CN114112437B - 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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- CN114112437B CN114112437B CN202111456402.9A CN202111456402A CN114112437B CN 114112437 B CN114112437 B CN 114112437B CN 202111456402 A CN202111456402 A CN 202111456402A CN 114112437 B CN114112437 B CN 114112437B
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- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
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Abstract
The invention relates to the technical field of vehicle control, and discloses a vehicle window data calibration method, device and equipment and a storage medium, wherein the method comprises the following steps: calculating the target window full-lifting data through a preset average 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 vehicle window lifting stroke data, the vehicle window area edge data, the target anti-clamping operation times and the vehicle window limiting stroke are respectively set, and the vehicle window detection target automatic lifting glass with the set data is compared with the vehicle window calibration and detection through the data with poor stability in the prior art, so that the accuracy of the vehicle window calibration data can be effectively improved, and the risk of abnormal vehicle window lifting function caused by single and batch differences of the vehicle door state is reduced.
Description
Technical Field
The invention relates to the technical field of vehicle control, in particular to a vehicle window data calibration method, device and equipment and a storage medium.
Background
With the rapid development of vehicle technology, the popularity of vehicles is rapidly increased, so that the vehicles become important vehicles in life, and the driving comfort of the vehicles can be effectively improved by applying electronic equipment, for example, the electric windows are controlled to ascend and descend by a motor controller, the electric windows have the advantages of simultaneously operating a plurality of windows and remote control, the window control is realized based on calibration data, the current calibration data cannot represent all data, the stability of the data is poor, and the influence of single or batch differences of the state of the vehicle door can cause abnormal glass lifting in the process of controlling the windows, for example, the windows are stopped when not closed to the top or are automatically overturned after being lifted to the top, so that the use of customers is influenced.
The foregoing is provided merely for the purpose of facilitating understanding of the technical solutions of the present invention and is not intended to represent an admission that the foregoing is prior art.
Disclosure of Invention
The invention mainly aims to provide a vehicle window data calibration method, device, equipment and storage medium, which aim to solve the problems that the accuracy of calibrating a vehicle window is low in the prior art, and the vehicle window is easily influenced by single or batch differences of the state of a vehicle door, so that the lifting function of the vehicle window is abnormal.
In order to achieve the above purpose, the invention provides a vehicle window data calibration method, which comprises 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 window full-lifting data through a preset average value calculation strategy to obtain window lifting travel data;
calculating the window lifting stroke data according to the waveform number of the vehicle by a preset edge number calculation strategy to obtain window area edge data;
determining the target anti-pinch operation times according to the target vehicle window semi-lifting data;
calculating the target vehicle window half-lifting data and the target anti-pinch operation times through a preset travel limit calculation strategy to obtain a vehicle window limit travel;
and respectively setting the window of the vehicle according to the window lifting stroke data, the window area edge data, the target anti-pinch operation times and the window limiting stroke, and detecting the target automatic lifting glass through the set window.
Optionally, the obtaining current window lifting data of the vehicle, and obtaining the target window half lifting data and the target window full lifting data according to the current window lifting data includes:
Acquiring current window lifting data of a vehicle, and carrying out average value calculation on the current window lifting data to obtain current average value data;
calculating the current mean value data and the current window lifting data through a preset variance calculation strategy to obtain current variance data;
and when the current variance data is smaller than or equal to a preset data threshold value, acquiring target vehicle window half lifting data and target vehicle window full lifting data according to the current vehicle window lifting data.
Optionally, after the calculating the current mean value data and the current window lifting data by the preset variance calculation strategy, the method further includes:
when the current variance data is larger than a preset data threshold value, grouping the current window lifting data to obtain a corresponding group data set;
calculating the occurrence probability and the average 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 window lifting data according to the target mean probability curve;
and screening the current window lifting data through the window lifting data value range to obtain target window half lifting data and target window full lifting data.
Optionally, the calculating the window lifting stroke data according to the waveform number of the vehicle by using a preset edge number calculating strategy to obtain window area edge data includes:
extracting a first data set mean value and a second data set mean value of the occurrence probability of the group data;
obtaining a data average value difference value according to the first data group average value and the second data group average value;
performing difference calculation on the data mean value difference value and the vehicle window lifting travel data to obtain an edge offset;
and carrying out summation calculation on 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 the target anti-pinch operation times according to the target vehicle window semi-lifting data includes:
extracting frequency data in the target vehicle window semi-lifting data;
constructing a target rectangular coordinate system according to the window lifting travel data, the travel data and the frequency data;
obtaining current travel anti-pinch data according to the target rectangular coordinate system, the vehicle window lifting travel data and the travel 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 calculating the target window half-lift data and the target anti-pinch operation times by a preset travel limit calculation strategy to obtain a window limit travel includes:
extracting travel data and target lifting data in the target vehicle window semi-lifting data;
determining target travel anti-pinch data according to the travel data and the target anti-pinch operation times;
generating a target limited 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 limiting stroke according to the target limiting stroke judging range.
