Intelligent door and window control system

Abstract

The invention relates to the technical field of intelligent home systems, in particular to an intelligent door and window control system. The method comprises the following steps: firstly, evaluating a reverse opening judgment result of the intelligent electric door at the current moment, and further analyzing and determining a correction coefficient of a motor parameter at the current moment in combination with a change relationship between historical travel positions and closing resistance coefficients of the intelligent electric door in all preset historical processes; and then the clamping danger coefficient at the current moment is obtained in combination with the change condition of the motor parameters in the current closing process, and finally the preset motor speed is adjusted in combination with the stroke position at the current moment to control the intelligent electric door to be opened reversely. Based on the universality of the clamping condition in the historical closing process of the intelligent electric door, the sensitivity of the closing hindering influence of the intelligent electric door at the current moment is corrected, so that the clamping danger coefficient is accurately determined to adjust the reverse opening speed of the intelligent electric door, the clamping danger is removed in time, and the control effect on the intelligent electric door is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of smart home systems, and in particular to a smart door and window control system. Background Art

[0002] Smart doors and windows are a type of door and window system based on advanced sensing technology, automated control technology, Internet connection technology and smart home platform, which aims to improve the comfort, safety and convenience of living or working environment. As a part of smart doors and windows, smart electric doors mainly realize automatic opening and closing of doors when entering and exiting areas. They also have anti-pinch function. When it detects that a person or object is pinched, the smart electric door will automatically open in the opposite direction to provide convenient, safe and efficient access control and passage services.

[0003] In the prior art, when it is determined that a person or object is clamped by a smart electric door, the smart electric door is usually controlled to open in the reverse direction based on a preset speed; however, selecting an appropriate reverse opening speed is crucial to timely eliminate the danger and balance the stable operation of the smart electric door; if the reverse opening speed is too fast, it may cause unnecessary mechanical loss or energy waste; if the reverse opening speed is too slow, the clamping danger may not be eliminated in time; an inappropriate reverse opening speed will seriously affect the control effect of the smart electric door. Summary of the invention

[0004] In order to solve the technical problem that the inappropriate reverse opening speed in the prior art leads to poor control effect of the intelligent electric door, the purpose of the present invention is to provide an intelligent door and window control system, and the technical solution adopted is as follows:

[0005] An intelligent door and window control system, the system comprising:

[0006] Run the data acquisition module: in each preset historical process, obtain the motor parameter curve of the intelligent electric door and the historical travel position when opening in the reverse direction. The preset historical process is the historical closing process corresponding to the reverse opening of the intelligent electric door; obtain the travel position of the intelligent electric door at the current moment;

[0007] Operation data analysis module: at the current moment, according to the change of the motor parameters of the intelligent electric door in the current closing process, obtain the reverse opening judgment result of the intelligent electric door; according to the change of each motor parameter curve, obtain the closing resistance coefficient of the intelligent electric door in the corresponding preset historical process; according to the change relationship between all the historical stroke positions and the closing resistance coefficient, combined with the reverse opening judgment result and the stroke position at the current moment, obtain the correction coefficient of the motor parameter at the current moment; at the current moment, according to the change of the motor parameters of the intelligent electric door in the current closing process and the correction coefficient, obtain the clamping risk coefficient;

[0008] Automatic control module: At the current moment, adjust the preset motor speed by using the clamping risk coefficient and the stroke position, and control the intelligent electric door to open in the reverse direction.

[0009] Further, the obtaining of the reverse opening determination result includes:

[0010] According to the differences in motor parameters at all adjacent acquisition moments within the preset historical period of the intelligent electric door at the current moment, obtain the motor parameter change coefficient during the current closing process;

[0011] When the motor parameter change coefficient is greater than the preset coefficient threshold, it is determined that the intelligent electric door needs to open in the reverse direction during the current closing process; when the motor parameter change coefficient is less than or equal to the preset coefficient threshold, it is determined that the intelligent electric door does not need to open in the reverse direction during the current closing process.

[0012] Further, the method for obtaining the closing resistance coefficient includes:

[0013] In each preset historical process, take the historical moment corresponding to when it is first determined that the intelligent electric door needs to open in the reverse direction as the reverse opening moment; after the reverse opening moment, according to the change situation of each motor parameter curve, obtain the clamping continuous sub-segment in each motor parameter curve;

[0014] According to the differences in motor parameters at all adjacent acquisition moments within each clamping continuous sub-segment, obtain the resistance continuous parameter; take the length of each clamping continuous sub-segment as the resistance continuous weight; weight the resistance continuous parameter by using the resistance continuous weight, and take the weighted result as the closing resistance coefficient in the corresponding preset historical process.

[0015] Further, the method for obtaining the clamping continuous sub-segment includes:

[0016] In each motor parameter curve, construct a sliding window with a preset length starting from the reverse opening moment, slide from the reverse opening moment with a preset step size, calculate the variance of the motor parameters within each sliding window, and take the starting point of the sliding window corresponding to the minimum variance as the end point of the clamping continuous sub-segment; take the reverse opening moment as the starting point of the clamping continuous sub-segment, and determine the clamping continuous sub-segment in combination with the end point.

