A method for assigning target measurement tasks in an optoelectronic network based on a KM algorithm

Abstract

The present application relates to the field of foundation multi-task multi-target joint measurement, in order to solve the problems of long distribution time, low efficiency, unreasonable matching mode planning and other problems of the existing joint measurement mode, and proposes a kind of photoelectric network target measurement task allocation method based on KM algorithm, comprising the following steps: S1, set the target observation task to H, observe H tasks by n devices, calculate the single operator of each device according to the set attribute of each device;S2, according to the calculation result of single operator, calculate the total scheduling operator when two devices are combined, and observe the grouping of n devices and H tasks, form the initial task-device combination;S3, construct task-device combination bipartite graph, and carry out maximum weight matching on the initial task-device combination through KM algorithm;S4, update the observation grouping of each device combination and H tasks, form the optimal matching result of the final task-device combination, and then complete the allocation of photoelectric network target measurement task.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of foundation multi-task multi-target joint measurement, and particularly relates to a photoelectric network target measurement task allocation method based on a KM algorithm. BACKGROUND

[0002] For the joint measurement scene of multiple devices and multiple tasks, a single intelligent measurement device is usually used for measurement, but due to the limited observation ability and observation distance of the single intelligent measurement device, the target cannot be tracked throughout the process. Therefore, single intelligent measurement devices with different sensors are combined into a photoelectric network to achieve joint measurement of the entire target trajectory through target task autonomous allocation. The specific idea of the measurement is as follows: multiple devices are respectively deployed at different sites, and the observation target and observation sequence of each device are reasonably planned and scheduled by the command center according to the task target characteristics, device attributes, and the time of random task occurrence, so as to achieve multi-target, omnidirectional, and high-efficiency networking detection.

[0003] Currently, the joint measurement task is generally solved by calculating the site in advance, and the position of the mobile device is deployed manually to achieve joint measurement of the entire trajectory of the target by the device. However, the measurement method of calculating the site in advance has limited intelligent level, low efficiency, and poor scalability. With the change of task target characteristics and the increase in the number of task targets, the measurement method has problems such as long measurement allocation time, low measurement efficiency, and unreasonable matching mode planning between task targets and measurement devices, which cannot meet the task requirements of rapid intelligent joint measurement of multiple tasks and multiple devices. SUMMARY

[0004] The purpose of the present application is to solve the problems of long measurement allocation time, low measurement efficiency, and unreasonable matching mode planning between task targets and measurement devices in the existing joint measurement method, and to propose a photoelectric network target measurement task allocation method based on a KM (kuhn-munkres) algorithm.

[0005] The design idea of the present application is as follows: a quantitative analysis model for joint rendezvous measurement is designed, a multi-theodolite joint detection task allocation algorithm is designed based on a KM algorithm by designing a scheduling operator, and the observation target and time of each device are reasonably planned and scheduled by the command center according to the task target characteristics, device attributes, and random task targets. Through fast and real-time task allocation and scheduling, multi-target, omnidirectional, and high-efficiency networking detection is achieved.

[0006] In order to achieve the above purpose, the technical solution proposed by the present application is as follows:

[0007] A photoelectric network target measurement task allocation method based on a KM algorithm, characterized by comprising the following steps:

[0008] S1, set the target observation task as H, observe the H tasks through n devices, calculate the single-item operator of each device according to the set attribute of each device; wherein, n≥2H;

[0009] S2, calculate the total scheduling operator when two devices are combined according to the calculation result of the single-item operator in step S1, and observe the grouping of n devices and H tasks according to the calculation results of the single-item operator and the total scheduling operator, to form an initial task-device combination;

[0010] S3, group the n devices two by two to form device combinations, construct a task-device combination bipartite graph, and perform maximum weight matching on the initial task-device combination through the KM algorithm;

[0011] S4, update the observation grouping of device combinations and H tasks according to the maximum weight matching result of step S3 to form the optimal matching result of the task-device combination, and further complete the allocation of the target measurement task of the optoelectronic network.