Optionally, the target limited stroke determination range includes a rising limited stroke determination range;
generating a target limited travel determination range according to the target travel anti-pinch data, the vehicle window lifting travel data and the target lifting data, wherein the target limited travel determination range comprises the following steps:
performing difference calculation on the target stroke anti-pinch data and the vehicle window lifting stroke data to obtain a first lifting range;
obtaining a second lifting range according to the window lifting travel data and the target lifting data;
and generating an ascending limit travel determination range according to the first ascending range, the second ascending range and the historical ascending limit travel determination range.
Optionally, the target limited travel determination range includes a descent limited travel determination range;
generating a target limited travel determination range according to the target travel anti-pinch data, the vehicle window lifting travel data and the target lifting data, wherein the target limited travel determination range comprises the following steps:
obtaining a first falling range according to the target travel anti-pinch data;
obtaining a second descending range according to the target lifting data;
and generating a descent limiting travel determination range according to the first descent range, the second descent range and the historical descent limiting travel determination range.
In addition, in order to achieve the above object, the present invention also provides a vehicle window data calibration device, 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 average value calculation module is used for calculating the target window full-lifting data through a preset average value calculation strategy to obtain window lifting travel data;
the edge number calculation module is used for calculating the window lifting travel data according to the waveform number of the vehicle through a preset edge number calculation strategy to obtain window area edge data;
The determining module is used for determining the target anti-pinch operation times according to the target vehicle window semi-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 travel limit calculation strategy to obtain a vehicle window limit travel;
and the calibration module is used for respectively setting the windows of the vehicle according to the window lifting stroke data, the window area edge data, the target anti-pinch operation times and the window limiting stroke, and detecting the target automatic lifting glass through the set windows.
In addition, in order to achieve the above object, the present invention also provides a vehicle window data calibration device, including: the system comprises a memory, a processor and a window data calibration program stored on the memory and capable of running on the processor, wherein the window data calibration program is configured to realize the window data calibration method.
In addition, in order to achieve the above object, the present invention also proposes a storage medium having stored thereon a window data calibration program which, when executed by a processor, implements the window data calibration method as described above.
According to the vehicle window data calibration method, the target vehicle window full-lifting data are calculated through a preset average value calculation strategy, the vehicle window lifting travel data are 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 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 vehicle window lifting stroke data, the vehicle window area edge data, the target anti-clamping operation times and the vehicle window limiting stroke are respectively set, and the vehicle window detection target automatic lifting glass with the set data is compared with the vehicle window calibration and detection through the data with poor stability in the prior art, so that the accuracy of the vehicle window calibration data can be effectively improved, and the risk of abnormal vehicle window lifting function caused by single and batch differences of the vehicle door state is reduced.
Drawings
FIG. 1 is a schematic diagram of a window data calibration device of a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a flowchart of a first embodiment of a window data calibration method according to the present invention;
FIG. 3 is a schematic diagram illustrating a data calibration and detection method according to an embodiment of the present invention;
FIG. 4 is a flowchart of a second embodiment of the window data calibration method of the present invention;
FIG. 5 is a graph illustrating a target mean probability curve according to an embodiment of the window data calibration method of the present invention;
FIG. 6 is a flowchart of a third embodiment of a window data calibration method according to the present invention;
FIG. 7 is a schematic functional block diagram of a first embodiment of the window data calibration device according to the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a vehicle window data calibration device in a hardware operation 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 (Central Processing Unit, CPU), a communication bus 1002, a user interface 1003, a network interface 1004, a memory 1005. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may further 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 high-speed random access Memory (Random Access Memory, RAM) Memory or a stable nonvolatile Memory (NVM), such as a disk Memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.
It will be appreciated by those skilled in the art that the configuration shown in fig. 1 is not limiting of the window data indexing apparatus and may include more or fewer components than shown, or certain components may be combined, or a different arrangement of components.
As shown in fig. 1, an operating system, a network communication module, a user interface module, and a window data calibration program may be included in the memory 1005 as one type of storage medium.
In the window data calibration device shown in fig. 1, the network interface 1004 is mainly used for data communication with a network integrated platform workstation; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the window data calibration device of the present invention may be provided in the window data calibration device, where the window data calibration device invokes a window data calibration program stored in the memory 1005 through the processor 1001, and executes the window data calibration method provided by the embodiment of the present 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 flowchart of a first embodiment of the window data calibration method according to the present invention.
In a first embodiment, the vehicle window data calibration method includes the steps of:
Step S10, current window lifting data of a vehicle are obtained, and target window half lifting data and target window full lifting data are obtained according to the current window lifting data.
It should be noted that, the execution body of the embodiment is a vehicle window data calibration device, and may be other devices that can implement the same or similar functions, for example, a vehicle-mounted device, etc., which is not limited in this embodiment, and in this embodiment, a vehicle-mounted device is taken as an example for description.
