CN110817627B - Equipment attribute calculation method based on acceleration sensor - Google Patents
Equipment attribute calculation method based on acceleration sensor Download PDFInfo
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- CN110817627B CN110817627B CN201911256317.0A CN201911256317A CN110817627B CN 110817627 B CN110817627 B CN 110817627B CN 201911256317 A CN201911256317 A CN 201911256317A CN 110817627 B CN110817627 B CN 110817627B
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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
- B66B13/00—Doors, gates, or other apparatus controlling access to, or exit from, cages or lift well landings
- B66B13/02—Door or gate operation
- B66B13/14—Control systems or devices
- B66B13/143—Control systems or devices electrical
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
- B66B3/00—Applications of devices for indicating or signalling operating conditions of elevators
- B66B3/02—Position or depth indicators
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Abstract
The invention discloses a method for calculating the attribute of equipment operated between stations based on the rule of an acceleration sensor, which comprises the following steps: acquiring acceleration data of the set direction of the equipment; and obtaining at least one of the type of the motion mode of the equipment, the maximum acceleration, the maximum deceleration, the maximum speed, the door opening time, the door closing time, the door width, the standby position, the distance between stations and the number of stations according to the acceleration data of the set direction of the equipment. The method for calculating the equipment attribute based on the acceleration sensor can acquire more attributes of the equipment; meanwhile, the accelerometer is arranged on the equipment door, so that the door opening and closing information of the equipment door can be obtained. Besides, with the aid of machine learning, the device can statistically acquire the maximum acceleration, the maximum deceleration, the maximum speed, the relative distance between the running stations and the number of stations of the device in various motion modes.
Description
Technical Field
The invention belongs to the technical field of elevator equipment, relates to an equipment attribute acquisition method, and particularly relates to an equipment attribute calculation method based on an acceleration sensor.
Background
At present, no uniform elevator monitoring system exists except that an elevator manufacturer and an elevator main board can directly obtain basic conditions and attributes of an elevator. The desire to obtain attributes of an elevator is to add additional equipment in addition to obtaining master board privileges from the elevator manufacturer.
In the prior art with an acceleration sensor, the available information is limited, and most of them can only detect the running speed and distance of the elevator by means of calculus. However, the elevator has different operation modes and different rated speeds and accelerations, and the traditional method is difficult to remove abnormal data and carry out a large amount of statistics. In addition, the traditional acceleration sensor additionally arranged in the elevator can only calculate the up-and-down movement of the elevator between floors, and other attributes of the elevator cannot be obtained.
In view of the above, there is an urgent need to design an equipment attribute obtaining method to overcome the above-mentioned defects of the existing elevator monitoring method.
Disclosure of Invention
The invention provides an equipment attribute calculation method based on an acceleration sensor, which can acquire more attributes of equipment.
In order to solve the technical problem, according to one aspect of the present invention, the following technical solutions are adopted:
an equipment attribute calculation method based on an acceleration sensor is disclosed, wherein the equipment is an elevator; the equipment attribute calculation method comprises the following steps:
step S0: determining three directions in the actual space represented by three axes according to information returned by the three-axis acceleration sensor; the x axis represents the moving direction between floors of the elevator, the y axis represents the opening and closing direction of the car door, and the z axis represents the direction vertical to the x axis and the y axis;
step S1: for the movement between floors of the elevator, namely the movement of the x axis, firstly, the unit LSB/g of the data of the x axis returned by the sensor is converted into m/s2Specifically, the conversion mode is that a is (k-b) × 9.8/b, wherein a is the converted acceleration value in meters per second, 9.8 is the value of the gravity acceleration g, b is the value of the x axis when the elevator is in a stationary period, and k is the value of the sensor at a certain moment;
step S2: after step S1, the x-axis data is a value of acceleration in a one-dimensional time series, and the unit is meter per second; the acceleration value has a positive value and a negative value and represents the direction of the acceleration; if the value of the acceleration is 0, the elevator does not move or is in a uniform motion state at the moment; because the elevator needs a certain time for acceleration and deceleration, the acceleration is positive or negative in a certain period of time; detecting continuous positive values and continuous negative values to obtain time periods when the continuous positive values or the continuous negative values appear; if this occurs, it indicates that the elevator has accelerated or decelerated in a certain direction;
step S3: the complete motion of the elevator for one time comprises one-time acceleration, one-time constant speed and one-time deceleration, and the detected time periods of continuous positive values and negative values are combined in pairs to determine the time period of the complete elevator motion for one time; the combination rule is that whether two continuous time periods of positive values/negative values exist in the data is detected, if so, the two time periods respectively belong to two complete elevator interlayer vertical motions; detecting the time period when the acceleration is 0, namely the time period when the elevator is not accelerated or decelerated; if the time period is longer than the set threshold, the elevator is considered to be in a static state instead of a constant speed state in the time period, so that the elevator moves vertically between two elevator floors twice in the time periods of continuous positive values and negative values at two ends of the stable section; after the anchor point is determined according to the rules, adjacent continuous non-0 acceleration time periods are combined pairwise to obtain complete time-varying acceleration data of vertical motion between elevator layers;
step S4: assuming a complete acceleration of the vertical movement between the elevator floors starting from 0 and ending at T, the speed of the elevator at time T is the integral of the acceleration over time from 0 to time T, i.e. the acceleration is measured as a function of the time
Wherein T is more than or equal to 0 and less than or equal to T and VtIs the velocity at time t, and a (t) is the acceleration at time t; after the speed at all the moments is calculated by the method, the data of the complete change of the speed of the vertical movement between the elevator floors along with the time is obtained; distance is the integral of velocity over time, similar to calculating a velocity curve;
