Auto parts management method, system and electronic equipment based on big data

Abstract

The embodiments of the present application provide a big data-based auto parts management method, system, and electronic device. The big data-based auto parts management method includes: obtaining maintenance data and auto parts data from each maintenance unit, performing big data-based data analysis, and obtaining information such as maintenance attributes and auto parts attributes corresponding to the maintenance unit, wherein the maintenance attributes include business type, and the auto parts attributes include auto parts circulation efficiency, high-frequency auto parts information, and high-demand auto parts information; updating the auto parts demand of the maintenance unit based on the above analysis results to uniformly allocate and schedule auto parts for each maintenance unit, and synchronizing the auto parts demand analysis results with upstream suppliers to improve the supply efficiency and circulation efficiency of parts, thereby improving maintenance efficiency.

Description

[0001] This application is a divisional application of the invention application with the application date of May 16, 2022, Chinese application number 202210525571.1, and invention name “A method and system for accessories management based on big data”. Technical Field

[0002] The present application relates to the field of computer and communication technology, and more specifically, to a method, system, computer-readable medium, and electronic device for managing automobile parts based on big data. Background Art

[0003] Automotive maintenance is a general term for vehicle maintenance and repair. It involves using technical means to troubleshoot a vehicle, identify the cause, and implement appropriate measures to eliminate the problem and restore it to a specified performance and safety standard. Currently, many repair shops experience delays due to parts shortages, logistics, and other management issues. These parts management issues lead to low maintenance efficiency and underutilized auto parts. Summary of the Invention

[0004] The embodiments of the present application provide a method and system for parts management based on big data, which can improve the supply efficiency and circulation efficiency of parts, and thus improve maintenance efficiency, at least to a certain extent.

[0005] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by practice of the present application.

[0006] According to one aspect of an embodiment of the present application, a big data-based parts management method is provided, including: acquiring maintenance data and auto parts data of a maintenance unit, wherein the maintenance data includes a maintenance type, and the auto parts data includes an auto parts model and its corresponding usage frequency; performing data analysis on the maintenance data and the auto parts data to obtain an analysis result, wherein the analysis result includes maintenance attributes and auto parts attributes corresponding to the maintenance unit, wherein the maintenance attributes include a business type, and the auto parts attributes include auto parts circulation efficiency, high-frequency auto parts information, and high-demand auto parts information; updating the auto parts demand of the maintenance unit based on the analysis result; and synchronizing the auto parts demand to the server corresponding to the auto parts supplier.

[0007] In some embodiments of the present application, based on the aforementioned scheme, the data analysis of the maintenance data and the auto parts data is performed to obtain analysis results, including: identifying the data types of the maintenance data and the auto parts data, and merging the maintenance data and the auto parts data based on the data types to obtain merged data; performing fitting analysis on the merged data to obtain distribution attributes corresponding to the merged data; and determining the maintenance attributes corresponding to the maintenance unit based on the distribution attributes; wherein the maintenance attributes include business types and the business scale and business frequency corresponding to each business type.

[0008] In some embodiments of the present application, based on the aforementioned scheme, the fitting analysis of the merged data to obtain the distribution attributes corresponding to the merged data includes: performing a logarithmic analysis on the merged data to determine the model parameters of each preset probability model corresponding to the merged data; determining the parameter difference based on the model parameters and their parameter means, and the parameter difference is used to measure the accuracy of the probability model; determining the model weight corresponding to the probability model based on the parameter difference; and determining the distribution attributes corresponding to the merged data based on the model weights corresponding to each of the probability models.

[0009] In some embodiments of the present application, based on the aforementioned scheme, the method further includes: obtaining a maintenance requirement triggered by a user, the maintenance requirement including maintenance object information and fault information; determining the target auto part required for this maintenance based on the maintenance object information and the fault information; obtaining a target maintenance unit corresponding to the target auto part from a database; and dispatching the maintenance requirement to the target maintenance unit.

[0010] In some embodiments of the present application, based on the aforementioned scheme, the method further includes: obtaining a maintenance requirement triggered by a user, the maintenance requirement including maintenance object information and fault information; determining the target auto parts and repair tools required for this maintenance based on the maintenance object information and the fault information; obtaining a target maintenance unit corresponding to the target auto parts and the repair tools from a database; and dispatching the maintenance requirement to the target maintenance unit.

