CN111090924A - Processing method for anti-pinch power window state information - Google Patents
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Abstract
The application discloses a processing method for anti-pinch power window state information, which comprises the following steps: collecting and storing vehicle window state sequence information; processing the car window state sequence information to generate a historical array; and repairing the window state sequence information of the historical array in real time. The accuracy of the car window state information is improved.
Description
Technical Field
The application relates to the field of automobile anti-pinch electric window controllers, in particular to a processing method for anti-pinch electric window state information.
Background
Since the advent of power windows, safety in use has become an important issue because the lift force generated when the window is raised is very large and can have serious consequences once clamping has occurred. The anti-pinch function has important significance for protecting passengers, and meanwhile, the rating of the safety of the vehicle can be improved, more and more vehicle enterprises with the anti-pinch function are installed on the vehicle windows, and the anti-pinch technology is greatly developed.
The anti-pinch technology can be divided into contact type and non-contact type in principle. Although the non-contact anti-pinch method can completely avoid the occurrence of clamping action, the non-contact anti-pinch method has high cost and is not generally applied because the hardware structure of the car window needs to be changed. And the hardware scheme and the algorithm of contact anti-pinch technology present the diversification, but the thinking of preventing pinching and judging is approximate: in the ascending process of the car window, when the controller judges that the car window is subjected to larger resistance in the anti-pinch area, the clamping is considered to occur at the moment, and the protection is realized by enabling the car window to move reversely to remove the clamping.
The contact type anti-pinch scheme essentially takes the output of a motor as the source of vehicle window state information, and realizes anti-pinch judgment by determining the position and stress of a vehicle window. Because the parameters of the vehicle windows of different models are generally different, and the vehicle windows are also accompanied by factors such as climate change, adhesive tape aging, mechanical wear, power supply fluctuation and the like in the using process, the system is changed, and the vehicle window state information is inaccurate and reliable.
Disclosure of Invention
The application aims to provide a processing method for anti-pinch power window state information, and accuracy of the window state information is improved.
The application discloses a processing method for anti-pinch power window state information, which comprises the following steps:
collecting and storing vehicle window state sequence information;
processing the car window state sequence information to generate a historical array;
and repairing the window state sequence information of the historical array in real time.
Optionally, the vehicle window state sequence information includes current information and vehicle window position information, and the step of repairing the vehicle window state sequence information of the historical array in real time includes:
smoothing current information of historical array vehicle window state sequence information;
eliminating peak data in the smoothed current information;
and repairing the data missing after the peak data in the current information is removed.
Optionally, the step of repairing the data missing after the peak data in the current information is removed includes:
constructing a data prediction model by using original current information before smoothing as a data basis and adopting a quadratic exponential smoothing method;
and replacing the eliminated peak data in the current information of the historical array window state sequence information by taking the prediction result of the data prediction model as the repair data.
Optionally, the patch data i* kThe calculation formula of (2) is as follows:
i* k＝ζe+ξer
wherein r is the number of prediction look-ahead periods equal to the number of base data to patch data i* kTime series number difference of (1). ZetaeAnd ξeIs an intermediate parameter variable.
Optionally, after the step of processing the vehicle window state sequence information and generating the history array, the method further includes the steps of:
updating the history array: and receiving the currently collected vehicle window state sequence information, and rejecting the old vehicle window state sequence information to complete the recording of the window state sequence information.
Optionally, after the step of repairing the vehicle window state sequence information of the historical array in real time, the method further includes:
verifying the repair effect of the vehicle window state sequence information of the real-time repair history array: and under the condition of the same window running, acquiring current information of the window state sequence information within a certain time, and comparing the current information with the current information of the window state sequence information before real-time repair to obtain the fluctuation improvement condition of the current after real-time repair.
Optionally, the vehicle window state sequence information includes current information and vehicle window position information, and the step of repairing the vehicle window state sequence information of the historical array in real time further includes:
and matching the current information and the window position information at the same moment.
