Automatic cost measuring and calculating system and method based on data medium station

Abstract

The invention discloses an automatic cost measuring and calculating system and method based on a data medium station, and belongs to the technical field of data medium stations. Aiming at the problems of research and development, purchase and production data separation, low non-standard material identification efficiency, inaccurate dynamic cost prediction and the like in a traditional method, the system realizes real-time linkage updating of manufacturing parameters and a bill of materials by constructing a multi-dimensional process characteristic dynamic coding mechanism, and breaks through a cross-system data barrier. And dynamically correcting a material and labor cost accounting reference by adopting a supply chain price fluctuation conduction model and combining real-time defect rate monitoring data of a production line. By establishing a flexible allocation strategy, indirect cost factors are intelligently matched based on production batch characteristics, and the cost allocation logic of multi-variety orders is optimized. According to the scheme, the problem of cost penetration management in customized production is effectively solved, the non-standard part recognition efficiency and the supply chain cooperation capability are remarkably improved, and accurate cost decision support is provided for small-batch agile manufacturing in the passenger car industry.

Description

Technical Field

[0001] The present invention relates to the technical field of data middle platform, and in particular to an automatic cost estimation system and method based on a data middle platform. Background Art

[0002] In the field of bus manufacturing, especially in the production of customized models, traditional cost measurement methods face multiple technical bottlenecks. At present, the industry generally has data islands between R&D, procurement, and production systems, which makes it impossible to update BOM versions and process parameters in real time. Taking ordinary bus production as an example, its body welding process involves more than 2,000 welding point parameters, and the painting process needs to match more than 6 coating formulas. It is difficult for traditional ERP systems to dynamically capture the impact of real-time defect rates on labor costs. Especially in the scenario where customers frequently change configurations, the existing system lacks an intelligent generation mechanism for non-standard material codes, and the procurement inquiry cycle is as long as more than 48 hours, which seriously affects the order response speed. At the same time, the forecast error of the total cost of the frame caused by price fluctuations of bulk materials such as steel often exceeds 12%, and the fixed allocation coefficient used in the traditional cost model cannot adapt to the production characteristics of multiple varieties and small batches. Industry research data shows that the cost overruns caused by insufficient supply chain collaboration in the bus manufacturing industry account for 5.2% of revenue, and it is urgent to build an automated measurement system driven by the data middle platform to achieve full-link cost penetration management. Summary of the invention

[0003] In order to solve the above technical problems, the technical solution adopted by the automatic cost estimation system and method based on the data middle station of the present invention includes the following steps: S1. Forecast order entry: The R&D management system generates basic product data, including vehicle model parameters and BOM structure, and pushes it to the data center; The data center performs data cleaning and standardization processing and establishes a part code mapping rule library; Simulate the process route through the offline development environment, generate a bill of materials prototype and associate it with the process parameter library; S2. Order design: After receiving the customer's order, the sales management system pushes the order data package containing the optional parts configuration to the data center; The data center calls the cost center module and performs the following design-related operations: S21: Dynamic material cost is calculated based on the material price fluctuation model, where the steel cost adopts the conduction formula C_frame=1.32P_steel+1850, C_frame is the total cost of the frame, P_steel is the spot price of hot-rolled coil, the coefficient 1.32 is the unit steel price fluctuation conduction coefficient, and 1850 is the fixed cost component, including tooling amortization, production line energy consumption and standard working hours; S22: Allocate manufacturing expenses according to the activity-based cost method, tooling amortization rate = (tooling purchase cost × reuse coefficient) / expected output; S23: Analyze the historical defect image library through convolutional neural network and generate a repair rate curve to correct labor costs; S3. Material Inquiry: The data center splits the material list into a standard parts purchase list and a non-standard parts inquiry list, where: (i) Standard parts list is directly connected to the supplier's inventory system to obtain real-time prices; (ii) The non-standard parts list sends technical parameters and price inquiry instructions to the SRM system via the ESB bus; Receive the tiered price data fed back by the procurement system: P_t=P_benchmark×(1+δ), where the δ value is dynamically adjusted based on the supplier's historical delivery qualification rate; S4. Data aggregation and cleaning: The data center pushes multi-dimensional cost data to the financial system, including direct material costs, dynamic labor costs, and amortization of R&D expenses. When it is detected that the BOM material quantity and process standard deviation are greater than 5%, the collaborative verification process among the R&D, production, and procurement departments is triggered. S5. Cost estimation: The financial system feeds back the price correction coefficient, basic price = (total cost × 1.18) / expected sales volume, and generates a quotation plan with an elastic range; performs cost deviation root cause analysis, and associates the timing consistency check between the R&D BOM version and the purchase order version; S6. Application of cost analysis: The data center extracts business snapshots from each system, generates golden data records, and performs the following analysis: S61: Cost tracing, establishing a penetrating mapping relationship table of order-BOM-supplier; S62: Fluctuation warning, when the promotion cost / revenue ratio is greater than 5%, a cross-system alarm is triggered; S63: Generates a multi-dimensional analysis report, including Monte Carlo simulation of order profit sensitivity analysis and cost composition Sankey diagram.

