CN117332922A - Full life cycle safety supervision method and system for special equipment - Google Patents
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Abstract
The invention discloses a full life cycle safety supervision method and system for special equipment, which can be applied to safety supervision of the special equipment. The special equipment full life cycle safety supervision method and system can solve the problems that the obtained safety risk information is incomplete and poor in timeliness, the safety inspection staff is excessively dependent on the executive force judgment and the occupational morality, and the management and control of the special equipment full life cycle safety risk are difficult to realize through limited several field detection by carrying out iterative optimization on the special equipment production process, the installation and debugging process, the using process, the maintenance, the detection and the scrapping of the special equipment full life cycle safety data, the special equipment production specification based on big data analysis, the special equipment safety critical monitoring boundary value determination based on big data analysis, the safety risk early warning based on the real-time comparison analysis of the special equipment critical part characteristic data and the compliance maintenance detection and monitoring based on the online video image identification analysis in the traditional special equipment detection.
Description
Technical Field
The invention relates to the field of special equipment safety supervision methods, in particular to a special equipment full life cycle safety supervision method and system.
Background
The special equipment refers to eight kinds of equipment which relate to boilers, pressure vessels, pressure pipelines, elevators, hoisting machinery, passenger ropeways, large amusement facilities and special motor vehicles in the field with high life safety and high danger. In order to ensure the safe operation of special equipment, the country has strict regulations on three links of production, use, inspection and detection of various special equipment, and the supervision of the whole process is implemented. However, the existing government supervision departments carry out the safety supervision mode of special equipment, namely, the special equipment production permission authentication inspection, and the security inspection workers in the special equipment detection center are commissioned to the special equipment use site to carry out the safety inspection and acceptance inspection and annual safety inspection on newly purchased special equipment, which belong to the periodic safety inspection with one time or overlarge time span, and the problems that the obtained potential safety hazard information of the special equipment is incomplete and poor in timeliness, excessively depends on the executive force, judgment force, occupational moral and the like of the security inspection workers, and the safety risk information of the whole life cycle of the special equipment is difficult to comprehensively and accurately grasp only through limited safety inspection for several times. Therefore, under the policy background of the national advanced digital economy, the needs of combining the whole life safety related data acquisition of the special equipment with the digital transformation of the special equipment, analyzing the acquired whole life safety related data of the special equipment by applying a big data analysis technology, constructing a whole life safety risk management model of the special equipment, comprehensively, accurately and real-timely researching and judging the potential safety hazards of the special equipment, and continuously improving the whole life cycle process from product design, manufacturing, installation and debugging to use of the special equipment on the basis, thereby reducing the risk of safety accidents to the greatest extent.
Disclosure of Invention
The invention aims to provide a full life cycle safety supervision method and system for special equipment, which can solve the problems that the prior government safety supervision department obtains safety related information incompletely and poorly in timeliness in the detection of the special equipment, and the safety risk information of the special equipment is difficult to comprehensively and accurately grasp through the prior safety acceptance detection and the annual periodic detection due to excessive dependence on the execution force, judgment force and occupational morality of security check staff.
In order to achieve the above object, the present invention provides the following solutions:
a full life cycle safety supervision method for special equipment, comprising: and collecting safety data of the whole life cycle of the special equipment, analyzing the safety data of the whole life cycle of the special equipment, and monitoring the safety of the whole life cycle of the special equipment.
The special equipment full life cycle safety data acquisition comprises special equipment production process data acquisition, special equipment installation and debugging data acquisition, special equipment use process data acquisition, special equipment maintenance data acquisition, special equipment detection data acquisition and special equipment failure rejection data acquisition. The special equipment production process data acquisition is to formulate a special equipment production quality inspection standard according to the special equipment safety performance requirement, and acquire all quality inspection data from part production to complete machine assembly in real time according to the standard by connecting a production enterprise quality management system database; the special equipment installation and debugging data acquisition refers to acquiring equipment operation data in the special equipment installation and debugging process by adopting the internet of things technology; the special equipment using process data acquisition refers to acquisition of real-time operation data such as special equipment load condition, operation time, operation environment, key part temperature, noise, vibration frequency, amplitude, electrical characteristics and the like by adopting the internet of things technology; the special equipment maintenance data acquisition means that special equipment maintenance and spare part receiving records are acquired through connecting an enterprise equipment management information system; the special equipment detection data acquisition means that an enterprise equipment manager fills in spot inspection records on line, a special equipment detection center worker fills in annual inspection reports on line, and a data acquisition system receives data; the special equipment failure scrapping data acquisition refers to the special equipment which is failed and scrapped, and the data information such as a special equipment manufacturer, a failure reason, a service life and the like is filled in on line by a use enterprise in real time; all the acquired data are stored in the special equipment safety big data platform in real time.
