CN116281479A - Elevator fault monitoring method and system based on Internet of things - Google Patents
Elevator fault monitoring method and system based on Internet of things Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
- B66B5/00—Applications of checking, fault-correcting, or safety devices in elevators
- B66B5/0006—Monitoring devices or performance analysers
- B66B5/0012—Devices monitoring the users of the elevator system
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
- B66B5/00—Applications of checking, fault-correcting, or safety devices in elevators
- B66B5/02—Applications of checking, fault-correcting, or safety devices in elevators responsive to abnormal operating conditions
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- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
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Abstract
The invention discloses an elevator fault monitoring method and system based on the Internet of things, wherein the elevator fault monitoring method based on the Internet of things comprises the following steps: a sensor is arranged in the elevator car, when the elevator door is closed, human body detection is carried out on the elevator car through the sensor, and if the human body is detected, a first signal is sent to the platform of the Internet of things; acquiring an image of the interior of an elevator car through an internet of things platform, and performing human body recognition; if a human body is identified and the residence time of the human body in the car is longer than the preset time, confirming that the current elevator has a trapped fault, and sending a second signal to an alarm to prompt a worker to perform trapped fault treatment; according to the elevator car detection system, the sensor is arranged on the elevator car for composite detection, and meanwhile, the human body recognition is performed again by combining the Internet of things and the image recognition technology, so that the accuracy of elevator personnel trapping event detection is improved, and when the elevator is detected to have personnel trapping faults, workers are prompted to perform personnel trapping fault processing through audible and visual alarm.
Description
Technical Field
The invention relates to the technical field of elevators, in particular to an elevator fault monitoring method and system based on the Internet of things.
Background
Today, elevators are widely used in production and life in large numbers, and faults and potential safety hazards of elevators are becoming more important. According to the control principle of the elevator, the car door of the elevator car performs periodic movement of opening and closing the door repeatedly, and when the car is occupied, the elevator door is completely closed and then is braked, stopped, leveled and opened within a specified time. Based on the control principle, if the elevator does not have door opening action within a certain time when the elevator door is closed and the elevator car runs, the elevator can be considered to be invalid and a trapping event occurs.
According to statistics, elevator faults are most commonly mainly elevator electric faults, including roof-rushing or squatting faults caused by electromagnetic interference, relay damage, safety switch damage and other reasons, so that trapped people or personal casualties are caused. The failure randomness of the actual elevator is very strong, and scientific prediction and accurate capture are difficult to carry out.
The existing elevator generally monitors whether trapped people exist by installing a camera and a sensor in the elevator and feeds the monitored information back to a background monitoring room, but the camera and the sensor are easily disturbed by the environment, so that whether the trapped people exist in the elevator or not cannot be detected, or a fault of the trapped people is wrongly reported, and the like.
Disclosure of Invention
The present invention has been made in view of the above-described problems occurring in the prior art.
In order to solve the technical problems, the invention provides the following technical scheme that: a sensor is arranged in the lift car, when the lift door is closed, the sensor is used for detecting the human body of the lift car, if the human body is detected, a first signal is sent to an Internet of things platform, and the Internet of things platform is connected with an image collector and a data processor; acquiring an image of the interior of an elevator car through the internet of things platform, and performing human body recognition; if the human body exists in the elevator car and the residence time of the human body in the elevator car is longer than the preset time, confirming that the current elevator has a trapped fault, and sending a second signal to an alarm to prompt a worker to perform trapped fault treatment; the Internet of things platform comprises an image collector and a data processor.
As a preferable scheme of the elevator fault monitoring method based on the Internet of things, the elevator fault monitoring method based on the Internet of things comprises the following steps: the sensor comprises a pyroelectric infrared sensor and a microwave radar sensor; the pyroelectric infrared sensor is provided with a gain circuit, an optical filter and at least one lens; wherein the gain circuit reduces the effects of pixel offset and noise by double sampling; the lens comprises glass and/or polycarbonate; the wavelength range of the light passing through the filter is 8-10 um.
As a preferable scheme of the elevator fault monitoring method based on the Internet of things, the elevator fault monitoring method based on the Internet of things comprises the following steps: the human body identification includes: preprocessing an image in an elevator car; the preprocessing comprises the steps of carrying out random scaling, rotation and mirror image processing on an image in the elevator car and marking a human body joint point; a human body recognition network is established, the preprocessed image is input into the human body recognition network for training, and training is stopped when the loss value is minimum; and carrying out human body recognition by using the trained human body recognition network.