According to the processing method, the accuracy of the car window state information is improved through theoretical demonstration and set theory analysis, theoretical guidance is provided for collection and processing of the car window state information, and preconditions are provided for developing an anti-pinch algorithm in the later stage and continuously optimizing and matching the anti-pinch algorithm.
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The accompanying drawings, which are included to provide a further understanding of the embodiments of the application, are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the principles of the application. It is obvious that the drawings in the following description are only some embodiments of the application, and that for a person skilled in the art, other drawings can be derived from them without inventive effort. In the drawings:
FIG. 1 is a schematic flow chart of the processing method of the present application;
FIG. 2 is a schematic diagram illustrating a history array updating method according to the present application;
FIG. 3 is a diagram of a historical array update state machine according to the present application;
FIG. 4 is another schematic flow chart of the process of the present application;
FIG. 5 is a schematic view of a sequence of states of window movement of the present application;
FIG. 6 is a schematic diagram of current information fluctuation according to the present application;
FIG. 7 is a schematic diagram of current information repair according to the present application;
FIG. 8 is a schematic diagram of real-time smoothing of a history array of the present application;
FIG. 9 is a schematic view of current information segmentation of the present application;
FIG. 10 is another schematic flow chart of the process of the present application;
FIG. 11 is a schematic diagram of a current information repair process according to the present application;
FIG. 12 is a comparison diagram of current information repair results according to the present application;
fig. 13 is a schematic diagram of the current information and the position information of the present application in time matching.
Detailed Description
It is to be understood that the terminology, the specific structural and functional details disclosed herein are for the purpose of describing particular embodiments only, and are representative, but that the present application may be embodied in many alternate forms and should not be construed as limited to only the embodiments set forth herein.
In the description of the present application, the terms "first", "second" are used for descriptive purposes only and are not to be construed as indicating relative importance or as implicitly indicating the number of technical features indicated. Thus, unless otherwise specified, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature; "plurality" means two or more. The terms "comprises" and "comprising," and any variations thereof, are intended to cover a non-exclusive inclusion, such that one or more other features, integers, steps, operations, elements, components, and/or combinations thereof may be present or added.
Further, terms of orientation or positional relationship indicated by "center", "lateral", "upper", "lower", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, are described based on the orientation or relative positional relationship shown in the drawings, are simply for convenience of description of the present application, and do not indicate that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present application.
Furthermore, unless expressly stated or limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly and may include, for example, fixed connections, removable connections, and integral connections; can be mechanically or electrically connected; either directly or indirectly through intervening media, or through both elements. The specific meaning of the above terms in the present application can be understood by those of ordinary skill in the art as appropriate.
The car window anti-pinch system is an important component of a car comfort system, and has the main functions that after a car window rises to clamp a barrier, the car window can be identified to be in a clamping state, the car window is made to roll back to release the clamped object, the motor is prevented from being burnt due to long-time stalling, and vehicle members are prevented from being pinched. Because the parameters of the vehicle windows of different models are generally different, and the vehicle windows are also accompanied by factors such as climate change, adhesive tape aging, mechanical wear, power supply fluctuation and the like in the using process, the system is changed. Therefore, a method for acquiring and repairing anti-pinch power window state information is needed, the output quantity of a sensor acquired by a controller is converted into stress information and position information corresponding to a state recognition condition, data filtering is needed to eliminate short-time large deviation caused by accidental interference in order to eliminate data fluctuation caused by system factors, and missing data is predicted in a secondary exponential smoothing mode to ensure curve continuity. And finally, the time sequence of the data in the historical array needs to be adjusted, and the data time sequence dislocation caused by the processing method is eliminated.
The present application is described in detail below with reference to the figures and alternative embodiments.
As shown in fig. 1, as an embodiment of the present application, a processing method of anti-pinch power window status information is disclosed, the processing method includes the steps of:
s1: collecting and storing vehicle window state sequence information;
s2: processing the car window state sequence information to generate a historical array;
s3: updating a history array;
s4: and repairing the window state sequence information of the historical array in real time.
