CN112960503B - A Modeling Method for Elevator Car Door Running Trajectory - Google Patents

Abstract

The invention discloses a method for modeling the running trajectory of an elevator car door, comprising: selecting characteristic parameters of opening and closing doors according to the movement process of the elevator car door; acquiring and processing sensor data during the movement process of the car door, and designing the values of the characteristic parameters Constraints, extract effective door opening and closing data according to the value constraints of the characteristic parameters; determine the door opening and closing signals and classify the data to establish a general door opening and closing model; according to the value range of the characteristic parameters, the opening and closing data are divided into groups; the number of times of opening and closing the door After reaching the threshold, perform statistical analysis on the values of characteristic parameters under each group during the operation process to obtain statistical analysis data; build a special switch door data judgment model according to the grouped data and statistical analysis data. Compare the real-time data generated by the elevator car door movement process with the special door opening and closing data judgment model, and judge whether the current car door is in the open, closed or fault state according to the comparison result, and report the fault when it is in the fault state.

Description

A Modeling Method for Elevator Car Door Running Trajectory

technical field

The invention relates to the field of elevators, in particular to a method for modeling the running trajectory of an elevator car door.

Background technique

The elevator door is a very important part of the elevator. There are two doors. The door that can be seen from outside the elevator and is fixed on each floor is called the hall door. . The elevator door switch is generally driven by the car door to open the hall door. Its stability, rationality, and safety are the key factors in determining the elevator grade. Therefore, the normal use and normal operation of the car door play a vital role in the safe operation of the elevator. However, in the prior art, the abnormality of the car door will only be notified to the relevant technicians when an accident or failure occurs, and the technicians need to judge the cause of the failure, so as to find a solution to the failure and deal with it. Before the failure is over, the user cannot use the elevator normally, which greatly reduces the user experience and brings great trouble to the technicians.

SUMMARY OF THE INVENTION

The present invention provides a method for modeling the running trajectory of an elevator car door to overcome the above technical problems.

A method for modeling the running trajectory of an elevator car door, comprising:

Step 1: According to the movement process of the elevator car door, select the characteristic parameters of the elevator door opening and closing;

Step 2: acquiring the data of the sensor during the movement of the elevator car door, preprocessing the sensor data, designing the value constraints of the characteristic parameters, and extracting the data according to the value constraints of the characteristic parameters to obtain effective door opening and closing data;

Step 3: Judging the door-opening signal and the door-closing signal according to the valid door-opening data, classifying the valid door-opening data according to the door-opening signal, and dividing it into the door-opening data and the door-closing data, and establishing a general door-opening model;

Step 4: Group the door opening data and the door closing data respectively according to the value range of the characteristic parameter;

Step 5: when the number of times of opening and closing the door reaches the threshold, perform statistical analysis on the values of the characteristic parameters under each group in the running process to obtain statistical analysis data and store them;

Step 6: According to the grouped door opening and closing data and statistical analysis data, construct an elevator-specific door opening and closing data judgment model;

Step 7: Compare the real-time data generated during the movement of the elevator car door with the special door opening and closing data judgment model, and judge according to the comparison result that the current elevator car door is in the open state, the door closed state or the fault state, and the fault occurs when it is in the fault state. report.

Preferably, constructing the elevator-specific door opening and closing data judgment model further includes calculating new characteristic parameter values by formula (1) and storing them in the door opening and closing data, recalculating the statistical analysis data of the group according to the new characteristic parameter values, and reconstructing the elevator special switch door data judgment model,

C _N+1 = C _N *Δ+C _N-1 *(1-Δ) (1)

Among them, C _N+1 represents the new characteristic parameter value, N is the number of times the elevator door is opened and closed, C _N represents the current characteristic parameter value when the elevator door is opened and closed N times every time and the movement of the elevator car door conforms to the elevator-specific door opening and closing data judgment model, C _N-1 represents the stored feature parameter value of the model, and Δ represents the weight value.

Preferably, the characteristic parameters of the elevator door opening and closing include positive acceleration peak value, positive acceleration area value, negative acceleration peak value, negative acceleration area value, positive acceleration time points, negative acceleration time points, and total acceleration time points.