[0017] Further, the method for obtaining the correction coefficient includes:

[0018] Divide the closing resistance coefficients in all preset historical processes into clusters of a preset number of different resistance levels; for each resistance level, fit a change relationship model between the historical travel position and the closing resistance coefficient according to all the closing resistance coefficients in the cluster and the historical travel position during reverse opening in each corresponding preset historical process.

[0019] Take the preset historical processes in which reverse opening occurs at the current travel position at the current moment as target historical processes, and also divide the closing resistance coefficients in all target historical processes into target clusters of a preset number of different resistance levels; according to each closing resistance coefficient in each target cluster and its fitting error in the change relationship model at the corresponding resistance level, obtain the representative resistance coefficient of each target cluster.

[0020] Obtain the current closing resistance coefficient according to the change of motor parameters during a preset historical period from the reverse opening moment to the current moment during the current closing process; according to the difference between the cluster centers of different target clusters and the current closing resistance coefficient, and combining the difference between the representative resistance coefficients of different target clusters, obtain the correction coefficient of the motor parameters at the current moment.

[0021] Further, the method for obtaining the representative resistance coefficient includes:

[0022] In each target cluster, substitute each closing resistance coefficient into the change relationship model at the corresponding resistance level, take the difference between the substitution value and the closing resistance coefficient as the fitting error, and take the negative correlation normalization result of the fitting error as the confidence weight.

[0023] Weight the corresponding closing resistance coefficient using the confidence weight, and take the weighted result as the confidence closing resistance coefficient; take the mean of the confidence closing resistance coefficients of all the closing resistance coefficients in the target cluster as the representative resistance coefficient of the target cluster.

[0024] Further, the method for obtaining the correction coefficient of the motor parameters at the current moment according to the difference between the cluster centers of different target clusters and the target cluster to which the current closing resistance coefficient belongs, and combining the difference between the representative resistance coefficients of different target clusters includes:

[0025] The preset number is 2; calculate the resistance difference between the current closing resistance coefficient and the cluster center of each target cluster, and take the target cluster corresponding to the minimum resistance difference as the representative cluster.

[0026] Take the sign of the difference between another said target cluster and the cluster center of the representative cluster as the sign of the correction coefficient; normalize the absolute value of the difference between the representative resistance coefficient of the representative cluster and another said target cluster, and after assigning the normalization result to the sign of the correction coefficient, use it as the correction coefficient.

[0027] Further, the method for obtaining the clamping risk coefficient includes:

[0028] Take the correction coefficient plus the constant 1 as the correction weight, use the correction weight to weight the coefficient of change of the motor parameters during the current closing process of the intelligent electric door, and take the weighted result as the clamping risk coefficient.

[0029] Further, the method for adjusting the preset motor speed includes:

[0030] Take the spatial distance between the stroke position and the preset closing position as the dangerous position parameter; take the normalization result of the ratio of the clamping risk coefficient to the dangerous position parameter as the adjustment amplitude parameter, and take the adjustment amplitude parameter plus the constant 1 as the adjustment weight; use the adjustment weight to weight the preset motor speed to obtain the adjusted motor speed.

[0031] Further, the method for obtaining the cluster includes:

[0032] Obtain all clusters based on the distance clustering algorithm.

[0033] The present invention has the following beneficial effects:

[0034] The present invention first obtains the motor parameter curve of the intelligent electric door in each preset historical process and the historical stroke position during reverse opening in the operation data acquisition module, and obtains the stroke position at the current moment, providing a data analysis basis for subsequent judgment on whether the intelligent electric door needs to be opened in reverse during the current closing process to further adjust the reverse opening speed; then, in the data analysis module, according to the change of the motor parameters of the intelligent electric door during the current closing process, the reverse opening determination result of the intelligent electric door at the current moment is obtained; according to the change of each motor parameter curve, the closing resistance coefficient of the intelligent electric door in the corresponding preset historical process is obtained, providing a reference basis for subsequent analysis and evaluation of the clamping risk coefficient at the current moment in combination with the current closing process; according to the change relationship between all historical stroke positions and the closing resistance coefficient, combined with the reverse opening determination result and the stroke position at the current moment, the correction coefficient of the motor parameters at the current moment is obtained, so as to facilitate subsequent adjustment of the sensitivity to the influence of the closing obstacle of the intelligent electric door at the current moment according to the correction coefficient, that is, to adjust the sensitivity to the change of the motor parameters of the intelligent electric door during the current closing process, so as to accurately obtain the clamping risk coefficient at the current moment; finally, in the automatic control module, the preset motor speed is adjusted by using the clamping risk coefficient and the stroke position, and the intelligent electric door is controlled to open in reverse. Based on the universality of the clamping situation during the historical closing process of the intelligent electric door, the present invention corrects the sensitivity to the influence of the closing obstacle of the intelligent electric door at the current moment, so as to accurately determine the clamping risk coefficient to adjust the reverse opening speed of the intelligent electric door, timely relieve the clamping risk, and improve the control effect of the intelligent electric door. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required to be used in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0036] Figure 1 The system block diagram of an intelligent door and window control system provided by an embodiment of the present invention;