As a preferable scheme of the elevator fault monitoring method based on the Internet of things, the elevator fault monitoring method based on the Internet of things comprises the following steps: the human body identification network comprises a feature extraction module, a coder and decoder and a multi-layer perceptron; extracting pixel characteristics from the preprocessed image through a characteristic extraction module; extracting spatial features from the preprocessed image by a codec; and carrying out alignment fusion on the space features and the pixel features through a multi-layer sensor, judging effective segmentation of the human body surface in the three-dimensional space, and identifying the human body.
As a preferable scheme of the elevator fault monitoring method based on the Internet of things, the elevator fault monitoring method based on the Internet of things comprises the following steps: the feature extraction module comprises an input layer, an undersampling layer, a first residual block, a second residual block, a third residual block, a fourth residual block, a fifth residual block, a sixth residual block, a first oversampling layer, a second oversampling layer and an output layer; four groups of residual blocks, namely a first residual block, a second residual block, a third residual block and a fourth residual block, are connected between the undersampling layer and the first oversampling layer, and a fifth residual block and a sixth residual block are connected between the output of the first oversampling layer and the input of the second oversampling layer.
As a preferable scheme of the elevator fault monitoring method based on the Internet of things, the elevator fault monitoring method based on the Internet of things comprises the following steps: the training comprises: defining a first loss function, a second loss function and a third loss function, taking the sum of the first loss function value and the second loss function value and the third loss function value as the average value, and stopping training when the loss value of the human body identification network is minimum;
wherein the first loss function L 1 The method comprises the following steps:
L 1 ＝(y 1 -y 1 ’) 2
wherein y is 1 For the actual output value of the feature extraction module, y 1 ' is the predicted output value of the feature extraction module;
second loss function L 2 The method comprises the following steps:
L 2 ＝(y 2 -y 2 ’) 2
wherein y is 2 Is the actual output value of the codec, y 2 ' is a predicted output value of the codec;
third loss function L 3 The method comprises the following steps:
L 3 ＝10(|y 3 -y 3 ’|-5)
wherein y is 3 Is the actual output value of the multi-layer sensor, y 3 ' is the predicted output value of the multi-layer sensor.
As a preferable scheme of the elevator fault monitoring method based on the Internet of things, the elevator fault monitoring method based on the Internet of things comprises the following steps: the alarm comprises: the warning lamp is continuously flashed and the alarm is sounded according to the preset time interval until the alarm is stopped after the third signal confirmed by the staff is received.
As a preferable scheme of the elevator fault monitoring system based on the internet of things, the invention comprises the following steps: comprising the following steps: a first detection module configured to perform setting of a sensor in the car, human body detection of the car by the sensor when the elevator door is closed, and if a human body is detected, transmitting a first signal to a second detection module; the second detection module is configured to acquire an image of the interior of the elevator car through the internet of things platform and perform human body identification; the early warning module is configured to execute the steps that if the existence of a human body in the car is recognized, and the residence time of the human body in the car is longer than the preset time, the occurrence of the trapped fault of the current elevator is confirmed, a second signal is sent to the alarm, the alarm continuously flashes a warning lamp and sounds a bell alarm according to the preset time interval, so that a worker is prompted to perform trapped fault processing, and the alarm is stopped until a third signal confirmed by the worker is received; the second detection module is an internet of things platform, and the internet of things platform comprises an image collector and a data processor.
As a preferable scheme of the elevator fault monitoring system based on the internet of things, the invention comprises the following steps: the sensor comprises a pyroelectric infrared sensor and a microwave radar sensor; the pyroelectric infrared sensor is provided with a gain circuit, an optical filter and at least one lens; wherein the gain circuit is configured to perform double sampling to reduce the effects of pixel offset and noise; the lens comprises glass and/or polycarbonate; the filter is configured to perform the passing light in a wavelength range of 8 to 10um.
As a preferable scheme of the elevator fault monitoring system based on the internet of things, the invention comprises the following steps: the second detection module is specifically configured to perform preprocessing on an image inside the elevator car; the preprocessing comprises the steps of carrying out random scaling, rotation and mirror image processing on an image in the elevator car and marking a human body joint point; a human body recognition network is established, the preprocessed image is input into the human body recognition network for training, and training is stopped when the loss value is minimum; and carrying out human body recognition by using the trained human body recognition network.
