Reconnaissance task decomposition and sensor resource allocation method and system

Abstract

The invention discloses a reconnaissance task decomposition and sensor resource allocation method, which comprises the following steps: S1, obtaining a task type issued by a superior and each parameter of a target, and constructing a task information database; s2, acquiring sensor resource parameters, and constructing a sensor resource information database; s3, the probability that the sensor finds the target is evaluated, and preparation is made for a subsequent sensor resource allocation task; s4, according to known information in a current scene, a hierarchical task network is adopted to decompose a task issued by a superior; and S5, sequentially executing each atomic task according to the task decomposition result, and generating a task resource allocation scheme in combination with the existing sensor resources. According to the method, the quality and stability of understanding are improved, reasonable and optimal configuration of reconnaissance resources is realized, and the quality and efficiency of reconnaissance decision making are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of network task decomposition, and in particular to a method and system for reconnaissance task decomposition and sensor resource allocation based on a hierarchical task network method. Background Art

[0002] Reconnaissance mission decomposition and sensor resource allocation are the core links of command and control and coordinated operations, and are key areas of modern military operations. Optimal reconnaissance perception and decision support can be achieved through reasonable mission planning and sensor scheduling. Reconnaissance mission decomposition refers to the scientific and reasonable decomposition of the complex superior combat mission into multiple executable subtasks based on the grasp of the environmental conditions and real-time situation, and the generation of a primary task list in order to clarify the combat objectives, better allocate resources, coordinate actions and improve combat efficiency. Task decomposition determines the specific tasks of each tactical unit, sensor and other resources, and is an indispensable part. Sensor resource allocation aims to optimize the use of sensors according to mission requirements and environmental changes, and maximize intelligence acquisition and reconnaissance perception effects. Reasonable sensor scheduling can significantly improve the timeliness and accuracy of intelligence, thereby supporting mission execution.

[0003] At present, traditional task decomposition mainly relies on the commander's experience and manual planning, which is inefficient and easily affected by human factors. In addition, as the reconnaissance environment changes, the task requirements may change significantly, and the task decomposition plan needs to be adjusted in real time. As the complexity of tasks and the number of sensors increase, especially when the real-time requirements of operations increase, the traditional static allocation method is inefficient and difficult to cope with complex tasks. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for reconnaissance task decomposition and sensor resource allocation, which solves the technical problems in the prior art such as low efficiency of task allocation and susceptibility to human factors.

[0005] To achieve the above object, the present invention provides the following technical solution: a method for reconnaissance task decomposition and sensor resource allocation, comprising the following steps:

[0006] S1. Obtain the task type issued by the superior, as well as all parameters such as the location, quantity, threat level, type and frequency band of the radiation source signal carried by the target, and build a task information database;

[0007] S2. Obtain all parameters of sensor resources, including type, quantity, location, working frequency band, status, detection range, etc., and build a sensor resource information database;

[0008] S3, evaluate the probability of the sensor finding the target and prepare for the subsequent sensor resource allocation task;

[0009] S4. Based on the known information in the current scene, a hierarchical task network is used to decompose the tasks issued by the superior;

[0010] S5: Execute each atomic task in sequence according to the task decomposition result of S4, and generate a task resource allocation plan based on the existing sensor resources.

[0011] In a specific embodiment, in S1, the task type includes multi-target positioning and single-target high-precision positioning. The various parameters of the target include the location, quantity, threat level, type and frequency band of the radiation source signal carried by the target; in S2, the sensor resource parameters include the type, quantity, location, working frequency band and detection range of the sensor resource.

[0012] In a specific embodiment, in S3, the probability of the sensor discovering the target includes:

[0013] The probability of a single sensor detecting a target:

[0014]

[0015] The probability of multiple sensors detecting a target:

[0016]

[0017] in,

[0018]

[0019] Where D is the detection range of the sensor; d is whether the target can be detected by the sensor; s c is the type of sensor resource, s c ∈{tz,lz},tz i is the ith communication sensor, lz j is the jth lightning detection sensor; i, j are the sensor numbers, i = 1, 2, ..., m, j = 1, 2, ..., n; s m is the type of radiation source carried by the target, s m ∈{tz,lz}; r is the distance between the target and the sensor; f m is the target frequency band; f c The sensor operating frequency band.

