CN114923107B - Escalator lubrication control method and device based on Internet of things and cluster analysis - Google Patents
Escalator lubrication control method and device based on Internet of things and cluster analysis Download PDFInfo
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- 238000005461 lubrication Methods 0.000 title claims abstract description 97
- 238000000034 method Methods 0.000 title claims abstract description 43
- 238000007621 cluster analysis Methods 0.000 title claims abstract description 34
- 230000007613 environmental effect Effects 0.000 claims abstract description 24
- 239000010687 lubricating oil Substances 0.000 claims description 33
- 238000012549 training Methods 0.000 claims description 20
- 230000001050 lubricating effect Effects 0.000 claims description 13
- 238000003860 storage Methods 0.000 claims description 11
- 238000004138 cluster model Methods 0.000 claims description 10
- 238000005516 engineering process Methods 0.000 claims description 7
- 238000007635 classification algorithm Methods 0.000 claims description 4
- 238000009833 condensation Methods 0.000 claims description 4
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- 239000003921 oil Substances 0.000 claims description 3
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- 230000006855 networking Effects 0.000 claims 1
- 238000012423 maintenance Methods 0.000 description 9
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Classifications
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
- F16N—LUBRICATING
- F16N29/00—Special means in lubricating arrangements or systems providing for the indication or detection of undesired conditions; Use of devices responsive to conditions in lubricating arrangements or systems
- F16N29/02—Special means in lubricating arrangements or systems providing for the indication or detection of undesired conditions; Use of devices responsive to conditions in lubricating arrangements or systems for influencing the supply of lubricant
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
- B66B27/00—Indicating operating conditions of escalators or moving walkways
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
- B66B31/00—Accessories for escalators, or moving walkways, e.g. for sterilising or cleaning
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
- F16N—LUBRICATING
- F16N2230/00—Signal processing
- F16N2230/02—Microprocessor; Microcomputer
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
- F16N—LUBRICATING
- F16N2250/00—Measuring
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
- F16N—LUBRICATING
- F16N2250/00—Measuring
- F16N2250/36—Viscosity
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
- F16N—LUBRICATING
- F16N2270/00—Controlling
- F16N2270/20—Amount of lubricant
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
- F16N—LUBRICATING
- F16N2270/00—Controlling
- F16N2270/20—Amount of lubricant
- F16N2270/30—Amount of lubricant intermittent
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
- F16N—LUBRICATING
- F16N2270/00—Controlling
- F16N2270/70—Supply
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B50/00—Energy efficient technologies in elevators, escalators and moving walkways, e.g. energy saving or recuperation technologies
Abstract
The invention discloses an escalator lubrication control method and device based on the Internet of things and cluster analysis, wherein the method comprises the following steps: acquiring operation condition information and environment characteristic information of a target escalator; inputting the operation condition information and the environmental characteristic information into a clustering model, and determining the category information of the target escalator; inputting the category information of the target escalator into an escalator mechanical condition identification model, and determining the category of the target mechanical condition of the target escalator; determining a lubrication mode of the target escalator according to the target mechanical condition category, and determining lubrication parameters according to the lubrication mode; and carrying out lubrication control on the target escalator according to the lubrication parameters. The invention improves the accuracy, saves energy and protects environment, and can be widely applied to the technical field of escalator control.
Description
Technical Field
The invention relates to the technical field of escalator control, in particular to an escalator lubrication control method and device based on the Internet of things and cluster analysis.
Background
At present, most of escalator adopts an automatic lubrication system to automatically and quantitatively refuel at fixed time, and the refuel amount required by a plurality of running tests of the escalator is set in the early stage. The problems of excessive lubrication, frequent maintenance and oiling work, maintenance cost increase, resource waste and environmental pollution, insufficient lubrication, increased dry abrasion probability, accelerated component aging and the like of the components after long-term operation are not considered, which are all small losses for maintenance, economy or environment, can occur because the initial working condition of the operating components is good.
Disclosure of Invention
Therefore, the embodiment of the invention provides the escalator lubrication control method and device with high accuracy, energy conservation and environmental protection based on the Internet of things and cluster analysis.
