CN118229309B - Traceability methods, devices and media for automotive parts - Google Patents
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Abstract
The embodiment of the application provides a tracing method, a tracing device and a tracing medium for automobile parts. Judging the type of the current texture according to the two-dimensional frequency domain characteristics; determining a texture extraction algorithm based on the type of the current texture, and outputting texture features; determining hidden electromagnetic fingerprint characteristics according to an electromagnetic field intensity distribution diagram of an RFID tag of the outer package of the automobile part; the traceable historical record is constructed according to the conventional information of the hidden electromagnetic fingerprint features, the texture features and the automobile parts so as to trace the automobile parts, at the moment, the texture features and the hidden electromagnetic fingerprint features are introduced so as to correlate the texture features and the hidden electromagnetic fingerprint features, the automobile parts are controlled along different directions through the texture features and the hidden electromagnetic fingerprint features, the multidirectional tracing of the automobile parts is realized, and meanwhile, the traceable historical record is constructed according to the conventional information of the hidden electromagnetic fingerprint features, the texture features and the automobile parts so as to trace the automobile parts.
Description
Technical Field
The application relates to the technical field of automobile parts, in particular to a tracing method, a tracing device and a tracing medium for automobile parts.
Background
Along with development of technology, automobile parts are applied to automobiles and need to be traced to the source in sales or production, at this moment, the automobile parts adopt a single strategy in the aspect of tracing to the source, and follow visual recognition technology, in visual recognition technology, shoot automobile parts to form automobile part images, determine the type of automobile parts based on automobile part images, thereby define the type of automobile parts, but the main utilization of tracing to the source is the outline of automobile parts, the characteristics such as fingerprint to automobile parts do not fully consider, lead to the comprehensively lower of tracing to the source of automobile parts.
Disclosure of Invention
The embodiment of the application provides a tracing method, a tracing device and a tracing medium for automobile parts, which introduce texture features and hidden electromagnetic fingerprint features at least to a certain extent so as to correlate the texture features and the hidden electromagnetic fingerprint features, control the automobile parts along different directions through the texture features and the hidden electromagnetic fingerprint features, realize multidirectional tracing of the automobile parts, and simultaneously construct a traceable history according to the hidden electromagnetic fingerprint features, the texture features and the past information of the automobile parts so as to trace the automobile parts, and ensure the tracing comprehensiveness and accuracy of the automobile parts.
Other features and advantages of the application will be apparent from the following detailed description, or may be learned by the practice of the application.
According to one aspect of the embodiment of the application, a tracing method of automobile parts is provided, and the tracing method is applied to the automobile parts; the tracing method of the automobile parts comprises the following steps:
Acquiring a micro-feature image of an automobile part, and determining two-dimensional frequency domain features based on the micro-feature image;
judging the type of the current texture according to the two-dimensional frequency domain characteristics;
Determining a texture extraction algorithm based on the type of the current texture, and outputting texture features;
acquiring an RFID tag electromagnetic field intensity distribution diagram of the outer package of the automobile part;
determining hidden electromagnetic fingerprint characteristics according to an electromagnetic field intensity distribution diagram of an RFID tag of the outer package of the automobile part;
and constructing a traceable historical record according to the hidden electromagnetic fingerprint characteristics, texture characteristics and the past information of the automobile parts so as to trace the source of the automobile parts.
Optionally, the acquiring the micro-feature image of the automobile part and determining the two-dimensional frequency domain feature based on the micro-feature image includes:
acquiring an overall image of the automobile part, and defining a texture area based on the overall image of the automobile part;
collecting micro-feature images of the automobile parts based on the microscope and the texture area;
Outputting an optimized image according to image processing of the micro-feature image;
And determining the two-dimensional frequency domain features based on the optimized image.
Optionally, the determining the type of the current texture according to the two-dimensional frequency domain features further includes:
A two-dimensional frequency domain characteristic is fixed;
Traversing the two-dimensional frequency domain features, and performing damage detection on the two-dimensional frequency domain features to define a damage area;
determining damaged features according to the damaged area and peripheral features of the damaged area, and optimizing two-dimensional frequency domain features according to the damaged features;
And judging the type of the current texture according to the two-dimensional frequency domain characteristics and the type learning model.