The invention has the beneficial effects that: according to the elevator car detection system, the sensor is arranged on the elevator car for composite detection, and meanwhile, the human body recognition is performed again by combining the Internet of things and the image recognition technology, so that the accuracy of elevator personnel trapping event detection is improved, and when the elevator is detected to have personnel trapping faults, workers are prompted to perform personnel trapping fault processing through audible and visual alarm.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
fig. 1 is a schematic diagram of a human body recognition network according to a first embodiment of the present invention;
fig. 2 is a schematic structural diagram of a feature extraction module according to a first embodiment of the invention.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
While the embodiments of the present invention have been illustrated and described in detail in the drawings, the cross-sectional view of the device structure is not to scale in the general sense for ease of illustration, and the drawings are merely exemplary and should not be construed as limiting the scope of the invention. In addition, the three-dimensional dimensions of length, width and depth should be included in actual fabrication.
Also in the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "upper, lower, inner and outer", etc. are based on the orientation or positional relationship shown in the drawings, are merely for convenience of describing the present invention and simplifying the description, and do not indicate or imply that the apparatus or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first, second, or third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected, and coupled" should be construed broadly in this disclosure unless otherwise specifically indicated and defined, such as: can be fixed connection, detachable connection or integral connection; it may also be a mechanical connection, an electrical connection, or a direct connection, or may be indirectly connected through an intermediate medium, or may be a communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
Example 1
Referring to fig. 1 to fig. 2, in a first embodiment of the present invention, there is provided an elevator fault monitoring method based on internet of things, including:
s1: the elevator door is provided with a sensor, when the elevator door is closed, the sensor is used for detecting the human body of the elevator car, and if the human body is detected, a first signal is sent to the platform of the Internet of things.
The sensor comprises a pyroelectric infrared sensor and a microwave radar sensor;
the pyroelectric infrared sensor is provided with a gain circuit, an optical filter and at least one lens; the gain circuit reduces the influence of pixel offset and noise through double sampling; the lens comprises glass and/or polycarbonate; the wavelength range of the light passing through the filter is 8-10 um.
The microwave radar sensor radiates microwaves into free space through the transmitting antenna, when electromagnetic waves encounter the movement of a human body, scattering phenomenon is generated on the surface of the human body, part of electromagnetic energy reaches the receiving antenna of the detector through the reflection of the surface of the human body, and electromagnetic parameters of reflected waves are detected through the signal processing circuit, so that human body induction is realized.
Preferably, the invention is based on the structural improvement of the traditional pyroelectric infrared sensor, reduces the external interference, and adopts the pyroelectric infrared sensor and the microwave radar sensor to carry out composite detection on the human body in the car, thereby not only solving the problem that the pyroelectric infrared sensor is easy to be interfered by a heat source, but also compensating the defect that the microwave radar sensor is afraid of vibration.
S2: and acquiring an image inside the elevator car through the platform of the Internet of things, and carrying out human body identification.
The internet of things platform comprises an image collector and a data processor, wherein the image collector is used for acquiring an image inside the elevator car, and the image inside the elevator car is further sent to the data processor for human body identification.
The data processor is embedded with a human body recognition algorithm, and the specific steps of the algorithm are as follows:
(1) Preprocessing an image in an elevator car; the preprocessing includes random scaling, rotation and mirroring of the images inside the elevator car and marking human body joints.
(2) And (3) establishing a human body recognition network, inputting the preprocessed image into the human body recognition network for training, and stopping training when the loss value is minimum.
Referring to fig. 1, the human body recognition network includes a feature extraction module, a codec, and a multi-layered perceptron; specific:
(1) extracting pixel characteristics from the preprocessed image through a characteristic extraction module;
referring to fig. 2, the feature extraction module includes an input layer, an undersampled layer, a first residual block, a second residual block, a third residual block, a fourth residual block, a fifth residual block, a sixth residual block, a first oversampled layer, a second oversampled layer, and an output layer;
four groups of residual blocks, namely a first residual block, a second residual block, a third residual block and a fourth residual block, are connected between the undersampling layer and the first oversampling layer, and a fifth residual block and a sixth residual block are connected between the output of the first oversampling layer and the input of the second oversampling layer.
