Cooperative strategy making and efficiency pre-evaluation method and system based on multiple factors

Abstract

The invention discloses a multi-factor-based collaborative strategy making and efficiency pre-evaluation method and system. The method comprises the following steps: analyzing a collaborative tracking task demand and a node working state; screening an effective node combination based on a detection capability matching criterion, and optimizing node selection through a constraint condition; a tracking strategy is formulated by combining target motion parameters, and master / slave node roles are distributed; constructing an efficiency estimation model by using multiple factors, and dynamically correcting an evaluation result through actual feedback data to form a closed-loop optimization mechanism; the system comprises a data receiving module, a task analysis module, a node selection module, a strategy making and issuing module and an efficiency estimation and evaluation module, and full-process automation from task analysis to dynamic adjustment is achieved. Through multi-factor weighted evaluation and feedback correction, the robustness of a collaborative strategy is improved, the resource utilization rate and the tracking precision are optimized, and the method is suitable for a multi-node collaborative detection task in a complex scene.

Description

Technical Field

[0001] The present invention belongs to the technical field of radar task scheduling, and in particular relates to a collaborative strategy formulation and effectiveness pre-evaluation method and system based on multiple factors. Background Art

[0002] Task scheduling strategies refer to the principles and methods used by radar task scheduling to receive and interpret task requests and schedule them for execution within a scheduling cycle. Different scheduling strategies can be used depending on the specific role and function of the radar system. Traditional scheduling strategies fall into two categories: template-based scheduling and real-time adaptive scheduling based on comprehensive priority calculation. With the rapid advancement of electronics and radar hardware technology, especially the widespread adoption of phased array radars, template-based task scheduling strategies are no longer able to fully utilize the potential of radar hardware. To further enhance radar scheduling performance, improve the flexibility of radar scheduling algorithms, and enhance their ability to adapt to complex detection environments, radar engineers have proposed adaptive task scheduling strategies. Adaptive task scheduling is a scheduling method that balances the time, energy, and computer resources required by various radar beam requests in real time within the radar design while meeting the priorities of different operating modes. This method selects an optimal radar task sequence for each scheduling interval.

[0003] As the target environment becomes increasingly complex and changeable, single-platform detection can no longer meet actual needs. Multi-node collaboration has become a trend, especially for collaborative tracking tasks, which can improve the tracking stability of high-threat, low, small, and slow targets, and play an important role in improving tracking quality. Existing Chinese patent document CN115494831B proposes a tracking method for autonomous human-machine intelligent collaboration, and CN112700158B proposes an algorithm performance evaluation method based on a multi-dimensional model. Neither of them estimates and evaluates the detection performance of collaborative tracking tasks. Moreover, for collaborative tracking tasks, both traditional and adaptive scheduling strategies lack an estimation and evaluation mechanism for collaborative tracking strategies. Radar system operators cannot directly obtain the execution effect of the strategy, which affects the operator's judgment of the current detection performance and the adjustment of the scheduling strategy. Summary of the Invention

[0004] The purpose of the present invention is to provide a collaborative strategy formulation and performance pre-evaluation method and system based on multiple factors, to improve the robustness of the collaborative strategy, optimize resource utilization and tracking accuracy, and realize multi-node collaborative detection tasks in complex scenarios.

[0005] To achieve the purpose of the present invention, on the one hand, the present invention provides a method for formulating collaborative strategies and pre-evaluating their effectiveness based on multiple factors, comprising the following steps:

[0006] S1. Analyze the data containing the collaborative tracking task through the task analysis model to obtain the collaborative task requirements;

[0007] S2. parse the data containing the radar status through the state extraction model to obtain the working status of the collaborative node;

[0008] S3, using the collaborative task requirements and the collaborative node working status to determine a valid node combination that meets the collaborative tracking task through a detection capability matching criterion;

[0009] S4. Formulate a tracking strategy for the collaborative tracking task based on the valid node combination;

[0010] S5. Estimating the effectiveness of the tracking strategy based on multiple factors to obtain an estimation result of the effectiveness of the tracking strategy;

[0011] S6. Sending the collaborative tracking strategy to the nodes of the best combination of collaborative nodes for execution. The master node receives the collaborative detection data sent by the slave node, and performs composite tracking on the collaborative target corresponding to the collaborative tracking task based on the collaborative detection data to obtain a collaborative tracking track.

