An unmanned ship cluster dynamic formation efficient cooperation method based on a multi-core central processing unit

Abstract

The application relates to the technical field of unmanned ship clusters, in particular to an unmanned ship cluster dynamic formation efficient cooperation method based on a multi-core central processing unit, which comprises the following steps: acquiring position information and operation directions of all unmanned ships, judging whether formation conditions are met, dispatching to a multi-core control flow, marking direction consistency deviation ships, identifying path intersection interference points, outputting direction change schemes, generating navigation control execution packages, and forming a dynamic formation instruction set. The application extracts the space interval between ships, the direction angle and the speed to construct classification standards, realizes grouping and regulation mapping, identifies path interference zones and aggregates conflict points, extracts adjustable intervals to generate direction change schemes, matches regulation parameters according to reconstruction priorities, forms control instructions containing heading and speed adjustment, synchronously executes the control instructions in combination with a multi-core structure, and improves scheduling accuracy and dynamic response accuracy.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned ship cluster, and in particular to an efficient coordination method for dynamic formation of unmanned ship cluster based on multi-core central processor. BACKGROUND

[0002] The technical field of unmanned ship cluster includes multiple technical directions, mainly involving autonomous control, cooperative formation, navigation obstacle avoidance, task allocation, and other aspects of unmanned ships. The core technologies include communication and cooperation between unmanned ships, cluster scheduling, path planning, sensor fusion, dynamic obstacle avoidance, and navigation control. By gathering multiple unmanned ships together and realizing cooperative operation, unmanned ship cluster technology can be applied to ocean monitoring, environmental protection, search and rescue, and other fields. In addition, this technology also covers efficient computing technology based on multi-core central processor, so that each ship in the cluster can work cooperatively in complex environments to achieve the predetermined task goal.

[0003] Among them, the efficient coordination method for dynamic formation of unmanned ship cluster based on multi-core central processor refers to the efficient cooperative control and formation adjustment of unmanned ship cluster by using multi-core processor. Mainly aiming at the formation and cooperative control problem of unmanned ship cluster in dynamic environment, a computing method based on multi-core central processor is proposed. Specifically, by distributing multiple computing tasks in the multi-core central processor, efficient computing and dynamic formation adjustment of each unmanned ship in the cluster are realized. The solution includes coordinating the path planning and task execution of each unmanned ship through the multi-core central processor, while ensuring real-time data sharing and execution strategy among cluster members, so as to realize the rapid response and efficient coordination of unmanned ship cluster in dynamic environment.

[0004] The existing technology focuses on task allocation and path control under multi-core architecture, but in the high-speed cooperative operation scene of ship cluster, it fails to centrally identify and respond to direction deviation, speed fluctuation, and path intersection position, lacks structured extraction means for trajectory overlap density, and is easy to form scheduling conflicts in path adjacent intersection areas, leading to path interference spread and regulation instability in task promotion, affecting the coherence of instruction response and path safety control ability in dynamic environment, and limiting the flexibility and accuracy of interference local adjustment in cluster formation process. SUMMARY

[0005] In order to solve the technical problems existing in the prior art, the embodiments of the present application provide an efficient coordination method for dynamic formation of unmanned ship cluster based on multi-core central processor. The technical solution is as follows:

[0006] An efficient coordination method for dynamic formation of unmanned ship cluster based on multi-core central processor, comprising the following steps:

[0007] S1: Obtain the position and operation direction of the unmanned ship in the sea area, perform clustering processing according to the spatial interval and the difference value of the direction angle, call the multi-core central processor to allocate the channel to dispatch the number of the task intensive group, construct the task navigation relationship mapping and store it in the control module, and generate a multi-core instruction dispatching basic diagram;

[0008] S2: Call the grouping information in the multi-core instruction dispatching basic diagram, extract the direction angle and speed value of each group of ships, compare whether the direction included angle and the speed difference value between them exceed the average deviation range, and mark the inconsistent ones with numbers, generate a parallel scheduling cooperation conflict label set;

[0009] S3: Call the ship number in the parallel scheduling cooperation conflict label set, extract the path line and navigation target position, judge the path intersection overlap interval, extract the trajectory concentrated paragraph according to the angle coincidence density, construct the conflict concentrated area, and generate a key conflict path convergence paragraph set;

[0010] S4: According to the intersection area in the key conflict path convergence paragraph set, extract the direction line of the related ships, judge whether there is a heading turning area, reconstruct the trajectory according to the avoidance direction, rearrange the task order and output the path adjustment suggestion, and generate a conflict path intervention instruction list.

[0011] As a further scheme of the application, the multi-core instruction dispatching basic diagram includes ship grouping number, core control channel identifier, task intensity classification, the parallel scheduling cooperation conflict label set includes direction deviation mark, speed incoherence mark, cooperation conflict number, the key conflict path convergence paragraph set includes path intersection point position, interference segment angle sequence, continuous conflict paragraph number, and the conflict path intervention instruction list includes direction change option, path reconstruction priority sequence and adjustment interval range.

[0012] As a further scheme of the application, the acquisition step of the multi-core instruction dispatching basic diagram is:

[0013] S101: Obtain the position and operation direction information of all unmanned ships in the sea area, extract the spatial coordinates and navigation direction angle of each ship, compare the spatial interval value and the direction included angle between any two ships, judge whether they are simultaneously less than the distance proximity threshold and the direction consistency threshold, and the ships meeting the conditions are classified into the same aggregation identification set to establish a formation aggregation identification mark matrix;

[0014] S102: Based on the ship set with all marks as aggregation state in the formation aggregation identification mark matrix, extract the member quantity and task point coordinate density range of each aggregation set, call the allocation channel of the corresponding core in the multi-core central processor according to the aggregation size and the task space overlap degree, perform multi-core dispatch on the ship set according to the task aggregation intensity, and generate a multi-core task docking number table;

[0015] S103: According to the core number and ship set mapping relationship in the multi-core task docking number table, the ship list in each number is correspondingly integrated with the task area number, the path target number and the actual number of each ship in each core number are extracted, a bidirectional matching comparison list of task numbers and ship numbers is established, and a multi-core instruction dispatching basic block diagram is generated.