[0012] Further, in step 1, the single-item operator includes a health state operator P i1 , a task priority operator P i2 , an observation distance effectiveness operator P i3 , a meeting accuracy operator P i4 , a task switching influence degree operator P i5 , and a task maneuverability matching operator P i6 .

[0013] Step S1 is specifically:

[0014] S1.1, set the target observation task as H, observe the H tasks through n devices, and calculate the health state operator P i1 of each device according to the total running time of each device from the start of the planned task to the current time when the target measurement task is called to execute, which satisfies the following formula:

[0015]

[0016] Wherein: i is any device in the n devices, is the running time of the i-th device, t now is the total running time from the start of the planned task to the current time;

[0017] S1.2, set the importance level table IM of each task in the H tasks;

[0018] Wherein, IM=[imp1, imp2,…, imp h ], imp h∈(0,1), h = 1, 2...H;

[0019] According to the task importance level table IM, the priority of each task is calculated i2 , which satisfies the following formula:

[0020]

[0021] Where s i = 0 indicates that the i-th device is idle, s i = 1 indicates that the i-th device is occupied; imp h_old is the importance level of the previous task, imp h_new is the importance level of the new task;

[0022] S1.3, according to the straight line distance between each device and the target observation task trajectory point, the observation distance validity operator P i3 between the target observation task and each device is calculated, which satisfies the following formula:

[0023]

[0024] Where d AT-TAR is the straight line distance between the i-th device and the current target trajectory point of the target observation task, d 有效 is the maximum detectable distance of the i-th device to the target observation task;

[0025] S1.4, set the device combination to (i,j), and calculate the intersection accuracy operator P i4 of device i according to the measurement intersection angle between the device combination (i,j) and the target observation task, which satisfies the following formula:

[0026]

[0027] Where: j is any one of the n devices except the i-th device, h is any one of the H tasks, a ihj ∈(0°, 180°), μ is the mean of the Gaussian distribution, μ = 90°, σ is the standard deviation of the Gaussian distribution, σ = 40; P ij4 is the intersection accuracy operator of the device combination (i,j); a ihj is the measurement intersection angle between the device combination (i,j) and the target observation task;

[0028] S1.5, set the task switching influence degree operator to P i5 , which satisfies the following formula:

[0029]

[0030] S1.6, according to the matching of the optical characteristics and the maneuverability requirements of each device observed in each task, set the matching table of task h and the i-th device as MAT, MAT = [mat h1 , mat h2 , …, mat hn ], mat hi ∈(0, 1);

[0031] Calculate the task maneuverability matching operator P i6 , which satisfies the following formula:

[0032] P i6 = mat hi .

[0033] Further, step S2 is specifically:

[0034] S2.1, calculate the total scheduling operator V h of the device combination (i, j) when observing task h, which satisfies the following formula:

[0035] V h (i, j) = p h ×eval h

[0036] Where p h is the magnitude operator, and eval h is the threshold operator;

[0037] S2.2, adjust the weight w(x i , y j ) of the total scheduling operator value V h of the uniform device combination (i, j) so that the magnitude operator p h satisfies the following formula:

[0038]

[0039]

[0040] Where w b is a larger weight value, w s is a smaller weight value, w b = 0.7, w s = 0.3, p i is the magnitude operator of device i, and p j is the magnitude operator of device j;

[0041] If the actual distance exceeds the detection distance of the device, the device combination (i, j) cannot work together, and the total scheduling operator value V h is 0, and the threshold operator eval h satisfies the following formula:

[0042] eval h = p i3 × p j3 ;

[0043] S2.3, randomly observing and grouping n devices and H tasks according to the calculation results of the single operator and the total scheduling operator to form an initial task-device combination.

