CN112960503A - Elevator car door running track modeling method - Google Patents
Elevator car door running track modeling method Download PDFInfo
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- CN112960503A CN112960503A CN202110302917.7A CN202110302917A CN112960503A CN 112960503 A CN112960503 A CN 112960503A CN 202110302917 A CN202110302917 A CN 202110302917A CN 112960503 A CN112960503 A CN 112960503A
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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
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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 a modeling method for an elevator car door running track, which comprises the following steps: selecting characteristic parameters for opening and closing the door according to the motion process of the elevator car door; acquiring and processing data of a sensor in the car door movement process, designing value restriction of characteristic parameters, and extracting effective door opening and closing data according to the value restriction of the characteristic parameters; judging a door opening and closing signal, classifying data, and establishing a general door opening and closing model; grouping the door opening and closing data according to the value range of the characteristic parameter; after the door opening and closing times reach a threshold value, carrying out statistical analysis on the values of the characteristic parameters under each group in the operation process to obtain statistical analysis data; and constructing a special door opening and closing data judgment model according to the grouped data and the statistical analysis data. And comparing real-time data generated in the motion process of the elevator car door with the special door opening and closing data judgment model, judging whether the current car door is in an open state, a closed state or a fault state according to a comparison result, and reporting the fault when the current car door is in the fault state.
Description
Technical Field
The invention relates to the field of elevators, in particular to a method for modeling an elevator car door running track.
Background
Elevator doors are an important part of elevators, and have two doors, one fixed to each floor, visible from outside the elevator, called landing doors, and one fixed to the car, visible from inside, called car doors, which move with the car. The elevator door is opened or closed by the car door. The stability, the reasonability and the safety of the elevator door are key factors for determining the grade of the elevator, so that the normal use and the normal operation of the elevator door play an important role in the safe operation of the elevator. However, in the prior art, the abnormality of the car door can be informed to relevant technical personnel only when the abnormality occurs to an accident or a fault, and the technical personnel need to judge the cause of the fault, so that a method for solving the fault is found, and a user cannot normally use the elevator before the fault is processed, so that the user experience is greatly reduced, and meanwhile, great trouble is brought to the technical personnel.
Disclosure of Invention
The invention provides a modeling method for an elevator car door running track, which aims to overcome the technical problems.
A modeling method for an elevator car door running track is characterized by comprising the following steps,
the method comprises the following steps: selecting characteristic parameters of opening and closing the elevator door according to the motion process of the elevator car door;
step two: acquiring data of a sensor in the motion process of an elevator car door, preprocessing the data of the sensor, designing value restriction of a characteristic parameter, and extracting the data according to the value restriction of the characteristic parameter to obtain effective door opening and closing data;
step three: judging door opening signals and door closing signals according to the effective door opening and closing data, classifying the effective door opening and closing data according to the door opening and closing signals, dividing the effective door opening and closing data into door opening data and door closing data, and establishing a general door opening and closing model;
step four: grouping the door opening data and the door closing data according to the value range of the characteristic parameter;
step five: when the door opening and closing times reach a threshold value, carrying out statistical analysis on the values of the characteristic parameters under each group in the operation process to obtain statistical analysis data and storing the statistical analysis data;
step six: according to the grouped door opening and closing data and the statistical analysis data, a door opening and closing data judgment model special for the elevator is constructed;
step seven: and comparing real-time data generated in the motion process of the elevator car door with the special door opening and closing data judgment model, judging whether the current elevator car door is in an open door state, a closed door state or a fault state according to a comparison result, and reporting a fault when the current elevator car door is in the fault state.
Preferably, the building of the door opening and closing data judgment model special for the elevator further comprises calculating a new characteristic parameter value through the formula (1) and storing the new characteristic parameter value in the door opening and closing data, recalculating statistical analysis data of the group according to the new characteristic parameter value, rebuilding the door opening and closing data judgment model special for the elevator,
CN+1＝CN*Δ+CN-1*(1-Δ) (1)
wherein C isN+1Representing a new characteristic parameter value, N being the number of times the elevator door is opened or closed, CNRepresenting the current characteristic parameter value C when the elevator door is opened and closed for N times and the elevator car door motion conforms to the door opening and closing data judgment model special for the elevatorN-1Representing the stored characteristic parameter values of the model, and Δ representing the weight values.
Preferably, the characteristic parameters of the opening and closing of the elevator door are selected to comprise a positive acceleration peak value, a positive acceleration area value, a negative acceleration peak value, a negative acceleration area value, a positive acceleration time point, a negative acceleration time point and an acceleration time total point.