at the moment, three pieces of data of the complete movement of all the elevators are obtained, namely data of the change of the acceleration relative to the time, data of the change of the speed relative to the time and data of the change of the movement distance of the elevator relative to the time under the complete movement of one elevator; assuming that the elevator moves completely for N times, 3N pieces of data are obtained;
step S5: because the elevator has different speed modes, the maximum speed which can be reached by the elevator is very low when the elevator moves for one floor, the speed is high when the elevator moves for several floors, and the elevator generally runs at half speed when being maintained; taking the maximum value of the speed curve of each time of complete operation of the elevator to obtain M maximum operation speeds; performing density-based clustering DBSCAN on the data of the M maximum running speeds, wherein the range radius depends on the precision of an algorithm and a sensor, and the density depends on the number of samples; obtaining A different maximum operation speed sets after clustering, wherein the A different maximum operation speed sets correspond to A classes of maximum operation speeds; finding out one complete motion corresponding to various elevators; summing and averaging the maximum running speeds of the various types respectively to obtain the maximum running speeds of the A types of motion modes; the maximum value of the absolute value of the first acceleration non-0 section of each type of acceleration curve is taken, and the average value is taken according to the type, so that the maximum acceleration of each type of motion of the elevator can be obtained; in the same way, the maximum value of the absolute value of the second acceleration non-0 section of the acceleration curve is taken, and the maximum deceleration of various types of motions of the elevator is obtained after the average according to the types;
step S6: calculating the y axis, namely the door opening and closing direction of the elevator car door, similar to the x axis, to obtain the curves of the door movement distance, the door speed and the door acceleration, and the door width is the maximum door movement distance of one-time door opening or door closing movement; however, one-time complete door opening/closing is divided into four processes of slow acceleration, deceleration and slow deceleration, and certain noise influence is generated when people go up and down after the door is opened; therefore, when a single door opening or closing curve is intercepted, an upper threshold and a lower threshold are set and divided into a data section continuously larger than the upper threshold and a data section continuously smaller than the lower threshold; because the middle constant speed section in the process of accelerating and decelerating the opening and closing of the elevator door is basically not available, if the difference time of the two data sections is small, the curve is a one-time door opening or closing curve;
step S7: carrying out accumulative calculation on the single running distance of the elevator, and obtaining the relative distance of all floors of the elevator to the bottommost layer by utilizing clustering; the vertical interlayer movement of the elevator with one complete x-axis is integrated, the distance curve of single operation of all the elevators is calculated, and the maximum value is the single operation distance of the elevator;
accumulating the single running distance, and taking the accumulated distance as a result to count dis [ i ], wherein i represents the ith result; when data of a long time is collected, the number of dis is enough, then clustering DBSCAN based on density is carried out on all dis to obtain several types of data which basically represent the number of floors of the building; averaging all similar objects to obtain the relative distance of each floor of the building; arranging all the average values from small to large, wherein the relative distance between the floor and the bottommost layer is obtained by subtracting the minimum average value from all the average values; obtaining the heights of all floors;
step S8: similarly, data in a certain longer time are collected, and the time period within the set time after the elevator stops is detected when the elevator is detected to perform x-axis vertical motion between floors every time, and whether the door is opened or closed can be detected by the y-axis; if the open and close door cannot be detected, the standby position of the elevator of the building is represented; the standby position is calculated by calculating the relative bottom height of the floor at several floors;
the following information of the elevator is obtained by all the steps: the type of elevator motion pattern, maximum acceleration, maximum deceleration and maximum speed, as well as the time taken for the elevator to open the door, the time taken for the elevator to close the door, the door width, the standby position, the relative bottom height of the floors and the number of floors.
According to another aspect of the invention, the following technical scheme is adopted: an acceleration sensor-based device attribute calculation method, the method comprising:
acquiring acceleration data of the set direction of the equipment;
obtaining at least one of the motion mode type, the maximum acceleration, the maximum deceleration, the maximum speed, the running distance and the running time of the equipment according to the acceleration data of the set direction of the equipment;
if the equipment has at least two stations and runs regularly, the method further comprises the step of obtaining the relative distance between the stations of the equipment and the number of the stations for running the equipment according to the acceleration data in the set direction of the equipment.
As an embodiment of the present invention, the method further includes: the elevator equipment has different speed modes, and the maximum value of the speed curve of the complete operation of the elevator of each equipment is taken to obtain M maximum operation speeds; performing density-based clustering DBSCAN on the data of the M maximum running speeds, wherein the range radius depends on the precision of an algorithm and a sensor, and the density depends on the number of samples;
a different maximum operation speed sets can be obtained after clustering, and the sets correspond to A classes of maximum operation speeds; finding out one complete motion of the elevator corresponding to various devices; summing and averaging the maximum running speeds of the various types respectively to obtain the maximum running speeds of the A types of motion modes; the maximum value of absolute values of the first acceleration non-0 section of each type of acceleration curve is taken, and the average value is taken according to the type, so that the maximum acceleration of each type of motion of the elevator equipment can be obtained; and in the same way, the maximum value of the absolute value of the second acceleration non-0 section of the acceleration curve is taken, and the maximum deceleration of various types of motions of the elevator equipment can be obtained after the maximum values are averaged according to the types.
