Data collection method, system and storage medium for smart buildings

Abstract

The present application relates to the field of data acquisition and processing, and in particular to a data acquisition method, system, and storage medium for smart buildings, including: periodically acquiring surveillance videos of corresponding areas through multiple camera units, and identifying identification information of each target object in each surveillance video; obtaining a current behavior probability value for each target object, and extracting key image frames and warning videos from the surveillance video based on the current behavior probability value for each target object; annotating the key image frames and warning videos, and mapping them to a three-dimensional map of the smart building according to the position of the camera unit; acquiring a complete movement path map for each target object; and sending the complete surveillance path map corresponding to the target object to a data receiving end. The present invention solves the problem of large amounts of surveillance data and the inability to obtain warning information in a timely manner, thereby reducing the amount of surveillance data and enabling timely acquisition of warning information.

Description

Technical Field

[0001] The present application relates to the field of data acquisition and processing, and in particular to a data acquisition method, system, and storage medium for smart buildings. Background Art

[0002] Security monitoring is a crucial component of modern smart buildings. With the advancement of technology, video surveillance systems have become a core component of building security management. However, existing video surveillance systems face several challenges and issues. They typically generate massive amounts of surveillance data. This data needs to be stored, processed, and analyzed to identify and respond to potential security threats. However, due to the huge amount of data, traditional monitoring systems often find it difficult to process and analyze all data in a timely manner, resulting in delays in early warning information and even the possibility of missing critical security events. Similar existing technologies include the Chinese patent publication number CN113310468A, which proposes a spatiotemporal information acquisition system, method and storage medium for monitoring targets. The system includes a video acquisition module for acquiring video image information of the monitoring area; a positioning module for locating the position information of the monitoring device; a distance perception module for calculating the distance information between the monitoring target and the monitoring device; a direction perception module for obtaining the direction information when the video acquisition module acquires the monitoring video; a time acquisition module for acquiring the current time information; and a central processing module for processing the acquired video image information, position information, distance information, direction information and time information to obtain the spatiotemporal information of the monitoring target, and displaying the spatiotemporal information and video image information in real time. The spatiotemporal information acquisition system for monitoring targets can quickly acquire the four-dimensional spatiotemporal information of the monitoring target, quickly locate the geographical location of the monitoring target on the map, and quickly find the target object. Similar prior art includes U.S. Patent Publication No. US11727580B2, which collects information about objects moving within an area of interest using AI-based video processing and analysis, object detection, and frame-by-frame tracking of moving objects. The system, method, and computer can be implemented as a platform including an analytics dashboard and a backend dashboard. The analytics dashboard displays the monitored camera feeds and analysis results for these feeds over time to the user through a user interface. The backend dashboard allows users to customize camera input settings and post-process pre-captured video files using system-provided algorithms, from which users can select. Both patents address methods for video data collection. However, with the increasing number of surveillance cameras, effectively managing and integrating data from multiple cameras becomes a challenge. Existing surveillance systems often lack intelligent processing capabilities, and due to the sheer volume of data, they cannot automatically identify and extract key information from the video data. This results in security personnel being unable to obtain timely warning information. Summary of the Invention

[0003] The present application provides a data collection method for smart buildings, the method comprising:

[0004] Step S1: periodically collecting surveillance videos of corresponding areas through multiple camera units, identifying multiple target objects in each surveillance video through a recognition model, and obtaining identification information of each target object;

[0005] Step S2: obtaining a current behavior probability value of each target object based on the identification information of each target object, the surveillance video, and the behavior probability distribution of each target object, and extracting a key image frame and a warning video of the surveillance video based on the current behavior probability value of each target object and a first similarity between the behavior feature of the target object and a preset behavior feature;

[0006] Step S3: Annotating the key image frames and the warning video with the identification information of the target object, the acquisition time, and the location information of the camera unit to construct a three-dimensional map of the smart building, mapping the location of the camera unit to the three-dimensional map, annotating the key image frames and the warning video in the three-dimensional map, and storing them in the storage node corresponding to the camera unit;

[0007] Step S4: The key image frames and the warning videos corresponding to each camera unit in the three-dimensional map are displayed in real time on the time coordinate axis according to the acquisition time and the corresponding time period, the monitoring movement path of each target object is obtained, and the target object is marked with a degree of danger in the three-dimensional map. The monitoring movement path of each target object is also supplemented according to the blind spot of each camera unit, so as to obtain a complete monitoring path map.

[0008] As a preferred technical solution of the present invention, step S1 includes:

[0009] The surveillance video of the corresponding area is periodically collected by multiple camera units in the smart building, and the video frame of each target object is separated from the surveillance video, and the video frame corresponding to each target object is input into the recognition model to obtain the recognition information of the target object, wherein the recognition model is a learning model trained with target data, and the target data is the physiological characteristics and motion characteristics of any target object that has appeared in the camera unit.