Preferably, the value restriction of the design characteristic parameter means that the number of positive acceleration time points is greater than 10, the area value of the positive acceleration is greater than 150, the number of negative acceleration time points is greater than 10, the area value of the negative acceleration is greater than 150, and the total number of acceleration time points is less than 300.
Preferably, the judging the door opening signal and the door closing signal according to the effective door opening and closing data comprises,
step one, determining a time interval, and acquiring effective door opening and closing data in the corresponding time interval;
step two, the time when the positive acceleration appears in the door opening and closing data is t1The time at which the negative acceleration occurs is t2If t is1Earlier than t2If it is, it is a door-opening signal1Later than t2The signal is a door closing signal.
Preferably, the step of grouping the door opening data and the door closing data respectively according to the value range of the characteristic parameter refers to selecting the total number of acceleration time points as the characteristic parameter, and dividing the grouping according to the value range of the total number of acceleration time points.
Preferably, the statistical analysis of the values of the characteristic parameters in each group during the operation process means to count the maximum value, the minimum value, the sum, the average value and the variation range of each characteristic parameter in the corresponding group.
The invention provides a modeling method for an elevator car door running track, which is characterized in that the data of each sensor in an elevator car door is acquired in real time, the running data of an elevator is analyzed in real time, the normal and fault states of the opening and closing of the elevator car door are accurately identified, and when the opening and closing of the elevator car door is in the fault state, the elevator car door is inspected, maintained and reported in real time, so that rescue guidance is provided for elevator managers or supervision departments.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart of a method for modeling an elevator car door movement track according to the present invention;
FIG. 2 is a waveform diagram of x-axis raw data of a three-axis acceleration sensor according to an embodiment of the present invention;
FIG. 3 is a waveform diagram of the digital median filter output according to an embodiment of the present invention;
FIG. 4 is a waveform diagram of the output of the first-order low-pass filter according to the embodiment of the present invention;
FIG. 5 is a waveform diagram of an output result of the algorithm for filtering out DC components according to the embodiment of the present invention;
fig. 6 is a characteristic parameter diagram of an elevator door opening process according to an embodiment of the present invention;
fig. 7 is an explanatory diagram of characteristic parameter values of the elevator-specific door opening and closing data determination model according to the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, 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 some, but not all, embodiments of the present invention. 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.
Fig. 1 is a flowchart of a method for modeling an elevator car door movement track according to the present invention, and as shown in fig. 1, the method of this embodiment may include:
at elevator sedan-chair door installation sensor to acquire the data that sedan-chair door switch door in-process sensor gathered, the sensor specifically includes: triaxial acceleration sensor, gyroscope sensor. The three-axis acceleration sensor and the gyroscope are used for measuring elevator operation posture related data such as an elevator door opening and closing state and characteristics, and the elevator car door can influence the data output by the sensor due to factors such as mechanical structure and passenger movement in the door opening and closing motion process. In order to accurately analyze the running track of the elevator car door, data output by the sensor needs to be preprocessed, and the data preprocessing mode adopted by the invention is a digital median filter, a first-order low-pass filter and the like.
In the invention, the data of all the sensors are read once every 20ms, in order to explain the mode of the invention for preprocessing the sensor data in detail, a 500points triaxial acceleration sensor data sample is selected for explanation, the source of the data is the original data generated by the movement of the car door in the process of opening and closing the door of the elevator for multiple times, and the unit of the data is mg.
The direction of opening and closing the door of the elevator is the horizontal movement (x axis) of the car door, and in the invention, the X axis raw data waveform diagram of the three-axis acceleration sensor is shown in figure 2.
After the data sampling and synthesis are completed, the digital median filter is entered first. The design of the digital median filter of the invention is as follows: and continuously sampling 27points, arranging the sizes of the 27points sampling values, and taking the intermediate value as the effective value at this time. Through the verification of a large amount of elevator operation data, when the number of sampling points is set to be 27points, the elevator operation characteristics can be accurately restored, and the sensor data fluctuation interference caused by accidental factors can be effectively overcome. Expressed by equation (1):
in the formula fi-v，…fi，…fi+vThe method is characterized in that the method is used for reading sensor original data every time, m is the total number of data points, m is required to be an odd number, the value m is 27, and i is the central position of a window. Y isiAnd the output result of the digital median filter of the current sampling is shown. Fig. 3 is a waveform diagram of the output result of the digital median filter in the invention.
