Automobile accessory transaction management method and system based on artificial intelligence and cloud computing

Abstract

The invention provides an automobile accessory transaction management method and system based on artificial intelligence and cloud computing, a computer readable medium and electronic equipment. The method comprises the following steps: for a transaction type corresponding to automobile accessory transaction information, preprocessing the automobile accessory transaction information to obtain processing data; based on the processing data of each maintenance manufacturer, generating a maintenance portrait corresponding to the maintenance manufacturer; determining a supply portrait of the automobile accessory supplier according to the historical transaction data of the supplier; after an inquiry request of a maintenance factory is received, based on a maintenance portrait, determining a target supplier portrait matched with the maintenance portrait; and determining a target supplier and quotation information corresponding to the target supplier according to inquiry information in the inquiry request and the matched target supplier portrait. The target supplier corresponding to the maintenance task is determined by matching the maintenance portrait with the supply portrait and combining the inquiry information, so that the automobile accessory transaction efficiency is improved, and the automobile accessory supply reliability in the maintenance process is ensured.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and more specifically, to an auto parts transaction management method, system, computer-readable medium, and electronic device based on artificial intelligence and cloud computing. Background Art

[0002] In the field of auto repair, auto parts often occupy a very important position in the auto aftermarket, and auto parts are often an important factor affecting the efficiency of auto maintenance and repair. Especially in the case of some large auto repair manufacturers, this often requires a large number and variety of auto parts, but the existing methods often cannot provide a scientific management method, which leads to the problem of low efficiency of auto parts management. Summary of the invention

[0003] The embodiments of the present application provide an auto parts transaction management method, system, computer-readable medium and electronic device based on artificial intelligence and cloud computing, which can solve the problem of low efficiency of auto parts management at least to a certain extent.

[0004] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by the practice of the present application.

[0005] According to one aspect of the present application, there is provided an auto parts transaction management method based on artificial intelligence and cloud computing, comprising:

[0006] Obtain auto parts transaction information from various repair manufacturers;

[0007] According to the transaction type corresponding to the auto parts transaction information, pre-processing the auto parts transaction information to obtain processed data;

[0008] Generate maintenance profiles corresponding to maintenance manufacturers based on the processing data of each maintenance manufacturer;

[0009] Determine the supply profile of auto parts suppliers based on their historical transaction data;

[0010] After receiving the inquiry request from the repair shop, based on the repair profile, determine the target supplier profile that matches the repair profile;

[0011] At least one target supplier and quotation information corresponding to the target supplier are determined according to the inquiry information in the inquiry request and the matched target supplier portrait.

[0012] In the present application, based on the above-mentioned solution, the obtaining of auto parts transaction information of each repair manufacturer includes: obtaining the auto parts transaction information of the repair manufacturer; and associating and storing the manufacturer identification of the repair manufacturer with the auto parts transaction information.

[0013] In the present application, based on the aforementioned scheme, the auto parts transaction information is pre-processed according to the transaction type corresponding to the auto parts transaction information to obtain processed data, including: identifying the transaction type corresponding to the auto parts transaction information; based on the transaction type, the auto parts transaction information is pre-processed to obtain processed data.

[0014] In the present application, based on the aforementioned scheme, the auto parts transaction information is pre-processed according to the transaction type corresponding to the auto parts transaction information to obtain processed data, including: based on the transaction type corresponding to the auto parts transaction information, obtaining a normal data range corresponding to the transaction type; based on the normal data range, the auto parts transaction information is pre-processed to obtain processed data.

[0015] In the present application, based on the aforementioned scheme, the auto parts transaction information is pre-processed according to the normal data range to obtain processed data, including: according to the normal data range, the auto parts transaction information is data cleansed to obtain cleaned data; according to the normal data range, the cleaned data is quantified to obtain processed data.

[0016] In the present application, based on the aforementioned scheme, the maintenance portrait corresponding to the maintenance manufacturer is generated based on the processing data of each maintenance manufacturer, including: based on the processing data of each maintenance manufacturer, the most frequent target auto parts, the most frequent maintenance time and the most frequent vehicle brand are extracted therefrom; based on the target auto parts, the maintenance time and the vehicle brand, a maintenance portrait of the maintenance manufacturer is generated.

[0017] In the present application, based on the aforementioned scheme, after predicting the subsequent auto parts estimation information of the repair manufacturer based on the repair portrait, it also includes: aggregating the auto parts estimation information of each repair manufacturer to generate production demand information; and sending the production demand information to the corresponding manufacturer.