[0010] As a preferred technical solution of the present invention, step S2 includes:

[0011] Step S21: extracting behavioral features of each target object through the surveillance video, the behavioral features including facial behavioral features and body behavioral features, obtaining a behavior type of the target object based on the behavioral features of the target object and behavioral data corresponding to the target object stored in a storage unit through the identification information, and obtaining a current behavior probability value of the target object based on the behavior probability distribution of the target object and the behavior type;

[0012] Step S22: When the current behavior probability value of each target object is greater than or equal to a first threshold, a random number generation unit randomly generates N time points within a corresponding time period of the surveillance video, and uses N surveillance images corresponding to the N time points as the key image frames;

[0013] When the current behavior probability value of any of the target objects is less than the first threshold, the target object is regarded as a suspected object, and the behavior characteristics of the suspected object are compared with the characteristics of the preset behavior, and a first similarity is obtained. When the first similarity is less than the set threshold, M surveillance images are extracted from the surveillance video on average as the key image frames, and the value of M is increased or decreased according to the increase or decrease of the first similarity. When the first similarity is greater than or equal to the set threshold, the surveillance video is saved, and an early warning message is sent to other camera units and a receiving terminal, wherein the early warning message includes the identification information of the suspected object, the current image of the suspected object, and the early warning mark, and the surveillance video including the target object captured by the camera unit and the other camera units is used as the early warning video, and the suspected object is rendered in the early warning video, wherein N is less than M.

[0014] As a preferred technical solution of the present invention, when the recognition model cannot obtain the recognition information of the target object, the feature information of the target object is extracted through the surveillance video including the target object, and the feature information includes facial features, body features and behavioral features. The feature information of the target object is also added to the recognition model, and recognition information is set for the target object, and the recognition information and feature information of the target object are sent to other camera units.

[0015] As a preferred technical solution of the present invention, step S3 includes:

[0016] Step S31: marking the identification information of each target object and the position information of the corresponding camera unit in each key image frame and the warning video, and also marking the acquisition time and the corresponding time of each video frame in each key image frame and the warning video;

[0017] Step S32: constructing a three-dimensional map of the smart building based on the design structure of the smart building, and mapping the position of each camera unit into the three-dimensional map;

[0018] Step S33: updating the key image frame and the warning video corresponding to each camera unit to the three-dimensional map in real time, and storing them in the storage node position corresponding to each camera unit.

[0019] As a preferred technical solution of the present invention, step S4 includes:

[0020] Step S41: mapping the key image frames acquired by each camera unit in the three-dimensional map onto the time coordinate axis according to acquisition time, and also mapping the warning video acquired by each camera unit onto the time coordinate axis;

[0021] Step S42: connecting the key image frames corresponding to the same target object or the position information of the camera units corresponding to the warning video in chronological order in the three-dimensional map, obtaining the monitored movement path of the target object, and marking the target object with a danger level in the three-dimensional map, wherein the higher the danger level, the more eye-catching the color of the monitored movement path;

[0022] Step S43: The monitoring movement path of each target object is supplemented and completed according to the blind spot of each camera unit according to the design structure of the smart building, so as to obtain the complete monitoring path map including the monitoring movement paths of all the target objects.

[0023] As a preferred technical solution of the present invention, step S4 further includes step S5:

[0024] When the communication load is less than or equal to the second threshold, the three-dimensional monitoring map consisting of the complete monitoring path map of all the target objects will be sent to the data collection end in real time. When the communication load is greater than the second threshold, the sending priority of the corresponding complete monitoring path map is set according to the danger level of each target object, wherein the higher the danger level, the higher the sending priority.

[0025] As a preferred technical solution of the present invention, when the current behavior probability value of the target object is greater than or equal to a first threshold, the corresponding danger level of the target object is level three danger; when the current behavior probability value of the target object is less than the first threshold, when the first similarity is less than a set threshold, the danger level of the target object is level two danger; when the first similarity is greater than or equal to the set threshold, the danger level of the target object is level one danger.

[0026] The present invention also provides a data acquisition system for smart buildings, the system being used to implement the above method, the system comprising:

[0027] Multiple camera units are used to periodically collect surveillance videos of corresponding areas;

[0028] an identification unit, configured to identify a plurality of target objects in each of the surveillance videos by using a recognition model, and obtain identification information of each of the target objects;

[0029] an acquisition unit, configured to obtain a current behavior probability value of each target object based on the identification information of each target object, the surveillance video, and the behavior probability distribution of each target object, and extract key image frames and warning videos of the surveillance video based on the current behavior probability value of each target object and a first similarity between the behavior feature of the target object and a preset behavior feature;

[0030] a mapping unit, configured to annotate the key image frames and the warning video with the identification information of the target object, the acquisition time, and the position information of the camera unit to construct a three-dimensional map of the smart building, map the position of the camera unit to the three-dimensional map, annotate the key image frames and the warning video in the three-dimensional map, and store them in a storage node corresponding to the camera unit;

[0031] The generating unit is used to display the key image frames and the warning videos corresponding to each camera unit in the three-dimensional map in real time on the time coordinate axis according to the acquisition time and the corresponding time period, obtain the monitoring movement path of each target object, mark the danger level of the target object in the three-dimensional map, and supplement the monitoring movement path of each target object according to the blind spot of each camera unit, so as to obtain a complete monitoring path map.

