CN111832832A - Community self-inspection system based on Internet of things - Google Patents
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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 autonomous management platform, wherein the hardware facilities and the centralized autonomous management platform are all provided with sensors of the Internet of things; and the information processing mechanism is used for processing the information transmitted by the information service mechanism and feeding back a 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) a plan can be learned, a solution can be recommended, and the problem processing efficiency is improved; 3) the labor cost of maintenance is reduced, the working difficulty of the inspection worker is reduced, and the personal safety of the inspection worker is improved; 4) the system has an automatic learning function, can optimize itself, and improves the prediction accuracy and the inspection work efficiency.
Description
Technical Field
The invention relates to the field of intelligent cells, in particular to a cell self-inspection system based on the Internet of things.
Background
The scientific and technological enabling service provides equipment state data analysis, potential safety hazard detection and prevention for urban infrastructure equipment, and enables a harmonious and beautiful social life environment to improve the happiness index of people.
How to change from labor-intensive to knowledge-intensive and service-intensive is a solution direction for solving the problems of difficult urban safety management, more application systems (difficult intercommunication, fragmentation and difficult decision making) and poor maintenance (offline, difficult access, untimely and the like) of infrastructure facilities and equipment.
Problems with conventional equipment facility maintenance and management: 1. the equipment facilities require manpower for patrolling, and redundant manpower cost is generated when the equipment normally operates most of time; and potential safety hazards exist in the process of no inspection, and the responsibility is great. 2. The maintenance of the damaged facilities usually needs professionals to report and find the professionals for repair, and the phenomenon of 'strike' exists in the equipment during the repair (for example, accidents are easily caused from the failure of a traffic light to the completion of the repair), so that the normal operation of the society is influenced. 3. The installation position of the facility equipment is hidden or not easy to reach, and the problem of frequent exploration (such as a community drainage and pollution discharge system and the like) is difficult to solve afterwards, so that the damage upgrading or irretrievable loss is caused.
Disclosure of Invention
In view of the above, the invention aims to provide a cell self-inspection system based on the internet of things, which reduces the labor cost of maintenance, reduces the working difficulty of inspection workers and improves the personal safety of the inspection workers.
The purpose of the invention is realized by the following technical scheme:
the utility model provides a district is from system of patrolling and examining based on thing networking, includes that 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 sensor of the Internet of things 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 a processing result to the information service mechanism in real time.
Further, the information processing mechanism includes a main server and a plurality of sub servers connected to the main server,
the main server is used for receiving the information of all types of the hardware facilities transmitted by the information service organization and distributing the information of each type of the 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 occur in the corresponding hardware facilities.
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 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 working environments of the corresponding hardware facilities, and each prediction model corresponds to one working environment.
Further, the information service organization 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 primary server and receiving the abnormal behavior information transmitted by the secondary server;
a file service module for storing the data for training the prediction model
The invention has the beneficial effects that:
the invention has the advantages that: 1) real-time data analysis, automatic inspection and automatic risk prediction can be achieved; 2) a plan can be learned, a solution can be recommended, and the problem processing efficiency is improved; 3) the labor cost of maintenance is reduced, the working difficulty of inspection workers is reduced, and the personal safety of the inspection workers is improved (toxic gas and liquid such as ultra-high temperature, ultra-low temperature, ultra-high pressure, ultra-high vacuum, ultra-strong magnetic field, ultra-weak magnetic field and other severe environments do not need to be in person every time); 4) the system has an automatic learning function, can optimize itself, and improves the prediction accuracy and the 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 objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
Drawings
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail with reference to the accompanying drawings, in which:
FIG. 1 is a schematic diagram of a hierarchy of the present invention;
FIG. 2 is a schematic structural view of the present invention;
FIG. 3 is a flow chart of the algorithm on which the predictive model is based;
fig. 4 is a process diagram of an implementation scheme of the 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 illustrative of the invention only and are not limiting upon the scope of the invention.
The embodiment provides a district is from system of patrolling and examining based on thing networking, as shown in fig. 1 and 2, including the multiple hardware facilities that all are equipped with thing networking sensor, the hardware facilities are district face identification floodgate machine, fan etc. to the equipment facilities that have thing networking sensor, what the transmission is sensor real-time status characteristic data, what this characteristic data completely characterized is sensor equipment running state (or represent data of this equipment performance or running state in theory, for example the fan passes back speed, acceleration, displacement, noise decibel etc. and represents this fan running state's characteristic data).
