Security device state determination method and device and readable storage medium

Abstract

The invention discloses a method and device for determining the state of safety equipment and a readable storage medium. The method comprises the steps that a scene image and a target feature of the safety equipment are acquired, the scene image is used for indicating a use scene where the safety equipment is located, and the target feature is used for indicating an appearance feature and / or a state feature of the safety equipment; based on the target feature, a target area is recognized from the scene image, and the target area comprises an area where the safety device is located; and based on the target area, determining a target state of the safety device, the target state being used for indicating that the safety device is in a safe wearing state or a non-wearing state. The technical problem that the use state of the safety equipment cannot be determined is solved.

Description

Technical Field

[0001] The present invention relates to the field of security monitoring technology, and in particular to a method, device and readable storage medium for determining the status of a security device. Background Art

[0002] Safety belts are crucial equipment for protecting workers during aerial work. They are the last line of defense for workers. Monitoring and determining the status of safety equipment is a key step in ensuring worker safety.

[0003] In related technologies, workers may accidentally lose the protection of their safety equipment during work due to various reasons, posing a significant safety risk. Currently, there is a lack of a method that can promptly and accurately determine whether workers working at heights have lost the protection of their safety equipment. Consequently, there is a technical problem of being unable to determine the usage status of safety equipment.

[0004] Currently, no effective solution has been proposed to the above-mentioned technical problem of being unable to determine the usage status of the safety equipment. Summary of the Invention

[0005] Embodiments of the present invention provide a method, apparatus, and readable storage medium for determining the status of a security device, so as to at least solve the technical problem of being unable to determine the usage status of a security device.

[0006] According to one aspect of an embodiment of the present invention, a method for determining the status of a safety device is provided. The method may include: acquiring a scene image and target features of the safety device, wherein the scene image indicates the usage scene of the safety device, and the target features indicate the appearance features and / or status features of the safety device; based on the target features, identifying a target area from the scene image, wherein the target area includes the area where the safety device is located; and determining a target state of the safety device based on the target area, wherein the target state indicates whether the safety device is in a safe wearing state or a non-wearing state.

[0007] Optionally, obtaining a scene image of the safety device includes: obtaining environmental information of the safety device, wherein the environmental information is used to indicate the weather environment and / or light environment in which the safety device is located; adjusting the acquisition angle of the acquisition device based on the environmental information; and controlling the acquisition device to acquire the scene image of the safety device according to the acquisition angle.

[0008] Optionally, based on the target features, a target area is identified from the scene image, including: segmenting the scene image to obtain multiple sub-scene images; extracting scene features from each sub-scene image; and in response to matching the scene features with the target features, determining the area of ​​the sub-scene image corresponding to the scene features as the target area.

[0009] Optionally, determining the target state of the safety device based on the target feature includes: inputting the target feature into a recognition model for analysis to obtain the target state of the safety device.

[0010] Optionally, the target feature is input into the recognition model for analysis to obtain the target state of the safety device, including: comparing the target feature with the preset feature to obtain a comparison result, wherein the preset feature is used to indicate the appearance feature and / or state feature of the preset safety device in a safe wearing state; in response to the comparison result being that the target feature matches the preset feature, determining that the target state is a safe wearing state.

[0011] Optionally, the method for determining the status of a security device further includes: preprocessing the scene image.

[0012] According to another aspect of an embodiment of the present invention, a device for determining the status of a safety device is provided. The device may include: an acquisition unit configured to acquire a scene image and target features of the safety device, wherein the scene image indicates the usage scene of the safety device, and the target features indicate the appearance features and / or status features of the safety device; an identification unit configured to identify a target area from the scene image based on the target features, wherein the target area includes the area where the safety device is located; and a determination unit configured to determine a target state of the safety device based on the target area, wherein the target state indicates whether the safety device is in a safe wearing state or a non-wearing state.

[0013] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is also provided, which includes a stored program, wherein when the program is executed by a processor, the device where the storage medium is located is controlled to execute the method for determining the status of the security device in the embodiment of the present invention.

[0014] According to another aspect of an embodiment of the present invention, a processor is provided, which is configured to run a program, wherein the program, when running, executes the method for determining the status of a security device according to an embodiment of the present invention.

[0015] According to another aspect of an embodiment of the present invention, a computer program product is provided, which includes computer instructions, and when executed by a processor, the computer instructions implement the method for determining the status of a security device in an embodiment of the present invention.

