CN114112437A - Vehicle window data calibration method, device, equipment and storage medium - Google Patents
Vehicle window data calibration method, device, equipment and storage medium Download PDFInfo
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- CN114112437A CN114112437A CN202111456402.9A CN202111456402A CN114112437A CN 114112437 A CN114112437 A CN 114112437A CN 202111456402 A CN202111456402 A CN 202111456402A CN 114112437 A CN114112437 A CN 114112437A
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Abstract
The invention relates to the technical field of vehicle control, and discloses a vehicle window data calibration method, a device, equipment and a storage medium, wherein the method comprises the following steps: calculating target window full-lifting data through a preset mean value calculation strategy, calculating window lifting travel data according to the waveform number of the vehicle through a preset edge number calculation strategy, determining target anti-pinch operation times according to the target window half-lifting data, and calculating the target window half-lifting data and the target anti-pinch operation times through a preset travel limit calculation strategy; the window lift stroke data, the regional edge data of window, the operation number of times and the door window restriction stroke of preventing pressing from both sides of target are set for respectively, and the automatic lift glass of window detection target through setting for data compares in prior art and marks the window through the relatively poor data of stability and detects, can effectively improve the accuracy of demarcating the window data, and then reduces the monomer of door state and the risk that the difference leads to window lift function to be unusual in batches.
Description
Technical Field
The invention relates to the technical field of vehicle control, in particular to a vehicle window data calibration method, device, equipment and storage medium.
Background
With the rapid development of vehicle technology, the popularity of vehicles is also rapidly increasing, so that the vehicles become more important vehicles in people's lives, the vehicles can effectively improve driving comfort by applying electronic equipment, for example, a power window, the power window controls the ascending and descending of the window through a motor controller, and has the advantages of simultaneously operating a plurality of windows and remote control, the window control is realized based on calibration data, the currently calibrated data cannot represent all data, the stability of the data is poor, and particularly, the abnormal glass lifting condition occurs in the process of controlling the window due to the influence of single vehicle door state or batch difference, for example, the window stops when not being closed to the top or automatically ascends to the top and then reverses, and the use of customers is influenced.
The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.
Disclosure of Invention
The invention mainly aims to provide a vehicle window data calibration method, a vehicle window data calibration device, vehicle window data calibration equipment and a storage medium, and aims to solve the problem that the vehicle window calibration accuracy is low and the vehicle window is easily influenced by single vehicle door state or batch difference, so that the vehicle window lifting function is abnormal.
In order to achieve the above object, the present invention provides a vehicle window data calibration method, which includes the following steps:
acquiring current window lifting data of a vehicle, and acquiring target window half-lifting data and target window full-lifting data according to the current window lifting data;
calculating the target vehicle window full-lifting data through a preset mean value calculation strategy to obtain vehicle window lifting travel data;
calculating the window lifting travel data according to the waveform number of the vehicle by using a preset edge number calculation strategy to obtain window area edge data;
determining the target anti-pinch operation times according to the target window half-lifting data;
calculating the target window half-lifting data and the target anti-pinch operation times through a preset stroke limit calculation strategy to obtain a window limit stroke;
and respectively setting the vehicle windows of the vehicle according to the vehicle window lifting stroke data, the vehicle window area edge data, the target anti-pinch operation times and the vehicle window limiting stroke, and detecting the target automatic lifting glass through the set vehicle windows.
Optionally, the obtaining current window lifting data of the vehicle, and obtaining target window half-lifting data and target window full-lifting data according to the current window lifting data, includes:
acquiring current window lifting data of a vehicle, and performing mean value calculation on the current window lifting data to obtain current mean value data;
calculating the current mean value data and the current car window lifting data through a preset variance calculation strategy to obtain current front difference data;
and when the current variance data is less than or equal to a preset data threshold value, obtaining target window half-lifting data and target window full-lifting data according to the current window lifting data.
Optionally, after the current mean value data and the current window lifting data are calculated through a preset variance calculation strategy to obtain current variance data, the method further includes:
when the front difference data is larger than a preset data threshold value, grouping the current car window lifting data to obtain a corresponding group data set;
calculating the occurrence probability of the group data and the mean value of the group data set;
fitting the occurrence probability of the group data and the group data mean value to obtain a target mean value probability curve;
determining a value range of the vehicle window lifting data according to the target mean value probability curve;
and screening the current car window lifting data through the car window lifting data value range to obtain target car window half-lifting data and target car window full-lifting data.
Optionally, the calculating the window lifting travel data according to the waveform number of the vehicle by using a preset edge number calculation strategy to obtain window area edge data includes:
extracting a first data group mean value and a second data group mean value of the occurrence probability of the group data;
obtaining a data mean value difference value according to the first data group mean value and the second data group mean value;
performing difference calculation on the data mean difference value and the car window lifting travel data to obtain edge offset;
and summing the edge offset according to the waveform number of the vehicle by a preset edge number calculation strategy to obtain the edge data of the vehicle window area.
Optionally, the determining a target anti-pinch operation number according to the target window half-lift data includes:
extracting the frequency data in the target vehicle window half-lifting data;
constructing a target rectangular coordinate system according to the car window lifting travel data, the travel data and the number data;
obtaining current stroke anti-pinch data according to the target rectangular coordinate system, the car window lifting stroke data and the stroke data;
and determining the target anti-pinch operation times through preset safety margin data, current travel anti-pinch data and vehicle window edge data.
Optionally, the target window half-lift data and the target anti-pinch operation times are calculated through a preset stroke limit calculation strategy to obtain a window limit stroke, including:
extracting stroke data and target lifting data in the target vehicle window half-lifting data;
determining target travel anti-pinch data according to the travel data and the target anti-pinch operation times;
generating a target limit travel judgment range according to the target travel anti-pinch data, the vehicle window lifting travel data and the target lifting data;
and obtaining the vehicle window limited travel according to the target limited travel judgment range.
