A method for intelligent building security monitoring

Abstract

This invention discloses an intelligent building security monitoring method, relating to the field of security monitoring technology. This intelligent building security monitoring method divides the public security area into standardized monitoring grid units, establishing a unified minimum scheduling unit for all network cameras and perimeter radars across the entire area. Then, by calculating the monitoring demand intensity of each monitoring grid unit, a unified quantitative basis is provided for the scheduling of equipment across the entire area. By traversing the device configuration grid by grid, for single-device scenarios, the baseline parameters corresponding to the maximum monitoring range are directly used as the optimal monitoring parameters. For dual-device scenarios, under unified collaborative constraints, the network camera parameters are first adjusted to identify areas not covered by video surveillance, and then the perimeter radar parameters are specifically adjusted to fill in the uncovered areas. This achieves standardized and unified scheduling of security equipment across the entire area, with no coverage gaps and no invalid overlaps, resulting in optimal collaborative control across the entire area.

Description

Technical Field

[0001] This invention relates to the field of security monitoring technology, specifically to an intelligent building security monitoring method. Background Technology

[0002] In practical applications of multi-device collaborative monitoring of network cameras and perimeter radar in intelligent building public security areas, existing technologies suffer from the following shortcomings: First, existing technologies lack a unified spatial scheduling unit and quantitative control benchmark for the entire area. Each network camera and perimeter radar typically employs an independent and decentralized control mode, lacking a standardized unified task scheduling benchmark, making it impossible to achieve unified allocation and coordinated control of collaborative tasks across devices. Second, existing technologies fail to establish rigid spatial coverage verification rules for multi-device collaborative scenarios, making it difficult to accurately match the effective monitoring range of different devices, easily leading to gaps in monitoring area coverage due to misaligned device parameter configurations. Third, existing technologies lack a collaborative control logic that prioritizes security coverage and optimizes resource efficiency step by step. Under the core premise of ensuring no blind spots in the entire area, it is impossible to simultaneously minimize monitoring overlap and optimize total device power consumption, thus failing to achieve optimal synergy between the overall security control effect and resource utilization efficiency in intelligent buildings.

[0003] Therefore, there is an urgent need for an intelligent building security monitoring method that can achieve unified grid-based scheduling of the entire public security area of ​​intelligent buildings, standardize parameter configuration for single and dual device scenarios based on quantitative control benchmarks, and achieve global optimization of coverage effect and resource consumption through progressive collaborative constraints, in order to solve the above-mentioned technical problems. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides an intelligent building security monitoring method that solves the problems of difficulty in unified task scheduling when multiple devices are monitoring collaboratively, easy occurrence of monitoring overlap or area coverage gaps, and inability to achieve optimal full-area collaborative monitoring.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an intelligent building security monitoring method, comprising the following steps: dividing the public security area into several monitoring grid units, and calculating the monitoring demand intensity of each monitoring grid unit.

[0006] Iterate through the area corresponding to each monitoring grid unit to check for network cameras and perimeter radar.

[0007] If only network cameras or perimeter radar exist, the baseline parameters corresponding to the maximum monitoring range are used as the optimal monitoring parameters, and the sampling frame rate is determined based on the intensity of monitoring needs.

[0008] If both network cameras and perimeter radar exist, then under the constructed cooperative constraints, the monitoring parameters of the network cameras are adjusted to determine the areas not covered by video surveillance, and the monitoring parameters of the perimeter radar are adjusted to determine the optimal cooperative parameters.

[0009] Compared with the prior art, the present invention has the following beneficial effects: (1) The present invention divides the public security area into a unified standard monitoring grid unit, establishes a unified minimum scheduling unit for all network cameras and perimeter radars in the whole area, and then provides a unified quantitative basis for the scheduling of equipment in the whole area by calculating the monitoring demand intensity of each monitoring grid unit; by traversing the equipment configuration of each grid and formulating parameter adjustment rules for single device and dual device scenarios, the present invention achieves the standardized and unified scheduling of security equipment in the whole area, with no coverage gaps and no invalid overlaps, and the optimal collaborative management of the whole area.

