Intelligent property management system

Abstract

The application discloses a kind of intelligent property management systems, specifically related to property management field, including property dispatch management module, monitoring management module, inspection path algorithm module, inspection execution module;Property dispatch management module is used to store the position of all cameras, sensors, property facilities, the state of property facilities and its maintenance record, and realize linkage through inspection path algorithm module;Monitoring management module is used to deploy camera, the deflection of execution camera, zoom, so that the monitoring range of camera covers the target area required to be inspected;Monitoring management module includes temperature and humidity sensor, smoke sensor, illumination sensor for real-time acquisition of property environment information;Inspection path algorithm module includes environment modeling, priority allocation, path dynamic optimization, perspective adjustment, adaptive learning;Through the technology such as environment modeling, priority allocation, path dynamic optimization of inspection path algorithm module, the dynamic adjustment of camera perspective and inspection path is realized.

Description

Technical Field

[0001] This invention relates to the field of property management technology, and more specifically, to an intelligent property management system. Background Technology

[0002] In the process of smart property management, monitoring is relied upon to ensure the safe operation and maintenance efficiency of equipment. Among them, equipment inspection is the core link. However, the inspection needs to cover different complex spaces within the building to ensure that key equipment such as computer rooms, elevators, and power supply are in normal condition.

[0003] In existing technologies, property management personnel typically rely on manual monitoring of surveillance screens to achieve the purpose of property monitoring and management. However, it is difficult to combine this with patrol path algorithm optimization, resulting in some areas not meeting the requirements for patrol coverage, thus creating monitoring blind spots and affecting security. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a smart property management system that uses an inspection path algorithm module to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a smart property management system, comprising a property dispatch management module, a monitoring management module, an inspection path algorithm module, and an inspection execution module;

[0006] The property dispatch and management module stores the location, status and maintenance records of all cameras, sensors and property facilities, and achieves linkage through the inspection path algorithm module;

[0007] The monitoring and management module is used to deploy cameras and perform camera deflection and zoom to ensure that the camera's monitoring range covers the target area to be inspected. The monitoring and management module includes temperature and humidity sensors, smoke sensors, and light sensors for real-time collection of property environmental information.

[0008] The inspection path algorithm module includes environmental modeling, priority allocation, dynamic path optimization, viewpoint adjustment, and adaptive learning. The inspection path algorithm module constructs a three-dimensional structure of the property management area through environmental modeling and calculates the field of view angle of the camera. The field of view angle is used to determine the initial monitoring coverage area.

[0009] The inspection path algorithm module calculates the inspection interval by prioritizing and combining it with the inspection time adjustment strategy, and adjusts the inspection order according to the task priority allocation.

[0010] The inspection path algorithm module dynamically optimizes the path and adjusts the inspection angle of the camera in real time. It adopts the shortest path priority to make the inspection task run on the required path. At the same time, it combines the hot spot area backtracking mechanism to increase the inspection frequency of areas with frequent failures.

[0011] The perspective adjustment in the inspection path algorithm module optimizes the monitoring coverage based on the error correction mechanism of the camera's horizontal rotation angle and pitch angle, and uses error compensation adjustment to make the monitoring perspective more stable.

[0012] The inspection path algorithm module optimizes the field of view and inspection interval by analyzing historical inspection data, so that the inspection path is optimized over time.

[0013] The inspection execution module receives instructions from the inspection path algorithm module and controls the camera to perform adjustment tasks. The camera integrates a pan-tilt control module, which is used to adjust the camera's deflection angle, rotation speed, and focal length.

[0014] In a preferred embodiment, environmental modeling includes constructing a 3D model of the property management area and calculating the required monitoring range of the cameras. At the same time, errors are detected through software debugging to prevent incorrect camera installation angles or spatial obstructions from affecting the field of view.

[0015] A 3D model of the property management area is expressed as follows:

[0016] ;

[0017] in Represents the three-dimensional coordinate point set of the property management area; For the first in the property The coordinates of each spatial sampling point; The number of all modeling points;

[0018] Calculate the camera's field of view;

[0019] ;

[0020] in For camera The angle of view; This represents the peak value of the camera's monitoring radius. The horizontal opening angle of the camera; Indicates the first During the software debugging, the camera was detected. Installation error; This represents the maximum allowable error.

