A real-time property security monitoring system under a smart city

Abstract

The application relates to the technical field of visual processing, and discloses a real-time property safety monitoring system for a smart city, which comprises a permission analysis module, a real-time permission acquisition module, a trajectory identification module, a space-time trajectory acquisition module and an abnormality judgment module, analyzes property work orders and owner registration data, obtains a dynamic authorization list, gives static entry prohibition tags to fire passages and equipment areas of a community, gives dynamic passing tags to a community grid map, and obtains a space-time grid map; the trajectories of moving targets in community videos are identified to obtain a mask sequence; the mask sequence is mapped to the space-time grid map to determine the space-time trajectories of the moving targets; the tag states of the grids covered by the space-time trajectories are extracted, the abnormal behaviors are judged according to the residence time of the moving targets in the static entry prohibition grids and the moving speed of the moving targets in the grids without effective dynamic passing tags, and an early warning event is output; the application can improve the safety monitoring efficiency of real-time properties.

Description

Technical Field

[0001] This invention relates to the field of visual processing technology, and in particular to a real-time property security monitoring system for smart cities. Background Technology

[0002] With the continuous advancement of smart city construction, the demand for refined and dynamic community property security monitoring is increasing. However, existing property security monitoring technologies are still based on static access control, which cannot dynamically adjust access authorization according to changes in service hours and service areas of property work orders within the community, as well as the actual distribution of permanent residents. Management is carried out only through fixed no-entry signs and access rules, resulting in a serious disconnect between community access management and actual operation scenarios. The generation and updating of dynamic authorization lists lack effective technical support, making it difficult to achieve precise access allocation in a grid-like manner within the community.

[0003] Meanwhile, existing property security monitoring systems lack the ability to analyze and integrate spatiotemporally for trajectory recognition and anomaly assessment of moving targets within the community. Trajectory extraction only obtains the spatial movement path of the target in the video without combining it with the time dimension to form a spatiotemporal trajectory. Furthermore, anomaly assessment is based solely on simple location boundary crossings, without considering key behavioral characteristics such as the duration of the moving target's stay in static restricted areas and the speed of movement in unauthorized areas. This results in low accuracy in identifying dangerous behaviors such as abnormal loitering and wandering, and is prone to false alarms and missed alarms. Insufficient data interaction and collaboration between monitoring modules also make it difficult for the overall real-time performance and intelligence level of the monitoring system to meet the monitoring requirements of property security in smart cities. Summary of the Invention

[0004] To achieve the above objectives, the present invention provides a real-time property security monitoring system for smart cities, characterized in that the system includes an access control module, a real-time access control module, a trajectory recognition module, a spatiotemporal trajectory acquisition module, and an anomaly detection module, wherein: The permission parsing module is used to parse the service hours, service areas and resident characteristics in the property work orders and owner registration data to obtain the community's dynamic authorization list; The real-time permission acquisition module is used to assign static no-entry tags to the fire lanes and equipment area grids of the community, and assign dynamic access tags that are only effective during the service period to the corresponding grids in the community grid map according to the dynamic authorization list, so as to obtain the spatiotemporal grid map of the community. The trajectory recognition module is used to identify the trajectory of moving targets in community videos and obtain a mask sequence of the moving targets; The spatiotemporal trajectory acquisition module is used to map the mask sequence onto the spatiotemporal grid map, determine the grid occupied by the moving target at consecutive time points, and obtain the spatiotemporal trajectory of the moving target; The anomaly detection module is used to extract the label status of the grid covered by the spatiotemporal trajectory, and determine the abnormal behavior of the moving target based on the dwell time of the moving target in the static restricted grid and the moving speed in the grid without effective dynamic access labels, and output a warning event.

[0005] In a preferred embodiment, when the permission parsing module parses the service hours, service areas, and resident characteristics from property work orders and owner registration data to obtain the community's dynamic authorization list, it is specifically used for: Extract service hours and service area boundary coordinates from property management work orders; Extract the identity identifiers of permanent residents and their associated property grid codes from the owner registration data; Spatially match the service area boundary coordinates with the property grid code to obtain the community grid block; The service period, the identity of the permanent resident, and the grid block are associated to obtain the authorized mapping entry for the community; The authorization mapping entries are merged according to the management partition of the community to obtain the dynamic authorization list of the community.

[0006] In a preferred embodiment, when the real-time access control module assigns static no-entry tags to the fire lanes and equipment area grids of the community, it is specifically used for: Extract the coordinates of fire hydrants, fire extinguisher boxes, electrical distribution cabinets, and emergency exit entrances / exits from the property facility management database of the community. Spatially register the coordinates of the facility locations with the community grid map to determine the grid blocks for the facility location coordinates; Assign static no-entry labels to the grid blocks of the facility location coordinates.

[0007] In a preferred embodiment, when the real-time permission acquisition module assigns dynamic access tags that are only valid during the service period to the corresponding grid in the community grid map according to the dynamic authorization list, and obtains the spatiotemporal grid map of the community, it is specifically used for: Traverse the authorized grid cell codes in the dynamic authorization list and locate the target grid cell to be authorized in the community grid map; Write a dynamic access tag to the target grid and use the service period as the valid timestamp attribute of the dynamic access tag; The target raster carrying the valid timestamp attribute is merged with the unweighted raster cells in the community raster map to obtain the spatiotemporal raster map of the community.

[0008] In a preferred embodiment, when the real-time permission acquisition module merges the target raster carrying the valid timestamp attribute with the unweighted raster cells in the community raster map to obtain the spatiotemporal raster map of the community, it is specifically used for: Obtain the real-time clock signal of the community and compare the real-time clock signal with the valid timestamp attribute; When the real-time clock signal enters the time range defined by the valid timestamp attribute, the dynamic access tag of the target grid is activated; When the real-time clock signal exceeds the time range defined by the valid timestamp attribute, the dynamic access tag of the target grid is revoked to restore the target grid to an unweighted state. Based on the activation and deactivation status of the dynamic access tags, the dynamic access tag layer in the community grid map is updated in real time, and a spatiotemporal grid map in which the tag status changes dynamically with the time axis is output.