[0018] In the present application, based on the aforementioned scheme, the target supplier portrait that matches the maintenance portrait is determined based on the maintenance portrait, including: extracting first feature data from the maintenance portrait of the maintenance manufacturer; extracting second feature data from the supply portrait of the auto parts supplier; and determining the target supplier portrait that matches the maintenance portrait based on matching the first feature data and the second feature data.

[0019] According to one aspect of the present application, an auto parts transaction management system based on artificial intelligence and cloud computing is provided, comprising:

[0020] An acquisition unit, used to acquire auto parts transaction information of various maintenance manufacturers;

[0021] A processing unit, configured to pre-process the auto parts transaction information according to the transaction type corresponding to the auto parts transaction information to obtain processed data;

[0022] A maintenance portrait unit, used to generate a maintenance portrait corresponding to the maintenance manufacturer based on the processing data of each maintenance manufacturer;

[0023] The supply profile unit is used to determine the supply profile of the auto parts supplier based on the supplier's historical transaction data;

[0024] A matching unit, configured to, upon receiving a price inquiry request from a repair shop, determine, based on the repair profile, a target supplier profile that matches the repair profile;

[0025] The quotation unit is used to determine at least one target supplier and the quotation information corresponding to the target supplier based on the inquiry information in the inquiry request and the matched target supplier portrait.

[0026] In the present application, based on the above-mentioned solution, after obtaining the auto parts transaction information of each repair manufacturer, it also includes: obtaining the manufacturer identification of the repair manufacturer; and associating and storing the manufacturer identification of the repair manufacturer with the auto parts transaction information.

[0027] In the present application, based on the aforementioned scheme, the auto parts transaction information is pre-processed according to the transaction type corresponding to the auto parts transaction information to obtain processed data, including: identifying the transaction type corresponding to the auto parts transaction information; based on the transaction type, the auto parts transaction information is pre-processed to obtain processed data.

[0028] In the present application, based on the aforementioned scheme, the auto parts transaction information is pre-processed according to the transaction type corresponding to the auto parts transaction information to obtain processed data, including: based on the transaction type corresponding to the auto parts transaction information, obtaining a normal data range corresponding to the transaction type; based on the normal data range, the auto parts transaction information is pre-processed to obtain processed data.

[0029] In the present application, based on the aforementioned scheme, the auto parts transaction information is pre-processed according to the normal data range to obtain processed data, including: according to the normal data range, the auto parts transaction information is data cleansed to obtain cleaned data; according to the normal data range, the cleaned data is quantified to obtain processed data.

[0030] In the present application, based on the aforementioned scheme, the maintenance portrait corresponding to the maintenance manufacturer is generated based on the processing data of each maintenance manufacturer, including: based on the processing data of each maintenance manufacturer, the most frequent target auto parts, the most frequent maintenance time and the most frequent vehicle brand are extracted therefrom; based on the target auto parts, the maintenance time and the vehicle brand, a maintenance portrait of the maintenance manufacturer is generated.

[0031] In the present application, based on the aforementioned scheme, after predicting the subsequent auto parts estimation information of the repair manufacturer based on the repair portrait, it also includes: aggregating the auto parts estimation information of each repair manufacturer to generate production demand information; and sending the production demand information to the corresponding manufacturer.

[0032] In the present application, based on the aforementioned scheme, the target supplier portrait that matches the maintenance portrait is determined based on the maintenance portrait, including: extracting first feature data from the maintenance portrait of the maintenance manufacturer; extracting second feature data from the supply portrait of the auto parts supplier; and determining the target supplier portrait that matches the maintenance portrait based on matching the first feature data and the second feature data.

[0033] According to one aspect of the present application, a computer-readable medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the auto parts transaction management method based on artificial intelligence and cloud computing as described in the above embodiment is implemented.

[0034] According to one aspect of the present application, an electronic device is provided, comprising: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the auto parts transaction management method based on artificial intelligence and cloud computing as described in the above embodiments.

[0035] According to one aspect of the present application, a computer program product or computer program is provided, the computer program product or computer program includes computer instructions, the computer instructions are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the auto parts transaction management method based on artificial intelligence and cloud computing provided in the above various optional implementations.

