Security video monitoring system based on big data

Abstract

This invention discloses a security video surveillance system based on big data, including a data acquisition module, a security detection module, and an early warning management module. The system is characterized by: the data acquisition module collecting visual images within the community, real-time weight of target objects inside elevators, and facial recognition data of community residents; the security detection module detecting whether community residents intend to push electric bicycles into elevators and whether target objects have crossed boundaries; and the early warning management module issuing alarms to residents intending to push electric bicycles into elevators and controlling the elevator to prevent them from carrying the bicycles. The data acquisition module, security detection module, and early warning management module are electrically connected to each other. The data acquisition module includes a vision module, a data input module, and a weighing module. The vision module captures real-time visual images of target areas within the community. This invention improves accuracy and security.

Description

Technical Field

[0001] This invention relates to the field of security video surveillance technology, specifically a security video surveillance system based on big data. Background Technology

[0002] With the rapid development of big data and the Internet of Things, they are being widely used, especially in the field of security video surveillance.

[0003] Currently, accidents caused by electric bicycles are frequent in residential buildings. Almost all communities prohibit electric bicycles from being parked in the connecting corridors between buildings. However, some people still secretly park their electric bicycles in the connecting corridors in front of their homes to charge them in order to save on charging costs, posing a huge safety hazard. Although most communities now have video surveillance systems for security monitoring, existing technologies mostly rely on manual inspection to monitor the surveillance footage. Due to the large number of surveillance images, manual inspection cannot detect problems in time, leading to safety hazards. Moreover, even if a few video surveillance systems use boundary detection methods to detect targets within the area, the system cannot mark the boundary boxes of the targets because the targets are at the edge of the detection area and the identified feature nodes are incomplete. This can easily lead to the system failing to detect the boundary boxes, resulting in inaccurate judgments and affecting the accuracy of the system's judgment. Therefore, it is necessary to design a big data-based security video surveillance system and method to improve accuracy and security. Summary of the Invention

[0004] The purpose of this invention is to provide a security video surveillance system and method based on big data to solve the problems mentioned in the background art.

[0005] To address the aforementioned technical problems, this invention provides the following technical solution: a security video surveillance system and method based on big data, comprising a data acquisition module, a security detection module, and an early warning management module, characterized in that: the data acquisition module is used to collect visual images within the community, the real-time weight of target objects inside the elevator, and comprehensive data from the facial recognition of community residents; the security detection module is used to detect whether community residents intend to push electric bicycles into the elevator and whether target objects have crossed boundaries; the early warning management module is used to issue an alarm to residents who are pushing electric bicycles into the elevator and control the elevator not to carry them; the data acquisition module, security detection module, and early warning management module are electrically connected to each other.

[0006] The vehicle detection module includes a first detection submodule, a second detection submodule, and a face recognition submodule. The first detection submodule is used to detect whether a resident waiting for the elevator intends to push an electric bicycle into the elevator. The second detection submodule is used to detect whether a resident is carrying a portable electric bicycle inside the elevator. The face recognition module is used to identify the resident's facial data and analyze the resident's address.

[0007] The region detection module includes an edge analysis submodule, a compensation analysis submodule, and a timing analysis submodule. The edge analysis submodule is used to identify the edge features of the target object and mark the edge box. The compensation analysis submodule is used to analyze the target object and complete the target object according to the features in the edge box of the target object. The timing analysis submodule is used to record the duration of the target object in the target region.

[0008] According to the above technical solution, the data acquisition module includes a vision module, a data input module, and a weighing module. The vision module is used to capture visual images of the target area in the community in real time. The data input module is used to input the visual images of the community residents into the system. The weighing module is used to measure the total weight carried by the elevator when the residents ride the elevator.

[0009] According to the above technical solution, the safety detection module includes a vehicle detection module, which is used to analyze whether there are any residents pushing electric bicycles into the elevator inside and outside the elevator, and adjust the operation of the elevator based on the analysis results.

[0010] According to the above technical solution, the security detection module also includes an area detection module, which is used to analyze whether there is a target object crossing the boundary in the target area, and to issue an alarm to the property manager based on the analysis results.