The full life cycle safety data analysis of the special equipment is as follows: firstly, carrying out data analysis by using collected special equipment production data, special equipment maintenance data and special equipment detection data set special equipment failure scrapping data and adopting correlation analysis algorithms such as stepwise regression and the like, finding out key production links and detection standard control values corresponding to equipment failure and part damage, and carrying out iterative optimization on special equipment production specifications according to the key production links and detection standard control values; secondly, the collected operation data, equipment maintenance part replacement data, daily point inspection data, detection center annual inspection data and other historical data of each special equipment of each enterprise are applied, and failure characteristic values such as temperature, noise and vibration characteristics of key parts of each special equipment under different load conditions are excavated by adopting an artificial intelligent regression algorithm and other big data analysis method, so that the safety boundary value of each key safety monitoring index is determined.
The full life cycle safety supervision of the special equipment is as follows: firstly, carrying out online auditing on collected detection data of each link of enterprise production according to continuously iterative updated special equipment production inspection standards, recording products of unqualified procedures, and tracking reject treatment processes of unqualified parts and the whole machine; secondly, installing and debugging field deployment video monitoring equipment, and sending a warning to enterprises according to the image recognition analysis of video information acquired by video monitoring, wherein the warning is sent out when the enterprises do not install and debug according to the specifications, and a rectification instruction is sent out in real time; thirdly, carrying out real-time comparison calculation on characteristic index data such as temperature, noise, vibration characteristics and the like of key parts of the special equipment and a safety boundary value, carrying out early warning on the special equipment exceeding the safety boundary value, and sending preventive maintenance inspection instructions to an enterprise to which the equipment belongs in real time; fourthly, on-site deployment of video monitoring equipment, according to image recognition analysis of video information acquired by video monitoring, giving a warning for spot inspection of enterprises on time without specification, and giving a rectification instruction in real time; fifthly, a detection program and a method are provided for the on-site detection work of the detection center staff according to national standard specifications, and the nonstandard behavior in the detection process is warned in real time through online video recording and video analysis.
A special equipment full life cycle safety supervision system comprising: the system comprises a software and hardware environment for forming a special equipment safety remote supervision system, a special equipment full life cycle mode data acquisition function module, a special equipment full life cycle data analysis function module and a special equipment full life cycle safety supervision function module.
The software and hardware environment of the special equipment full life cycle safety supervision system comprises an operating system, a big data storage analysis platform, software environments such as application programs of all functional modules, a data acquisition device, a network running the software system, a computing and storage component and other hardware environments, wherein the hardware environments can be provided as a group of cloud computing resources or as physical equipment.
The special equipment full life cycle safety data acquisition functional module refers to an application program comprising the following functions: firstly, acquiring data in the production process of special equipment, namely formulating production quality inspection specifications of the special equipment according to the safety performance requirements of the special equipment, and acquiring all quality inspection data from the production of parts to the assembly of a complete machine in real time according to the specifications by connecting a quality management system database of a production enterprise; secondly, acquiring installation and debugging data of special equipment, namely acquiring equipment operation data in the installation and debugging process of the special equipment by adopting the internet of things technology; thirdly, acquiring data in the using process of the special equipment, namely acquiring real-time operation data such as special equipment load condition, operation time, operation environment, key part temperature, noise, vibration frequency, amplitude, electrical characteristics and the like by adopting the internet of things technology; fourth, special equipment maintenance data acquisition, namely, acquiring special equipment maintenance and spare part receiving records by connecting an enterprise equipment management information system; fifthly, special equipment detection data acquisition, namely, on-line filling of spot check records by enterprise equipment management personnel, on-line filling of annual check reports by special equipment detection center staff, and data receiving by a data acquisition system; sixthly, collecting failure scrapped data of the special equipment, namely, real-time online filling data information of manufacturers, failure reasons, service life and the like of the special equipment for the failed scrapped special equipment by a use enterprise; and storing all the acquired data into a safety big data platform of the special equipment in real time.
