Elevator safety management system and method based on Internet of Things

Abstract

The invention discloses an elevator safety management system and method based on the Internet of Things, the elevator safety management method is achieved based on the elevator safety management system, the elevator safety management system comprises an edge computing terminal and a camera shooting assembly deployed in an elevator, and the edge computing terminal is connected with the camera shooting assembly. The elevator safety management method comprises the following steps that image data of the camera shooting assembly is obtained, and the image content of the image data is an internal scene of an elevator car; screening the image data to obtain target image data; whether a forbidden target exists in the target image data or not is recognized, and the forbidden target is an article forbidden to enter the elevator; and when the forbidden target exists, corresponding warning information is generated and sent out. Through combination of the edge computing terminal and the camera shooting assembly, real-time monitoring, image data processing, forbidden target recognition and warning information generation of the internal scene of the elevator are achieved, the safety and reliability of the elevator are improved, accidents are reduced, and safer and more convenient elevator taking experience is provided for passengers.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to an elevator safety management system and method based on the Internet of Things. Background Art

[0002] On the one hand, elevators have brought great convenience to people's travel, but on the other hand, they have also caused many safety problems. At present, through elevator monitoring, it can not only effectively avoid the frequent occurrence of abnormal behaviors of passengers, but also ensure that elevator failures are observed in time after they occur, thereby reducing the harm caused. Traditional monitoring requires manual supervision. If one person is watching multiple monitors at the same time, negligence is easy to occur. If the quality of manual monitoring is to be improved, manpower can only be increased, and the cost will also increase accordingly. Therefore, many manual monitoring has now become a decoration. When an accident occurs, the elevator management personnel cannot discover it through monitoring in the first time, which makes the elevator monitoring lose its role in ensuring elevator safety. Summary of the invention

[0003] In order to overcome the above technical defects, the present invention provides an elevator safety management system and method based on the Internet of Things.

[0004] In order to solve the above problems, the present invention is implemented according to the following technical solutions:

[0005] In a first aspect, the present invention provides an elevator safety management method based on the Internet of Things, the elevator safety management method is implemented based on an elevator safety management system, the elevator safety management system includes an edge computing terminal and a camera component deployed in the elevator, the edge computing terminal is connected to the camera component; the elevator safety management method includes the following steps:

[0006] Acquire image data of the camera assembly, wherein the image content of the image data is an internal scene of an elevator car;

[0007] Screening the image data to obtain target image data;

[0008] Identify whether there is a prohibited object in the target image data, wherein the prohibited object is an object prohibited from entering the elevator;

[0009] When there is a prohibited target, a corresponding warning message is generated and issued.

[0010] In combination with the first aspect, the present invention further provides a first preferred implementation manner of the first aspect, specifically, screening the image data comprises the following steps:

[0011] Read the current frame graphics data;

[0012] Calculate the optical flow of the current frame image data;

[0013] Determining whether the optical flow is greater than a preset threshold;

[0014] When the optical flow is greater than a preset threshold, the current frame of graphic data is used as target image data and output, and the next frame of image data is read;

[0015] When the optical flow is less than a preset threshold, the next frame of image data is read.

[0016] In combination with the first aspect, the present invention further provides a second preferred implementation manner of the first aspect, specifically, the optical flow is calculated by the following method:

[0017] Establishing the optical flow constraint equation: I x u+I y v+I t =0; where I x and I y is the gradient of the image in the x and y directions, u and v are the components of the optical flow in the x and y directions, I t is the change of the image at time t; I x ,I y and I t It can be calculated from the image;

[0018] Establish the matrix equation of 9 pixels in the 3×3 range

[0019] The matrix equation Convert to tensor matrix

[0020]

[0021] According to the tensor matrix, the optical flow (u, v) of the pixel is calculated by accumulating the partial derivatives of the neighborhood pixels in three dimensions and performing matrix operations.

[0022] In combination with the first aspect, the present invention further provides a third preferred implementation manner of the first aspect. Specifically, the elevator safety management method further includes a door blocking event detection step, and the door blocking event detection step includes:

[0023] Get the door opening detection signal sent by the elevator;

[0024] The timing starts when the door opening detection signal is obtained and stops when the door closing detection signal sent by the elevator is obtained;

[0025] Determine in real time whether the accumulated time value is greater than a preset time threshold, where the time value is in seconds;

[0026] When the time value is greater than a preset time threshold, identifying whether there is an obstacle at a preset target position in the image data, wherein the preset target position is a door of an elevator car;

[0027] When an obstacle is identified at the preset target position, a door blocking event detection result is generated;

[0028] When it is identified that there is no obstacle at the preset target position, an elevator door abnormality result is generated.