[0016] In an embodiment of the present invention, a scene image and target features of a safety device are acquired, wherein the scene image is used to indicate the usage scene of the safety device, and the target features are used to indicate the appearance features and / or status features of the safety device; based on the target features, a target area is identified from the scene image, wherein the target area includes the area where the safety device is located; based on the target area, a target state of the safety device is determined, wherein the target state is used to indicate whether the safety device is in a safe wearing state or not worn state. In other words, the present invention achieves the purpose of real-time monitoring of the safety device by analyzing the acquired scene image and target features of the safety device to determine the target state of the safety device, thereby solving the technical problem of being unable to determine the usage state of the safety device and achieving the technical effect of determining the usage state of the safety device. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0018] Figure 1 is a flow chart of a method for determining the status of a security device according to an embodiment of the present invention;

[0019] Figure 2 This is a flow chart of a method for intelligently tracking, photographing and identifying the wearing status of workers' safety belts in aerial work scenarios according to an embodiment of the present invention;

[0020] Figure 3 2 is a schematic diagram of a system for intelligently tracking, photographing and identifying the wearing status of workers' safety belts in aerial work scenarios according to an embodiment of the present invention;

[0021] Figure 4 2 is a schematic diagram of a device for determining a state of a security device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only 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 ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0023] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, functional component or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, functional components or devices.

[0024] According to an embodiment of the present invention, an embodiment of a method for determining the status of a security device is 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.

[0025] Figure 1 FIG. 1 is a flow chart of a method for determining the status of a security device according to an embodiment of the present invention. Figure 1 As shown, the method may include the following steps:

[0026] Step S101: Acquire scene images and target features of security devices.

[0027] In the technical solution provided in step S101 of the present invention, the scene image is used to indicate the usage scenario of the safety device, and the target features are used to indicate the appearance and / or status characteristics of the safety device. The target features may include color, shape, texture, and other features, and the safety device may be at least a safety belt and a safety lanyard.

[0028] In this embodiment, the scene image and target features of the security device are obtained, for example, by using a camera to obtain the scene image and using target features pre-set in a database. This is merely an example and does not limit the specific method of obtaining the scene image and target features of the security device.

[0029] For example, cameras can be installed in locations that effectively capture workers working at height, such as fixed brackets or movable robotic arms around the work area. The cameras feature high resolution (e.g., 4K and above), automatic focus adjustment, and a wide field of view, ensuring clear capture of the worker's entire body and key areas. The cameras also have built-in advanced photosensitive elements that can keenly perceive changes in light, providing the basis for light recognition triggering photography, thereby capturing scene images of safety equipment based on the camera.

[0030] For example, for a safety belt, the target feature could be color (e.g., orange, yellow, etc.), with a specific range in the HSV color space set for color segmentation. For a safety rope, the target feature could be thickness, color (which may be different from the safety belt), and texture.

[0031] Step S102: identifying a target area from the scene image based on the target features.

[0032] In the technical solution provided in the above step S102 of the present invention, the target area includes the area where the security device is located.

[0033] In this embodiment, after obtaining the target features of the security device in step S101, a target area is identified from the scene image based on the target features. For example, the target area can be identified from the scene image based on the target features using methods such as Hough transform. This is merely an example and does not limit the specific method of identifying the target area.

[0034] For example, for seat belts, color segmentation can be performed by setting a specific range in the HSV color space based on common colors (such as orange and yellow). Furthermore, methods such as the Hough transform can be used to detect long strips of shape, and the periodicity of the seat belt's texture can be combined to further identify the seat belt area, i.e., the target area.

[0035] Step S103: determining a target state of the security device based on the target area.

[0036] In the technical solution provided in the above step S103 of the present invention, the target state is used to indicate whether the safety device is in a safe wearing state or a non-wearing state.

[0037] In this embodiment, after identifying the target area from the scene image in step S102, the target state of the security device is determined based on the target area. For example, a machine learning algorithm is used to determine the target state of the security device. This is merely an example and does not limit the specific method for determining the target state of the security device.

[0038] For example, a machine learning algorithm (such as a convolutional neural network (CNN)) trained with a large amount of labeled data is used to identify the status of safety equipment in the target area, ensuring accurate judgment of the wearing status of seat belts and safety ropes.

[0039] It should be noted that the above embodiment can be implemented by an intelligent tracking and photographing system for high-altitude work safety belt identification.

