Abnormal information indication method and device, computer equipment and medium

Abstract

The invention relates to the technical field of intelligent locks, and discloses an abnormal information indication method and device, computer equipment and a medium, and the method comprises the steps: receiving environment data reported by an intelligent lock cluster distributed in a preset region; determining an abnormal aggregation region in a spatial topological graph of a preset region according to the environmental data, and predicting a diffusion condition of the abnormal aggregation region to obtain an abnormal diffusion path; and generating corresponding escape information based on the abnormal gathering area and the abnormal diffusion path. The problem that traditional static escape indication cannot dynamically adjust escape information and indication content based on real-time sensing data of an intelligent lock is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of smart locks, and in particular to an abnormal information indication method, device, computer equipment and medium. Background Art

[0002] In densely populated buildings, traditional fire safety exit signs are mostly static and fixed, providing only preset escape routes and failing to address dynamic risks such as fire spread and smoke diffusion at a fire scene. With the widespread adoption of intelligent IoT technology, smart door locks, widely deployed within buildings, have become important environmental sensing nodes. Integrated temperature, smoke, and other sensors within the locks can collect local environmental data in real time, and they feature distributed deployment and clear location coordinates. However, existing technologies have not yet fully utilized the cluster sensing capabilities of smart door locks. Traditional solutions still rely on the simple linkage of single-point fire alarm systems and indicator devices. This makes it impossible for emergency evacuation systems to build global risk situational awareness based on multi-point environmental data, making it difficult to dynamically assess channel safety and adjust escape directions.

[0003] Existing technologies limit the application of smart door locks to single-point alarm responses, lacking collaborative analysis and spatial topological correlation of sensor data from multiple locks. For example, it's impossible to identify abnormal areas by clustering temperature and smoke parameters reported by a cluster of smart door locks, nor is it possible to simulate risk diffusion paths based on building structures. Consequently, escape information planning still relies on static, pre-set logic and fails to dynamically mitigate real-time risks. Furthermore, traditional signage lacks data interaction with smart door locks, preventing real-time switching of guidance directions based on changing risks. This can lead evacuees into newly created dangerous areas due to the spread of fire, resulting in inefficient evacuation and secondary risks. Summary of the Invention

[0004] In view of this, the embodiments of the present invention provide an abnormal information indication method, device, computer equipment and medium to solve the problem that traditional static escape indication cannot dynamically adjust the escape information and indication content based on the real-time perception data of the smart lock.

[0005] In a first aspect, an embodiment of the present invention provides a method for indicating abnormal information, the method comprising:

[0006] Receive environmental data reported by smart lock clusters distributed in a preset area;

[0007] Determine an abnormal gathering area in the spatial topology map of the preset area according to the environmental data, and predict the diffusion of the abnormal gathering area to obtain an abnormal diffusion path;

[0008] Corresponding escape information is generated based on the abnormality gathering area and the abnormality diffusion path.

[0009] Furthermore, the method further comprises:

[0010] Obtain information reported by the smart lock cluster in real time;

[0011] Escape information is updated in real time based on reported information.

[0012] Furthermore, determining an abnormal clustering area in a spatial topological map of the preset area according to the environmental data includes:

[0013] identifying anomalous features in the environmental data;

[0014] Extracting the position code corresponding to the abnormal feature, and locating the smart lock coordinate point corresponding to the position code in the spatial topology map of the preset area;

[0015] Cluster analysis is performed on the coordinate points of the smart lock to obtain abnormal clustering areas.

[0016] Furthermore, the prediction of the diffusion of the abnormal accumulation area to obtain the abnormal diffusion path includes:

[0017] Analyze the changes in environmental data in the abnormal clustering area and obtain the gradient matrix;

[0018] Based on the building structure in the spatial topology map and the gradient matrix, the physical diffusion law of the abnormal factors in the abnormal aggregation area is simulated to obtain the abnormal diffusion path.

[0019] Furthermore, the generating corresponding escape information based on the abnormality gathering area and the abnormality diffusion path includes:

[0020] Based on the abnormal aggregation area and the abnormal diffusion path, demarcating a traversable area in the spatial topology map;

[0021] In the passable area, corresponding escape information is generated with the center point of the abnormal gathering area as the starting point and the exit location information in the spatial topology map as the end point.

[0022] Furthermore, the method further comprises:

[0023] Determining the relative positional relationship between the escape information and each of the prompt devices in the spatial topology map of the preset area;

[0024] Allocating a corresponding sub-segment of escape information to each prompt device according to the relative position relationship, and determining corresponding prompt content based on the sub-segment;

[0025] The prompt device is controlled to perform a switching operation according to the prompt content.

[0026] Furthermore, determining corresponding prompt content based on the sub-segment includes:

[0027] Obtaining the direction vector and the coordinates of the next path point of the sub-segment in the spatial topology graph;

[0028] generating directional information according to the direction vector, and calculating guidance parameters based on the coordinates of the next path point;

[0029] Corresponding prompt content is determined based on the directional information and the guidance parameters.

[0030] In a second aspect, an embodiment of the present invention provides an abnormal information indication system, the system comprising: a smart lock cluster, a cloud server;

[0031] The smart lock cluster is distributed in a preset area and is used to collect environmental data in the preset area and report the environmental data to the cloud server;

[0032] The cloud server is used to receive environmental data reported by the smart lock cluster; determine the abnormal aggregation area in the spatial topology map of the preset area based on the environmental data, and predict the diffusion of the abnormal aggregation area to obtain the abnormal diffusion path; generate corresponding escape information based on the abnormal aggregation area and the abnormal diffusion path.

[0033] Furthermore, the system also includes: a prompt device, which is used to receive the escape information and dynamically switch to display the prompt content.

