;

[0080] where, represents the total number of expanded target points, represents the Gaussian function corresponding to the th expanded target point, represents the specific position of the th target point, represents the distribution range of the th target point, represents the unoptimized target search probability map, The value of is the target search probability at represents the optimized target search probability map.

[0081] In one example, the optimized target search probability map can be as shown in Figure 3 . Compared with the target search probability map ( Figure 2 ), the information contained in the optimized target search probability map is more accurate. Correspondingly,Figure 3 The X-axis in it represents the abscissa of the search area corresponding to the target search task, the Y-axis represents the ordinate of the search area corresponding to the target search task, and the Z-axis represents any position. The corresponding optimized target search probability.

[0082] Step 105: Conduct non-parametric data-driven estimation in the optimized target search probability map, and then analyze each cluster to quantitatively extract several valuable search areas.

[0083] In a specific implementation, after optimizing the target search probability map, the server needs to conduct non-parametric data-driven estimation in the optimized target search probability map and then analyze each cluster to quantitatively extract several valuable search areas. Cluster extraction can identify the cluster areas of potential targets, thereby concentrating search resources and avoiding ineffective searches. In a marine environment, targets are usually non-uniformly distributed, so cluster extraction can help the unmanned system focus on several key areas to maximize search efficiency. By analyzing the clusters, the system can determine the high-probability areas (valuable search areas) and prioritize access. This process not only reduces the overall search time but also increases the detection success rate of the targets. In addition, cluster extraction also provides a basis for subsequent strategy adjustment to ensure that the unmanned system can respond flexibly in a dynamic environment.

[0084] In one example, the server first conducts non-parametric data-driven estimation in the optimized target search probability map through the following formula to obtain the non-parametric data-driven estimation result:

[0085] ;

[0086] where represents the number of environmental changes, is the preset probability drop value due to environmental changes, represents the non-parametric data-driven estimation result.

[0087] Subsequently, based on the non-parametric data-driven estimation result, the server analyzes each cluster to ensure that each cluster contains adjacent points with positive values and all clusters are isolated from each other, thereby quantitatively extracting several valuable search areas. Among them, as increases, gradually decreases, and the size of the valuable search area also shrinks accordingly.

[0088] In one example, the process of cluster extraction can be as Figure 4 shown, and the valuable search areas extracted by the server can be as Figure 5 shown, inFigure 4 and Figure 5 where the X-axis represents the abscissa of the search area corresponding to the target search task, and the Y-axis represents the ordinate of the search area corresponding to the target search task Figure 4 and Figure 5 both clearly divide into two connected domains, namely, connected domain 1 and connected domain 2

[0089] Step 106, instruct the unmanned marine cluster to perform target search according to the priority order of each valuable search area, where the greater the target probability of the valuable search area, the higher its corresponding priority

[0090] In a specific implementation, after the server extracts each valuable search area, it can instruct the unmanned marine cluster to perform target search according to the priority order of each valuable search area, where the greater the target probability of the valuable search area, the higher its corresponding priority

[0091] In an example, to further improve the search efficiency, multiple mobile marine unmanned systems can work collaboratively. These unmanned systems dynamically adjust their respective search paths and task assignments by sharing real-time data and the optimized target search probability map. This way of cluster collaboration enables the unmanned systems to cooperate efficiently within a vast sea area and ensures flexible response in a complex environment. In actual operation, communication and collaboration among the unmanned systems are crucial. By establishing a reliable communication network, the system can achieve real-time data sharing, ensuring that each unmanned system can obtain the latest environmental information and target probability map. This information sharing not only improves the decision-making ability of individual unmanned systems but also enhances the collaborative effect of the entire cluster

[0092] During the search process, the sensors built into the unmanned system can continuously monitor changes in the marine environment, including water flow, temperature, acoustic signals, etc. These real-time data will be fed back to the control center (server), enabling the unmanned system to adjust the search strategy in a timely manner according to environmental changes. For example, if the environmental conditions in a certain area change drastically, the unmanned system can immediately evaluate the possibility of the existence of targets in that area and thus decide whether to adjust the search path or strategy. Through such real-time monitoring, the unmanned system can continuously optimize the target probability map and search strategy, making the search process more intelligent and efficient. This flexibility ensures that the unmanned system can maintain an efficient target search ability in a complex and changing marine environment