[0037] Figure 2 The flowchart of a method for obtaining a correction coefficient provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0038] To further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following will, in conjunction with the accompanying drawings and preferred embodiments, detail the specific implementation manner, structure, features and effects of an intelligent door and window control system proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0039] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0040] The following will specifically describe the specific solution of an intelligent door and window control system provided by the present invention with reference to the accompanying drawings.

[0041] Please refer to Figure 1 , which shows a system block diagram of an intelligent door and window control system provided by an embodiment of the present invention. The system mainly includes an operation data acquisition module 101, an operation data analysis module 102, and an automatic control module 103.

[0042] The operation data acquisition module 101, in each preset historical process, obtains the motor parameter curve of the intelligent electric door and the historical stroke position during reverse opening. The preset historical process is the corresponding historical closing process when the intelligent electric door opens in reverse; obtains the stroke position of the intelligent electric door at the current moment.

[0043] It should be noted that the embodiments of the present invention take the intelligent electric door as an example for analysis and description. Implementers can also use it for the control of intelligent electric windows with the same or similar working principles; the intelligent electric door targeted in the embodiments of the present invention is a translational automatic door, including but not limited to horizontally opening and closing electric retractable doors in double-opening or single-opening forms, vertically opening and closing electric doors, etc.; such electric doors are controlled to open and close by a motor.

[0044] To perform clamping risk control on the intelligent electric door and adaptively adjust the reverse opening speed, in each closing process of the intelligent electric door, the embodiments of the present invention will analyze the change of the motor parameters of the intelligent electric door, providing a data analysis basis for subsequent judgment on whether the intelligent electric door needs to open in reverse during the current closing process to further adjust the reverse opening speed; among them, reverse opening means that when the intelligent electric door is blocked by an external object during the closing process, it automatically triggers relevant control programs to open in reverse, which is a prior art and will not be elaborated herein.

[0045] It should be noted that the process of the intelligent electric door from the fully open state to the fully closed state is a complete closing process. In the abnormal closing process, there may be special situations such as continuous opening or reverse opening, which are regarded as the intermediate process of the closing process; the walking distance between the position where the intelligent electric door is in the fully open state and the position where it is in the fully closed state is the travel distance of this closing process.

[0046] In an embodiment of the present invention, first, all the processes with reverse opening are screened out from all the historical closing processes of the intelligent electric door, and these processes are used as the preset historical processes. In each preset historical process, the current on the motor of the intelligent electric door is collected by using a Hall sensor, the collection frequency is set to 100 Hz, and the collected currents are sorted according to the collection order to fit the motor parameter curve; at the same time, an encoder on the motor shaft is used to obtain the historical travel position at the time of reverse opening in each preset historical process, and the travel position of the intelligent electric door at the current moment in the current closing process.

[0047] It should be noted that the travel position refers to the position of the side of the intelligent electric door that may hold an object during the moving process starting from the fully open state, and its position is relative to the position where the intelligent electric door is in the fully open state.

[0048] It should be noted that obtaining the motor current by using a Hall sensor and obtaining the travel position of the intelligent electric door by using an encoder are both well-known technologies and will not be elaborated here; in other embodiments, the implementer can also obtain other types of motor parameters such as motor speed instead of the motor current, and can also customize the collection frequency.

[0049] It should be noted that the historical closing processes of only one intelligent electric door may be less, and the implementer can synchronously collect the relevant information of the historical closing processes of other intelligent electric doors of the same model for reference.

[0050] The operation data analysis module 102, at the current moment, obtains the reverse opening determination result of the intelligent electric door according to the change of the motor parameters in the current closing process of the intelligent electric door; obtains the closing resistance coefficient of the intelligent electric door in the corresponding preset historical process according to the change situation of each motor parameter curve; obtains the correction coefficient of the motor parameters at the current moment according to the change relationship between all the historical travel positions and the closing resistance coefficients, combined with the reverse opening determination result and the travel position at the current moment; at the current moment, obtains the clamping danger coefficient according to the change situation of the motor parameters and the correction coefficient in the current closing process of the intelligent electric door.

[0051] Considering that when there is a clamping situation in the intelligent electric door, the clamped obstacles such as people or objects will affect the further closing of the intelligent electric door, which may cause changes in the originally stable motor parameters; based on this, in an embodiment of the present invention, it will be evaluated whether there is a clamping risk in the intelligent electric door at the current moment, that is, whether it needs to be opened in reverse; when it needs to be opened in reverse, subsequent analysis will be carried out to adaptively adjust the reverse opening speed; when it does not need to be opened in reverse, the intelligent electric door will continue to operate according to the set control program.

