阅读说明：本技术 一种基于装配式建筑的标识解析系统 (Identification analysis system based on assembly type building ) 是由 程路熙 李秀华 李辉 明钊 于 2021-04-26 设计创作，主要内容包括：本发明公开一种基于装配式建筑的标识解析系统,包括标识生成模块、二维码扫描及信息输入设备、标识信息获取模块、边缘服务器、云平台；本发明可以减少人工干预,避免由于手工记账导致的数据不准确、采集不及时,以及数据安全等问题。(The invention discloses an identification analysis system based on an assembly type 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, wherein the two-dimensional code scanning and information input equipment is connected with the identification generation module; the invention can reduce manual intervention and avoid the problems of inaccurate data, untimely acquisition, data safety and the like caused by manual accounting.)

1. An identification analysis system based on an assembly type building is characterized in that: the system 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 generates a two-dimensional code with identification information for recording complete equipment identification information; after the two-dimensional code is generated, displaying the two-dimensional code on corresponding equipment;

a user scans the two-dimensional code through the two-dimensional code scanning and information input equipment to obtain initial identification code information of the equipment and transmits the initial identification code information to the identification information acquisition module;

a user inputs a maintenance state information code and a 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 code information, the equipment maintenance state information code and the use state information code to obtain identification code information and transmits the identification code information to the edge server;

the edge server restores the identification code information into readable identification information according to a digital code 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 a cloud platform;

the cloud platform restores the identification coding information into identification information according to a digital coding rule;

the cloud platform writes the identification information of the same equipment into the same equipment database; all the equipment databases are stored in a total database of the cloud platform;

and 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 sends an alarm to the two-dimensional code scanning and information input equipment if the identification information has the data abnormality.

2. The assembly 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 updating time and system date; the using state of the equipment comprises using, not using, failure 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 of updating the identification information is the last time of generating the identification information recorded by the identification information acquisition module;

the initial identification information comprises the geographical position of the equipment, the type of the equipment, the manufacturer of the equipment, the use of the equipment, the number of parts of the equipment and the unit to which the equipment belongs.

3. The assembly building based identification resolution system of claim 1, wherein: the digital coding rules uniformly convert the characters into n-system digital codes.

4. The assembly building based identification resolution system according to claim 1, wherein the step of classifying the identification information by the edge server according to the equipment classification rule is:

1) taking any one of the initial identification information as a classification standard, and classifying the devices with the same initial identification information into one class;

2) and classifying the identification information corresponding to the same kind of equipment into one kind.

5. The assembly building based identification resolution system of claim 1, wherein each equipment database further stores a neural network for predicting equipment status;

when the cloud platform writes current identification information into an equipment database, the equipment database stores the identification information and inputs the identification information into a 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, overdue equipment maintenance and equipment maintenance requirement.

6. The assembly building-based logo resolution system according to claim 5, wherein the neural network is a trained neural network;

the step of training the neural network comprises:

1) building a neural network, wherein the neural network comprises an input layer, a hidden layer and an output layer;

2) the cloud platform acquires equipment data located in a building area within T time; the device data includes a device tag and identification information; the equipment label comprises normal equipment maintenance, overdue equipment maintenance and equipment maintenance requirement;

3) randomly dividing the device data into a training data set and a testing data set;

4) training the neural network by using a training data set to obtain a trained neural network;

5) inputting the test data set into a trained neural network to obtain an equipment state prediction result; and if the accuracy of the neural network prediction result is greater than the preset threshold value P, ending, otherwise, returning to the step 2).

7. The assembly building based identification resolution system of claim 1, wherein: the system also comprises an early warning module;

the early warning module stores; displaying the geographical position of each device on a map; map with building area

The early warning module classifies and marks the equipment on the map according to the identification information, wherein the marking classification comprises normal equipment maintenance, overdue equipment maintenance and equipment maintenance requirement;

and when the equipment marking type is the equipment maintenance overdue and the equipment needs maintenance, the early warning module sends an alarm to the two-dimensional code scanning and information input equipment.