It should be understood that the current window-up and down data refers to data when the glass of the test target number of vehicles is up and down, and includes all data during the up or down of the glass, for example, data that the glass is stopped without being up to the top, the glass is further up after being up to the top, the glass is automatically down to the top, and the like, and the current window-up and down data is 3 bytes of data, respectively: the 1 st byte data represents a data category, the 2 nd byte data represents position data, and the 3 rd byte data represents the number of operations.
It is understood that the target window full lift data refers to data that the glass is fully lifted from the bottom to the top of the window at one time or fully lowered from the top to the bottom of the window at one time, the data type of the target window full lift data is 01, and the target window half lift data refers to data that the glass is not fully lifted from the bottom to the top of the window at one time or fully lowered from the top to the bottom of the window at one time, and the data type of the target window half lift data is 02.
And step S20, calculating the target window full-lifting data through a preset average value calculation strategy to obtain window lifting travel data.
It should be understood that after the target window full lift data is obtained, the target window full lift data is calculated by a preset average calculation strategy, so as to obtain window lift travel data, for example, the number of data of the current window lift data is N, i is a data number, then the window lift travel data S,
and step S30, calculating the window lifting travel data according to the waveform number of the vehicle through a preset edge number calculation strategy to obtain window area edge data.
It can be understood that the window area edge data refers to edge data of an anti-pinch function area of glass in the rising direction, and is specifically obtained by calculating window rising and falling stroke data through a preset edge number calculation strategy and a waveform number of a vehicle, wherein the waveform number corresponds to a downward target distance of the upper edge of the adhesive tape, and when the target distance is 4mm, the corresponding waveform number is X 4mm 。
Further, step S30 includes: extracting a first data set mean value and a second data set mean value of the occurrence probability of the group data; obtaining a data average value difference value according to the first data group average value and the second data group average value; performing difference calculation on the data mean value difference value and the vehicle window lifting travel data to obtain an edge offset; and carrying out summation calculation on 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 group occurrences and the total number of groups, the first data group mean refers to the mean value of the data in the first data group, the second data group mean refers to the mean value of the data in the second data group, and the data mean difference is determined based on 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 number of waveforms, i.e., window area edge data b=1/2X 4mm In practical situations, the edge offset needs to be considered, and the edge offset is calculated based on the difference between the data mean value difference and the vehicle window lifting travel data, specifically: offset l=window lift travel data S-data mean difference E (X P(Xj)MAX )-E(X P(Xj)MIN ) Further, window area edge data B=1/2X calculated through a preset edge number calculation strategy 4mm+ L＝1/2X 4mm+ S-(E(X P(Xj)MAX )-E(X P(Xj)MIN ))。
And step S40, determining the target anti-pinch operation times according to the target vehicle window semi-lifting data.
It is understood that the target travel anti-pinch data refers to travel data under the target anti-pinch operation number, and after the target window half-lift data is obtained, the target anti-pinch operation number is determined by the target window half-lift data.
Further, step S40 includes: extracting frequency data in the target window semi-lifting data, and constructing a target rectangular coordinate system according to window lifting travel data, travel data and frequency data; obtaining current travel anti-pinch data according to the target rectangular coordinate system, the vehicle window lifting travel data and the travel data; and determining the target anti-pinch operation times through preset safety margin data, current travel anti-pinch data and window area edge data.
It should be understood that the target number of anti-pinch operations refers to the maximum number of allowable anti-pinch operations in the case where the glass is not on top, and the target number of anti-pinch operations needs to be set because the maximum number of allowable operations affects the maximum allowable accumulated error amount, which may cause inversion or may not rise to the top.
It can be understood that after the target window half-lift data is obtained, the frequency data in the target window half-lift data is extracted as the abscissa Z of the rectangular coordinate system i The window lifting travel data and the travel data are subjected to difference, and the absolute value of the difference data is taken as the ordinate |Y of a rectangular coordinate system i -S|, constructing a target rectangular coordinate system by the abscissa and the ordinate, and at this time, setting |Y i -s|=b, B being window area edge data, obtaining a maximum anti-pinching operation number and a minimum anti-pinching operation number, taking into account preset safety margin data, which may be 20%, then the target anti-pinching operation number d=minimum anti-pinching operation number Z e *80％。
And S50, calculating the target vehicle window half-lifting data and the target anti-pinch operation times through a preset travel limit calculation strategy to obtain a vehicle window limit travel.
It should be understood that the window-limiting stroke refers to a stroke range in which the glass is limited in the ascending or descending direction, and includes an ascending-limiting stroke and a descending-limiting stroke.
In the specific implementation, after the target vehicle window half-lifting data and the target anti-pinch operation times are obtained, the target vehicle window half-lifting data and the target anti-pinch operation times are calculated through a preset travel limit calculation strategy, and the vehicle window limit travel is obtained.
And step S60, respectively setting the windows of the vehicle according to the window lifting stroke data, the window area edge data, the target anti-pinch operation times and the window limiting stroke, and detecting the target automatic lifting glass through the set windows.