[0052] Preferably, in an embodiment of the present invention, the method for obtaining the reverse opening determination result includes:

[0053] According to the differences in the motor parameters at all adjacent acquisition moments within the preset historical period of the intelligent electric door at the current moment, obtain the motor parameter change coefficient during the current closing process;

[0054] When the motor parameter change coefficient is greater than the preset coefficient threshold, it is determined that the intelligent electric door needs to be opened in reverse during the current closing process; when the motor parameter change coefficient is less than or equal to the preset coefficient threshold, it is determined that the intelligent electric door does not need to be opened in reverse during the current closing process.

[0055] As an example, set the preset historical period to the previous 2 seconds of the current moment, and obtain all the motor parameters of the intelligent electric door within the previous 2 seconds of the current moment; take the average value of the absolute values of the differences in the motor parameters at all adjacent acquisition moments as the motor parameter change coefficient; the larger the motor parameter change coefficient, the more likely it is affected by obstacles during the current closing process, and the greater the possibility of clamping. Then, use the preset coefficient threshold to determine whether it needs to be opened in reverse;

[0056] Among them, the method for obtaining the preset coefficient threshold is specifically: in all the historical closing processes of the intelligent electric door, regard the normal closing process without clamping as the reference closing process; calculate the absolute value of the difference in the motor parameters at adjacent acquisition moments in each reference closing process, and select the maximum value from all the absolute values of the differences in all reference closing processes as the preset coefficient threshold. The preset coefficient threshold reflects the extreme situation of the motor parameter change during the normal closing process; the implementer can also choose the mode or median, etc., which will not be elaborated here.

[0057] Considering that in different preset closing processes, different clamped obstacles have different influences on the closing obstruction of the intelligent electric door, and different closing obstruction influences can be reflected in the changes of the motor parameters; based on this, the embodiment of the present invention will obtain the closing resistance coefficient of the intelligent electric door during the corresponding preset historical process according to the change situation of each motor parameter curve; the closing resistance coefficient reflects the influence of the closing obstruction received by the intelligent electric door, providing a reference basis for subsequent analysis and evaluation of the clamping risk coefficient at the current moment in combination with the current closing process.

[0058] Preferably, in one embodiment of the present invention, considering that during the clamping process, the clamped object will cause the motor parameters to change in order to resist the clamping, until the obstacle gives up resistance and the motor parameters become stable; the process of resisting the clamping is taken as the clamping continuation sub-segment, and the longer the clamping continuation sub-segment and the more drastic the change of the motor parameters therein, the stronger the resistance of the clamped object is, and the greater the closing ability of the intelligent electric door is; therefore, the method for obtaining the closing resistance coefficient includes:

[0059] In each preset historical process, the historical moment corresponding to the first time when it is determined that the intelligent electric door needs to be opened in the reverse direction is used as the reverse opening moment; after the reverse opening moment, the clamping duration sub-segment in each motor parameter curve is obtained according to the change of each motor parameter curve;

[0060] According to the difference of motor parameters at all adjacent acquisition moments in each clamping duration subsegment, the resistance duration parameter is obtained; the length of each clamping duration subsegment is used as the resistance duration weight; the resistance duration parameter is weighted by the resistance duration weight, and the weighted result is used as the closing resistance coefficient in the corresponding preset historical process.

[0061] It should be noted that the method for determining whether the smart electric door needs to be opened in reverse in each preset historical process is consistent with the method for obtaining the reverse opening determination result mentioned above and will not be repeated here.

[0062] Among them, in a preferred embodiment of the present invention, the method for obtaining the clamping continuous sub-segment includes:

[0063] In each motor parameter curve, a sliding window of preset length is constructed with the reverse start-up moment as the starting point, and it slides from the reverse start-up moment with a preset step size. The variance of the motor parameters in each sliding window is calculated, and the starting point of the sliding window corresponding to the minimum variance is used as the end point of the clamping duration sub-segment; the reverse start-up moment is used as the starting point of the clamping duration sub-segment, and the clamping duration sub-segment is determined in combination with the end point.

[0064] As an example, first, the preset length is set to 10, that is, 10 motor parameters can be included in the sliding window; starting from the reverse start moment, slide on the motor parameter curve with a step size of 1 to obtain the variance of the motor parameters in each sliding window; when the variance reaches the minimum, it is determined that the motor parameters tend to be stable, so as to determine the clamping continuation sub-segment; in other examples, the implementer can also set the preset length by himself, and can also set a threshold value. When the corresponding variance of the sliding window is less than the threshold value for the first time, it is determined that the starting point of the sliding window is the end point of the clamping continuation sub-segment;

[0065] Then, within each clamping duration sub - segment, the average value of the absolute differences of the motor parameters at all adjacent acquisition moments is used as the resistance duration coefficient. The larger the average value of the absolute differences, the greater the impact of the closing obstruction of the clamped object. The longer the length of the clamping duration sub - segment, the longer the duration for which the clamped object resists closing, which also indicates a greater impact on the closing obstruction from the side. Therefore, the resistance duration weight is multiplied and combined with the resistance duration parameter to obtain the closing resistance coefficient corresponding to the preset historical process.

