CN116664518A - Fire control access door closer abnormality detection method and system and electronic equipment - Google Patents
Fire control access door closer abnormality detection method and system and electronic equipment Download PDFInfo
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Abstract
The application discloses a fire control channel door closer abnormality detection method, a fire control channel door closer abnormality detection system and electronic equipment, wherein the fire control channel door closer abnormality detection method comprises the following steps: patrol detection is carried out on the fire fighting channel through the robot, shooting information of the door closer is obtained, and the shooting information is pushed to the background; acquiring the connection relation of the position and the confidence coefficient of the bearing key point of the door closer and the bearing key point according to the acquired shooting information of the door closer; calculating parameters for logic judgment according to the acquired connection relation between the position and the confidence coefficient of the bearing key point of the door closer and the bearing key point; and adopting a neural network model to carry out logic judgment on the parameters so as to judge whether the door closer is abnormal or not. The abnormal detection method of the fire control access door closer reduces the labor cost, improves the detection efficiency and greatly improves the inefficacy of the whole scheme.
Description
Technical Field
The application relates to the technical field of fire door closer detection, in particular to a fire passage door closer abnormality detection method, a fire passage door closer abnormality detection system and electronic equipment.
Background
In places such as buildings, abnormal phenomena such as damage to the fire door closer or no closing of the fire door can occur. When emergency situations such as fire disaster and the like need emergency lifesaving, whether the fire door is closed perfectly is an important condition for guaranteeing survival staff. If the door closer is damaged or if the door is not fully closed, this can increase and exacerbate the loss caused by the fire.
At present, patrol inspection is carried out on areas such as buildings or markets by security personnel or related personnel at regular intervals, and naked eyes are used for judging whether a door closer is damaged, whether a fire door can be tightly closed or not and the like. There are no other means or techniques for judgment or assistance.
The existing scheme has high labor cost, and personnel can easily generate misjudgment and missed judgment when distinguishing by naked eyes, so that the overall efficiency is low, and the timeliness is low. This would be limited to the camera position if a fixed camera plus image analysis were used, also making application very difficult.
Disclosure of Invention
The application aims to provide a fire control access door closer abnormality detection method, a fire control access door closer abnormality detection system and a new technical scheme of electronic equipment, which at least can solve the problems of high cost, low efficiency, difficult application and the like in the prior art.
In a first aspect of the present application, a fire-fighting access door closer anomaly detection method is provided, comprising:
patrol detection is carried out on the fire fighting channel through the robot, shooting information of the door closer is obtained, and the shooting information is pushed to the background;
acquiring the connection relation of the position and the confidence coefficient of the bearing key point of the door closer and the bearing key point according to the acquired shooting information of the door closer;
calculating parameters for logic judgment according to the acquired connection relation between the position and the confidence coefficient of the bearing key point of the door closer and the bearing key point;
and adopting a neural network model to carry out logic judgment on the parameters so as to judge whether the door closer is abnormal or not.
Optionally, the step of obtaining the shooting information of the door closer by patrol detection of the fire fighting access by the robot includes:
setting a robot patrol route, and setting snap shots on the patrol route;
and controlling the robot to patrol according to the patrol route.
Optionally, the robot is provided with a camera, and when the robot patrols to the snapshot point, shooting information is obtained.
Optionally, the step of acquiring the connection relationship between the bearing key point position, the confidence coefficient and the bearing key point of the door closer according to the acquired shooting information of the door closer includes:
detecting a position of the door closer using a detector;
determining whether a bounding box of the door closer is detected according to the detected position of the door closer;
and detecting the bearing key points of the door closer according to the detected bounding box so as to obtain the connection relation of the bearing key point positions, the confidence degrees and the bearing key points of the door closer.
Optionally, in the step of logically judging the parameters, the door closer abnormality is judged when at least half of the logically judged parameters exceed a threshold.
Optionally, the step of calculating the parameter for logic determination includes:
calculating the angle of a bearing connecting point of the bearing key point;
and calculating the pixel distance of the bearing connection point according to the angle of the bearing connection point to obtain a parameter for logic judgment so as to judge whether the door closer is abnormal or not.