[0044] Further, step S3 is specifically:

[0045] S3.1, grouping n devices two by two to form device combinations (i, j); setting device combinations (i, j) as set X and H tasks as set Y, the number of elements of set X and set Y is unified as N, and If the number of tasks is small, the task H is supplemented, and the connection weight of the supplemented task and each device combination is 0;

[0046] S3.2, constructing a bipartite graph G (V, E) through the connection edges and nodes between set X and set Y, the bipartite graph G (V, E) satisfies the following formula:

[0047] G (V, E) = (X, Y): X = {x1, x2,..., x i ,...x N}, Y = {y1, y2,..., y j ,...y N};

[0048] Wherein, V is all nodes in the bipartite graph, E is the connection edge between all nodes in the bipartite graph, x i is the node on set X, and y j is the node on set Y;

[0049] The matching edge weight corresponding to the connection node pair of x i and y j is w (x i , y j ); The connection edge weight of each edge on set X and set Y is marked as p ij = w (x i , y j );

[0050] S3.3, by adding adjustable node value l (x i ) to each node x i on set X in the bipartite graph G (V, E) and adding adjustable node value l (y j ) to each node y j on set Y, so that it always satisfies l (x i ) + l (yj ) = w(x i ,y j ), and find an augmenting path by adjusting the node value l(V i ) to maximize the matching of the initial task-device combination.

[0051] Further, step S3.3 is specifically as follows:

[0052] S3.3.1, initialize all adjustable node values l(y j ) of the set Y in the bipartite graph G(V, E) to 0, and the corresponding adjustable node values l(x i ) of the set X are the maximum weight values, which satisfy the following formula:

[0053]

[0054] The sum of the nodes of the set X and the set Y satisfies the following formula:

[0055]

[0056] The nodes of the bipartite graph satisfying the above adjustable node value relationship are denoted as l;

[0057] S3.3.2, take the connecting edges of the set X and the set Y satisfying the equal condition of the following formula to form the initial bipartite graph G(V, E l ), and denote it as the equal subgraph;

[0058]

[0059] Obtain the matching M (M ∈ E l ) of the initial bipartite graph G(V, E l );

[0060] S3.3.3, according to the KM lemma, obtain the perfect matching of the equal subgraph formed by the marked nodes l, and the corresponding matching M is the maximum weight matching.

[0061] Further, step S3.3.3 is specifically as follows:

[0062] A, judge whether the matching M of the equal subgraph of the obtained initial bipartite graph G(V, E l ) is a perfect matching, that is, whether all nodes V belong to the matching M, and the matching edge is the most under the matching mode;

[0063] If yes, the matching M satisfies the KM lemma, and the maximum weight matching, that is, the optimal matching result of the task-device combination, is obtained, and the algorithm process is ended;

[0064] If not, for the initial bipartite graph G(V, E l) that are not matched, u e X, and form an initial set S = {u} and where is an empty set, X, define N l (s) is the set of unmatched nodes, as follows: N l (u) = {v: (u, v) e E l}, N l (s) = U u∈S N l (u);

[0065] B, when N l (s) = T, update the node value and N l (s), so that N l (s) ≠ T, produce an augmented path;

[0066]

[0067]

[0068] where, α l is the node change amount;

[0069] C, when N l (s) ≠ T, select y j e N l (s) - T;

[0070] If y j has not been matched, produce an augmented path with the existing match, and realize the match of y j in the graph G (V, E l );

[0071] If y j has been matched, the matched point is set as z, then S = S U {z} and T = T U {y j};

[0072] Check if it is a perfect match, if so, it is the maximum weight matching, end the matching, if not, return to B, until it is a perfect match.

[0073] The beneficial effects of the present application are:

[0074] The application discloses a target measurement task allocation method based on a KM algorithm for an optoelectronic network.

[0075] The application can accurately obtain device health states, task priorities, observation distance effectiveness, rendezvous precision, task switching influence degrees and the like by calculating single operators of each device, can obtain measurement effect evaluation indexes of a target by a combined device by calculating total scheduling operators of the combined device, and can select matching results of the device combination and the task target by the KM algorithm, so that the overall device combination measurement effect is optimal, and multi-target, omnidirectional and high-efficiency network detection can be quickly and accurately completed.

[0076] The application can convert the target allocation problem in the measurement task into a maximum weight matching problem by constructing a bipartite graph of the device combination and the task target, can calculate the overall optimal task target allocation result by the KM algorithm, can find the globally optimal matching scheme, and can quickly and real-timely perform task allocation and scheduling. BRIEF DESCRIPTION OF DRAWINGS

[0077] Figure 1 A flowchart of the target measurement task allocation method based on the KM algorithm for the optoelectronic network in the embodiment of the application is shown in the figure.

