Multi-means networking specific target search task planning method and system

Abstract

The invention discloses a multi-means networking specific target search task planning method and system. According to the multi-means networking specific target searching task planning method provided by the invention, the searching of the specific target is divided into three modes, namely a large-range searching mode, a small-range positioning mode and an accurate tracking mode, different planning strategies are provided for the three modes, and conversion conditions are set among the three strategies, so that the searching efficiency of the specific target is improved. When conditions are met, a search mode can be changed, and task planning is dynamically performed. According to the method, the situation of a search target and input external information are analyzed, and a search mode required by the search target is judged. And performing task planning according to the search mode, the search target information and the mode means capability, forming a search scheme, generating a search task and supporting means for searching. Searching situation data are accessed in real time, a scheme is dynamically planned and adjusted according to the situation data, searching of a specific target is completed, and searching accuracy and efficiency are improved.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and more specifically, to a method and system for planning a multi-means network-based target search task. Background Technology

[0002] For specific aerial targets that are high-speed, highly maneuverable, and possess advanced electronic countermeasures capabilities, traditional single-site active radio detection technology faces significant bottlenecks in application. Due to the targets' extreme maneuverability, single-site equipment often struggles to maintain stable illumination, only acquiring sparse, discontinuous, discrete points. This discontinuity of data makes it difficult for filtering algorithms to converge, failing to construct smooth and reliable target trajectories. Furthermore, limited by the physical line-of-sight and fixed coverage area of ​​a single station, once the target leaves the detection range, it is easily lost, creating a detection blind zone. Therefore, the search mode relying on isolated, single-method equipment can no longer meet the requirements for continuous tracking of high-value targets in complex electromagnetic environments.

[0003] To overcome the aforementioned limitations, multi-source heterogeneous sensor networking and collaborative search technology has emerged. This technology is no longer limited to single active detection, but deeply integrates non-cooperative detection of target communication radiation signals, passive localization, and other multi-dimensional feature information. By constructing a distributed sensing network, the system achieves a wide-area extension of the detection range in space, effectively eliminating single-station blind spots; in time, it utilizes the spatial diversity characteristics of multiple nodes to ensure continuous target acquisition from different perspectives. Especially in areas where multi-station capabilities overlap, the accuracy and robustness of state estimation are significantly improved through spatiotemporal data correlation and fusion processing, achieving "seamless" tracking of highly maneuverable targets.

[0004] Building upon this foundation, search resource optimization planning technology becomes a core element in enhancing system efficiency. Addressing the dynamically changing target trajectory and limited sensor resources, this technology establishes a scientific task allocation and path planning model. By scheduling the detection timing and beam pointing of each node, it not only avoids resource redundancy and conflicts but also achieves a dual leap in search efficiency and positioning accuracy even in complex interference environments. Summary of the Invention

[0005] This invention overcomes the shortcomings of existing technologies in network target search and provides a multi-means network-based specific target search task planning method and system, which is expected to solve the problems existing in the prior art.

[0006] To address the aforementioned technical problems, one aspect of the present invention provides a multi-method network-based method for planning specific target search tasks:

[0007] A multi-method network-based target search task planning method includes the following steps:

[0008] Build a knowledge base for search data;

[0009] Select a specific target to search for, enter the target information, and generate a search request;

[0010] Based on the search requirements and combined with knowledge base data, determine the search pattern;

[0011] According to the determined search pattern, plan multi-demand network search tasks and generate search tasks from search demands;

[0012] Execute search tasks, obtain search data, analyze the search data, and complete the search or adjust the search mode according to the analysis results;

[0013] When the search pattern needs to be changed, the step of determining the search pattern is re-executed, and subsequent steps are executed until the search is completed.

[0014] A further technical solution is that the construction of the search data knowledge base specifically includes the following steps:

[0015] Construct a target knowledge base, the content of which includes target basic knowledge, dynamic parameters, communication system, interference system, and frequently active area;

[0016] Construct a target feature library, which includes target feature types, feature parameters, feature occurrence frequency, and feature occurrence regions;

[0017] Construct a search method knowledge base, which includes basic information about the methods, deployment locations of the methods, the scope of the methods' capabilities, and the search modes of the methods;

[0018] Construct a knowledge base for key regions, which includes regional location, regional type, and regional importance level.

