Intelligent security system integrating video monitoring and patrol path optimization

Abstract

The invention relates to the field of artificial intelligence, in particular to an intelligent security system integrating video monitoring and patrol path optimization, which comprises a video collection module, a patrol path optimization module, a data analysis and prediction module and a control and communication module. The dual-enhanced chimpanzee optimization algorithm is used for initial path planning and optimization, so that the local optimal solution problem is effectively avoided, and a more globally optimal path is found; in a double-enhanced chimpanzee optimization algorithm, global search enhancement is performed by introducing an enhancement factor R1, a search space is expanded, local search enhancement is performed by introducing an enhancement factor R2, more detailed exploration is performed in a local area of the search space, the quality of an existing solution is optimized, and the precision and efficiency of local search are improved.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence, and specifically refers to an intelligent security system integrating video surveillance and patrol path optimization. Background Art

[0002] With the development of artificial intelligence, it has been gradually applied to security systems, but unoptimized patrol paths may lead to unreasonable scheduling of patrol personnel and equipment, and fail to detect and respond to emergencies in a timely manner, affecting the safety of activities. Traditional path planning methods have problems of low efficiency and high energy consumption, and are prone to falling into local optimal solutions, resulting in inefficient patrol paths, increased patrol time and path length, and reduced patrol efficiency. Unoptimized paths will cause patrol equipment to consume more energy on unnecessary paths, shorten the service life and working time of the equipment, and increase maintenance and operating costs; ordinary path planning methods have problems of slow convergence speed, insufficient global search and local search capabilities, and lack of global search capabilities of enhancement factors. The algorithm is prone to fall into local optimal solutions during the search process, making it difficult to find the global optimal solution, reducing the effectiveness of the algorithm and the quality of path planning. There is a lack of global search of R1 and R2 enhancement factors, and the algorithm's search range is limited, making it difficult to fully explore the global range, affecting the algorithm's global search performance and the discovery of the optimal solution. Summary of the invention

[0003] In view of the above situation, the present invention provides an intelligent security system integrating video surveillance and patrol path optimization. In view of the problems of low efficiency and high energy consumption in traditional path planning methods, the present invention uses a double-enhanced chimpanzee optimization algorithm in the patrol path optimization module to perform initial path planning and optimization, effectively avoiding the problem of local optimal solution, finding a more globally optimal path, making the patrol route more efficient and able to cover more key areas. The patrol path after optimization reduces the patrol time and path length, improves the patrol efficiency, and enables the security system to complete the patrol task in a shorter time. By adding double-enhancement technology, the patrol path is dynamically adjusted, the system's ability to cope with environmental changes and potential threats is enhanced, and the flexibility and timeliness of the patrol task are ensured. In view of the problems of slow convergence speed and insufficient global search and local search capabilities in ordinary path planning methods, the present invention introduces two enhancement factors in the double-enhanced chimpanzee optimization algorithm, introduces the enhancement factor R1 to perform global search enhancement, expands the search space, introduces the enhancement factor R2 to perform local search enhancement, performs more detailed exploration in the local area of ​​the search space, optimizes the quality of existing solutions, and improves the accuracy and efficiency of local search.

[0004] The intelligent security system integrating video monitoring and patrol path optimization provided by the present invention includes a video collection module, a patrol path optimization module, a data analysis and prediction module, and a control and communication module, and specifically includes the following contents:

[0005] The video collection module uses a camera to collect video and performs basic processing, including image enhancement, picture stabilization and denoising, to obtain analysis results;

[0006] The patrol path optimization module uses a double-enhanced chimpanzee optimization algorithm to perform initial path planning and optimization;

[0007] The data analysis and prediction module performs behavior analysis based on the basically processed video and analysis results;

[0008] The control and communication module realizes data transmission and information sharing among modules.