8. The assembly building based identification resolution system of claim 7, wherein: the early warning module also classifies and labels the equipment according to the equipment state prediction result.

9. The assembly building based identification resolution system of claim 7, wherein: and the map with classified labeling on the equipment is a visual map.

10. The assembly 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; data beyond the normal standard range exists in the identification information; and the normal standard range is set according to equipment delivery information.

Technical Field

The invention relates to the field of big data, in particular to an identification analysis system based on an assembly type building.

Background

With the continuous development of the information-oriented industry in China and the construction of related Internet applications in the construction industry, the intellectualization of the construction site gradually becomes an important development direction in the future construction field. In the field of intelligent construction sites, 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 construction equipment, the phenomena of lack of unified coding, scattered sources, manual account keeping and the like generally exist in the intelligent construction site data, and the phenomena are serious due to manual intervention, so that the problems of incomplete data acquisition, non-unified sources, time consumption in calculation, difficult analysis, low transmission safety and the like exist in the data, the effective utilization rate of information is extremely low, the intelligent degree of the construction is low, the maintenance of the construction equipment is difficult to efficiently expand, and the construction loss of the construction site is easily caused.

Disclosure of Invention

The invention aims to provide an identification analysis system based on an assembly type 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. And 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 on corresponding equipment. The digital coding rules uniformly convert the characters into n-system digital codes.

The initial identification information comprises the geographical position of the equipment, the type of the equipment, the manufacturer of the equipment, the use of the equipment, 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 updating time and system date. The using state of the equipment comprises using, not using, failure and maintenance. The equipment maintenance state comprises maintained state, in-service state and maintenance waiting state.

The system date is generated by the identification information acquisition module according to the date of the current identification analysis system. The last time of updating the identification information is recorded by the last time of generating the identification information by the identification information acquisition module.

The user scans the two-dimensional code through the two-dimensional code scanning and information input equipment, obtains the initial identification code information of the equipment, and transmits the initial identification code information to the identification information obtaining 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 code information, the equipment maintenance state information code and the use state information code to obtain identification code information, and transmits the identification code 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 edge server classifies the identification information according to the equipment classification rule, and the method comprises the following steps:

I) and classifying the devices 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 same kind of equipment into one kind.

And the edge server encodes the preprocessed identification information into identification coding information again according to the 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 equipment into the same equipment database. All device databases are stored in a total database of the cloud platform.

And 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 sends an alarm to the two-dimensional code scanning and information input equipment if the identification information has the data abnormality.

The data exception types include: the identification information data format is different from the historical data format; the identification information has data missing; data beyond the normal standard range exists in the identification information; and the normal standard range is set according to equipment delivery information.

Each device database also stores neural networks used to predict device states.

When the cloud platform writes the 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, overdue equipment maintenance and equipment maintenance requirement.

The neural network is a trained neural network.

The step of training the neural network comprises:

1) and constructing a neural network which comprises an input layer, a hidden layer and an output layer.

2) And the cloud platform acquires the equipment data located in the building area within the T time. The device data includes a device tag and identification information. The equipment label comprises normal equipment maintenance, overdue equipment maintenance and equipment maintenance requirement.

3) The device data is randomly divided into a training data set and a testing data set.

4) And training the neural network by using the training data set to obtain the trained neural network.

5) And inputting the test data set into the trained neural network to obtain the equipment state prediction result. And if the accuracy of the neural network prediction result is greater than the preset threshold value P, ending, otherwise, returning to the step 2).

An identification analysis system based on an assembly type building further comprises an early warning module.

The early warning module stores a building area map. The geographical location of each device is displayed on the map.

The early warning module classifies and marks the equipment according to the identification information, and the marking classification comprises normal equipment maintenance, overdue equipment maintenance and equipment maintenance requirement.

And when the equipment marking type is the equipment maintenance overdue and the equipment needs maintenance, the early warning module sends an alarm to the two-dimensional code scanning and information input equipment.

