CN113920720A - Highway tunnel equipment fault processing method and device and electronic equipment - Google Patents
Highway tunnel equipment fault processing method and device and electronic equipment Download PDFInfo
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- CN113920720A CN113920720A CN202111093251.5A CN202111093251A CN113920720A CN 113920720 A CN113920720 A CN 113920720A CN 202111093251 A CN202111093251 A CN 202111093251A CN 113920720 A CN113920720 A CN 113920720A
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
- G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/16—Anti-collision systems
Abstract
The application discloses a method and a device for processing faults of highway tunnel equipment and electronic equipment, belonging to the technical field of maintenance of highway tunnel equipment, wherein the method for processing the faults of the highway tunnel equipment comprises the following steps: acquiring real-time sensing data acquired by highway tunnel equipment; inputting real-time sensing data into a trained multidimensional deep neural network so as to enable a neuron layer to carry out weighted calculation to obtain a classification result; and if the classification result is the fault type, sending out an alarm message according to the fault type. The method avoids manual inspection, can quickly analyze and determine the fault equipment, ensures the normal operation of the highway tunnel equipment, and reduces the occurrence of highway tunnel traffic accidents.
Description
Technical Field
The application belongs to the technical field of maintenance of highway tunnel equipment, and particularly relates to a method and a device for processing faults of highway tunnel equipment and electronic equipment.
Background
The highway tunnel is a passage specially used for automobile transportation. Along with the development of social economy and production, a great number of expressways appear, and a higher standard is provided for the maintenance technology of road equipment.
Due to the fact that the highway tunnel is difficult to inspect, fault equipment cannot be detected and found timely.
Disclosure of Invention
The application aims to provide a method and a device for processing faults of highway tunnel equipment and electronic equipment so as to solve the problem that the highway tunnel fault equipment is difficult to detect and discover.
According to a first aspect of embodiments of the present application, there is provided a method for handling a failure of a highway tunnel device, the method may include:
acquiring real-time sensing data acquired by highway tunnel equipment;
inputting real-time sensing data into a trained multidimensional deep neural network so as to enable a neuron layer to carry out weighted calculation to obtain a classification result;
and if the classification result is the fault type, sending out an alarm message according to the fault type.
In some optional embodiments of the present application, the acquiring of the real-time sensing data collected by the highway tunnel equipment includes at least one of:
acquiring real-time sensing data acquired by highway tunnel lighting equipment;
acquiring real-time sensing data acquired by expressway tunnel ventilation equipment;
acquiring real-time sensing data acquired by highway tunnel traffic guidance equipment;
acquiring real-time sensing data acquired by highway tunnel environment monitoring equipment;
acquiring real-time sensing data acquired by fire fighting equipment of the highway tunnel;
acquiring real-time sensing data acquired by highway tunnel network monitoring equipment;
acquiring real-time sensing data acquired by expressway tunnel video monitoring equipment;
acquiring real-time sensing data acquired by emergency telephone equipment in a highway tunnel;
and acquiring real-time sensing data acquired by the emergency broadcasting equipment of the highway tunnel.
In some optional embodiments of the present application, the trained multidimensional deep neural network is obtained by training:
acquiring historical sensing data acquired by highway tunnel equipment;
classifying and labeling historical sensing data to obtain a training sample set;
and training the multidimensional deep neural network by using the training sample set to obtain the trained multidimensional deep neural network.
In some optional embodiments of the present application, the obtaining of the historical sensing data collected by the highway tunnel equipment includes:
acquiring historical sensing data acquired by highway tunnel lighting equipment;
acquiring historical sensing data acquired by expressway tunnel ventilation equipment;
acquiring historical sensing data acquired by highway tunnel traffic guidance equipment;
acquiring historical sensing data acquired by highway tunnel environment monitoring equipment;
acquiring historical sensing data acquired by fire fighting equipment of a highway tunnel;
acquiring historical sensing data acquired by expressway tunnel network monitoring equipment;
acquiring historical sensing data acquired by expressway tunnel video monitoring equipment;
acquiring historical sensing data acquired by emergency telephone equipment in a highway tunnel; and
historical sensing data collected by the expressway tunnel emergency broadcasting equipment is obtained.