[0036] In the technical solution of the present application, the auto parts transaction information of each maintenance manufacturer is obtained; the auto parts transaction information is preprocessed to obtain processing data for the transaction type corresponding to the auto parts transaction information; the maintenance portrait corresponding to the maintenance manufacturer is generated based on the processing data of each maintenance manufacturer; the supply portrait of the auto parts supplier is determined based on the supplier's historical transaction data; after receiving the inquiry request from the maintenance plant, the target supplier portrait that matches the maintenance portrait is determined based on the maintenance portrait; at least one target supplier and the quotation information corresponding to the target supplier are determined based on the inquiry information in the inquiry request and the matched target supplier portrait. By preprocessing the auto parts transaction information and constructing a maintenance portrait based on the obtained processing data, and determining the supply portrait based on the supplier's historical transaction data, the target supplier corresponding to the maintenance task is determined by matching the maintenance portrait with the supply portrait and combining the inquiry information, thereby improving the efficiency of auto parts transactions and ensuring the reliability of auto parts supply during the maintenance process.

[0037] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present application, and together with the specification are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0039] Figure 1 The flowchart of an auto parts transaction management method based on artificial intelligence and cloud computing according to an embodiment of the present application is schematically shown.

[0040] Figure 2 The flowchart of obtaining auto parts transaction information of each maintenance manufacturer according to an embodiment of the present application is schematically shown.

[0041] Figure 3 A schematic diagram of an auto parts transaction management system based on artificial intelligence and cloud computing according to an embodiment of the present application is schematically shown.

[0042] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown. DETAILED DESCRIPTION

[0043] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be more comprehensive and complete and fully convey the concept of the example embodiments to those skilled in the art.

[0044] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present application. However, those skilled in the art will appreciate that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, known methods, systems, implementations or operations are not shown or described in detail to avoid blurring the various aspects of the application.

[0045] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0046] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0047] The implementation details of the technical solution of the embodiment of the present application are described in detail below:

[0048] With the rapid development of the digital economy, the scale of transactions continues to expand, the transaction process has become more complex, and the needs have become more and more diversified. This makes the existing transaction management methods seem inadequate. At the same time, the continuous advancement of artificial intelligence and cloud computing technologies has also provided more feasible solutions for transaction management. At present, the market is highly competitive, but due to the high technical threshold of transaction management and the relatively low industry concentration, the quality of products and services on the market is unstable, and the user experience is not satisfactory. There are many problems with traditional transaction management methods, such as low efficiency and error-prone manual processing. At the same time, for enterprises, how to achieve rapid analysis and precise control of transaction data is also a difficult problem. By combining industry characteristics and customer needs, this application will launch a transaction management system based on artificial intelligence and cloud computing to improve transaction management efficiency and reduce management costs.

[0049] In one embodiment of the present application, a transaction management system based on artificial intelligence and cloud computing technology is introduced to realize digital management of the entire transaction process. The system will include order management module, payment management module, logistics management module, data analysis and prediction module, user management module, system management module, etc., and automatically identify and process data through intelligent algorithms, greatly improving the efficiency of transaction data processing.

[0050] In the transaction management system, we will add intelligent analysis and prediction functions to provide enterprises with more accurate predictions and decision-making support through the analysis and mining of historical data. At the same time, we will also add monitoring and risk assessment of transaction behaviors to help users quickly discover potential risks and problems. According to user habits and needs, we will design an adaptive user interface to make the system easier and more convenient to use, and improve user experience and satisfaction. We use cloud architecture to provide users with efficient, stable and secure services. At the same time, we also pay attention to the scalability of the system, and can flexibly add business modules and functional expansions according to customer needs.

[0051] Figure 1 A flowchart of an auto parts transaction management method based on artificial intelligence and cloud computing according to an embodiment of the present application is shown. Figure 1 As shown, the auto parts transaction management method based on artificial intelligence and cloud computing includes at least steps S110 to S160, which are described in detail as follows:

[0052] In step S110, the auto parts transaction information of each maintenance manufacturer is obtained.

[0053] In one embodiment of the present application, auto parts transaction information of a maintenance manufacturer is obtained, and a manufacturer identification of the maintenance manufacturer is associated with the auto parts transaction information and stored.

[0054] In an embodiment of the present application, the auto parts transaction information of the maintenance manufacturer is first obtained through a wireless network. In this embodiment, the auto parts transaction information includes information such as the auto parts identification, transaction time, and transaction manufacturer.

[0055] After obtaining the auto parts transaction information of the maintenance manufacturer, the manufacturer identification of the maintenance manufacturer and the auto parts transaction information are associated and stored. In this way, the reliability of information storage is guaranteed, and the query and processing of information is facilitated.

