Automobile one-dimensional virtual matching method and device based on big data platform

Abstract

The invention discloses an automobile one-dimensional virtual matching method and device based on a big data platform. The method comprises the following steps: determining an influence factor of a to-be-matched target; according to a preset virtual matching rule, collecting part data of the influence factor; determining calculation logic according to the influence factors; and according to the calculation logic and the part data, calculating to obtain a target deviation value of the target to be matched. The analysis efficiency of the automobile assembly size problem can be improved.

Description

Technical Field

[0001] The present invention relates to the field of automobile testing, and in particular to a one-dimensional virtual matching method and device for an automobile based on a big data platform. Background Art

[0002] With the rapid development of the modern automobile industry, market competition is becoming increasingly fierce. Improving automobile quality and shortening the development cycle are the keys for automobile manufacturers to win the market competition. In the manufacturing process, controlling the size of parts is an important aspect of quality control. How to eliminate matching defects to ensure higher vehicle manufacturing quality has always been a difficult problem for automobile manufacturers. The traditional method of automobile dimensional engineering is to use a gauge to evaluate the size of the parts.

[0003] At present, when analyzing the appearance size and abnormal noise problems of the whole vehicle, such as a crack, engineers usually list all the factors that affect the crack based on the thinking of one-dimensional dimension chain, and collect the actual measurement data of each part involved on site, and then list all the data in EXCEL, and calculate through the function formula provided by EXCEL, so as to decompose the actual size problem encountered in the installation, find all the factors affecting the problem, and compare and refine the single-piece measurement data in detail to see if it is consistent with the actual fault site, so as to check whether the influencing factors are found completely.

[0004] However, this method will result in data being collected and analyzed only when problems arise. Moreover, collecting and organizing data takes a lot of time, and the efficiency of problem analysis is extremely low, which greatly affects the efficiency of normal automobile production and testing. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, the present invention provides a one-dimensional virtual matching method and device for automobiles based on a big data platform, which can improve the analysis efficiency of automobile assembly size problems.

[0006] An embodiment of the present invention provides a one-dimensional virtual matching method for a car based on a big data platform, comprising the following steps:

[0007] Determine the influencing factors of the target to be matched;

[0008] According to a preset virtual matching rule, collecting part data of the influencing factor;

[0009] Determine the calculation logic according to the influencing factors;

[0010] According to the calculation logic and the part data, a target deviation value of the target to be matched is calculated.

[0011] Furthermore, the determining of the influencing factor of the target to be matched specifically includes:

[0012] The one-dimensional dimension chain of the target to be matched is calculated, and all the influencing factors are determined according to the one-dimensional dimension chain.

[0013] Preferably, after determining the impact factor, the method further comprises:

[0014] According to a preset template, all the influencing factors are input into the preset template to obtain an influence table; wherein the preset template is a visualization template with a fixed format.

[0015] Furthermore, collecting the part data of the influencing factor according to the preset virtual matching rule specifically includes:

[0016] According to the data sampling method defined by the preset virtual matching rule, the part data of the influencing factor is collected from the preset big data platform; wherein, the data sampling method includes sampling according to the latest data, sampling according to a fixed time period and random sampling, and the part data includes the base, vehicle model, part number, measurement point and size data corresponding to the part.

[0017] Preferably, the preset big data platform includes a big data platform based on CM4D operation logic, and the part data is uploaded to the preset big data platform according to a preset format.

[0018] Furthermore, determining the calculation logic according to the influencing factor specifically includes:

[0019] Determining a detection direction corresponding to the influencing factor according to the measuring point of the influencing factor;

[0020] When the detection direction is opposite to the preset deviation direction, the dimensional data of the influencing factor is multiplied by -1 when calculating the target deviation value; wherein the preset deviation direction is specifically: the direction in which the part contour material increases is positive, and the direction in which the part contour material decreases is negative.

[0021] Furthermore, the target deviation value of the target to be matched is calculated according to the calculation logic and the part data, specifically including:

[0022] A deviation calculation formula is determined according to the calculation logic, and the dimension data is substituted into the deviation calculation formula to calculate the target deviation value.

[0023] Preferably, after obtaining the target deviation value, the method further comprises:

[0024] When the target deviation value exceeds the preset deviation threshold, an early warning message is sent.

[0025] Another embodiment of the present invention provides a one-dimensional virtual matching device for automobiles based on a big data platform, comprising: an influencing factor module, a calculation logic module, a collection module, and a calculation module;

[0026] The impact factor module is used to determine the impact factor of the target to be matched;

[0027] The calculation logic module is used to determine the calculation logic according to the influencing factors;

[0028] The acquisition module is used to collect the part data of the influencing factor according to a preset virtual matching rule;

[0029] The calculation module is used to calculate the target deviation value of the target to be matched according to the calculation logic and the part data.

