Digital warehousing system for automobile part storage

Abstract

The invention relates to the technical field of artificial intelligence, and discloses a digital warehousing system for automobile part storage, the system comprises a data acquisition module, a data storage module, a data analysis module and a visual management module, multi-source automobile part data are acquired and subjected to data verification to obtain verification data, and the verification data are stored in a database; the verification data is uploaded to the data storage module; performing chain storage on the received verification data to obtain storage information; performing part demand prediction on the received storage information to obtain a demand result, and performing storage analysis on the storage information by using the demand result to obtain a storage layout; and performing visual display by utilizing the received storage layout, the storage information and the demand result to obtain a visual result, and generating a storage management report based on the visual result and the storage information. The problem that an existing digital warehousing system for automobile part storage is inaccurate in inventory management can be solved.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a digital warehousing system for storing automobile parts. Background Art

[0002] With the rapid development of the automotive industry, the types and quantity of auto parts have increased significantly, which has brought great challenges to the storage management of parts. Both automakers and parts suppliers are faced with the need to efficiently manage a large number of parts to meet the just-in-time production of production lines and the diversified needs of the market.

[0003] However, traditional warehouse management systems rely heavily on manual operations and empirical judgment, which can easily lead to inaccurate data, improper inventory management, and waste of resources. For large-scale auto parts warehouses, manual operations make it difficult to achieve refined management and real-time monitoring of parts. In addition, the wide variety of parts and their different volumes, weights, and storage requirements increase the complexity of management. Therefore, the current warehouse management system still has the problem of inaccurate management. Summary of the invention

[0004] The present invention provides a digital storage system for storing automobile parts, and its main purpose is to solve the problem of inaccurate management of an existing digital storage system for storing automobile parts.

[0005] To achieve the above-mentioned purpose, the present invention provides a digital storage system for automobile parts storage, comprising: a data acquisition module, a data storage module, a data analysis module, and a visual management module, wherein:

[0006] A data acquisition module, used for acquiring multi-source automobile parts data, performing data verification on the multi-source automobile parts data to obtain verification data, and uploading the verification data to the data storage module;

[0007] A data storage module, used to store the received verification data in a chain manner to obtain storage information;

[0008] A data analysis module, used to perform component demand forecasting on the received storage information to obtain demand results, and perform storage analysis on the storage information using the demand results to obtain a storage layout;

[0009] The visualization management module is used to perform visualization using the received storage layout, storage information and demand results to obtain visualization results, and generate a storage management report based on the visualization results and the storage information.

[0010] Optionally, when the data acquisition module performs the function of acquiring multi-source automobile parts data, performing data verification on the multi-source automobile parts data, and obtaining verification data, it is specifically used to:

[0011] Performing an integrity check on the multi-source automobile parts data to obtain an integrity check result;

[0012] Performing a repeatability check on the multi-source automobile parts data to obtain a repeatability result;

[0013] Using the integrity check result and the repeatability check result, the multi-source automobile parts data is corrected to obtain corrected data;

[0014] The correction data is standardized to obtain verification data.

[0015] Optionally, when the data storage module performs the function of chain-storing the received verification data to obtain the storage information, it is specifically used to:

[0016] Packaging the verification data to obtain packaged data;

[0017] Calculating a hash value for the packaged data using an encrypted hash algorithm;

[0018] Constructing chain data using the hash value and the packaged data;

[0019] Distributed storage is performed on the chain data to obtain a storage result;

[0020] A consensus mechanism is performed on the storage result to obtain a consensus result;

[0021] The consensus result is synchronized to obtain storage information.

[0022] Optionally, when the data storage module performs the function of calculating a hash value for the packaged data using an encrypted hash algorithm, it is specifically used to:

[0023] Divide the packaged data into message blocks to obtain divided data blocks;

[0024] Perform message expansion on the divided data blocks to obtain extended data, wherein the message expansion is performed using the following formula:

[0025] D t =σ0(W t-15 )+W t-7 +σ1(W t-2 )+W t-16 (16 <t<63);

[0026] Among them, Dt is the extended data corresponding to the t-th partitioned data block, σ0 and σ1 are bitwise operators, t is the index sequence, and W t-15 For the t-15th partitioned data block, W t-7 For the t-7th partition data block, W t-2 For the t-2th partitioned data block, W t-16 Divide the data block for the t-16th partition;

[0027] Iteratively calculating the extended data to obtain a compressed value;

[0028] The compressed value is concatenated with the divided data block to obtain a hash value.