[0029] In combination with the first aspect, the present invention further provides a fourth preferred implementation of the first aspect, specifically, identifying whether there is an obstacle at a preset target position in the image data specifically includes:

[0030] Retrieve the preset cropping template;

[0031] Using a preset cropping template to crop the image data to obtain a cropped image;

[0032] Identify the elevator door outline in the cropped image;

[0033] Compare the standard profile information with the elevator door profile to obtain the obstacle detection result.

[0034] In combination with the first aspect, the present invention further provides a fifth preferred implementation of the first aspect. Specifically, the elevator safety management method further includes a fire event detection step, and the fire event detection step includes:

[0035] Acquire image data of the camera component in real time;

[0036] Preprocessing the image data;

[0037] identifying smoke texture features and flame color features in the image data;

[0038] Combining the smoke texture feature and the flame color feature into a feature vector;

[0039] Inputting the feature vector into a preset fire detection model to obtain a fire probability value output by the preset fire detection model;

[0040] Based on the fire probability value, a fire detection result is output.

[0041] In a second aspect, the present invention further provides an elevator safety management system based on the Internet of Things, the elevator safety management system comprising:

[0042] A camera assembly, wherein the camera assembly is deployed in the elevator car;

[0043] An edge computing terminal, wherein the edge technology terminal is connected to the camera assembly;

[0044] A monitoring backend, the monitoring backend is connected to the camera assembly and the edge computing terminal;

[0045] Among them, the edge computing terminal is used to execute the elevator safety management method based on the Internet of Things described in the first aspect.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] The present invention provides an elevator safety management method based on the Internet of Things, which is implemented based on an elevator safety management system. The elevator safety management system includes an edge computing terminal and a camera component deployed in the elevator, and the edge computing terminal is connected to the camera component. The elevator safety management method includes the following steps: acquiring image data of the camera component, the image content of the image data is the internal scene of the elevator car; screening the image data to obtain target image data; identifying whether there is a prohibited target in the target image data, the prohibited target is an item prohibited from entering the elevator; when there is a prohibited target, generating and issuing a corresponding warning message.

[0048] The present invention realizes real-time monitoring of the internal scenes of elevators, image data processing, prohibited target recognition and generation of warning information through the combination of edge computing terminals and camera components, effectively preventing the occurrence of elevator accidents, helping to improve the safety and reliability of elevators and reduce the occurrence of accidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The specific embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings, wherein:

[0050] Figure 1 It is a flow chart of an elevator safety management method based on the Internet of Things of the present invention;

[0051] Figure 2 It is a system composition diagram of an elevator safety management system based on the Internet of Things of the present invention. DETAILED DESCRIPTION

[0052] The following specifically illustrates the implementation mode of the present invention in conjunction with the accompanying drawings. The embodiments are provided for illustrative purposes only and cannot be understood as limiting the present invention. The accompanying drawings are provided for reference and illustration only and do not constitute a limitation on the scope of patent protection of the present invention, because many changes can be made to the present invention without departing from the spirit and scope of the present invention.

[0053] On the one hand, elevators have brought great convenience to people's travel, but on the other hand, they have also caused many safety problems. At present, through elevator monitoring, it can not only effectively avoid the frequent occurrence of abnormal behaviors of passengers, but also ensure that elevator failures are observed in time after they occur, thereby reducing the harm caused. Traditional monitoring requires manual supervision. If one person is watching multiple monitors at the same time, negligence is easy to occur. If the quality of manual monitoring is to be improved, manpower can only be increased, and the cost will also increase accordingly. Therefore, many manual monitoring has now become a decoration. When an accident occurs, the elevator management personnel cannot discover it through monitoring in the first time, which makes the elevator monitoring lose its role in ensuring elevator safety.