[0012] S7. Parsing the data containing the state feedback using a state feedback extraction model to obtain collaborative node state feedback, wherein the collaborative node state feedback includes a data rate corresponding to the actual working mode of the node and a collaborative tracking track quality fed back by the master node. Simultaneously, a working mode-data rate mapping relationship is used to obtain the node data rate, and the collaborative tracking track quality is obtained from the collaborative tracking track.

[0013] S8. The estimated information is corrected based on the data rate corresponding to the actual working mode of the node and the quality of the cooperative tracking track, and finally a cooperative tracking strategy evaluation result is obtained.

[0014] On the other hand, the present invention also provides a system for collaborative strategy formulation and effectiveness pre-evaluation method based on multiple factors, including the following modules:

[0015] Data receiving module, used to receive collaborative node status, status feedback information and collaborative tracking tasks;

[0016] Task parsing module, used to parse collaborative tracking task parameters;

[0017] State processing module, used to process collaborative node status;

[0018] Node selection module, used to select valid node combinations;

[0019] Strategy formulation module, used to formulate collaborative tracking strategies;

[0020] Strategy estimation module, used to estimate the collaborative tracking strategy;

[0021] Strategy issuing module, used to issue collaborative tracking strategies;

[0022] The strategy evaluation module is used to evaluate the collaborative tracking strategy.

[0023] A non-transitory computer-readable storage medium stores computer instructions, wherein the computer instructions are used to enable the computer to execute the above-mentioned collaborative strategy formulation and performance pre-evaluation method based on multiple factors.

[0024] A computer program product includes computer program instructions. When the computer program instructions are executed on a computer, the computer is caused to execute the above-mentioned collaborative strategy formulation and effectiveness pre-evaluation method based on multiple factors.

[0025] Compared with the existing technology, the significant progress of the present invention is that it improves the robustness of the collaborative strategy, optimizes resource utilization and tracking accuracy through multi-factor weighted evaluation and feedback correction, and is suitable for multi-node collaborative detection tasks in complex scenarios.

[0026] In order to more clearly illustrate the functional characteristics and structural parameters of the present invention, further description is given below with reference to the accompanying drawings and specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0028] Figure 1 It is a flow chart of the steps of the present invention. DETAILED DESCRIPTION

[0029] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0030] The present invention is a collaborative strategy formulation and effectiveness pre-evaluation method based on multiple factors, combined with Figure 1 , including the following steps:

[0031] S1. Parse the data containing the collaborative tracking task through the task analysis model to obtain the collaborative task requirements, including the type and location of the collaborative target corresponding to the collaborative tracking task;

[0032] S2. Analyze the data containing the radar status through the state extraction model to obtain the working status of the collaborative node, including the radar working mode and detection range;

[0033] S3, using the collaborative task requirements and the collaborative node working status to determine a valid node combination that meets the collaborative tracking task through a detection capability matching criterion;

[0034] S4. Formulate a tracking strategy for the collaborative tracking task based on the valid node combination, the tracking strategy including the optimal combination of collaborative nodes, a recommended working mode for each node, and a node role for each node, where the node roles are divided into master nodes and slave nodes;

[0035] S5. Estimating the effectiveness of the tracking strategy based on multiple factors to obtain an estimation result of the effectiveness of the tracking strategy;

[0036] S6. Sending the collaborative tracking strategy to the nodes of the best combination of collaborative nodes for execution. The master node receives the collaborative detection data sent by the slave node, and performs composite tracking on the collaborative target corresponding to the collaborative tracking task based on the collaborative detection data to obtain a collaborative tracking track.

[0037] S7. Parsing the data containing the state feedback using a state feedback extraction model to obtain collaborative node state feedback, wherein the collaborative node state feedback includes a data rate corresponding to the actual working mode of the node and a collaborative tracking track quality fed back by the master node. Simultaneously, a working mode-data rate mapping relationship is used to obtain the node data rate, and the collaborative tracking track quality is obtained from the collaborative tracking track.

