Operation scheduling method and system applied to security patrol car in closed park
By using high-precision maps and hierarchical clustering algorithms to predict the operation path of patrol vehicles in security patrols in closed parks and establishing task scheduling functions, the problem of resource waste when multiple patrol vehicles patrol at the same time is solved, and efficient patrol resource utilization and operation scheduling are achieved.
Patent Information
- Application Number
- CN202411897753.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-23
- Publication Date
- 2025-04-15
AI Technical Summary
In security patrols in closed parks, multiple patrol vehicles are prone to waste of resources when patrolling at the same time. For example, multiple vehicles patroll the same area at the same time, resulting in ineffective utilization of patrol resources.
By obtaining high-precision map data of the closed park, a region division function and a local feature extraction algorithm are constructed, the operation path of the patrol vehicle is predicted, and a task scheduling function is established to achieve accurate operation task scheduling of multiple patrol vehicles.
The operation efficiency and quality of security patrol vehicles are improved, the waste of patrol resources is avoided, and efficient time and space coverage of closed parks is ensured.
Smart Images
Figure CN119831244A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of security patrol vehicles, and in particular to an operation scheduling method and system for security patrol vehicles in a closed park. Background Art
[0002] For various security scenarios, such as patrolling scenic spots and closed park patrols, patrol cars are used instead of manual patrols to reduce the manpower burden of patrol personnel and prevent accidental personal injuries to patrol personnel. Considering that the above-mentioned closed parks occupy a large area, a small number of patrol cars cannot effectively cover the park in time and space, so multiple patrol cars are used to patrol at the same time.
[0003] Without the introduction of a patrol car cooperation mechanism, during the actual patrol process, each patrol car will choose its own patrol route based on its current status. It is possible that multiple patrol cars will patrol the same chemical plant area at the same time, which will cause a waste of patrol resources. In order to improve resource utilization and ensure the effectiveness of patrols, multiple patrol cars are required to avoid the above-mentioned problems as much as possible when patrolling at the same time, which requires a certain scheduling mechanism for multiple patrol cars during the patrol process. Summary of the invention
[0004] In view of the above problems, the present invention provides an operation scheduling method and system for closed campus security patrol vehicles, which not only improves the operation efficiency and quality of the security patrol vehicles, but also can accurately schedule operation tasks for multiple security patrol vehicles.
[0005] In order to achieve the above-mentioned purpose and other related purposes, the technical solution provided by the present invention is as follows: a method for scheduling operations of security patrol vehicles in a closed park, the method comprising:
[0006] R1. Obtain data information of a high-precision map of a closed park, construct a region division function Q of the closed park, divide the high-precision map of the park, and obtain data information of a high-precision map of the closed park after division;
[0007] R2. Based on the data information of the high-precision map of the closed park after the division, the local feature extraction algorithm based on hierarchical clustering is used to extract local features of the high-precision map of the closed park to obtain the data information of the local features of the high-precision map of the closed park after the division;
[0008] R3. Based on the data information of the local features of the high-precision map of the closed park after the division, a security patrol car operation path prediction model is constructed to predict the operation path of the security patrol car, and the data information of the predicted operation path of the security patrol car is obtained;
[0009] R4. Based on the data information of the predicted operation path of the security patrol car, establish a task scheduling function W of the security patrol car, schedule the operation of the security patrol car, and output the data information of the scheduling of the operation task of the security patrol car.
[0010] Furthermore, the area division function Q of the closed park is:
[0011]
[0012] Among them, x is the data information of the high-precision map of the closed park, and α1, α2 and α3 are the regional division factors of the high-precision map of the park.
[0013] Furthermore, the regional division factors α1, α2 and α3 of the high-precision map of the park are,
[0014]
[0015]
[0016] Among them, x is the data information of the high-precision map of the closed park.
[0017] Furthermore, in step R2, the local feature extraction algorithm based on hierarchical clustering is used to extract local features from the high-precision map of the closed park, including:
[0018] R21. Based on the data information of the high-precision map of the closed park after the division, a hierarchical clustering function W of the high-precision map of the closed park is established.
[0019]
[0020] Among them, y is the data information of the high-precision map of the closed park after division, β1, β2 and β3 are the hierarchical clustering factors of the high-precision map of the closed park, and the high-precision map of the closed park is hierarchically clustered to obtain the data information of the high-precision map of the closed park after hierarchical clustering;
[0021] R22. Based on the data information of the high-precision map of the closed park after hierarchical clustering, a local feature extraction function U of the high-precision map of the closed park is established.
