CN111302173A - Elevator steel wire rope abnormity detection method, device and equipment and readable storage medium - Google Patents
Elevator steel wire rope abnormity detection method, device and equipment and readable storage medium Download PDFInfo
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- CN111302173A CN111302173A CN201911170166.7A CN201911170166A CN111302173A CN 111302173 A CN111302173 A CN 111302173A CN 201911170166 A CN201911170166 A CN 201911170166A CN 111302173 A CN111302173 A CN 111302173A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
- B66B5/00—Applications of checking, fault-correcting, or safety devices in elevators
- B66B5/0006—Monitoring devices or performance analysers
- B66B5/0018—Devices monitoring the operating condition of the elevator system
- B66B5/0031—Devices monitoring the operating condition of the elevator system for safety reasons
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
- B66B5/00—Applications of checking, fault-correcting, or safety devices in elevators
- B66B5/02—Applications of checking, fault-correcting, or safety devices in elevators responsive to abnormal operating conditions
Abstract
The invention discloses an elevator steel wire rope abnormity detection method, device, equipment and readable storage medium, wherein a sensor is arranged close to the steel wire rope, and the method comprises the following steps: collecting detection data by the sensor; acquiring the detection data, and judging whether the detection data is abnormal data; and judging the state of the elevator steel wire rope by combining a preset data learning library according to the judgment result of the detection data. The elevator control system can effectively master the state of the steel wire rope of the elevator at any time, and reduces the potential safety hazard of the elevator in use.
Description
Technical Field
The invention relates to the technical field of elevator detection, in particular to a method, a device and equipment for detecting abnormity of an elevator steel wire rope and a readable storage medium.
Background
The elevator brings great convenience for people to go upstairs and downstairs, but the elevator is inevitable to have a fault in the running process, especially the elevator steel wire rope plays a role in dragging the elevator car, once the elevator steel wire rope is cracked or broken, serious safety accidents are caused, and the elevator steel wire rope needs to be detected and maintained.
However, at present, the detection of the elevator steel wire rope is completed through artificial regular detection and maintenance, and the artificial detection does not have real-time performance, so that the state of the elevator steel wire rope is difficult to effectively master in real time, and hidden danger is brought to safe use of the elevator.
The above is only for the purpose of assisting understanding of the technical solutions of the present application, and does not represent an admission that the above is prior art.
Disclosure of Invention
Therefore, aiming at the problem that the existing detection for the elevator steel wire rope does not have real-time performance through artificial detection, the state of the elevator steel wire rope is difficult to effectively master in real time, and potential safety hazards are brought to the use of an elevator, a method, a device, equipment and a readable storage medium for detecting the abnormality of the elevator steel wire rope are needed to be provided, the state of the elevator steel wire rope can be effectively master in real time, and the potential safety hazards in the use of the elevator are reduced.
In order to achieve the above object, the present invention provides an elevator rope anomaly detection method, in which a sensor is disposed near the rope, the method comprising:
collecting detection data by the sensor;
acquiring the detection data, and judging whether the detection data is abnormal data;
and judging the state of the elevator steel wire rope by combining a preset data learning library according to the judgment result of the detection data.
Optionally, the step of collecting detection data by the sensor comprises:
detecting the elevator steel wire rope through the sensor to obtain detection data;
and transmitting the detection data to a cloud platform so as to judge the abnormity of the detection data.
Optionally, the step of acquiring the detection data and determining whether the detection data is abnormal data includes:
acquiring the detection data, and inputting the detection data into a preset decision tree model;
and judging whether the detection data are abnormal data or not.
Optionally, the sensor includes a first photoelectric sensor, a second photoelectric sensor, a third photoelectric sensor and a vibration sensor, and the step of acquiring the detection data and inputting the detection data into a preset decision tree model includes:
acquiring the detection data, and converting the detection data into corresponding numerical expressions, wherein 0 represents no detection signal, and 1 represents detection signal;
and inputting the numerical expression into a decision tree model, sequentially passing through a root node and a branch node of the decision tree, and judging whether the detection data is abnormal data.