[0019] A further technical solution involves the following steps: Selecting a specific target, inputting target information, and generating a search request.

[0020] Select the specific target to search from the knowledge base;

[0021] Set the search time period and minimum search duration;

[0022] Set the search area for the target. If the area information cannot be obtained, you can leave the search area blank.

[0023] Generate search requests based on the input information.

[0024] A further technical solution involves determining the search pattern based on search requirements and knowledge base data, specifically including the following steps:

[0025] Retrieve search data over a period of time from databases and real-time memory data;

[0026] Retrieve knowledge data of the target from the knowledge base, filter the search data according to the target knowledge data, filter out the search data related to the target, and process the search data;

[0027] If the processed target search data can generate high-precision, high-time-efficiency target historical trajectory information, then the current search mode is determined to be the precise tracking search mode.

[0028] If the processed target search data cannot generate a continuous trajectory, the target activity is analyzed and the search mode is adjusted.

[0029] A further technical solution involves analyzing the target activity and adjusting the search mode, specifically including the following steps:

[0030] Acquire the target's location information over a period of time. If no target location information is available, generate a regular search area. The regular search area is set by the user, and the search mode is determined to be a wide-range search mode.

[0031] If location information exists, the target's activity area is predicted according to the target prediction algorithm to generate a search area; the search is judged according to the predicted search area. If it is greater than a preset threshold, the search mode is a large-area search mode, otherwise the search mode is a small-area search mode.

[0032] A further technical solution involves generating a search task from a search request, specifically including the following steps:

[0033] Define search parameters, which include search requirements, search methods, and search tasks;

[0034] Define the constraints for the task planning;

[0035] Planning search pattern strategies;

[0036] Plan the search tasks and output the final list of search tasks.

[0037] A further technical solution is that the planning and search pattern strategy includes any one or more of the following strategies;

[0038] A large-scale planning strategy generates two or more types of search tasks with large-scale search patterns to meet the needs of large-scale search.

[0039] In addition to planning and executing tasks with precise tracking and search patterns, the small-scale planning strategy should generate as many search tasks as possible while meeting the constraints.

[0040] Accurately track search planning strategies and generate search tasks using all means that meet the constraints of task planning.

[0041] A further technical solution is that the planning of the search task and the output of the final search task list specifically includes the following steps;

[0042] After completing the large-scale planning strategy, small-scale planning strategy, and precise tracking search planning strategy, and all the demand planning, the demand for the small-scale search mode is re-planned. The search methods that still have free time and meet the constraints and free time can be used to execute the demand are selected to generate new search tasks and add the tasks to the planned search task list.

[0043] After completing the secondary planning of the small-scale search mode requirements, the secondary planning of the large-scale search mode requirements is carried out. The search methods that still have idle time and meet the constraints and idle time can be used to execute the requirements are selected to generate new search tasks and add the tasks to the planned search task list.

[0044] After completing the secondary planning, the time for the search task in the precise tracking mode is increased. After the time is increased, the subsequent tasks are postponed. If the time limit is met, the time for the precise search task is adjusted and saved to the task list.

[0045] After adjusting the search time for the precise tracking mode, increase the search time for the small-scale search mode. After increasing the time, adjust the subsequent tasks. If the time limit is met, adjust the precise search task time and save it to the task list.

[0046] After adjusting the search time for small-scale search mode tasks, increase the time for large-scale search mode tasks. After increasing the time, adjust the subsequent tasks accordingly. If the time limit is met, adjust the time for precise search tasks and save it to the task list.

[0047] Output the final list of search tasks.

[0048] A further technical solution is that when the search mode needs to be changed, the step of determining the search mode is re-executed, and subsequent steps are executed until the search is completed, specifically including the following steps;

[0049] Access data from various sources, standardize the data, and store the results in the database;

[0050] Save the latest data to memory and clean up data that exceeds the time limit in real time;

[0051] The search data in real time is evaluated according to rules to determine whether the search pattern determination process can be restarted. If the conditions are met, the search pattern determination process is restarted.