[0011] In some embodiments of the present application, based on the aforementioned scheme, the analysis results include inventory parts information indicating that the inventory is greater than a set threshold; performing data analysis on the maintenance data and the auto parts data, and after obtaining the analysis results, further comprising: querying the parts demand information corresponding to the inventory parts information; obtaining the target maintenance unit that publishes the parts demand information; generating transfer information based on the inventory parts information, and sending the transfer information to the target maintenance unit.

[0012] In some embodiments of the present application, based on the aforementioned solution, after synchronizing the auto parts demand to the server corresponding to the auto parts supplier, it also includes: obtaining the auto parts production progress and supply information fed back by the server; generating a maintenance schedule based on the auto parts production progress and the supply information.

[0013] According to one aspect of an embodiment of the present application, a big data-based parts management system is provided, including:

[0014] An acquisition unit, configured to acquire maintenance data and auto parts data of a maintenance unit, wherein the maintenance data includes a maintenance type, and the auto parts data includes an auto parts model and its corresponding usage frequency;

[0015] an analysis unit, configured to perform data analysis on the maintenance data and the auto parts data to obtain analysis results, wherein the analysis results include maintenance attributes and auto parts attributes corresponding to the maintenance unit, wherein the maintenance attributes include business type, and the auto parts attributes include auto parts circulation efficiency, high-frequency auto parts information, and high-demand auto parts information;

[0016] an updating unit, configured to update the auto parts demand of the maintenance unit based on the analysis result;

[0017] The synchronization unit is used to synchronize the auto parts demand to the server corresponding to the auto parts supplier.

[0018] In some embodiments of the present application, based on the aforementioned scheme, the data analysis of the maintenance data and the auto parts data is performed to obtain analysis results, including: identifying the data types of the maintenance data and the auto parts data, and merging the maintenance data and the auto parts data based on the data types to obtain merged data; performing fitting analysis on the merged data to obtain distribution attributes corresponding to the merged data; and determining the maintenance attributes corresponding to the maintenance unit based on the distribution attributes; wherein the maintenance attributes include business types and the business scale and business frequency corresponding to each business type.

[0019] In some embodiments of the present application, based on the aforementioned scheme, the fitting analysis of the merged data to obtain the distribution attributes corresponding to the merged data includes: performing a logarithmic analysis on the merged data to determine the model parameters of each preset probability model corresponding to the merged data; determining the parameter difference based on the model parameters and their parameter means, and the parameter difference is used to measure the accuracy of the probability model; determining the model weight corresponding to the probability model based on the parameter difference; and determining the distribution attributes corresponding to the merged data based on the model weights corresponding to each of the probability models.

[0020] In some embodiments of the present application, based on the aforementioned scheme, the method further includes: obtaining a maintenance requirement triggered by a user, the maintenance requirement including maintenance object information and fault information; determining the target auto part required for this maintenance based on the maintenance object information and the fault information; obtaining a target maintenance unit corresponding to the target auto part from a database; and dispatching the maintenance requirement to the target maintenance unit.

[0021] In some embodiments of the present application, based on the aforementioned scheme, the method further includes: obtaining a maintenance requirement triggered by a user, the maintenance requirement including maintenance object information and fault information; determining the target auto parts and repair tools required for this maintenance based on the maintenance object information and the fault information; obtaining a target maintenance unit corresponding to the target auto parts and the repair tools from a database; and dispatching the maintenance requirement to the target maintenance unit.

[0022] In some embodiments of the present application, based on the aforementioned scheme, the analysis results include inventory parts information indicating that the inventory is greater than a set threshold; performing data analysis on the maintenance data and the auto parts data, and after obtaining the analysis results, further comprising: querying the parts demand information corresponding to the inventory parts information; obtaining the target maintenance unit that publishes the parts demand information; generating transfer information based on the inventory parts information, and sending the transfer information to the target maintenance unit.

[0023] In some embodiments of the present application, based on the aforementioned solution, after synchronizing the auto parts demand to the server corresponding to the auto parts supplier, it also includes: obtaining the auto parts production progress and supply information fed back by the server; generating a maintenance schedule based on the auto parts production progress and the supply information.

[0024] According to one aspect of an embodiment 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 accessory management method based on big data as described in the above embodiment is implemented.

[0025] According to one aspect of an embodiment of the present application, an electronic device is provided, comprising: one or more processors; a storage device for storing one or more programs, which, when executed by the one or more processors, enables the one or more processors to implement the big data-based accessory management method as described in the above embodiments.

[0026] According to one aspect of an embodiment of the present application, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the big data-based accessory management method provided in the various optional implementations described above.