The internet of things is a network which connects any article with the internet according to an 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 to exchange and communicate information so as to realize intelligent identification, positioning, tracking, monitoring and management.
The sensor is a device and an element which can convert various physical quantities, chemical quantities and biomass in nature into measurable electric signals. The sensor belongs to the nerve endings of the Internet of things: 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, ray radiation sensors, thermosensitive sensors, 24GHz radar sensors.
The system comprises a centralized autonomous management platform, wherein the sensor of the Internet of things transmits related information to the platform, and the platform classifies all the information so as to predict whether each type of hardware facility can generate an abnormal event. The centralized autonomous management platform comprises:
and the information service mechanism is used for distributing the information transmitted by the sensor of the Internet of things in real time and receiving the feedback information. The information service mechanism realizes the functions through a data acquisition service module, a platform service module and a file service module.
The data acquisition service module is used for acquiring information which expresses the performance or the running state of the sensors of the internet of things and is transmitted by 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 an analog electric signal into a digital signal, quantitatively storing and preprocessing: the analog electrical signals are converted from various changing physical quantities, such as strain, temperature, pressure, vibration, etc., by corresponding sensors.
For example smoke sensors: smoke sensors are commonly used for both chemical and optical detection. The former uses the radioactive americium 241 element, and the positive and negative ions generated in the ionization state move directionally under the action of an electric field to generate stable voltage and current. Once smoke enters the sensor, normal movement of positive ions and negative ions is influenced, corresponding changes are generated in voltage and current, and the intensity of the smoke can be judged through calculation. The light can be fully irradiated on the photosensitive material under normal conditions through the photosensitive material, and stable voltage and current are generated. Once smoke enters the sensor, normal irradiation of light 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 is matched with a computer to realize itinerant detection, real-time control, 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 an 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, and the data analysis processing is a process of extracting valuable information from a large amount of raw data, namely converting the data into the information. The input data in various forms are mainly processed and sorted, and the process comprises the whole process of evolution and derivation of collection, storage, processing, classification, merging, calculation, sorting, conversion, retrieval and propagation of the data. For example, data uploaded by the face barrier is received, the data is analyzed, and the analysis result is executed corresponding to a corresponding instruction.
The file service module is used for storing data of the training prediction model, and 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 secondary servers.
The main server receives the information transmitted by the platform service module, classifies the information, and sends each piece of sub-information to the corresponding sub-server, namely, each sub-server processes the information of hardware facilities of a single type, 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.
And each secondary server is provided with a self-inspection model, and the self-inspection model is used for predicting whether the corresponding hardware facilities have abnormal behaviors or not. The operation principle of the self-inspection model is shown in fig. 3 and comprises a maintenance management module and a prediction module. The maintenance management module is not specific hardware, and means that a fault type is obtained according to analysis of abnormal information, and a matched action is executed. This operator may be the device itself, such as restarting, powering off, pressurizing, etc. Or may be human to maintain.
The prediction module is configured with a prediction model, and the prediction model has a deep learning function. The prediction model corresponds to the same type of equipment under the same working environment, and all the equipment is processed in a centralized mode. All equipment self-inspection models are processed in parallel. The same equipment sets the same model in the same working environment, and the data collected in the working process trains the same model W, different working environments and corresponding 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, etc., conventional and mature stable network structures. Each self-inspection equipment model corresponds to an optimized structure and has the function of self-adaptive change (time-limited or real-time transfer learning and model updating), so that the system is in a progressive state all the time during the whole service period. Even if all hardware equipment is aged and replaced, the learned model can still be placed on other equipment or platforms, and the accuracy and the stability are revealed.
And the maintenance management module is used for receiving the process information of 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 in a state that the fan does not work, a VGG network model is adopted to predict the fan; and aiming at the face recognition gate, a depth residual error 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 capabilities of self-decision making and self-regulation, the training data is richer and richer along with time and in the fault processing process, the model is suitable for the working environment of equipment and the fault occurrence and is more and more skillful, and the establishment method of the prediction model is briefly described for the face recognition gate machine below which is determined by adopting a deep learning model mechanism.
The human face barrier gate consists of two parts, namely a human face recognition instrument and a gate machine.
X _ Data { "the face recognition instrument works normally, the gate is half open and half closed", "the face recognition instrument works normally, the gate works normally", "the face recognition instrument works normally, the gate is always open", "the face recognition instrument works normally, the gate is always closed", "the face recognition instrument fails, the gate does not work", … … }
Y _ Data { "gate self-control restart", "whole 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 serves as an input layer of the whole deep network. Y _ Data represents the set of schema types that abstract which measures are collected, and is the output of the training network. A self-decision-making model { W } for processing the running state of equipment and fault maintenance under the current working environment is established through a large amount of data, and a hidden layer in the model adopts a residual error network structure and is combined with a full-connection network.