[0034] In a third aspect, an embodiment of the present invention provides a computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, computer instructions being stored in the memory, and the processor executing the method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0035] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the method of the first aspect or any corresponding embodiment thereof.

[0036] The method provided in the embodiments of the present application has the following beneficial effects:

[0037] The method provided in the embodiment of the present application receives environmental data reported by the smart lock cluster, and can utilize the distributed deployment and real-time perception characteristics of the smart lock to obtain multi-point environmental parameters (such as temperature, smoke concentration, etc.) in the preset area, providing a data basis for global risk analysis; based on the environmental data, the abnormal aggregation area is determined in the spatial topology map and the abnormal diffusion path is predicted, which can combine the building structure and data change trends to realize the spatial positioning and dynamic deduction of risks, avoiding the limitations of traditional single-point detection; based on the abnormal aggregation area and the abnormal diffusion path, escape information is generated, which can dynamically avoid real-time risks and optimize escape routes, which is more in line with the actual situation on the scene than the static preset path; after obtaining the escape information, the prompt content of the prompt device in the preset area can be dynamically switched according to the escape information, and the real-time guidance of the prompt device can ensure that the escaping personnel adjust the direction in time to avoid entering the dangerous area by mistake, and finally achieve deep coordination between the perception capability of the smart lock and the dynamic interaction of the prompt device, thereby improving the safety, flexibility and efficiency of emergency evacuation. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. 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 paying any creative work.

[0039] Figure 1 is a flow chart of an abnormal information indication method according to an embodiment of the present invention;

[0040] Figure 2 is a schematic diagram of a planar layout of a preset area according to an embodiment of the present invention;

[0041] Figure 3 is a schematic diagram of the communication relationship between the smart lock cluster and the prompt device according to an embodiment of the present invention;

[0042] Figure 4 is a structural block diagram of an abnormal information indication system according to an embodiment of the present invention;

[0043] Figure 5 Schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0044] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of the present invention.

[0045] According to an embodiment of the present invention, an abnormal information indication method, apparatus, computer equipment and medium are provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0046] In this embodiment, a method for indicating abnormal information is provided. Figure 1 is a flow chart of an abnormal information indication method according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0047] Step S11, receiving environmental data reported by a cluster of smart locks distributed in a preset area.

[0048] In the embodiment of the present application, the smart lock, as a front-end sensing node, can not only collect information through integrated multi-dimensional sensors such as temperature, smoke, humidity, and harmful gas concentration, but also send information to the smart lock after detecting an abnormality through an independent smoke sensor installed in the house; at the same time, the camera equipped with the smart lock can also obtain relevant visual information. Combined with the natural physical distribution characteristics of smart locks in the building, a high-density coverage environmental monitoring network is formed. These smart locks use wired or wireless communication methods (such as LoRa, ZigBee, Wi-Fi, etc.) to efficiently transmit real-time environmental parameters on both sides of the door, door lock status, battery power, life detection data, and precise location coding to the cloud server through wireless transmission or direct connection mode between the door lock and the gateway. In particular, the independent backup power supply (such as a lithium battery) equipped with the smart lock ensures that it can continue to work in the event of a strong power outage, ensuring uninterrupted collection of key fire data. After receiving these heterogeneous data, the cloud server uses a time series database for storage and preprocessing, providing accurate and comprehensive environmental perception basis for subsequent abnormal area identification, diffusion path prediction, and dynamic escape information planning based on spatial topology.

[0049] As an example, a schematic diagram of the floor plan of a preset area is shown as follows: Figure 2As shown, each floor is identified by the room location in the form [nA] (n represents the number of floors). Floors are connected by corridors, and multiple safety exits are located at the intersection of corridors and floors. This layout creates pre-set areas. In practice, smart lock clusters distributed in these rooms collect environmental data such as temperature and smoke concentration inside and outside the door through their own sensors, receiving information transmitted by indoor sensors, or capturing images from cameras. This data is then reported to the fire control cloud platform. Based on this data, the fire control cloud platform identifies abnormal clusters in spatial topology maps similar to this layout and predicts their diffusion paths. Based on this result, a safe escape message is planned. Finally, according to the generated escape message, the prompt content of the prompt devices in the pre-set area is switched to guide personnel to evacuate safely.

[0050] It should be noted that the communication relationship between the smart lock cluster and the prompt device in the channel is as follows: Figure 3 As shown, smart locks distributed in different locations can transmit environmental data collected through various channels to the cloud server through wireless or wired communication, realizing the function of receiving environmental data reported by the smart lock cluster.

[0051] Step S12: determining an abnormal gathering area in a spatial topological map of a preset area according to the environmental data, and predicting the diffusion of the abnormal gathering area to obtain an abnormal diffusion path.

[0052] In the embodiment of the present application, first, the abnormal features in the environmental data are identified, such as sudden temperature rise, excessive smoke concentration, abnormal concentration of harmful gases, etc. These abnormal features reflect potential fire hazards; the position code corresponding to the abnormal features is extracted, and the corresponding smart lock coordinate points are located in the spatial topology map of the preset area, and the discrete abnormal data is mapped to the specific location of the building; then, the smart lock coordinate points are processed using a cluster analysis algorithm to accurately determine the abnormal cluster area and effectively lock the fire source or dangerous area. Then, the changes in the environmental data in the abnormal cluster area are analyzed to generate a gradient matrix that characterizes the data change trend. The matrix quantifies the diffusion trend of abnormal factors such as temperature and smoke; combined with the building structure in the spatial topology map (such as walls, passages, doors and windows, etc. that block or guide airflow and fire), the physical diffusion law of the abnormal factors is simulated based on the gradient matrix, and then the abnormal diffusion path is predicted. This process not only realizes the spatial positioning of the dangerous area, but also grasps the spread trend of the fire or hazardous substances through dynamic simulation, providing a reliable basis for the subsequent generation of scientific and reasonable escape information. Compared with traditional static monitoring and fuzzy prejudgment, it greatly improves the accuracy and timeliness of emergency response.