[0040] In the above steps S101 to S103 of the present invention, a scene image and target features of the safety device are obtained, wherein the scene image is used to indicate the usage scene of the safety device, and the target features are used to indicate the appearance features and / or status features of the safety device; based on the target features, a target area is identified from the scene image, wherein the target area includes the area where the safety device is located; based on the target area, a target state of the safety device is determined, wherein the target state is used to indicate whether the safety device is in a safe wearing state or not worn state. In other words, the present invention determines the target state of the safety device by analyzing the obtained scene image and target features of the safety device, thereby achieving the purpose of real-time monitoring of the safety device, thereby solving the technical problem of being unable to determine the usage state of the safety device and realizing the technical effect of determining the usage state of the safety device.

[0041] The above method of this embodiment is further introduced below.

[0042] As an optional embodiment, obtaining a scene image of a safety device includes: obtaining environmental information of the safety device, wherein the environmental information is used to indicate the weather environment and / or light environment in which the safety device is located; adjusting the acquisition angle of the acquisition device based on the environmental information; and controlling the acquisition device to acquire the scene image of the safety device according to the acquisition angle.

[0043] In this embodiment, the environmental information of the safety device is obtained, for example, by using a photosensitive sensor.

[0044] For example, a photosensor continuously monitors ambient light. When workers are working at height, changes in their position can cause changes in light reflection, obstruction, and other conditions. Photosensors can keenly sense these light changes. When the amplitude of the light change exceeds the set threshold, a trigger signal is immediately sent to the system. After receiving the trigger signal, the intelligent tracking and photographing system activates the high-definition camera function to photograph workers wearing safety equipment, thereby obtaining environmental information about the safety equipment. Assuming the initial light intensity is I0 and the current light intensity is I, the calculation formula for the light change amplitude is shown in the following formula (1):

[0045] ΔI=|I-I0| (1)

[0046] When ΔI ≥ T (where T is the set light change threshold), the camera is triggered to take a photo.

[0047] Optionally, the acquisition angle of the acquisition device is adjusted according to the environmental information, so that the scene image of the security device is acquired according to the acquisition angle.

[0048] For example, the camera uses human body recognition technology to quickly determine the position and outline of the operator through image analysis. A deep learning-based human body detection algorithm can be used to accurately identify the position of the human body in the image. Based on the human body position information, the acquisition angle is determined. The camera automatically adjusts the focal length based on the acquisition angle to ensure clear imaging of the operator's body, especially the waist safety belt and the installation position of the protection rope, that is, to capture the scene image of the safety equipment. For example, the lens group is driven by a motor to adjust the focal length to achieve the best imaging effect. The camera exposure time t exp The choice of aperture size f can be adjusted according to the ambient light intensity and shooting requirements to ensure image quality. The following formula (2) can be used to calculate the appropriate exposure time:

[0049]

[0050] Among them, is the sensitivity N of the camera, and I is the ambient light intensity.

[0051] As an optional embodiment, based on target features, a target area is identified from a scene image, including: segmenting the scene image to obtain multiple sub-scene images; extracting scene features in each sub-scene image; in response to matching the scene features with the target features, determining the area of ​​the sub-scene image corresponding to the scene features as the target area.

[0052] In this embodiment, the scene image is segmented to obtain a plurality of sub-scene images. For example, the scene image is segmented using a method such as a neural network, and similar pixels are segmented into the same sub-scene image.

[0053] Optionally, after obtaining multiple sub-scene images, scene features in each sub-scene image are extracted, and the scene features of each sub-scene image are matched with the target features. When the scene feature matches the target feature, it means that the scene feature is similar to the target feature, that is, there is a safety device corresponding to the target feature in the sub-scene image corresponding to the scene feature. Based on this, the area of ​​the sub-scene image corresponding to the scene feature can be determined as the target area.

[0054] As an optional embodiment, determining the target state of the security device based on the target feature includes: inputting the target feature into a recognition model for analysis to obtain the target state of the security device.

[0055] In this embodiment, the target features are input into a recognition model for analysis to obtain the target state of the security device, wherein the recognition model may be a machine learning algorithm trained with a large amount of labeled data.

[0056] Optionally, by analyzing the target features through the recognition model, it can be determined whether the safety device is in the unworn state or the safe worn state, thereby achieving the purpose of monitoring the safety device.

[0057] As an optional embodiment, the target feature is input into the recognition model for analysis to obtain the target state of the safety device, including: comparing the target feature with the preset feature to obtain a comparison result, wherein the preset feature is used to indicate the appearance feature and / or state feature of the preset safety device in a safe wearing state; in response to the comparison result being that the target feature matches the preset feature, determining that the target state is a safe wearing state.

