CN111832832B - District self-inspection system based on thing networking - Google Patents
District self-inspection system based on thing networking Download PDFInfo
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Abstract
The invention discloses a community self-inspection system based on the Internet of things, which comprises a plurality of hardware facilities and a centralized independent management platform, wherein the hardware facilities are respectively provided with an Internet of things sensor, the centralized independent management platform comprises an information service mechanism and is used for distributing information transmitted by the Internet of things sensor in real time and receiving feedback information; and the information processing mechanism is used for processing the information transmitted by the information service mechanism and feeding back the processing result to the information service mechanism in real time. The invention has the advantages that: 1) Real-time data analysis, automatic inspection and automatic risk prediction can be achieved; 2) The scheme can be learned, the solution is recommended, and the problem processing efficiency is improved; 3) The labor cost of maintenance is reduced, the work difficulty of inspection workers is reduced, and the personal safety of the inspection workers is improved; 4) The automatic learning system has an automatic learning function, can be self-optimized, and improves prediction accuracy and inspection work efficiency.
Description
Technical Field
The invention relates to the field of intelligent communities, in particular to a community self-inspection system based on the Internet of things.
Background
The technology energized service provides equipment state data analysis for urban infrastructure equipment, potential safety hazard detection and prevention, and a harmonious and good social life environment is provided for improving the happiness index of people.
How to change from labor-intensive to knowledge-intensive and service-intensive is a solution to the problem of difficult urban safety management, more application systems (difficult intercommunication, fragmentation, difficult decision making), poor maintenance (off-line, difficult access, untimely, etc.) of infrastructure equipment.
Problems with traditional equipment maintenance and management: 1. the equipment facility inspection requires manpower, and the equipment normally operates most of the time, so that redundant manpower cost is generated; and the potential safety hazard exists in the inspection, so that the responsibility is great. 2. The equipment is often required to be repaired by professionals during the repair of the equipment, and the professional personnel is often required to report the equipment to repair the equipment, so that the normal operation of society is affected due to the fact that the equipment is 'strike' during the repair period (for example, traffic light faults are easy to cause accidents during the repair period). 3. The installation position of facility equipment is hidden or not easy to reach, is difficult to frequently explore (such as a community drainage and pollution discharge system and the like), and is often solved after the fact, so that damage upgrading or irrecoverable loss is caused.
Disclosure of Invention
Therefore, the invention aims to provide a community self-inspection system based on the Internet of things, which reduces the labor cost of maintenance, reduces the work difficulty of inspection workers and improves the personal safety of the inspection workers.
The invention aims at realizing the following technical scheme:
a district self-inspection system based on the Internet of things comprises a plurality of hardware facilities and a centralized autonomous management platform, wherein the hardware facilities are respectively provided with an Internet of things sensor, and the centralized autonomous management platform comprises
The information service mechanism is used for distributing information transmitted by the Internet of things sensor in real time and receiving feedback information;
and the information processing mechanism is used for processing the information transmitted by the information service mechanism and feeding back the processing result to the information service mechanism in real time.
Further, the information processing mechanism comprises a main server and a plurality of secondary servers connected with the main server,
the primary server is used for receiving information of all types of hardware facilities transmitted by the information service mechanism and distributing information of each type of hardware facilities to each secondary server.
Further, the secondary server is configured with a self-inspection model, and the self-inspection model is used for predicting whether abnormal behaviors of the corresponding hardware facilities occur.
Further, the self-inspection model comprises a maintenance management module and a prediction module;
the prediction module is configured with a prediction model, and the prediction model has a deep learning function;
the prediction management module is used for receiving the process information of manual intervention and transmitting the process information to the prediction module so as to optimize the prediction model.
Further, the number of the prediction models is the same as the number of the types of the corresponding working environments of the hardware facilities, and each prediction model corresponds to one working environment.
Further, the information service mechanism includes:
the data acquisition service module is used for receiving information transmitted by all the sensors of the Internet of things;
the platform service module is used for transmitting all information to the main server and receiving abnormal behavior information transmitted by the secondary server;
a file service module for storing data for training the prediction model
The beneficial effects of the invention are as follows:
the invention has the advantages that: 1) Real-time data analysis, automatic inspection and automatic risk prediction can be achieved; 2) The scheme can be learned, the solution is recommended, and the problem processing efficiency is improved; 3) The labor cost of maintenance is reduced, the work difficulty of the patrol workers is reduced, and the personal safety of the patrol workers is improved (such as toxic gas and liquid, such as ultra-high temperature, ultra-high pressure, ultra-high vacuum, ultra-strong magnetic field, ultra-weak magnetic field and other severe environments do not need to be brought into the field in person each time); 4) The automatic learning system has an automatic learning function, can be self-optimized, and improves prediction accuracy and inspection work efficiency.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objects and other advantages of the invention may be realized and obtained by means of the instrumentalities and combinations particularly pointed out in the specification.
