Pricing engine construction method based on bulk commodity transportation cost calculation formula

Abstract

The invention relates to the technical field of logistics, in particular to a pricing engine construction method based on a bulk commodity transportation cost calculation formula. The method comprises the following steps: constructing a logistics service provider database; data collection and attribute labeling are carried out on bulk commodities, a commodity attribute metadata set is obtained, and the commodity attribute metadata set comprises commodity name data, commodity transportation weight data, shipping address data and receiving address data; according to the commodity name data, the commodity transportation weight data, the shipping address data and the receiving address data, carrying out transportation condition analysis on the bulk commodity to obtain commodity transportation condition data; and carrying out transportation mode selection on bulk commodities according to the commodity transportation condition data and the commodity transportation weight data to obtain a target commodity transportation mode. According to the invention, the data change can be responded in real time, and the dynamic and real-time performance of cost calculation is realized.

Description

Technical Field

[0001] The present invention relates to the field of logistics technology, and in particular to a pricing engine construction method based on a bulk commodity transportation cost calculation formula. Background Art

[0002] In the logistics industry, the calculation of transportation costs has always been an important issue faced by enterprises. Traditional methods of calculating transportation costs often rely on static pricing models that cannot flexibly adapt to market changes and the diversity of transportation conditions. With the increase in the variety of goods and the diversification of transportation needs, traditional pricing methods have obvious deficiencies in accuracy, efficiency and adaptability. For example, different commodities have different physical and chemical properties and requirements for transportation environments, but static models often cannot distinguish these differences in detail, resulting in inaccurate calculation of transportation costs.

[0003] In addition, existing pricing methods often ignore the safety and specificity of the transportation process when dealing with bulk commodity transportation. For example, energy commodities, basic raw materials and agricultural commodities face different safety risks and environmental adaptability issues during transportation, but traditional pricing methods rarely incorporate these factors into cost calculations, making it impossible to fully evaluate transportation costs. Furthermore, the choice of logistics service providers is also an important factor affecting transportation costs. However, traditional pricing methods often do not systematically consider the service scope of logistics service providers, the distribution of service sites, and the optimization of transportation routes, which are crucial to reducing transportation costs and improving transportation efficiency. Summary of the invention

[0004] Based on this, it is necessary for the present invention to provide a pricing engine construction method based on the bulk commodity transportation cost calculation formula to solve at least one of the above technical problems.

[0005] To achieve the above purpose, a pricing engine construction method based on a bulk commodity transportation cost calculation formula includes the following steps: Step S1: construct a logistics service provider database; collect data and label attributes of bulk commodities to obtain a commodity attribute metadata set, wherein the commodity attribute metadata set includes commodity name data, commodity transportation weight data, shipping address data, and receiving address data; Step S2: Analyze the transportation conditions of bulk commodities according to commodity name data, commodity transportation weight data, shipping address data and receiving address data to obtain commodity transportation condition data; Step S3: selecting a transportation mode for the bulk commodity according to the commodity transportation condition data and the commodity transportation weight data to obtain a target commodity transportation mode; matching and selecting a logistics service provider for the bulk commodity according to the commodity transportation condition data and the target commodity transportation mode based on a logistics service provider database to obtain target logistics service provider data; Step S4: Based on the logistics service provider database and according to the target commodity transportation mode and the target logistics service provider data, the transportation path analysis of the bulk commodity is performed to obtain the commodity transportation path data; based on the commodity transportation path data, the transportation resource demand analysis of the bulk commodity is performed to obtain the transportation resource demand data; Step S5: construct a transportation cost calculation formula for bulk commodities according to the target commodity transportation mode, commodity transportation route data and transportation resource demand data to obtain the transportation cost calculation formula; construct a transportation pricing engine for bulk commodities based on the transportation cost calculation formula to obtain a dynamic transportation pricing engine.

[0006] The present invention significantly improves the accuracy of transportation cost calculation by comprehensively considering the construction of a logistics service provider database, detailed data collection and attribute labeling of bulk commodities. By dynamically analyzing transportation conditions and selecting appropriate transportation modes, the adaptability of the pricing model is enhanced, enabling it to flexibly respond to the variability of market and transportation conditions. In addition, the present invention particularly strengthens the consideration of safety risks and environmental adaptability during transportation, ensuring the comprehensiveness of cost calculation and compliance with actual transportation needs. At the same time, by optimizing the selection of logistics service providers, transportation costs are further reduced and transportation efficiency is improved. The constructed dynamic transportation pricing engine can respond to data changes in real time, realizing the dynamic and real-time nature of cost calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings: Figure 1 A schematic flow chart of the steps of a method for constructing a pricing engine based on a bulk commodity transportation cost calculation formula according to an embodiment is shown.

[0008] Figure 2 A detailed flow chart of step S3 of an embodiment is shown.