Optionally, the data learning library includes an abnormal database and a normal database, and the step of determining the state of the elevator steel wire rope according to the determination result of the detection data and by combining a preset data learning library includes:
if the detection data belong to abnormal data, comparing the detection data with the abnormal data in the abnormal database, judging the abnormal state of the elevator steel wire rope, and warning and reminding through a warning device;
if the detection data belong to normal data, comparing the detection data with the normal data in the normal database, judging the normal state of the elevator steel wire rope, and keeping the normal operation of the elevator.
Optionally, the step of determining the state of the elevator steel wire rope by combining a preset data learning library according to the determination result of the detection data includes:
if the detection data belong to abnormal data, updating the detection data to the abnormal database;
and if the detection data belong to normal data, updating the detection data to the normal database.
In order to achieve the above object, the present invention also provides an elevator rope abnormality detection device, including:
a collection module for collecting detection data by the sensor;
the judging module is used for acquiring the detection data and judging whether the detection data are abnormal data;
and the comparison module is used for judging the state of the elevator steel wire rope by combining a preset data learning library according to the judgment result of the detection data.
Optionally, the collecting module is further configured to detect the elevator steel wire rope through the sensor, and acquire detection data; and transmitting the detection data to a cloud platform so as to judge the abnormity of the detection data.
In addition, in order to achieve the above object, the present invention also provides an elevator rope abnormality detection apparatus including: the elevator wire rope abnormity detection program is stored on the memory and can be operated on the processor; the elevator wire rope abnormality detection program when executed by the processor implements the steps of the elevator wire rope abnormality detection method as described above.
In addition, in order to achieve the above object, the present invention also provides a readable storage medium having stored thereon an elevator rope abnormality detection program, which when executed by a processor, implements the steps of the elevator rope abnormality detection method as described above.
According to the technical scheme, the sensor is arranged at a position close to the elevator steel wire rope, the running condition of the steel wire rope can be detected through the sensor, the detection data is obtained, whether the detection data are abnormal data or not is analyzed through judgment of the detection data, in addition, the abnormal data or normal data of the steel wire rope under various conditions are stored in a preset data learning library, the state of the elevator steel wire rope can be timely judged through comparison of the detection data and the abnormal data or normal data, the sensor obtains the detection data in a continuous process, and each time one detection data is obtained, a corresponding result can be timely obtained through comparison and judgment of the data learning library, so that the running state of the elevator steel wire rope is mastered at all times, and the potential safety use hazard of the elevator is reduced.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the structures shown in the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for detecting an abnormality of an elevator rope according to a first embodiment of the present invention;
fig. 2 is a schematic flow chart of an elevator wire rope abnormality detection method according to a second embodiment of the present invention;
fig. 3 is a schematic flow chart of an elevator wire rope abnormality detection method according to a third embodiment of the present invention;
fig. 4 is a schematic flow chart of a method for detecting an abnormality of an elevator rope according to a fourth embodiment of the present invention;
fig. 5 is a schematic flow chart of a fifth embodiment of the elevator rope abnormality detection method of the present invention;
fig. 6 is a schematic flow chart of a sixth embodiment of an elevator rope abnormality detection method according to the present invention;
fig. 7 is a schematic flow chart of a decision tree principle in the elevator wire rope abnormality detection method of the present invention;
FIG. 8 is a schematic view showing the installation position of an inductor in an elevator rope abnormality detection method according to the present invention;
fig. 9 is a schematic structural view of an elevator rope abnormality detection device according to the present invention.
The reference numbers illustrate:
| reference numerals | Name (R) | Reference numerals | Name (R) |
| 100 | |
500 | |
| 200 | |
510 | |
| 300 | |
511 | |
| 400 | Updating module |
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that all the directional indicators (such as up, down, left, right, front, and rear … …) in the embodiment of the present invention are only used to explain the relative position relationship between the components, the movement situation, etc. in a specific posture (as shown in the drawing), and if the specific posture is changed, the directional indicator is changed accordingly.