[0009] Furthermore, in the patrol path optimization module, the double-enhanced chimpanzee optimization algorithm is used to perform initial path planning and optimization, which specifically includes the following steps:

[0010] Step S1: Initialization, determine the size of the patrol area, use a grid map to represent the patrol area, and define the key nodes that need to be patrolled;

[0011] Step S2: Generate an initial population, randomly generate a set of initial paths, each path represents a patrol route, the patrol route consists of key nodes that need to be patrolled, and the node order represents the patrol order;

[0012] Step S3: path evaluation, selecting path individuals to participate in optimization;

[0013] Step S4: optimization stage;

[0014] Step S5: local search, applying a local search algorithm to adjust the path;

[0015] Step S6: Apply the double enhancement technique.

[0016] Further, step S3 specifically includes the following steps:

[0017] Step S31: Determine the key nodes, determine all the key nodes in a path, and calculate the sum of the distances between the key nodes. The formula used is as follows: ;

[0018] in, is the sum of distances, is the total number of key nodes, , It is The coordinates of the key nodes in the grid map, , It is The coordinates of the key nodes in the grid map;

[0019] Step S32: Calculate coverage, where coverage is the ratio of the number of key points in a path to the total number of key points;

[0020] Step S33: energy consumption calculation, calculating energy consumption according to the path length, setting an energy consumption factor, and the energy consumption is the product of the path length and the energy consumption factor;

[0021] Step S34: Calculate the fitness function, define the fitness function, and use the following formula: ;

[0022] in, is the fitness function, is the weight of the path length, is the weight of coverage, is the coverage rate, is the weight of energy consumption, It is energy consumption;

[0023] Step S35: Calculate the fitness of each path, and select the path with the highest fitness as the current path;

[0024] Further, step S4 specifically includes the following steps:

[0025] Step S41: Initialize parameters, define population size, maximum number of iterations and initial path position, the population size is the number of path individuals participating in optimization, and the initial position is the position of the initial path individual;

[0026] Step S42: Position update: for each path, the key node sequence is updated according to the following formula: ;

[0027] in, For the The population after the individual updates of the paths, is the initial path position, , is a random vector;

[0028] Step S43: Disturbance after position update, in A path is randomly selected and reversed. The formula used is as follows: ;

[0029] in, For the The population after the path individuals are reversed, For the The starting point of the path, For the The end point of the path;

[0030] Step S44: randomly exchange key nodes. Randomly select two key nodes in each path and exchange their positions. The formula used is as follows:

[0031] ;

[0032] in, For the The population after the path individuals exchange key nodes, is the function of the exchange operation, There are two key nodes;

[0033] Step S45: The perturbation after exchange, Randomly select a path from the above and perform the reverse operation again to obtain ;

[0034] Further, step S6 specifically includes the following steps:

[0035] Step S61: Calculate the enhancement factor. Set the enhancement factor and perform the calculation. The formula used is as follows: ; ;

[0036] in, , is the enhancement factor, , is a random number in the interval [0,1];

[0037] Step S62: Global search enhancement: perform global search enhancement on each path individual, using the enhancement factor Perform global search adjustments using the following formula: ;

[0038] in, For the The path after the individual global enhancement of the path, Represents the current The path positions of the path individuals, is the optimal path individual in the current population;

[0039] Step S63: local search enhancement, performing local search enhancement on each path individual:

[0040] Use enhancement factor Perform local search adjustments on the current path using the following formula: ; ;

[0041] in, For the The path after the individual local enhancement of the path, is the neighboring path individual of the current path individual;

[0042] Step S64: Select the optimal path and use the fitness function to evaluate the The path of individual global enhancement and the The path after the individual local enhancement of the paths is selected, and the path with high fitness is selected as the optimal path.

[0043] The beneficial effects achieved by the present invention using the above scheme are as follows:

[0044] (1) In the patrol path optimization module, the present invention uses the double-enhanced chimpanzee optimization algorithm to perform initial path planning and optimization, effectively avoiding the problem of local optimal solutions and finding a more globally optimal path. The patrol route is more efficient and can cover more key areas. The patrol path after optimization reduces the patrol time and path length, improves the patrol efficiency, and enables the security system to complete the patrol task in a shorter time. By adding the double-enhancement technology, the patrol path is dynamically adjusted, the system's ability to respond to environmental changes and potential threats is enhanced, and the flexibility and timeliness of the patrol task are ensured.