[0009] In a preferred embodiment, when the trajectory recognition module identifies the trajectory of a moving target in a community video to obtain a mask sequence of the moving target, it is specifically used for: Extract a continuous sequence of video frames from the community's real-time video stream; The foreground pixel region is separated from the background image of each frame in the video frame sequence, and the foreground pixel region is segmented to obtain the instance segmentation mask of the moving target. Based on the spatial overlap and appearance feature similarity of the instance segmentation mask between adjacent frames in the video frame sequence, cross-frame association matching is performed on the moving target, and a globally unique trajectory identifier is assigned to the successfully matched moving target; According to the collection timestamp order, the instance segmentation masks corresponding to the moving targets carrying the same trajectory identifier in consecutive time frames are arranged to generate the mask sequence of the moving targets.

[0010] In a preferred embodiment, when the spatiotemporal trajectory acquisition module performs the operation of mapping the mask sequence to the spatiotemporal grid map, determining the grid occupied by the moving target at consecutive time points, and obtaining the spatiotemporal trajectory of the moving target, it is specifically used for: Extract the bottom center pixel coordinates of each frame mask from the mask sequence; Input the coordinates of the bottom edge center pixel into a preset coordinate transformation mapping table, and query the physical coordinate system position corresponding to the coordinates of the bottom edge center pixel; Spatial registration is performed between the physical coordinate system position and the spatiotemporal grid map to locate the grid cell in which the physical coordinate system position falls. The trajectory identifier of the moving target is associated and bound with the grid cell and the corresponding timestamp; The grid cells associated with the trajectory identifier are arranged in chronological order according to the timestamps to generate the spatiotemporal trajectory of the moving target.

[0011] In a preferred embodiment, when the anomaly detection module extracts the label status of the grid covered by the spatiotemporal trajectory, and determines the abnormal behavior of the moving target and outputs a warning event based on the dwell time of the moving target in the static restricted grid and the movement speed in the grid without a valid dynamic access label, it is specifically used to: Extract grid cells carrying static no-entry tags from the spatiotemporal trajectory to generate a static no-entry grid for the moving target; The timestamp corresponding to the first time the moving target falls into the static no-entry grid is taken as the starting time point; The timestamp corresponding to when the moving target leaves the static no-entry grid is taken as the termination time point; Calculate the time difference between the termination time point and the start time point to generate the dwell time of the moving target within the static no-entry grid; The dwell time is compared with a preset restricted dwell time. If the dwell time exceeds the restricted dwell time, it is determined that the moving target has experienced an abnormal stay event in the static restricted area.

[0012] In a preferred embodiment, when the anomaly detection module determines the abnormal behavior of the moving target and outputs a warning event based on the dwell time of the moving target within a static restricted area grid and its movement speed within a grid without a valid dynamic access tag, it is specifically used for: Grid cells that do not carry dynamic access tags and whose dynamic access tags are not activated are selected from the spatiotemporal trajectory to generate unauthorized grid trajectory segments of the moving target; Extract the center coordinates of the first and last grid cells of the unauthorized grid trajectory segment, and obtain the timestamps corresponding to the center coordinates of the first and last grid cells. Calculate the spatial distance between the center coordinates of the first and last grid cells; Calculate the time difference between the timestamp corresponding to the center coordinates of the last grid cell and the timestamp corresponding to the center coordinates of the first grid cell; Divide the spatial distance by the time difference to generate the moving speed of the moving target within the unauthorized grid trajectory segment; The moving speed is compared with a preset low-speed anomaly threshold. If the moving speed is lower than the low-speed anomaly threshold, it is determined that the moving target has experienced an unauthorized area abnormal wandering event.

[0013] In a preferred embodiment, when the permission resolution module obtains the community's dynamic authorization list, it is further specifically used for: Continuously monitor the incremental work order message queue pushed by the community's property management platform; When a new work order is received, the new service time period, the boundary coordinates of the new service area, and the identity credentials of the new executor are extracted from the new work order. Spatially match the boundary coordinates of the newly added service area with the community grid map to determine the newly authorized grid cell code in the community grid map; The newly added service period, the newly added executor identity credential, and the newly authorized grid cell code are associated to generate a new authorization mapping entry for the community, and the new authorization mapping entry is written into the dynamic authorization list; When a work order cancellation instruction is received, the work order identifier to be cancelled is extracted from the work order cancellation instruction. Based on the work order identifier to be cancelled, the corresponding authorization mapping entry is located in the dynamic authorization list, and the corresponding authorization mapping entry is deleted from the dynamic authorization list. The updated dynamic authorization list is pushed to the real-time permission acquisition module to trigger the re-authorization of the dynamic access tag.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention generates a dynamic authorization list by parsing property work orders and owner registration data, assigns dynamic access tags with timestamps and static prohibition tags to community grids, forming a spatiotemporal grid map. It can also update the authorization list in real time according to the addition and deletion of work orders and adjust the tag status synchronously, realizing grid-based precise authorization, and making access control highly adaptable to the spatiotemporal changes of the actual service scenarios in the community.

[0015] 2. This invention transforms the trajectory of a moving target into a spatiotemporal trajectory, abandoning the traditional single method of determining location boundaries. It combines the duration of stay within a static restricted area grid and the movement speed within an unauthorized area grid to determine anomalies from multiple dimensions. This can accurately identify dangerous behaviors such as abnormal loitering and wandering, effectively reducing the false alarm and missed alarm rates of monitoring, and realizing intelligent and refined monitoring and early warning of community safety risks. Attached Figure Description

[0016] Figure 1 This invention provides a system architecture diagram for a real-time property security monitoring system in a smart city, as shown in one embodiment of the present invention. The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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 belong to some, but not all, embodiments of the present invention. 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.

[0018] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “said” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0019] Depending on the context, the word "if" or "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0020] Furthermore, the timing of the steps in the following method embodiments is merely an example and not a strict limitation.

[0021] In practice, the server-side equipment deployed in a real-time property security monitoring system for smart cities may consist of one or more devices. This system can be implemented as a business instance, a virtual machine, or hardware devices. For example, it can be implemented as a business instance deployed on one or more devices in a cloud node. Simply put, it can be understood as software deployed on a cloud node, providing real-time property security monitoring for smart cities to various user terminals. Alternatively, it can be implemented as a virtual machine deployed on one or more devices in a cloud node, with application software installed to manage various user terminals. Or, it can also be implemented as a server composed of numerous identical or different types of hardware devices, with one or more hardware devices configured to provide real-time property security monitoring for smart cities to various user terminals.