[0058] In this embodiment, the target feature is compared with the preset feature to obtain a comparison result. The preset feature may be, for example, a pre-set texture feature of a safety belt or a color feature of a safety rope, etc. This is merely an example and does not limit the specific content of the preset feature.

[0059] Optionally, after determining the comparison result, when the comparison result is that the target feature matches the preset feature, it means that the state of the safety device corresponding to the target feature is consistent with the safe wearing state of the safety device corresponding to the preset feature. Based on this, the target state can be determined to be the safe wearing state.

[0060] As an optional embodiment, the method for determining the status of a security device further includes: pre-processing the scene image.

[0061] In this embodiment, the scene image is preprocessed, wherein the preprocessing may at least include grayscale conversion, noise reduction and other operations.

[0062] For example, for grayscale processing, a weighted average method is used to convert a color image into a grayscale image. For example, for an RGB color image, the grayscale value G of each pixel can be obtained by the following formula (3):

[0063] G=0.299R+0.587G+0.114B (3)

[0064] For noise reduction, we use Gaussian filtering and other methods to remove noise from the image. Gaussian filtering performs weighted averaging on each pixel in the image and its neighboring pixels, with the weights determined by the Gaussian function. Let the image be I(x,y), and the image after Gaussian filtering is I filtered The calculation formula for (x,y) can be expressed using the following formula (4):

[0065]

[0066] Where σ is the standard deviation of the Gaussian distribution and r is the radius of the filter window.

[0067] It should be noted that the above embodiment can be implemented by an intelligent tracking and photographing system for high-altitude work safety belt identification.

[0068] In this embodiment, a scene image and target features of a safety device are acquired, wherein the scene image is used to indicate the usage scene of the safety device, and the target features are used to indicate the appearance features and / or status features of the safety device; based on the target features, a target area is identified from the scene image, wherein the target area includes the area where the safety device is located; based on the target area, a target state of the safety device is determined, wherein the target state is used to indicate whether the safety device is in a safe wearing state or not wearing state. In other words, the present invention achieves the purpose of real-time monitoring of the safety device by analyzing the acquired scene image and target features of the safety device to determine the target state of the safety device, thereby solving the technical problem of being unable to determine the usage state of the safety device and achieving the technical effect of determining the usage state of the safety device.

[0069] The technical solutions of the embodiments of the present invention are described below with reference to preferred implementation methods.

[0070] Safety belts are crucial equipment for protecting workers during aerial work. They are the last line of defense for workers. Monitoring and determining the status of safety equipment is a key step in ensuring worker safety.

[0071] In related technologies, workers may accidentally lose the protection of their safety equipment during work due to various reasons, posing a significant safety risk. Currently, there is a lack of a method that can promptly and accurately determine whether workers working at heights have lost the protection of their safety equipment. Consequently, there is a technical problem of being unable to determine the usage status of safety equipment.

[0072] Currently, no effective solution has been proposed to the above-mentioned technical problem of being unable to determine the usage status of the safety equipment.

[0073] However, an embodiment of the present invention proposes a method for intelligently tracking, photographing and identifying the wearing of safety belts by workers in high-altitude work scenarios. It automatically triggers photography through light recognition, automatically focuses according to the position of the human body, and accurately identifies the installation position of the safety belt and protection rope, promptly detects incorrect wearing and issues an alarm, solves the technical problem of being unable to determine the usage status of safety equipment, and achieves the technical effect of determining the usage status of safety equipment.

[0074] The following is a further introduction to the embodiments of the present invention.

[0075] Figure 2This is a flowchart of a method for intelligently tracking, photographing, and identifying the wearing status of workers' safety belts in aerial work scenarios according to an embodiment of the present invention. The identification method includes the following steps:

[0076] Step S201: Acquire an image of a security device and perform pre-processing.

[0077] In this embodiment, the photosensor continuously monitors the ambient light. When the operator is working at high altitude, the change in his position will cause changes in light reflection, obstruction, etc. The photosensor can keenly perceive these light changes. When the amplitude of the light change exceeds the set threshold, a trigger signal is immediately sent to the system. After receiving the trigger signal, the intelligent tracking and photographing system starts the high-definition camera photography function and photographs the operator wearing the safety equipment, thereby obtaining the environmental information of the safety equipment. Assuming the initial light intensity is I0 and the current light intensity is I, the calculation formula for the light change amplitude is shown in the above formula (1), which will not be repeated here.

