Multi-source information fusion ERP (Enterprise Resource Planning) management method and system for automobile disassembly

Abstract

The invention discloses a multi-source information fusion ERP management method and system for automobile disassembly, and belongs to the technical field of information management, and the method comprises the steps: building a supplier exclusive archive library, and generating a supplier code; generating a bill according to the settlement standard in the supplier code association file; the wagon balance is started for weighing, and the edge gateway carries out grading preprocessing on weight data to generate double tags and pushes the double tags to the ERP; matching multi-dimensional data through a low-code residual evaluation micro-plug-in, generating a dynamic residual score, and writing the dynamic residual score into an inventory label; a basic BOM is automatically matched according to a disassembling task data source, dynamic adjustment is carried out according to a non-standard condition, and a personalized disassembling list is generated; after parts are disassembled, code scanning is carried out to input information, collection and payment amount is automatically checked according to a sales accessory bill, a supplier settlement mode is associated, and environmental protection data are linked to trigger multi-level early warning. According to the method, through strong coupling data linkage, a whole-process closed loop is achieved, efficient error reduction and quality improvement are achieved, and data-driven continuous upgrading is achieved.

Description

Technical Field

[0001] This invention belongs to the field of information management technology, specifically a multi-source information fusion ERP management method and system for automobile dismantling. Background Technology

[0002] The current automotive dismantling industry faces the challenges of complex vehicle models and numerous non-standard scenarios. Traditional management models suffer from data silos, with information on suppliers, vehicles, and inventory being scattered.

[0003] Traditional automotive dismantling management models lack a unified data anchor. Supplier qualifications, vehicle information, and settlement standards are stored in isolation, requiring repetitive manual entry and verification, which easily leads to information errors and efficiency losses. Residual value assessment relies on manual experience, failing to incorporate dynamic data such as market conditions and dismantling costs, making it difficult to accurately calculate part value and resulting in an imbalance in the priority of dismantling high-residual-value parts. For non-standard scenarios such as vehicle modification and parts damage, the use of fixed dismantling process BOMs cannot dynamically adjust procedures, resulting in poor adaptability. The lack of environmental compliance and data anomaly early warning mechanisms makes it susceptible to compliance risks due to inadequate environmental treatment, and issues such as weight deviations and residual value fluctuations are difficult to detect in a timely manner, hindering the formation of a data-driven decision-making loop and limiting overall management efficiency and profitability. Summary of the Invention

[0004] To overcome the shortcomings of existing technologies, this invention proposes a multi-source information fusion ERP management method for automobile dismantling. This invention primarily addresses the problem in automobile dismantling management where the lack of integration with dynamic data such as market conditions and dismantling costs makes it difficult to accurately calculate the value of parts.

[0005] The multi-source information fusion ERP management method for automobile dismantling provided by the present invention includes: S1: Based on the needs of automobile dismantling business, a supplier-specific archive is built in the ERP system, and qualifications, cooperative categories, settlement methods are entered and supplier codes are generated.

[0006] S2: Enter the vehicle model, condition, and supplier information for vehicles returning from other locations into the supplier code, and automatically generate an invoice by associating it with the settlement standards in the file. After verification, initiate a payment request and output a verified and valid invoice.

[0007] S3: After a vehicle enters the warehouse, the weighbridge is activated for weighing. The edge gateway performs hierarchical preprocessing of the weight data, generates dual tags, and pushes them to the ERP system. It also associates the preceding vehicle information to generate an entry order and outputs real-time inventory data.

[0008] S4: Based on real-time inventory data, the low-code residual value assessment micro-plugin matches multi-dimensional data, generates dynamic residual value scores, writes them into inventory tags, and outputs the data source for the dismantling task.

[0009] S5: Automatically matches the basic BOM based on the data source of the disassembly task, dynamically adjusts for non-standard cases, and generates a personalized disassembly order. After disassembly, the parts are scanned and information is entered, and a sales parts invoice is output.

[0010] S6: Automatically reconcile payment and receipt amounts based on sales parts invoices, associate supplier settlement methods, trigger multi-level early warnings by linking environmental data, and output financial vouchers and anomaly lists.

[0011] S7: Build a two-layer intelligent dashboard based on full-process data to display task breakdown and performance data, generate value-added decision reports and push optimization suggestions in reverse, and output visualized decision data and optimization instructions.

[0012] According to the multi-source information fusion ERP management method for automobile disassembly provided by the present invention, the specific steps for generating supplier codes in step S1 are as follows: S11: Determine the core requirements of the automotive dismantling business for supplier management and the information dimensions and management standards that the archive should cover, and output a list of requirements for building the archive.

[0013] S12: Based on the archive building requirements list, create a supplier-specific archive basic module in the ERP system, configure the archive data storage fields and module access permissions, and output a blank archive framework.

[0014] S13: Enter the supplier qualification documents, scope of cooperative product categories, and settlement method details into the blank archive framework, and output the basic information of the supplier.

[0015] S14: Based on the supplier's basic information, the system automatically matches information features according to the coding rules of the ERP system, generates a unique and associated exclusive code for the supplier information, and outputs the supplier profile data.

[0016] According to the multi-source information fusion ERP management method for automobile dismantling provided by the present invention, the specific steps for outputting and verifying valid invoices in step S2 are as follows: S21: Enter vehicle model, vehicle condition, and supplier information and bind them with the supplier code, then output cross-regional return information.

[0017] S22: Based on the return information from other locations, automatically match the preset settlement standards in the supplier's file, extract the pricing rules and settlement methods corresponding to the vehicle model, and generate an initial invoice.

[0018] S23: Based on the initial invoice, verify the vehicle information, settlement standard matching degree, and amount accuracy in the invoice. If there are no errors, trigger the ERP system payment application process and output the approved valid invoice.

[0019] According to the multi-source information fusion ERP management method for automobile dismantling provided by the present invention, the specific steps for outputting real-time inventory data in step S3 are as follows: S31: Based on the vehicle basic information in the valid invoice, initiate a vehicle entry application in the ERP system, trigger the weighbridge system to weigh the vehicle and collect the total vehicle weight data, and output the raw weighing data.

