CN116562855B - Maintenance management system of internet of things for key equipment in seamless steel tube production - Google Patents
Maintenance management system of internet of things for key equipment in seamless steel tube production Download PDFInfo
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- 238000004519 manufacturing process Methods 0.000 title claims abstract description 320
- 238000012423 maintenance Methods 0.000 title claims abstract description 130
- 229910000831 Steel Inorganic materials 0.000 title claims abstract description 98
- 239000010959 steel Substances 0.000 title claims abstract description 98
- 238000012544 monitoring process Methods 0.000 claims abstract description 66
- 238000004458 analytical method Methods 0.000 claims abstract description 64
- 238000004364 calculation method Methods 0.000 claims abstract description 21
- 238000000034 method Methods 0.000 claims abstract description 5
- 230000002159 abnormal effect Effects 0.000 claims description 29
- 238000011156 evaluation Methods 0.000 claims description 12
- 230000010354 integration Effects 0.000 claims description 12
- 238000012545 processing Methods 0.000 claims description 9
- 238000004891 communication Methods 0.000 claims description 3
- 239000013618 particulate matter Substances 0.000 claims description 2
- 238000007405 data analysis Methods 0.000 abstract description 9
- 238000004088 simulation Methods 0.000 description 6
- 230000000694 effects Effects 0.000 description 5
- 239000003570 air Substances 0.000 description 3
- 230000004888 barrier function Effects 0.000 description 2
- 238000010622 cold drawing Methods 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 238000012795 verification Methods 0.000 description 2
- 239000012080 ambient air Substances 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000005520 cutting process Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000001125 extrusion Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005096 rolling process Methods 0.000 description 1
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G16Y40/00—IoT characterised by the purpose of the information processing
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- G16Y40/00—IoT characterised by the purpose of the information processing
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- G16Y40/00—IoT characterised by the purpose of the information processing
- G16Y40/40—Maintenance of things
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- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
Abstract
The invention discloses an internet of things maintenance management system for key equipment in seamless steel tube production, and belongs to the technical field of equipment maintenance management; performing staged calculation analysis on all production equipment of different seamless steel pipe production sequences, and evaluating and classifying the operation states of the production equipment in a sequential monitoring period by performing simultaneous calculation on data of all aspects of operation and maintenance of the production equipment; the method comprises the steps of evaluating and classifying the overall operation state of a plurality of production equipment by carrying out simultaneous calculation on the staged analysis results of the production equipment; dynamic pushing management is implemented on different overall operation states of all production equipment corresponding to the same seamless steel pipe production sequence; the invention is used for solving the technical problems that in the prior art, different layers of monitoring and data analysis cannot be implemented on different production equipment of different seamless steel pipe production sequences, and dynamic maintenance management is implemented on important equipment of seamless steel pipe production according to the data analysis result of the whole layer.
Description
Technical Field
The invention relates to the technical field of equipment maintenance management, in particular to an internet of things maintenance management system for key equipment in seamless steel pipe production.
Background
The production method of seamless steel pipes is roughly classified into a skew rolling method (Meng Nasi man method) and an extrusion method, and the production equipment involved includes a hydraulic cold drawing machine, a cold drawing pipe machine, a pipe straightening machine, a bar steel pipe straightening machine, a roll finishing machine, a coil straightening cutting machine, a steel pipe straightening machine, and the like.
When the existing maintenance management scheme of the seamless steel tube production key equipment is implemented, most of the maintenance management scheme is still stopped on processing and recording in enterprises, so that data among enterprises form data islands in the aspect of equipment maintenance management, staged monitoring and data analysis and integral monitoring and data analysis and sharing cannot be implemented on different production equipment of different seamless steel tube production sequences, dynamic maintenance management cannot be implemented on the seamless steel tube production key equipment of different enterprises according to data analysis results, and the integral effect of the maintenance management of the seamless steel tube production key equipment is poor.
Disclosure of Invention
The invention aims to provide an internet of things maintenance management system for key equipment in seamless steel pipe production, which is used for solving the technical problems that in the prior art, different layers of monitoring and data analysis cannot be implemented on different production equipment in different seamless steel pipe production sequences, and dynamic maintenance management is implemented on the key equipment in seamless steel pipe production according to the data analysis result of the whole layer.