[0038] S8. The estimated information is corrected based on the data rate corresponding to the actual working mode of the node and the quality of the cooperative tracking track, and finally a cooperative tracking strategy evaluation result is obtained.

[0039] The collaborative tracking task in step S1 is:

[0040] T m (B m , Rt m , N m , Lon m , Lat m , H m , V x , V y , V z );

[0041] Among them, m is the task number, T m For collaborative tracking tasks, B m is the bandwidth required for the task, Rt mis the data rate required for the task, N m is the number of collaborative nodes required for the task, Lon m is the longitude of the collaborative target, Lat m is the collaborative target latitude, H m is the collaborative target height, V x is the velocity component of the collaborative target on the X axis, V y V is the velocity component of the collaborative target on the Y axis, z The Z-axis velocity component of the collaborative target.

[0042] The working state W of the collaborative node in step S2 cs Including node longitude and latitude (Lon i , Lat i , H i ), node working mode combination W i =[W i1 W i2 … W iQ ], and the effective bandwidth between nodes

[0043]

[0044] Among them, Q is the number of working modes supported by the i-th node, M is the total number of collaborative nodes, Lon i is the longitude of the i-th node, Lat i is the latitude of the i-th node, H i is the height of the i-th node;

[0045] W iq =[Rt iq ,(R iqs , R iqe ), (A iqs , A iqe ), (E iqs , E iqe )];

[0046] Among them, W iq is the qth working mode of the i-th node, Rt iq is the detection data rate, (R iqs , R iqe ) is the detection distance range, (A iqs , A iqe )Detection azimuth range, (E iqs , E iqe ) Detection elevation range, B ij is the effective bandwidth between the i-th node and the j-th node.

[0047] The step S3 specifically includes the following steps:

[0048] 3-1. According to the longitude and latitude of the cooperative target and the longitude and latitude of the detection node, determine the distance R of the cooperative target relative to the i-th node mi , elevation angle E mi and direction A mi ;

[0049] 3-2. Establish constraints:

[0050]

[0051] Among them, Node vm Node is a valid node combination. vm =[Node1, Node2,...,Node K ], K is the total number of nodes that meet the conditions;

[0052] λ1, λ2, γ1, γ2, η1, η2 are detection efficiency guarantee coefficients, and λ1R iqs <λ2R iqe ,γ1E iqs <γ2E iqe ,η1A iqs <η2A iqe ;

[0053] In the above constraints, the detection data rate Rt iq The maximum value is taken to ensure the uniqueness of the selected node working mode, which is used as the working mode recommended for the collaborative nodes to execute in the strategy.

[0054] 3-3. According to the constraints, traverse all nodes and select the valid node combination that meets the conditions. vm .

[0055] The step S4 specifically includes the following steps:

[0056] 4-1. Establish new constraints:

[0057]

[0058] Among them, Node bm is the best combination of collaborative nodes, N m Node is the number of nodes required for collaborative tasks. i is the i-th node, Node j is the jth node, B ij is the effective bandwidth between the i-th node and the j-th node;

[0059] 4-2. According to the constraints of step 4-1, traverse Node bm All nodes in the node list, select the nodes that meet the conditions cm, and based on the collaborative goal and Node cm Sort the nodes by the distance between the nodes from small to large, and let Node cm The number of nodes is N cm , select the first N m Nodes as a combination bm , if Node cm The number of nodes is less than N m , then Node cm That is the combination Node bm , let Node bm The number of nodes is N bm ,Right now

[0060] 4-3. According to the speed V of the cooperative target x 、V y 、V z Calculate relative to Node bm The radial velocity V of the i-th node in ri , according to V ri The value of the node bm The nodes in the grid are sorted. The larger the radial velocity, the greater the threat level of the coordinated target relative to a certain node. The node with the largest radial velocity is selected as the master node, and the remaining nodes are slave nodes.

[0061] The collaborative tracking strategy effectiveness estimation result E in step S5 r :

[0062]

[0063] Among them, Rt si Set the data rate corresponding to the working mode of the i-th node in the collaborative strategy.