In some optional embodiments of the present application, the classifying and labeling the historical sensing data to obtain a training sample set includes:
determining a subspace of a fault label according to a fault value range of historical sensing data;
and determining the multi-source data association relation of the target fault label according to the target fault case in the historical sensing data to obtain a labeled sample.
In some optional embodiments of the present application, the classifying and labeling the historical sensing data to obtain a training sample set further includes:
and cutting the marked sample into sample blocks of smaller blocks to obtain a training sample set.
In some optional embodiments of the present application, training the multidimensional deep neural network by using a training sample set to obtain a trained multidimensional deep neural network, including:
carrying out compression coding on the training sample set to obtain a compressed sample set;
and the compressed sample set carries out deep learning training on the multidimensional deep neural network, and learns and adjusts the structure and weight parameters of the multidimensional deep neural network by adopting a gradient descent method according to the index of the loss function to obtain the trained multidimensional deep neural network.
According to a second aspect of embodiments of the present application, there is provided an apparatus for handling a failure of a highway tunnel device, the apparatus may include:
the acquisition module is used for acquiring real-time sensing data acquired by the highway tunnel equipment;
the classification module is used for inputting the real-time sensing data into the trained multi-dimensional deep neural network so as to enable the neuron layer to carry out weighting calculation to obtain a classification result;
and the alarm module is used for sending out an alarm message according to the fault type if the classification result is the fault type.
According to a third aspect of embodiments of the present application, there is provided an electronic apparatus, which may include:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to execute the instructions to implement the method of highway tunnel equipment fault handling as shown in any embodiment of the first aspect.
According to a fourth aspect of the embodiments of the present application, there is provided a storage medium, in which instructions are executed by a processor of an information processing apparatus or a server to cause the information processing apparatus or the server to implement the highway tunnel equipment failure processing method as shown in any one of the embodiments of the first aspect.
The technical scheme of the application has the following beneficial technical effects:
the method comprises the steps of acquiring real-time sensing data acquired by highway tunnel equipment; inputting real-time sensing data into a trained multidimensional deep neural network so as to enable a neuron layer to carry out weighted calculation to obtain a classification result; and if the classification result is the fault type, sending out an alarm message according to the fault type. The method avoids manual inspection, can quickly analyze and determine the fault equipment, ensures the normal operation of the highway tunnel equipment, and reduces the occurrence of highway tunnel traffic accidents.
Drawings
FIG. 1 is a flow chart of a method for handling a failure of a highway tunnel equipment in an exemplary embodiment of the present application;
FIG. 2 is a flow chart of the training and application of a multi-dimensional deep neural network in an exemplary embodiment of the present application;
FIG. 3 is a schematic structural diagram of a fault handling device of the highway tunnel equipment in an exemplary embodiment of the present application;
FIG. 4 is a schematic diagram of a deep learning and warning system for highway tunnel inspection data according to an exemplary embodiment of the present application;
FIG. 5 is a schematic diagram of an electronic device in an exemplary embodiment of the present application;
fig. 6 is a schematic diagram of a hardware structure of an electronic device in an exemplary embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is further described in detail below with reference to the accompanying drawings in combination with the detailed description. It should be understood that the description is intended to be exemplary only, and is not intended to limit the scope of the present application. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present application.
In the drawings, a schematic diagram of a layer structure according to an embodiment of the application is shown. The figures are not drawn to scale, wherein certain details are exaggerated and possibly omitted for clarity. The shapes of various regions, layers, and relative sizes and positional relationships therebetween shown in the drawings are merely exemplary, and deviations may occur in practice due to manufacturing tolerances or technical limitations, and a person skilled in the art may additionally design regions/layers having different shapes, sizes, relative positions, as actually required.
It is to be understood that the embodiments described are only a few embodiments of the present application and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In the description of the present application, it is noted that the terms "first", "second", and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In addition, the technical features mentioned in the different embodiments of the present application described below may be combined with each other as long as they do not conflict with each other.