[0022]
[0023] Among them, z is the data information of the high-precision map of the closed park after hierarchical clustering, δ1, δ2 and δ3 are the local feature extraction factors of the high-precision map of the closed park;
[0024] R23. Based on the local feature extraction function U of the high-precision map of the closed park, local feature extraction is performed on the high-precision map of the closed park to obtain data information on the local features of the high-precision map of the closed park after division.
[0025] Furthermore, the constraints of the local feature extraction factors δ1, δ2 and δ3 of the high-precision map of the closed park are:
[0026]
[0027] Furthermore, the hierarchical clustering factors β1, β2 and β3 of the high-precision map of the closed park are,
[0028]
[0029] Among them, y is the data information of the high-precision map of the closed park after division.
[0030] Furthermore, in step R3, the construction of the operation path prediction model of the security patrol car and the prediction of the operation path of the security patrol car include:
[0031] R31. Based on the data information of the local features of the high-precision map of the divided closed park, a data set of local features of the high-precision map of the divided closed park is constructed;
[0032] R32. Input the local feature data set of the high-precision map of the divided closed park into the operation path prediction model of the security patrol car for training and learning, and determine the prediction function G of the operation path of the security patrol car.
[0033]
[0034] Among them, r is the data set of local features of the high-precision map of the divided closed park, γ1, γ2 and γ3 are the prediction factors of the operation path of the security patrol car, and the trained security patrol car operation path prediction model is obtained;
[0035] R33. Based on the trained security patrol car's operating path prediction model, the data information of the local features of the high-precision map of the divided closed park is input to predict the security patrol car's operating path, and the data information of the predicted security patrol car's operating path is obtained.
[0036] Furthermore, the constraint function f of the prediction factors γ1, γ2 and γ3 of the operation path of the security patrol car is,
[0037]
[0038] The value range of the constraint function f is (0,1).
[0039] Furthermore, the task scheduling function W of the security patrol car is:
[0040]
[0041] Among them, h is the data information of the predicted operation path of the security patrol car, and η1, η2 and η3 are the operation scheduling factors of the security patrol car.
[0042] In order to achieve the above-mentioned purpose and other related purposes, the present invention also provides a job scheduling system applied to a closed campus security patrol car, including a computer device, which is programmed or configured to execute any one of the steps of the job scheduling method applied to a closed campus security patrol car.
[0043] The present invention has the following positive effects:
[0044] 1. The present invention divides the high-precision map of the closed park by constructing a regional division function Q of the closed park, and extracts local features from the high-precision map of the closed park by combining a local feature extraction algorithm based on hierarchical clustering. This can not only accurately divide the park, thereby improving the accuracy of the operation scheduling of the security patrol car, but also further improve the patrol efficiency of the security patrol car.
[0045] 2. The present invention predicts the operation path of the security patrol car by constructing an operation path prediction model of the security patrol car, and schedules the operation of the security patrol car by combining the establishment of a task scheduling function W of the security patrol car. This not only improves the operation efficiency and quality of the security patrol car, but also enables accurate scheduling of operation tasks for multiple security patrol cars. BRIEF DESCRIPTION OF THE DRAWINGS
[0046] Figure 1 It is a schematic diagram of the method flow of the present invention;
[0047] Figure 2 It is a schematic diagram of the process of the local feature extraction algorithm based on hierarchical clustering of the present invention;
[0048] Figure 3 The present invention is a flowchart of constructing an operation path prediction model for a security patrol vehicle. DETAILED DESCRIPTION
[0049] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.
[0050] Example 1: Figure 1 As shown, a method for scheduling operations of security patrol vehicles in a closed park is provided, the method comprising:
[0051] R1. Obtain data information of a high-precision map of a closed park, construct a region division function Q of the closed park, divide the high-precision map of the park, and obtain data information of a high-precision map of the closed park after division;
[0052] R2. Based on the data information of the high-precision map of the closed park after the division, the local feature extraction algorithm based on hierarchical clustering is used to extract local features of the high-precision map of the closed park to obtain the data information of the local features of the high-precision map of the closed park after the division;
[0053] R3. Based on the data information of the local features of the high-precision map of the closed park after the division, a security patrol car operation path prediction model is constructed to predict the operation path of the security patrol car, and the data information of the predicted operation path of the security patrol car is obtained;
[0054] R4. Based on the data information of the predicted operation path of the security patrol car, establish a task scheduling function W of the security patrol car, schedule the operation of the security patrol car, and output the data information of the scheduling of the operation task of the security patrol car.
[0055] In this embodiment, the area division function Q of the closed park is:
[0056]
[0057] Among them, x is the data information of the high-precision map of the closed park, and α1, α2 and α3 are the regional division factors of the high-precision map of the park.
[0058] In this embodiment, the area division factors α1, α2 and α3 of the high-precision map of the park are:
[0059]
[0060]
[0061] Among them, x is the data information of the high-precision map of the closed park.