[0021] Establish a mechanism to prevent errors in software debugging and detection;

[0022] ;

[0023] in The actual installation angle detected by the camera; This refers to the theoretical installation angle of the camera; This is the error accumulation factor, which is used to determine the error correction rate;

[0024] like The camera angle can then be adjusted via the gimbal control module.

[0025] ;

[0026] in Indicates camera Adjusted rotation angle.

[0027] In a preferred embodiment, the priority allocation in the inspection path algorithm module is used to dynamically adjust the inspection sequence based on the historical failure rate of the inspection area, dynamic environmental changes, and equipment aging level, so that the required area is monitored with priority.

[0028] ;

[0029] ;

[0030] in For the region Inspection risk score; These are weight parameters; For the region Historical failure rate; For the region Dynamic environmental changes; For the region Equipment aging level; Scoring software debugging errors; Correction factor for software debugging; For the region The actual inspection time interval in actual inspection tasks; For the region The ideal inspection time interval is calculated based on inspection requirements; This is the adjustment coefficient for inspection time error.

[0031] In a preferred embodiment, the path dynamic optimization calculation in the inspection path algorithm module aims to find the path from the current monitoring state. To the target monitoring status The ideal adjustment path;

[0032] ;

[0033] in For the first Round calculation from camera to camera The cost of adjusting the ideal inspection route; For camera Rotate to camera Minimum angle adjustment required for the position; For risk weight parameters; For camera The inspection risk score for the corresponding area; Indicates the current monitoring status; Indicates the target monitoring status; Calculate the error value for the path detected during software debugging; This is the path error correction coefficient;

[0034] During the dynamic optimization of the path, the system analyzes historical inspection paths through software debugging and calculates the deviation between the actual inspection path and the theoretical inspection path.

[0035] ;

[0036] in For the first Error value of the inspection path; The actual inspection path distance adjusted for the camera; The ideal inspection path calculated by the system; This is the path error adjustment factor.

[0037] In a preferred embodiment, the viewpoint adjustment in the inspection path algorithm module is used to enable the camera to adjust its horizontal rotation angle and pitch angle according to changes in the environment during the inspection task.

[0038] ;

[0039] ;

[0040] in For camera The horizontal rotation angle in actual inspection tasks; For camera The theoretical horizontal rotation angle calibrated during the installation and commissioning phase; For camera The elevation angle during actual inspection tasks; For camera The theoretical pitch angle calibrated during the installation and commissioning phase; For camera The angle of view; This represents the peak value of the camera's monitoring radius. , This is the rotation error adjustment coefficient.

[0041] In a preferred embodiment, the adaptive learning in the inspection path algorithm module optimizes the camera status based on historical inspection data, so that the inspection task is continuously optimized over time; the optimization is based on historical data fitting and error compensation to calculate the optimized monitoring angle and inspection interval.

[0042] ;

[0043] in For the first Optimized camera The angle of view; No. Camera during inspection Calculated field of view angle; For the region Inspection risk score; For camera Adjust to camera Adjust the path distance for the location; For the first Error correction value for the next software test; This is the error correction factor; This is the learning rate.

[0044] In a preferred embodiment, the system further includes an inspection task execution monitoring module. The inspection task execution monitoring module establishes an inspection task execution deviation evaluation model based on three factors. Based on the inspection task execution deviation evaluation model, the module calculates the execution deviation value. If the execution deviation value is greater than the execution deviation threshold, it is determined to be an execution deviation and an abnormal alarm is triggered.

[0045] The three factors include path deviation, coverage area, and task execution. The path deviation factor is used to calculate the difference between the actual inspection path of the camera and the ideal path, and to assess whether the inspection task is executed according to the predetermined trajectory.

[0046] The coverage area factor is used to compare the actual monitoring range with the theoretical coverage area to detect whether there are any long-term uninspected areas.

[0047] Task execution factors are used to monitor camera rotation accuracy, zoom adjustment error, and inspection time deviation to assess the consistency of inspection task execution.