[0053] Step S13: Generate corresponding escape information based on the abnormality gathering area and the abnormality diffusion path.

[0054] In the embodiment of the present application, firstly, a determined abnormal gathering area and a predicted abnormal diffusion path are given. In the spatial topology map of the preset area, by excluding the coverage of dangerous areas such as fire and smoke, a safe and passable area is accurately delineated, effectively avoiding the risks that may be encountered during the evacuation process. Then, the center point of the abnormal gathering area is used as the evacuation starting point, and the location information of each exit in the spatial topology map is combined to set it as the evacuation end point. By calling the improved Dijkstra path planning algorithm and comprehensively considering the real-time status of stairs and elevators, one or more optimal escape information is generated in the passable area. The escape information includes but is not limited to: escape routes, safe hiding areas, safe exit locations, rescue routes determined based on vital signs detection by smart door locks, and environmental disaster distribution information corresponding to the building. This process makes full use of the multi-dimensional data and spatial topology analysis results collected by the smart door lock group. It can not only dynamically adapt to changes in the fire scene, but also combine the characteristics of the building structure to plan an evacuation route for escapees that is both safe and efficient. Compared with traditional fixed escape routes, it greatly improves the rationality and reliability of the evacuation plan, laying a solid foundation for the subsequent safe evacuation of personnel through prompt equipment.

[0055] In an embodiment of the present application, the method also includes: obtaining information reported by the smart lock cluster in real time; and updating the escape information in real time according to the reported information.

[0056] It's important to note that the information reported by the smart lock cluster comes from a variety of sources, including but not limited to video streams generated by door lock cameras and environmental data detected by various sensors built into the door locks, such as temperature, smoke, and hazardous gas concentrations. Furthermore, information exchanged between door locks via wireless or wired communication, such as abnormal conditions and changes in access status detected by adjacent door locks, is also collected and relayed. This multi-dimensional information complements each other, providing comprehensive and accurate data support for real-time updates of escape information.

[0057] As an example, the method for updating escape information may include: obtaining the video stream of the camera in the smart lock cluster; determining the population density of each channel in the preset area based on the video stream; dynamically adjusting the escape information according to the population density to obtain adjusted escape information; and updating the prompt content of the prompt device in the preset area according to the adjusted escape information.

[0058] Specifically, the cameras in the smart lock cluster continuously collect video data. Through the built-in communication module, using wireless transmission technologies such as Wi-Fi, Bluetooth Mesh, 4G / 5G, or wired transmission methods such as Ethernet, the video stream is packaged into a data format that conforms to network transmission protocols (such as RTMP and HLS) and uploaded to the cloud server in real time. During the transmission process, data compression algorithms are used to reduce bandwidth usage, and encryption technology is used to ensure the security of video data, ensuring that the cloud server can stably and quickly obtain the live images captured by each smart lock camera.

[0059] Secondly, after receiving the video stream, the cloud server first decodes the video data and restores it to the original image frame. It then uses deep learning algorithms in computer vision, such as the YOLO target detection model, to identify people in the image. Using density map estimation, it generates a heat map of people density based on the distribution of people. By analyzing the heat map, it calculates the number of people per unit area in each channel within the preset area. Combined with parameters such as channel width and length, it ultimately determines the density level of each channel (e.g., unobstructed, slightly congested, or severely congested), providing a quantitative basis for subsequent decision-making.

[0060] Subsequently, the population density is compared and analyzed with the existing escape information. If the escape information shows that the population density is too high, the dynamic path adjustment mechanism is immediately activated. Based on the spatial topology of the building and combined with real-time fire information (such as the fire source location and diffusion trend determined by the temperature and smoke concentration data reported by the smart door lock sensor), the improved Dijkstra algorithm, A* algorithm and other path planning algorithms are called to set passages with excessive population density and areas affected by the fire to an impassable state, recalculate the shortest and safest path from each area to be evacuated to the safe exit, and generate an adjusted escape information plan.

[0061] Finally, the cloud server converts the adjusted escape information plan into control instructions for the prompt device. The instructions include light color (red / green), dynamic arrow direction, text content (such as safety exit to the left, no passage), voice prompt information, etc. Through a wired or wireless communication network, the instructions are sent to each prompt device in the preset area according to the transmission path of cloud server-gateway-prompt device, or cloud server-prompt device. After receiving the instructions, the prompt device drives the internal LED lighting module, arrow display module, voice playback module and other hardware devices to update the display content and voice prompts in real time, and guide people to evacuate quickly according to the adjusted escape information in an intuitive and clear manner.

[0062] Step A2: extract the position code corresponding to the abnormal feature, and locate the smart lock coordinate point corresponding to the position code in the spatial topology map of the preset area.

[0063] Specifically, for the identified abnormal data, the cloud server extracts the unique location code it carries (such as "F3-E-3B" represents room 3B on the east side of the 3rd floor) and maps it to the preset building space topology map. This topology map is built based on CAD drawings and includes physical structures such as floors, passages, and emergency exits. Each smart lock corresponds to a precise coordinate point (X, Y, Z) in the map. If the smart lock is not equipped with a positioning module, the platform estimates the location through gateway signal strength or multi-device triangulation, and calibrates it in combination with environmental features in the camera video stream (such as corridor signs and room numbers) to ensure that the positioning accuracy of the abnormal point in the topology map reaches the meter level.