[0027] In the technical solutions provided in some embodiments of the present application, a big data-based parts classification management system and method obtains maintenance data and auto parts data of each maintenance unit, performs big data-based data analysis, and obtains information such as maintenance attributes and auto parts attributes corresponding to the maintenance unit through analysis, wherein the maintenance attributes include business type, and the auto parts attributes include auto parts circulation efficiency, high-frequency auto parts information, and high-demand auto parts information; based on the above analysis results, the auto parts demand of the maintenance unit is updated to uniformly allocate and schedule auto parts for each maintenance unit, and at the same time, the auto parts demand analysis results are synchronized to upstream suppliers to improve the supply efficiency and circulation efficiency of parts, thereby improving maintenance efficiency.

[0028] 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

[0029] The accompanying drawings are incorporated into and constitute a part of the specification, illustrating embodiments consistent with the present application and, together with the specification, explaining the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and those skilled in the art can derive other drawings based on these drawings without inventive effort. In the drawings:

[0030] Figure 1 The flowchart of the big data-based accessories management method according to one embodiment of the present application is schematically shown.

[0031] Figure 2 The flowchart of data processing according to one embodiment of the present application is schematically shown.

[0032] Figure 3 A block diagram of a big data-based parts management system according to an embodiment of the present application is schematically shown.

[0033] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing an electronic device according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0034] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many 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 thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art.

[0035] 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 so as to provide a full understanding of the embodiments of the present application. However, it will be appreciated by those skilled in the art 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, devices, implementations or operations are not shown or described in detail to avoid blurring the various aspects of the application.

[0036] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically separate entities. That is, these functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0037] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, while others may be combined or partially combined. Therefore, the actual execution order may vary depending on the actual situation.

[0038] The following is a detailed description of the implementation details of the technical solution of the embodiment of the present application:

[0039] Figure 1 FIG2 shows a flowchart of a method for managing accessories based on big data according to an embodiment of the present application. Figure 1 As shown, the big data-based accessories management method includes at least steps S110 to S140, which are described in detail as follows:

[0040] In step S110 , the maintenance data and auto parts data of the maintenance unit are acquired, wherein the maintenance data includes the maintenance type, and the auto parts data includes the auto parts model and its corresponding usage frequency.

[0041] In one embodiment of the present application, maintenance data and auto parts data from a maintenance organization are collected to analyze the maintenance organization's performance. In this embodiment, the maintenance data includes information such as the type of maintenance, the time of maintenance, and the cost of maintenance. The auto parts data includes the auto parts models associated with the maintenance services provided by the maintenance organization, as well as the usage frequency of each auto part model. In this embodiment, the usage frequency can be measured in monthly or weekly units.

[0042] In one embodiment of the present application, Figure 2 As shown, obtain the maintenance data and auto parts data of the maintenance unit, including:

[0043] S210, acquiring maintenance data and auto parts data from a database of a maintenance unit based on a set time period;

[0044] S220, identifying data identifiers of the maintenance data and auto parts data;

[0045] S230, storing data belonging to the same data identifier in one storage unit;

[0046] S240, identifying and deleting duplicate data in the storage unit;

[0047] S250, acquiring a set data range based on the data identifier corresponding to the storage unit;

[0048] S260: Detect abnormal data based on the data range, and delete the abnormal data.

[0049] In one embodiment of the present application, maintenance data and auto parts data are obtained from the database of a maintenance unit by presetting a time period, wherein the time period can be one week, one month, and the like. The data identifiers of the maintenance data and auto parts data are then identified, and the data identifiers corresponding to the above data can be identified by detecting the data type. The data belonging to the same data identifier are then stored in a storage unit, and the storage unit in this embodiment can be a folder, and the like. Data with the same data identifier and the same value in a storage unit are identified as duplicate data, and the duplicate data are deleted to reduce redundant data. Abnormal data is then detected based on the data range corresponding to the data identifier in the storage unit to delete the abnormal data.

[0050] In one embodiment of the present application, in step S260, detecting abnormal data based on the data range and deleting the abnormal data includes:

[0051] determining, based on a minimum value and a maximum value in the data range, a detection parameter corresponding to the data in the storage unit;

[0052] If the detection parameter is greater than or equal to the set abnormality threshold, the data is determined to be abnormal data;

[0053] Delete the abnormal data.

[0054] In one embodiment of the present application, based on the minimum value R in the data range min and the maximum value R max , and the data value of the data in the storage unit , determine the detection parameter Par_test corresponding to the data in the storage unit as:

[0055] P ;in, Represents the detection factor.