[0048] In a preferred embodiment, the path deviation error is calculated based on the path deviation factor, and the proposed path deviation error is... The coverage area error is calculated based on coverage area factors, and the proposed coverage area error is: ;Calculate the task execution error based on task execution factors, and set the task execution error as follows: ;

[0049] ;

[0050] ;

[0051] ;

[0052] in This refers to the number of cameras in the system. For camera Actual inspection route points and theoretical inspection path points The Bézier curve matching error between them; This is the maximum path deviation value allowed by the system; The actual installation angle detected by the camera; This refers to the theoretical installation angle of the camera; This represents the extreme value of the camera's rotation angle; For the region The actual inspection time interval in actual inspection tasks; For the region The ideal inspection time interval is calculated based on inspection requirements; The peak value of the allowable inspection time deviation; This is the path deviation weighting factor, which is used to dynamically adjust the degree of influence of path error on the final execution deviation.

[0053] in This represents the total number of inspection areas. For the first The actual coverage area of ​​each monitored zone; For the first Theoretical calculated coverage area of ​​each monitoring zone; This represents the area of ​​the intersection between the actual monitored area and the theoretical monitored area. For the first The cumulative risk score of each monitored area that has not been inspected in the past; This is a weighting factor for coverage error;

[0054] in For camera Actual and theoretical zoom ratios; This represents the peak zoom ratio of the camera. This is the weighting factor for task execution.

[0055] In a preferred embodiment, a patrol task execution deviation evaluation model is established based on three factors; the execution deviation value is calculated based on the patrol task execution deviation evaluation model; and the proposed execution deviation value is... ;

[0056] ;

[0057] in The nonlinear adjustment value representing the path deviation error; The nonlinear adjustment value representing the coverage area error; The nonlinear adjustment value representing the task execution error; This is the error attenuation factor; They are respectively , , Importance weights; This is the normalization factor.

[0058] The technical effects and advantages of this invention are as follows:

[0059] 1. By employing environmental modeling, priority allocation, and dynamic path optimization technologies in the inspection path algorithm module, the dynamic adjustment of camera view and inspection path is achieved. Traditional inspection systems rely on manual path planning, which may lead to blind spots in monitoring coverage. In contrast, this invention uses a path optimization algorithm based on 3D environmental modeling to calculate the optimal inspection route, ensuring that the field of view of the monitoring camera maximizes coverage of key areas. At the same time, the short path priority mechanism and hotspot area backtracking mechanism can intelligently adjust monitoring tasks, increase the inspection frequency of areas with frequent faults, and ensure the safety of property management and the reliability of equipment operation and maintenance.

[0060] 2. By introducing a software debugging and error prevention mechanism, the system can detect camera installation errors and make real-time corrections. For example, in the 3D modeling stage, the deviation between the theoretical installation angle and the actual measured angle of the camera is calculated, and dynamic adjustments are made based on the error accumulation coefficient to ensure that the monitoring range and the calculation path remain consistent.

[0061] 3. The inspection task execution monitoring module can calculate the execution deviation value in real time and monitor the execution accuracy of the inspection task. Based on the evaluation of path deviation factors, coverage area factors and task execution factors, the system can identify problems such as the error between the actual inspection path and the ideal path, insufficient monitoring coverage, and camera rotation accuracy deviation. Once the system detects that the inspection task deviation value exceeds the preset threshold, it will trigger an alarm and adjust the inspection parameters, thereby improving the reliability and adaptability of the entire property management system. Attached Figure Description

[0062] Figure 1 This is a schematic diagram of the system modules of the present invention. Detailed Implementation

[0063] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0064] Refer to the instruction manual appendix Figure 1 An embodiment of the present invention provides a smart property management system, which includes a property scheduling management module, a monitoring management module, an inspection path algorithm module, and an inspection execution module.

[0065] The property dispatch and management module stores the location, status and maintenance records of all cameras, sensors and property facilities, and achieves linkage through the inspection path algorithm module;

[0066] The monitoring and management module is used to deploy cameras and perform camera deflection and zoom to ensure that the camera's monitoring range covers the target area to be inspected. The monitoring and management module includes temperature and humidity sensors, smoke sensors, and light sensors for real-time collection of property environmental information.

[0067] The inspection path algorithm module includes environmental modeling, priority allocation, dynamic path optimization, viewpoint adjustment, and adaptive learning. The inspection path algorithm module constructs a three-dimensional structure of the property management area through environmental modeling and calculates the field of view angle of the camera. The field of view angle is used to determine the initial monitoring coverage area.

[0068] The inspection path algorithm module calculates the inspection interval by prioritizing and combining it with the inspection time adjustment strategy, and adjusts the inspection order according to the task priority allocation, so that cameras in high-risk areas are given priority in the inspection task.