[0064] Step A3: Perform cluster analysis on the smart lock coordinate points to obtain abnormal clustering areas.

[0065] Specifically, the cloud server uses a density clustering algorithm (such as DBSCAN) to spatially cluster the coordinate points of abnormal smart locks, sets the neighborhood radius (such as abnormal points within 5 meters on the same floor) and the minimum number of samples (such as 3 adjacent abnormal points), and divides spatially adjacent abnormal points into abnormal cluster areas. For example, if the smart locks in rooms 3B, 3C, and 3D on the east side of the 3rd floor simultaneously report high temperature and smoke exceeding the standard, and the coordinate points form a continuous cluster in the topology map, the area is marked as an abnormal cluster area; if sporadic abnormal points appear on adjacent floors, they are considered potential diffusion areas. The clustering results are visualized in different colors in the topology map, intuitively presenting the distribution and intensity of the fire.

[0066] In the embodiment of the present application, the diffusion of the abnormal aggregation area is predicted to obtain the abnormal diffusion path, including steps B1-B2:

[0067] Step B1: Analyze the changes in environmental data in the abnormal clustering area to obtain a gradient matrix.

[0068] Specifically, a spatiotemporal gradient analysis is performed on the multi-dimensional environmental data (such as temperature, smoke concentration, and harmful gas content) in the abnormal clustering area. First, the detection value of each smart lock is extracted according to the time series (such as every 5 seconds), and the change rate of adjacent time points (such as the temperature change rate ΔT / Δt, the smoke concentration change rate ΔC / Δt) is calculated to form a dynamic gradient field in the area. For example, if the temperature in the east area of the 3rd floor rises from 80°C (at time t1) to 95°C (at time t2), and the temperature in the west area only rises by 5°C, then the temperature gradient value of this area in the gradient matrix is significantly higher than that of the surrounding area. At the same time, combined with the spatial position relationship, the physical distance between adjacent smart locks is calculated to generate a spatial gradient matrix to characterize the diffusion intensity distribution of abnormal factors (such as heat and smoke) within the building plane. This matrix quantifies the directionality (such as the temperature gradient points to the direction where the high temperature grows fastest) and speed of fire spread in a numerical way, providing a data basis for subsequent diffusion simulation.

[0069] Step B2: Based on the building structure and gradient matrix in the spatial topology map, the physical diffusion law of the abnormal factors in the abnormal aggregation area is simulated to obtain the abnormal diffusion path.

[0070] Specifically, the cloud server combines the structural features in the building space topology map (such as the location and permeability of walls, doors, windows, and stairwells) and uses the gradient matrix to drive the physical diffusion model (such as the smoke diffusion model or heat conduction model based on fluid mechanics). For example, if the topology map shows that there are open doors and windows in a certain area (the door lock status is abnormally open), it is considered the main channel for the spread of fire, and the diffusion coefficient in the gradient matrix increases accordingly; if there is a closed wall, the diffusion path is automatically bypassed. Through finite element analysis or particle swarm simulation, the diffusion trajectory of abnormal factors (such as high-temperature airflow, toxic smoke) in the future time period (such as within 30 seconds) is predicted to generate a dynamic diffusion path. The path is superimposed on the topology map as a visual curve, marking the location of the diffusion front and the risk level (such as red areas are high-risk diffusion paths, yellow is medium risk), and is updated in real time to reflect the latest environmental data changes, providing dynamic obstacle information for escape information planning.

[0071] In an embodiment of the present application, corresponding escape information is generated based on the abnormal aggregation area and the abnormal diffusion path, including: based on the abnormal aggregation area and the abnormal diffusion path, a passable area is delineated in the spatial topology map; in the passable area, the center point of the abnormal aggregation area is used as the starting point and the exit location information in the spatial topology map is used as the end point to generate corresponding escape information.

[0072] Specifically, the identified abnormal gathering areas and their predicted diffusion paths are set as prohibited areas, and areas such as walls that were originally inaccessible in the building and unopened fire doors are marked. After excluding these dangerous areas, the remaining corridors, stairs, unaffected rooms and other areas constitute the passable areas and are marked in the topology map. The geometric center point of the abnormal gathering area is used as the evacuation starting point, and the safe exit position marked in the spatial topology map is used as the end point. Call the path planning algorithm (such as the improved Dijkstra algorithm) to search for the shortest escape information from the starting point to the end point in the passable area to avoid dangerous areas. At the same time, the video stream of the smart door lock camera is combined to analyze the density of people in the channel. If there is congestion in a certain section of the corridor, the path is replanned to ensure evacuation efficiency.

[0073] As an example, suppose the cloud server receives data reported by smart door locks in rooms 3B, 3C, and 3D on the east side of the third floor of a building. The data shows a temperature of 85°C and a smoke concentration of 600 ppm in room 3B, a temperature of 90°C and a smoke concentration of 700 ppm in room 3C, and a temperature of 88°C and a smoke concentration of 650 ppm in room 3D. The data is accompanied by the location codes "F3-E-3B," "F3-E-3C," and "F3-E-3D." A 3D spatial topology map is constructed based on the building's CAD drawings. The coordinates of the smart lock in room 3B are determined to be (X1, Y1, Z1), and the corridor is marked as passable, while the wall is marked as impassable.