Preferably, the value constraints of the design feature parameters refer to the number of positive acceleration time points>10, the positive acceleration area value>150, the number of negative acceleration time points>10, the negative acceleration area value>150, and the total number of acceleration time points<300 .

Preferably, judging the door opening signal and the door closing signal according to valid door opening and closing data includes:

Step 1: Determine the time interval, and obtain valid door opening and closing data within the corresponding time interval;

Step 2, the time of positive acceleration in the door opening and closing data is t ₁ , and the time of negative acceleration is t ₂ , if t ₁ is earlier than t ₂ , it is the door opening signal, and if t ₁ is later than t ₂ , it is Close the door signal.

Preferably, grouping the door opening data and the door closing data according to the value range of the characteristic parameter means selecting the total number of acceleration time points as the characteristic parameter, and dividing into groups according to the value range of the total acceleration time point number.

Preferably, performing statistical analysis on the values of the characteristic parameters in each group during the running process refers to counting the maximum value, minimum value, sum, average value, and variation range of each characteristic parameter in the corresponding group.

The invention provides a method for modeling the running trajectory of an elevator car door. By acquiring the data of each sensor in the elevator car door in real time, and analyzing the running data of the elevator in real time, the accurate identification of the normal and fault states of the elevator car door opening and closing door is realized. , When the car door switch is in a fault state, it will check and maintain and report the fault in real time to provide rescue guidance for elevator managers or supervisory departments.

Description of drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the accompanying drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description These are some embodiments of the present invention, and for those of ordinary skill in the art, other drawings can also be obtained from these drawings without any creative effort.

Fig. 1 is the flow chart of the elevator car door running trajectory modeling method of 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 the digital median filter output result waveform diagram of the embodiment of the present invention;

4 is a waveform diagram of an output result of a first-order low-pass filter according to an embodiment of the present invention;

5 is a waveform diagram of an output result of an algorithm for filtering out DC components according to an embodiment of the present invention;

Fig. 6 is the characteristic parameter chart of the elevator door opening process of the embodiment of the present invention;

FIG. 7 is an explanatory diagram of characteristic parameter values of an elevator-specific door opening and closing data judgment model according to an embodiment of the present invention.

Detailed ways

In order to make the purposes, 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 accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments These are some embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

Fig. 1 is a flowchart of a method for modeling the running trajectory of an elevator car door according to the present invention. As shown in Fig. 1 , the method of this embodiment may include:

A sensor is installed on the elevator car door, and the data collected by the sensor in the process of opening and closing the car door is obtained. The sensor specifically includes: a three-axis acceleration sensor and a gyroscope sensor. The three-axis accelerometer and gyroscope are used to measure the data related to the elevator's running attitude, such as the status and characteristics of the elevator's door opening and closing. During the door-opening and closing movement of the elevator car door, due to the mechanical structure and the movement of passengers and other factors, the output of the sensor will be affected. Data has an impact. In order to accurately analyze the running track of the elevator car door, it is necessary to preprocess the data output by the sensor.

In the present invention, the data of all sensors is read every 20ms. In order to describe in detail the method of the present invention to preprocess the sensor data, the present invention selects 500 points of three-axis acceleration sensor data samples for description. The source of this data is the elevator multiple times. The raw data generated by the movement of the car door during the door opening and closing process, the data unit is mg.

The direction of the elevator door opening and closing is the horizontal movement of the car door (x-axis). In the present invention, FIG. 2 is a waveform diagram of the x-axis raw data of the three-axis acceleration sensor.

After completing the data sampling and synthesis, first enter the digital median filter. The design of the digital median filter in the present invention is as follows: continuous sampling of 27 points, and arrangement of the sampled values of the 27 points, taking the median value as the current effective value. After the verification of a large number of elevator operation data, when the sampling points are set to 27 points, it can not only accurately restore the elevator operation characteristics, but also effectively overcome the fluctuation interference of sensor data caused by accidental factors. It is expressed by formula (1) as:

In the formula, f _iv , ... f _i , ... f _i+v are the original data of the sensor read each time, m is the total number of data points, and m is required to be an odd number, in the present invention, the value is m=27, and i is the center of the window Location. Y _i represents the output result of the digital median filter of this sampling. In the present invention, FIG. 3 is a waveform diagram of the output result of the digital median filter.