The window state sequence information is collected by a sensor (such as a Hall sensor) and stored in the memory of the window controller. The vehicle window state sequence information is the basis of vehicle window state identification, but is easily interfered by the environment, and brings difficulty to anti-pinch judgment. The anti-pinch parameters are used as the key of state identification, the acquisition process is complex, the production efficiency of anti-pinch car windows is reduced, and the possibility of making mistakes in the production process is improved. The adaptation method of the vehicle window to the environment, which depends on the empirical value, also reduces the reliability of the anti-pinch function when the vehicle window faces a variable environment. In order to solve the problems, the processing method provided by the application improves the accuracy of the car window state information through theoretical demonstration and set theory analysis, provides theoretical guidance for collection and processing of the car window state information, and provides precondition for developing an anti-pinch algorithm in the later stage and continuously optimizing and matching the anti-pinch algorithm.
In step S1, the window state sequence Z is a set of sensor information, collectively reflecting the state change of the window continuously over a long time, and can be expressed as follows:
Z＝{I，P} (1)
wherein I is current information and P is position information, which are respectively defined as follows:
I＝{i(tk)} (k＝1，2，…，N) (2)
P＝{p(tk)} (k＝1，2，…，N) (3)
in the formulae (2) and (3), i (t)k) For the current sample value, p (t)k) Is the number of high levels of Hall pulse, where p (t)k) Reflects a change in window position. t is tkAnd k is the time sequence number of the sampling point at the sampling moment, and the time interval of the adjacent sampling points is delta t. Thus tkIs as defined in formula (4):
z(tk)＝{i(tk)，p(tk)} (k＝1，2，…，N)(4)
in step 2, since the memory of the window controller is generally small, the storage of all the historical state information cannot be realized, and meanwhile, the window state information is recorded by adopting a fixed-length historical array in consideration of the processing speed of zero clearing during updating. The history array is a proper subset of the corresponding state sequence. Respectively defining current history arrays ILAnd location history array PLAs shown in formulas (6) and (7).
IL＝{IM，FL} (6)
PL＝{PM，PL} (7)
Wherein IMAnd PMIs a data segment, withLAnd PLCollectively referred to as a history array. M is the length of the data segment, FLAnd PLFor the count flag, the initial value is 0, which represents the number of times the history array is populated with state information, with a population interval Δ t. I isMAnd PMThe definitions are shown in formulas (8) and (9):
IM＝{im}(m＝1，2，…，M) (8)
PM＝{pm}(m＝1，2，…，M) (9)
history array IMIs equal to and PMThe relationship with the state sequence I, P and the corresponding relationship between its elements and the state information are shown in equations (10), (11) and (12):
IM∈I，PM∈P (10)
fm＝i{tk+m-M}(m＝1，2，…，M) (11)
pm＝p{tk+m-M}(m＝1，2，…，M) (12)
in the step of S3, the history array is updated: and receiving the currently collected vehicle window state sequence information, and rejecting the old vehicle window state sequence information to complete the recording of the window state sequence information. Specifically, by pushing currently acquired sensor data and eliminating earlier data, a historical array can be updated, and the recording of the latest section of state information is completed. Using current history array IMFor example, the history array is updated as shown in FIG. 2.
In view of consistency of window state recording, it is undesirable that data of window ascending and descending exist in the history array at the same time, and the history array needs to be cleared when the window switches the running direction. Defining a window operating state SSAs shown in formulas (2-13). At SSS needs to be changed in case of not equal to 0SWhen the value of (1) is S, S is first addedSSet to 0 before it can be set to the target value.
The historical array can be divided into a plurality of states according to the running condition of the car window, the conversion among the states needs to meet certain conditions, and an updating state machine for designing the historical array is shown in fig. 3.