[0066] Considering that the degree of influence of the clamped object on the closing obstruction of the door when being clamped is not exactly equivalent to its degree of danger when being clamped; for example, a physically strong person may subconsciously apply a large force to block the closing of the intelligent electric door when being clamped, but this does not mean that the degree of danger they are in is high; while a thin and weak child or elderly person may only be able to apply a small force to block the closing of the door when being clamped, but this does not mean that the degree of danger they are in is low. Therefore, it is not possible to directly evaluate the clamping danger degree brought by the intelligent electric door at the current moment or during reverse opening based on the change of the motor parameter curve in the current closing process.

[0067] Also considering that the impact of the obstacle on the closing obstruction of the intelligent electric door is also affected by its position. The closer the position is to the fully closed state, the greater the possible clamping danger degree it may be in, and the relatively larger the closing resistance coefficient may be. Based on the change relationship between the impact on the closing obstruction of the door and its position in a large number of preset historical processes, the relationship between the impact on the closing obstruction of the door and the clamping danger degree under general rules can be roughly analyzed. Furthermore, the clamping danger coefficient of the intelligent electric door in the current closing process can be evaluated by combining the stroke position at the current moment.

[0068] Therefore, in the embodiment of the present invention, first, based on the reverse opening determination result at the current moment, according to the change relationship between all historical stroke positions and the closing resistance coefficient, combined with the stroke position at the current moment, the correction coefficient of the motor parameters at the current moment is obtained, and then the motor parameters at the current moment are corrected, so as to accurately evaluate the clamping danger coefficient at the current moment.

[0069] Preferably, in an embodiment of the present invention, the method for obtaining the correction coefficient includes:

[0070] Please refer to Figure 2 , which shows a flowchart of a method for obtaining a correction coefficient provided by an embodiment of the present invention, specifically including:

[0071] Step S201: Divide the closing resistance coefficients in all preset historical processes into clusters with a preset number of different resistance levels; for each resistance level, based on all the closing resistance coefficients in the cluster and the historical stroke positions during reverse opening in each corresponding preset historical process, fit a variation relationship model between the historical stroke position and the closing resistance coefficient.

[0072] Considering that objects to be clamped with the same property may have different reactions to the closing obstruction of the door at the same stroke position. For example, some people have greater strength and greater closing obstruction ability, while some people have smaller strength and smaller closing obstruction ability. Therefore, in a preferred embodiment of the present invention, first, based on the K-means algorithm and a preset K value, cluster the closing resistance coefficients in all preset historical processes to obtain clusters with a preset number of different resistance levels; where the preset K value is the same as the preset number, both are 2; the resistance level can be regarded as the magnitude of the closing resistance coefficient corresponding to the cluster center.

[0073] Then, in the cluster under each resistance level, based on the linear regression algorithm, fit the functional relationship between all the closing resistance coefficients and the corresponding historical stroke positions to obtain a variation relationship model between the historical stroke position and the closing resistance coefficient; the variation relationship model under each resistance level reflects the influence of the closing obstruction of a certain type of object to be clamped on the intelligent electric door by the stroke position under general rules, preparing for accurately evaluating the clamping risk coefficient at the current moment.

[0074] It should be noted that both the K-means algorithm and the linear regression algorithm are already well-known prior arts to those skilled in the art and will not be elaborated here; in other embodiments, the implementer can also set other preset K values or preset numbers, and can also use other clustering algorithms to divide the closing resistance coefficients.

[0075] Step S202: Use the preset historical processes with reverse opening occurring at the stroke position at the current moment as target historical processes, and also divide the closing resistance coefficients in all target historical processes into target clusters with a preset number of different resistance levels; according to each closing resistance coefficient in each target cluster and its fitting error in the variation relationship model under the corresponding resistance level, obtain the representative resistance coefficient of each target cluster.

[0076] Considering that the closing resistance coefficients of the preset historical processes with the same stroke position as the current moment can provide relevant references for evaluating the clamping risk degree at the current moment. Therefore, in an embodiment of the present invention, first, screen out the target historical processes from all preset historical processes, and further based on the clustering method described in step S201, divide the closing resistance coefficients in all target historical processes into target clusters with 2 different resistance levels.