[0009] Figure 3 A detailed flow chart of step S5 of an embodiment is shown. DETAILED DESCRIPTION

[0010] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0011] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0012] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0013] To achieve this, please refer to Figures 1 to 3 The present invention provides a pricing engine construction method based on a bulk commodity transportation cost calculation formula, comprising the following steps: Step S1: construct a logistics service provider database; collect data and label attributes of bulk commodities to obtain a commodity attribute metadata set, wherein the commodity attribute metadata set includes commodity name data, commodity transportation weight data, shipping address data, and receiving address data; Step S2: Analyze the transportation conditions of bulk commodities according to commodity name data, commodity transportation weight data, shipping address data and receiving address data to obtain commodity transportation condition data; Step S3: selecting a transportation mode for the bulk commodity according to the commodity transportation condition data and the commodity transportation weight data to obtain a target commodity transportation mode; matching and selecting a logistics service provider for the bulk commodity according to the commodity transportation condition data and the target commodity transportation mode based on a logistics service provider database to obtain target logistics service provider data; Step S4: Based on the logistics service provider database and according to the target commodity transportation mode and the target logistics service provider data, the transportation path analysis of the bulk commodity is performed to obtain the commodity transportation path data; based on the commodity transportation path data, the transportation resource demand analysis of the bulk commodity is performed to obtain the transportation resource demand data; Step S5: construct a transportation cost calculation formula for bulk commodities according to the target commodity transportation mode, commodity transportation route data and transportation resource demand data to obtain the transportation cost calculation formula; construct a transportation pricing engine for bulk commodities based on the transportation cost calculation formula to obtain a dynamic transportation pricing engine.

[0014] The present invention significantly improves the accuracy of transportation cost calculation by comprehensively considering the construction of a logistics service provider database, detailed data collection and attribute labeling of bulk commodities. By dynamically analyzing transportation conditions and selecting appropriate transportation modes, the adaptability of the pricing model is enhanced, enabling it to flexibly respond to the variability of market and transportation conditions. In addition, the present invention particularly strengthens the consideration of safety risks and environmental adaptability during transportation, ensuring the comprehensiveness of cost calculation and compliance with actual transportation needs. At the same time, by optimizing the selection of logistics service providers, transportation costs are further reduced and transportation efficiency is improved. The constructed dynamic transportation pricing engine can respond to data changes in real time, realizing the dynamic and real-time nature of cost calculation.

[0015] In this embodiment, a structured database is first created using database management software (such as MySQL) to store detailed information of logistics service providers. Next, a data collection system is developed to use web crawlers (such as Scrapy) and APIs to capture commodity names, transportation weights, shipping addresses, and receiving address data from different logistics platforms and enterprise resource planning (ERP) systems, and store them in commodity attribute metadata sets. Subsequently, a data analysis tool (such as Python's Pandas library) is used to analyze the commodity attribute metadata set, and the commodity transportation condition data is determined according to the commodity characteristics and transportation requirements. Combining the commodity transportation condition data and the commodity transportation weight data, a decision tree algorithm or a logistic regression model is used to select the most suitable transportation mode to form a target commodity transportation mode. In the logistics service provider database, by executing SQL queries, a suitable logistics service provider is screened out according to the commodity transportation condition data and the target commodity transportation mode, and the target logistics service provider data is obtained. Then, a path planning algorithm (such as Dijkstra or A* algorithm) and GIS software (such as ArcGIS) are used to analyze the transportation path of bulk commodities and generate commodity transportation path data. Furthermore, based on the commodity transportation path data, the transportation resource demand is evaluated using cost analysis tools (such as Microsoft Excel or professional logistics cost analysis software) to obtain transportation resource demand data. Combining the target commodity transportation mode, commodity transportation path data and transportation resource demand data, a multi-factor cost composition model is constructed, and statistical analysis software (such as SPSS) is used for parameterized formula conversion to form a transportation cost calculation formula. Finally, based on the transportation cost calculation formula, a dynamic transportation pricing engine is developed, and an online pricing tool is created using programming languages ​​(such as Python) and web frameworks (such as Django or Flask), allowing users to enter relevant parameters and calculate transportation costs in real time. The pricing engine will be embedded in the logistics information system to achieve automation and real-time response, providing logistics service providers and commodity customers with instant transportation cost estimates.

[0016] Preferably, step S1 comprises the following steps: Step S11: Design an attribute table for the logistics service provider to obtain the service provider attribute table; Specifically, you can determine which attributes are key features of logistics service providers, such as service scope, transportation capacity, service site distribution, and historical service quality evaluation. Use requirements analysis tools, such as UML use case diagrams or ER diagrams, to identify and define these attributes. Next, use spreadsheet software (such as Microsoft Excel) to create an attribute table template, list all identified attributes, and define the data type and value range for each attribute. After completing the attribute table design, you will eventually get the service provider attribute table.