With the continuous improvement of highway infrastructure and the networking of road equipment, maintenance inspection personnel can acquire real-time sensing data of highway tunnel equipment.
The method for processing the failure of the highway tunnel equipment provided by the embodiment of the present application is described in detail below with reference to the accompanying drawings through specific embodiments and application scenarios thereof.
As shown in fig. 1, in a first aspect of the embodiments of the present application, there is provided a method for handling a failure of an expressway tunnel device, where the method may include:
s110: acquiring real-time sensing data acquired by highway tunnel equipment;
s120: inputting real-time sensing data into a trained multidimensional deep neural network so as to enable a neuron layer to carry out weighted calculation to obtain a classification result;
s130: and if the classification result is the fault type, sending out an alarm message according to the fault type.
The method avoids manual inspection, can quickly analyze and determine the fault equipment, ensures the normal operation of the highway tunnel equipment, and reduces the occurrence of highway tunnel traffic accidents.
In order to more clearly describe the present embodiment, the following describes the above steps respectively:
first, step S110: and acquiring real-time sensing data acquired by the highway tunnel equipment.
The highway tunnel equipment in this step may include lighting equipment, ventilation equipment, traffic guidance equipment, environmental monitoring equipment, fire fighting equipment, network monitoring equipment, video monitoring equipment, emergency telephone equipment, and emergency broadcasting equipment.
Next is step S120: and inputting the real-time sensing data into the trained multidimensional deep neural network so as to enable the neuron layer to carry out weighted calculation to obtain a classification result.
The construction process of the well-trained multidimensional deep neural network in the step comprises the following steps:
building a trained historical data import module of the lighting equipment of the highway tunnel of the multidimensional deep neural network, and importing the collected historical data of the lighting equipment of the highway tunnel;
constructing a historical data import module of ventilation equipment of the highway tunnel, and importing collected historical data of the ventilation equipment of the highway tunnel;
the method comprises the steps that a historical data import module of traffic guidance equipment of the highway tunnel is constructed, and collected historical data of the traffic guidance equipment of the highway tunnel is imported;
the method comprises the steps that a historical data import module of the environment monitoring equipment of the highway tunnel is constructed, and collected historical data of the environment monitoring equipment of the highway tunnel is imported;
building a historical data import module of fire fighting equipment of the highway tunnel, and importing the collected historical data of the fire fighting equipment of the highway tunnel;
building a historical data import module of the network monitoring equipment of the highway tunnel, and importing the collected historical data of the network monitoring equipment of the highway tunnel;
building a historical data import module of the video monitoring equipment of the highway tunnel, and importing the collected historical data of the video monitoring equipment of the highway tunnel;
the method comprises the steps that a historical data import module of emergency telephone equipment of the highway tunnel is built, and collected historical data of the emergency telephone equipment of the highway tunnel is imported;
the method comprises the steps that a historical data import module of the emergency broadcasting equipment of the highway tunnel is built, and collected historical data of the emergency broadcasting equipment of the highway tunnel is imported;
and analyzing the relation between the historical data and the real-time sensing data of each device, and constructing a data model of the highway tunnel inspection.
And constructing a trained historical data learning training module of the multidimensional deep neural network, inputting the historical data into the multidimensional deep neural network, performing deep learning on the failure rule in the historical data, and optimizing the structure and weight of the trained multidimensional deep neural network.
The trained multidimensional deep neural network consists of an expressway tunnel inspection data input layer, a convolution layer, a pooling layer, a full-connection layer and an output layer, and historical data and real-time sensing data of expressway tunnel inspection are used for the expressway tunnel inspection data input layer.
Finally, step S130: and if the classification result is the fault type, sending out an alarm message according to the fault type.
The method comprises the steps of constructing a trained multidimensional deep neural network real-time sensing data calculation and fault warning module based on a trained multidimensional deep neural network, inputting real-time sensing data into the trained multidimensional deep neural network, calculating a fault judgment result of the real-time sensing data, and sending a fault warning message if the fault judgment result is a fault.