[0078] Figure 2 A bipartite graph in step S3 in the embodiment of the application is shown in the figure.

[0079] Figure 3 A device station and target trajectory diagram when the target to be measured is a UAV, a missile and a UAV cluster in the measurement task allocation in the embodiment of the application is shown in the figure.

[0080] Figure 4 A matching condition diagram of different time, different targets and device combinations when the target to be measured is a UAV, a missile and a UAV cluster in the measurement task allocation in the embodiment of the application is shown in the figures.

[0081] (a) is a matching condition diagram at t11.

[0082] (b) is a matching condition diagram at t15.

[0083] (c) is a matching situation schematic diagram at t31 time;

[0084] (d) is a matching situation schematic diagram at t79 time;

[0085] (e) is a matching situation schematic diagram at t151 time;

[0086] (f) is a matching situation schematic diagram at t275 time. DETAILED DESCRIPTION

[0087] In the multi-task multi-target joint detection process, the detection idea of the embodiment is: first, the total operator of each group of devices detecting each task is calculated respectively. Since each observation target has multiple device combinations satisfying the observation condition, but different observation target characteristics requirements and device preferences are different, the state and coordinate position of the device combination are different, and the total operator is calculated. The operator value is used as the connection weight of the task and the device, and the maximum weight matching result of the bipartite graph composed of the task and the device can be obtained, that is, the measurement device combination for all tasks is found, and the total scheduling operator of multiple tasks is maximized. When the number of tasks and the number of device combinations are not equal, in order to apply the KM algorithm to solve the maximum weight matching of the bipartite graph, the following processing can be performed: supplement the points in the set with fewer points, so that the number of points on both sides is the same, and then set the weight of the non-existent edge to be infinitely small, and convert it into a maximum weight perfect matching problem, so as to apply the KM algorithm to solve.

[0088] As shown in Figure 1 , a photoelectric network target measurement task allocation method based on the KM algorithm comprises the following steps:

[0089] S1, set the target observation task to H, observe the H tasks through n devices, and calculate the single operator of each device according to the set attribute of each device; wherein n≥2H;

[0090] S1.1, set the target observation task to H, observe the H tasks through n devices, and calculate the health state operator P of each device according to the total running time of each device from the beginning of the planning task to the current time when it is called to execute the target measurement task. i1 , which satisfies the following formula:

[0091]

[0092] Wherein: i is any device in n devices, is the running time of the i-th device, t now is the total running time from the beginning of the planning task to the current time;

[0093] S1.2, set the importance level table IM of each task in the H tasks;

[0094] where IM = [imp1, imp2, …, impn], impn∈(0, 1), h = 1, 2, …, H. h h

[0095] According to the task importance level table IM, the priority of each task operator P i2 is calculated, which satisfies the following formula:

[0096]

[0097] where s i i = 0 indicates that the ith device is idle, s i i = 1 indicates that the ith device is occupied; imp h_old is the importance level of the previous task, and imp h_new is the importance level of the new task.

[0098] S1.3, according to the straight-line distance between each device and the target observation task trajectory point, the observation distance validity operator P i3 between the target observation task and each device is calculated, which satisfies the following formula:

[0099]

[0100] where d AT-TAR i is the straight-line distance between the ith device and the current target trajectory point of the target observation task, and d 有效 i is the maximum detectable distance of the ith device to the target observation task.

[0101] S1.4, set the device combination to (i, j), and calculate the intersection accuracy operator P i4 of device i according to the measurement intersection angle between the device combination (i, j) and the target observation task, which satisfies the following formula:

[0102]

[0103] where j is any one of the n devices except the ith device, h is any one of the H tasks, a ihj ∈(0°, 180°), μ = 90°, σ = 40, μ is the mean of the Gaussian distribution, and σ is the standard deviation of the Gaussian distribution; P ij4 is the intersection accuracy operator of the device combination (i, j); a ihj is the measurement intersection angle between the device combination (i, j) and the target observation task.

