CN113762445B - Identification analysis system based on assembled building - Google Patents
Identification analysis system based on assembled building Download PDFInfo
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- CN113762445B CN113762445B CN202110454843.9A CN202110454843A CN113762445B CN 113762445 B CN113762445 B CN 113762445B CN 202110454843 A CN202110454843 A CN 202110454843A CN 113762445 B CN113762445 B CN 113762445B
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Classifications
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06K—GRAPHICAL DATA READING; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K19/00—Record carriers for use with machines and with at least a part designed to carry digital markings
- G06K19/06—Record carriers for use with machines and with at least a part designed to carry digital markings characterised by the kind of the digital marking, e.g. shape, nature, code
- G06K19/06009—Record carriers for use with machines and with at least a part designed to carry digital markings characterised by the kind of the digital marking, e.g. shape, nature, code with optically detectable marking
- G06K19/06037—Record carriers for use with machines and with at least a part designed to carry digital markings characterised by the kind of the digital marking, e.g. shape, nature, code with optically detectable marking multi-dimensional coding
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06K—GRAPHICAL DATA READING; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K17/00—Methods or arrangements for effecting co-operative working between equipments covered by two or more of main groups G06K1/00 - G06K15/00, e.g. automatic card files incorporating conveying and reading operations
- G06K17/0022—Methods or arrangements for effecting co-operative working between equipments covered by two or more of main groups G06K1/00 - G06K15/00, e.g. automatic card files incorporating conveying and reading operations arrangements or provisious for transferring data to distant stations, e.g. from a sensing device
- G06K17/0025—Methods or arrangements for effecting co-operative working between equipments covered by two or more of main groups G06K1/00 - G06K15/00, e.g. automatic card files incorporating conveying and reading operations arrangements or provisious for transferring data to distant stations, e.g. from a sensing device the arrangement consisting of a wireless interrogation device in combination with a device for optically marking the record carrier
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- G—PHYSICS
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- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
Abstract
The invention discloses an identification analysis system based on an assembled building, which comprises an identification generation module, two-dimensional code scanning and information input equipment, an identification information acquisition module, an edge server and a cloud platform; the invention can reduce manual intervention and avoid the problems of inaccurate data, untimely acquisition, data safety and the like caused by manual accounting.
Description
Technical Field
The invention relates to the field of big data, in particular to an identification analysis system based on an assembled building.
Background
Along with the sustainable development of the informatization industry in China and the construction of related Internet application in the building industry, the building site intellectualization gradually becomes an important development direction in the future building field. In the field of construction site intellectualization, a plurality of problems such as massive real-time data processing, rapid information synchronization and the like exist; meanwhile, higher requirements are also put forward on data acquisition in the field. However, in the data acquisition process of the building equipment, the phenomena of lack of unified coding, source dispersion, manual accounting and the like of intelligent building site data generally exist, and the phenomena of insufficient data acquisition, non-uniform sources, time consumption in calculation, difficulty in analysis, low transmission safety and the like are caused due to serious manual intervention, so that the effective utilization rate of information is extremely low, the intelligent degree of building construction is low, the maintenance of the building equipment is difficult to be effectively unfolded, and the building site construction loss is easy to cause.
Disclosure of Invention
The invention aims to provide an identification analysis system based on an assembled building, which comprises an identification generation module, two-dimensional code scanning and information input equipment, an identification information acquisition module, an edge server and a cloud platform.
The identification generation module acquires initial identification information of a plurality of devices in a building area and sequentially numbers the devices. The identification generation module encodes the initial identification information and the equipment number of the equipment according to a digital encoding rule to obtain initial identification encoding information and generate a two-dimensional code. And after the two-dimensional code is generated, displaying the two-dimensional code on corresponding equipment. The digital coding rule uniformly converts characters into n-system digital codes.
The initial identification information comprises the geographic position of the equipment, the equipment type, the equipment manufacturer, the equipment application, the number of parts of the equipment and the unit to which the equipment belongs.
The identification information comprises initial identification information, equipment use state, equipment maintenance state, last identification information update time and system date. The equipment use state comprises use, unused, fault and maintenance. The equipment maintenance state comprises maintained, in-repair and to-be-repaired.
The system date is generated by the identification information acquisition module according to the date of the current identification analysis system. The last time the identification information was updated is recorded by the identification information acquisition module as the last time the identification information was generated.
The user scans the two-dimension code through the two-dimension code scanning and information input device, acquires the initial identification coding information of the device, and transmits the initial identification coding information to the identification information acquisition module.