[0020] In a specific embodiment, S4 includes the following steps:

[0021] S41. Select a multi-target positioning task or a single-target high-precision positioning task according to the task type;

[0022] S42. Select the next task according to the task constraint method:

[0023] In the multi-target positioning task: the constraint method 0 is that the target signal type is only the communication signal, the constraint method 1 is that the target signal type is only the radar signal, and the constraint method 2 is that the target signal type includes both communication signals and radar signals;

[0024] In the single-target high-precision positioning task: the constraint method 0 is that the target signal type is only the communication signal, and the constraint method 1 is that the target signal type is only the radar signal;

[0025] S43, splitting the task into executable atomic tasks according to the constraint conditions and task information; the atomic tasks include the available communication sensors for each target, determining the number of communication sensors to be used, allocating communication sensors to multiple targets, and allocating communication sensors to a single target.

[0026] In a specific embodiment, S5 includes the following steps:

[0027] S51, determining a set of sensor resources available for each target;

[0028] S52, determining the amount of sensor resources used by each target;

[0029] S53. Allocate sensor resources to each target.

[0030] In a specific embodiment, in S52, the number of the targets is a single target and multiple targets;

[0031] The number of sensor resources used by a single target is the number of all available resources in the set of sensor resources available to the target; or,

[0032] Calculate the highest precision macc for simultaneous positioning of n resources n , n∈[2,size(S)-1], when |macc n+1 -macc n | is less than the set value k, then num = m, where macc m =min(macc n ,macc n+1 ); if the condition is not met, then num = m, where macc m =min(macc n );

[0033] Where S is the set of sensor resources available to the target; size(S) is the number of elements in the set of sensor resources available to the target; num is the number of sensor resources used by a single target; m is the number of sensor resources that achieves the highest accuracy; macc mIt is the highest accuracy under the set value k;

[0034] The number of sensor resources used by multiple targets is as follows: 2 available sensor resources are allocated to each target; if the number of sensor resources available to a target is less than 2, the positioning task of the target is abandoned and no sensor resources are allocated to it.

[0035] In a specific embodiment, in S53, if the number of sensor resources used is the number size(S) of elements in the set of sensor resources available to the target, the single-target optimal allocation solution is to allocate all resources in the set S of sensor resources available to the target; otherwise, the solution that simultaneously locates the highest accuracy using num resources is the optimal allocation solution;

[0036] In the formula, num is the number of sensor resources used by a single target;

[0037] Multi-target allocation sensor resource allocation includes the following steps:

[0038] S531, based on the sensor resource set available for each target, using a genetic algorithm to randomly generate a certain number of chromosomes as an initial population, wherein the chromosomes are initial candidate allocation schemes;

[0039] S532, calculating the fitness value of each chromosome in the current population;

[0040] S433, select, crossover and mutate the initial population to obtain the next generation population;

[0041] S534, the next generation population obtained after the above steps is disturbed by a simulated annealing method to generate a new solution, and the fitness values ​​of the solutions before and after the disturbance are calculated respectively to obtain an increment Δf between the initial solution and the new solution;

[0042] S535, apply the Meteopolis rule to determine whether to accept the new solution. If Δf<0, accept the new solution and go to S536; otherwise, generate a random number ξ=U(0,1). If Then accept the new solution;

[0043] S536, judging whether thermal equilibrium is reached, if so, proceeding to the next step, otherwise proceeding to S534;

[0044] S537, perform cooling operation: when the temperature T i If the temperature is lower than the set threshold, the annealing process ends; otherwise, T i+1 =αT i , α∈(0,1), i=i+1, continue to S534 for annealing;

[0045] S538, determine whether the termination condition is met, if not, go to S532; otherwise, the individual with the largest fitness value in the current population is output as the optimal solution to the problem, representing the best allocation plan for sensor allocation.

