Method for intelligently recommending logistics modes and resources according to contract data
Through intelligent contract data analysis and machine learning algorithms, the problem of inefficient logistics methods and resource recommendations in the existing technology is solved, efficient and flexible supply chain management is achieved, and logistics costs are reduced.
Patent Information
- Application Number
- CN202510325435.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-19
- Publication Date
- 2025-07-04
AI Technical Summary
In the existing logistics and supply chain management, it is difficult for enterprises to use contract data through intelligent means to efficiently recommend logistics methods and resources, resulting in inefficient and difficult to cope with complex market demands and dynamic changes in supply chain environments.
Through the data acquisition and integration module, external customer demand and analysis module, unknown terminal analysis module and intelligent recommendation module, combined with machine learning algorithms, intelligent analysis and recommendation of contract data, customer demand and supply chain paths are realized, and the optimal supply path and price are generated.
It improves supply chain management efficiency, reduces logistics costs, and enhances the flexibility and intelligence of the system, so as to quickly respond to dynamically changing market demand.
Smart Images
Figure CN120258676A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of artificial intelligence technology, and specifically relates to a method for intelligently recommending logistics methods and resources based on contract data. Background Art
[0002] In existing logistics and supply chain management, enterprises usually rely on manual experience or simple rules to select logistics methods and resources. This method is inefficient and difficult to cope with complex market demands and dynamic supply chain environments. In addition, existing systems lack in-depth analysis and utilization of contract data and cannot make intelligent recommendations based on customer needs, cost structures, and supply chain paths. Therefore, there is an urgent need for a method that can intelligently recommend logistics methods and resources based on contract data to improve supply chain efficiency and reduce costs. Summary of the Invention
[0003] Aiming at the deficiencies in the prior art, the purpose of the present invention is to provide a method for intelligently recommending logistics methods and resources based on contract data, which realizes intelligent recommendation of logistics methods and resources by integrating contract data, external customer needs, supply chain paths, and cost structures.
[0004] To achieve the above purpose, the present invention is realized through the following technical solutions: A logistics method and resource recommendation system for intelligent recommendation based on contract data, including a data collection and integration module, an external customer demand and analysis module, an unknown terminal parsing module, and an intelligent recommendation module. The data collection and integration module collects contract data and performs data integrity analysis according to data label consistency. The external customer demand and analysis module, through the demand analysis module, combines the existing terminal database, analyzes customer needs, supply chain paths, and cost structures, learns and validates the existing supply chain paths and cost structures, and recommends the optimal supply path and price. The unknown terminal parsing module performs address parsing, product parsing, supplier parsing, transportation resource parsing, transportation path parsing, and cost analysis on unknown terminals, and based on the parsing results, recommends the optimal supply path and price. The intelligent recommendation module intelligently recommends logistics methods and resources according to contract data, external customer needs, and supply chain paths, combines the cost analysis results, and continuously optimizes the recommendation results through machine learning algorithms to improve the accuracy and efficiency of the recommendation.
[0005] Preferably, the data collection includes a procurement information collection module, a logistics information collection module, and a financial cost accounting module.
[0006] Preferably, the procurement information collection module extracts procurement orders, supplier information, contract terms, and cost data from the procurement system. Collect relevant information of suppliers, such as geographical location, transportation capacity, historical cooperation records, etc.
[0007] Preferably, the logistics information collection module collects data such as transportation resources, transportation routes, and supplier information. This module works in collaboration with the external customer demand and procurement information collection module to ensure the comprehensiveness and real-time nature of the data.
[0008] Preferably, the financial cost accounting module conducts a detailed analysis of transportation costs, including transportation expenses, warehousing expenses, labor expenses, etc. Through cost analysis, the system can recommend the most cost-effective logistics methods and resource allocation plans.
[0009] A method for intelligently recommending logistics methods and resources based on contract data, comprising the following steps:
[0010] 1. Collect and integrate contract data and conduct data integrity analysis;
[0011] 2. Analyze external customer demands, and combine with the existing terminal database to conduct supply chain path and cost structure verification;
[0012] 3. Analyze unknown terminals to generate recommended supply paths and prices;
[0013] 4. Based on contract data, customer demands, and supply chain paths, intelligently recommend logistics methods and resources.
[0014] Preferably, the data integrity analysis is achieved through data label consistency verification.
[0015] Preferably, step 2 specifically involves parsing customer demands, including transportation timeliness, cost budget, and cargo types, combining with the historical supply chain paths and cost structures in the existing terminal database, and using machine learning algorithms for verification to identify the optimal paths and cost structures.
[0016] Preferably, step 3 includes address parsing, product parsing, supplier parsing, transportation resource parsing, transportation route parsing, and cost analysis.
[0017] Preferably, step 4 is based on machine learning and optimization algorithms to construct an intelligent recommendation model, generate the optimal logistics methods and resource allocation plans, and according to the model calculation results, generate recommendation results and display them to users through the user interface.
[0018] The present invention has the following beneficial effects:
[0019] 1. By intelligently recommending logistics methods and resources, it reduces manual intervention and improves the efficiency of supply chain management.