[0077] Then, based on the variation relationship model, the confidence of each closing resistance coefficient in the target cluster relative to the general law can be evaluated, and then the representative resistance coefficient in the target cluster can be obtained, preparing for subsequent correction of the motor parameters at the current moment, accurately evaluating its closing hindrance effect, and further accurately evaluating the clamping risk coefficient;

[0078] In a preferred embodiment of the present invention, the method for obtaining the representative resistance coefficient includes:

[0079] In each target cluster, substitute each closing resistance coefficient into the variation relationship model under the corresponding resistance level, take the difference between the substitution value and the closing resistance coefficient as the fitting error, and take the negative correlation normalization result of the fitting error as the confidence weight;

[0080] Use the confidence weight to weight the corresponding closing resistance coefficient, and take the weighted result as the confidence closing resistance coefficient; take the mean of the confidence closing resistance coefficients of all closing resistance coefficients in each target cluster as the representative resistance coefficient of each target cluster.

[0081] As an example, taking any target cluster as an example, the calculation formula for the representative resistance coefficient is:

[0082] where D is the representative resistance coefficient of the target cluster; i is the serial number of the closing resistance coefficient in the target cluster; I is the total number of closing resistance coefficients in the target cluster; τ i is the confidence weight of the i-th closing resistance coefficient; F i is the i-th closing resistance coefficient; τ i ×F i is the confidence closing resistance coefficient of the i-th closing resistance coefficient.

[0083] Among them, the method for obtaining the confidence weight is: take the reciprocal of the fitting error corresponding to each closing resistance coefficient and normalize it with negative correlation to obtain the confidence weight; it should be noted that when the fitting error is 0, taking the reciprocal is meaningless, and the confidence weight is directly set to 1; in other examples, other negative correlation normalization means can also be used, such as taking the fitting error as the exponent in the negative exponential function, etc., which will not be elaborated here;

[0084] In the above formula, the fitting error is used to adjust the confidence reference significance of the closing resistance parameter to obtain the confidence closing resistance coefficient, and then the mean value is taken to obtain the representative center of the corresponding target cluster, that is, the representative resistance coefficient; in other examples, the implementer can also take the mode or median, etc.

[0085] Step S203: Obtain the current closing resistance coefficient according to the change of the motor parameters within a preset historical period from the reverse opening moment to the current moment during the current closing process; obtain the correction coefficient of the motor parameters at the current moment according to the difference between the cluster centers of different target clusters and the current closing resistance coefficient, in combination with the difference between the representative resistance coefficients of different target clusters.

[0086] Considering the change of the motor parameters during the current closing process can preliminarily estimate the current influence on the closing obstruction of the intelligent electric door, that is, the current closing resistance coefficient; therefore, first obtain the current closing resistance coefficient, and then the resistance level to which the current closing resistance coefficient belongs can be judged. Then, in combination with the difference between different target clusters corresponding to the travel position at the current moment, obtain the correction coefficient, so as to facilitate the subsequent correction of the evaluation of the change of the motor parameters at the current moment, and indirectly correct the current closing resistance coefficient, so as to accurately obtain the clamping risk coefficient at the current moment.

[0087] In a preferred embodiment of the present invention, the method for obtaining the correction coefficient includes:

[0088] The preset quantity is 2; calculate the resistance difference between the current closing resistance coefficient and the cluster center of each target cluster, and take the target cluster corresponding to the minimum resistance difference as the representative cluster;

[0089] Take the difference sign between the cluster center of the other target cluster and the representative cluster as the correction coefficient sign; normalize the absolute value of the difference between the representative resistance coefficient of the representative cluster and the representative resistance coefficient of the other target cluster, and after assigning the normalized result to the correction coefficient sign, use it as the correction coefficient.

[0090] As an example, the calculation formula of the correction coefficient is:

[0091] a = flag(Q′ o - Q o ) × tanh(|D′ o - D o |); where a is the correction coefficient; Q o is the cluster center of the representative cluster; Q′ o is the cluster center of the other target cluster except the representative cluster; flag(Q′ o - Q o ) is the correction coefficient sign; D′ o is the representative resistance coefficient of the other target cluster except the representative cluster; D o is the representative resistance coefficient of the representative cluster; tanh() is the hyperbolic tangent function for normalization.

[0092] In the above formula, first obtain the representative cluster corresponding to the current closing resistance coefficient. The representative cluster reflects the resistance level to which the current closing resistance coefficient belongs from the side. When the resistance level of the current closing resistance coefficient is relatively large, the sensitivity to subsequent changes in motor parameters can be appropriately reduced, that is, the risk sensitivity to the influence of a large closing obstacle is reduced to avoid overreaction. When the resistance level of the current closing coefficient is relatively small, the sensitivity to changes in motor parameters can be appropriately increased, that is, the risk sensitivity to the influence of a small closing obstacle is increased to avoid potential danger awareness. Based on this, when the cluster center of the representative cluster is smaller than the cluster center of another target cluster, the sign of the correction coefficient is positive, and vice versa. Then, take the absolute value of the difference between the representative resistance coefficients of the representative cluster and the other target cluster as the reference amplitude for correction. Multiply the reference amplitude for correction by the corresponding sign of the correction coefficient to obtain the correction coefficient.

[0093] After obtaining the correction coefficient, the clamping risk coefficient at the current moment can be further obtained in combination with the changes in motor parameters during the current closing process of the intelligent electric door.