[0062] In this embodiment, if Figure 2 As shown, in step R2, the local feature extraction algorithm based on hierarchical clustering is used to extract local features from the high-precision map of the closed park, including:
[0063] R21. Based on the data information of the high-precision map of the closed park after the division, a hierarchical clustering function W of the high-precision map of the closed park is established.
[0064]
[0065] Among them, y is the data information of the high-precision map of the closed park after division, β1, β2 and β3 are the hierarchical clustering factors of the high-precision map of the closed park, and the high-precision map of the closed park is hierarchically clustered to obtain the data information of the high-precision map of the closed park after hierarchical clustering;
[0066] R22. Based on the data information of the high-precision map of the closed park after hierarchical clustering, a local feature extraction function U of the high-precision map of the closed park is established.
[0067]
[0068] Among them, z is the data information of the high-precision map of the closed park after hierarchical clustering, δ1, δ2 and δ3 are the local feature extraction factors of the high-precision map of the closed park;
[0069] R23. Based on the local feature extraction function U of the high-precision map of the closed park, local feature extraction is performed on the high-precision map of the closed park to obtain data information on the local features of the high-precision map of the closed park after division.
[0070] In this embodiment, the constraints of the local feature extraction factors δ1, δ2 and δ3 of the high-precision map of the closed park are:
[0071]
[0072] In this embodiment, the hierarchical clustering factors β1, β2 and β3 of the high-precision map of the closed park are,
[0073]
[0074] Among them, y is the data information of the high-precision map of the closed park after division.
[0075] Embodiment 2: Based on the operation scheduling method applied to a closed park security patrol car in Embodiment 1, the present invention is further illustrated and described below.
[0076] like Figure 1 As shown, Figure 1 As shown, a method for scheduling operations of security patrol vehicles in a closed park is provided, the method comprising:
[0077] R1. Obtain data information of a high-precision map of a closed park, construct a region division function Q of the closed park, divide the high-precision map of the park, and obtain data information of a high-precision map of the closed park after division;
[0078] R2. Based on the data information of the high-precision map of the closed park after the division, the local feature extraction algorithm based on hierarchical clustering is used to extract local features of the high-precision map of the closed park to obtain the data information of the local features of the high-precision map of the closed park after the division;
[0079] R3. Based on the data information of the local features of the high-precision map of the closed park after the division, a security patrol car operation path prediction model is constructed to predict the operation path of the security patrol car, and the data information of the predicted operation path of the security patrol car is obtained;
[0080] R4. Based on the data information of the predicted operation path of the security patrol car, establish a task scheduling function W of the security patrol car, schedule the operation of the security patrol car, and output the data information of the scheduling of the operation task of the security patrol car.
[0081] In this embodiment, if Figure 3 As shown, in step R3, the construction of the operation path prediction model of the security patrol car and the prediction of the operation path of the security patrol car include:
[0082] R31. Based on the data information of the local features of the high-precision map of the divided closed park, a data set of local features of the high-precision map of the divided closed park is constructed;
[0083] R32. Input the local feature data set of the high-precision map of the divided closed park into the operation path prediction model of the security patrol car for training and learning, and determine the prediction function G of the operation path of the security patrol car.
[0084]
[0085] Among them, r is the data set of local features of the high-precision map of the divided closed park, γ1, γ2 and γ3 are the prediction factors of the operation path of the security patrol car, and the trained security patrol car operation path prediction model is obtained;
[0086] R33. Based on the trained security patrol car's operating path prediction model, the data information of the local features of the high-precision map of the divided closed park is input to predict the security patrol car's operating path, and the data information of the predicted security patrol car's operating path is obtained.
[0087] In this embodiment, the constraint function f of the prediction factors γ1, γ2 and γ3 of the operation path of the security patrol car is,
[0088]
[0089] The value range of the constraint function f is (0,1).
[0090] In this embodiment, the task scheduling function W of the security patrol car is:
[0091]
[0092] Among them, h is the data information of the predicted operation path of the security patrol car, and η1, η2 and η3 are the operation scheduling factors of the security patrol car.
[0093] In this embodiment, the present invention provides a job scheduling system for a closed campus security patrol car, including a computer device that is programmed or configured to execute any one of the steps of the job scheduling method for a closed campus security patrol car.
[0094] In this embodiment, the present invention further provides a computer-readable storage medium storing a computer program programmed or configured to execute any one of the above-described job scheduling for closed campus security patrol vehicles.
[0095] Any reference to memory, storage, database or other media used in the embodiments provided in the present application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM) and memory bus dynamic RAM (RDRAM).
[0096] In summary, the present invention not only improves the operating efficiency and quality of security patrol vehicles, but also enables accurate scheduling of operating tasks for multiple security patrol vehicles.