The aim of the invention can be achieved by the following technical scheme:
the maintenance management system comprises an equipment monitoring data statistics module, a maintenance management module and a maintenance management module, wherein the equipment monitoring data statistics module is used for monitoring and data statistics of different production equipment of all seamless steel pipe production enterprises to obtain an equipment monitoring statistics set;
the equipment state evaluation module is used for sequentially carrying out staged evaluation and classification on the running states of different production equipment according to the equipment monitoring statistical set to obtain an equipment state analysis set; comprising the following steps:
traversing the equipment monitoring statistics set to sequentially obtain the equipment monitoring data of all production equipment corresponding to different production sequences of the seamless steel pipe, and processing the equipment monitoring data of all production equipment; grouping production equipment with the same equipment model, and counting the total grouping number after grouping;
sequentially extracting and combining the keywords of the historical maintenance logs corresponding to the production equipment in different groups to obtain a historical maintenance keyword library;
counting the total number of maintenance according to the corresponding historical maintenance time of the production equipment and marking as WZ; performing digital processing on each maintenance reason of the history to obtain a corresponding fault weight and marking the fault weight as GQ; marking the total loss amount caused by the corresponding fault of each maintenance of the history corresponding to the production equipment as GS; acquiring a corresponding sequence monitoring period according to the sequence of seamless steel pipe production, extracting numerical values of various marked data and acquiring a corresponding operation stability coefficient Yw of production equipment through calculation when operation stability analysis is carried out on the production equipment corresponding to the production sequence in the sequence monitoring period;
evaluating and classifying the operation states of the corresponding production equipment in the sequential monitoring period according to the operation stability coefficient to obtain excellent operation state equipment, normal operation state equipment or abnormal operation state equipment;
all running stability coefficients, excellent running state equipment, normal running state equipment and abnormal running state equipment corresponding to the same seamless steel pipe production sequence form local equipment analysis data, and all local equipment analysis data corresponding to the seamless steel pipe production sequence form equipment state analysis sets and are uploaded to a cloud platform;
the equipment state tracking and verifying module is used for tracking and integrating analysis on the state analysis results of the production equipment in a plurality of subsequent sequential monitoring periods to obtain an equipment state integration analysis set;
and the equipment maintenance management module is used for carrying out dynamic pushing prompt on the operation maintenance of different production equipment in the seamless steel pipe production sequence according to the equipment state integration analysis set in sequence, so as to realize cross-region and cross-type sharing and communication of maintenance management of key equipment in seamless steel pipe production.
Preferably, the step of obtaining the device monitoring statistics set includes:
numbering and marking different production equipment according to the production sequence of the seamless steel pipes;
when basic information of different production equipment is monitored in sequence according to the serial numbers, equipment models corresponding to the production equipment and the production time are obtained, historical maintenance time, historical maintenance logs, historical maintenance time and historical maintenance reasons of each time corresponding to the production equipment are obtained according to the equipment models, and the total loss caused by fault correspondence of each time of historical maintenance is obtained;
the equipment model corresponding to the production equipment, the production time, the corresponding historical maintenance log for each time, the historical maintenance reasons for each time and the loss total amount caused by the corresponding fault of each time of the historical maintenance form equipment monitoring data;
and equipment monitoring data corresponding to all production equipment of all seamless steel pipe production sequences form an equipment monitoring statistical set and are uploaded to the cloud platform.
Preferably, the calculation formula of the operation stability coefficient Yw is:the method comprises the steps of carrying out a first treatment on the surface of the Wherein, jy is a basic influence factor corresponding to production equipment;
the basic influence factors corresponding to the production equipment are obtained by the following steps:
sequentially acquiring corresponding operation time according to the corresponding production time of production equipment in different groups and the current real-time Beijing time, and marking the operation time as YS;
counting the total maintenance times according to the historical maintenance time corresponding to the production equipment and marking the total maintenance times as HZ;
extracting the number of each item of marked dataValues and pass through the formulaCalculating and obtaining a basic influence factor Jy corresponding to production equipment; wherein, BS is the standard evaluation period corresponding to the production sequence of the seamless steel pipe where the production equipment is located.
Preferably, the production equipment corresponding to the operation stability coefficient smaller than the operation stability threshold is marked as excellent operation state equipment;
marking production equipment corresponding to the operation stability coefficient which is not smaller than the operation stability threshold and not larger than the operation stability threshold by Y% as normal operation state equipment; y is a real number greater than one hundred;
and marking the production equipment corresponding to the operation stability coefficient which is larger than the operation stability threshold value Y% as abnormal operation state equipment.
