Vehicle interior matching method and system

Abstract

The present invention provides a vehicle interior matching method and system, the method comprising: receiving demand information input by a user in real time, and performing semantic analysis processing on the demand information to generate a corresponding target demand text; performing a full scan on the target demand text to detect a number of target keywords correspondingly contained in the target demand text, and performing feature conversion processing on each of the target keywords to generate a number of corresponding feature vectors; performing integration processing on the several feature vectors to generate a corresponding feature chain in real time, and performing digital processing on the feature chain to generate a corresponding feature code, and matching the target interior accessories corresponding to the user in real time in a preset interior database according to the feature code. The present invention can effectively improve the matching efficiency of the interior, and correspondingly improve the user experience.

Description

Technical Field

[0001] The present invention relates to the field of automobile technology, and in particular to a vehicle interior matching method and system. Background Art

[0002] With the advancement of science and technology and the rapid development of productivity, cars have become popular in people's daily lives and have become one of the indispensable means of transportation for people's daily travel, greatly facilitating people's lives.

[0003] With the rapid popularization of new energy vehicles and the rapid development of the automobile industry, users' overall experience of using vehicles, especially personalized demand for automobile interiors, has shown an explosive growth trend. Consumers are no longer satisfied with traditional, single interior choices, but prefer to customize exclusive interior solutions based on personal preferences, lifestyles and cultural backgrounds.

[0004] Furthermore, the mainstream car interior design and matching methods on the market currently rely mainly on preset rules or relatively simple algorithms. These traditional methods usually include keyword searches, simple recommendation systems, or matching mechanisms based on specific parameters. However, such systems have significant limitations in practical applications, especially when dealing with complex user needs. It is difficult to accurately grasp the user's true intentions and preferences, which correspondingly reduces the user experience. Summary of the invention

[0005] Based on this, the purpose of the present invention is to provide a vehicle interior matching method and system to solve the significant limitations of the prior art in practical applications, especially the difficulty in accurately grasping the user's true intentions and preferences when dealing with complex user needs.

[0006] The first aspect of the embodiment of the present invention proposes:

[0007] A vehicle interior matching method, wherein the method comprises:

[0008] Receive demand information input by users in real time, and perform semantic analysis on the demand information to generate corresponding target demand text;

[0009] Performing a full scan on the target demand text to detect a number of target keywords correspondingly contained in the target demand text, and performing feature conversion processing on each of the target keywords to generate a number of corresponding feature vectors;

[0010] The feature vectors are integrated to generate a corresponding feature chain in real time, and the feature chain is digitally processed to generate a corresponding feature code, and the target interior accessories corresponding to the user are matched in real time in a preset interior database according to the feature code.

[0011] The beneficial effect of the present invention is that by receiving the demand information input by the user in real time, the real demand of each user can be obtained accordingly. Based on this, by further parsing and processing the current demand information, the feature vector for subsequent matching can be further obtained. Furthermore, a feature code for final identification can be further generated, and the target interior accessories corresponding to the current user can be directly matched in real time in the preset interior database through the feature code, which greatly improves the user experience.

[0012] Furthermore, the step of performing feature conversion processing on each of the target keywords to generate a number of corresponding feature vectors includes:

[0013] When a number of target keywords are acquired in real time, a corresponding target identifier is added to each target keyword;

[0014] Based on the target identifier, target character strings corresponding to each target keyword are detected in sequence, and each target character string is input into a preset conversion model to generate a plurality of feature vectors accordingly.

[0015] Furthermore, the step of inputting each of the target character strings into a preset conversion model to generate a plurality of the feature vectors includes:

[0016] When each target character string is acquired in real time, the preset conversion model is correspondingly called out in the preset model database;

[0017] When the preset conversion model is acquired in real time, the parsing layer, the conversion layer and the output layer sequentially included in the preset conversion model are detected one by one;

[0018] Each of the target character strings is sequentially passed through the parsing layer, the conversion layer, and the output layer to generate each of the feature vectors accordingly.