[0017] Step S12: constructing a database architecture for the logistics service provider according to the service provider attribute table to obtain a provider database architecture; Specifically, you can use database design tools, such as ER / Studio or MySQL Workbench, to create a conceptual model to convert the attributes in the service provider attribute table into tables and fields in the database. Next, define the data type and constraints of each field, such as primary key, foreign key, and uniqueness constraint. Then, perform logical design to determine the relationship between tables, such as one-to-many or many-to-many relationships, and create the corresponding database table structure accordingly, and finally obtain the provider database architecture.

[0018] Step S13: collecting attribute data of the logistics service provider according to the service provider attribute table to obtain a service provider attribute data set; Specifically, the data items that need to be collected can be determined based on the service provider attribute table. Then, market research can be conducted to collect and verify these attribute information through questionnaires or interviews with logistics service providers, or a data collection application can be developed. Programming languages ​​such as Python combined with web crawler technology can be used to automatically extract data from the official website or public database of logistics service providers. For unstructured data, text analysis tools such as NLTK or spaCy are needed to parse and extract key information. For structured data, it can be directly obtained through API or database query. Finally, the service provider attribute dataset is obtained.

[0019] Step S14: performing data mapping and filling on the service provider attribute table based on the service provider attribute data set to obtain a filled service provider attribute table; Specifically, you can use data mapping tools, such as Talend or Informatica, to define mapping rules between service provider attribute datasets and service provider attribute tables. These rules will guide the tool on how to convert the collected raw data and map it to the corresponding fields in the service provider attribute table. For example, use SQL statements to update records in the database, or use Python's Pandas library to operate data frames. After completing these operations, you will eventually get a populated service provider attribute table.

[0020] Step S15: Based on the filled service provider attribute table, the logistics service provider database is integrated according to the provider database architecture to obtain a logistics service provider database; Specifically, you can use database management tools such as phpMyAdmin or SQL Server ManagementStudio to connect to the target database. Then, according to the provider database schema, execute SQL scripts or use database integration tools such as Apache NiFi or Talend to batch import and populate the service provider attribute table. Finally, you will get a complete and updated logistics service provider database.

[0021] Step S16: Data collection and attribute labeling are performed on bulk commodities to obtain a commodity attribute metadata set, wherein the commodity attribute metadata set includes commodity name data, commodity transportation weight data, shipping address data, and receiving address data.

[0022] Specifically, you can determine the commodity attributes that need to be collected, such as commodity name, shipping weight, shipping address, and delivery address. Then, use web crawler tools such as Scrapy or BeautifulSoup to automatically collect this data from e-commerce platforms, logistics information websites, or enterprise resource planning (ERP) systems. For non-digital data sources, such as paper documents or PDF files, you can use OCR (Optical Character Recognition) technology, combined with tools such as AdobeAcrobat or Tesseract, to convert text data. Next, use data annotation tools such as LabelImg or custom annotation software to classify and annotate the collected data. For example, commodities can be classified into different categories based on their physical or chemical properties. Finally, the annotated data is stored in a database or data warehouse to form a commodity attribute metadata set, which will contain all the necessary information.

[0023] The present invention not only improves the organization and storage efficiency of logistics service provider information through the optimized database architecture, but also speeds up data retrieval and update. Comprehensive data collection provides rich information for evaluating and selecting logistics service providers, making service matching more accurate. The reliability of the database is further improved through the accuracy of data mapping and filling, reducing the risk of data errors. The integrated database construction provides a unified data support platform for transportation mode selection and cost calculation, enhancing the data-driven ability of decision-making. In addition, through the comprehensive analysis of the key attributes of bulk commodities, the calculation of transportation costs is more in line with the actual commodity characteristics, and the relevance and accuracy of pricing are improved. In summary, the present invention promotes the efficiency, accuracy and personalization of logistics services.

[0024] Preferably, step S2 comprises the following steps: Step S21: Identify the commodity category of the bulk commodity according to the commodity name data to obtain commodity category data, wherein the commodity category data is any one of energy category data, basic raw material category data and agricultural product category data; Specifically, a reference database containing various product names and their corresponding categories can be constructed. Text processing tools, such as Python's NLTK library, are used to segment and standardize product names so that they can be matched with entries in the reference database. Next, by writing a classification algorithm, a machine learning framework such as scikit-learn can be used to train a classification model that can identify the product category to which the product name belongs based on the keywords in the product name. Once the model is trained, it is applied to new product name data to identify and obtain product category data. In addition, regular expressions can be used to match specific product name patterns.

[0025] Step S22: if the commodity category data is energy category data, then the bulk commodity is identified as an energy commodity according to the commodity name data to obtain energy commodity type data, wherein the energy commodity type data is any one of liquid energy, gaseous energy and solid energy; otherwise, directly execute step S28; Specifically, if the commodity category data is energy category data, it is necessary to further identify the specific types of energy commodities. First, update the reference database, including the specific commodity names and characteristics of liquid energy, gaseous energy, and solid energy. Then, use a text processing and classification method similar to step S21, but this time use a classification model for energy commodities. This model needs to be retrained or adjusted to identify specific keywords and patterns of energy commodities. For example, deep learning techniques, such as convolutional neural networks (CNN) or recurrent neural networks (RNN), can be used to process more complex text data and improve recognition accuracy. Once the type of energy commodity is identified, the energy commodity type data can be obtained. If step S21 does not obtain energy category data, skip this step and go directly to step S28.