[0052] A further technical solution is that the determination of whether the search mode determination process can be repeated is specifically determined by the following conditions: when any one of the following conditions is met, the search mode determination is repeated.

[0053] When searching in a broad search pattern, the specific target is found for the first time.

[0054] In a small-scale search mode, a specific target is searched continuously for a short period of time, forming a continuous track.

[0055] When searching in a small-range search mode, if the target is not found within a time exceeding the set target disappearance time threshold;

[0056] When searching in precise tracking mode, if the specific target is not found within the set time period;

[0057] When the target's movement trend has exceeded the search range of the current set of search methods in any search mode.

[0058] In another aspect, the present invention provides a multi-means network-based target search task planning system.

[0059] A multi-method network-based target search task planning system includes a data knowledge base, a task planning component, and a search execution component;

[0060] The data knowledge base is used to store search target information;

[0061] The task planning component is used to generate search requirements, determine search patterns, generate search tasks, determine whether search results meet the conditions for changing search patterns, and decide whether to output results or re-execute the steps for determining search patterns and subsequent steps.

[0062] The search execution component is used to perform search tasks.

[0063] Compared with existing technologies, this invention has at least the following beneficial effects: The task planning method for searching specific targets using a multi-means network provided by this invention divides the search for specific targets into three modes: large-scale search mode, small-scale positioning mode, and precise tracking mode. Different planning strategies are applied to these three modes, and conversion conditions are set between them. When these conditions are met, the search mode can be changed, allowing for dynamic task planning. This method models the capabilities of different means and categorizes these capabilities according to the three different search modes. By analyzing the situation of the search target and input external information, the required search mode is determined. Task planning is then performed according to the search mode, target information, and the capabilities of the means, forming a search plan, generating a search task, and supporting the means in the search. Real-time access to search situation data and dynamic planning and adjustment of the plan based on the situation data completes the search for specific targets, improving search accuracy and efficiency. Attached Figure Description

[0064] Figure 1 This is a schematic diagram of the overall process of Example 1;

[0065] Figure 2 This is a flowchart of step S3;

[0066] Figure 3 This is a flowchart of step S4. Detailed Implementation

[0067] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0068] Example 1.

[0069] A multi-method network-based target search task planning method, see [link to relevant documentation]. Figure 1 This includes the following steps:

[0070] S1: Build a knowledge base for search data;

[0071] In a further preferred embodiment, the construction of the search data knowledge base specifically includes the following steps:

[0072] S12: Construct a target knowledge base, the content of which includes target basic knowledge, dynamic parameters, communication system, interference system, and frequently active area;

[0073] S13: Construct a target feature library, which includes target feature types, feature parameters, feature occurrence counts, and feature occurrence regions;

[0074] S14: Construct a search method knowledge base, which includes basic information about the methods, deployment locations of the methods, capability range of the methods, and search modes of the methods;

[0075] S15: Construct a knowledge base for key regions, which includes regional location, regional type, and regional importance level.

[0076] S2: Select a specific target to search, enter the target information, and generate a search request;

[0077] In a further preferred embodiment, the step of selecting a specific target, inputting target information, and generating a search request specifically includes the following steps:

[0078] S21: Select the specific target to be searched from the knowledge base;

[0079] S22: Set the search time period (including search start time and search end time) and minimum search duration information;

[0080] S23: Set the search area for the target. If the area cannot be obtained, you can leave the search area blank.

[0081] S24: Generate search requirements based on the input information.

[0082] S3: Based on the search requirements and in conjunction with knowledge base data, determine the search pattern. See [link to relevant documentation]. Figure 2 ;

[0083] In a further preferred embodiment, determining the search pattern based on search requirements and knowledge base data specifically includes the following steps:

[0084] S31: Retrieve search data for a period of time from the database and real-time memory data;

[0085] S32: Obtain knowledge data of the target to be searched from the knowledge base, filter the search data according to the target knowledge data, filter out the search data related to the target, and process the search data, including data fusion, abnormal data removal, target historical trajectory generation, etc.

