A fast target search method based on greedy algorithm under weak perception conditions

Abstract

The present invention discloses a method for fast target search based on a greedy algorithm under weak perception conditions, the method comprising step 1, obtaining a node set N={i|i=1,2,...,n} according to the intersections in the city map, where n represents the total number of nodes; step 2, when moving from the starting point position to the nearest node, obtaining the relative position of the current position and the target node according to the signal strength; step 3, after reaching the next node, based on the relative position of the current node and the target node, updating the probabilities of all the nodes to obtain the result after probability update; step 4, obtaining the position of the target node according to the result after probability update. The method of the present invention realizes fast search for targets under weak perception conditions, especially the introduced probability map accelerates the algorithm convergence speed, so that the specific position of the target can be quickly and accurately searched.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent search algorithms, and in particular relates to a fast target search method based on a greedy algorithm under weak perception conditions. Background Art

[0002] There are many methods available for searching and locating criminals, finding the source of leaks of hazardous chemicals, and locating scientific expeditions in the field. For example, cellular networking is used to locate criminals' mobile phone signals, global imaging is used to identify the source of danger, and GPS (Global Positioning System) is used to locate the collars of scientific expedition subjects. Most of these methods rely on powerful perception capabilities.

[0003] However, in practice, such strong perception capabilities may not be available, and sometimes there are multiple constraints (such as road accessibility issues, etc.). From the existing research results, there is no research on fast target search methods under weak perception conditions.

[0004] Therefore, providing a method that can perform fast target search under weak perception conditions has become an urgent problem to be solved. Summary of the invention

[0005] In order to solve the above problems existing in the prior art, the present invention provides a fast target search method based on a greedy algorithm under weak perception conditions. The technical problem to be solved by the present invention is achieved through the following technical solutions:

[0006] A fast target search method based on a greedy algorithm under weak perception conditions, the fast target search method comprising:

[0007] Step 1: Obtain a node set N = {i|i = 1, 2, ..., n} according to the intersections in the city map, where n represents the total number of nodes;

[0008] Step 2: when moving from the starting point to the nearest node, the relative position between the current position and the target node is obtained according to the signal strength;

[0009] Step 3: After reaching the next node, based on the relative position between the current node and the target node, the probabilities of all the nodes are updated to obtain the updated probability results;

[0010] Step 4: Obtain the position of the target node according to the probability updated result.

[0011] In one embodiment of the present invention, step 1 comprises:

[0012] Each intersection in the city map is a node i, and all the nodes constitute the node set, wherein the weight of the edge between two nodes is the real distance between them. Before starting the search, the probability p(i)=0 that all the nodes are nodes to be searched is set.

[0013] In one embodiment of the present invention, step 2 comprises:

[0014] Set the starting point position, move from the starting point position to the nearest node, and during the movement, determine the relative position of the current position and the target node according to the change in signal strength, the relative position is that the target node is behind the current position or the target node is in front of the current position.

[0015] In one embodiment of the present invention, determining the relative position between the current position and the target node according to the change in signal strength includes:

[0016] When moving from the starting point position to the nearest node, if the signal becomes stronger from weak, the target node is located in front of the current position; if the signal becomes weaker from strong, the target node is located behind the current position.

[0017] In one embodiment of the present invention, step 3 comprises:

[0018] After reaching the next node, the probabilities of all the nodes are updated using the signal determined during the movement to determine whether it is in front of or behind the body. If the signal is in front of the body, the probabilities of all the nodes in front of the current node are increased by 1, and the probabilities of all the nodes behind the current node remain unchanged. If the signal is behind the body, the probabilities of all the nodes behind the current node are increased by 1, and the probabilities of all the nodes in front of the current node remain unchanged.

[0019] In one embodiment of the present invention, step 4 comprises:

[0020] The position of the target node is obtained according to the highest probability among all the nodes.

[0021] In one embodiment of the present invention, obtaining the position of the target node according to the highest probability among all the nodes includes:

[0022] Step 4.1, obtain the highest probability max(p(i))=k among all the nodes;

[0023] Step 4.2, if |N p＝k |=1, then the node with probability k is the target node. If |N p＝k|≥2, then find the node closest to the current position among the nodes with probability k as the next node, and execute step 3 until the target node is determined, where |N p＝k |=1 means the number of nodes with probability k is 1, |N p＝k |≥2 means the number of nodes with probability k is greater than or equal to 2.