It can be understood that after the window lift stroke data, the window area edge data, the target anti-pinching operation number and the window limiting stroke are obtained, the window lift stroke data, the window area edge data, the target anti-pinching operation number and the window limiting stroke are respectively set at the window of the vehicle, at this time, the window after the setting is passed through the automatic lift function of the window detection glass, referring to fig. 3, fig. 3 is the window after the window area edge data and the window limiting stroke are calibrated, a is the lift limiting stroke determination range, B is the window area edge data, S is the window lift stroke data, C is the lowering limiting stroke determination range, and the whole detection result is divided into 6 cases, namely case 1: after detecting that the glass position exceeds 0 point A waveforms, the vehicle can continue to rise, and then the driving is stopped, and the vehicle window position fault and the learning sign fault code are reported; case 2: when the position of the glass is detected to be more than 0 point but less than A waveforms, blocking rotation occurs, 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, 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 greater than B, blocking occurs, and anti-clamping is triggered; case 5: when the glass position is detected to be beyond the bottom position after calibration and self-learning but not beyond C-S waveforms, blocking is generated, and the abnormality is not triggered at this time; case 6: when the glass position is detected to be not blocked after exceeding the C-S waveforms of the calibrated and self-learned bottommost position, the driving is stopped, and the vehicle window position fault and the learning sign fault code are reported.
According to the method, the full-lifting data of the target window are calculated through a preset average value calculation strategy, window lifting travel data are 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 half-lifting data of the target window, and the half-lifting data of the target window and the target anti-pinch operation times are calculated through a preset travel limit calculation strategy; the vehicle window lifting stroke data, the vehicle window area edge data, the target anti-clamping operation times and the vehicle window limiting stroke are respectively set, and the vehicle window detection target automatic lifting glass with the set data is compared with the vehicle window calibration and detection through the data with poor stability in the prior art, so that the accuracy of the vehicle window calibration data can be effectively improved, and the risk of abnormal vehicle window lifting functions caused by single and batch differences of the vehicle door state is reduced.
In an embodiment, as shown in fig. 4, a second embodiment of the vehicle window data calibration method according to the present invention is provided based on the first embodiment, and the step S10 includes:
step S101, current window lifting data of a vehicle are obtained, and average value calculation is carried out on the current window lifting data to obtain current average value data.
It should be understood that the current average value data refers to average value data of current window lifting data, and when the current window lifting data of the vehicle is obtained, the current window lifting data is calculated through an average value formula, specifically:
wherein N is the data quantity of the current window lifting data, i is the data number,x is the current mean value data i Refers to current window lift data.
Step S102, calculating the current mean value data and the current window lifting data through a preset variance calculation strategy to obtain current variance data.
It may be understood that the preset variance calculation policy refers to a policy for calculating a variance of data, and after the current mean value data and the current window lifting data are obtained, the current variance data is calculated through the preset variance calculation policy, which specifically includes:
wherein, the liquid crystal display device comprises a liquid crystal display device,x is the current mean value data i The current 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 current variance data is larger than a preset data threshold value, grouping the current window lifting data to obtain a corresponding group data set; calculating the occurrence probability and the average 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 window lifting data according to the target mean probability curve; and screening the current window lifting data through the window lifting data value range to obtain target window half lifting data and target window full lifting data.
It should be appreciated that when the current variance data is greater than the preset data threshold, indicating that the current window lift data is unstable at this time, to avoid differences in door batch caused by the instability of the window lift data, the current window lift data is grouped according to grouping logic, the total of j groups, the group data set is composed of j groups of lift data, the grouping logic is X j ＝X min And (5*j) calculating the occurrence probability of each group of data in the group data set to obtain the occurrence probability of the group data, calculating the average value of the group data, fitting the occurrence probability 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 through the window lifting data value range determined by the target average value probability curve to obtain the target 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 ofAt this time, only 90% of the function is needed, namely +.>And X is b -X c X corresponding to minimum value c And X b Namely, the window lifting data value range determined by the target mean probability curve is [ X ] b ,X c ]At this time throughThe window lifting data value range screens the current window lifting data to obtain the window lifting data value range [ X ] meeting the window lifting data value range b ,X c ]The lifting data of the target window half lifting data and the target window full lifting data.
And step S103, when the current variance data is smaller than or equal to a preset data threshold, acquiring target vehicle window half lifting data and target vehicle window full lifting data according to the current vehicle window lifting data.
It should be understood that the preset data threshold refers to a theoretical data stability minimum value, and may be 100 or other data thresholds according to a value defined by a vehicle door size error, which is not limited in this embodiment and is exemplified by 100 depending on the vehicle door size error. After the current variance data is obtained, judging whether the current variance data is larger than a preset data threshold value, if so, indicating that the current window lifting data is stable, wherein the current window lifting data are all available at the moment, and obtaining target window half lifting data and target window full lifting data according to the current window lifting data.