[0056] In the above process, after obtaining the auto parts transaction information of each maintenance manufacturer, the manufacturer ID of the maintenance manufacturer and the auto parts transaction information are associated and stored. This is convenient for subsequent information tracing. For example, if it is necessary to retrieve the historical transaction information of the maintenance manufacturer, the historical auto parts transaction information can be directly extracted based on the manufacturer ID of the maintenance manufacturer.

[0057] Optionally, in this embodiment, the auto parts transaction information of each maintenance manufacturer can also be obtained through artificial intelligence, automatic identification and acquisition.

[0058] Cloud computing technology will play an important role in the technical solution of this application. Its main principle is to deliver computing resources and services to users on demand through the network, and provide continuously expanding functions and services. Application scenarios include virtualization, elastic scaling, cloud storage, big data processing, etc. For example, associating and storing the manufacturer's identification and auto parts transaction information of the repair manufacturer on the cloud platform can provide enterprises with efficient, stable and secure services. Cloud computing technology is an Internet-based computing method that uses the powerful computing and storage resources of the cloud to provide users with on-demand, flexible and highly reliable computing services. Cloud computing technology is usually divided into three service models: Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). Among them, IaaS provides virtualized servers, networks and storage resources; PaaS provides development environment, database, middleware and other services; SaaS provides applications such as email, online office suites, etc. Cloud computing technology has brought huge commercial value and social benefits by improving the utilization and flexibility of computing resources and reducing IT costs and risks of enterprises and users.

[0059] Artificial intelligence is a branch of computer science that attempts to understand the essence of intelligence and produce a new type of intelligent machine that can respond in a similar way to human intelligence. Research in this field includes robotics, speech recognition, image recognition, natural language processing, and expert systems. Since the birth of artificial intelligence, the theory and technology have become increasingly mature, and the application field has continued to expand. It can be imagined that the technological products brought by artificial intelligence in the future will be the "container" of human wisdom. Artificial intelligence can simulate the information process of human consciousness and thinking. Artificial intelligence is not human intelligence, but it can think like humans and may also exceed human intelligence.

[0060] In step S120, the auto parts transaction information is pre-processed according to the transaction type corresponding to the auto parts transaction information to obtain processed data.

[0061] In one embodiment of the present application, after the auto parts transaction information is acquired, the transaction type corresponding to the auto parts transaction information is identified, and then the auto parts transaction information is pre-processed according to the transaction type to obtain processed data.

[0062] In one embodiment of the present application, Figure 2As shown, for the transaction type corresponding to the auto parts transaction information, the auto parts transaction information is preprocessed to obtain processed data, including:

[0063] S210, identifying the transaction type corresponding to the auto parts transaction information;

[0064] S220: Pre-process the auto parts transaction information based on the transaction type to obtain processed data.

[0065] Specifically, when we identify the transaction type corresponding to the auto parts transaction information, we first perform character matching based on the set transaction identifier, determine the transaction characters in the auto parts transaction information, and calculate the transaction identifier Tra_sin = {x i} and the characters in the auto parts transaction information Tra_cha = {y i The similarity parameter Par_sim of} is:

[0066]

[0067] Wherein, k represents the number of characters, and ω represents the similarity factor. In the above process, the similarity parameter between the transaction identifier and the characters in the auto parts transaction information is calculated to evaluate the similarity between the characters in the auto parts transaction information and the transaction identifier, and then the character corresponding to the maximum similarity parameter is obtained as the transaction type.

[0068] It should be noted that the transaction identifiers in this embodiment correspond one to one with the transaction types.

[0069] In one embodiment of the present application, the auto parts transaction information is pre-processed according to the transaction type corresponding to the auto parts transaction information to obtain processed data, including:

[0070] Based on the transaction type corresponding to the auto parts transaction information, obtaining a normal data range corresponding to the transaction type;

[0071] According to the normal data range, the auto parts transaction information is preprocessed to obtain processed data.

[0072] Specifically, the transaction types in this embodiment correspond one-to-one to the normal data range, and the corresponding normal data range can be obtained by searching through the transaction type.

[0073] Specifically, the auto parts transaction information is preprocessed according to the normal data range to obtain processed data, including:

[0074] According to the normal data range, the auto parts transaction information is cleansed to obtain cleansed data;

[0075] According to the normal data range, the cleaned data is quantified to obtain processed data.

[0076] In an embodiment of the present application, after determining the transaction type, the auto parts transaction information is preprocessed to obtain processed data. In this embodiment, the preprocessing of the auto parts transaction information includes operations such as data cleaning and data quantification.