[0064] The collaborative tracking strategy evaluation result A of step S8 r :

[0065]

[0066] Among them, the collaborative tracking track quality Q t ≤7, Rt ir is the data rate corresponding to the actual working mode of the i-th node, κ1, κ2, κ3, κ4 are weight coefficients, and κ1+κ2+κ3+κ4=1.

[0067] The cooperative tracking track quality is a parameter carried by the cooperative tracking track. 5 or 7 is the general maximum value in the industry. In this method, it is 7.

[0068] The present invention provides a system for collaborative strategy formulation and effectiveness pre-evaluation based on multiple factors, comprising the following modules:

[0069] Data receiving module, used to receive collaborative node status, status feedback information and collaborative tracking tasks;

[0070] Task parsing module, used to parse collaborative tracking task parameters;

[0071] State processing module, used to process collaborative node status;

[0072] Node selection module, used to select valid node combinations;

[0073] Strategy formulation module, used to formulate collaborative tracking strategies;

[0074] Strategy estimation module, used to estimate the collaborative tracking strategy;

[0075] Strategy issuing module, used to issue collaborative tracking strategies;

[0076] The strategy evaluation module is used to evaluate the collaborative tracking strategy.

[0077] The specific implementation methods of each module of the above system are the same as the aforementioned collaborative strategy formulation and performance pre-assessment method, and will not be repeated here.

[0078] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0079] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A collaborative strategy formulation and effectiveness pre-evaluation method based on multiple factors, characterized by: The following steps are involved: S1. Analyze the data containing the collaborative tracking task through the task analysis model to obtain the collaborative task requirements; S2. parse the data containing the radar status through the state extraction model to obtain the working status of the collaborative node; S3, using the collaborative task requirements and the collaborative node working status to determine a valid node combination that meets the collaborative tracking task through a detection capability matching criterion; S4. Formulate a tracking strategy for the collaborative tracking task based on the valid node combination; S5. Estimating the effectiveness of the tracking strategy based on multiple factors to obtain an estimation result of the effectiveness of the tracking strategy; S6. Sending the collaborative tracking strategy to the nodes of the best combination of collaborative nodes for execution. The master node receives the collaborative detection data sent by the slave node, and performs composite tracking on the collaborative target corresponding to the collaborative tracking task based on the collaborative detection data to obtain a collaborative tracking track. S7. Parsing the data containing the state feedback using a state feedback extraction model to obtain collaborative node state feedback, wherein the collaborative node state feedback includes a data rate corresponding to the actual working mode of the node and a collaborative tracking track quality fed back by the master node. Simultaneously, a working mode-data rate mapping relationship is used to obtain the node data rate, and the collaborative tracking track quality is obtained from the collaborative tracking track. S8. The estimated information is corrected based on the data rate corresponding to the actual working mode of the node and the quality of the cooperative tracking track, and finally a cooperative tracking strategy evaluation result is obtained.

2. A collaborative strategy formulation and effectiveness pre-evaluation method based on multiple factors according to claim 1, characterized in that: The collaborative tracking task in step S1 is: T m (B m ,Rt m ,N m ,Lon m ,Lat m ,H m ,V x ,V y ,V z ); Among them, m is the task number, T m For collaborative tracking tasks, B m is the bandwidth required for the task, Rt m is the data rate required for the task, N m is the number of collaborative nodes required for the task, Lon m is the longitude of the collaborative target, Lat m is the collaborative target latitude, H m is the collaborative target height, V x is the velocity component of the collaborative target on the X axis, V y V is the velocity component of the collaborative target on the Y axis, z The Z-axis velocity component of the collaborative target.

3. The method for collaborative strategy formulation and effectiveness pre-evaluation based on multiple factors according to claim 2, characterized in that: The working state W of the collaborative node in step S2 cs Including node longitude and latitude (Lon i , Lat i , H i ), node working mode combination W i =[W i1 W i2 …W iQ ], and the effective bandwidth between nodes Among them, Q is the number of working modes supported by the i-th node, M is the total number of collaborative nodes, Lon i is the longitude of the i-th node, Lat i is the latitude of the i-th node, H i is the height of the i-th node; W iq =[Rt iq ,(R iqs ,R iqe ),(A iqs ,A iqe ),(E iqs ,E iqe )]; Among them, W iq is the qth working mode of the i-th node, Rt iq is the detection data rate, (R iqs , R iqe ) is the detection distance range, (A iqs , A iqe )Detection azimuth range, (E iqs , E iqe ) Detection elevation range, B ij is the effective bandwidth between the i-th node and the j-th node.