[0086] S33: If the processed target search data can generate high-precision, high-time-efficiency target historical trajectory information, then the current search mode is determined to be the precise tracking search mode.

[0087] S34: If the processed target search data cannot generate a continuous trajectory, analyze the target activity and adjust the search mode.

[0088] In a further preferred embodiment, the analysis of target activity and adjustment of the search mode specifically includes the following steps:

[0089] S341: Obtain the location information of the target over a period of time. If there is no target location information, generate a regular search area. The regular search area is set by the user and can be multiple areas. Set the search mode to a wide-range search mode.

[0090] S342: If location information exists, predict the target's activity area according to the target prediction algorithm to generate a search area; judge according to the predicted search area. If it is greater than the preset threshold, the search mode is a large-area search mode, otherwise the search mode is a small-area search mode.

[0091] S4: Plan multi-demand network search tasks according to the determined search pattern, and generate search tasks from search demands;

[0092] In a further preferred embodiment, generating a search task from a search request specifically includes the following steps:

[0093] S41: Define search parameters, including search requirements, search methods, and search tasks;

[0094]

[0095] In the formula, the search parameters include a general definition, specifically:

[0096] D represents the set of search requests;

[0097] R represents the set of search methods in the knowledge base;

[0098] RT refers to the type of search method;

[0099] O represents a specific set of targets in the knowledge base;

[0100] G represents geographic space;

[0101] SM stands for Search Pattern Set;

[0102] ST represents the set of search tasks.

[0103] The search requirements are specifically defined as follows:

[0104] d represents the search requirement;

[0105] o represents a specific target for the search needs;

[0106] ts represents the start time of the search time range for the demand;

[0107] te is the end time of the search time range; t is the minimum search duration.

[0108] 'a' represents the search area for the desired information;

[0109] sm is a search pattern where the demand is defined.

[0110] The search methods are specifically defined as follows:

[0111]

[0112]

[0113] In the formula, r represents the search method;

[0114] sml is a set of search patterns that can be executed by the search method, containing multiple search patterns sm;

[0115] c represents the search method and search scope;

[0116] rt indicates the type of search method.

[0117] The search task is specifically defined as follows:

[0118]

[0119] In the formula, st represents the search task;

[0120] ts is the task start time;

[0121] te represents the task completion time;

[0122] 'a' represents the search area for the search task;

[0123] r represents the means of executing the task;

[0124] sm is the task search mode;

[0125] d represents the source requirement of the task.

[0126] S42: Define the constraints for task planning;

[0127] For example, a search task originates from and is related to a search requirement, including that the start time of the search task is greater than or equal to the start time of the search requirement, the end time of the search task is less than or equal to the end time of the search requirement, the execution duration of the search task is greater than or equal to the minimum search duration of the search requirement, the search area of ​​the search task is the search area of ​​the search requirement, and the search pattern of the search task is the search pattern of the search requirement. These conditions are expressed by the following formula:

[0128]

[0129] In the formula, st represents the search task;

[0130] ts is the task start time;

[0131] te represents the task completion time;

[0132] 'a' represents the search area for the search task;

[0133] sm is the task search mode;

[0134] ST represents the set of search tasks;

[0135] d represents the source requirement of the task.

[0136] by For example, this formula means that the start time of the task must be greater than the start time of the task's source requirement;

[0137] The formula indicates that the task's end time must be less than the end time required by the task's source.

[0138] The formula indicates that the execution time of the search task is greater than or equal to the minimum search time required for the search.

[0139] The formula indicates that the search area for the search task is the search area for the search requirements;

[0140] The formula indicates that the search pattern of the search task is the search pattern of the search requirement;

[0141] Search methods can only search for requests with the same search pattern, that is:

[0142]

[0143] In the formula, st represents the search task;

[0144] sm is the task search mode;

[0145] r represents the means of executing the task;

[0146] sml is a set of search patterns that can be executed by the search method, containing multiple search patterns sm;

[0147] ST represents the set of search tasks;

[0148] d represents the source requirement of the task.

[0149] The search method can only search areas within the search range or intersecting areas, that is:

[0150]

[0151] In the formula, st represents the search task;

[0152] 'a' represents the search area for the search task;

[0153] r represents the means of executing the task;

[0154] c represents the search method and search scope;

[0155] ST represents the set of search tasks;

[0156] Area is the area where intersections are calculated.

