CN116127604B - Method and system for processing anti-collision data of automobile - Google Patents
Method and system for processing anti-collision data of automobile Download PDFInfo
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Abstract
The invention relates to the technical field of test data statistics, and particularly discloses an automobile anti-collision data processing method and system, wherein the method comprises the steps of determining a sampling area and a sampling grid thereof in an automobile model according to maintenance records; installing an acquisition sensor based on a sampling grid, and receiving acquisition data of the acquisition sensor in real time; generating a time domain graph according to the acquired data, and determining the correlation degree of each acquired data based on the time domain graph; classifying and identifying the acquired data according to the correlation degree, and determining collision characteristics; the collision features are corrected based on the actual collision scenario. According to the invention, sampling points are determined in the automobile model according to the pre-counted maintenance data, the collected data is acquired based on the sampling points, the collected data is subjected to similarity matching in the time domain, the collected data is further classified, collision features are generated according to the similar collected data, and the collision features are verified and corrected based on actual conditions, so that the collision test data with extremely high fit with reality can be obtained.
Description
Technical Field
The invention relates to the technical field of test data statistics, in particular to an automobile anti-collision data processing method and system.
Background
The collision process of the automobile needs to be tested, and as the collision process is an instantaneous process, the change condition of each part is extremely severe and rapid in one collision process, the data acquisition difficulty is very high, in addition, the cost of one collision test is very high, and if the data acquisition is not comprehensive enough, the cost utilization rate can be very low.
Therefore, how to obtain the data generated in the collision process more comprehensively and timely is a technical problem to be solved by the technical scheme of the invention.
Disclosure of Invention
The invention aims to provide an automobile anti-collision data processing method and system, which are used for solving the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a method of processing automotive crash-proof data, the method comprising:
determining a sampling area and a sampling grid thereof in the automobile model according to the maintenance record;
installing an acquisition sensor based on a sampling grid, and receiving acquisition data of the acquisition sensor in real time; the acquired data contains time information;
generating a time domain graph according to the acquired data, and determining the correlation degree of each acquired data based on the time domain graph;
classifying and identifying the acquired data according to the correlation degree, and determining collision characteristics; the collision features include mechanical and kinematic parameters during a collision;
and generating a theoretical collision model based on the collision characteristics, comparing the theoretical collision model with an actual collision scene, and correcting the collision characteristics according to the comparison result.
As a further scheme of the invention: the step of determining the sampling area and the sampling grid thereof in the automobile model according to the maintenance record comprises the following steps:
establishing a connection channel with a research database, and reading a stored automobile model;
acquiring after-sales information of the same type of automobiles in real time, inquiring maintenance records in the after-sales information, and determining the damage probability of different parts according to the maintenance records;
determining a sampling area and a sampling grid thereof in the automobile model according to the damage probability of different parts;
wherein the size of the cells in the sampling grid is inversely proportional to the probability of damage.
As a further scheme of the invention: the step of installing the acquisition sensor based on the sampling grid and receiving acquisition data of the acquisition sensor in real time comprises the following steps:
randomly selecting a preset number of target units in a sampling grid to serve as installation points;
sequentially taking the installation points as centers, calculating the number of other installation points in a preset radius range, and marking the installation point serving as the center as a core point based on the number;
classifying all the installation points based on the core points to obtain a classification array;
circularly executing and determining final installation points according to the classifying array, and receiving acquisition data of the acquisition sensor in real time;
the index of the classifying array is a core point index, and the element values in the classifying array are the number of similar installation points; the classifying array contains noise point elements; and when the distances between a certain installation point and all the core points are larger than a preset distance threshold value, marking the installation point as a noise point.
As a further scheme of the invention: the step of generating a time domain graph according to the acquired data and determining the correlation degree of each acquired data based on the time domain graph comprises the following steps:
counting the acquired data received by all the acquisition sensors based on the same coordinate axis;
converting the acquired data into a jump edge signal according to the acquired data derivative characteristic;
inputting the jump edge signal into a preset coloring model, and outputting a jump signal diagram corresponding to the acquired data;
and selecting jump signal diagrams corresponding to the two acquired data, comparing the two jump signal diagrams, and calculating the correlation degree of the two acquired data.