[0010] (2) For single-device scenarios, the present invention directly uses the benchmark parameters corresponding to the maximum monitoring range as the optimal monitoring parameters, which avoids coverage gaps in the single-device control area from the execution level. At the same time, it matches the sampling frame rate based on the intensity of monitoring demand, avoiding ineffective performance redundancy. For dual-device scenarios, under the unified collaborative constraints, the network camera parameters are first adjusted to clarify the video monitoring coverage area, and then the perimeter radar parameters are adjusted to fill the coverage area. This avoids the problem of coverage gaps in dual-device collaboration and controls monitoring overlap through collaborative constraints. Attached Figure Description

[0011] Figure 1 This is a flowchart of the intelligent building security monitoring method of the present invention.

[0012] Figure 2 This is a flowchart illustrating the process of determining the sampling frame rate in the intelligent building security monitoring method of the present invention.

[0013] Figure 3 This is a flowchart for determining the optimal collaborative parameters in the intelligent building security monitoring method of the present invention. Detailed Implementation

[0014] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. Please refer to the accompanying drawings. Figure 1 The present invention provides a technical solution: an intelligent building security monitoring method, comprising the following steps: S1, dividing the public security area into several monitoring grid units, and calculating the monitoring demand intensity of each monitoring grid unit.

[0015] In order to achieve unified quantification of continuous space in the overall management and control of public security in intelligent buildings, and to match security management with the physical structure of the building, and to have fixed spatial anchor points for multi-device collaboration, it is necessary to divide the public security area into several monitoring grid units. The specific process is as follows: S101, take the physical closed boundary of the public security area as the outer boundary constraint of the overall division, and the physical separation boundary of the functional area as the internal rigid constraint of the division.

[0016] S102. Perform Delaunay 3D meshing. Perform compliance verification for each generated tetrahedral spatial element. If the verification passes, mark the tetrahedral spatial element as a monitoring mesh element. Otherwise, adjust the meshing node positions and regenerate the tetrahedral spatial element.

[0017] The adjustment of the partition node position is based on the Delaunay partitioning circumscribed sphere criterion. The adjustment step of the partition node is 0.1m. After the adjustment, the distance between the node and the adjacent node must be not less than 0.5m. Under the premise of not breaking the outer boundary constraints and internal rigid constraints, the position of the partition node corresponding to the tetrahedral spatial element is finely adjusted. The adjustment process is existing technology and will not be described in detail here.

[0018] Both the physical closed boundary of the public security area and the physical separation boundary of the functional area are extracted from the three-dimensional building structure vector data with the building absolute coordinate system corresponding to the public security area. The physical closed boundary of the public security area is the three-dimensional continuous closed boundary of the inherent enclosure structure of the public security area, and the physical separation boundary of the functional area is the three-dimensional continuous closed boundary of the inherent physical separation between different functional zones within the public security area.

[0019] The Delaunay 3D meshing is performed based on the established Delaunay 3D meshing calculation model. The establishment process is as follows: in the architectural absolute coordinate system, all characteristic corner points and inflection points of the outer boundary constraints and internal rigid constraints are discretized into meshing constraint nodes, and all closed surfaces of the outer boundary constraints and internal rigid constraints are discretized into constraint boundary surfaces. The global space enclosed by the outer boundary constraints is used as the meshing solution domain, which serves as the Delaunay 3D meshing calculation model.

[0020] Furthermore, the compliance verification includes verifying whether all vertices and boundaries of the tetrahedral space unit do not exceed the outer boundary constraints or cross the internal rigid constraints.

[0021] Verify that the overlap volume between the tetrahedral spatial cell and all generated monitoring mesh cells is 0. The overlap volume is obtained by calculating the spatial union of the monitoring mesh cells using Boolean operations, which is the spatial volume of the overlapping portion of two monitoring mesh cells.

[0022] Compliance is defined as not exceeding the outer boundary constraints, not crossing the internal rigid constraints, and having an overlap volume of 0.

[0023] This embodiment breaks down a continuous public security area into several spatially non-overlapping tetrahedral monitoring grid units, providing a unified and unique spatial benchmark for subsequent monitoring intensity calculations, security equipment parameter adjustments, and multi-device collaborative linkage.

[0024] The process of calculating the monitoring demand intensity of each monitoring grid unit is as follows: S103, mapping the continuous and uninterrupted security events before the current moment to the monitoring grid unit to which the occurrence location belongs.