Optionally, the determining a texture extraction algorithm based on the type of the current texture and outputting texture features includes:
Determining the type of the current texture;
Defining a plurality of associated algorithms based on the type of the current texture and the texture database;
correlating the type of the current texture with a plurality of correlation algorithms, and defining a matching coefficient;
sequentially sequencing a plurality of associated algorithms according to the size of the matching coefficient, and determining a texture extraction algorithm;
outputting texture features based on a texture extraction algorithm and two-dimensional frequency domain features;
And further importing the texture features into a convolutional neural network, and carrying out matching classification with a priori feature library to finally obtain a matching result.
Optionally, the acquiring the electromagnetic field intensity distribution map of the RFID tag of the outer package of the automobile part includes:
Extracting the outer package of the automobile parts by adopting multiple antennas to respond to the RFID tag;
And in the response process of the RFID tag, acquiring an electromagnetic field intensity distribution diagram of the RFID tag of the outer package of the automobile part.
Optionally, the determining the hidden electromagnetic fingerprint feature according to the electromagnetic field intensity distribution diagram of the RFID tag of the outer package of the automobile part includes:
defining electromagnetic field intensity distribution characteristics based on the RFID tag electromagnetic field intensity distribution map;
Outputting a dimension parameter based on a dimension analysis of the electromagnetic field intensity distribution characteristics;
Defining a plurality of estimated characteristics according to each dimension parameter and the CNN multi-layer neural network;
And determining hidden electromagnetic fingerprint features based on the plurality of estimated features and the priori feature library.
Optionally, the constructing a traceable history according to the hidden electromagnetic fingerprint features, texture features and past information of the automobile parts, so as to trace the automobile parts, includes:
freeze hiding electromagnetic fingerprint features and texture features;
correlating the hidden electromagnetic fingerprint features and texture features, and defining correlation coefficients;
Determining the type of the automobile part based on the hidden electromagnetic fingerprint features, the texture features and the association coefficients;
matching corresponding past information according to the types of the automobile parts;
And constructing a traceable history record based on the prior information of the hidden electromagnetic fingerprint features, texture features and automobile parts.
Optionally, the traceable history record is constructed according to the hidden electromagnetic fingerprint features, texture features and previous information of the automobile parts, so as to trace the source of the automobile parts, and the method further comprises:
Freeze traceable history;
Constructing an automobile part anti-counterfeiting traceability platform system based on traceable histories and types of automobile parts;
And tracing the source of the automobile parts according to the anti-counterfeiting tracing platform system of the automobile parts.
According to an aspect of an embodiment of the present application, there is provided a tracing apparatus for an automobile part, including:
the acquisition module is used for acquiring the micro-feature image of the automobile part and determining the two-dimensional frequency domain feature based on the micro-feature image;
The judging module is used for judging the type of the current texture according to the two-dimensional frequency domain characteristics;
the texture feature module is used for determining a texture extraction algorithm based on the type of the current texture and outputting texture features;
the distribution map module is used for acquiring an RFID tag electromagnetic field intensity distribution map of the outer package of the automobile part;
the electromagnetic fingerprint feature module is used for determining hidden electromagnetic fingerprint features according to an electromagnetic field intensity distribution diagram of the RFID tag of the outer package of the automobile part;
And the traceability module is used for constructing a traceable historical record according to the hidden electromagnetic fingerprint characteristics, the texture characteristics and the past information of the automobile parts so as to trace the automobile parts.
According to an aspect of an embodiment of the present application, there is provided an electronic apparatus including: one or more processors; and the storage device is used for storing one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors are enabled to realize the tracing method of the automobile parts according to the embodiment.
According to an aspect of embodiments of the present application, there is provided a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the tracing method for the automobile parts provided in the above embodiment.