[0029] Optionally, when executing the function of constructing chain data using the hash value and the packaged data, the data storage module is specifically used to:

[0030] Arrange the packaged data according to the hash value sequence to obtain arranged data;

[0031] Filtering out target data from the arranged data in sequence;

[0032] Inserting a timestamp into the target data to obtain data to be processed;

[0033] Chain data is created according to the data to be processed and the corresponding hash value.

[0034] Optionally, when the data storage module performs the function of performing consensus mechanism verification on the storage result to obtain the consensus result, it is specifically used to:

[0035] Filtering out the data blocks to be verified from the stored results in sequence;

[0036] Performing hash calculation on the data block to be verified to obtain a verification result;

[0037] The verification result is compared with the hash value corresponding to the stored result to obtain a consensus result.

[0038] Optionally, when the data analysis module performs the function of forecasting the demand for parts on the received storage information to obtain the demand result, it is specifically used to:

[0039] Filtering out historical data from the stored information, performing vector encoding on the historical data, and obtaining a data vector;

[0040] Performing difference calculation on the data vector to obtain a stationary sequence;

[0041] Performing autoregression calculation on the stationary sequence to obtain a predicted value;

[0042] Performing weighted averaging on the predicted values ​​to obtain an average result;

[0043] The average result is subjected to reverse difference calculation to obtain the required result.

[0044] Optionally, when the data analysis module performs the function of performing storage analysis on the storage information using the demand result to obtain a storage layout, it is specifically used to:

[0045] Sorting the demand results to obtain demand data;

[0046] Calculating the storage priority of the storage information using the demand data;

[0047] The storage space is allocated to the storage information according to the storage priority to obtain a storage layout.

[0048] Optionally, when the visualization management module performs the function of visually displaying the received storage layout, the storage information, and the demand result to obtain the visualization result, it is specifically used to:

[0049] Generate inventory information according to the demand result and the storage information;

[0050] Displaying the storage layout in a plan view to obtain a warehouse layout diagram;

[0051] The inventory information is added to the corresponding warehouse layout diagram to obtain a visualization result.

[0052] Optionally, when the visualization management module performs the function of generating a storage management report based on the visualization result and the storage information, it is specifically used to:

[0053] extracting inventory information and warehouse log information from the stored information;

[0054] Performing data integration on the inventory information, the warehouse log information, and the visualization result to obtain an integration result;

[0055] The integration result is filled into a preset report template to obtain a storage management report.

[0056] The present invention not only improves the accuracy and reliability of the prediction by predicting the demand for parts based on the storage information, but also realizes automation and systematization, promoting the intelligent decision-making and operational efficiency of the enterprise; after integrating the inventory information, warehouse logs and visualization results, it can provide the management with comprehensive inventory status and operation records, helping them to make more scientific decisions and optimize inventory management strategies. Therefore, the digital warehousing system for automobile parts storage proposed by the present invention can solve the problem of inaccurate management of the existing digital warehousing system for automobile parts storage. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 A functional module diagram of a digital warehousing system for storing automobile parts provided by one embodiment of the present invention;

[0058] Figure 2 A schematic diagram of a flow chart of hash value calculation provided by an embodiment of the present invention;

[0059] Figure 3 A schematic diagram of a demand forecasting process provided by an embodiment of the present invention;

[0060] Figure 4 A schematic diagram of a process flow of a digital warehousing system for storing automobile parts provided by one embodiment of the present invention.

[0061] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0062] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0063] Reference Figure 1 As shown, it is a functional module diagram of a digital warehousing system for storing automobile parts provided by one embodiment of the present invention. In this embodiment, the digital warehousing system 100 for storing automobile parts can be installed in an electronic device. According to the functions implemented, the digital warehousing system 100 for storing automobile parts may include a data acquisition module 101, a data storage module 102, a data analysis module 103 and a visual management module 104. The module described in the present invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by a processor of an electronic device and can complete fixed functions, which are stored in the memory of the electronic device.

[0064] In the embodiment of the present invention, the data acquisition module 101 includes collecting multi-source automobile parts data, performing data verification on the multi-source automobile parts data to obtain verification data, and uploading the verification data to the data storage module.

[0065] In the embodiment of the present invention, the data storage module 102 includes chain storage of the received verification data to obtain storage information.

