Optimization control method and system for intelligent carrying of automobile bumpers

Abstract

The invention provides an optimal control method and system for intelligent carrying of automobile bumpers, and relates to the technical field of bumper carrying, the method comprises the steps that a bumper carrying database is constructed, the bumper carrying database comprises bumper models and bumper carrying positions, and the bumper models and the bumper carrying positions are stored in the bumper carrying database; the models of the bumpers are in one-to-one correspondence with the carrying positions; obtaining first image acquisition data; obtaining a feature recognition result; performing bumper model matching on the target bumper to obtain a target bumper model; obtaining a carrying position matching result; the material type of the target bumper is obtained, and a clamp type matching result is obtained; generating a control instruction; and carrying control is conducted on the target bumper. The technical problems that in the prior art, due to the fact that the recognition accuracy of bumper images is not high and clamps are selected improperly, the carrying position is not accurate in the bumper carrying process, and the protection effect on bumpers is not good are solved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of bumper transportation, and in particular to an optimization control method and system for intelligent transportation of automobile bumpers. Background Art

[0002] The production process of automobile bumpers mainly includes injection molding, painting, grinding and polishing, assembly, warehousing and other processes. After injection molding, the bumper injection molded parts need to be classified by type and stored in the designated area of ​​the buffer warehouse to facilitate the retrieval of the bumper in the next link. From its production process, it can be seen that the bumper has to be moved many times.

[0003] At present, the prior art has technical problems such as low accuracy in identifying the bumper image and improper selection of the clamp, which results in inaccurate transport position during bumper transport and poor protection effect on the bumper. Summary of the invention

[0004] The present invention provides an optimization control method and system for intelligent transportation of automobile bumpers, which are used to solve the technical problems existing in the prior art, such as low recognition accuracy of bumper images and improper selection of clamps, resulting in inaccurate transportation positions during bumper transportation and poor protection of the bumper.

[0005] According to a first aspect of the present disclosure, an optimization control method for intelligent transportation of automobile bumpers is provided, comprising: constructing a bumper transportation database, wherein the bumper transportation database includes bumper models and transportation positions of bumpers, and the bumper models correspond to the transportation positions one by one; performing image acquisition of a target bumper through the image acquisition device to obtain first image acquisition data; performing feature recognition based on the first image acquisition data to obtain a feature recognition result; performing bumper model matching on the target bumper according to the feature recognition result to obtain a target bumper model; performing transportation position matching based on the bumper model and the transportation position in the bumper transportation database according to the target bumper model to obtain a transportation position matching result; obtaining a material type of the target bumper, and performing fixture type matching based on the material type to obtain a fixture type matching result; generating a control instruction according to the transportation position matching result and the fixture type matching result; and performing transportation control on the target bumper according to the control instruction.

[0006] According to a second aspect of the present disclosure, there is provided an optimization control system for intelligent transportation of automobile bumpers, comprising:

[0007] According to a third aspect of the present disclosure, there is provided an electronic device, including:

[0008] at least one processor; and

[0009] a memory communicatively connected to the at least one processor; wherein,

[0010] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method described in the first aspect.

[0011] According to an optimization control method for intelligent transportation of automobile bumpers adopted by the present disclosure, a bumper transportation database is constructed, wherein the bumper transportation database includes bumper models and transportation positions of bumpers, and the bumper models correspond to the transportation positions one by one; an image of a target bumper is acquired by the image acquisition device to obtain first image acquisition data; feature recognition is performed based on the first image acquisition data to obtain a feature recognition result; based on the feature recognition result, bumper model matching is performed on the target bumper to obtain a target bumper model; and based on the target bumper model, the transportation position is matched based on the bumper model and the transportation position in the bumper transportation database. Obtaining a transport position matching result; obtaining the material type of the target bumper, and matching the fixture type based on the material type to obtain a fixture type matching result; generating a control instruction according to the transport position matching result and the fixture type matching result; and controlling the transport of the target bumper according to the control instruction. The present invention constructs a bumper transport database, performs image capture of the target bumper through the image capture device, performs feature extraction and recognition on the bumper image, matches the transport position in the bumper transport database according to the recognition result, and matches the fixture type at the same time, thereby controlling the transport of the bumper, improving the accuracy of the bumper transport process, and achieving the technical effect of protecting the bumper.