In the technical scheme provided by some embodiments of the application, a micro-feature image of an automobile part is obtained, and a two-dimensional frequency domain feature is determined based on the micro-feature image; judging the type of the current texture according to the two-dimensional frequency domain characteristics; determining a texture extraction algorithm based on the type of the current texture, and outputting texture features; acquiring an RFID tag electromagnetic field intensity distribution diagram of the outer package of the automobile part; determining hidden electromagnetic fingerprint characteristics according to an electromagnetic field intensity distribution diagram of an RFID tag of the outer package of the automobile part; the traceable historical record is constructed according to the conventional information of the hidden electromagnetic fingerprint features, the texture features and the automobile parts, so that the texture features and the hidden electromagnetic fingerprint features are introduced at the moment so as to correlate the texture features and the hidden electromagnetic fingerprint features, the automobile parts are controlled along different directions through the texture features and the hidden electromagnetic fingerprint features, the multidirectional traceability of the automobile parts is realized, and meanwhile, the traceable historical record is constructed according to the conventional information of the hidden electromagnetic fingerprint features, the texture features and the automobile parts so as to trace the automobile parts, and the traceability comprehensiveness and the accuracy of the traceability of the automobile parts are ensured.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application. It is evident that the drawings in the following description are only some embodiments of the present application and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art. In the drawings:
FIG. 1 illustrates a flow diagram of a method of tracing automotive parts according to one embodiment of the application;
FIG. 2 shows a schematic flow chart of S110 in FIG. 1;
FIG. 3 shows a schematic flow chart of S120 in FIG. 1;
FIG. 4 shows a schematic flow chart of S130 in FIG. 1;
fig. 5 shows a schematic flow chart of S140 in fig. 1;
FIG. 6 shows a schematic flow chart of S150 in FIG. 1;
FIG. 7 illustrates a block diagram of a method apparatus for tracing automotive parts according to one embodiment of the application;
Fig. 8 shows a schematic diagram of a computer system suitable for use in implementing an embodiment of the application.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the application. One skilled in the relevant art will recognize, however, that the application may be practiced without one or more of the specific details, or with other methods, components, devices, steps, etc. In other instances, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the application.
The block diagrams depicted in the figures are merely functional entities and do not necessarily correspond to physically separate entities. That is, the functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
The flow diagrams depicted in the figures are exemplary only, and do not necessarily include all of the elements and operations/steps, nor must they be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be synthesized or partially synthesized, so that the order of actual execution may be changed according to actual situations.
Fig. 1 shows a flow diagram of a method for tracing an automotive part according to an embodiment of the application. Referring to fig. 1 to 8, the tracing method of the automobile parts is applied to the automobile parts.
The tracing method of the automobile parts at least comprises the following steps of S110 to S140, wherein the detailed description is as follows:
In step S110, a micro-feature image of the automobile part is obtained, and a two-dimensional frequency domain feature is determined based on the micro-feature image;
In step S120, the type of the current texture is determined according to the two-dimensional frequency domain features;
in step S130, a texture extraction algorithm is determined based on the type of the current texture, and texture features are output;
In step S140, an RFID tag electromagnetic field intensity distribution map of the exterior packaging of the automobile parts is acquired;
In step S150, determining hidden electromagnetic fingerprint characteristics according to an electromagnetic field intensity distribution diagram of an RFID tag of the outer package of the automobile part;
In step S160, a traceable history is constructed according to the hidden electromagnetic fingerprint features, texture features and previous information of the automobile parts, so as to trace the source of the automobile parts.
In the embodiment of the application, a micro-feature image of an automobile part is obtained, and a two-dimensional frequency domain feature is determined based on the micro-feature image; judging the type of the current texture according to the two-dimensional frequency domain characteristics; determining a texture extraction algorithm based on the type of the current texture, and outputting texture features; acquiring an RFID tag electromagnetic field intensity distribution diagram of the outer package of the automobile part; determining hidden electromagnetic fingerprint characteristics according to an electromagnetic field intensity distribution diagram of an RFID tag of the outer package of the automobile part; the traceable historical record is constructed according to the conventional information of the hidden electromagnetic fingerprint features, the texture features and the automobile parts, so that the texture features and the hidden electromagnetic fingerprint features are introduced at the moment so as to correlate the texture features and the hidden electromagnetic fingerprint features, the automobile parts are controlled along different directions through the texture features and the hidden electromagnetic fingerprint features, the multidirectional traceability of the automobile parts is realized, and meanwhile, the traceable historical record is constructed according to the conventional information of the hidden electromagnetic fingerprint features, the texture features and the automobile parts so as to trace the automobile parts, and the traceability comprehensiveness and the accuracy of the traceability of the automobile parts are ensured.