The preprocessed image is input to the feature extraction module from the input layer, half of the image is input to the fifth residual block, the other half of the image is input to the undersampling layer, then sequentially passes through the first residual block, the second residual block, the third residual block, the fourth residual block and the first oversampling layer, features output by the first oversampling layer are added with features output by the fifth residual block, the added features are input to the sixth residual block, and finally, the oversampling is performed through the second oversampling layer, and pixel features are output through the output layer.
(2) Extracting spatial features from the preprocessed image by a codec; the codec may use a network model such as SEQ2SEQ, AE, VAE, which is not described herein.
(3) The space features and the pixel features are aligned and fused through the multi-layer perceptron, effective segmentation of the human body surface in the three-dimensional space is judged, and the human body is identified;
the working principle of the multilayer perceptron is as follows: and fitting an implicit surface function M (x) to the input feature x, so as to obtain the probability P=M (x) that the corresponding position under the feature is positioned in the human body surface, and judging the effective segmentation of the human body surface in the three-dimensional space to identify the human body.
Further, in order to improve the accuracy of human body recognition, training is required to be performed on the human body recognition network, and when the loss value is minimum, the training is stopped, specifically including:
defining a first loss function, a second loss function and a third loss function, taking the sum of the first loss function value and the second loss function value and the third loss function value as the average value, and stopping training when the loss value of the human body identification network is minimum;
wherein the first loss function L is defined based on the square error loss 1 The method comprises the following steps:
L 1 ＝(y 1 -y 1 ’) 2
wherein y is 1 For the actual output value of the feature extraction module, y 1 ' is the predicted output value of the feature extraction module;
defining a second loss function L based on the square error loss 2 The method comprises the following steps:
L 2 ＝(y 2 -y 2 ’) 2
wherein y is 2 Is the actual output value of the codec, y 2 ' is a predicted output value of the codec;
defining a third loss function L based on Huber losses 3 The method comprises the following steps:
L 3 ＝10(|y 3 -y 3 ’|-5)
wherein y is 3 Is the actual output value of the multi-layer sensor, y 3 ' is the predicted output value of the multi-layer sensor.
Preferably, the invention defines corresponding loss functions on each structure of the human body identification network to carry out supervision training, thereby ensuring normal updating of bottom layer parameters and improving the identification accuracy of the human body identification network.
(3) And carrying out human body recognition by using the trained human body recognition network.
S3: if the human body exists in the elevator car and the residence time of the human body in the elevator car is longer than the preset time, the current elevator is confirmed to have a trapped fault, and a second signal is sent to the alarm to prompt the staff to perform trapped fault treatment.
The warning lamp is continuously flashed and the alarm is sounded according to the preset time interval until the third signal confirmed by the staff is received, namely, after the staff receives the response of the warning lamp and the alarm, the third signal is sent to the alarm, and the alarm is stopped.
Example 2
Unlike the first embodiment, this embodiment provides an elevator fault monitoring system based on the internet of things, which includes,
a first detection module configured to perform setting of a sensor in the car, detecting a human body of the car through the sensor when the elevator door is closed, and if the human body is detected, transmitting a first signal to a second detection module sensor including a pyroelectric infrared sensor and a microwave radar sensor; the pyroelectric infrared sensor is provided with a gain circuit, an optical filter and at least one lens; wherein the gain circuit is configured to perform double sampling to reduce the effects of pixel offset and noise; the lens comprises glass and/or polycarbonate; the filter is configured to perform the passing light in a wavelength range of 8 to 10um.
The second detection module is configured to acquire an image of the interior of the elevator car through the terminal application layer and perform human body identification;
the early warning module is configured to execute the steps that if a human body is identified and the time of the human body staying in the elevator car is longer than the preset time, the current elevator is confirmed to have a trapping fault, a second signal is sent to the alarm, the early warning module is configured to execute the steps that if the human body is identified to exist in the elevator car and the time of the human body staying in the elevator car is longer than the preset time, the current elevator is confirmed to have a trapping fault, the second signal is sent to the alarm, the alarm continuously flashes a warning lamp and sends a ringing alarm according to the preset time interval to prompt a worker to perform trapping fault processing, and after a third signal confirmed by the worker is received, namely, the worker receives the response of the warning lamp and the alarm, the third signal is sent to the alarm, and the alarm is stopped.