[0078] Optionally, the camera uses human body recognition technology to quickly determine the position and outline of the operator through image analysis. A human body detection algorithm based on deep learning can be used to accurately identify the position of the human body in the image. Based on the human body position information, the acquisition angle is determined. The camera automatically adjusts the focal length based on the acquisition angle to ensure clear imaging of the operator's body, especially the waist safety belt and the installation position of the protection rope, that is, to capture the scene image of the safety equipment. For example, the lens group is driven by a motor to adjust the focal length to achieve the best imaging effect. The camera exposure time t exp The choice of aperture size f can be adjusted according to the ambient light intensity and shooting requirements to ensure image quality. The above formula (2) can be used to calculate the appropriate exposure time, which will not be repeated here.

[0079] Optionally, after receiving the trigger signal, the camera's photo function is activated, and the camera takes pictures according to pre-set parameters (such as exposure time, aperture size, etc.). The camera exposure time and aperture size can be adjusted according to the ambient light intensity and shooting requirements to ensure image quality. The following formula (5) can be used to calculate the appropriate exposure time:

[0080]

[0081] Among them, is the k constant, S is the sensitivity of the camera, and I is the ambient light intensity.

[0082] Optionally, for grayscale processing, a weighted average method is used to convert the color image into a grayscale image. For example, for an RGB color image, the grayscale value G of each pixel can be obtained by the aforementioned formula (3), which will not be repeated here.

[0083] Optionally, for noise reduction, a method such as Gaussian filtering can be used to remove noise from the image. Gaussian filtering performs a weighted average of each pixel in the image and its neighboring pixels, with the weight determined by a Gaussian function. Let the image be I(x,y), and the image after Gaussian filtering is I filtered The calculation formula of (x, y) can be expressed using the aforementioned formula (4), which will not be repeated here.

[0084] Step S202 : Identify the safety belt and the safety rope from the pre-processed image.

[0085] In this embodiment, feature extraction: The image processor pre-processes the captured image, including grayscale conversion, noise reduction, etc. Then, features such as color, shape, and texture are used to extract areas that may be safety belts and safety ropes.

[0086] Alternatively, for seat belts, color segmentation can be performed by setting a specific range in the HSV color space based on common colors (e.g., orange, yellow, etc.). At the same time, methods such as the Hough transform can be used to detect long strip shapes, and the seat belt area can be further confirmed by combining the periodicity of the seat belt's texture.

[0087] Alternatively, a safety rope may be extracted based on its specific thickness, color (which may be different from a safety belt), and texture features. For example, a safety rope is typically thinner than a safety belt and may be a specific blue or gray in color.

[0088] Optionally, color features can be used to perform color segmentation in the image based on the specific color of the protection rope (such as blue or gray). The HSV range of the corresponding color can be set to extract the area that may be the protection rope.

[0089] Alternatively, using edge detection algorithms (such as Canny Edge Detection) to detect edges in the image, the width of the edges is then analyzed to identify areas that may be safety ropes. Safety ropes are typically thinner than seatbelts, so a width threshold is set to filter out areas that meet the safety rope thickness characteristics. Let the edge width be w. When w ≤ W (where W is the safety rope width threshold), the area is considered to be a possible safety rope area.

[0090] Step S203: Determine the wearing status of the safety belt and the safety rope.

[0091] In this embodiment, feature analysis is performed on the extracted safety belt and belay rope areas and compared with pre-stored correct wearing patterns. This determines whether the safety belt is wrapped around the worker's body and is correctly fastened, and whether the belay rope is properly connected and loose. Machine learning algorithms (such as convolutional neural networks (CNNs)) trained on large amounts of labeled data are used for recognition, ensuring accurate judgment of the safety belt and belay rope wearing status.

[0092] Optionally, collect a large dataset of images showing both properly worn and unworn safety belts and harnesses. Divide the dataset into training, validation, and test sets. Use the training set to train the CNN model, adjusting the model weights through backpropagation to achieve good accuracy on the validation set.

[0093] Optionally, the extracted images of the seat belt and safety rope areas are input into a trained CNN model, and the model outputs a classification result of the wearing status of the seat belt and safety rope (worn correctly, worn incorrectly, or not worn).

[0094] Step S204: alarm and data transmission.

[0095] In this embodiment, if the image processor determines that a worker is not wearing a safety belt or harness correctly, the system immediately triggers an alarm. This alarm can be an audible alarm, sounding loudly through a speaker installed at the work site, or a signal alarm, which sends a notification of improper wearing to a receiving device (such as a mobile phone or a dedicated monitoring terminal) carried by an on-site supervisor.