[0094] Preferably, in an embodiment of the present invention, the method for obtaining the clamping risk coefficient includes:

[0095] Take the correction coefficient plus the constant 1 as the correction weight, and weight the coefficient of change of the motor parameters during the current closing process of the intelligent electric door with the correction weight, and take the weighted result as the clamping risk coefficient.

[0096] As an example, the calculation steps of the coefficient of change of the motor parameters during the current closing process are the same as those described in the method for obtaining the reverse opening determination result, and will not be repeated here. Specifically, multiply the correction weight by the coefficient of change of the motor parameters during the current closing process, and take the product as the clamping risk coefficient.

[0097] The automatic control module 103 adjusts the preset motor speed according to the clamping risk coefficient and the stroke position at the current moment, and controls the intelligent electric door to open in reverse.

[0098] After obtaining the clamping risk coefficient, the original fixed preset motor speed can be adaptively adjusted in combination with the stroke position at the current moment, so as to adaptively control the intelligent electric door to open in reverse according to the actual situation at the current moment.

[0099] Preferably, in an embodiment of the present invention, the method for adjusting the preset motor speed includes:

[0100] Take the spatial distance between the stroke position and the preset closing position as the risk position parameter; take the normalized result of the ratio of the clamping risk coefficient to the risk position parameter as the adjustment amplitude parameter, and take the adjustment amplitude parameter plus the constant 1 as the adjustment weight; weight the preset motor speed with the adjustment weight to obtain the adjusted motor speed.

[0101] As an example, the adjusted motor speed is calculated as: Among them, v′ is the adjusted motor speed; v is the preset motor speed; norm() is the standard normalization function; Ye is the clamping hazard coefficient at the current moment; d is the dangerous position parameter; is the adjustment amplitude parameter.

[0102] It should be noted that the preset closing position refers to the position of the smart electric door when it is in a fully closed state. If the smart electric door is clamped, the travel position at the current moment will not be able to coincide with the preset closing position, that is, the dangerous position parameter cannot be 0, and it is always meaningful as a denominator; the preset motor speed needs to be determined in combination with the specific implementation scenario and motor design parameters, and its determination is already a prior art and will not be repeated here.

[0103] In the above formula, when the spatial distance between the stroke position and the preset closed position is smaller, the dangerous position parameter is larger, and at the same time, the clamping risk coefficient is larger, which makes the adjustment amplitude parameter larger, and then the adjusted motor speed is relatively larger, and the reverse opening is faster to quickly eliminate the clamping risk.

[0104] In summary, the present invention first obtains the motor parameter curve of the intelligent electric door in each preset historical process and the historical stroke position when opening in reverse, and obtains the stroke position at the current moment; then evaluates the reverse opening judgment result of the intelligent electric door at the current moment, and obtains the closing resistance coefficient of the intelligent electric door in each preset historical process, and further combines the change relationship between all historical stroke positions and closing resistance coefficients, analyzes and determines the correction coefficient of the motor parameters at the current moment, and then combines the change of the motor parameters of the intelligent electric door in the current closing process to obtain the clamping risk coefficient at the current moment; finally, the clamping risk coefficient and stroke position are used to adjust the preset motor speed to control the reverse opening of the intelligent electric door. Based on the universality of the clamping situation in the historical closing process of the intelligent electric door, the present invention corrects the sensitivity of the closing obstruction of the intelligent electric door at the current moment, so as to accurately determine the clamping risk coefficient to adjust the reverse opening speed of the intelligent electric door, eliminate the clamping risk in time, and improve the control effect of the intelligent electric door.

[0105] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0106] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. An intelligent door and window control system, characterized in that The system includes: An operating data acquisition module: In each preset historical process, obtain the motor parameter curve of the intelligent electric door and the historical stroke position during reverse opening. The preset historical process is the corresponding historical closing process when the intelligent electric door opens in reverse; obtain the stroke position of the intelligent electric door at the current moment; An operating data analysis module: At the current moment, according to the change of the motor parameters during the current closing process of the intelligent electric door, obtain the reverse opening determination result of the intelligent electric door; according to the change of each motor parameter curve, obtain the closing resistance coefficient of the intelligent electric door during the corresponding preset historical process; according to the change relationship between all the historical stroke positions and the closing resistance coefficient, combine the reverse opening determination result and the stroke position at the current moment to obtain the correction coefficient of the motor parameters at the current moment; at the current moment, according to the change of the motor parameters during the current closing process of the intelligent electric door and the correction coefficient, obtain the clamping danger coefficient; An automatic control module: At the current moment, use the clamping danger coefficient and the stroke position to adjust the preset motor speed and control the intelligent electric door to open in reverse.