As an embodiment of the invention, the apparatus is an elevator; the method further comprises the following steps: the elevator vertical interlayer movement with one complete elevator interlayer movement direction is integrated, the distance curve of single operation of all elevators is calculated, and the maximum value is the single operation distance of the elevator;
accumulating the single running distance, and taking the accumulated distance as a result to count dis [ i ], wherein i represents the ith result; when data of a long time is collected, the number of dis is enough, then clustering DBSCAN based on density is carried out on all dis to obtain several types of data which basically represent the number of floors of the building; averaging all similar objects to obtain the relative distance of each floor of the building; arranging all the average values from small to large, wherein the relative distance between the floor and the bottommost layer is obtained by subtracting the minimum average value from all the average values; the heights of all floors are obtained.
As an embodiment of the invention, the apparatus is an elevator; the method further comprises the following steps: processing acceleration data of the elevator car door in the door opening and closing direction to obtain curves of door movement distance, door speed and door acceleration, wherein the door width is the maximum door movement distance of one-time door opening or door closing movement; the one-time complete door opening/closing is divided into four processes of slow acceleration, deceleration and slow deceleration, and certain noise influence is generated when people go up and down after the door is opened; when a single door opening or closing curve is intercepted, an upper threshold and a lower threshold are set and divided into a data section continuously larger than the upper threshold and a data section continuously smaller than the lower threshold; the middle constant speed section in the acceleration and deceleration process of the opening and closing of the elevator door is basically not available, and if the difference time of the acceleration and deceleration data sections of the opening and closing of the elevator door is smaller than a set value, a one-time door opening or closing curve is obtained.
As an embodiment of the invention, the apparatus is an elevator; the method further comprises the following steps: collecting data in a certain longer time, detecting the time period within the set time after the elevator stops when the elevator is detected to perform x-axis vertical motion between floors every time, and detecting whether the door can be opened or closed by a y-axis; if the open and close door cannot be detected, the standby position of the elevator of the building is represented; the standby position is calculated at several floors by calculating the relative floor height of the floor.
As an embodiment of the present invention, the apparatus includes a door; the method further comprises the following steps: and obtaining at least one of the time for opening the door, the time for closing the door, the door width and the standby position of the equipment according to the acceleration data of the set direction of the equipment.
As an embodiment of the invention, the apparatus is an elevator; the method comprises the following steps: acquiring triaxial acceleration data of equipment; determining three directions in the actual space represented by three axes according to information returned by the three-axis acceleration sensor; the x-axis represents the direction of movement between elevator floors, the y-axis represents the direction of opening and closing of the car doors, and the z-axis represents the direction perpendicular to the x-and y-axes.
As an embodiment of the invention, the apparatus is an elevator; the method comprises the following steps: for the movement between floors of the elevator, namely the movement of the x axis, firstly, the unit LSB/g of the data of the x axis returned by the sensor is converted into m/s2Specifically, the conversion mode is that a is (k-b) × 9.8/b, wherein a is the converted acceleration value in meters per second, 9.8 is the value of the gravity acceleration g, b is the value of the x axis when the elevator is in a stationary period, and k is the value of the sensor at a certain moment.
As an embodiment of the invention, the apparatus is an elevator; the method comprises the following steps: the movement direction data between the elevator floors is the value of the acceleration under a one-dimensional time sequence, and the unit is meter per second; the acceleration value has a positive value and a negative value and represents the direction of the acceleration; if the value of the acceleration is 0, the elevator does not move or is in a uniform motion state at the moment;
detecting continuous positive values and continuous negative values to obtain time periods when the continuous positive values or the continuous negative values appear; if this occurs, it indicates that the elevator has accelerated or decelerated in a certain direction.
As an embodiment of the invention, the apparatus is an elevator; the method comprises the following steps: the complete motion of the elevator for one time comprises one-time acceleration, one-time constant speed and one-time deceleration, and the detected time periods of continuous positive values and negative values are combined in pairs to determine the time period of the complete elevator motion for one time;
the combination rule is that whether two continuous time periods of positive values/negative values exist in the data is detected, if so, the two time periods respectively belong to two complete elevator interlayer vertical motions; detecting the time period when the acceleration is 0, namely the time period when the elevator is not accelerated or decelerated; if the time period is longer than the set threshold, the elevator is considered to be in a static state instead of a constant speed state in the time period, so that the elevator moves vertically between two elevator floors twice in the time periods of continuous positive values and negative values at two ends of the stable section; after the anchor point is determined according to the rules, adjacent continuous non-0 acceleration time periods are combined pairwise to obtain the time-varying acceleration data of the vertical motion between elevator layers.
As an embodiment of the invention, the apparatus is an elevator; assuming a complete acceleration of the vertical movement between the elevator floors starting from 0 and ending at T, the speed of the elevator at time T is the integral of the acceleration over time from 0 to time T, i.e. the acceleration is measured as a function of the time
Wherein T is more than or equal to 0 and less than or equal to T and VtIs the velocity at time t, and a (t) is the acceleration at time t; after the speed at all the moments is calculated by the method, the data of the change of the speed of the complete elevator floor vertical motion along with the time is obtained, and the distance of the complete elevator floor vertical motion is the integral of the speed on the time;
at the moment, three pieces of data of the complete motion of all the elevators are obtained, namely data of the change of the acceleration relative to time, data of the change of the speed relative to time and data of the change of the motion distance of the elevator relative to time under the complete motion of one elevator; assuming that the elevator has moved N times in total, 3N pieces of data are obtained.