[0004] As a further solution of the present invention, the data cleaning in step S1 includes: The number of body welding points and coating area parameters are standardized; When it is detected that the BOM material quantity and process standard deviation is greater than 5%, the collaborative verification process among the R&D, production and procurement departments is automatically triggered; Establish a process parameter mapping rule base between electrophoretic coating temperature threshold and assembly cycle time.

[0005] As a further solution of the present invention, the procurement coordination in step S3 includes: A three-dimensional scoring model is used to make purchasing decisions for passenger car axles. The scoring dimensions include: (1) Price-quality ratio: a weighted score of the supplier’s quote and historical defect rate; (2) Delivery efficiency coefficient: calculated based on the logistics routing optimization model; (3) Strategic cooperation level: automatically matched according to the tiered discount agreement; When the bidding results generate a draft procurement contract, a budget reservation instruction is simultaneously sent to the ERP system.

[0006] As a further solution of the present invention, dynamic labor cost calculation adopts: Scrap loss coefficient = baseline value + k*(number of defects on the day / output on the day); The k value is dynamically adjusted according to the stability of the production line. When the sensor detects that the welding defect rate is >3% for 2 consecutive hours, the k value is automatically increased from 1.0 to 1.2.

[0007] As a further solution of the present invention, an automatic cost calculation system based on a data middle platform includes: Data middle platform module: integrates material price fluctuation model, activity-based cost allocation algorithm and CNN defect analysis model, stores solder joint density-man-hour coefficient comparison table and coating area-coating consumption mapping rules; R&D management module: supports multi-version BOM structure comparison and process route conflict detection, and generates tooling interference reports; Procurement collaboration module: performs non-standard parts technical parameter analysis, step-by-step quotation optimization, and procurement contract risk warning; Financial accounting module: real-time reception of MES workstation-level energy consumption data, WMS line-side inventory turnover rate, and TMS logistics path cost; Verification control module: Establish order-BOM-purchase batch mapping chain, deploy defect rate sudden increase warning and bulk material replacement plan deduction mechanism; As a further solution of the present invention, the procurement collaboration module includes an intelligent bidding unit that performs the following functions: (a) Automatic parser for technical parameters of non-standard parts, extracting key procurement indicators such as axle load capacity; (b) a step-by-step quotation optimizer that generates the optimal purchase batch size based on the supplier’s capacity load rate; The contract risk early warning unit detects the deviation threshold between the purchase price and the market benchmark price.

[0008] As a further solution of the present invention, the verification control module includes an anomaly detection unit, which deploys the following monitoring strategies: (α) When the daily defect rate suddenly increases by Δ>15%, the process parameter rollback mechanism is triggered; (β) When the bulk material purchase price exceeds the futures hedging threshold, the alternative plan deduction is initiated; Visual reporting engine that generates interactive dashboards including Sankey diagrams of cost components and heat maps of profit sensitivity.