[0011] According to the above technical solution, the early warning management module includes an elevator adjustment module and an early warning module. The elevator adjustment module is used to adjust the elevator's operating mode based on the vehicle analysis results inside and outside the elevator. The early warning module is used to issue alarms to the property manager based on the vehicle detection results and the area detection results.

[0012] According to the above technical solution, the security video surveillance method mainly includes the following steps:

[0013] Step S1: Collect visual images of the target area in real time through the vision module, input the facial data of the community residents into the system through the data input module, and measure the weight carried by the elevator in real time through the weighing module.

[0014] Step S2: After the resident presses the elevator button, the system sends an electrical signal to trigger the vehicle detection module to start, and begins to analyze whether the resident intends to push an electric bicycle into the elevator, and adjusts the operation of the elevator according to the analysis results;

[0015] Step S3: When detecting the target area, the system starts the area detection module, begins to analyze the target objects in the target area, and determines whether the target objects have crossed the boundary based on the analysis results;

[0016] Step S4: When a resident intends to push an electric bicycle into the elevator or when the target object crosses the boundary, the system will issue an alarm through the early warning module to remind the property manager of the potential safety hazard.

[0017] According to the above technical solution, step S2 further includes the following steps:

[0018] Step S21: Retrieve the visual image of the target area, scan and identify the contour feature nodes in the visual image, connect the human contour feature nodes and the electric bicycle contour nodes respectively to construct a contour model, compare it with the electric bicycle contour model in the database, if the similarity is greater than the system set threshold, then mark that the owner has the intention to push the electric bicycle into the elevator, the system issues an alarm through the warning module to remind the owner not to push the electric bicycle into the elevator, and controls the elevator not to stop on this floor through the elevator adjustment module. When the similarity is less than the system set threshold, the system continues to detect.

[0019] Step S22: Identify the number of electric bicycles and people in the image. If the number of electric bicycles is less than the number of people waiting, the elevator will operate normally. If it is detected that a resident is pushing an electric bicycle into the elevator, the elevator will be controlled to enter overload mode, and the warning module will remind the resident again not to push the electric bicycle into the elevator. Otherwise, the elevator will start normally.

[0020] Step S23: Obtain the visual image captured by the visual module inside the elevator, identify the outline nodes of the electric bicycle in the visual image, and when the number of identified outline nodes is greater than or equal to the system-set threshold, the system controls the elevator to enter the overload mode and reminds the owner again through the warning module not to push the electric bicycle into the elevator.

[0021] According to the above technical solution, step S23 further includes the following steps:

[0022] Step S231: When the identified contour node is less than the threshold, the sleeve diameter and cuff diameter at the owner's forearm are identified. If the difference between the sleeve diameter and the cuff diameter is greater than the system-set threshold, the owner's weight influence coefficient α is retrieved from the system database based on the difference. The owner's height and waist circumference are measured. The standard weight K1 and the waist circumference influence coefficient β are retrieved from the database based on the height and waist circumference. The owner's estimated weight M = K1 * α * β is calculated using the formula, where M represents the owner's estimated weight. The difference W between the elevator's load capacity and the sum of the owners' weights is calculated using the formula. If the difference W is greater than the system-set threshold, a portable electric bicycle is marked as being present in the elevator. Otherwise, the detection continues.

[0023] When a resident leaves the elevator, the system calculates the difference between the current weight the elevator is carrying and the sum of the weights of all the residents inside. If the difference is less than a minimum threshold, it means that the resident carrying the portable electric scooter has exited the elevator. The system retrieves the visual images inside the elevator, scans and identifies the resident's facial data, identifies the resident's address, and issues an alarm to remind the property manager to advise the resident. Otherwise, the system continues to monitor.

[0024] According to the above technical solution, step S3 further includes the following steps;

[0025] Step S31: Retrieve the visual image of the target region, scan and identify the feature nodes of all target objects in the image, compare the feature nodes of the target objects with the database, select all models with similarity greater than the set threshold to complete the target object, overlap and fuse all models, identify the edge nodes of the fused model, select the model with a rectangle based on the edge nodes, establish a coordinate system, identify the coordinates of the edge box vertices, and draw multiple tangents through the edge box vertices. If the tangent intersects with the surface where the target detection area is located, mark the target object as out of bounds pending; otherwise, mark it as not out of bounds.