[0026] Step S23: Analyze the types of potential transportation safety accidents of bulk commodities according to the commodity name data to obtain data on the types of potential transportation safety accidents; Specifically, a database containing different types of goods and their potential safety risks can be established. Then, text analysis tools are used to analyze the product name data to identify keywords or phrases that indicate specific safety risks. For example, some chemical names contain words that indicate their flammability or corrosiveness. Next, decision trees or logistic regression classification algorithms are used to combine product attributes and transportation conditions to predict and classify potential safety accident types. Finally, the analysis results are organized into data on potential transportation safety accident types.

[0027] Step S24: Determine the safety device requirements for bulk commodities according to the data on the types of potential transportation safety accidents, and obtain commodity transportation safety device requirement data; Specifically, the required safety device type can be determined by consulting relevant safety specifications and standards based on the data on the types of potential safety accidents in transportation. For example, if the goods are flammable liquids, fire protection devices or explosion-proof devices are required. Next, database query tools, such as SQL queries, are used to retrieve safety device information that matches the risk type from the safety device database. Once the required safety devices are determined, the detailed information of these devices is recorded in the safety device requirements data for commodity transportation.

[0028] Step S25: If the energy commodity type data indicates that the bulk commodity is liquid energy or gaseous energy, the corrosion characteristics of the bulk commodity are collected to obtain commodity corrosion characteristic data, and corrosion-resistant containers are selected for the bulk commodity according to the commodity corrosion characteristic data to obtain corrosion resistance data of the transport container; otherwise, step S26 is directly executed; Specifically, if the energy commodity type data indicates that the commodity is a liquid energy or a gaseous energy, it is necessary to collect corrosion characteristics. Obtain the chemical properties and corrosiveness information of the commodity through laboratory testing or consulting the Material Safety Data Sheet (MSDS). Then, use chemical analysis software, such as ChemOffice or similar tools, to analyze these data to determine the degree of corrosion of the commodity to different materials. Based on the analysis results, evaluate the available container materials and select containers that can resist the corrosion characteristics of the commodity. This process involves querying the material database and communicating with material suppliers to determine the availability and cost of container materials. Once a corrosion-resistant container is selected, record its material type and corrosion resistance to form the corrosion resistance data of the transport container.

[0029] Step S26: determining the transport container type of the bulk commodity according to the corrosion resistance data of the transport container to obtain first transport container type data; Specifically, the suitability of different types of containers can be evaluated based on the corrosion resistance data of the transport container. Use container design software, such as AutoCAD or SolidWorks, to evaluate the structural integrity and capacity of the container. At the same time, consider the storage and transportation conditions of the goods, such as temperature, pressure, and vibration, to ensure that the selected container type can meet these conditions. After determining the container type, record its specifications and characteristics to form the first transport container type data.

[0030] Step S27: determining the sealing requirement of the bulk commodity according to the commodity name data, obtaining container sealing data, and recording the commodity transportation safety device requirement data, the first transportation container type data and the container sealing data, or the first transportation container type data and the container sealing data as commodity transportation condition data; Specifically, the necessity of preventing leakage during transportation can be evaluated based on the commodity name data and container sealing data. Use risk assessment tools to determine the sealing requirements in combination with the physical and chemical properties of the commodity. For example, for volatile or toxic commodities, highly sealed containers are required. Next, according to the sealing requirements, select or design corresponding sealing technologies and components, such as using special sealing gaskets or sealants. Then, use 3D modeling and simulation software to simulate the sealing performance of the container to ensure that the design meets the requirements. Finally, the commodity transportation safety device demand data, the first transportation container type data and the container sealing data are integrated to form commodity transportation condition data.

[0031] Step S28: Analyze the transportation conditions of the bulk commodities according to the commodity name data and the commodity category data to obtain commodity transportation condition data.

[0032] Specifically, please refer to the sub-steps of step S28 for the detailed implementation process of this embodiment.

[0033] The present invention uses customized safety measures to identify and equip necessary safety devices for potential safety accident types of different commodities, thereby ensuring the safety of the transportation process. In particular, for liquid or gaseous energy commodities, the present invention takes into account the corrosion characteristics, realizes the adaptive selection of corrosion-resistant containers, and ensures the integrity and safety of commodity transportation. In addition, the present invention also optimizes the configuration of transport containers, determines the appropriate container type according to the characteristics of the commodities and transportation requirements, including sealing requirements, and meets the specific requirements of different commodities. Finally, through comprehensive transportation condition analysis, detailed commodity transportation condition data is formed, which provides an important basis for formulating reasonable transportation plans and cost calculations. These measures not only enhance risk management capabilities and reduce the probability of accidents, but also improve transportation efficiency and the reliability of commodity delivery.