The invention provides an escalator lubrication control method based on the Internet of things and cluster analysis, which comprises the following steps:
acquiring operation condition information and environment characteristic information of a target escalator;
inputting the operation condition information and the environmental characteristic information into a clustering model, and determining the category information of the target escalator;
inputting the category information of the target escalator into an escalator mechanical condition identification model, and determining the category of the target mechanical condition of the target escalator;
determining a lubrication mode of the target escalator according to the target mechanical condition category, and determining lubrication parameters according to the lubrication mode;
and carrying out lubrication control on the target escalator according to the lubrication parameters.
Optionally, the acquiring the operation condition information and the environmental characteristic information of the target escalator includes at least one of the following:
acquiring the pitch of the target escalator;
acquiring the roller diameter of the target escalator;
acquiring the inner width of the inner section of the target escalator;
obtaining the diameter of a refined shaft of the target escalator;
acquiring the pin shaft length of the target escalator;
acquiring the height of an inner chain plate of the target escalator;
acquiring the thickness of a chain plate of the target escalator;
obtaining the ultimate tensile load of the target escalator;
obtaining the average tensile load of the target escalator;
acquiring the length of each meter of the target escalator;
acquiring a lubricating oil viscosity index of the target escalator;
acquiring the lubricating oil condensation point of the target escalator;
acquiring the flash point of the lubricating oil of the target escalator;
obtaining the demulsification time of the lubricating oil of the target escalator;
acquiring the environmental temperature information of the target escalator;
acquiring the environmental humidity information of the target escalator;
acquiring the environmental wind information of the target escalator;
acquiring the environment sand information of the target escalator;
and acquiring the environmental rainwater information of the target escalator.
Optionally, the method further comprises a training step of the cluster model, the step comprising:
the working condition characteristics and the environment characteristics of the escalator are obtained through the internet of things technology, and an escalator training sample is constructed;
performing cluster analysis training on the escalator training sample to obtain a cluster model; the clustering model can determine staircase categories corresponding to different working condition characteristics;
assigning the same label to the same category obtained by clustering; wherein all the escalators under each label represent the same type of mechanical condition; the lubrication control model of all the staircase under the same label is the same.
Optionally, the method further comprises a training step of the staircase mechanical condition identification model, the step comprising:
determining different types of staircase sample sets according to the clustering model;
training a staircase mechanical condition recognition model according to different staircase sample sets and by combining classification algorithms with supervised learning; the escalator mechanical condition identification model is used for determining a mechanical condition type label of the escalator.
Optionally, the method further comprises a step of evaluating the stability of the cluster model, the step comprising at least one of:
calculating intra-class and inter-class distances between the different classes of the escalator, and determining the clustering stability according to the intra-class and inter-class distances;
dividing an original sample set into a plurality of subsets, respectively clustering the subsets, comparing clustering results between the original sample set and the subsets, and determining clustering stability according to the comparison results;
adding random interference noise into the original data, comparing cluster analysis results before and after the random interference noise is added, and determining the stability of clusters according to the comparison results;
and carrying out cluster analysis on new characteristic data randomly projected by the original data in a low-dimensional space, comparing cluster analysis results before and after projection, and determining the stability of the clusters according to the comparison results.
Optionally, the determining lubrication parameters according to the lubrication mode includes at least one of:
determining the oil filling amount of the lubricating oil according to the lubricating mode;
determining the oiling frequency of the lubricating oil according to the lubricating mode;
and determining the oiling time of the lubricating oil according to the lubricating mode.
Another aspect of the embodiment of the present invention further provides an escalator lubrication control device based on internet of things and cluster analysis, including:
the first module is used for acquiring the operation condition information and the environment characteristic information of the target escalator;
the second module is used for inputting the operation condition information and the environment characteristic information into a clustering model and determining the category information of the target escalator;
a third module, configured to input category information of the target escalator into an escalator mechanical condition recognition model, and determine a target mechanical condition category of the target escalator;
a fourth module for determining a lubrication mode of the target escalator according to the target mechanical condition category, and determining lubrication parameters according to the lubrication mode;
and the fifth module is used for controlling the lubrication of the target escalator according to the lubrication parameters.