In addition, the descriptions related to "first", "second", etc. in the present invention are only for descriptive purposes and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
In the present invention, unless otherwise expressly stated or limited, the terms "connected," "secured," and the like are to be construed broadly, and for example, "secured" may be a fixed connection, a removable connection, or an integral part; can be mechanically or electrically connected; they may be directly connected or indirectly connected through intervening media, or they may be connected internally or in any other suitable relationship, unless expressly stated otherwise. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
In addition, the technical solutions in the embodiments of the present invention may be combined with each other, but it must be based on the realization of those skilled in the art, and when the technical solutions are contradictory or cannot be realized, such a combination of technical solutions should not be considered to exist, and is not within the protection scope of the present invention.
Referring to fig. 1, according to a first embodiment of the present invention, an elevator rope abnormality detection method includes:
step S10, collecting detection data through a sensor;
specifically, in the process of moving an elevator dragged by an elevator steel wire rope, the steel wire rope is abraded in different degrees, for example, the thickness of the steel wire rope changes, the vibration condition of the steel wire rope possibly changes, the detection data of the steel wire rope can be effectively measured through a vibration sensor, in addition, protruding points are arranged at intervals of the steel wire rope, the change condition of the protruding points of the steel wire rope can be shot and recorded through a photoelectric sensor, and whether the steel wire rope breaks or not can be detected.
Step S20, acquiring detection data, and judging whether the detection data is abnormal data;
specifically, the detection data detected by the sensor is sorted and stored, and since the detection data obtained by the sensor is a continuous real-time process, whether the obtained detection data is abnormal or not can be obtained by analyzing and judging the detection data.
And step S30, judging the state of the elevator steel wire rope by combining a preset data learning library according to the judgment result of the detection data.
The detection data of the elevator steel wire rope under various running conditions are stored in the data learning base, so that the detection data and the data in the data learning base are compared and analyzed according to the data learning base, and the state of the elevator brake can be accurately judged. The core of data processing is the Python big data processing technology, and Python is a computer programming language. The language is an object-oriented dynamic type language and can be operated on Linux, Windows, Android and Mac OS operating systems. The method is light and efficient, is provided with various operation libraries, and can realize full-time rapid operation and automatic machine learning of complex data by combining a database technology, a cloud computing technology and a machine learning technology. And processing, analyzing and comparing the abnormal data set by using a Python big data technology and an algorithm. A large amount of abnormal data are collected and imported into the system, training data processing and classification are carried out, a non-abnormal data learning base is formed, through expansion of the learning base, classification and alarm can be carried out on steel wire rope abnormity of different degrees, and finally the effects of automatic detection, early warning and alarm on the steel wire rope abnormity are achieved.
In the technical scheme of the embodiment, a sensor is arranged at a position close to the elevator steel wire rope, the running condition of the steel wire rope can be detected through the sensor, detection data can be obtained, whether the detection data are abnormal data or not is analyzed through judgment of the detection data, in addition, the abnormal data or normal data of the steel wire rope under various conditions are stored in a preset data learning library, the state of the elevator steel wire rope can be timely judged through comparison of the detection data and the abnormal data or normal data, the sensor obtains the detection data in a continuous process, and each detection data is obtained, a corresponding result can be timely judged through comparison of the data learning library, so that the running state of the elevator steel wire rope is mastered at all times, and the potential safety use hazard of the elevator is reduced.
Referring to fig. 2, in a second embodiment of the present invention, based on the first embodiment of the present invention, the step S10 of collecting the detection data by the sensor includes:
step S110, detecting an elevator steel wire rope through a sensor to obtain detection data;
specifically, in the process of moving an elevator dragged by an elevator steel wire rope, the steel wire rope is abraded to different degrees, for example, the thickness of the steel wire rope changes, the vibration condition of the steel wire rope may change, the detection data of the steel wire rope can be effectively measured through a vibration sensor, in addition, protruding points are arranged at intervals of the steel wire rope, the change condition of the protruding points of the steel wire rope can be shot and recorded through a photoelectric sensor, and therefore the real-time detection data of the elevator can be obtained.
And step S120, transmitting the detection data to a cloud platform so as to judge the abnormity of the detection data.