2. The intelligent door and window control system according to claim 1, characterized in that The obtaining of the reverse opening determination result includes: According to the difference of the motor parameters at all adjacent acquisition moments during the preset historical period of the intelligent electric door at the current moment, obtain the motor parameter change coefficient during the current closing process; When the motor parameter change coefficient is greater than the preset coefficient threshold, it is determined that the intelligent electric door needs to open in reverse during the current closing process; when the motor parameter change coefficient is less than or equal to the preset coefficient threshold, it is determined that the intelligent electric door does not need to open in reverse during the current closing process.

3. An intelligent door and window control system according to claim 1, characterized in that, The method for obtaining the closing resistance coefficient includes: In each preset historical process, take the historical moment corresponding to the first determination that the intelligent electric door needs to open in reverse as the reverse opening moment; after the reverse opening moment, according to the change of each motor parameter curve, obtain the clamping continuous sub-segment in each motor parameter curve; According to the difference of the motor parameters at all adjacent acquisition moments in each clamping continuous sub-segment, obtain the resistance continuous parameter; take the length of each clamping continuous sub-segment as the resistance continuous weight; weight the resistance continuous parameter with the resistance continuous weight, and take the weighted result as the closing resistance coefficient in the corresponding preset historical process.

4. An intelligent door and window control system according to claim 3, characterized in that, The method for obtaining the clamping continuous sub-segment includes: In each motor parameter curve, construct a sliding window with a preset length starting from the reverse opening moment, slide from the reverse opening moment with a preset step length, calculate the variance of the motor parameters in each sliding window, and take the starting point of the sliding window corresponding to the minimum variance as the end point of the clamping continuous sub-segment; take the reverse opening moment as the starting point of the clamping continuous sub-segment, and combine the end point to determine the clamping continuous sub-segment.

5. An intelligent door and window control system according to claim 1, characterized in that The method for obtaining the correction coefficient includes: Cluster the closing resistance coefficients in all preset historical processes into clusters of a preset number of different resistance levels; for each resistance level, fit a change relationship model between the historical travel position and the closing resistance coefficient according to all the closing resistance coefficients in the cluster and the historical travel positions during reverse opening in the preset historical process corresponding to each closing resistance coefficient. Take the preset historical processes in which reverse opening occurs at the travel position at the current moment as target historical processes, and also cluster the closing resistance coefficients in all target historical processes into target clusters of a preset number of different resistance levels; according to each closing resistance coefficient in each target cluster and its fitting error in the change relationship model at the corresponding resistance level, obtain the representative resistance coefficient of each target cluster. Obtain the current closing resistance coefficient according to the change of the motor parameters during a preset historical period from the reverse opening moment to the current moment in the current closing process; according to the difference between the cluster centers of different target clusters and the current closing resistance coefficient, and in combination with the difference between the representative resistance coefficients of different target clusters, obtain the correction coefficient of the motor parameters at the current moment.

6. The intelligent door and window control system according to claim 5, characterized in that, The method for obtaining the representative resistance coefficient includes: In each target cluster, substitute each closing resistance coefficient into the change relationship model at the corresponding resistance level, take the difference between the substitution value and the closing resistance coefficient as the fitting error, and take the negative correlation normalization result of the fitting error as the confidence weight. Weight the corresponding closing resistance coefficient using the confidence weight, and take the weighted result as the confidence closing resistance coefficient; take the mean of the confidence closing resistance coefficients of all the closing resistance coefficients in the target cluster as the representative resistance coefficient of the target cluster.

7. An intelligent door and window control system according to claim 5, characterized in that, The method for obtaining the correction coefficient of the motor parameters at the current moment according to the difference between the cluster centers of different target clusters and the target cluster to which the current closing resistance coefficient belongs, and in combination with the difference between the representative resistance coefficients of different target clusters includes: The preset number is 2; calculate the resistance difference between the current closing resistance coefficient and the cluster center of each target cluster, and take the target cluster corresponding to the minimum resistance difference as the representative cluster. Take the difference sign between the cluster center of the other target cluster and the representative cluster as the correction coefficient sign; normalize the absolute value of the difference between the representative resistance coefficient of the representative cluster and the other target cluster, and after assigning the normalization result to the correction coefficient sign, take it as the correction coefficient.

8. The intelligent door and window control system according to claim 2, wherein The method for obtaining the clamping danger coefficient includes: Take the correction coefficient plus the constant 1 as the correction weight, and weight the motor parameter change coefficient of the intelligent electric door during the current closing process using the correction weight, and take the weighted result as the clamping danger coefficient.

9. The intelligent door and window control system according to claim 1, characterized in that, The method for adjusting the preset motor speed includes: Take the spatial distance between the stroke position and the preset closed position as the dangerous position parameter; take the normalized result of the ratio of the clamping danger coefficient to the dangerous position parameter as the adjustment amplitude parameter, and take the adjustment amplitude parameter plus the constant 1 as the adjustment weight; use the adjustment weight to weight the preset motor speed to obtain the adjusted motor speed.

10. An intelligent door and window control system according to claim 5, characterized in that, The method for obtaining the clusters includes: Obtain all clusters based on the distance clustering algorithm.

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