[0016] As a further scheme of the present application, the process of establishing the formation aggregation identification marker matrix is specifically:

[0017] Based on the spatial coordinates of each ship, the Euclidean distance between the geometric center points of any two ships is calculated as the spatial interval value;

[0018] Based on the sailing direction angle of each ship, the included angle between the sailing direction vectors of any two ships is calculated as the direction included angle;

[0019] The distance proximity threshold is a safety distance dynamically set according to the unmanned ship hull size, the current sailing speed and the hydrological conditions of the sea area;

[0020] The direction consistency threshold is an allowed deviation angle set according to the requirement of direction coordination of the cluster in executing the task;

[0021] The formation aggregation identification marker matrix is a square matrix, the number of rows and columns of the square matrix is equal to the number of all unmanned ships in the sea area, and the matrix elements are used to mark whether any two ships meet the condition of belonging to the same aggregation identification set.

[0022] As a further scheme of the present application, the acquisition step of the parallel scheduling cooperation conflict label set is:

[0023] S201: Call the ship grouping result in the multi-core instruction dispatching basic block diagram, extract the heading angle and moving speed information of all ships in each group, compare the direction angles between each two ships, calculate the relative included angle value and record the ship numbers exceeding the direction consistency deviation threshold, establish a direction difference identification list according to the numbers, and generate a heading deviation number list;

[0024] S202: According to all the marked ship numbers in the heading deviation number list, further extract the speed information of the corresponding ships and judge the difference value with the average speed value in the group, select the ship numbers whose speed change amplitude exceeds the speed stability reference range, and combine them with the direction deviation number set to form a new set, generate a motion characteristic abnormal number set;

[0025] S203: Call all the ship numbers in the motion characteristic anomaly number set, classify the groups corresponding to each number, put the ship numbers with motion characteristic abnormal behavior in each group into the conflict label table, and construct the corresponding conflict type label item according to the ship number. Build a number and conflict type two-item mapping table, generate a parallel scheduling cooperation conflict label set.

[0026] As a further scheme of the application, the acquisition step of the key conflict path convergence paragraph set is:

[0027] S301: Call all the ship numbers labeled in the parallel scheduling cooperation conflict label set, extract the task target point and current path coordinate sequence of the corresponding ship, construct a complete path trajectory set according to the geographic coordinates of the path point and the navigation target position, and combine and aggregate all the trajectory sets to establish a path trajectory coordinate summary list;

[0028] S302: According to the path line combination recorded in the path trajectory coordinate summary list, detect the intersection points of any two trajectories, extract the overlapping area of the path segment and identify the number of intersection points and their interval distribution, extract the angle information of each intersection point in the overlapping segment and judge the direction consistency, jointly evaluate the angle coincidence and spatial concentration value, and generate a path intersection concentration trend value group;

[0029] S303: Call all the trend values in the path intersection concentration trend value group, mark the path paragraph whose continuous concentration degree exceeds the concentration degree identification benchmark, extract the ship number corresponding to the path line and form a one-to-one correspondence with its trajectory paragraph, construct a continuous intersection segment set with associated numbers, and generate a key conflict path convergence paragraph set.

[0030] As a further scheme of the application, the process of jointly evaluating the angle coincidence and spatial concentration value to generate the path intersection concentration trend value group is:

[0031] For any two intersecting path trajectory lines, extract the tangent vector of each path trajectory line at the intersection point, calculate the included angle between the tangent vectors, and take the cosine value of the included angle as the angle coincidence;

[0032] Divide the geographical space covered by the path trajectory coordinate summary list into a preset grid area, count the number of path intersection points contained in each grid area, and take the number of path intersection points as the spatial concentration value;

[0033] For each grid area, weight the sum of the spatial concentration value and the arithmetic mean of the angle coincidence of all intersection points in the area, and the result is the trend value corresponding to the grid area in the path intersection concentration trend value group.

[0034] The concentration identification benchmark is dynamically determined according to the statistical distribution characteristics of all trend values in the path intersection concentration trend value group.

[0035] As a further scheme of the present application, the step of obtaining the conflict path intervention instruction list is:

[0036] S401: According to all conflict point coordinates in the key conflict path convergence paragraph set, extract the current direction line and trajectory segment angle sequence of the corresponding ship of each conflict point, calculate the included angle between each group of paths and compare the difference, judge whether the angle difference is lower than the path convergence angle judgment benchmark, obtain the path set that meets the angle close condition, and generate a direction parallel path set;

[0037] S402: Call the path number recorded in the direction parallel path set, extract the current spatial position and turning interval information of the corresponding ship of each path, calculate the intersection ratio between the turning angle operable range and the current path density value, filter the path number with the intersection ratio in the adjustable interval, and rearrange the operable path according to the path number, generate a direction change sorting list;

[0038] S403: Based on the path number set rearranged in the direction change sorting list, extract the change angle information and actual path node index of the adjustable direction of each path, bind the change direction and path number one by one to form a direction adjustment combination, construct a pairing list of ship number and adjustment direction, output all combination contents and unified number identification, and generate a conflict path intervention instruction list.

[0039] As a further scheme of the present application, the method further comprises:

[0040] S5: Call the adjustment content in the conflict path intervention instruction list, match the ship number and the original path control state, construct a direction and speed update instruction package, write into a task transmission channel, and output according to the core number, to generate a cluster dynamic formation execution instruction set;

[0041] The cluster dynamic formation execution instruction set comprises a control instruction package number, a heading update parameter, and a speed adjustment data.