The second detection module is an internet of things platform, and the internet of things platform comprises an image collector and a data processor, and is specifically configured to perform preprocessing on images inside an elevator car; the preprocessing comprises the steps of carrying out random scaling, rotation and mirror image processing on the images in the elevator car and marking human body joint points; a human body recognition network is established, the preprocessed image is input into the human body recognition network for training, and training is stopped when the loss value is minimum; and carrying out human body recognition by using the trained human body recognition network.
It should be appreciated that embodiments of the invention may be implemented or realized by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer readable memory. The methods may be implemented in a computer program using standard programming techniques, including a non-transitory computer readable storage medium configured with a computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner, in accordance with the methods and drawings described in the specific embodiments. Each program may be implemented in a high level procedural or object oriented programming language to communicate with a computer system. However, the program(s) can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language. Furthermore, the program can be run on a programmed application specific integrated circuit for this purpose.
Furthermore, the operations of the processes described herein may be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The processes (or variations and/or combinations thereof) described herein may be performed under control of one or more computer systems configured with executable instructions, and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications), by hardware, or combinations thereof, collectively executing on one or more processors. The computer program includes a plurality of instructions executable by one or more processors.
Further, the method may be implemented in any type of computing platform operatively connected to a suitable computing platform, including, but not limited to, a personal computer, mini-computer, mainframe, workstation, network or distributed computing environment, separate or integrated computer platform, or in communication with a charged particle tool or other imaging device, and so forth. Aspects of the invention may be implemented in machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, optical read and/or write storage medium, RAM, ROM, etc., such that it is readable by a programmable computer, which when read by a computer, is operable to configure and operate the computer to perform the processes described herein. Further, the machine readable code, or portions thereof, may be transmitted over a wired or wireless network. When such media includes instructions or programs that, in conjunction with a microprocessor or other data processor, implement the steps described above, the invention described herein includes these and other different types of non-transitory computer-readable storage media. The invention also includes the computer itself when programmed according to the methods and techniques of the present invention. The computer program can be applied to the input data to perform the functions described herein, thereby converting the input data to generate output data that is stored to the non-volatile memory. The output information may also be applied to one or more output devices such as a display. In a preferred embodiment of the invention, the transformed data represents physical and tangible objects, including specific visual depictions of physical and tangible objects produced on a display.
As used in this application, the terms "component," "module," "system," and the like are intended to refer to a computer-related entity, either hardware, firmware, a combination of hardware and software, or software in execution. For example, the components may be, but are not limited to: a process running on a processor, an object, an executable, a thread of execution, a program, and/or a computer. By way of example, both an application running on a computing device and the computing device can be a component. One or more components may reside within a process and/or thread of execution and a component may be localized on one computer and/or distributed between two or more computers. Furthermore, these components can execute from various computer readable media having various data structures thereon. The components may communicate by way of local and/or remote processes such as in accordance with a signal having one or more data packets (e.g., data from one component interacting with another component in a local system, distributed system, and/or across a network such as the internet with other systems by way of the signal).
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
Claims (10)
1. The elevator fault monitoring method based on the Internet of things is characterized by comprising the following steps of:
a sensor is arranged in the lift car, when the lift door is closed, human body detection is carried out on the lift car through the sensor, and if the human body is detected, a first signal is sent to the platform of the Internet of things;
acquiring an image of the interior of an elevator car through the internet of things platform, and performing human body recognition;
if the human body exists in the elevator car and the residence time of the human body in the elevator car is longer than the preset time, confirming that the current elevator has a trapped fault, and sending a second signal to an alarm to prompt a worker to perform trapped fault treatment;
the Internet of things platform comprises an image collector and a data processor.
2. The elevator fault monitoring method based on the internet of things according to claim 1, wherein the sensor comprises a pyroelectric infrared sensor and a microwave radar sensor;
the pyroelectric infrared sensor is provided with a gain circuit, an optical filter and at least one lens;
wherein the gain circuit reduces the effects of pixel offset and noise by double sampling; the lens comprises glass and/or polycarbonate; the wavelength range of the light passing through the filter is 8-10 um.