[0054] For this purpose, refer to Figure 1 , an embodiment of the present invention provides an elevator safety management method based on the Internet of Things, the elevator safety management method is implemented based on an elevator safety management system, the elevator safety management system includes an edge computing terminal and a camera component deployed in the elevator, and the edge computing terminal is connected to the camera component. The elevator safety management method includes the following steps: acquiring image data of the camera component, the image content of the image data is the internal scene of the elevator car; screening the image data to obtain target image data; identifying whether there is a prohibited target in the target image data, the prohibited target is an item prohibited from entering the elevator; when there is a prohibited target, generating and issuing a corresponding warning message.

[0055] like Figure 1-2 As shown, the preferred structure of an elevator safety management system based on the Internet of Things shown in the present invention. In the present invention, the elevator safety management system includes:

[0056] A camera assembly is deployed in the elevator car.

[0057] An edge computing terminal, wherein the edge technology terminal is connected to the camera assembly;

[0058] A monitoring backend, the monitoring backend is connected to the camera assembly and the edge computing terminal;

[0059] Among them, the edge computing terminal is implemented in the form of hardware and / or software to execute and implement the elevator safety management method based on the Internet of Things described in the present invention.

[0060] In the specific implementation, the camera component is responsible for real-time monitoring of the situation inside the elevator car, capturing passenger behavior, environmental changes, and potential safety hazards (such as fire, abnormal behavior, etc.). The camera should be installed on the top or side of the elevator car to ensure that it can cover the entire interior of the car and the elevator door. Among them, a high-definition camera is preferably used, which supports night vision function to adapt to different lighting conditions. It supports real-time video streaming and can transmit captured video data and image data to the edge computing terminal.

[0061] In the specific implementation, the monitoring background is responsible for centralized management and monitoring of the safety status of the elevator, receiving data and alarm information from the edge computing terminal, and also storing and archiving the video data and image data of the camera component. In the specific implementation, the monitoring background is connected to the edge computing terminal / camera component by wireless or wired connection.

[0062] Specifically, the monitoring background has the following functions:

[0063] User interface: Provides a visual monitoring interface that displays the real-time status, video stream and alarm information of each elevator.

[0064] Data storage: Store historical monitoring data, alarm records and event logs for subsequent analysis and auditing.

[0065] Alarm system: Based on the analysis results of the edge computing terminal, the alarm is automatically triggered and relevant personnel (such as maintenance personnel, security personnel, etc.) are notified.

[0066] Furthermore, the monitoring background can also be expanded to support remote monitoring and management, allowing managers to access the monitoring system anytime and anywhere through mobile devices or computers. Other sensors (such as smoke sensors, temperature sensors, etc.) can be integrated to achieve multi-dimensional security monitoring.

[0067] refer to Figure 1 The present invention provides a flow chart of an elevator safety management method. The elevator safety management method is executed by the above-mentioned elevator safety management system, which can be implemented in the form of hardware and / or software. Figure 1 As shown, the elevator safety management method includes:

[0068] S100: Acquire image data of the camera assembly, where the image content of the image data is an internal scene of an elevator car;

[0069] S200: Screening the image data to obtain target image data;

[0070] S300: Identify whether there is a prohibited object in the target image data, where the prohibited object is an object prohibited from entering the elevator;

[0071] S400: When there is a prohibited target, a corresponding warning message is generated and issued.

[0072] Specifically, the present invention describes in detail each step of the elevator safety management method:

[0073] S100: Acquire image data of the camera assembly, wherein the image content of the image data is an internal scene of an elevator car.

[0074] In the present invention, the purpose of the camera assembly is to capture the real-time video stream inside the elevator car and provide image data for subsequent safety detection.

[0075] S200: Screening the image data to obtain target image data.

[0076] In the present invention, by extracting key image data from the original video stream, unnecessary data processing can be reduced and system efficiency can be improved. It is understandable that the amount of data generated by elevator monitoring 24 hours a day is very large, including large sections of repeated images of empty cars, and it is not necessary to detect whether there are electric cars in all of them. To this end, the present invention provides a technical solution that can effectively reduce the amount of calculation of edge technology terminals by pre-screening these images using the optical flow method. The images are pre-screened based on the optical flow method, and only images that may contain prohibited targets are further detected.

[0077] To this end, the screening of the image data specifically includes the following steps:

[0078] S210: Read the current frame graphic data.

[0079] S220: Calculate the optical flow of the current frame image data.