[0042] As a further scheme of the present application, the step of obtaining the cluster dynamic formation execution instruction set is:

[0043] S501: Call the sorted adjustment items in the conflict path intervention instruction list, extract the ship number and heading change instruction value bound by each adjustment item, match the corresponding path control state according to the ship number, judge whether the heading value in the path control state and the adjustment item exist conflict position, record the control state mark value corresponding to the matched successful item, and generate a path state matching result set;

[0044] S502: According to the control state mark value in the path state matching result set, extract the ship number and current speed information bound by each mark value, combine the target speed value in the previous stage adjustment item, calculate the speed deviation value between the current speed and the adjustment speed, and compare and screen the deviation value with the allowed adjustment amplitude reference, retain the ship number and corresponding speed within the allowed adjustment range, and generate an instruction integration data list;

[0045] S503: Call all ship numbers and updated control items recorded in the instruction integration data list, extract the heading adjustment value and speed update value, group and reorganize them according to the multi-core processor core number, build a navigation control execution data structure package, and write it into the multi-core task buffer channel according to the number order, and generate a cluster dynamic formation execution instruction set.

[0046] The technical scheme provided by the embodiment of the application has at least the following beneficial effects:

[0047] In the application, the spatial interval between ships, the direction angle and the speed information are extracted to construct a classification standard, the efficient mapping of ship grouping and regulation relationship is realized, the angle coincidence density is recognized to identify the path interference section and aggregate the conflict point position, the adjustable interval is extracted to construct the direction change combination scheme, the regulation parameters are matched according to the path reconstruction priority, the control instruction containing the heading and speed adjustment content is formed, the multi-core structure is synchronized to execute, the task scheduling accuracy is improved, the path cross interference is quickly decoupled, the cluster internal coordination action is stably connected, and the precision control of the path intervention pertinence and dynamic response is effectively enhanced. BRIEF DESCRIPTION OF DRAWINGS

[0048] Figure 1 The method flowchart of the application is provided;

[0049] Figure 2 The acquisition flowchart of the multi-core instruction dispatching basic block diagram of the application is provided;

[0050] Figure 3 The acquisition flowchart of the parallel scheduling cooperation conflict label set of the application is provided;

[0051] Figure 4 The acquisition flowchart of the key conflict path gathering paragraph set of the application is provided;

[0052] Figure 5Flow chart for acquiring conflict path intervention instruction list of the present application;

[0053] Figure 6 Flow chart for acquiring cluster dynamic formation execution instruction set of the present application. DETAILED DESCRIPTION

[0054] The technical solutions in the present application will be described below with reference to the drawings.

[0055] In the embodiments of the present application, the words such as "example", "for example" are used to represent as an example, illustration or description. Any embodiment or design scheme described as "example" in the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. In fact, the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present application, the meaning expressed by "and / or" can be both, or can be one of the two options.

[0056] In the embodiments of the present application, "image" and "picture" can be used interchangeably at times. It should be pointed out that when the distinction is not emphasized, the meanings expressed are consistent. "Of", "corresponding" and "corresponding" can be used interchangeably at times. It should be pointed out that when the distinction is not emphasized, the meanings expressed are consistent.

[0057] In the embodiments of the present application, sometimes the subscript such as W1 can be written in the form of non-subscript such as W1. When the distinction is not emphasized, the meanings expressed are consistent.

[0058] In order to make the technical problems, technical solutions and advantages to be solved by the present application more clear, the following will be described in detail with reference to the drawings and specific embodiments.

[0059] Please refer to Figure 1 The present application provides a technical solution: a kind of unmanned ship cluster dynamic formation efficient cooperation method based on multi-core central processing unit, comprising the following steps:

[0060] S1: the position and operation direction information of all unmanned ships in sea area are acquired, whether the space interval and direction angle difference value between ships meet the formation gathering condition is judged, each core distribution channel in multi-core central processing unit is called, ship set is dispatched according to task density and is distributed to each core control process, navigation task and number mapping list between each region is constructed, and multi-core instruction dispatching basic block diagram is generated;

[0061] S2: Call the ship grouping result in the multi-core instruction dispatching basic framework, extract the heading angle and moving speed information of each ship in the group, compare each ship with the mean value range of the group according to the direction angle change range and the speed increase and decrease amplitude, mark the corresponding ship number according to whether the direction repulsion or speed inconsistency occurs, construct the direction consistency deviation identification table corresponding to all numbers, and generate the parallel scheduling cooperation conflict label set;

[0062] S3: Call the ship number marked in the parallel scheduling cooperation conflict label set, extract the coordinate trajectory of the navigation target and path line, identify whether the paths of each ship overlap in space, filter the paragraphs in the track set as intersection interference points by extracting the angle coincidence density between the conflict intersection points, and generate the key conflict path convergence paragraph set according to the degree of continuous distribution of the interference points.

[0063]

[0064] S5: Call the sorted adjustment item in the conflict path intervention instruction list, match the current path control state according to the ship number, call the core synchronization construction function in the multi-core central processor execution structure, extract the heading adjustment line and speed update value in the instruction content, combine all the contents into a navigation control execution package, and write it into the task issuing buffer channel to generate the cluster dynamic formation execution instruction set.

[0065] The multi-core instruction dispatching basic framework includes ship grouping number, core control channel identification, task intensity classification, the parallel scheduling cooperation conflict label set includes direction deviation mark, speed inconsistency mark, cooperation conflict number, the key conflict path convergence paragraph set includes path intersection point position, interference segment angle sequence, continuous conflict paragraph number, the conflict path intervention instruction list includes direction change option, path reconstruction priority sequence, adjustment interval range, and the cluster dynamic formation execution instruction set includes control instruction package number, heading update parameter, and speed adjustment data.