The basic principle of the depth residual error neural network is that matrix multiplication of an input layer and a middle layer and residual error structure and pooling are performed, input passes through a full connection layer, a residual error network layer, an output full connection layer and a softmax layer to obtain output Y, back propagation of the output Y is performed according to a cross entropy loss function, and parameter change of the whole model is adjusted by conforming to a function derivative chain rule.
The important levels comprise:
ReLu layer: (x) max (0, x) | f' (x) ═ 0
SoftMax layer:
cross entropy loss function L (y, a) ═ Σiyilog(ai)
Chain rule of complex function derivatives: let f and g be derivable functions for x, the composite function y ═ f [ g (x) ].
Derivative of complex function y with respect to x
The network formula is expressed as:
X*[resnet resnet]＝y-------(2)
y*Wfc→Softmax＝Y-------(3)
wherein, ResNet structure in the network structure is as follows:
that is, in the formula (3), y1 ═ F (X, { Wi }) + WsX
Finally, the event data transmitted by the equipment sensor is abstracted to generate a complete event representation data set x, a high-matching applicable model of a specific scene and equipment is established corresponding to the solution type Y
And the prediction management module is used for receiving and modifying the default setting operation information and transmitting the default setting operation information to the prediction module so as to optimize the prediction model.
For example, when one of all secondary servers determines that a face recognition error occurs in a next period of a face recognition gate, the gate is opened, and self-processing cannot be performed, the information is transmitted to the platform service module, the platform service module gives an alarm, manual intervention is started, after a maintenance worker arrives, the face recognition gate performs manual line maintenance and the whole machine is restarted, then X _ Data corresponds to { face recognition error, the gate is opened }, Y _ Data corresponds to { manual line maintenance and whole machine restart }, and the maintenance management module receives the process information, transmits the process information to the file service module, stores the Data, inputs the Data into the existing prediction model, and optimizes the Data. The data and operational procedures (this regulation) are stored as new blocks as new regulations for the current class of equipment. Namely a block chain type storage mode.
Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered by the claims of the present invention.
Claims (6)
1. The utility model provides a district is from system of patrolling and examining based on thing networking which characterized in that: the system comprises various hardware facilities and a centralized autonomous management platform which are all provided with sensors of the Internet of things, wherein the centralized autonomous management platform comprises
The information service mechanism is used for distributing information transmitted by the sensor of the Internet of things 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 a processing result to the information service mechanism in real time.
2. The internet of things-based cell self-inspection system according to claim 1, wherein: the information processing mechanism includes a main server and a plurality of sub servers connected to the main server,
the main server is used for receiving the information of all types of the hardware facilities transmitted by the information service organization and distributing the information of each type of the hardware facilities to each secondary server.
3. The internet of things-based cell self-inspection system according to claim 2, wherein: the secondary server is provided with a self-inspection model, and the self-inspection model is used for predicting whether abnormal behaviors occur in the corresponding hardware facilities.
4. The internet of things-based cell self-inspection system according to claim 3, wherein: 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 process information of manual intervention and transmitting the process information to the prediction module so as to optimize the prediction model.
5. The internet of things-based cell self-inspection system according to claim 4, wherein: the number of the prediction models is the same as the number of the types of the working environments of the corresponding hardware facilities, and each prediction model corresponds to one working environment.
6. The internet of things-based cell self-inspection system according to claim 5, wherein:
the information service organization 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 primary server and receiving the abnormal behavior information transmitted by the secondary server;
and the file service module is used for storing data for training the prediction model.