Optionally, the fire control access door closer abnormality detection method further includes: and when the door closer is judged to be abnormal, an alarm is sent out.
In a second aspect of the present application, a fire-fighting access door closer abnormality detection system is provided, which is applied to the fire-fighting access door closer abnormality detection method described in the foregoing embodiment, and the system includes:
the first acquisition module is used for carrying out patrol detection on the fire fighting channel through the robot, acquiring shooting information of the door closer and pushing the shooting information to the background;
the second acquisition module is used for acquiring the connection relation of the bearing key point position, the confidence coefficient and the bearing key point of the door closer according to the acquired shooting information of the door closer;
the calculation module is used for calculating parameters for logic judgment according to the acquired connection relation between the position and the confidence coefficient of the bearing key point of the door closer and the bearing key point;
and the judging module adopts a neural network model to carry out logic judgment on the parameters so as to judge whether the door closer is abnormal or not.
In a third aspect of the present application, there is provided an electronic apparatus comprising: a processor and a memory, in which computer program instructions are stored, wherein the computer program instructions, when executed by the processor, cause the processor to perform the steps of the fire door closer anomaly detection method described in the above embodiments.
In a fourth aspect of the present application, there is provided a computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of the fire access door closer anomaly detection method described in the above embodiments.
According to the abnormal detection method for the fire control access door closer, the robot is used for patrol detection, shooting information of the door closer is obtained, the connection relation between the position and the confidence coefficient of the bearing key point of the door closer and the bearing key point is obtained, and parameters for logic judgment are calculated, so that whether the door closer and the fire control door are normally closed or not is judged. The abnormal detection method of the fire control access door closer reduces the labor cost, improves the detection efficiency and greatly improves the inefficacy of the whole scheme.
Other features of the present application and its advantages will become apparent from the following detailed description of exemplary embodiments of the application, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description, serve to explain the principles of the application.
FIG. 1 is a fire access door closer anomaly detection method according to an embodiment of the present application;
FIG. 2 is a logic block diagram of a fire access door closer anomaly detection method according to an embodiment of the present application;
FIG. 3 is a schematic view of a robotic inspection according to an embodiment of the application;
FIG. 4 is a schematic illustration of the calculation of the bearing keypoint locations of a door closer according to an embodiment of the application;
fig. 5 is a schematic diagram of the operation of an electronic device according to an embodiment of the application.
Reference numerals:
a robot 10;
a fire door 20;
a snapshot point 30;
a door closer 40;
a processor 201;
a memory 202; an operating system 2021; an application 2022;
a network interface 203;
an input device 204;
a hard disk 205;
a display device 206.
Detailed Description
Various exemplary embodiments of the present application will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present application unless it is specifically stated otherwise.
The following description of at least one exemplary embodiment is merely exemplary in nature and is in no way intended to limit the application, its application, or uses.
Techniques, methods, and apparatus known to one of ordinary skill in the relevant art may not be discussed in detail, but are intended to be part of the specification where appropriate.
In all examples shown and discussed herein, any specific values should be construed as merely illustrative, and not a limitation. Thus, other examples of exemplary embodiments may have different values.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further discussion thereof is necessary in subsequent figures.
The abnormality detection method of the fire-fighting access door closer 40 according to the embodiment of the present application is specifically described below with reference to the accompanying drawings.
As shown in fig. 1, the abnormality detection method of the fire-fighting access door closer 40 according to the embodiment of the present application includes:
s1, patrol detection is carried out on a fire fighting channel through a robot 10, shooting information of a door closer 40 is obtained, and the shooting information is pushed to the background;
s2, acquiring the connection relation between the position and the confidence of the bearing key point of the door closer 40 and the bearing key point according to the acquired shooting information of the door closer 40;
s3, calculating parameters for logic judgment according to the acquired connection relation between the position and the confidence coefficient of the bearing key point of the door closer 40 and the bearing key point;
s4, adopting a neural network model to carry out logic judgment on the parameters so as to judge whether the door closer 40 is abnormal or not.