[0022] In terms of implementation, a real-time property security monitoring system for smart cities and its user terminal are mutually compatible. Specifically, if the real-time property security monitoring system for smart cities is implemented as an application installed on a cloud service platform, then the user terminal acts as a client establishing a communication connection with that application; or if the real-time property security monitoring system for smart cities is implemented as a website, then the user terminal acts as a webpage; or if the real-time property security monitoring system for smart cities is implemented as a cloud service platform, then the user terminal acts as a mini-program within an instant messaging application.

[0023] like Figure 1 The figure shown is a system architecture diagram of a real-time property security monitoring system for smart cities provided by an embodiment of the present invention.

[0024] The real-time property security monitoring system 100 for smart cities described in this invention can be installed on a cloud server. In terms of implementation, it can be used as one or more service devices, or as an application installed on the cloud (e.g., a mobile service operator's server, server cluster, etc.), or it can be developed as a website. Depending on the functions implemented, the real-time property security monitoring system 100 for smart cities may include a permission parsing module 101, a real-time permission acquisition module 102, a trajectory recognition module 103, a spatiotemporal trajectory acquisition module 104, and an anomaly judgment module 105. The modules described in this invention can also be called units, referring to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, stored in the memory of the electronic device.

[0025] In this embodiment of the invention, in a real-time property security monitoring system for smart cities, each of the above-mentioned modules can be implemented independently and can call other modules. Here, "calling" can be understood as one module connecting to multiple modules of another type and providing corresponding services to those connected modules. The real-time property security monitoring system for smart cities provided by this embodiment of the invention allows for adjustment of the system's applicability by adding modules and directly calling them, without modifying the program code. This enables cluster-based horizontal expansion, achieving the goal of quickly and flexibly expanding the real-time property security monitoring system for smart cities. In practical applications, the above modules can be set in the same device or different devices, or they can be set in virtual devices, such as service instances in a cloud server.

[0026] The following describes, with reference to specific embodiments, the various components and specific workflows of a real-time property security monitoring system for smart cities: The permission parsing module 101 is used to parse the service time period, service area and resident characteristics in the property work order and owner registration data to obtain the community's dynamic authorization list; In this embodiment of the invention, when the permission parsing module parses the service hours, service areas, and resident characteristics in the property work orders and owner registration data to obtain the community's dynamic authorization list, it is specifically used for: Extract service hours and service area boundary coordinates from property management work orders; Extract the identity identifiers of permanent residents and their associated property grid codes from the owner registration data; Spatially match the service area boundary coordinates with the property grid code to obtain the community grid block; The service period, the identity of the permanent resident, and the grid block are associated to obtain the authorized mapping entry for the community; The authorization mapping entries are merged according to the management partition of the community to obtain the dynamic authorization list of the community.

[0027] When the permission parsing module obtains the dynamic authorization list from the community, it is also specifically used for: Continuously monitor the incremental work order message queue pushed by the community's property management platform; When a new work order is received, the new service time period, the boundary coordinates of the new service area, and the identity credentials of the new executor are extracted from the new work order. Spatially match the boundary coordinates of the newly added service area with the community grid map to determine the newly authorized grid cell code in the community grid map; The newly added service period, the newly added executor identity credential, and the newly authorized grid cell code are associated to generate a new authorization mapping entry for the community, and the new authorization mapping entry is written into the dynamic authorization list; When a work order cancellation instruction is received, the work order identifier to be cancelled is extracted from the work order cancellation instruction. Based on the work order identifier to be cancelled, the corresponding authorization mapping entry is located in the dynamic authorization list, and the corresponding authorization mapping entry is deleted from the dynamic authorization list. The updated dynamic authorization list is pushed to the real-time permission acquisition module to trigger the re-authorization of the dynamic access tag.

[0028] The process iterates through each line of text in the property management work order, identifying the specific start and end times of the service. These times are then integrated to form a unique and fixed service period. Simultaneously, the process identifies the geographical location descriptions of the service coverage area boundaries and converts these descriptions into corresponding boundary coordinates. This ensures that the extracted boundary coordinates accurately correspond to the entire service area. Finally, the service period and service area boundary coordinates are precisely extracted from the property management work order.

[0029] Each complete registration record in the owner registration data is reviewed one by one. In each registration record, the specific text content marking the identity information of the permanent resident is accurately located and extracted directly as the identity identifier of the permanent resident. At the same time, in the same registration record, the raster code text content corresponding to the property directly bound to the permanent resident is accurately located. The extracted identity identifier of the permanent resident is fixedly associated with the property raster code one-to-one, ensuring that each identity identifier of the permanent resident corresponds to a unique property raster code. Finally, the identity identifier of the permanent resident and the associated property raster code are completely extracted from the owner registration data.

[0030] The extracted service area boundary coordinates are completely delineated according to the actual geographic space range to form a closed and unique service area spatial range. The actual geographic space location corresponding to each extracted property raster code is checked one by one to determine whether the geographic space location corresponding to the property raster code falls completely within the closed spatial range delineated by the service area boundary coordinates. All independent geographic units corresponding to property raster codes that fall completely within this spatial range are uniformly confirmed. Each confirmed independent geographic unit is a single raster unit. All single raster units that meet the conditions are combined together, and finally, the community's raster block is obtained by spatial matching between the service area boundary coordinates and the property raster codes.

[0031] First, determine the property grid code corresponding to each grid block. Then, find the resident identity card bound to the property grid code. At the same time, determine the service period for the property work order to be extracted for the grid block. Then, establish a fixed binding association between the determined service period, resident identity card and grid block to ensure that each binding relationship is unique and indivisible. The complete information set formed by this fixed binding association finally yields the community's authorized mapping entry.

[0032] First, clarify the actual geographical coverage of each pre-divided management zone within the community, determine the total geographical space corresponding to each management zone, match each authorized mapping entry to the corresponding management zone according to the geographical space of the raster block it contains, centrally organize and merge all authorized mapping entries belonging to the same management zone, remove duplicate authorized mapping entries, retain the unique and valid entry information, and after completing the merging and integration of all authorized mapping entries according to each management zone of the community, the final dynamic authorization list of the community is obtained.