[0020] S32: Based on the original weighing data, perform hierarchical preprocessing through the edge gateway to distinguish between the weight data of core components and the whole vehicle, generate dual tags, push them to the ERP system in real time, and output standard weight data.

[0021] S33: Automatically associates and matches standard weight data with previous vehicle basic information, and the ERP system generates an inbound order containing weight details according to preset rules, synchronously updates the system material ledger, and outputs real-time inventory data.

[0022] According to the multi-source information fusion ERP management method for automobile disassembly provided by the present invention, the specific steps for outputting standard weight data in step S32 are as follows: The raw weighing data is transmitted to the edge gateway data processing module, where hierarchical preprocessing is used to set the hierarchical thresholds for the weight of core components and the weight of the whole vehicle, and output hierarchical preprocessing rule parameters.

[0023] Based on the hierarchical preprocessing rule parameters, the edge gateway filters and calibrates the raw weighing data, distinguishes between the precise weight data of core components and the reference weight data of the whole vehicle, and outputs hierarchical weight data.

[0024] Based on the graded weight data, the stability and accuracy of data transmission are verified through a dedicated transmission channel between the edge gateway and the ERP system, and standard weight data is output.

[0025] According to the multi-source information fusion ERP management method for automobile dismantling provided by the present invention, the specific steps for outputting the dismantling task data source in step S4 are as follows: S41: Based on real-time inventory data, connect to the low-code residual value assessment micro-plugin in the ERP system, import the vehicle model, weight tag, and part status from the inventory data into the low-code residual value assessment micro-plugin's dedicated data interface and assessment algorithm engine, and output the inventory data to be assessed.

[0026] S42: Based on the micro-plugin for the inventory data to be evaluated, match the preset market data, dismantling cost parameters, and part residual value coefficients, complete the residual value calculation according to the algorithm, and output dynamic residual value scoring inventory data.

[0027] S43: Write the score into the dedicated label field of the corresponding inventory data based on the dynamic residual value score inventory data, and complete the data filtering by associating with the dismantling priority rules to form the data source for the dismantling task.

[0028] According to the multi-source information fusion ERP management method for automobile dismantling provided by the present invention, the specific steps in step S42 for outputting dynamic residual value scoring inventory data are as follows: Based on the inventory data to be evaluated, the micro-plugin algorithm engine automatically retrieves the preset market information database, matches the market circulation price and fluctuation coefficient of the current vehicle model parts, and outputs market information matching parameters.

[0029] Based on market conditions, the matching parameter plugin extracts preset dismantling cost parameters and part residual value coefficients. Based on the weight tags and part status in the inventory data to be evaluated, a residual value calculation algorithm model is constructed.

[0030] The residual value of a single vehicle and its core components is accurately calculated using a residual value calculation algorithm model. The calculation results are then linked to inventory data to output inventory data with dynamic residual value scores.

[0031] According to the multi-source information fusion ERP management method for automobile disassembly provided by the present invention, the specific steps for outputting the sales parts invoice in step S5 are as follows: S51: Based on the data source of the dismantling task, retrieve the preset basic dismantling process BOM library in the ERP system, match the vehicle model and residual value priority in the data source, and output the basic dismantling BOM scheme.

[0032] S52: Based on the basic dismantling BOM solution, the system automatically identifies non-standard features in the data source, triggers preset dynamic BOM adjustment rules, supplements or deletes dismantling procedures, and outputs personalized dismantling task sheets.

[0033] S53: Based on the personalized disassembly task order, guide the disassembly of parts, enter the status and residual value information of parts by scanning with RFID, and automatically summarize the data to output the sales parts bill.

[0034] According to the multi-source information fusion ERP management method for automobile dismantling provided by the present invention, the specific steps for outputting financial vouchers and anomaly lists in step S6 are as follows: S61: Based on the sales parts invoice, extract the payment and receipt details from the ERP system's financial module, match the supplier codes in the invoice with the corresponding settlement methods, and output the invoice verification data.

[0035] S62: Based on the billing verification data, screen for parts sales orders that have not completed environmental compliance treatment, and generate a technical early warning list of orders with abnormal environmental treatment.

[0036] S63: Calculate the actual weight deviation rate and residual value fluctuation based on the order technical warning list and billing verification data, and determine whether the actual weight deviation rate and residual value fluctuation exceed the preset threshold. If so, trigger the corresponding level of data anomaly technical warning.

[0037] S64: Generate standardized financial settlement technical vouchers based on bill reconciliation data, order technical warning list, and abnormal technical warning, and output an abnormal handling technical list.

[0038] This invention also provides a multi-source information fusion ERP management system for automobile dismantling, including: The file building module is used to build a supplier-specific file database in the ERP system based on the needs of automobile dismantling business, and to enter qualifications, cooperative product categories, settlement methods and generate supplier codes.

[0039] The bill generation module is used to input the vehicle model, condition, and supplier information for vehicles returning from other locations into the supplier code, and automatically generate a bill by associating it with the settlement standards in the file. After verification, a payment request is initiated, and a valid bill is output.

[0040] The weighing and warehousing module is used to start weighing on the weighbridge after a vehicle enters the warehouse. The edge gateway performs hierarchical preprocessing of the weight data, generates dual tags, and pushes them to the ERP system. It also associates the preceding vehicle information to generate an warehousing order and outputs real-time inventory data.

[0041] The residual value assessment module is used to match multi-dimensional data with low-code residual value assessment micro-plugins based on real-time inventory data, generate dynamic residual value scores and write them into inventory tags, and output the data source for the dismantling task.

[0042] The disassembly order generation module automatically matches the basic Bill of Materials (BOM) based on the disassembly task data source, dynamically adjusts for non-standard cases, and generates a personalized disassembly order. After disassembly, parts are scanned and information is entered, and a sales parts invoice is output.

[0043] The settlement early warning module is used to automatically verify the amount of payment and receipt based on the sales parts invoice, associate the supplier's settlement method, trigger multi-level early warnings by linking environmental data, and output financial vouchers and anomaly lists.

[0044] The decision optimization module is used to build a two-layer intelligent dashboard based on full-process data, display the breakdown of tasks and performance data, generate value-added decision reports and push optimization suggestions back, and output visualized decision data and optimization instructions.