[0012] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the present disclosure or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0014] Figure 1A schematic flow chart of an optimization control method for intelligent transportation of a vehicle bumper provided in an embodiment of the present disclosure;

[0015] Figure 2 A schematic diagram of the structure of an optimization control system for intelligent transportation of automobile bumpers provided in an embodiment of the present disclosure;

[0016] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present disclosure.

[0017] Explanation of the reference numerals: bumper transport database construction module 11, image acquisition module 12, feature recognition module 13, bumper model matching module 14, transport position matching module 15, fixture type matching module 16, control instruction generation module 17, transport control module 18, electronic device 800, processor 801, memory 802, bus 803. DETAILED DESCRIPTION

[0018] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0019] In order to solve the technical problems existing in the prior art of inaccurate bumper image recognition accuracy and improper clamp selection, resulting in inaccurate bumper position during bumper transportation and poor bumper protection, the inventors of the present invention have, through creative work, obtained an optimized control method and system for intelligent transportation of automobile bumpers disclosed in the present invention.

[0020] Embodiment 1

[0021] Figure 1 FIG. 1 is a diagram of an optimization control method for intelligent transportation of automobile bumpers provided in an embodiment of the present application. The method is applied to an optimization control system for intelligent transportation of automobile bumpers. The system is connected to an image acquisition device for communication, such as Figure 1 As shown, the method includes:

[0022] Step S100: constructing a bumper transport database, wherein the bumper transport database includes bumper models and bumper transport positions, and the bumper models correspond to the transport positions one by one;

[0023] Specifically, the above-mentioned image acquisition equipment includes but is not limited to cameras, camcorders and other equipment. The bumper model refers to the specifications of the car bumper, applicable vehicle models and other information. The bumper transportation position refers to the position where the bumper of each model of car needs to be transported to. Different bumper models have different corresponding transportation positions. The bumper model and the transportation position have a one-to-one correspondence, that is, each model of bumper has a corresponding transportation position.

[0024] Step S200: performing image acquisition of a target bumper through the image acquisition device to obtain first image acquisition data;

[0025] Specifically, the target bumper is a bumper that needs to be moved. The image of the target bumper is captured by an image capture device to obtain first image capture data, which includes image data of the target bumper.

[0026] Step S300: performing feature recognition based on the first image acquisition data to obtain a feature recognition result;

[0027] The feature recognition is performed based on the first image acquisition data to obtain a feature recognition result. Step S300 of the embodiment of the present application further includes:

[0028] Step S310: performing image preprocessing on the first image acquisition data to obtain an image preprocessing result;

[0029] Step S320: extracting a region of interest from the image preprocessing result using the minimum boundary moment to obtain a region extraction result;

[0030] Step S330: performing feature recognition of the target bumper based on the region extraction result to obtain the feature recognition result.

[0031] The image preprocessing is performed on the first image acquisition data to obtain an image preprocessing result. In the embodiment of the present application, step S310 further includes:

[0032] Step S311: performing grayscale processing on the first image acquisition data to obtain grayscale image data;

[0033] Step S312: performing image noise reduction processing on the grayscale image data to obtain the image preprocessing result.

[0034] Specifically, feature recognition is performed based on the first image acquisition data. Specifically, image processing and feature extraction can be performed on the first image acquisition data by a computer to obtain a feature recognition result.

[0035] Specifically, the first image acquisition data is subjected to image preprocessing to obtain an image preprocessing result. When the original image data is processed, the memory occupied will be relatively large, which affects the processing speed. Image preprocessing can improve the operation speed and avoid image distortion. Further, the image preprocessing result is subjected to region of interest extraction using the minimum boundary moment to obtain a region extraction result. In simple terms, the constituent points of the outer contour of the bumper are first found from the image, and then the minimum rectangle containing all the outer contours is calculated and the minimum rectangle is drawn, thereby obtaining a region extraction result, thereby avoiding interference from the background image. It should be noted that image preprocessing and region of interest extraction using the minimum boundary moment can be implemented by computer programming, such as by using the library function in OpenCV. Further, feature recognition of the target bumper is performed based on the region extraction result to obtain the feature recognition result. That is, the minimum rectangular area containing only the bumper is obtained by extracting the minimum boundary moment. The feature recognition result can be obtained by only performing feature recognition on the minimum area. The feature recognition result includes the shape, size, style, etc. of the bumper.