[0033] After generating the community dynamic authorization list, the permission resolution module maintains a constant connection with the data transmission channel of the community property management platform. It performs real-time monitoring of the incremental work order message queue pushed by the property management platform at fixed time intervals, checking each message to be pushed in the queue one by one to ensure that new work order messages and work order cancellation messages are received in a timely and complete manner, without any message omissions or listening interruptions.

[0034] After successfully receiving a new work order pushed by the property management platform, the permission resolution module sorts out all the information items in the new work order one by one. It accurately identifies the start and end times of the service from the time description item of the work order and organizes them into a new service time period. It sorts out the geographical boundary information from the area description item of the work order one by one and converts it into the boundary coordinates of the new service area. It extracts the unique identification information from the personnel execution item of the work order as the identity certificate of the new executor. It completely and accurately extracts the new service time period, the boundary coordinates of the new service area, and the identity certificate of the new executor from the new work order.

[0035] The extracted boundary coordinates of the new service area are connected sequentially according to the actual geographical location to form a complete and closed service space. Then, the geographical location of this closed service space is compared with that of all grid cells in the community grid map one by one to confirm whether the actual spatial location of each grid cell is completely within the closed service space. The codes corresponding to all grid cells that meet the spatial ownership conditions are uniformly confirmed, and finally the codes of the newly authorized grid cells in the community grid map are determined.

[0036] The newly confirmed service time slots are directly mapped to the identity credentials of the newly added executors. Then, this combination of information is deeply bound to the newly authorized grid cell code, so that the newly added service time slots, the newly added executor identity credentials, and the newly authorized grid cell code form a unique and complete information combination. This information combination is the community's new authorization mapping entry. The generated new authorization mapping entries are then added to the community's dynamic authorization list in an orderly manner according to the original format specifications of the dynamic authorization list, thus completing the expansion and update of the list content.

[0037] After receiving a work order cancellation instruction from the property management platform, the permission resolution module reads all the text information in the work order cancellation instruction and extracts the work order identifier to uniquely specify the work order to be cancelled. Then, using the work order identifier to be cancelled as the search basis, it iterates through each authorization mapping entry in the community's dynamic authorization list. Through information comparison, it accurately locates the authorization mapping entry that matches the work order identifier to be cancelled. After the location is completed, the corresponding authorization mapping entry is completely removed from the dynamic authorization list, completing the entry deletion operation.

[0038] After confirming that the dynamic authorization list has completed the operation of adding new authorization mapping entries or deleting corresponding authorization mapping entries, the permission resolution module determines the current dynamic authorization list as the updated dynamic authorization list. Then, it pushes the updated dynamic authorization list to the real-time permission acquisition module through a dedicated data transmission channel. Upon receiving the updated dynamic authorization list, the real-time permission acquisition module immediately initiates the re-authorization process of the dynamic access tag to complete the synchronous update of permissions.

[0039] The real-time permission acquisition module 102 is used to assign static no-entry tags to the fire lanes and equipment area grids of the community, and assign dynamic access tags that are only effective during the service period to the corresponding grids in the community grid map according to the dynamic authorization list, so as to obtain the spatiotemporal grid map of the community. In this embodiment of the invention, when the real-time permission acquisition module assigns static no-entry tags to the fire lanes and equipment area grids of the community, it is specifically used for: Extract the coordinates of fire hydrants, fire extinguisher boxes, electrical distribution cabinets, and emergency exit entrances / exits from the property facility management database of the community. Spatially register the coordinates of the facility locations with the community grid map to determine the grid blocks for the facility location coordinates; Assign static no-entry labels to the grid blocks of the facility location coordinates.

[0040] When the real-time permission acquisition module assigns a dynamic access tag, valid only during the service period, to the corresponding grid in the community grid map based on the dynamic authorization list, and obtains the spatiotemporal grid map of the community, it is specifically used for: Traverse the authorized grid cell codes in the dynamic authorization list and locate the target grid cell to be authorized in the community grid map; Write a dynamic access tag to the target grid and use the service period as the valid timestamp attribute of the dynamic access tag; The target raster carrying the valid timestamp attribute is merged with the unweighted raster cells in the community raster map to obtain the spatiotemporal raster map of the community.

[0041] When the real-time permission acquisition module merges the target raster carrying the valid timestamp attribute with the unweighted raster cells in the community raster map to obtain the spatiotemporal raster map of the community, it is specifically used for: Obtain the real-time clock signal of the community and compare the real-time clock signal with the valid timestamp attribute; When the real-time clock signal enters the time range defined by the valid timestamp attribute, the dynamic access tag of the target grid is activated; When the real-time clock signal exceeds the time range defined by the valid timestamp attribute, the dynamic access tag of the target grid is revoked to restore the target grid to an unweighted state. Based on the activation and deactivation status of the dynamic access tags, the dynamic access tag layer in the community grid map is updated in real time, and a spatiotemporal grid map in which the tag status changes dynamically with the time axis is output.

[0042] The system iterates through all facility information entries stored in the community property facility management database, identifying and confirming the facility category for each entry. It specifically filters out facilities categorized as fire hydrants, fire extinguisher boxes, electrical distribution cabinets, and emergency exit entrances / exits. The system then precisely extracts the corresponding actual geographical location coordinates from each filtered facility entry. All extracted coordinate data is then organized and aggregated. Finally, the system extracts the complete coordinates of the fire hydrants, fire extinguisher boxes, electrical distribution cabinets, and emergency exit entrances / exits from the community property facility management database.

[0043] The coordinates of each extracted facility location are meticulously compared with the actual geographical boundaries of each pre-divided grid in the community grid map. By directly comparing the spatial location, it is determined which grid's geographical area each facility location coordinate falls within, ensuring that each facility location coordinate corresponds to only one grid area. The unique grid area corresponding to each facility location coordinate is officially determined as the grid block of that facility location coordinate, thus completing the spatial registration operation between the facility location coordinates and the community grid map.

[0044] Each grid cell with the confirmed location coordinates of facility points after spatial registration is individually marked. In accordance with the unified standards for community security management, a static no-entry label that is valid for a long time and will not change on its own is added to each marked grid cell, so that these grid cells will always maintain the basic management status of not allowing unauthorized personnel to enter from the initial setup.