The user inputs the maintenance state information code and the use state information code of the equipment in the two-dimension code scanning and information input equipment and transmits the codes to the identification information acquisition module.
The identification information acquisition module integrates the initial identification coding information, the equipment maintenance state information coding and the use state information coding to obtain identification coding information, and transmits the identification coding information to the edge server.
And the edge server restores the identification coding information into the identification information according to the digital coding rule.
The edge server preprocesses the identification information according to the equipment classification rule; the preprocessing comprises format verification, data cleaning, data classification and formatting;
the step of classifying the identification information by the edge server according to the equipment classification rule comprises the following steps:
i) And classifying the equipment with the same initial identification information into one class by taking any one of the initial identification information as a classification standard.
II) classifying the identification information corresponding to the similar equipment into one type.
And the edge server re-encodes the preprocessed identification information into identification coding information according to a digital coding rule and stores the identification coding information. And the edge server uploads the identification coding information to the cloud platform.
And the cloud platform restores the identification coding information into the identification information according to the digital coding rule.
And the cloud platform writes the identification information of the same device into the same device database. All device databases are stored in a total database of the cloud platform.
The cloud platform compares the currently received identification information with historical data in the equipment database, judges whether the identification information has data abnormality, and if so, sends an alarm to the two-dimensional code scanning and information input equipment.
The data exception types include: the identification information data format is different from the historical data format; the identification information has data missing; the identification information contains data exceeding the normal standard range; and setting the normal standard range according to factory information of the equipment.
Each device database also stores a neural network for predicting device status.
When the cloud platform writes current identification information into the equipment database, the equipment database stores the identification information, and the identification information is input into the neural network to obtain an equipment state prediction result. The equipment state prediction result comprises early warning information; the early warning information comprises normal equipment maintenance, equipment maintenance exceeding period and equipment maintenance required.
The neural network is a trained neural network.
The step of training the neural network comprises:
1) The neural network is built and comprises an input layer, a hidden layer and an output layer.
2) And the cloud platform acquires the equipment data in the building area within the time T. The device data includes a device tag and identification information. The equipment label comprises normal equipment maintenance, equipment maintenance exceeding and equipment maintenance required.
3) The device data is randomly divided into a training data set and a test data set.
4) And training the neural network by using the training data set to obtain a trained neural network.
5) And inputting the test data set into the trained neural network to obtain a device state prediction result. If the accuracy of the neural network prediction result is greater than the preset threshold value P, ending, otherwise, returning to the step 2).
The identification analysis system based on the assembled building further comprises an early warning module.
And the early warning module stores a building area map. The geographic location of each device is displayed on a map.
The early warning module classifies and marks the equipment according to the identification information, and the marking category comprises normal equipment maintenance, equipment maintenance exceeding period and equipment maintenance required.
When the equipment labeling category is that equipment maintenance is out of date and equipment is required to be maintained, the early warning module sends an alarm to the two-dimensional code scanning and information input equipment.
And the early warning module also classifies and marks the equipment according to the equipment state prediction result.
The map with the equipment classified and marked is a visual map.
The invention has the technical effects that the invention provides the identification analysis system based on the assembly type building equipment, and the system encodes the assembly type building materials, equipment and equipment states by utilizing the identification analysis method and is used for automatically recording various data of the equipment; and uploading the device data to an edge server, carrying out data identification and processing on the device data by using the edge server, and uploading the data to a cloud. The invention can reduce manual intervention and avoid the problems of inaccurate data, untimely acquisition, data safety and the like caused by manual accounting.
According to the invention, the data are analyzed at the cloud, and then the data are intelligently analyzed through the cloud, for example, the data are processed by a regression analysis method. Based on the method and combining with Web technology, an intelligent early warning system capable of visualizing the equipment state in real time and dynamically is established so as to quickly and accurately find out problems and locate problems. In addition, the system can timely send mails and short messages for early warning, and can provide basis for decision makers, so that the loss of construction of a construction site is avoided or reduced.
Drawings
FIG. 1 is a flow chart of an application of an identification resolution system;
FIG. 2 is a flow chart of the early warning module;
FIG. 3 is a flow chart for predictive maintenance of a device using visual content;
FIG. 4 is a frame diagram of an identification resolution system.
Detailed Description
The present invention is further described below with reference to examples, but it should not be construed that the scope of the above subject matter of the present invention is limited to the following examples. Various substitutions and alterations are made according to the ordinary skill and familiar means of the art without departing from the technical spirit of the invention, and all such substitutions and alterations are intended to be included in the scope of the invention.