[0080] In the present invention, the motion changes in the image sequence are detected by calculating the optical flow to determine whether a dynamic event occurs. The motion information of the object can be understood by calculating the optical flow generated between images. In the car, the camera is stationary relative to the car, so it can be considered that the movement of the object in the car is directly mapped to the monitoring video and becomes the optical flow. By calculating the optical flow of the elevator monitoring video, the movement of the object in the car can be obtained.

[0081] In some examples, a suitable optical flow algorithm is selected, such as the Lucas-Kanade method, the Horn-Schunck method, or a deep learning-based optical flow estimation method.

[0082] In a specific implementation, the optical flow is calculated by the following method:

[0083] S221: Establishing the optical flow constraint equation: I x u+I y v+I t =0; where I x and I y is the gradient of the image in the x and y directions, u and v are the components of the optical flow in the x and y directions, I t is the change of the image at time t; I x ,I y and I t It can be calculated from the image.

[0084] S222: Establish the matrix equation of 9 pixels in the 3×3 range

[0085] It can be understood that a linear system of equations is built for each pixel and its 8 surrounding neighbors. For each pixel, a 3×3 neighborhood around it is selected. For each pixel in the neighborhood, a linear equation is constructed using the optical flow constraint equation.

[0086] S223: Transform the matrix equation Convert to tensor matrix

[0087]

[0088] It can be understood that the linear equations of the local neighborhood are extended to the entire image to form a tensor matrix. The specific implementation includes:

[0089] Tensor construction: Combine all the linear equations of the local neighborhood to form a large tensor using matrix A and vector b:

[0090]

[0091] The dimension of A is 3N×3, where N is the number of pixels in the image; the dimension of b is 3N×1.

[0092] S224: Using a matrix based on the tensor, the optical flow (u, v) of the pixel is calculated by accumulating partial derivatives of the neighboring pixel points in three dimensions and performing matrix operations.

[0093] Partial derivative accumulation: For each pixel, accumulate the partial derivatives of all pixels in its neighborhood in the x, y, and time dimensions.

[0094] Matrix operations: Use least squares or other numerical optimization techniques to solve for a tensor using matrix A and vector b:

[0095] The resulting u and v vectors represent the optical flow components of each pixel.

[0096] S230: Determine whether the optical flow is greater than a preset threshold.

[0097] It can be understood that by comparing the optical flow size with the preset threshold, it is determined whether the current frame image contains significant motion changes. The optical flow size threshold is set according to the elevator car scene and experience.

[0098] S240: When the optical flow is greater than a preset threshold, the current frame of graphic data is used as target image data and output, and the next frame of image data is read.

[0099] S250: When the optical flow is less than a preset threshold, read the next frame of image data.

[0100] Specifically, the calculated optical flow is compared with a preset threshold value. If the optical flow size exceeds the threshold value, it is considered that the current frame image contains significant motion changes.

[0101] The purpose of screening images is to filter out images that do not need to be detected, improve detection efficiency, and save detection calculations. When observing the surveillance video of the elevator, it is found that there are long periods of static images with no people. Performing forbidden target detection on such images will cause a lot of resource waste. Therefore, it is necessary to improve detection efficiency and avoid repeated detection by eliminating the images that are continuously static, that is, the images without moving targets.

[0102] S300: Identify whether there is a prohibited object in the target image data, where the prohibited object is an object that is prohibited from entering the elevator.

[0103] In the present invention, prohibited items mainly include electric vehicles, motorcycles, electric vehicle batteries, etc. In the specific implementation, an object recognition algorithm can be used to identify whether there are electric vehicles, motorcycles, and electric vehicle batteries in the image data. The object recognition algorithm can train the model by using a large amount of image data of marked prohibited objects to more accurately identify prohibited objects.

[0104] S400: When there is a prohibited target, a corresponding warning message is generated and issued.

[0105] In specific implementation, the edge computing terminal will transmit the warning information to the monitoring background, and then notify the elevator maintenance personnel and safety management personnel. This information may include the type of warning, the time of occurrence, the specific location of the elevator, etc., so that the management personnel can respond quickly and take appropriate measures.

[0106] In another implementation, while issuing a warning message, the elevator's stop mechanism can also be triggered to prevent the elevator from continuing to operate before the prohibited object is removed. This can be achieved by sending a signal to the elevator control system, which instructs the elevator to stop or safely stop at the nearest floor and open the elevator door.