[0077] Specifically, in this embodiment, after the normal data range is obtained, the auto parts transaction information is cleaned. Specifically, abnormal data in the auto parts transaction information is determined according to the normal data range, and the abnormal data is deleted to obtain cleaned data.

[0078] Then, the cleaned data is quantified according to the normal data range. The specific steps include:

[0079] Determine the quantization range according to the normal data range and the quantization parameter;

[0080] The cleaned data is quantified according to the quantification range to obtain processed data.

[0081] Specifically, in this embodiment, the quantization range [Pmin, Pmax] is determined according to the normal data range [Dmin, Dmax] and the quantization parameter Par_lia as follows:

[0082] [Pmin,Pmax]=Par_lia·[Dmin,Dmax]

[0083] Then, the cleaned data Dat_lea is quantized based on the quantization range [Pmin, Pmax], and the processed data Dat_dea is obtained as follows:

[0084]

[0085] Wherein, μ represents the quantization factor. The above process determines the quantization data range for quantization based on the normal data range, and then quantizes the cleaned data based on the quantization data range to obtain processed data, thereby improving the reliability of the data and ensuring the efficiency of subsequent data applications.

[0086] In step S130, a maintenance profile corresponding to each maintenance manufacturer is generated based on the processing data of each maintenance manufacturer.

[0087] In this embodiment, after the processed data is generated, the most frequently used target auto parts are extracted from the processed data, that is, the auto parts logos that are frequently used; the most frequently used maintenance times are extracted from the processed data, that is, the peak maintenance times; the most frequently used vehicle brands are extracted from the processed data, that is, the vehicle brands that frequently need maintenance, and then a maintenance profile of the maintenance manufacturer is generated based on the above-mentioned target auto parts, the maintenance time, the vehicle brand and other information.

[0088] In one embodiment of the present application, based on the processing data of each maintenance manufacturer, a maintenance profile corresponding to the maintenance manufacturer is generated, including:

[0089] Based on the processed data of each maintenance manufacturer, the most frequently used target auto parts, the most frequently used maintenance time and the most frequently used vehicle brand are extracted;

[0090] A maintenance profile of the maintenance manufacturer is generated based on the target auto part, the maintenance time and the vehicle brand.

[0091] Maintenance portraits are an effective tool for outlining maintenance vendors and manufacturers through big data cloud computing, as well as linking user demands with design directions. In actual operations, we often use the most simple and life-like words to transform and connect the user's attributes, behaviors, and expected data. As a virtual representative of the actual user, the manufacturer role formed by the maintenance portrait is not constructed independently of the product and the market. The manufacturer role formed needs to be representative and can represent the main audience and target group of the product.

[0092] The maintenance portrait can improve decision-making efficiency. In the process of parts trading or production, there are many participants in each link, and disagreements are always inevitable. The decision-making efficiency undoubtedly affects the progress of the parts application project. The maintenance portrait comes from the research on the target manufacturer. When all individuals involved in the auto parts application discuss and make decisions based on the same users, it is easy to constrain all parties to stay in the same general direction, improve the efficiency of decision-making, and then improve the transaction efficiency of auto parts.

[0093] Machine learning technology is an artificial intelligence technology based on statistical learning theory. It can learn from a large amount of data and automatically discover patterns and rules in the data. In the transaction management system, machine learning technology can be used for customer profiling, product recommendations, risk assessment and other work. By analyzing and learning historical data, more accurate predictions and decision support can be provided.

[0094] In step S140, the supply profile of the auto parts supplier is determined based on the supplier's historical transaction data.

[0095] In one embodiment of the present application, historical transaction data of each supplier is obtained, and a supply profile of the auto parts supplier is determined based on the historical transaction data. In this embodiment, the historical transaction data includes transaction time, transaction type, transaction method, and the type and number of the parts therein, as well as maintenance time, maintenance personnel and other information. By obtaining the above information, the auto parts supplier can be profiled to generate a supply profile of the auto parts supplier.

[0096] In this embodiment, the supply profile of the auto parts supplier is generated in the above manner, which facilitates the attribute management of each auto parts supplier and is used to determine the target supplier profile that matches the maintenance profile after determining the maintenance needs.

[0097] In step S150, after receiving the price inquiry request from the repair shop, a target supplier profile matching the repair profile is determined based on the repair profile.