[0020] 2. Cost reduction: By optimizing the supply chain paths and cost structures, it reduces logistics costs.
[0021] 3. Enhance flexibility: It can quickly adjust the recommendation strategy according to the dynamically changing market demands and supply chain environment.
[0022] 4. Provide accurate recommendation results based on contract data and customer requirements to improve the quality of decision-making. BRIEF DESCRIPTION OF THE DRAWINGS
[0023] The present invention will be described in detail below in conjunction with the drawings and specific embodiments;
[0024] Figure 1 It is a flowchart of the method of the present invention. SPECIFIC EMBODIMENTS
[0025] To make the technical means, creative features, achieved purposes and effects of the present invention easy to understand, the present invention will be further described below in conjunction with specific embodiments.
[0026] Refer to Figure 1 , the following technical solutions are adopted in this specific embodiment: An intelligent logistics mode and resource recommendation system based on contract data, including a data collection and integration module, an external customer demand and analysis module, an unknown terminal parsing module, and an intelligent recommendation module. The data collection and integration module collects contract data and performs data integrity analysis according to data label consistency; the external customer demand and analysis module, through the demand analysis module, combines the existing terminal database, analyzes customer demands, supply chain paths and cost structures, learns and verifies the existing supply chain paths and cost structures, and recommends the optimal supply path and price; the unknown terminal parsing module performs address parsing, product parsing, supplier parsing, transportation resource parsing, transportation path parsing and cost analysis on unknown terminals, and based on the parsing results, recommends the optimal supply path and price; the intelligent recommendation module intelligently recommends logistics modes and resources according to contract data, external customer demands and supply chain paths, combines the cost analysis results, and continuously optimizes the recommendation results through machine learning algorithms to improve the accuracy and efficiency of the recommendation.
[0027] It should be noted that the data collection and integration module performs data collection and preprocessing, extracts logistics-related data from the contract management system, including goods type, weight, volume, transportation distance, delivery time, customer requirements, etc. The data is cleaned, standardized and classified to ensure the accuracy and consistency of the data.
[0028] It should be noted that the external customer demand and analysis module is used to parse customer demands, including address parsing, product parsing, supplier parsing, transportation resource parsing and transportation path parsing. Through this module, the system can accurately understand customer demands and provide basic data for subsequent recommendations.
[0029] In addition, the data collection includes a procurement information collection module, a logistics information collection module, and a financial cost accounting module. The procurement information collection module extracts procurement order information from the procurement system, including the type, quantity, supplier information, procurement time, delivery location, etc. of the procured items. Collect relevant information about suppliers, such as the geographical location, transportation capacity, historical cooperation records, credit ratings, etc. of the suppliers. Extract the key terms in the procurement contract, such as delivery time, transportation method requirements, cost budget, liability for breach of contract, etc. Collect the cost data generated during the procurement process, including procurement price, transportation cost, warehousing cost, customs duties, etc. The logistics information collection module collects data such as transportation resources, transportation routes, and supplier information. This module works in coordination with external customer requirements and the procurement information collection module to ensure the comprehensiveness and real-time nature of the data. The financial cost accounting module conducts a detailed analysis of transportation costs, including transportation expenses, warehousing expenses, labor costs, etc. Through cost analysis, the system can recommend the most cost-effective logistics methods and resource allocation plans.
[0030] This specific implementation method uses machine learning algorithms to verify the known terminal database and the original supply chain path and cost structure to ensure the accuracy of the data and the reliability of the model. Through machine learning verification, the system can continuously optimize the recommendation model and improve the accuracy of the recommendation.
[0031] Based on the analysis results of the above modules, the system recommends the optimal supply path and price. The recommendation results comprehensively consider various factors such as customer requirements, transportation resources, and cost structure to ensure that the recommended plan not only meets customer needs but also has economic efficiency.
[0032] A method for intelligently recommending logistics methods and resources based on contract data specifically includes the following steps:
[0033] 1. Collect and integrate contract data and conduct data integrity analysis
[0034] 1.1 Data collection: Extract logistics-related data from the contract management system, including the type of goods, weight, volume, transportation distance, delivery time, customer requirements, etc.
[0035] 1.2 Data integration: Integrate the collected contract data to ensure the integrity and consistency of the data.
[0036] 1.3 Data integrity analysis: Through data label consistency verification, ensure the accuracy and reliability of the data. Data label consistency verification includes verifying the key fields (such as goods information, customer information, transportation requirements, etc.) in the contract data to ensure that the data format and content meet the preset standards.
[0037] 2. Analyze external customer requirements and conduct supply chain path and cost structure verification in combination with the existing terminal database
[0038] 2.1 External customer demand analysis: Analyze customer demands, including transportation timeliness, cost budget, cargo types, etc.
[0039] 2.2 Supply chain path and cost structure verification: Combine the historical supply chain paths and cost structures in the existing terminal database and use machine learning algorithms for verification. Through the machine learning model, the system can identify the optimal paths and cost structures in the historical data and provide references for current demands.
[0040] 3. Analyze unknown terminals to generate recommended supply paths and prices
[0041] 3.1 Address parsing: Parse the specific transportation origin and destination based on the address information provided by the customer.