[0066] Please refer to Figure 2 The acquisition steps of the multi-core instruction dispatching basic framework are as follows:

[0067] ​S101: Obtain the position and operation direction information of all unmanned ships in the sea area, extract the spatial coordinates and sailing direction angle of each ship, compare the spatial interval value and direction angle between any two ships, judge whether they are simultaneously less than the distance proximity threshold and the direction consistency threshold, and the ships meeting the conditions are classified into the same aggregation identification set to establish a formation aggregation identification marker matrix;

[0068] The position and operation direction information uploaded by all four unmanned ships in the sea area in real time is obtained, specifically, the two-dimensional spatial coordinates (x, y) and the sailing direction angle θ with the north direction as 0 degrees of each ship are extracted, and the information is shown in Table 1. The spatial interval value and direction angle between any two ships, i.e., unmanned ship i and unmanned ship j, are compared and processed. Based on the spatial coordinates of each ship and , the Euclidean distance between the geometric centers of the two ships is calculated to obtain the spatial interval value, and based on the sailing direction angle of each ship and , the angle between the sailing direction vectors of the two ships is calculated to obtain the direction angle, and it is judged whether is less than the distance proximity threshold and is less than the direction consistency threshold . The distance proximity threshold is a safety distance dynamically set according to the ship body size, current sailing speed and hydrological conditions of the sea area. The specific setting process is as follows: the basic value is 2 times the average length of the ship body, i.e. , the speed additional value is the average speed value of the current formation, i.e. knots, and each knot of speed increases the safety distance by 1.5 m, and the additional value is , the hydrological additional value is increased by 5 m when the wave height is 1-2 m and 10 m when the wave height is greater than 2 m according to the real-time monitoring of the sea wave height, and the current sea area wave height is 1.5 m, so the additional value is , The direction consistency threshold is the allowed deviation angle set according to the requirement of heading coordination of the cluster for the sea patrol task. Through statistical analysis of 1000 historical patrol task data, it is found that when the heading angle is less than 8 degrees, the formation coordination efficiency is higher than 95%, so the value is set to . Taking unmanned ship 1 and unmanned ship 2 as an example, the spatial interval value is , and the direction angle is , since and The unmanned ship 1 and the unmanned ship 2 meet the aggregation condition, all ships meeting the condition are classified into the same aggregation identification set, the aggregation set is identified as {unmanned ship 1, unmanned ship 2, unmanned ship 4}, the unmanned ship 3 is independent, and a 4x4 formation aggregation identification marking matrix is established accordingly If the unmanned ship i and the unmanned ship j meet the aggregation condition, the matrix element and is marked as 1, otherwise, it is marked as 0, and the diagonal element is marked as 1, thereby generating the formation aggregation identification marking matrix.

[0069] Table 1: Initial state information table of unmanned ships

[0070] Unmanned ship number Spatial coordinate x (m) Spatial coordinate y (m) Heading direction angle (°) Navigation speed (knots) 1 100 50 45 10.0 2 120 70 50 10.5 3 400 300 120 12.0 4 110 85 48 9.8

[0071] As shown in Table 1, the table records the running state parameters of the 4 unmanned ships in the sea area at the initial moment, providing a data basis for subsequent formation identification and conflict detection.

[0072] S102: Based on the ship set marked as an aggregation state in the formation aggregation identification marking matrix, the number of members in each aggregation set and the coordinate density range of the task point are extracted, and according to the aggregation size and the task space overlap degree, the corresponding core of the multi-core central processor is called to dispatch the channel, and the multi-core core task docking number table is generated according to the task aggregation intensity.

[0073] Based on the ship set marked as an aggregation state in the formation aggregation identification marking matrix, that is, {unmanned ship 1, unmanned ship 2, unmanned ship 4} and the independent {unmanned ship 3}, the number of members in each aggregation set and the coordinate density range of the task point are extracted, wherein the number of members in the first aggregation set is 3, the coordinate range of the task area A is (80, 40) to (160, 120), and the area contains 15 task points, and the task point coordinate density is calculated as per square meter, the number of members in the second aggregation set is 1, the coordinate range of the task area B is (380, 280) to (420, 320), and the area contains 3 task points, and the coordinate density is per square meter, according to the aggregation size and the task space overlap degree, the corresponding core of a multi-core central processor with 4 cores is called to dispatch the channel, and a task aggregation intensity index is set, wherein is the number of ships in the set, is the task point coordinate density, the aggregation intensity of the first group is calculated as , and the aggregation intensity of the second group is , and the aggregation intensity values are sorted from high to low, that is and are sequentially assigned to the processor cores with consecutive numbers, that is, the first group {unmanned ship 1, unmanned ship 2, unmanned ship 4} is assigned to core 1, and the second group {unmanned ship 3} is assigned to core 2. The ship set is dispatched to multiple cores according to the task aggregation strength, and a multi-core task docking number table is generated.

[0074] S103: According to the core number and ship set mapping relationship in the multi-core task docking number table, the ship list and task area number in each number are correspondingly integrated, the path target number and actual number of each ship in each core number are extracted, a bidirectional matching comparison list of task number and ship number is established, and a multi-core instruction dispatching basic block diagram is generated.

[0075] According to the core number and ship set mapping relationship established in the multi-core task docking number table, that is, core 1 corresponds to {unmanned ship 1, unmanned ship 2, unmanned ship 4}, and core 2 corresponds to {unmanned ship 3}, the ship list and task area number in each number are correspondingly integrated, the task of core 1 is specified as task area A, and the task of core 2 is specified as task area B. The path target number and actual number of each ship in each core number are extracted, for example, in core 1, the path target sequence number of unmanned ship 1 is P101, P102, P103, the path target sequence number of unmanned ship 2 is P201, P202, and the path target sequence number of unmanned ship 4 is P401, P402, P403. These path target numbers are bound to the actual identification numbers (ID1, ID2, ID4) of the ships, a bidirectional matching comparison list of task number and ship number is established, which clearly records the complete corresponding relationship of "core 1-task area A-{ID1: [P101, P102, P103], ID2: [P201, P202], ID4: [P401, P402, P403]}" and "core 2-task area B-{ID3: [P301, P302]}", and a multi-core instruction dispatching basic block diagram is generated.

[0076] Please refer to Figure 3 The acquisition step of the parallel scheduling cooperation conflict label set is:

[0077] S201: Call the ship grouping result in the multi-core instruction dispatching basic block diagram, extract the heading angle and moving speed information of all ships in each group, compare the direction angles between each ship, calculate the relative included angle value, and record the ship number that exceeds the direction consistency deviation threshold. According to the number, a direction difference identification list is established, and a heading deviation number list is generated.