[0045] Following the complete record order of the dynamic authorization list, all authorization information is reviewed one by one, and all registered authorized raster unit codes in the list are fully traversed. Then, based on each authorized raster unit code, the actual geographical area that matches it is accurately found in the community raster map. All successfully matched geographical areas are uniformly determined as target raster cells that need to be granted permissions.

[0046] Within all target grids that have been accurately located and are awaiting authorization, add dynamic access tags that can be adjusted in real time according to changes in permissions. At the same time, directly bind the exclusive service period corresponding to the target grid to the dynamic access tag, and officially set the service period as the valid timestamp attribute of the dynamic access tag, thus completing the fixed association setting between the dynamic access tag and the valid timestamp attribute.

[0047] The system continuously collects real-time clock signals generated during community system operation to ensure that the real-time clock signals are always fully synchronized with the actual time in reality. Then, it directly compares the real-time collected clock signals with the valid timestamp attribute of the dynamic access tag bound to the target grid to clearly determine the correspondence between real-time time and the time range limited by the valid timestamp attribute.

[0048] After comparing the real-time clock signal with the valid timestamp attribute, when the specific time corresponding to the real-time clock signal is within the complete range from the start time to the end time specified by the valid timestamp attribute, the dynamic access tag corresponding to the target grid is immediately adjusted to the enabled state that can be used normally, and the activation operation of the dynamic access tag of the target grid is completed.

[0049] After comparing the real-time clock signal with the valid timestamp attribute, if the specific time corresponding to the real-time clock signal is earlier than the start time specified by the valid timestamp attribute or later than the end time specified by the valid timestamp attribute, the usage permission of the dynamic access tag corresponding to the target grid is immediately terminated, and the target grid is directly restored to its original unauthorized state without any access permissions.

[0050] Based on the actual active or deactivated state of the dynamic access tag of each target grid, the display content of all dynamic access tag layers in the community grid map is adjusted in real time, and the tag status information in the layer is continuously updated in sync with the continuous progress of the timeline, ultimately outputting a spatiotemporal grid map in which the tag status changes dynamically with the timeline.

[0051] The trajectory recognition module 103 is used to identify the trajectory of a moving target in a community video and obtain a mask sequence of the moving target. In this embodiment of the invention, when the trajectory recognition module identifies the trajectory of a moving target in a community video and obtains a mask sequence of the moving target, it is specifically used for: Extract a continuous sequence of video frames from the community's real-time video stream; The foreground pixel region is separated from the background image of each frame in the video frame sequence, and the foreground pixel region is segmented to obtain the instance segmentation mask of the moving target. Based on the spatial overlap and appearance feature similarity of the instance segmentation mask between adjacent frames in the video frame sequence, cross-frame association matching is performed on the moving target, and a globally unique trajectory identifier is assigned to the successfully matched moving target; According to the collection timestamp order, the instance segmentation masks corresponding to the moving targets carrying the same trajectory identifier in consecutive time frames are arranged to generate the mask sequence of the moving targets.

[0052] The system continuously receives real-time video stream data from various monitoring devices within the community, maintaining uninterrupted video stream reception throughout. It captures each independent frame completely, frame by frame, according to the video stream's generation and transmission sequence, ensuring no frame is missed. All captured frames are then arranged sequentially according to their actual time progression, guaranteeing tight connections between adjacent frames without frame loss, duplication, or disordered order. Finally, a continuous sequence of video frames is extracted from the community's real-time video stream.

[0053] For each independent video frame in the video frame sequence, each pixel in the frame is compared and identified one by one. First, the background image part that maintains a fixed position and shape in the frame is identified. Then, all pixels in the current frame that are significantly different from the background image are screened and extracted one by one. These extracted difference pixels are combined to form a complete foreground pixel region, thus completing the separation of the foreground pixel region from the background image. Then, the separated foreground pixel region is precisely delineated according to the actual independent moving individuals, completely distinguishing the pixel region corresponding to each independent moving target and forming a clear closed contour mark, finally obtaining the instance segmentation mask of the moving target.

[0054] Two sets of video frames that are temporally adjacent are selected from the video frame sequence. All instance segmentation masks in the preceding and following frames are compared one by one. First, it is determined whether there are overlapping areas between the spatial positions of the instance segmentation masks in the preceding and following frames to determine the spatial overlap between the two sets of masks. Then, the appearance contours, overall color distribution, surface texture features, and other appearance contents of the moving targets corresponding to the two sets of masks are compared to determine the appearance feature similarity between the two sets of masks. Combining the results of the dual determination of spatial overlap and appearance feature similarity, the masks belonging to the same moving target are confirmed and cross-frame association matching of the moving target is completed. Finally, each matched moving target is assigned a unique identifier that will never be repeated throughout the entire video frame sequence. This identifier is the globally unique trajectory identifier.

[0055] Based on the actual chronological order of the acquisition timestamps of each frame in the video frame sequence, the trajectory identifiers corresponding to the instance segmentation masks in all video frames are uniformly classified and organized. All instance segmentation masks with the same trajectory identifier are screened and grouped together. These instance segmentation masks belonging to the same moving target are arranged in strict order from earliest to latest according to the acquisition timestamps, so that the arrangement order of the masks is completely consistent with the actual motion sequence of the moving target in continuous time frames. All the arranged instance segmentation masks are combined in an orderly manner to form a complete sequence content, and finally the mask sequence of the moving target is generated.

[0056] The spatiotemporal trajectory acquisition module 104 is used to map the mask sequence to the spatiotemporal grid map, determine the grid occupied by the moving target at consecutive time points, and obtain the spatiotemporal trajectory of the moving target. In this embodiment of the invention, when the spatiotemporal trajectory acquisition module performs the operation of mapping the mask sequence to the spatiotemporal grid map, determining the grid occupied by the moving target at consecutive time points, and obtaining the spatiotemporal trajectory of the moving target, it is specifically used for: Extract the bottom center pixel coordinates of each frame mask from the mask sequence; Input the coordinates of the bottom edge center pixel into a preset coordinate transformation mapping table, and query the physical coordinate system position corresponding to the coordinates of the bottom edge center pixel; Spatial registration is performed between the physical coordinate system position and the spatiotemporal grid map to locate the grid cell in which the physical coordinate system position falls. The trajectory identifier of the moving target is associated and bound with the grid cell and the corresponding timestamp; The grid cells associated with the trajectory identifier are arranged in chronological order according to the timestamps to generate the spatiotemporal trajectory of the moving target.