When the vehicle window is static, the vehicle window is in an initial state, and the data section I of the historical arrayMIs empty. Window state S after issuing a control commandSNot equal to 0, in IMIf not, the sensor data i (t) continues to be usedk) Filling IM(ii) a When I isMFilling up M data, and performing first-in first-out data updating circulation. When the window stops in operation, SSWhen equal to 0, empty IMThat is, the history array is restored to the initial state with all the element values being 0, and the next S waiting is performedSAnd (3) arrival of time not equal to 0. The current information is only used as an object for analysis, and the position data is updated in the same way.
Because the current fluctuation is extremely irregular when the window motor is started, when the window state is identified by using the historical array in the follow-up process, only the counting mark F is usedLAnd PLGreater than M, i.e., the history array is in a loop state, the data is valid.
As shown in fig. 4, the window state sequence information includes current information and window position information, and the step of S4 repairing the window state sequence information of the history array in real time includes:
s41: smoothing current information of historical array vehicle window state sequence information;
s42: eliminating peak data in the smoothed current information;
s43: and repairing the data missing after the peak data in the current information is removed.
The hardware scheme and the algorithm of contact anti-pinch technology present the diversification, but the thinking of anti-pinch judgement is approximate: in the ascending process of the car window, when the controller judges that the car window is subjected to larger resistance in the anti-pinch area, the clamping is considered to occur at the moment, and the protection is realized by enabling the car window to move reversely to remove the clamping. Although various contact type anti-pinch schemes are different in implementation of anti-pinch judgment, the anti-pinch judgment is realized by determining the position and stress of a vehicle window by taking the output of a motor as the source of vehicle window state information. The method is based on a current Hall pulse anti-pinch technology, and the position of the car window is detected by using signals of a Hall sensor and is car window position information; meanwhile, by serially connecting a sampling resistor in a motor circuit, the armature current of the motor can be indirectly obtained through the voltage at the two ends of the sampling resistor, and the current information is obtained. The stress of the car window is approximately in linear relation with the current of the motor. On the basis, whether the clamping of the window occurs is judged by detecting whether the integral value of the motor current curve in a period of time exceeds a set threshold value.
The state information of the vehicle window may change irregularly due to the change of temperature and humidity, and the influence of sand, dust, vibration and the like on the vehicle window. And recording the start-stop process of the vehicle window from the bottom to the top by temporarily prolonging the length M of the historical array under the use condition that the sealing strip is aged and foreign matters are clamped into the guide groove. The complete state sequence is shown in fig. 5. It can be seen that the window position changes relatively regularly over time, but the current exhibits irregular fluctuations.
Fig. 5 is partially enlarged as shown in fig. 6. It can be seen that there are two irregular cases of current with sawtooth-like fluctuations as shown in fig. 6(a) and spikes as shown in fig. 6(b), and the black dots mark the main current sampling points that form the spike data. The amplitude of the sawtooth-shaped fluctuation is small, and the whole process of the running of the car window is mainly caused by the change of the friction force of the contact surface of the car window and the sealing strip in the running process, the fluctuation of a power supply system and the manufacturing error of the car window; the peak data is usually generated when a foreign object exists in the window frame or the friction strip is largely deformed or the automobile is violently bumpy.
Because the current is the information source for detecting the stress of the car window, the accuracy of the stress condition in the clamping identification can be influenced by the two irregular conditions, the influence on the car window state identification is larger, the complexity of a car window control algorithm can be increased, and the reliability of the control is reduced. Because the anti-pinch judgment is only needed to be carried out in the ascending process of the car window, the current information is processed in real time in the ascending process of the car window, the fluctuation of the current signal is restrained, the peak is removed, the missing data is reconstructed, and the smoother current change trend is extracted, as shown in fig. 7.
In the steps of S31 and S32, the fluctuation of current information can be eliminated in real time through smoothing processing in the running process of the car window, peak data are further removed, an accurate and reliable data source is provided for the state identification of the car window, and the method is a precondition and a necessary basis for developing an anti-pinch electric car window system.
Specifically, in the step of S31, since the sawtooth fluctuation of the current exists throughout the window running, the problem is first solved. Taking into account historical data I in real-time processingMIs limited, and a smooth track I 'is obtained by adopting a moving average method'M＝{i′m}(m＝1，2，…，M)。I′MOf latest data i'MThe calculation is shown in equation (14).