[0046] In a specific embodiment, the specific method of S531 is: randomly allocating sensor resources to each target in order according to the threat level from high to low, and adding them to the allocated sensor resource set:

[0047]

[0048] Y=Y∪C i ;

[0049] Where C is the resource set of the final single-target solution; Y is the allocated sensor resource set; S i is the set of sensor resources available to the i-th target; size(S) is the number of elements in the set of sensor resources available to the target; x is the first sensor selected; y is the second sensor selected; is an empty set;

[0050] The specific method of S532 is:

[0051] Calculate the probability that the target can be successfully located:

[0052]

[0053] l>0 means it can be successfully located, l=0 means it cannot be successfully located;

[0054] Determine the number of locations weighted by target threat level:

[0055]

[0056] Determine the average accuracy of multi-target positioning weighted by target threat level:

[0057]

[0058] The fitness value is:

[0059] f=kf1+(1-k)f2;

[0060] In the formula, w i The threat level for each target; i is the probability that the target can be successfully located; g(C i ) is the positioning accuracy of the ith target; p+q is the gene position of the chromosome, that is, the total number of targets; k is the set value.

[0061] In a specific embodiment, the specific method of S533 is:

[0062] The process of selecting an operation is as follows:

[0063] The selection operation is carried out by pairing crossover inheritance. The selection mechanism is fitness proportional selection mechanism. The selection probability of each chromosome is:

[0064]

[0065] The process of crossover operation is as follows:

[0066] Randomly select two points in the two selected parent chromosomes to determine the matching crossover gene string. After exchanging the gene string between the two points to obtain two new chromosomes, they are compared with the original chromosomes and the excellent individuals are preserved.

[0067] The mutation operation process is as follows:

[0068] A substitution mutation-based method is adopted, that is, a replacement position is randomly selected, and the gene information at the current position is replaced with the gene information of a current individual that meets the constraints, so as to obtain a new chromosome, which is then compared with the original chromosome to preserve the excellent individuals.

[0069] A reconnaissance mission decomposition and sensor resource allocation system, characterized in that the system is used to implement a reconnaissance mission decomposition and sensor resource allocation method, including:

[0070] Information acquisition module: used to obtain sensor resource parameters; obtain the task type issued by the superior, as well as the location, quantity, threat level of the target, and the type and frequency band of the radiation source signal carried by the target;

[0071] Database module: including sensor resource information database and task information database;

[0072] Evaluation module: used to evaluate the probability of the sensor detecting the target;

[0073] Decomposition module: Based on the known information in the current scene, a hierarchical task network is used to decompose the tasks issued by the superior;

[0074] Allocation module: Execute each atomic task in sequence according to the task decomposition results, and generate a task resource allocation plan based on the existing sensor resources.

[0075] The present invention also provides an electronic device, comprising a memory and a processor, wherein the processor is used to implement the steps of the reconnaissance task decomposition and sensor resource allocation method when executing a computer management program stored in the memory.

[0076] The present invention also provides a computer-readable storage medium on which a computer management program is stored. When the computer management program is executed by a processor, the steps of the reconnaissance task decomposition and sensor resource allocation method are implemented.

[0077] Compared with the prior art, the present invention has the following beneficial effects:

[0078] The present invention constructs a database of sensor resource information and task information, adopts a hierarchical task network to decompose tasks issued by superiors, decomposes complex reconnaissance tasks into atomic tasks of multiple levels, and transforms high-level goals into low-level executable tasks through a clear hierarchical structure, making task planning more systematic, structured, and easy to understand and implement.

[0079] In the process of implementing atomic tasks, the present invention adopts a method combining genetic algorithm and simulated annealing algorithm to seek the global optimal solution for resource allocation problem, improve the quality and stability of the solution, and realize rapid rational and optimal allocation of resources. The present invention combines the task decomposition based on hierarchical task network with the optimization of genetic-simulated annealing algorithm, and can provide a reconnaissance task decomposition and resource allocation method with clear structure, flexible and efficient, rapid convergence and strong adaptability, which helps to improve the quality and efficiency of decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 It is a schematic diagram of the process of the present invention;

[0081] Figure 2 It is a schematic diagram of the framework of the task decomposition part of the present invention;

[0082] Figure 3 A schematic diagram of a process for allocating sensor resources to a single target of the present invention;

[0083] Figure 4 A schematic diagram of a flow chart for allocating sensor resources to multiple targets according to the present invention. DETAILED DESCRIPTION

[0084] 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.