[0056] In this embodiment, after the detection parameter is calculated, the detection parameter is compared with the set abnormality threshold. If the detection parameter is greater than or equal to the set abnormality threshold, the data is determined to be abnormal data and deleted. If the detection parameter is less than the abnormality threshold, it is determined to be normal data and no processing is performed.

[0057] The above method improves the reliability of the data to be analyzed by removing redundant and abnormal data from the acquired data, thereby ensuring the accuracy of the analysis results obtained later.

[0058] In step S120, data analysis is performed on the maintenance data and the auto parts data to obtain analysis results, which include maintenance attributes and auto parts attributes corresponding to the maintenance unit, wherein the maintenance attributes include business type, and the auto parts attributes include auto parts circulation efficiency, high-frequency auto parts information, and high-demand auto parts information.

[0059] In one embodiment of the present application, acquired maintenance data and auto parts data are analyzed to obtain maintenance attributes and auto parts attributes corresponding to the maintenance unit. In this embodiment, the maintenance attributes are used to represent the maintenance business of the maintenance unit, which may include business type, maintenance cost-effectiveness, and applicable population, etc. The auto parts attributes are used to represent the auto parts situation of the maintenance unit, which may include auto parts circulation efficiency, high-frequency auto parts information, and high-demand auto parts information, etc.

[0060] In one embodiment of the present application, step S120 performs data analysis on the maintenance data and the auto parts data to obtain analysis results, including S310 to S330:

[0061] S310, identifying data types of the maintenance data and the auto parts data, and merging the maintenance data and the auto parts data based on the data types to obtain merged data;

[0062] S320, performing fitting analysis on the merged data to obtain distribution attributes corresponding to the merged data;

[0063] S330: Determine maintenance attributes corresponding to the maintenance unit based on the distribution attributes; wherein the maintenance attributes include business types and business scale and business frequency corresponding to each business type.

[0064] In one embodiment of the present application, based on the data types of maintenance data and auto parts data, data of the same or similar type are merged to obtain merged data. The above method can still reduce data redundancy and improve data analysis efficiency.

[0065] In one embodiment of the present application, in step S320, fitting analysis is performed on the merged data to obtain distribution attributes corresponding to the merged data, including:

[0066] Performing a logarithmic analysis on the combined data to determine model parameters of each preset probability model corresponding to the combined data;

[0067] Determining parameter differences based on model parameters and their parameter means, wherein the parameter differences are used to measure the accuracy of the probability model;

[0068] Determining a model weight corresponding to the probability model based on the parameter difference;

[0069] Based on the model weights corresponding to the probability models, a distribution attribute corresponding to the merged data is determined.

[0070] In the above embodiment, at least two probability models are preset for comprehensively determining the distribution properties of the merged data. The merged data is subjected to a logarithmic analysis to determine the model parameters of the merged data corresponding to each of the preset probability models, including:

[0071] Corresponding data values ​​based on merged data , the amount of data to be merged , and the probability factor of the probability model k , determine the model parameters :

[0072]

[0073] in, represents the model factor, i represents the identifier of the probability model, represents the likelihood operation, Represents logarithmic operation.

[0074] In the above embodiment, based on the model parameters and its parameter mean , determine the parameter difference for:

[0075]

[0076] Since different probability models have different degrees of fitting to the combined data, in this embodiment, the difference between the parameter mean and the model parameter is used as the parameter difference to measure the accuracy of the probability model.

[0077] In the above embodiment, the accuracy of the probability model is measured by the parameter difference, so as to determine the model weight by the accuracy, that is, the model weight corresponding to the probability model is determined based on the parameter difference. :

[0078]

[0079] in, Represents the weight factor. The above scheme uses parameter differences to make the probability model with higher accuracy have a higher model weight, thereby improving the accuracy of data fitting.

[0080] In the above embodiment, based on the model weights corresponding to the probability models, weighted processing is performed based on the fitting results of the probability models and their corresponding model weights to comprehensively determine the distribution properties corresponding to the merged data. Alternatively, the distribution properties corresponding to the merged data can be obtained by fitting only the probability model with the highest model weight.

[0081] After determining the distribution attributes corresponding to the merged data, the corresponding maintenance attributes are determined based on the corresponding relationship between the distribution attributes and the maintenance attributes. In this embodiment, the maintenance attributes can include overhaul attributes, maintenance attributes, or quick repair attributes, etc.; the auto parts attributes can include large parts attributes, high-end parts attributes, or maintenance attributes, etc., and can also include auto parts circulation efficiency, high-frequency auto parts information, and high-demand auto parts information.