[0078] The ship grouping result in the multi-core instruction dispatching basic block diagram is called, specifically the ship group {UAV1, UAV2, UAV4} handled by core 1, the heading angle and moving speed information of all ships in the group are extracted, which are shown in Table 1, the direction angles between each ship in the group are compared pairwise, that is, the heading angles of UAV1 and UAV2, UAV1 and UAV4, and UAV2 and UAV4 are compared, the relative included angle values are calculated, , , and the ship numbers exceeding the direction consistency offset threshold are recorded, the threshold is 50% of the aforementioned direction consistency threshold , that is , which is based on simulation experiments, it is found that after the formation of the formation, when the internal heading disturbance exceeds half of the initial threshold, the conflict risk increases by 60%, the calculated relative included angle value is compared with the threshold, , , it is found that the relative included angle between UAV1 and UAV2 exceeds the direction consistency offset threshold, so the numbers of UAV1 and UAV2 are recorded, and a direction difference identification list is established according to the numbers, a heading offset number list is generated, that is, {1, 2}.

[0079] S202: According to all the marked ship numbers in the heading offset number list, the speed information of the corresponding ships is extracted and compared with the average speed value in the group to filter out the ship numbers whose speed change amplitude exceeds the speed stability reference range, and combine them with the direction offset number set to form a new set, and generate a motion characteristic abnormal number set;

[0080] According to all the marked ship numbers in the heading offset number list {1, 2}, the speed information of the corresponding ships is extracted from Table 1, that is, the speed of UAV1 is 10.0 knots, and the speed of UAV2 is 10.5 knots, and the difference value is compared with the average speed value in the group {UAV1, 2, 4}, the average speed of the group is 10.25 knots, the speed difference value is calculated, 0.25 knots, 0.5 knots, 1.0 knots The ship numbers whose speed change amplitude exceeds the speed stability reference range are filtered out, the reference range is set to 3% of the average speed, that is 1.0 knots 1.5 knots, 2.0 knots The speed variation range of the unmanned ship 2 exceeds the reference range, the unmanned ship 2 is screened out, and the direction offset number set {1, 2} generated in the previous step is merged to form a new set {1, 2}, and an abnormal motion characteristic number set is generated.

[0081] S203: All ship numbers in the abnormal motion characteristic number set are called, and the groups corresponding to the numbers are classified. The ship numbers with abnormal motion characteristic behaviors in each group are classified into the conflict marking table, and the corresponding conflict type label items are constructed according to the ship numbers. A number and conflict type double-item mapping table is established, and a parallel scheduling cooperation conflict label set is generated.

[0082] All ship numbers in the abnormal motion characteristic number set {1, 2} are called, and the groups corresponding to the numbers are classified. Since numbers 1 and 2 belong to the formation group managed by core 1, the two ship numbers with abnormal motion characteristic behaviors are classified into the conflict marking table of this group, and the corresponding conflict type label items are constructed according to the ship numbers. Specifically, the label constructed for the unmanned ship 1 is “heading angle difference”, because it is only detected in step S201. The label constructed for the unmanned ship 2 is “heading angle difference and speed fluctuation”, because it is detected in steps S201 and S202. Accordingly, a number and conflict type double-item mapping table is established to clearly indicate that the unmanned ship 1 corresponds to “heading angle difference” and the unmanned ship 2 corresponds to “heading angle difference and speed fluctuation”. A parallel scheduling cooperation conflict label set is generated.

[0083] Please refer to Figure 4 The acquisition step of the key conflict path convergence paragraph set is:

[0084] S301: All ship numbers marked in the parallel scheduling cooperation conflict label set are called, and the task target point and current path coordinate sequence of the corresponding ship are extracted. According to the geographic coordinates of the path point and the navigation target position, a complete path trajectory line set is constructed. All trajectory sets are combined and aggregated to establish a path trajectory coordinate summary list.

[0085] All ship numbers marked in the parallel scheduling cooperation conflict label set, i.e. {1, 2}, are called, and the task target point and current path coordinate sequence of the corresponding ship are extracted. The path coordinate sequence of the unmanned ship 1 is , and the path coordinate sequence of the unmanned ship 2 is . According to the geographic coordinates of the path point and the navigation target position, a complete path trajectory line set is constructed, i.e. a set containing trajectory lines and . All trajectory sets are combined and aggregated, and all coordinate points are summarized according to the ship number and order to establish a path trajectory coordinate summary list.

[0086] S302: According to the path line combination recorded in the path trajectory coordinate summary list, the intersection of the spatial segments of any two trajectories is detected, the overlapping area of the path segments is extracted, and the number of intersections and their interval distribution are identified. The angle information of each intersection in the overlapping segment is extracted and the direction consistency is judged. The angle overlap and spatial concentration value are called to jointly evaluate, and the path intersection concentration trend value group is generated.

[0087] According to the path line combination recorded in the path trajectory coordinate summary list, , , the intersection of the spatial segments of any two trajectories is detected, and it is found that the second segment (from (110, 60) to (120, 70)) of and the starting point (120, 70) of exist intersection, the overlapping area of the path segments is extracted, and the number of intersections and their interval distribution are identified. The number of intersections is 1, located at the coordinate (120, 70). The angle information of each intersection in the overlapping segment is extracted and the direction consistency is judged. The angle overlap and spatial concentration value are called to jointly evaluate, and the path intersection concentration trend value group is generated. and , the tangent vector of each trajectory line at the intersection (120, 70) is extracted, the direction of vector is from (110, 60) to (120, 70), that is , the direction of vector is from (120, 70) to (115, 80), that is , the included angle between the two tangent vectors is calculated, and the cosine value of the included angle is taken as the angle overlap , the geographical space covered by the path trajectory coordinate summary list is divided into preset grid areas, the number of path intersections contained in each grid area is counted, and the intersection (120, 70) falls into the grid numbered (6, 4). The number of intersections in this grid is 1, which is the spatial concentration value of this grid. and are set to 0.6 and 0.4 according to historical data analysis, then the trend value is obtained, which is taken as the trend value corresponding to the grid (6, 4) in the path intersection concentration trend value group, and the path intersection concentration trend value group is generated.