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Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102403794A (en) * | 2011-09-26 | 2012-04-04 | 国网信息通信有限公司 | Integrated management system of intelligent power grid |
| CN103825944A (en) * | 2014-02-25 | 2014-05-28 | 黄河科技学院 | Transformer substation safety management system based on Internet of things |
| CN106790344A (en) * | 2016-11-09 | 2017-05-31 | 北京理工大学 | A kind of Internet of Things movement E Laboratory platforms of embedded Web control terminal |
| CN106980883A (en) * | 2017-02-17 | 2017-07-25 | 杭州润缘信息科技有限公司 | With reference to cloud computing and the cruising inspection system and method for Internet of Things |
| US20180046869A1 (en) * | 2016-08-10 | 2018-02-15 | Surround.IO Corporation | Method and Apparatus for Providing Information Via Collected and Stored Metadata Using Inferred Attentional Model |
| CN108174293A (en) * | 2017-12-01 | 2018-06-15 | 咪咕视讯科技有限公司 | Information processing method and device, server and storage medium |
| CN108474866A (en) * | 2018-03-23 | 2018-08-31 | 深圳市锐明技术股份有限公司 | A kind of manhole cover loss detecting system and method based on deep learning |
| CN108805008A (en) * | 2018-04-19 | 2018-11-13 | 江苏理工学院 | A kind of community's vehicle security system based on deep learning |
| CN108831098A (en) * | 2018-06-09 | 2018-11-16 | 深圳市中电数通智慧安全科技股份有限公司 | A kind of wisdom garden emergency warning system |
| CN109189806A (en) * | 2018-09-07 | 2019-01-11 | 广东智信信息科技股份有限公司 | A kind of Campus Security Internet of Things aware platform |
| CN109377585A (en) * | 2018-08-09 | 2019-02-22 | 上海安悦节能技术有限公司 | Factory's public affairs match facility intelligent operation cruising inspection system and its method for inspecting |
| CN109544724A (en) * | 2019-02-22 | 2019-03-29 | 新誉轨道交通科技有限公司 | A kind of intelligent blower cruising inspection system and its working method |
| CN110059631A (en) * | 2019-04-19 | 2019-07-26 | 中铁第一勘察设计院集团有限公司 | The contactless monitoring defect identification method of contact net |
| CN110779573A (en) * | 2019-10-30 | 2020-02-11 | 重庆瑞坤科技发展股份有限公司 | Wisdom district thing networking data acquisition box |
-
2020
- 2020-07-21 CN CN202010706803.4A patent/CN111832832B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102403794A (en) * | 2011-09-26 | 2012-04-04 | 国网信息通信有限公司 | Integrated management system of intelligent power grid |
| CN103825944A (en) * | 2014-02-25 | 2014-05-28 | 黄河科技学院 | Transformer substation safety management system based on Internet of things |
| US20180046869A1 (en) * | 2016-08-10 | 2018-02-15 | Surround.IO Corporation | Method and Apparatus for Providing Information Via Collected and Stored Metadata Using Inferred Attentional Model |
| CN106790344A (en) * | 2016-11-09 | 2017-05-31 | 北京理工大学 | A kind of Internet of Things movement E Laboratory platforms of embedded Web control terminal |
| CN106980883A (en) * | 2017-02-17 | 2017-07-25 | 杭州润缘信息科技有限公司 | With reference to cloud computing and the cruising inspection system and method for Internet of Things |
| CN108174293A (en) * | 2017-12-01 | 2018-06-15 | 咪咕视讯科技有限公司 | Information processing method and device, server and storage medium |
| CN108474866A (en) * | 2018-03-23 | 2018-08-31 | 深圳市锐明技术股份有限公司 | A kind of manhole cover loss detecting system and method based on deep learning |
| CN108805008A (en) * | 2018-04-19 | 2018-11-13 | 江苏理工学院 | A kind of community's vehicle security system based on deep learning |
| CN108831098A (en) * | 2018-06-09 | 2018-11-16 | 深圳市中电数通智慧安全科技股份有限公司 | A kind of wisdom garden emergency warning system |
| CN109377585A (en) * | 2018-08-09 | 2019-02-22 | 上海安悦节能技术有限公司 | Factory's public affairs match facility intelligent operation cruising inspection system and its method for inspecting |
| CN109189806A (en) * | 2018-09-07 | 2019-01-11 | 广东智信信息科技股份有限公司 | A kind of Campus Security Internet of Things aware platform |
| CN109544724A (en) * | 2019-02-22 | 2019-03-29 | 新誉轨道交通科技有限公司 | A kind of intelligent blower cruising inspection system and its working method |
| CN110059631A (en) * | 2019-04-19 | 2019-07-26 | 中铁第一勘察设计院集团有限公司 | The contactless monitoring defect identification method of contact net |
| CN110779573A (en) * | 2019-10-30 | 2020-02-11 | 重庆瑞坤科技发展股份有限公司 | Wisdom district thing networking data acquisition box |
Non-Patent Citations (1)
| Title |
|---|
| 陈湘军: "综合深度特征的异常交通事件视频解析及应用系统", 《中国优秀博士学位论文全文数据库》, no. 4, pages 034 - 10 * |
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