[0107] In another preferred implementation, the elevator safety management method further includes a door blocking event detection step, and the door blocking event detection step includes:

[0108] S1000: Obtain the door opening detection signal sent by the elevator.

[0109] In specific implementation, the elevator control system will send a door opening detection signal when the door is fully opened. The safety management system receives this signal through the interface with the elevator control system. The interface can be a hardware signal line or a network communication interface to ensure the real-time and accuracy of the signal.

[0110] In elevator technology, the open and closed status of elevator doors is usually detected by sensors on the door. These sensors can be photoelectric sensors, infrared sensors or mechanical switches, etc., which trigger signals when the door is opened. The elevator control system generates a door opening detection signal based on the input of the sensor or the feedback signal of the door motor.

[0111] S2000: The timing starts when a door opening detection signal is obtained and stops when a door closing detection signal from the elevator is obtained.

[0112] After receiving the door open detection signal, the edge computing terminal of the security management system starts a timer. The timer will continue to run until the system receives the door close detection signal, which usually occurs when the door is fully closed. The accuracy of the timer needs to be high enough to ensure the accuracy of time measurement. After receiving the door open detection signal, the security management system starts a timer. The timer will continue to run until the system receives the door close detection signal, which usually occurs when the door is fully closed. The accuracy of the timer needs to be high enough to ensure the accuracy of time measurement.

[0113] It is understood that, similar to the door opening detection, the closed state of the elevator door is usually detected by sensors installed on the door. These sensors may include contact switches, photoelectric sensors or proximity sensors, etc., which trigger signals when the door is fully closed. The elevator control system generates a door closing detection signal based on the input of the sensor or the feedback signal of the door motor.

[0114] In another preferred implementation, when a door opening command is received from the elevator, the timer is reset. The door opening command refers to a command from a user to open the elevator door by pressing a button on a control panel in the elevator car. The edge computing terminal is connected to the control system of the elevator so as to detect the action of the user pressing the door opening button.

[0115] S3000: Determine in real time whether the accumulated time value is greater than a preset time threshold, where the time value is in seconds.

[0116] In the present invention, the safety management system continuously monitors the time value of the timer and compares it with a preset time threshold. This threshold is set by the system according to the elevator usage specifications and safety requirements. For example, if the elevator door does not close within 30 seconds, it is considered that a door blocking event or elevator door failure has occurred.

[0117] S4000: When the time value is greater than a preset time threshold, identify whether there is an obstacle at a preset target position in the image data, where the preset target position is a door of an elevator car.

[0118] In a specific implementation, once the door remains open for longer than a preset threshold, the system triggers the camera component to capture image data at the elevator door. Then, image processing techniques (such as edge detection, color recognition, and deep learning models) are used to analyze the image to identify whether there is an object blocking the door from closing.

[0119] In a specific implementation, identifying whether there is an obstacle at a preset target position in the image data specifically includes:

[0120] S4100: Retrieve the preset cropping template.

[0121] S4200: Crop the image data using a preset cropping template to obtain a cropped image.

[0122] S4300: Identify the outline of an elevator door in the cropped image.

[0123] In the specific implementation, it is identified by the following method:

[0124] The cropped image is converted to a grayscale image and output to reduce computational complexity.

[0125] The guided filter is used to enhance the edge information in the grayscale image in order to maintain the sharpness of the edge. In a specific implementation, a guided filter is applied to process the grayscale image, and the filter can adjust the filter strength according to the local statistical characteristics of the image to enhance the edge information.

[0126] The Canny algorithm is used to detect the elevator door edge in the grayscale image and output the elevator door contour image. The Canny edge detection algorithm is a popular edge detection method that can provide good detection results and robustness.

[0127] S4400: Compare the standard profile information with the elevator door profile to obtain an obstacle detection result.

[0128] In a specific implementation, the standard profile information is usually generated based on the actual profile of the elevator door. In a specific implementation, the detected elevator door profile is compared with the standard profile using an image comparison algorithm, such as template matching, feature point matching, or geometric comparison method. The difference between the elevator door profile and the standard profile is analyzed, including deviations in shape, size, and position.