4. The method for collaborative strategy formulation and effectiveness pre-evaluation based on multiple factors according to claim 3, characterized in that: The step S3 specifically includes the following steps: 3-1. According to the longitude and latitude of the cooperative target and the longitude and latitude of the detection node, determine the distance R of the cooperative target relative to the i-th node mi , elevation angle E mi and direction A mi ; 3-2. Establish constraints: Among them, Node vm Node is a valid node combination. vm =[Node1, Node2,...,Node K ], K is the total number of nodes that meet the conditions; λ1, λ2, γ1, γ2, η1, η2 are detection efficiency guarantee coefficients, and λ1R iqs <λ2R iqe ,γ1E iqs <γ2E iqe ,η1A iqs <η2A iqe ; In the above constraints, the detection data rate Rt iq The maximum value is taken, and this working mode is used as the working mode recommended for the collaborative nodes in the strategy. 3-3. According to the constraints, traverse all nodes and select the valid node combination that meets the conditions. vm .

5. The method for formulating collaborative strategies and pre-evaluating effectiveness based on multiple factors according to claim 4, characterized in that: The step S4 specifically includes the following steps: 4-1. Establish new constraints: Among them, Node bm is the best combination of collaborative nodes, N m Node is the number of nodes required for collaborative tasks. i is the i-th node, Node j is the jth node, B ij is the effective bandwidth between the i-th node and the j-th node; 4-2. According to the constraints of step 4-1, traverse Node bm All nodes in the node list, select the nodes that meet the conditions cm , and based on the collaborative goal and Node cm Sort the nodes by the distance between the nodes from small to large, and let Node cm The number of nodes is N cm , select the first N m Nodes as a combination bm , if Node cm The number of nodes is less than N m , then Node cm That is the combination Node bm , let Node bm The number of nodes is N bm ,Right now 4-3. According to the speed V of the cooperative target x 、V y 、V z Calculate relative to Node bm The radial velocity V of the i-th node in ri , according to V ri The value of the node bm The nodes in the grid are sorted. The larger the radial velocity, the greater the threat level of the coordinated target relative to a certain node. The node with the largest radial velocity is selected as the master node, and the remaining nodes are slave nodes.

6. The method for collaborative strategy formulation and effectiveness pre-evaluation based on multiple factors according to claim 5, characterized in that: The collaborative tracking strategy effectiveness estimation result E in step S5 r : Among them, Rt si Set the data rate corresponding to the working mode of the i-th node in the collaborative strategy.

7. The method for collaborative strategy formulation and effectiveness pre-evaluation based on multiple factors according to claim 5, characterized in that: The collaborative tracking strategy evaluation result A of step S8 r : Among them, the collaborative tracking track quality Q t ≤7, Rt ir is the data rate corresponding to the actual working mode of the i-th node, κ1, κ2, κ3, κ4 are weight coefficients, and κ1+κ2+κ3+κ4=1.

8. A system for collaborative strategy formulation and effectiveness pre-evaluation method based on multiple factors according to any one of claims 1 to 7, characterized in that: Includes the following modules: Data receiving module, used to receive collaborative node status, status feedback information and collaborative tracking tasks; Task parsing module, used to parse collaborative tracking task parameters; State processing module, used to process collaborative node status; Node selection module, used to select valid node combinations; Strategy formulation module, used to formulate collaborative tracking strategies; Strategy estimation module, used to estimate the collaborative tracking strategy; Strategy issuing module, used to issue collaborative tracking strategies; The strategy evaluation module is used to evaluate the collaborative tracking strategy.

9. A non-transitory computer-readable storage medium, characterized in that The non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 7.

10. A computer program product comprising computer program instructions, characterized in that When the computer program instructions are executed on a computer, the computer is caused to perform the method according to any one of claims 1 to 7.

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