[0032] The present invention also provides a computer-readable storage medium having instructions stored thereon, and the above-mentioned method is implemented when the instructions are executed by a processor.

[0033] The present invention periodically captures surveillance video using multiple camera units, and uses a recognition model to identify target objects in the video, obtaining their identification information. By analyzing the target object's behavior probability value, key image frames and warning videos are extracted, annotated, and mapped onto a three-dimensional map of the smart building, thereby obtaining a complete movement path diagram of the target object. This method effectively solves the problem of large amounts of surveillance data and the inability to obtain warning information in a timely manner, reducing the amount of surveillance data and enabling timely warning information. Furthermore, through similarity analysis of behavior probability values and behavioral characteristics, key image frames are intelligently screened, reducing the burden of data storage and processing while improving monitoring efficiency. When the recognition model cannot obtain target object information, the system can extract the target object's feature information and update the recognition model, enhancing the system's recognition capabilities. Furthermore, by annotating key image frames and warning videos on the three-dimensional map, real-time updates and queries are facilitated, improving the usability and intuitiveness of surveillance data. The method also dynamically adjusts the data transmission strategy based on the communication load and the target object's level of danger, ensuring that information on high-risk targets is prioritized when communication pressure is high, thereby improving the efficiency of emergency response. Through the mutual coordination of the above technical solutions, through intelligent processing and analysis of monitoring video data, the amount of data collection and transmission is reduced, and key data is retained to ensure timely acquisition of early warning information, realize effective monitoring and management of target objects in smart buildings, improve the intelligence level and response speed of the monitoring system, and provide strong support for the safe operation of smart buildings. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0035] Figure 1 This is a flow chart of a data collection method for smart buildings in an embodiment of the present application;

[0036] Figure 2 This is a structural diagram of the data acquisition system for smart buildings in an embodiment of the present application. DETAILED DESCRIPTION

[0037] In the specification and claims of this application and the accompanying drawings, the terms "first," "second," "third," "fourth," and so on (if any) are used to distinguish similar objects and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that shown or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus that includes a series of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, product, or apparatus.

[0038] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 As shown, the data collection method for smart buildings in the embodiment of the present application includes:

[0039] Step S1: periodically collecting surveillance videos of corresponding areas through multiple camera units, identifying multiple target objects in each surveillance video through a recognition model, and obtaining identification information of the multiple target objects;

[0040] Specifically, the above-mentioned multiple camera units are arranged in the above-mentioned smart building to periodically collect the above-mentioned surveillance video in the corresponding area, and the video frame of each of the above-mentioned target objects is separated from the above-mentioned surveillance video. Each of the above-mentioned video frames is also input into the above-mentioned recognition model to obtain the identification information of the above-mentioned target object. Through the above-mentioned technical solution, the identification information of each of the above-mentioned target objects can be identified based on the above-mentioned surveillance video, laying the foundation for further obtaining the behavior of each of the above-mentioned target objects.

[0041] Step S2: obtaining a current behavior probability value of each target object based on the identification information of each target object, the surveillance video, and the behavior probability distribution of each target object, and extracting a key image frame and a warning video of the surveillance video based on the current behavior probability value of each target object and a first similarity between the behavior feature of the target object and a preset behavior feature;

[0042] Specifically, behavioral features of each of the target objects are extracted through the surveillance video, and the behavioral features include facial behavioral features and limb behavioral features. The behavioral features are compared with the behavioral data of the target object to obtain the behavior type of the target object. Furthermore, according to the behavior type of the target object and the behavior probability distribution of the target object, the current behavior probability value corresponding to the current behavior type of the target object is obtained. When the current behavior probability values are greater than or equal to the first threshold, it is considered that each of the target objects is in a normal state. Therefore, N surveillance images corresponding to N randomly generated time points are used as the key image frames. When the current behavior probability value of any of the target objects is less than When the first threshold is reached, the target object whose current behavior probability value is less than the first threshold is regarded as the suspected object. The behavior characteristics of the suspected object are compared with the characteristics of the preset behavior, and the first similarity is obtained. M surveillance images are extracted based on the first similarity as the key image frames. When the first similarity is greater than the set threshold, it is considered that the current behavior of the suspected object is more harmful. At this time, the surveillance video is saved and the warning information is sent to other monitoring units. Through the technical solution, not only can the corresponding key image frames be obtained based on the current behavior probability value of the target object and the first similarity with the preset behavior, but also the amount of data can be reduced, and warnings can be issued to suspected objects with higher risks.