In this embodiment, a server of the highway tunnel inspection data deep learning and warning system needs to be constructed;
a server of the highway tunnel inspection data deep learning and warning system is a computation center of the highway tunnel inspection data deep learning and warning, comprises a GPU, a large-capacity external memory and a large-capacity memory, and provides sufficient GPU computational power and memory capacity support for the highway tunnel inspection data deep learning and warning.
The well-trained multidimensional deep neural network adopted by the embodiment can be popularized to other inspection data machine learning systems, and is used for realizing machine learning, deep analysis and emergency treatment of inspection data.
In some optional embodiments of the present application, the acquiring of the real-time sensing data collected by the highway tunnel equipment includes at least one of:
acquiring real-time sensing data acquired by highway tunnel lighting equipment;
acquiring real-time sensing data acquired by expressway tunnel ventilation equipment;
acquiring real-time sensing data acquired by highway tunnel traffic guidance equipment;
acquiring real-time sensing data acquired by highway tunnel environment monitoring equipment;
acquiring real-time sensing data acquired by fire fighting equipment of the highway tunnel;
acquiring real-time sensing data acquired by highway tunnel network monitoring equipment;
acquiring real-time sensing data acquired by expressway tunnel video monitoring equipment;
acquiring real-time sensing data acquired by emergency telephone equipment in a highway tunnel;
and acquiring real-time sensing data acquired by the emergency broadcasting equipment of the highway tunnel.
In some optional embodiments of the present application, the trained multidimensional deep neural network is obtained by training:
acquiring historical sensing data acquired by highway tunnel equipment;
classifying and labeling historical sensing data to obtain a training sample set;
and training the multidimensional deep neural network by using the training sample set to obtain the trained multidimensional deep neural network.
Inputting historical data into a multidimensional deep neural network, deeply learning the failure rule in the historical data, and optimizing and training the structure and weight of the multidimensional deep neural network; as shown in fig. 2, the training and application of the multidimensional deep neural network includes:
(1) classifying historical data, labeling, determining a subspace of common fault labels according to a common fault value range of single sensor data, and determining a multi-source data association relation of a specific fault label according to a specific fault case in the historical data;
(2) generating input samples from historical data and labels thereof, and cutting each sample into sample blocks of smaller blocks;
(3) constructing a multi-dimensional deep neural network, firstly carrying out compression coding on each sample block, then carrying out deep learning training on the multi-dimensional deep neural network, learning and adjusting the structure and weight parameters of the multi-dimensional deep neural network by adopting a gradient descent method according to the index of a loss function, and then decoding the training result;
(4) inputting the real-time sensing data into the trained multidimensional deep neural network, calculating a fault judgment result of the real-time sensing data, and sending a fault alarm message if the fault judgment result is a fault.
According to the method, the multidimensional deep neural network can be used for deeply learning the inspection data of the highway tunnel, the multidimensional deep neural network can give an alarm according to the deep learning result, and the effectiveness and the intelligence of the inspection of the highway tunnel are improved.
In some optional embodiments of the present application, obtaining the historical sensing data collected by the highway tunnel equipment comprises at least one of:
acquiring historical sensing data acquired by highway tunnel lighting equipment;
acquiring historical sensing data acquired by expressway tunnel ventilation equipment;
acquiring historical sensing data acquired by highway tunnel traffic guidance equipment;
acquiring historical sensing data acquired by highway tunnel environment monitoring equipment;
acquiring historical sensing data acquired by fire fighting equipment of a highway tunnel;
acquiring historical sensing data acquired by expressway tunnel network monitoring equipment;
acquiring historical sensing data acquired by expressway tunnel video monitoring equipment;
acquiring historical sensing data acquired by emergency telephone equipment in a highway tunnel;
historical sensing data collected by the expressway tunnel emergency broadcasting equipment is obtained.
In some optional embodiments of the present application, the classifying and labeling the historical sensing data to obtain a training sample set includes:
determining a subspace of a fault label according to a fault value range of historical sensing data;
and determining the multi-source data association relation of the target fault label according to the target fault case in the historical sensing data to obtain a labeled sample.