The invention has the beneficial effects that: the device attribute calculation method based on the acceleration sensor can acquire more attributes of the device.
The currently-used method for calculating speed and distance based on a three-axis accelerometer only performs unit conversion and simple integration on the obtained data of the accelerometer to obtain one-dimensional data about speed and distance based on a time series. The method takes the single elevator interlayer vertical motion of the elevator as a motion unit, calculates the elevator attributes including the maximum acceleration and the like of the single motion unit, collects a large amount of single motions, namely collects all the attributes under the single motions, and then calculates, thereby having statistical significance. After theoretical study and study are carried out on the speed regulation modes of the elevators, the fact that one elevator operation mode can be multiple (the elevator with a lower floor number can only have one speed regulation mode) is found, different motion modes of the elevators are firstly classified before the statistical significance processing is carried out on the attributes of each elevator, and therefore the attributes under different elevator operation modes are obtained.
Meanwhile, the accelerometer of the method is installed on the door of the equipment (such as an elevator car), so that the door opening and closing information of the equipment door can be obtained, which is not related to the prior art. The method also makes a brief description of the steps for calculating the relevant attributes of the equipment door.
In addition, with the aid of machine learning, the method can obtain the maximum acceleration, the maximum deceleration, the maximum speed, the relative distance between the running stations and the number of stations of the equipment in various motion modes in a statistical mode.
Drawings
Fig. 1 is a flowchart of a device attribute calculation method according to an embodiment of the present invention.
Fig. 2 is a one-dimensional data smoothing graph of acceleration and speed of vertical movement between elevator floors in an ideal state along with time.
Fig. 3 is a graph of acceleration and velocity of an elevator car door switch over time in an ideal state.
Detailed Description
Preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
For a further understanding of the invention, reference will now be made to the preferred embodiments of the invention by way of example, and it is to be understood that the description is intended to further illustrate features and advantages of the invention, and not to limit the scope of the claims.
The description in this section is for several exemplary embodiments only, and the present invention is not limited only to the scope of the embodiments described. It is within the scope of the present disclosure and protection that the same or similar prior art means and some features of the embodiments may be interchanged.
The invention discloses an equipment attribute calculation method based on an acceleration sensor, which comprises the following steps: acquiring acceleration data of the set direction of the equipment; and obtaining at least one of the motion mode type, the maximum acceleration, the maximum speed, the running distance and the running time of the equipment according to the acceleration data of the set direction of the equipment. If the equipment has at least two stations and runs regularly, the method further comprises the step of obtaining the relative distance between the stations of the equipment and the number of the stations for running the equipment according to the acceleration data in the set direction of the equipment.
In one embodiment of the invention, the apparatus is an elevator.
In an embodiment of the present invention, the method further includes: and obtaining at least one of the time for opening the door, the time for closing the door, the door width and the standby position of the elevator according to the acceleration data of the set direction of the equipment.
In one embodiment of the invention the device property calculation method is used to obtain elevator run properties. FIG. 1 is a flow chart of a method for calculating device attributes according to an embodiment of the present invention; referring to fig. 1, in an embodiment of the present invention, the method for calculating the device attribute includes the following steps:
step S0: according to the information returned by the three-axis acceleration sensor, three directions in the actual space represented by the three axes can be basically determined. In general, the x-axis represents the direction of movement between elevator floors, the y-axis represents the direction of opening and closing of the car door, and the z-axis represents the direction perpendicular to the x and y-axes.
Step S1: for the inter-floor movement of the elevator, namely the movement of an x axis, firstly converting unit LSB/g of data returned by a sensor of the elevator into m/s2, specifically converting the unit LSB/g into unit LSB/g in a way that a is (k-b) 9.8/b, wherein a is an acceleration value after conversion, the unit is meter per second, 9.8 is a value of gravity acceleration g, b is a value of the x axis when the elevator is in a stationary period, and k is a value of the sensor at a certain moment.
Step S2: after step S1, the x-axis data is a one-dimensional time-series value of acceleration in units of meters per second. The acceleration values have positive and negative values and represent the direction of acceleration. If the value of the acceleration is 0, it means that the elevator is not moving or in a state of uniform motion at this time. Because the elevator needs a certain time to accelerate and decelerate, the acceleration is positive or negative in a certain period of time. And detecting continuous positive values and continuous negative values to obtain the time periods when the continuous positive values or the continuous negative values occur. If this occurs, it indicates that the elevator has accelerated or decelerated in a certain direction.
Step S3: because the complete movement of the elevator comprises one-time acceleration, one-time constant speed and one-time deceleration, the detected time periods of continuous positive values and negative values are combined pairwise to determine the time period of the complete movement of the elevator. The combination rule is that whether two continuous time periods of positive values/negative values exist in the data is detected, if so, the two time periods respectively belong to two complete elevator interlayer vertical motions; and detecting the time period when the acceleration is 0, namely the time period when the elevator is not accelerated or decelerated. If the time period is longer than 45 seconds, the elevator is considered to be in a static state instead of a constant speed state in the time period, so that two times of vertical movement between elevator floors belong to two times of continuous positive and negative values at two ends of the stable section. After the anchor point is determined according to the rules, adjacent continuous non-0 acceleration time periods are combined pairwise to obtain the time-varying acceleration data of the vertical motion between elevator layers.