Optionally, the target limit stroke determination range includes an ascending limit stroke determination range;
according to target stroke anti-pinch data, window lift stroke data and target lift data generate target limit stroke decision range, include:
performing difference calculation on the target travel anti-pinch data and the vehicle window lifting travel data to obtain a first lifting range;
obtaining a second lifting range according to the car window lifting travel data and the target lifting data;
and generating an ascending limit travel judgment range according to the first ascending range, the second ascending range and the historical ascending limit travel judgment range.
Optionally, the target limit stroke determination range includes a descent limit stroke determination range;
according to target stroke anti-pinch data, window lift stroke data and target lift data generate target limit stroke decision range, include:
obtaining a first descending range according to the target travel anti-pinch data;
obtaining a second descending range according to the target ascending and descending data;
and generating a descending limit travel judgment range according to the first descending range, the second descending range and the historical descending limit travel judgment range.
In addition, in order to achieve the above object, the present invention further provides a vehicle window data calibration apparatus, including:
the acquisition module is used for acquiring current window lifting data of the vehicle and acquiring target window half-lifting data and target window full-lifting data according to the current window lifting data;
the mean value calculation module is used for calculating the target car window full-lifting data through a preset mean value calculation strategy to obtain car window lifting travel data;
the edge number calculation module is used for calculating the car window lifting travel data according to the waveform number of the car through a preset edge number calculation strategy to obtain car window area edge data;
the determining module is used for determining the target anti-pinch operation times according to the target window half-lifting data;
the thread limit calculation module is used for calculating the target vehicle window half-lifting data and the target anti-pinch operation times through a preset stroke limit calculation strategy to obtain a vehicle window limit stroke;
and the calibration module is used for respectively setting the vehicle windows of the vehicle according to the vehicle window lifting stroke data, the vehicle window area edge data, the target anti-pinch operation times and the vehicle window limiting stroke, and detecting the target automatic lifting glass through the set vehicle windows.
In addition, in order to achieve the above object, the present invention further provides a vehicle window data calibration apparatus, including: the vehicle window data calibration system comprises a memory, a processor and a vehicle window data calibration program stored on the memory and capable of running on the processor, wherein the vehicle window data calibration program is configured to realize the vehicle window data calibration method.
In addition, in order to achieve the above object, the present invention further provides a storage medium, which stores a window data calibration program, and when the window data calibration program is executed by a processor, the window data calibration program implements the window data calibration method as described above.
According to the vehicle window data calibration method, the target vehicle window full-lifting data is calculated through a preset mean value calculation strategy, the vehicle window lifting travel data is calculated according to the waveform number of a vehicle through a preset edge number calculation strategy, the target anti-pinch operation times are determined according to the target vehicle window half-lifting data, and the target vehicle window half-lifting data and the target anti-pinch operation times are calculated through a preset travel limit calculation strategy; the window lift stroke data, the regional edge data of window, the operation number of times and the door window restriction stroke of preventing pressing from both sides of target are set for respectively, and the automatic lift glass of window detection target through setting for data compares in prior art and marks the window through the relatively poor data of stability and detects, can effectively improve the accuracy of demarcating the window data, and then reduces the monomer of door state and the risk that the difference leads to window lift function to be unusual in batches.
Drawings
FIG. 1 is a schematic structural diagram of a vehicle window data calibration device of a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a first embodiment of a vehicle window data calibration method according to the present invention;
FIG. 3 is a schematic diagram of data calibration detection according to an embodiment of the vehicle window data calibration method of the present invention;
FIG. 4 is a schematic flow chart illustrating a second embodiment of a vehicle window data calibration method according to the present invention;
FIG. 5 is a schematic diagram of a target mean probability curve according to an embodiment of the vehicle window data calibration method of the present invention;
FIG. 6 is a schematic flow chart illustrating a third exemplary embodiment of a vehicle window data calibration method according to the present invention;
fig. 7 is a functional module schematic diagram of the first embodiment of the vehicle window data calibration device.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a vehicle window data calibration device in a hardware operating environment according to an embodiment of the present invention.
As shown in fig. 1, the window data calibration apparatus may include: a processor 1001, such as a Central Processing Unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a Wireless interface (e.g., a Wireless-Fidelity (Wi-Fi) interface). The Memory 1005 may be a Random Access Memory (RAM) Memory, or may be a Non-Volatile Memory (NVM), such as a disk Memory. The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the configuration shown in fig. 1 does not constitute a limitation of the window data calibration apparatus and may include more or fewer components than those shown, or some components in combination, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is a storage medium, may include therein an operating system, a network communication module, a user interface module, and a window data calibration program.
In the vehicle window data calibration device shown in fig. 1, the network interface 1004 is mainly used for data communication with the network integrated platform workstation; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 of the vehicle window data calibration device may be arranged in the vehicle window data calibration device, and the vehicle window data calibration device calls the vehicle window data calibration program stored in the memory 1005 through the processor 1001 and executes the vehicle window data calibration method provided by the embodiment of the invention.
Based on the hardware structure, the embodiment of the vehicle window data calibration method is provided.
Referring to fig. 2, fig. 2 is a schematic flow chart of a vehicle window data calibration method according to a first embodiment of the present invention.
In a first embodiment, the vehicle window data calibration method includes the following steps:
and step S10, acquiring current window lifting data of the vehicle, and acquiring target window half lifting data and target window full lifting data according to the current window lifting data.