The early warning module also classifies and labels the equipment according to the equipment state prediction result.

And the map with classified labeling on the equipment is a visual map.

The technical effect of the invention is undoubtedly that the invention provides an identification analysis system based on the fabricated building equipment, which utilizes an identification analysis method to encode the fabricated building materials, equipment and equipment states for the system to automatically record various data of the equipment; and then, uploading the equipment data to an edge server, performing data identification and processing on the equipment 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.

The data is analyzed at the cloud end, and then the data is intelligently analyzed through the cloud end, for example, the data is processed by using a regression analysis method. Based on the method and combined with Web technology, an intelligent early warning system capable of dynamically visualizing the equipment state in real time is established so as to rapidly and accurately find problems and locate the problems. In addition, the system can send mails and short messages in time 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 identity resolution system;

FIG. 2 is a flow chart of the early warning module;

FIG. 3 is a flow diagram of predictive maintenance of a device using visual content;

FIG. 4 is a block diagram of an identity resolution system.

Detailed Description

The present invention is further illustrated by the following examples, but it should not be construed that the scope of the above-described subject matter is limited to the following examples. Various substitutions and alterations can be made without departing from the technical idea of the invention and the scope of the invention is covered by the present invention according to the common technical knowledge and the conventional means in the field.

Example 1:

referring to fig. 1 to 4, an identifier resolution system based on an assembly type building includes an identifier generation module, a two-dimensional code scanning and information input device, an identifier 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. And 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 on corresponding equipment. And after the two-dimensional code is generated, the two-dimensional code is not changed. The digital coding rules uniformly convert the characters into n-system digital codes. The construction area is a construction site area where the prefabricated building is located.

The initial identification information comprises the geographical position of the equipment, the type of the equipment, the manufacturer of the equipment, the use of the equipment, 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 updating time and system date. The using state of the equipment comprises using, not using, failure and maintenance. The equipment maintenance state comprises maintained state, in-service state and maintenance waiting state.

The equipment use state and the equipment maintenance state are represented in a numerical mode;

such as 20210101115210, 11 o' clock 52 minutes 10 seconds 1 month 1 day 2020.

The using states of the equipment comprise 4 states in total, namely in use, non-use, fault and maintenance, each state represents the current state of the equipment and can be identified by a numerical value of 0-3:

0: use of

1: is not used

2: fault 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 the equipment can be marked by 0-2:

0: has been repaired

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 of updating the identification information is recorded by the last time of generating the identification information by the identification information acquisition module.

The user scans the two-dimensional code through the two-dimensional code scanning and information input equipment, obtains the initial identification code information of the equipment, and transmits the initial identification code information to the identification information obtaining 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 code information, the equipment maintenance state information code and the use state information code to obtain identification code information, and transmits the identification code 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 edge server classifies the identification information according to the equipment classification rule, and the method comprises the following steps:

I) and classifying the devices 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 same kind of equipment into one kind.

And the edge server encodes the preprocessed identification information into identification coding information again according to the 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 equipment into the same equipment database. All device databases are stored in a total database of the cloud platform.

The cloud platform analyzes data in the equipment database, compares the data with historical data for training, identifies whether problems such as data formats, data loss, data value non-conformity with standards or data abnormal values exist, and sends an alarm to the two-dimensional code scanning and information input equipment if the problems exist.

Each device database also stores neural networks used to predict device states.

When the cloud platform writes the 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, overdue equipment maintenance and equipment maintenance requirement.

The neural network is a trained neural network.

The step of training the neural network comprises:

1) and constructing a neural network which comprises an input layer, a hidden layer and an output layer.

2) And the cloud platform acquires the equipment data located in the building area within the T time. The device data includes a device tag and identification information. The equipment label comprises normal equipment maintenance, overdue equipment maintenance and equipment maintenance requirement.

3) The device data is randomly divided into a training data set and a testing data set.

4) And training the neural network by using the training data set to obtain the trained neural network.