[0069] The inspection path algorithm module dynamically optimizes the path and adjusts the inspection angle of the camera in real time. It adopts the shortest path priority to make the inspection task run on the required path. At the same time, it combines the hot spot area backtracking mechanism to increase the inspection frequency of areas with frequent failures.

[0070] The perspective adjustment in the inspection path algorithm module optimizes the monitoring coverage based on the error correction mechanism of the camera's horizontal rotation angle and pitch angle, and uses error compensation adjustment to stabilize the monitoring perspective, reduce blind spots, and improve inspection accuracy.

[0071] The inspection path algorithm module optimizes the field of view and inspection interval by analyzing historical inspection data, so that the inspection path is optimized over time.

[0072] The inspection execution module receives instructions from the inspection path algorithm module and controls the camera to perform adjustment tasks. The camera integrates a pan-tilt control module, which is used to adjust the camera's deflection angle, rotation speed, and focal length.

[0073] Environmental modeling includes constructing a 3D model of the property management area and calculating the required monitoring range of the cameras. At the same time, it involves debugging and detecting errors through software to prevent incorrect camera installation angles and spatial obstructions from affecting the field of view.

[0074] A 3D model of the property management area is expressed as follows:

[0075]

[0076] in Represents the three-dimensional coordinate point set of the property management area. Used to build spatial models to ensure that inspection path calculations can accurately match building structures and monitoring needs; For the first in the property The coordinates of each spatial sampling point; The number of all modeling points, used to determine the modeling accuracy;

[0077] Calculate the camera's field of view;

[0078] ;

[0079] in For camera The angle of view; This represents the peak value of the camera's monitoring radius. The horizontal opening angle of the camera; Indicates the first During the software debugging, the camera was detected. Installation error; For the maximum allowable error, Used for calibrating the monitoring area;

[0080] Establish a mechanism to prevent errors in software debugging and detection; Indicates the first During the software debugging, the camera was detected. Installation error;

[0081] ;

[0082] in The actual installation angle detected by the camera; This refers to the theoretical installation angle of the camera; This is the error accumulation factor, which is used to determine the error correction rate;

[0083] like The camera angle can then be adjusted via the gimbal control module.

[0084] ;

[0085] in Indicates camera Adjusted rotation angle Used to compensate for installation errors or optimize monitoring perspective; if the error is too large, the system will trigger an alarm, requiring manual re-inspection.

[0086] The priority allocation in the inspection path algorithm module is used to dynamically adjust the inspection sequence based on the historical failure rate of the inspection area, dynamic environmental changes, and equipment aging level, so that the required areas are monitored first.

[0087] ;

[0088] ;

[0089] in For the region Inspection risk score, The higher the value, the higher the inspection priority required for that area; These are weight parameters, which are used to adjust... , , The degree of influence on priority; For the region Historical failure rate; For the region The dynamic environmental changes, including factors such as population density and equipment load, affect monitoring needs. For the region The higher the aging level of the equipment, the greater the probability of failure, and the more frequent the inspections should be. The software debugging error score is given on the first iteration of the software debugging error test. The calculation error of the current inspection priority is calculated during the next inspection. If the error is too large, the priority allocation is corrected. This is the software debugging correction factor, which is used to determine the degree of impact of errors on inspection priority. Indicates the inspection priority error in the first place. Correction value after round calculation; For the region The actual inspection time interval in actual inspection tasks; For the region The ideal inspection time interval is calculated based on inspection requirements; This is the inspection time error adjustment coefficient, used to control the impact of inspection time error on inspection priority;

[0090] To prevent errors in the calculation of inspection priorities, the system monitors and dynamically corrects inspection priority errors through software debugging.

[0091] Error detection: Before calculating the inspection path, the software simulates multiple inspection cycles and records the results for each area. and The deviation; if the deviation value is too large, that is If so, adjust the inspection priority; among which The allowable error threshold;

[0092] Error correction: when This indicates that the inspection priority for this area has been incorrectly set too high, and the inspection frequency should be reduced.

[0093] ;

[0094] in For the new inspection interval; The time adjustment factor is used to prevent instability in inspections caused by sudden changes; when This indicates that the inspection priority for this area has been incorrectly set too low, and the inspection frequency should be increased.