In some optional embodiments of the present application, the classifying and labeling the historical sensing data to obtain a training sample set further includes:
and cutting the marked sample into sample blocks of smaller blocks to obtain a training sample set.
In some optional embodiments of the present application, training the multidimensional deep neural network by using a training sample set to obtain a trained multidimensional deep neural network, including:
carrying out compression coding on the training sample set to obtain a compressed sample set;
and the compressed sample set carries out deep learning training on the multidimensional deep neural network, and learns and adjusts the structure and weight parameters of the multidimensional deep neural network by adopting a gradient descent method according to the index of the loss function to obtain the trained multidimensional deep neural network.
As shown in fig. 3, in a second aspect of the embodiments of the present application, there is provided an apparatus for handling a failure of a highway tunnel device, which may include:
the acquisition module is used for acquiring real-time sensing data acquired by the highway tunnel equipment;
the classification module is used for inputting the real-time sensing data into the trained multi-dimensional deep neural network so as to enable the neuron layer to carry out weighting calculation to obtain a classification result;
and the alarm module is used for sending out an alarm message according to the fault type if the classification result is the fault type.
The fault processing device for the expressway tunnel equipment is constructed by the following steps:
constructing a real-time sensing data acquisition module of ventilation equipment of the highway tunnel, and acquiring and preprocessing the real-time sensing data of the ventilation equipment of the highway tunnel; constructing a trained real-time sensing data acquisition module of the lighting equipment of the highway tunnel of the multidimensional deep neural network, and acquiring and preprocessing the real-time sensing data of the lighting equipment of the highway tunnel;
the method comprises the steps that a real-time sensing data acquisition module of traffic guidance equipment of the highway tunnel is constructed, and real-time sensing data of the traffic guidance equipment of the highway tunnel are collected and preprocessed;
the method comprises the steps of constructing a real-time sensing data acquisition module of the environment monitoring equipment of the highway tunnel, and acquiring and preprocessing the real-time sensing data of the environment monitoring equipment of the highway tunnel;
constructing a real-time sensing data acquisition module of fire fighting equipment of the highway tunnel, and acquiring and preprocessing the real-time sensing data of the fire fighting equipment of the highway tunnel;
the method comprises the steps that a real-time sensing data acquisition module of the network monitoring equipment for constructing the highway tunnel collects and preprocesses real-time sensing data of the network monitoring equipment for the highway tunnel;
the method comprises the steps that a real-time sensing data acquisition module of the video monitoring equipment of the expressway tunnel is constructed, and real-time sensing data of the video monitoring equipment of the expressway tunnel are collected and preprocessed;
the method comprises the steps that a real-time sensing data acquisition module of the emergency telephone equipment of the expressway tunnel is constructed, and the real-time sensing data of the emergency telephone equipment of the expressway tunnel is collected and preprocessed;
the method comprises the steps that a real-time sensing data acquisition module of the emergency broadcasting equipment of the expressway tunnel is constructed, and real-time sensing data of the emergency broadcasting equipment of the expressway tunnel are collected and preprocessed;
and analyzing the relation between the historical data and the real-time sensing data of each device, and constructing a data model of the highway tunnel inspection.
It should be noted that, in the method for processing a failure of an expressway tunnel device provided in the embodiment of the present application, the execution main body may be an expressway tunnel device failure processing apparatus, or a control module of the method for processing a failure of an expressway tunnel device in the expressway tunnel device failure processing apparatus. The method for performing the fault processing on the highway tunnel equipment by using the fault processing device for the highway tunnel equipment in the embodiment of the application is taken as an example, and the fault processing device for the highway tunnel equipment provided by the embodiment of the application is described.
The fault processing device of the highway tunnel equipment in the embodiment of the application can be a device, and can also be a component, an integrated circuit or a chip in a terminal. The device can be mobile electronic equipment or non-mobile electronic equipment. By way of example, the mobile electronic device may be a mobile phone, a tablet computer, a notebook computer, a palm top computer, a vehicle-mounted electronic device, a wearable device, an ultra-mobile personal computer (UMPC), a netbook or a Personal Digital Assistant (PDA), and the like, and the non-mobile electronic device may be a server, a Network Attached Storage (NAS), a Personal Computer (PC), a Television (TV), a teller machine or a self-service machine, and the like, and the embodiments of the present application are not particularly limited.