[0157] Only one task can be executed at a time using the same search method, and there are intervals between tasks, i.e.:

[0158]

[0159]

[0160] In the formula, for example, The formula represents the total time window occupied by performing these two tasks using the same search method;

[0161] Indicates the start time and end time of the task;

[0162] st represents the search task;

[0163] ts is the task start time;

[0164] te represents the task completion time;

[0165] r represents the means of executing the task;

[0166] ST represents the set of search tasks;

[0167] Max is used to calculate the maximum value.

[0168] S43: Planning search pattern strategies, see [link / reference] Figure 3 ;

[0169] The planning search pattern strategy includes any one or more of the following strategies;

[0170] Large-scale planning strategy: To meet the needs of large-scale search, generate two or more types of search tasks with large-scale search patterns;

[0171] Small-scale planning strategy: In addition to planning and executing tasks with precise tracking search patterns, for small-scale search patterns, as many search tasks as possible should be generated while meeting the constraints.

[0172] Precise tracking search planning strategy: Generate search tasks using all means that meet the task planning constraints.

[0173] For example, the requirements for the precise tracking mode are planned one by one. The search methods that can be searched are selected according to the constraints. The idle time of the search methods and the time arrangement of the requirements are combined. The time is set according to the minimum search time of the requirements and the strategy of executing the task as soon as possible. The search task for the target of the requirement is generated by the search method and the task is added to the planned search task list.

[0174] Plan the requirements for small-scale search patterns, plan each requirement in the requirement list one by one, filter out the search methods that can be searched based on constraints, and combine the idle time of the search methods with the time arrangement of the requirements. Set the time according to the minimum search time of the requirements and the strategy of executing the task as soon as possible. Calculate the coverage of the selected methods and the search area, select the search method with the highest coverage, generate the search task for the requirement target for that search method, and add the task to the planned search task list.

[0175] Planning is done for the demand of a wide range of search patterns. Each demand in the demand list is planned one by one. Search methods that can be searched are selected based on constraints. The idle time of search methods and the time arrangement of demand are combined. The time is set according to the minimum search time of demand and the strategy of executing tasks as soon as possible. The coverage of the selected methods and search area is calculated. The search method with the highest coverage is selected, and the search task for the demand target is generated by the search method. The task is added to the planned search task list.

[0176] S44: Plan the search tasks and output the final list of search tasks.

[0177] In a further preferred embodiment, the planning of the search task and the output of the final search task list specifically includes the following steps;

[0178] S441: After completing the large-scale planning strategy, small-scale planning strategy and precise tracking search planning strategy, and all the demand planning, the demand for the small-scale search mode is re-planned. The search method that still has free time and meets the constraints and free time can be used to execute the demand is selected to generate a new search task and add the task to the planned search task list.

[0179] S442: After completing the secondary planning of the small-scale search mode requirements, perform secondary planning of the large-scale search mode requirements, select the search methods that still have idle time and meet the constraints and idle time to execute the requirements, generate new search tasks, and add the tasks to the planned search task list.

[0180] S443: After completing the secondary planning, increase the time of the search task in the precise tracking mode. After increasing the time, adjust the subsequent tasks. If the time limit is met, adjust the time of the precise search task and save it to the task list.

[0181] S444: After completing the time adjustment for the search task in the precise tracking mode, increase the time for the search task in the small-scale search mode. After increasing the time, adjust the subsequent tasks. If the time limit is met, adjust the time for the small-scale search task and save it to the task list.

[0182] S445: After adjusting the search time for the small-scale search mode, increase the time for the large-scale search mode. After increasing the time, shift the subsequent tasks to the next task. If the time limit is met, adjust the time for the large-scale search task and save it to the task list.

[0183] S446: Output the final list of search tasks.

[0184] S5: Execute the search task, obtain search data, analyze the search data, and complete the search or adjust the search mode according to the analysis results;

[0185] S6: When the search mode needs to be changed, re-execute step S3 to determine the search mode, and then execute subsequent steps until the search is completed.