In other words, in the method for detecting the abnormality of the fire-fighting access door closer 40 according to the embodiment of the present application, first, referring to fig. 1, patrol detection may be performed on the fire-fighting access by the robot 10, and shooting information of the door closer 40 may be obtained and pushed to the background. Relevant data is collected through the round trip patrol of the robot 10, patrol efficiency is improved, and labor cost is saved. Then, the connection relationship between the bearing key point position, the confidence level and the bearing key point of the door closer 40 can be obtained according to the obtained shooting information of the door closer 40. The parameters for the logical judgment can then be calculated from the acquired bearing key point positions, confidence levels and connection relations of the bearing key points of the door closer 40. Finally, a neural network model may be used to logically determine the parameters to determine if the door closer 40 is abnormal.
The application obtains the corresponding information through the processing means of deep learning and then judges the image. The analysis flow of the whole image is summarized in a simple way mainly by three steps: detection of the door closer 40, detection of key points of the door closer 40 and comprehensive logic judgment. In deep learning, the YOLOv5 method is used to locate the door closer 40 row on the fire door 20 in the image. To reduce the time consumption of detection, the application modifies the YOLOV5 original backbone network into a lightweight network like mobiletv 3. One part of training data of the model is network open source, and the other part is self-collection and labeling. In order to improve the universality and detection capability of the model, the Focus structure in YOLOv5 can be eliminated, and the training of Multiscale can be performed by changing the training and reasoning picture input of the model into the ratio of the resolution ratio of 4:3 of video. By means of the detector, it is possible to initially determine whether the door closer 40 is abnormal, and if the door closer 40 is not checked, the fire safety door has a high probability that the door closer 40 is not installed, i.e. it is determined that an abnormality has occurred.
Therefore, according to the method for detecting the abnormality of the fire control access door closer 40 in the embodiment of the application, the robot 10 is utilized to carry out patrol detection, the shooting information of the door closer 40 is obtained, the connection relation between the bearing key point position, the confidence coefficient and the bearing key point of the door closer 40 is obtained, and the parameters for logic judgment are calculated, so as to judge whether the door closer 40 and the fire control door 20 are normally closed. The abnormal detection method of the fire control access door closer 40 reduces the labor cost, improves the detection efficiency and greatly improves the invalidation of the whole scheme.
According to an embodiment of the present application, the step of obtaining the photographing information of the door closer 40 by patrol detection of the fire passage by the robot 10 includes:
setting a patrol route of the robot 10, and setting a snapshot point 30 on the patrol route;
according to the patrol route, the robot 10 is controlled to patrol.
The robot 10 is provided with a camera, and when the robot 10 patrols to the snapshot point 30, shooting information is acquired.
That is, as shown in fig. 3, in the process of performing patrol detection on a fire passage by the robot 10 to acquire photographing information of the door closer 40, a patrol route of the robot 10 may be set, and the snapshot point 30 may be set on the patrol route. Then, the robot 10 may be controlled to patrol according to the patrol route. The robot 10 is provided with a camera, and when the robot 10 patrols to the snapshot point 30, shooting information is acquired.
The detection method is based on the inspection robot 10, and the camera is arranged at the head of the robot 10, and image analysis is carried out by continuously inspecting the point positions arranged at the fire-fighting channel back and forth and collecting videos after the point positions in real time and pushing the videos to the background algorithm server. The image analysis mainly performs abnormality judgment on the door closer 40 of the fire door 20 by means of target detection and key point detection.
When the robot 10 makes a patrol in a fixed scene environment, we can make a patrol of the scene in a reciprocating cycle according to a certain annular path, and the patrol route can be set manually (when setting, the route must pass through the fire door 20 area). In the patrol process, the robot 10 performs snapshot operation according to a preset point position, namely, the robot 10 stays at a fixed point position and performs video stream snapshot on a fire fighting channel scene by using a camera arranged at the head of the robot, and then the video is transmitted to a background algorithm server through a network module for analysis and early warning prompt.