[0057] The complete data of each frame of the mask in the mask sequence is retrieved sequentially according to time. The overall external contour of the single frame mask is identified and located in all directions. The specific area and complete shape range of the mask in the current video frame are clearly defined. Then, the horizontal bottom boundary line of the mask is accurately locked. The leftmost and rightmost endpoint pixel positions of this bottom boundary line are determined one by one. A unique core pixel point is selected in the middle of the two endpoint pixels. The horizontal and vertical pixel position information of the core pixel point in the current video frame is completely extracted. The same extraction operation is performed on all frames one by one according to the predetermined arrangement order of the mask sequence. Finally, the bottom center pixel coordinates of each frame mask are extracted from the mask sequence.

[0058] The bottom center pixel coordinates of each frame mask extracted from the mask sequence are sent one by one into a coordinate transformation mapping table that has been created and permanently stored in advance based on the actual monitoring scene and physical space layout of the community. This coordinate transformation mapping table completely records the one-to-one correspondence between all pixel coordinates in the video frame and the actual physical space location of the community. Starting from the first record in the mapping table, the table searches for a record that is completely consistent with the current input bottom center pixel coordinates. After finding a unique matching record, the actual physical space orientation and location information corresponding to the record are directly read. This information is the physical coordinate system position corresponding to the current bottom center pixel coordinates.

[0059] The physical coordinate system position obtained after each frame of masking is compared one by one with the actual physical spatial boundary range of each grid cell in the spatiotemporal grid map. It is determined whether the physical coordinate system position is within the complete range of the physical boundary of the currently compared grid cell. Grid cells that do not contain the position are gradually eliminated until the unique grid cell that completely contains the physical coordinate system position is found. The spatial registration of the physical coordinate system position and the spatiotemporal grid map is completed through the precise correspondence and matching of the physical spatial position, and finally the grid cell in which the physical coordinate system position falls is clearly identified.

[0060] First, identify the globally unique trajectory identifier of the moving target corresponding to the current processing frame. Then, accurately obtain the original timestamp information generated by the mask of this frame during video capture. At the same time, match the unique grid unit determined after spatial registration. Bind the trajectory identifier of the moving target, the locked grid unit, and the corresponding original timestamp information in a fixed one-to-one correspondence, so that the trajectory identifier, grid unit, and timestamp in each set of bound data form an inseparable and stable association.

[0061] All grid cells bound to the same trajectory identifier and their corresponding timestamp information are comprehensively collected. The order in which the timestamps in each set of bound data are actually generated is used as the sole criterion for arrangement. These grid cells belonging to the same trajectory identifier are arranged in order from earliest to latest time, so that the arrangement order of the grid cells completely matches the actual movement time order of the moving target in the community. The arranged continuous grid cells are combined into a complete position change sequence, and finally the spatiotemporal trajectory of the moving target is generated.

[0062] The anomaly detection module 105 is used to extract the label status of the grid covered by the spatiotemporal trajectory, and determine the abnormal behavior of the moving target and output a warning event based on the dwell time of the moving target in the static restricted grid and the moving speed in the grid without effective dynamic access labels.

[0063] In this embodiment of the invention, when the anomaly judgment module extracts the label status of the grid covered by the spatiotemporal trajectory, and determines the abnormal behavior of the moving target and outputs a warning event based on the dwell time of the moving target in the static restricted grid and the moving speed in the grid without a valid dynamic access label, it is specifically used for: Extract grid cells carrying static no-entry tags from the spatiotemporal trajectory to generate a static no-entry grid for the moving target; The timestamp corresponding to the first time the moving target falls into the static no-entry grid is taken as the starting time point; The timestamp corresponding to when the moving target leaves the static no-entry grid is taken as the termination time point; Calculate the time difference between the termination time point and the start time point to generate the dwell time of the moving target within the static no-entry grid; The dwell time is compared with a preset restricted dwell time. If the dwell time exceeds the restricted dwell time, it is determined that the moving target has experienced an abnormal stay event in the static restricted area.

[0064] When the anomaly detection module determines the abnormal behavior of the moving target and outputs a warning event based on the duration of the moving target's stay in the static restricted area and its movement speed in the area without a valid dynamic access tag, it is specifically used for: Grid cells that do not carry dynamic access tags and whose dynamic access tags are not activated are selected from the spatiotemporal trajectory to generate unauthorized grid trajectory segments of the moving target; Extract the center coordinates of the first and last grid cells of the unauthorized grid trajectory segment, and obtain the timestamps corresponding to the center coordinates of the first and last grid cells. Calculate the spatial distance between the center coordinates of the first and last grid cells; Calculate the time difference between the timestamp corresponding to the center coordinates of the last grid cell and the timestamp corresponding to the center coordinates of the first grid cell; Divide the spatial distance by the time difference to generate the moving speed of the moving target within the unauthorized grid trajectory segment; The moving speed is compared with a preset low-speed anomaly threshold. If the moving speed is lower than the low-speed anomaly threshold, it is determined that the moving target has experienced an unauthorized area abnormal wandering event.

[0065] Each grid cell in the spatiotemporal trajectory of the moving target is thoroughly and meticulously examined in chronological order. For each individual grid cell, it is confirmed whether it carries a static no-entry tag pre-assigned according to community safety management rules. All grid cells that have been repeatedly verified to carry static no-entry tags are selected one by one. These selected grid cells are uniformly organized, collected, and sorted to ensure that no grid cell carrying a static no-entry tag is missed. Finally, all grid cells carrying static no-entry tags are accurately extracted from the spatiotemporal trajectory of the moving target, and a complete static no-entry grid of the moving target is generated.

[0066] Among all the timestamps associated with the static no-entry grid of the moving target, all timestamps are compared and sorted one by one according to the natural order of their generation. The earliest timestamp is then precisely selected. This earliest timestamp corresponds precisely to the actual moment when the moving target first enters the static no-entry grid, which is also the initial time when the moving target appears in the static no-entry grid. This earliest timestamp is then directly determined as the starting time point when the moving target first falls into the static no-entry grid.