It should be noted that, the execution subject of the present embodiment is a vehicle window data calibration device, and may also be other devices that can achieve the same or similar functions, such as a vehicle, and the present embodiment is not limited to this, and in the present embodiment, a vehicle is taken as an example for description.
It should be understood that the current window lift data refers to data when the glass of the vehicle of the test target number is lifted, and the current window lift data includes all data during the lifting or lowering process of the glass, for example, data that the glass stops without being lifted to the top, the glass continues to be lifted after being lifted to the top, the glass automatically descends after being lifted to the top, and the like, and the current window lift data is 3 bytes of data, which are respectively: the 1 st byte data indicates the data type, the 2 nd byte data indicates the position data, and the 3 rd byte data indicates the number of operations.
It is understood that the target window full-lift data refers to data of which the glass is completely lifted from the bottom to the top of the window at one time or completely lowered from the top to the bottom of the window at one time, the data category of the target window full-lift data is 01, the target window half-lift data refers to data of which the glass is not completely lifted from the bottom to the top of the window at one time or is not completely lowered from the top to the bottom of the window at one time, and the data category of the target window half-lift data is 02.
And step S20, calculating the target window full-lifting data through a preset mean value calculation strategy to obtain window lifting travel data.
It should be understood that after the target window full-lifting data is obtained, the target window full-lifting data is calculated through a preset mean value calculation strategy, and window lifting travel data is obtained, for example, the data quantity of the current window lifting data is N, i is a data number, then the window lifting travel data S,
and step S30, calculating the vehicle window lifting travel data according to the waveform number of the vehicle by a preset edge number calculation strategy to obtain the vehicle window area edge data.
It can be understood that the window area edge data refers to the number of edges of the anti-pinch function area of the glass in the ascending directionSpecifically, the vehicle window lifting travel data are obtained by calculating the vehicle window lifting travel data through a preset edge number calculation strategy and the waveform number of the vehicle, the waveform number corresponds to the downward target distance of the upper edge of the rubber strip, and when the target distance is 4mm, the corresponding waveform number is X4mm。
Further, step S30 includes: extracting a first data group mean value and a second data group mean value of the occurrence probability of the group data; obtaining a data mean value difference value according to the first data group mean value and the second data group mean value; performing difference calculation on the data mean difference value and the car window lifting travel data to obtain edge offset; and summing the edge offset according to the waveform number of the vehicle by a preset edge number calculation strategy to obtain the edge data of the vehicle window area.
It should be understood that the group data occurrence probability refers to the probability of occurrence of each data group in the traversal process after grouping, the first data group is the group with the highest occurrence probability, the second data group is the group with the lowest occurrence probability, the group data occurrence probability is determined by the number of occurrences of the group and the total number of the groups, the first data group mean refers to the average value of the data in the first data group, and similarly, the second data group mean refers to the average value of the data in the second data group, and the data mean difference value is determined according to the first data group mean value and the second data group mean value, since in an ideal state, the window area edge data is only related to the waveform number, that is, the window area edge data B is 1/2X4mmIn practical cases, the edge offset needs to be considered, and the edge offset is obtained by difference calculation based on the data mean difference and the vehicle window lifting travel data, and specifically includes: offset L is vehicle window lifting travel data S-data mean difference E (X)P(Xj)MAX)-E(XP(Xj)MIN) And further calculating window area edge data B which is 1/2X through a preset edge number calculation strategy4mm+L＝1/2X4mm+S-(E(XP(Xj)MAX)-E(XP(Xj)MIN))。
And step S40, determining the target anti-pinch operation times according to the target window half-lift data.
The target window half-lifting data are used for determining the target anti-pinch operation times.
Further, step S40 includes: extracting the frequency data in the target vehicle window half-lifting data, and constructing a target rectangular coordinate system according to the vehicle window lifting travel data, the travel data and the frequency data; obtaining current stroke anti-pinch data according to the target rectangular coordinate system, the car window lifting stroke data and the stroke data; and determining the target anti-pinch operation times through preset safety margin data, current travel anti-pinch data and vehicle window region edge data.
It should be understood that the target number of anti-pinch operations refers to the maximum number of operations allowed to prevent pinching in the case of a glass not reaching the top, and the target number of anti-pinch operations is set because the maximum number of operations affects the cumulative error amount that can be accommodated at the maximum, and exceeding the cumulative error amount that can be accommodated at the maximum causes the glass to reverse or not rise to the top.
It can be understood that after the target window half-lift data is obtained, the number data in the target window half-lift data is extracted as the abscissa Z of the rectangular coordinate systemiThe window lifting travel data and the travel data are subjected to difference making, and the absolute value of the difference making data is used as a vertical coordinate Y of a rectangular coordinate systemiS, constructing a target rectangular coordinate system through the horizontal coordinate and the vertical coordinate, and then enabling YiObtaining maximum anti-pinch operation times and minimum anti-pinch operation times by taking into account preset safety margin data, wherein the preset safety margin data can be 20%, and obtaining target anti-pinch operation times D as minimum anti-pinch operation times Ze*80％。
And step S50, calculating the target window half-lifting data and the target anti-pinch operation times through a preset stroke limit calculation strategy to obtain a window limit stroke.
It should be understood that the window limit stroke refers to a stroke range in which the glass is limited in the ascending or descending direction, and the window limit stroke includes an ascending limit stroke and a descending limit stroke.
In the specific implementation, after the target window half-lifting data and the target anti-pinch operation times are obtained, the target window half-lifting data and the target anti-pinch operation times are calculated through a preset stroke limit calculation strategy, and the window limit stroke is obtained.
And step S60, respectively setting the windows of the vehicle according to the window lifting travel data, the window area edge data, the target anti-pinch operation times and the window limiting travel, and detecting the target automatic lifting glass through the set windows.