Another aspect of the embodiment of the present invention further provides an escalator lubrication control system based on internet of things and cluster analysis, including:
the GPS positioning device is used for acquiring the position information of the target escalator and sending the position information to the main control board;
the information acquisition device of the Internet of things is used for acquiring the position information of the target escalator from the main control board and acquiring the operation condition information and the environment characteristic information of the target escalator according to the position information; transmitting the operation condition information and the environment characteristic information to a main control board;
the main control board is used for receiving, processing and sending related signals and sending lubricating oil control instructions to the oiling device according to the triggered control signals;
the edge computing device is used for acquiring operation condition information and environment characteristic information from the main control board, and then establishing a clustering model and an escalator mechanical condition recognition model; the clustering model is used for determining category information of the target escalator, and the escalator mechanical condition identification model is used for determining a target mechanical condition category of the target escalator;
and the oiling device is used for responding to the lubricating oil control instruction sent by the main control board to carry out lubricating control.
Another aspect of the embodiment of the invention also provides an electronic device, which includes a processor and a memory;
the memory is used for storing programs;
the processor executes the program to implement the method as described above.
Another aspect of the embodiments of the present invention also provides a computer-readable storage medium storing a program that is executed by a processor to implement a method as described above.
Embodiments of the present invention also disclose a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The computer instructions may be read from a computer-readable storage medium by a processor of a computer device, and executed by the processor, to cause the computer device to perform the foregoing method.
The embodiment of the invention acquires the operation condition information and the environment characteristic information of the target escalator; inputting the operation condition information and the environmental characteristic information into a clustering model, and determining the category information of the target escalator; inputting the category information of the target escalator into an escalator mechanical condition identification model, and determining the category of the target mechanical condition of the target escalator; determining a lubrication mode of the target escalator according to the target mechanical condition category, and determining lubrication parameters according to the lubrication mode; and carrying out lubrication control on the target escalator according to the lubrication parameters. The invention improves the accuracy, saves energy and protects environment.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, 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 an accessory lubrication control system according to an embodiment of the present invention;
fig. 2 is a flowchart illustrating steps of an additional lubrication control method according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
Because most of the escalator at present adopts an automatic lubrication system, automatic oiling with fixed time and fixed quantity is performed, and the oiling amount required by the operation test of the escalator for multiple times is set in the early stage. The problems of excessive lubrication, frequent maintenance and oiling work, maintenance cost increase, resource waste and environmental pollution, insufficient lubrication, increased dry abrasion probability, accelerated component aging and the like of the components after long-term operation are not considered, which are all small losses for maintenance, economy or environment, can occur because the initial working condition of the operating components is good. Aiming at the problems existing in the prior art, the invention combines the internet of things technology and the cluster analysis technology, optimizes the escalator automatic lubrication control system, performs unsupervised machine learning based on the working condition characteristics and the environmental characteristics of the outdoor escalator, and performs oiling lubrication according to the requirements. The scheme comprises a main control board, a GPS positioning device, an information acquisition device of the Internet of things, an edge calculation device and an oiling device.
Specifically, the embodiment of the invention provides an escalator lubrication control method based on the Internet of things and cluster analysis,
comprising the following steps:
acquiring operation condition information and environment characteristic information of a target escalator;
inputting the operation condition information and the environmental characteristic information into a clustering model, and determining the category information of the target escalator;
inputting the category information of the target escalator into an escalator mechanical condition identification model, and determining the category of the target mechanical condition of the target escalator;
determining a lubrication mode of the target escalator according to the target mechanical condition category, and determining lubrication parameters according to the lubrication mode;
and carrying out lubrication control on the target escalator according to the lubrication parameters.
Optionally, the acquiring the operation condition information and the environmental characteristic information of the target escalator includes at least one of the following:
acquiring the pitch of the target escalator;
acquiring the roller diameter of the target escalator;
acquiring the inner width of the inner section of the target escalator;
obtaining the diameter of a refined shaft of the target escalator;
acquiring the pin shaft length of the target escalator;
acquiring the height of an inner chain plate of the target escalator;
acquiring the thickness of a chain plate of the target escalator;
obtaining the ultimate tensile load of the target escalator;
obtaining the average tensile load of the target escalator;
acquiring the length of each meter of the target escalator;
acquiring a lubricating oil viscosity index of the target escalator;
acquiring the lubricating oil condensation point of the target escalator;
acquiring the flash point of the lubricating oil of the target escalator;
obtaining the demulsification time of the lubricating oil of the target escalator;
acquiring the environmental temperature information of the target escalator;
acquiring the environmental humidity information of the target escalator;
acquiring the environmental wind information of the target escalator;
acquiring the environment sand information of the target escalator;
and acquiring the environmental rainwater information of the target escalator.