5) And inputting the test data set into the trained neural network to obtain the equipment state prediction result. And if the accuracy of the neural network prediction result is greater than the preset threshold value P, ending, otherwise, returning to the step 2).

An identification analysis system based on an assembly type building further comprises an early warning module.

The early warning module stores a building area map. The geographical location of each device is displayed on the map.

The early warning module classifies and marks the equipment according to the identification information, and the marking classification comprises normal equipment maintenance, overdue equipment maintenance and equipment maintenance requirement.

And when the equipment marking type is the equipment maintenance overdue and the equipment needs maintenance, the early warning module sends an alarm to the two-dimensional code scanning and information input equipment.

The early warning module also classifies and labels the equipment according to the equipment state prediction result.

And the map with classified labeling on the equipment is a visual map.

Example 2:

referring to fig. 1 to 4, an application method of an assembly building based identification resolution system includes the steps of:

1) the method comprises the following steps of identifying the state of the equipment of the prefabricated building, coding the equipment number and the equipment state, and providing support for various data of the automatic recording equipment of the system, wherein the steps comprise:

1.1) encoding the equipment and the state information thereof by using an identification analysis method, generating a rule corresponding to a 29-bit 16-system equipment code (the code comprises the province and city of the equipment, the company name, the construction site, the equipment type, the position, the application, the state and the like), as shown in the following table,

where bits 20-23 represent device status parameters. The coding rule for coding the device number and the device status is preset.

1.2) according to a code generation rule, generating a two-dimensional code by using the first 19 bits, using the last 10 bits as reserved bits, and manually inputting the reserved bits when equipment is checked, wherein the 26 th to 27 th bits are not required to be input for the last maintenance time stored by the system, the last two bits are the current time, and recording the time if a confirmation button of an interface is clicked.

2) Based on the edge data acquisition and the edge calculation of the industrial internet, the step is used for acquiring equipment data monitored in real time and carrying out edge preprocessing on the data, and specifically comprises the following steps:

equipment maintenance state data: the two-dimensional code is identified by manual scanning (equipment maintenance state code is manually input and confirmation is clicked) to carry out equipment networking;

the system automatically uploads the equipment data to an edge server;

according to the identification coding rule, identifying the equipment data by using an edge server;

classifying the identified equipment data and preprocessing the data according to different service logics (such as single equipment maintenance time interval, equipment failure 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 data inconsistency, data omission and the like), untimely acquisition, safety of data transmission and the like caused by manual accounting are avoided;

3) the cloud platform is used for carrying out coding analysis, data analysis and data mining on the equipment data 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;

the cloud platform is used for analyzing, analyzing and counting the equipment data, if data abnormity is found, related personnel can be informed to process the data in a mode of sending WeChat or short message, and accurate data maintenance is achieved;

according to the historical data of the equipment, data mining (such as methods of neural networks, decision trees, association rules and the like) is carried out through a cloud platform, and data models in different states of the equipment are established and used for predicting the state of the equipment so as to realize preventive maintenance on the equipment;

4) by utilizing a Web technology, an intelligent early warning system capable of dynamically visualizing the equipment state in real time is established, and the method mainly comprises the following 3 steps:

4.1) compiling a 3D map of the construction site, namely the position of the equipment of each link in the construction site;

4.2) importing data from the cloud into the 3D map of the construction site by using the web,

if the equipment is green, the maintenance of the equipment is normal;

if the yellow color indicates the maintenance overdue of the equipment, the equipment is notified through WeChat or mail;

if the color is red, the device maintenance alarm is indicated (namely the device must be maintained), and the alarm is notified through WeChat or mail.

And performing predictive maintenance on the equipment by the equipment operation and maintenance personnel and the equipment operator according to the visual content.

By the method, automatic acquisition of equipment data is realized, data is recorded without depending on a manual accounting mode, manual intervention is reduced, efficient acquisition, data analysis and visual intelligent early warning of equipment state data are achieved, and production loss of a construction site is avoided.