[0042] 3.2 Product parsing: Analyze the attributes of the cargo such as type, weight, volume, etc. to determine the suitable transportation mode.
[0043] 3.3 Supplier parsing: Screen suitable suppliers according to the cargo type and transportation requirements.
[0044] 3.4 Transportation resource parsing: Analyze the available transportation resources (such as vehicles, ships, airplanes, etc.) to determine the availability and capacity of the resources.
[0045] 3.5 Transportation path parsing: Combine the address, product, and transportation resources to generate possible transportation paths.
[0046] 3.6 Cost analysis: Conduct cost analysis on each possible transportation path, including transportation costs, warehousing costs, labor costs, etc.
[0047] 3.7 Generate recommended supply paths and prices: Based on the above parsing results, generate the optimal supply path and price recommendations.
[0048] 4. Based on contract data, customer demands, and supply chain paths, intelligently recommend logistics methods and resources
[0049] 4.1 Intelligent recommendation model: Build an intelligent recommendation model based on machine learning and optimization algorithms. The model inputs include contract data, customer demands, and supply chain paths, and the output is the recommended logistics method and resource allocation plan.
[0050] 4.2 Generation of recommendation results: Generate the optimal logistics method and resource allocation plan according to the model calculation results and display them to the user through the user interface.
[0051] 4.3 User feedback and model optimization: Users can adjust the recommendation results according to the actual situation, and the system will record the user feedback and use it for further optimization of the model.
[0052] This specific implementation realizes the intelligent recommendation of logistics methods and resources by integrating contract data, external customer requirements, and supply chain paths. The present invention can improve the efficiency of supply chain management, reduce logistics costs, and enhance the flexibility and intelligence level of the system.
[0053] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and what is described in the above embodiments and the specification is only to illustrate the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of the present invention claimed is defined by the appended claims and their equivalents.
Claims
1. An intelligent logistics method and resource recommendation system based on contract data, characterized in that, It includes a data collection and integration module, an external customer demand and analysis module, an unknown terminal parsing module, and an intelligent recommendation module. The data collection and integration module collects contract data and performs data integrity analysis based on data tag consistency. The external customer demand and analysis module, through the demand analysis module, combines the existing terminal database to analyze customer demands, supply chain paths, and cost structures, conducts learning verification on the existing supply chain paths and cost structures, and recommends the optimal supply path and price. The unknown terminal parsing module performs address parsing, product parsing, supplier parsing, transportation resource parsing, transportation path parsing, and cost analysis on unknown terminals, and based on the parsing results, recommends the optimal supply path and price. The intelligent recommendation module, according to the contract data, external customer demands, and supply chain paths, combines the cost analysis results, intelligently recommends logistics methods and resources, and continuously optimizes the recommendation results through machine learning algorithms to improve the accuracy and efficiency of the recommendation.
2. The intelligent logistics method and resource recommendation system according to claim 1, characterized in that The data collection includes a procurement information collection module, a logistics information collection module, and a financial cost accounting module.
3. The intelligent logistics method and resource recommendation system according to claim 2, wherein The procurement information collection module extracts procurement orders, supplier information, contract terms, and cost data from the procurement system; collects relevant information about suppliers, such as geographical location, transportation capacity, and historical cooperation records.
4. An intelligent logistics method and resource recommendation system according to contract data as claimed in claim 2, characterized in that, The logistics information collection module collects transportation resource, transportation path, and supplier information data.
5. An intelligent logistics method and resource recommendation system based on contract data according to claim 2, characterized in that The financial cost accounting module conducts a detailed analysis of transportation costs, including transportation expenses, warehousing expenses, and labor expenses.
6. A method for intelligently recommending logistics methods and resources based on contract data, characterized in that, It includes the following steps: (1) Collect and integrate contract data and perform data integrity analysis; (2) Analyze external customer demands and conduct supply chain path and cost structure verification in combination with the existing terminal database; (3) Parse unknown terminals to generate recommended supply paths and prices; (4) Based on contract data, customer demands, and supply chain paths, intelligently recommend logistics methods and resources.
7. The method for intelligently recommending a logistics method and resources according to contract data according to claim 6, wherein, The data integrity analysis is achieved through data tag consistency verification.
8. A method for intelligently recommending logistics methods and resources according to contract data as claimed in claim 6, characterized in that, The specific content of step (2) is to parse customer demands, including transportation timeliness, cost budget, and cargo types, combine the historical supply chain paths and cost structures in the existing terminal database, and use machine learning algorithms for verification to identify the optimal path and cost structure.
9. The method for intelligently recommending a logistics method and resources according to contract data as claimed in claim 6, wherein Step (3) includes address parsing, product parsing, supplier parsing, transportation resource parsing, transportation path parsing, and cost analysis.
10. The method for intelligently recommending a logistics method and resources according to contract data according to claim 6, characterized in that, Step (4) is based on machine learning and optimization algorithms to build an intelligent recommendation model, generate the optimal logistics method and resource allocation plan, and according to the calculation results of the model, generate recommendation results and display them to users through the user interface.