Preferably, the working steps of the device state tracking verification module include:
when the overall operation states of all production equipment corresponding to the production sequence are sequentially evaluated according to the production sequence of the seamless steel pipes, traversing analysis data of all local equipment corresponding to the production equipment in N sequence monitoring periods, counting the total number of the excellent operation state equipment, the normal operation state equipment and the abnormal operation state equipment, and setting the total number as a first state total number YZZ, a second state total number EZZ and a third state total number SZZ respectively; n is a positive integer;
the production sequence name of the production equipment and the corresponding production sequence weight are obtained and marked as SQ; extracting the first state total number, the second state total number, the third state total number and the numerical value of the production sequence weight of the corresponding marks of the production equipment, and obtaining the corresponding running state coefficient Yz of the production equipment through calculation;
when the overall operation state of the production equipment corresponding to the production sequence is evaluated according to the operation state coefficient, matching and classifying the operation state coefficient and the corresponding operation state range to obtain the equipment with excellent overall operation state, the equipment with normal overall operation state or the equipment with abnormal overall operation state;
all running state coefficients corresponding to the same seamless steel tube production sequence, and equipment with excellent overall running state, equipment with normal overall running state and equipment with abnormal overall running state form equipment tracking overall analysis data, and all equipment tracking overall analysis data corresponding to the same seamless steel tube production sequence form equipment state integration analysis set and are uploaded to a cloud platform.
Preferably, the calculation formula of the operation state coefficient Yz is:。
preferably, the production equipment corresponding to the operation state coefficient smaller than the minimum value of the operation state range is marked as the whole operation state excellent equipment;
marking the production equipment corresponding to the operation state coefficient which is not smaller than the minimum value of the operation state range and not larger than the maximum value of the operation state range as the whole operation state normal equipment;
and marking the production equipment corresponding to the operation state coefficient larger than the maximum value of the operation state range as the whole operation state abnormal equipment.
Preferably, the working steps of the equipment maintenance management module include:
sequentially acquiring equipment tracking overall analysis data associated with all production equipment corresponding to the seamless steel pipe production sequence according to the equipment state integration analysis set, traversing the equipment tracking overall analysis data to acquire marks of overall operation state excellent equipment, overall operation state normal equipment or overall operation state abnormal equipment corresponding to all production equipment;
acquiring a historical maintenance keyword library corresponding to the overall operation state excellent equipment, marking the historical maintenance keyword library as a selected maintenance keyword library, and acquiring operation environment parameters corresponding to the overall operation state excellent equipment and marking the operation environment parameters as selected operation environment parameters;
and pushing the selected maintenance keyword library corresponding to the excellent equipment in the overall operation state and the selected operation environment parameters to the normal equipment in the overall operation state and the production enterprises corresponding to the abnormal equipment in the overall operation state in a self-adaptive manner.
Preferably, if all production equipment corresponding to the production sequence of the seamless steel pipes does not have excellent equipment in the overall operation state, pushing a historical maintenance keyword library and operation environment parameters corresponding to the production equipment of equipment in the normal overall operation state to a production enterprise corresponding to equipment in the abnormal overall operation state.
Preferably, the operating environment parameters consist of the ambient temperature, ambient humidity, ambient air pressure, ambient noise and air particulate concentration at which the device is operating.
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, through carrying out staged calculation analysis on all production equipment of different seamless steel pipe production sequences, and carrying out simultaneous calculation on data of all aspects of operation and maintenance of the production equipment to evaluate and classify the operation state of the production equipment in a sequential monitoring period, the production states in the corresponding stages of all production equipment of different seamless steel pipe production sequences can be intuitively and efficiently obtained, reliable data support can be provided for the whole production state analysis and dynamic maintenance of the production equipment corresponding to all production equipment of the subsequent different seamless steel pipe production sequences, and the whole effect of staged monitoring analysis of all production equipment of different seamless steel pipe production sequences is improved.
According to the invention, the integral operation states of the production equipment are evaluated and classified by carrying out simultaneous calculation on the staged analysis results of the production equipment, so that errors existing in single-stage production equipment state analysis can be reduced, the accuracy of integral operation state analysis corresponding to the production equipment can be effectively improved, the integral operation states of different production equipment corresponding to different seamless steel pipe production sequences can be intuitively and efficiently obtained, reliable data support can be provided for dynamic maintenance management of different production equipment corresponding to different subsequent seamless steel pipe production sequences, and the diversity and reliability of monitoring data analysis of the production equipment are improved.