[0034] Preferably, step S28 comprises the following steps: Step S281: If the commodity category data is basic raw material category data, physical and chemical property data of the bulk commodity are collected to obtain raw material physical property data and raw material chemical property data; Specifically, if the commodity category data is the basic raw material category data, physical and chemical property data collection is required. This involves using scientific instruments and laboratory tests to obtain the physical properties of raw materials, such as density, melting point, boiling point, hardness, and chemical properties, such as flammability, reactivity, and corrosiveness. Data recording software can be used to organize and store these test results. For example, you can use spreadsheet software to record experimental data, or use professional chemical data analysis software, such as ChemDraw or ChemWindow, to input and analyze chemical structures and properties. After the test is completed, the collected data is organized into raw material physical property data and raw material chemical property data.

[0035] Step S282: Determine the characteristics of the bulk commodity transport container according to the physical characteristic data and the chemical characteristic data of the raw materials to obtain the commodity transport container characteristic data; Specifically, the characteristics of the transport container suitable for these characteristics can be determined based on the physical property data of the raw materials and the chemical property data of the raw materials. This includes but is not limited to the material, shape, size, and compressive strength of the container. Use material selection software, such as MATWeb or Granta Design, to screen container materials suitable for the characteristics of the raw materials. At the same time, use computer-aided design (CAD) software, such as SolidWorks or AutoCAD, to design the geometry of the container to ensure that it can meet the storage and transportation requirements of the raw materials. Finally, record the characteristic data of the designed commodity transport container.

[0036] Step S283: Perform environmental sensitivity analysis on bulk commodities according to the chemical property data of raw materials to obtain commodity environmental sensitivity data; Specifically, environmental sensitivity analysis can be performed on the chemical property data of raw materials to determine the sensitivity of the product to environmental factors. This includes sensitivity assessments to temperature, humidity, light, and oxygen environmental factors. Environmental simulation software, such as ANSYS or ABAQUS, can be used to simulate the stability of raw materials under different environmental conditions. In addition, chemical kinetics simulation software, such as Chemkin or Reaxys, can be used to predict the chemical reactions that will occur in raw materials under different environmental conditions. Through these analyses, the environmental sensitivity data of the product is obtained.

[0037] Step S284: determining the meteorological conditions for safe transportation of bulk commodities according to the commodity environmental sensitivity data, and obtaining meteorological conditions data for safe transportation; Specifically, appropriate meteorological conditions can be determined based on the environmental sensitivity data of the goods. This includes but is not limited to temperature range, humidity level, and air pressure conditions. Use meteorological data analysis tools, such as Weather Analytics or ClimateData Online, to collect and analyze meteorological data on different transportation routes. Combined with the environmental sensitivity of the raw materials, determine the extreme meteorological conditions that should be avoided during transportation. For example, if the raw materials are sensitive to humidity, high humidity areas should be avoided; if they are sensitive to temperature, extreme high or low temperature environments should be avoided. Finally, the meteorological condition data for safe transportation is obtained.

[0038] Step S285: selecting the type of bulk commodity transport container according to the commodity transport container characteristic data and the commodity environmental sensitivity data, obtaining second transport container type data, and recording the safe transport meteorological condition data and the second transport container type data as commodity transport condition data; Specifically, the appropriate type of transport container can be selected by combining the commodity transport container characteristic data with the commodity environmental sensitivity data. Use logistics planning software, such as SAP Logistics or Oracle Transportation Management, to evaluate the effect of different transport container types on raw material protection. At the same time, consider the sensitivity of raw materials to meteorological conditions and select containers that can provide appropriate protection during transportation. For example, if the raw materials need to be transported at a specific temperature, a container with temperature control function needs to be selected. Finally, the data of the selected second transport container type is recorded and combined with the safe transportation meteorological conditions data to form commodity transportation condition data.

[0039] Step S286: If the commodity category data is agricultural product category data, the transportation condition analysis of the bulk commodity is performed according to the commodity name data to obtain commodity transportation condition data.

[0040] Specifically, please refer to the sub-steps of step S286 for the detailed implementation process of this embodiment.

[0041] The present invention provides solid data support for the scientific selection of transport containers and the reasonable setting of transport conditions through the understanding of the physical and chemical properties of basic raw materials. It optimizes the selection of transport containers, ensuring that the specific needs of different raw materials are met, while enhancing the analysis of the environmental sensitivity of commodities and identifying key environmental factors that affect the stability of commodities. In addition, the present invention ensures transportation safety and reduces transportation risks by determining suitable meteorological conditions. The refined transportation condition data not only provides support for accurate pricing and transportation management, but also improves the professionalism and effectiveness of agricultural product transportation, taking into account the specific attributes of perishable and seasonal agricultural products. Finally, the present invention supports sustainable logistics practices, adopts environmentally friendly transportation methods, and reduces the impact on the environment.