[0082] In one embodiment of the present application, the analysis result includes information about accessories in stock that indicates the inventory is greater than a set threshold, that is, information about accessories with a large inventory.

[0083] In step S120, after analyzing the maintenance data and the auto parts data and obtaining the analysis results, the method further includes:

[0084] Querying the spare parts demand information corresponding to the stock spare parts information;

[0085] Obtain the target maintenance unit that publishes the parts demand information;

[0086] Generate goods transfer information based on the inventory parts information, and send the goods transfer information to the target maintenance unit.

[0087] Specifically, this embodiment first searches the entire network for parts demand information related to stock parts, identifying repair shops that are short of or in need of the parts. It then obtains the target repair shops corresponding to the parts demand information, generates stock transfer information based on the stock parts information, and proactively sends this information to the target repair shops. This approach can be used for unified stock transfers between repair shops, improving parts utilization.

[0088] In step S130 , the auto parts demand of the maintenance unit is updated based on the analysis result.

[0089] In one embodiment of the present application, after the analysis results are generated, the auto parts demand of the maintenance unit is updated based on the analysis results. For example, when the maintenance attribute of the maintenance unit is an overhaul attribute and the auto parts attribute is a large accessories attribute, its corresponding auto parts demand is updated to the auto parts demand corresponding to the large accessories. It can also be specific to the corresponding accessory model and accessory identification, etc., to achieve the purpose of importing the analysis results into production needs.

[0090] In step S140, the auto parts demand is synchronized to the server corresponding to the auto parts supplier.

[0091] In one embodiment of the present application, after the auto parts demand is generated, the auto parts demand is synchronized to the server corresponding to the auto parts supplier to remind the auto parts supplier of the auto parts demand of the maintenance unit and supply based on the auto parts demand to ensure the reliability of the use of accessories.

[0092] In one embodiment of the present application, after synchronizing the auto parts demand to the server corresponding to the auto parts supplier in step S140, the following steps are further included:

[0093] Obtaining auto parts production progress and supply information fed back by the server;

[0094] A maintenance schedule is generated based on the auto parts production progress and the supply information.

[0095] Specifically, in this embodiment, after the auto parts supplier obtains the auto parts demand, it will feedback the auto parts production progress and supply information to the maintenance unit to notify the maintenance unit to adjust and arrange the production and maintenance progress based on the above information to ensure the efficiency of production and maintenance.

[0096] In one embodiment of the present application, the method further includes:

[0097] Acquire a maintenance demand triggered by a user, wherein the maintenance demand includes maintenance object information and fault information;

[0098] Determining target auto parts required for this maintenance based on the maintenance object information and the fault information;

[0099] Obtaining a target maintenance unit corresponding to the target auto part from a database;

[0100] The maintenance request is dispatched to the target maintenance unit.

[0101] Specifically, after a user triggers a maintenance request, the server obtains information about the repair target and the fault. The repair target information may include the vehicle model and configuration, while the fault information may include the faulty part and the specific fault condition. Based on this information, the server determines the target auto part required for repairing the repair target and retrieves the target repair unit corresponding to the target auto part from a database. The server then sends the repair request to the target repair unit and provides feedback to the user. This solution effectively utilizes available maintenance resources, ensuring resource utilization while improving maintenance efficiency.

[0102] In one embodiment of the present application, a maintenance demand triggered by a user is obtained, wherein the maintenance demand includes maintenance object information and fault information;

[0103] Determining target auto parts and repair tools required for this repair based on the repair object information and the fault information;

[0104] Obtaining from a database a target maintenance unit that comprehensively corresponds to the target auto part and the maintenance tool;

[0105] The maintenance request is dispatched to the target maintenance unit.

[0106] Specifically, after a user triggers a maintenance request, the server obtains information about the repair target and the fault. The repair target information may include the vehicle model and configuration, while the fault information may include the faulty part and the specific fault condition. Based on this information, the server determines the target auto part and repair tools required to repair the repair target. The server then retrieves the target repair unit corresponding to the target auto part and repair tools from a database, sends the repair request to the target repair unit, and provides feedback to the user. This solution effectively manages maintenance resources, improving maintenance efficiency and reliability.

[0107] The following describes an embodiment of the device of the present application, which can be used to implement the big data-based accessory management method described in the above embodiment of the present application. For details not disclosed in the embodiment of the device of the present application, please refer to the embodiment of the big data-based accessory management method described in the above embodiment of the present application.

[0108] Figure 3 A block diagram of a big data-based accessories management system according to an embodiment of the present application is shown.