[0043] Step S3: Annotating the key image frames and the warning video with the identification information of the target object, the acquisition time, and the location information of the camera unit to construct a three-dimensional map of the smart building, mapping the location of the camera unit to the three-dimensional map, annotating the key image frames and the warning video in the three-dimensional map, and storing them in the storage node corresponding to the camera unit;

[0044] Specifically, by marking the identification information of each target object and the location information of the corresponding camera unit in each of the above-mentioned key image frames and warning videos, and also marking the acquisition time of the key image frame and the corresponding time of each video frame in the warning video, a three-dimensional map of the above-mentioned smart building is constructed based on the design structure of the above-mentioned smart building, and each of the above-mentioned camera units is updated to the above-mentioned three-dimensional map, and the key image frames and warning videos corresponding to each of the above-mentioned camera units are updated to the above-mentioned three-dimensional map, and the key image frames and the above-mentioned warning videos are stored in the storage nodes of the corresponding camera units, so as to facilitate the analysis of the movement path of each target object. Through the above-mentioned technical solution, the foundation is laid for further analysis and acquisition of the movement path map of each target object.

[0045] Step S4: The key image frames and the warning videos corresponding to each camera unit in the three-dimensional map are displayed in real time on the time coordinate axis according to the acquisition time and the corresponding time period, the monitoring movement path of each target object is obtained, and the target object is marked with a degree of danger in the three-dimensional map. The monitoring movement path of each target object is also supplemented according to the blind spot of each camera unit, so as to obtain a complete monitoring path map.

[0046] Specifically, by mapping the key image frames corresponding to each camera unit in the three-dimensional map onto the time coordinate axis according to the acquisition time, and also mapping the warning video corresponding to each camera unit onto the time coordinate axis, and connecting the position information of the target object in chronological order to obtain the monitoring movement path of the target object, and also marking the degree of danger of each target object, and displaying its monitoring movement path according to its degree of danger, and also supplementing the integrity of the monitoring movement path of each target object through the design structure of the smart building, through the above technical solution, a complete monitoring path map including all target objects can be obtained, which is convenient for analyzing the movement path of the suspected object when warning information appears.

[0047] Furthermore, the step S1 includes:

[0048] The surveillance video of the corresponding area is periodically collected by multiple camera units in the smart building, and the video frame of each target object is separated from the surveillance video, and the video frame corresponding to each target object is input into the recognition model to obtain the recognition information of the target object, wherein the recognition model is a learning model trained with target data, and the target data is the physiological characteristics and motion characteristics of any target object that has appeared in the camera unit.

[0049] Specifically, the plurality of camera units arranged in the smart building periodically capture the surveillance video in the corresponding area, separate the video frames of each target object from the surveillance video, and input each video frame into the recognition model to obtain the recognition information of the target object, wherein the recognition model is a learning model trained by the target object, that is, when the target object first appears in the first surveillance video, the physiological characteristics and behavioral characteristics of the target object are obtained through the first surveillance video, the physiological characteristics include facial features, height and limb proportions, and movement characteristics such as gait characteristics and habitual limb movement characteristics, and the facial features and movement characteristics of the target object are used as target data to train the recognition model. The recognition model can also generate multi-angle recognition images of the target object through the target data of the target object, and generate recognition information for the target object. When the video frame is input into the recognition model, the target object can be accurately identified even if the video frame is not clear enough or partially occluded. Through the above technical solution, the recognition information of each target object in the surveillance video can be identified, laying the foundation for further obtaining the behavior of each target object.

[0050] Furthermore, the step S2 includes:

[0051] Step S21: extracting behavioral features of each target object through the surveillance video, the behavioral features including facial behavioral features and body behavioral features, obtaining a behavior type of the target object based on the behavioral features of the target object and behavioral data corresponding to the target object stored in a storage unit through the identification information, and obtaining a current behavior probability value of the target object based on the behavior probability distribution of the target object and the behavior type;

[0052] Specifically, behavioral characteristics of each of the target objects are extracted through the above-mentioned surveillance video, and the above-mentioned behavioral characteristics include facial behavioral characteristics and limb behavioral characteristics. The above-mentioned facial behavioral characteristics include at least: tension, relaxation, anxiety, anger and happiness, and the above-mentioned limb characteristics include at least gestures, speed of limb movements and repetitiveness of limb movements, and also include the distance between the target object and other people. The above-mentioned behavioral characteristics are compared with the behavioral data of the above-mentioned target object to obtain the above-mentioned behavior type of the above-mentioned target object. In addition, based on the above-mentioned behavior type of the above-mentioned target object and the behavior probability distribution of the above-mentioned target object, the probability value corresponding to the current behavior type of the above-mentioned target object, that is, the current behavior probability value, is obtained. Through the above-mentioned technical solution, the current behavior probability value of each of the above-mentioned target objects can be accurately obtained, thereby laying a foundation for determining the number of key image frames obtained in the corresponding time period of the above-mentioned surveillance video according to the above-mentioned current behavior probability value.