Specifically, the sensor transmits detected data obtained through detection to a cloud platform or a background server through a wireless network, for example, the data are transmitted through a 4G or 5G communication network, wherein the cloud platform or the background server is provided with a storage unit, the cloud platform or the background server stores the received detected data into the storage unit, and then the detected data is extracted and compared with data in a data learning library, so that the detected data of the elevator steel wire rope obtained through real-time detection can be judged and classified, and the current running state of the elevator is obtained.
Referring to fig. 3, based on the first embodiment of the present invention, a third embodiment of the present invention is proposed, in which the step S20 of acquiring the detection data and determining whether the detection data is abnormal data includes:
step S210, acquiring detection data, and inputting the detection data into a preset decision tree model;
the decision tree model is a tree-like judgment graph, and the abnormal result of the detection data is obtained by inputting the detection data into the decision tree model and carrying out layer-by-layer screening judgment.
In step S220, it is determined whether the detected data is abnormal data.
Specifically, the decision tree model includes a root node and a plurality of branch nodes, and corresponding results are output to the detection data through screening and judging of each layer of nodes of the decision tree model until the analysis and judgment are completed.
In addition, the decision tree model is characterized in that a sample is collected to construct a sample set D, information Gain (D, a) of each attribute a is calculated on the basis of the sample set D, V values are set for the discrete attribute a, and the sample set D is divided by using the a, so that V branch nodes are generated, wherein the V branch nodes comprise all values of the attribute a in the D, namely the values of the attribute a are avSample of (2), denoted as DvThen, then
Wherein the ratio of the kth sample in the current sample set D is pkThen Ent (D) of D is defined as:
determining a root node of the decision tree attribute through the Gain (D, a) value, and performing information Gain (D) on a second node through the root node attribute1And a), circularly operating in such a way until all attribute branches are calculated and constructed.
Referring to fig. 4, based on the third embodiment of the present invention, a fourth embodiment of the present invention is proposed, in which the sensor includes a first photo sensor, a second photo sensor, a third photo sensor and a vibration sensor, the step S210 of obtaining the detection data and inputting the detection data into the preset decision tree model includes:
step S211, acquiring detection data, and converting the detection data into corresponding numerical expressions, wherein 0 represents no detection signal, and 1 represents detection signal;
specifically, the change condition of the protruding point of the steel wire rope is calculated through a first photoelectric sensor, a second photoelectric sensor and a third photoelectric sensor, and the vibration sensor is arranged close to the steel wire rope, so that the vibration data of the steel wire rope can be effectively measured, for example, when the sensor detects the steel wire rope, a return value mark of the first photoelectric sensor is 1 when the first photoelectric sensor works, that is, 1 represents that a signal is detected, a return value mark of no value is 0, that is, 0 represents that no signal is detected, and the detection conditions of other sensors can be calculated, wherein the first photoelectric sensor, the second photoelectric sensor, the third photoelectric sensor and the vibration sensor do not detect signals and are marked as (0, 0, 0), and the first photoelectric sensor, the second photoelectric sensor, the third photoelectric sensor and the vibration sensor all detect signals and are marked as (1, 1, 1, 1), from which the constructed sample set D is known to be
Step S212, inputting the numerical expression into the decision tree model, sequentially passing through the root node to the branch node of the decision tree, and determining whether the detected data is abnormal data.
For example, as shown in fig. 7, when the vibration data is relatively more important, the node determined from the vibration data is used as a root node, and the vibration sensor determines that the wire rope is abnormal when detecting the vibration signal, if the vibration sensor does not detect the vibration signal, the branch node determines that the signal detected by the first photoelectric sensor is abnormal, if the vibration sensor detects the signal, the branch node determines that the signal is abnormal, and if the vibration sensor does not detect the signal, the next branch node determines that the signal detected by the second photoelectric sensor is abnormal, if the vibration sensor detects the signal, the next branch node determines that the signal detected by the third photoelectric sensor is abnormal, and if the vibration sensor detects the signal, the next branch node determines that the signal is abnormal, and if the vibration sensor does not detect the signal, the next branch.