[0088] S303: Call all trend values in the trend value group in the path intersection set, mark the path segment where the continuous concentration exceeds the concentration identification benchmark, extract the ship number corresponding to the path line of this part and form a one-to-one correspondence with the trajectory segment, build a continuous intersection segment set with associated numbers, and generate a key conflict path convergence segment set;

[0089] Call all trend values in the trend value group in the path intersection set, mark the path segment where the continuous concentration exceeds the concentration identification benchmark, which is dynamically determined according to the statistical distribution characteristics of all trend values in the trend value group in the path intersection set. Specifically, the mean and standard deviation of all non-zero trend values are calculated, and the benchmark value is set to the mean plus one standard deviation. Assuming that the mean calculated in the current scenario is 0.4 and the standard deviation is 0.25, the benchmark value is , the trend value of the grid (6, 4) Therefore, the path segment corresponding to this grid is marked as a conflict segment, and the path line of this part, i.e. (110, 60) to (120, 70) and (120, 70) to (115, 80) are extracted, and the corresponding ship numbers {1, 2} are found to form a one-to-one correspondence with the trajectory segment, building a continuous intersection segment set with associated numbers, i.e. {number 1: [(110, 60)-(120, 70)], number 2: [(120, 70)-(115, 80)]}, and generating a key conflict path convergence segment set.

[0090] Please refer to Figure 5 The acquisition step of the conflict path intervention instruction list is:

[0091] S401: According to all conflict point coordinates in the key conflict path convergence segment set, extract the current direction line and trajectory segment angle sequence of the corresponding ship of each conflict point, calculate and compare the difference of the included angle between each group of paths, judge whether the angle difference is less than the path convergence angle determination benchmark, obtain the path set that meets the angle close condition, and generate the direction parallel path set;

[0092] According to the conflict point coordinates (120, 70) in the key conflict path convergence segment set, extract the current direction line and trajectory segment angle sequence of ships 1 and 2 corresponding to the conflict point. The direction angle of ship 1 in the conflict segment is , and the direction angle of ship 2 in the conflict segment is Calculate and compare the difference of the included angle between the two groups of paths, and the angle difference is , judge whether the angle difference is less than the path convergence angle determination benchmark, which is set to Because the intersection less than 90 degrees is considered as a high-risk convergence rather than a simple vertical crossing, the value is obtained by analyzing the traffic accident data, the accident rate of the intersection angle in the 30-90 degree interval is 3 times of that in the 90-150 degree interval, because , the angle approach condition is met, the path set satisfying the angle approach condition is obtained, that is, , the direction parallel path set is generated.

[0093] S402: Call the path number recorded in the direction parallel path set, extract the current spatial position and turning interval information of the corresponding ship of each path, calculate the intersection ratio between the turning angle operable range and the current path density value, screen the path number whose intersection ratio is in the adjustable interval, and rearrange the operable paths according to the path number, and generate a direction change sorting list;

[0094] Call the path number recorded in the direction parallel path set , extract the current spatial position (100, 50) and (120, 70) of the corresponding unmanned ship 1 and 2 and the turning interval information, which is determined by the performance parameters of the unmanned ship, such as the maximum rudder angle of 30 degrees and the minimum turning radius of 25 meters, and calculate the turning angle operable range, the operable range of unmanned ship 1 is , the operable range of unmanned ship 2 is , and the current path density value is calculated, which is defined as the number of paths in the circular area with a radius of twice the ship length (20 meters) centered at the conflict point. There are 2 paths in the current area, and the density value is 2. Calculate the intersection ratio between the turning angle operable range and the current path density value, which is defined as the product of the operable range and the density value. The ratio of unmanned ship 1 is , and the ratio of unmanned ship 2 is , screen the path number whose intersection ratio is in the adjustable interval, and the adjustable interval is set to [100, 200], which is obtained by simulation test. A ratio that is too low indicates that the adjustment space is insufficient, and a ratio that is too high indicates that the adjustment may trigger new complex situations. The ratios of unmanned ship 1 and 2 are both in the interval, so they can be adjusted. Rearrange the operable paths according to the path number, which is arranged in the order of [1, 2] here, and generate a direction change sorting list.

[0095] S403: Based on the rearranged path number set in the direction change sorting list, extract the change angle information of the adjustable direction of each path and the actual path node index, bind the change direction and the path number one by one to form a direction adjustment combination, construct a pairing list of ship number and adjustment direction, output all combination contents and unified number identification, and generate a conflict path intervention instruction list;

[0096] Based on the rearranged path number set in the direction change sorting list [1,2], the change angle information of the adjustable direction for each path and the actual path node index are extracted. For unmanned vessel 1, its course is turned to the right. To avoid the intersection, the changed course is This adjustment applies to all nodes after path node index 1 starting from (100,50). For unmanned vessel 2, no adjustment is needed; the direction will be changed. Each vessel is bound to path number 1 to form a direction adjustment combination. A pairing list of vessel numbers and adjustment directions is constructed, i.e., {number 1:+10°}. All combination contents are output and uniformly numbered as Intervention-001, generating a list of conflict path intervention instructions.

[0097] Please see Figure 6 The steps for obtaining the instruction set for dynamic cluster formation execution are as follows:

[0098] S501: Call the sorted adjustment items in the conflict path intervention instruction list, extract the vessel number and course change instruction value bound to each adjustment item, match the corresponding path control status according to the vessel number, determine whether there is a conflict between the course value and the adjustment item in the path control status, record the control status flag value corresponding to the successfully matched item, and generate a path status matching result set.

[0099] Invoke the sorted adjustment item in the conflict path intervention instruction list, namely Intervention-001, and extract the vessel number 1 bound to this adjustment item and the course change instruction value. Based on vessel number 1, its corresponding path control status is matched. This status is read from the unmanned vessel's control system and includes the current set course. Determine the heading value recorded in the path control status. If there is a conflict with the adjustment item, there is no conflict here. Record the control status flag value corresponding to the successfully matched item. The flag value is a boolean value TRUE. Generate the path status matching result set, i.e., {number 1: TRUE}.