[0009] Useful explanation: The present invention constructs an intelligent cost estimation system through a data middle platform, establishes a dynamic coding mechanism based on multi-dimensional process characteristics, breaks through the barriers of R&D, procurement and production data, realizes real-time linkage between manufacturing parameters and bill of materials, and improves the recognition of non-standard parts and the efficiency of supply chain collaboration. Integrate real-time data of production lines with market price fluctuations, build a dynamic cost transmission model, and enhance the accuracy of bulk material cost prediction. By correlating manufacturing defect rates with resource consumption, dynamically calibrate labor costs and quality losses. In view of the characteristics of small batch orders, a flexible allocation strategy is adopted to intelligently match production batches with cost drivers and optimize the allocation of indirect costs. This solution breaks through the limitations of data silos and static modeling of traditional systems, provides full-process cost decision support for the agile production of multiple varieties of buses, and helps companies improve cost control and market responsiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 This is a flowchart of the steps of an automatic cost estimation system and method based on a data middle platform of the present invention; Figure 2 This is a system architecture diagram of an automatic cost estimation system and method based on a data middle station in the present invention. DETAILED DESCRIPTION

[0011] The present invention will be further described in detail below in conjunction with the embodiments.

[0012] See also Figure 1 As shown in the figure, an automatic cost calculation method based on the data center is shown in the figure. The specific steps are as follows: S1. Forecast order entry: The R&D management system generates basic product data, including vehicle model parameters and BOM structure, and pushes it to the data center; The data center performs data cleaning and standardization processing and establishes a part code mapping rule library; Simulate the process route through the offline development environment, generate a bill of materials prototype and associate it with the process parameter library; S2. Order design: After receiving the customer's order, the sales management system pushes the order data package containing the optional parts configuration to the data center; The data center calls the cost center module and performs the following design-related operations: S21: Dynamic material cost is calculated based on the material price fluctuation model, where the steel cost adopts the conduction formula C_frame=1.32P_steel+1850, C_frame is the total cost of the frame, P_steel is the spot price of hot-rolled coil, the coefficient 1.32 is the unit steel price fluctuation conduction coefficient, and 1850 is the fixed cost component, including tooling amortization, production line energy consumption and standard working hours; S22: Allocate manufacturing expenses according to the activity-based cost method, tooling amortization rate = (tooling purchase cost × reuse coefficient) / expected output; S23: Analyze the historical defect image library through convolutional neural network and generate a repair rate curve to correct labor costs; S3. Material Inquiry: The data center splits the material list into a standard parts purchase list and a non-standard parts inquiry list, where: (i) Standard parts list is directly connected to the supplier's inventory system to obtain real-time prices; (ii) The non-standard parts list sends technical parameters and price inquiry instructions to the SRM system via the ESB bus; Receive the tiered price data fed back by the procurement system: P_t=P_benchmark×(1+δ), where the δ value is dynamically adjusted based on the supplier's historical delivery qualification rate; S4. Data aggregation and cleaning: The data center pushes multi-dimensional cost data to the financial system, including direct material costs, dynamic labor costs, and amortization of R&D expenses. When it is detected that the BOM material quantity and process standard deviation are greater than 5%, the collaborative verification process among the R&D, production, and procurement departments is triggered. S5. Cost estimation: The financial system feeds back the price correction coefficient, basic price = (total cost × 1.18) / expected sales volume, and generates a quotation plan with an elastic range; performs cost deviation root cause analysis, and associates the timing consistency check between the R&D BOM version and the purchase order version; S6. Application of cost analysis: The data center extracts business snapshots from each system, generates golden data records, and performs the following analysis: S61: Cost tracing, establishing a penetrating mapping relationship table of order-BOM-supplier; S62: Fluctuation warning, when the promotion cost / revenue ratio is greater than 5%, a cross-system alarm is triggered; S63: Generates a multi-dimensional analysis report, including Monte Carlo simulation of order profit sensitivity analysis and cost composition Sankey diagram.

[0013] Furthermore, the data cleaning in step S1 includes: The number of body welding points and coating area parameters are standardized; When it is detected that the BOM material quantity and process standard deviation is greater than 5%, the collaborative verification process among the R&D, production and procurement departments is automatically triggered; Establish a process parameter mapping rule base between electrophoretic coating temperature threshold and assembly cycle time.