[0085] Embodiment 1:

[0086] See also Figure 1 , a reconnaissance task decomposition and sensor resource allocation method, comprising the following steps:

[0087] S1. Obtain the task type issued by the superior, as well as all parameters such as the location, quantity, threat level, type and frequency band of the radiation source signal carried by the target, and build a task information database;

[0088] S2. Obtain all parameters of sensor resources, including type, quantity, location, working frequency band, status, detection range, etc., and build a sensor resource information database;

[0089] S3. Evaluate the probability of the sensor detecting the target and prepare for the subsequent sensor resource allocation task.

[0090] The mission types include multi-target positioning and single-target high-precision positioning; the type of radiation source carried by the target m Including two types of sensors: communication detection sensors and lightning detection sensors. m ∈{tz,lz}.

[0091] Since it is necessary to consider whether the detection range, type, and frequency band of the sensor match the distance of the target from the sensor, and the type and frequency band of the carried radiation source, the probability of the sensor detecting the target includes:

[0092] Since the attenuation of electromagnetic waves in space is proportional to the square of the distance, the probability of a single sensor detecting a target is:

[0093]

[0094] When there are n sensors searching for the same target, assuming they are independent of each other, the probability of them jointly finding the target is:

[0095]

[0096] in,

[0097]

[0098] Where D is the detection range of the sensor; d is whether the target can be detected by the sensor; s c is the type of sensor resource, s c ∈{tz,lz},tz i is the ith communication sensor, lz j is the jth lightning detection sensor; i, j are the sensor numbers, i = 1, 2, ..., m, j = 1, 2, ..., n; s m is the type of radiation source carried by the target, s m ∈{tz,lz}; r is the distance between the target and the sensor; f m is the target frequency band; f c is the sensor operating frequency band; x i is the probability that the i-th sensor discovers the target alone.

[0099] S4. Based on the known information in the current scene, a hierarchical task network is used to decompose the tasks issued by the superior. Figure 2 As shown, the specific steps are as follows:

[0100] S41: Selecting a corresponding composite task according to the task type, wherein the composite task includes multi-target positioning and single-target high-precision positioning.

[0101] S42: Select the next task according to the constraint method in the composite task.

[0102] In the multi-target positioning task:

[0103] The condition of constraint method0 is that the target signal type is only communication signal; the condition of constraint method1 is that the target signal type is only radar signal; the condition of constraint method2 is that the target signal type includes both communication signal and radar signal.

[0104] In the single target high-precision positioning task:

[0105] The condition of constraint method0 is that the target signal type is only communication signal, and the condition of constraint method1 is that the target signal type is only radar signal.

[0106] S43: split the task into executable atomic tasks according to the constraint conditions and task information. The atomic tasks include the available communication sensors for each target, determining the number of communication sensors to be used, allocating communication sensors to multiple targets, allocating communication sensors to a single target, etc.

[0107] S5: Execute each atomic task in turn according to the task decomposition result of S5, and generate the best resource allocation plan for the task in combination with the existing sensor resources.

[0108] The following steps are involved:

[0109] S51, determining a set of sensor resources available for each target; the number of targets includes single target and multiple targets.

[0110] The number of single targets is (taking the common detection sensor as an example):

[0111]

[0112] The number of multiple targets is (taking communication detection sensors and mine detection sensors as examples):

[0113] Define that there are p communication sensor targets in the air that need to be located, and q mine detection sensor targets that need to be located; then,

[0114]

[0115] Where S is the set of sensor resources available to the target; is the set of sensor resources available to the i-th target; Whether the target can be detected by the i-th communication sensor; Whether the target can be detected by the jth communication sensor; Whether the target can be detected by the kth mine detection sensor; tz i is the ith communication sensor; tz j is the jth communication sensor; lz k is the kth lightning detection sensor; i = 1, 2, ..., m + n; j = 1, 2, ..., p; k = 1, 2, ..., q.