[0098] After receiving the inquiry request from the repair shop, the target supplier profile that matches the repair profile is determined based on the repair profile. The above process specifically includes the following steps:

[0099] Extracting first feature data from the maintenance profile of the maintenance manufacturer;

[0100] Extracting second feature data from the supply profile of the auto parts supplier;

[0101] Based on matching the first feature data and the second feature data, a target supplier profile that matches the maintenance profile is determined.

[0102] Specifically, in this embodiment, the first characteristic data is first extracted from the maintenance profile of the maintenance manufacturer, including information such as maintenance parts, parts quantity, and maintenance methods, and then the second characteristic data is extracted based on the supply profile of the auto parts supplier, including information such as parts name, parts data, and maintenance methods. Then, matching is performed based on the first characteristic data and the second characteristic data to determine the target supplier profile that matches the maintenance profile.

[0103] In step S160, at least one target supplier and quotation information corresponding to the target supplier are determined based on the inquiry information in the inquiry request and the matched target supplier portrait.

[0104] In one embodiment of the present application, after determining the target supplier portrait, at least one target supplier that meets the inquiry information and the target supplier portrait, and the quotation information corresponding to the target supplier are determined based on the inquiry information in the inquiry request and the target supplier portrait.

[0105] In one embodiment of the present application, after generating a maintenance profile, we can predict the maintenance manufacturer's future auto parts forecast information based on the maintenance profile. This may include demand information for various types of auto parts, such as demand time, demand quantity, etc. Then, the production demand information is sent to the corresponding target supplier.

[0106] Optionally, the production demand information in this embodiment may include quantity information, model number and other information of each auto part.

[0107] In the technical solution of the present application, the auto parts transaction information of each maintenance manufacturer is obtained; the auto parts transaction information is preprocessed to obtain processing data for the transaction type corresponding to the auto parts transaction information; the maintenance portrait corresponding to the maintenance manufacturer is generated based on the processing data of each maintenance manufacturer; the supply portrait of the auto parts supplier is determined based on the supplier's historical transaction data; after receiving the inquiry request from the maintenance plant, the target supplier portrait that matches the maintenance portrait is determined based on the maintenance portrait; at least one target supplier and the quotation information corresponding to the target supplier are determined based on the inquiry information in the inquiry request and the matched target supplier portrait. By preprocessing the auto parts transaction information and constructing a maintenance portrait based on the obtained processing data, and determining the supply portrait based on the supplier's historical transaction data, the target supplier corresponding to the maintenance task is determined by matching the maintenance portrait with the supply portrait and combining the inquiry information, thereby improving the efficiency of auto parts transactions and ensuring the reliability of auto parts supply during the maintenance process.

[0108] The following describes an embodiment of the device of the present application, which can be used to execute the auto parts transaction management method based on artificial intelligence and cloud computing in the above-mentioned embodiment of the present application. It can be understood that the device can be a computer program (including program code) running in a computer device, for example, the device is an application software; the device can be used to execute the corresponding steps in the method provided in the 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 auto parts transaction management method based on artificial intelligence and cloud computing in the above-mentioned embodiment of the present application.

[0109] Figure 3 A block diagram of an auto parts transaction management system based on artificial intelligence and cloud computing according to an embodiment of the present application is shown.

[0110] Reference Figure 3 As shown, according to an embodiment of the present application, an auto parts transaction management system based on artificial intelligence and cloud computing includes:

[0111] The acquisition unit 310 is used to acquire the auto parts transaction information of each maintenance manufacturer;

[0112] The processing unit 320 is used to pre-process the auto parts transaction information according to the transaction type corresponding to the auto parts transaction information to obtain processed data;

[0113] A maintenance portrait unit 330, for generating a maintenance portrait corresponding to the maintenance manufacturer based on the processing data of each maintenance manufacturer;

[0114] A supply profile unit 340 is used to determine a supply profile of an auto parts supplier based on the supplier's historical transaction data;

[0115] The matching unit 350 is used to determine, based on the maintenance profile, a target supplier profile that matches the maintenance profile after receiving the inquiry request from the maintenance plant;

[0116] The quotation unit 360 is used to determine at least one target supplier and the quotation information corresponding to the target supplier according to the inquiry information in the inquiry request and the matched target supplier portrait.

[0117] In the present application, based on the above-mentioned solution, after obtaining the auto parts transaction information of each repair manufacturer, it also includes: obtaining the manufacturer identification of the repair manufacturer; and associating and storing the manufacturer identification of the repair manufacturer with the auto parts transaction information.