[0019] Furthermore, the step of sequentially passing each of the target strings through the parsing layer, the conversion layer, and the output layer to generate each of the feature vectors includes:

[0020] Input the target character string into the parsing layer accordingly, and detect the target number of bytes corresponding to the target character string in real time through the parsing layer;

[0021] The target number of bytes is converted into corresponding feature values ​​in real time through the conversion layer, and the feature values ​​are vectorized to generate the feature vectors accordingly, and the feature vectors are outputted accordingly through the output layer.

[0022] Furthermore, the step of digitizing the feature chain to generate a corresponding feature code includes:

[0023] When the feature chain is acquired in real time, the feature chain is fully scanned to detect the starting point and the ending point corresponding to the feature chain;

[0024] Within the range of the starting point and the end point, a number of characteristic numbers correspondingly contained in the characteristic chain are detected in real time, and the characteristic code is correspondingly generated according to the characteristic numbers.

[0025] Furthermore, the step of generating the feature code according to the feature digits includes:

[0026] When a number of the characteristic numbers are obtained in real time, a corresponding target two-dimensional space is created in real time through a preset program;

[0027] An adapted target two-dimensional coordinate system is created in real time in the target two-dimensional space, and each of the characteristic numbers is mapped to the target two-dimensional coordinate system to generate a number of corresponding connection points, and the characteristic code is generated in real time according to the number of corresponding connection points.

[0028] Furthermore, the step of generating the feature code in real time according to the plurality of connection points includes:

[0029] When a plurality of connection points are acquired in real time, each of the connection points is sequentially connected in the target two-dimensional coordinate system to generate a plurality of connection lines correspondingly, each of the connection lines being a straight line;

[0030] The target slope values ​​corresponding to each of the connecting lines are calculated one by one, and each of the target slope values ​​is integrated to generate the feature code accordingly.

[0031] The second aspect of the embodiment of the present invention proposes:

[0032] A vehicle interior matching system, wherein the system comprises:

[0033] The acquisition module is used to receive the demand information input by the user in real time and perform semantic analysis on the demand information to generate the corresponding target demand text;

[0034] A processing module, used for performing a full scan on the target demand text to detect a number of target keywords correspondingly contained in the target demand text, and performing feature conversion processing on each of the target keywords to generate a number of corresponding feature vectors;

[0035] The control module is used to integrate and process the plurality of feature vectors to generate a corresponding feature chain in real time, and to digitally process the feature chain to generate a corresponding feature code, and to match the target interior accessories corresponding to the user in a preset interior database in real time according to the feature code.

[0036] Furthermore, the processing module is specifically used for:

[0037] When a number of target keywords are acquired in real time, a corresponding target identifier is added to each target keyword;

[0038] Based on the target identifier, target character strings corresponding to each target keyword are detected in sequence, and each target character string is input into a preset conversion model to generate a plurality of feature vectors accordingly.

[0039] Furthermore, the processing module is specifically used for:

[0040] When each target character string is acquired in real time, the preset conversion model is correspondingly called out in the preset model database;

[0041] When the preset conversion model is acquired in real time, the parsing layer, the conversion layer and the output layer sequentially included in the preset conversion model are detected one by one;

[0042] Each of the target character strings is sequentially passed through the parsing layer, the conversion layer, and the output layer to generate each of the feature vectors accordingly.

[0043] Furthermore, the processing module is specifically used for:

[0044] Input the target character string into the parsing layer accordingly, and detect the target number of bytes corresponding to the target character string in real time through the parsing layer;

[0045] The target number of bytes is converted into corresponding feature values ​​in real time through the conversion layer, and the feature values ​​are vectorized to generate the feature vectors accordingly, and the feature vectors are outputted accordingly through the output layer.

[0046] Furthermore, the processing module is specifically used for:

[0047] When the feature chain is acquired in real time, the feature chain is fully scanned to detect the starting point and the ending point corresponding to the feature chain;

[0048] Within the range of the starting point and the end point, a number of characteristic numbers correspondingly contained in the characteristic chain are detected in real time, and the characteristic code is correspondingly generated according to the characteristic numbers.