[0025] S104. Calculate the ratio of the total frequency of events to the maximum total frequency of events for each monitoring grid unit to obtain the initial demand value.

[0026] S105. The ratio of the cumulative duration of events in each monitoring grid unit to the total duration of the data window is used as a correction coefficient, and multiplied by the initial demand value to obtain the monitoring demand intensity.

[0027] The intensity of monitoring demand quantifies the frequency and cumulative duration of security events occurring within a specified statistical time window for a given monitoring grid unit. The value ranges from 0 to 1. A value closer to 1 indicates a higher frequency and longer cumulative duration of historical security events within the corresponding monitoring grid unit, necessitating an increase in the sampling frame rate of network cameras and perimeter radar. The sampling frame rate is inversely proportional to the sampling period; a higher frame rate results in a shorter sampling period.

[0028] S2. Iterate through the area corresponding to each monitoring grid unit to check whether there are network cameras and perimeter radars.

[0029] The specific process is as follows: assign a unique grid identification code to each monitoring grid unit, traverse all monitoring grid units that have completed subdivision and verification one by one, first retrieve the spatial closed boundary information of a single monitoring grid unit with the building absolute coordinate system, then retrieve the unique identification codes of all online network cameras and perimeter radars, as well as the pre-bound grid identification codes, and quickly filter the target devices belonging to the monitoring grid units through the grid identification codes.

[0030] S3. If only network cameras or perimeter radar exist, the baseline parameters corresponding to the maximum monitoring range shall be used as the optimal monitoring parameters, and the sampling frame rate shall be determined based on the intensity of monitoring demand.

[0031] The process of obtaining the benchmark parameters corresponding to the maximum monitoring range is as follows: S301, traverse all adjustable parameter combinations of network cameras or perimeter radars, and calculate the corresponding theoretical monitoring space range of the device for each adjustable parameter combination.

[0032] S302. From the theoretical monitoring space of the equipment, the spatial intersection with the three-dimensional closed boundary of obstacles and the area exceeding the spatial boundary of the monitoring grid unit are removed to obtain the effective monitoring range. Among them, the three-dimensional closed boundary of fixed structures such as walls, columns, and equipment cabinets are regarded as obstacles.

[0033] S303. Calculate the spatial volume of the effective monitoring range and determine the adjustable parameter combination corresponding to the maximum spatial volume as the benchmark parameter corresponding to the maximum monitoring range.

[0034] Adjustable parameter combinations are obtained by taking the adjustable range of each adjustable parameter in the inherent physical parameters of the security equipment as the unique boundary, and using the factory-fixed inherent minimum adjustable step size as the minimum iteration unit, and performing a full permutation and combination of each adjustable parameter. For example, the minimum adjustable step size for the horizontal or vertical rotation angle of a network camera is 0.5°, the focal length is 0.1mm, and the effective imaging distance is 0.5m; the minimum adjustable step size for the horizontal or vertical detection angle of a perimeter radar is 0.5°, and the detection distance is 1m. The above values ​​can be adjusted according to the specific model of the equipment.

[0035] Adjustable parameters for network cameras include horizontal rotation angle, vertical rotation angle, focal length, and inherent effective imaging range. Adjustable parameters for perimeter radar include horizontal detection angle, vertical detection angle, detection range, and inherent effective detection range.

[0036] The theoretical monitoring space range of the calculated equipment is based on existing technology, using a network camera as an example: Taking the three-dimensional coordinates of the network camera's installation point as the apex of a cone, the spatial orientation of the cone axis in the building's absolute coordinate system is determined by the horizontal and vertical rotation angles corresponding to the adjustable parameter combinations. Based on the inherent correspondence between the focal length corresponding to the adjustable parameter combinations, the factory-fixed focal length of the network camera, and the field of view, the horizontal and vertical field of view angles are determined. Using the upper and lower limits of the network camera's inherent effective imaging distance as the axial length boundaries of the cone axis, a closed three-dimensional cone space is generated, representing the theoretical monitoring space range of the equipment corresponding to the adjustable parameter combinations. The calculation process for the theoretical monitoring space range of perimeter radar equipment is not detailed here.