The above-mentioned determination process of the numerical marker formed by the detection signal through the decision tree model is not limited to the illustrated embodiment, and the embodiment is used to illustrate the principle of the decision tree, and the user can reset the decision tree model according to the specific situation and the determination criteria.
Referring to fig. 5, based on the first embodiment of the present invention, a fifth embodiment of the present invention is proposed, in which the data learning library includes an abnormal database and a normal database, and the step S30 of determining the state of the elevator rope by combining the preset data learning library according to the determination result of the detection data includes:
step S310, if the detected data belong to abnormal data, comparing the detected data with the abnormal data in the abnormal database, judging the abnormal state of the elevator steel wire rope, and warning and reminding through a warning device;
the warning device can adopt one of sound and light principles, or can combine the two principles, such as prompt sound warning and red display light flashing mode, so as to prompt maintenance personnel to carry out maintenance and repair.
And step S320, if the detected data belongs to normal data, comparing the detected data with the normal data in a normal database, judging the normal state of the elevator steel wire rope, and keeping the normal operation of the elevator. The elevator is controlled to keep the existing working state under the condition that the elevator is judged not to have a fault, so that the running state of the steel wire rope of the elevator can be mastered at any time, and potential safety hazards brought to the use of the elevator are reduced.
Referring to fig. 6, a sixth embodiment of the present invention is proposed on the basis of the fifth embodiment of the present invention, wherein the step S30 of determining the state of the elevator rope by combining a preset data learning library according to the determination result of the detection data includes:
step S40, if the detected data belongs to abnormal data, updating the detected data to an abnormal database;
specifically, after whether the detected data belong to abnormal data or not is obtained, the detected data are stored in an abnormal database to update the abnormal database, wherein the abnormal database is updated by comparing and checking whether the existing abnormal data are the same as the detected data or not, if the abnormal data are the same, the original data are directly covered or renamed and stored, if the abnormal data do not exist, the detected data are directly stored in the abnormal database, and by updating the abnormal database in real time, accurate judgment on the state of the elevator brake can be guaranteed, and classification and analysis of various conditions of the elevator are perfected.
In step S50, if the detected data belongs to normal data, the detected data is updated to a normal database.
Specifically, after whether the detected data belong to normal data or not is obtained, the detected data are stored in a normal database to update the normal database, wherein the normal database is updated by comparing and checking whether the existing normal data are the same as the detected data or not, if the existing normal data are the same as the detected data, the original data are directly covered, or the detected data are renamed and stored, if the existing normal data are not the same as the detected data, the detected data are directly stored in the normal database, and through real-time updating of the normal database, accurate judgment on the state of the elevator brake can be guaranteed, and classification and analysis of various situations of the elevator are perfected.
Referring to fig. 9, the present invention also provides an elevator wire rope abnormality detection apparatus, including: a collection module 100, a judgment module 200 and a comparison module 300.
The collection module 100 is used for collecting detection data through the sensors; specifically, in the process of moving an elevator dragged by an elevator steel wire rope, the steel wire rope is abraded in different degrees, for example, the thickness of the steel wire rope changes, the vibration condition of the steel wire rope possibly changes, the detection data of the steel wire rope can be effectively measured through a vibration sensor, in addition, protruding points are arranged at intervals of the steel wire rope, the change condition of the protruding points of the steel wire rope can be shot and recorded through a photoelectric sensor, and whether the steel wire rope breaks or not can be detected.
The judging module 200 is configured to obtain the detection data, and judge whether the detection data is abnormal data; specifically, the detection data detected by the sensor is sorted and stored, and since the detection data obtained by the sensor is a continuous real-time process, whether the obtained detection data is abnormal or not can be obtained by analyzing and judging the detection data.