[0109] Reference Figure 3 As shown, according to an embodiment of the present application, a big data-based parts management system 300 includes:

[0110] An acquisition unit 310 is configured to acquire maintenance data and auto parts data of a maintenance unit, wherein the maintenance data includes a maintenance type, and the auto parts data includes an auto parts model and its corresponding usage frequency;

[0111] An analysis unit 320 is configured to perform data analysis on the maintenance data and the auto parts data to obtain analysis results, wherein the analysis results include maintenance attributes and auto parts attributes corresponding to the maintenance unit, wherein the maintenance attributes include business type, and the auto parts attributes include auto parts circulation efficiency, high-frequency auto parts information, and high-demand auto parts information;

[0112] An updating unit 330, configured to update the auto parts demand of the maintenance unit based on the analysis result;

[0113] The synchronization unit 340 is used to synchronize the auto parts demand to the server corresponding to the auto parts supplier.

[0114] In some embodiments of the present application, based on the aforementioned scheme, the obtaining of the maintenance data and auto parts data of the maintenance unit includes: obtaining the maintenance data and auto parts data from the database of the maintenance unit based on a set time period; identifying the data identifiers of the maintenance data and auto parts data; storing data belonging to the same data identifier in a storage unit; identifying and deleting duplicate data in the storage unit; obtaining a set data range based on the data identifier corresponding to the storage unit; detecting abnormal data based on the data range, and deleting the abnormal data.

[0115] In some embodiments of the present application, based on the aforementioned scheme, detecting abnormal data based on the data range and deleting the abnormal data include: determining the detection parameter corresponding to the data in the storage unit based on the minimum value and the maximum value in the data range; if the detection parameter is greater than or equal to the set abnormal threshold, determining that the data is abnormal data; and deleting the abnormal data.

[0116] In some embodiments of the present application, based on the aforementioned scheme, the data analysis of the maintenance data and the auto parts data is performed to obtain analysis results, including: identifying the data types of the maintenance data and the auto parts data, and merging the maintenance data and the auto parts data based on the data types to obtain merged data; performing fitting analysis on the merged data to obtain distribution attributes corresponding to the merged data; and determining the maintenance attributes corresponding to the maintenance unit based on the distribution attributes; wherein the maintenance attributes include business types and the business scale and business frequency corresponding to each business type.

[0117] In some embodiments of the present application, based on the aforementioned scheme, the fitting analysis of the merged data to obtain the distribution attributes corresponding to the merged data includes: performing a logarithmic analysis on the merged data to determine the model parameters of each preset probability model corresponding to the merged data; determining the parameter difference based on the model parameters and their parameter means, and the parameter difference is used to measure the accuracy of the probability model; determining the model weight corresponding to the probability model based on the parameter difference; and determining the distribution attributes corresponding to the merged data based on the model weights corresponding to each of the probability models.

[0118] In some embodiments of the present application, based on the aforementioned scheme, the method further includes: obtaining a maintenance requirement triggered by a user, the maintenance requirement including maintenance object information and fault information; determining the target auto part required for this maintenance based on the maintenance object information and the fault information; obtaining a target maintenance unit corresponding to the target auto part from a database; and dispatching the maintenance requirement to the target maintenance unit.

[0119] In some embodiments of the present application, based on the aforementioned scheme, the method further includes: obtaining a maintenance requirement triggered by a user, the maintenance requirement including maintenance object information and fault information; determining the target auto parts and repair tools required for this maintenance based on the maintenance object information and the fault information; obtaining a target maintenance unit corresponding to the target auto parts and the repair tools from a database; and dispatching the maintenance requirement to the target maintenance unit.

[0120] In some embodiments of the present application, based on the aforementioned scheme, the analysis results include inventory parts information indicating that the inventory is greater than a set threshold; performing data analysis on the maintenance data and the auto parts data, and after obtaining the analysis results, further comprising: querying the parts demand information corresponding to the inventory parts information; obtaining the target maintenance unit that publishes the parts demand information; generating transfer information based on the inventory parts information, and sending the transfer information to the target maintenance unit.

[0121] In some embodiments of the present application, based on the aforementioned solution, after synchronizing the auto parts demand to the server corresponding to the auto parts supplier, it also includes: obtaining the auto parts production progress and supply information fed back by the server; generating a maintenance schedule based on the auto parts production progress and the supply information.