3. The elevator fault monitoring method based on the internet of things according to claim 2, wherein the human body recognition comprises:
preprocessing an image in an elevator car; the preprocessing comprises the steps of carrying out random scaling, rotation and mirror image processing on an image in the elevator car and marking a human body joint point;
a human body recognition network is established, the preprocessed image is input into the human body recognition network for training, and training is stopped when the loss value is minimum;
and carrying out human body recognition by using the trained human body recognition network.
4. The elevator fault monitoring method based on the internet of things according to claim 3, wherein the human body recognition network comprises a feature extraction module, a codec and a multi-layer sensor;
extracting pixel characteristics from the preprocessed image through a characteristic extraction module;
extracting spatial features from the preprocessed image by a codec;
and carrying out alignment fusion on the space features and the pixel features through a multi-layer sensor, judging effective segmentation of the human body surface in the three-dimensional space, and identifying the human body.
5. The elevator fault monitoring method based on the internet of things of claim 4, wherein the feature extraction module comprises an input layer, an undersampled layer, a first residual block, a second residual block, a third residual block, a fourth residual block, a fifth residual block, a sixth residual block, a first oversampled layer, a second oversampled layer, and an output layer;
four groups of residual blocks, namely a first residual block, a second residual block, a third residual block and a fourth residual block, are connected between the undersampling layer and the first oversampling layer, and a fifth residual block and a sixth residual block are connected between the output of the first oversampling layer and the input of the second oversampling layer.
6. The internet of things-based elevator fault monitoring method according to claim 4 or 5, wherein the training comprises:
defining a first loss function, a second loss function and a third loss function, taking the sum of the first loss function value and the second loss function value and the third loss function value as the average value, and stopping training when the loss value of the human body identification network is minimum;
wherein the first loss function L 1 The method comprises the following steps:
L 1 ＝(y 1 -y 1 ’) 2
wherein y is 1 For the actual output value of the feature extraction module, y 1 ' is the predicted output value of the feature extraction module;
second loss function L 2 The method comprises the following steps:
L 2 ＝(y 2 -y 2 ’) 2
wherein y is 2 Is the actual output value of the codec, y 2 ' is a predicted output value of the codec;
third loss function L 3 The method comprises the following steps:
L 3 ＝10(|y 3 -y 3 ’|-5)
wherein y is 3 Is the actual output value of the multi-layer sensor, y 3 ' is the predicted output value of the multi-layer sensor.
7. The elevator fault monitoring method based on the internet of things according to claim 1, wherein the alarm comprises:
the warning lamp is continuously flashed and the alarm is sounded according to the preset time interval until the alarm is stopped after the third signal confirmed by the staff is received.
8. Elevator fault monitoring system based on thing networking, characterized by, include:
a first detection module configured to perform setting of a sensor in the car, human body detection of the car by the sensor when the elevator door is closed, and if a human body is detected, transmitting a first signal to a second detection module;
the second detection module is configured to acquire an image of the interior of the elevator car through the internet of things platform and perform human body identification;
the early warning module is configured to execute the steps that if the existence of a human body in the car is recognized, and the residence time of the human body in the car is longer than the preset time, the occurrence of the trapped fault of the current elevator is confirmed, a second signal is sent to the alarm, the alarm continuously flashes a warning lamp and sounds a bell alarm according to the preset time interval, so that a worker is prompted to perform trapped fault processing, and the alarm is stopped until a third signal confirmed by the worker is received;
the second detection module is an internet of things platform, and the internet of things platform comprises an image collector and a data processor.
9. The internet of things-based elevator fault monitoring system of claim 8, wherein the sensor comprises a pyroelectric infrared sensor and a microwave radar sensor;
the pyroelectric infrared sensor is provided with a gain circuit, an optical filter and at least one lens;
wherein the gain circuit is configured to perform double sampling to reduce the effects of pixel offset and noise; the lens comprises glass and/or polycarbonate; the filter is configured to perform the passing light in a wavelength range of 8 to 10um.
10. The internet of things-based elevator fault monitoring system of claim 8 or 9, wherein the second detection module is specifically configured to perform preprocessing of an image of an interior of an elevator car; the preprocessing comprises the steps of carrying out random scaling, rotation and mirror image processing on an image in the elevator car and marking a human body joint point; a human body recognition network is established, the preprocessed image is input into the human body recognition network for training, and training is stopped when the loss value is minimum; and carrying out human body recognition by using the trained human body recognition network.
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