As a further scheme of the invention: the step of classifying and identifying the collected data according to the correlation degree and determining collision characteristics comprises the following steps:
sequentially taking each acquired data as a reference, and inquiring the correlation degree of the acquired data and other acquired data;
comparing the correlation with a preset correlation threshold, and marking corresponding acquired data when the correlation reaches the preset correlation threshold;
counting the marked collected data to obtain classified data;
inputting the classifying data into a preset recognition model to obtain collision characteristics;
after the angelica data are generated, inquiring the correlation degree of each acquired data in the unlabeled acquired data and the classified data to obtain a correlation degree array; and expanding the classified data according to the relevance array.
As a further scheme of the invention: the step of generating a theoretical collision model based on the collision characteristics, comparing the theoretical collision model with an actual collision scene, and correcting the collision characteristics according to the comparison result comprises the following steps:
inquiring a theoretical collision model in a preset collision model library according to the collision characteristics;
and comparing the theoretical collision model with the actual collision scene, and correcting collision characteristics according to the comparison result.
The technical scheme of the invention also provides an automobile anti-collision data processing system, which comprises:
the sampling parameter determining module is used for determining a sampling area and a sampling grid thereof in the automobile model according to the maintenance record;
the data receiving module is used for installing the acquisition sensor based on the sampling grid and receiving the acquisition data of the acquisition sensor in real time; the acquired data contains time information;
the correlation calculation module is used for generating a time domain graph according to the acquired data and determining the correlation of each acquired data based on the time domain graph;
the classification and identification module is used for classifying and identifying the acquired data according to the correlation degree and determining collision characteristics; the collision features include mechanical and kinematic parameters during a collision;
and the characteristic correction module is used for generating a theoretical collision model based on the collision characteristics, comparing the theoretical collision model with an actual collision scene, and correcting the collision characteristics according to the comparison result.
As a further scheme of the invention: the sampling parameter determining module comprises:
the model reading unit is used for establishing a connecting channel with the research and development database and reading the stored automobile model;
the probability determining unit is used for acquiring after-sales information of the same type of automobiles in real time, inquiring maintenance records in the after-sales information and determining the damage probability of different parts according to the maintenance records;
the processing execution unit is used for determining a sampling area and a sampling grid thereof in the automobile model according to the damage probability of different parts;
wherein the size of the cells in the sampling grid is inversely proportional to the probability of damage.
As a further scheme of the invention: the data receiving module includes:
the selecting unit is used for randomly selecting a preset number of target units in the sampling grid to serve as installation points;
the core point determining unit is used for sequentially taking the installation points as centers, calculating the number of other installation points in a preset radius range, and marking the installation point serving as the center as a core point based on the number;
the point location classifying unit is used for classifying all the installation points based on the core points to obtain a classifying array;
the receiving and executing unit is used for circularly executing and determining a final installation point position according to the classifying array, and receiving the acquired data of the acquisition sensor in real time;
the index of the classifying array is a core point index, and the element values in the classifying array are the number of similar installation points; the classifying array contains noise point elements; and when the distances between a certain installation point and all the core points are larger than a preset distance threshold value, marking the installation point as a noise point.
As a further scheme of the invention: the correlation calculation module includes:
the data statistics unit is used for counting the acquired data received by all the acquisition sensors based on the same coordinate axis;
the signal conversion unit is used for converting the acquired data into jump edge signals according to the acquired data derivative characteristics;
the signal diagram output unit is used for inputting the jump edge signal into a preset coloring model and outputting a jump signal diagram corresponding to the acquired data;
and the signal diagram comparison unit is used for selecting the jump signal diagrams corresponding to the two acquired data, comparing the two jump signal diagrams and calculating the correlation degree of the two acquired data.
Compared with the prior art, the invention has the beneficial effects that: according to the invention, sampling points are determined in the automobile model according to the pre-counted maintenance data, the collected data is acquired based on the sampling points, the collected data is subjected to similarity matching in the time domain, the collected data is further classified, collision features are generated according to the similar collected data, and the collision features are verified and corrected based on actual conditions, so that the collision test data with extremely high fit with reality can be obtained.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following description will briefly introduce the drawings that are needed in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the present invention.
FIG. 1 is a flow chart diagram of a method of processing automotive crash data.
Fig. 2 is a first sub-flowchart of a method of processing vehicle crash data.
Fig. 3 is a second sub-flowchart of the method of processing vehicle crash data.
Fig. 4 is a third sub-flowchart of the method for processing the crash data of the automobile.
Fig. 5 is a fourth sub-flowchart of the method for processing the crash data of the automobile.
Fig. 6 is a fifth sub-flowchart of a method of processing vehicle crash data.
FIG. 7 is a block diagram of the constituent architecture of an automotive crash data processing system.