[0037] The spatial volume is calculated based on the three-dimensional geometric volume of the monitoring space range of the equipment, which is existing technology and will not be elaborated further.

[0038] like Figure 2 As shown, the process of determining the sampling frame rate based on the intensity of monitoring demand is as follows: multiply the intensity of monitoring demand by the maximum value of the rated sampling frame rate range of the network camera to obtain the initial sampling frame rate.

[0039] The initial sampling frame rate is constrained within the network camera's rated sampling frame rate range to obtain the optimal sampling frame rate for that network camera. The constraint rules are as follows: if the initial sampling frame rate is lower than the minimum value of the rated sampling frame rate range, the minimum value is taken as the optimal sampling frame rate. If the initial sampling frame rate is higher than the maximum value of the rated sampling frame rate range, the maximum value is taken as the optimal sampling frame rate. If the initial sampling frame rate is within the rated sampling frame rate range, the initial sampling frame rate is taken as the optimal sampling frame rate.

[0040] The sampling frame rate is obtained by inputting the intensity of monitoring demand into the sampling frame rate calculation formula of the perimeter radar. The calculation formula is as follows: ,in The sampling frame rate of the perimeter radar. This represents the minimum value within the rated sampling frame rate range of the perimeter radar. This represents the maximum value within the rated sampling frame rate range of the perimeter radar. This is the difference between the maximum and minimum values ​​within the rated sampling frame rate range of the perimeter radar. To monitor the intensity of demand.

[0041] For network cameras, the sampling frame rate is usually measured in fps (frames per second). The higher the value, the more video frames are captured per unit of time, resulting in smoother footage and stronger capabilities for capturing and documenting fast-moving targets and sudden abnormal events. For perimeter radar, the sampling frame rate is usually measured in Hz (times per second). The higher the value, the more times moving targets are detected and scanned in the monitored area per unit of time, resulting in higher tracking accuracy for moving targets.

[0042] S4. If both network cameras and perimeter radar exist, then under the constructed cooperative constraints, adjust the monitoring parameters of the network cameras to determine the areas not covered by video surveillance, and adjust the monitoring parameters of the perimeter radar to determine the optimal cooperative parameters.

[0043] To ensure that there are no blind spots in the public security area, minimize invalid overlapping coverage, reduce the total power consumption of the system, and achieve the optimal balance between security monitoring effect and resource efficiency, the collaborative constraint is: ① The volume difference between the union volume of the effective coverage areas of network cameras and perimeter radar and the volume difference between the volume of the three-dimensional monitoring grid unit to which they belong is 0.

[0044] ② The overlap volume of the effective detection coverage of network cameras and perimeter radar is minimized.

[0045] ③ The total operating power consumption of network cameras and perimeter radar is the lowest.

[0046] In this embodiment, constraint ① is first set as a rigid hard constraint, which forces the effective coverage area of ​​the two devices to completely fill the monitoring grid unit from a three-dimensional spatial geometry perspective, eliminating blind spots in security monitoring. Then, constraint ② is set as a constraint for spatial resource optimization, which minimizes the overlap volume of the network camera and perimeter radar coverage while meeting the rigid requirement of full coverage, avoiding the waste of computing power and bandwidth resources caused by ineffective and repeated monitoring. Finally, constraint ③ is set as a closing constraint for operating cost optimization, which minimizes the total operating power consumption of the two devices while satisfying the first two constraints. The ultimate goal is to achieve a globally optimal balance between system resource consumption and operating cost while absolutely ensuring the effectiveness of security control.

[0047] like Figure 3 As shown, the process of determining the optimal coordination parameters is as follows: S401, traverse all adjustable parameter combinations of the network camera, and calculate the corresponding spatial volume for each adjustable parameter combination.

[0048] S402. Sort all adjustable parameter combinations in descending order of spatial volume to generate a candidate parameter queue.

[0049] S403. Take the current adjustable parameter combination from the candidate parameter queue in descending order and calculate the corresponding effective coverage area of ​​the camera.

[0050] S404. Calculate the closed space within the three-dimensional monitoring grid unit that is not effectively covered by the camera using Boolean operations, and use it as the current video surveillance uncovered area.