[0129] In another specific implementation, the contour matching algorithm uses dynamic time warping (DTW) or Hausdorff distance to measure the similarity between two contours. This method can determine the degree of contour matching by calculating the minimum distance between the two.

[0130] Hausdorff distance: Calculate the maximum and minimum distance between the standard contour and the elevator door contour. If the distance is less than the preset threshold, it is considered that there is no obstacle in the elevator door.

[0131] S5000: When an obstacle is identified at the preset target position, a door blocking event detection result is generated.

[0132] It is understood that if the image analysis confirms that there is an obstruction, the system will generate a detection result of the door blocking event. This may include detailed information such as the type, size, and location of the obstruction. The system will record this information in the log, trigger an alarm or notify the management staff to prompt the user's improper behavior.

[0133] S6000: When it is identified that there is no obstacle at the preset target position, an elevator door abnormality result is generated.

[0134] In another specific implementation, if the image analysis shows that there is no obstruction, but the door is still not closed for a long time, the system will generate a detection result of an abnormal elevator door. This may indicate a mechanical failure or other non-blocking problem with the elevator door. The system will also record this information and notify maintenance personnel to check and repair it.

[0135] In the entire door blocking event detection process, the safety management system needs to be closely integrated with the elevator control system and be able to respond and process the state changes of the elevator door in real time. Through this automated detection and response mechanism, the operation safety of the elevator can be effectively improved and safety accidents caused by door blocking events or car door failures can be reduced.

[0136] In a preferred implementation, the elevator safety management method further includes a fire event detection step, and the fire event detection step includes:

[0137] S10: Acquire image data of the camera assembly in real time.

[0138] S20: Preprocessing the image data.

[0139] In one specific implementation, preprocessing includes:

[0140] Denoising: Use a filter (such as a Gaussian filter) to reduce the noise in an image.

[0141] Enhance: Adjust the contrast and brightness of an image to make features more distinct.

[0142] Normalization: Normalize the image to reduce the impact of lighting changes on subsequent detection.

[0143] S30: Identify smoke texture features and flame color features in the image data.

[0144] In a specific implementation, image processing techniques (such as gray level co-occurrence matrix GLCM, Laplacian transform, etc.) are used to extract the texture features of the smoke. On the other hand, the typical color (such as red, yellow) area of ​​the flame is identified through color space conversion (such as from RGB to HSV), and the color histogram or other color features are extracted.

[0145] In the specific implementation, smoke texture feature extraction:

[0146] Gray Level Co-occurrence Matrix (GLCM): Calculates the gray level co-occurrence matrix of an image, which is a statistical method used to describe the spatial relationship of pixels in an image. Extract texture features such as contrast, uniformity, energy, and entropy from the GLCM.

[0147] Laplacian transform: Apply the Laplacian operator to the image for edge detection to identify the outline of smoke. The density and distribution of edges are counted as supplementary features of smoke texture.

[0148] In the specific implementation, flame color feature extraction:

[0149] Color space conversion: Convert the image from RGB color space to HSV color space because color features in HSV space are easier to extract.

[0150] Color threshold segmentation: Set thresholds for typical flame colors (such as red and yellow) to identify possible flame areas in the image. Extract the color histogram within these areas and count the red and yellow pixel ratios.

[0151] Color moment: Calculate the color moment (such as mean, standard deviation) of the flame area to describe the characteristics of the color distribution.

[0152] S40: Combining the smoke texture feature and the flame color feature into a feature vector.

[0153] In the specific implementation, the extracted smoke texture features and flame color features are combined into a feature vector. This vector will be used as the input of the fire detection model, containing all the necessary information for fire probability assessment.

[0154] In one example, four features (contrast, uniformity, energy, and entropy) have been extracted from the smoke texture features, and three features (red pixel ratio, yellow pixel ratio, and color variance) have been extracted from the flame color features, and these features are synthesized into a feature vector.

[0155] S50: Inputting the feature vector into a preset fire detection model to obtain a fire probability value output by the preset fire detection model.

[0156] In a specific implementation, selecting or training a machine learning model suitable for fire detection, such as a support vector machine (SVM), a random forest or a deep learning model, is a common technical means in the art.

[0157] S60: Outputting a fire detection result based on the fire probability value.