[0074] The cloud server judges that the temperature exceeds 60°C and the smoke concentration is greater than 500ppm, which is an abnormality. The data of rooms 3B, 3C, and 3D meet the standards. The position code is extracted, and the three smart lock coordinate points (X1, Y1, Z1), (X2, Y2, Z2), and (X3, Y3, Z3) are located in the topology map. The DBSCAN algorithm (neighborhood radius 5 meters, minimum sample number 2) is used for clustering and marked as abnormal cluster areas. Analysis found that the temperature in room 3B rises by 8°C every 5 minutes, and in room 3C by 10°C. The temperature and smoke concentration gradients are calculated to generate a gradient matrix. Combined with the building structure with the doors and windows of room 3B open and the doors and windows of room 3C closed, the fluid mechanics model is used to simulate the diffusion of smoke, and the diffusion path for the next 30 seconds is marked with orange lines in the topology map.

[0075] The red abnormal gathering area and the orange diffusion path are set as no-pass areas, and the walls and other impassable areas are marked. The remaining corridors, stairs and other areas are set as green passable areas. Taking the geometric center point (X0, Y0, Z0) of the abnormal gathering area as the starting point, the coordinates of the east exit of the first floor (X e ,Y e ,Z e ) is the destination. The modified Dijkstra algorithm is used. If stair 1 is out of service due to a fire, stair 2 is automatically selected, generating an evacuation route with multiple path nodes. If the camera video stream shows congestion in a corridor, the route is replanned.

[0076] In an embodiment of the present application, the method further includes: switching the prompt content of the prompt device in the preset area according to the escape information.

[0077] It should be noted that: First, within a spatial topology map of a pre-defined area, the relative positional relationship between escape information and each warning device is determined using geographic information system technology, clarifying the specific role of each warning device in the evacuation route. Subsequently, based on this relative positional relationship, an escape information subsegment is assigned to each warning device. By calculating the direction vector and the coordinates of the next path point within the spatial topology map, and combining the building layout with the movement logic of personnel, directional information and guidance parameters are generated. This in turn determines the corresponding warning content, such as "Turn left to the emergency exit" or "This passage is closed, please detour," or other graphic or voice instructions. Finally, via a wireless transmission channel or direct connection to a cloud server, the warning content is transmitted to the warning device, such as a user's mobile phone, a fire warning device, or other smart door lock. The warning device then displays the escape instructions and controls operations such as light color switching (red / green warning), arrow direction adjustment, and text information updates, ensuring that escaping personnel receive accurate and clear escape instructions in real time.

[0078] In an embodiment of the present application, an escape indication method based on a smart door lock is also introduced and applied to the smart door lock. The process includes: the smart door lock cluster receives prompt content containing escape guidance information sent by the cloud server, and the prompt content includes but is not limited to escape direction instructions and channel status information; the smart door lock parses the received prompt content to obtain a parsing result, wherein the parsing result includes the instruction type and key information; according to the instruction type and key information, the corresponding component on the smart door lock is controlled to perform the prompt operation.

[0079] In an embodiment of the present application, the corresponding components on the smart door lock are controlled to perform prompt operations according to the instruction type and key information, including: if the instruction type is a visual prompt type, the display device on the smart door lock is controlled to switch the display content; if the instruction type is an auditory prompt type, the voice device on the smart door lock is controlled to perform a broadcast operation; if the instruction type is a position sensing type, the sensing device on the smart door lock is controlled to monitor the location information of the escaped person.

[0080] Specifically, the smart door lock combines camera image recognition and human posture analysis technology to monitor the movement status of escaping personnel in real time. This movement status can include body movements and facial expressions, and judge the person's psychological state and action intentions. When abnormal behavior such as hesitation or wandering is detected, more detailed voice guidance is triggered, such as a detailed description of the distance to the nearby emergency exit and surrounding landmarks, to assist personnel in making quick decisions. During the smart door lock's visual prompt control process, the smart door lock can also be linked to the lighting system at the entrance of the passage. When interpreted as a passage closure instruction, the lighting in the passage is switched to red strobe mode to strengthen the danger warning.

[0081] As an example, suppose a fire occurs in a large office building. The cloud server will generate escape guidance information and send prompts to the smart door lock cluster in the office building.

[0082] First, the smart door lock cluster receives prompts from the cloud server, including "Turn right to Area B safety exit, the passage is currently clear" and "The passage to Area C is closed due to fire, please detour." The prompts are parsed. The "Turn right to Area B safety exit, the passage is currently clear" instruction type is visual and auditory, with the key information being the escape direction: turn right, the destination is Area B safety exit, and the passage is clear. The "Area C passage is closed due to fire, please detour" instruction type also includes visual and auditory prompts, with the key information being the closed passage to Area C, requiring a detour.

[0083] Then, according to the instruction type and key information, the corresponding components on the smart door lock are controlled to perform prompt operations:

[0084] For visual instructions, smart door locks installed in accessible passageways (toward the Zone B emergency exit) will immediately change their external display background to green, displaying "Turn right to Zone B emergency exit" in large white font, and display a right-pointing arrow icon. Smart door locks located at the entrance to Zone C will change their display background to red, flashing "Zone C passage closed, please detour." For auditory instructions, smart door locks located in the Zone B emergency exit will use their built-in voice module to repeat the announcement "Turn right to Zone B emergency exit, passage clear" in a steady, rapid tone. Smart door locks located at the entrance to Zone C will play a warning sound and announce "Zone C passage closed, please detour." For location-based instructions, smart door locks use built-in sensors to detect approaching people. When a person approaches, the smart door lock adjusts the volume and speed of the voice announcement based on the preset sensing range and its own position within the office building topology map. For example, when a person is close to the door lock, lower the volume and slow down the speaking speed to clearly broadcast the instructions; when the person is far away, increase the volume and speed up the speaking speed to attract the person's attention.

[0085] During real-time monitoring, smart door locks combine camera image recognition and human posture analysis technology to monitor escaping individuals. If a person is spotted lingering near a smart door lock, exhibiting hesitation in their movements and a tense expression, the smart door lock will determine that the individual is struggling to make a decision and proactively trigger more detailed voice guidance: "You are approximately 20 meters from the emergency exit in Area B. Turn right along the current corridor. You will see a clearly marked exit after passing the third office. Please proceed as soon as possible."