[0186] In a further preferred embodiment, when the search mode needs to be changed, the step of determining the search mode is re-executed, and subsequent steps are executed until the search is completed. This specifically includes the following steps:

[0187] S61: Access data searched through various means, standardize the data, and store the processing results in the database;

[0188] S62: Saves the latest data to memory and cleans up data that exceeds the time limit in real time;

[0189] S63: Perform rule evaluation on real-time search data to determine whether the search pattern determination process can be restarted. If the conditions are met, the search pattern determination process can be restarted.

[0190] The determination of whether the search pattern determination process can be repeated is exemplified by the following specific conditions: the search pattern determination is repeated when any one of the following conditions is met.

[0191] When searching in a broad search pattern, the specific target is found for the first time.

[0192] In a small-scale search mode, a specific target is searched continuously for a short period of time, forming a continuous track.

[0193] When searching in a small-range search mode, if the target is not found within a time exceeding the set target disappearance time threshold;

[0194] When searching in precise tracking mode, if the specific target is not found within the set time period;

[0195] When the target's movement trend has exceeded the search range of the current set of search methods in any search mode.

[0196] Example 2.

[0197] A multi-method network-based target search task planning system includes a data knowledge base, a task planning component, and a search execution component;

[0198] The data knowledge base is used to store search target information;

[0199] The task planning component is used to generate search requirements, determine search patterns, generate search tasks, determine whether search results meet the conditions for changing search patterns, and decide whether to output results or re-execute the steps for determining search patterns and subsequent steps.

[0200] The search execution component is used to perform search tasks.

[0201] The second embodiment is used to execute the multi-means networking specific target search task planning method described in the first embodiment.

[0202] Although the invention has been described herein with reference to illustrative embodiments, it should be understood that many other modifications and implementations can be devised by those skilled in the art, which will fall within the scope and spirit of the principles disclosed herein. More specifically, various variations and modifications can be made to the components and / or layout of the subject matter combination within the scope of this disclosure. Besides variations and modifications to the components and / or layout, other uses will be apparent to those skilled in the art.

Claims

1. A multi-method network-based target search task planning method, characterized in that, Includes the following steps: Build a knowledge base for search data; Select a specific target to search for, enter the target information, and generate a search request; Based on the search requirements and combined with knowledge base data, determine the search pattern; According to the determined search pattern, plan multi-demand network search tasks and generate search tasks from search demands; Execute search tasks, obtain search data, analyze the search data, and complete the search or adjust the search mode according to the analysis results; When the search pattern needs to be changed, the step of determining the search pattern is re-executed, and subsequent steps are executed until the search is completed.

2. The multi-means networking specific target search task planning method as described in claim 1, characterized in that, The construction of the search data knowledge base specifically includes the following steps: Construct a target knowledge base, the content of which includes target basic knowledge, dynamic parameters, communication system, interference system, and frequently active area; Construct a target feature library, which includes target feature types, feature parameters, feature occurrence frequency, and feature occurrence regions; Construct a search method knowledge base, which includes basic information about the methods, deployment locations of the methods, capability range of the methods, and search modes of the methods; Construct a knowledge base for key regions, which includes regional location, regional type, and regional importance level.

3. The multi-means networking specific target search task planning method as described in claim 1, characterized in that, The process of selecting a specific target, inputting target information, and generating a search request includes the following steps: Select the specific target to search from the knowledge base; Set the search time period and minimum search duration; Set the search area for the target. If the area information cannot be obtained, you can leave the search area unset. Generate search requests based on the input information.

4. The multi-means networking specific target search task planning method as described in claim 1, characterized in that, The process of determining the search pattern based on search requirements and knowledge base data includes the following steps: Retrieve search data over a period of time from databases and real-time memory data; Retrieve knowledge data of the target from the knowledge base, filter the search data according to the target knowledge data, filter out the search data related to the target, and process the search data; If the processed target search data can generate high-precision, high-time-efficiency target historical trajectory information, then the current search mode is determined to be the precise tracking search mode. If the processed target search data cannot generate a continuous trajectory, the target activity is analyzed and the search mode is adjusted.