[0104] S1.5, set the task switching influence degree operator P i5 , which satisfies the following formula:

[0105] ​​

[0106] S1.6. Based on the matching of the optical characteristics and mobility requirements of each device for each mission observation, the matching table between mission h and the i-th device is set as MAT, MAT = [mat h1 ,mat h2 ,…,mat hn ], mat hi ∈(0,1);

[0107] Computation task mobility matching operator P i6 It satisfies the following formula:

[0108] P i6 =mat hi .

[0109] S2. Based on the calculation results of the individual operators in step S1, calculate the overall scheduling operator when combining two devices. Based on the calculation results of the individual operators and the overall scheduling operator, randomly observe and group the n devices and H tasks to form the initial task-device combination.

[0110] S2.1, The overall scheduling operator V for the computing device combination (i,j) during observation task h. h It satisfies the following formula:

[0111] V h (i,j)=p h ×eval h

[0112] Where, p h For the magnitude operator, eval h For threshold operators;

[0113] S2.2, Adjusting weight w(x) i ,y j The total scheduling operator value V of the uniform equipment combination (i,j) h This makes the magnitude operator p h Satisfy the following formula:

[0114]

[0115]

[0116] Among them, w b For larger weight values, w s For smaller weight values, w b =0.7, w s =0.3, p i For the quantity operator of device i, p j For the quantity operator of device j;

[0117] If the actual distance exceeds the detection distance of the device, the device combination (i, j) cannot work together, at which time the total scheduling operator value V h is 0, eval h The threshold operator satisfies the following formula:

[0118] eval h = p i3 x p j3 ;

[0119] S2.3, according to the calculation results of the single operator and the total scheduling operator, randomly observe the grouping of n devices and H tasks to form an initial task-device combination.

[0120] S3, group the n devices two by two to form device combinations, construct a task-device combination bipartite graph, and perform maximum weight matching on the initial task-device combination through the KM algorithm;

[0121] S3.1, group the n devices two by two to form device combinations (i, j); set device combinations (i, j) as set X, and the H tasks as set Y, unify the number of elements of set X and set Y to N, and If the number of tasks is small, then the tasks H are supplemented, and the connection weight of the supplemented tasks and each device combination is 0;

[0122] S3.2, construct a bipartite graph G (V, E) through the connection edges and nodes between set X and set Y, which satisfies the following formula:

[0123] G (V, E) = (X, Y): X = {x1, x2,..., x i ,...x N}, Y = {y1, y2,..., y j ,...y N};

[0124] Wherein, V is all nodes in the bipartite graph, E is the connection edge between all nodes in the bipartite graph, x i is the node on set X, and y j is the node on set Y;

[0125] The matching edge weight corresponding to the connection node pair of x i and y j is w (x i , y j ); the connection edge weight of each edge on set X and set Y is marked as p ij = w (x i , y j );

[0126] S3.3, By defining each node x on the set X in the bipartite graph G(V,E) i Add adjustable node value l(x) i ), each node y in set Y j Add adjustable node value l(y) j ), so that it always satisfies l(x) i )+l(y j )=w(x i ,y j ), and by adjusting the node value l(V) i Find augmenting paths and perform maximum weight matching on the initial task-device combination.

[0127] S3.3.1, Set the adjustable node values ​​l(y) of set Y in the bipartite graph G(V,E). j If all nodes in set X are initialized to 0, then the adjustable node value l(x) corresponding to set X is... i The maximum weight value is given by the following formula:

[0128]

[0129] The sum of the nodes of set X and set Y satisfies the following equation:

[0130]

[0131] The bipartite graph node that satisfies the above adjustable node value relationship is denoted as l;

[0132] S3.3.2. Take the edges connecting set X and set Y that satisfy the following equality condition to form an initial bipartite graph G(V,E). l ), and denote them as equal subgraphs;

[0133]

[0134] Obtain the initial bipartite graph G(V,E) l Matching M (M∈E) l );

[0135] S3.3.3 According to the KM lemma, if a perfect matching exists in the equal subgraphs formed by a node l in a bipartite graph after marking, then the corresponding matching M is the maximum weight matching.