[0024] In one embodiment of the present invention, finding a node closest to the node with probability k as the next node includes:

[0025] The Floyd algorithm is used to find the node closest to the current position among the nodes with probability k as the next node.

[0026] In one embodiment of the present invention, the signal is a mobile phone signal.

[0027] Beneficial effects of the present invention:

[0028] The present invention uses each intersection as a node and judges whether the hidden location of the criminal's mobile phone is in front of or behind the current location according to the strength of the mobile phone signal, thereby judging each node as a possible hiding location of the criminal's mobile phone, thereby realizing rapid search for targets under weak perception conditions. In particular, the introduced probability map accelerates the convergence speed of the algorithm, thereby being able to quickly and accurately search for the specific location of the target.

[0029] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a flowchart of a fast target search method based on a greedy algorithm under weak perception conditions provided by an embodiment of the present invention;

[0031] Figure 2 It is a flowchart of another fast target search method based on a greedy algorithm under weak perception conditions provided by an embodiment of the present invention;

[0032] Figure 3 It is a schematic diagram for explaining the front and back provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0033] The present invention is further described in detail below with reference to specific embodiments, but the embodiments of the present invention are not limited thereto.

[0034] Embodiment 1

[0035] See also Figure 1 and Figure 2 , Figure 1is a flowchart of a fast target search method based on a greedy algorithm under weak perception conditions provided by an embodiment of the present invention, Figure 2 It is a flow chart of another method for rapid target search based on a greedy algorithm under weak perception conditions provided by an embodiment of the present invention. For the problem of locating the mobile phone signal of criminals in the city, the known conditions of the present invention are that the location of the criminal's mobile phone signal does not change temporarily, the city map is known, and the change of the signal can only be qualitatively perceived in motion (the strength of the target signal cannot be quantitatively perceived). Under the conditions as described above, it is necessary to achieve rapid positioning of the criminal's mobile phone signal. An embodiment of the present invention provides a method for rapid target search based on a greedy algorithm under weak perception conditions, and the method for rapid target search includes steps 1 to 4, wherein:

[0036] Step 1: Obtain a node set N = {i|i = 1, 2, ..., n} according to the intersections in the city map, where n represents the total number of nodes.

[0037] Specifically, each intersection in the city map is a node i, and all nodes constitute a node set N = {i|i = 1, 2, ..., n}. The city map is converted into a topological map, in which the weight of the edge between two nodes is the real distance between them. The adjacency matrix G of the topological map is further obtained. Assuming that the criminal's mobile phone is hidden at a certain node, the probability p(i) = 0 that all nodes are the hiding places of the criminal's mobile phone at the beginning. At this time, N p＝0 represents the set of nodes with probability 0, |N p＝0 |=n means that the number of nodes with probability 0 is n.

[0038] Step 2: When moving from the starting point to the nearest node, the relative position between the current position and the target node is obtained based on the signal strength, where the target node is the node where the criminal's mobile phone is hidden.

[0039] Specifically, first set the starting point position, which can be any position, and then move from the starting point position to the nearest node. During the movement, determine the relative position of the current position and the target node according to the change in signal strength. The relative position is that the target node is behind the current position or the target node is in front of the current position.

[0040] In a specific embodiment, determining the relative position between the current position and the target node according to the change in signal strength includes:

[0041] When moving from the starting point to the nearest node, if the signal changes from weak to strong, the target node is located in front of the current position; if the signal changes from strong to weak, the target node is located behind the current position.

[0042] See also Figure 3The circles in the figure represent the intersection nodes in the city. The arrows represent the process of moving from the current node to the next node. Figure 3 The left figure shows that when the moving direction is away from the target node, the perceived signal changes from strong to weak, which means that the target is roughly behind you. Therefore, all nodes behind you may be target nodes, that is, the probability of the target behind you increases. Figure 3 The figure on the right in the middle shows that when the moving direction is close to the target node, the perceived signal changes from weak to strong, and it can be judged that the general direction of the target is in front of the body. Therefore, all nodes in front of the body may be target nodes, that is, the probability of the target in front of the body increases.

[0043] Step 3: After reaching the next node, the probabilities of all nodes are updated based on the relative positions of the current node and the target node to obtain the updated probability results.

[0044] Specifically, after reaching the next node, the signal determined during the movement to be located in front of or behind the body is used to update the probabilities of all nodes. If the signal is in front of the body, the probabilities of all nodes in front of the current node are increased by 1, and the probabilities of all nodes behind the current node remain unchanged. If the signal is behind the body, the probabilities of all nodes behind the current node are increased by 1, and the probabilities of all nodes in front of the current node remain unchanged.