[0067] Among all the timestamp information bound to the static no-entry grid of the moving target, all timestamps are compared and sorted one by one according to the natural order of their generation. The latest timestamp is then precisely selected. This latest timestamp corresponds precisely to the actual time when the moving target leaves the static no-entry grid, which is also the final time the moving target stays in the static no-entry grid. This latest timestamp is then directly determined as the termination time point corresponding to the moving target leaving the static no-entry grid.

[0068] By directly comparing the determined termination time point with the start time point according to the normal flow of actual time, the actual time length between the two time points is accurately calculated. This calculated time length is the total duration for which the moving target stays inside the static no-entry grid. By calculating the time difference between the termination time point and the start time point through this direct comparison method, the dwell time of the moving target inside the static no-entry grid is finally generated.

[0069] The system directly compares the dwell time of the generated moving target within the static restricted area with the restricted dwell tolerance time that is pre-set and permanently stored according to community management regulations. This clearly and accurately determines whether the actual dwell time exceeds the restricted dwell tolerance time limit. When the actual dwell time clearly exceeds the restricted dwell tolerance time limit, it is directly determined that the moving target has experienced an abnormal dwelling event in the static restricted area.

[0070] A comprehensive and detailed traversal and verification is performed on each grid cell in the spatiotemporal trajectory of the moving target, arranged sequentially in time. For each individual grid cell, it is checked whether it carries a dynamic access tag and whether the corresponding dynamic access tag is in a normal active state. All grid cells that neither carry a dynamic access tag nor have a dynamic access tag activated are filtered out one by one. Then, these filtered grid cells are arranged in an orderly manner according to the original time sequence of the spatiotemporal trajectory to ensure the temporal continuity of the trajectory segment. Finally, the corresponding grid cells are accurately selected from the spatiotemporal trajectory of the moving target to generate the complete unauthorized grid trajectory segment of the moving target.

[0071] The system accurately locates the first and last grid cells in the unauthorized grid trajectory segment of a moving target, arranged chronologically, and extracts their center coordinates. Simultaneously, it acquires the corresponding acquisition timestamp associated with the first and last grid cells. Then, it accurately locates the last grid cell in the same chronological order, extracts its center coordinates, and simultaneously acquires the corresponding acquisition timestamp associated with the last grid cell. This process fully extracts the center coordinates of the first and last grid cells in the unauthorized grid trajectory segment and accurately acquires their corresponding timestamps.

[0072] The center coordinates of the first and last grid cells are extracted and compared with the actual spatial location in accordance with the actual physical spatial layout rules and geographical location of the community. The straight-line length between the two center coordinates in the actual physical space of the community is accurately calculated to ensure that the distance calculation conforms to the actual geographical situation of the community. The spatial distance between the center coordinates of the first and last grid cells is calculated by this on-site comparison and calculation method.

[0073] The timestamp corresponding to the center coordinates of the last extracted raster cell is directly compared with the timestamp corresponding to the center coordinates of the first raster cell according to the normal flow of time. The actual time length between the two timestamps is accurately calculated to ensure that the time difference calculation is accurate. The time difference between the timestamp corresponding to the center coordinates of the last raster cell and the timestamp corresponding to the center coordinates of the first raster cell is calculated by this direct comparison calculation method.

[0074] The calculated spatial distance and time difference are directly correlated numerically. The actual physical length of the spatial distance is divided by the actual time length of the time difference. The result accurately reflects the moving speed of the target within the trajectory segment, ensuring that the speed calculation fits the actual motion situation. The moving speed of the target within the unauthorized grid trajectory segment is generated through this direct calculation method.

[0075] The movement speed of the generated moving target within the unauthorized grid trajectory segment is directly compared with the low-speed anomaly threshold that the system has pre-set and permanently fixed according to the community's normal passage standards. This clearly and accurately determines whether the actual value of the movement speed is lower than the set value of the low-speed anomaly threshold. When the actual value of the movement speed is clearly lower than the set value of the low-speed anomaly threshold, it is directly determined that the moving target has experienced an unauthorized area abnormal wandering event.

[0076] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0077] This application embodiment can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0078] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A real-time property security monitoring system for smart cities, characterized in that, The system includes a permission parsing module, a real-time permission acquisition module, a trajectory recognition module, a spatiotemporal trajectory acquisition module, and an anomaly detection module, wherein: The permission parsing module is used to parse the service hours, service areas and resident characteristics in the property work orders and owner registration data to obtain the community's dynamic authorization list; The real-time permission acquisition module is used to assign static no-entry tags to the fire lanes and equipment area grids of the community, and assign dynamic access tags that are only effective during the service period to the corresponding grids in the community grid map according to the dynamic authorization list, so as to obtain the spatiotemporal grid map of the community. The trajectory recognition module is used to identify the trajectory of moving targets in community videos and obtain a mask sequence of the moving targets; The spatiotemporal trajectory acquisition module is used to map the mask sequence onto the spatiotemporal grid map, determine the grid occupied by the moving target at consecutive time points, and obtain the spatiotemporal trajectory of the moving target; The anomaly detection module is used to extract the label status of the grid covered by the spatiotemporal trajectory, and based on the dwell time of the moving target in the static restricted grid and the moving speed in the grid without effective dynamic access labels, determine the abnormal behavior of the moving target and output a warning event.

2. The real-time property security monitoring system for smart cities as described in claim 1, characterized in that, When the permission parsing module parses the service hours, service areas, and resident characteristics from property management work orders and owner registration data to obtain the community's dynamic authorization list, it is specifically used for: Extract service hours and service area boundary coordinates from property management work orders; Extract the identity identifiers of permanent residents and their associated property grid codes from the owner registration data; Spatially match the service area boundary coordinates with the property grid code to obtain the community grid block; The service period, the identity of the permanent resident, and the grid block are associated to obtain the authorized mapping entry for the community; The authorization mapping entries are merged according to the management partition of the community to obtain the dynamic authorization list of the community.

3. The real-time property security monitoring system for smart cities as described in claim 1, characterized in that, When the real-time access control module assigns static no-entry tags to the fire lanes and equipment area grids of the community, it is specifically used for: Extract the coordinates of fire hydrants, fire extinguisher boxes, electrical distribution cabinets, and emergency exit entrances / exits from the property facility management database of the community. Spatially register the coordinates of the facility locations with the community grid map to determine the grid blocks for the facility location coordinates; Assign static no-entry labels to the grid blocks of the facility location coordinates.