The special equipment full life cycle safety data analysis functional module refers to an application program comprising the following functions: firstly, carrying out data analysis by using collected special equipment production data, special equipment maintenance data, special equipment detection data and special equipment failure scrapping data and adopting correlation analysis algorithms such as stepwise regression and the like, finding out critical production link detection standard control values corresponding to equipment failure and part damage, and carrying out iterative optimization on special equipment production specifications according to the critical production link detection standard control values; secondly, the collected operation data, equipment maintenance part replacement data, daily point inspection data, detection center annual inspection data and other historical data of each special equipment of each enterprise are applied, and failure characteristic values such as temperature, noise and vibration characteristics of key parts of each special equipment under different load conditions are excavated by adopting an artificial intelligent regression algorithm and other big data analysis method, so that the safety boundary value of each key safety monitoring index is determined.
The special equipment full life cycle safety supervision function module refers to an application program comprising the following functions: firstly, carrying out online auditing on collected detection data of each link of enterprise production according to continuously iterative updated special equipment production inspection standards, recording products of unqualified procedures, and tracking reject treatment processes of unqualified parts and the whole machine; secondly, deploying video monitoring equipment on site, according to image recognition analysis of video information acquired by video monitoring, warning that enterprises do not install and debug according to specifications, and sending a rectification instruction in real time; thirdly, carrying out real-time comparison calculation on characteristic index data such as temperature, noise, vibration characteristics and the like of key parts of the special equipment and a safety boundary value, carrying out early warning on the special equipment exceeding the safety boundary value, and sending preventive maintenance inspection instructions to an enterprise to which the equipment belongs in real time; fourthly, on-site deployment of video monitoring equipment, according to image recognition analysis of video information acquired by video monitoring, giving a warning for spot inspection of enterprises on time without specification, and giving a rectification instruction in real time; fifthly, a detection program and a method are provided for the on-site detection work of the detection center staff according to national standard specifications, and the nonstandard behavior in the detection process is warned in real time through online video recording and video analysis.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic diagram of a full life cycle safety supervision method for special equipment according to an embodiment of the present invention.
Detailed Description
The following description of the technical solutions according to the embodiments of the present invention will be given with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide a safety supervision method and system for a full life cycle of special equipment, which can solve the problems that the safety supervision method for the traditional special equipment is incomplete in acquiring safety hidden danger information and poor in timeliness, excessively depends on the executive force, judgment force, occupational morality and the like of security check staff, and is difficult to comprehensively and accurately grasp the safety risk information of the full life cycle of the special equipment through limited safety detection for several times.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
Example 1
Fig. 1 is a schematic diagram of implementation steps of a full life cycle safety supervision method for a special device according to an embodiment of the present invention; as shown in fig. 1, the present invention provides a method for monitoring and managing the whole life cycle of special equipment, which comprises the following steps:
step 101: and collecting safety related data of the whole life cycle of the special equipment, wherein the safety related data comprise equipment production process data, equipment installation and debugging process data, equipment use process data, equipment maintenance data, equipment detection data and equipment failure rejection data.
Step 102: and carrying out data analysis by using the collected special equipment production data, special equipment maintenance data, special equipment detection data and special equipment failure scrapping data and adopting correlation analysis algorithms such as stepwise regression and the like to find out a critical production link detection standard control value corresponding to equipment failure and part damage, and carrying out iterative optimization on the special equipment production specification according to the critical production link detection standard control value.
Step 103: and excavating failure characteristic values such as temperature, noise and vibration characteristics of key parts of various special equipment under different load conditions by using collected special equipment operation data, equipment maintenance part replacement data, daily point inspection data, detection center annual inspection data and other historical data of various enterprises and adopting a large data analysis method such as an artificial intelligent regression algorithm, so as to determine the safety boundary value of various key safety monitoring indexes.
Step 104: and (3) carrying out on-line auditing on the collected detection data of each link of enterprise production according to the production inspection standard of the continuously iterative updated special equipment, recording the products produced in the unqualified working procedure, and tracking the reject processing process of unqualified parts and the whole machine.
Step 105: and according to the image recognition analysis of the video information collected by video monitoring, warning is sent out when the enterprise is not installed and debugged according to the specifications, and a rectifying and modifying instruction is sent out in real time.
Step 106: and carrying out real-time comparison calculation on characteristic index data such as temperature, noise, vibration characteristics and the like of key parts of the special equipment and a safety boundary value, carrying out early warning on the special equipment exceeding the safety boundary value, and sending preventive maintenance inspection instructions to an enterprise to which the equipment belongs in real time.