In step S110, a microfeature image of the automotive component is acquired, and a two-dimensional frequency domain feature is determined based on the microfeature image.
Step S111, acquiring an overall image of the automobile part, and defining a texture area based on the overall image of the automobile part;
Step S112, collecting micro-feature images of the automobile parts based on the microscope and the texture area;
step S113, outputting an optimized image according to the image processing of the micro-feature image;
and step S114, determining the two-dimensional frequency domain characteristics based on the optimized image.
In the embodiment of the application, the whole image of the automobile part is acquired so as to conveniently carry out region regulation and control according to the whole image of the automobile part and introduce texture features, and at the moment, the texture region is defined based on the whole image of the automobile part so as to conveniently further carry out microscopic detection according to the texture region and finish the rapid processing of the local region, thereby improving the tracing efficiency of the automobile part.
Further, the micro-feature images of the automobile parts are acquired based on the microscope and the texture region, so that the micro-feature images of the automobile parts are introduced, the micro-feature images of the automobile parts are further subjected to image processing, at this time, an optimized image is output according to the image processing of the micro-feature images, so that two-dimensional frequency domain features are determined based on the optimized image, and further, two-dimensional frequency domain features are introduced, so that the type of the current texture is judged based on the two-dimensional frequency domain features.
In step S120, the type of the current texture is determined according to the two-dimensional frequency domain features.
Step S121, fixing two-dimensional frequency domain features;
step S122, traversing the two-dimensional frequency domain features, and performing damage detection on the two-dimensional frequency domain features to define a damage area;
Step S123, determining damaged features according to the damaged area and the peripheral features of the damaged area, and optimizing two-dimensional frequency domain features according to the damaged features;
and step S124, judging the type of the current texture according to the two-dimensional frequency domain characteristics and the type learning model.
In the embodiment of the application, the two-dimensional frequency domain features are fixed so as to be convenient for traversing the two-dimensional frequency domain features, so that the two-dimensional frequency domain features are examined, at the moment, the two-dimensional frequency domain features are traversed, the two-dimensional frequency domain features are subjected to damage detection so as to define damaged areas, and the damaged areas are further controlled so as to be convenient for extracting current textures in the damaged areas.
Specifically, determining damaged features according to damaged areas and peripheral features of the damaged areas, and optimizing two-dimensional frequency domain features according to the damaged features; and judging the type of the current texture according to the two-dimensional frequency domain characteristics and the type learning model. At this time, the type learning model is trained based on the past two-dimensional frequency domain features and the type of the current texture.
In step S130, a texture extraction algorithm is determined based on the type of the current texture, and texture features are output.
In one embodiment of the application, the specific steps are as follows:
step S131, determining the type of the current texture;
step S132, defining a plurality of associated algorithms based on the type of the current texture and a texture database;
step S133, associating the type of the current texture with a plurality of associated algorithms, and defining a matching coefficient;
Step S134, sequentially sequencing a plurality of associated algorithms according to the size of the matching coefficient, and determining a texture extraction algorithm;
Step S135, outputting texture features based on a texture extraction algorithm and two-dimensional frequency domain features;
and step S136, further importing the texture features into a convolutional neural network, and carrying out matching classification with a priori feature library to finally obtain a matching result.
In the embodiment of the application, the type of the current texture is related to the texture database, so that a plurality of related algorithms are defined based on the type of the current texture and the texture database, and the plurality of related algorithms are screened to select a proper algorithm.