Description of the embodiments
In order to make the technical problems, technical schemes and beneficial effects to be solved more clear, the invention is further described in detail below with reference to the accompanying drawings and embodiments. 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.
Example 1
Fig. 1 is a flow chart of an automotive anti-collision data processing method, and in an embodiment of the invention, the method includes:
step S100: determining a sampling area and a sampling grid thereof in the automobile model according to the maintenance record;
in the collision test process of an automobile, a plurality of acquisition devices need to be installed, and the whole collision process can be evaluated by the acquired data based on the acquisition devices acquiring the data generated in the collision process. The installation process of the acquisition equipment needs to consider maintenance records of the same type of automobiles, so that the position where the acquisition equipment is easy to damage is determined, and the sampling area and the number of sampling points are determined at the position where the acquisition equipment is easy to damage, wherein the number of the sampling points is represented by a sampling grid; the sampling grid is the grid applied to the automobile model.
Step S200: installing an acquisition sensor based on a sampling grid, and receiving acquisition data of the acquisition sensor in real time; the acquired data contains time information;
when the sampling grid is well determined, the acquisition sensor is installed based on the sampling grid, and the acquisition sensor can record data generated in the collision process of the automobile in real time.
Step S300: generating a time domain graph according to the acquired data, and determining the correlation degree of each acquired data based on the time domain graph;
and counting the data acquired by all the acquisition sensors, and performing correlation analysis to judge which data have larger mutual influence degree, namely the correlation degree.
Step S400: classifying and identifying the acquired data according to the correlation degree, and determining collision characteristics; the collision features include mechanical and kinematic parameters during a collision;
selecting data with larger relativity, identifying the data, and determining collision characteristics, wherein the collision characteristics are mechanical characteristics and kinematic characteristics in the collision process; the collision characteristic is the final data which the technical scheme of the invention wants to obtain.
Step S500: generating a theoretical collision model based on the collision characteristics, comparing the theoretical collision model with an actual collision scene, and correcting the collision characteristics according to the comparison result;
the collision characteristics predicted by the data acquired by the acquisition sensors may have errors, for example, if a certain acquisition sensor fails, the corresponding acquisition data is made wrong, so that the collision characteristics are inconsistent with the actual characteristics; therefore, verification of collision features from actual collision scenarios is also required.
FIG. 2 is a first sub-flowchart of a method for processing vehicle crash data, wherein the steps for determining a sampling area and a sampling grid thereof in a vehicle model according to a maintenance record include:
step S101: establishing a connection channel with a research database, and reading a stored automobile model;
the automobile model is generated and recorded in the research and development process of the automobile, and a connecting channel with a research and development database is established, so that the stored automobile model can be read.
Step S102: acquiring after-sales information of the same type of automobiles in real time, inquiring maintenance records in the after-sales information, and determining the damage probability of different parts according to the maintenance records;
the after-sales data of any type of automobile can be counted in real time, after-sales information of the same type of automobile is obtained, maintenance records in the after-sales information are inquired, and parts which are easy to damage can be clearly determined according to the maintenance records.
Step S103: determining a sampling area and a sampling grid thereof in the automobile model according to the damage probability of different parts;
wherein the size of the cells in the sampling grid is inversely proportional to the probability of damage.
Determining sampling areas in an automobile model by different parts, counting the damage probability of all parts in an area, and further determining a sampling grid, wherein the finer the granularity of the sampling grid is, the more data are collected, and the more accurate the collision analysis process is.
FIG. 3 is a second sub-flowchart of an automotive crash-proof data processing method, wherein the steps of installing the acquisition sensor based on the sampling grid and receiving the acquisition data of the acquisition sensor in real time include:
step S201: randomly selecting a preset number of target units in a sampling grid to serve as installation points;
step S202: sequentially taking the installation points as centers, calculating the number of other installation points in a preset radius range, and marking the installation point serving as the center as a core point based on the number;
step S203: classifying all the installation points based on the core points to obtain a classification array;
step S204: circularly executing and determining final installation points according to the classifying array, and receiving acquisition data of the acquisition sensor in real time;
the index of the classifying array is a core point index, and the element values in the classifying array are the number of similar installation points; the classifying array contains noise point elements; and when the distances between a certain installation point and all the core points are larger than a preset distance threshold value, marking the installation point as a noise point.
The above specifically defines the process of determining the mounting point, which is a cyclic process, and selects the most suitable distribution situation of the mounting point in the preset number of cycles.