[0136] A. Determine the initial bipartite graph G(V,E) obtained. l Is the matching M of the equal subgraphs in the equation a perfect matching, i.e., are all nodes V all belonging to matching M? Under this matching method, the matching edges are the most.

[0137] If yes, the matching M satisfies the KM lemma, and the maximum weight matching, i.e. the optimal matching result of task-device combination, is obtained, and the algorithm flow is ended.

[0138] If no, for the unmatched node u in the initial bipartite graph G(V, E l ), u∈set X, and an initialization set S={u} and is formed, where is an empty set, set X, N l (s) is defined as the set of unmatched nodes, as follows: N l (u)={v:(u,v)∈E l}, N l (s)=∪ u∈S N l (u);

[0139] B. When N l (s)=T, the node value and N l (s) are updated, so that N l (s)≠T, and an augmented path is generated;

[0140]

[0141]

[0142] where α l is the node change amount;

[0143] C. When N l (s)≠T, y j ∈N l (s)-T is selected;

[0144] If y j is not matched, an augmented path is generated with the existing matching, and the matching of y l is realized in the graph G(V, E j );

[0145] If y j is already matched, and the matched point is z, then S=S∪{z} and T=T∪{y j};

[0146] It is checked whether it is a perfect matching. If yes, it is a maximum weight matching, and the matching is ended. If no, it returns to B, until it is a perfect matching.

[0147] S4. The maximum weight matching result according to step S3 can be periodically updated with the observation grouping of H task-device combinations, and the optimal matching result of task-device combination is formed, and the allocation of the photovoltaic network target measurement task is completed.

[0148] This embodiment is to measure the size of the measurement area is 2.5km*100km, the target to be measured for unmanned aerial vehicle, bomb and unmanned aerial vehicle cluster measurement task allocation, in different locations, 10 theodolites with different observation ability, the real-time trajectory measurement of all targets in the observation area, in the northeast coordinate system, theodolite layout position and equipment characteristics are shown in Table 1, wherein the coordinate origin is (99.01°, 43.01°, 0) in the LBH coordinate system;

[0149] Table 1 Theodolite station position and characteristics

[0150]

[0151]

[0152] The target to be measured is an airplane, a bomb, and an unmanned aerial vehicle, which appears at different times with different speeds, and the target parameters and equipment preferences are shown in Table 2.

[0153] Table 2 Task attributes of the target to be measured

[0154]

[0155] First, initialize the 10 theodolites, initialize the station position of each theodolite, so that each theodolite selects three adjacent theodolites to form a rendezvous measurement device combination, a total of five measurement combinations; Because new target points may appear at any time in the measurement environment, and the trajectory of the target point to be measured changes in real time with time, therefore, set a period T to perform device combination measurement task redistribution; At time nT, the command center calculates the total scheduling operator of each device combination for each target point according to the coordinate position and characteristics of all device combinations, and the trajectory point coordinates of all measurement tasks at the current time.

[0156] As shown in Figure 3 the device station and target trajectory, the theodolites are randomly arranged on both sides of the measurement area, and in the task time period, the observation area contains four targets, and the airplane, bomb 1, bomb 2 and unmanned aerial vehicle appear at times t1, t12, t50 and t20 respectively; Therefore, the real-time scheduling of 10 devices and 4 targets is performed through the task allocation algorithm.

[0157] Construct a bipartite graph of device combinations and tasks, and the connection weight is the total scheduling operator corresponding to each device combination and target point, and find the maximum weight matching result through the KM algorithm;

[0158] Figure 4The matching conditions of different time (t11, t15, t31, t79, t151, t275), different targets and equipment combinations are respectively shown in (a)-(f); after the unmanned aerial vehicle target appears, the unmanned aerial vehicle target can be allocated to the intelligent agent combination (1, 2) through the task matching algorithm; the experimental results show that through the task allocation algorithm, the intelligent planning and deployment and measurement of multiple targets and temporary targets can be realized, the measurement results can be further quantified by using the scheduling operator, the sum of all equipment scheduling operators in the task re-allocation process is maximized, and thus the optimal task allocation and scheduling scheme is found.