[0045] Step 4: Get the position of the target node based on the probability update result.

[0046] Specifically, the location of the target node is obtained according to the highest probability among all nodes.

[0047] In a specific embodiment, obtaining the position of the target node according to the highest probability among all nodes includes:

[0048] Step 4.1, obtain the highest probability max(p(i))=k among all nodes;

[0049] Step 4.2, if |N p＝k |=1, then the node with probability k is the target node. If |N p＝k |≥2, then find the node closest to the current position among the nodes with probability k as the next node, and repeat steps 3 and 4 until the target node is determined, where |N p＝k |=1 means the number of nodes with probability k is 1, |N p＝k |≥2 means the number of nodes with probability k is greater than or equal to 2.

[0050] Further, finding the node closest to the current position among the nodes with probability k as the next node includes:

[0051] Use the Floyd algorithm to find the node closest to the current position among the nodes with probability k as the next node.

[0052] The method of the present invention can be programmed as follows:

[0053]

[0054]

[0055] The present invention uses each intersection as a node and judges whether the hidden location of the criminal's mobile phone is in front of or behind the current location according to the strength of the mobile phone signal, thereby judging each node as a possible hiding location of the criminal's mobile phone, thereby realizing rapid search for targets under weak perception conditions. In particular, the introduced probability map accelerates the convergence speed of the algorithm, thereby being able to quickly and accurately search for the specific location of the target.

[0056] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0057] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristic data points described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristic data points described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification.

[0058] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the scope of protection of the present invention.

Claims

1. A fast target search method based on a greedy algorithm under weak perception conditions, characterized in that: The fast target search method comprises: Step 1: Get the node set based on the intersections in the city map , n represents the total number of nodes; Step 2: when moving from the starting point to the nearest node, the relative position between the current position and the target node is obtained according to the signal strength; Step 3: After reaching the next node, based on the relative position between the current node and the target node, the probabilities of all the nodes are updated to obtain the updated probability results; Step 4: Obtain the position of the target node according to the result after the probability update; The step 2 comprises: The starting point position is set, and the vehicle moves from the starting point position to the nearest node. During the movement, the relative position between the current position and the target node is determined according to the change in signal strength, where the target node is behind the current position or in front of the current position. Determine the relative position between the current position and the target node based on the change in signal strength, including: When moving from the starting point to the nearest node, if the signal becomes stronger from weak, the target node is located in front of the current position; if the signal becomes weaker from strong, the target node is located behind the current position; The step 3 comprises: After reaching the next node, the probabilities of all the nodes are updated using the signal determined during the movement to determine whether it is in front of or behind the body. If the signal is in front of the body, the probabilities of all the nodes in front of the current node are increased by 1, and the probabilities of all the nodes behind the current node remain unchanged. If the signal is behind the body, the probabilities of all the nodes behind the current node are increased by 1, and the probabilities of all the nodes in front of the current node remain unchanged.

2. The fast target search method based on the greedy algorithm under weak perception conditions according to claim 1 is characterized in that: The step 1 comprises: Each intersection in the city map is a node i , all the nodes constitute the node set, where the weight of the edge between two nodes is the real distance between them. Before starting the search, set the probability of all the nodes being the nodes to be searched .

3. The fast target search method based on greedy algorithm under weak perception conditions according to claim 2 is characterized in that: The step 4 comprises: The position of the target node is obtained according to the highest probability among all the nodes.

4. The fast target search method based on the greedy algorithm under weak perception conditions according to claim 3 is characterized in that: Obtaining the location of the target node according to the highest probability among all the nodes includes: Step 4.1: Get the highest probability among all the nodes ; Step 4.2: If , then the probability is k The node of is the target node. , then the probability is k Find the node closest to the current position from the nodes as the next node, and execute step 3 until the target node is determined, wherein, The number of nodes with probability k is 1, Indicates that the number of nodes with probability k is greater than or equal to 2.

5. The fast target search method based on greedy algorithm under weak perception conditions according to claim 4 is characterized in that: In the probability k Finding the node closest to the current position as the next node among the nodes includes: The Floyd algorithm is used to find the node closest to the current position among the nodes with probability k as the next node.

6. The fast target search method based on greedy algorithm under weak perception conditions according to claim 1 is characterized in that: The signal is a mobile phone signal.

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