Example 1:
referring to fig. 1 to 4, an identification analysis system based on an assembled building comprises an identification generation module, two-dimensional code scanning and information input equipment, an identification information acquisition module, an edge server and a cloud platform.
The identification generation module acquires initial identification information of a plurality of devices in a building area and sequentially numbers the devices. The identification generation module encodes the initial identification information and the equipment number of the equipment according to a digital encoding rule to obtain initial identification encoding information and generate a two-dimensional code. And after the two-dimensional code is generated, displaying the two-dimensional code on corresponding equipment. After the two-dimensional code is generated, the two-dimensional code is not changed. The digital coding rule uniformly converts characters into n-system digital codes. The building area is a building site area where the assembled building is located.
The initial identification information comprises the geographic position of the equipment, the equipment type, the equipment manufacturer, the equipment application, the number of parts of the equipment and the unit to which the equipment belongs.
The identification information comprises initial identification information, equipment use state, equipment maintenance state, last identification information update time and system date. The equipment use state comprises use, unused, fault and maintenance. The equipment maintenance state comprises maintained, in-repair and to-be-repaired.
The equipment use state and the equipment maintenance state are represented in a numerical mode;
such as 20210101115210, namely 11 points 1.1.2020 for 10 seconds.
The use state of the equipment totally comprises 4 states, namely, in use, unused, fault and maintenance, each state represents the current state of the equipment, and can be identified by numerical values of 0-3:
0: using
1: unused and not used
2: failure of
3: in maintenance
The equipment maintenance state comprises 3 states, namely maintenance, in-maintenance and to-be-maintained, each state represents the current maintenance state of the equipment, and can be identified by 0-2:
0: has been maintained
1: in maintenance
2: to be maintained
The system date is generated by the identification information acquisition module according to the date of the current identification analysis system. The last time the identification information was updated is recorded by the identification information acquisition module as the last time the identification information was generated.
The user scans the two-dimension code through the two-dimension code scanning and information input device, acquires the initial identification coding information of the device, and transmits the initial identification coding information to the identification information acquisition module.
The user inputs the maintenance state information code and the use state information code of the equipment in the two-dimension code scanning and information input equipment and transmits the codes to the identification information acquisition module.
The identification information acquisition module integrates the initial identification coding information, the equipment maintenance state information coding and the use state information coding to obtain identification coding information, and transmits the identification coding information to the edge server.
And the edge server restores the identification coding information into the identification information according to the digital coding rule.
The edge server preprocesses the identification information according to the equipment classification rule; the preprocessing comprises format verification, data cleaning, data classification and formatting;
the step of classifying the identification information by the edge server according to the equipment classification rule comprises the following steps:
i) And classifying the equipment with the same initial identification information into one class by taking any one of the initial identification information as a classification standard.
II) classifying the identification information corresponding to the similar equipment into one type.
And the edge server re-encodes the preprocessed identification information into identification coding information according to a digital coding rule and stores the identification coding information. And the edge server uploads the identification coding information to the cloud platform.
And the cloud platform restores the identification coding information into the identification information according to the digital coding rule.
And the cloud platform writes the identification information of the same device into the same device database. All device databases are stored in a total database of the cloud platform.
The cloud platform analyzes data in the equipment database, and through comparison training with historical data, whether the problems such as data format, data loss, data value failing to meet the standard or abnormal value exists in the data or not is identified, and if the problems exist, an alarm is sent to the two-dimensional code scanning and information input equipment.
Each device database also stores a neural network for predicting device status.
When the cloud platform writes current identification information into the equipment database, the equipment database stores the identification information, and the identification information is input into the neural network to obtain an equipment state prediction result. The equipment state prediction result comprises early warning information; the early warning information comprises normal equipment maintenance, equipment maintenance exceeding period and equipment maintenance required.
The neural network is a trained neural network.
The step of training the neural network comprises:
1) The neural network is built and comprises an input layer, a hidden layer and an output layer.
2) And the cloud platform acquires the equipment data in the building area within the time T. The device data includes a device tag and identification information. The equipment label comprises normal equipment maintenance, equipment maintenance exceeding and equipment maintenance required.
3) The device data is randomly divided into a training data set and a test data set.
4) And training the neural network by using the training data set to obtain a trained neural network.
5) And inputting the test data set into the trained neural network to obtain a device state prediction result. If the accuracy of the neural network prediction result is greater than the preset threshold value P, ending, otherwise, returning to the step 2).
The identification analysis system based on the assembled building further comprises an early warning module.
And the early warning module stores a building area map. The geographic location of each device is displayed on a map.