Optionally, the method further comprises a training step of the cluster model, the step comprising:
the working condition characteristics and the environment characteristics of the escalator are obtained through the internet of things technology, and an escalator training sample is constructed;
performing cluster analysis training on the escalator training sample to obtain a cluster model; the clustering model can determine staircase categories corresponding to different working condition characteristics;
assigning the same label to the same category obtained by clustering; wherein all the escalators under each label represent the same type of mechanical condition; the lubrication control model of all the staircase under the same label is the same.
Optionally, the method further comprises a training step of the staircase mechanical condition identification model, the step comprising:
determining different types of staircase sample sets according to the clustering model;
training a staircase mechanical condition recognition model according to different staircase sample sets and by combining classification algorithms with supervised learning; the escalator mechanical condition identification model is used for determining a mechanical condition type label of the escalator.
Optionally, the method further comprises a step of evaluating the stability of the cluster model, the step comprising at least one of:
calculating intra-class and inter-class distances between the different classes of the escalator, and determining the clustering stability according to the intra-class and inter-class distances;
dividing an original sample set into a plurality of subsets, respectively clustering the subsets, comparing clustering results between the original sample set and the subsets, and determining clustering stability according to the comparison results;
adding random interference noise into the original data, comparing cluster analysis results before and after the random interference noise is added, and determining the stability of clusters according to the comparison results;
and carrying out cluster analysis on new characteristic data randomly projected by the original data in a low-dimensional space, comparing cluster analysis results before and after projection, and determining the stability of the clusters according to the comparison results.
Optionally, the determining lubrication parameters according to the lubrication mode includes at least one of:
determining the oil filling amount of the lubricating oil according to the lubricating mode;
determining the oiling frequency of the lubricating oil according to the lubricating mode;
and determining the oiling time of the lubricating oil according to the lubricating mode.
Another aspect of the embodiment of the present invention further provides an escalator lubrication control device based on internet of things and cluster analysis, including:
the first module is used for acquiring the operation condition information and the environment characteristic information of the target escalator;
the second module is used for inputting the operation condition information and the environment characteristic information into a clustering model and determining the category information of the target escalator;
a third module, configured to input category information of the target escalator into an escalator mechanical condition recognition model, and determine a target mechanical condition category of the target escalator;
a fourth module for determining a lubrication mode of the target escalator according to the target mechanical condition category, and determining lubrication parameters according to the lubrication mode;
and the fifth module is used for controlling the lubrication of the target escalator according to the lubrication parameters.
Another aspect of the embodiment of the present invention further provides an escalator lubrication control system based on internet of things and cluster analysis, including:
the GPS positioning device is used for acquiring the position information of the target escalator and sending the position information to the main control board;
the information acquisition device of the Internet of things is used for acquiring the position information of the target escalator from the main control board and acquiring the operation condition information and the environment characteristic information of the target escalator according to the position information; transmitting the operation condition information and the environment characteristic information to a main control board;
the main control board is used for receiving, processing and sending related signals and sending lubricating oil control instructions to the oiling device according to the triggered control signals;
the edge computing device is used for acquiring operation condition information and environment characteristic information from the main control board, and then establishing a clustering model and an escalator mechanical condition recognition model; the clustering model is used for determining category information of the target escalator, and the escalator mechanical condition identification model is used for determining a target mechanical condition category of the target escalator;
and the oiling device is used for responding to the lubricating oil control instruction sent by the main control board to carry out lubricating control.
Another aspect of the embodiment of the invention also provides an electronic device, which includes a processor and a memory;
the memory is used for storing programs;
the processor executes the program to implement the method as described above.