To further generate usable data, the result output by the digital median filter also needs to be input to a first order low pass filter. The advantage of applying the first-order low-pass filter is that the output value of the current filtering mainly depends on the output value of the previous filtering, and the contribution of the current sampling value to the filtering output is relatively small, but the current sampling value has a correction function on the output value of the current filtering. The motion characteristic of the elevator is a regular acceleration and deceleration process, so that the characteristic that the input value changes slowly is met, and the use scene of the low-pass filter is matched. The design of the first-order low-pass filter is as follows: the coefficient of the first order low pass filter is set to 0.3. Expressed by equation (2):
Y(n)＝αX(n)+(1-α)Y(n-1) (2)
where x (n) is the current sampling value, Y (n-1) is the last low-pass filter output value, Y (n) is the current low-pass filter output value, α is the filter coefficient, and the value in the present invention is α ═ 0.3. Fig. 4 is a waveform diagram of the output result of the first-order low-pass filter in the invention.
When the elevator internet of things system is installed on an elevator, the horizontal and vertical postures of the three-axis acceleration sensor and the gyroscope cannot be required forcibly for simpler construction, so that the data output by the three-axis acceleration sensor and the gyroscope have direct current components introduced by angle deviation relative to the gravity direction, and the measurement result of the system is adversely affected. In order to filter out the influence of the direct current component, the specific method of the invention is as follows: and collecting 1000points of output data of the first-order low-pass filter, namely sampling time of 20s, sorting the data size of the 1000points, and if the difference value between the maximum value and the minimum value is less than 10, judging that the system is in a static or uniform motion state, and filtering out direct-current components. The filtering method is to average the 1000points data, and the value is the value of the DC component existing in the system. Expressed by equation (3):
Wherein D represents the value of the dc component in the system, y (n) is the output data of the first-order low-pass filter, and the number of data points in the invention is n-1000.
FIG. 5 is a waveform diagram of the output result of 500points data sample through the algorithm of filtering out DC component. As can be seen from the waveform plot, the data fluctuates up and down the zero axis, consistent with the expected results.
And after the data preprocessing process is finished, entering a modeling process of a special door opening and closing model of the elevator. In the process of opening and closing the door of the elevator, the operation posture can be in five states of static, accelerating, uniform speed, decelerating and static. Elevators with smaller doors may not include a constant speed process but must include acceleration and deceleration processes. Therefore, the invention takes the acceleration and deceleration processes of opening and closing the door as the characteristic model to carry out high-intelligent autonomous learning training and establish the special opening and closing door data judgment model for the elevator.
According to the motion process of the elevator car door, characteristic parameters of the opening and closing of the elevator door are selected, and the total 7 characteristic parameters of the opening and closing of the elevator door used for modeling are respectively as follows: the method comprises the steps of firstly, obtaining a positive acceleration peak value (+ gPeak), secondly, obtaining a positive acceleration area value (+ gArea), thirdly, obtaining a negative acceleration peak value (-gPeak), fourthly, obtaining a negative acceleration area value (-gArea), fifthly, obtaining a positive acceleration time point (+ gPoint), sixthly, obtaining a negative acceleration time point (-gPoint), and seventhly, obtaining an acceleration time total point (btwPNPoint). The preprocessed data are within +/-10 mg, belong to data in a static or uniform speed state, are not counted into the characteristic parameters, and the preprocessed data are out of +/-10 mg and are counted into the characteristic parameters.
In the invention, figure 6 is a characteristic parameter chart of an elevator door opening process, two horizontal transverse lines in the chart are used for representing that the preprocessed data are within +/-10 mg, belong to data in a static or uniform speed state, are not counted into the characteristic parameters, and the preprocessed data are out of +/-10 mg and are counted into the characteristic parameters. When the pre-processed data is more than 10mg, the characteristic parameters are included, and when the pre-processed data is less than-10 mg, the characteristic parameters are included. The calculation formula of the forward acceleration time point is (4),
+gPoint＝②-① (4)
the elevator car door is changed from a static or uniform speed state to a positive acceleration increasing starting state as a starting time point number, and the elevator car door is changed from the positive acceleration decreasing starting state to the static or uniform speed state as an ending time point number.
The calculation formula of the forward acceleration time point is (5),
-gPoint＝④-③ (5)
the elevator car door is changed from a static or uniform speed state to a negative acceleration increasing starting state to serve as a starting time point number, and the elevator car door is changed from the negative acceleration increasing starting state to the static or uniform speed state to serve as an ending time point number.