[0045] The multi-source information fusion ERP management method for automobile dismantling provided by this invention has the following beneficial effects: 1. This invention uses supplier codes as the core data anchor point to construct a strongly correlated link where the preceding output is the subsequent input, connecting all business processes such as supplier profile building, remote vehicle settlement, weighing and warehousing, residual value assessment, dismantling task generation, and financial settlement early warning. Data at each stage does not require manual secondary entry and verification; it is automatically matched and transferred through preset system rules, completely eliminating information silos. This significantly reduces the error rate caused by manual operation and substantially improves the overall operational efficiency of the automotive dismantling business from supplier onboarding to financial settlement, adapting to the complex vehicle models and large data volumes characteristic of the industry.

[0046] 2. This invention integrates a low-code residual value assessment micro-plugin. Through quantitative formulas combined with market conditions, dismantling costs, part condition, and weight dual-label data, it achieves dynamic and accurate calculation of the residual value of single parts and the entire vehicle. The calculation results directly serve as the basis for prioritizing dismantling, ensuring the implementation of a high-residual-value-first dismantling strategy. Simultaneously, the system automatically completes core operations such as supplier code generation, invoice matching and calculation, dynamic BOM adjustment, and RFID barcode scanning for part information entry, replacing the traditional experience-driven manual assessment and operation mode. This not only improves dismantling revenue but also promotes the accurate recycling and reuse of core parts, maximizing resource value.

[0047] 3. This invention embeds environmental compliance verification into the core financial settlement process, automatically triggering early warnings for parts orders that have not completed environmental treatment. Simultaneously, it calculates weight deviation rates and residual value fluctuations using quantitative formulas, triggering multi-level warnings for data exceeding thresholds, accurately mitigating the risks of stringent environmental regulations and abnormal business data in the automotive dismantling industry. Furthermore, it constructs a two-tiered visualization system of workstation dashboards and management dashboards. The management dashboard generates three value-added decision reports based on full-process data mining: dismantling efficiency, inventory turnover, and residual value maximization. Optimization suggestions in these reports can be pushed back to the dismantling task order generation stage, forming a closed loop of data collection, analysis, decision-making, and process optimization, driving continuous iteration and upgrading of business processes. Attached Figure Description

[0048] The invention will now be further described with reference to the accompanying drawings.

[0049] Figure 1 This is a flowchart illustrating the steps of the multi-source information fusion ERP management method for automobile disassembly provided in this embodiment of the invention. Figure 2 This is a flowchart of the multi-source information fusion ERP management method for automobile dismantling provided in an embodiment of the present invention; Figure 3 This is a module diagram of the multi-source information fusion ERP management system for automobile disassembly provided in an embodiment of the present invention. Detailed Implementation

[0050] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below according to specific embodiments.

[0051] like Figures 1 to 3 As shown in the embodiment of the present invention, the multi-source information fusion ERP management method for automobile dismantling provides the following methods: S1: Based on the needs of the automotive dismantling business, a dedicated supplier database is built in the ERP system, and qualifications, cooperative product categories, settlement methods are entered and supplier codes are generated.

[0052] S11: Determine the core requirements of the automotive dismantling business for supplier management and the information dimensions and management standards that the archive should cover, and output a list of requirements for building the archive.

[0053] Based on the pain points of the entire process of supplier access, cooperation, and settlement in the automotive dismantling business, we identified core needs such as qualification review, product category matching, and payment term management, and generated a needs survey checklist. Based on this checklist, we defined the information dimensions that the database should cover, including qualification certificates, cooperative product categories, and settlement terms. We then formulated data entry, update, and verification standards, and generated a database construction needs checklist.

[0054] S12: Based on the archive building requirements list, create a supplier-specific archive basic module in the ERP system, configure the archive data storage fields and module access permissions, and output a blank archive framework.

[0055] Based on the requirements list for building the archive, create a dedicated module for supplier profiles in the ERP system, plan the data storage fields for basic information, qualification documents, cooperation agreements, etc., and output the module field configuration scheme. Based on the module field configuration scheme, set access permissions for different roles such as module administrator, operator, and viewer, limit the scope of data modification, query, and export operations, and output a blank archive framework.

[0056] S13: Enter the supplier qualification documents, scope of cooperative product categories, and settlement method details into the blank archive framework, and output the basic information of the supplier.

[0057] Based on the blank archive framework, enter the supplier's business license, dismantling qualification, and other certificate information sequentially according to the preset fields, ensuring that the qualification documents and fields correspond accurately, and output a subset of supplier qualification information. Based on the supplier qualification information subset, continue to enter the range of scrapped vehicle types, settlement cycle, and payment method details for cooperation, and integrate them to form complete basic supplier information.

[0058] S14: Based on the supplier's basic information, information features are automatically matched using the ERP system's coding generation rules to generate a unique and associated exclusive code for the supplier's information.

[0059] Based on the supplier's basic information, core features such as the supplier's location, product categories, and onboarding time are extracted. These features are then matched against the ERP system's preset coding generation rules to output the coding feature matching results. Based on these matching results, a unique supplier-specific code is automatically generated using the system algorithm. This code is then linked to the supplier's basic information, outputting a complete supplier profile with the unique code.

[0060] S2: Enter the vehicle model, condition, and supplier information for vehicles returning from other locations into the supplier code, and automatically generate an invoice by associating it with the settlement standards in the file. After verification, initiate a payment request and output a verified and valid invoice.

[0061] S21: Enter vehicle model, vehicle condition, and supplier information and bind them with the supplier code, then output cross-regional return information.

[0062] Based on the supplier-specific code generated previously, the corresponding supplier file is retrieved from the off-site return vehicle module of the ERP system. The information entry point is located, and the code-associated entry interface is output. Using this interface, core information such as the specific vehicle model, vehicle condition level, and full name of the supplier is filled in, ensuring the fields match the system's preset format. The vehicle information to be bound is then output. Based on this information, the system's built-in association function is used to map and bind the information to the supplier code, verifying the relevance and uniqueness of the information. Finally, the bound off-site return vehicle information is output.