[0118] In the present application, based on the aforementioned scheme, the auto parts transaction information is pre-processed according to the transaction type corresponding to the auto parts transaction information to obtain processed data, including: identifying the transaction type corresponding to the auto parts transaction information; based on the transaction type, the auto parts transaction information is pre-processed to obtain processed data.

[0119] In the present application, based on the aforementioned scheme, the auto parts transaction information is pre-processed according to the transaction type corresponding to the auto parts transaction information to obtain processed data, including: based on the transaction type corresponding to the auto parts transaction information, obtaining a normal data range corresponding to the transaction type; based on the normal data range, the auto parts transaction information is pre-processed to obtain processed data.

[0120] In the present application, based on the aforementioned scheme, the auto parts transaction information is pre-processed according to the normal data range to obtain processed data, including: according to the normal data range, the auto parts transaction information is data cleansed to obtain cleaned data; according to the normal data range, the cleaned data is quantified to obtain processed data.

[0121] In the present application, based on the aforementioned scheme, the maintenance portrait corresponding to the maintenance manufacturer is generated based on the processing data of each maintenance manufacturer, including: based on the processing data of each maintenance manufacturer, the most frequent target auto parts, the most frequent maintenance time and the most frequent vehicle brand are extracted therefrom; based on the target auto parts, the maintenance time and the vehicle brand, a maintenance portrait of the maintenance manufacturer is generated.

[0122] In the present application, based on the aforementioned scheme, after predicting the subsequent auto parts estimation information of the repair manufacturer based on the repair portrait, it also includes: aggregating the auto parts estimation information of each repair manufacturer to generate production demand information; and sending the production demand information to the corresponding manufacturer.

[0123] In the present application, based on the aforementioned scheme, the target supplier portrait that matches the maintenance portrait is determined based on the maintenance portrait, including: extracting first feature data from the maintenance portrait of the maintenance manufacturer; extracting second feature data from the supply portrait of the auto parts supplier; and determining the target supplier portrait that matches the maintenance portrait based on matching the first feature data and the second feature data.

[0124] In the technical solution of the present application, the auto parts transaction information of each maintenance manufacturer is obtained; the auto parts transaction information is preprocessed to obtain processing data for the transaction type corresponding to the auto parts transaction information; the maintenance portrait corresponding to the maintenance manufacturer is generated based on the processing data of each maintenance manufacturer; the supply portrait of the auto parts supplier is determined based on the supplier's historical transaction data; after receiving the inquiry request from the maintenance plant, the target supplier portrait that matches the maintenance portrait is determined based on the maintenance portrait; at least one target supplier and the quotation information corresponding to the target supplier are determined based on the inquiry information in the inquiry request and the matched target supplier portrait. By preprocessing the auto parts transaction information and constructing a maintenance portrait based on the obtained processing data, and determining the supply portrait based on the supplier's historical transaction data, the target supplier corresponding to the maintenance task is determined by matching the maintenance portrait with the supply portrait and combining the inquiry information, thereby improving the efficiency of auto parts transactions and ensuring the reliability of auto parts supply during the maintenance process.

[0125] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown.

[0126] It should be noted that Figure 4 The computer system 400 of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0127] like Figure 4 As shown, the computer system 400 includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 402 or the program loaded from the storage part 408 to the random access memory (RAM) 403, such as executing the method described in the above embodiment. In the RAM 403, various programs and data required for system operation are also stored. The CPU 401, ROM 402 and RAM 403 are connected to each other through a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0128] The following components are connected to the I / O interface 405: an input section 406 including a keyboard, a mouse, etc.; an output section 407 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the I / O interface 405 as needed. A removable medium 411, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 410 as needed so that a computer program read therefrom is installed into the storage section 408 as needed.

[0129] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication section 409, and / or installed from a removable medium 411. When the computer program is executed by a central processing unit (CPU) 401, various functions defined in the system of the present application are executed.

[0130] It should be noted that the computer-readable medium shown in the embodiment of the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by an instruction execution system, device or device or used in combination with it. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a computer-readable computer program is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate, or transmit programs for use by or in conjunction with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0131] The flowchart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present application. Wherein, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0132] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. The names of these units do not, in some cases, constitute limitations on the units themselves.

[0133] According to one aspect of the present application, a computer program product or a computer program is provided, the computer program product or the computer program comprising computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the methods provided in the above-mentioned various optional implementations.

[0134] As another aspect, the present application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiment; or may exist independently without being assembled into the electronic device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by an electronic device, the electronic device implements the method described in the above embodiment.

[0135] It should be noted that, although several modules or units of the equipment for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into being embodied by multiple modules or units.