Specifically, the type of the current texture is associated with a plurality of associated algorithms, and a matching coefficient is defined; sequentially sorting a plurality of associated algorithms according to the size of the matching coefficient, determining a texture extraction algorithm, introducing the matching coefficient at the moment, taking the matching coefficient as an influence factor so as to conveniently screen the algorithms along the matching coefficient, and sequentially sorting the plurality of associated algorithms according to the size of the matching coefficient, and determining the texture extraction algorithm; outputting texture features based on a texture extraction algorithm and two-dimensional frequency domain features; the texture features are further led into a convolutional neural network and are matched and classified with the priori feature library, so that a matching result is finally obtained, the final texture is determined, and the accuracy of the texture features is ensured.
In step S140, an RFID tag electromagnetic field intensity distribution map of the exterior package of the automobile parts is acquired.
In an embodiment of the present application, the tracing method for the automobile part further includes:
S141, extracting the outer package of the automobile parts by adopting multiple antennae to respond to the RFID tag;
and step S142, acquiring an RFID tag electromagnetic field intensity distribution diagram of the outer package of the automobile part in the RFID tag response process.
In the embodiment of the application, the multi-antenna is used for responding to the outer package of the automobile part, at the moment, the multi-antenna is used for extracting the outer package of the automobile part to carry out RFID tag response so as to be convenient for tracking the RFID tag, at the moment, in the RFID tag response process, the RFID tag electromagnetic field intensity distribution diagram of the outer package of the automobile part is obtained so as to be convenient for introducing the RFID tag electromagnetic field intensity distribution diagram of the outer package of the automobile part, thereby carrying out management and control on the RFID tag electromagnetic field intensity distribution diagram of the outer package of the automobile part and further determining the hidden electromagnetic fingerprint characteristics.
In step S150, the hidden electromagnetic fingerprint feature is determined from the RFID tag electromagnetic field intensity profile of the exterior packaging of the automobile part.
In an embodiment of the present application, the tracing method for the automobile part further includes:
Step S151, defining electromagnetic field intensity distribution characteristics based on an RFID tag electromagnetic field intensity distribution map;
Step S152, outputting dimension parameters based on dimension analysis of electromagnetic field intensity distribution characteristics;
step S153, defining a plurality of estimated characteristics according to each dimension parameter and the CNN multi-layer neural network;
and step S154, determining hidden electromagnetic fingerprint features based on a plurality of estimated features and a priori feature library.
In the embodiment of the application, an RFID tag electromagnetic field intensity distribution diagram is introduced so as to define electromagnetic field intensity distribution characteristics based on the RFID tag electromagnetic field intensity distribution diagram, thereby fixing the electromagnetic field intensity distribution characteristics and outputting dimensional parameters based on dimensional analysis of the electromagnetic field intensity distribution characteristics; at this time, defining a plurality of estimated characteristics according to each dimension parameter and the CNN multi-layer neural network; and determining the hidden electromagnetic fingerprint characteristics based on the plurality of estimated characteristics and the priori characteristic library, so that the accuracy of the hidden electromagnetic fingerprint characteristics is ensured.
In step S160, a traceable history is constructed according to the hidden electromagnetic fingerprint features, texture features and previous information of the automobile parts, so as to trace the source of the automobile parts.
In the embodiment of the application, the texture features and the hidden electromagnetic fingerprint features are introduced so as to be convenient for associating the texture features and the hidden electromagnetic fingerprint features, and the automobile parts are controlled along different directions by the texture features and the hidden electromagnetic fingerprint features, so that the multidirectional tracing of the automobile parts is realized, and meanwhile, the traceable history record is constructed according to the hidden electromagnetic fingerprint features, the texture features and the past information of the automobile parts so as to trace the automobile parts, and the tracing comprehensiveness and accuracy of the automobile parts are ensured.
Thus, the electromagnetic fingerprint features and texture features are hidden by freeze; correlating the hidden electromagnetic fingerprint features and texture features, and defining correlation coefficients; determining the type of the automobile part based on the hidden electromagnetic fingerprint features, the texture features and the association coefficients; matching corresponding past information according to the types of the automobile parts; constructing a traceable history record based on the prior information of the hidden electromagnetic fingerprint features, texture features and automobile parts; freeze traceable history; constructing an automobile part anti-counterfeiting traceability platform system based on traceable histories and types of automobile parts; and tracing the source of the automobile parts according to the anti-counterfeiting tracing platform system of the automobile parts.