[0051] S405. Traverse all adjustable parameter combinations of the perimeter radar and retain the radar parameter combinations whose effective detection coverage completely covers the area not currently covered by the video surveillance, as the candidate parameter set. (If the current radar candidate parameter set is not empty, it means that there is a parameter combination that satisfies constraint ①, and the queue traversal stops. If the current radar candidate parameter set is empty, it means that the current group of camera parameters cannot satisfy constraint ①, and the next smaller adjustable parameter combination is taken from the candidate parameter queue.)

[0052] S406. Calculate the overlap volume between each radar parameter combination in the candidate parameter set and the adjustable parameter combination of the current network camera, and select the radar parameter subset with the smallest overlap volume.

[0053] S407. Select the radar parameter combination with the minimum total operating power consumption from the radar parameter subset, and integrate it with the adjustable parameter combination of the current network camera to obtain the optimal collaborative parameters.

[0054] This embodiment avoids exhaustive calculation of all parameter combinations of cameras and radar by sorting the data in descending order of coverage volume and stopping traversal once the first feasible solution is found, thus reducing computational overhead. While ensuring full coverage, it minimizes the waste of video storage, bandwidth, and computing resources caused by ineffective overlapping coverage of dual devices, and simultaneously minimizes the total operating power consumption of network cameras and perimeter radar. Without sacrificing security control effectiveness, it achieves globally optimal utilization of system resources.

[0055] It should be noted that if the candidate parameter set is empty after traversing all parameter combinations in the candidate parameter queue, the adjustable parameters of the network camera will be adjusted one by one according to the inherent minimum adjustable step size fixed at the factory, generating new adjustable parameter combinations and adding them to the end of the candidate parameter queue.

[0056] This embodiment addresses the scenario where, after traversing all native adjustable parameter combinations in the candidate parameter queue, no feasible solution satisfies the rigid constraint of full coverage by both devices. It eliminates the risk of monitoring blind spots caused by the lack of available parameter combinations. Furthermore, the design of supplementing the newly generated adjustable parameter combinations to the end of the candidate parameter queue fully preserves the original optimization logic of traversing in descending order of effective coverage volume. This not only provides a safety net for feasible solutions but also does not disrupt the original efficiency optimization design that prioritizes parameters with large coverage volume and reduces the computational load of optimization.

[0057] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0058] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0059] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0060] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0061] Finally, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for intelligent building security monitoring, characterized in that, Includes the following steps: The public security area is divided into several monitoring grid units, and the monitoring demand intensity of each monitoring grid unit is calculated. Iterate through the area corresponding to each monitoring grid unit to check for network cameras and perimeter radar; If only network cameras or perimeter radar exist, the baseline parameters corresponding to the maximum monitoring range are used as the optimal monitoring parameters, and the sampling frame rate is determined based on the intensity of monitoring demand. If both network cameras and perimeter radar exist, then under the constructed cooperative constraints, the parameters of the network cameras are adjusted to determine the areas not covered by video surveillance, and the parameters of the perimeter radar are adjusted to determine the optimal cooperative parameters.

2. The intelligent building security monitoring method according to claim 1, characterized in that, The process of dividing the public security area into several monitoring grid units is as follows: The physical closed boundary of the public security area serves as the outer boundary constraint for the whole domain division, while the physical separation boundary of the functional area serves as the internal rigid constraint for the division. Perform Delaunay 3D meshing. For each tetrahedral spatial element generated, a compliance check is performed. If the check passes, the tetrahedral spatial element is marked as a monitoring mesh element; otherwise, the meshing node positions are adjusted and the tetrahedral spatial element is regenerated.

3. The intelligent building security monitoring method according to claim 2, characterized in that, The compliance verification includes: Verify that all vertices and boundaries of the tetrahedral spatial element do not exceed the outer boundary constraints and do not cross the internal rigid constraints. Verify that the overlap volume between the tetrahedral spatial cell and all generated monitoring mesh cells is 0.

4. The intelligent building security monitoring method according to claim 1, characterized in that, The process of calculating the monitoring demand intensity for each monitoring grid unit is as follows: Map the continuous security events that occurred before the current moment to the monitoring grid unit to which the location belongs; The initial demand value is obtained by calculating the ratio of the total frequency of events to the maximum total frequency of events for each monitoring grid unit. The ratio of the cumulative duration of events in each monitoring grid unit to the total duration of the data window is used as a correction factor, and multiplied by the initial demand value to obtain the monitoring demand intensity.