[0122] In the technical solutions provided in some embodiments of the present application, a big data-based parts classification management system and method obtains maintenance data and auto parts data of each maintenance unit, performs big data-based data analysis, and obtains information such as maintenance attributes and auto parts attributes corresponding to the maintenance unit through analysis, wherein the maintenance attributes include business type, and the auto parts attributes include auto parts circulation efficiency, high-frequency auto parts information, and high-demand auto parts information; based on the above analysis results, the auto parts demand of the maintenance unit is updated to uniformly allocate and schedule auto parts for each maintenance unit, and at the same time, the auto parts demand analysis results are synchronized to upstream suppliers to improve the supply efficiency and circulation efficiency of parts, thereby improving maintenance efficiency.

[0123] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing an electronic device according to an embodiment of the present application is shown.

[0124] 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.

[0125] like Figure 4 As shown, computer system 400 includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes, such as the methods described in the above embodiments, based on programs stored in read-only memory (ROM) 402 or programs loaded from storage 408 into random access memory (RAM) 403. RAM 403 also stores various programs and data required for system operation. CPU 401, ROM 402, and RAM 403 are interconnected via bus 404. An input / output (I / O) interface 405 is also connected to bus 404.

[0126] The following components are connected to the I / O interface 405: an input section 406 including a keyboard, mouse, and the like; an output section 407 including devices such as a cathode ray tube (CRT), a liquid crystal display (LCD), and speakers; a storage section 408 including devices such as a hard disk; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card or a modem. 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. Removable media 411, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 410 as needed, so that computer programs read from the media can be installed in the storage section 408 as needed.

[0127] In particular, according to embodiments of the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for executing the methods illustrated in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via the communication section 409 and / or installed from removable media 411. When executed by the central processing unit (CPU) 401, the computer program performs the various functions defined in the system of the present application.

[0128] It should be noted that the computer-readable medium described in the embodiments of the present application may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present application, a computer-readable signal medium may include a data signal transmitted in baseband or as part of a carrier wave, which carries a computer-readable computer program. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. A computer program embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, or any suitable combination thereof.

[0129] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. Among them, each box in the flowchart or block diagram can represent a module, program segment, or part of the code, and the above-mentioned module, program segment, or part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that 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 the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0130] 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. In some cases, the names of these units do not constitute limitations on the units themselves.

[0131] According to one aspect of the present application, a computer program product or computer program is provided, the computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in the various optional implementations described above.

[0132] As another aspect, the present application further provides a computer-readable medium, which may be included in the electronic device described in the above embodiments, or may exist independently without being incorporated into the electronic device. The computer-readable medium carries one or more programs, and when the one or more programs are executed by the electronic device, the electronic device implements the method described in the above embodiments.

[0133] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiment of the application, the features and functions of two or more modules or units described above can be concretized in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be concretized.

[0134] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the embodiments 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 a number of 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 embodiments of the present application.

[0135] Those skilled in the art will readily conceive of other embodiments of the present application after considering the specification and practicing the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of this application and include common knowledge or customary techniques in the art that are not disclosed herein.

[0136] It should be understood that the present application is not limited to the exact structures 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. A big data-based accessories management method, characterized in that: include: Acquire maintenance data and auto parts data of a maintenance unit, wherein the maintenance data includes maintenance types, and the auto parts data includes auto parts models and their corresponding usage frequencies; Performing data analysis on the maintenance data and the auto parts data to obtain analysis results, the analysis results including maintenance attributes and auto parts attributes corresponding to the maintenance unit, the auto parts attributes including auto parts circulation efficiency, high-frequency auto parts information, and high-demand auto parts information; updating the auto parts demand of the maintenance unit based on the analysis result; Synchronizing the auto parts demand to the corresponding server of the auto parts supplier; The maintenance data and the auto parts data are analyzed to obtain analysis results, including: identifying data types of the maintenance data and the auto parts data, and merging the maintenance data and the auto parts data based on the data types to obtain merged data; Performing fitting analysis on the combined data to obtain distribution attributes corresponding to the combined data; Based on the distribution attributes, determining the maintenance attributes corresponding to the maintenance unit; wherein the maintenance attributes include business types and business scale and business frequency corresponding to each business type; The fitting analysis is performed on the merged data to obtain the distribution attributes corresponding to the merged data, including: Performing a logarithmic analysis on the combined data to determine model parameters of each preset probability model corresponding to the combined data; Determining parameter differences based on model parameters and their parameter means, wherein the parameter differences are used to measure the accuracy of the probability model; Determining a model weight corresponding to the probability model based on the parameter difference; Based on the model weights corresponding to the probability models, weighted processing is performed in combination with the fitting results of the probability models to comprehensively determine the distribution attributes corresponding to the merged data; The step of performing a logarithmic analysis on the combined data to determine the model parameters of each preset probability model corresponding to the combined data includes: Based on the data value val_data corresponding to the merged data, the data volume data_n of the merged data, and the probability factor k of the probability model, the model parameter Moe_p(i) is determined: Moe_p(i)=-β·In[Li (k|val_data)]+In(data_n) Where β represents the model factor, i represents the identifier of the probability model, Li (·) represents the likelihood operation, and In(·) represents the logarithmic operation; Based on the model parameter Moe_p(i) and its parameter mean Moe_avr, the parameter difference ΔMoe_p(i) is determined as: ΔMoe_p(i)=Moe_p(i)-Moe_avr.