The fault handling device of the highway tunnel equipment in the embodiment of the application can be a device with an operating system. The operating system may be an Android (Android) operating system, an ios operating system, or other possible operating systems, and embodiments of the present application are not limited specifically.
The fault processing apparatus for highway tunnel equipment provided in the embodiment of the present application can implement each process implemented in the method embodiment of fig. 1, and is not described here again to avoid repetition.
In another aspect of the embodiments of the present application, a deep learning and warning system for highway tunnel inspection data is provided, which combines a highway tunnel processing system and a model training system, as shown in fig. 4, and includes: the historical data importing module of the lighting equipment of the highway tunnel is used for acquiring the collected historical data of the lighting equipment of the highway tunnel; the real-time sensing data acquisition module of the lighting equipment of the highway tunnel is used for acquiring and preprocessing the real-time sensing data of the lighting equipment of the highway tunnel; the historical data importing module of the ventilation equipment of the highway tunnel is used for acquiring the collected historical data of the ventilation equipment of the highway tunnel; the real-time sensing data acquisition module of the ventilation equipment of the highway tunnel is used for acquiring and preprocessing the real-time sensing data of the ventilation equipment of the highway tunnel; the historical data importing module of the traffic guidance equipment of the highway tunnel is used for acquiring the collected historical data of the traffic guidance equipment of the highway tunnel; the real-time sensing data acquisition module of the traffic guidance equipment of the highway tunnel is used for acquiring and preprocessing the real-time sensing data of the traffic guidance equipment of the highway tunnel; the historical data import module of the environment monitoring equipment of the expressway tunnel is used for acquiring the collected historical data of the environment monitoring equipment of the expressway tunnel; the real-time sensing data acquisition module of the environment monitoring equipment of the expressway tunnel is used for acquiring and preprocessing the real-time sensing data of the environment monitoring equipment of the expressway tunnel; the fire fighting equipment historical data import module of the highway tunnel is used for acquiring the acquired historical data of the fire fighting equipment of the highway tunnel; the real-time sensing data acquisition module of the fire fighting equipment of the highway tunnel is used for acquiring and preprocessing the real-time sensing data of the fire fighting equipment of the highway tunnel; the historical data import module of the network monitoring equipment of the expressway tunnel is used for acquiring the collected historical data of the network monitoring equipment of the expressway tunnel; the system comprises a real-time sensing data acquisition module of the network monitoring equipment of the expressway tunnel, a real-time sensing data acquisition module of the expressway tunnel; the historical data import module of the video monitoring equipment of the expressway tunnel is used for acquiring the collected historical data of the video monitoring equipment of the expressway tunnel; the real-time sensing data acquisition module of the video monitoring equipment of the expressway tunnel is used for acquiring and preprocessing the real-time sensing data of the video monitoring equipment of the expressway tunnel; the historical data import module of the emergency telephone equipment of the expressway tunnel is used for acquiring the collected historical data of the emergency telephone equipment of the expressway tunnel; the real-time sensing data acquisition module of the emergency telephone equipment of the expressway tunnel is used for acquiring and preprocessing the real-time sensing data of the emergency telephone equipment of the expressway tunnel; the historical data import module of the emergency broadcasting equipment of the highway tunnel is used for acquiring the acquired historical data of the emergency broadcasting equipment of the highway tunnel; the real-time sensing data acquisition module of the emergency broadcasting equipment of the highway tunnel is used for acquiring and preprocessing the real-time sensing data of the emergency broadcasting equipment of the highway tunnel; the multi-dimensional deep neural network historical data learning training module is used for inputting the historical data into the multi-dimensional deep neural network, deeply learning the failure rule in the historical data and optimally training the structure and weight of the multi-dimensional deep neural network; the multidimensional deep neural network real-time sensing data calculation and fault warning module is used for inputting real-time sensing data into a trained multidimensional deep neural network, calculating a fault judgment result of the real-time sensing data, and sending a fault warning message if the fault judgment result is a fault.