Step 107: and according to the image recognition analysis of the video information collected by video monitoring, carrying out spot inspection on the enterprises according to the specifications on time to give out a warning, and sending out a rectifying instruction in real time.
Step 108: the method comprises the steps of providing prompt of detection programs and methods for on-site detection work of detection center staff according to national standard standards, and warning nonstandard behaviors in the detection process in real time through online video recording and video analysis.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (9)
1. The full life cycle safety supervision method for the special equipment is characterized by comprising the following steps of: and collecting safety data of the whole life cycle of the special equipment, analyzing the safety data of the whole life cycle of the special equipment, and monitoring the safety of the whole life cycle of the special equipment.
2. The special equipment full life cycle safety data acquisition of claim 1, which means that: firstly, acquiring data in the production process of special equipment, namely formulating production quality inspection specifications of the special equipment according to the safety performance requirements of the special equipment, and acquiring all quality inspection data from the production of parts to the assembly of a complete machine in real time according to the specifications by connecting a quality management system database of a production enterprise; the special equipment installation and debugging data acquisition refers to acquiring equipment operation data in the special equipment installation and debugging process by adopting the internet of things technology; secondly, acquiring data in the using process of the special equipment, namely acquiring real-time operation data such as special equipment load condition, operation time, operation environment, key part temperature, noise, vibration frequency, amplitude, electrical characteristics and the like by adopting the internet of things technology; thirdly, collecting maintenance data of the special equipment, namely collecting maintenance records of the special equipment and a spare part leading record through connecting an enterprise equipment management information system; fourth, special equipment detection data acquisition, namely, filling in spot inspection records on line by enterprise equipment management personnel, filling in annual inspection reports on line by special equipment detection center staff, and receiving data by a data acquisition system; fifthly, collecting failure and scrapping data of the special equipment, namely, filling data information of manufacturers, failure reasons, service life and the like of the special equipment on line in real time for the special equipment which is failed and scrapped by a use enterprise; and finally, storing all the acquired data into a special equipment safety big data platform in real time.
3. The special equipment full life cycle safety data analysis of claim 1, which refers to: firstly, carrying out data analysis by using collected special equipment production data, special equipment maintenance data and special equipment detection data set special equipment failure scrapping data and adopting correlation analysis algorithms such as stepwise regression and the like, finding out key production links and detection standard control values corresponding to equipment failure and part damage, and carrying out iterative optimization on special equipment production specifications according to the key production links and detection standard control values; secondly, the collected operation data, equipment maintenance part replacement data, daily point inspection data, detection center annual inspection data and other historical data of each special equipment of each enterprise are applied, and failure characteristic values such as temperature, noise and vibration characteristics of key parts of each special equipment under different load conditions are excavated by adopting a big data analysis method such as an artificial intelligent regression algorithm, so that the safety boundary value of each key index is determined.
4. The special equipment full life cycle safety supervision according to claim 1, which means: firstly, carrying out online auditing on collected detection data of each link of enterprise production according to continuously iterative updated special equipment production inspection standards, recording products of unqualified procedures, and tracking reject treatment processes of unqualified parts and the whole machine; secondly, installing and debugging field deployment video monitoring equipment, and sending a warning to enterprises according to the image recognition analysis of video information acquired by video monitoring, wherein the warning is sent out when the enterprises do not install and debug according to the specifications, and a rectification instruction is sent out in real time; thirdly, carrying out real-time comparison calculation on characteristic index data such as temperature, noise, vibration characteristics and the like of key parts of the special equipment and a safety boundary value, carrying out early warning on the special equipment exceeding the safety boundary value, and sending preventive maintenance inspection instructions to an enterprise to which the equipment belongs in real time; fourthly, on-site deployment of video monitoring equipment, according to image recognition analysis of video information acquired by video monitoring, giving a warning for spot inspection of enterprises on time without specification, and giving a rectification instruction in real time; fifthly, a detection program and a method are provided for the on-site detection work of the detection center staff according to national standard specifications, and the nonstandard behavior in the detection process is warned in real time through online video recording and video analysis.
5. A special equipment full life cycle safety supervision system, comprising: the system comprises a software and hardware environment for forming a special equipment safety remote supervision system, a special equipment full life cycle mode data acquisition function module, a special equipment full life cycle data analysis function module and a special equipment full life cycle safety supervision function module.