It can be understood that, after obtaining door window lift stroke data, the regional marginal data of door window, operation number of times and door window restriction stroke are prevented pressing from both sides to the target, with door window lift stroke data, the regional marginal data of door window, operation number of times and door window restriction stroke are prevented pressing from both sides to the target are set up respectively in the door window of vehicle, the automatic lift function of door window detection glass after the rethread was set for this moment, refer to fig. 3, fig. 3 is the door window after demarcating regional marginal data of door window and the door window restriction stroke, a judges the scope for the ascending restriction stroke, B is the regional marginal data of door window, S is door window lift stroke data, C judges the scope for the descending restriction stroke, divide into 6 kinds of circumstances with whole testing result, be condition 1 respectively: when the glass position is detected to exceed 0 point A waveforms, the glass can continuously rise, the driving is stopped, and the vehicle window position fault and the learning sign fault code are reported; case 2: when the glass position is detected to exceed 0 point but be less than A waveforms, the locked rotor is generated, and the position is directly reset to 0 point; case 3: when the glass position is detected to be less than 0 point and the waveform number is less than B, locked rotor occurs, and the glass position is directly reset to 0 point; case 4: when the glass position is detected to be less than 0 point and the waveform number is larger than B, the anti-pinch is triggered; case 5: when the fact that the glass position exceeds the bottommost position after calibration and self-learning but does not exceed C-S waveforms is detected, locked rotor occurs, and abnormality cannot be triggered at this time; case 6: and when the glass position is detected to be not locked after exceeding the C-S waveforms at the calibration and self-learning bottommost position, stopping driving, and reporting the position fault of the car window and learning the mark fault code.
In the embodiment, the target window full-lifting data is calculated through a preset mean value calculation strategy, the window lifting travel data is calculated according to the waveform number of the vehicle through a preset edge number calculation strategy, the target anti-pinch operation times are determined according to the target window half-lifting data, and the target window half-lifting data and the target anti-pinch operation times are calculated through a preset travel limit calculation strategy; the window lift stroke data, the regional edge data of window, the operation number of times and the door window restriction stroke of preventing pressing from both sides of target are set for respectively, and the window detects the automatic lift glass of target through the window of setting for data, compares in prior art and marks the window through the relatively poor data of stability and detects, can effectively improve the accuracy of demarcating the window data, and then reduces the monomer of door state and the unusual risk of window lift function that the difference leads to in batches.
In an embodiment, as shown in fig. 4, a second embodiment of the vehicle window data calibration method according to the present invention is proposed based on the first embodiment, and the step S10 includes:
and S101, acquiring current window lifting data of the vehicle, and carrying out mean value calculation on the current window lifting data to obtain current mean value data.
It should be understood that the current mean data refers to mean data of current window lift data, and when the current window lift data of the vehicle is obtained, the calculation is performed through a mean formula, specifically:
wherein N is the data quantity of the current car window lifting data, i is the data number,
for current mean data, XiRefers to the current window lift data.
And S102, calculating the current mean value data and the current window lifting data through a preset variance calculation strategy to obtain current front difference data.
It can be understood that the preset variance calculation strategy refers to a strategy for calculating data variance, and after the current mean data and the current window lifting data are obtained, the current front variance data is calculated through the preset variance calculation strategy, specifically:
wherein the content of the first and second substances,
for current mean data, XiThe window lifting data are referred to, and N is the data quantity of the current window lifting data.
Further, after step S102, the method further includes: when the front difference data is larger than a preset data threshold value, grouping the current car window lifting data to obtain a corresponding group data set; calculating the occurrence probability of the group data and the mean value of the group data set; fitting the occurrence probability of the group data and the group data mean value to obtain a target mean value probability curve; determining a value range of the vehicle window lifting data according to the target mean value probability curve; and screening the current car window lifting data through the car window lifting data value range to obtain target car window half-lifting data and target car window full-lifting data.
It should be understood that when the current variance data is greater than the preset data threshold, it indicates that the current car window lifting data at the moment is unstable, and to avoid car door batch difference caused by unstable car window lifting data, the current car window lifting data are grouped according to grouping logic, which is j groups in total, and a group data set is composed of j groups of lifting data, where the grouping logic is Xj＝Xmin+5 × j, calculating the probability of each group of data in the group data set to obtain the probability of occurrence of the group data, calculating the average value of the group data, fitting the probability of occurrence of the group data and the average value of the group data to obtain a target average value probability curve, and screening the current window lifting data according to the window lifting data value range determined by the target average value probability curve to obtain the target window lifting dataAnd the standard window half-lifting data and the target window full-lifting data.
Referring to FIG. 5, FIG. 5 is a schematic diagram of a target mean probability curve corresponding to a function of
In this case, only 90% of the function is needed, i.e.
And Xb-XcX corresponding to the minimum valuecAnd XbThat is, the window lifting data value range determined by the target mean probability curve is [ X ]b,Xc]And at the moment, the current car window lifting data is screened through the car window lifting data value range to obtain the value range [ X ] satisfying the car window lifting data value rangeb,Xc]The lifting data of (1), namely target window half lifting data and target window full lifting data.
And S103, when the current variance data is smaller than or equal to a preset data threshold value, obtaining target window half-lifting data and target window full-lifting data according to the current window lifting data.
It is understood that the preset data threshold refers to a theoretical data stability minimum value, and is a value defined according to a vehicle door dimension error, and the preset data threshold may be 100, or may be other data thresholds, depending on the vehicle door dimension error, and this embodiment does not limit this, and is described by taking 100 as an example. After the current front difference data are obtained, whether the current front difference data are larger than a preset data threshold value or not is judged, if the current front difference data are smaller than or equal to the preset data threshold value, the current car window lifting data are stable, the current car window lifting data are all available at the moment, and the target car window half lifting data and the target car window full lifting data are obtained according to the current car window lifting data.