[0095] ;

[0096] Reduce the inspection interval, allowing the area to be inspected more frequently; among which This indicates the maximum allowable error value for inspection priority. If this value is exceeded, the system will reduce the inspection frequency of that area to prevent excessive inspection and waste of resources. This indicates the minimum allowable error for inspection priority. If the error is lower than this value, the system will increase the inspection frequency for that area to ensure that critical areas are adequately monitored.

[0097] Dynamic optimization: After each round of inspections, the system recalculates. and We continuously adjust the inspection task scheduling to optimize the inspection path.

[0098] The goal of the dynamic path optimization calculation in the inspection path algorithm module is to find the path from the current monitoring state. To the target monitoring status The optimal ideal adjustment path is to minimize the path cost while taking into account regional risk scoring and software debugging error correction.

[0099] ;

[0100] in For the first Round calculation from camera to camera The cost of adjusting the optimal ideal inspection path; For camera Rotate to camera Minimum angle adjustment required for the position; The risk weight parameter is used to determine the degree of impact of regional risk on route planning. For camera The higher the risk score of the corresponding area, the higher the priority of the route. Indicates the current monitoring status; Indicates the target monitoring status; The error value is calculated for the path detected by the software debugging, which represents the deviation between the actual inspection path and the theoretically calculated path. If the error is too large, the inspection path is corrected. This is the path error correction coefficient, used to control the magnitude of the impact of errors on path calculation;

[0101] During the dynamic optimization of the path, the system analyzes historical inspection paths through software debugging and calculates the deviation between the actual inspection path and the theoretical inspection path.

[0102] ;

[0103] in For the first Error value of the inspection path; The actual inspection path distance adjusted for the camera; The ideal and optimal inspection path is calculated by the system; This is the path error adjustment factor, used to determine the rate of error correction; when software debugging detects a large error between the actual path and the theoretical path (i.e., ,in (Based on the allowable error threshold), the system will automatically adjust the inspection path;

[0104] The path correction methods for the above scheme include:

[0105] If the path error is too large, it is represented as: Reduce the range of path adjustments to make camera adjustments more precise and avoid blind spots caused by path deviation.

[0106] ;

[0107] If the path error is small, it is represented as: Increase the range of path adjustments so that cameras can complete inspection tasks faster and improve monitoring efficiency.

[0108] ;

[0109] in The adjusted inspection route; Path adjustment factor, used to prevent path instability caused by mutations; This is the maximum allowable threshold for path error. If this value is exceeded, the system needs to recalculate the inspection path to prevent significant deviations. This is the minimum allowable threshold for path error. If the error is lower than this value, the system needs to appropriately increase the path adjustment to avoid insufficient adjustment affecting the inspection efficiency.

[0110] The viewpoint adjustment in the inspection path algorithm module is used to enable the camera to adjust its horizontal rotation angle and pitch angle according to changes in the environment during the inspection task.

[0111] ;

[0112] ;

[0113] in For camera The horizontal rotation angle in actual inspection tasks; For camera The theoretical horizontal rotation angle calibrated during the installation and commissioning phase; For camera The elevation angle during actual inspection tasks; For camera The theoretical pitch angle calibrated during the installation and commissioning phase; For camera The angle of view; This represents the peak value of the camera's monitoring radius. , These are rotation error adjustment coefficients, used to correct the horizontal rotation angle and pitch angle, respectively;

[0114] The software debugging and error prevention mechanisms during perspective adjustment include:

[0115] Error detection; Before the inspection task is executed, the system collects the current horizontal and vertical angles of the camera and calculates the deviation between the actual adjustment angle and the theoretical value:

[0116] ;

[0117] ;

[0118] If the error exceeds the set threshold Enter the error correction process;

[0119] Error correction includes horizontal rotation angle correction and pitch angle correction;

[0120] Horizontal rotation angle correction:

[0121] ;

[0122] Pitch angle correction:

[0123] ;

[0124] in These are the corrected rotation angles; This is the error correction factor. The adjustment rate is used to control error correction and prevent over-adjustment from causing frequent camera shake. This represents the maximum allowable error. Indicates the first During patrol inspections, the camera Horizontal rotation angle error; Indicates the first During patrol inspections, the camera Pitch angle error.

[0125] The adaptive learning in the inspection path algorithm module optimizes the camera status based on historical inspection data, so that the inspection task is continuously optimized over time; the optimization is based on historical data fitting and error compensation to calculate the optimized monitoring angle and inspection interval.