According to the invention, through implementing dynamic pushing management on different overall operation states of all production equipment corresponding to the same seamless steel pipe production sequence, the information barrier between maintenance data of the internet of things of the key equipment in the production of the seamless steel pipe in the existing scheme can be broken, so that the maintenance data of the internet of things of the key equipment can be efficiently and conveniently shared and mutually learned, and the overall effect of maintenance management of the internet of things of the key equipment in the production of the seamless steel pipe can be improved.
Drawings
The invention is further described below with reference to the accompanying drawings.
Fig. 1 is a block diagram of a maintenance management system of an internet of things of a seamless steel tube production key device.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described 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.
As shown in FIG. 1, the invention discloses a maintenance management system of an Internet of things for key equipment in seamless steel pipe production, which comprises an equipment monitoring data statistics module, an equipment state evaluation module, an equipment state tracking verification module, an equipment maintenance management module, a cloud platform and a database;
the equipment monitoring data statistics module is used for monitoring and data statistics of different production equipment of all seamless steel pipe production enterprises to obtain an equipment monitoring statistics set; comprising the following steps:
numbering and marking different production equipment according to the production sequence of the seamless steel pipes;
when basic information of different production equipment is monitored in sequence according to the serial numbers, equipment models corresponding to the production equipment and the production time are obtained, historical maintenance time, historical maintenance logs, historical maintenance time and historical maintenance reasons of each time corresponding to the production equipment are obtained according to the equipment models, and the total loss caused by fault correspondence of each time of historical maintenance is obtained;
the unit of time is accurate to minutes, the unit of loss total amount is ten thousand yuan, and the production benefit corresponding to the total time length of stopping the production equipment can be estimated; the equipment types of all production equipment corresponding to the same seamless steel pipe production sequence can be different;
the equipment model corresponding to the production equipment, the production time, the corresponding historical maintenance log for each time, the historical maintenance reasons for each time and the loss total amount caused by the corresponding fault of each time of the historical maintenance form equipment monitoring data;
the equipment monitoring data corresponding to all production equipment of all seamless steel pipe production sequences form an equipment monitoring statistical set and are uploaded to a cloud platform;
in the embodiment of the invention, by implementing monitoring and data statistics on all enterprises in seamless steel pipe production and all equipment corresponding to all production sequences, reliable data support can be provided for production state analysis of different production equipment in different subsequent production sequences.
The equipment state evaluation module is used for sequentially carrying out staged evaluation and classification on the running states of different production equipment according to the equipment monitoring statistical set to obtain an equipment state analysis set; comprising the following steps:
traversing the equipment monitoring statistics set to sequentially obtain the equipment monitoring data of all production equipment corresponding to different production sequences of the seamless steel pipe, and processing the equipment monitoring data of all production equipment; grouping production equipment with the same equipment model, and counting the total grouping number after grouping;
sequentially extracting and combining the keywords of the historical maintenance logs corresponding to the production equipment in different groups to obtain a historical maintenance keyword library;
the keyword extraction is implemented by the conventional keyword extraction algorithm, and specific steps are not described herein; the historical maintenance keyword library is obtained by extracting and combining the keywords of each maintenance log of the history, so that reliable data support can be provided for the dynamic maintenance of production equipment of different subsequent production enterprises;
counting the total number of maintenance according to the corresponding historical maintenance time of the production equipment and marking as WZ; performing digital processing on each maintenance reason of the history to obtain a corresponding fault weight and marking the fault weight as GQ; the step of performing digital processing on the historical maintenance reasons each time comprises the following steps:
obtaining a fault type corresponding to each maintenance reason of the history, and performing traversal comparison on the obtained fault type and a fault type-weight table prestored in a database to obtain a corresponding fault type weight;
the fault type-weight table comprises a plurality of different fault types and corresponding fault type weights, wherein one corresponding fault type weight is preset for the different fault types, and the specific numerical value of the fault type weight can be obtained through simulation by simulation software according to fault big data corresponding to production equipment;
marking the total loss amount caused by the corresponding fault of each maintenance of the history corresponding to the production equipment as GS; acquiring corresponding sequence monitoring periods according to the sequence of seamless steel pipe production, wherein the units of the sequence monitoring periods are days, specific numerical values can be obtained through simulation by simulation software according to maintenance big data of the corresponding production sequence, and when running stability analysis is carried out on production equipment of the corresponding production sequence in the sequence monitoring periods, the numerical values of all marked data are extracted and are represented by a formulaCalculating and obtaining an operation stability coefficient Yw corresponding to production equipment; wherein, jy is a basic influence factor corresponding to production equipment;