[0049] Furthermore, the processing module is specifically used for:

[0050] When a number of the characteristic numbers are obtained in real time, a corresponding target two-dimensional space is created in real time through a preset program;

[0051] An adapted target two-dimensional coordinate system is created in real time in the target two-dimensional space, and each of the characteristic numbers is mapped to the target two-dimensional coordinate system to generate a number of corresponding connection points, and the characteristic code is generated in real time according to the number of corresponding connection points.

[0052] Furthermore, the processing module is specifically used for:

[0053] When a plurality of connection points are acquired in real time, each of the connection points is sequentially connected in the target two-dimensional coordinate system to generate a plurality of connection lines correspondingly, each of the connection lines being a straight line;

[0054] The target slope values ​​corresponding to each of the connecting lines are calculated one by one, and each of the target slope values ​​is integrated to generate the feature code accordingly.

[0055] The third aspect of the embodiment of the present invention proposes:

[0056] A computer comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the vehicle interior matching method as described above is implemented when the processor executes the computer program.

[0057] The fourth aspect of the embodiments of the present invention proposes:

[0058] A readable storage medium stores a computer program, wherein the program, when executed by a processor, implements the vehicle interior matching method as described above.

[0059] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 A flow chart of a vehicle interior matching method provided by a first embodiment of the present invention;

[0061] Figure 2 This is a structural block diagram of a vehicle interior matching system provided in the third embodiment of the present invention.

[0062] The following specific implementation manner will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0063] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Several embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0064] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be a central element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a central element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0065] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0066] See also Figure 1 , which shows the vehicle interior matching method provided by the first embodiment of the present invention. The vehicle interior matching method provided by this embodiment can objectively and accurately match the automobile interior accessories required by the user, thereby correspondingly improving the user experience.

[0067] Specifically, this embodiment provides:

[0068] A vehicle interior matching method comprises the following steps:

[0069] Step S10, receiving demand information input by the user in real time, and performing semantic analysis on the demand information to generate a corresponding target demand text;

[0070] Step S20, performing a full scan on the target demand text to detect a number of target keywords correspondingly contained in the target demand text, and performing feature conversion processing on each of the target keywords to generate a number of corresponding feature vectors;

[0071] Step S30, integrating the plurality of feature vectors to generate a corresponding feature chain in real time, and digitally processing the feature chain to generate a corresponding feature code, and matching the target interior accessories corresponding to the user in real time in a preset interior database according to the feature code.

[0072] Specifically, in this embodiment, it should be noted that in order to objectively and accurately match the interior accessories required by the user and the corresponding interior combination scheme, it is necessary to accurately obtain the actual needs of the user and perform accurate parsing. Based on this, the server set up in the background can receive the corresponding demand information input by the user in real time. Specifically, the demand information can include the color and model of the accessories required by the user. Based on this, the present invention will further perform corresponding semantic parsing on the current demand information, and can parse out the required target demand text accordingly. Based on this, the current target demand text is immediately scanned in its entirety, so that several target keywords correspondingly contained in the current target demand text can be further detected.

[0073] Furthermore, in order to accurately complete subsequent matching, it is necessary to avoid interference caused by external information. Based on this, the present invention will further perform feature conversion processing on the current target keyword, and be able to convert the required feature vector. Furthermore, the current feature vector is immediately integrated and processed to further generate the required feature chain. On this basis, it is only necessary to perform corresponding digital processing on the current feature chain in real time to finally generate a feature code for matching, and finally use the feature code to match the target interior accessories corresponding to the current user in real time in a pre-set interior database, so as to meet the needs of different users respectively and improve the user experience accordingly.

[0074] Second embodiment

[0075] Furthermore, the step of performing feature conversion processing on each of the target keywords to generate a number of corresponding feature vectors includes:

[0076] When a number of target keywords are acquired in real time, a corresponding target identifier is added to each target keyword;

[0077] Based on the target identifier, target character strings corresponding to each target keyword are detected in sequence, and each target character string is input into a preset conversion model to generate a plurality of feature vectors accordingly.