Step S4: assuming a complete acceleration of the vertical movement between the elevator floors starting from 0 and ending at T, the speed of the elevator at time T is the integral of the acceleration over time from 0 to time T, i.e. the acceleration is measured as a function of the time
Wherein T is more than or equal to 0 and less than or equal to T and VtThe velocity at time t, and a (t) the acceleration at time t. After the speed at all times is calculated by the method, the data of the complete time-varying speed of the vertical movement between the elevator floors along with the time can be obtained. Distance is the integral of velocity over time, similar to calculating a velocity curve.
At the moment, three pieces of data of the complete movement of all the elevators are obtained, namely data of the change of the acceleration relative to the time, data of the change of the speed relative to the time and data of the change of the movement distance of the elevator relative to the time under the complete movement of one elevator. Assuming that the elevator has moved N times in total, 3N pieces of data are obtained.
Step S5: because the elevator has different speed modes, the maximum speed which can be reached by the elevator is very small when the elevator moves for one floor, the speed is very high when the elevator moves for several floors, and the elevator generally runs at half speed when being maintained. Therefore, the maximum value is taken for the speed curve of each complete running of the elevator, and N maximum running speeds are obtained. The data of the N maximum running speeds are subjected to density-based clustering DBSCAN, wherein two parameters are that the range radius depends on the accuracy of an algorithm and a sensor, and the density depends on the number of samples. After clustering, several different sets of maximum operating speeds may be obtained. It is assumed that there are two classes of maximum operating speed, one class and two, from which a complete movement of the elevator corresponding to one class and two can be found. And respectively carrying out summation and average on the maximum running speeds of the first type and the second type to obtain the maximum running speeds of the two types of motion modes. And taking the maximum value of the absolute value of the first acceleration non-0 section of the acceleration curves of the first class and the second class, and averaging according to the classes to obtain the respective maximum acceleration of the two types of motions of the elevator. And in the same way, the maximum value of the absolute value of the second acceleration non-0 section of the acceleration curve is taken, and the maximum deceleration of the two types of motions of the elevator can be obtained after the maximum deceleration is averaged according to the types.
Step S6: the calculation of the y axis, namely the door opening and closing direction of the elevator car door, is similar to the x axis, the curves of the door movement distance, the door speed and the door acceleration can be obtained, and the door width is the maximum door movement distance of one-time door opening or door closing movement. However, one-time complete door opening/closing is divided into four processes of slow acceleration, deceleration and slow deceleration, and certain noise influence can be generated when people get on and off after the door is opened. Therefore, when a single door opening or closing curve is intercepted, an upper threshold and a lower threshold are set and are divided into a data section continuously larger than the upper threshold and a data section continuously smaller than the lower threshold. Because the middle constant speed section in the process of accelerating and decelerating the opening and closing of the elevator door is not basically available, if the difference time of the two data sections is small, the curve is a one-time opening or closing door curve. This part is negligible because slow acceleration and slow deceleration are not significant and are difficult to detect.
Step S7: carrying out accumulative calculation on the single running distance of the elevator, and obtaining the relative distance of all floors of the elevator to the bottommost layer by utilizing clustering; the above-mentioned vertical interlayer movement of the elevator which is once complete to the x axis is integrated, the distance curve of single operation of all the elevators is calculated, and the maximum value is the single operation distance of the elevator.
And accumulating the single running distance, and taking the accumulated distance as a result to count dis [ i ], wherein i represents the ith result. When data is collected for a long time, the number of dis is enough, and then density-based clustering (DBSCAN) is carried out on all dis, so that several types can be obtained, and the several types basically represent the number of floors of the building. And averaging all the same classes, and obtaining the relative distance of each floor of the building. And arranging all the average values from small to large, wherein the relative distance between the floor and the bottommost layer is obtained by subtracting the minimum average value from all the average values. The height of all floors can thus be obtained.
Step S8: and similarly, collecting data in a certain longer time, and detecting the time period within 15 seconds after the elevator stops when the elevator is detected to perform x-axis vertical motion between floors every time, wherein whether the door is opened or closed can be detected by the y-axis. If the open door cannot be detected, this means that the elevator of the building has a standby position. The standby position can be calculated from the relative floor height of the calculated floor at several floors.
The following information of the elevator can be obtained by all the steps: the elevator has several movement modes, the maximum acceleration and the maximum speed of which are respectively, and the time taken for opening the door, the time taken for closing the door, the door width, the standby position, the relative bottommost height of the floors and the floor number of the elevator.
Because elevators have several different operating modes, the behavior is different in acceleration and different in maximum speed. The traditional method has no machine learning, can only be regarded as the same motion mode to be calculated, and has no classification or classification by manpower. And thus are statistically less significant. In step S5 of the method, different operation modes of the elevator can be separated by a clustering method of unsupervised learning, and statistics after classification has statistical significance. The same applies to step S7 for calculating the floor heights, and the floor height of each floor and the total number of floors can be calculated in an unsupervised learning manner. This is difficult for traditional algorithms to compute and analyze using large amounts of data statistics; after the floor height is calculated, it is an additional function to calculate whether the elevator has a standby position (step S8).