According to the embodiment, the current window lifting data of the vehicle are obtained, and mean value calculation is carried out on the current window lifting data to obtain current mean value data; calculating the current mean value data and the current window lifting data through a preset variance calculation strategy to obtain current variance data; when the current variance data is smaller than or equal to a preset data threshold value, acquiring target vehicle window half lifting data and target vehicle window full lifting data according to the current vehicle window lifting data; according to the method, the average value of the current window lifting data is calculated, the current average value data is calculated according to a preset variance calculation strategy, whether the calculated current variance data is smaller than or equal to a preset data threshold value 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 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 provided based on the first embodiment, and the step S50 includes:
step S501, extracting travel data and target lifting data in the target window half-lifting data.
It can be understood that the travel data refers to travel data of moving glass in a rising or falling process, the target lifting data refers to maximum lifting data in target window half-lifting data, specifically, after the target window half-lifting data is obtained, the travel data of the target window half-lifting data is extracted, the target window half-lifting data is sorted according to a sequence from large to small, and the target lifting data is extracted from the sorted target window half-lifting data.
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 travel anti-pinch data refers to travel data under the target anti-pinch operation number, that is, the travel data is Y, the target anti-pinch operation number is D, and the target travel anti-pinch data is Y D 。
Step S503, generating a target limited travel determination range according to the target travel anti-pinch data, the window lifting travel data and the target lifting data.
It is to be understood that, after the target stroke pinch prevention data, the window lift stroke data, and the target lift data are obtained, a target limit stroke determination range including an ascent limit stroke determination range and a descent limit stroke determination range is determined in accordance with the target stroke pinch prevention data, the window lift stroke data, and the target lift data.
Further, step S503 includes: performing difference calculation on the target stroke anti-pinch data and the vehicle window lifting stroke data to obtain a first lifting range; obtaining a second lifting range according to the window lifting travel data and the target lifting data; and generating an ascending limit travel determination range according to the first ascending range, the second ascending range and the historical ascending limit travel determination range.
Should be treatedAfter the target stroke anti-pinch data and the window lifting stroke data are obtained, the target stroke anti-pinch data and the window lifting stroke data are differenced to obtain a first lifting range, for example, the first lifting range is A1, and the target stroke anti-pinch data is Y D If the window lift stroke data is S, a1=s-Y D A second rising range is obtained by the window lift travel data and the target lift data, for example, the second rising range is A2, and the target lift data is X MAX A2= (S-X) MAX ) 120% of the historical ascending limit travel determination range is a determination range determined according to the historical ascending data of the glass, specifically-S5%, and the ascending limit travel determination range is A & gtor-S5% and A is equal to or greater than-S5%<S-Y D And A is<(S-X MAX )*120％。
Further, step S503 includes: obtaining a first falling range according to the target travel anti-pinch data; obtaining a second descending range according to the target lifting data; and generating a descent limiting travel determination range according to the first descent range, the second descent range and the historical descent limiting travel determination range.
It will be appreciated that the first drop range is derived from the target travel anti-pinch data, i.e. the first drop range c1=y D And obtaining a second falling range according to the target lifting data, namely a second falling range C2=X MAX *120%, the history glass descent limiting travel determination range is a determination range determined according to history glass descent data, specifically s×105%, and the descent limiting travel determination range is c.ltoreq.s×105% and C>Y D And C>X MAX *120％。
And step S504, obtaining the vehicle window limiting stroke according to the target limiting stroke judging 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, specifically, a numerical value is selected in the target limited travel determination range as the window limited travel.
In the embodiment, stroke data and target lifting data in the target vehicle window half-lifting data are extracted; determining target travel anti-pinch data according to the travel data and the target anti-pinch operation times; generating a target limited 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 limiting stroke according to the target limiting stroke judging range; according to the method, the device and the system, the target stroke anti-pinch data are determined through the stroke data and the target anti-pinch operation times, then the target limiting stroke judging range is generated according to the target stroke anti-pinch data, the vehicle window lifting stroke data and the target lifting data, and the vehicle window limiting stroke is selected according to the target limiting stroke judging range, so that accuracy of obtaining the vehicle window limiting stroke can be effectively improved.
In addition, the embodiment of the invention also provides a storage medium, wherein the storage medium stores a window data calibration program, and the window data calibration program realizes the steps of the window data calibration method when being executed by a processor.
Because the storage medium adopts all the technical schemes of all the embodiments, the storage medium has at least all the beneficial effects brought by the technical schemes of the embodiments, and the description is omitted here.
In addition, referring to fig. 7, an embodiment of the present invention further provides a vehicle window data calibration device, where the vehicle window data calibration device 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-up and down data refers to data when the glass of the test target number of vehicles is up and down, and includes all data during the up or down of the glass, for example, data that the glass is stopped without being up to the top, the glass is further up after being up to the top, the glass is automatically down to the top, and the like, and the current window-up and down data is 3 bytes of data, respectively: the 1 st byte data represents a data category, the 2 nd byte data represents position data, and the 3 rd byte data represents the number of operations.