[0066] In the embodiment of the present invention, the data analysis module 103 includes performing component demand forecasting on the received storage information to obtain demand results, and performing storage analysis on the storage information using the demand results to obtain a storage layout.

[0067] In the embodiment of the present invention, the visualization management module 104 includes using the received storage layout, the storage information and the demand result for visualization display to obtain a visualization result, and generating a storage management report based on the visualization result and the storage information.

[0068] In detail, the modules described in the digital warehousing system 100 for storing automobile parts described in the embodiment of the present invention adopt the same technical means as the digital warehousing system for storing automobile parts described in the accompanying drawings when in use, and can produce the same technical effects, which will not be repeated here.

[0069] In the following, in combination with specific embodiments, each component and specific workflow of the digital storage system for storing automotive parts are described respectively:

[0070] The data acquisition module 101 is used to acquire multi-source automobile parts data, perform data verification on the multi-source automobile parts data to obtain verification data, and upload the verification data to the data storage module.

[0071] In the embodiment of the present invention, the multi-source automobile parts data refers to automobile-related parts data collected in real time from multiple sources, including Internet of Things (IoT) devices, warehouse sensors, logistics data, supplier information, and historical sales records, etc. The parts data includes the name, model, specification, inventory quantity, production date, shelf life, supplier information, logistics status, storage and outbound records, historical usage, and maintenance and replacement records of the parts, etc. The parts data can fully reflect the various attributes and states of automobile parts.

[0072] In the embodiment of the present invention, by collecting multi-source automobile parts data, it is possible to integrate parts data from different channels and improve the comprehensiveness of the data. By performing data verification on the multi-source automobile parts data, the accuracy and completeness of the collected data can be ensured and the impact of erroneous data can be reduced.

[0073] In the embodiment of the present invention, the function of collecting multi-source automobile parts data, performing data verification on the multi-source automobile parts data, and obtaining verification data is specifically used for:

[0074] Performing an integrity check on the multi-source automobile parts data to obtain an integrity check result;

[0075] Performing a repeatability check on the multi-source automobile parts data to obtain a repeatability result;

[0076] Using the integrity check result and the repeatability check result, the multi-source automobile parts data is corrected to obtain corrected data;

[0077] The correction data is standardized to obtain verification data.

[0078] In an embodiment of the present invention, by screening whether the multi-source automobile parts data contains all necessary fields (such as part number, name, specification, source, etc.) and blank fields, the multi-source automobile parts data corresponding to the blank fields are screened out to obtain the integrity check result.

[0079] In the embodiment of the present invention, key fields (such as part numbers) are identified on the multi-source automobile parts data to obtain repeatable results.

[0080] In the embodiment of the present invention, data correction refers to filling the missing fields found in the integrity check with the data adjacent to the fields to ensure the completeness of the records; based on the results of the duplication check, duplicate records are deleted or merged to ensure the uniqueness of each component in the data set.

[0081] In the embodiment of the present invention, standardization processing refers to unifying the data formats of all fields (such as date format, number format) to ensure data consistency.

[0082] In the embodiment of the present invention, data verification is performed to ensure that the collected data is accurate, thereby improving the data quality.

[0083] The data storage module 102 is used to store the received verification data in a chain manner to obtain storage information.

[0084] In the embodiment of the present invention, the security and integrity of the data can be guaranteed by storing the verified data in a structured manner.

[0085] In the embodiment of the present invention, when the data storage module performs the function of chain-storing the received verification data to obtain the storage information, it is specifically used to:

[0086] Packaging the verification data to obtain packaged data;

[0087] Calculating a hash value for the packaged data using an encrypted hash algorithm;

[0088] Constructing chain data using the hash value and the packaged data;

[0089] Distributed storage is performed on the chain data to obtain a storage result;

[0090] A consensus mechanism is performed on the storage result to obtain a consensus result;

[0091] The consensus result is synchronized to obtain storage information.

[0092] In an embodiment of the present invention, data conversion is performed using a data conversion tool (such as Python's json library or a data integration tool), the verification data is converted into a unified format (such as JSON, XML), the converted data is grouped by category or preset conditions, for example, grouped by component type, and finally the grouped data is converted into a data packet to obtain packaged data, the component types include engine, transmission, suspension system, braking system, electrical system, body parts, interior parts, tires and lights, etc.