In the technical scheme provided by some embodiments of the application, a micro-feature image of an automobile part is obtained, and a two-dimensional frequency domain feature is determined based on the micro-feature image; judging the type of the current texture according to the two-dimensional frequency domain characteristics; determining a texture extraction algorithm based on the type of the current texture, and outputting texture features; acquiring an RFID tag electromagnetic field intensity distribution diagram of the outer package of the automobile part; determining hidden electromagnetic fingerprint characteristics according to an electromagnetic field intensity distribution diagram of an RFID tag of the outer package of the automobile part; the traceable historical record is constructed according to the conventional information of the hidden electromagnetic fingerprint features, the texture features and the automobile parts, so that the texture features and the hidden electromagnetic fingerprint features are introduced at the moment so as to correlate the texture features and the hidden electromagnetic fingerprint features, the automobile parts are controlled along different directions through the texture features and the hidden electromagnetic fingerprint features, the multidirectional traceability of the automobile parts is realized, and meanwhile, the traceable historical record is constructed according to the conventional information of the hidden electromagnetic fingerprint features, the texture features and the automobile parts so as to trace the automobile parts, and the traceability comprehensiveness and the accuracy of the traceability of the automobile parts are ensured.
The following describes an embodiment of the device of the present application, which may be used to perform the tracing method of the automobile parts in the above embodiment of the present application. For details not disclosed in the embodiment of the device of the present application, please refer to the embodiment of the tracing method for the automobile parts.
Fig. 7 shows a block diagram of a traceability device for automobile parts according to an embodiment of the application.
Referring to fig. 7, a tracing device for an automobile part according to an embodiment of the present application includes:
the acquiring module 210 is configured to acquire a micro-feature image of an automobile part, and determine a two-dimensional frequency domain feature based on the micro-feature image;
a judging module 220, configured to judge a type of a current texture according to the two-dimensional frequency domain feature;
A texture feature module 230 for determining a texture extraction algorithm based on the type of the current texture and outputting texture features;
A profile module 240 for obtaining an RFID tag electromagnetic field intensity profile of the exterior packaging of the automotive part;
the electromagnetic fingerprint feature module 250 is used for determining hidden electromagnetic fingerprint features according to an electromagnetic field intensity distribution diagram of an RFID tag of the outer package of the automobile part;
The traceability module 260 is configured to construct a traceable history according to the hidden electromagnetic fingerprint features, texture features and previous information of the automobile parts, so as to trace the automobile parts.
According to an aspect of the embodiments of the present application, there is provided a computer readable medium having stored thereon a computer program which, when executed by a processor, implements a method for tracing an automotive part as described in the above embodiments.
In one embodiment of the present application, there is also provided an electronic device including:
one or more processors;
And the storage device is used for storing one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors are enabled to realize the tracing method of the automobile parts according to the previous embodiment.
In one example, FIG. 8 illustrates a schematic diagram of a computer system suitable for use in implementing an electronic device of an embodiment of the present application.
It should be noted that, the computer system of the electronic device shown in fig. 8 is only an example, and should not impose any limitation on the functions and the application scope of the embodiments of the present application.
As shown in fig. 8, the computer system includes a central processing unit (Central Processing Unit, CPU) 301 (i.e., a processor as described above) that can perform various appropriate actions and processes, such as performing the tracing method for the automobile parts described in the above embodiments, according to a program stored in a Read-Only Memory (ROM) 302 or a program loaded from a storage portion 308 into a random access Memory (Random Access Memory, RAM) 303. It should be understood that RAM303 and ROM302 are just described as storage devices. In the RAM303, various programs and data required for the system operation are also stored. The CPU 301, ROM302, and RAM303 are connected to each other through a bus 304. An Input/Output (I/O) interface 305 is also connected to bus 304.