[0078] Furthermore, the step of inputting each of the target character strings into a preset conversion model to generate a plurality of the feature vectors includes:

[0079] When each target character string is acquired in real time, the preset conversion model is correspondingly called out in the preset model database;

[0080] When the preset conversion model is acquired in real time, the parsing layer, the conversion layer and the output layer sequentially included in the preset conversion model are detected one by one;

[0081] Each of the target character strings is sequentially passed through the parsing layer, the conversion layer, and the output layer to generate each of the feature vectors accordingly.

[0082] Furthermore, the step of sequentially passing each of the target strings through the parsing layer, the conversion layer, and the output layer to generate each of the feature vectors includes:

[0083] Input the target character string into the parsing layer accordingly, and detect the target number of bytes corresponding to the target character string in real time through the parsing layer;

[0084] The target number of bytes is converted into corresponding feature values ​​in real time through the conversion layer, and the feature values ​​are vectorized to generate the feature vectors accordingly, and the feature vectors are outputted accordingly through the output layer.

[0085] Furthermore, the step of digitizing the feature chain to generate a corresponding feature code includes:

[0086] When the feature chain is acquired in real time, the feature chain is fully scanned to detect the starting point and the ending point corresponding to the feature chain;

[0087] Within the range of the starting point and the end point, a number of characteristic numbers correspondingly contained in the characteristic chain are detected in real time, and the characteristic code is correspondingly generated according to the characteristic numbers.

[0088] Furthermore, the step of generating the feature code according to the feature digits includes:

[0089] When a number of the characteristic numbers are obtained in real time, a corresponding target two-dimensional space is created in real time through a preset program;

[0090] An adapted target two-dimensional coordinate system is created in real time in the target two-dimensional space, and each of the characteristic numbers is mapped to the target two-dimensional coordinate system to generate a number of corresponding connection points, and the characteristic code is generated in real time according to the number of corresponding connection points.

[0091] Furthermore, the step of generating the feature code in real time according to the plurality of connection points includes:

[0092] When a plurality of connection points are acquired in real time, each of the connection points is sequentially connected in the target two-dimensional coordinate system to generate a plurality of connection lines correspondingly, each of the connection lines being a straight line;

[0093] The target slope values ​​corresponding to each of the connecting lines are calculated one by one, and each of the target slope values ​​is integrated to generate the feature code accordingly.

[0094] In addition, in the present embodiment, it is also necessary to explain that after the required target keywords are acquired in real time through the above steps, in order to be able to objectively and accurately generate the required feature vectors, it is necessary to further perform secondary processing on the current target keywords. Preferably, in order to facilitate subsequent identification, the present invention will first add corresponding target identifiers to each current target keyword. Based on this, according to the sequence of the current target identifiers, the target character strings corresponding to each current target keyword are detected in real time. At the same time, the adaptive conversion model is called out in real time in the preset model database. Based on this, in order to facilitate subsequent conversion, the internal corresponding parsing layer, conversion layer and output layer of the current conversion model will be further detected at this time. In the actual conversion process, it is only necessary to detect the target number of bytes corresponding to each target character string in real time through the parsing layer, and further convert it into the corresponding feature value in real time through the conversion layer. Based on this, the subsequent vectorization processing is further completed, and each feature vector can be finally generated for subsequent processing.

[0095] Furthermore, after the required feature chain is further obtained through the above steps, in order to accurately generate the required feature code to improve the matching accuracy, specifically, the present invention will first detect the starting point and the ending point of the current feature chain, and further detect the corresponding feature numbers contained in the current feature chain within the range of the current starting point and the ending point. Based on this, the required target two-dimensional coordinate system is further generated in the target two-dimensional space created in real time. At the same time, each current feature number is mapped to the current target two-dimensional coordinate system to generate corresponding connection points. On this basis, it is only necessary to connect each current connection point in a straight line in sequence to further generate several required connection lines. Based on this, the target slope value corresponding to each current connection line is finally calculated in real time, and the current target slope values ​​are immediately integrated and processed, so that the required feature code can be finally obtained, and the subsequent matching is further completed, which greatly improves the user experience.