Firstly, randomly selecting some installation points in a sampling grid, then taking the installation points as the center, calculating the number of the surrounding installation points, and marking the central installation points as core points when the number reaches a certain degree; finally, classifying all the installation points by the core points, wherein the classification principle is that the distance between the installation point of the non-core point and each core point is calculated, and when the shortest distance meets the preset requirement, the non-core point and the corresponding core point are classified; and when the shortest distance does not meet the preset requirement, marking the non-core point as a noise point.
Fig. 4 is a third sub-flowchart of an automotive anti-collision data processing method, where the step of generating a time domain graph according to the acquired data and determining the correlation degree of each acquired data based on the time domain graph includes:
step S301: counting the acquired data received by all the acquisition sensors based on the same coordinate axis;
and counting the acquired data received by all the acquisition sensors on the same coordinate axis to obtain a signal diagram containing a plurality of curves.
Step S302: converting the acquired data into a jump edge signal according to the acquired data derivative characteristic;
and deriving the acquired data (the order of derivation is determined by staff according to the situation), further determining the change condition of the acquired data, and converting each curve into a jump edge signal based on the change condition.
Step S303: inputting the jump edge signal into a preset coloring model, and outputting a jump signal diagram corresponding to the acquired data;
the jump edge signal is a very simple signal, and can be easily converted into an image containing color values through a preset rule, for example, the high level and the low level are represented by the preset color values, and the duration is represented by the distance, so that a blank image can be colored, and the jump edge signal can be converted into the image according to a certain coloring sequence.
Step S304: selecting two jump signal graphs corresponding to the acquired data, comparing the two jump signal graphs, and calculating the correlation degree of the two acquired data;
the images are compared, so that the similarity of the characteristics can be rapidly judged, the similarity represents that the changes of the two acquired data are almost synchronous and only have time difference; how much of the similar content corresponds to the degree of correlation.
FIG. 5 is a fourth sub-flowchart of an automotive crash-proof data processing method, wherein the step of classifying and identifying the collected data according to the correlation, and determining the crash features includes:
step S401: sequentially taking each acquired data as a reference, and inquiring the correlation degree of the acquired data and other acquired data;
step S402: comparing the correlation with a preset correlation threshold, and marking corresponding acquired data when the correlation reaches the preset correlation threshold;
step S403: counting the marked collected data to obtain classified data;
all the collected data with the correlation degree reaching the preset condition with a certain collected data are selected and classified.
Step S404: inputting the classifying data into a preset recognition model to obtain collision characteristics;
and uniformly identifying the data of one type to obtain collision characteristics. Regarding the mapping relation between the collected data and the collision characteristics, it needs to be specifically explained that the function of the collecting sensor is to collect the data generated in the collision process, the data has what meaning, and represents what situation is the collision characteristics, and the staff counts samples in advance, so that it is determined that for the technical scheme of the invention, the identification model is the default existing technical scheme.
In practice, the recognition model itself is not complex, and can be preset by a sample-fitting method.
It is worth mentioning that after the angelica data are generated, the relevance of each acquired data in the unlabeled acquired data and the classified data is inquired to obtain a relevance array; and expanding the classified data according to the relevance array.
In the classifying process, some acquired data and most acquired data in a certain class of acquired data have higher correlation, and at the moment, the acquired data can be classified even if the correlation between the acquired data and the reference acquired data does not reach a preset condition.
Fig. 6 is a fifth sub-flowchart of an automotive crash-proof data processing method, wherein the steps of generating a theoretical crash model based on the crash features, comparing the theoretical crash model with an actual crash scene, and correcting the crash features according to the comparison result include:
step S501: inquiring a theoretical collision model in a preset collision model library according to the collision characteristics;
the theoretical collision model can be inquired in a predetermined collision model library according to the collision characteristics, wherein the theoretical collision model refers to a preset theoretical collision scene and is generated by staff in a statistical simulation mode in advance.
Step S502: comparing the theoretical collision model with the actual collision scene, and correcting collision characteristics according to the comparison result;
acquiring an actual collision scene, judging whether the difference between the actual collision scene and a theoretical collision scene is large enough, and if the difference is within a certain degree, carrying out proper adjustment on collision characteristics according to the difference (presetting an adjustment coefficient, wherein different adjustment coefficients correspond to different difference degrees); if the difference exceeds a certain degree, warning information is generated to inform the staff to detect the application process of the acquisition sensor or the acquisition data.