5. The multi-means networking specific target search task planning method as described in claim 4, characterized in that, The analysis of target activity and adjustment of the search mode specifically includes the following steps: Acquire the target's location information over a period of time. If no target location information is available, generate a regular search area. The regular search area is set by the user, and the search mode is determined to be a wide-range search mode. If location information exists, the target's activity area is predicted according to the target prediction algorithm to generate a search area; the search is judged according to the predicted search area. If it is greater than a preset threshold, the search mode is a large-area search mode, otherwise the search mode is a small-area search mode.

6. The multi-means networking specific target search task planning method as described in claim 1, characterized in that, The process of generating a search task from a search request specifically includes the following steps: Define search parameters, including search requirements, search methods, and search tasks; Define the constraints for the task planning; Planning search pattern strategies; Plan the search tasks and output the final list of search tasks.

7. The multi-means networking specific target search task planning method as described in claim 6, characterized in that, The planning search pattern strategy includes any one or more of the following strategies; A large-scale planning strategy generates two or more types of search tasks with large-scale search patterns to meet the needs of large-scale search. In addition to planning and executing tasks with precise tracking and search patterns, the small-scale planning strategy aims to generate as many search tasks as possible while meeting the constraints of the small-scale search pattern. Accurately track search planning strategies and generate search tasks using all means that meet the constraints of task planning.

8. The multi-means networking specific target search task planning method as described in claim 7, characterized in that, The planning and search task, and the output of the final search task list, specifically includes the following steps; After completing the large-scale planning strategy, small-scale planning strategy, and precise tracking search planning strategy, and all the demand planning, the demand for the small-scale search mode is re-planned. The search methods that still have free time and meet the constraints and free time can be used to execute the demand are selected to generate new search tasks and add the tasks to the planned search task list. After completing the secondary planning of the small-scale search mode requirements, the secondary planning of the large-scale search mode requirements is carried out. The search methods that still have idle time and meet the constraints and idle time can be used to execute the requirements are selected to generate new search tasks and add the tasks to the planned search task list. After completing the secondary planning, the time for the search task in the precise tracking mode is increased. After the time is increased, the subsequent tasks are postponed. If the time limit is met, the time for the precise search task is adjusted and saved to the task list. After adjusting the search time for the precise tracking mode, increase the search time for the small-scale search mode. After increasing the time, adjust the subsequent tasks. If the time limit is met, adjust the precise search task time and save it to the task list. After adjusting the search time for small-scale search mode tasks, increase the time for large-scale search mode tasks. After increasing the time, shift the subsequent tasks to the next task. If the time limit is met, adjust the time for the precise search task and save it to the task list. Output the final list of search tasks.

9. The multi-means networking specific target search task planning method as described in claim 1, characterized in that, When the search mode needs to be changed, the step of determining the search mode is re-executed, and subsequent steps are executed until the search is completed. Specifically, this includes the following steps: Access data from various sources, standardize the data, and store the results in the database; Save the latest data to memory and clean up data that exceeds the time limit in real time; The search data in real time is evaluated according to rules to determine whether the search pattern determination process should be restarted. If the conditions are met, the search pattern determination process is restarted.

10. The multi-means networking specific target search task planning method as described in claim 9, characterized in that, The specific condition for determining whether to re-perform the search mode determination process is that the search mode determination is re-performed when any of the following conditions are met; When searching in a broad search pattern, the specific target is found for the first time. In a small-scale search mode, a specific target is searched continuously for a short period of time, forming a continuous track. When searching in a small-range search mode, if the target is not found within a time exceeding the set target disappearance time threshold; When searching in precise tracking mode, if the specific target is not found within the set time period; When the target's movement trend has exceeded the search range of the current set of search methods in any search mode.

11. A multi-method network-based target search task planning system, characterized in that, This includes a data knowledge base, task planning components, and search execution components; The data knowledge base is used to store search target information; The task planning component is used to generate search requirements, determine search patterns, generate search tasks, determine whether search results meet the conditions for changing search patterns, and decide whether to output results or re-execute the steps for determining search patterns and subsequent steps. The search execution component is used to perform search tasks.

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