[0093] In one example, after completing the target search task, the server can also analyze and evaluate the results of the entire search process, and conduct a comprehensive evaluation based on the estimated rewards, sailing time, and coverage time of each valuable search area, respectively obtaining the comprehensive evaluation values of each valuable search area, so as to continuously optimize future search strategies. This continuous learning and optimization mechanism not only improves the success rate of the search task, but also provides reliable support for subsequent ocean exploration and resource development. By analyzing historical data, the server can identify the key factors affecting search efficiency and make adjustments.

[0094] For example, if it is found that a certain search strategy is ineffective under specific conditions, the server will be able to record this experience and improve it in future tasks. This self-optimization ability enables the unmanned system to maintain high search efficiency in a changing environment.

[0095] In one example, as Figure 6 shown, the search accuracy of the unmanned system continuously improves over time, as Figure 7 shown, the cumulative reward value of the search task also continuously increases over time.

[0096] In one example, the server records the comprehensive evaluation value of the th valuable search area as , The calculation process of

[0097] can be expressed by the formula:

[0098] where represents the estimated reward of the th valuable search area, calculated based on the probability integral of each point in the th valuable search area, represents the sailing time of the th valuable search area, represents the coverage time of the th valuable search area.

[0099] In this embodiment, the acquired prior information is first comprehensively analyzed to obtain several target points corresponding to the current target search task. Subsequently, Gaussian modeling is performed according to the specific positions and distribution ranges of the target points. Using the Parzen window theory and non-parametric data-driven estimation, the Gaussian functions obtained by modeling are superimposed to generate a target search probability map corresponding to the current target search task. Then, the prior set is expanded based on the reliability of each target point, and the target search probability map is optimized based on the sum of the Gaussian functions of the expanded target points obtained by the expansion. Non-parametric data-driven estimation is performed on the optimized target search probability map, and then each cluster is analyzed to quantitatively extract several valuable search regions. Finally, the unmanned marine cluster is instructed to perform target search according to the priority order of each valuable search region. Such a search process can not only significantly improve the efficiency and accuracy of target search, but also provide a more intelligent solution for target recognition in complex marine environments. Through the full utilization of prior information, the scientific construction of the target probability map, the effective application of cluster extraction, and the collaborative work of unmanned systems, this embodiment has opened up a new path for the field of marine target search. In the future, with the continuous progress of technology, especially the development of artificial intelligence and big data analysis technologies, this method is expected to further improve its intelligence level and achieve more accurate and efficient target search. This will not only provide strong technical support for the development and protection of marine resources, but also provide important data bases and tools for marine scientific research. This embodiment has broad application prospects and can play an important role in multiple fields, including but not limited to marine resource exploration, environmental monitoring, marine safety and rescue, etc., and has extremely high social value and economic benefits. Through continuous adjustment and optimization in practical applications, a method for target search of an unmanned marine cluster based on a prior map proposed in this embodiment will become an important technical support in the future field of marine exploration and provide a strong guarantee for realizing more intelligent marine management and development.

[0100] The step division of the above various methods is only for clear description. When implemented, they can be combined into one step or some steps can be split into multiple steps. As long as the same logical relationship is included, they are all within the protection scope of this application; adding insignificant modifications or introducing insignificant designs to the algorithm or process, but not changing the core design of its algorithm and process are all within the protection scope of this application.

[0101] Another embodiment of this application proposes a target search system for an unmanned marine cluster based on a prior map. The details of the target search system for an unmanned marine cluster based on a prior map proposed in this embodiment are specifically described below. The following content is only implementation details provided for convenient understanding and is not necessary for implementing this example. Figure 8It is a schematic diagram of a maritime unmanned cluster target search system based on a prior map proposed in this embodiment, including: a prior analysis module 201, a Gaussian modeling module 202, a target search probability map generation module 203, a target search probability map optimization module 204, a valuable search area extraction module 205, and a search execution module 206.