According to the embodiment, the current window lifting data of the vehicle is obtained, and the mean value of the current window lifting data is calculated to obtain the current mean value data; calculating the current mean value data and the current car window lifting data through a preset variance calculation strategy to obtain current front difference data; when the current variance data is smaller than or equal to a preset data threshold value, obtaining target window half-lifting data and target window full-lifting data according to the current window lifting data; according to the method, the mean value of the current window lifting data is calculated, then the current mean value data is calculated according to the preset variance calculation strategy, whether the calculated current variance data is smaller than or equal to the preset data threshold value or not is judged, if yes, the target window half-lifting data and the target window full-lifting data are obtained based on the current window lifting data, and therefore the accuracy of obtaining the target window half-lifting data and the target window full-lifting data can be effectively improved.
In an embodiment, as shown in fig. 6, a third embodiment of the vehicle window data calibration method according to the present invention is proposed based on the first embodiment, and the step S50 includes:
and S501, extracting stroke data and target lifting data in the target window half-lifting data.
It can be understood that the stroke data refers to stroke data of the glass moving in the ascending or descending process, the target ascending and descending data refers to maximum ascending and descending data in the target window half-ascending and descending data, specifically, after the target window half-ascending and descending data is obtained, the stroke data of the target window half-ascending and descending data is extracted, the target window half-ascending and descending data is sorted according to the sequence from large to small, and the target ascending and descending data is extracted from the sorted target window half-ascending and descending data.
And step S502, determining target travel anti-pinch data according to the travel data and the target anti-pinch operation times.
It should be understood that the target trip anti-pinch data refers to trip data of the number of times of target anti-pinch operations, i.e. the trip data is Y, the number of times of target anti-pinch operations is D, and the target trip anti-pinch data is YD。
And S503, generating a target limit travel judgment range according to the target travel anti-pinch data, the car window lifting travel data and the target lifting data.
It can be understood that after the target travel anti-pinch data, the window lifting travel data and the target lifting data are obtained, a target limited travel judgment range is determined jointly according to the target travel anti-pinch data, the window lifting travel data and the target lifting data, and the target limited travel judgment range comprises an ascending limited travel judgment range and a descending limited travel judgment range.
Further, step S503 includes: performing difference calculation on the target travel anti-pinch data and the vehicle window lifting travel data to obtain a first lifting range; obtaining a second lifting range according to the car window lifting travel data and the target lifting data; and generating an ascending limit travel judgment range according to the first ascending range, the second ascending range and the historical ascending limit travel judgment range.
It should be appreciated that after the target trip anti-pinch data and the window lift trip data are obtained, the target trip anti-pinch data and the window lift trip data are subtracted to obtain a first ascending range, for example, the first ascending range is A1, and the target trip anti-pinch data is YDIf the window lift stroke data is S, a1 is S-YDA second ascending range is obtained by the window ascending and descending stroke data and the target ascending and descending data, for example, the second ascending range is a2, and the target ascending and descending data is XMAXThen a2 ═ S-XMAX) 120%, wherein the historical rise limit travel judgment range is a judgment range determined according to the historical glass rise data, specifically-S5%, and the rise limit travel judgment range is A ≧ S5% and A<S-YDAnd A is<(S-XMAX)*120％。
Further, step S503 includes: obtaining a first descending range according to the target travel anti-pinch data; obtaining a second descending range according to the target ascending and descending data; and generating a descending limit travel judgment range according to the first descending range, the second descending range and the historical descending limit travel judgment range.
It is understood that the first drop range, i.e., the first drop range C1 — Y, is obtained from the target trip anti-pinch dataDAnd obtaining a second descending range according to the target ascending and descending data, namely a second descending range C2 being equal to XMAX120%, the historical descending limit travel judgment range is based on the historical glassA judgment range determined by the descending data is S x 105%, and the descending limit travel judgment range is C less than or equal to S x 105% and C>YDAnd C>XMAX*120％。
And step S504, obtaining the vehicle window limited stroke according to the target limited stroke judgment range.
It should be understood that after the target limited travel determination range is obtained, the window limited travel is determined according to the target limited travel determination range, and specifically, a value is selected from the target limited travel determination range as the window limited travel.
The embodiment extracts the stroke data and the target lifting data in the target window half-lifting data; determining target travel anti-pinch data according to the travel data and the target anti-pinch operation times; generating a target limit travel judgment range according to the target travel anti-pinch data, the vehicle window lifting travel data and the target lifting data; obtaining a vehicle window limited travel according to the target limited travel judgment range; because this embodiment is through the data of target stroke and target anti-pinch number of times of operation confirm target stroke anti-pinch data, then according to target stroke anti-pinch data, door window lift stroke data and target lift data generation target restriction stroke judge the scope, again according to target restriction stroke judge the scope select door window restriction stroke to can effectively improve the accuracy that obtains door window restriction stroke.
In addition, an embodiment of the present invention further provides a storage medium, where the storage medium stores a vehicle window data calibration program, and the vehicle window data calibration program, when executed by a processor, implements the steps of the vehicle window data calibration method described above.
Since the storage medium adopts all technical solutions of all the embodiments, at least all the beneficial effects brought by the technical solutions of the embodiments are achieved, and no further description is given here.
In addition, referring to fig. 7, an embodiment of the present invention further provides a vehicle window data calibration apparatus, where the vehicle window data calibration apparatus includes:
the acquiring module 10 is configured to acquire current window lifting data of a vehicle, and obtain target window half-lifting data and target window full-lifting data according to the current window lifting data.