Drawings
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings, in which:
FIG. 1 is a schematic diagram of the hierarchy of the present invention;
FIG. 2 is a schematic diagram of the structure of the present invention;
FIG. 3 is a flow chart of an algorithm on which a predictive model is based;
fig. 4 is a process diagram of an implementation of the present invention.
Detailed Description
Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. It should be understood that the preferred embodiments are presented by way of illustration only and not by way of limitation.
The embodiment provides a community self-inspection system based on the internet of things, as shown in fig. 1 and 2, which comprises a plurality of hardware facilities each provided with an internet of things sensor, wherein the hardware facilities are a community face recognition gate, a fan and the like, and aiming at equipment facilities with the internet of things sensor, real-time state characteristic data of the sensor are transmitted, and the characteristic data completely represent the running state of the sensor equipment (or theoretically represent the performance or running state of the equipment, such as the speed, acceleration, displacement, noise decibels and the like transmitted by the fan) and represent the running state of the fan.
The internet of things is a network which is used for realizing intelligent identification, positioning, tracking, monitoring and management by connecting any article with the internet according to a agreed protocol through information sensing equipment such as Radio Frequency Identification (RFID), an infrared sensor, a global positioning system, a laser scanner and the like and carrying out information exchange and communication.
The sensor is a device and an element for converting various physical quantities, chemical quantities and biomass in nature into measurable electric signals. The sensor belongs to the nerve ending of the internet of things: the sensors are classified according to their use: pressure-sensitive and force-sensitive sensors, position sensors, liquid level sensors, energy consumption sensors, speed sensors, radar sensors, acceleration sensors, radiation sensors, heat-sensitive sensors, 24GHz radar sensors.
And the centralized autonomous management platform is used for transmitting related information to the internet of things sensor, classifying all the information by the platform and predicting whether each type of hardware facility can generate an abnormal event. The centralized autonomous management platform comprises:
the information service mechanism is used for distributing information transmitted by the Internet of things sensor in real time and receiving feedback information. The information service mechanism realizes the functions of the information service mechanism through three modules, namely a data acquisition service module, a platform service module and a file service module.
The data acquisition service module is used for acquiring information expressing the performance or the running state of all the sensors of the Internet of things in real time, wherein the information comprises all types of information. The data acquisition equipment is a device for converting analog electric signals into numbers, quantitatively storing and preprocessing: the analog electrical signal is converted from various changing physical quantities, such as strain, temperature, pressure, vibration, etc., by corresponding sensors.
Such as smoke sensors: there are two common types of smoke sensors, chemical detection and optical detection. The former uses radioactive americium 241 element, and positive and negative ions generated in ionization state directionally move under the action of electric field to generate stable voltage and current. Once smoke enters the sensor, normal movement of positive ions and negative ions is affected, voltage and current are correspondingly changed, and the intensity of the smoke can be judged through calculation. The latter passes through the photosensitive material and light can be fully irradiated on the photosensitive material under normal conditions, so that stable voltage and current are generated. Once smoke enters the sensor, normal irradiation of light rays is affected, so that fluctuating voltage and current are generated, and the intensity of the smoke can be judged through calculation.
The data acquisition device and the computer can realize the tour detection, the real-time control, the data processing and the like. The data acquisition device generally comprises a preamplifier, a sampling switch circuit, a sampling hold circuit, an analog-digital conversion circuit, a digital-analog conversion circuit, a logic control circuit, a storage device and the like.
The platform service module is used for receiving the information transmitted by the data acquisition service module, pushing the information to the information processing mechanism, receiving the abnormal warning processed and pushed by the information processing mechanism, and sending an alarm.
The platform service performs data analysis processing on the received data, wherein the data analysis processing is a process of extracting valuable information from a large amount of original data, namely converting the data into information. The input data in various forms are processed and arranged, and the process comprises the whole process of collecting, storing, processing, classifying, merging, calculating, sequencing, converting, searching and propagating evolution and deduction of the data. For example, data uploaded by a face barrier is received, the data is analyzed, and corresponding instructions corresponding to the analysis result are executed.