[0102] The prior analysis module 201 is used to obtain prior information, comprehensively analyze the obtained prior information, and obtain several target points corresponding to the current target search task. Among them, the prior information includes historical search data, environmental monitoring records, and expert analysis opinions, and the several target points form a prior set corresponding to the current target search task.

[0103] The Gaussian modeling module 202 is used to perform Gaussian modeling based on the specific positions and distribution ranges of each target point to obtain several Gaussian functions.

[0104] The target search probability map generation module 203 is used to superimpose each Gaussian function by using the Parzen window theory and non-parametric data-driven estimation to generate a target search probability map corresponding to the current target search task.

[0105] The target search probability map optimization module 204 is used to expand the prior set based on the reliability of each target point, and optimize the target search probability map based on the sum of the Gaussian functions of the expanded target points obtained by the expansion to obtain an optimized target search probability map.

[0106] The valuable search area extraction module 205 is used to perform non-parametric data-driven estimation in the optimized target search probability map, and then analyze each cluster to quantitatively extract several valuable search areas.

[0107] The search execution module 206 is used to instruct the maritime unmanned cluster to perform target search according to the priority order of each valuable search area. Among them, the greater the target probability of the valuable search area, the higher its corresponding priority.

[0108] It is worth mentioning that each module involved in this embodiment is a logical module. In practical applications, a logical unit can be a physical unit, a part of a physical unit, or can be implemented by a combination of multiple physical units. In addition, in order to highlight the innovative part of this application, units that are not closely related to solving the technical problems proposed in this application are not introduced in this embodiment, but this does not mean that there are no other units in this embodiment.

[0109] It is not difficult to find that this embodiment is a system embodiment corresponding to the above method embodiment. This embodiment can be implemented in cooperation with the above method embodiment. The relevant technical details and technical effects mentioned in the above method embodiment are still valid in this embodiment. To avoid repetition, they will not be elaborated here. Correspondingly, the relevant technical details mentioned in this embodiment can also be applied to the above method embodiment.

[0110] Another embodiment of the present application proposes an electronic device, and its specific structure can be as Figure 9 shown, including: at least one processor 301; and a memory 302 communicatively connected to the at least one processor 301; wherein, the memory 302 stores instructions executable by the at least one processor 301, and the instructions are executed by the at least one processor 301 to enable the at least one processor 301 to execute a method for searching for targets of unmanned marine clusters based on a priori graphs as described in the above method embodiment.

[0111] Among them, the memory and the processor are connected by a bus. The bus can include any number of interconnected buses and bridges, and the bus connects various circuits of one or more processors and memories together. The bus can also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits together, which are well known in the art, so they will not be further described herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be an element or multiple elements, such as multiple receivers and transmitters, and provides a unit for communicating with various other devices on the transmission medium. The data processed by the processor is transmitted over the wireless medium through the antenna. Further, the antenna also receives data and transmits the data to the processor.

[0112] The processor is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interface, voltage regulation, power management, and other control functions. The memory can be used to store data used by the processor when executing operations.

[0113] Another embodiment of the present application proposes a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, it can implement a method for searching for targets of unmanned marine clusters based on a priori graphs as described in the above method embodiment.

[0114] That is, those skilled in the art can understand that all or part of the steps in the methods of the above embodiments can be completed by instructing relevant hardware through a program. The program is stored in a storage medium, including several instructions to enable a device (which can be a single-chip microcomputer, a chip, etc.) or a processor to execute all or part of the steps of the methods described in various embodiments of the present application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, ROM (Read-Only Memory), RAM (Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0115] Those of ordinary skill in the art can understand that the above embodiments are specific embodiments for implementing the present application. In actual applications, various changes can be made to them in form and details without departing from the spirit and scope of the present application.