In summary, the method for calculating the device attribute based on the acceleration sensor can obtain more attributes of the device.
The currently-used method for calculating speed and distance based on a three-axis accelerometer only performs unit conversion and simple integration on the obtained data of the accelerometer to obtain one-dimensional data about speed and distance based on a time series. The method takes the single elevator interlayer vertical motion of the elevator as a motion unit, calculates the elevator attributes including the maximum acceleration and the like of the single motion unit, collects a large amount of single motions, namely collects all the attributes under the single motions, and then calculates, thereby having statistical significance. After theoretical study and study are carried out on the speed regulation modes of the elevators, the fact that one elevator operation mode can be multiple (the elevator with a lower floor number can only have one speed regulation mode) is found, different motion modes of the elevators are firstly classified before the statistical significance processing is carried out on the attributes of each elevator, and therefore the attributes under different elevator operation modes are obtained.
Meanwhile, the accelerometer of the method is installed on the door of the equipment (such as an elevator car), so that the door opening and closing information of the equipment door can be obtained, which is not related to the prior art. The method also makes a brief description of the steps for calculating the relevant attributes of the equipment door.
In addition, with the aid of machine learning, the method can obtain the maximum acceleration, the maximum deceleration, the maximum speed, the relative distance between the running stations and the number of stations of the equipment in various motion modes in a statistical mode.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The description and applications of the invention herein are illustrative and are not intended to limit the scope of the invention to the embodiments described above. Variations and modifications of the embodiments disclosed herein are possible, and alternative and equivalent various components of the embodiments will be apparent to those skilled in the art. It will be clear to those skilled in the art that the present invention may be embodied in other forms, structures, arrangements, proportions, and with other components, materials, and parts, without departing from the spirit or essential characteristics thereof. Other variations and modifications of the embodiments disclosed herein may be made without departing from the scope and spirit of the invention.
Claims (10)
1. An equipment attribute calculation method based on an acceleration sensor is characterized in that the equipment is an elevator; the equipment attribute calculation method comprises the following steps:
step S0: determining three directions in the actual space represented by three axes according to information returned by the three-axis acceleration sensor; the x axis represents the moving direction between floors of the elevator, the y axis represents the opening and closing direction of the car door, and the z axis represents the direction vertical to the x axis and the y axis;
step S1: for the movement between floors of the elevator, namely the movement of the x axis, firstly, the unit LSB/g of the data of the x axis returned by the sensor is converted into m/s2Specifically, the conversion mode is that a is (k-b) × 9.8/b, wherein a is the converted acceleration value in meters per second, 9.8 is the value of the gravity acceleration g, b is the value of the x axis when the elevator is in a stationary period, and k is the value of the sensor at a certain moment;
step S2: after step S1, the x-axis data is a value of acceleration in a one-dimensional time series, and the unit is meter per second; the acceleration value has a positive value and a negative value and represents the direction of the acceleration; if the value of the acceleration is 0, the elevator does not move or is in a uniform motion state at the moment; because the elevator needs a certain time for acceleration and deceleration, the acceleration is positive or negative in a certain period of time; detecting continuous positive values and continuous negative values to obtain a time period when the continuous positive values or the continuous negative values appear; if this occurs, it indicates that the elevator has accelerated or decelerated in a certain direction;
step S3: the complete motion of the elevator for one time comprises one-time acceleration, one-time constant speed and one-time deceleration, the detected time periods of continuous positive values and negative values are combined in pairs, and the time period of the complete elevator for one time is determined; the combination rule is that whether two continuous time periods of positive values/negative values exist in the data is detected, if so, the two time periods respectively belong to two complete elevator interlayer vertical motions; detecting the time period when the acceleration is 0, namely the time period when the elevator is not accelerated or decelerated; if the time period is longer than the set threshold, the elevator is considered to be in a static state instead of a constant speed state in the time period, so that the elevator moves vertically between two elevator floors twice in the time periods of continuous positive values and negative values at two ends of the stable section; after the anchor point is determined according to the rules, adjacent continuous non-0 acceleration time periods are combined pairwise to obtain complete time-varying acceleration data of vertical motion between elevator layers;
step S4: assuming a complete acceleration of the vertical movement between the elevator floors starting from 0 and ending at T, the speed of the elevator at time T is the integral of the acceleration over time from 0 to time T, i.e. the acceleration is measured as a function of the time
Wherein T is more than or equal to 0 and less than or equal to T and VtIs the velocity at time t, and a (t) is the acceleration at time t; after the speed at all the moments is calculated by the method, the data of the complete change of the speed of the vertical movement between the elevator floors along with the time is obtained; distance is the integral of velocity over time, similar to calculating a velocity curve;
at the moment, three pieces of data of the complete movement of all the elevators are obtained, namely data of the change of the acceleration relative to the time, data of the change of the speed relative to the time and data of the change of the movement distance of the elevator relative to the time under the complete movement of one elevator; assuming that the elevator moves completely for N times, 3N pieces of data are obtained;