The file service module is used for storing data for training the prediction model, and the file service module can be a database.
The system also comprises an information processing mechanism, which is used for receiving and processing the information transmitted by the information service mechanism and feeding back the processing result to the information service mechanism in real time.
The information processing mechanism achieves the technical purpose through the main server and the plurality of times of servers.
The main server receives the information transmitted by the platform service module, classifies the information into categories, and transmits each piece of sub information to the corresponding sub server, namely, each sub server processes the information of a single type of hardware facility, for example, the first sub server is used for processing a fan, and the second sub server is used for processing a face recognition gate.
Each secondary server is configured with a self-inspection model, and the self-inspection model is used for predicting whether abnormal behaviors of the corresponding hardware facilities occur. The operation principle of the self-inspection model is shown in fig. 3, and the self-inspection model comprises a maintenance management module and a prediction module. The maintenance management module is not specific hardware, and is used for analyzing abnormal information to obtain fault types and executing matched actions. This actor may be the device itself, such as a restart, a power down, a pressurization, etc. Or may be a person for maintenance.
The prediction module is configured with a prediction model, and the prediction model has a deep learning function. The prediction model corresponds to a model corresponding to the same type of equipment in the same working environment, and all the equipment are processed in a centralized way. All the equipment self-inspection models are processed in parallel. The same model is set in the same working environment of the same equipment, the data collected in the working process train the same model W, and different working environments correspond to different models; different devices correspond to different models.
Algorithm design as shown in fig. 4, practical methods include, but are not limited to, multiple linear regression, deep convolutional neural networks, and so on, conventional and mature stable network structures. Each self-inspection equipment model corresponds to an optimized structure, has a self-adaptive change function (time-limited or real-time transfer learning, model updating) and enables the system to be in an advanced state all the time during the whole service period. Even if all hardware devices are aged and replaced, the learned model can be still put on other devices or platforms, so that accuracy and stability are revealed.
The maintenance management module is used for receiving the process information of the manual intervention, inputting the process information into the prediction model and correcting the prediction model.
For example, for a fan, a linear programming model is adopted to predict the fan, and a VGG network model is adopted to predict the fan in a state that the fan does not work; and aiming at the face recognition gate, a depth residual neural network model is adopted to predict the working state of the face recognition gate. The mechanism is as follows: the original data is an actual processing scheme in life, the original data is used as training, the model has the self-decision and self-adjustment capability, training data is more and more abundant in the process of processing faults along with time, the model is more and more proficient in adapting to the working environment of the equipment and the fault occurrence, and the method for establishing the prediction model of the face recognition gate is briefly described below, which is determined by adopting a model mechanism of deep learning.
The face barrier consists of two parts, a number face recognition instrument and a gate.
X_Data= { "face recognition instrument normally works, gate half-open and half-close", "face recognition instrument normally works, gate normal works", "face recognition instrument normal works, gate is always open", "face recognition instrument normal works, gate is always closed", "face recognition instrument fault, gate is not working", … … }
Y_Data= { "Gate self-control restart", "complete machine self-switching power supply", "manual line maintenance", "face recognition instrument automatic restart", "face recognition instrument self-repair update", … … }
The Data set x_data is an abstract Data set corresponding to the detected fault phenomenon as an input layer of the whole depth network. Y_Data represents which measure abstract set of solution types to collect as output of the training network. Through a large amount of data, a self-decision model { W } for processing the running state of equipment and maintenance of fault overhaul in the current working environment is established, and a hidden layer in the model adopts a residual error network structure and a combination of a fully-connected network.
The basic principle of the depth residual neural network is that the matrix multiplication of an input layer and an intermediate layer and the residual structure and pooling are formed, the input passes through a full-connection layer, a residual network layer, an output full-connection layer and a softmax layer to obtain an output Y, and the back propagation of the output Y adjusts the parameter change of the whole model by conforming to a function derivative chain rule according to a cross entropy loss function.
Wherein the importance layers include:
ReLu layer: f (x) =max (0, x) |f' (x) =0
SoftMax layer:
cross entropy loss function L (y, a) = - Σ i y i log(a i )
Chain law of derivative of complex function: let f and g be the derivative functions with respect to x, the complex function y=f [ g (x) ].