the basic influence factors corresponding to the production equipment are obtained by the following steps:
sequentially acquiring corresponding operation time according to the corresponding production time of production equipment in different groups and the current real-time Beijing time, and marking the operation time as YS; the unit of the operation time length is a day;
counting the total maintenance times according to the historical maintenance time corresponding to the production equipment and marking the total maintenance times as HZ;
extracting the numerical value of each item of marked data and passing through a formulaCalculating and obtaining a basic influence factor Jy corresponding to production equipment; wherein, BS is the standard evaluation period corresponding to the production sequence of seamless steel pipes where production equipment is located; the standard evaluation period is given in days, and specific numerical values can be obtained through simulation by simulation software according to maintenance big data of corresponding production equipment;
the operation stability coefficient is a numerical value for performing simultaneous calculation on data of various aspects of operation and maintenance of the production equipment to evaluate a staged operation state of the production equipment in a sequential monitoring period; the larger the operation stability coefficient is, the worse the corresponding production equipment is in the staged operation state in the sequential monitoring period;
furthermore, the basic influence factor is a numerical value for performing simultaneous calculation on each item of data of the operation aspect and the maintenance aspect of the production equipment to perform overall evaluation on the aspects thereof; the larger the basic influence factor is, the more excellent the corresponding operation equipment is, for example, faults can be prevented or timely found;
when the operation states of the corresponding production equipment in the sequential monitoring period are evaluated and classified according to the operation stability coefficients, marking the production equipment corresponding to the operation stability coefficients smaller than the operation stability threshold as excellent operation state equipment; the operation stability threshold value can be obtained through simulation by simulation software according to production big data corresponding to the production sequence of the seamless steel tube;
marking production equipment corresponding to the operation stability coefficient which is not smaller than the operation stability threshold and not larger than the operation stability threshold by Y% as normal operation state equipment; y is a real number greater than one hundred;
marking production equipment corresponding to an operation stability coefficient greater than an operation stability threshold Y% as abnormal operation state equipment;
all running stability coefficients, excellent running state equipment, normal running state equipment and abnormal running state equipment corresponding to the same seamless steel pipe production sequence form local equipment analysis data, and all local equipment analysis data corresponding to the seamless steel pipe production sequence form equipment state analysis sets and are uploaded to a cloud platform;
according to the embodiment of the invention, the operation stability coefficient is obtained by carrying out staged calculation analysis on all production equipment of different seamless steel pipe production sequences, the operation state of the production equipment in a sequential monitoring period is evaluated and classified according to the operation stability coefficient by carrying out simultaneous calculation on the data of each aspect of operation and maintenance of the production equipment, so that the production states in the corresponding stages of all production equipment of different seamless steel pipe production sequences can be intuitively and efficiently obtained, reliable data support can be provided for the whole production state analysis and the dynamic maintenance of the production equipment corresponding to all production equipment of the subsequent different seamless steel pipe production sequences, and the whole effect of staged monitoring analysis of all production equipment of different seamless steel pipe production sequences is improved.
The equipment state tracking and verifying module is used for tracking and integrating analysis on the state analysis results of the production equipment in a plurality of subsequent sequential monitoring periods to obtain an equipment state integration analysis set; comprising the following steps:
when the overall operation states of all production equipment corresponding to the production sequence are sequentially evaluated according to the production sequence of the seamless steel pipes, traversing analysis data of all local equipment corresponding to the production equipment in N sequence monitoring periods, counting the total number of the excellent operation state equipment, the normal operation state equipment and the abnormal operation state equipment, and setting the total number as a first state total number YZZ, a second state total number EZZ and a third state total number SZZ respectively; n is a positive integer;
the production sequence names of the production equipment are obtained, different production sequence names are set to correspond to different production sequence weights, the specific numerical value of the production sequence weights can be obtained through simulation of production big data corresponding to the production sequence of the seamless steel pipe through simulation software, and the obtained production sequence names are subjected to traversal comparison with all production sequence names prestored in a database to obtain corresponding production sequence weights and marked as SQ; extracting the values of the first state total number, the second state total number, the third state total number and the production sequence weight of the corresponding marks of the production equipment and passing through a formulaCalculating and obtaining an operation state coefficient Yz corresponding to production equipment;
it should be noted that the running state coefficient is a numerical value for performing simultaneous calculation on the staged analysis results of the plurality of production devices to evaluate the overall running state of the production devices; the larger the operation state coefficient is, the worse the whole operation state of the corresponding production equipment is;