[0116] S52, determining the amount of sensor resources used by each target;

[0117] The number of sensor resources used by a single target is the number of all available resources in the set of sensor resources available to the target; or,

[0118] Calculate the highest precision macc for simultaneous positioning of n resources n , n∈[2,size(S)-1], when |macc n+1 -macc n | is less than the set value k, then num = m, where macc m =min(macc n ,macc n+1 ); if the condition is not met, then num = m, where macc m =min(macc n );

[0119] Where S is the set of sensor resources available to the target; size(S) is the number of elements in the set of sensor resources available to the target; num is the number of sensor resources used by a single target; m is the number of sensor resources that achieves the highest accuracy; macc m It is the highest accuracy under the setting value k.

[0120] The number of sensor resources used by the multiple targets is:

[0121] To ensure the mission success rate and maximize resource utilization, each target is allocated 2 available sensor resources; if the number of available sensor resources for a target is less than 2, the positioning task of the target is abandoned and no sensor resources are allocated to it.

[0122] S53, allocate sensor resources to each target and obtain the best allocation solution, such as Figure 3 shown.

[0123] If the number of sensor resources used by a single target is size(S), the best allocation scheme is to allocate all resources in S; otherwise, the best allocation scheme is to take num resources and locate the highest accuracy at the same time.

[0124] Multi-target allocation of sensor resources Figure 4 As shown, the following steps are included:

[0125] S531, set the gene of the chromosome to p+q bits (the total number of targets), each gene bit is the number of the sensor resource selected for positioning of the target, based on the set of sensor resources available for each target, use the genetic algorithm to randomly generate a certain number of chromosomes as the initial population, representing a set of initial candidate allocation schemes. The population size is popsize, and the crossover probability is P c , the mutation probability is P m , the initial temperature is T0.

[0126] In a specific embodiment, sensor resources are randomly allocated to each target in sequence according to the principle of threat level from high to low, and added to the allocated sensor resource set Y.

[0127]

[0128] Y=Y∪C i ;

[0129] Where C is the resource set of the final single-target solution; Y is the allocated sensor resource set; S i is the set of sensor resources available to the i-th target; size(S) is the number of elements in the set of sensor resources available to the target; x is the first sensor selected; y is the second sensor selected; is an empty set;

[0130] S532. Calculate the fitness value of each chromosome (each individual) in the current population.

[0131] l represents the probability that the target can be successfully located. l>0 means that it may be successfully located, and l=0 means that it cannot be successfully located.

[0132]

[0133] The number of locations weighted by target threat level:

[0134]

[0135] The average accuracy of multi-target positioning weighted by target threat level:

[0136]

[0137] The fitness value is:

[0138] f=kf1+(1-k)f2;

[0139] In the formula, w i is the threat level of each target, w i ∈{1,2,3}, the larger the number, the higher the threat level; l i is the probability that the target can be successfully located; g(C i ) is the positioning accuracy of the ith target; p+q is the gene position of the chromosome, that is, the total number of targets; k is the set value, k=0.9.

[0140] S533. Perform selection, crossover and mutation on the initial population to obtain the next generation population.

[0141] The process of selecting an operation is as follows:

[0142] The selection operation is carried out by pairing crossover inheritance. The selection mechanism is fitness proportional selection mechanism (fitnessproportional mode 1), also called roulette wheel or Monte Carlo selection. The selection probability of each chromosome is:

[0143]

[0144] In the formula, f i is; popsize is the population size.

[0145] The probability of selection is proportional to its fitness value, and the chromosome with a large fitness value is more likely to be selected. At the same time, the best individual preservation method (elitistmod.e1) is used to copy the chromosome with the largest fitness value in the population directly to the next generation without pairing and crossover, so as to ensure that the maximum fitness value of the new generation population will not be less than that of the previous generation.