[0086] In addition, in terms of visual prompt control, the smart door lock at the entrance of the channel in Zone C, in addition to displaying the closed prompt on the display screen, can also link with the lighting system at the entrance of the channel to switch the lighting in the channel to red strobe mode, further strengthening the danger warning visually and preventing escapees from mistakenly entering the closed channel.

[0087] This process not only realizes the visualization of escape information planning results, but also dynamically updates prompt content through real-time data collected by smart locks (such as population density and fire situation changes), significantly improving the flexibility and reliability of emergency evacuation guidance, and effectively avoiding escapees from mistakenly entering dangerous areas or low evacuation efficiency due to information lag.

[0088] In the embodiment of the present application, dynamically switching the prompt content of the prompt device in the preset area according to the escape information includes the following steps C1-C3:

[0089] Step C1: determining the relative position relationship between the escape information and each prompt device in a spatial topology map of a preset area.

[0090] Specifically, in the spatial topology map of the preset area, the generated escape information is first converted into a digital coordinate sequence (such as the spatial coordinates of the starting point, turning point, and end point), and the physical position coordinates of all prompt devices are loaded at the same time (such as the specific position of the prompt device in the building plan). Through geometric algorithms (such as vector angle calculation, shortest distance measurement), the spatial correlation between each escape information and the surrounding prompt devices is analyzed, for example, to determine whether the prompt device is located at a necessary node, branch intersection or key turning point of the path, and to mark the direction of the path relative to the prompt device (such as front, left, right). In addition, the system will also identify obstacles (such as walls, elevator shafts) between the escape information and the prompt device to ensure the accuracy of the relative position relationship and provide a spatial benchmark for the precise allocation of subsequent prompt content.

[0091] Step C2: assigning a corresponding sub-segment of escape information to each prompt device according to the relative position relationship, and determining corresponding prompt content based on the sub-segment.

[0092] Specifically, the escape information is divided into multiple sub-segments according to the relative position relationship, and each sub-segment corresponds to one or more prompt devices. For example, the prompt device at the stairwell may correspond to the sub-segment of "from the current position to the stair entrance", while the prompt device at the corner of the corridor corresponds to the sub-segment of "turn to the right channel". For each sub-segment, its key features are extracted: direction vector: the direction of travel (such as 30° east of north) is calculated by the coordinate difference between the starting point and the end point of the sub-segment, which is used to generate the arrow indicating the direction; the coordinates of the next path point: the coordinates of the end point of the sub-segment, which are used to calculate the distance and turning angle (i.e., guidance parameters) to the next prompt device.

[0093] Prompt content is generated based on these features: if the sub-segment is a straight path, the prompt device displays "Safety Exit Forward" + a green arrow; if the sub-segment requires a turn, it displays "Turn right / left to the passage" + a dynamic arrow turn; if the path ahead is blocked (such as a new smoke diffusion area), it displays "No entry, back up" + a red cross, and triggers a voice reminder simultaneously.

[0094] Step C3: Control the prompt device to perform a switching operation according to the prompt content.

[0095] Specifically, via wired or wireless communication protocols, the cloud server sends control commands containing prompt content to each prompt device. Upon receiving the command, the prompt device parses the display parameters (such as light color, arrow direction, text content, and voice broadcast content) and drives the hardware modules to execute the switch: Lighting system: The red and green LED lights switch colors according to the command (green for go, red for no go); Dynamic arrow module: A motor drives the arrow to point in a specified direction (such as left, right, up, or down), or displays an animated arrow on the OLED screen; Voice module: Plays a pre-recorded guidance voice (such as "Please follow the arrow to the safe exit"), with the volume automatically adjusting based on ambient noise; Text display module: The LCD screen scrolls and displays real-time updated escape instructions (such as "There is a fire on the 3rd floor, please go up to the 5th floor corridor"). Furthermore, the prompt device's feedback signals (such as power status and command execution confirmation) are monitored in real time. In the event of communication interruption or device failure, the system automatically switches to a preset emergency mode (such as displaying the last valid command or a solid green arrow) to ensure the continuity of evacuation guidance.

[0096] In an embodiment of the present application, the corresponding prompt content is determined based on the sub-segment, including: obtaining the direction vector and the coordinates of the next path point of the sub-segment in the spatial topology map; generating directional information according to the direction vector, and calculating the guidance parameters based on the coordinates of the next path point; and determining the corresponding prompt content based on the directional information and the guidance parameters.

[0097] Specifically, first, obtain the direction vector and next path point coordinates of the sub-segment in the spatial topology map. The spatial topology map abstracts the accessible areas and nodes (such as the location of smart door locks) in the building into a digital graph structure. For the generated escape information, it is broken down into multiple sub-segments, each of which corresponds to an actual route. By calculating the coordinate difference between the starting point and the end point of the sub-segment, the direction vector can be obtained, which represents the physical direction of the path; and the coordinates of the next path point, that is, the coordinates of the end point of the sub-segment, represent the next target location that the escaper needs to go to after arriving at the end point of the current sub-segment. These two sets of data lay the spatial foundation for the subsequent guidance generation.

[0098] Next, directional information is generated based on the direction vector, and guidance parameters are calculated based on the coordinates of the next path point. The direction vector will be converted into understandable semantic directional information, such as the east direction corresponds to "forward", the northeast direction corresponds to "left front", etc., and accurate directional guidance is generated by combining different scenarios of two-dimensional plane and three-dimensional space. Based on the coordinates of the next path point, the system calculates guidance parameters such as distance parameters (the distance from the current prompt device to the next path point), steering angle parameters (steering angle between adjacent sub-segments) and risk level parameters (the degree of risk assessed in combination with the data reported by the door lock). These parameters quantify the spatial characteristics and risk status of the escape information.