Another aspect of the embodiments of the present invention also provides a computer-readable storage medium storing a program that is executed by a processor to implement a method as described above.
Embodiments of the present invention also disclose a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The computer instructions may be read from a computer-readable storage medium by a processor of a computer device, and executed by the processor, to cause the computer device to perform the foregoing method.
The following describes the specific implementation of the present invention in detail with reference to the drawings of the specification:
as shown in fig. 1: the automatic lubrication control system of the outdoor escalator comprises a main control board, a GPS positioning device, an information acquisition device of the Internet of things, an edge calculation device and an oiling device.
The main control board is used for receiving, processing and sending signals, and can be a controller formed by a microcomputer board, a PLC, a singlechip, an embedded system and the like.
The GPS positioning device is mainly used for acquiring position coordinate signals of the outdoor escalator and transmitting the position coordinate signals to the main control board.
The internet of things information acquisition device is used for connecting the internet, and control network data transmission is carried out through the main control board, and mainly has two aspects of information acquisition: firstly, acquiring GPS positioning information of an outdoor escalator from a main control board, thereby acquiring the positioning environmental characteristics (including temperature, humidity, wind power and the like) of the escalator; second, the operating characteristics of the escalator lubrication component (including drive chain pitch, roller diameter, lubricant viscosity index, etc.) are obtained.
The edge computing device is mainly used for data analysis and establishment of a machine learning model, characteristic data obtained by the Internet of things are transmitted to the edge computing device through the main control board to carry out data preprocessing, characteristic analysis and unsupervised machine learning, escalator samples with similar characters are classified through a cluster analysis method, different escalator category labels are finally formed, and the category of the escalator samples newly added in the future can be rapidly judged and returned to corresponding labels.
The oiling device is used for finally executing the target, and the main control board is used for controlling the lubricating oil mode, so that the escalator lubricating process is realized.
As shown in fig. 2, the outdoor escalator lubrication control system is a flow chart. The prior art mainly focuses on the optimization of the oiling device, has less research on the oiling intensity, generally adopts a single periodic oiling mode, does not consider the requirements of the escalator, and does not relate to a control method of the oiling intensity and frequency. Some outdoor escalators cannot guarantee optimal lubrication of the escalator in a severe environment due to the general oiling mode, so that aging of the escalator is accelerated, excessive lubrication is performed in a good environment, and waste of lubricating oil is caused.
According to the escalator automatic lubrication system, the escalator real-time data is obtained to adjust the automatic lubrication system, and the lubrication frequency of the escalator is controlled, so that the dry wear probability of parts is reduced, the lubrication resource waste is reduced, the cost is reduced, the maintenance working pressure is reduced, and the environmental pollution is reduced. Firstly, working condition and environment characteristic information in the running process of an outdoor escalator are obtained through the technology of the Internet of things, wherein the working condition characteristics comprise driving chain characteristics (comprising pitch, roller diameter, inner section inner width, finish shaft diameter, pin shaft length, inner chain plate height, chain plate thickness, ultimate tensile load, average tensile load and length/meter weight) and lubricating oil indexes (comprising viscosity index, condensation point, flash point and demulsification time) used, and the environment characteristics comprise temperature, humidity, wind power, sand dust and rainwater. A total of 19 features of the outdoor escalator are obtained.
Based on the characteristics, cluster analysis is carried out on a large number of escalator samples, the cluster stability is used as a final optimization target, the optimal category number is obtained, lubrication modes with different intensities are set for different clustered categories, the optimal lubrication effect of the escalator is ensured in all the different modes, for example, vibration and noise are reduced to the minimum during machine part operation, the attachment rate of lubricating oil is highest, and the like. When evaluating the effect of cluster analysis, the algorithm is required to relatively stabilize the class distribution obtained by aggregation after the original data is learned, and the class distribution cannot be changed along with the noise, the random sample set and the model iteration number, for example, in the stability evaluation step in the flow chart 2, the stability of the escalator sample cluster needs to be evaluated, and the following aspects can be considered: firstly, the separation condition of the classes is measured, and the smaller the intra-class distance is, the larger the inter-class distance is, the higher the stability is; secondly, dividing an original sample set into a plurality of subsets, clustering the subsets respectively, and comparing clustering results of the original sample set and the subsets, wherein the smaller the difference is, the higher the stability is; thirdly, the clustering analysis results before and after adding random interference noise to the original data are compared, and the clustering model with smaller interference noise is higher in relative stability; fourth, cluster analysis is performed on new feature data randomly projected by original data in a low-dimensional space, and a cluster model which does not change along with the projection space is higher in relative stability. Therefore, the clustering stability is improved from the directions, and model parameters in an unsupervised learning process, such as the number of categories of aggregation, convergence mode and optimization of a feature expression method, are optimized.