The following components are connected to the I/O interface 305: an input section 306 including a keyboard, a mouse, and the like; an output portion 307 including a Cathode Ray Tube (CRT), a Liquid crystal display (Liquid CRYSTAL DISPLAY, LCD), and a speaker, etc.; a storage section 308 including a hard disk or the like; and a communication section 309 including a network interface card such as a LAN (Local Area Network ) card, a modem, or the like. The communication section 309 performs communication processing via a network such as the internet. The drive 310 is also connected to the I/O interface 305 as needed. A removable medium 311 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed on the drive 310 as needed, so that a computer program read therefrom is installed into the storage section 308 as needed.
In particular, according to embodiments of the present application, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising a computer program for performing the method shown in the flowchart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 309, and/or installed from the removable medium 311. When executed by a Central Processing Unit (CPU) 301, performs the various functions defined in the system of the present application.
It should be noted that, the computer readable medium shown in the embodiments of the present application may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-Only Memory (ROM), an erasable programmable read-Only Memory (Erasable Programmable Read Only Memory, EPROM), a flash Memory, an optical fiber, a portable compact disc read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
In the present application, however, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with a computer-readable computer program embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. A computer program embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. Where each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units involved in the embodiments of the present application may be implemented by software, or may be implemented by hardware, and the described units may also be provided in a processor. Wherein the names of the units do not constitute a limitation of the units themselves in some cases.
As another aspect, the present application also provides a computer-readable medium that may be contained in the electronic device described in the above embodiment; or may exist alone without being incorporated into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to implement the method for tracing an automobile part described in the above embodiment.
It should be noted that although in the above detailed description several modules or units of a device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functions of two or more modules or units described above may be embodied in one module or unit in accordance with embodiments of the application. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present application may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, and includes several instructions to cause a computing device (may be a personal computer, a server, a touch terminal, or a network device, etc.) to perform the method according to the embodiments of the present application.
Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains.
It is to be understood that the application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (9)
1. The tracing method for the automobile parts is characterized by being applied to the automobile parts; the tracing method of the automobile parts comprises the following steps:
Acquiring a micro-feature image of an automobile part, and determining two-dimensional frequency domain features based on the micro-feature image;
judging the type of the current texture according to the two-dimensional frequency domain characteristics;
Determining a texture extraction algorithm based on the type of the current texture, and outputting texture features;
acquiring an RFID tag electromagnetic field intensity distribution diagram of the outer package of the automobile part;
Determining hidden electromagnetic fingerprint characteristics according to an electromagnetic field intensity distribution diagram of an RFID tag of the outer package of the automobile part; defining electromagnetic field intensity distribution characteristics based on the RFID tag electromagnetic field intensity distribution map; outputting a dimension parameter based on a dimension analysis of the electromagnetic field intensity distribution characteristics; defining a plurality of estimated characteristics according to each dimension parameter and the CNN multi-layer neural network; determining hidden electromagnetic fingerprint features based on a plurality of estimated features and a priori feature library;
and constructing a traceable historical record according to the hidden electromagnetic fingerprint characteristics, texture characteristics and the past information of the automobile parts so as to trace the source of the automobile parts.
2. The method for tracing an automotive component according to claim 1, wherein the acquiring a microfeature image of the automotive component and determining the two-dimensional frequency domain feature based on the microfeature image comprises:
acquiring an overall image of the automobile part, and defining a texture area based on the overall image of the automobile part;
collecting micro-feature images of the automobile parts based on the microscope and the texture area;
Outputting an optimized image according to image processing of the micro-feature image;
And determining the two-dimensional frequency domain features based on the optimized image.
3. The method for tracing an automobile part according to claim 2, wherein the determining the type of the current texture according to the two-dimensional frequency domain features comprises:
A two-dimensional frequency domain characteristic is fixed;
Traversing the two-dimensional frequency domain features, and performing damage detection on the two-dimensional frequency domain features to define a damage area;
determining damaged features according to the damaged area and peripheral features of the damaged area, and optimizing two-dimensional frequency domain features according to the damaged features;
And judging the type of the current texture according to the two-dimensional frequency domain characteristics and the type learning model.