[0136] Through the description of the above implementation methods, it is easy for those skilled in the art to understand that the example implementation methods described here can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the implementation methods of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the implementation methods of the present application.

[0137] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the embodiments disclosed herein. The present application is intended to cover any variations, uses or adaptations of the present application, which follow the general principles of the present application and include common knowledge or customary technical means in the art that are not disclosed in the present application.

[0138] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. An auto parts transaction management method based on artificial intelligence and cloud computing, characterized in that: include: Obtain auto parts transaction information from various repair manufacturers; According to the transaction type corresponding to the auto parts transaction information, pre-processing the auto parts transaction information to obtain processed data; Generate maintenance profiles corresponding to maintenance manufacturers based on the processing data of each maintenance manufacturer; Determine the supply profile of auto parts suppliers based on their historical transaction data; After receiving the inquiry request from the repair shop, based on the repair profile, determine the target supplier profile that matches the repair profile; At least one target supplier and quotation information corresponding to the target supplier are determined according to the inquiry information in the inquiry request and the matched target supplier portrait.

2. The method according to claim 1, characterized in that: After obtaining the auto parts transaction information of each maintenance manufacturer, the method further includes: Obtaining the manufacturer identification of the maintenance manufacturer; The manufacturer identification of the maintenance manufacturer is associated with the auto parts transaction information and stored.

3. The method according to claim 1, characterized in that The preprocessing of the auto parts transaction information according to the transaction type corresponding to the auto parts transaction information to obtain the processed data includes: Identify the transaction type corresponding to the auto parts transaction information; Based on the transaction type, the auto parts transaction information is preprocessed to obtain processed data.

4. The method according to claim 1, characterized in that: The preprocessing of the auto parts transaction information according to the transaction type corresponding to the auto parts transaction information to obtain the processed data includes: Based on the transaction type corresponding to the auto parts transaction information, obtaining a normal data range corresponding to the transaction type; According to the normal data range, the auto parts transaction information is preprocessed to obtain processed data.

5. The method according to claim 4, characterized in that The preprocessing of the auto parts transaction information according to the normal data range to obtain processed data includes: According to the normal data range, the auto parts transaction information is cleansed to obtain cleansed data; According to the normal data range, the cleaned data is quantified to obtain processed data.

6. The method according to claim 1, characterized in that The generating of a maintenance profile corresponding to the maintenance manufacturer based on the processed data of each maintenance manufacturer includes: Based on the processed data of each maintenance manufacturer, the most frequently used target auto parts, the most frequently used maintenance time and the most frequently used vehicle brand are extracted; A maintenance profile of the maintenance manufacturer is generated based on the target auto part, the maintenance time and the vehicle brand.

7. The method according to claim 1, characterized in that The determining, based on the maintenance profile, a target supplier profile that matches the maintenance profile includes: Extracting first characteristic data from the maintenance profile of the maintenance manufacturer; the first characteristic data includes maintenance parts, the number of parts, and maintenance methods; Extracting second characteristic data from the supply profile of the auto parts supplier; the second characteristic data includes the name of the parts, the data of the parts, and the maintenance method; Based on matching the first feature data and the second feature data, a target supplier profile that matches the maintenance profile is determined.

8. An auto parts transaction management system based on artificial intelligence and cloud computing, characterized in that: include: An acquisition unit, used to acquire auto parts transaction information of various maintenance manufacturers; A processing unit, configured to pre-process the auto parts transaction information according to the transaction type corresponding to the auto parts transaction information to obtain processed data; A maintenance portrait unit, used to generate a maintenance portrait corresponding to the maintenance manufacturer based on the processing data of each maintenance manufacturer; The supply profile unit is used to determine the supply profile of the auto parts supplier based on the supplier's historical transaction data; A matching unit, configured to, upon receiving a price inquiry request from a repair shop, determine, based on the repair profile, a target supplier profile that matches the repair profile; The quotation unit is used to determine at least one target supplier and the quotation information corresponding to the target supplier based on the inquiry information in the inquiry request and the matched target supplier portrait.

9. The method according to claim 8, characterized in that Also includes: Obtaining the manufacturer identification of the maintenance manufacturer; The manufacturer identification of the maintenance manufacturer is associated with the auto parts transaction information and stored.

10. The method according to claim 8, characterized in that The processing unit is also used for: Identify the transaction type corresponding to the auto parts transaction information; Based on the transaction type, the auto parts transaction information is preprocessed to obtain processed data.

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