Claims

1. A method for allocating target measurement tasks in an optoelectronic network based on the KM algorithm, characterized in that, Includes the following steps: S1. Set the target observation tasks to H, and use n devices to observe the H tasks. Calculate the individual operators for each device based on the set attributes of each device; where n≥2H. The single operator includes the health status operator P. i1 Task priority operator P i2 Observation distance effectiveness operator P i3 Intersection accuracy operator P i4 The task switching impact operator is P. i5 Task mobility matching operator P i6 ; S2. Based on the calculation results of the individual operators in step S1, calculate the overall scheduling operator when combining two devices. Based on the calculation results of the individual operators and the overall scheduling operator, observe and group the n devices and H tasks to form the initial task-device combination. Step S2 is as follows: S2.1, The overall scheduling operator V for the computing device combination (i,j) during observation task h. h It satisfies the following formula: V h (i,j)=p h ×eval h Where i is any one of the n devices, j is any one of the n devices except the i-th device, and p h For the magnitude operator, eval h Here, is the threshold operator; h is any one of the H tasks; S2.2, Adjusting weight w(x) i ,y j The total scheduling operator value V of the uniform equipment combination (i,j) h This makes the magnitude operator p h Satisfy the following formula: Among them, w b For larger weight values, w s For smaller weight values, w b =0.7, w s =0.3, p i For the quantity operator of device i, p j P is the quantity operator for device j; sk This is the k-th single-item operator for the s-th device; If the actual distance exceeds the detection range of the device, the device combination (i,j) cannot work together. In this case, the total scheduling operator value V h If it is 0, eval h The threshold operator satisfies the following equation: eval h =P i3 ×P j3 ; Where: P i3 For the observation distance effectiveness operator of device i, P j3 For the observation distance validity operator of device j; S2.

3. Based on the calculation results of the individual operators and the overall scheduling operator, randomly observe and group the n devices and H tasks to form the initial task-device combination; S3. Divide the n devices into pairs to form For each device combination, construct a task-device combination bipartite graph, and use the KM algorithm to perform maximum weight matching on the initial task-device combination; S4. Update based on the maximum weight matching result of step S3. The optimal matching result of task-equipment combination is formed by grouping the observations of one device combination with H tasks, thereby completing the allocation of the target measurement tasks of the optoelectronic network.

2. The method for allocating target measurement tasks in an optoelectronic network based on the KM algorithm according to claim 1, characterized in that: Step S1 is as follows: S1.

1. Set the target observation tasks to H, and use n devices to observe the H tasks. Calculate the health status operator P for each device based on the total running time of each device from the start of the planned task to the current time when it is called to execute the target measurement task. i1 It satisfies the following formula: Where i is any one of the n devices. Let t be the running time of the i-th device. now This represents the total running time from the start of the planned task to the current moment. S1.2, Set the importance level table IM for each of the H tasks; Where IM = [imp1, imp2, ..., imp h ], imp h ∈(0,1), h=1,2,...,H; Based on the task importance ranking table IM, calculate the priority operator P for each task. i2 It satisfies the following formula: Among them, s i A value of 0 indicates that the i-th device is idle, s i A value of 1 indicates that the i-th device is occupied; imp h_old Based on the importance level of the previous task, imp h_new The importance level of the new task; S1.

3. Based on the straight-line distance between each device and the target observation mission trajectory point, calculate the observation distance effectiveness operator P between the target observation mission and each device. i3 It satisfies the following formula: Where, d AT-TAR Let d be the straight-line distance between the i-th device and the current target trajectory point of the target observation mission. 有效 Let be the maximum detectable distance of the i-th device for observing this target; S1.

4. Set the equipment combination as (i,j). Based on the measurement intersection angle between the equipment combination (i,j) and the target observation task, calculate the intersection accuracy operator P of equipment i. i4 It satisfies the following formula: Where: j is any device among the n devices except the i-th device, h is any task among the H tasks, α ihj ∈(0°, 180°), μ is the mean of the Gaussian distribution, μ = 90°, σ is the standard deviation of the Gaussian distribution, σ = 40; P ij4 For the intersection accuracy operator of the device combination (i,j); α ihj Let be the measurement intersection angle between the equipment combination (i,j) and the target observation task; S1.5, Set the task switching impact operator to P i5 It satisfies the following formula: S1.