The highway tunnel inspection data deep learning and warning system can be popularized to other inspection data machine learning systems and is used for realizing machine learning, deep analysis and emergency treatment of inspection data.
Optionally, as shown in fig. 5, an electronic device 500 is further provided in this embodiment of the present application, and includes a processor 501, a memory 502, and a program or an instruction stored in the memory 502 and executable on the processor 501, where the program or the instruction is executed by the processor 501 to implement each process of the above-mentioned highway tunnel device fault handling method embodiment, and can achieve the same technical effect, and in order to avoid repetition, it is not described here again.
It should be noted that the electronic devices in the embodiments of the present application include the mobile electronic device and the non-mobile electronic device described above.
Fig. 6 is a schematic diagram of a hardware structure of an electronic device implementing an embodiment of the present application.
The electronic device 600 includes, but is not limited to: a radio frequency unit 601, a network module 602, an audio output unit 603, an input unit 604, a sensor 605, a display unit 606, a user input unit 607, an interface unit 608, a memory 609, a processor 610, and the like.
Those skilled in the art will appreciate that the electronic device 600 may further comprise a power source (e.g., a battery) for supplying power to the various components, and the power source may be logically connected to the processor 610 through a power management system, so as to implement functions of managing charging, discharging, and power consumption through the power management system. The electronic device structure shown in fig. 6 does not constitute a limitation of the electronic device, and the electronic device may include more or less components than those shown, or combine some components, or arrange different components, and thus, the description is omitted here.
It is to be understood that, in the embodiment of the present application, the input Unit 604 may include a Graphics Processing Unit (GPU) 6041 and a microphone 6042, and the Graphics Processing Unit 6041 processes image data of a still picture or a video obtained by an image capturing apparatus (such as a camera) in a video capturing mode or an image capturing mode. The display unit 606 may include a display panel 6061, and the display panel 6061 may be configured in the form of a liquid crystal display, an organic light emitting diode, or the like. The user input unit 607 includes a touch panel 6071 and other input devices 6072. A touch panel 6071, also referred to as a touch screen. The touch panel 6071 may include two parts of a touch detection device and a touch controller. Other input devices 6072 may include, but are not limited to, a physical keyboard, function keys (e.g., volume control keys, switch keys, etc.), a trackball, a mouse, and a joystick, which are not described in detail herein. The memory 609 may be used to store software programs as well as various data including, but not limited to, application programs and an operating system. The processor 610 may integrate an application processor, which primarily handles operating systems, user interfaces, applications, etc., and a modem processor, which primarily handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 610.
The embodiment of the present application further provides a readable storage medium, where a program or an instruction is stored on the readable storage medium, and when the program or the instruction is executed by a processor, the program or the instruction implements each process of the above highway tunnel device fault processing method embodiment, and can achieve the same technical effect, and in order to avoid repetition, details are not repeated here.
The processor is the processor in the electronic device described in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and so on.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element. Further, it should be noted that the scope of the methods and apparatus of the embodiments of the present application is not limited to performing the functions in the order illustrated or discussed, but may include performing the functions in a substantially simultaneous manner or in a reverse order based on the functions involved, e.g., the methods described may be performed in an order different than that described, and various steps may be added, omitted, or combined. In addition, features described with reference to certain examples may be combined in other examples.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application may be embodied in the form of a computer software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal (such as a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present application.
While the present embodiments have been described with reference to the accompanying drawings, it is to be understood that the invention is not limited to the precise embodiments described above, which are meant to be illustrative and not restrictive, and that various changes may be made therein by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A method for processing faults of highway tunnel equipment is characterized by comprising the following steps:
acquiring real-time sensing data acquired by highway tunnel equipment;
inputting the real-time sensing data into a trained multidimensional deep neural network so as to enable a neuron layer to carry out weighted calculation to obtain a classification result;
and if the classification result is a fault type, sending out an alarm message according to the fault type.