[0146] The process of crossover operation is as follows:

[0147] Randomly select two points in the two selected parent chromosomes to determine the matching crossover gene string. After exchanging the gene string between the two points to obtain two new chromosomes, they are compared with the original chromosomes to save the excellent individuals. If the offspring obtained after the crossover operation does not meet the specified constraints, the corresponding adjustment strategy needs to be further adopted.

[0148] In a specific embodiment, assume that the chromosomes of the two parents are z1 and z2, and the position of the randomly initialized crossover point is x. x The elements in belong to Y z2 , then it is necessary to adjust the resources of the conflict outside the overlapping area.

[0149] The specific adjustment method is: in the available sensor resource set C of the target where the conflicting resource is located, reselect a sensor resource that has not been used by the chromosome to replace the conflicting resource. If no replaceable resource can be found, reselect the position of the intersection to perform the crossover operation.

[0150] The mutation operation process is as follows:

[0151] A substitution mutation-based method is adopted, that is, a replacement position is randomly selected, and the gene information at the current position is replaced with the gene information of a current individual that meets the constraints, so as to obtain a new chromosome, which is then compared with the original chromosome to preserve the excellent individuals.

[0152] In a specific embodiment, assume that the chromosome to be mutated is z, and a mutation point x is randomly generated. In order to ensure that the newly generated chromosome after mutation meets the conditions, first check how many elements in the available sensor resource set C corresponding to the target of this mutation point are not in the set Y. z If there are two or more, randomly select two of them to perform mutation operation on the chromosome; if there is only one, randomly select one element to perform mutation operation; if there is none, regenerate the mutation point and repeat the above work.

[0153] S534, the next generation population obtained after the above steps is perturbed by simulated annealing method (such as exchange operations) to generate a new solution, and the fitness values ​​of the solutions before and after the perturbation are calculated respectively, and the increment Δf=f between the initial solution and the new solution is obtained. j -f i ;f i is the fitness value of the initial solution; f j is the fitness value of the new solution.

[0154] S535, apply the Meteopolis rule to determine whether to accept the new solution. If Δf<0, accept the new solution and go to S536; otherwise, generate a random number ξ=U(0,1). If Accept the new solution.

[0155] S536, determine whether thermal equilibrium is reached (reaching the preset number of internal cycles), if so, go to the next step, otherwise go to S534.

[0156] S537, perform cooling operation: when the temperature T at time i i If the temperature is lower than the set threshold, the annealing process ends; otherwise, T i+1 =αT i , α∈(0,1), i=i+1, and continue to S534 for annealing.

[0157] S538. Determine whether the termination condition (maximum number of iterations) is met. If not, go to S532; otherwise, the individual with the largest fitness value in the current population is output as the optimal solution to the problem, representing the best allocation plan for sensor allocation.

[0158] Embodiment 2:

[0159] The present invention also provides a reconnaissance mission decomposition and sensor resource allocation system, the system is used to implement the steps of the reconnaissance mission decomposition and sensor resource allocation method, including:

[0160] Information acquisition module: used to obtain sensor resource parameters; obtain the task type issued by the superior, as well as the location, quantity, threat level of the target, and the type and frequency band of the radiation source signal carried by the target;

[0161] Database module: including sensor resource information database and task information database;

[0162] Evaluation module: used to evaluate the probability of the sensor detecting the target;

[0163] Decomposition module: Based on the known information in the current scene, a hierarchical task network is used to decompose the tasks issued by the superior;

[0164] Allocation module: Execute each atomic task in sequence according to the task decomposition results, and generate a task resource allocation plan based on the existing sensor resources.

[0165] Embodiment 3

[0166] The present invention also provides a device, including a memory and a processor, wherein the processor is used to implement the steps of the reconnaissance task decomposition and sensor resource allocation method when executing a computer management program stored in the memory.

[0167] Embodiment 4

[0168] The present invention also provides a computer-readable storage medium on which a computer management program is stored. When the computer management program is executed by a processor, the steps of the reconnaissance task decomposition and sensor resource allocation method are implemented.