Derivative of the complex function y to x
The network formula is expressed as:
X*[resnet resnet]＝y-------(2)
y*Wfc→Softmax＝Y-------(3)
wherein, the ResNet structure in the network structure is as follows:
that is, in the formula (3), y1=f (X, { Wi }) +w s X
Finally, through abstract processing of event data transmitted by the device sensor, a complete event representation data set x is generated, a solution category Y is corresponded, and a high-matching applicable model of a targeted scene and the device is established
The prediction management module is used for receiving and modifying default setting operation information, transmitting the default setting operation information to the prediction module and optimizing the prediction model.
For example, when one of all secondary servers considers that the face recognition gate will have face recognition error in the next period, the gate is opened and cannot be processed by self, the information is transmitted to the platform service module, the platform service module alarms, human intervention is started, after a maintainer arrives, the face recognition gate is subjected to manual line maintenance and the whole machine is restarted, X_Data corresponds to { face recognition error, gate is opened }, Y_Data corresponds to { manual line maintenance and whole machine restarting }, the maintenance management module receives the process information, transmits the process information to the file service module to store the Data, and inputs the Data to the existing prediction model to optimize the Data. The data and the operation flow (this regulation) are stored as new blocks as new regulations for the current kind of equipment. Namely a block chain type storage mode.
Finally, it is 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 modifications and equivalents may be made thereto without departing from the spirit and scope of the present invention, which is intended to be covered by the claims of the present invention.
Claims (2)
1. A district self-inspection system based on thing networking, its characterized in that: including multiple hardware facilities and centralized autonomic management platform that all dispose thing networking sensor, centralized autonomic management platform includes:
the information service mechanism is used for distributing information transmitted by the Internet of things sensor in real time and receiving feedback information;
the information processing mechanism is used for processing the information transmitted by the information service mechanism and feeding back the processing result to the information service mechanism in real time,
wherein the information processing mechanism comprises a main server and a plurality of secondary servers connected with the main server,
the primary server is used for receiving the information of all types of hardware facilities transmitted by the information service mechanism and distributing the information of each type of hardware facilities to each secondary server;
the secondary server is configured with a self-inspection model, and the self-inspection model is used for predicting whether abnormal behaviors of the corresponding hardware facilities occur or not;
the self-inspection model comprises a maintenance management module and a prediction module;
the prediction module is configured with a prediction model, and the prediction model has a deep learning function;
the maintenance management module is used for receiving the process information of manual intervention and transmitting the process information to the prediction module so as to optimize the prediction model;
the information service mechanism comprises:
the data acquisition service module is used for receiving information transmitted by all the sensors of the Internet of things;
the platform service module is used for transmitting all information to the main server and receiving abnormal behavior information transmitted by the secondary server;
the file service module is used for storing data for training the prediction model;
the hardware facility comprises a face barrier gate, wherein the face barrier gate comprises a face recognition instrument and a gate;
x_data= { "face recognition instrument normally works, gate half-open and half-close", "face recognition instrument normally works, gate normally works", "face recognition instrument normally works, gate always opens", "face recognition instrument normally works, gate always closes", "face recognition instrument fails, gate does not work" };
y_data= { "gate self-control restart", "complete machine self-switching power supply", "manual line maintenance", "face recognition instrument automatic restart", "face recognition instrument self-repair update" };
the Data set X_Data is an abstract Data set corresponding to the detected fault phenomenon and is used as an input layer of the whole depth network; Y_Data represents the abstract set of solution types of which measures are collected and is the output of the training network; through a large amount of data, a self-decision model { W } for processing the running state of equipment and maintenance of fault overhaul in the current working environment is established, and a hidden layer in the model adopts a residual error network structure and is combined with a fully-connected network;
the prediction management module is used for receiving and modifying default setting operation information, transmitting the default setting operation information to the prediction module and optimizing a prediction model;
when one of all secondary servers considers that the face recognition gate has face recognition errors in the next period, the gate is opened and cannot be subjected to self-processing, the information is transmitted to the platform service module, the platform service module gives an alarm, human intervention is started, after a maintainer arrives, the face recognition gate is subjected to manual line maintenance and the whole machine is restarted, X_Data corresponds to { face recognition errors, the gate is opened }, Y_Data corresponds to { manual line maintenance and whole machine restarting }, the maintenance management module receives the process information, transmits the process information to the file service module to store the Data, and inputs the Data to the existing prediction model to optimize the Data.
2. The internet of things-based cell self-inspection system of claim 1, wherein: the number of the prediction models is the same as the number of the types of the corresponding working environments of the hardware facilities, and each prediction model corresponds to one working environment.
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