[0014] Furthermore, the procurement coordination in step S3 includes: A three-dimensional scoring model is used to make purchasing decisions for passenger car axles. The scoring dimensions include: (I) Price-quality ratio: a weighted score of the supplier’s quote and historical defect rate; (II) Delivery time efficiency coefficient: calculated based on the logistics routing optimization model; (III) Strategic cooperation level: automatically matched according to the tiered discount agreement; When the bidding results generate a draft procurement contract, a budget reservation instruction is simultaneously sent to the ERP system.

[0015] Furthermore, dynamic labor cost calculation adopts: Scrap loss coefficient = baseline value + k*(number of defects on the day / output on the day); The k value is dynamically adjusted according to the stability of the production line. When the sensor detects that the welding defect rate is >3% for 2 consecutive hours, the k value is automatically increased from 1.0 to 1.2.

[0016] For further information, see Figure 2 As shown, an automatic cost estimation system based on a data middle platform includes: Data middle platform module: integrates material price fluctuation model, activity-based cost allocation algorithm and CNN defect analysis model, stores solder joint density-man-hour coefficient comparison table and coating area-coating consumption mapping rules; R&D management module: supports multi-version BOM structure comparison and process route conflict detection, and generates tooling interference reports; Procurement collaboration module: performs non-standard parts technical parameter analysis, step-by-step quotation optimization, and procurement contract risk warning; Financial accounting module: real-time reception of MES workstation-level energy consumption data, WMS line-side inventory turnover rate, and TMS logistics path cost; Verification control module: Establish order-BOM-purchase batch mapping chain, deploy defect rate sudden increase warning and bulk material replacement plan deduction mechanism; Furthermore, the procurement collaboration module includes an intelligent bidding unit that performs the following functions: (a) Automatic parser for technical parameters of non-standard parts, extracting key procurement indicators such as axle load capacity; (b) a step-by-step quotation optimizer that generates the optimal purchase batch size based on the supplier’s capacity load rate; The contract risk early warning unit detects the deviation threshold between the purchase price and the market benchmark price.

[0017] Furthermore, the verification control module includes an anomaly detection unit, which deploys the following monitoring strategies: (α) When the daily defect rate suddenly increases by Δ>15%, the process parameter rollback mechanism is triggered; (β) When the bulk material purchase price exceeds the futures hedging threshold, the alternative plan deduction is initiated; Visual reporting engine that generates interactive dashboards including Sankey diagrams of cost components and heat maps of profit sensitivity.

[0018] The above description is only an embodiment of this specification and is not intended to limit this specification. For those skilled in the art, this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this specification should be included in the scope of the claims of this application.

Claims

1. An automatic cost calculation method based on a data center, characterized in that: The following steps are involved: S1. Forecast order entry: The R&D management system generates basic product data, including vehicle model parameters and BOM structure, and pushes it to the data center; The data center performs data cleaning and standardization processing and establishes a part code mapping rule library; Simulate the process route through the offline development environment, generate a bill of materials prototype and associate it with the process parameter library; S2. Order design: After receiving the customer's order, the sales management system pushes the order data package containing the optional parts configuration to the data center; The data center calls the cost center module and performs the following design-related operations: S21: Dynamic material cost is calculated based on the material price fluctuation model, where the steel cost adopts the conduction formula C_frame=1.32P_steel+1850, C_frame is the total cost of the frame, P_steel is the spot price of hot-rolled coil, the coefficient 1.32 is the unit steel price fluctuation conduction coefficient, and 1850 is the fixed cost component, including tooling amortization, production line energy consumption and standard working hours; S22: Allocate manufacturing expenses according to the activity-based cost method, tooling amortization rate = (tooling purchase cost × reuse coefficient) / expected output; S23: Analyze the historical defect image library through convolutional neural network and generate a repair rate curve to correct labor costs; S3. Material Inquiry: The data center splits the material list into a standard parts purchase list and a non-standard parts inquiry list, where: (i) Standard parts list is directly connected to the supplier's inventory system to obtain real-time prices; (ii) The non-standard parts list sends technical parameters and price inquiry instructions to the SRM system via the ESB bus; Receive the tiered price data fed back by the procurement system: P_t=P_benchmark×(1+δ), where the δ value is dynamically adjusted based on the supplier's historical delivery qualification rate; S4. Data aggregation and cleaning: The data center pushes multi-dimensional cost data to the financial system, including direct material costs, dynamic labor costs, and R&D expense amortization. When it is detected that the BOM material quantity and process standard deviation are greater than 5%, the collaborative verification process among the R&D, production, and procurement departments is triggered. S5. Cost estimation: The financial system feeds back the price correction coefficient, basic price = (total cost × 1.18) / expected sales volume, and generates a quotation plan with an elastic range; performs cost deviation root cause analysis, and associates the timing consistency check between the R&D BOM version and the purchase order version; S6. Application of cost analysis: The data center extracts business snapshots from each system, generates golden data records, and performs the following analysis: S61: Cost tracing, establishing a penetrating mapping relationship table of order-BOM-supplier; S62: Fluctuation warning, when the promotion cost / revenue ratio is greater than 5%, a cross-system alarm is triggered; S63: Generates a multi-dimensional analysis report, including Monte Carlo simulation of order profit sensitivity analysis and cost composition Sankey diagram.