[0036] Specifically, the first image acquisition data is a three-channel color image. In image processing, images are calculated in the form of matrices. The three-channel image data uses three elements to represent the pixel value of a pixel point for storage, which occupies a large amount of memory during processing and limits the processing speed. The grayscale image can reduce the difficulty of image processing and improve the efficiency of image processing. The image color can be converted to the grayscale image through the library function provided by the OpenCV library, and then the grayscale image data is subjected to image denoising to obtain the image preprocessing result. The source image obtained from the work site will always have a certain noise influence, and there will also be noise during the image transmission process. Therefore, the image needs to be subjected to denoising before image recognition to reduce the interference of various noises, reduce the amount of calculation in the subsequent recognition process, and increase the accuracy of recognition. Here, the common filters such as the mean filter (Blur), the median filter (Median Blur), and the Gaussian filter (GaussianBlur) provided by OpenCV can be selected to eliminate the noise mixed in the image, thereby obtaining the image preprocessing result.

[0037] Step S400: performing bumper model matching on the target bumper according to the feature recognition result to obtain the target bumper model;

[0038] Specifically, according to the feature recognition result, the target bumper is matched with the bumper model to obtain the target bumper model. In other words, according to the feature recognition result of the target bumper, the bumper model is matched to determine the target bumper model information.

[0039] Step S500: matching the transport position according to the target bumper model, based on the bumper model and the transport position in the bumper transport database, to obtain a transport position matching result;

[0040] Specifically, according to the target bumper model, the transport position is matched based on the bumper model and the transport position in the bumper transport database. In simple terms, the bumper model that is the same as the target bumper model is screened and matched in the bumper transport database. The bumper model and the transport position in the bumper transport database are one-to-one corresponding. According to the matched bumper model, the corresponding transport position is screened out to obtain the transport position matching result.

[0041] Step S600: acquiring the material type of the target bumper, and matching the fixture type based on the material type to obtain a fixture type matching result;

[0042] The material type of the target bumper is obtained, and the fixture type is matched based on the material type to obtain a fixture type matching result. In this embodiment of the present application, step S600 further includes:

[0043] Step S610: constructing a bumper material type set;

[0044] Step S620: constructing a fixture type set according to the bumper material type set, wherein data in the fixture type set has a first corresponding relationship with data in the bumper material type set;

[0045] Step S630: obtaining the material type of the target bumper, filtering the bumper material type set, and obtaining a material type filtering result;

[0046] Step S640: According to the material type screening result, based on the first corresponding relationship, matching the handling fixture within the fixture type set is performed to obtain the fixture type matching result.

[0047] Specifically, the material type of the target bumper is obtained. The material type refers to the material used to manufacture the bumper, such as steel plate, plastic, aluminum alloy, etc., and then the fixture type is matched according to the material type. In other words, different types of fixtures are used when transporting bumpers of different material types. Furniture matching is performed according to the material type of the target bumper to obtain the fixture type matching result.

[0048] Specifically, a bumper material type set is constructed, and the bumper material type set includes multiple material type data, such as steel plate, plastic, aluminum alloy, etc. According to the bumper material type set, a fixture type set is constructed, and the fixture type set includes multiple fixture types corresponding to the bumper material types, wherein the data in the fixture type set has a first corresponding relationship with the data in the bumper material type set. In other words, one data in the fixture type set corresponds to one data in the bumper material type set. Further, the material type of the target bumper is obtained, and the bumper material type set is filtered according to the material type of the target bumper to filter out the material type that is the same as the material type of the target bumper, thereby obtaining a material type filtering result, and then according to the material type filtering result, based on the first corresponding relationship, the handling fixture is matched in the fixture type set, that is, the fixture type corresponding to the material type filtering result is filtered out in the fixture type set to obtain a fixture type matching result.

[0049] Step S700: generating a control instruction according to the transport position matching result and the fixture type matching result;

[0050] Specifically, a control instruction is generated according to the transport position matching result and the fixture type matching result. The control instruction is an instruction for transport control and is used to control the transport machine to select a suitable transport fixture to transport the target bumper to the corresponding position.

[0051] Step S800: Controlling the transport of the target bumper according to the control instruction.

[0052] Specifically, according to the control instruction, the target bumper is transported and controlled, thereby completing the bumper transport work.