[0026] Step S32: Identify the markers in the target object. If there is an out-of-bounds pending marker in the target object, start the timing module; otherwise, continue the detection. When the timing ends, perform an out-of-bounds detection on the target object. If the target object is still out of bounds, confirm that the target object is out of bounds and issue an alarm to remind the property manager to handle it properly. Otherwise, mark the target object as not out of bounds and continue the detection.

[0027] According to the above technical solution, in step S4, the detection results of the first detection submodule and the second detection submodule are identified. If the detection results show that a resident is pushing an electric bicycle into the elevator, the elevator will be adjusted to not respond to the floor command, and an alarm will be issued through the early warning module to remind the property manager to give a warning. Otherwise, the system will continue to detect, thereby preventing residents from secretly bringing electric bicycles upstairs, which could lead to a fire, and further improving the safety of the community.

[0028] Compared with existing technologies, the beneficial effects achieved by this invention are as follows: By detecting whether residents are pushing electric bicycles while waiting for the elevator, and thus controlling the elevator to avoid passing through that floor, this invention can prevent residents from secretly bringing their electric bicycles to the corridors to charge, thereby reducing the risk of fire and greatly improving the safety of the community. By detecting the presence of electric bicycles again inside the elevator, this invention can prevent residents from pressing the elevator button in advance and then pushing their electric bicycles into the elevator after it arrives, thus avoiding the first detection and allowing them to bring their electric bicycles upstairs, which could lead to safety hazards. This invention can further improve safety. By comparing the real-time load capacity of the elevator with the total weight of the residents, this invention can prevent residents from secretly bringing their electric bicycles upstairs, thereby reducing safety hazards in the community and greatly improving the safety of the community. By analyzing whether the target object is in the same space as the target area and re-detecting the target object that has crossed the boundary after the timer ends, this invention can avoid the influence of environmental factors on the accuracy of the target object detection results. It can also prevent the system from alarming when the target object only stays briefly, causing the target object to have left before the property manager can handle the situation, thus wasting the property manager's energy and time. This invention can further improve the accuracy of the system detection. Attached Figure Description

[0029] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0030] Figure 1 This is a schematic diagram of the system module composition of the present invention. Detailed Implementation

[0031] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] Please see Figure 1This invention provides a technical solution: a security video surveillance system and method based on big data, including a data acquisition module, a security detection module, and an early warning management module. The data acquisition module is used to collect visual images within the community, the real-time weight of target objects inside the elevator, and comprehensive data from the facial recognition of community residents. The security detection module is used to detect whether community residents intend to push electric bicycles into the elevator and whether target objects have crossed boundaries. The early warning management module is used to issue an alarm to residents who are pushing electric bicycles into the elevator and control the elevator to prevent them from carrying the bicycles. The data acquisition module, security detection module, and early warning management module are electrically connected to each other.

[0033] The vehicle detection module includes a first detection submodule, a second detection submodule, and a face recognition submodule. The first detection submodule is used to detect whether a resident waiting for the elevator intends to push an electric bicycle into the elevator. The second detection submodule is used to detect whether a resident is carrying a portable electric bicycle inside the elevator. The face recognition module is used to identify the resident's facial data and analyze the resident's address.

[0034] The region detection module includes an edge analysis submodule, a compensation analysis submodule, and a timing analysis submodule. The edge analysis submodule is used to identify the edge features of the target object and mark the edge box. The compensation analysis submodule is used to analyze the target object and complete the target object based on the features in the edge box of the target object. The timing analysis submodule is used to record the duration of the target object in the target region.

[0035] The data acquisition module includes a vision module, a data entry module, and a weighing module. The vision module is used to capture visual images of the target area in the community in real time. The data entry module is used to enter the visual images of the community residents into the system. The weighing module is used to measure the total weight carried by the elevator when the residents ride the elevator.