The comparison module 300 is configured to determine the state of the elevator steel wire rope according to the determination result of the detection data in combination with a preset data learning library. The detection data of the elevator steel wire rope under various running conditions are stored in the data learning base, so that the detection data and the data in the data learning base are compared and analyzed according to the data learning base, and the state of the elevator brake can be accurately judged. The core of data processing is the Python big data processing technology, and Python is a computer programming language. The language is an object-oriented dynamic type language and can be operated on Linux, Windows, Android and Mac OS operating systems. The method is light and efficient, is provided with various operation libraries, and can realize full-time rapid operation and automatic machine learning of complex data by combining a database technology, a cloud computing technology and a machine learning technology. And processing, analyzing and comparing the abnormal data set by using a Python big data technology and an algorithm. A large amount of abnormal data are collected and imported into the system, training data processing and classification are carried out, a non-abnormal data learning base is formed, through expansion of the learning base, classification and alarm can be carried out on steel wire rope abnormity of different degrees, and finally the effects of automatic detection, early warning and alarm on the steel wire rope abnormity are achieved.
In the technical scheme, a sensor is arranged at a position close to the elevator steel wire rope, the running condition of the steel wire rope can be detected through the sensor, detection data can be obtained, whether the detection data are abnormal data or not is analyzed through judgment of the detection data, in addition, the abnormal data or normal data of the steel wire rope under various conditions are stored in a preset data learning library, the state of the elevator steel wire rope can be timely judged through comparison of the detection data and the abnormal data or normal data, the sensor obtains the detection data in a continuous process, and each detection data is obtained, a corresponding result can be timely obtained through comparison and judgment of the data learning library, so that the running state of the elevator steel wire rope is mastered at any time, and the potential safety use hazard of the elevator is reduced.
Further, the collecting module 100 is further configured to detect the elevator steel wire rope through the sensor, and acquire detection data; and transmitting the detection data to a cloud platform so as to judge the abnormity of the detection data. In addition, protruding points are arranged at intervals of a certain distance on the steel wire rope, and the change situation of the protruding points of the steel wire rope can be shot and recorded by a photoelectric sensor so as to acquire the real-time detection data of the elevator. The sensor transmits detected data obtained through detection to a cloud platform or a background server through a wireless network, for example, the data are transmitted through a 4G or 5G communication network, wherein the cloud platform or the background server is provided with a storage unit, the cloud platform or the background server stores the received detected data into the storage unit, and then the detected data is extracted and compared with data in a data learning library, so that the detected data of the elevator steel wire rope obtained through real-time detection can be judged and classified, and the current operation state of the elevator is obtained.
Further, the determining module 200 is further configured to obtain detection data, and input the detection data into a preset decision tree model; the decision tree model is a tree-like judgment graph, and the abnormal result of the detection data is obtained by inputting the detection data into the decision tree model and carrying out layer-by-layer screening judgment.
The determining module 200 is further configured to determine whether the detected data is abnormal data. Specifically, the decision tree model includes a root node and a plurality of branch nodes, and corresponding results are output to the detection data through screening and judging of each layer of nodes of the decision tree model until the analysis and judgment are completed.
In addition, the decision tree model is characterized in that a sample is collected to construct a sample set D, information Gain (D, a) of each attribute a is calculated on the basis of the sample set D, V values are set for the discrete attribute a, and the sample set D is divided by using the a, so that V branch nodes are generated, wherein the V branch nodes comprise all values of the attribute a in the D, namely the values of the attribute a are avSample of (2), denoted as DvThen, then
Wherein the ratio of the kth sample in the current sample set D is pkThen Ent (D) of D is defined as:
determining a root node of the decision tree attribute through the Gain (D, a) value, and performing information Gain (D) on a second node through the root node attribute1And a), circularly operating in such a way until all attribute branches are calculated and constructed.
Further, the sensor includes a first photoelectric sensor, a second photoelectric sensor, a third photoelectric sensor and a vibration sensor, and the determining module 200 is further configured to obtain detection data, and convert the detection data into corresponding numerical expressions, where 0 represents no detection signal and 1 represents a detection signal; specifically, the change condition of the protruding point of the steel wire rope is calculated through a first photoelectric sensor, a second photoelectric sensor and a third photoelectric sensor, and the vibration sensor is arranged close to the steel wire rope, so that the vibration data of the steel wire rope can be effectively measured, for example, when the sensor detects the steel wire rope, a return value mark of the first photoelectric sensor is 1 when the first photoelectric sensor works, that is, 1 represents that a signal is detected, a return value mark of no value is 0, that is, 0 represents that no signal is detected, and the detection conditions of other sensors can be calculated, wherein the first photoelectric sensor, the second photoelectric sensor, the third photoelectric sensor and the vibration sensor do not detect signals and are marked as (0, 0, 0), and the first photoelectric sensor, the second photoelectric sensor, the third photoelectric sensor and the vibration sensor all detect signals and are marked as (1, 1, 1, 1), from which the constructed sample set D is known to be
The determining module 200 is further configured to input the numerical expression into the decision tree model, sequentially pass through the root node to the branch node of the decision tree, and determine whether the detected data is abnormal data.