[0093] See also Figure 2 As shown, in the embodiment of the present invention, when the data storage module performs the function of calculating the hash value of the packaged data using the encrypted hash algorithm, it is specifically used to:

[0094] S21, dividing the packaged data into message blocks to obtain divided data blocks;

[0095] S22, performing message expansion on the divided data blocks to obtain extended data;

[0096] S23, performing iterative calculation on the extended data to obtain a compressed value;

[0097] S24. Concatenate the compressed value with the divided data block to obtain a hash value.

[0098] In detail, the packed data is divided into pieces according to a preset size (eg, 512 bits), and a plurality of data blocks are generated in sequence to form a set of divided data blocks.

[0099] In detail, each data block is extracted in turn from the set of divided data blocks for message expansion. For the first 16 divided data blocks, the extended data is obtained by extracting the 32-bit words of the divided data blocks, and the remaining divided data blocks are expanded using a given formula to generate corresponding extended data.

[0100] In detail, the following formula is used for message expansion:

[0101] D t =σ0(W t-15 )+W t-7 +σ1(Wt-2 )+W t-16 (16 <t<63);

[0102] Among them, D t is the extended data corresponding to the t-th partitioned data block, σ0 and σ1 are bitwise operators, t is the index sequence, and W t-15 For the t-15th partitioned data block, W t-7 For the t-7th partition data block, W t-2 For the t-2th partitioned data block, W t-16 Partition data block for the t-16th time.

[0103] In detail, according to predefined constants and specific compression algorithms (such as SHA, MD5, etc.), the extended data is iteratively processed. Each iteration uses the current value of the extended data and the previous compressed value for combined calculation. After the iteration is completed, the final result is the compressed value.

[0104] In detail, the compressed value and the block identifier corresponding to the corresponding divided data block are extracted, and the compressed value and the divided data block are concatenated using byte concatenation, string concatenation, etc. The concatenated result is the final hash value, which is usually expressed in a specific encoding method (such as hexadecimal).

[0105] In the embodiment of the present invention, when the data storage module executes the function of constructing the chain data using the hash value and the packaged data, it is specifically used to:

[0106] Arrange the packaged data according to the hash value sequence to obtain arranged data;

[0107] Filtering out target data from the arranged data in sequence;

[0108] Inserting a timestamp into the target data to obtain data to be processed;

[0109] Chain data is created according to the data to be processed and the corresponding hash value.

[0110] In detail, the packaged data is sorted based on the order of the hash values, and a sorting algorithm (such as quick sort, merge sort, etc.) can be used to sort the data to obtain arranged data.

[0111] In detail, the arranged data are screened one by one according to the order in which the data are arranged, and the screened data are the target data.

[0112] In detail, a timestamp is generated using the system current time, which is usually formatted into a readable date and time format (such as "YYYY-MM-DD HH:MM"). For each target data, the generated timestamp is inserted into the data, which can be added as a new field or attribute. The data collection after the timestamp is inserted forms a new data structure, which is called the data to be processed.

[0113] Specifically, first, determine the structure of the chain data. Each chain data should contain the following three parts: the data to be processed, the hash value corresponding to the data, and the hash value of the previous data to be processed. Then, connect the hash value of the previous data to be processed in each chain data with the previous chain data to obtain a complete chain structure.

[0114] In the embodiment of the present invention, distributed storage refers to distributing chain data to selected nodes in a preset block network, ensuring that each node saves part of the chain or the data of the entire chain, and backing up the same data on multiple nodes.

[0115] In the embodiment of the present invention, when the data storage module performs the function of performing consensus mechanism verification on the storage result to obtain the consensus result, it is specifically used to:

[0116] Filtering out the data blocks to be verified from the stored results in sequence;

[0117] Performing hash calculation on the data block to be verified to obtain a verification result;

[0118] The verification result is compared with the hash value corresponding to the stored result to obtain a consensus result.

[0119] In detail, the data blocks in the storage result are sequentially screened out from the usage condition query or programming logic as the data blocks to be verified, and each data block in the storage result is traversed in a loop.

[0120] Specifically, the data block to be verified is converted into a string format, and a new hash value is calculated using an encrypted hash algorithm for the data to be verified in the string format, and the hash value is the verification result.