Claims

1. A method for reconnaissance mission decomposition and sensor resource allocation, characterized in that: The following steps are involved: S1. Obtain the task type and various parameters of the target issued by the superior, and build a task information database; S2, obtaining sensor resource parameters and building a sensor resource information database; S3, evaluate the probability of the sensor finding the target and prepare for the subsequent sensor resource allocation task; S4. Based on the known information in the current scene, a hierarchical task network is used to decompose the tasks issued by the superior; S5: Execute each atomic task in sequence according to the task decomposition result of S4, and generate a task resource allocation plan based on the existing sensor resources.

2. A method for reconnaissance mission decomposition and sensor resource allocation according to claim 1, characterized in that: In S1, the task types include multi-target positioning and single-target high-precision positioning; the various parameters of the target include the target's location, quantity, threat level, type and frequency band of the radiation source signal carried by the target; in S2, the sensor resource parameters include the type, quantity, location, working frequency band and detection range of sensor resources.

3. The method for reconnaissance mission decomposition and sensor resource allocation according to claim 1, characterized in that: In S3, the probability of the sensor finding the target includes: The probability of a single sensor detecting a target: The probability of multiple sensors detecting a target: in, Where D is the detection range of the sensor; d is whether the target can be detected by the sensor; s c is the type of sensor resource, s c ∈{tz,iz},tz i is the ith communication sensor, lz j is the jth lightning detection sensor; i, j are the sensor numbers, i = 1, 2, ..., m, j = 1, 2, ..., n; s m is the type of radiation source carried by the target, s m ∈{tz,lz}; r is the distance between the target and the sensor; f m is the target frequency band; f c The sensor operating frequency band.

4. A method for reconnaissance mission decomposition and sensor resource allocation according to claim 1, characterized in that: S4 includes the following steps: S41. Select a multi-target positioning task or a single-target high-precision positioning task according to the task type; S42. Select the next task according to the task constraint method: In the multi-target positioning task: the constraint method 0 is that the target signal type is only the communication signal, the constraint method 1 is that the target signal type is only the radar signal, and the constraint method 2 is that the target signal type includes both communication signals and radar signals; In the single-target high-precision positioning task: the condition of constraint method 0 is that the target signal type is only communication signal, and the condition of constraint method 1 is that the target signal type is only radar signal; S43, splitting the task into executable atomic tasks according to the constraint conditions and task information; the atomic tasks include the available communication sensors for each target, determining the number of communication sensors to be used, allocating communication sensors to multiple targets, and allocating communication sensors to a single target.

5. The method for reconnaissance mission decomposition and sensor resource allocation according to claim 1, characterized in that: S5 includes the following steps: S51, determining a set of sensor resources available for each target; S52, determining the amount of sensor resources used by each target; S53. Allocate sensor resources to each target.

6. A method for reconnaissance mission decomposition and sensor resource allocation according to claim 5, characterized in that: In S52, the number of the targets is either a single target or multiple targets; The number of sensor resources used by a single target is the number of all available resources in the set of sensor resources available to the target; or, Calculate the highest precision macc for simultaneous positioning of n resources n , n∈[2, size(S)-1], when |macc n+1 -macc n | is less than the set value k, then num = m, where macc m =min(macc n ,macc n+1 ); if the condition is not met, then num = m, where macc m =min(macc n ); Where S is the set of sensor resources available to the target; size(S) is the number of elements in the set of sensor resources available to the target; num is the number of sensor resources used by a single target; m is the number of sensor resources that achieves the highest accuracy; macc m It is the highest accuracy under the set value k; The number of sensor resources used by multiple targets is as follows: 2 available sensor resources are allocated to each target; if the number of sensor resources available to a target is less than 2, the positioning task of the target is abandoned and no sensor resources are allocated to it.