For example, if the vibration data is relatively more important, the node for determining the vibration data is taken as a root node, the vibration sensor determines that the wire rope is abnormal when detecting the vibration signal, if the signal is not detected, the branch node determines the signal detected by the first photoelectric sensor, if the signal is detected, the signal is abnormal, if the signal is not detected, the next-layer branch node determines the signal detected by the second photoelectric sensor, if the signal is detected, the signal is abnormal, if the signal is not detected, the next-layer branch node determines the signal detected by the third photoelectric sensor, if the signal is detected, the signal is abnormal, and if the signal is not detected, the signal is not abnormal.
The above-mentioned determination process of the numerical marker formed by the detection signal through the decision tree model is not limited to the illustrated embodiment, and the embodiment is used to illustrate the principle of the decision tree, and the user can reset the decision tree model according to the specific situation and the determination criteria.
In addition, as shown in fig. 8, two brackets 510 are oppositely arranged between two pulleys 500 of the elevator, mounting positions 511 are arranged on the brackets 510, a first photoelectric sensor, a second photoelectric sensor and a third photoelectric sensor are arranged on the mounting positions 511 in parallel, a vibration sensor is arranged between the two oppositely arranged mounting positions 511, and a sensing part of the vibration sensor is abutted against the elevator steel wire rope, so that the abnormal condition of the steel wire rope can be effectively measured.
Further, the data learning base includes an abnormal database and a normal database, and the comparison module 300 is further configured to compare the detection data with abnormal data in the abnormal database if the detection data belongs to the abnormal data, determine what abnormal state the elevator steel wire rope is in, and perform warning and reminding through the warning device; the warning device can adopt one of sound and light principles, or can combine the two principles, such as prompt sound warning and red display light flashing mode, so as to prompt maintenance personnel to carry out maintenance and repair. And the comparison module 300 is further configured to compare the detection data with normal data in a normal database if the detection data belongs to the normal data, determine what normal state the elevator steel wire rope is in, and keep the elevator running normally. The elevator is controlled to keep the existing working state under the condition that the elevator is judged not to have a fault, so that the running state of the steel wire rope of the elevator can be mastered at any time, and potential safety hazards brought to the use of the elevator are reduced.
Further, the elevator steel wire rope abnormity detection device further comprises an updating module 400, wherein the updating module 400 is used for updating the detection data to an abnormity database if the detection data belong to abnormity data; specifically, after whether the detected data belong to abnormal data or not is obtained, the detected data are stored in an abnormal database to update the abnormal database, wherein the abnormal database is updated by comparing and checking whether the existing abnormal data are the same as the detected data or not, if the abnormal data are the same, the original data are directly covered or renamed and stored, if the abnormal data do not exist, the detected data are directly stored in the abnormal database, and by updating the abnormal database in real time, accurate judgment on the state of the elevator brake can be guaranteed, and classification and analysis of various conditions of the elevator are perfected.
The updating module 400 is further configured to update the detection data to the normal database if the detection data belongs to the normal data. Specifically, after whether the detected data belong to normal data or not is obtained, the detected data are stored in a normal database to update the normal database, wherein the normal database is updated by comparing and checking whether the existing normal data are the same as the detected data or not, if the existing normal data are the same as the detected data, the original data are directly covered, or the detected data are renamed and stored, if the existing normal data are not the same as the detected data, the detected data are directly stored in the normal database, and through real-time updating of the normal database, accurate judgment on the state of the elevator brake can be guaranteed, and classification and analysis of various situations of the elevator are perfected.