Through cluster analysis, the staircase categories with different working condition characteristics are trained, the same label is given to the same category obtained through clustering, different categories are distinguished by using the label, and all staircase samples are regarded as the same type of mechanical condition under the same label, so that the oiling mode of the staircase samples is consistent. After the clustering model is trained in the early stage, different types of staircase sample sets are obtained, then the staircase mechanical condition recognition model training is carried out based on different types of staircase data and combined with a classification algorithm (supervised learning), then when a new test sample is added, the staircase mechanical condition recognition model is used for distinguishing, a mechanical condition type label to which the new test sample belongs is recognized, and then the label is returned to a main control board, so that a lubrication control system outputs a lubrication mode corresponding to the label. Therefore, the running performance of the escalator is improved, the lubricating oil resource is fully utilized, the cost consumption is reduced, and the mechanical abrasion is slowed down.
The lubrication mode is determined by actual condition clustering, the component characteristics and the environmental characteristics of different escalators are different, similar commonalities can be found through unsupervised machine learning and clustering is carried out based on the difference characteristics among the classes, so that samples with similar conditions are classified into a certain class, in each class of aggregation, the given lubrication mode can optimize the running state of the escalator, different lubrication modes have pertinence, and lubrication parameters (such as oiling amount, oiling frequency, oiling duration and the like) are set according to different mechanical condition class labels. In addition, through repeated iteration experiments for many times, the clustering stability is used as an evaluation standard, the clustering effect of the escalator samples is optimized, and the type of the escalator samples can be rapidly judged when one escalator sample is newly added in the later period, so that the corresponding lubrication mode is used.
In summary, compared with the prior art, the invention has the following advantages:
1. the accuracy is higher, only need the staircase class label of earlier stage training, through obtaining outdoor staircase real-time characteristic data after putting into use, can distinguish out the lubrication mode of what kind of grades should be carried out fast, make the lubrication procedure of outdoor staircase more accurate, improve lubricated effect, reduce moving part dry abrasion probability, increase of service life, alleviate maintenance personnel work pressure.
2. The automatic lubrication system is energy-saving and environment-friendly, is different from the fixed and unchanged oiling frequency in the automatic lubrication system of the existing method, makes a decision through the operation working condition and the environmental characteristics of the escalator, can automatically adjust the oiling frequency according to the escalator requirement, avoids the problem of unreasonable resource utilization caused by excessive or insufficient oiling amount in the operation process of the outdoor escalator, improves the resource utilization rate, reduces the environmental pollution, reduces the frequency of the maintenance personnel lubricating oil replacement work, and saves the cost.
In some alternative embodiments, the functions/acts noted in the block diagrams may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Furthermore, the embodiments presented and described in the flowcharts of the present invention are provided by way of example in order to provide a more thorough understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed, and in which sub-operations described as part of a larger operation are performed independently.
Furthermore, while the invention is described in the context of functional modules, it should be appreciated that, unless otherwise indicated, one or more of the described functions and/or features may be integrated in a single physical device and/or software module or one or more functions and/or features may be implemented in separate physical devices or software modules. It will also be appreciated that a detailed discussion of the actual implementation of each module is not necessary to an understanding of the present invention. Rather, the actual implementation of the various functional modules in the apparatus disclosed herein will be apparent to those skilled in the art from consideration of their attributes, functions and internal relationships. Accordingly, one of ordinary skill in the art can implement the invention as set forth in the claims without undue experimentation. It is also to be understood that the specific concepts disclosed are merely illustrative and are not intended to be limiting upon the scope of the invention, which is to be defined in the appended claims and their full scope of equivalents.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that: many changes, modifications, substitutions and variations may be made to the embodiments without departing from the spirit and principles of the invention, the scope of which is defined by the claims and their equivalents.