[0036] The safety detection module includes a vehicle detection module, which analyzes whether residents are pushing electric bicycles into the elevator, both inside and outside the elevator, and adjusts the elevator's operation based on the analysis results.

[0037] The security detection module also includes an area detection module, which analyzes whether there are any targets crossing the boundary in the target area and sends an alarm to the property manager based on the analysis results.

[0038] The early warning management module includes an elevator adjustment module and an early warning module. The elevator adjustment module is used to adjust the elevator's operating mode based on the analysis results of vehicles inside and outside the elevator, while the early warning module is used to issue alarms to the property manager based on vehicle detection results and area detection results.

[0039] Security video surveillance methods mainly include the following steps:

[0040] Step S1: Collect visual images of the target area in real time through the vision module, input the facial data of the community residents into the system through the data input module, and measure the weight carried by the elevator in real time through the weighing module.

[0041] Step S2: After the resident presses the elevator button, the system sends an electrical signal to trigger the vehicle detection module to start, and begins to analyze whether the resident intends to push an electric bicycle into the elevator, and adjusts the operation of the elevator according to the analysis results;

[0042] Step S3: When detecting the target area, the system starts the area detection module, begins to analyze the target objects in the target area, and determines whether the target objects have crossed the boundary based on the analysis results;

[0043] Step S4: When a resident intends to push an electric bicycle into the elevator or when the target object crosses the boundary, the system will issue an alarm through the early warning module to remind the property manager of the potential safety hazard.

[0044] Step S2 further includes the following steps:

[0045] Step S21: Retrieve the visual image of the target area, scan and identify the contour feature nodes in the visual image, connect the human contour feature nodes and the electric bicycle contour nodes respectively to construct a contour model, compare it with the electric bicycle contour model in the database, if the similarity is greater than the system set threshold, then mark that the owner has the intention to push the electric bicycle into the elevator, the system issues an alarm through the warning module to remind the owner not to push the electric bicycle into the elevator, and controls the elevator not to stop on this floor through the elevator adjustment module. When the similarity is less than the system set threshold, the system continues to detect. By detecting whether the owner is pushing the electric bicycle while the owner is waiting for the elevator, and then controlling the elevator not to pass through this floor, it can prevent owners who take chances from secretly bringing the electric bicycle to the corridor to charge, thereby reducing the risk of fire and greatly improving the safety of the community.

[0046] Step S22: Identify the number of electric bicycles and people in the image. If the number of electric bicycles is less than the number of people waiting, the elevator will operate normally. If it is detected that a resident is pushing an electric bicycle into the elevator, the elevator will be controlled to enter overload mode, and the warning module will remind the resident again not to push the electric bicycle into the elevator. Otherwise, the elevator will start normally, thereby preventing electric bicycles from going upstairs, reducing safety hazards and improving safety.

[0047] Step S23: Obtain the visual image captured by the visual module inside the elevator, identify the outline nodes of the electric bicycle in the visual image. When the number of identified outline nodes is greater than or equal to the system's set threshold, it indicates that the electric bicycle has entered the elevator. The system controls the elevator to enter overload mode and reminds the owner again through the warning module not to push the electric bicycle into the elevator. By detecting whether there is an electric bicycle inside the elevator again, the system can prevent the owner from pressing the elevator button in advance and then pushing the electric bicycle into the elevator after the elevator arrives, thus avoiding the first detection and allowing the owner to still bring the electric bicycle upstairs, which would pose a safety hazard. This greatly improves safety.

[0048] Step S23 further includes the following steps:

[0049] Step S231: When the identified contour node is less than the threshold, the sleeve diameter and cuff diameter at the owner's forearm are identified. If the difference between the sleeve diameter and the cuff diameter is greater than the system-set threshold, the owner's weight influence coefficient α is retrieved from the system database based on the difference. The owner's height and waist circumference are measured. The standard weight K1 and the waist circumference influence coefficient β are retrieved from the database based on the height and waist circumference. The owner's estimated weight M = K1 * α * β is calculated using the formula, where M represents the owner's estimated weight. The difference W between the elevator's load capacity and the sum of the owners' weights is calculated using the formula. If the difference W is greater than the system-set threshold, a portable electric bicycle is marked as being present in the elevator. Otherwise, the detection continues.