It should be understood that the current window lift data refers to data when the glass of the vehicle of the test target number is lifted, and the current window lift data includes all data during the lifting or lowering process of the glass, for example, data that the glass stops without being lifted to the top, the glass continues to be lifted after being lifted to the top, the glass automatically descends after being lifted to the top, and the like, and the current window lift data is 3 bytes of data, which are respectively: the 1 st byte data indicates the data type, the 2 nd byte data indicates the position data, and the 3 rd byte data indicates the number of operations.
It is understood that the target window full-lift data refers to data of which the glass is completely lifted from the bottom to the top of the window at one time or completely lowered from the top to the bottom of the window at one time, the data category of the target window full-lift data is 01, the target window half-lift data refers to data of which the glass is not completely lifted from the bottom to the top of the window at one time or is not completely lowered from the top to the bottom of the window at one time, and the data category of the target window half-lift data is 02.
And the mean value calculating module 20 is used for calculating the target car window full-lifting data through a preset mean value calculating strategy to obtain car window lifting travel data.
It should be understood that after the target window full-lifting data is obtained, the target window full-lifting data is calculated through a preset mean value calculation strategy, and window lifting travel data is obtained, for example, the data quantity of the current window lifting data is N, i is a data number, then the window lifting travel data S,
and the edge number calculating module 30 is configured to calculate the window lifting travel data according to the waveform number of the vehicle by using a preset edge number calculating strategy to obtain the window area edge number.
It is understood that the window edge data refers to the edge of the functional area that the glass is protected from pinching in the upward directionThe edge data are obtained by calculating the lifting travel data of the car window through a preset edge number calculation strategy and the waveform number of the car, the waveform number corresponds to the downward target distance of the upper edge of the rubber strip, and when the target distance is 4mm, the corresponding waveform number is X4mm。
Further, the edge number calculating module 30 is further configured to extract a first data group mean value and a second data group mean value of the group data occurrence probability; obtaining a data mean value difference value according to the first data group mean value and the second data group mean value; performing difference calculation on the data mean difference value and the car window lifting travel data to obtain edge offset; and summing the edge offset according to the waveform number of the vehicle by a preset edge number calculation strategy to obtain the edge data of the vehicle window area.
It should be understood that the group data occurrence probability refers to the probability of occurrence of each data group in the traversal process after grouping, the first data group is the group with the highest occurrence probability, the second data group is the group with the lowest occurrence probability, the group data occurrence probability is determined by the number of occurrences of the group and the total number of the groups, the first data group mean refers to the average value of the data in the first data group, and similarly, the second data group mean refers to the average value of the data in the second data group, and the data mean difference value is determined according to the first data group mean value and the second data group mean value, since in an ideal state, the window area edge data is only related to the waveform number, that is, the window area edge data B is 1/2X4mmIn practical cases, the edge offset needs to be considered, and the edge offset is obtained by difference calculation based on the data mean difference and the vehicle window lifting travel data, and specifically includes: offset L is vehicle window lifting travel data S-data mean difference E (X)P(Xj)MAX)-E(XP(Xj)MIN) And further calculating window area edge data B which is 1/2X through a preset edge number calculation strategy4mm+L＝1/2X4mm+S-(E(XP(Xj)MAX)-E(XP(Xj)MIN))。
And the determining module 40 is used for determining the target anti-pinch operation times according to the target window half-lifting data.
The target window half-lifting data are used for determining the target anti-pinch operation times.
Further, the determining module 40 is further configured to extract frequency data in the target window half-lifting data, and construct a target rectangular coordinate system according to the window lifting travel data, the travel data, and the frequency data; obtaining current stroke anti-pinch data according to the target rectangular coordinate system, the car window lifting stroke data and the stroke data; and determining the target anti-pinch operation times through preset safety margin data, current travel anti-pinch data and vehicle window region edge data.
It should be understood that the target number of anti-pinch operations refers to the maximum number of operations allowed to prevent pinching in the case of a glass not reaching the top, and the target number of anti-pinch operations is set because the maximum number of operations affects the cumulative error amount that can be accommodated at the maximum, and exceeding the cumulative error amount that can be accommodated at the maximum causes the glass to reverse or not rise to the top.
It can be understood that after the target window half-lift data is obtained, the number data in the target window half-lift data is extracted as the abscissa Z of the rectangular coordinate systemiThe window lifting travel data and the travel data are subjected to difference making, and the absolute value of the difference making data is used as a vertical coordinate Y of a rectangular coordinate systemiS, constructing a target rectangular coordinate system through the horizontal coordinate and the vertical coordinate, and then enabling YiObtaining maximum anti-pinch operation times and minimum anti-pinch operation times by taking into account preset safety margin data, wherein the preset safety margin data can be 20%, and obtaining target anti-pinch operation times D as minimum anti-pinch operation times Ze*80％。
And the thread limit calculation module 50 is used for calculating the target vehicle window half-lifting data and the target anti-pinch operation times through a preset stroke limit calculation strategy to obtain a vehicle window limit stroke.
It should be understood that the window limit stroke refers to a stroke range limit stroke of the glass in a rising or falling direction, and includes a rising limit stroke and a falling limit stroke.
In the specific implementation, after the target window half-lifting data and the target anti-pinch operation times are obtained, the target window half-lifting data and the target anti-pinch operation times are calculated through a preset stroke limit calculation strategy, and the window limit stroke is obtained.
And the calibration module 60 is used for respectively setting the windows of the vehicle according to the window lifting travel data, the window area edge data, the target anti-pinch operation times and the window limiting travel, and detecting the target automatic lifting glass through the set windows.