The method comprises the steps of acquiring preprocessed sensor data, designing value restriction of characteristic parameters, and enabling the door closing process and the door opening process of the elevator to be different in acceleration direction but the same in type of the characteristic parameters. Before establishing the special feature model, effective door opening and closing data needs to be found out from the preprocessed sensor data, namely, the door opening and closing features are collected by using a general door opening and closing model. The judgment of the general door opening and closing model in the invention is as follows: the acceleration time point number (+ gPoint) >10, the positive acceleration area value (+ gArea) >150, the negative acceleration time point number (-gPoint) >10, the negative acceleration area value (-gArea) >150, and the total acceleration time point number (btwppnpoint) < 300.
Since the sampling rate is 20ms/Point, the general switching gate model can be described as: the MEMS sensor collects motion information from the direction of the elevator car door, within 300Points (6s), positive acceleration and negative acceleration occur firstly, and the motion information meeting the numerical requirement is an effective door opening signal; the first negative acceleration occurs, the second positive acceleration occurs, and what meets the above numerical requirements is an effective door-closing signal.
Due to the difference of mechanical structures or resistance of the elevator hall door, the opening and closing processes of the elevator at different floors can be different, and the opening and closing processes at different floors are different visually. The system is intended to achieve the purpose of adapting to different floor characteristics of the same elevator, and after effective door opening and closing characteristic data are collected by using a general door opening and closing model, a special door opening and closing data judgment model of the elevator needs to be established. The invention establishes 5 groups of characteristic parameters of opening and closing the door at most for the door opening process and the door closing process of the elevator respectively.
The characteristic parameter which can intuitively embody the door opening and closing time is the total point number of acceleration time (btwpnPPoint), so the 5 groups of characteristic parameters established by the invention are distinguished by the difference of the total point number of the acceleration time. When the difference of the total Points (btwppnpoint) of the acceleration time is more than 20Points (400ms), the acceleration time is stored as different door opening and closing judgment models.
The elevator Internet of things system is installed behind an elevator, the elevator running process is accompanied with a plurality of door opening and closing processes, when the door opening and closing times N acquired by the system are 100 times, the maximum value Max, the minimum value Min and the total value Sum of each parameter in 7 characteristic parameters are acquired, then the average value Mean of each parameter is calculated, and the variation Range is Max-Min.
And storing the calculated maximum 5 groups of 7 characteristic parameters of 5 parameter values in a storage module of the system. After the storage is successful, the establishment of the special door opening and closing data judgment model of the elevator is successful. In the invention, fig. 7 is an explanatory diagram of characteristic parameter values of a data judgment model of a special opening and closing door of an elevator.
And in the following every elevator car door motion process, the system can be compared with a special door opening and closing model in the storage module, and compared parameters comprise whether 7 characteristic parameters of the current elevator car door motion are in a parameter value variation Range (Mean +/-variation Range), so that the opening and closing state and the opening and closing characteristics of the elevator car door are accurately detected, and the related faults of the elevator car door are reported when the comparison results are different.
Meanwhile, if the motion process of the secondary elevator car door accords with the special model, the high-intelligence autonomous learning function can continuously record the characteristic parameter value acquired by the secondary motion, and when the door opening and closing times reach N (equal to 100), the current parameter value is recorded into the special model according to a certain weight, and the special model is recalculated and stored once, so that the purpose of finely adjusting the characteristic parameter value is achieved. The autonomous learning function collects each motion process of the elevator, and the data acquisition precision is higher and higher. The weight value set by the present invention is 0.05. Expressed as follows by the mathematical formula:
CN+1＝CN*Δ+CN-1*(1-Δ) (6)
wherein C isN+1Representing a new characteristic parameter value, CNRepresenting the current parameter value, CN-1Representing the stored characteristic parameter values of the system. Δ represents a weight value, and in the present invention, Δ is 0.05.