2. The method according to claim 1, characterized in that The method further comprises: Acquire a maintenance demand triggered by a user, wherein the maintenance demand includes maintenance object information and fault information; Determining target auto parts required for this maintenance based on the maintenance object information and the fault information; Obtaining a target maintenance unit corresponding to the target auto part from a database; The maintenance request is dispatched to the target maintenance unit.

3. The method according to claim 1, characterized in that The method further comprises: Acquire a maintenance demand triggered by a user, wherein the maintenance demand includes maintenance object information and fault information; Determining target auto parts and repair tools required for this repair based on the repair object information and the fault information; Obtaining from a database a target maintenance unit that comprehensively corresponds to the target auto part and the maintenance tool; The maintenance request is dispatched to the target maintenance unit.

4. The method according to claim 1, wherein The analysis result includes spare parts inventory information indicating that the inventory is greater than a set threshold; After analyzing the maintenance data and the auto parts data and obtaining the analysis results, the method further includes: Querying the spare parts demand information corresponding to the stock spare parts information; Obtain the target maintenance unit that publishes the parts demand information; Generate goods transfer information based on the inventory parts information, and send the goods transfer information to the target maintenance unit.

5. The method according to claim 1, wherein After synchronizing the auto parts demand to the corresponding server of the auto parts supplier, the following steps are also included: Obtaining auto parts production progress and supply information fed back by the server; A maintenance schedule is generated based on the auto parts production progress and the supply information.

6. A spare parts management system based on big data, characterized in that: include: An acquisition unit, configured to acquire maintenance data and auto parts data of a maintenance unit, wherein the maintenance data includes a maintenance type, and the auto parts data includes an auto parts model and its corresponding usage frequency; an analysis unit, configured to perform data analysis on the maintenance data and the auto parts data to obtain analysis results, wherein the analysis results include maintenance attributes and auto parts attributes corresponding to the maintenance unit, wherein the auto parts attributes include auto parts circulation efficiency, high-frequency auto parts information, and high-demand auto parts information; an updating unit, configured to update the auto parts demand of the maintenance unit based on the analysis result; A synchronization unit, configured to synchronize the auto parts demand to a server corresponding to the auto parts supplier; The maintenance data and the auto parts data are analyzed to obtain analysis results, including: identifying data types of the maintenance data and the auto parts data, and merging the maintenance data and the auto parts data based on the data types to obtain merged data; Performing fitting analysis on the combined data to obtain distribution attributes corresponding to the combined data; Based on the distribution attributes, determining the maintenance attributes corresponding to the maintenance unit; wherein the maintenance attributes include business types and business scale and business frequency corresponding to each business type; The fitting analysis is performed on the merged data to obtain the distribution attributes corresponding to the merged data, including: Performing a logarithmic analysis on the combined data to determine model parameters of each preset probability model corresponding to the combined data; Determining parameter differences based on model parameters and their parameter means, wherein the parameter differences are used to measure the accuracy of the probability model; Determining a model weight corresponding to the probability model based on the parameter difference; Based on the model weights corresponding to the probability models, weighted processing is performed in combination with the fitting results of the probability models to comprehensively determine the distribution attributes corresponding to the merged data; The step of performing a logarithmic analysis on the combined data to determine the model parameters of each preset probability model corresponding to the combined data includes: Based on the data value val_data corresponding to the merged data, the data volume data_n of the merged data, and the probability factor k of the probability model, the model parameter Moe_p(i) is determined: Moe_p(i)=-β·In[Li (k|val_data)]+In(data_n) Where β represents the model factor, i represents the identifier of the probability model, Li (·) represents the likelihood operation, and In(·) represents the logarithmic operation; Based on the model parameter Moe_p(i) and its parameter mean Moe_avr, the parameter difference ΔMoe_p(i) is determined as: ΔMoe_p(i)=Moe_p(i)-Moe_avr.

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