[0045] (2) The present invention introduces two enhancement factors into the dual enhanced chimpanzee optimization algorithm. The enhancement factor R1 is introduced to enhance the global search and expand the search space. The enhancement factor R2 is introduced to enhance the local search and conduct a more detailed exploration in the local area of ​​the search space, thereby optimizing the quality of the existing solution and improving the accuracy and efficiency of the local search. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 A schematic diagram of a module of an intelligent security system integrating video surveillance and patrol route optimization provided by the present invention.

[0047] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention. DETAILED DESCRIPTION

[0048] 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.

[0049] In the description of the present invention, it should be understood that terms such as “upper”, “lower”, “front”, “back”, “left”, “right”, “top”, “bottom”, “inside” and “outside” indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore should not be understood as limiting the present invention.

[0050] Example 1, see Figure 1 The intelligent security system integrating video monitoring and patrol path optimization provided by the present invention includes a video collection module, a patrol path optimization module, a data analysis and prediction module, and a control and communication module, which specifically includes the following contents:

[0051] The video collection module uses a camera to collect video and performs basic processing, including image enhancement, picture stabilization and denoising, to obtain analysis results;

[0052] The patrol path optimization module uses a double-enhanced chimpanzee optimization algorithm to perform initial path planning and optimization;

[0053] The data analysis and prediction module performs behavior analysis based on the basically processed video and analysis results;

[0054] The control and communication module realizes data transmission and information sharing among modules.

[0055] In this embodiment, a high-definition camera Hikvision DS-2CD2043G0-I4MP Outdoor IP Camera is selected in the video collection module and installed in key locations in the monitoring area to ensure coverage of all important monitoring areas. The camera device continuously collects monitoring videos through the network or local storage device.

[0056] Embodiment 2: Based on the above embodiment, in the patrol path optimization module, the double enhanced chimpanzee optimization algorithm is used to perform initial path planning and optimization, which specifically includes the following steps:

[0057] Step S1: Initialization, determine the size of the patrol area, use a grid map to represent the patrol area, and define the key nodes that need to be patrolled;

[0058] Step S2: Generate an initial population, randomly generate a set of initial paths, each path represents a patrol route, the patrol route consists of key nodes that need to be patrolled, and the node order represents the patrol order;

[0059] Step S3: path evaluation, selecting path individuals to participate in optimization;

[0060] Step S4: optimization stage;

[0061] Step S5: local search, applying a local search algorithm to adjust the path;

[0062] Step S6: Apply the double enhancement technique.

[0063] Through the above operations, the double-enhanced chimpanzee optimization algorithm is used for initial path planning and optimization, which effectively avoids the problem of local optimal solutions and finds a more globally optimal path. The patrol route is more efficient and can cover more key areas. The optimized patrol path reduces patrol time and path length, improves patrol efficiency, and enables the security system to complete patrol tasks in a shorter time. By adding double-enhancement technology, the patrol path is dynamically adjusted, which enhances the system's ability to respond to environmental changes and potential threats, ensuring the flexibility and timeliness of patrol tasks.

[0064] Embodiment 3, based on the above embodiment, step S3 specifically includes the following steps:

[0065] Step S31: Determine the key nodes, determine all the key nodes in a path, and calculate the sum of the distances between the key nodes. The formula used is as follows: ;

[0066] in, is the sum of distances, is the total number of key nodes, , It is The coordinates of the key nodes in the grid map, , It is The coordinates of the key nodes in the grid map;

[0067] Step S32: Calculate coverage, where coverage is the ratio of the number of key points in a path to the total number of key points;

[0068] Step S33: energy consumption calculation, calculating energy consumption according to the path length, setting an energy consumption factor, and the energy consumption is the product of the path length and the energy consumption factor;

[0069] Step S34: Calculate the fitness function, define the fitness function, and use the following formula: ;

[0070] in, is the fitness function, is the weight of the path length, is the weight of coverage, is the coverage rate, is the weight of energy consumption, It is energy consumption;

[0071] Step S35: Calculate the fitness of each path and select the path with the highest fitness as the current path.