[0126] ;

[0127] in For the first Optimized camera The angle of view; No. Camera during inspection Calculated field of view angle; For the region Inspection risk score; For camera Adjust to camera Adjust the path distance for the location; For the first The software test error correction value is used to correct calculation errors during the optimization process. This is the error correction factor, which is used to ensure that error correction meets long-term optimization requirements. The learning rate is used to control the step size of the field of view angle adjustment during the inspection path optimization process, ensuring that the adjustment range is not too large or too small, so as to achieve stable optimization. This indicates the total number of cameras or monitoring nodes within the inspection area;

[0128] Software debugging error prevention mechanisms include:

[0129] Error analysis: The system records the camera adjustment path, zoom parameters, and monitoring coverage area during each inspection, and calculates the error between the actual adjustment value and the theoretical optimal value.

[0130] Error correction: If the software debugging detects an error in the inspection path... Then adjust and optimize the strategy:

[0131] ;

[0132] in, This indicates the maximum allowable error threshold during the inspection path optimization process. If the error exceeds this value, the system needs to adjust the inspection parameters to prevent the accumulation of monitoring deviations and reduce the range of field of view adjustments to avoid over-optimization leading to the loss of monitoring areas.

[0133] Adaptive optimization: By combining historical inspection data, the system uses deep learning methods (such as LSTM and reinforcement learning) to continuously train and optimize the inspection path, enabling the system to have long-term learning capabilities and improve monitoring accuracy.

[0134] It also includes an inspection task execution supervision module, which establishes an inspection task execution deviation evaluation model based on three factors; it calculates the execution deviation value based on the inspection task execution deviation evaluation model, and if the execution deviation value is greater than the execution deviation threshold, it is determined to be an execution deviation and triggers an abnormal alarm;

[0135] The three factors include path deviation, coverage area, and task execution. The path deviation factor is used to calculate the difference between the actual inspection path of the camera and the ideal path, and to assess whether the inspection task is executed according to the predetermined trajectory.

[0136] The coverage area factor is used to compare the actual monitoring range with the theoretical coverage area to detect whether there are any long-term uninspected areas.

[0137] Task execution factors are used to monitor camera rotation accuracy, zoom adjustment error, and inspection time deviation to assess the consistency of inspection task execution.

[0138] The path deviation error is calculated based on the path deviation factor, and the proposed path deviation error is: The coverage area error is calculated based on coverage area factors, and the proposed coverage area error is: ;Calculate the task execution error based on task execution factors, and set the task execution error as follows: ;

[0139] ;

[0140] ;

[0141] ;

[0142] in This refers to the number of cameras in the system. For camera Actual inspection route points and theoretical inspection path points The Bézier curve matching error between them; This is the maximum path deviation value allowed by the system; The actual installation angle detected by the camera; This refers to the theoretical installation angle of the camera; This represents the extreme value of the camera's rotation angle; For camera The actual and theoretical inspection time interval; The peak value of the allowable inspection time deviation; This is the path deviation weighting factor, which is used to dynamically adjust the impact of path errors on the final execution deviation. It is adjusted according to the complexity of the environment.

[0143] in This represents the total number of inspection areas. For the first The actual coverage area of ​​each monitored zone; For the first Theoretical calculation of the optimal coverage area for each monitoring zone; This represents the area where the actual monitored area intersects with the theoretical monitored area, which is also the area that is successfully covered. For the first The cumulative risk score for historical non-inspection of each monitored area, with an increased weight for areas that have not been inspected for a long time. As a weighting factor for coverage error, it dynamically adjusts the degree of impact of coverage error based on the risk level and environmental complexity of the region;

[0144] in For camera Actual and theoretical zoom ratios; This represents the peak zoom ratio of the camera. As a weighting factor for task execution, if a camera makes multiple errors in historical inspections, its weight will automatically increase, making its impact stronger.