While the preferred embodiment of the present invention has been described in detail, the present invention is not limited to the embodiments described above, and those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention, and these equivalent modifications or substitutions are included in the scope of the present invention as defined in the appended claims.
Claims (7)
1. The escalator lubrication control method based on the Internet of things and cluster analysis is characterized by comprising the following steps of:
acquiring operation condition information and environment characteristic information of a target escalator;
inputting the operation condition information and the environmental characteristic information into a clustering model, and determining the category information of the target escalator;
inputting the category information of the target escalator into an escalator mechanical condition identification model, and determining the category of the target mechanical condition of the target escalator;
determining a lubrication mode of the target escalator according to the target mechanical condition category, and determining lubrication parameters according to the lubrication mode;
according to the lubrication parameters, the lubrication control is carried out on the target escalator;
the method for acquiring the operation condition information and the environment characteristic information of the target escalator comprises at least one of the following steps:
acquiring the pitch of the target escalator;
acquiring the roller diameter of the target escalator;
acquiring the inner width of the inner section of the target escalator;
obtaining the diameter of a refined shaft of the target escalator;
acquiring the pin shaft length of the target escalator;
acquiring the height of an inner chain plate of the target escalator;
acquiring the thickness of a chain plate of the target escalator;
obtaining the ultimate tensile load of the target escalator;
obtaining the average tensile load of the target escalator;
acquiring the length of each meter of the target escalator;
acquiring a lubricating oil viscosity index of the target escalator;
acquiring the lubricating oil condensation point of the target escalator;
acquiring the flash point of the lubricating oil of the target escalator;
obtaining the demulsification time of the lubricating oil of the target escalator;
acquiring the environmental temperature information of the target escalator;
acquiring the environmental humidity information of the target escalator;
acquiring the environmental wind information of the target escalator;
acquiring the environment sand information of the target escalator;
acquiring the environmental rainwater information of the target escalator;
the method further comprises a training step of the cluster model, which comprises the following steps:
the working condition characteristics and the environment characteristics of the escalator are obtained through the internet of things technology, and an escalator training sample is constructed;
performing cluster analysis training on the escalator training sample to obtain a cluster model; the clustering model can determine staircase categories corresponding to different working condition characteristics;
assigning the same label to the same category obtained by clustering; wherein all the escalators under each label represent the same type of mechanical condition; the lubrication control models of all the escalators under the same label are the same; the method also comprises a training step of the staircase mechanical condition identification model, and the step comprises the following steps:
determining different types of staircase sample sets according to the clustering model;
training a staircase mechanical condition recognition model according to different staircase sample sets and by combining classification algorithms with supervised learning; the escalator mechanical condition identification model is used for determining a mechanical condition type label of the escalator.
2. The escalator lubrication control method based on the internet of things and cluster analysis according to claim 1, further comprising the step of evaluating the stability of the cluster model, the step comprising at least one of:
calculating intra-class and inter-class distances between the different classes of the escalator, and determining the clustering stability according to the intra-class and inter-class distances;
dividing an original sample set into a plurality of subsets, respectively clustering the subsets, comparing clustering results between the original sample set and the subsets, and determining clustering stability according to the comparison results;
adding random interference noise into the original data, comparing cluster analysis results before and after the random interference noise is added, and determining the stability of clusters according to the comparison results;
and carrying out cluster analysis on new characteristic data randomly projected by the original data in a low-dimensional space, comparing cluster analysis results before and after projection, and determining the stability of the clusters according to the comparison results.
3. The escalator lubrication control method based on the internet of things and cluster analysis according to claim 1, wherein the determining lubrication parameters according to the lubrication mode comprises at least one of the following:
determining the oil filling amount of the lubricating oil according to the lubricating mode;
determining the oiling frequency of the lubricating oil according to the lubricating mode;
and determining the oiling time of the lubricating oil according to the lubricating mode.