Wherein lmThe actual demand is 5, which is an actual empirical value. lmIf the value is too large, the calculated amount is large i'MThe hardware of the car window can quickly respond only by calculating in a short time; lmToo small a value, the smoothing effect is not good. Each time obtaining a new smoothed value i'MPost, real-time to i'MAnd (6) updating. Smooth track i'MThe generation is shown in fig. 8. When i'MIn the filling state, the first four elements are directly put into i 'without calculation due to the missing of calculation data'M. Thereafter, each time new current information data i (t) is obtainedk) That is, a smoothed value i 'is calculated according to the formula (14)'mAnd push it into I'M. Track I 'to be smoothed'MAnd after the filling of all the elements is completed, entering a circulation state. According to the meaning of moving average, the average value corresponds to the time of calculating the midpoint of the data, i'mShould be im-2Corresponding to the smoothed value at the time, therefore smoothing the trajectory I'MRelative to IMThere is a delay of 2 at, which problem will be addressed in the following of the present application.
In step S32, history array I is utilizedMAfter calculating the smoothed trajectory of the obtained current, consider the smoothed trajectory I'MWith respect to the original data IMCalculating the sampled value i (t)k) Deviation value d from smooth trajectorykAs shown in equation (15).
Deviation value dkThe range data is spike data. As can be seen from fig. 5, the current changes more drastically during the initial motor start-up of the window lift, but deviates from the value d during the subsequent steady operationkShould be within a reasonable range. Consider measuring current information i (t) using this rangek) To identify i (t)k) Whether spike data. The current information I is first segmented as shown in fig. 9.
The time length of the starting stage is q delta t, the time length of the starting stage is different according to different actual electrodes, when the electrodes are started, a current peak exists, actual calibration measurement is needed, the time length of the starting stage is obtained, and the time lengths of the starting stages of motors of different models are different. In the stage, the rationality judgment is not carried out on the current information, and d does not need to be calculatedk. From tqInitially, calculate the deviation value of the following lambda data and get tλAt the beginning, every time the current information i (t) is collectedk) Then, the mean value D of the deviation values of the previous (k-q) points is calculatedkAs shown in formula (16).
Reasonably determining the number of sampling points in the accumulation stage to consider DkReflect i (t)k) Deviation value d ofkStatistically reasonable variation range, defined as cumulative deviation, to prevent direct use of DkTo generate false judgment, D pair is requiredkWeighting to take aDkAs dkWherein a > 1. When d iskWhen the formula (17) is satisfied, i (t) at this time is considered to bek) Possibly a spike.
dk＞aDk(17)
In consideration of the calculation capability of the controller single chip microcomputer, it is difficult to complete the calculation shown in the formula (16) within the time Δ t, and therefore the formula (16) is changedForm, using iterative method to calculate DkSuch as formula (18)
As shown. By saving and updating the accumulated deviation DkAnd the calculation amount of each time is reduced.
The current increases significantly from near top dead centre to the final stop at the stop, so that d is calculated in this segmentkMay be greater than the set reasonable upper limit aDkHowever, the information at this stage is characteristic information that inevitably appears when the window is in normal operation, and should not be removed. It is intuitive and visible that when the current has a peak, the number of sampling points is small, and the current sampling value i (t) isk) Exhibit rapid increases and decreases; but are dense in samples, i (t), near top dead centerk) Continues to increase. The pause condition for setting spike elimination is thus as shown in equation (19).
Φ＝0(sign[i(tx+1)-i(tx)])＝1，x∈[k，k+l]，k＞q) (19)
On the premise that equation (17) is satisfied, when Φ is 0, the removal of the spike is temporarily stopped; when the current information does not satisfy the continuous rising condition of equation (19), Φ becomes 1, and at this time, spike elimination can be performed. The maximum interference duration I Δ t is determined by using I, and if the value is too small, the wider peak removing capability is weakened, but if the value is too large, the larger judgment delay is caused, and the maximum interference duration I Δ t needs to be reasonably set according to the actual I Δ t and the sampling interval Δ t.