[0042] Preferably, step S286 includes the following steps: Step S2861: if the commodity category data is agricultural product category data, then collect agricultural product basic data for the bulk commodity according to the commodity name data to obtain agricultural product basic attribute data, wherein the agricultural product basic attribute data includes variety data and harvest date data; Specifically, if the commodity category data is agricultural product category data, it is necessary to collect basic agricultural product data. This includes using agricultural information management software or databases to record the variety and harvest date of the commodity. For example, barcode or RFID technology can be used to scan the labels on agricultural products to automatically obtain variety information and harvest dates. In addition, for information that cannot be obtained by scanning, data can be recorded by manual input using spreadsheet software such as Microsoft Excel. During the data collection process, it is necessary to ensure the accuracy and completeness of the information, which involves communication with farmers or suppliers to obtain detailed agricultural product information. After the collection is completed, the variety data and harvest date data are organized into basic attribute data of agricultural products.

[0043] Step S2862: measuring the physical properties of agricultural products for bulk commodities to obtain the physical property data of agricultural products, wherein the physical property data of agricultural products at least includes temperature sensitivity data, humidity adaptability data and gas exchange rate data; Specifically, special measuring instruments can be used to measure the physical properties of agricultural products, such as temperature recorders, hygrometers and gas analyzers. For example, a data recorder can be used to continuously monitor the temperature changes of agricultural products under different environmental conditions to determine their temperature sensitivity. For the acquisition of humidity adaptability data, a high-precision humidity sensor can be used for measurement. The measurement of gas exchange rate needs to be carried out in a controlled environment, using gas analysis equipment to monitor the respiration of agricultural products. Finally, the physical property data of agricultural products are obtained, wherein the physical property data of agricultural products at least includes temperature sensitivity data, humidity adaptability data and gas exchange rate data.

[0044] Step S2863: Analyze the chemical properties of bulk commodities to obtain chemical property data of agricultural products; Specifically, agricultural products can be analyzed for chemical properties, which involve nutrients, pesticide residues, and potential chemical pollutants. Analytical instruments such as high performance liquid chromatography (HPLC) and gas chromatography-mass spectrometry (GC-MS) can be used to detect the chemical components in agricultural products. For example, HPLC can be used to analyze sugars, organic acids and other components in agricultural products, while GC-MS is suitable for detecting pesticide residues and volatile compounds. In the process of chemical property analysis, professional chemical analysis software is needed to process and interpret the data obtained by the instrument, such as Chromeleon or MassHunter. Through these analyses, the chemical property data of agricultural products can be obtained.

[0045] Step S2864: Conduct agricultural product microbial testing on bulk commodities to obtain agricultural product microbial community data; Specifically, it can be carried out in a laboratory environment, using microbial culture and analysis techniques. First, by sampling and preparing agricultural product samples, they are then inoculated into culture media using sterile techniques. Next, microorganisms are observed and counted using a microscope, or automated microbial analysis systems, such as BactiFlow or VITEK 2, are used for rapid detection and identification. In addition, modern molecular biology techniques, such as PCR and gene sequencing, can be used to identify and analyze specific types and community structures of microorganisms. Through these tests, agricultural product microbial community data can be obtained.

[0046] Step S2865: Based on the preset preservation technology library and according to the physical property data of agricultural products, the chemical property data of agricultural products and the microbial community of agricultural products, the preservation technology parameters of the transportation process are obtained; Specifically, a database containing different preservation technical parameters and technical characteristics can be established, and this database can be stored using a relational database management system, such as MySQL or PostgreSQL. Then, use data analysis software, such as Python's Pandas library, to analyze the physical properties, chemical properties, and microbial community data of agricultural products. Based on these data, determine the appropriate preservation technology by querying the preservation technology library. For example, if the agricultural product has a high respiration rate, it is necessary to select a preservation technology with the function of adjusting the gas concentration. Finally, record the selected preservation technology parameters to obtain the preservation technology parameters during transportation.

[0047] Step S2866: simulate and predict the preservation effect of bulk commodities according to the agricultural product basic attribute data, agricultural product physical property data, agricultural product chemical property data, agricultural product microbial community data and transportation process preservation technical parameters to obtain preservation effect prediction data; Specifically, simulation software can be used to achieve this, such as Comsol Multiphysics or ANSYS, which can simulate the preservation effect of agricultural products under different preservation technologies. First, input the basic attribute data, physical property data, chemical property data, microbial community data and preservation technology parameters of agricultural products into the simulation model. Then, run the simulation model to simulate the preservation effect of agricultural products during transportation. The simulation results, such as temperature changes, humidity changes and microbial growth, will be used to predict the preservation effect. These prediction data are organized into preservation effect prediction data.