[0099] Finally, the corresponding prompt content is determined based on the directional information and guidance parameters. The system uses a rule engine to combine the directional information, guidance parameters and the hardware capabilities of the safety exit prompt device to generate multimodal escape guidance. In the light display, a green arrow indicates the direction of safe passage, a red cross indicates prohibited passage, and the arrow direction is adjusted in real time according to the direction vector; the text and voice prompts follow the format of [direction guidance] + [distance / risk warning] + [target location], for example, "8 meters to the right front to the staircase on the 2nd floor, pass at a low speed in the smoke." If the path changes due to the fire, the prompt content will automatically switch to the backup route guidance. At the same time, ensure that the content of adjacent prompt devices remains consistent to ensure that escapees can receive continuous and effective guidance, ultimately achieving dynamic and accurate escape guidance, and improving the efficiency and safety of personnel evacuation in emergency situations.

[0100] In the embodiment of the present application, a distributed dynamic response network is built between the smart lock cluster and the prompt device, and two-way collaboration is achieved through the following technologies:

[0101] First, the smart lock monitors its own sensor data (such as battery voltage ≤ 3.2V, communication signal strength ≤ -85dBm) and environmental risk levels (such as temperature ≥ 70°C or CO concentration ≥ 100ppm) in real time, generating a device health index and a regional risk index. When the index exceeds the threshold, it automatically triggers a communication link switch: it prioritizes establishing a direct channel (≤ 15 meters) with a nearby notification device using Bluetooth Mesh or LoRa ad hoc networking, and secondarily uses a gateway for relay, ensuring command transmission latency ≤ 500ms.

[0102] Secondly, after receiving the prompt content sent by the cloud server, the prompt device broadcasts a path verification request (including the target exit code and path node sequence) to smart locks within a radius of 10 meters. If it detects that the door is abnormally locked or the passage is blocked (no one passes for 5 consecutive seconds), a path failure alarm (with the device location code and failure reason code) will be immediately sent to the prompt device and the cloud server.

[0103] Finally, upon receiving a route failure alert, the device's built-in route decision module: Based on a preloaded building topology, it uses the A* algorithm to recalculate a detour route. It then obtains environmental data for alternate pathways (e.g., smoke concentration ≤ 300 ppm, indicating safety) from nearby smart locks. It then switches to a local route within 300 milliseconds, illuminating the alternate direction arrow (flashing yellow to indicate a temporary change). Simultaneously, the corrected route and decision log are asynchronously reported to the cloud server, achieving cloud-edge policy synchronization.

[0104] In this embodiment, an abnormal information indication system is provided, which includes: a smart lock cluster and a cloud server;

[0105] Smart lock clusters are distributed in a preset area and are used to collect environmental data within the preset area and report the environmental data to the cloud server;

[0106] The cloud server is used to receive environmental data reported by the smart lock cluster; determine the abnormal aggregation area in the spatial topology map of the preset area based on the environmental data, and predict the diffusion of the abnormal aggregation area to obtain the abnormal diffusion path; generate corresponding escape information based on the abnormal aggregation area and abnormal diffusion path.

[0107] Specifically, the smart lock cluster collects real-time environmental data inside and outside the door through built-in sensors (such as temperature and smoke sensors) or cameras, encodes the data (including location code, parameter values, etc.), and transmits it to the cloud server via wireless (such as Wi-Fi, LoRa) or wired methods. After receiving the data, the cloud server first identifies abnormal features (such as smoke concentration exceeding the standard), extracts the smart lock coordinate points corresponding to the abnormal data, and uses a clustering algorithm to determine the abnormal cluster area (such as the fire point on a certain floor); then analyzes the changing gradient of the environmental data in the area (such as the temperature diffusion trend), and simulates the diffusion path of the abnormal factor in combination with the building structure (such as the direction of the channel); finally, excludes the dangerous area in the spatial topology map, takes the center point of the abnormal cluster area as the starting point and the safe exit as the end point, and uses the path planning algorithm to generate escape information including escape routes, safe avoidance areas, etc.

[0108] In an embodiment of the present application, the system further includes: a prompt device for receiving escape information and dynamically switching to display prompt content.

[0109] Specifically, the cloud server determines the relative position of the escape information and the prompt device (such as fire warning signs, smart door lock display screens) based on the spatial topology map, and assigns an escape information sub-segment (such as guidance for a certain section of the channel) to each device. By calculating the direction vector of the sub-segment (such as "turn right") and the coordinates of the next path point, the directional information and guidance parameters (such as 30 meters from the exit) are generated and converted into prompt content. The instructions are then sent to the prompt device through the communication network, controlling it to update the display content in real time through lights, arrows, text or voice, and guide people to evacuate according to the planned path. If the smart lock cluster reports new data in real time (such as congestion caused by new personnel), the cloud server will re-optimize the escape information and synchronize it to all prompt devices for dynamic adjustment.

[0110] In this embodiment, an abnormal information indication device is also provided, which is used to implement the above-mentioned embodiments and preferred embodiments. The details already described will not be repeated here. As used below, the term "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0111] This embodiment provides an abnormal information indicating device, such as Figure 4 As shown, including:

[0112] Receiving module 41, used to receive environmental data reported by the smart lock cluster distributed in a preset area;

[0113] A determination module 42 is configured to determine an abnormal aggregation area in a spatial topology map of a preset area based on environmental data, and to predict the diffusion of the abnormal aggregation area to obtain an abnormal diffusion path;

[0114] The generation module 43 generates corresponding escape information based on the abnormality gathering area and the abnormality diffusion path.