step S5: because the elevator has different speed modes, the maximum speed which can be reached by the elevator is very low when the elevator moves for one floor, the speed is high when the elevator moves for several floors, and the elevator generally runs at half speed when being maintained; taking the maximum value of the speed curve of each time of complete operation of the elevator to obtain M maximum operation speeds; performing density-based clustering DBSCAN on the data of the M maximum running speeds, wherein the range radius depends on the precision of an algorithm and a sensor, and the density depends on the number of samples; obtaining A different maximum operation speed sets after clustering, wherein the A different maximum operation speed sets correspond to A classes of maximum operation speeds; finding out one complete motion corresponding to various elevators; summing and averaging the maximum running speeds of the various types respectively to obtain the maximum running speeds of the A types of motion modes; the maximum value of the absolute value of the first acceleration non-0 section of each type of acceleration curve is taken, and the average value is taken according to the type, so that the maximum acceleration of each type of motion of the elevator can be obtained; in the same way, the maximum value of the absolute value of the second acceleration non-0 section of the acceleration curve is taken, and the maximum deceleration of various types of motions of the elevator is obtained after the average according to the types;
step S6: calculating the y axis, namely the door opening and closing direction of the elevator car door, similar to the x axis, to obtain the curves of the door movement distance, the door speed and the door acceleration, and the door width is the maximum door movement distance of one-time door opening or door closing movement; however, one-time complete door opening/closing is divided into four processes of slow acceleration, deceleration and slow deceleration, and certain noise influence is generated when people go up and down after the door is opened; therefore, when a single door opening or closing curve is intercepted, an upper threshold and a lower threshold are set and divided into a data section continuously larger than the upper threshold and a data section continuously smaller than the lower threshold; because the middle constant speed section in the process of accelerating and decelerating the opening and closing of the elevator door is basically not available, if the difference time of the two data sections is small, the curve is a one-time door opening or closing curve;
step S7: carrying out accumulative calculation on the single running distance of the elevator, and obtaining the relative distance of all floors of the elevator to the bottommost layer by utilizing clustering; integrating the once complete elevator vertical interlayer movement of the x axis, calculating and taking the distance curve of single operation of all elevators, wherein the maximum value is the single operation distance of the elevators;
accumulating the single running distance, and taking the accumulated distance as a result to count dis [ i ], wherein i represents the ith result; when data of a long time is collected, the number of dis is enough, then clustering DBSCAN based on density is carried out on all dis to obtain several types of data which basically represent the number of floors of the building; averaging all similar objects to obtain the relative distance of each floor of the building; arranging all the average values from small to large, wherein the relative distance between the floor and the bottommost layer is obtained by subtracting the minimum average value from all the average values; obtaining the heights of all floors;
step S8: similarly, data in a certain longer time are collected, and the time period within the set time after the elevator stops is detected when the elevator is detected to perform x-axis vertical motion between floors every time, and whether the door is opened or closed can be detected by the y-axis; if the open and close door cannot be detected, the standby position of the elevator of the building is represented; the standby position is calculated by calculating the relative bottom height of the floor at several floors;
the following information of the elevator is obtained by the steps: the type of elevator motion pattern, maximum acceleration, maximum deceleration and maximum speed, as well as the time taken for the elevator to open the door, the time taken for the elevator to close the door, the door width, the standby position, the relative bottom height of the floors and the number of floors.
2. An acceleration sensor-based device attribute calculation method, characterized by comprising:
acquiring acceleration data of the set direction of the equipment;
obtaining at least one of the motion mode type, the maximum acceleration, the maximum deceleration, the maximum speed, the running distance and the running time of the equipment according to the acceleration data of the set direction of the equipment;
if the equipment has at least two stations and runs regularly, the method also comprises the steps of obtaining the relative distance between the stations of the equipment and the number of the stations for running the equipment according to the acceleration data in the set direction of the equipment;
the method further comprises the following steps: the equipment has different speed modes, and the maximum value of the speed curve of each time of complete operation of the equipment is taken to obtain M maximum operation speeds; performing density-based clustering DBSCAN on the M data with the maximum operating speed;
obtaining A different maximum operation speed sets after clustering, wherein the A different maximum operation speed sets correspond to A classes of maximum operation speeds; finding out one complete motion corresponding to various devices; summing and averaging the maximum running speeds of the various types respectively to obtain the maximum running speeds of the A types of motion modes;
taking the maximum value of the absolute value of the first acceleration non-0 section of each type of acceleration curve, and averaging according to the type to obtain the maximum acceleration of each type of motion of the equipment; in the same way, the maximum value of the absolute value of the second acceleration non-0 section of the acceleration curve is taken, and the maximum deceleration of various types of motion of the equipment can be obtained after the maximum value is averaged according to the types;
the equipment is an elevator; the method further comprises the following steps: the elevator vertical interlayer movement with one complete elevator interlayer movement direction is integrated, the distance curve of single operation of all elevators is calculated, and the maximum value is the single operation distance of the elevator;
accumulating the single running distance, and taking the accumulated distance as a result to count dis [ i ], wherein i represents the ith result; when data of a long time is collected, the number of dis is enough, then clustering DBSCAN based on density is carried out on all dis to obtain several types of data which basically represent the number of floors of the building; averaging all similar objects to obtain the relative distance of each floor of the building; arranging all the average values from small to large, wherein the relative distance between the floor and the bottommost layer is obtained by subtracting the minimum average value from all the average values; the heights of all floors are obtained.