4. An apparatus for applying the escalator lubrication control method based on the internet of things and cluster analysis as claimed in any one of claims 1-3, comprising:
the first module is used for acquiring the operation condition information and the environment characteristic information of the target escalator;
the second module is used for inputting the operation condition information and the environment characteristic information into a clustering model and determining the category information of the target escalator;
a third module, configured to input category information of the target escalator into an escalator mechanical condition recognition model, and determine a target mechanical condition category of the target escalator;
a fourth module for determining a lubrication mode of the target escalator according to the target mechanical condition category, and determining lubrication parameters according to the lubrication mode;
and the fifth module is used for controlling the lubrication of the target escalator according to the lubrication parameters.
5. Staircase lubrication control system based on thing networking and cluster analysis, its characterized in that includes:
the GPS positioning device is used for acquiring the position information of the target escalator and sending the position information to the main control board;
the information acquisition device of the Internet of things is used for acquiring the position information of the target escalator from the main control board and acquiring the operation condition information and the environment characteristic information of the target escalator according to the position information; transmitting the operation condition information and the environment characteristic information to a main control board;
the main control board is used for receiving, processing and sending related signals and sending lubricating oil control instructions to the oiling device according to the triggered control signals;
the edge computing device is used for acquiring operation condition information and environment characteristic information from the main control board, and then establishing a clustering model and an escalator mechanical condition recognition model; the clustering model is used for determining category information of the target escalator, and the escalator mechanical condition identification model is used for determining a target mechanical condition category of the target escalator;
and the oiling device is used for responding to the lubricating oil control instruction sent by the main control board to carry out lubricating control.
6. An electronic device comprising a processor and a memory;
the memory is used for storing programs;
the processor executing the program implements the method of any one of claims 1 to 3.
7. A computer-readable storage medium, characterized in that the storage medium stores a program that is executed by a processor to implement the method of any one of claims 1 to 3.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN205892487U (en) * | 2016-08-11 | 2017-01-18 | 苏州速菱电梯有限公司 | Automatic staircase lubricating arrangement |
| CN106586796A (en) * | 2016-11-15 | 2017-04-26 | 王蕊 | System and method for monitoring state of escalator |
| CN208964360U (en) * | 2018-10-11 | 2019-06-11 | 苏州江南嘉捷电梯有限公司 | A kind of staircase automatic lubrication installation |
| CA3063580A1 (en) * | 2018-12-17 | 2020-06-17 | 10353744 Canada Ltd. | Classifier training method and apparatus, electronic device and computer readable medium |
| CN113987905A (en) * | 2020-12-18 | 2022-01-28 | 天津市特种设备监督检验技术研究院(天津市特种设备事故应急调查处理中心) | Escalator braking force intelligent diagnosis system based on deep belief network |
| CN215797860U (en) * | 2021-04-02 | 2022-02-11 | 上海三菱电梯有限公司 | Detection apparatus for automatic staircase main drive chain lacks oily state |
-
2022
- 2022-05-05 CN CN202210480987.6A patent/CN114923107B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN205892487U (en) * | 2016-08-11 | 2017-01-18 | 苏州速菱电梯有限公司 | Automatic staircase lubricating arrangement |
| CN106586796A (en) * | 2016-11-15 | 2017-04-26 | 王蕊 | System and method for monitoring state of escalator |
| CN208964360U (en) * | 2018-10-11 | 2019-06-11 | 苏州江南嘉捷电梯有限公司 | A kind of staircase automatic lubrication installation |
| CA3063580A1 (en) * | 2018-12-17 | 2020-06-17 | 10353744 Canada Ltd. | Classifier training method and apparatus, electronic device and computer readable medium |
| CN113987905A (en) * | 2020-12-18 | 2022-01-28 | 天津市特种设备监督检验技术研究院(天津市特种设备事故应急调查处理中心) | Escalator braking force intelligent diagnosis system based on deep belief network |
| CN215797860U (en) * | 2021-04-02 | 2022-02-11 | 上海三菱电梯有限公司 | Detection apparatus for automatic staircase main drive chain lacks oily state |
Non-Patent Citations (1)
| Title |
|---|
| 自动扶梯主驱动链条性能测试分析与润滑保养;庞旭旻;仇润鹤;;科技与创新;20160205(03);10-11 * |
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