[0096] Optionally, the system can transmit data such as image data, location information (if there is a relevant positioning information source), and timestamps of workers who do not wear the mask correctly to the monitoring center or the terminal device of the manager. This data can be used for subsequent safety records, responsibility tracing, etc.

[0097] Optionally, when the tracking camera system identifies a worker not wearing a seatbelt, the control module triggers an alarm and also sends a signal to the lighting device to emit a specific warning light source. For example, the light element can emit a rapidly flashing red light, or switch to a color and frequency that is significantly different from the normal lighting pattern to attract the attention of the worker and surrounding personnel.

[0098] Optionally, if the system determines that a worker is not properly wearing a safety belt or harness, it triggers an alarm and sounds a loud siren through a speaker. The speaker can be installed in a conspicuous location at the work site, such as at the edge of the work platform or at the entrance, so that workers can hear the alarm immediately. The speaker's volume and pitch should be adjusted to ensure clear hearing in noisy work environments.

[0099] Optionally, information about improper wear can be sent to a receiving device carried by on-site supervisors, such as a mobile phone or a dedicated monitoring terminal. The receiving device can connect to the system via a wireless network and receive alerts and image data in real time. Supervisors can use the receiving device to monitor workers' wearability and take timely corrective measures. Furthermore, the receiving device can be configured with alerts, such as vibration or sound, to ensure supervisors are promptly alerted to alerts.

[0100] Figure 3 This is a schematic diagram of a system for intelligently tracking, photographing and identifying the wearing of safety belts by workers in high-altitude work scenarios according to an embodiment of the present invention. The intelligent tracking, photographing and identifying system 300 includes: a high-definition camera 301, an image processor 302, a storage device 303 and a photosensor 304.

[0101] High-definition camera 301: Installed in a location that effectively captures workers working at height, such as on a fixed support or a movable robotic arm around the work area. The camera features high resolution (e.g., 4K or higher), automatic focus adjustment, and a wide field of view, ensuring clear images of the worker's entire body and key areas. The camera's advanced built-in photosensitive element is highly sensitive to light changes, providing the foundation for light recognition triggering and capturing images.

[0102] Image processor 302: Connects to the HD camera, receives image data, and performs real-time processing and analysis. It uses a high-performance digital signal processor (DSP) or graphics processing unit (GPU) to ensure fast data processing speed.

[0103] Storage device 303: used to store raw image data and analysis result data. A large-capacity solid-state drive (SSD) or flash memory card can be used to support fast read and write operations.

[0104] Light sensor 304: Installed near the camera or integrated with the camera, it senses the intensity and changes of the surrounding light. When the light changes meet the preset conditions, it triggers the camera to take a photo.

[0105] In this embodiment, the HD camera 301 is mounted on a fixed bracket using a sturdy metal bracket secured to the building structure or the edge of the work platform using expansion bolts or welding. Ensure the bracket can withstand the weight of the camera and potential external forces such as wind. During installation, adjust the camera's angle and height to provide the optimal viewing angle for the worker.

[0106] Optionally, for HD camera 301, select 4K or higher resolution based on actual needs to ensure clear images of the worker's safety belt and harness installation locations. In scenarios requiring high image quality, a higher resolution camera can be selected to maintain clarity when zooming in for analysis.

[0107] Automatic Focus Adjustment Range: Set an appropriate automatic focus adjustment range based on the size of the work area and the range of the operator's movements. For larger work areas, choose a wider focus adjustment range to ensure clear imaging at varying distances. Also, adjust the focus adjustment speed and accuracy based on actual conditions to ensure fast and accurate focusing.

[0108] Optionally, adjust the sensitivity of the sensor based on the lighting conditions at the work site. In low-light environments, you can increase the sensitivity of the sensor to ensure clear images. However, excessive sensitivity may increase image noise, so a balance needs to be struck between sensitivity and image quality.

[0109] Optionally, a movable arm can be installed on large equipment such as a tower crane or hoist using bolts or bayonet connections. During installation, ensure that the arm's range of motion covers the entire work area and can stably maintain the camera's position and orientation.

[0110] Optionally, the light sensor 304, if integrated with the camera, can be mounted directly in the same manner as the camera. If mounted independently, it can be secured in a suitable location near the camera using a small bracket or glue, ensuring that the light-sensitive surface of the light sensor faces the work area and can accurately sense changes in light.