It is understood that the target window full lift data refers to data that the glass is fully lifted from the bottom to the top of the window at one time or fully lowered from the top to the bottom of the window at one time, the data type of the target window full lift data is 01, and the target window half lift data refers to data that the glass is not fully lifted from the bottom to the top of the window at one time or fully lowered from the top to the bottom of the window at one time, and the data type of the target window half lift data is 02.
The average calculation module 20 is configured to calculate the target window full-lift data by using a preset average calculation policy, so as to obtain window lift travel data.
It should be understood that after the target window full lift data is obtained, the target window full lift data is calculated by a preset average calculation strategy, so as to obtain window lift travel data, for example, the number of data of the current window lift data is N, i is a data number, then the window lift travel data S,
the edge number calculating module 30 is configured to calculate the window lifting stroke data according to the waveform number of the vehicle by using a preset edge number calculating strategy, so as to obtain the window area edge number.
It can be understood that the window area edge data refers to edge data of an anti-pinch function area of glass in the rising direction, and is specifically obtained by calculating window rising and falling stroke data through a preset edge number calculation strategy and a waveform number of a vehicle, wherein the waveform number corresponds to a downward target distance of the upper edge of the adhesive tape, and when the target distance is 4mm, the corresponding waveform number is X 4mm 。
Further, the edge number calculating module 30 is further configured to extract a first data set average value and a second data set average value of the occurrence probability of the group data; obtaining a data average value difference value according to the first data group average value and the second data group average value; performing difference calculation on the data mean value difference value and the vehicle window lifting travel data to obtain an edge offset; and carrying out summation calculation on 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 group occurrences and the total number of groups, the first data group mean refers to the mean value of the data in the first data group, the second data group mean refers to the mean value of the data in the second data group, and the data mean difference is determined based on 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 number of waveforms, i.e., window area edge data b=1/2X 4mm In practical situations, the edge offset needs to be considered, and the edge offset is calculated based on the difference between the data mean value difference and the vehicle window lifting travel data, specifically: offset l=window lift travel data S-data mean difference E (X P(Xj)MAX )-E(X P(Xj)MIN ) Further, window area edge data B=1/2X calculated through a preset edge number calculation strategy 4mm+ L＝1/2X 4mm+ S-(E(X P(Xj)MAX )-E(X P(Xj)MIN ))。
And the determining module 40 is used for determining the target anti-pinch operation times according to the target vehicle window semi-lifting data.
It is understood that the target travel anti-pinch data refers to travel data under the target anti-pinch operation number, and after the target window half-lift data is obtained, the target anti-pinch operation number is determined by the target window half-lift data.
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 stroke data, the stroke data and the frequency data; obtaining current travel anti-pinch data according to the target rectangular coordinate system, the vehicle window lifting travel data and the travel data; and determining the target anti-pinch operation times through preset safety margin data, current travel anti-pinch data and window area edge data.
It should be understood that the target number of anti-pinch operations refers to the maximum number of allowable anti-pinch operations in the case where the glass is not on top, and the target number of anti-pinch operations needs to be set because the maximum number of allowable operations affects the maximum allowable accumulated error amount, which may cause inversion or may not rise to the top.
It can be understood that after the target window half-lift data is obtained, the frequency data in the target window half-lift data is extracted as the abscissa Z of the rectangular coordinate system i The window lifting travel data and the travel data are subjected to difference, and the absolute value of the difference data is taken as the ordinate |Y of a rectangular coordinate system i -S|, constructing a target rectangular coordinate system by the abscissa and the ordinate, and at this time, setting |Y i -s|=b, B being window area edge data, obtaining a maximum anti-pinching operation number and a minimum anti-pinching operation number, taking into account preset safety margin data, which may be 20%, then the target anti-pinching operation number d=minimum anti-pinching operation number Z e *80％。
The thread limit calculation module 50 is configured to calculate the target window half-lift data and the target anti-pinch operation times according to a preset travel limit calculation policy, so as to obtain a window limit travel.
It should be understood that the window limiting stroke refers to a glass stroke range limiting stroke in the ascending or descending direction, including an ascending limiting stroke and a descending limiting stroke.
In the specific implementation, after the target vehicle window half-lifting data and the target anti-pinch operation times are obtained, the target vehicle window half-lifting data and the target anti-pinch operation times are calculated through a preset travel limit calculation strategy, and the vehicle window limit travel is obtained.
The calibration module 60 is configured to set windows of the vehicle according to the window lifting stroke data, window area edge data, target anti-pinching operation times and window limiting strokes, and detect target automatic lifting glass through the set windows.