[0063] S22: Based on the return information from other locations, automatically match the preset settlement standards in the supplier's file, extract the pricing rules and settlement methods corresponding to the vehicle model, and generate an initial invoice.

[0064] Based on the completed cross-regional vehicle return information, the supplier code and vehicle model data are extracted. The corresponding supplier's settlement standard file is retrieved from the ERP system's supplier database, and the settlement standard retrieval results are output. Based on the settlement standard retrieval results, and according to the vehicle model matching preset pricing rules, the pricing coefficient is adjusted according to the vehicle condition level. Simultaneously, details such as the settlement cycle and payment method are extracted, and a pricing and settlement parameter set is output. Based on the output pricing and settlement parameter set, and using implicit data such as the quantity and weight of cross-regional vehicle returns, the total amount is automatically calculated using the system algorithm, generating an initial invoice containing details and a summary.

[0065] S23: Based on the initial invoice, verify the vehicle information, settlement standard matching degree, and amount accuracy in the invoice. If there are no errors, trigger the ERP system payment application process and output the approved valid invoice.

[0066] Based on the generated initial invoice, verify the consistency between the vehicle model, vehicle condition, supplier information, and out-of-town return information within the invoice, check the matching degree of the pricing rules, and output the information consistency verification result. Based on the information consistency verification result, if the information is correct, further verify the amount calculation logic and numerical accuracy; if errors exist, initiate the modification process and retain traceability records, outputting the invoice review result. Based on the output invoice review result, trigger the ERP system payment application process for approved invoices, fill in the application number, payment time, and other information, submit for review, and output the approved valid invoice.

[0067] S3: After a vehicle enters the warehouse, the weighbridge system is activated for weighing. The weight data is pre-processed in a hierarchical manner through an edge gateway, generating dual tags of accurate data and reference data, which are then pushed to the ERP system in real time. Based on the vehicle's basic information in the approved invoice, the system automatically associates the data with the dual-tag weighing data to generate an entry slip and outputs real-time inventory data.

[0068] S31: Based on the vehicle basic information in the valid invoice, initiate a vehicle entry application in the ERP system, trigger the weighbridge system to weigh the vehicle and collect the total vehicle weight data, and output the raw weighing data.

[0069] Based on the previously output valid invoices, extract core basic information such as vehicle model, supplier, and vehicle condition level to ensure that the data is complete and error-free, and output the basic information set of vehicles to be added to the database.

[0070] Based on the basic information set of the vehicles to be put into storage, the system initiates a vehicle entry application in the warehouse management module of the ERP system, fills in auxiliary information such as application number and entry time, and after submitting the application, triggers the system linkage instruction and outputs the weighbridge weighing trigger signal.

[0071] Based on the weighbridge's trigger signal, the weighbridge system is activated to accurately collect the vehicle's weight, initially binding the weight value with the vehicle's basic information set, verifying the integrity of the collected data, and outputting the original weighing data with the vehicle's identification.

[0072] S32: Based on the original weighing data, perform hierarchical preprocessing through the edge gateway to distinguish between the weight data of core components and the whole vehicle, generate dual tags, push them to the ERP system in real time, and output standard weight data.

[0073] The raw weighing data is transmitted to the edge gateway data processing module, where hierarchical preprocessing is used to set the hierarchical thresholds for the weight of core components and the weight of the whole vehicle, and output hierarchical preprocessing rule parameters.

[0074] Based on the hierarchical preprocessing rule parameters, the edge gateway filters and calibrates the raw weighing data, distinguishes between the precise weight data of core components and the reference weight data of the whole vehicle, and outputs hierarchical weight data.

[0075] Based on the graded weight data, the stability and accuracy of data transmission are verified through a dedicated transmission channel between the edge gateway and the ERP system, and standard weight data is output.

[0076] S33: Automatically associates and matches standard weight data with previous vehicle basic information, and the ERP system generates an inbound order containing weight details according to preset rules, synchronously updates the system material ledger, and outputs real-time inventory data.

[0077] Based on the standard weight data with dual labels output in the previous step, the basic information set of vehicles to be put into storage is retrieved from the ERP system. The data is automatically matched using the system's built-in association algorithm, the consistency of the matched fields is verified, and the weight-vehicle information associated dataset is output.

[0078] Based on the weight-vehicle information association dataset, the ERP system automatically fills in vehicle information, weight details, and entry time according to the preset entry order generation rules, generates standardized vehicle entry orders, and outputs entry order files with weight tags.

[0079] Based on the inbound order file with weight tags, the material management ledger in the ERP system is updated synchronously, the vehicle status is marked as inbound, the inventory quantity and weight data are updated, the ledger data is refreshed in real time, and the real-time inventory data with weight tags is output.

[0080] S4: Based on real-time inventory data, and through low-code residual value assessment micro-interpolation, automatically match vehicle model, part status, and market conditions to generate a dynamic residual value score. The residual value score is then written to the inventory data tag, outputting the dismantling task data source.

[0081] S41: Based on real-time inventory data, connect to the low-code residual value assessment micro-plugin in the ERP system, import the vehicle model, weight tag, and part status from the inventory data into the low-code residual value assessment micro-plugin's dedicated data interface and assessment algorithm engine, and output the inventory data to be assessed.

[0082] Based on the real-time inventory data output previously, locate the interface entry point for the low-code residual value assessment micro-plugin in the ERP system's functional modules, configure the data transmission protocol and field mapping rules, and output the plugin interface parameter configuration set.

[0083] Based on the plugin integration parameter configuration set, extract core fields such as vehicle model, weight dual tags, and part status from real-time inventory data, complete data format conversion and cleaning according to interface requirements, and output a standardized dataset to be transmitted.

[0084] Based on the standardized dataset to be transmitted, the data is imported into the evaluation algorithm engine through the micro-plugin's dedicated data interface. The integrity of the data transmission and the field matching degree are verified, and the loaded inventory data to be evaluated is output.

[0085] S42: Based on the micro-plugin for the inventory data to be evaluated, match the preset market data, dismantling cost parameters, and part residual value coefficients, complete the residual value calculation according to the algorithm, and output dynamic residual value scoring inventory data.