[0111] Optionally, set an appropriate light change threshold based on the light conditions at the work site and the activities of the workers. If the light changes frequently at the work site, the threshold can be lowered to increase system sensitivity. However, a too low threshold may lead to false triggers, so a balance needs to be struck between sensitivity and accuracy.

[0112] Optionally, adjust the response time of the photosensor to quickly detect changes in light and trigger a photo. A response time that is too short may cause the system to trigger too frequently, wasting resources; a response time that is too long may result in missing critical photo opportunities. Therefore, adjustments should be made based on actual conditions to ensure the system can respond promptly to light changes.

[0113] Optionally, the image processor 302 and storage device 303 control box can be placed on a stable surface or mounted on a wall. The devices within the control box should be properly arranged and secured to prevent loosening and damage during transportation and use. Cables connecting the various devices should be neatly routed and secured to avoid clutter that could affect normal operation.

[0114] Optionally, image processor 302 parameters, including data processing speed, can be configured appropriately based on the camera's resolution and frame rate, as well as the system's real-time requirements. If large amounts of image data need to be processed in real time, a high-performance image processor can be selected and its parameters adjusted to increase processing speed. For example, the number of processor cores can be increased, the clock frequency can be increased, or the algorithm can be optimized.

[0115] Optionally, feature extraction parameters: For seatbelt and lanyard feature extraction, appropriate parameters need to be set based on the actual situation. For example, for color segmentation, the HSV color space range can be adjusted based on the actual color of the seatbelt and lanyard. For shape and texture feature extraction, the corresponding algorithm parameters can be adjusted based on the shape and texture characteristics of the seatbelt and lanyard to improve feature extraction accuracy.

[0116] Optionally, regarding storage device 303 parameters, storage capacity: select an appropriate storage capacity based on the duration of the operation and the size of the image data. If the operation is long or requires storage of a large amount of image data, a high-capacity solid-state drive or flash memory card can be selected. Also, consider the read and write speed of the storage device to ensure rapid storage and retrieval of image data.

[0117] Optionally, set the data retention period based on security management requirements. Generally, image data should be retained for a certain period of time so that it can be reviewed and analyzed when needed. However, excessive retention periods can consume significant storage space, so a balance needs to be struck between storage space and data retention period.

[0118] In this embodiment, photo taking is automatically triggered by light recognition, automatic focus is achieved according to the position of the human body, and the installation position of the safety belt and protection rope is accurately identified. Incorrect wearing is discovered in time and an alarm is issued, which solves the technical problem of being unable to determine the usage status of the safety equipment and achieves the technical effect of determining the usage status of the safety equipment.

[0119] According to an embodiment of the present invention, a device for determining the status of a security device is also provided. It should be noted that the device for determining the status of a security device can be used to execute the method for determining the status of a security device in the method embodiment.

[0120] Figure 4 FIG. 1 is a schematic diagram of a device for determining the state of a security device according to an embodiment of the present invention. Figure 4 As shown, the apparatus 400 for determining the state of the security device may include: an acquiring unit 401 , an identifying unit 402 , and a determining unit 403 .

[0121] The acquisition unit 401 is configured to acquire a scene image and target features of a security device, wherein the scene image is used to indicate a usage scene of the security device, and the target features are used to indicate appearance features and / or status features of the security device.

[0122] The identification unit 402 is configured to identify a target area from the scene image based on the target features, wherein the target area includes an area where the security device is located.

[0123] The determining unit 403 is configured to determine a target state of the safety device based on the target area, wherein the target state is used to indicate whether the safety device is in a safe wearing state or a non-wearing state.

[0124] Optionally, the acquisition unit 401 may include: an acquisition module for acquiring environmental information of the safety device, wherein the environmental information is used to indicate the weather environment and / or light environment in which the safety device is located; an adjustment module for adjusting the acquisition angle of the acquisition device based on the environmental information; and an acquisition module for controlling the acquisition device to acquire scene images of the safety device according to the acquisition angle.

[0125] Optionally, the recognition unit 402 may include: a segmentation module for segmenting the scene image to obtain multiple sub-scene images; an extraction module for extracting scene features from each sub-scene image; and a matching module for determining the area of ​​the sub-scene image corresponding to the scene feature as the target area in response to matching the scene feature with the target feature.

[0126] Optionally, the determination unit 403 further includes: an analysis module, configured to input the target feature into the recognition model for analysis to obtain the target state of the security device.