From the above analysis, a peak point identification condition epsilon (i (t) of the current information can be summarizedk) Is represented by the formula (20)
For the acquired raw current information i (t)k) If it satisfies epsilon (i (t)k) 1) is considered as abnormal information caused by an external disturbance, and needs to be discarded without being stored in the history array IMAnd (4) the following steps.
As shown in fig. 10, the step of S43 repairing the missing data after removing the spike data in the current information includes:
s431: constructing a data prediction model by using original current information before smoothing as a data basis and adopting a quadratic exponential smoothing method;
s432: and replacing the eliminated peak data in the current information of the historical array window state sequence information by taking the prediction result of the data prediction model as the repair data.
After the peak data is removed, the missing data needs to be repaired to maintain the consistency of the data record. In order to retain more information and improve real-time performance, the data for performing the patch calculation is based on the original current information IMBut not smooth current information i'M. In order to reduce the calculation amount and fully utilize the short-term variation trend of the data, a data prediction model is constructed for prediction in a quadratic exponential smoothing mode, and the prediction result is used as a repair value, namely repair data. For the identified spike data i (t)k) Which patches data i* kThe calculation is shown in equation (21).
i* k＝ζe+ξer (21)
Where r is the number of prediction look-ahead periods, equal to the base data to patch data i* kTime series number difference of (1). ZetaeAnd ξeIs an intermediate parameter variable, and is calculated as shown in formulas (22) and (23)
Wherein
Is the first exponential smoothing value of the k-r period,
the second exponential smoothing value in the k-r period, α is the smoothing coefficient, α ∈ (0, 1).
And
is calculated as shown in equations (24) and (25).
In consideration of the rapidity of the repair, r is 1, α is 0.6, the change of the current curve is predicted mainly by using the latest data, and the repair data i is* kI filling into the history array as new current informationMAfter that, the replacement of the original spike data is realized.
Further, after the step of S4 repairing the window state sequence information of the history array in real time, the method further includes:
s5: verifying the repair effect of the vehicle window state sequence information of the real-time repair history array: and under the condition of the same window running, acquiring current information of the window state sequence information within a certain time, and comparing the current information with the current information of the window state sequence information before real-time repair to obtain the fluctuation improvement condition of the current after real-time repair.
From the above analysis, a complete section of history array I collected after the window is startedMAfter the occurrence of the spike data, the complete repair process can be described as shown in fig. 11, and in other cases, the repair process is only smoothed as shown in fig. 8.
Two kinds of main irregular fluctuation of the current can be solved by continuously smoothing the current information and eliminating peaks in the acquisition process. After the real-time repairing method is adopted, the acquired current information is shown in fig. 12(a) under the condition that the window operation condition is not changed.
As can be seen from fig. 12(a), the trend of the current information is more significant after the real-time restoration is performed. As shown in fig. 12(b), it can be seen that the repair process significantly suppresses current fluctuation, eliminates peaks, improves curve smoothness, and provides a reliable current data source for subsequent state identification and calculation.
Further, the S4 window state sequence information includes current information and window position information, and the step of repairing the window state sequence information of the history array in real time further includes:
s6: and matching the current information and the window position information at the same moment.
When real-time processing is carried out on the current historical array, certain processing methods need the current i (t)k) The generated result does not represent the current time in engineering but the state of a certain historical time. This results in a temporal offset of the window position information from the processed current information. Information describing the state at the same time needs to be paired to guarantee the state point z (t)k) The various information in (1) are unified in time. Defining the current history array subjected to the complete restoration treatment as a rehabilitation current IQAs shown in equation (26).
IQ{qm}(m＝1，2，···，M) (26)
IQWill substitute IMAnd is combined with PMConstitute the historical information source that carries out window state identification and some data calculation. Due to the moving average processing, qmRelative to imThere is a delay of 2 at. Position P of vehicle windowMThe data is not processed and can be used as a reference for time sequence pairing. The process of eliminating the information misalignment caused by this delay is shown in fig. 13.