[0145] A patrol task execution deviation assessment model is established based on three factors. The execution deviation value is calculated based on this model, and a proposed execution deviation value is determined. ;

[0146] ;

[0147] in It is a non-linear adjustment factor that makes different deviations change dynamically during calculation, preventing a single factor from excessively affecting the overall result; The nonlinear adjustment value represents the path deviation error, and is used to calculate the degree of matching between the actual inspection path and the ideal path of the camera. It represents the nonlinear adjustment value of the coverage area error, which measures the degree of overlap between the actual monitoring range and the theoretical coverage range; The non-linear adjustment value represents the task execution error, which is calculated by comprehensively considering the deviations of the camera rotation angle, zoom ratio, and inspection time. This is the error attenuation factor, used to smooth the impact of different errors on the final calculated value; They are respectively , , The importance weight can be dynamically adjusted over time; This is a normalization factor used to ensure that the calculation results are within a stable range, making the model more adaptable.

[0148] The above description is merely 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 smart property management system, comprising a property dispatch management module, a monitoring management module, an inspection path algorithm module, and an inspection execution module, characterized in that: The property dispatch and management module stores the location, status and maintenance records of all cameras, sensors and property facilities, and achieves linkage through the inspection path algorithm module; The monitoring and management module is used to deploy cameras and perform camera deflection and zoom to ensure that the camera's monitoring range covers the target area to be inspected. The monitoring and management module includes temperature and humidity sensors, smoke sensors, and light sensors for real-time collection of property environmental information. The inspection path algorithm module includes environment modeling, priority allocation, dynamic path optimization, perspective adjustment, and adaptive learning; The inspection path algorithm module constructs a three-dimensional structure of the property management area through environmental modeling and calculates the field of view angle of the camera. The field of view angle is used to determine the initial monitoring coverage area. The inspection path algorithm module calculates the inspection interval by prioritizing and combining it with the inspection time adjustment strategy, and adjusts the inspection order according to the task priority allocation. The inspection path algorithm module dynamically optimizes the path and adjusts the inspection angle of the camera in real time. It adopts the shortest path priority to make the inspection task run on the required path. At the same time, it combines the hot spot area backtracking mechanism to increase the inspection frequency of areas with frequent failures. The perspective adjustment in the inspection path algorithm module optimizes the monitoring coverage based on the error correction mechanism of the camera's horizontal rotation angle and pitch angle, and uses error compensation adjustment to make the monitoring perspective more stable. The inspection path algorithm module optimizes the field of view and inspection interval by analyzing historical inspection data, so that the inspection path is optimized over time. The inspection execution module receives instructions from the inspection path algorithm module and controls the camera to perform adjustment tasks. The camera integrates a pan-tilt control module, which is used to adjust the camera's deflection angle, rotation speed, and focal length.

2. The intelligent property management system according to claim 1, characterized in that: Environmental modeling includes constructing a 3D model of the property management area and calculating the required monitoring range of the cameras. At the same time, it involves debugging and detecting errors through software to prevent incorrect camera installation angles and spatial obstructions from affecting the field of view. A 3D model of the property management area is expressed as follows: ； in Represents the three-dimensional coordinate point set of the property management area; For the first in the property The coordinates of each spatial sampling point; The number of all modeling points; Calculate the camera's field of view; ； in For camera The angle of view; This represents the peak value of the camera's monitoring radius. The horizontal opening angle of the camera; Indicates the first During the software debugging, the camera was detected. Installation error; This represents the maximum allowable error. Establish a mechanism to prevent errors in software debugging and detection; ； in The actual installation angle detected by the camera; This refers to the theoretical installation angle of the camera; This is the error accumulation factor, which is used to determine the error correction rate; like The camera angle can then be adjusted via the gimbal control module. ； in Indicates camera Adjusted rotation angle.

3. The intelligent property management system according to claim 2, characterized in that: The priority allocation in the inspection path algorithm module is used to dynamically adjust the inspection sequence based on the historical failure rate of the inspection area, dynamic environmental changes, and equipment aging level, so that the required areas are monitored first. ； ； in For the region Inspection risk score; These are weight parameters; For the region Historical failure rate; For the region Dynamic environmental changes; For the region Equipment aging level; Scoring software debugging errors; Correction factor for software debugging; For the region The actual inspection time interval in actual inspection tasks; For the region The ideal inspection time interval is calculated based on inspection requirements; This is the adjustment coefficient for inspection time error.