6. The software and hardware environment of the special equipment full life cycle safety supervision system according to claim 5, comprising an operating system, a big data storage analysis platform, software environments such as application programs of each functional module, and hardware environments such as a data acquisition device, a network running the software system, a computing and storage component, and the hardware environments can be provided as a set of cloud computing resources or as physical equipment.
7. The special equipment full life cycle safety data collection function module according to claim 5, which is an application program comprising the following functions: firstly, acquiring data in the production process of special equipment, namely formulating production quality inspection specifications of the special equipment according to the safety performance requirements of the special equipment, and acquiring all quality inspection data from the production of parts to the assembly of a complete machine in real time according to the specifications by connecting a quality management system database of a production enterprise; secondly, acquiring installation and debugging data of special equipment, namely acquiring equipment operation data in the installation and debugging process of the special equipment by adopting the internet of things technology; thirdly, acquiring data in the using process of the special equipment, namely acquiring real-time operation data such as special equipment load condition, operation time, operation environment, key part temperature, noise, vibration frequency, amplitude, electrical characteristics and the like by adopting the internet of things technology; fourth, special equipment maintenance data acquisition, namely, acquiring special equipment maintenance and spare part receiving records by connecting an enterprise equipment management information system; fifthly, special equipment detection data acquisition, namely, on-line filling of spot check records by enterprise equipment management personnel, on-line filling of annual check reports by special equipment detection center staff, and data receiving by a data acquisition system; sixthly, collecting failure scrapped data of the special equipment, namely, real-time online filling data information of manufacturers, failure reasons, service life and the like of the special equipment for the failed scrapped special equipment by a use enterprise; and storing all the acquired data into a safety big data platform of the special equipment in real time.
8. The special equipment full life cycle safety data analysis function module according to claim 5, which is an application program comprising the following functions: firstly, carrying out data analysis by using collected special equipment production data, special equipment maintenance data, special equipment detection data and special equipment failure scrapping data and adopting correlation analysis algorithms such as stepwise regression and the like, finding out critical production link detection standard control values corresponding to equipment failure and part damage, and carrying out iterative optimization on special equipment production specifications according to the critical production link detection standard control values; secondly, the collected operation data, equipment maintenance part replacement data, daily point inspection data, detection center annual inspection data and other historical data of each special equipment of each enterprise are applied, and failure characteristic values such as temperature, noise and vibration characteristics of key parts of each special equipment under different load conditions are excavated by adopting a big data analysis method such as an artificial intelligent regression algorithm, so that the safety boundary value of each key index is determined.
9. The special equipment full life cycle safety supervision function module according to claim 5, which refers to an application program comprising the following functions: firstly, carrying out online auditing on collected detection data of each link of enterprise production according to continuously iterative updated special equipment production inspection standards, recording products of unqualified procedures, and tracking reject treatment processes of unqualified parts and the whole machine; secondly, deploying video monitoring equipment on site, according to image recognition analysis of video information acquired by video monitoring, warning that enterprises do not install and debug according to specifications, and sending a rectification instruction in real time; thirdly, carrying out real-time comparison calculation on characteristic index data such as temperature, noise, vibration characteristics and the like of key parts of the special equipment and a safety boundary value, carrying out early warning on the special equipment exceeding the safety boundary value, and sending preventive maintenance inspection instructions to an enterprise to which the equipment belongs in real time; fourthly, on-site deployment of video monitoring equipment, according to image recognition analysis of video information acquired by video monitoring, giving a warning for spot inspection of enterprises on time without specification, and giving a rectification instruction in real time; fifthly, a detection program and a method are provided for the on-site detection work of the detection center staff according to national standard specifications, and the nonstandard behavior in the detection process is warned in real time through online video recording and video analysis.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202311290770.XA CN117332922A (en) | 2023-10-08 | 2023-10-08 | Full life cycle safety supervision method and system for special equipment |
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| CN202311290770.XA CN117332922A (en) | 2023-10-08 | 2023-10-08 | Full life cycle safety supervision method and system for special equipment |
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| CN117938478A (en) * | 2024-01-11 | 2024-04-26 | 广东尚坤工业科技有限公司 | Special equipment box remote operation and maintenance method and system based on Internet of things technology |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117938478A (en) * | 2024-01-11 | 2024-04-26 | 广东尚坤工业科技有限公司 | Special equipment box remote operation and maintenance method and system based on Internet of things technology |
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