In some embodiments of the present application, the step of obtaining the connection relationship between the bearing key point position, the confidence coefficient and the bearing key point of the door closer 40 according to the obtained shooting information of the door closer 40 includes:
detecting the position of the door closer 40 using a detector;
determining whether a bounding box of the door closer 40 is detected according to the detected position of the door closer 40;
and detecting the bearing key points of the door closer 40 according to the detected bounding box to obtain the connection relation of the bearing key point positions, the confidence degrees and the bearing key points of the door closer 40.
In the step of logically judging the parameters, it is judged that the door closer 40 is abnormal when at least half of the logically judged parameters exceed the threshold.
The step of calculating parameters for logic determination includes:
calculating the angle of a bearing connecting point of the bearing key point;
based on the angle of the bearing connection point, the pixel distance of the bearing connection point is calculated, and a parameter for logic judgment is obtained to judge whether the door closer 40 is abnormal.
The method further comprises the steps of: upon determining that the door closer 40 is abnormal, an alarm is issued.
That is, based on the inspection robot 10, the camera is placed on the head of the robot 10, and image analysis is performed by continuously inspecting the point location set at the fire-fighting channel back and forth and collecting the video at the time of the point location in real time and pushing the video to the background algorithm server. The image analysis mainly performs abnormality judgment on the door closer 40 of the fire door 20 by means of target detection and key point detection.
Specific fire channels or areas with fire safety doors are patrol by the robot 10, and video streams of fixed-point snapshots are collected in real time and pushed to the background for analysis.
When the robot 10 makes a patrol in a fixed scene environment, we can make a patrol of the scene in a reciprocating cycle according to a certain annular path, and the patrol route can be set manually (when setting, the route must pass through the fire door 20 area). In the patrol process, the robot 10 performs snapshot operation according to a preset point position, namely, the robot 10 stays at a fixed point position and performs video stream snapshot on a fire fighting channel scene by using a camera arranged at the head of the robot, and then the video is transmitted to a background algorithm server through a network module for analysis and early warning prompt.
And acquiring corresponding information by using a processing means for acquiring a real-time video image through deep learning and then judging the corresponding information in the image. The analysis flow of the whole image is summarized in a simple way mainly by three steps: detection of the door closer 40, detection of key points of the door closer 40 and comprehensive logic judgment.
(1) Detection of door closer 40
The present application uses the YOLOv5 method in deep learning to locate the door closer 40 on the fire door 20 in the image. To reduce the time consuming detection, the YOLOV5 original backbone network was modified to be a lightweight network like mobiletv 3. One part of training data of the model is network open source, and the other part is self-collection and labeling. To improve the universality and detectability of the model, we cull the Focus structure in YOLOv5 and change the training and inferred picture input of the model to the ratio of resolution ratio 4:3 of the video for Multiscale training. By means of the detector, it is possible to initially determine whether the door closer 40 is abnormal, and if the door closer 40 is not checked, the fire safety door has a high probability that the door closer 40 is not installed, i.e. it is determined that an abnormality has occurred.
(2) Key point detection of door closer 40
For convenience of subsequent logical judgment, the key point positions of the three bearings on the door closer 40 are detected by using a neural network model method. The present application uses the Heatmap method in OpenPose in deep learning to key the bearing point of the door closer 40. By means of the neural network we can obtain the bearing key point position, the confidence level and the bearing connection relation of the key points of each door closer 40 of each frame. To reduce time consumption, shuffleNetV2 was used as a backbone model for the neural network. One part of training data of the model is acquired by a web crawler, and the other part is acquired and marked by self. To increase the accuracy of the model, the model downsampling ratio is changed to 2 and the input image resolution size is changed to 256 x 256, with a ratio of 1:1 approximating the actual aspect ratio of the door closer 40.