[0121] Specifically, the hash value corresponding to each data block to be verified is extracted from the stored results, and the hash calculation result of the data block to be verified is compared with the original hash value in the stored results. The comparison can be completed by direct equality judgment. If the hash values ​​are consistent, it means that the data block to be verified has not been tampered with, and it is marked as "verification passed". If the hash values ​​are inconsistent, it means that the data block to be verified may have been tampered with, and it is marked as "verification failed".

[0122] In an embodiment of the present invention, whether the data is valid is determined based on the consensus result. If the consensus result is that the verification is passed, the data on all nodes is updated to ensure data consistency. Each node confirms the synchronization status of its data and records successful and failed synchronization operations. If the consensus result is that the verification fails, the data is deleted.

[0123] In the embodiment of the present invention, by adopting chain storage, the data access speed can be increased, the data access time can be reduced, the system response capability can be improved, and the storage efficiency can be optimized.

[0124] The data analysis module 103 is used to perform component demand forecasting on the received storage information to obtain demand results, and perform storage analysis on the storage information using the demand results to obtain a storage layout.

[0125] In the embodiment of the present invention, by performing demand forecasting on the received storage information, stocking and production plans can be made according to the market demand for parts and components, storage space can be reasonably allocated, inventory costs can be reduced, and resource utilization can be improved.

[0126] See also Figure 3 As shown, in the embodiment of the present invention, when the data analysis module performs the function of forecasting the demand for parts on the received storage information and obtaining the demand result, it is specifically used to:

[0127] S31, filtering out historical data from the stored information, performing vector encoding on the historical data, and obtaining a data vector;

[0128] S32, performing differential calculation on the data vector to obtain a stationary sequence;

[0129] S33, performing autoregression calculation on the stationary sequence to obtain a predicted value;

[0130] S34, performing weighted averaging on the predicted values ​​to obtain an average result;

[0131] S35. Perform reverse differential calculation on the average result to obtain the required result.

[0132] In an embodiment of the present invention, relevant historical data, such as historical parts sales records, inventory data, etc., are filtered out from the stored information, and the filtered data is converted into a vector format and converted into a data vector through one-hot encoding.

[0133] In the embodiment of the present invention, a stationary sequence is obtained by calculating the difference between the current value and the previous value of the data vector.

[0134] In the embodiment of the present invention, the following formula can be used for autoregressive calculation:

[0135]

[0136] Among them, K n is the predicted value corresponding to the nth data in the stationary sequence, K n-1 is the predicted value corresponding to the n-1th data in the stationary sequence, K n-2 is the predicted value corresponding to the n-2th data in the stationary sequence, And c is the preset fitting value.

[0137] In the embodiment of the present invention, the reverse difference calculation can be performed using the following formula:

[0138]

[0139] Among them, Y m is the mth requirement result, where m is the time index value. is the m-1th corresponding historical data, Y m ′ is the average result corresponding to the mth demand result.

[0140] In the embodiment of the present invention, when the data analysis module performs the function of performing storage analysis on the storage information using the demand result to obtain the storage layout, it is specifically used to:

[0141] Sorting the demand results to obtain demand data;

[0142] Calculating the storage priority of the storage information using the demand data;

[0143] The storage space is allocated to the storage information according to the storage priority to obtain a storage layout.

[0144] In the embodiment of the present invention, relevant fields such as part number, demand quantity, demand time, etc. are extracted from the demand results, the demand results are sorted according to a preset demand sorting standard, and the sorted results are saved as a new data set to form demand data. For example, the demand quantity can be selected as the main sorting basis, and after sorting, parts with large demand will be ranked first.

[0145] In the embodiment of the present invention, relevant fields such as part number, current inventory, storage cost, etc. are extracted from the storage information, and the storage priority is calculated by formula or model using the demand data and the data in the storage information, and the calculated storage priority information is associated with the relevant storage information to form a new data set. For example: priority = (demand × weight 1) + (current inventory × weight 2) + ...

[0146] In the embodiment of the present invention, the maximum capacity and allocation limit of each storage area corresponding to the actual warehouse layout and available space are obtained, and storage space is allocated item by item from high to low according to the calculated storage priority. High-priority parts should be preferentially allocated to locations close to the shipping area and with high storage space utilization.

[0147] In the embodiment of the present invention, through data analysis, the changes in demand for parts can be accurately grasped, the market response can be made in advance, and the demand can be accurately predicted; and the warehouse resources can be reasonably allocated based on the stored data to optimize inventory management.