4. The intelligent property management system according to claim 3, characterized in that: The goal of the dynamic path optimization calculation in the inspection path algorithm module is to find the path from the current monitoring state. To the target monitoring status The ideal adjustment path; ； in For the first Round calculation from camera to camera The cost of adjusting the ideal inspection route; For camera Rotate to camera Minimum angle adjustment required for the position; For risk weight parameters; For camera The inspection risk score for the corresponding area; Indicates the current monitoring status; Indicates the target monitoring status; Calculate the error value for the path detected during software debugging; This is the path error correction coefficient; During the dynamic optimization of the path, the system analyzes historical inspection paths through software debugging and calculates the deviation between the actual inspection path and the theoretical inspection path. ； in For the first Error value of the inspection path; The actual inspection path distance adjusted for the camera; The ideal inspection path calculated by the system; This is the path error adjustment factor.

5. The intelligent property management system according to claim 4, characterized in that: The viewpoint adjustment in the inspection path algorithm module is used to enable the camera to adjust its horizontal rotation angle and pitch angle according to changes in the environment during the inspection task. ； ； in For camera The horizontal rotation angle in actual inspection tasks; For camera The theoretical horizontal rotation angle calibrated during the installation and commissioning phase; For camera The elevation angle during actual inspection tasks; For camera The theoretical pitch angle calibrated during the installation and commissioning phase; For camera The angle of view; This represents the peak value of the camera's monitoring radius. , This is the rotation error adjustment coefficient.

6. A smart property management system according to claim 5, characterized in that: The adaptive learning in the inspection path algorithm module optimizes the camera status based on historical inspection data, so that the inspection task is continuously optimized over time; the optimization is based on historical data fitting and error compensation to calculate the optimized monitoring angle and inspection interval. ； in For the first Optimized camera The angle of view; No. Camera during inspection Calculated field of view angle; For the region Inspection risk score; For camera Adjust to camera Adjust the path distance for the location; For the first Error correction value for the next software test; This is the error correction factor; This is the learning rate.

7. A smart property management system according to claim 6, characterized in that: It also includes an inspection task execution supervision module, which establishes an inspection task execution deviation evaluation model based on three factors; it calculates the execution deviation value based on the inspection task execution deviation evaluation model, and if the execution deviation value is greater than the execution deviation threshold, it is determined to be an execution deviation and triggers an abnormal alarm; The three factors include path deviation, coverage area, and task execution. The path deviation factor is used to calculate the difference between the actual inspection path of the camera and the ideal path, and to assess whether the inspection task is carried out according to the predetermined trajectory. The coverage area factor is used to compare the actual monitoring range with the theoretical coverage area to detect whether there are any long-term uninspected areas. Task execution factors are used to monitor camera rotation accuracy, zoom adjustment error, and inspection time deviation to assess the consistency of inspection task execution.

8. The intelligent property management system according to claim 7, characterized in that: The path deviation error is calculated based on the path deviation factor, and the proposed path deviation error is: The coverage area error is calculated based on coverage area factors, and the proposed coverage area error is: ;Calculate the task execution error based on task execution factors, and set the task execution error as follows: ; ； ； ； in This refers to the number of cameras in the system. For camera Actual inspection route points and theoretical inspection path points The Bézier curve matching error between them; This is the maximum path deviation value allowed by the system; The actual installation angle detected by the camera; This refers to the theoretical installation angle of the camera; This represents the extreme value of the camera's rotation angle; For the region The actual inspection time interval in actual inspection tasks; For the region The ideal inspection time interval is calculated based on inspection requirements; The peak value of the allowable inspection time deviation; This is the path deviation weighting factor, which is used to dynamically adjust the degree of influence of path error on the final execution deviation. in This represents the total number of inspection areas. For the first The actual coverage area of ​​each monitored zone; For the first Theoretical calculated coverage area of ​​each monitoring zone; This represents the area of ​​the intersection between the actual monitored area and the theoretical monitored area. For the first The cumulative risk score of each monitored area that has not been inspected in the past; This is a weighting factor for coverage error; in For camera Actual and theoretical zoom ratios; This represents the peak zoom ratio of the camera. This is the weighting factor for task execution.

9. A smart property management system according to claim 8, characterized in that: A patrol task execution deviation assessment model is established based on three factors. The execution deviation value is calculated based on this model, and a proposed execution deviation value is determined. ; ； in The nonlinear adjustment value representing the path deviation error; The nonlinear adjustment value representing the coverage area error; The nonlinear adjustment value representing the task execution error; This is the error attenuation factor; They are respectively , , Importance weights; This is the normalization factor.

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