The parameters for logic judgment can be obtained through a certain calculation according to the obtained key point positions, and finally whether the door closer 40 is normal or whether the fire door 20 is tightly closed can be judged through logic judgment. The specific calculation method is as follows:
referring to FIG. 4, bearing attachment point angle alpha is calculated: the included angle between the line segment CD and the line segment DE can be calculated and is recorded as alpha; the angle between AB and CD was calculated and noted as beta. Angle calculation formula we use a vector point multiplication formula to assist in the calculation:
A·B＝|A|*|B|*cos(alpha)
bearing connection point pixel distance d is calculated: the distance between the wire sections CD can be calculated; denoted as d1; calculating the distance between the AB's and recording as d2; the distance between DE was calculated and noted as d3. The calculated distance formula is the Euclidean distance of the two-dimensional plane.
Depending on the actual use of the door closer 40, it may be found that if the fire door 20 is open, and the greater the opening the greater the alpha angle, the less the beta angle; and both the ratio values d1/d2 and d3/d2 become large. Based on this characteristic, we can determine whether the door closer 40 is abnormally damaged or whether the fire door 20 is in an open state by defining a certain threshold. The specific abnormality determination logic is that if at least two of the four conditions alpha >60 °, beta <60 °, d1/d2<1.1, d2/d2<0.9 are satisfied, it is determined that the door closer 40 is abnormal, or that the fire door 20 is not normally closed.
The application uses the robot 10 to inspect and take a snap shot with fixed points and analyze the images of the door closer 40 and the fire safety door, thereby judging whether the abnormal closing condition of the door closer 40 and the fire safety door occurs. The scheme of detecting by using security personnel is broken through, so that the overall efficiency is higher, and meanwhile, the problems of false alarm, missing report and the like caused by manpower can be well reduced.
According to a second aspect of the present application, a fire door closer 40 anomaly detection system is provided, which is applied to the fire door closer 40 anomaly detection method in the above embodiment, and the system includes a first acquisition module, a second acquisition module, a calculation module and a judgment module. Specifically, the first obtaining module is configured to obtain, through patrol detection of the robot 10 on the fire fighting access, shooting information of the door closer 40, and push the shooting information to the background. The second obtaining module is configured to obtain, according to the obtained shooting information of the door closer 40, a connection relationship between a bearing key point position, a confidence coefficient, and a bearing key point of the door closer 40. The calculation module is used for calculating parameters for logic judgment according to the acquired positions and confidence levels of the bearing key points of the door closer 40 and the connection relation of the bearing key points. The judging module adopts a neural network model to logically judge the parameters so as to judge whether the door closer 40 is abnormal or not.
The application uses the robot 10 to inspect and take a snap shot with fixed points and analyze the images of the door closer 40 and the fire safety door, thereby judging whether the abnormal closing condition of the door closer 40 and the fire safety door occurs. The scheme of detecting by using security personnel is broken through, so that the overall efficiency is higher, and meanwhile, the problems of false alarm, missing report and the like caused by manpower can be well reduced.
According to a third aspect of the present application, there is also provided an electronic apparatus comprising: a processor 201 and a memory 202, wherein computer program instructions are stored in the memory 202, wherein the computer program instructions, when executed by the processor 201, cause the processor 201 to perform the steps of the fire door closer 40 anomaly detection method in the above-described embodiment.
Further, as shown in fig. 5, the electronic device further comprises a network interface 203, an input device 204, a hard disk 205, and a display device 206.
The interfaces and devices described above may be interconnected by a bus architecture. The bus architecture may include any number of interconnected buses and bridges. One or more central processing units 201 (CPUs), in particular represented by processor 201, and various circuits of one or more memories 202, represented by memories 202, are connected together. The bus architecture may also connect various other circuits together, such as peripheral devices, voltage regulators, and power management circuits. It is understood that a bus architecture is used to enable connected communications between these components. The bus architecture includes, in addition to a data bus, a power bus, a control bus, and a status signal bus, all of which are well known in the art and therefore will not be described in detail herein.
The network interface 203 may be connected to a network (e.g., the internet, a local area network, etc.), and may obtain relevant data from the network and store the relevant data in the hard disk 205.