[0072] In this embodiment, the patrol area is a 10x10 grid map. There are five key nodes that need to be patrolled, with coordinates of (1,1), (3,4), (5,5), (7,8), and (9,9). The initial path is randomly generated, [(1,1)->(3,4)->(7,8)->(5,5)->(9,9)].

[0073] Compute the sum of distances: (1,1)->(3,4): ; (3,4)->(7,8): ; (7,8)->(5,5): ; (5,5)->(9,9): ; ;

[0074] The total number of key nodes is 5, the number of key nodes in the path is 5, and the coverage is 1.0;

[0075] If the energy consumption factor is set to 0.1, the energy consumption is 1.854;

[0076] Set weights, =0.4, =0.4, =0.2, the calculated total fitness is 0.0508.

[0077] Embodiment 4, based on the above embodiment, step S4 specifically includes the following steps:

[0078] Step S41: Initialize parameters, define population size, maximum number of iterations and initial path position, the population size is the number of path individuals participating in optimization, and the initial position is the position of the initial path individual;

[0079] Step S42: Position update: for each path, the key node sequence is updated according to the following formula: ;

[0080] in, For the The population after the individual updates of the paths, is the initial path position, , is a random vector;

[0081] Step S43: Disturbance after position update, in A path is randomly selected and reversed. The formula used is as follows: ;

[0082] in, For the The population after the path individuals are reversed, For the The starting point of the path, For the The end point of the path;

[0083] Step S44: randomly exchange key nodes. Randomly select two key nodes in each path and exchange their positions. The formula used is as follows: ;

[0084] in, For the The population after the path individuals exchange key nodes, is the function of the exchange operation, There are two key nodes;

[0085] Step S45: The perturbation after exchange, Randomly select a path from the above and perform the reverse operation again to obtain .

[0086] Embodiment 5, based on the above embodiment, step S6 specifically includes the following steps:

[0087] Step S61: Calculate the enhancement factor. Set the enhancement factor and perform the calculation. The formula used is as follows: ; ;

[0088] in, , is the enhancement factor, , is a random number in the interval [0,1];

[0089] Step S62: Global search enhancement: perform global search enhancement on each path individual, using the enhancement factor Perform global search adjustments using the following formula:

[0090] ;

[0091] in, For the The path after the individual global enhancement of the path, Represents the current The path positions of the path individuals, is the optimal path individual in the current population;

[0092] Step S63: local search enhancement, performing local search enhancement on each path individual:

[0093] Use enhancement factor Perform local search adjustments on the current path using the following formula: ; ;

[0094] in, For the The path after the individual local enhancement of the path, is the neighboring path individual of the current path individual;

[0095] Step S64: Select the optimal path and use the fitness function to evaluate the The path of individual global enhancement and the The path after the individual local enhancement of the paths is selected, and the path with high fitness is selected as the optimal path.

[0096] By performing the above operations, two enhancement factors are introduced into the dual enhanced chimpanzee optimization algorithm. The enhancement factor R1 is introduced to enhance the global search and expand the search space. The enhancement factor R2 is introduced to enhance the local search and conduct a more detailed exploration in the local area of ​​the search space, thereby optimizing the quality of the existing solution and improving the accuracy and efficiency of the local search.

[0097] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0098] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

[0099] The present invention and its embodiments are described above, and such description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if ordinary technicians in the field are inspired by it, without departing from the purpose of the invention, they can design a structure and embodiment similar to the technical solution without creativity, which should belong to the protection scope of the present invention.