[0053] Based on the above analysis, it can be known that the present disclosure provides an optimization control method for the intelligent transportation of automobile bumpers. In this embodiment, a bumper transportation database is constructed, the image of the target bumper is captured by the image acquisition device, and the bumper image is subjected to feature extraction and recognition. The transportation position is matched in the bumper transportation database according to the recognition result, and the fixture type is matched at the same time, so as to control the transportation of the bumper, improve the accuracy of the bumper transportation process, and achieve the technical effect of protecting the bumper.

[0054] Among them, step S900 of the embodiment of the present application also includes:

[0055] Step S910: Detecting whether the target bumper has a damaged area according to the first image acquisition data;

[0056] Step S920: If the target bumper has the damaged area, a repair instruction is issued;

[0057] Step S930: Inspect the target bumper according to the inspection instruction.

[0058] Specifically, based on the first image acquisition data, it is detected whether there is a damaged area on the target bumper. The damaged area refers to whether the bumper has paint loss, scratches, dents and other damaged areas. Here, you can choose to use a convolution kernel to perform image processing on the first image acquisition data to determine whether there is a damaged area. The damaged features of the bumper image with the damaged area can be extracted. The damaged area is used as a standard feature, and the first image acquisition data is divided into multiple sub-areas. The sub-areas of the first image acquisition data are compared according to the standard features. The convolution kernels of the standard features and the multiple sub-areas can be calculated. By comparing the sizes of the convolution kernels, it is determined whether there is a damaged area in the first image acquisition data. If the target bumper has a damaged area, a maintenance instruction is issued. The maintenance instruction is an instruction used to remind staff to inspect and repair the bumper. According to the maintenance instruction, the staff is reminded to repair the damaged area of ​​the target bumper.

[0059] Among them, step S1000 of the embodiment of the present application also includes:

[0060] Step S1010: acquiring the bumper transport speed;

[0061] Step S1020: determining whether the bumper transport speed is within a preset transport speed range;

[0062] Step S1030: if the bumper transport speed is not within the preset transport speed range, obtaining a difference between the bumper transport speed and the preset transport speed;

[0063] Step S1040: adjusting the bumper transport speed according to the difference value.

[0064] Specifically, the bumper transport speed is collected and obtained. The bumper transport speed refers to the number of bumpers transported per unit time, which can be obtained by dividing the total number of transported bumpers by the transport time. It is judged whether the bumper transport speed is within the preset transport speed range. The preset transport speed range is the transport speed threshold range set according to actual demand. If the bumper transport speed is not within the preset transport speed range, it means that the transport speed is too fast or too slow. The difference between the bumper transport speed and the preset transport speed is obtained. The difference value can be obtained by subtracting the preset transport speed from the bumper transport speed. The difference value can be positive or negative, indicating whether the bumper transport speed is too large or too small. Then, the bumper transport speed is adjusted according to the difference value. The transport speed is appropriately increased or decreased according to the difference value to control the bumper transport speed.

[0065] Embodiment 2

[0066] Based on the same inventive concept as the optimization control method for intelligent transportation of automobile bumpers in the aforementioned embodiment, Figure 2 As shown, the present application also provides an optimization control system for intelligent transportation of automobile bumpers, the system is communicatively connected with an image acquisition device, and the system includes:

[0067] A bumper transport database construction module 11, wherein the bumper transport database construction module 11 is used to construct a bumper transport database, wherein the bumper transport database includes bumper models and transport positions of bumpers, and the bumper models correspond to the transport positions one by one;

[0068] An image acquisition module 12, the image acquisition module 12 is used to acquire an image of a target bumper through the image acquisition device to obtain first image acquisition data;

[0069] A feature recognition module 13, wherein the feature recognition module 13 is used to perform feature recognition based on the first image acquisition data to obtain a feature recognition result;

[0070] A bumper model matching module 14, the bumper model matching module 14 is used to perform bumper model matching on the target bumper according to the feature recognition result to obtain a target bumper model;

[0071] A transport position matching module 15, the transport position matching module 15 is used to match the transport position according to the target bumper model, based on the bumper model and the transport position in the bumper transport database, to obtain a transport position matching result;

[0072] A fixture type matching module 16, the fixture type matching module 16 is used to obtain the material type of the target bumper, and match the fixture type based on the material type to obtain a fixture type matching result;

[0073] A control instruction generating module 17, wherein the control instruction generating module 17 is used to generate a control instruction according to the transport position matching result and the fixture type matching result;

[0074] The transport control module 18 is used to control the transport of the target bumper according to the control instruction.