Input device 204 may receive various instructions entered by an operator and send to processor 201 for execution. The input device 204 may include a keyboard or pointing device (e.g., a mouse, a trackball, a touch pad, or a touch screen, among others).
A display device 206 may display results obtained by the execution of instructions by the processor 201.
The memory 202 is used for storing programs and data necessary for the operation of the operating system 2021, and data such as intermediate results in the calculation process of the processor 201.
It will be appreciated that the memory 202 in embodiments of the application can be either volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory. The nonvolatile memory may be Read Only Memory (ROM), programmable Read Only Memory (PROM), erasable Programmable Read Only Memory (EPROM), electrically Erasable Programmable Read Only Memory (EEPROM), or flash memory, among others. Volatile memory can be Random Access Memory (RAM), which acts as external cache memory. The memory 202 of the apparatus and methods described herein is intended to comprise, without being limited to, these and any other suitable types of memory 202.
In some implementations, the memory 202 stores the following elements, executable modules or data structures, or a subset thereof, or an extended set thereof: an operating system 2021 and application programs 2022.
The operating system 2021 contains various system programs, such as a framework layer, a core library layer, a driver layer, and the like, for implementing various basic services and processing hardware-based tasks. The application programs 2022 include various application programs 2022, such as a Browser (Browser), for implementing various application services. The program implementing the method of the embodiment of the present application may be contained in the application program 2022.
The processor 201 performs the steps of the fire door closer 40 anomaly detection method according to the above-described embodiment when calling and executing the application 2022 and data stored in the memory 202, specifically, the program or instructions stored in the application 2022.
The method disclosed in the above embodiment of the present application may be applied to the processor 201 or implemented by the processor 201. The processor 201 may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in the processor 201 or by instructions in the form of software. The processor 201 may be a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or may implement or perform the methods, steps, and logic blocks disclosed in embodiments of the present application. A general purpose processor may be a microprocessor or the processor 201 may be any conventional processor 201 or the like. The steps of the method disclosed in connection with the embodiments of the present application may be embodied directly in the execution of a hardware decoding processor, or in the execution of a combination of hardware and software modules in a decoding processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in the memory 202, and the processor 201 reads the information in the memory 202 and, in combination with its hardware, performs the steps of the method described above.
It is to be understood that the embodiments described herein may be implemented in hardware, software, firmware, middleware, microcode, or a combination thereof. For a hardware implementation, the processing units may be implemented within one or more Application Specific Integrated Circuits (ASICs), digital Signal Processors (DSPs), digital Signal Processing Devices (DSPDs), programmable Logic Devices (PLDs), field Programmable Gate Arrays (FPGAs), general purpose processors, controllers, micro-controllers, microprocessors, other electronic units designed to perform the functions of the application, or a combination thereof.
For a software implementation, the techniques herein may be implemented with modules (e.g., procedures, functions, and so on) that perform the functions herein. The software codes may be stored in the memory 202 and executed by the processor 201. The memory 202 may be implemented within the processor 201 or external to the processor 201.
Specifically, the processor 201 is further configured to read the computer program and perform the steps of predicting a stake pocket method and outputting answers to questions asked by the user.
In a fourth aspect of the present application, there is also provided a computer-readable storage medium storing a computer program, which when executed by the processor 201, causes the processor 201 to perform the steps of the abnormality detection method of the fire door closer 40 of the above-described embodiment.
In the several embodiments provided in the present application, it should be understood that the disclosed methods and apparatus may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of elements is merely a logical functional division, and there may be additional divisions of actual implementation, e.g., multiple elements or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may be physically included separately, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in hardware plus software functional units.
The integrated units implemented in the form of software functional units described above may be stored in a computer readable storage medium. The software functional unit is stored in a storage medium, and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform part of the steps of the transceiving method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
While certain specific embodiments of the application have been described in detail by way of example, it will be appreciated by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the application. It will be appreciated by those skilled in the art that modifications may be made to the above embodiments without departing from the scope and spirit of the application. The scope of the application is defined by the appended claims.