4. A method of tracing an automotive part according to claim 3, wherein said determining a texture extraction algorithm based on the type of current texture and outputting texture features comprises:
Determining the type of the current texture;
Defining a plurality of associated algorithms based on the type of the current texture and the texture database;
correlating the type of the current texture with a plurality of correlation algorithms, and defining a matching coefficient;
sequentially sequencing a plurality of associated algorithms according to the size of the matching coefficient, and determining a texture extraction algorithm;
outputting texture features based on a texture extraction algorithm and two-dimensional frequency domain features;
And further importing the texture features into a convolutional neural network, and carrying out matching classification with a priori feature library to finally obtain a matching result.
5. The method for tracing an automotive part according to claim 4, wherein the acquiring an RFID tag electromagnetic field intensity profile of an outer package of an automotive part comprises:
Extracting the outer package of the automobile parts by adopting multiple antennas to respond to the RFID tag;
And in the response process of the RFID tag, acquiring an electromagnetic field intensity distribution diagram of the RFID tag of the outer package of the automobile part.
6. The method for tracing a vehicle component according to claim 1, wherein the constructing a traceable history according to the hidden electromagnetic fingerprint features, the texture features and the past information of the vehicle component to trace the vehicle component comprises:
freeze hiding electromagnetic fingerprint features and texture features;
correlating the hidden electromagnetic fingerprint features and texture features, and defining correlation coefficients;
Determining the type of the automobile part based on the hidden electromagnetic fingerprint features, the texture features and the association coefficients;
matching corresponding past information according to the types of the automobile parts;
And constructing a traceable history record based on the prior information of the hidden electromagnetic fingerprint features, texture features and automobile parts.
7. The method for tracing a vehicle component according to claim 6, wherein said constructing a traceable history according to the hidden electromagnetic fingerprint features, the texture features and the past information of the vehicle component to trace the vehicle component further comprises:
Freeze traceable history;
Constructing an automobile part anti-counterfeiting traceability platform system based on traceable histories and types of automobile parts;
And tracing the source of the automobile parts according to the anti-counterfeiting tracing platform system of the automobile parts.
8. The utility model provides a device of tracing to source of car spare part which characterized in that includes:
the acquisition module is used for acquiring the micro-feature image of the automobile part and determining the two-dimensional frequency domain feature based on the micro-feature image;
The judging module is used for judging the type of the current texture according to the two-dimensional frequency domain characteristics;
the texture feature module is used for determining a texture extraction algorithm based on the type of the current texture and outputting texture features;
the distribution map module is used for acquiring an RFID tag electromagnetic field intensity distribution map of the outer package of the automobile part;
The electromagnetic fingerprint feature module is used for determining hidden electromagnetic fingerprint features according to an electromagnetic field intensity distribution diagram of the RFID tag of the outer package of the automobile part; defining electromagnetic field intensity distribution characteristics based on the RFID tag electromagnetic field intensity distribution map; outputting a dimension parameter based on a dimension analysis of the electromagnetic field intensity distribution characteristics; defining a plurality of estimated characteristics according to each dimension parameter and the CNN multi-layer neural network; determining hidden electromagnetic fingerprint features based on a plurality of estimated features and a priori feature library;
And the traceability module is used for constructing a traceable historical record according to the hidden electromagnetic fingerprint characteristics, the texture characteristics and the past information of the automobile parts so as to trace the automobile parts.
9. A computer readable medium, on which a computer program is stored, characterized in that the computer program, when executed by a processor, implements a method of tracing an automotive part according to any one of claims 1 to 7.
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| CN119887233A (en) * | 2024-12-23 | 2025-04-25 | 苏州嘉绅报废汽车回收拆解有限公司 | Vehicle disassembled part traceability method, device, terminal and storage medium |
| CN120725696B (en) * | 2025-08-18 | 2025-11-18 | 南京海关工业产品检测中心 | Multi-source fusion tracing method and system for automobile parts based on distributed block chain |
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