[0097] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.
Claims
1. A method for scheduling operations of security patrol vehicles in a closed park, characterized in that: The method comprises: R1. Obtain data information of a high-precision map of a closed park, construct a region division function Q of the closed park, divide the high-precision map of the park, and obtain data information of a high-precision map of the closed park after division; R2. Based on the data information of the high-precision map of the closed park after the division, the local feature extraction algorithm based on hierarchical clustering is used to extract local features of the high-precision map of the closed park to obtain the data information of the local features of the high-precision map of the closed park after the division; R3. Based on the data information of the local features of the high-precision map of the closed park after the division, a security patrol car operation path prediction model is constructed to predict the operation path of the security patrol car, and the data information of the predicted operation path of the security patrol car is obtained; R4. Based on the data information of the predicted operation path of the security patrol car, establish a task scheduling function W of the security patrol car, schedule the operation of the security patrol car, and output the data information of the scheduling of the operation task of the security patrol car.
2. The operation scheduling method for closed park security patrol vehicles according to claim 1 is characterized in that: The regional division function Q of the closed park is, Among them, x is the data information of the high-precision map of the closed park, and α1, α2 and α3 are the regional division factors of the high-precision map of the park.
3. The operation scheduling method for closed park security patrol vehicles according to claim 2 is characterized in that: The regional division factors α1, α2 and α3 of the high-precision map of the park are, Among them, x is the data information of the high-precision map of the closed park.
4. The operation scheduling method for closed park security patrol vehicles according to claim 1 is characterized in that: In step R2, the local feature extraction algorithm based on hierarchical clustering is used to extract local features from the high-precision map of the closed park, including: R21. Based on the data information of the high-precision map of the closed park after the division, a hierarchical clustering function W of the high-precision map of the closed park is established. Among them, y is the data information of the high-precision map of the closed park after division, β1, β2 and β3 are the hierarchical clustering factors of the high-precision map of the closed park, and the high-precision map of the closed park is hierarchically clustered to obtain the data information of the high-precision map of the closed park after hierarchical clustering; R22. Based on the data information of the high-precision map of the closed park after hierarchical clustering, a local feature extraction function U of the high-precision map of the closed park is established. Among them, z is the data information of the high-precision map of the closed park after hierarchical clustering, δ1, δ2 and δ3 are the local feature extraction factors of the high-precision map of the closed park; R23. Based on the local feature extraction function U of the high-precision map of the closed park, local feature extraction is performed on the high-precision map of the closed park to obtain data information on the local features of the high-precision map of the closed park after division.
5. The operation scheduling method for closed park security patrol vehicles according to claim 4 is characterized in that: The constraints of the local feature extraction factors δ1, δ2 and δ3 of the high-precision map of the closed park are:
6. The operation scheduling method for closed park security patrol vehicles according to claim 4 is characterized in that: The hierarchical clustering factors β1, β2 and β3 of the high-precision map of the closed park are, Among them, y is the data information of the high-precision map of the closed park after division.
7. The operation scheduling method for closed park security patrol vehicles according to claim 1 is characterized in that: In step R3, the construction of the operation path prediction model of the security patrol car and the prediction of the operation path of the security patrol car include: R31. Based on the data information of the local features of the high-precision map of the divided closed park, a data set of local features of the high-precision map of the divided closed park is constructed; R32. Input the local feature data set of the high-precision map of the divided closed park into the operation path prediction model of the security patrol car for training and learning, and determine the prediction function G of the operation path of the security patrol car. Among them, r is the data set of local features of the high-precision map of the divided closed park, γ1, γ2 and γ3 are the prediction factors of the operation path of the security patrol car, and the trained security patrol car operation path prediction model is obtained; R33. Based on the trained security patrol car's operating path prediction model, the data information of the local features of the high-precision map of the divided closed park is input to predict the security patrol car's operating path, and the data information of the predicted security patrol car's operating path is obtained.
8. The operation scheduling method for closed park security patrol vehicles according to claim 7 is characterized in that: The constraint function f of the prediction factors γ1, γ2 and γ3 of the operation path of the security patrol car is, The value range of the constraint function f is (0,1).
9. The operation scheduling method for closed park security patrol vehicles according to claim 1 is characterized in that: The task scheduling function W of the security patrol car is: Among them, h is the data information of the predicted operation path of the security patrol car, and η1, η2 and η3 are the operation scheduling factors of the security patrol car.
10. An operation scheduling system for closed campus security patrol vehicles, including computer equipment, characterized in that: The computer device is programmed or configured to execute the steps of the operation scheduling method applied to a closed campus security patrol car as described in any one of claims 1 to 9.