[0053] Step S22: When the current behavior probability value of each target object is greater than or equal to a first threshold, a random number generation unit randomly generates N time points within a corresponding time period of the surveillance video, and uses N surveillance images corresponding to the N time points as the key image frames;

[0054] When the current behavior probability value of any of the target objects is less than the first threshold, the target object is regarded as a suspected object, and the behavior characteristics of the suspected object are compared with the characteristics of the preset behavior, and a first similarity is obtained. When the first similarity is less than the set threshold, M surveillance images are extracted from the surveillance video on average as the key image frames, and the value of M is increased or decreased according to the increase or decrease of the first similarity. When the first similarity is greater than or equal to the set threshold, the surveillance video is saved, and an early warning message is sent to other camera units, wherein the early warning message includes the identification information of the suspected object, the current image of the suspected object and the early warning mark, and the surveillance video including the target object captured by the camera unit and the other camera units is used as the early warning video, and the suspected object is rendered in the early warning video, and the rendered early warning video is sent to the receiving end, wherein N is less than M.

[0055] Specifically, when the current behavior probability value of each of the target objects in the surveillance video is greater than or equal to the first threshold, that is, the current behavior probability value of each of the target objects is consistent with the usual behavior, it is considered that each of the target objects is in a normal state. Therefore, the random number generation unit randomly generates N time points within the corresponding time period of the surveillance video, and uses the N surveillance images corresponding to the N time points as the key image frames. Since the N surveillance images are randomly acquired, they can better reflect the behavior process of each target object within the time period, which is convenient for playback and viewing, and also reduces the amount of data. The value of N is proportional to the length of the corresponding time period of the surveillance video. In the surveillance video, when the current behavior probability value of any of the target objects is less than the first threshold, the target object with the current behavior probability value less than the first threshold is regarded as the suspected object, that is, the behavior of the suspected object is quite different from the usual behavior. However, at this time, it cannot be said that the current behavior of the suspected object is suspicious. Therefore, the behavioral characteristics of the suspected object are also compared with the characteristics of the preset behavior, wherein the preset behavior, for example, is destructive behavior, forced entry into unauthorized areas, attempts to conceal the true identity, etc., and a first similarity is obtained. When the first similarity is less than the set threshold, it is considered that although the suspected object is quite different from the usual behavior, the harm is not particularly great, and because of the above The greater the first similarity, the more harmful the behavior of the suspected object is. Therefore, when the first similarity changes, the value of M also changes. The greater the first similarity, the greater the value of M, and vice versa. The smaller the first similarity, the smaller the value of M. M monitoring images are extracted at the same time interval as the key image frames. Not only can the behavior process of the target object be reflected according to the key image frames based on the current behavior dangerousness of the target object, but the amount of data can also be reduced. When the first similarity is greater than the set threshold, it is considered that the current behavior of the suspected object is more harmful. At this time, the monitoring video is saved and the warning information is sent to other monitoring units so that other monitoring units can quickly Identify the above-mentioned suspicious objects, timely monitor the moving paths of the above-mentioned target objects, and issue timely warnings. Render the suspected objects in the warning video, and send the rendered warning video to the above-mentioned receiving end, so that users can take corresponding measures and collect evidence based on the above-mentioned warning video. When there are multiple suspected objects, adjust the value of M based on the maximum value of the first similarities corresponding to the multiple suspected objects, wherein the values of N and M are both positive integers greater than or equal to 1. Through the above-mentioned technical solution, not only can the corresponding key image frames be obtained based on the current behavior probability value of the target object and the first similarity with the preset behavior to record and reflect the behavior process of the target object, but also the amount of data can be reduced, and warnings can be issued for suspected objects with higher risks.

[0056] Furthermore, when the recognition model cannot obtain the recognition information of the target object, the feature information of the target object is extracted through the surveillance video including the target object, and the feature information includes physiological features and behavioral features. The target object's angle discrimination information is also generated based on the feature information, and the feature information and the discrimination information of the target object are added to the recognition model for learning, and the recognition information of the target object is generated, and the recognition information and the feature information of the target object are also sent to other camera units.

[0057] Specifically, when the above-mentioned recognition model cannot obtain the recognition information of the above-mentioned target object, that is, the above-mentioned target object may be entering the surveillance video corresponding to the above-mentioned camera unit for the first time, and the above-mentioned recognition model does not have the recognition features of the above-mentioned target object, therefore, the physiological characteristics and behavioral characteristics of the above-mentioned target object are extracted through the above-mentioned surveillance video, wherein the above-mentioned physiological characteristics include facial features, height and limb proportions, movement characteristics such as gait characteristics and habitual limb movement characteristics, and multi-angle identification information of the above-mentioned target object is generated through the above-mentioned physiological characteristics and behavioral characteristics. Even if the above-mentioned target object is not very clear in the surveillance video or the dress has changed, it can be accurately identified through the above-mentioned identification information. The identification information of the above-mentioned target object is also generated, and the above-mentioned identification information and feature information of the above-mentioned target object are sent to other camera units, so that other camera units have unified identification information for the same target object and can identify the above-mentioned target object in time. Through the above-mentioned technical solution, the above-mentioned feature information and identification information of the above-mentioned target object can be obtained when the above-mentioned target object appears for the first time, thereby laying the foundation for accurate and timely identification of the above-mentioned target object.