[0048] Step S2867: selecting a transport fresh-keeping device for the bulk commodity according to the fresh-keeping effect prediction data to obtain transport fresh-keeping device data, wherein the transport fresh-keeping device data at least includes fresh-keeping device type data, temperature control parameters, and humidity control parameters; Specifically, you can use spreadsheet software or professional logistics planning software to analyze the fresh-keeping effect prediction data and determine the type of fresh-keeping device required. For example, if the prediction data shows that temperature and humidity need to be strictly controlled, you need to choose a fresh-keeping device with precise temperature and humidity control functions. Then, based on these requirements, query the fresh-keeping device database or use online resources to select a specific device. Finally, record the type of fresh-keeping device selected, temperature control parameters, and humidity control parameters to form the transportation fresh-keeping device data.

[0049] Step S2868: Select the transport container type for the bulk commodity according to the transport freshness-keeping device data, obtain the third transport container type data, and record the transport freshness-keeping device data and the third transport container type data as commodity transport condition data.

[0050] Specifically, CAD software or logistics simulation software can be used to evaluate the adaptability of different container types to the fresh-keeping device. Then, the temperature and humidity control requirements of the fresh-keeping device are considered according to the transport fresh-keeping device data, and a container that can meet these requirements is selected. For example, if the fresh-keeping device needs to maintain a specific temperature range, a container with an insulation layer needs to be selected. In addition, the sealing, durability and easy handling characteristics of the container also need to be considered. After the selection is completed, the type data of the third transport container is recorded, and the transport fresh-keeping device data is integrated with the third transport container type data to form the commodity transportation condition data.

[0051] The present invention provides a solid foundation for the scientific transportation and preservation of agricultural products by comprehensively collecting basic data of agricultural products, carefully analyzing their physical properties, deeply understanding their chemical properties, and accurately evaluating microbial communities. The intelligent matching of preservation technology combined with simulation prediction not only improves the preservation effect, but also provides data support for adjusting preservation measures. The freshness and safety of agricultural products during transportation are further ensured by accurately selecting preservation devices and optimized transport containers. These measures comprehensively improve the transportation quality of agricultural products, reduce losses, and support the efficient operation of the agricultural product supply chain by ensuring the stability of the supply chain.

[0052] Preferably, step S3 comprises the following steps: Step S31: if the commodity transportation weight data is greater than or equal to the preset bulk transportation mass threshold, the railway transportation mode, the sea transportation mode, the road transportation mode and the inland waterway transportation mode are used as the initial commodity transportation mode set; Specifically, if the commodity transportation weight data is greater than or equal to the preset bulk transportation mass threshold, the rail, sea, road and inland waterway transportation modes are marked as the initial commodity transportation mode set. This set can be represented in the program through an array or list data structure.

[0053] Step S32: If the commodity transportation weight data is less than the preset bulk transportation mass threshold, the air transportation mode, highway transportation mode, light rail transportation mode and short-distance shipping mode are used as the initial commodity transportation mode set; Specifically, if the commodity transportation weight data is less than the preset bulk transportation mass threshold, aviation, highway, light rail and short-sea shipping are used as the initial commodity transportation mode set.

[0054] Step S33: Screening the initial commodity transportation mode set according to the commodity transportation condition data (including transportation container type data and container sealing data or transportation meteorological environment data or transportation preservation device) to obtain the target commodity transportation mode; Specifically, data analysis tools, such as Python's Pandas library, can be used to process and analyze the transportation condition data of commodities, which include but are not limited to the physical properties, chemical properties, and transportation safety requirements of the commodities. Then, the initial set of transportation modes is screened based on these condition data using decision trees, logistic regression, or expert system methods. For example, if the commodity is highly corrosive, certain incompatible transportation modes need to be excluded. In addition, multi-objective optimization algorithms, such as genetic algorithms or simulated annealing algorithms, can be used to select the most cost-effective transportation mode. Finally, the screened transportation mode will be used as the target commodity transportation mode.

[0055] Step S34: Based on the logistics service provider database, the logistics service provider is matched and selected for the bulk commodities according to the shipping address data, the receiving address data, the commodity transportation condition data and the target commodity transportation mode to obtain the target logistics service provider data.

[0056] Specifically, please refer to the sub-steps of step S34 for the detailed implementation process of this embodiment.

[0057] The present invention significantly improves transportation efficiency and ensures the adaptability and flexibility of transportation methods by intelligently selecting transportation modes that match the characteristics of the goods. The further screening process accurately determines the target transportation mode according to the specific transportation conditions, which enhances the pertinence of this solution. The precise matching of service providers by utilizing the logistics service provider database ensures high quality and efficiency of logistics services. At the same time, this process fully considers the safety requirements of the goods, reduces transportation risks, and improves safety. The present invention also enhances service personalization, meets the market demand for differentiated logistics services, and improves transportation response speed by quickly responding to market changes. Finally, the present invention strengthens the coordination of various links in the supply chain and improves the operational efficiency and responsiveness of the entire supply chain.