The early warning module classifies and marks the equipment according to the identification information, and the marking category comprises normal equipment maintenance, equipment maintenance exceeding period and equipment maintenance required.
When the equipment labeling category is that equipment maintenance is out of date and equipment is required to be maintained, the early warning module sends an alarm to the two-dimensional code scanning and information input equipment.
And the early warning module also classifies and marks the equipment according to the equipment state prediction result.
The map with the equipment classified and marked is a visual map.
Example 2:
referring to fig. 1 to 4, an application method of an identification analysis system based on an assembled building includes the steps:
1) Identifying the status of the assembled building equipment, the step encoding the equipment number and the equipment status to provide support for the system to automatically record various data of the equipment, the steps include:
1.1 The device and its state information are encoded by the identification analysis method, the generation rule corresponds to 29 bit 16 system device code (the code includes province and city where the device is located, company name, construction site, device type, position, use, state, etc.), as shown in the following table,
wherein bits 20-23 represent the device state parameters. The encoding rule for encoding the device number and the device state is preset.
1.2 According to the code generation rule, generating a two-dimensional code from the first 19 bits, wherein the last 10 bits are reserved bits, and the two-dimensional code is required to be manually input when the equipment is checked, wherein the 26 th to 27 th bits are the last maintenance time saved by the system and are not required to be input, the last two bits are the current time, and if a confirmation button of an interface is clicked, the time is recorded.
2) The method is characterized by comprising the following steps of collecting equipment data monitored in real time based on edge data collection and edge calculation of an industrial Internet, and carrying out edge preprocessing on the data, wherein the steps are as follows:
device maintenance status data: the two-dimensional code (the maintenance state code of the manual input equipment is input manually and the click confirmation) is identified by manual scanning to carry out equipment networking;
the system automatically uploads the device data to the edge server;
according to the identification coding rule, using an edge server to identify the equipment data;
classifying and preprocessing the identified equipment data according to different service logics (such as single equipment maintenance time interval, equipment faults and the like);
the edge server reversely carries out identification analysis node coding storage on the preprocessed data and uploads the data to the cloud platform;
the safety of data transmission is ensured by an identification analysis method;
by the method, manual intervention in the data acquisition process can be reduced, and the problems of inaccurate data (such as inconsistent data, missing data and the like), untimely acquisition, safety of data transmission and the like caused by manual accounting are avoided;
3) Performing coding analysis, data analysis and data mining on equipment data by utilizing a cloud platform so as to realize preventive maintenance on the equipment;
fusing data of different parts of the same equipment, establishing a single-equipment full database, and establishing a data warehouse of all the equipment;
analyzing, analyzing and counting the equipment data by utilizing a cloud platform, and if the data is found to be abnormal, informing related personnel to process by sending a WeChat or a short message mode, so that the accurate maintenance of the data is realized;
according to the historical data of the equipment, data mining (such as a neural network, a decision tree, association rules and other methods) is carried out through a cloud platform, and a data model of the equipment in different states is established and used for predicting the state of the equipment so as to realize preventive maintenance of the equipment;
4) By utilizing the Web technology, an intelligent early warning system capable of visualizing the equipment state dynamically in real time is established, and is mainly divided into the following 3 steps:
4.1 A 3D map of the construction site is compiled, namely, the position of equipment of each link is specifically on the construction site;
4.2 Using the web, importing data from the cloud into a 3D map of the worksite,
if the equipment is green, indicating that the equipment maintenance is normal;
if the color is yellow, indicating that the maintenance of the equipment is out of date, and notifying through WeChat or mail;
if the color is red, the device maintenance alarm (namely, the device must be maintained) is notified through WeChat or mail.
The equipment operation and maintenance personnel and the equipment operators perform predictive maintenance on the equipment according to the visual contents.
By the method, automatic acquisition of equipment data is realized, the data is not recorded by means of manual accounting, manual intervention is reduced, efficient acquisition, data analysis and visualized intelligent early warning of equipment state data are achieved, and production loss of a construction site is avoided.