Example 2
Fig. 7 is a block diagram of a component structure of an automotive anti-collision data processing system, in which the system 10 includes:
the sampling parameter determining module 11 is used for determining a sampling area and a sampling grid thereof in the automobile model according to the maintenance record;
a data receiving module 12, configured to install the acquisition sensor based on the sampling grid, and receive the acquisition data of the acquisition sensor in real time; the acquired data contains time information;
a correlation calculation module 13, configured to generate a time domain graph according to the acquired data, and determine a correlation of each acquired data based on the time domain graph;
the classification and identification module 14 is used for classifying and identifying the acquired data according to the correlation degree and determining collision characteristics; the collision features include mechanical and kinematic parameters during a collision;
the feature correction module 15 is configured to generate a theoretical collision model based on the collision feature, compare the theoretical collision model with an actual collision scene, and correct the collision feature according to the comparison result.
The sampling parameter determination module 11 includes:
the model reading unit is used for establishing a connecting channel with the research and development database and reading the stored automobile model;
the probability determining unit is used for acquiring after-sales information of the same type of automobiles in real time, inquiring maintenance records in the after-sales information and determining the damage probability of different parts according to the maintenance records;
the processing execution unit is used for determining a sampling area and a sampling grid thereof in the automobile model according to the damage probability of different parts;
wherein the size of the cells in the sampling grid is inversely proportional to the probability of damage.
The data receiving module 12 includes:
the selecting unit is used for randomly selecting a preset number of target units in the sampling grid to serve as installation points;
the core point determining unit is used for sequentially taking the installation points as centers, calculating the number of other installation points in a preset radius range, and marking the installation point serving as the center as a core point based on the number;
the point location classifying unit is used for classifying all the installation points based on the core points to obtain a classifying array;
the receiving and executing unit is used for circularly executing and determining a final installation point position according to the classifying array, and receiving the acquired data of the acquisition sensor in real time;
the index of the classifying array is a core point index, and the element values in the classifying array are the number of similar installation points; the classifying array contains noise point elements; and when the distances between a certain installation point and all the core points are larger than a preset distance threshold value, marking the installation point as a noise point.
The correlation calculation module 13 includes:
the data statistics unit is used for counting the acquired data received by all the acquisition sensors based on the same coordinate axis;
the signal conversion unit is used for converting the acquired data into jump edge signals according to the acquired data derivative characteristics;
the signal diagram output unit is used for inputting the jump edge signal into a preset coloring model and outputting a jump signal diagram corresponding to the acquired data;
and the signal diagram comparison unit is used for selecting the jump signal diagrams corresponding to the two acquired data, comparing the two jump signal diagrams and calculating the correlation degree of the two acquired data.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
Claims (10)
1. A method for processing vehicle crash-proof data, the method comprising:
determining a sampling area and a sampling grid thereof in the automobile model according to the maintenance record;
installing an acquisition sensor based on a sampling grid, and receiving acquisition data of the acquisition sensor in real time; the acquired data contains time information;
generating a time domain graph according to the acquired data, and determining the correlation degree of each acquired data based on the time domain graph;
classifying and identifying the acquired data according to the correlation degree, and determining collision characteristics; the collision features include mechanical and kinematic parameters during a collision;
and generating a theoretical collision model based on the collision characteristics, comparing the theoretical collision model with an actual collision scene, and correcting the collision characteristics according to the comparison result.
2. The method for processing vehicle crash data as set forth in claim 1, wherein the step of determining the sampling area and the sampling grid thereof in the vehicle model based on the maintenance record comprises:
establishing a connection channel with a research database, and reading a stored automobile model;
acquiring after-sales information of the same type of automobiles in real time, inquiring maintenance records in the after-sales information, and determining the damage probability of different parts according to the maintenance records;
determining a sampling area and a sampling grid thereof in the automobile model according to the damage probability of different parts;
wherein the size of the cells in the sampling grid is inversely proportional to the probability of damage.
3. The method for processing collision avoidance data of a vehicle according to claim 1, wherein the step of installing the acquisition sensor based on the sampling grid and receiving the acquisition data of the acquisition sensor in real time comprises:
randomly selecting a preset number of target units in a sampling grid to serve as installation points;
sequentially taking the installation points as centers, calculating the number of other installation points in a preset radius range, and marking the installation point serving as the center as a core point based on the number;
classifying all the installation points based on the core points to obtain a classification array;
circularly executing and determining final installation points according to the classifying array, and receiving acquisition data of the acquisition sensor in real time;
the index of the classifying array is a core point index, and the element values in the classifying array are the number of similar installation points; the classifying array contains noise point elements; and when the distances between a certain installation point and all the core points are larger than a preset distance threshold value, marking the installation point as a noise point.