2. According to claim 1, the automatic cost calculation method based on the data center is characterized in that: The data cleaning in step S1 includes: The number of body welding points and coating area parameters are standardized; When it is detected that the BOM material quantity and process standard deviation is greater than 5%, the collaborative verification process among the R&D, production and procurement departments is automatically triggered; Establish a process parameter mapping rule base between electrophoretic coating temperature threshold and assembly cycle time.

3. According to claim 1, the automatic cost calculation method based on the data center is characterized in that: The procurement coordination of step S3 includes: A three-dimensional scoring model is used to make purchasing decisions for passenger car axles. The scoring dimensions include: (1) Price-quality ratio: a weighted score of the supplier’s quote and historical defect rate; (2) Delivery efficiency coefficient: calculated based on the logistics routing optimization model; (3) Strategic cooperation level: automatically matched according to the tiered discount agreement; When the bidding results generate a draft procurement contract, a budget reservation instruction is simultaneously sent to the ERP system.

4. According to claim 1, the automatic cost calculation method based on the data center is characterized in that: The dynamic labor cost calculation adopts: Scrap loss coefficient = baseline value + k*(number of defects on the day / output on the day); The k value is dynamically adjusted according to the stability of the production line. When the sensor detects that the welding defect rate is >3% for 2 consecutive hours, the k value is automatically increased from 1.0 to 1.

2.

5. An automatic cost calculation system based on a data center, characterized in that: The system comprises: Data middle platform module: integrates material price fluctuation model, activity-based cost allocation algorithm and CNN defect analysis model, stores solder joint density-man-hour coefficient comparison table and coating area-coating consumption mapping rules; R&D management module: supports multi-version BOM structure comparison and process route conflict detection, and generates tooling interference reports; Procurement collaboration module: performs non-standard parts technical parameter analysis, step-by-step quotation optimization, and procurement contract risk warning; Financial accounting module: real-time reception of MES workstation-level energy consumption data, WMS line-side inventory turnover rate, and TMS logistics path cost; Verification control module: Establish order-BOM-purchase batch mapping chain, deploy defect rate sudden increase warning and bulk material alternative solution deduction mechanism.

6. The automatic cost calculation system based on the data middle platform according to claim 5 is characterized in that: The procurement collaboration module includes an intelligent bidding unit that performs the following functions: (a) Automatic parser for technical parameters of non-standard parts, extracting key procurement indicators such as axle load capacity; (b) a step-by-step quotation optimizer that generates the optimal purchase batch size based on the supplier’s capacity load rate; The contract risk early warning unit detects the deviation threshold between the purchase price and the market benchmark price.

7. The automatic cost calculation system based on data middle platform according to claim 5 is characterized in that: The verification control module includes an anomaly detection unit, which deploys the following monitoring strategies: (α) When the daily defect rate suddenly increases by Δ>15%, the process parameter rollback mechanism is triggered; (β) When the bulk material purchase price exceeds the futures hedging threshold, the alternative plan deduction is initiated; Visual reporting engine that generates interactive dashboards including Sankey diagrams of cost components and heat maps of profit sensitivity.

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