when the overall operation state of the production equipment corresponding to the production sequence is evaluated according to the operation state coefficient, matching and classifying the operation state coefficient and the corresponding operation state range; the operation state range can be obtained through simulation by simulation software according to production big data corresponding to the production sequence of the seamless steel tube;
marking the production equipment corresponding to the operation state coefficient smaller than the minimum value of the operation state range as the whole operation state excellent equipment;
marking the production equipment corresponding to the operation state coefficient which is not smaller than the minimum value of the operation state range and not larger than the maximum value of the operation state range as the whole operation state normal equipment;
marking production equipment corresponding to an operation state coefficient larger than the maximum value of the operation state range as whole operation state abnormal equipment;
all running state coefficients corresponding to the same seamless steel tube production sequence, and equipment with excellent overall running state, equipment with normal overall running state and equipment with abnormal overall running state form equipment tracking overall analysis data, and all equipment tracking overall analysis data corresponding to the same seamless steel tube production sequence form equipment state integration analysis set and are uploaded to a cloud platform;
in the embodiment of the invention, the operation state coefficients are obtained by carrying out simultaneous calculation on the staged analysis results of a plurality of production devices, and the overall operation states of the production devices are evaluated and classified according to the operation state coefficients, so that the error existing in the single stage of the state analysis of the production devices can be reduced, the accuracy of the overall operation state analysis corresponding to the production devices can be effectively improved, the overall operation states of different production devices corresponding to different seamless steel pipe production sequences can be intuitively and efficiently obtained, reliable data support can be provided for dynamic maintenance management of different production devices corresponding to different subsequent seamless steel pipe production sequences, and the diversity and reliability of the monitoring data analysis of the production devices are improved.
The equipment maintenance management module is used for carrying out dynamic pushing prompt on the operation maintenance of different production equipment in the seamless steel pipe production sequence according to the equipment state integration analysis set; comprising the following steps:
sequentially acquiring equipment tracking overall analysis data associated with all production equipment corresponding to the seamless steel pipe production sequence according to the equipment state integration analysis set, traversing the equipment tracking overall analysis data to acquire marks of overall operation state excellent equipment, overall operation state normal equipment or overall operation state abnormal equipment corresponding to all production equipment;
acquiring a historical maintenance keyword library corresponding to the overall operation state excellent equipment, marking the historical maintenance keyword library as a selected maintenance keyword library, and acquiring operation environment parameters corresponding to the overall operation state excellent equipment and marking the operation environment parameters as selected operation environment parameters;
the operation environment parameters comprise the environment temperature, the environment humidity, the environment air pressure, the environment noise and the air particulate matter concentration when the equipment operates, and the monitoring and the data statistics are implemented through the existing internet of things technology;
the selected maintenance keyword library corresponding to the excellent equipment in the overall operation state and the selected operation environment parameters are adaptively pushed to production enterprises corresponding to the normal equipment in the overall operation state and the abnormal equipment in the overall operation state, so that the operation and maintenance of the production equipment can be pertinently learned and adjusted by the production enterprises corresponding to the normal equipment in the overall operation state and the abnormal equipment in the overall operation state;
if all production equipment corresponding to the production sequence of the seamless steel pipes does not have excellent equipment in the overall operation state, the historical maintenance keyword library and the operation environment parameters corresponding to the production equipment of the equipment in the normal overall operation state are pushed to the production enterprises corresponding to the equipment in the abnormal overall operation state, and cross-region and cross-type sharing and communication of maintenance management of key equipment in the production of the seamless steel pipes can be achieved.
According to the embodiment of the invention, the information barriers between the maintenance data of the internet of things of the key equipment in the production of the seamless steel pipe in the existing scheme can be broken through carrying out dynamic pushing management on different overall operation states of all production equipment corresponding to the same production sequence of the seamless steel pipe, so that the maintenance data of the internet of things of the key equipment can be efficiently and conveniently shared and learned, and the overall effect of the maintenance management of the internet of things of the key equipment in the production of the seamless steel pipe can be improved.
In addition, the formulas related in the above are all formulas for removing dimensions and taking numerical calculation, and are one formula which is obtained by acquiring a large amount of data and performing software simulation through simulation software and is closest to the actual situation.
In the several embodiments provided by the present invention, it should be understood that the disclosed system may be implemented in other ways. For example, the above-described embodiments of the invention are merely illustrative, and for example, the division of modules is merely a logical function division, and other manners of division may be implemented in practice.