Claims

1. A method for searching for maritime unmanned cluster targets based on a priori maps, characterized in that Including: Obtain prior information, comprehensively analyze the obtained prior information, and obtain several target points corresponding to the current target search task; wherein, the prior information includes historical search data, environmental monitoring records, and expert analysis opinions, and the several target points form a prior set corresponding to the current target search task; Perform Gaussian modeling based on the specific positions and distribution ranges of each target point to obtain several Gaussian functions; Use the Parzen window theory and non-parametric data-driven estimation to superimpose each Gaussian function to generate a target search probability map corresponding to the current target search task; Expand the prior set based on the reliability of each target point, and optimize the target search probability map based on the sum of the Gaussian functions of the expanded target points obtained by the expansion to obtain an optimized target search probability map; Perform non-parametric data-driven estimation in the optimized target search probability map, and then analyze each cluster to quantitatively extract several valuable search areas; Instruct the unmanned marine cluster to perform target search according to the priority order of each valuable search area; wherein, the greater the target probability of the valuable search area, the higher its corresponding priority; Expand the prior set based on the reliability of each target point, and optimize the target search probability map based on the sum of the Gaussian functions of the expanded target points obtained by the expansion to obtain an optimized target search probability map, including: Based on the preset maximum expansion number and the reliability of each target point, each target point is expanded respectively to obtain a number of expanded target points. The number of expanded target points obtained by expanding different target points is different. The number of expanded target points obtained by expanding the th target point is expressed by the formula: ； ； Among them, represents the reliability of the th target point, is the preset maximum expansion number at represents the number of expanded target points obtained by expanding based on the th target point; Perform Gaussian modeling based on the specific positions and distribution ranges of each expanded target point to obtain Gaussian functions corresponding to each expanded target point, and calculate the sum of the Gaussian functions of each expanded target point; Optimize the target search probability map based on the sum of the Gaussian functions of each expanded target point through the following formula to obtain an optimized target search probability map; ； ； Among them, represents the total number of extended target points, represents the Gaussian function corresponding to the th extended target point, represents the specific position of the th target point, represents the distribution range of the th target point, represents the unoptimized target search probability map, The value at represents the optimized target search probability map.

2. The method for searching for maritime unmanned cluster targets based on a prior graph according to claim 1, characterized in that The prior information further includes relevant literature and unvalidated indication reports, and there is a chance that the unvalidated indication reports contain preliminary clues of the existence of the target; The historical search data is used to provide the frequency and location of the target appearance, providing a basis for evaluating whether there are potential targets in the search area. The environmental monitoring records are used to provide the impact of changes in marine conditions on the target distribution. The changes in marine conditions include but are not limited to tides, flow rates, and temperatures. The expert analysis opinions are used to provide a professional perspective for the interpretation of relevant literature and unvalidated indication reports.

3. The method for searching for maritime unmanned cluster targets based on a prior graph according to claim 1, wherein After obtaining the prior information, comprehensively analyzing the obtained prior information, and obtaining several target points corresponding to the current target search task, the method further includes: Define three key attributes for each target point, namely the specific location, reliability, and distribution range. Denote the specific location of the th target point as , the reliability as , and the distribution range as ; The specific position is used to characterize the central position of the expected target and is obtained based on an evaluation of the historical search data; The reliability is used to characterize the credibility of the specific position of the target point and is a quantitatively evaluated probability of the expected target appearing at the specific position, obtained by analyzing the historical search data; The distribution range is used to characterize the possible range of the area where the expected target exists, and is set based on the characteristics of the expected target and the environmental conditions in the environmental monitoring records to ensure a comprehensive understanding of the potential distribution of the expected target.

4. The method for searching for maritime unmanned cluster targets based on a prior graph according to claim 3, wherein Perform Gaussian modeling based on the specific positions and distribution ranges of each target point to obtain several Gaussian functions, which is achieved through the following formula: ； ； Among them, represents the Gaussian function corresponding to the th target point, represents the specific position of the th target point, represents the distribution range of the th target point, is the total number of target points, is the independent variable, representing any position within the search area corresponding to the target search task.