[0030] Furthermore, the impact factor module is used to determine the impact factor of the target to be matched, specifically including:

[0031] The one-dimensional dimension chain of the target to be matched is calculated, and all the influencing factors are determined according to the one-dimensional dimension chain.

[0032] Compared with the prior art, the beneficial effects of the present invention are:

[0033] Based on the thinking of one-dimensional dimension chain, the influencing factors of the target to be matched are sorted out in advance, and the target to be matched is analyzed in advance according to the customized virtual matching rules. This can predict possible problems in the assembly process in advance, and can also improve the analysis efficiency of the problem after the assembly dimension problem occurs. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 A schematic flow chart of a one-dimensional virtual matching method for a car based on a big data platform provided in one embodiment of the present invention.

[0035] Figure 2 A schematic diagram of a visualization template of an impact factor provided by an embodiment of the present invention.

[0036] Figure 3 A schematic diagram of formulating a virtual matching rule provided by an embodiment of the present invention.

[0037] Figure 4 A schematic diagram of a parts data online big data platform provided in one embodiment of the present invention.

[0038] Figure 5 A schematic diagram of uploading dimension data provided by an embodiment of the present invention.

[0039] Figure 6 A schematic diagram of a calculation logic provided by an embodiment of the present invention.

[0040] Figure 7 A custom schematic diagram of a calculation formula provided by an embodiment of the present invention.

[0041] Figure 8 A flowchart of another preferred embodiment of a one-dimensional virtual matching method for a car based on a big data platform provided by the present invention.

[0042] Fig. 9 A schematic structural diagram of a one-dimensional virtual matching device for a car based on a big data platform is provided in accordance with another embodiment of the present invention. DETAILED DESCRIPTION

[0043] The drawings are for illustrative purposes only and should not be construed as limiting the present patent;

[0044] It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0045] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0046] Reference Figure 1 , is a flow chart of a one-dimensional virtual matching method for a car based on a big data platform provided by an embodiment of the present invention, comprising the following steps:

[0047] S1: Determine the influencing factors of the target to be matched;

[0048] S2: collecting part data of the influencing factors according to a preset virtual matching rule;

[0049] S3: Determine the calculation logic according to the influencing factors;

[0050] S4: Calculate the target deviation value of the target to be matched according to the calculation logic and the part data.

[0051] For step S1, specifically, determining the influencing factor of the target to be matched specifically includes:

[0052] The one-dimensional dimension chain of the target to be matched is calculated, and all the influencing factors are determined according to the one-dimensional dimension chain.

[0053] In a preferred embodiment, in actual work, the target to be matched may be a slit. For this slit, it is necessary to decompose all the influencing factors that have an impact on the slit according to the idea of ​​a one-dimensional dimension chain.

[0054] In the existing technology, engineers generally list all the factors that affect the seam based on the thinking of one-dimensional dimension chain, collect the actual measurement data of each part involved on site, and then list all the data in EXCEL, and calculate through the function formula provided by EXCEL, so as to decompose the dimensional problems actually encountered in loading, find all the factors affecting the problem, and compare in detail whether the single-piece measurement data is consistent with the actual fault site, so as to check whether the influencing factors are found completely.

[0055] It is not difficult to see that in the existing solutions, problems are always solved and analyzed only after they occur, which leads to the following problems: the efficiency of problem analysis is very low and it is easy to cause production line stoppage; it is difficult to sort out the problem factors on site and the problem is difficult to deal with; it is difficult to collect measurement data and requires a large amount of manpower to measure on site; and the timeliness is poor. Therefore, in view of this defect of the prior art, the present invention greatly improves the speed and timeliness of problem analysis by making plans in advance and determining the influencing factors of each target to be matched.

[0056] Preferably, after determining the impact factor, the method further comprises:

[0057] According to a preset template, all the influencing factors are input into the preset template to obtain an influence table; wherein the preset template is a visualization template with a fixed format.

[0058] In a preferred embodiment, referring to Figure 2 , is a schematic diagram of a visualization template of an impact factor provided by an embodiment of the present invention. Figure 2 It can be seen that after obtaining the influencing factors, all of these influencing factors can be put into a pre-made visualization template with a fixed format to visualize all the influencing factors, so that users or maintenance engineers can see the influencing factors related to the target to be matched at a glance.

[0059] For step S2, specifically, collecting the part data of the influencing factor according to the preset virtual matching rule specifically includes:

[0060] According to the data sampling method defined by the preset virtual matching rule, the part data of the influencing factor is collected from the preset big data platform; wherein, the data sampling method includes sampling according to the latest data, sampling according to a fixed time period and random sampling, and the part data includes the base, vehicle model, part number, measurement point and size data corresponding to the part.