The modules illustrated as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, i.e., may be located in one place, or may be distributed over a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist alone physically, or two or more modules may be integrated into one module. The integrated modules may be implemented in hardware or in hardware plus software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the essential characteristics thereof.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (10)
1. The maintenance management system of the internet of things for the key equipment in the seamless steel pipe production is characterized by comprising an equipment monitoring data statistics module, wherein the equipment monitoring data statistics module is used for monitoring and data statistics of different production equipment of all seamless steel pipe production enterprises to obtain an equipment monitoring statistics set;
the equipment state evaluation module is used for sequentially carrying out staged evaluation and classification on the running states of different production equipment according to the equipment monitoring statistical set to obtain an equipment state analysis set; comprising the following steps:
traversing the equipment monitoring statistics set to sequentially obtain equipment monitoring data of all production equipment corresponding to different production sequences of the seamless steel pipe, grouping the production equipment with the same equipment model when the equipment monitoring data of all the production equipment are subjected to data processing, and counting the total grouping number after grouping;
sequentially extracting and combining the keywords of the historical maintenance logs corresponding to the production equipment in different groups to obtain a historical maintenance keyword library;
counting the total number of maintenance according to the corresponding historical maintenance time of the production equipment and marking as WZ; performing digital processing on each maintenance reason of the history to obtain a corresponding fault weight and marking the fault weight as GQ; marking the total loss amount caused by the corresponding fault of each maintenance of the history corresponding to the production equipment as GS; acquiring a corresponding sequence monitoring period according to the sequence of seamless steel pipe production, extracting numerical values of various marked data and acquiring a corresponding operation stability coefficient Yw of production equipment through calculation when operation stability analysis is carried out on the production equipment corresponding to the production sequence in the sequence monitoring period;
evaluating and classifying the operation states of the corresponding production equipment in the sequential monitoring period according to the operation stability coefficient to obtain excellent operation state equipment, normal operation state equipment or abnormal operation state equipment;
all running stability coefficients, excellent running state equipment, normal running state equipment and abnormal running state equipment corresponding to the same seamless steel pipe production sequence form local equipment analysis data, and all local equipment analysis data corresponding to the seamless steel pipe production sequence form equipment state analysis sets and are uploaded to a cloud platform;
the equipment state tracking and verifying module is used for tracking and integrating analysis on the state analysis results of the production equipment in a plurality of subsequent sequential monitoring periods to obtain an equipment state integration analysis set;
and the equipment maintenance management module is used for carrying out dynamic pushing prompt on the operation maintenance of different production equipment in the seamless steel pipe production sequence according to the equipment state integration analysis set in sequence, so as to realize cross-region and cross-type sharing and communication of maintenance management of key equipment in seamless steel pipe production.
2. The maintenance management system of the internet of things for producing key equipment for seamless steel pipes according to claim 1, wherein the step of obtaining the equipment monitoring statistics set comprises the steps of:
numbering and marking different production equipment according to the production sequence of the seamless steel pipes;
when basic information of different production equipment is monitored in sequence according to the serial numbers, equipment models corresponding to the production equipment and the production time are obtained, historical maintenance time, historical maintenance logs, historical maintenance time and historical maintenance reasons of each time corresponding to the production equipment are obtained according to the equipment models, and the total loss caused by fault correspondence of each time of historical maintenance is obtained;
the equipment model corresponding to the production equipment, the production time, the corresponding historical maintenance log for each time, the historical maintenance reasons for each time and the loss total amount caused by the corresponding fault of each time of the historical maintenance form equipment monitoring data;
and equipment monitoring data corresponding to all production equipment of all seamless steel pipe production sequences form an equipment monitoring statistical set and are uploaded to the cloud platform.
3. The maintenance management system of the internet of things for the production key equipment of the seamless steel pipe according to claim 1, wherein a calculation formula of the operation stability coefficient Yw is as follows:the method comprises the steps of carrying out a first treatment on the surface of the Wherein, jy is a basic influence factor corresponding to production equipment;
the basic influence factors corresponding to the production equipment are obtained by the following steps:
sequentially acquiring corresponding operation time according to the corresponding production time of production equipment in different groups and the current real-time Beijing time, and marking the operation time as YS;
counting the total maintenance times according to the historical maintenance time corresponding to the production equipment and marking the total maintenance times as HZ; extracting the numerical value of each item of marked data and passing through a formulaCalculating and obtaining a basic influence factor Jy corresponding to production equipment; wherein, BS is the standard evaluation period corresponding to the production sequence of the seamless steel pipe where the production equipment is located.