It can be understood that after obtaining the window area edge data and the window limit stroke, the window area edge data and the window limit stroke are respectively calibrated on the window of the vehicle, and at this time, the automatic lifting function of the glass is detected through the calibrated window, referring to fig. 3, where fig. 3 is the window after calibrating the window area edge data and the window limit stroke, and the whole detection result is divided into 6 cases, which are respectively case 1: when the glass position is detected to exceed 0 point A waveforms, the glass can continuously rise, the driving is stopped, and the vehicle window position fault and the learning sign fault code are reported; case 2: when the glass position is detected to exceed 0 point but be less than A waveforms, the locked rotor is generated, and the position is directly reset to 0 point; case 3: when the glass position is detected to be less than 0 point and the waveform number is less than B, locked rotor occurs, and the glass position is directly reset to 0 point; case 4: when the glass position is detected to be less than 0 point and the waveform number is larger than B, the anti-pinch is triggered; case 5: when the fact that the glass position exceeds the bottommost position after calibration and self-learning but does not exceed C-S waveforms is detected, locked rotor occurs, and abnormality cannot be triggered at this time; case 6: and when the glass position is detected to be not locked after exceeding the C-S waveforms at the calibration and self-learning bottommost position, stopping driving, and reporting the position fault of the car window and learning the mark fault code.
In the embodiment, the target window full-lifting data is calculated through a preset mean value calculation strategy, the window lifting travel data is calculated according to the waveform number of the vehicle through a preset edge number calculation strategy, the target anti-pinch operation times are determined according to the target window half-lifting data, and the target window half-lifting data and the target anti-pinch operation times are calculated through a preset travel limit calculation strategy; the window lift stroke data, the regional edge data of window, the operation number of times and the door window restriction stroke of preventing pressing from both sides of target are set for respectively, and the window detects the automatic lift glass of target through the window of setting for data, compares in prior art and marks the window through the relatively poor data of stability and detects, can effectively improve the accuracy of marking the window data, and then reduces the monomer of door state or the unusual risk of window lift function that the difference leads to in batches.
It should be noted that the above-described work flows are only exemplary, and do not limit the scope of the present invention, and in practical applications, a person skilled in the art may select some or all of them to achieve the purpose of the solution of the embodiment according to actual needs, and the present invention is not limited herein.
In addition, technical details that are not described in detail in this embodiment may refer to the vehicle window data calibration method provided in any embodiment of the present invention, and are not described herein again.
In an embodiment, the obtaining module 10 is further configured to obtain current window lifting data of a vehicle, and perform mean value calculation on the current window lifting data to obtain current mean value data; calculating the current mean value data and the current car window lifting data through a preset variance calculation strategy to obtain current front difference data; and when the current variance data is less than or equal to a preset data threshold value, obtaining target window half-lifting data and target window full-lifting data according to the current window lifting data.
In an embodiment, the obtaining module 10 is further configured to group the current vehicle window lifting data when the current front difference data is greater than a preset data threshold, so as to obtain a corresponding group data set; calculating the occurrence probability of the group data and the mean value of the group data set; fitting the occurrence probability of the group data and the group data mean value to obtain a target mean value probability curve; determining a value range of the vehicle window lifting data according to the target mean value probability curve; and screening the current car window lifting data through the car window lifting data value range to obtain target car window half-lifting data and target car window full-lifting data.
In an embodiment, the edge number calculating module 30 is further configured to extract a first data group mean and a second data group mean of the occurrence probability of the group data; obtaining a data mean value difference value according to the first data group mean value and the second data group mean value; performing difference calculation on the data mean difference value and the car window lifting travel data to obtain edge offset; and summing the edge offset according to the waveform number of the vehicle by a preset edge number calculation strategy to obtain the edge data of the vehicle window area.
In an embodiment, the determining module 40 is further configured to extract time data in the target window half-lift data; constructing a target rectangular coordinate system according to the car window lifting travel data, the travel data and the number data; obtaining current stroke anti-pinch data according to the target rectangular coordinate system, the car window lifting stroke data and the stroke data; and determining the target anti-pinch operation times through preset safety margin data, current travel anti-pinch data and vehicle window edge data.
In an embodiment, the thread limit calculation module 50 is further configured to extract stroke data and target lifting data in the target window half-lifting data; determining target travel anti-pinch data according to the travel data and the target anti-pinch operation times; generating a target limit travel judgment range according to the target travel anti-pinch data, the vehicle window lifting travel data and the target lifting data; and obtaining the vehicle window limited travel according to the target limited travel judgment range.
In an embodiment, the thread limit calculating module 50 is further configured to perform difference calculation on the target stroke anti-pinch data and the window lifting stroke data to obtain a first lifting range; obtaining a second lifting range according to the car window lifting travel data and the target lifting data; and generating an ascending limit travel judgment range according to the first ascending range, the second ascending range and the historical ascending limit travel judgment range.
In one embodiment, the thread limit calculation module 50 is further configured to obtain a first descending range according to the target journey anti-pinch data; obtaining a second descending range according to the target ascending and descending data; and generating a descending limit travel judgment range according to the first descending range, the second descending range and the historical descending limit travel judgment range.
Other embodiments or methods of implementing the window data calibration device of the present invention can refer to the above embodiments, and are not intended to be redundant.
Further, it is to be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., Read Only Memory (ROM)/RAM, magnetic disk, optical disk), and includes several instructions for enabling a terminal device (e.g., a mobile phone, a computer, an all-in-one platform workstation, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (11)
1. The vehicle window data calibration method is characterized by comprising the following steps:
acquiring current window lifting data of a vehicle, and acquiring target window half-lifting data and target window full-lifting data according to the current window lifting data;
calculating the target vehicle window full-lifting data through a preset mean value calculation strategy to obtain vehicle window lifting travel data;
calculating the window lifting travel data according to the waveform number of the vehicle by using a preset edge number calculation strategy to obtain window area edge data;
determining the target anti-pinch operation times according to the target window half-lifting data;
calculating the target window half-lifting data and the target anti-pinch operation times through a preset stroke limit calculation strategy to obtain a window limit stroke;
and respectively setting the vehicle windows of the vehicle according to the vehicle window lifting stroke data, the vehicle window area edge data, the target anti-pinch operation times and the vehicle window limiting stroke, and detecting the target automatic lifting glass through the set vehicle windows.