5. The intelligent building security monitoring method according to claim 1, characterized in that, The process for obtaining the baseline parameters corresponding to the maximum monitoring range is as follows: Iterate through all adjustable parameter combinations of network cameras or perimeter radars, and calculate the corresponding theoretical monitoring space range of the device for each adjustable parameter combination. The effective monitoring range is obtained by removing the spatial intersection with the three-dimensional closed boundary of the obstacle and the area beyond the spatial boundary of the monitoring grid unit from the theoretical monitoring space range of the equipment. Calculate the spatial volume of the effective monitoring range and determine the adjustable parameter combination corresponding to the maximum spatial volume, which serves as the benchmark parameter corresponding to the maximum monitoring range.

6. The intelligent building security monitoring method according to claim 1, characterized in that, The collaborative constraint condition is as follows: The volume difference between the union of the effective coverage areas of network cameras and perimeter radar and the volume of their respective three-dimensional monitoring grid cells is 0. The overlap volume of the effective detection coverage of webcams and perimeter radar is minimized; The combined power consumption of the webcam and perimeter radar is the lowest.

7. The intelligent building security monitoring method according to claim 1, characterized in that, The process of adjusting the parameters of the network camera to determine the areas not covered by video surveillance, and adjusting the parameters of the perimeter radar to determine the optimal coordination parameters is as follows: Iterate through all adjustable parameter combinations of the network camera and calculate the corresponding spatial volume for each parameter combination; All adjustable parameter combinations are sorted in descending order of spatial volume to generate a candidate parameter queue. Retrieve the current adjustable parameter combination from the candidate parameter queue in descending order and calculate the corresponding effective coverage area of ​​the camera; Boolean operations are used to calculate the closed space within the three-dimensional monitoring grid unit that is not effectively covered by the camera, which is then considered as the current video surveillance uncovered area. Iterate through all adjustable parameter combinations of the perimeter radar and retain the radar parameter combinations whose effective detection coverage can cover the area not currently covered by video surveillance as a candidate parameter set; Based on the candidate parameter queue and candidate parameter set, the monitoring parameters of network cameras and perimeter radar are adjusted to determine the optimal cooperative parameters.

8. The intelligent building security monitoring method according to claim 7, characterized in that, The process of adjusting the monitoring parameters of network cameras and perimeter radar based on candidate parameter queues and candidate parameter sets to determine the optimal collaborative parameters is as follows: Calculate the overlap volume between each radar parameter combination in the candidate parameter set and the adjustable parameter combination of the current network camera, and filter out the radar parameter subset with the smallest overlap volume. The radar parameter combination with the minimum total operating power consumption is selected from the subset of radar parameters and integrated with the adjustable parameter combination of the current network camera to obtain the optimal collaborative parameters.

9. The intelligent building security monitoring method according to claim 7, characterized in that, If the candidate parameter set is empty after traversing all adjustable parameter combinations in the candidate parameter queue, then the parameters of the network camera are adjusted sequentially using the inherent minimum adjustable step size fixed at the factory, generating new adjustable parameter combinations and adding them to the tail of the candidate parameter queue.

10. The intelligent building security monitoring method according to claim 1, characterized in that, The process of determining the sampling frame rate based on the intensity of monitoring demand is as follows: The initial sampling frame rate is obtained by multiplying the intensity of the monitoring demand by the maximum value of the rated sampling frame rate range of the network camera. By constraining the initial sampling frame rate within the rated sampling frame rate range of the network camera, the optimal sampling frame rate of the network camera is obtained. The sampling frame rate is obtained by inputting the intensity of monitoring demand into the sampling frame rate calculation formula of the perimeter radar. The calculation formula is as follows: ,in The sampling frame rate of the perimeter radar. This represents the minimum value within the rated sampling frame rate range of the perimeter radar. This represents the maximum value within the rated sampling frame rate range of the perimeter radar. This is the difference between the maximum and minimum values ​​within the rated sampling frame rate range of the perimeter radar. To monitor the intensity of demand.

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