[0061] Preferably, the preset big data platform includes a big data platform based on CM4D operation logic, and the part data is uploaded to the preset big data platform according to a preset format.

[0062] In a preferred embodiment, referring to Figure 3 , is a schematic diagram of formulating a virtual matching rule provided by an embodiment of the present invention. Figure 3 It can be seen that the formulation of virtual matching rules is actually the definition of the data sampling method. Each part has measured data in each time period, so the optional sampling methods include: matching by the latest data, matching by production in a fixed time period for loading, random calculation or one-to-one calculation according to time, etc. Users can customize the sampling method according to actual needs.

[0063] After the virtual matching rules are formulated, it is necessary to sample the corresponding part data of the influencing factors from the preset big data platform. In this preferred embodiment, the preset big data platform is a big data platform redeveloped according to the CM4D operation logic, which contains the 3D Drive tool based on its own needs and IT development. Figure 4 , is a schematic diagram of a big data platform for uploading parts data provided by an embodiment of the present invention. After the parts are produced, whether they are from suppliers or self-made parts in the factory, they need to fill in the base, model, part number, measurement point and other information in a fixed format and then upload them to the big data platform. In addition, the actual measured size data of the parts also needs to be uploaded to the preset big data platform. Figure 5 , which is a schematic diagram of uploading dimension data provided by an embodiment of the present invention.

[0064] For step S3, specifically, determining the calculation logic according to the influencing factor specifically includes:

[0065] Determining a detection direction corresponding to the influencing factor according to the measuring point of the influencing factor;

[0066] When the detection direction is opposite to the preset deviation direction, the dimensional data of the influencing factor is multiplied by -1 when calculating the target deviation value; wherein the preset deviation direction is specifically: the direction in which the part contour material increases is positive, and the direction in which the part contour material decreases is negative.

[0067] In a preferred embodiment, referring to Figure 6 , is a schematic diagram of a calculation logic provided by an embodiment of the present invention. Figure 6 It can be seen that, taking the virtual matching of the fender X-direction welding adjustment as an example, it is necessary to unify the deviation direction when sorting out the calculation logic.

[0068] The process of sorting out the calculation logic specifically includes: taking the direction of increasing part contour material as positive and the direction of decreasing part contour material as negative, to determine whether the inspection data needs to be corrected (if necessary, multiply the formula by -1). For example, the door is the inspection fixture data, and the direction of increasing inspection data material is negative, so it needs to be multiplied by -1 to correct the direction; while the direction of increasing body inspection data material is positive, which is consistent, so it does not need to be multiplied by -1 to correct the direction.

[0069] For step S4, specifically, the target deviation value of the target to be matched is calculated according to the calculation logic and the part data, specifically including:

[0070] A deviation calculation formula is determined according to the calculation logic, and the dimension data is substituted into the deviation calculation formula to calculate the target deviation value.

[0071] Preferably, after obtaining the target deviation value, the method further comprises:

[0072] When the target deviation value exceeds the preset deviation threshold, an early warning message is sent.

[0073] In a preferred embodiment, referring to Figure 7 , is a self-defined schematic diagram of a calculation formula provided by an embodiment of the present invention. Figure 7 It can be seen that after the calculation logic is sorted out, the deviation calculation formula for calculating the deviation value can be customized according to the calculation logic. Then, the size data corresponding to each part is substituted into the deviation calculation formula, and the target deviation value can be automatically calculated.

[0074] Different targets to be matched may have different tolerance requirements. When the target deviation value obtained exceeds the preset deviation threshold of the tolerance requirement, an early warning message will be sent to the engineer in the form of an email or the like.

[0075] In summary, the present invention is based on the existing big data dimension platform, and redevelops new functions on the existing system logic. It directly carries the measurement data of parts at all levels through the big data platform, and newly develops the cross-part measurement data fusion function, and then adds custom formula calculations. It directly makes the corresponding visualization templates and plans the calculation logic in advance on the big data platform, urges the upload of parts data at all levels, and after ensuring the authenticity of the data, the system automatically calculates and outputs the corresponding virtual matching report, thereby fundamentally solving the problem of low analysis efficiency in the prior art.

[0076] In another preferred embodiment, referring to Figure 8 , which is a flow chart of another preferred embodiment of a one-dimensional virtual matching method for a car based on a big data platform provided by the present invention. Figure 8It can be seen that the technical solution adopted by this preferred embodiment is: relying on the dimension big data platform, uploading the parts of each level to the dimension platform; then uploading the actual measurement data of the parts of each level to form a measurement database; then sorting out all the influencing factors (for parts, they are related measurement points) with the thinking of one-dimensional dimension chain; then making a visual template on the platform based on these influencing factors, and defining the calculation formula; finally defining the virtual matching rules. In this way, the system can virtually match when the data is uploaded, calculate the results in advance, and predict the problem.