[0115] Furthermore, the device also includes: an update module for obtaining information reported by the smart lock cluster in real time; and updating the escape information in real time according to the reported information.

[0116] Furthermore, the determination module 42 includes an identification submodule and a prediction submodule:

[0117] The recognition submodule is used to identify abnormal features in environmental data; extract the position codes corresponding to the abnormal features, and locate the smart lock coordinate points corresponding to the position codes in the spatial topology map of the preset area; perform cluster analysis on the smart lock coordinate points to obtain the abnormal clustering area.

[0118] The prediction submodule is used to analyze the changes in environmental data in the abnormal aggregation area and obtain the gradient matrix; based on the building structure and gradient matrix in the spatial topology map, the physical diffusion law of the abnormal factors in the abnormal aggregation area is simulated to obtain the abnormal diffusion path.

[0119] Furthermore, the generation module 43 is used to delineate a traversable area in the spatial topology map based on the abnormal aggregation area and the abnormal diffusion path; in the traversable area, the corresponding escape information is generated with the center point of the abnormal aggregation area as the starting point and the exit location information in the spatial topology map as the end point.

[0120] Furthermore, the device also includes: a switching module, which is used to determine the relative position relationship between the escape information and each prompt device in the spatial topology map of the preset area; assign a corresponding sub-segment of the escape information to each prompt device according to the relative position relationship, and determine the corresponding prompt content based on the sub-segment; control the prompt device to perform the switching operation according to the prompt content.

[0121] Furthermore, the switching module is also used to obtain the direction vector and the coordinates of the next path point of the sub-segment in the spatial topology map; generate directional information based on the direction vector, and calculate the guidance parameters based on the coordinates of the next path point; determine the corresponding prompt content based on the directional information and the guidance parameters.

[0122] See also Figure 5 , Figure 5 is a structural diagram of a computer device provided by an optional embodiment of the present invention, such as Figure 5 As shown, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components utilize different buses to communicate with each other and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in the memory or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Equally, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system).

[0123] The processor 10 may be a central processing unit, a network processor, or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0124] The memory 20 stores instructions that can be executed by at least one processor 10, so as to enable at least one processor 10 to execute the method shown in the above embodiment.

[0125] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created based on the use of a computer device for displaying a small program landing page, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0126] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0127] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0128] The embodiment of the present invention also provides a computer-readable storage medium. The above-mentioned method according to the embodiment of the present invention can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

[0129] Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention. Such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for indicating abnormal information, characterized in that: The method comprises: Receive environmental data reported by smart lock clusters distributed in a preset area; Determine an abnormal gathering area in the spatial topology map of the preset area according to the environmental data, and predict the diffusion of the abnormal gathering area to obtain an abnormal diffusion path; Corresponding escape information is generated based on the abnormality gathering area and the abnormality diffusion path.

2. The method according to claim 1, characterized in that The method further comprises: Obtain information reported by the smart lock cluster in real time; Escape information is updated in real time based on reported information.

3. The method according to claim 1, characterized in that Determining an abnormal clustering area in a spatial topological map of the preset area according to the environmental data includes: identifying anomalous features in the environmental data; Extracting the position code corresponding to the abnormal feature, and locating the smart lock coordinate point corresponding to the position code in the spatial topology map of the preset area; Cluster analysis is performed on the coordinate points of the smart lock to obtain abnormal clustering areas.

4. The method according to claim 1, wherein The step of predicting the diffusion of the abnormal aggregation area to obtain the abnormal diffusion path includes: Analyze the changes in environmental data in the abnormal clustering area and obtain the gradient matrix; Based on the building structure in the spatial topology map and the gradient matrix, the physical diffusion law of the abnormal factors in the abnormal aggregation area is simulated to obtain the abnormal diffusion path.

5. The method according to claim 1, wherein The generating corresponding escape information based on the abnormality gathering area and the abnormality diffusion path includes: Based on the abnormal aggregation area and the abnormal diffusion path, demarcating a traversable area in the spatial topology map; In the passable area, corresponding escape information is generated with the center point of the abnormal gathering area as the starting point and the exit location information in the spatial topology map as the end point.

6. The method according to claim 1, characterized in that The method further comprises: Determining the relative positional relationship between the escape information and each prompt device in the spatial topology map of the preset area; Allocating a corresponding sub-segment of escape information to each prompt device according to the relative position relationship, and determining corresponding prompt content based on the sub-segment; The prompt device is controlled to perform a switching operation according to the prompt content.

7. The method according to claim 6, characterized in that The determining of corresponding prompt content based on the sub-segment includes: Obtaining the direction vector and the coordinates of the next path point of the sub-segment in the spatial topology graph; generating directional information according to the direction vector, and calculating guidance parameters based on the coordinates of the next path point; Corresponding prompt content is determined based on the directional information and the guidance parameters.

8. An abnormal information indication system, characterized in that: The system includes: a smart lock cluster and a cloud server; The smart lock cluster is distributed in a preset area and is used to collect environmental data in the preset area and report the environmental data to the cloud server; The cloud server is used to receive environmental data reported by the smart lock cluster; determine the abnormal aggregation area in the spatial topology map of the preset area based on the environmental data, and predict the diffusion of the abnormal aggregation area to obtain the abnormal diffusion path; generate corresponding escape information based on the abnormal aggregation area and the abnormal diffusion path.

9. The system according to claim 8, characterized in that The system further comprises: a prompt device, which is used to receive the escape information and dynamically switch and display prompt content.

10. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method according to any one of claims 1 to 7 by executing the computer instructions.

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