[0075] Furthermore, the system further comprises:

[0076] An image preprocessing module, the image preprocessing module is used to perform image preprocessing on the first image acquisition data to obtain an image preprocessing result;

[0077] An area of ​​interest extraction module, the area of ​​interest extraction module is used to extract the area of ​​interest from the image preprocessing result using the minimum boundary moment to obtain an area extraction result;

[0078] A feature recognition result acquisition module is used to perform feature recognition of the target bumper based on the region extraction result to obtain the feature recognition result.

[0079] Furthermore, the system further comprises:

[0080] A grayscale processing module, the grayscale processing module is used to perform grayscale processing on the first image acquisition data to obtain grayscale image data;

[0081] The image noise reduction processing module is used to perform image noise reduction processing on the grayscale image data to obtain the image preprocessing result.

[0082] Furthermore, the system further comprises:

[0083] A bumper material type set building module, wherein the bumper material type set building module is used to build a bumper material type set;

[0084] A fixture type set construction module, the fixture type set construction module is used to construct a fixture type set according to the bumper material type set, wherein the data in the fixture type set has a first corresponding relationship with the data in the bumper material type set;

[0085] A material type screening module, the material type screening module is used to obtain the material type of the target bumper, screen the bumper material type set, and obtain a material type screening result;

[0086] A handling fixture matching module is used to filter the results according to the material type, match the handling fixtures within the fixture type set based on the first corresponding relationship, and obtain the fixture type matching result.

[0087] Furthermore, the system further comprises:

[0088] A damaged area detection module, the damaged area detection module is used to detect whether there is a damaged area on the target bumper according to the first image acquisition data;

[0089] A maintenance instruction acquisition module, the maintenance instruction acquisition module is used to issue a maintenance instruction if the target bumper has the damaged area;

[0090] A bumper repair module is used to repair the target bumper according to the repair instruction.

[0091] Furthermore, the system further comprises:

[0092] A transport speed acquisition module, wherein the transport speed acquisition module is used to acquire the transport speed of the bumper;

[0093] A transport speed determination module, the transport speed determination module is used to determine whether the bumper transport speed is within a preset transport speed range;

[0094] a difference value acquisition module, the difference value acquisition module being used to acquire a difference value between the bumper transport speed and the preset transport speed if the bumper transport speed is not within the preset transport speed range;

[0095] A transport speed adjustment module is used to adjust the transport speed of the bumper according to the difference value.

[0096] The specific example of the optimization control method for intelligent transportation of automobile bumpers in the aforementioned embodiment 1 is also applicable to the optimization control system for intelligent transportation of automobile bumpers in this embodiment. Through the aforementioned detailed description of the optimization control method for intelligent transportation of automobile bumpers, those skilled in the art can clearly know the optimization control system for intelligent transportation of automobile bumpers in this embodiment, so for the sake of brevity of the specification, it will not be described in detail here. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0097] Embodiment 3

[0098] Figure 3 is a schematic diagram according to the third embodiment of the present disclosure, Figure 3 As shown, the electronic device 800 in the present disclosure may include: a processor 801 and a memory 802 .

[0099] The memory 802 is used to store programs. The memory 802 may include volatile memory, such as random access memory (RAM), such as static random access memory (SRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), etc. The memory may also include nonvolatile memory, such as flash memory. The memory 802 is used to store computer programs (such as applications, functional modules, etc. that implement the above methods), computer instructions, etc. The above computer programs, computer instructions, etc. may be partitioned and stored in one or more memories 802. And the above computer programs, computer instructions, data, etc. may be called by the processor 801.

[0100] The above-mentioned computer programs, computer instructions, etc. may be stored in partitions in one or more memories 802 . And the above-mentioned computer programs, computer instructions, etc. may be called by the processor 801 .

[0101] The processor 801 is used to execute the computer program stored in the memory 802 to implement each step of the method involved in the above embodiment.

[0102] For details, please refer to the relevant description in the previous method embodiment.

[0103] The processor 801 and the memory 802 may be independent structures or integrated structures. When the processor 801 and the memory 802 are independent structures, the memory 802 and the processor 801 may be coupled and connected via a bus 803 .

[0104] The electronic device of this embodiment can execute the technical solution in the above method, and its specific implementation process and technical principle are the same, which will not be repeated here.

[0105] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0106] According to an embodiment of the present disclosure, the present disclosure also provides a computer program product, which includes: a computer program, the computer program is stored in a readable storage medium, at least one processor of an electronic device can read the computer program from the readable storage medium, and at least one processor executes the computer program so that the electronic device executes the solution provided by any of the above embodiments.