[0086] Based on the previously output inventory data to be evaluated, the micro-plugin algorithm engine automatically retrieves the preset market information database, matches the market circulation price and fluctuation coefficient of the current vehicle model parts, and outputs market information matching parameters.

[0087] The market price adjustment formula is expressed as:

[0088] In the formula, The real-time market benchmark price for the parts. This is the basic market guidance price for the parts. This is the market price volatility coefficient.

[0089] Based on market conditions and matching parameters, the micro-plugin continues to extract preset dismantling cost parameters and part residual value coefficients, and constructs a residual value calculation algorithm model based on the weight tags and part status in the inventory data to be evaluated.

[0090] The formula for quantifying energy consumption costs through dismantling is as follows:

[0091] In the formula, The energy consumption cost per unit weight of disassembly parts. The total energy consumption per unit weight of parts disassembled. Energy-to-money conversion factor, This is an additional factor for equipment loss. This refers to the weight of the part.

[0092] The residual value of a single vehicle and its core components is accurately calculated using a residual value calculation algorithm model. The calculation results are then linked to inventory data to output inventory data with dynamic residual value scores.

[0093] The core formula for calculating the dynamic residual value of a single part is expressed as follows:

[0094] In the formula, For the dynamic residual value of a single part, The real-time market benchmark price for the parts. Energy consumption cost per unit weight of disassembly parts This is a correction factor for the condition of the part. This refers to the weight of the part.

[0095] The formula for evaluating the dynamic residual value of a vehicle is as follows:

[0096] In the formula, To score the dynamic residual value of the whole vehicle, Let be the dynamic residual value of the i-th part. Let i be the value weight of the i-th part. This represents the theoretical maximum residual value of all parts of the same vehicle model.

[0097] S43: Write the score into the dedicated label field of the corresponding inventory data based on the dynamic residual value score inventory data, and complete the data filtering by associating with the dismantling priority rules to form the data source for the dismantling task.

[0098] Based on the inventory data with dynamic residual value scores output previously, the scores are written into the exclusive label field of the corresponding vehicle inventory data in the inventory label management module of the ERP system, completing the update and binding of data labels, and outputting inventory data with score labels.

[0099] Based on the inventory data with rating labels, the system uses preset dismantling priority rules, sets filtering conditions according to dimensions such as rating level and parts scarcity, prioritizes the inventory vehicles, and outputs a priority ranking result set.

[0100] Based on the priority sorting result set of the output, integrate vehicle basic information, residual value score, dismantling priority and other contents, and complete data classification and structuring according to business needs to form a dismantling task data source that can be directly used.

[0101] S5: Based on the dismantling task data source with residual value priority, the system automatically matches the basic dismantling process BOM. If non-standard situations such as vehicle modification or parts damage are identified, the system automatically triggers the dynamic adjustment rules of the BOM to generate a personalized dismantling task order. After dismantling, the status and residual value are recorded by RFID scanning. When the sales process is initiated, the system automatically captures the data to generate an order and outputs a sales parts invoice with non-standard identification.

[0102] S51: Based on the data source of the dismantling task, retrieve the preset basic dismantling process BOM library in the ERP system, match the vehicle model and residual value priority in the data source, and output the basic dismantling BOM scheme.

[0103] Based on the disassembly task data source output in the previous step, the preset basic disassembly process BOM library is retrieved in the process management module of the ERP system. The vehicle classification dimension, process coding rules and residual value association logic of the BOM data in the library are clarified, and the BOM library retrieval rule set is output.

[0104] Based on the output BOM library retrieval rule set, core matching fields such as vehicle model and residual value priority are extracted from the dismantling task data source. These fields are then accurately matched with standard data in the basic dismantling process BOM library to select suitable basic process lists and output preliminary BOM matching results.

[0105] Based on the preliminary matching results of the output BOM, verify the matching degree between process integrity and residual value priority, eliminate redundant processes and supplement the preconditions for disassembling core parts, integrate them to form a structured process and material list, and output the basic disassembly BOM scheme.

[0106] S52: Based on the basic dismantling BOM solution, the system automatically identifies non-standard features in the data source, triggers preset dynamic BOM adjustment rules, supplements or deletes dismantling procedures, and outputs personalized dismantling task sheets.

[0107] Based on the basic disassembly BOM solution output in the previous step, non-standard feature information is retrieved from the disassembly task data source in the ERP system to identify differentiated attributes such as vehicle modification, part damage, and abnormal residual value, and a non-standard feature identification report is output.

[0108] Based on the output non-standard feature identification report, the system automatically triggers the preset BOM dynamic adjustment rule library, matches the corresponding process addition, deletion or optimization strategies for different non-standard features, generates adjustment instruction parameters, and outputs the BOM adjustment plan.

[0109] Based on the output BOM adjustment plan, the basic disassembly BOM plan is modified in a targeted manner, special disassembly tooling usage procedures are added, invalid inspection steps are deleted, the execution sequence and quality requirements of each procedure are clarified, and a personalized disassembly task sheet is output.

[0110] S53: Based on the personalized disassembly task order, guide the disassembly of parts, enter the status and residual value information of parts by scanning with RFID, and automatically summarize the data to output the sales parts bill.

[0111] Based on the personalized disassembly task sheet output in the previous step, guide on-site personnel to carry out the parts disassembly operation according to the process, bind a unique RFID tag to each disassembled part, record the process completion status and operator information in real time, and output the RFID-bound disassembled parts ledger.

[0112] Based on the output RFID-bound dismantled parts ledger, the core data such as the condition of the parts, residual value level, and specifications are entered through a barcode scanning device. The consistency between the entered information and the task order requirements is verified, and a standardized parts information dataset is output.

[0113] Based on the output standardized parts information dataset, the ERP system automatically matches the preset parts pricing rules and sales bill template, summarizes information such as parts details, unit price, and total price, generates a complete document containing settlement terms, and outputs the sales parts bill.

[0114] S6: Based on sales invoices for non-standard parts, automatically verify payment amounts and match supplier codes with settlement methods. Simultaneously, link environmental monitoring data to trigger alerts for sales orders of parts for which environmental treatment has not been completed. Trigger multi-level alerts for data such as weight deviations and abnormal residual values, and output financial settlement vouchers with environmental compliance verification and an anomaly handling list.