[0100] S502: Based on the control status marker values ​​in the path status matching result set, extract the vessel number and current speed information bound to each marker value, combine it with the target speed value in the previous stage adjustment item, calculate the speed deviation value between the current speed and the adjusted speed, compare and filter the deviation value with the allowable adjustment range benchmark, retain the vessel number and corresponding speed within the allowable adjustment range, and generate an instruction integration data list.

[0101] According to the control state flag value in the path state matching result set, that is, {number 1: TRUE}, the number 1 bound with the flag value and the current speed information, that is, 10.0 knots, are extracted, and the target speed value in the previous stage adjustment item is combined. Since this intervention only adjusts the heading, the target speed value remains unchanged, that is, 10.0 knots. The speed deviation value between the current speed and the adjusted speed is calculated The deviation value is compared with the allowed adjustment amplitude reference, which is set to 0.5 knots, that is, the speed change of a single instruction adjustment should not exceed 0.5 knots to ensure smooth sailing. Since , the condition is met, the number 1 of the ship and the corresponding speed 10.0 knots are retained, and the instruction integration data list is generated, which contains {number: 1, heading adjustment: +10°, speed update: 10.0 knots}.

[0102] S503: Call all ship numbers and updated control items recorded in the instruction integration data list, extract the heading adjustment value and the speed update value, group and reorganize them according to the multi-core processor core number, build a navigation control execution data structure package, and write it into the multi-core task buffer channel in the order of the number to generate a cluster dynamic formation execution instruction set;

[0103] Call all ship numbers and updated control items recorded in the instruction integration data list, that is, {number: 1, heading adjustment: +10°, speed update: 10.0 knots}, extract the heading adjustment value and the speed update value 10.0 knots, group and reorganize them according to the multi-core processor core number, since the unmanned ship 1 is managed by core 1, the instruction is classified into the task group of core 1, a navigation control execution data structure package containing the target ship ID, the heading control amount and the speed control amount is built, which is {Target_ID: 1, Command: {Heading_Control: 55, Speed_Control: 10.0}}, and is written into the multi-core task buffer channel in the order of the number. The scheduler of core 1 reads and sends it to the underlying controller of unmanned ship 1 to generate a cluster dynamic formation execution instruction set.

[0104] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for efficient collaborative dynamic formation of unmanned surface vessel swarms based on multi-core central processing units, characterized in that, Includes the following steps: S1: Obtain the position and operating direction of the unmanned vessel in the sea area, perform clustering based on spatial interval and azimuth angle difference, call the multi-core central processing unit allocation channel to assign numbers to the dense task groups, construct the task navigation relationship mapping and store it in the control module, and generate a multi-core instruction dispatch basic block diagram. S2: Call the grouping information in the multi-core instruction dispatch basic block diagram, extract the direction angle and speed value of each group of ships, compare whether the direction angle and speed difference between them exceed the average deviation range, number and label those that are inconsistent, and generate a set of parallel scheduling cooperation conflict labels. S3: Call the ship number in the parallel scheduling and cooperation conflict label set, extract the path line and the navigation target position, determine the path intersection and overlap interval, extract the trajectory concentration segment according to the angle overlap density, construct the conflict concentration area, and generate the key conflict path convergence segment set. The steps for obtaining the set of key conflict path convergence paragraphs are as follows: S301: Call all the ship numbers marked in the parallel scheduling cooperation conflict tag set, extract the task target point and current path coordinate sequence of the corresponding ship, construct a complete set of path trajectory lines based on the geographical coordinates of the path points and the location of the navigation target, combine and aggregate all trajectory sets, and establish a summary list of path trajectory coordinates. S302: Based on the path line combinations recorded in the path trajectory coordinate summary list, perform intersection detection on the spatial segments of any two trajectories, extract the overlapping area of ​​the path segments and identify the number of intersections and their interval distribution, extract the angle information of each intersection point in the overlapping segment and judge the direction consistency, call the angle overlap amount and spatial concentration value for joint evaluation, and generate a path intersection convergence trend value group. S303: Call all trend values ​​in the trend value group of the path intersection, mark the path segments whose continuous concentration exceeds the concentration identification benchmark, extract the ship number corresponding to the path line and form a one-to-one correspondence with its trajectory segment, construct a set of continuous intersection segments with associated numbers, and generate a set of key conflict path convergence segments. The process of jointly evaluating the overlap of the calling angle and the spatial concentration value to generate a set of convergent trend values ​​for path intersection is as follows: For any two intersecting path trajectories, extract the tangent vector of each path trajectories at the intersection point, calculate the angle between the tangent vectors, and use the cosine of the angle as the angle coincidence value; The geographic space covered by the summary list of path trajectory coordinates is divided into preset grid areas, and the number of path intersections contained in each grid area is counted. The number of path intersections is used as the spatial set value. For each grid region, its spatial concentration value is weighted and summed with the arithmetic mean of the angular coincidence of all intersections within that region. The result is used as the trend value corresponding to the grid region in the path intersection concentration trend value group. The concentration identification criterion is dynamically determined based on the statistical distribution characteristics of all trend values ​​in the central trend value group of the path intersection. S4: Based on the convergence area of ​​the key conflict path convergence segment, extract the direction lines of relevant ships, determine whether there is a course change area, reconstruct the trajectory according to the avoidance direction, rearrange the task order and output path adjustment suggestions, and generate a conflict path intervention instruction list.

2. The efficient collaborative method for dynamic formation of unmanned surface vessel swarms based on a multi-core central processing unit as described in claim 1, characterized in that: The multi-core instruction dispatch basic diagram includes ship grouping number, nuclear control channel identifier, and task density classification. The parallel scheduling cooperation conflict label set includes direction deviation marker, speed discontinuity marker, and cooperation conflict number. The key conflict path convergence segment set includes path intersection location, interference segment angle sequence, and continuous conflict segment number. The conflict path intervention instruction list includes direction change option, path reconstruction priority sequence, and adjustment interval range.