[0148] The visualization management module 104 is used to perform visualization using the received storage layout, storage information and demand results to obtain visualization results, and generate a storage management report based on the visualization results and the storage information.

[0149] In the embodiment of the present invention, complex data and analysis results become more intuitive through visual display, and the visual results can help enterprises monitor storage conditions in real time and help optimize management processes and strategies.

[0150] In the embodiment of the present invention, when the visualization management module performs the function of visually displaying the received storage layout, the storage information, and the demand result to obtain the visualization result, it is specifically used to:

[0151] Generate inventory information according to the demand result and the storage information;

[0152] Displaying the storage layout in a plan view to obtain a warehouse layout diagram;

[0153] The inventory information is added to the corresponding warehouse layout diagram to obtain a visualization result.

[0154] In an embodiment of the present invention, future demand results are obtained from demand forecasts, and the current inventory status (such as existing inventory, incoming inventory, outgoing inventory, etc.) is obtained from stored information, and the inventory information is calculated using: inventory information = current inventory - demand forecast.

[0155] In an embodiment of the present invention, a plan view corresponding to the actual situation of the warehouse is obtained, including areas such as shelves, storage areas, and passages. Different storage areas are marked in the plan view according to the storage layout to obtain a warehouse layout map.

[0156] In the embodiment of the present invention, the calculated inventory information is superimposed on the plan view, and the corresponding inventory quantity and status information are marked next to each storage area.

[0157] In the embodiment of the present invention, when the visualization management module performs the function of generating a storage management report based on the visualization result and the storage information, it is specifically used to:

[0158] extracting inventory information and warehouse log information from the stored information;

[0159] Performing data integration on the inventory information, the warehouse log information, and the visualization result to obtain an integration result;

[0160] The integration result is filled into a preset report template to obtain a storage management report.

[0161] In an embodiment of the present invention, a preset query statement is used to extract inventory information and warehouse log information from the stored information, and the query result is received and parsed into an easy-to-process data structure, such as a table form.

[0162] In the embodiment of the present invention, the inventory information, warehouse log information and visualization results are matched through common fields and merged into a unified data set to obtain an integrated result.

[0163] In an embodiment of the present invention, the computer loads a preset report template, ensures that the template contains a title, summary, data analysis, etc., fills the integrated data into the corresponding positions item by item according to the structure of the template, saves the completed report in the required file format (such as PDF, DOCX, XLSX), and stores it in a designated location.

[0164] In the embodiment of the present invention, the visualization result makes complex data simple and easy to understand, improves the efficiency of information transmission, and at the same time, the generation of storage management reports can provide a clear management basis, thereby realizing accurate management of inventory.

[0165] like Figure 4 FIG. 1 is a flow chart of a digital warehousing method for storing automobile parts provided by an embodiment of the present invention, which is applied to the digital warehousing system for storing automobile parts. In the embodiment of the present invention, the digital warehousing method for storing automobile parts includes:

[0166] S401, collecting multi-source automobile parts data, performing data verification on the multi-source automobile parts data to obtain verification data, and uploading the verification data to the data storage module;

[0167] S402, chain-storing the received verification data to obtain storage information;

[0168] S403, performing component demand forecasting on the received storage information to obtain demand results, and performing storage analysis on the storage information using the demand results to obtain a storage layout;

[0169] S404: Perform visual display using the received storage layout, storage information, and demand results to obtain a visualization result, and generate a storage management report based on the visualization result and the storage information.

[0170] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology.

[0171] Among them, Artificial Intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0172] In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or systems stated in the system can also be implemented by one unit or system through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

[0173] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A digital storage system for automobile parts storage, characterized in that: The system includes: a data acquisition module, a data storage module, a data analysis module, and a visual management module, wherein: A data acquisition module, used for acquiring multi-source automobile parts data, performing data verification on the multi-source automobile parts data to obtain verification data, and uploading the verification data to the data storage module; A data storage module, used to store the received verification data in a chain manner to obtain storage information; A data analysis module, used to perform component demand forecasting on the received storage information to obtain demand results, and perform storage analysis on the storage information using the demand results to obtain a storage layout; The visualization management module is used to perform visualization using the received storage layout, storage information and demand results to obtain visualization results, and generate a storage management report based on the visualization results and the storage information.