[0115] S61: Based on the sales parts invoice, extract the payment and receipt details from the ERP system's financial module, match the supplier codes in the invoice with the corresponding settlement methods, and output the invoice verification data.

[0116] Based on the previously output sales parts invoice, locate the entry point for extracting payment and receipt data in the financial accounting module of the ERP system, filter the core detailed fields such as the amount receivable, the amount received, and the amount payable in the invoice, and output the original dataset of payment and receipt amounts.

[0117] Based on the original dataset of payment and receipt amounts, the supplier code information is extracted from the invoice, associated with the settlement method parameters in the supplier archive of the ERP system, and the settlement rules such as payment period, payment channel, and tax rate are matched to output the settlement rule matching result set.

[0118] Based on the output settlement rule matching result set, the payment and receipt details are bound and verified with the settlement rules to check the consistency between the amount calculation logic and the settlement terms, and integrated to form structured financial reconciliation data, and the bill reconciliation data is output.

[0119] S62: Based on the billing verification data, screen for parts sales orders that have not completed environmental compliance treatment, and generate a technical early warning list of orders with abnormal environmental treatment.

[0120] Based on the previously output bill verification data, extract the parts sales order details, parts categories and corresponding environmental disposal requirements, associate them with the disposal completion records in the ERP system's environmental monitoring database, and output the order-environmental disposal matching dataset.

[0121] Based on the output order-environmental disposal matching dataset, the system automatically screens parts sales orders that have not completed environmental compliance testing or submitted disposal certificates, marks key information such as order number, parts category, and type of violation, and outputs a preliminary list of environmental violation orders.

[0122] Based on the initial screening list of environmental violation orders, the reasons for the incomplete environmental disposal of the orders are manually reviewed, and information such as the violation level and rectification requirements are supplemented. A standardized early warning document is generated, and a technical early warning list of orders with abnormal environmental disposal is output.

[0123] S63: Calculate the actual weight deviation rate and residual value fluctuation based on the order technical warning list and billing verification data, and determine whether the actual weight deviation rate and residual value fluctuation exceed the preset threshold. If so, trigger the corresponding level of data anomaly technical warning.

[0124] Based on the previously output order technical warning list and billing verification data, extract core data such as the theoretical weight, actual weighed weight, theoretical residual value, and actual calculated residual value of the parts in the order, and output the basic dataset for deviation calculation.

[0125] Based on the output deviation calculation dataset, calculate the actual weight deviation rate and residual value fluctuation value according to the formulas, and output the deviation and fluctuation calculation results. The formula for calculating the weight deviation rate is expressed as: Weight deviation rate = |actual weight - theoretical weight| / theoretical weight × 100%.

[0126] Residual value fluctuation = |Actual residual value - Theoretical residual value| / Theoretical residual value × 100%.

[0127] The calculated deviation and fluctuation values ​​are compared with the preset threshold range of the ERP system. If the value exceeds the threshold, a level 1, 2, or 3 data anomaly warning is triggered according to the severity, and the graded anomaly warning result is output.

[0128] S64: Generate standardized financial settlement technical vouchers based on bill reconciliation data, order technical warning list, and abnormal technical warning, and output an abnormal handling technical list.

[0129] Based on the previously output bill reconciliation data, order technical warning list, and graded abnormal warning results, retrieve the standardized settlement voucher template from the ERP system's financial voucher module, fill in the amount details, settlement method, warning flags, and other information, and output the financial settlement technical voucher to be reviewed.

[0130] Based on the output financial settlement technical documents pending review, integrate all abnormal information such as environmental disposal anomalies, weight deviations, and residual value fluctuations, classify and organize them according to the type of anomaly, clarify the responsible party, rectification period, handling suggestions, etc., and output an anomaly information classification list.

[0131] Based on the output list of abnormal information categories, associate and bind it with financial settlement technical vouchers, verify the consistency of information between the vouchers and the list, form a complete financial settlement and abnormal handling document package, and output an abnormal handling technical list.

[0132] S7: Integrates supplier, settlement, weighing, sales, finance, and environmental data from the entire upstream process to build a two-tiered intelligent dashboard. The workstation dashboard displays real-time task breakdown, inventory alerts, and residual value priorities. The management dashboard not only presents performance and turnover efficiency but also generates three value-added decision reports through multi-source data mining. Simultaneously, optimization suggestions are pushed back to the task breakdown generation stage, forming a closed-loop optimization of data, process, and decision, outputting visualized decision data and reverse optimization instructions.

[0133] First, based on the supplier, settlement, weighing, sales, finance, and environmental data from the entire process, ETL tools are used to clean, integrate, and standardize the data, removing duplicate and abnormal data, and outputting a unified format of the entire integrated dataset.

[0134] Based on the integrated dataset, the data dimensions are split according to the display requirements of workstation dashboards and management dashboards. Workstation-side fields such as real-time task breakdown and inventory alerts are defined, as well as management-side fields such as performance and turnover efficiency, and a dashboard data classification scheme is output.

[0135] Based on the dashboard data classification scheme, a data transmission channel for workstation dashboards is established to push real-time task breakdown, inventory warnings, and residual value priority data to the workstation display interface in real time, complete the visualization configuration of the workstation dashboards, and output the real-time display data of the workstation dashboards.

[0136] Based on the real-time data displayed on the workstation dashboard, a management dashboard data model is built, and basic visualization charts are generated by integrating performance indicators and turnover efficiency data. At the same time, data correlation patterns are analyzed through multi-source data mining algorithms to output basic display data and mining results for the management dashboard.

[0137] Based on the basic data displayed on the management dashboard and the mining results, three value-added decision reports are generated: dismantling efficiency optimization, inventory turnover improvement, and residual value maximization. These reports are then embedded into the management dashboard interface, outputting complete management dashboard data including the value-added reports.

[0138] Based on the complete management dashboard data including the value-added report, the core optimization suggestions in the report are extracted, a mapping rule is constructed between the suggestions and the task order generation process, the optimization suggestions are transformed into specific parameter instructions, and the reverse optimization parameter set is output.