4. The real-time property security monitoring system for smart cities as described in claim 3, characterized in that, When the real-time permission acquisition module assigns a dynamic access tag, valid only during the service period, to the corresponding grid in the community grid map based on the dynamic authorization list, and obtains the spatiotemporal grid map of the community, it is specifically used for: Traverse the authorized grid cell codes in the dynamic authorization list and locate the target grid cell to be authorized in the community grid map; Write a dynamic access tag to the target grid and use the service period as the valid timestamp attribute of the dynamic access tag; The target raster carrying the valid timestamp attribute is merged with the unweighted raster cells in the community raster map to obtain the spatiotemporal raster map of the community.

5. A real-time property security monitoring system for smart cities as described in claim 4, characterized in that, When the real-time permission acquisition module merges the target raster carrying the valid timestamp attribute with the unweighted raster cells in the community raster map to obtain the spatiotemporal raster map of the community, it is specifically used for: Obtain the real-time clock signal of the community and compare the real-time clock signal with the valid timestamp attribute; When the real-time clock signal enters the time range defined by the valid timestamp attribute, the dynamic access tag of the target grid is activated; When the real-time clock signal exceeds the time range defined by the valid timestamp attribute, the dynamic access tag of the target grid is revoked to restore the target grid to an unweighted state. Based on the activation and deactivation status of the dynamic access tags, the dynamic access tag layer in the community grid map is updated in real time, and a spatiotemporal grid map in which the tag status changes dynamically with the time axis is output.

6. A real-time property security monitoring system for smart cities as described in claim 1, characterized in that, When the trajectory recognition module identifies the trajectories of moving targets in community videos and obtains the mask sequence of the moving targets, it is specifically used for: Extract a continuous sequence of video frames from the community's real-time video stream; The foreground pixel region is separated from the background image of each frame in the video frame sequence, and the foreground pixel region is segmented to obtain the instance segmentation mask of the moving target. Based on the spatial overlap and appearance feature similarity of the instance segmentation mask between adjacent frames in the video frame sequence, cross-frame association matching is performed on the moving target, and a globally unique trajectory identifier is assigned to the successfully matched moving target; According to the collection timestamp order, the instance segmentation masks corresponding to the moving targets carrying the same trajectory identifier in consecutive time frames are arranged to generate the mask sequence of the moving targets.

7. A real-time property security monitoring system for smart cities as described in claim 1, characterized in that, When the spatiotemporal trajectory acquisition module maps the mask sequence to the spatiotemporal grid map, determines the grid occupied by the moving target at consecutive time points, and obtains the spatiotemporal trajectory of the moving target, it is specifically used for: Extract the bottom center pixel coordinates of each frame mask from the mask sequence; Input the coordinates of the bottom edge center pixel into a preset coordinate transformation mapping table, and query the physical coordinate system position corresponding to the coordinates of the bottom edge center pixel; Spatial registration is performed between the physical coordinate system position and the spatiotemporal grid map to locate the grid cell in which the physical coordinate system position falls. The trajectory identifier of the moving target is associated and bound with the grid cell and the corresponding timestamp; The grid cells associated with the trajectory identifier are arranged in chronological order according to the timestamps to generate the spatiotemporal trajectory of the moving target.

8. A real-time property security monitoring system for smart cities as described in claim 1, characterized in that, When the anomaly detection module extracts the label status of the grid covered by the spatiotemporal trajectory, and determines the abnormal behavior of the moving target and outputs a warning event based on the dwell time of the moving target in the static restricted grid and the movement speed in the grid without a valid dynamic access label, it is specifically used for: Extract grid cells carrying static no-entry tags from the spatiotemporal trajectory to generate a static no-entry grid for the moving target; The timestamp corresponding to the first time the moving target falls into the static no-entry grid is taken as the starting time point; The timestamp corresponding to when the moving target leaves the static no-entry grid is taken as the termination time point; Calculate the time difference between the termination time point and the start time point to generate the dwell time of the moving target within the static no-entry grid; The dwell time is compared with a preset restricted dwell time. If the dwell time exceeds the restricted dwell time, it is determined that the moving target has experienced an abnormal stay event in the static restricted area.

9. A real-time property security monitoring system for smart cities as described in claim 8, characterized in that, When the anomaly detection module determines the abnormal behavior of the moving target and outputs a warning event based on the duration of the moving target's stay in the static restricted area and its movement speed in the area without a valid dynamic access tag, it is specifically used for: Grid cells that do not carry dynamic access tags and whose dynamic access tags are not activated are selected from the spatiotemporal trajectory to generate unauthorized grid trajectory segments of the moving target; Extract the center coordinates of the first and last grid cells of the unauthorized grid trajectory segment, and obtain the timestamps corresponding to the center coordinates of the first and last grid cells. Calculate the spatial distance between the center coordinates of the first and last grid cells; Calculate the time difference between the timestamp corresponding to the center coordinates of the last grid cell and the timestamp corresponding to the center coordinates of the first grid cell; Divide the spatial distance by the time difference to generate the moving speed of the moving target within the unauthorized grid trajectory segment; The moving speed is compared with a preset low-speed anomaly threshold. If the moving speed is lower than the low-speed anomaly threshold, it is determined that the moving target has experienced an unauthorized area abnormal wandering event.

10. A real-time property security monitoring system for smart cities as described in claim 1, characterized in that, When the permission parsing module obtains the dynamic authorization list from the community, it is also specifically used for: Continuously monitor the incremental work order message queue pushed by the community's property management platform; When a new work order is received, the new service time period, the boundary coordinates of the new service area, and the identity credentials of the new executor are extracted from the new work order. Spatially match the boundary coordinates of the newly added service area with the community grid map to determine the newly authorized grid cell code in the community grid map; The newly added service period, the newly added executor identity credential, and the newly authorized grid cell code are associated to generate a new authorization mapping entry for the community, and the new authorization mapping entry is written into the dynamic authorization list; When a work order cancellation instruction is received, the work order identifier to be cancelled is extracted from the work order cancellation instruction. Based on the work order identifier to be cancelled, the corresponding authorization mapping entry is located in the dynamic authorization list, and the corresponding authorization mapping entry is deleted from the dynamic authorization list. The updated dynamic authorization list is pushed to the real-time permission acquisition module to trigger the re-authorization of the dynamic access tag.

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