[0058] Furthermore, step S3 includes:

[0059] Step S31: marking the identification information of each target object and the position information of the corresponding camera unit in each key image frame and the warning video, and also marking the acquisition time and the corresponding time of each video frame in each key image frame and the warning video;

[0060] Step S32: constructing a three-dimensional map of the smart building based on the design structure of the smart building, and mapping the position of each camera unit into the three-dimensional map;

[0061] Step S33: updating the key image frame and the warning video corresponding to each camera unit to the three-dimensional map in real time, and storing them in the storage node position corresponding to each camera unit.

[0062] Specifically, by marking the identification information of each target object and the location information of the corresponding camera unit in each of the above-mentioned key image frames and warning videos, and also marking the acquisition time of the key image frame and the corresponding time of each video frame in the warning video, a three-dimensional map of the above-mentioned smart building is constructed based on the design structure of the above-mentioned smart building, and each of the above-mentioned camera units is updated to the above-mentioned three-dimensional map, and the key image frames and warning videos corresponding to each of the above-mentioned camera units are updated to the above-mentioned three-dimensional map, and the key image frames and the above-mentioned warning videos are stored in the storage nodes of the corresponding camera units, so as to facilitate the analysis of the movement path of each target object. Through the above-mentioned technical solution, the foundation is laid for further analysis and acquisition of the movement path map of each target object.

[0063] Furthermore, the step S4 includes:

[0064] Step S41: mapping the key image frames acquired by each camera unit in the three-dimensional map onto the time coordinate axis according to acquisition time, and also mapping the warning video acquired by each camera unit onto the time coordinate axis;

[0065] Step S42: connecting the key image frames corresponding to the same target object or the position information of the camera units corresponding to the warning video in chronological order in the three-dimensional map, obtaining the monitored movement path of the target object, and marking the target object with a danger level in the three-dimensional map, wherein the higher the danger level, the more eye-catching the color of the monitored movement path;

[0066] Step S43: The monitoring movement path of each target object is supplemented and completed according to the blind spot of each camera unit according to the design structure of the smart building, so as to obtain the complete monitoring path map including the monitoring movement paths of all the target objects.

[0067] Specifically, the key image frames corresponding to each camera unit in the three-dimensional map are mapped onto the time coordinate axis according to the acquisition time, and the warning video corresponding to each camera unit is also mapped onto the time coordinate axis. The position information of each target object is extracted in chronological order through the key image frames and the warning video, and the position information of the target object is connected in chronological order to obtain the monitored movement path of the target object. The danger level of each target object is also marked, and its monitored movement path is displayed according to its danger level. The more serious the danger level, the more eye-catching its monitored movement path, which is convenient for the user at the data receiving end to view and take corresponding measures in time. However, since the camera unit may have a monitoring blind spot, it is impossible to obtain a complete monitored movement path map of the target object. Therefore, the design structure of the smart building is used to obtain each channel of the blind spot, so as to complete the monitored movement path of each target object, and the supplemented path part is displayed by a dotted line. There may be multiple supplemented paths. Through the above technical solution, a complete monitored path map including all target objects can be obtained, which is convenient for analyzing the movement path of the suspected object when an early warning occurs.

[0068] Furthermore, the step S4 further includes a step S5:

[0069] When the communication load is less than or equal to the second threshold, the three-dimensional monitoring map consisting of the complete monitoring path map of all the target objects is sent to the data collection end in real time. When the communication load is greater than the second threshold, the sending priority of the corresponding complete monitoring path map is set according to the danger level of each target object, wherein the higher the danger level, the higher the sending priority.

[0070] Specifically, when the above-mentioned communication load is less than or equal to the above-mentioned second threshold, that is, when the communication load is small, that is, when the communication pressure is small, a three-dimensional monitoring map composed of a complete monitoring path map of all the above-mentioned target objects is sent to the above-mentioned data collection end in real time. When the above-mentioned communication load is greater than the above-mentioned second threshold, that is, when the communication pressure is large, the complete monitoring path map corresponding to the above-mentioned target object is sent according to the sending priority of the above-mentioned target object. The higher the danger level of the above-mentioned target object, the higher the corresponding sending priority. Through the above-mentioned technical solution, the data collection unit can obtain the complete monitoring path map of the target object with a higher danger level in time, so as to facilitate the adoption of corresponding measures.

[0071] Furthermore, when the current behavior probability value of the target object is greater than or equal to the first threshold, the corresponding danger level of the target object is level three danger; when the current behavior probability value of the target object is less than the first threshold, when the first similarity is less than the set threshold, the danger level of the target object is level two danger; when the first similarity is greater than or equal to the set threshold, the danger level of the target object is level one danger.