6. Based on the matching of the optical characteristics and mobility requirements of each device for each mission observation, the matching table between mission h and the i-th device is set as MAT, MAT = [mat h1 ,mat h2 ,…,mat hn ], mat hi ∈(0,1); Computation task mobility matching operator P i6 It satisfies the following formula: P i6 =food hi 。 3. The method for allocating target measurement tasks in an optoelectronic network based on the KM algorithm according to claim 2, characterized in that, Step S3 is as follows: S3.

1. Divide the n devices into pairs to form... Each device combination (i,j); set Let there be a set X of device combinations (i,j) and a set Y of H tasks. Unify the number of elements in both sets X and Y to N, and... If there are few tasks, then complete task H, and the connection weight of the completed task and each device combination is 0; S3.2 Construct a bipartite graph G(V,E) using the connecting edges and nodes between sets X and Y. The bipartite graph G(V,E) satisfies the following equation: G(V,E)=(X,Y):X={x1,x2...,x i ,...x N }, Y={y1,y2,...,y j ,...and N }; Where V represents all nodes in the bipartite graph, E represents the connecting edges between all nodes in the bipartite graph, and x i Let y be a node on set X. j A node in set Y; x i With y j The weight of the matching edge corresponding to the connecting node is w(x) i ,y j The weight of each edge connecting set X and set Y is labeled as p. ij =w(x i ,y j ); S3.3, By defining each node x on the set X in the bipartite graph G(V,E) i Add adjustable node value l(x) i ), each node y in set Y j Add adjustable node value l(y) j ), so that it always satisfies l(x) i )+l(y j )=w(x i ,y j ), and by adjusting the node value l(V) i Find augmenting paths and perform maximum weight matching on the initial task-device combination.

4. The method for allocating target measurement tasks in an optoelectronic network based on the KM algorithm according to claim 3, characterized in that, Step S3.3 specifically includes: S3.3.1, Set the adjustable node values ​​l(y) of set Y in the bipartite graph G(V,E). j If all nodes in set X are initialized to 0, then the adjustable node value l(x) corresponding to set X is... i The maximum weight value is given by the following formula: The sum of the nodes of set X and set Y satisfies the following equation: The bipartite graph node that satisfies the above adjustable node value relationship is denoted as l; S3.3.

2. Take the edges connecting set X and set Y that satisfy the following equality condition to form an initial bipartite graph G(V,E). l ), and denote them as equal subgraphs; Obtain the initial bipartite graph G(V,E) l Matching M (M∈E) l ); S3.3.3 According to the KM lemma, if a perfect matching exists in the equal subgraphs formed by a node l in a bipartite graph after marking, then the corresponding matching M is the maximum weight matching.

5. The method for allocating target measurement tasks in an optoelectronic network based on the KM algorithm according to claim 4, characterized in that, Step S3.3.3 specifically refers to: A. Determine the initial bipartite graph G(V,E) obtained. l Is the matching M of the equal subgraphs in the equation a perfect matching, i.e., are all nodes V all belonging to matching M? Under this matching method, the matching edges are the most. If so, then the matching M satisfies the KM lemma, obtaining the maximum weight matching, which is the optimal matching result of the task-device combination, and the algorithm process ends; If not, then for the initial bipartite graph G(V,E) l Unmatched nodes u, u∈setX, are identified in set X, and an initialization set S = {u} is formed. Define N l (s) is the set of unmatched nodes, as follows: N l (u)={v:(u,v)∈E l },N l (s)=∪ u∈S N l (u); B, when N l When (s) = T, update the node value and N. l (s) makes N l (s)≠T, resulting in an augmenting path; Where, α l For node changes; C. When N l When (s)≠T, choose y j ∈N l (s)-T; If y j No match was generated, but an augmenting path was created with the existing match in graph G(V,E). l Implement y in ) j Matching; If y j A match has been found. Let the matching point be z. Then S = S∪{z}, T = T∪{y} j }; Check if it is a perfect match. If it is, it is the maximum weight match, and the matching ends. Otherwise, return to B, until it is a perfect match.

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