4. The maintenance management system of the internet of things for the production key equipment of the seamless steel pipe according to claim 3, wherein the production equipment corresponding to the operation stability coefficient smaller than the operation stability threshold is marked as excellent operation state equipment;
marking production equipment corresponding to the operation stability coefficient which is not smaller than the operation stability threshold and not larger than the operation stability threshold by Y% as normal operation state equipment; y is a real number greater than one hundred;
and marking the production equipment corresponding to the operation stability coefficient which is larger than the operation stability threshold value Y% as abnormal operation state equipment.
5. The maintenance management system of the internet of things for producing key equipment for seamless steel pipes according to claim 1, wherein the working steps of the equipment state tracking and verifying module comprise:
when the overall operation states of all production equipment corresponding to the production sequence are sequentially evaluated according to the production sequence of the seamless steel pipes, traversing analysis data of all local equipment corresponding to the production equipment in N sequence monitoring periods, counting the total number of the excellent operation state equipment, the normal operation state equipment and the abnormal operation state equipment, and setting the total number as a first state total number YZZ, a second state total number EZZ and a third state total number SZZ respectively; n is a positive integer;
the production sequence name of the production equipment and the corresponding production sequence weight are obtained and marked as SQ; extracting the first state total number, the second state total number, the third state total number and the numerical value of the production sequence weight of the corresponding marks of the production equipment, and obtaining the corresponding running state coefficient Yz of the production equipment through calculation;
when the overall operation state of the production equipment corresponding to the production sequence is evaluated according to the operation state coefficient, matching and classifying the operation state coefficient and the corresponding operation state range to obtain the equipment with excellent overall operation state, the equipment with normal overall operation state or the equipment with abnormal overall operation state;
all running state coefficients corresponding to the same seamless steel tube production sequence, and equipment with excellent overall running state, equipment with normal overall running state and equipment with abnormal overall running state form equipment tracking overall analysis data, and all equipment tracking overall analysis data corresponding to the same seamless steel tube production sequence form equipment state integration analysis set and are uploaded to a cloud platform.
6. The maintenance management system of the internet of things for producing key equipment for seamless steel pipes according to claim 5, wherein the calculation formula of the operation state coefficient Yz is as follows:。
7. the maintenance management system of the internet of things for producing key equipment for seamless steel pipes according to claim 5, wherein production equipment corresponding to an operation state coefficient smaller than the minimum value of the operation state range is marked as integral operation state excellent equipment;
marking the production equipment corresponding to the operation state coefficient which is not smaller than the minimum value of the operation state range and not larger than the maximum value of the operation state range as the whole operation state normal equipment;
and marking the production equipment corresponding to the operation state coefficient larger than the maximum value of the operation state range as the whole operation state abnormal equipment.
8. The maintenance management system of the internet of things for producing key equipment for seamless steel pipes according to claim 5, wherein the working steps of the equipment maintenance management module include:
sequentially acquiring equipment tracking overall analysis data associated with all production equipment corresponding to the seamless steel pipe production sequence according to the equipment state integration analysis set, traversing the equipment tracking overall analysis data to acquire marks of overall operation state excellent equipment, overall operation state normal equipment or overall operation state abnormal equipment corresponding to all production equipment;
acquiring a historical maintenance keyword library corresponding to the overall operation state excellent equipment, marking the historical maintenance keyword library as a selected maintenance keyword library, and acquiring operation environment parameters corresponding to the overall operation state excellent equipment and marking the operation environment parameters as selected operation environment parameters;
and pushing the selected maintenance keyword library corresponding to the excellent equipment in the overall operation state and the selected operation environment parameters to the normal equipment in the overall operation state and the production enterprises corresponding to the abnormal equipment in the overall operation state in a self-adaptive manner.
9. The maintenance management system of the internet of things for the key equipment in the production of the seamless steel pipes according to claim 8, wherein if all production equipment corresponding to the production sequence of the seamless steel pipes does not have excellent equipment in the overall operation state, a historical maintenance keyword library and operation environment parameters corresponding to the production equipment of normal equipment in the overall operation state are pushed to a production enterprise corresponding to abnormal equipment in the overall operation state.
10. The maintenance management system of the internet of things for the production and emphasis equipment of the seamless steel pipe according to claim 8, wherein the operation environment parameter is composed of the environment temperature, the environment humidity, the environment air pressure, the environment noise and the air particulate matter concentration when the equipment is operated.
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