[0139] Based on the reverse optimization parameter set, it is pushed to the task decomposition generation stage, matched and integrated with the task decomposition data source, the task decomposition generation rules are adjusted, the data-process-decision closed-loop optimization is completed, and the visualized decision data and reverse optimization instructions are output.

[0140] like Figure 3 As shown, the present invention also provides a multi-source information fusion ERP management system for automobile disassembly, comprising: The file building module is used to build a supplier-specific file database in the ERP system based on the needs of automobile dismantling business, and to enter qualifications, cooperative product categories, settlement methods and generate supplier codes.

[0141] The bill generation module is used to input the vehicle model, condition, and supplier information for vehicles returning from other locations into the supplier code, and automatically generate a bill by associating it with the settlement standards in the file. After verification, a payment request is initiated, and a valid bill is output.

[0142] The weighing and warehousing module is used to start weighing on the weighbridge after a vehicle enters the warehouse. The edge gateway performs hierarchical preprocessing of the weight data, generates dual tags, and pushes them to the ERP system. It also associates the preceding vehicle information to generate an warehousing order and outputs real-time inventory data.

[0143] The residual value assessment module is used to match multi-dimensional data with low-code residual value assessment micro-plugins based on real-time inventory data, generate dynamic residual value scores and write them into inventory tags, and output the data source for the dismantling task.

[0144] The disassembly order generation module automatically matches the basic Bill of Materials (BOM) based on the disassembly task data source, dynamically adjusts for non-standard cases, and generates a personalized disassembly order. After disassembly, parts are scanned and information is entered, and a sales parts invoice is output.

[0145] The settlement early warning module is used to automatically verify the amount of payment and receipt based on the sales parts invoice, associate the supplier's settlement method, trigger multi-level early warnings by linking environmental data, and output financial vouchers and anomaly lists.

[0146] The decision optimization module is used to build a two-layer intelligent dashboard based on full-process data, display the breakdown of tasks and performance data, generate value-added decision reports and push optimization suggestions back, and output visualized decision data and optimization instructions.

[0147] In summary, this embodiment provides a multi-source information fusion ERP management method and system for automotive dismantling. By integrating a low-code residual value assessment micro-plugin, and using quantitative formulas combined with market conditions, dismantling costs, part status, and weight dual-label data, it achieves dynamic and accurate calculation of the residual value of single parts and the whole vehicle. The calculation results are directly used as the basis for dismantling priority ranking, ensuring the implementation of the high residual value priority dismantling strategy. Simultaneously, the system automatically completes core operations such as supplier code generation, invoice matching and calculation, dynamic BOM adjustment, and RFID barcode scanning for part information entry, replacing the traditional experience-driven manual assessment and operation mode. This not only improves dismantling revenue but also promotes the accurate recycling and reuse of core parts, maximizing resource value.

[0148] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments or some parts of embodiments.

[0149] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. These modifications or substitutions do not cause the essence of the corresponding technical solutions to depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multi-source information fusion ERP management method for automobile dismantling, characterized in that, include: S1: Based on the needs of the automotive dismantling business, build a dedicated supplier database in the ERP system, enter qualifications, cooperative product categories, settlement methods and generate supplier codes; S2: Enter the vehicle model, vehicle condition, and supplier of the vehicle returning from another location into the supplier code, automatically generate an invoice by associating it with the settlement standard in the file; after verification, initiate a payment application and output the verified and valid invoice; S3: After a vehicle enters the warehouse, the weighbridge is activated to weigh the vehicle. The edge gateway performs hierarchical preprocessing of the weight data to generate dual tags and pushes them to the ERP system. It also associates the preceding vehicle information to generate an entry order and outputs real-time inventory data. S4: Based on the real-time inventory data, match multi-dimensional data through the low-code residual value assessment micro-plugin, generate dynamic residual value scores and write them into inventory tags, and output the data source for the dismantling task. S5: Automatically match the basic BOM based on the disassembly task data source, dynamically adjust for non-standard cases, and generate a personalized disassembly order; scan the barcode to enter information after disassembly of parts, and output the sales parts bill. S6: Automatically verify the payment amount based on the sales parts invoice, associate the supplier's settlement method, trigger multi-level early warnings by linking environmental data, and output financial vouchers and anomaly list; S7: Build a two-layer intelligent dashboard based on full-process data to display task breakdown and performance data, generate value-added decision reports and push optimization suggestions in reverse, and output visualized decision data and optimization instructions.

2. The multi-source information fusion ERP management method for automobile dismantling according to claim 1, characterized in that: In step S1, the specific steps for generating the supplier code are as follows: S11: Determine the core requirements of the automotive dismantling business for supplier management and the information dimensions and management standards that the archive should cover, and output a list of requirements for building the archive. S12: Based on the archive building requirements list, create a supplier-specific archive basic module in the ERP system, configure archive data storage fields and module access permissions, and output a blank archive framework. S13: Enter the supplier qualification documents, scope of cooperative product categories, and settlement method details into the blank archive framework, and output the basic information of the supplier; S14: Based on the supplier basic information, automatically match information features through the ERP system coding generation rules to generate a unique and associated exclusive code for the supplier information, and output supplier profile data.

3. The multi-source information fusion ERP management method for automobile dismantling according to claim 1, characterized in that: In step S2, the specific steps for outputting the verified valid invoices are as follows: S21: Enter vehicle model, vehicle condition, and supplier information and bind it with the supplier code; output vehicle return information from other locations. S22: Based on the out-of-town return information, automatically match the preset settlement standards in the supplier's file, extract the pricing rules and settlement methods corresponding to the vehicle model, and generate an initial invoice; S23: Verify the accuracy of vehicle information and settlement standard matching based on the initial invoice. If there are no errors, the ERP system will process the payment application and output a valid invoice.