To further generate usable data, the output of the digital median filter is also fed into a first-order low-pass filter. The advantage of applying a first-order low-pass filter is that the output value of this filter mainly depends on the output value of the last filter, and the contribution of this sampling value to the filter output is relatively small, but at the same time it has the ability to affect the filter output value of this time. correction effect. Since the motion characteristic of the elevator is a regular acceleration and deceleration process, it conforms to the slow change of the input value and matches the usage scenario of the low-pass filter. The design of the first-order low-pass filter in the present invention is to set the coefficient of the first-order low-pass filter to 0.3. It is expressed by formula (2) as:

Y(n)=αX(n)+(1-α)Y(n-1) (2)

Where X(n) is the sampling value of this time, Y(n-1) is the output value of the last low-pass filter, Y(n) is the output value of the low-pass filter this time, α is the filter coefficient, this time In the invention, the value is α=0.3. In the present invention, FIG. 4 is a waveform diagram of the output result of the first-order low-pass filter.

When the elevator IoT system is installed in the elevator, in order to make the construction easier, the horizontal and vertical attitudes of the three-axis acceleration sensor and the gyroscope cannot be forced, so the data output by the three-axis acceleration sensor and the gyroscope will have an angular deviation relative to the direction of gravity. Introduced DC components and adversely affect the measurement results of the system. In order to filter out the influence of the DC component, the specific method of the present invention is: collect 1000 points of output data of the first-order low-pass filter, that is, the sampling time of 20 seconds, and sort the data size of 1000 points, if the difference between the maximum value and the minimum value is If it is less than 10, it is judged that the system is in a static or uniform motion state, and the DC component can be filtered out. The filtering method is to average the 1000points data, and this value is the value of the DC component existing in the system. It is expressed by formula (3) as:

其中Y(n)_max-Y(n)_min<10 (3)

where Y(n) _max -Y(n) _min <10 (3)

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 present invention is n=1000.

In the present invention, FIG. 5 is a waveform diagram of the output result of the 500-point data sample filtered to remove the DC component. It can be seen from the waveform diagram that the data fluctuates up and down the zero axis, which is in line with the expected results.

After completing the data preprocessing process, enter the modeling process of the elevator-specific door opening and closing model. During the door opening and closing process of the elevator, the running posture will experience five states: static, acceleration, constant speed, deceleration, and static. An elevator with a small car door may not include a constant speed process, but must include an acceleration and deceleration process. Therefore, the present invention takes the acceleration and deceleration process of door opening and closing as a characteristic model, carries out high-intelligence self-learning training, and establishes a special door opening and closing data judgment model for this elevator.

According to the movement process of the elevator car door, the characteristic parameters of the elevator door opening and closing are selected. The acceleration and deceleration characteristic parameters of the elevator door opening and closing used for modeling in the present invention are a total of 7, which are: (1) positive acceleration peak value (+gPeak), ② Positive acceleration area value (+gArea), ③ Negative acceleration peak value (-gPeak), ④ Negative acceleration area value (-gArea), ⑤ Positive acceleration time points (+gPoint), ⑥ Negative acceleration time points ( -gPoint), ⑦The total number of acceleration time points (btwPNPoint). If the preprocessed data is within ±10mg, it belongs to the static or constant speed state, and is not included in the characteristic parameter. If the preprocessed data is outside the ±10mg, it is included in the characteristic parameter.