[0107] It should be understood that the various forms of the above-described processes may be used to reorder, add, or delete steps. For example, the steps described in this disclosure may be performed in parallel, sequentially, or in a different order.

[0108] As long as the expected results of the technical solutions disclosed in this disclosure can be achieved, this document does not impose any limitations here.

[0109] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. An optimization control method for intelligent transportation of automobile bumpers, characterized in that: The method is applied to an optimization control system for intelligent transportation of automobile bumpers, the system is communicatively connected with an image acquisition device, and the method comprises: Constructing a bumper transport database, wherein the bumper transport database includes bumper models and bumper transport positions, and the bumper models correspond to the transport positions one by one; Capturing an image of a target bumper by using the image acquisition device to obtain first image acquisition data; Performing feature recognition based on the first image acquisition data to obtain a feature recognition result; According to the feature recognition result, performing bumper model matching on the target bumper to obtain the target bumper model; According to the target bumper model, based on the bumper model and the transport position in the bumper transport database, matching the transport position to obtain a transport position matching result; Acquire the material type of the target bumper, and match the fixture type based on the material type to obtain a fixture type matching result; Generate a control instruction according to the handling position matching result and the fixture type matching result; According to the control instruction, the target bumper is transported and controlled.

2. The method according to claim 1, characterized in that The performing feature recognition based on the first image acquisition data to obtain a feature recognition result includes: Performing image preprocessing on the first image acquisition data to obtain an image preprocessing result; Extracting a region of interest from the image preprocessing result using a minimum boundary moment to obtain a region extraction result; The feature recognition of the target bumper is performed based on the region extraction result to obtain the feature recognition result.

3. The method according to claim 2, characterized in that The performing image preprocessing on the first image acquisition data to obtain an image preprocessing result includes: Performing grayscale processing on the first image acquisition data to obtain grayscale image data; Perform image noise reduction processing on the grayscale image data to obtain the image preprocessing result.

4. The method according to claim 1, characterized in that The obtaining of the material type of the target bumper and matching of the fixture type based on the material type to obtain a fixture type matching result includes: Construct a bumper material type collection; Constructing a fixture type set according to the bumper material type set, wherein the data in the fixture type set has a first corresponding relationship with the data in the bumper material type set; Obtaining the material type of the target bumper, filtering the bumper material type set, and obtaining a material type filtering result; According to the material type screening result, based on the first corresponding relationship, matching of the handling fixture is performed within the fixture type set to obtain the fixture type matching result.

5. The method according to claim 1, characterized in that The method further comprises: Detecting whether the target bumper has a damaged area according to the first image acquisition data; If the target bumper has the damaged area, issuing a repair instruction; The target bumper is inspected and maintained according to the inspection instruction.

6. The method according to claim 1, characterized in that The method further comprises: Collect and obtain the bumper transport speed; Determining whether the bumper transport speed is within a preset transport speed range; If the bumper transport speed is not within the preset transport speed range, obtaining a difference between the bumper transport speed and the preset transport speed; The bumper transport speed is adjusted according to the difference value.

7. An optimization control system for intelligent transportation of automobile bumpers, characterized in that: The system is connected in communication with the image acquisition device, and the system comprises: A bumper transport database construction module, the bumper transport database construction module is used to construct a bumper transport database, wherein the bumper transport database includes bumper models and bumper transport positions, and the bumper models correspond to the transport positions one by one; An image acquisition module, the image acquisition module is used to acquire an image of a target bumper through the image acquisition device to obtain first image acquisition data; A feature recognition module, the feature recognition module is used to perform feature recognition based on the first image acquisition data to obtain a feature recognition result; A bumper model matching module, the bumper model matching module is used to perform bumper model matching on the target bumper according to the feature recognition result to obtain the target bumper model; A transport position matching module, the transport position matching module is used to match the transport position according to the target bumper model, based on the bumper model and the transport position in the bumper transport database, to obtain a transport position matching result; A fixture type matching module, the fixture type matching module is used to obtain the material type of the target bumper, and match the fixture type based on the material type to obtain a fixture type matching result; A control instruction generation module, the control instruction generation module is used to generate a control instruction according to the handling position matching result and the fixture type matching result; A transport control module is used to control the transport of the target bumper according to the control instruction.

8. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 6.

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