[0072] The present invention also provides a data acquisition system for smart buildings, which is used to implement the above method, such as Figure 2 As shown, the system includes:

[0073] Multiple camera units are used to periodically collect surveillance videos of corresponding areas;

[0074] an identification unit, configured to identify a plurality of target objects in each of the surveillance videos by using a recognition model, and obtain identification information of each of the target objects;

[0075] an acquisition unit, configured to obtain a current behavior probability value of each target object based on the identification information of each target object, the surveillance video, and the behavior probability distribution of each target object, and extract key image frames and warning videos of the surveillance video based on the current behavior probability value of each target object and a first similarity between the behavior feature of the target object and a preset behavior feature;

[0076] a mapping unit, configured to annotate the key image frames and the warning video with the identification information of the target object, the acquisition time, and the position information of the camera unit to construct a three-dimensional map of the smart building, map the position of the camera unit to the three-dimensional map, annotate the key image frames and the warning video in the three-dimensional map, and store them in a storage node corresponding to the camera unit;

[0077] The generating unit is used to display the key image frames and the warning videos corresponding to each camera unit in the three-dimensional map in real time on the time coordinate axis according to the acquisition time and the corresponding time period, obtain the monitoring movement path of each target object, mark the danger level of the target object in the three-dimensional map, and supplement the monitoring movement path of each target object according to the blind spot of each camera unit, so as to obtain a complete monitoring path map.

[0078] The present invention also provides a computer-readable storage medium having instructions stored thereon, and the above-mentioned method is implemented when the instructions are executed by a processor.

[0079] In summary, the present invention periodically captures surveillance video using multiple camera units, and uses a recognition model to identify target objects in the video, obtaining their identification information. By analyzing the target object's behavior probability value, key image frames and warning videos are extracted, annotated, and mapped to a three-dimensional map of the smart building, thereby obtaining a complete movement path map of the target object. This method effectively solves the problem of large amounts of surveillance data and the inability to obtain warning information in a timely manner, reducing the amount of surveillance data and enabling timely warning information. Furthermore, through similarity analysis of behavior probability values and behavioral characteristics, key image frames are intelligently screened, reducing the burden of data storage and processing while improving monitoring efficiency. When the recognition model cannot obtain target object information, the system can extract the target object's feature information and update the recognition model, enhancing the system's recognition capabilities. Furthermore, by annotating key image frames and warning videos in the three-dimensional map, real-time updates and queries are facilitated, improving the usability and intuitiveness of surveillance data. The method also dynamically adjusts the data transmission strategy based on the communication load and the target object's level of danger, ensuring that information on high-risk targets is prioritized when communication pressure is high, thereby improving the efficiency of emergency response. Through the mutual coordination of the above technical solutions, through intelligent processing and analysis of monitoring video data, the amount of data collection and transmission is reduced, and key data is retained to ensure timely acquisition of early warning information, realize effective monitoring and management of target objects in smart buildings, improve the intelligence level and response speed of the monitoring system, and provide strong support for the safe operation of smart buildings.

[0080] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0081] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program code.

[0082] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A data collection method for smart buildings, characterized in that: The method comprises: Step S1: periodically collecting surveillance videos of corresponding areas through multiple camera units, identifying multiple target objects in each surveillance video through a recognition model, and obtaining identification information of each target object; Step S2: obtaining a current behavior probability value of each target object based on the identification information of each target object, the surveillance video, and the behavior probability distribution of each target object, and extracting a key image frame and a warning video of the surveillance video based on the current behavior probability value of each target object and a first similarity between the behavior feature of the target object and a preset behavior feature; Step S3: Annotating the key image frames and the warning video with the identification information of the target object, the acquisition time, and the location information of the camera unit to construct a three-dimensional map of the smart building, mapping the location of the camera unit to the three-dimensional map, annotating the key image frames and the warning video in the three-dimensional map, and storing them in the storage node corresponding to the camera unit; Step S4: The key image frames and the warning videos corresponding to each camera unit in the three-dimensional map are displayed in real time on the time coordinate axis according to the acquisition time and the corresponding time period, and the monitoring movement path of each target object is obtained, and the danger level of the target object is marked in the three-dimensional map. The monitoring movement path of each target object is also supplemented according to the blind spot of each camera unit, so as to obtain a complete monitoring path map.

2. The method according to claim 1, characterized in that The step S1 comprises: The surveillance video of the corresponding area is periodically collected by multiple camera units in the smart building, and the video frame of each target object is separated from the surveillance video, and the video frame corresponding to each target object is input into the recognition model to obtain the recognition information of the target object, wherein the recognition model is a learning model trained with target data, and the target data is the physiological characteristics and motion characteristics of any target object that has appeared in the camera unit.