[0096] See also Figure 2 , the third embodiment of the present invention provides:

[0097] A vehicle interior matching system, wherein the system comprises:

[0098] The acquisition module is used to receive the demand information input by the user in real time and perform semantic analysis on the demand information to generate the corresponding target demand text;

[0099] A processing module, used for performing a full scan on the target demand text to detect a number of target keywords correspondingly contained in the target demand text, and performing feature conversion processing on each of the target keywords to generate a number of corresponding feature vectors;

[0100] The control module is used to integrate and process the plurality of feature vectors to generate a corresponding feature chain in real time, and to digitally process the feature chain to generate a corresponding feature code, and to match the target interior accessories corresponding to the user in a preset interior database in real time according to the feature code.

[0101] Furthermore, the processing module is specifically used for:

[0102] When a number of target keywords are acquired in real time, a corresponding target identifier is added to each target keyword;

[0103] Based on the target identifier, target character strings corresponding to each target keyword are detected in sequence, and each target character string is input into a preset conversion model to generate a plurality of feature vectors accordingly.

[0104] Furthermore, the processing module is specifically used for:

[0105] When each target character string is acquired in real time, the preset conversion model is correspondingly called out in the preset model database;

[0106] When the preset conversion model is acquired in real time, the parsing layer, the conversion layer and the output layer sequentially included in the preset conversion model are detected one by one;

[0107] Each of the target character strings is sequentially passed through the parsing layer, the conversion layer, and the output layer to generate each of the feature vectors accordingly.

[0108] Furthermore, the processing module is specifically used for:

[0109] Input the target character string into the parsing layer accordingly, and detect the target number of bytes corresponding to the target character string in real time through the parsing layer;

[0110] The target number of bytes is converted into corresponding feature values ​​in real time through the conversion layer, and the feature values ​​are vectorized to generate the feature vectors accordingly, and the feature vectors are outputted accordingly through the output layer.

[0111] Furthermore, the processing module is specifically used for:

[0112] When the feature chain is acquired in real time, the feature chain is fully scanned to detect the starting point and the ending point corresponding to the feature chain;

[0113] Within the range of the starting point and the end point, a number of characteristic numbers correspondingly contained in the characteristic chain are detected in real time, and the characteristic code is correspondingly generated according to the characteristic numbers.

[0114] Furthermore, the processing module is specifically used for:

[0115] When a number of the characteristic numbers are obtained in real time, a corresponding target two-dimensional space is created in real time through a preset program;

[0116] An adapted target two-dimensional coordinate system is created in real time in the target two-dimensional space, and each of the characteristic numbers is mapped to the target two-dimensional coordinate system to generate a number of corresponding connection points, and the characteristic code is generated in real time according to the number of corresponding connection points.

[0117] Furthermore, the processing module is specifically used for:

[0118] When a plurality of connection points are acquired in real time, each of the connection points is sequentially connected in the target two-dimensional coordinate system to generate a plurality of connection lines correspondingly, each of the connection lines being a straight line;

[0119] The target slope values ​​corresponding to each of the connecting lines are calculated one by one, and each of the target slope values ​​is integrated to generate the feature code accordingly.

[0120] A fourth embodiment of the present invention provides a computer, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the vehicle interior matching method as described above when executing the computer program.

[0121] A fifth embodiment of the present invention provides a readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the vehicle interior matching method as described above.

[0122] In summary, the vehicle interior matching method and system provided in the above embodiments of the present invention can match corresponding vehicle interior accessories according to user needs in real time, thereby improving the user experience accordingly.

[0123] It should be noted that the above modules can be functional modules or program modules, and can be implemented by software or hardware. For modules implemented by hardware, the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0124] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0125] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0126] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0127] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0128] The above-described embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the present invention. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the attached claims.