3. The acceleration-sensor-based device attribute calculation method according to claim 2, characterized in that:
the method further comprises the following steps: processing acceleration data of the elevator car door in the door opening and closing direction to obtain curves of door movement distance, door speed and door acceleration, wherein the door width is the maximum door movement distance of one-time door opening or door closing movement; the one-time complete door opening/closing is divided into four processes of slow acceleration, deceleration and slow deceleration, and certain noise influence is generated when people go up and down after the door is opened; when a single door opening or closing curve is intercepted, an upper threshold and a lower threshold are set and divided into a data section continuously larger than the upper threshold and a data section continuously smaller than the lower threshold; the middle constant speed section in the acceleration and deceleration process of the opening and closing of the elevator door is basically not available, and if the difference time of the acceleration and deceleration data sections of the opening and closing of the elevator door is smaller than a set value, a one-time door opening or closing curve is obtained.
4. The acceleration-sensor-based device attribute calculation method according to claim 2, characterized in that:
the method further comprises the following steps: collecting data in a certain longer time, detecting the time period within the set time after the elevator stops when the elevator is detected to perform x-axis vertical motion between floors every time, and detecting whether the door can be opened or closed by a y-axis; if the open and close door cannot be detected, the standby position of the elevator of the building is represented; the standby position is calculated at several floors by calculating the relative floor height of the floor.
5. The acceleration-sensor-based device attribute calculation method according to claim 2, characterized in that:
the apparatus includes a door; the method further comprises the following steps: and obtaining at least one of the time for opening the door, the time for closing the door, the door width and the standby position of the equipment according to the acceleration data of the set direction of the equipment.
6. The acceleration-sensor-based device attribute calculation method according to claim 2, characterized in that:
the method comprises the following steps: acquiring triaxial acceleration data of equipment; determining three directions in the actual space represented by three axes according to information returned by the three-axis acceleration sensor; the x-axis represents the direction of movement between elevator floors, the y-axis represents the direction of opening and closing of the car doors, and the z-axis represents the direction perpendicular to the x-and y-axes.
7. The acceleration-sensor-based device attribute calculation method according to claim 3, characterized in that:
the method comprises the following steps: for the movement between floors of the elevator, namely the movement of the x axis, firstly, the unit LSB/g of the data of the x axis returned by the sensor is converted into m/s2Specifically, the conversion mode is that a is (k-b) × 9.8/b, wherein a is the converted acceleration value in meters per second, 9.8 is the value of the gravity acceleration g, b is the value of the x axis when the elevator is in a stationary period, and k is the value of the sensor at a certain moment.
8. The acceleration-sensor-based device attribute calculation method according to claim 2, characterized in that:
the method comprises the following steps: the movement direction data between the elevator floors is the value of the acceleration under a one-dimensional time sequence, and the unit is meter per second; the acceleration value has a positive value and a negative value and represents the direction of the acceleration; if the value of the acceleration is 0, the elevator does not move or is in a uniform motion state at the moment;
detecting continuous positive values and continuous negative values to obtain a time period when the continuous positive values or the continuous negative values appear; if this occurs, it indicates that the elevator has accelerated or decelerated in a certain direction.
9. The acceleration-sensor-based device attribute calculation method according to claim 2, characterized in that:
the method comprises the following steps: the complete motion of the elevator for one time comprises one-time acceleration, one-time constant speed and one-time deceleration, the detected time periods of continuous positive values and negative values are combined in pairs, and the time period of the complete elevator for one time is determined;
the combination rule is that whether two continuous time periods of positive values/negative values exist in the data is detected, if so, the two time periods respectively belong to two complete elevator interlayer vertical motions; detecting the time period when the acceleration is 0, namely the time period when the elevator is not accelerated or decelerated; if the time period is longer than the set threshold, the elevator is considered to be in a static state instead of a constant speed state in the time period, so that the elevator moves vertically between two elevator floors twice in the time periods of continuous positive values and negative values at two ends of the stable section; after the anchor point is determined according to the rules, adjacent continuous non-0 acceleration time periods are combined pairwise to obtain the time-varying acceleration data of the vertical motion between elevator layers.
10. The acceleration-sensor-based device attribute calculation method according to claim 2, characterized in that:
the equipment is an elevator; assuming a complete acceleration of the vertical movement between the elevator floors starting from 0 and ending at T, the speed of the elevator at time T is the integral of the acceleration over time from 0 to time T, i.e. the acceleration is measured as a function of the time
Wherein T is more than or equal to 0 and less than or equal to T and VtIs the velocity at time t, and a (t) is the acceleration at time t; after the speed at all the moments is calculated by the method, the data of the change of the speed of the complete elevator floor vertical motion along with the time is obtained, and the distance of the complete elevator floor vertical motion is the integral of the speed on the time;
at the moment, three pieces of data of the complete motion of all the elevators are obtained, namely data of the change of the acceleration relative to time, data of the change of the speed relative to time and data of the change of the motion distance of the elevator relative to time under the complete motion of one elevator; assuming that the elevator has moved N times in total, 3N pieces of data are obtained.
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| CN116969286B (en) * | 2023-09-20 | 2023-12-26 | 杭州海康威视数字技术股份有限公司 | Floor positioning method and device |
| CN117864890B (en) * | 2024-01-12 | 2024-06-11 | 广州励心物联科技有限公司 | Non-invasive elevator floor position calculation method, system and equipment |
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