2. The vehicle window data calibration method according to claim 1, wherein the obtaining current window lifting data of the vehicle and obtaining target window half-lifting data and target window full-lifting data according to the current window lifting data comprises:
acquiring current window lifting data of a vehicle, and performing mean value calculation on the current window lifting data to obtain current mean value data;
calculating the current mean value data and the current car window lifting data through a preset variance calculation strategy to obtain current front difference data;
and when the current variance data is less than or equal to a preset data threshold value, obtaining target window half-lifting data and target window full-lifting data according to the current window lifting data.
3. The vehicle window data calibration method according to claim 2, wherein after the current mean value data and the current vehicle window lifting data are calculated by a preset variance calculation strategy to obtain current variance data, the method further comprises:
when the front difference data is larger than a preset data threshold value, grouping the current car window lifting data to obtain a corresponding group data set;
calculating the occurrence probability of the group data and the mean value of the group data set;
fitting the occurrence probability of the group data and the group data mean value to obtain a target mean value probability curve;
determining a value range of the vehicle window lifting data according to the target mean value probability curve;
and screening the current car window lifting data through the car window lifting data value range to obtain target car window half-lifting data and target car window full-lifting data.
4. The vehicle window data calibration method according to claim 1, wherein the step of calculating the vehicle window lifting travel data according to the waveform number of the vehicle by using a preset edge number calculation strategy to obtain the vehicle window area edge data comprises the following steps:
extracting a first data group mean value and a second data group mean value of the occurrence probability of the group data;
obtaining a data mean value difference value according to the first data group mean value and the second data group mean value;
performing difference calculation on the data mean difference value and the car window lifting travel data to obtain edge offset;
and summing the edge offset according to the waveform number of the vehicle by a preset edge number calculation strategy to obtain the edge data of the vehicle window area.
5. The vehicle window data calibration method as claimed in claim 1, wherein the determining a target anti-pinch operation number according to the target window half-lift data comprises:
extracting the frequency data in the target vehicle window half-lifting data;
constructing a target rectangular coordinate system according to the car window lifting travel data, the travel data and the number data;
obtaining current stroke anti-pinch data according to the target rectangular coordinate system, the car window lifting stroke data and the stroke data;
and determining the target anti-pinch operation times through preset safety margin data, current travel anti-pinch data and vehicle window edge data.
6. The vehicle window data calibration method as claimed in claim 1, wherein the step of calculating the target vehicle window half-lift data and the target anti-pinch operation times through a preset stroke limit calculation strategy to obtain the vehicle window limit stroke comprises the following steps:
extracting stroke data and target lifting data in the target vehicle window half-lifting data;
determining target travel anti-pinch data according to the travel data and the target anti-pinch operation times;
generating a target limit travel judgment range according to the target travel anti-pinch data, the vehicle window lifting travel data and the target lifting data;
and obtaining the vehicle window limited travel according to the target limited travel judgment range.
7. The window data calibration method according to claim 6, wherein the target limited stroke determination range includes an ascending limited stroke determination range;
according to target stroke anti-pinch data, window lift stroke data and target lift data generate target limit stroke decision range, include:
performing difference calculation on the target travel anti-pinch data and the vehicle window lifting travel data to obtain a first lifting range;
obtaining a second lifting range according to the car window lifting travel data and the target lifting data;
and generating an ascending limit travel judgment range according to the first ascending range, the second ascending range and the historical ascending limit travel judgment range.
8. The window data calibration method according to claim 6, wherein the target limited stroke determination range includes a descent limited stroke determination range;
according to target stroke anti-pinch data, window lift stroke data and target lift data generate target limit stroke decision range, include:
obtaining a first descending range according to the target travel anti-pinch data;
obtaining a second descending range according to the target ascending and descending data;
and generating a descending limit travel judgment range according to the first descending range, the second descending range and the historical descending limit travel judgment range.
9. A vehicle window data calibration device is characterized by comprising:
the acquisition module is used for acquiring current window lifting data of the vehicle and acquiring target window half-lifting data and target window full-lifting data according to the current window lifting data;
the mean value calculation module is used for calculating the target car window full-lifting data through a preset mean value calculation strategy to obtain car window lifting travel data;
the edge number calculation module is used for calculating the car window lifting travel data according to the waveform number of the car through a preset edge number calculation strategy to obtain car window area edge data;
the determining module is used for determining the target anti-pinch operation times according to the target window half-lifting data;
the thread limit calculation module is used for calculating the target vehicle window half-lifting data and the target anti-pinch operation times through a preset stroke limit calculation strategy to obtain a vehicle window limit stroke;
and the calibration module is used for respectively setting the vehicle windows of the vehicle according to the vehicle window lifting stroke data, the vehicle window area edge data, the target anti-pinch operation times and the vehicle window limiting stroke, and detecting the target automatic lifting glass through the set vehicle windows.
10. A vehicle window data calibration apparatus, comprising: a memory, a processor and a window data calibration program stored on the memory and executable on the processor, the window data calibration program being configured to implement the window data calibration method according to any one of claims 1 to 8.
11. A storage medium having stored thereon a window data calibration program which, when executed by a processor, implements a window data calibration method according to any one of claims 1 to 8.
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