[0127] Optionally, the analysis module may include: a comparison submodule, used to compare the target feature with the preset feature to obtain a comparison result, wherein the preset feature is used to indicate the appearance feature and / or state feature of the preset safety device in a safe wearing state; a matching submodule, used to determine that the target state is a safe wearing state in response to the comparison result that the target feature matches the preset feature.

[0128] Optionally, the apparatus 400 for determining the state of a security device may further include: a pre-processing unit configured to pre-process the scene image.

[0129] In this embodiment, a scene image and target features of a safety device are acquired, wherein the scene image is used to indicate the usage scene of the safety device, and the target features are used to indicate the appearance features and / or status features of the safety device; based on the target features, a target area is identified from the scene image, wherein the target area includes the area where the safety device is located; based on the target area, a target state of the safety device is determined, wherein the target state is used to indicate whether the safety device is in a safe wearing state or not wearing state. In other words, the present invention achieves the purpose of real-time monitoring of the safety device by analyzing the acquired scene image and target features of the safety device to determine the target state of the safety device, thereby solving the technical problem of being unable to determine the usage state of the safety device and achieving the technical effect of determining the usage state of the safety device.

[0130] According to an embodiment of the present invention, a computer-readable storage medium is further provided. The storage medium includes a stored program, wherein the program executes the method for determining the status of a security device in the method embodiment.

[0131] According to an embodiment of the present invention, a processor is further provided. The processor is configured to run a program. When the program is run, the method for determining the status of a security device in the method embodiment is executed.

[0132] According to an embodiment of the present invention, a computer program product is further provided. The computer program product includes computer instructions. When the computer instructions are executed by a processor, the method for determining the status of a security device in the method embodiment is implemented.

[0133] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0134] In the above embodiments of the present invention, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0135] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0136] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple units. Some or all of the units may be selected to achieve the purpose of the present embodiment according to actual needs.

[0137] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0138] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent functional component, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software functional component, which is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0139] The above are only preferred embodiments of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for determining the state of a safety device, characterized in that: include: Acquire a scene image and a target feature of a security device, wherein the scene image is used to indicate a usage scene of the security device, and the target feature is used to indicate an appearance feature and / or a state feature of the security device; Based on the target feature, identifying a target area from the scene image, wherein the target area includes an area where the safety device is located; Based on the target area, a target state of the safety device is determined, wherein the target state is used to indicate whether the safety device is in a safe wearing state or a non-wearing state.

2. The method according to claim 1, characterized in that Get scene images of security equipment, including: Acquiring environmental information of the safety device, wherein the environmental information is used to indicate a weather environment and / or a light environment in which the safety device is located; Based on the environmental information, adjusting the collection angle of the collection device; According to the acquisition angle, the acquisition device is controlled to acquire the scene image of the safety device.

3. The method according to claim 1, characterized in that Based on the target feature, identifying a target area from the scene image includes: Segmenting the scene image to obtain a plurality of sub-scene images; Extracting scene features from each of the sub-scene images; In response to the scene feature matching the target feature, a region of the sub-scene image corresponding to the scene feature is determined as the target region.

4. The method according to claim 1, characterized in that Determining a target state of the safety device based on the target feature includes: The target feature is input into a recognition model for analysis to obtain the target state of the safety device.

5. The method according to claim 4, characterized in that Inputting the target feature into a recognition model for analysis to obtain the target state of the safety device includes: Comparing the target feature with a preset feature to obtain a comparison result, wherein the preset feature is used to indicate the preset appearance feature and / or the state feature of the safety device in the safe wearing state; In response to the comparison result that the target feature matches the preset feature, the target state is determined to be the safe wearing state.

6. The method according to any one of claims 1 to 5, characterized in that: The method further comprises: The scene image is preprocessed.

7. A device for determining the state of a safety device, characterized in that: include: An acquisition unit, used to acquire a scene image and a target feature of a security device, wherein the scene image is used to indicate a usage scene of the security device, and the target feature is used to indicate an appearance feature and / or a state feature of the security device; an identification unit, configured to identify a target area from the scene image based on the target feature, wherein the target area includes an area where the security device is located; A determining unit is used to determine a target state of the safety device based on the target area, wherein the target state is used to indicate that the safety device is in a safe wearing state or a non-wearing state.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed by a processor, the device where the storage medium is located is controlled to execute the method according to any one of claims 1 to 6.

9. A processor, characterized in that: The processor is used to run a program, wherein the program executes the method according to any one of claims 1 to 6 when running.

10. A computer program product, characterized in that The computer program product comprises computer instructions, which implement the method according to any one of claims 1 to 6 when executed by a processor.

Images