Claims

1. A vehicle interior matching method, characterized in that: The method comprises: Receive demand information input by users in real time, and perform semantic analysis on the demand information to generate corresponding target demand text; Performing a full scan on the target demand text to detect a number of target keywords correspondingly contained in the target demand text, and performing feature conversion processing on each of the target keywords to generate a number of corresponding feature vectors; Integrate the feature vectors to generate a corresponding feature chain in real time, digitally process the feature chain to generate a corresponding feature code, and match the target interior accessories corresponding to the user in a preset interior database in real time according to the feature code; The step of performing feature conversion processing on each of the target keywords to generate a number of corresponding feature vectors includes: When a number of target keywords are acquired in real time, a corresponding target identifier is added to each target keyword; Detecting target strings corresponding to each of the target keywords in sequence based on the target identifier, and inputting each of the target strings into a preset conversion model to generate a number of feature vectors accordingly; The step of inputting each of the target character strings into a preset conversion model to generate a plurality of the feature vectors includes: When each target character string is acquired in real time, the preset conversion model is correspondingly called out in the preset model database; When the preset conversion model is acquired in real time, the parsing layer, the conversion layer and the output layer sequentially included in the preset conversion model are detected one by one; Passing each of the target character strings through the parsing layer, the conversion layer, and the output layer in sequence to generate each of the feature vectors accordingly; The step of sequentially passing each of the target strings through the parsing layer, the conversion layer, and the output layer to generate each of the feature vectors includes: Input the target character string into the parsing layer accordingly, and detect the target number of bytes corresponding to the target character string in real time through the parsing layer; The target number of bytes is converted into corresponding feature values ​​in real time through the conversion layer, and the feature values ​​are vectorized to generate the feature vectors accordingly, and the feature vectors are outputted accordingly through the output layer.

2. The vehicle interior matching method according to claim 1, characterized in that: The step of digitizing the feature chain to generate a corresponding feature code includes: When the feature chain is acquired in real time, the feature chain is fully scanned to detect the starting point and the ending point corresponding to the feature chain; Within the range of the starting point and the end point, a number of characteristic numbers correspondingly contained in the characteristic chain are detected in real time, and the characteristic code is correspondingly generated according to the characteristic numbers.

3. The vehicle interior matching method according to claim 2, characterized in that: The step of generating the feature code according to the feature digits comprises: When a number of the characteristic numbers are obtained in real time, a corresponding target two-dimensional space is created in real time through a preset program; An adapted target two-dimensional coordinate system is created in real time in the target two-dimensional space, and each of the characteristic numbers is mapped to the target two-dimensional coordinate system to generate a number of corresponding connection points, and the characteristic code is generated in real time according to the number of corresponding connection points.

4. The vehicle interior matching method according to claim 3, characterized in that: The step of generating the feature code in real time according to the plurality of connection points comprises: When a plurality of connection points are acquired in real time, each of the connection points is sequentially connected in the target two-dimensional coordinate system to generate a plurality of connection lines correspondingly, each of the connection lines being a straight line; The target slope values ​​corresponding to each of the connecting lines are calculated one by one, and each of the target slope values ​​is integrated to generate the feature code accordingly.

5. A vehicle interior matching system, characterized in that: For implementing the vehicle interior matching method according to any one of claims 1 to 4, the system comprises: The acquisition module is used to receive the demand information input by the user in real time and perform semantic analysis on the demand information to generate the corresponding target demand text; A processing module, used for performing a full scan on the target demand text to detect a number of target keywords correspondingly contained in the target demand text, and performing feature conversion processing on each of the target keywords to generate a number of corresponding feature vectors; The control module is used to integrate and process the plurality of feature vectors to generate a corresponding feature chain in real time, and to digitally process the feature chain to generate a corresponding feature code, and to match the target interior accessories corresponding to the user in a preset interior database in real time according to the feature code.

6. A computer comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the vehicle interior matching method according to any one of claims 1 to 4 is implemented.

7. A readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the vehicle interior matching method as described in any one of claims 1 to 4 is implemented.

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