[0077] Reference Fig. 9 , is a schematic diagram of a structure of a one-dimensional virtual matching device for automobiles based on a big data platform provided by another embodiment of the present invention, comprising: an influencing factor module 101, a calculation logic module 102, a collection module 103 and a calculation module 104;

[0078] The impact factor module 101 is used to determine the impact factor of the target to be matched;

[0079] The calculation logic module 102 is used to determine the calculation logic according to the impact factor;

[0080] The acquisition module 103 is used to acquire the part data of the influencing factor according to a preset virtual matching rule;

[0081] The calculation module 104 is used to calculate the target deviation value of the target to be matched according to the calculation logic and the part data.

[0082] Furthermore, the impact factor module 101 is used to determine the impact factor of the target to be matched, specifically including:

[0083] The one-dimensional dimension chain of the target to be matched is calculated, and all the influencing factors are determined according to the one-dimensional dimension chain.

[0084] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the embodiments here. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A one-dimensional virtual matching method for automobiles based on a big data platform, characterized in that: The steps include: Determine the influencing factors of the target to be matched; According to a preset virtual matching rule, collecting part data of the influencing factor; Determine the calculation logic according to the influencing factors; According to the calculation logic and the part data, a target deviation value of the target to be matched is calculated.

2. The one-dimensional virtual matching method for automobiles based on a big data platform as claimed in claim 1, characterized in that: The determining of the influencing factor of the target to be matched specifically includes: The one-dimensional dimension chain of the target to be matched is calculated, and all the influencing factors are determined according to the one-dimensional dimension chain.

3. The one-dimensional virtual matching method for automobiles based on a big data platform as claimed in claim 2, characterized in that: After determining the impact factor, it also includes: According to a preset template, all the influencing factors are input into the preset template to obtain an influence table; wherein the preset template is a visualization template with a fixed format.

4. The one-dimensional virtual matching method for automobiles based on a big data platform as claimed in claim 1, characterized in that: The collecting of the part data of the influencing factor according to the preset virtual matching rule specifically includes: According to the data sampling method defined by the preset virtual matching rule, the part data of the influencing factor is collected from the preset big data platform; wherein, the data sampling method includes sampling according to the latest data, sampling according to a fixed time period and random sampling, and the part data includes the base, vehicle model, part number, measurement point and size data corresponding to the part.

5. The one-dimensional virtual matching method for automobiles based on a big data platform as claimed in claim 4, characterized in that: The preset big data platform includes a big data platform based on CM4D operation logic, and the part data is uploaded to the preset big data platform according to a preset format.

6. The one-dimensional virtual matching method for automobiles based on a big data platform as claimed in claim 4, characterized in that: Determining the calculation logic according to the influencing factor specifically includes: Determining a detection direction corresponding to the influencing factor according to the measuring point of the influencing factor; When the detection direction is opposite to the preset deviation direction, the dimensional data of the influencing factor is multiplied by -1 when calculating the target deviation value; wherein the preset deviation direction is specifically: the direction in which the part contour material increases is positive, and the direction in which the part contour material decreases is negative.

7. The one-dimensional virtual matching method for automobiles based on a big data platform as claimed in claim 1, characterized in that: The step of calculating the target deviation value of the target to be matched according to the calculation logic and the part data specifically includes: A deviation calculation formula is determined according to the calculation logic, and the dimension data is substituted into the deviation calculation formula to calculate the target deviation value.

8. The one-dimensional virtual matching method for automobiles based on a big data platform as claimed in claim 7, characterized in that: After obtaining the target deviation value, the method further includes: When the target deviation value exceeds the preset deviation threshold, an early warning message is sent.

9. A one-dimensional virtual matching device for automobiles based on a big data platform, characterized in that: include: Impact factor module, calculation logic module, acquisition module and calculation module; The impact factor module is used to determine the impact factor of the target to be matched; The calculation logic module is used to determine the calculation logic according to the influencing factors; The acquisition module is used to collect the part data of the influencing factor according to a preset virtual matching rule; The calculation module is used to calculate the target deviation value of the target to be matched according to the calculation logic and the part data.

10. The one-dimensional virtual matching device for automobiles based on a big data platform as claimed in claim 9, characterized in that: The impact factor module is used to determine the impact factor of the target to be matched, specifically including: The one-dimensional dimension chain of the target to be matched is calculated, and all the influencing factors are determined according to the one-dimensional dimension chain.

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