Claims (10)
1. The method for detecting the abnormality of the fire control passage door closer is characterized by comprising the following steps of:
patrol detection is carried out on the fire fighting channel through the robot, shooting information of the door closer is obtained, and the shooting information is pushed to the background;
acquiring the connection relation of the position and the confidence coefficient of the bearing key point of the door closer and the bearing key point according to the acquired shooting information of the door closer;
calculating parameters for logic judgment according to the acquired connection relation between the position and the confidence coefficient of the bearing key point of the door closer and the bearing key point;
and adopting a neural network model to carry out logic judgment on the parameters so as to judge whether the door closer is abnormal or not.
2. The fire door closer anomaly detection method of claim 1, wherein the step of obtaining the photographed information of the door closer by patrol detection of the fire passage by the robot comprises:
setting a robot patrol route, and setting snap shots on the patrol route;
and controlling the robot to patrol according to the patrol route.
3. The fire access door closer anomaly detection method according to claim 2, wherein the robot is provided with a camera, and shooting information is acquired when the robot patrols to the snapshot point.
4. The fire door closer anomaly detection method according to claim 1, wherein the step of acquiring the connection relation of the bearing key point position, the confidence level and the bearing key point of the door closer according to the acquired photographing information of the door closer comprises:
detecting a position of the door closer using a detector;
determining whether a bounding box of the door closer is detected according to the detected position of the door closer;
and detecting the bearing key points of the door closer according to the detected bounding box so as to obtain the connection relation of the bearing key point positions, the confidence degrees and the bearing key points of the door closer.
5. The fire door closer abnormality detection method according to claim 4, wherein in the step of logically judging the parameters, the door closer abnormality is judged when at least half of logically judged parameters exceed a threshold value.
6. The fire access door closer anomaly detection method of claim 5, wherein the step of calculating parameters for logic determination comprises:
calculating the angle of a bearing connecting point of the bearing key point;
and calculating the pixel distance of the bearing connection point according to the angle of the bearing connection point to obtain a parameter for logic judgment so as to judge whether the door closer is abnormal or not.
7. The fire access door closer anomaly detection method of claim 1, further comprising: and when the door closer is judged to be abnormal, an alarm is sent out.
8. A fire access door closer anomaly detection system for use in a fire access door closer anomaly detection method according to any one of claims 1 to 7, the system comprising:
the first acquisition module is used for carrying out patrol detection on the fire fighting channel through the robot, acquiring shooting information of the door closer and pushing the shooting information to the background;
the second acquisition module is used for acquiring the connection relation of the bearing key point position, the confidence coefficient and the bearing key point of the door closer according to the acquired shooting information of the door closer;
the calculation module is used for calculating parameters for logic judgment according to the acquired connection relation between the position and the confidence coefficient of the bearing key point of the door closer and the bearing key point;
and the judging module adopts a neural network model to carry out logic judgment on the parameters so as to judge whether the door closer is abnormal or not.
9. An electronic device, comprising: a processor and a memory having stored therein computer program instructions, wherein the computer program instructions, when executed by the processor, cause the processor to perform the steps of the fire access door closer anomaly detection method of any one of claims 1-7.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which, when executed by a processor, causes the processor to perform the steps of the fire access door closer anomaly detection method of any one of claims 1 to 7.
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| CN202310635116.1A CN116664518A (en) | 2023-05-31 | 2023-05-31 | Fire control access door closer abnormality detection method and system and electronic equipment |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117576491A (en) * | 2024-01-17 | 2024-02-20 | 浙江新再灵科技股份有限公司 | Elevator door fault detection method, elevator door fault occurrence rate prediction method and device |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117576491A (en) * | 2024-01-17 | 2024-02-20 | 浙江新再灵科技股份有限公司 | Elevator door fault detection method, elevator door fault occurrence rate prediction method and device |
| CN117576491B (en) * | 2024-01-17 | 2024-04-26 | 浙江新再灵科技股份有限公司 | Elevator door fault detection method, elevator door fault occurrence rate prediction method and device |
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