[0050] When a resident leaves the elevator, the system calculates the difference between the elevator's current load capacity and the sum of the resident's weight inside. If the difference is less than a minimum threshold, it indicates that the resident carrying a portable electric bicycle has exited the elevator. The system retrieves visual images from inside the elevator, scans and identifies the resident's face, identifies the resident's address, and issues an alarm to remind the property manager to advise the resident against it. Otherwise, the system continues to monitor. By comparing the elevator's real-time load capacity with the sum of the resident's weight, the system can prevent residents from secretly carrying electric bicycles upstairs, thereby reducing security risks in the community and greatly improving its safety.

[0051] Step S3 further includes the following steps;

[0052] Step S31: Retrieve the visual image of the target region, scan and identify the feature nodes of all target objects in the image, compare the feature nodes of the target objects with the database, select all models with similarity greater than the set threshold to complete the target object, overlap and fuse all models, identify the edge nodes of the fused model, select the model with a rectangle based on the edge nodes, establish a coordinate system, identify the coordinates of the edge box vertices, and draw multiple tangents through the edge box vertices. If the tangent has several points with the surface where the target detection area is located, mark the target object as out of bounds pending; otherwise, mark it as not out of bounds.

[0053] Step S32: Identify the markers in the target object. If there is a boundary-crossing pending marker in the target object, start the timing module; otherwise, continue detection. When the timer ends, perform boundary-crossing detection on the target object. If the target object is still boundary-crossing, it is confirmed that the target object is boundary-crossing, and the system issues an alarm to remind the property manager to handle it carefully. Otherwise, mark the target object as not boundary-crossing and continue detection. By analyzing whether the target object is in the same space as the target area and re-detecting the boundary-crossing target object after the timer ends, the influence of environmental factors on the accuracy of the target object detection results can be avoided. At the same time, it can also avoid the system alarm being triggered when the target object only stays briefly, causing the target object to leave before the property manager can handle it, thus wasting the property manager's energy and time, thereby greatly improving the accuracy of the system detection.

[0054] In step S4, the detection results of the first detection submodule and the second detection submodule are identified. If the detection results show that a resident is pushing an electric bicycle into the elevator, the elevator will be adjusted to not respond to the floor command, and an alarm will be issued through the warning module to remind the property manager to give a warning. Otherwise, the system will continue to detect, thereby preventing residents from secretly bringing electric bicycles upstairs and causing fires, and further improving the safety of the community.