The beneficial effects of the whole are as follows:
the elevator door opening and closing data judgment system can establish a special elevator door opening and closing data judgment model, accurately monitor the operation condition of the elevator door opening and closing each time, perform inspection and maintenance and fault reporting in real time when the elevator door opening and closing is in a fault state, and meanwhile, install a sound acquisition device in the elevator, can acquire sound information in the elevator before and after the fault occurs in real time, judge whether people are trapped and provide rescue guidance for elevator managers or supervision departments.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (7)
1. A modeling method for an elevator car door running track is characterized by comprising the following steps,
the method comprises the following steps: selecting characteristic parameters of opening and closing the elevator door according to the motion process of the elevator car door;
step two: acquiring data of a sensor in the motion process of an elevator car door, preprocessing the data of the sensor, designing value restriction of a characteristic parameter, and extracting the data according to the value restriction of the characteristic parameter to obtain effective door opening and closing data;
step three: judging door opening signals and door closing signals according to the effective door opening and closing data, classifying the effective door opening and closing data according to the door opening and closing signals, dividing the effective door opening and closing data into door opening data and door closing data, and establishing a general door opening and closing model;
step four: grouping the door opening data and the door closing data according to the value range of the characteristic parameter;
step five: when the door opening and closing times reach a threshold value, carrying out statistical analysis on the values of the characteristic parameters under each group in the operation process to obtain statistical analysis data and storing the statistical analysis data;
step six: according to the grouped door opening and closing data and the statistical analysis data, a door opening and closing data judgment model special for the elevator is constructed;
step seven: and comparing real-time data generated in the motion process of the elevator car door with the special door opening and closing data judgment model, judging whether the current elevator car door is in an open door state, a closed door state or a fault state according to a comparison result, and reporting a fault when the current elevator car door is in the fault state.
2. The modeling method for the elevator car door running track according to claim 1, wherein the building of the elevator door opening and closing data judgment model further comprises calculating a new characteristic parameter value through formula (1) and storing the new characteristic parameter value in the door opening and closing data, recalculating statistical analysis data of the group in which the new characteristic parameter value is stored according to the new characteristic parameter value, rebuilding the elevator door opening and closing data judgment model,
CN+1＝CN*Δ+CN-1*(1-Δ) (I)
wherein C isN+1Representing a new characteristic parameter value, N being the number of times the elevator door is opened or closed, CNRepresenting the current characteristic parameter value C when the elevator door is opened and closed for N times and the elevator car door motion conforms to the door opening and closing data judgment model special for the elevatorN-1Representing the stored characteristic parameter values of the model, and Δ representing the weight values.
3. The modeling method for the running track of the elevator car door according to claim 1, wherein the selected characteristic parameters for opening and closing the elevator car door comprise a positive acceleration peak value, a positive acceleration area value, a negative acceleration peak value, a negative acceleration area value, a positive acceleration time point, a negative acceleration time point and an acceleration time total point.
4. The modeling method for the elevator car door running track according to claim 1, characterized in that the value constraints of the design characteristic parameters are that the number of positive acceleration time points is greater than 10, the area value of positive acceleration is greater than 150, the number of negative acceleration time points is greater than 10, the area value of negative acceleration is greater than 150, and the total number of acceleration time points is less than 300.
5. The modeling method of elevator car door motion trajectory according to claim 1, wherein said determining the door opening signal and the door closing signal according to the effective door opening and closing data comprises,
step one, determining a time interval, and acquiring effective door opening and closing data in the corresponding time interval;
step two, the time when the positive acceleration appears in the door opening and closing data is t1The time at which the negative acceleration occurs is t2If t is1Earlier than t2If it is, it is a door-opening signal1Later than t2The signal is a door closing signal.
6. The modeling method for the elevator car door running track according to claim 1, wherein the step of grouping the door opening data and the door closing data respectively according to the value range of the characteristic parameter refers to the step of selecting the total number of acceleration time points as the characteristic parameter and dividing the groups according to the value range of the total number of acceleration time points.
7. The modeling method for the running track of the elevator car door according to claim 1, wherein the statistical analysis of the values of the characteristic parameters in each group in the running process means that the maximum value, the minimum value, the sum, the average value and the variation range of each characteristic parameter in the corresponding group are counted.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113401758A (en) * | 2021-06-28 | 2021-09-17 | 广州鲁邦通物联网科技有限公司 | Elevator door opening and closing fault detection method |
| CN113401758B (en) * | 2021-06-28 | 2023-02-17 | 广州鲁邦通物联网科技股份有限公司 | Elevator door opening and closing fault detection method |
| CN113581961A (en) * | 2021-08-10 | 2021-11-02 | 江苏省特种设备安全监督检验研究院 | Automatic fault identification method for elevator hall door |
| CN114809856A (en) * | 2022-04-29 | 2022-07-29 | 上海思岚科技有限公司 | Method and equipment for determining motion state of electric control door during opening and closing |
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| CN112960503B (en) | 2022-09-13 |
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