2. A digital storage system for automobile parts storage as claimed in claim 1, characterized in that: When the data acquisition module performs the function of collecting multi-source automobile parts data, performing data verification on the multi-source automobile parts data, and obtaining verification data, it is specifically used to: Performing an integrity check on the multi-source automobile parts data to obtain an integrity check result; Performing a repeatability check on the multi-source automobile parts data to obtain a repeatability result; Using the integrity check result and the repeatability check result, the multi-source automobile parts data is corrected to obtain corrected data; The correction data is standardized to obtain verification data.

3. A digital storage system for automobile parts storage as claimed in claim 1, characterized in that: When the data storage module performs the function of chain-storing the received verification data to obtain the storage information, it is specifically used to: Packaging the verification data to obtain packaged data; Calculate a hash value for the packaged data using an encrypted hash algorithm; Constructing chain data using the hash value and the packaged data; Distributed storage is performed on the chain data to obtain a storage result; A consensus mechanism is performed on the storage result to obtain a consensus result; The consensus result is synchronized to obtain storage information.

4. A digital storage system for automobile parts storage as claimed in claim 3, characterized in that: When the data storage module performs the function of calculating the hash value of the packaged data using the encrypted hash algorithm, it is specifically used to: Divide the packaged data into message blocks to obtain divided data blocks; Perform message expansion on the divided data blocks to obtain extended data, wherein the message expansion is performed using the following formula: D t =σ0(W t-15 )+W t-7 +σ1(W t-2 )+W t-16 (16<t<63); Among them, D t is the extended data corresponding to the t-th partitioned data block, σ0 and σ1 are bitwise operators, t is the index sequence, and W t-15 For the t-15th partitioned data block, W t-7 For the t-7th partitioned data block, W t-2 For the t-2th partitioned data block, W t-16 Divide the data block for the t-16th partition; Iteratively calculating the extended data to obtain a compressed value; The compressed value is concatenated with the divided data block to obtain a hash value.

5. A digital storage system for automobile parts storage as claimed in claim 3, characterized in that: When the data storage module executes the function of constructing chain data using the hash value and the packaged data, it is specifically used to: Arrange the packaged data according to the hash value sequence to obtain arranged data; Filtering out target data from the arranged data in sequence; Inserting a timestamp into the target data to obtain data to be processed; Chain data is created according to the data to be processed and the corresponding hash value.

6. A digital storage system for automobile parts storage as claimed in claim 3, characterized in that: When the data storage module performs the function of performing consensus mechanism verification on the storage result to obtain the consensus result, it is specifically used to: Filtering out the data blocks to be verified from the stored results in sequence; Performing hash calculation on the data block to be verified to obtain a verification result; The verification result is compared with the hash value corresponding to the stored result to obtain a consensus result.

7. A digital storage system for automobile parts storage as claimed in claim 1, characterized in that: When the data analysis module performs the function of forecasting the demand for parts and components on the received storage information and obtaining the demand result, it is specifically used to: Filtering out historical data from the stored information, performing vector encoding on the historical data, and obtaining a data vector; Performing difference calculation on the data vector to obtain a stationary sequence; Performing autoregression calculation on the stationary sequence to obtain a predicted value; Performing weighted averaging on the predicted values ​​to obtain an average result; The average result is subjected to reverse difference calculation to obtain the required result.

8. A digital storage system for automobile parts storage as claimed in claim 1, characterized in that: When the data analysis module performs the function of performing storage analysis on the storage information using the demand result to obtain the storage layout, it is specifically used to: Sorting the demand results to obtain demand data; Calculating the storage priority of the storage information using the demand data; The storage space is allocated to the storage information according to the storage priority to obtain a storage layout.

9. A digital storage system for automobile parts storage as claimed in claim 1, characterized in that: When the visualization management module performs the function of visually displaying the received storage layout, the storage information, and the demand result to obtain the visualization result, it is specifically used to: Generate inventory information according to the demand result and the storage information; Displaying the storage layout in a plan view to obtain a warehouse layout diagram; The inventory information is added to the corresponding warehouse layout diagram to obtain a visualization result.

10. A digital storage system for automobile parts storage as claimed in claim 1, characterized in that: When the visualization management module performs the function of generating a storage management report based on the visualization result and the storage information, the visualization management module is specifically used to: extracting inventory information and warehouse log information from the stored information; Performing data integration on the inventory information, the warehouse log information, and the visualization result to obtain an integration result; The integration result is filled into a preset report template to obtain a storage management report.

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