[0158] In the specific implementation, a fire probability threshold (e.g. 0.5) is set. When the fire probability value output by the model exceeds this threshold, a fire is considered to be detected. If the fire probability exceeds the threshold, the system will output the fire detection result and trigger the corresponding alarm and emergency response measures. The time, probability value and related image data of the fire detection are recorded for subsequent audit and system optimization.

[0159] For other structures of the elevator safety management system and method based on the Internet of Things described in this embodiment, refer to the prior art.

[0160] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Therefore, any modification, equivalent change and modification made to the above embodiment according to the technical essence of the present invention without departing from the technical solution of the present invention shall still fall within the scope of the technical solution of the present invention.

Claims

1. An elevator safety management method based on the Internet of Things, characterized in that: The elevator safety management method is implemented based on an elevator safety management system, which includes an edge computing terminal and a camera component deployed in the elevator, and the edge computing terminal is connected to the camera component; the elevator safety management method includes the following steps: Acquire image data of the camera assembly, wherein the image content of the image data is an internal scene of an elevator car; Screening the image data to obtain target image data; Identify whether there is a prohibited object in the target image data, wherein the prohibited object is an object prohibited from entering the elevator; When there is a prohibited target, a corresponding warning message is generated and issued.

2. The elevator safety management method based on the Internet of Things according to claim 1 is characterized in that: The screening of the image data specifically comprises the following steps: Read the current frame graphics data; Calculate the optical flow of the current frame image data; Determining whether the optical flow is greater than a preset threshold; When the optical flow is greater than a preset threshold, the current frame of graphic data is used as target image data and output, and the next frame of image data is read; When the optical flow is less than a preset threshold, the next frame of image data is read.

3. The elevator safety management method based on the Internet of Things according to claim 2 is characterized in that: The optical flow is calculated by the following method: Establishing the optical flow constraint equation: I u u+I y v+I t =0; where I x and I y is the gradient of the image in the x and y directions, u and v are the components of the optical flow in the x and y directions, I t is the change of the image at time t; I x ,I y and I t It can be calculated from the image; Establish the matrix equation of 9 pixels in the 3×3 range The matrix equation Convert to tensor matrix According to the tensor matrix, the optical flow (u, v) of the pixel is calculated by accumulating the partial derivatives of the neighborhood pixels in three dimensions and performing matrix operations.

4. The elevator safety management method based on the Internet of Things according to claim 1 is characterized in that: The elevator safety management method further includes a door blocking event detection step, and the door blocking event detection step includes: Get the door opening detection signal sent by the elevator; The timing starts when the door opening detection signal is obtained and stops when the door closing detection signal sent by the elevator is obtained; Determine in real time whether the accumulated time value is greater than a preset time threshold, where the time value is in seconds; When the time value is greater than a preset time threshold, identifying whether there is an obstacle at a preset target position in the image data, wherein the preset target position is a door of an elevator car; When an obstacle is identified at the preset target position, a door blocking event detection result is generated; When it is identified that there is no obstacle at the preset target position, an elevator door abnormality result is generated.

5. The elevator safety management method based on the Internet of Things according to claim 4 is characterized in that: Identify whether there is an obstacle at the preset target position in the image data, including: Retrieve the preset cropping template; Using a preset cropping template to crop the image data to obtain a cropped image; Identify the elevator door outline in the cropped image; Compare the standard profile information with the elevator door profile to obtain the obstacle detection result.

6. The elevator safety management method based on the Internet of Things according to claim 1 is characterized in that: The elevator safety management method further comprises a fire event detection step, wherein the fire event detection step comprises: Acquire image data of the camera component in real time; Preprocessing the image data; identifying smoke texture features and flame color features in the image data; Combining the smoke texture feature and the flame color feature into a feature vector; Inputting the feature vector into a preset fire detection model to obtain a fire probability value output by the preset fire detection model; Based on the fire probability value, a fire detection result is output.

7. An elevator safety management system based on the Internet of Things, characterized in that: The elevator safety management system comprises: A camera assembly, wherein the camera assembly is deployed in the elevator car; An edge computing terminal, wherein the edge technology terminal is connected to the camera assembly; A monitoring backend, the monitoring backend is connected to the camera assembly and the edge computing terminal; Wherein, the edge computing terminal is used to execute the elevator safety management method based on the Internet of Things as described in any one of claims 1 to 6.

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