At PMAnd IQAll having entered the circulation state, I is first introducedQShifting q by 2 Δ t along time axismAnd pm-2Are matched, andlatest element q that will achieve a matchMAnd pM-2As the information of the current time k. According to l'MIn the generation process of (3), it can be seen that the first q is obtained when k is 5MThe window state point can be rewritten into a form expressed by the history array element as shown in formula (27), thereby generating z (t)k) Is a nominal current time state point, but its state information actually corresponds to the time k-2, i.e. it is relative to the real time tkThere is a 2 at delay.
z(tk)＝(qM，pM-2)(k＝5，6，…，N) (27)
The information that cannot be aligned after translation is mismatch information at that time, and cannot be used to generate a state point. Carry out the position information PMAnd a healing current IQAfter the time of (c) is matched, 2 data are in the mismatched section, when k > M +4, P isMAnd IQWhen the system is in a circulation state, the number of state points at a certain moment is reduced from M to M-2. By choosing a suitable value of M, this effect can be neglected. :
it should be noted that, the limitations of each step in the present disclosure are not considered to limit the order of the steps without affecting the implementation of the specific embodiments, and the steps written in the foregoing may be executed first, or executed later, or even executed simultaneously, and as long as the present disclosure can be implemented, all the steps should be considered as belonging to the protection scope of the present application.
The foregoing is a more detailed description of the present application in connection with specific alternative embodiments, and the specific implementations of the present application are not to be considered limited to these descriptions. For those skilled in the art to which the present application pertains, several simple deductions or substitutions may be made without departing from the concept of the present application, and all should be considered as belonging to the protection scope of the present application.
Claims (7)
1. The processing method for the anti-pinch power window state information is characterized by comprising the following steps of:
collecting and storing vehicle window state sequence information;
processing the car window state sequence information to generate a historical array; and
and repairing the window state sequence information of the historical array in real time.
2. The method for processing anti-pinch power window state information according to claim 1, wherein the window state sequence information comprises current information and window position information, and the step of repairing the window state sequence information of the historical array in real time comprises the following steps of:
smoothing current information of historical array vehicle window state sequence information;
eliminating peak data in the smoothed current information; and
and repairing the data missing after the peak data in the current information is removed.
3. The method of claim 2, wherein the step of repairing the missing data after the peak data in the current information is removed comprises:
constructing a data prediction model by using original current information before smoothing as a data basis and adopting a quadratic exponential smoothing method; and
and replacing the eliminated peak data in the current information of the historical array window state sequence information by taking the prediction result of the data prediction model as the repair data.
4. The method of claim 3, wherein the repair data i is a value obtained by processing the status information of the power window against pinching* kThe calculation formula of (2) is as follows:
i* k＝ξe+ξer
wherein r is the number of prediction look-ahead periods equal to the number of base data to patch data i* kξ time series number differenceeAnd ξeIs an intermediate parameter variable.
5. The method for processing anti-pinch power window state information according to claim 1, wherein the step of processing the window state sequence information and generating the history array further comprises the following steps:
updating the history array: and receiving the currently collected vehicle window state sequence information, and rejecting the old vehicle window state sequence information to complete the recording of the window state sequence information.
6. The method for processing anti-pinch power window state information according to claim 1, wherein the step of repairing the window state sequence information of the historical array in real time further comprises the following steps:
verifying the repair effect of the vehicle window state sequence information of the real-time repair history array: and under the condition of the same window running, acquiring current information of the window state sequence information within a certain time, and comparing the current information with the current information of the window state sequence information before real-time repair to obtain the fluctuation improvement condition of the current after real-time repair.
7. The method for processing anti-pinch power window state information according to claim 1, wherein the window state sequence information comprises current information and window position information, and the step of repairing the window state sequence information of the historical array in real time further comprises the following steps:
and matching the current information and the window position information at the same moment.
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