5. A method for searching for maritime unmanned cluster targets based on a prior map according to claim 1, characterized in that Perform non-parametric data-driven estimation in the optimized target search probability map, and then analyze each cluster to quantitatively extract several valuable search regions, including: Perform non-parametric data-driven estimation in the optimized target search probability map through the following formula to obtain the non-parametric data-driven estimation result: ； Among them, represents the number of environmental changes, is a preset probability decrease value caused by environmental changes, represents the non-parametric data-driven estimation result; Based on the non-parametric data-driven estimation results, each cluster is analyzed to ensure that each cluster contains adjacent points with positive values , and all clusters are isolated from each other, so as to quantitatively extract several valuable search regions; Among them, as increases, gradually decreases, and the size of the valuable search area shrinks accordingly.

6. A method for searching for maritime unmanned cluster targets based on a prior map according to any one of claims 1 to 5, characterized in that, After instructing the unmanned marine cluster to perform target search according to the priority order of each valuable search region, the method further includes: Based on the estimated return, sailing time, and coverage time of each valuable search area, a comprehensive evaluation is carried out to obtain the comprehensive evaluation value of each valuable search area. Denote the comprehensive evaluation value of the th valuable search area as , The calculation process is expressed by the formula as follows: ； Among them, represents the estimated return of the th valuable search area, which is calculated based on the probability integral of each point in the th valuable search area, represents the sailing time of the th valuable search area, represents the coverage time of the th valuable search area.

7. An unmanned maritime cluster target search system based on a prior map, characterized in that, Including: A prior analysis module, configured to obtain prior information, comprehensively analyze the obtained prior information, and obtain several target points corresponding to the current target search task. The prior information includes historical search data, environmental monitoring records, and expert analysis opinions, and the several target points form a prior set corresponding to the current target search task; A Gaussian modeling module, configured to perform Gaussian modeling according to the specific positions and distribution ranges of the target points to obtain several Gaussian functions; A target search probability map generation module, configured to use the Parzen window theory and non-parametric data-driven estimation to superimpose the Gaussian functions to generate a target search probability map corresponding to the current target search task; A target search probability map optimization module, configured to expand the prior set based on the reliability of each target point, and optimize the target search probability map based on the sum of the Gaussian functions of the expanded target points obtained by the expansion to obtain an optimized target search probability map; A valuable search region extraction module, configured to perform non-parametric data-driven estimation in the optimized target search probability map, and then analyze each cluster to quantitatively extract several valuable search regions; A search execution module, configured to instruct the unmanned marine cluster to perform target search according to the priority order of each valuable search region, where the greater the target probability of the valuable search region, the higher its corresponding priority; Expand the prior set based on the reliability of each target point, and optimize the target search probability map based on the sum of the Gaussian functions of the expanded target points obtained by the expansion to obtain an optimized target search probability map, including: Based on the preset maximum expansion number and the reliability of each target point, each target point is expanded respectively to obtain a number of expanded target points. The number of expanded target points obtained by expanding different target points is different. For the th target point, the number of expanded target points obtained by expansion is expressed by the formula: ； ； Among them, represents the reliability of the th target point, is the preset maximum expansion number at represents the number of expanded target points obtained by expanding based on the th target point; Perform Gaussian modeling according to the specific positions and distribution ranges of the expanded target points to obtain Gaussian functions corresponding to the expanded target points, and calculate the sum of the Gaussian functions of the expanded target points; Optimize the target search probability map based on the sum of the Gaussian functions of the expanded target points through the following formula to obtain an optimized target search probability map; ； ； Among them, represents the total number of extended target points, represents the Gaussian function corresponding to the th extended target point, represents the specific position of the th target point, represents the distribution range of the th target point, represents the unoptimized target search probability map, The value at represents the optimized target search probability map.

8. An electronic device, characterized in that, Including: At least one processor; And a memory communicatively connected to the at least one processor; Wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a method for target search of an unmanned marine cluster based on a prior map as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it can implement a method for target search of an unmanned marine cluster based on a prior map as described in any one of claims 1 to 6.

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