[0055] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0056] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A security video surveillance method based on big data, characterized in that: It uses a big data-based security video surveillance system, which includes a data acquisition module, a security detection module, and an early warning management module. The data acquisition module is used to collect visual images within the community, the real-time weight of target objects inside the elevator, and comprehensive data from the facial recognition of community residents. The security detection module is used to detect whether community residents intend to push electric bicycles into the elevator and whether target objects have crossed the boundary. The early warning management module is used to issue an alarm to residents who are pushing electric bicycles into the elevator and control the elevator not to carry them. The data acquisition module, security detection module, and early warning management module are electrically connected to each other. The safety detection module includes a vehicle detection module, which is used to analyze whether there are any residents pushing electric bicycles into the elevator inside and outside the elevator, and adjust the operation of the elevator based on the analysis results; The security detection module also includes an area detection module, which is used to analyze whether there are any target objects crossing the boundary in the target area and to issue an alarm to the property manager based on the analysis results. The vehicle detection module includes a first detection submodule, a second detection submodule, and a face recognition submodule. The first detection submodule is used to detect whether a resident waiting for the elevator intends to push an electric bicycle into the elevator. The second detection submodule is used to detect whether a resident is carrying a portable electric bicycle inside the elevator. The face recognition submodule is used to identify the resident's facial data and analyze the resident's address. The region detection module includes an edge analysis submodule, a compensation analysis submodule, and a timing analysis submodule. The edge analysis submodule is used to identify the edge features of the target object and mark the edge box. The compensation analysis submodule is used to analyze the target object and complete the target object according to the features in the edge box of the target object. The timing analysis submodule is used to record the duration of the target object in the target region. The data acquisition module includes a vision module, a data input module, and a weighing module. The vision module is used to capture visual images of the target area within the community in real time. The data input module is used to input the visual images of the community residents into the system. The weighing module is used to measure the total weight carried by the elevator when the residents ride the elevator. The early warning management module includes an elevator adjustment module and an early warning module. The elevator adjustment module is used to adjust the elevator's operating mode based on the vehicle analysis results inside and outside the elevator. The early warning module is used to issue alarms to the property manager based on the vehicle detection results and the area detection results. The security video surveillance method includes the following steps: Step S1: Collect visual images of the target area in real time through the vision module, input the facial data of the community residents into the system through the data input module, and measure the weight carried by the elevator in real time through the weighing module. Step S2: After the resident presses the elevator button, the system sends an electrical signal to trigger the vehicle detection module to start, and begins to analyze whether the resident intends to push an electric bicycle into the elevator, and adjusts the operation of the elevator according to the analysis results; Step S3: When detecting the target area, the system starts the area detection module, begins to analyze the target objects in the target area, and determines whether the target objects have crossed the boundary based on the analysis results; Step S4: When a resident intends to push an electric bicycle into the elevator or the target object crosses the boundary, the system will issue an alarm through the early warning module to remind the property manager of the potential safety hazard. Step S2 further includes the following steps: Step S21: Retrieve the visual image of the target area, scan and identify the contour feature nodes in the visual image, connect the human contour feature nodes and the electric bicycle contour nodes respectively to construct a contour model, compare it with the electric bicycle contour model in the database, if the similarity is greater than the system set threshold, then mark that the owner has the intention to push the electric bicycle into the elevator, the system issues an alarm through the warning module to remind the owner not to push the electric bicycle into the elevator, and controls the elevator not to stop on this floor through the elevator adjustment module. When the similarity is less than the system set threshold, the system continues to detect. Step S22: Identify the number of electric bicycles and people in the image. If the number of electric bicycles is less than the number of people waiting, the elevator will operate normally. If it is detected that a resident is pushing an electric bicycle into the elevator, the elevator will be controlled to enter overload mode, and the warning module will remind the resident again not to push the electric bicycle into the elevator. Otherwise, the elevator will start normally. Step S23: Obtain the visual image captured by the visual module inside the elevator, identify the outline nodes of the electric bicycle in the visual image, and when the number of identified outline nodes is greater than or equal to the system-set threshold, the system controls the elevator to enter the overload mode and reminds the owner again through the warning module not to push the electric bicycle into the elevator. Step S23 further includes the following steps: Step S231: When the identified contour node is less than the threshold, the sleeve diameter and cuff diameter at the owner's forearm are identified. If the difference between the sleeve diameter and the cuff diameter is greater than the system-set threshold, the owner's weight influence coefficient α is retrieved from the system database based on the difference. The owner's height and waist circumference are measured. The standard weight K1 and the waist circumference influence coefficient β are retrieved from the database based on the height and waist circumference. The owner's estimated weight M = K1 * α * β is calculated using the formula, where M represents the owner's estimated weight. The difference W between the elevator's load capacity and the sum of the owners' weights is calculated using the formula. If the difference W is greater than the system-set threshold, a portable electric bicycle is marked as being present in the elevator. Otherwise, the detection continues. When a resident leaves the elevator, the system calculates the difference between the current weight carried by the elevator and the sum of the weights of the residents inside. If the difference is less than the minimum threshold, it means that the resident carrying the portable electric vehicle has exited the elevator. The system retrieves the visual images inside the elevator, scans and identifies the resident's facial data, identifies the resident's address, and issues an alarm to remind the property manager to advise the resident. Otherwise, the system continues to monitor. In step S4, the detection results of the first detection submodule and the second detection submodule are identified. If the detection results show that a resident is pushing an electric bicycle into the elevator, the elevator will be adjusted to not respond to the floor command, and an alarm will be issued through the early warning module to remind the property manager to give a warning. Otherwise, the system will continue to detect, thereby preventing residents from secretly bringing electric bicycles upstairs and causing fires, and further improving the safety of the community.

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