4. The method for processing vehicle collision avoidance data according to claim 1, wherein the step of generating a time domain map from the acquired data and determining the correlation of each acquired data based on the time domain map comprises:
counting the acquired data received by all the acquisition sensors based on the same coordinate axis;
converting the acquired data into a jump edge signal according to the acquired data derivative characteristic;
inputting the jump edge signal into a preset coloring model, and outputting a jump signal diagram corresponding to the acquired data;
and selecting jump signal diagrams corresponding to the two acquired data, comparing the two jump signal diagrams, and calculating the correlation degree of the two acquired data.
5. The method for processing vehicle collision avoidance data according to claim 1, wherein the step of classifying and identifying the collected data according to the degree of correlation, and determining the collision feature comprises:
sequentially taking each acquired data as a reference, and inquiring the correlation degree of the acquired data and other acquired data;
comparing the correlation with a preset correlation threshold, and marking corresponding acquired data when the correlation reaches the preset correlation threshold;
counting the marked collected data to obtain classified data;
inputting the classifying data into a preset recognition model to obtain collision characteristics;
after the angelica data are generated, inquiring the correlation degree of each acquired data in the unlabeled acquired data and the classified data to obtain a correlation degree array; and expanding the classified data according to the relevance array.
6. The method for processing vehicle collision avoidance data according to claim 1, wherein the step of generating a theoretical collision model based on the collision features, comparing the theoretical collision model with an actual collision scene, and correcting the collision features according to the comparison result comprises:
inquiring a theoretical collision model in a preset collision model library according to the collision characteristics;
and comparing the theoretical collision model with the actual collision scene, and correcting collision characteristics according to the comparison result.
7. An automotive crash-proof data processing system, said system comprising:
the sampling parameter determining module is used for determining a sampling area and a sampling grid thereof in the automobile model according to the maintenance record;
the data receiving module is used for installing the acquisition sensor based on the sampling grid and receiving the acquisition data of the acquisition sensor in real time; the acquired data contains time information;
the correlation calculation module is used for generating a time domain graph according to the acquired data and determining the correlation of each acquired data based on the time domain graph;
the classification and identification module is used for classifying and identifying the acquired data according to the correlation degree and determining collision characteristics; the collision features include mechanical and kinematic parameters during a collision;
and the characteristic correction module is used for generating a theoretical collision model based on the collision characteristics, comparing the theoretical collision model with an actual collision scene, and correcting the collision characteristics according to the comparison result.
8. The automotive crash-proof data processing system of claim 7, wherein said sampling parameter determination module comprises:
the model reading unit is used for establishing a connecting channel with the research and development database and reading the stored automobile model;
the probability determining unit is used for acquiring after-sales information of the same type of automobiles in real time, inquiring maintenance records in the after-sales information and determining the damage probability of different parts according to the maintenance records;
the processing execution unit is used for determining a sampling area and a sampling grid thereof in the automobile model according to the damage probability of different parts;
wherein the size of the cells in the sampling grid is inversely proportional to the probability of damage.
9. The automotive crash-proof data processing system of claim 7, wherein said data receiving module comprises:
the selecting unit is used for randomly selecting a preset number of target units in the sampling grid to serve as installation points;
the core point determining unit is used for sequentially taking the installation points as centers, calculating the number of other installation points in a preset radius range, and marking the installation point serving as the center as a core point based on the number;
the point location classifying unit is used for classifying all the installation points based on the core points to obtain a classifying array;
the receiving and executing unit is used for circularly executing and determining a final installation point position according to the classifying array, and receiving the acquired data of the acquisition sensor in real time;
the index of the classifying array is a core point index, and the element values in the classifying array are the number of similar installation points; the classifying array contains noise point elements; and when the distances between a certain installation point and all the core points are larger than a preset distance threshold value, marking the installation point as a noise point.
10. The automotive crash-proof data processing system of claim 7, wherein said correlation calculation module comprises:
the data statistics unit is used for counting the acquired data received by all the acquisition sensors based on the same coordinate axis;
the signal conversion unit is used for converting the acquired data into jump edge signals according to the acquired data derivative characteristics;
the signal diagram output unit is used for inputting the jump edge signal into a preset coloring model and outputting a jump signal diagram corresponding to the acquired data;
and the signal diagram comparison unit is used for selecting the jump signal diagrams corresponding to the two acquired data, comparing the two jump signal diagrams and calculating the correlation degree of the two acquired data.
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