In the present invention, Fig. 6 is the characteristic parameter chart of the elevator door opening process. In the figure, two horizontal horizontal lines are used to indicate that the preprocessed data is within ±10mg, which belongs to the static or constant speed state, and is not included in the characteristic parameter. If the preprocessed data is outside ±10mg, it is included in the characteristic parameter. That is, when the preprocessed data is greater than 10mg, it is included in the characteristic parameter, and when the preprocessed data is less than -10mg, it is included in the characteristic parameter. The formula for calculating the number of positive acceleration time points is (4),

+gPoint=②-① (4)

Among them, ① is the start time point when the elevator car door changes from the static or constant speed state to the positive acceleration increasing start state, and ② is the end time point when the elevator car door changes from the positive acceleration decreasing start state to the static or constant speed state.

The calculation formula of the number of positive acceleration time points is (5),

-gPoint=④-③ (5)

Among them, ③ is the start time point when the elevator car door changes from the static or constant speed state to the negative acceleration increasing start state, and ④ is the end time point when the elevator car door changes from the negative acceleration decreasing start state to the static or constant speed state.

Obtain the preprocessed sensor data and design the value constraints of the characteristic parameters. The difference between the door closing process and the door opening process of the elevator is that the acceleration direction is different, but the types of characteristic parameters are the same. Before establishing a special feature model, it is necessary to find out the valid door opening and closing data from the preprocessed sensor data, that is, first use the general opening and closing door model to collect the door opening and closing features. The judgment of the general door opening and closing model in the present invention is: the number of positive acceleration time points (+gPoint)>10, the positive acceleration area value (+gArea)>150, the number of negative acceleration time points (-gPoint)>10, the negative acceleration The acceleration area value (-gArea)>150, and the total acceleration time points (btwPNPoint)<300.

Since the sampling rate is 20ms/Point, the general door opening and closing model can be described as: the MEMS sensor collects the motion information from the direction of the elevator car door, within 300Points (6s), the positive acceleration occurs first, and then the negative acceleration occurs, And the one that meets the above numerical requirements is the effective door opening signal; the negative acceleration first appears, then the positive acceleration occurs, and the one that meets the above numerical requirements is the effective door closing signal.

Due to the difference in the mechanical structure or the resistance of the elevator hall door, the door opening and closing process of the elevator on different floors may be different, which is intuitively reflected in the different door opening and closing times on different floors. In order to achieve the purpose of adapting to the characteristics of different floors of the same elevator, the system needs to establish a special door-opening data judgment model for this elevator after collecting valid door-opening characteristic data using the general door-opening model. The present invention establishes up to 5 groups of door opening and closing characteristic parameters respectively for the door opening process and the door closing process of the elevator.

The characteristic parameter that can intuitively reflect the time of opening and closing the door is the total number of acceleration time points (btwPNPoint), so the five groups of characteristic parameters established by the present invention are distinguished by the difference of the total number of acceleration time points. When the difference between the total number of acceleration time points (btwPNPoint) is more than 20Points (400ms), it is stored as a different door switch judgment model.

After the elevator Internet of Things system of the present invention is installed in the elevator, the elevator operation process will be accompanied by multiple door opening and closing processes. ①Maximum value Max, ②Minimum value Min, ③Total value Sum, and then calculate the average value of each parameter ④Mean=Sum/N, variation range ⑤Range=Max-Min.

Store at most 5 groups*7 characteristic parameters*5 parameter values calculated in the storage module of the system. After the storage is successful, the elevator's dedicated door opening and closing data judgment model is established successfully. In the present invention, FIG. 7 is an explanatory diagram of the characteristic parameter values of the elevator-specific door opening and closing data judgment model.

For each subsequent elevator door movement process, the system will compare it with the special door opening and closing model in the storage module. The compared parameters include whether the 7 characteristic parameters of the elevator door movement are within the range of parameter value changes ( Mean ± range of variation), so as to accurately detect the opening and closing status and characteristics of the elevator car door, and report the elevator car door related faults when the comparison results are different.