The present invention also provides an elevator wire rope abnormality detection apparatus, including: the elevator wire rope abnormity detection program is stored on the memory and can be operated on the processor; the elevator wire rope abnormality detection program when executed by the processor implements the steps of the elevator wire rope abnormality detection method as described above.
The specific implementation of the elevator steel wire rope abnormality detection device can refer to the embodiments of the elevator steel wire rope abnormality detection method, and details are not repeated herein.
The invention also provides a readable storage medium on which an elevator wire rope abnormality detection program is stored, which when executed by a processor implements the steps of the elevator wire rope abnormality detection method as described above.
The specific implementation of the readable storage medium of the present invention may refer to the above embodiments of the elevator rope anomaly detection method, and will not be described herein again.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) as described above and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications and equivalents of the present invention, which are made by the contents of the present specification and the accompanying drawings, or directly/indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. An elevator wire rope abnormality detection method characterized in that a sensor is provided near the wire rope, the method comprising:
collecting detection data by the sensor;
acquiring the detection data, and judging whether the detection data is abnormal data;
and judging the state of the elevator steel wire rope by combining a preset data learning library according to the judgment result of the detection data.
2. The elevator rope abnormality detection method according to claim 1, wherein the step of collecting detection data by the sensor includes:
detecting the elevator steel wire rope through the sensor to obtain detection data;
and transmitting the detection data to a cloud platform so as to judge the abnormity of the detection data.
3. The method for detecting an abnormality in an elevator wire rope according to claim 1, wherein the step of acquiring the detection data and determining whether the detection data is abnormal data includes:
acquiring the detection data, and inputting the detection data into a preset decision tree model;
and judging whether the detection data are abnormal data or not.
4. The method of claim 3, wherein the sensors include a first photo sensor, a second photo sensor, a third photo sensor and a vibration sensor, and the step of acquiring the detection data and inputting the detection data into a preset decision tree model includes:
acquiring the detection data, and converting the detection data into corresponding numerical expressions, wherein 0 represents no detection signal, and 1 represents detection signal;
and inputting the numerical expression into a decision tree model, sequentially passing through a root node and a branch node of the decision tree, and judging whether the detection data is abnormal data.
5. The method for detecting the abnormality of the elevator wire rope according to claim 1, wherein the data learning library includes an abnormality database and a normal database, and the step of determining the state of the elevator wire rope by combining a preset data learning library according to the determination result of the detection data includes:
if the detection data belong to abnormal data, comparing the detection data with the abnormal data in the abnormal database, judging the abnormal state of the elevator steel wire rope, and warning and reminding through a warning device;
if the detection data belong to normal data, comparing the detection data with the normal data in the normal database, judging the normal state of the elevator steel wire rope, and keeping the normal operation of the elevator.
6. The method for detecting an abnormality of an elevator wire rope according to claim 5, wherein the step of determining the state of the elevator wire rope based on the result of the determination of the detection data in combination with a preset data learning library comprises the steps of:
if the detection data belong to abnormal data, updating the detection data to the abnormal database;
and if the detection data belong to normal data, updating the detection data to the normal database.
7. An elevator wire rope abnormality detection device, characterized in that the detection device comprises:
a collection module for collecting detection data by the sensor;
the judging module is used for acquiring the detection data and judging whether the detection data are abnormal data;
and the comparison module is used for judging the state of the elevator steel wire rope by combining a preset data learning library according to the judgment result of the detection data.
8. The elevator wire rope abnormality detection apparatus according to claim 7, wherein said collection module is further configured to detect said elevator wire rope by said sensor, and acquire detection data; and transmitting the detection data to a cloud platform so as to judge the abnormity of the detection data.
9. An elevator wire rope abnormality detection apparatus, characterized by comprising: the elevator wire rope abnormity detection program is stored on the memory and can be operated on the processor; the elevator rope abnormality detection program when executed by the processor implements the steps of the elevator rope abnormality detection method according to any one of claims 1 to 6.
10. A readable storage medium having stored thereon an elevator rope anomaly detection program that, when executed by a processor, implements the steps of the elevator rope anomaly detection method according to any one of claims 1-6.
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