It can be understood that after the window area edge data and the window limiting stroke are obtained, the window area edge data and the window limiting stroke are respectively calibrated at the window of the vehicle, at this time, the automatic lifting function of the glass is detected by the calibrated window, referring to fig. 3, fig. 3 is the window after the window area edge data and the window limiting stroke are calibrated, and the whole detection result is divided into 6 cases, namely case 1: after detecting that the glass position exceeds 0 point A waveforms, the vehicle can continue to rise, and then the driving is stopped, and the vehicle window position fault and the learning sign fault code are reported; case 2: when the position of the glass is detected to be more than 0 point but less than A waveforms, blocking rotation occurs, 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, 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 greater than B, blocking occurs, and anti-clamping is triggered; case 5: when the glass position is detected to be beyond the bottom position after calibration and self-learning but not beyond C-S waveforms, blocking is generated, and the abnormality is not triggered at this time; case 6: when the glass position is detected to be not blocked after exceeding the C-S waveforms of the calibrated and self-learned bottommost position, the driving is stopped, and the vehicle window position fault and the learning sign fault code are reported.
According to the method, the full-lifting data of the target window are calculated through a preset average value calculation strategy, window lifting travel data are 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 half-lifting data of the target window, and the half-lifting data of the target window and the target anti-pinch operation times are calculated through a preset travel limit calculation strategy; the vehicle window lifting stroke data, the vehicle window area edge data, the target anti-clamping operation times and the vehicle window limiting stroke are respectively set, and the vehicle window detection target automatic lifting glass with the set data is compared with the vehicle window calibration and detection through the data with poor stability in the prior art, so that the accuracy of the vehicle window calibration data can be effectively improved, and the risk of abnormal vehicle window lifting functions caused by single or batch differences of vehicle door states is further reduced.
It should be noted that the above-described working procedure is merely illustrative, and does not limit the scope of the present invention, and in practical application, a person skilled in the art may select part or all of them according to actual needs to achieve the purpose of the embodiment, which is not limited herein.
In addition, technical details not described in detail in the present embodiment may refer to the vehicle window data calibration method provided in any embodiment of the present invention, which is not described herein.
In an embodiment, the obtaining module 10 is further configured to obtain current window lifting data of the vehicle, and perform average calculation on the current window lifting data to obtain current average data; calculating the current mean value data and the current window lifting data through a preset variance calculation strategy to obtain current variance data; and when the current variance data is smaller than or equal to a preset data threshold value, acquiring target vehicle window half lifting data and target vehicle window full lifting data according to the current vehicle window lifting data.
In an embodiment, the obtaining module 10 is further configured to group the current window lifting data to obtain a corresponding group data set when the current variance data is greater than a preset data threshold; calculating the occurrence probability and the average 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 window lifting data according to the target mean probability curve; and screening the current window lifting data through the window lifting data value range to obtain target window half lifting data and target window full lifting data.
In one embodiment, the edge number calculating module 30 is further configured to extract a first data set average value and a second data set average value of the occurrence probability of the group data; obtaining a data average value difference value according to the first data group average value and the second data group average value; performing difference calculation on the data mean value difference value and the vehicle window lifting travel data to obtain an edge offset; and carrying out summation calculation on 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 the frequency data in the target window half-lift data; constructing a target rectangular coordinate system according to the window lifting travel data, the travel data and the frequency data; obtaining current travel anti-pinch data according to the target rectangular coordinate system, the vehicle window lifting travel data and the travel 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 calculating module 50 is further configured to extract travel data and target lift data in the target window half-lift data; determining target travel anti-pinch data according to the travel data and the target anti-pinch operation times; generating a target limited 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 limiting stroke according to the target limiting stroke judging range.
In an embodiment, the thread limit calculating module 50 is further configured to perform a difference calculation on the target stroke anti-clipping data and the window lifting stroke data to obtain a first lifting range; obtaining a second lifting range according to the window lifting travel data and the target lifting data; and generating an ascending limit travel determination range according to the first ascending range, the second ascending range and the historical ascending limit travel determination range.
In one embodiment, the thread limit calculating module 50 is further configured to obtain a first descent range according to the target trip anti-pinch data; obtaining a second descending range according to the target lifting data; and generating a descent limiting travel determination range according to the first descent range, the second descent range and the historical descent limiting travel determination range.
Other embodiments of the vehicle window data calibration device or the implementation method thereof can refer to the above method embodiments, and are not redundant here.
Furthermore, it should 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 one … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. Read Only Memory)/RAM, magnetic disk, optical disk) and including several instructions for causing a terminal device (which may be a mobile phone, a computer, an integrated platform workstation, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (10)
1. The vehicle window data calibration method is characterized by comprising the following steps of:
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 window full-lifting data through a preset average value calculation strategy to obtain window lifting travel data;
calculating the window lifting stroke data according to the waveform number of the vehicle by a preset edge number calculation strategy to obtain window area edge data;
determining the target anti-pinch operation times according to the target vehicle window semi-lifting data;
calculating the target vehicle window half-lifting data and the target anti-pinch operation times through a preset travel limit calculation strategy to obtain a vehicle window limit travel;
respectively setting the window of the vehicle according to the window lifting stroke data, the window area edge data, the target anti-pinch operation times and the window limiting stroke, and detecting the target automatic lifting glass through the set window;
the obtaining the current window lifting data of the vehicle, and obtaining the target window half lifting data and the target window full lifting data according to the current window lifting data comprises the following steps:
Acquiring current window lifting data of a vehicle, and carrying out average value calculation on the current window lifting data to obtain current average value data;
calculating the current mean value data and the current window lifting data through a preset variance calculation strategy to obtain current variance data;
and when the current variance data is smaller than or equal to a preset data threshold value, acquiring target vehicle window half lifting data and target vehicle window full lifting data according to the current vehicle window lifting data.