At the same time, if the movement process of the current elevator car door conforms to the special model, the high-intelligence self-learning function will continue to record the characteristic parameter values collected during the current movement, and when the door opening and closing times reaches N=100 times, the current parameter The value is included in the special model according to a certain weight, and the special model is recalculated and stored once, so as to achieve the purpose of fine-tuning the characteristic parameter value. The self-learning function collects every movement process of the elevator, and the data collection accuracy will become higher and higher. The weight value set by the present invention is 0.05. Mathematically expressed as:

C _N+1 =C _N *Δ+C _N-1 *(1-Δ) (6)

Among them, C _N+1 represents the new characteristic parameter value, C _N represents the current parameter value, and C _N-1 represents the characteristic parameter value stored in the system. Δ represents a weight value, and in the present invention, the value is Δ=0.05.

Overall beneficial effects:

The invention can establish a special elevator door opening and closing data judgment model, accurately monitor the operation status of the elevator door opening and closing each time, when the car door switch is in a fault state, perform inspection and maintenance and fault reporting in real time, and install a sound collection device in the elevator at the same time. , which can collect the sound information in the elevator before and after the fault occurs in real time, judge whether there are people trapped, and provide rescue guidance for elevator managers or supervisory departments.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, but not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: The technical solutions described in the foregoing embodiments can still be modified, or some or all of the technical features thereof can be equivalently replaced; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the technical solutions of the embodiments of the present invention. scope.

Claims (2)

1. an elevator car door running trajectory modeling method, is characterized in that, comprises, Step 1: According to the movement process of the elevator car door, select the characteristic parameters of the elevator door opening and closing; The characteristic parameters of the elevator door opening and closing include positive acceleration peak value, positive acceleration area value, negative acceleration peak value, negative acceleration area value, positive acceleration time points, negative acceleration time points, and total acceleration time points; Step 2: Acquire the data of the sensor during the movement of the elevator car door, preprocess the sensor data, design the value constraints of the characteristic parameters, and extract the data according to the value constraints of the characteristic parameters to obtain effective door opening and closing data; The value constraints of the design feature parameters are that the number of positive acceleration time points > 10, the positive acceleration area value > 150, the number of negative acceleration time points > 10, the negative acceleration area value > 150, and the total number of acceleration time points < 300; Step 3: Judging the door-opening signal and the door-closing signal according to the valid door-opening data, classifying the valid door-opening data according to the door-opening signal, and dividing it into the door-opening data and the door-closing data, and establishing a general door-opening model; The judging of the door opening signal and the door closing signal according to the valid door opening and closing data includes: 1. Determine the time interval and obtain valid door opening data within the corresponding time interval; 2. The time of positive acceleration in the door opening and closing data is t ₁ , and the time of negative acceleration is t ₂ . If t ₁ is earlier than t ₂ , it is the door opening signal; if t ₁ is later than t ₂ , it is the door closing signal. Signal; Step 4: Group the door opening data and the door closing data respectively according to the value range of the characteristic parameter; The grouping of 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 into groups according to the value range of the total acceleration time point number; Step 5: when the number of times of opening and closing the door reaches the threshold, perform statistical analysis on the values of the characteristic parameters under each group in the running process to obtain statistical analysis data and store them; The performing statistical analysis on the values of the characteristic parameters under each group in the running process refers to the statistics of the maximum value, the minimum value, the sum, the average value, and the variation range of each characteristic parameter in the corresponding grouping; Step 6: According to the grouped door opening and closing data and statistical analysis data, construct the elevator special door opening and closing data judgment model; According to the result, judge that the current elevator car door is in the open state, the door closed state or the fault state, and report the fault when it is in the fault state. 2. a kind of elevator car door running trajectory modeling method according to claim 1 is characterized in that, described constructing elevator special opening and closing door data judgment model also comprises calculating new characteristic parameter value by formula (1) and storing in switch In the door data, the statistical analysis data of the group is recalculated according to the new characteristic parameter value, and the judgment model of the elevator door opening and closing data is reconstructed. C _N+1 =C _N *Δ+C _N -1 *(1-Δ) (1) Among them, C _N+1 represents the new characteristic parameter value, N is the number of times the elevator door is opened and closed, C _N represents the current characteristic parameter value when the elevator door is opened and closed N times every time and the movement of the elevator car door conforms to the elevator-specific door opening and closing data judgment model, C _N-1 represents the stored feature parameter value of the model, and Δ represents the weight value.

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