3. The efficient collaborative method for dynamic formation of unmanned surface vessel swarms based on a multi-core central processing unit as described in claim 1, characterized in that, The steps for obtaining the basic block diagram of the multi-core instruction dispatch are as follows: S101: Obtain the position and operation direction information of all unmanned vessels in the sea area, extract the spatial coordinates and navigation direction angles of each vessel, compare the spatial interval value and the direction angle between any two vessels, and determine whether they are both less than the distance proximity threshold and the direction consistency threshold. Vessels that meet the conditions are included in the same cluster identification set, and a formation cluster identification mark matrix is ​​established. S102: Based on all the ship sets marked as clustered in the formation cluster identification mark matrix, extract the number of members and the range of task point coordinate density of each cluster set. According to the cluster size and task space overlap, call the allocation channel of the corresponding core in the multi-core central processing unit, and perform multi-core allocation of the ship set according to the task aggregation intensity to generate a multi-core core task docking number table. S103: Based on the mapping relationship between the core number and the ship set in the multi-core core task docking number table, the ship list and task area number in each number are integrated accordingly. The path target number and actual number of each ship in each group of core numbers are extracted to establish a two-way matching comparison list between task number and ship number, and a basic block diagram for multi-core instruction dispatch is generated.

4. The efficient collaborative method for dynamic formation of unmanned surface vessel swarms based on a multi-core central processing unit as described in claim 3, characterized in that: The process of establishing the formation cluster identification marker matrix is ​​as follows: Based on the spatial coordinates of each ship, the Euclidean distance between the geometric center points of any two ships is calculated and used as the spatial interval value. Based on the sailing direction angle of each ship, calculate the angle between the sailing direction vectors of any two ships, and use it as the direction angle; The distance approach threshold is a safe distance dynamically set based on the size of the unmanned vessel, its current speed, and the hydrological conditions of the sea area. The directional consistency threshold is an allowable deviation angle set based on the requirements of the cluster's task execution for heading coordination; The formation cluster identification marker matrix is ​​a square matrix, and the number of rows and columns of the square matrix is ​​equal to the total number of unmanned vessels in the sea area. The matrix elements are used to mark whether any two vessels meet the conditions of belonging to the same cluster identification set.

5. The efficient collaborative method for dynamic formation of unmanned surface vessel swarms based on a multi-core central processing unit as described in claim 1, characterized in that, The steps for obtaining the parallel scheduling cooperation conflict label set are as follows: S201: Call the multi-core instruction dispatch basic block diagram to distribute the ship grouping results, extract the heading angle and speed information of all ships in each group, compare the heading angles of each ship pairwise, calculate the relative angle value and record the ship number that exceeds the direction consistency offset threshold, establish a direction difference identification list based on the number, and generate a heading offset number list. S202: Based on all the marked vessel numbers in the heading offset number list, extract the speed information of the corresponding vessel and compare it with the average speed value in the group to filter out the vessel numbers whose speed change exceeds the speed stability benchmark range, and merge them with the heading offset number set to form a new set, generating a motion characteristic abnormal number set. S203: Call all the ship numbers in the set of abnormal motion characteristics, classify the groups corresponding to each number, add the ship numbers with abnormal motion characteristics in each group to the conflict label table, construct the corresponding conflict type label item according to the ship number, establish a two-way mapping table between number and conflict type, and generate a set of parallel scheduling cooperation conflict labels.

6. The efficient collaborative method for dynamic formation of unmanned surface vessel swarms based on a multi-core central processing unit as described in claim 1, characterized in that, The steps for obtaining the list of conflict path intervention instructions are as follows: S401: Based on the coordinates of all conflict points in the set of key conflict path convergence segments, extract the current direction line and trajectory segment angle sequence of the corresponding ships for each conflict point, calculate and compare the angle between each set of paths, determine whether the angle difference is lower than the path convergence angle judgment benchmark, obtain the set of paths that meet the angle closeness condition, and generate a set of parallel paths in the direction. S402: Call the path number recorded in the set of parallel paths in the direction, extract the current spatial position and turning interval information of the ship corresponding to each path, calculate the cross ratio between the operable range of the turning angle and the current path density value, filter the path number whose cross ratio is in the adjustable range, and rearrange the operable paths according to the path number to generate a direction change sorting list. S403: Based on the rearranged set of path numbers in the direction change sorting list, extract the change angle information of the adjustable direction of each path and the actual path node index, bind the change direction with the path number to form a direction adjustment combination, construct a pairing list of ship number and adjustment direction, output all combination contents and uniformly number them, and generate a conflict path intervention instruction list.

7. The efficient collaborative method for dynamic formation of unmanned surface vessel swarms based on a multi-core central processing unit as described in claim 1, characterized in that, The method further includes: S5: Call the adjustment content in the conflict path intervention instruction list, match the ship number with the original path control status, construct the direction and speed update instruction package, write it into the task transmission channel, organize and output it according to the core number, and generate a cluster dynamic formation execution instruction set. The cluster dynamic formation execution instruction set includes control instruction packet number, heading update parameters, and speed adjustment data.

8. The efficient collaborative method for dynamic formation of unmanned surface vessel swarms based on a multi-core central processing unit as described in claim 7, characterized in that, The steps for obtaining the cluster dynamic formation execution instruction set are as follows: S501: Call the sorted adjustment items in the conflict path intervention instruction list, extract the vessel number and course change instruction value bound to each adjustment item, match the corresponding path control status according to the vessel number, determine whether there is a conflict between the course value and the adjustment item in the path control status, record the control status flag value corresponding to the successfully matched item, and generate a path status matching result set. S502: Based on the control status marker values ​​in the path status matching result set, extract the ship number and current speed information bound to each marker value, combine it with the target speed value in the previous stage adjustment item, calculate the speed deviation value between the current speed and the adjusted speed, compare and filter the deviation value with the allowable adjustment range benchmark, retain the ship number and corresponding speed within the allowable adjustment range, and generate an instruction integration data list. S503: Call the instruction to integrate all ship numbers and updated control items recorded in the data list, extract the heading adjustment value and speed update value, group and reorganize them according to the multi-core processor core number, construct the navigation control execution data structure package, and write it into the multi-core task buffer channel in numerical order to generate a cluster dynamic formation execution instruction set.

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