4. The multi-source information fusion ERP management method for automobile dismantling according to claim 1, characterized in that: In step S3, the specific steps for outputting real-time inventory data are as follows: S31: Based on the vehicle basic information in the verified valid bill, initiate a vehicle entry application in the ERP system, trigger the weighbridge system to weigh the vehicle weight data, and output the raw weighing data. S32: Based on the original weighing data, perform hierarchical preprocessing through the edge gateway to distinguish between the weight data of core components and the whole vehicle, generate dual tags, push them to the ERP system in real time, and output standard weight data; S33: The standard weight data is automatically associated and matched with the preceding vehicle basic information. The ERP system generates an inbound order containing weight details according to preset rules, updates the system material ledger synchronously, and outputs real-time inventory data.

5. The multi-source information fusion ERP management method for automobile dismantling according to claim 4, characterized in that: In step S32, the specific steps for outputting the standard weight data are as follows: The original weighing data is transmitted to the edge gateway data processing module, where hierarchical preprocessing is used to set the hierarchical thresholds for the weight of core components and the weight of the whole vehicle, and output hierarchical preprocessing rule parameters. According to the graded preprocessing rule parameters, the edge gateway filters and calibrates the raw weighing data, distinguishes between the precise weight data of core components and the reference weight data of the whole vehicle, and outputs graded weight data. Based on the graded weight data, the stability and accuracy of data transmission are verified through a dedicated transmission channel between the edge gateway and the ERP system, and standard weight data is output.

6. The multi-source information fusion ERP management method for automobile dismantling according to claim 1, characterized in that: In step S4, the specific steps for outputting the data source for the disassembly task are as follows: S41: Based on the real-time inventory data, connect to the low-code residual value assessment micro-plugin in the ERP system, import the vehicle model, weight tag, and part status from the inventory data into the dedicated data interface and assessment algorithm engine of the low-code residual value assessment micro-plugin, and output the inventory data to be assessed. S42: Based on the micro-plugin of the inventory data to be evaluated, match the preset market data, dismantling cost parameters, and part residual value coefficient, complete the residual value calculation according to the algorithm, and output dynamic residual value scoring inventory data; S43: Based on the dynamic residual value scoring inventory data, write the score into the exclusive label field of the corresponding inventory data, associate the dismantling priority rules to complete the data filtering, and form the dismantling task data source.

7. The multi-source information fusion ERP management method for automobile dismantling according to claim 6, characterized in that: In step S42, the specific steps for outputting dynamic residual value scoring inventory data are as follows: Based on the inventory data to be evaluated, the micro-plugin algorithm engine automatically retrieves the preset market information database, matches the market circulation price and fluctuation coefficient of the current vehicle model parts, and outputs the market information matching parameters. Based on market conditions, the matching parameter micro-plugin extracts preset dismantling cost parameters and part residual value coefficients, and constructs a residual value calculation algorithm model based on the weight tags and part status in the inventory data to be evaluated. The residual value of a single vehicle and its core components is accurately calculated using a residual value calculation algorithm model. The calculation results are then linked to inventory data to output inventory data with dynamic residual value scores.

8. The multi-source information fusion ERP management method for automobile dismantling according to claim 1, characterized in that: In step S5, the specific steps for outputting the sales parts invoice are as follows: S51: Based on the data source of the dismantling task, retrieve the preset basic dismantling process BOM library in the ERP system, match the vehicle model and residual value priority in the data source, and output the basic dismantling BOM scheme. S52: Identify non-standard features in the data source based on the basic disassembly BOM scheme, trigger the preset BOM dynamic adjustment rules, supplement or delete disassembly procedures, and output a personalized disassembly task sheet. S53: Based on the personalized disassembly task order, guide the disassembly of parts, enter the status and residual value information of parts by scanning with RFID, and automatically summarize the data to output a sales parts bill.

9. The multi-source information fusion ERP management method for automobile dismantling according to claim 1, characterized in that: In step S6, the specific steps for outputting financial vouchers and the exception list are as follows: S61: Based on the sales parts invoice, extract the payment details from the ERP system's financial module, match the supplier code in the invoice with the corresponding settlement method, and output the invoice verification data. S62: Based on the billing verification data, screen the sales orders of parts that have not completed environmental compliance treatment, and generate a technical early warning list of orders with abnormal environmental treatment; S63: Calculate the actual weight deviation rate and residual value fluctuation value based on the order technical warning list and bill verification data, and determine whether the actual weight deviation rate and residual value fluctuation value exceed the preset threshold. If so, trigger the corresponding level of data anomaly technical warning. S64: Generate standardized financial settlement technical vouchers based on the bill reconciliation data, order technical early warning list, and abnormal technical early warning, and output an abnormal handling technical list.

10. A multi-source information fusion ERP management system for automobile dismantling, which adopts the multi-source information fusion ERP management method for automobile dismantling as described in any one of claims 1 to 9, characterized in that, The management system includes: The file building module is used to build a supplier-specific file database in the ERP system based on the needs of automobile dismantling business, and to enter qualifications, cooperative product categories, settlement methods and generate supplier codes; The bill generation module is used to input the vehicle model, vehicle condition, and supplier of the vehicle returning from another location into the supplier code, automatically associate the settlement standard in the file to generate a bill; after verification, it initiates a payment application and outputs the verified valid bill; The weighing and warehousing module is used to start the weighbridge weighing after the vehicle enters the warehouse. The edge gateway performs hierarchical preprocessing of the weight data to generate dual tags and pushes them to the ERP. It also generates an inbound order by associating it with the preceding vehicle information and outputs real-time inventory data. The residual value assessment module is used to match multi-dimensional data with low-code residual value assessment micro-plugin based on the real-time inventory data, generate dynamic residual value scores and write them into inventory tags, and output the data source for dismantling tasks. The disassembly order module is used to automatically match the basic BOM based on the disassembly task data source, dynamically adjust for non-standard cases, and generate a personalized disassembly order; after the parts are disassembled, the information is scanned and entered, and the sales parts bill is output. The settlement early warning module is used to automatically verify the amount of payment and receipt based on the sales parts bill, associate the supplier's settlement method, trigger multi-level early warnings by linking environmental data, and output financial vouchers and anomaly lists. The decision optimization module is used to build a two-layer intelligent dashboard based on full-process data, display the breakdown of tasks and performance data, generate value-added decision reports and push optimization suggestions back, and output visualized decision data and optimization instructions.

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