7. A method for reconnaissance mission decomposition and sensor resource allocation according to claim 5, characterized in that: In S53, if the number of sensor resources used is the number size(S) of elements in the set of sensor resources available to the target, the single-target optimal allocation solution is to allocate all resources in the set S of sensor resources available to the target; otherwise, the solution that simultaneously locates the highest accuracy using num resources is the optimal allocation solution; In the formula, num is the number of sensor resources used by a single target; Multi-target allocation sensor resource allocation includes the following steps: S531, based on the sensor resource set available for each target, using a genetic algorithm to randomly generate a certain number of chromosomes as an initial population, wherein the chromosomes are initial candidate allocation schemes; S532, calculating the fitness value of each chromosome in the current population; S533, select, crossover and mutate the initial population to obtain the next generation population; S534, the next generation population obtained after the above steps is disturbed by a simulated annealing method to generate a new solution, and the fitness values ​​of the solutions before and after the disturbance are calculated respectively to obtain an increment Δf between the initial solution and the new solution; S535, apply the Meteopolis rule to determine whether to accept the new solution, if Δf<0, then accept the new solution and go to S536; otherwise generate a random number ξ=U(0,1), if it satisfies Then accept the new solution; S536, judging whether thermal equilibrium is reached, if so, proceeding to the next step, otherwise proceeding to S534; S537, perform cooling operation: when the temperature T i If the temperature is lower than the set threshold, the annealing process ends; otherwise, T i+1 =αT i , α∈(0,1), i=i+1, continue to S534 for annealing; S538, determine whether the termination condition is met, if not, go to S532; otherwise, the individual with the largest fitness value in the current population is output as the optimal solution to the problem, representing the best allocation plan for sensor allocation.

8. A method for reconnaissance mission decomposition and sensor resource allocation according to claim 7, characterized in that: The specific method of S531 is: randomly allocate sensor resources to each target in order from high to low threat levels, and add them to the allocated sensor resource set: Y=Y∪C i ; Where C is the resource set of the final single-target solution; Y is the allocated sensor resource set; S i is the set of sensor resources available to the i-th target; size(S) is the number of elements in the set of sensor resources available to the target; x is the first sensor selected; y is the second sensor selected; is an empty set; The specific method of S532 is: Calculate the probability that the target can be successfully located: l>0 means it may be successfully located, l=0 means it cannot be successfully located; Determine the number of locations weighted by target threat level: Determine the average accuracy of multi-target positioning weighted by target threat level: The fitness value is: f=kf1+(1-k)f2; In the formula, w i The threat level for each target; i is the probability that the target can be successfully located; g(C i ) is the positioning accuracy of the ith target; p+q is the gene position of the chromosome, that is, the total number of targets; k is the set value.

9. A method for reconnaissance mission decomposition and sensor resource allocation according to claim 7, characterized in that: The specific method of S533 is: The process of selecting an operation is as follows: The selection operation is carried out by pairing crossover inheritance. The selection mechanism is fitness proportional selection mechanism. The selection probability of each chromosome is: The process of crossover operation is as follows: Randomly select two points in the two selected parent chromosomes to determine the matching crossover gene string. After exchanging the gene string between the two points to obtain two new chromosomes, they are compared with the original chromosomes and the excellent individuals are preserved. The mutation operation process is as follows: A substitution mutation-based method is adopted, that is, a replacement position is randomly selected, and the gene information at the current position is replaced with the gene information of a current individual that meets the constraints, so as to obtain a new chromosome, which is then compared with the original chromosome to preserve the excellent individuals.

10. A reconnaissance mission decomposition and sensor resource allocation system, characterized in that: The system is used to implement a reconnaissance task decomposition and sensor resource allocation method as described in any one of claims 1 to 9, comprising: Information acquisition module: used to obtain sensor resource parameters; obtain the task type issued by the superior, as well as the location, quantity, threat level of the target, and the type and frequency band of the radiation source signal carried by the target; Database module: including sensor resource information database and task information database; Evaluation module: used to evaluate the probability of the sensor detecting the target; Decomposition module: Based on the known information in the current scene, a hierarchical task network is used to decompose the tasks issued by the superior; Allocation module: Execute each atomic task in sequence according to the task decomposition results, and generate a task resource allocation plan based on the existing sensor resources.

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