Claims (7)
1. An identification analytic system based on assembled building, its characterized in that: the device comprises an identification generation module, two-dimensional code scanning and information input equipment, an identification information acquisition module, an edge server and a cloud platform;
the identification generation module acquires initial identification information of a plurality of devices in a building area and sequentially numbers the plurality of devices; the identification generation module encodes the initial identification information and the equipment number of the equipment according to a digital encoding rule to obtain initial identification encoding information, and generates a two-dimensional code with the identification information for recording complete equipment identification information; after the two-dimensional code is generated, displaying the two-dimensional code on corresponding equipment;
the user scans the two-dimensional code through the two-dimensional code scanning and information input device, acquires the initial identification coding information of the device, and transmits the initial identification coding information to the identification information acquisition module;
the user inputs the maintenance state information code and the use state information code of the equipment in the two-dimensional code scanning and information input equipment and transmits the maintenance state information code and the use state information code to the identification information acquisition module;
the identification information acquisition module integrates the initial identification coding information, the equipment maintenance state information coding and the use state information coding to obtain identification coding information, and transmits the identification coding information to the edge server;
the edge server restores the identification coding information into readable identification information according to the digital coding rule;
the edge server preprocesses the identification information according to the equipment classification rule; the preprocessing comprises format verification, data cleaning, data classification and formatting;
the edge server encodes the preprocessed identification information into identification coding information again according to a digital coding rule, and performs classified storage; the edge server uploads the identification coding information to the cloud platform;
the cloud platform restores the identification coding information into identification information according to the digital coding rule;
the cloud platform writes the identification information of the same device into the same device database; all equipment databases are stored in a total database of the cloud platform;
the cloud platform compares the currently received identification information with historical data in the equipment database, judges whether the identification information has data abnormality, and if so, sends an alarm to the two-dimensional code scanning and information input equipment;
each device database also stores a neural network for predicting the status of the device;
when the cloud platform writes current identification information into the equipment database, the equipment database stores the identification information and inputs the identification information into the neural network to obtain an equipment state prediction result; the equipment state prediction result comprises early warning information; the early warning information comprises normal equipment maintenance, equipment maintenance exceeding and equipment maintenance required;
the neural network is a trained neural network;
the step of training the neural network comprises:
1) Building a neural network, which comprises an input layer, a hidden layer and an output layer;
2) The cloud platform acquires equipment data in a building area within the time T; the device data comprises a device tag and identification information; the equipment label comprises normal equipment maintenance, equipment maintenance exceeding and equipment maintenance required;
3) Randomly dividing the device data into a training data set and a test data set;
4) Training the neural network by using the training data set to obtain a trained neural network;
5) Inputting the test data set into a trained neural network to obtain a device state prediction result; if the accuracy of the neural network prediction result is greater than a preset threshold value P, ending, otherwise, returning to the step 2);
the step of classifying the identification information by the edge server according to the equipment classification rule comprises the following steps:
i) Classifying the equipment with the same initial identification information into one type by taking any one of the initial identification information as a classification standard;
II) classifying the identification information corresponding to the similar equipment into one type.
2. The fabricated building-based identification resolution system of claim 1, wherein: the identification information comprises initial identification information, equipment use state, equipment maintenance state, last identification information update time and system date; the equipment use state comprises use, unused, fault and maintenance; the equipment maintenance state comprises maintained, in-service and to-be-maintained;
the system date is generated by the identification information acquisition module according to the date of the current identification analysis system; the last time the identification information is updated is recorded by the identification information acquisition module;
the initial identification information comprises the geographic position of the equipment, the equipment type, the equipment manufacturer, the equipment application, the number of parts of the equipment and the unit to which the equipment belongs.
3. The fabricated building-based identification resolution system of claim 1, wherein: the digital coding rule uniformly converts characters into n-system digital codes.
4. The fabricated building-based identification resolution system of claim 1, wherein: the system also comprises an early warning module;
the early warning module stores; displaying the geographic position of each device on the map; map with building area
The early warning module classifies and marks the equipment on the map according to the identification information, and the marking category comprises normal equipment maintenance, equipment maintenance exceeding period and equipment maintenance required;
when the equipment labeling category is that equipment maintenance is out of date and equipment is required to be maintained, the early warning module sends an alarm to the two-dimensional code scanning and information input equipment.
5. The building-based identification resolution system of claim 4, wherein: and the early warning module also classifies and marks the equipment according to the equipment state prediction result.
6. The building-based identification resolution system of claim 4, wherein: the map with the equipment classified and marked is a visual map.
7. The fabricated building-based identification resolution system of claim 1, wherein: the data exception types include: the identification information data format is different from the historical data format; the identification information has data missing; the identification information contains data exceeding the normal standard range; and setting the normal standard range according to factory information of the equipment.
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| CN112101588A (en) * | 2020-09-04 | 2020-12-18 | 重庆工业大数据创新中心有限公司 | Equipment maintenance management method based on industrial Internet |
| CN112347804A (en) * | 2020-10-27 | 2021-02-09 | 任玉海 | Bar code/two-dimensional code analysis method |
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