Claims

1. Intelligent security system integrating video surveillance and patrol route optimization, characterized by: It includes video collection module, patrol path optimization module, data analysis and prediction module, control and communication module, and specifically includes the following contents: The video collection module uses a camera to collect video and performs basic processing, including image enhancement, picture stabilization and denoising, to obtain analysis results; The patrol path optimization module uses a double-enhanced chimpanzee optimization algorithm to perform initial path planning and optimization; The data analysis and prediction module performs behavior analysis based on the basically processed video and analysis results; The control and communication module realizes data transmission and information sharing between modules; In the patrol path optimization module, the double-enhanced chimpanzee optimization algorithm is used for initial path planning and optimization, which specifically includes the following steps: Step S1: Initialization, determine the size of the patrol area, use a grid map to represent the patrol area, and define the key nodes that need to be patrolled; Step S2: Generate an initial population, randomly generate a set of initial paths, each path represents a patrol route, the patrol route consists of key nodes that need to be patrolled, and the node order represents the patrol order; Step S3: path evaluation, selecting path individuals to participate in optimization; Step S4: optimization stage; Step S5: local search, applying a local search algorithm to adjust the path; Step S6: Apply the double enhancement technique.

2. The intelligent security system integrating video surveillance and patrol route optimization according to claim 1, characterized in that: Step S3 specifically includes the following steps: Step S31: Determine the key nodes, determine all the key nodes in a path, and calculate the sum of the distances between the key nodes. The formula used is as follows: ； in, is the sum of distances, is the total number of key nodes, , It is The coordinates of the key nodes in the grid map, , It is The coordinates of the key nodes in the grid map; Step S32: Calculate coverage, where coverage is the ratio of the number of key points in a path to the total number of key points; Step S33: energy consumption calculation, calculating energy consumption according to the path length, setting an energy consumption factor, and the energy consumption is the product of the path length and the energy consumption factor; Step S34: Calculate the fitness function, define the fitness function, and use the following formula: ； in, is the fitness function, is the weight of the path length, is the weight of coverage, is the coverage rate, is the weight of energy consumption, It is energy consumption; Step S35: Calculate the fitness of each path and select the path with the highest fitness as the current path.

3. The intelligent security system integrating video surveillance and patrol route optimization according to claim 2, characterized in that: Step S4 specifically includes the following steps: Step S41: Initialize parameters, define population size, maximum number of iterations and initial path position, the population size is the number of path individuals participating in optimization, and the initial position is the position of the initial path individual; Step S42: Position update: for each path, the key node sequence is updated according to the following formula: ； in, For the The population after the individual updates of the paths, is the initial path position, , is a random vector; Step S43: Disturbance after position update, in A path is randomly selected and reversed. The formula used is as follows: ； in, For the The population after the path individuals are reversed, For the The starting point of the path, For the The end point of the path; Step S44: randomly exchange key nodes. Randomly select two key nodes in each path and exchange their positions. The formula used is as follows: ； in, For the The population after the path individuals exchange key nodes, is the function of the exchange operation, There are two key nodes; Step S45: The perturbation after exchange, Randomly select a path from the above and perform the reverse operation again to obtain .

4. The intelligent security system integrating video surveillance and patrol route optimization according to claim 3 is characterized in that: Step S6 specifically includes the following steps: Step S61: Calculate the enhancement factor. Set the enhancement factor and perform the calculation. The formula used is as follows: ； ； in, , is the enhancement factor, , is a random number in the interval [0,1]; Step S62: Global search enhancement: perform global search enhancement on each path individual, using the enhancement factor Perform global search adjustments using the following formula: ； in, For the The path after the individual global enhancement of the path, Represents the current The path positions of the path individuals, is the optimal path individual in the current population; Step S63: local search enhancement, performing local search enhancement on each path individual: Use enhancement factor Perform local search adjustments on the current path using the following formula: ； ； in, For the The path after the individual local enhancement of the path, is the neighboring path individual of the current path individual; Step S64: Select the optimal path and use the fitness function to evaluate the The path of individual global enhancement and the The path after the individual local enhancement of the paths is selected, and the path with high fitness is selected as the optimal path.

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