3. The method according to claim 1, characterized in that , the step S2 comprises: Step S21: extracting behavioral features of each target object through the surveillance video, the behavioral features including facial behavioral features and body behavioral features, obtaining a behavior type of the target object based on the behavioral features of the target object and behavioral data corresponding to the target object stored in a storage unit through the identification information, and obtaining a current behavior probability value of the target object based on the behavior probability distribution of the target object and the behavior type; Step S22: When the current behavior probability value of each target object is greater than or equal to a first threshold, a random number generation unit randomly generates N time points within a corresponding time period of the surveillance video, and uses N surveillance images corresponding to the N time points as the key image frames; When the current behavior probability value of any of the target objects is less than the first threshold, the target object is regarded as a suspected object, and the behavior characteristics of the suspected object are compared with the characteristics of the preset behavior, and a first similarity is obtained. When the first similarity is less than the set threshold, M surveillance images are extracted from the surveillance video on average as the key image frames, and the value of M is increased or decreased according to the increase or decrease of the first similarity. When the first similarity is greater than or equal to the set threshold, the surveillance video is saved, and an early warning message is sent to other camera units and a receiving terminal, wherein the early warning message includes the identification information of the suspected object, the current image of the suspected object, and the early warning mark, and the surveillance video including the target object captured by the camera unit and the other camera units is used as the early warning video, and the suspected object is rendered in the early warning video, wherein N is less than M.

4. The method according to claim 1, characterized in that When the recognition model cannot obtain the recognition information of the target object, the feature information of the target object is extracted through the surveillance video including the target object, and the feature information includes facial features, body features and behavioral features. The feature information of the target object is also added to the recognition model, and recognition information is set for the target object, and the recognition information and feature information of the target object are sent to other camera units.

5. The method according to claim 1, characterized in that , the step S3 includes: Step S31: marking the identification information of each target object and the position information of the corresponding camera unit in each key image frame and the warning video, and also marking the acquisition time and the corresponding time of each video frame in each key image frame and the warning video; Step S32: constructing a three-dimensional map of the smart building based on the design structure of the smart building, and mapping the position of each camera unit into the three-dimensional map; Step S33: updating the key image frame and the warning video corresponding to each camera unit to the three-dimensional map in real time, and storing them in the storage node position corresponding to each camera unit.

6. The method according to claim 1, characterized in that , the step S4 comprises: Step S41: mapping the key image frames acquired by each camera unit in the three-dimensional map onto the time coordinate axis according to acquisition time, and also mapping the warning video acquired by each camera unit onto the time coordinate axis; Step S42: connecting the key image frames corresponding to the same target object or the position information of the camera units corresponding to the warning video in chronological order in the three-dimensional map, obtaining the monitored movement path of the target object, and marking the target object with a danger level in the three-dimensional map, wherein the higher the danger level, the more eye-catching the color of the monitored movement path; Step S43: The monitoring movement path of each target object is supplemented and completed according to the blind spot of each camera unit according to the design structure of the smart building, so as to obtain the complete monitoring path map including the monitoring movement paths of all the target objects.

7. The method according to claim 1, characterized in that , the step S4 further includes a step S5: When the communication load is less than or equal to the second threshold, the three-dimensional monitoring map consisting of the complete monitoring path map of all the target objects will be sent to the data collection end in real time. When the communication load is greater than the second threshold, the sending priority of the corresponding complete monitoring path map is set according to the danger level of each target object, wherein the higher the danger level, the higher the sending priority.

8. The method according to claim 1, characterized in that When the current behavior probability value of the target object is greater than or equal to a first threshold value, the corresponding danger level of the target object is level three danger; when the current behavior probability value of the target object is less than the first threshold value, when the first similarity is less than a set threshold value, the danger level of the target object is level two danger; when the first similarity is greater than or equal to the set threshold value, the danger level of the target object is level one danger.

9. A data acquisition system for smart buildings, the system being used to implement the method according to any one of claims 1 to 8, characterized in that: The system comprises: Multiple camera units are used to periodically collect surveillance videos of corresponding areas; an identification unit, configured to identify a plurality of target objects in each of the surveillance videos by using a recognition model, and obtain identification information of each of the target objects; an acquisition unit, configured to obtain a current behavior probability value of each target object based on the identification information of each target object, the surveillance video, and the behavior probability distribution of each target object, and extract key image frames and warning videos of the surveillance video based on the current behavior probability value of each target object and a first similarity between the behavior feature of the target object and a preset behavior feature; a mapping unit, configured to annotate the key image frames and the warning video with the identification information of the target object, the acquisition time, and the position information of the camera unit to construct a three-dimensional map of the smart building, map the position of the camera unit to the three-dimensional map, annotate the key image frames and the warning video in the three-dimensional map, and store them in a storage node corresponding to the camera unit; The generating unit is used to display the key image frames and the warning videos corresponding to each camera unit in the three-dimensional map in real time on the time coordinate axis according to the acquisition time and the corresponding time period, obtain the monitoring movement path of each target object, mark the danger level of the target object in the three-dimensional map, and supplement the monitoring movement path of each target object according to the blind spot of each camera unit, so as to obtain a complete monitoring path map.

10. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by a processor, the method according to any one of claims 1 to 8 is implemented.

Images