2. The vehicle window data calibration method according to claim 1, wherein the calculating the current mean value data and the current vehicle window lifting data by a preset variance calculation strategy, after obtaining current variance data, further comprises:
when the current variance data is larger than a preset data threshold value, grouping the current window lifting data to obtain a corresponding group data set;
calculating the occurrence probability and the average 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 window lifting data according to the target mean probability curve;
And screening the current window lifting data through the window lifting data value range to obtain target window half lifting data and target window full lifting data.
3. The vehicle window data calibration method according to claim 1, wherein the calculating the vehicle window lifting stroke data according to the waveform number of the vehicle by the preset edge number calculation strategy to obtain vehicle window area edge data comprises:
extracting a first data set mean value and a second data set mean value of the occurrence probability of the group data;
obtaining a data average value difference value according to the first data group average value and the second data group average value;
performing difference calculation on the data mean value difference value and the vehicle window lifting travel data to obtain an edge offset;
and carrying out summation calculation on 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.
4. The vehicle window data calibration method according to claim 1, wherein the determining the target anti-pinch operation times according to the target vehicle window semi-lifting data comprises:
extracting frequency data in the target vehicle window semi-lifting data;
constructing a target rectangular coordinate system according to the window lifting travel data, the travel data and the frequency data;
Obtaining current travel anti-pinch data according to the target rectangular coordinate system, the vehicle window lifting travel data and the travel data;
and determining the target anti-pinch operation times through preset safety margin data, current travel anti-pinch data and vehicle window edge data.
5. The vehicle window data calibration method according to claim 1, wherein the calculating the target vehicle window half-lift data and the target anti-pinch operation times by the preset travel limit calculation strategy to obtain a vehicle window limit travel comprises:
extracting travel data and target lifting data in the target vehicle window semi-lifting data;
determining target travel anti-pinch data according to the travel data and the target anti-pinch operation times;
generating a target limited 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 limiting stroke according to the target limiting stroke judging range.
6. The vehicle window data calibration method according to claim 5, wherein the target limited travel determination range includes a rising limited travel determination range;
generating a target limited travel determination range according to the target travel anti-pinch data, the vehicle window lifting travel data and the target lifting data, wherein the target limited travel determination range comprises the following steps:
Performing difference calculation on the target stroke anti-pinch data and the vehicle window lifting stroke data to obtain a first lifting range;
obtaining a second lifting range according to the window lifting travel data and the target lifting data;
and generating an ascending limit travel determination range according to the first ascending range, the second ascending range and the historical ascending limit travel determination range.
7. The vehicle window data calibration method according to claim 5, wherein the target limited travel determination range includes a descent limited travel determination range;
generating a target limited travel determination range according to the target travel anti-pinch data, the vehicle window lifting travel data and the target lifting data, wherein the target limited travel determination range comprises the following steps:
obtaining a first falling range according to the target travel anti-pinch data;
obtaining a second descending range according to the target lifting data;
and generating a descent limiting travel determination range according to the first descent range, the second descent range and the historical descent limiting travel determination range.
8. The utility model provides a door window data calibration device which characterized in that, door window data calibration device includes:
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 average value calculation module is used for calculating the target window full-lifting data through a preset average value calculation strategy to obtain window lifting travel data;
the edge number calculation module is used for calculating the window lifting travel data according to the waveform number of the vehicle through a preset edge number calculation strategy to obtain window area edge data;
the determining module is used for determining the target anti-pinch operation times according to the target vehicle window semi-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 travel limit calculation strategy to obtain a vehicle window limit travel;
the calibration module is used for respectively setting the windows of the vehicle according to the window lifting stroke data, the window area edge data, the target anti-pinch operation times and the window limiting stroke, and detecting the target automatic lifting glass through the set windows;
the acquisition module is also used for acquiring current window lifting data of the vehicle, and carrying out average value calculation on the current window lifting data to obtain current average value data; calculating the current mean value data and the current window lifting data through a preset variance calculation strategy to obtain current variance data; and when the current variance data is smaller than or equal to a preset data threshold value, acquiring target vehicle window half lifting data and target vehicle window full lifting data according to the current vehicle window lifting data.
9. A window data calibration device, characterized in that the window data calibration device comprises: a memory, a processor and a window data calibration program stored on the memory and executable on the processor, the window data calibration program configured to implement the window data calibration method of any one of claims 1 to 7.
10. A storage medium having stored thereon a window data calibration program which when executed by a processor implements the window data calibration method of any one of claims 1 to 7.
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