2. The method for handling the fault of the expressway tunnel equipment according to claim 1, wherein the acquiring of the real-time sensing data collected by the expressway tunnel equipment comprises at least one of the following steps:
acquiring real-time sensing data acquired by highway tunnel lighting equipment;
acquiring real-time sensing data acquired by expressway tunnel ventilation equipment;
acquiring real-time sensing data acquired by highway tunnel traffic guidance equipment;
acquiring real-time sensing data acquired by highway tunnel environment monitoring equipment;
acquiring real-time sensing data acquired by fire fighting equipment of the highway tunnel;
acquiring real-time sensing data acquired by highway tunnel network monitoring equipment;
acquiring real-time sensing data acquired by expressway tunnel video monitoring equipment;
acquiring real-time sensing data acquired by emergency telephone equipment in a highway tunnel;
and acquiring real-time sensing data acquired by the emergency broadcasting equipment of the highway tunnel.
3. The method for handling the fault of the expressway tunnel equipment according to claim 1, wherein the trained multidimensional deep neural network is obtained by training through the following method:
acquiring historical sensing data acquired by highway tunnel equipment;
classifying and labeling the historical sensing data to obtain a training sample set;
and training the multidimensional deep neural network by using the training sample set to obtain the trained multidimensional deep neural network.
4. The method for handling the fault of the expressway tunnel equipment according to claim 3, wherein the acquiring historical sensing data collected by the expressway tunnel equipment comprises:
acquiring historical sensing data acquired by highway tunnel lighting equipment;
acquiring historical sensing data acquired by expressway tunnel ventilation equipment;
acquiring historical sensing data acquired by highway tunnel traffic guidance equipment;
acquiring historical sensing data acquired by highway tunnel environment monitoring equipment;
acquiring historical sensing data acquired by fire fighting equipment of a highway tunnel;
acquiring historical sensing data acquired by expressway tunnel network monitoring equipment;
acquiring historical sensing data acquired by expressway tunnel video monitoring equipment;
acquiring historical sensing data acquired by emergency telephone equipment in a highway tunnel; and
historical sensing data collected by the expressway tunnel emergency broadcasting equipment is obtained.
5. The method for processing the fault of the expressway tunnel equipment according to claim 3, wherein the classifying and labeling the historical sensing data to obtain a training sample set comprises:
determining a subspace of a fault label according to a fault value range of historical sensing data;
and determining the multi-source data association relation of the target fault label according to the target fault case in the historical sensing data to obtain a labeled sample.
6. The method for processing the fault of the expressway tunnel equipment according to claim 5, wherein the classifying and labeling the historical sensing data to obtain a training sample set further comprises:
and cutting the marked sample into sample blocks of smaller blocks to obtain the training sample set.
7. The method for processing the fault of the expressway tunnel equipment according to claim 3, wherein training the multidimensional deep neural network by using the training sample set to obtain the trained multidimensional deep neural network comprises:
carrying out compression coding on the training sample set to obtain a compressed sample set;
and performing deep learning training on the multidimensional deep neural network by compressing the sample set, and learning and adjusting the structure and weight parameters of the multidimensional deep neural network by adopting a gradient descent method according to the index of the loss function to obtain the trained multidimensional deep neural network.
8. A highway tunnel equipment fault handling device, characterized by includes:
the acquisition module is used for acquiring real-time sensing data acquired by the highway tunnel equipment;
the classification module is used for inputting the real-time sensing data into a trained multi-dimensional deep neural network so as to enable a neuron layer to carry out weighting calculation to obtain a classification result;
and the alarm module is used for sending out an alarm message according to the fault type if the classification result is the fault type.
9. An electronic device, comprising: comprising a processor, a memory and a program or instructions stored on said memory and executable on said processor, said program or instructions when executed by said processor implementing the steps of the method of highway tunnel equipment fault handling according to any one of claims 1-7.
10. A readable storage medium, on which a program or instructions are stored, which when executed by a processor, implement the steps of the highway tunneling apparatus fault handling method according to any one of claims 1-7.
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