[0058] Preferably, step S34 includes the following steps: Step S341: performing condition matching screening on the logistics service provider database according to the commodity transportation condition data and the target commodity transportation mode to obtain a preliminary matching service provider list; Specifically, the database query language SQL can be used to query the logistics service provider database to retrieve those service providers that can meet the conditions for the transportation of goods. For example, if the goods require special temperature control or safety measures, the query will include these conditions as screening parameters. Then, using data matching tools, such as Python's Pandas library, the target commodity transportation mode is matched with the transportation capacity of the service provider to determine which service providers can provide the required transportation services. Through these screening conditions, a preliminary list of matching service providers is generated, which can be stored and managed in the form of a spreadsheet or database table.

[0059] Step S342: Defining the transportation area of ​​the bulk commodity according to the shipping address data and the receiving address data to obtain target transportation range data; Specifically, the shipping address and delivery address data can be entered through geographic information system (GIS) software, such as ArcGIS or QGIS. The address is converted into geographic coordinates using the geocoding function of the GIS software. Then, the transportation route is drawn on the map based on these coordinates, and the key geographic areas through which the transportation path passes are identified. By analyzing these areas, the starting point, end point and any transfer points of the transportation are determined, thereby defining the target transportation range. This range can be defined through the regional analysis tool of the GIS software and exported as the target transportation range data.

[0060] Step S343: searching the service scope of the logistics service providers in the preliminary matching service provider list based on the logistics service provider database to obtain a service provider service scope data set; Specifically, you can use database management tools, such as Microsoft SQL Server Management Studio, to access the logistics service provider database. Then, based on the target transportation range data, execute SQL queries to retrieve the service range information of the service provider. This includes but is not limited to the geographic areas that the service provider can cover, the locations of the service sites or branches it owns. Use data visualization tools, such as Tableau or Power BI, to compare and analyze the service range data with the target transportation range data to identify which service providers' service ranges match the target transportation range. Finally, organize this information into a service provider service range data set.

[0061] Step S344: screening the service provider range of the preliminary matching service provider list according to the target transportation range data and the service provider service range data set to obtain a screened service provider list; Specifically, you can use spreadsheet software such as Microsoft Excel or database query tools to load the target transportation range data and the service provider service range data set. Then, by writing SQL queries or using Excel's VLOOKUP and other functions, match the two data sets to filter out those service providers whose service ranges fully or partially cover the target transportation range. Next, use data filtering tools, such as Python's Pandas library, to further analyze and filter the service provider list and exclude those service providers whose service ranges do not meet the requirements. Finally, save the filtered service provider list as a filtered service provider list.

[0062] Step S345: performing service site distribution statistics on the logistics service providers in the screened service provider list based on the logistics service provider database to obtain a service site distribution data set; Specifically, you can access the logistics service provider database and use SQL queries or NoSQL queries (depending on the database type) to retrieve the service site information of each service provider. Then, use GIS software or map service APIs, such as Google Maps API or Bing Maps API, to visualize the geographic locations of the service sites and calculate their geographical distribution. In addition, you can use statistical analysis software, such as SPSS or R language, to analyze the distribution density and coverage of service sites. The statistical results are organized into a service site distribution data set, which includes the number of service sites for each service provider, their location, and the proximity to the main transportation routes.

[0063] Step S346: Optimize the distance of service providers according to the preset service site distance threshold, shipping address data, service site distribution data set and the screening service provider list to obtain target logistics service provider data.

[0064] Specifically, a service site distance threshold can be determined, which can be set based on transportation costs, time efficiency or other business rules. Then, using GIS software or online map service API, the distance from each service site to the shipping address is calculated based on the shipping address data and the service site distribution data set of the service provider. Next, using data analysis tools such as Excel's data analysis toolkit or Python's SciPy library, the distance data is sorted and filtered to select those service sites whose distances are within the preset threshold. Finally, based on these distance data, the best logistics service provider is selected from the list of screened service providers, and the detailed information of these service providers and their service site data are integrated into the target logistics service provider data.

[0065] The present invention quickly screens out service providers that meet the conditions for transporting goods through efficient preliminary matching of logistics service provider selection. The targeted selection of service providers is ensured by clearly defined transportation areas, and the applicability of services is further improved by accurate service range retrieval. Through refined screening of the service provider range, combined with optimized statistics of service site distribution, data support is provided for evaluating the service capabilities and response speed of service providers. Through the intelligent optimization of service provider distance strategy, the nearest service provider is selected according to preset thresholds and shipping addresses, effectively reducing transportation costs and improving efficiency. The accurately determined target logistics service provider takes into account the service scope, service site distribution, and service distance, ensuring high quality and efficiency of logistics services.

[0066] Preferably, step S4 comprises the following steps: Step S41: extracting global transport paths from the logistics service provider database according to the target logistics service provider data, and extracting transport modes corresponding to the transport paths to obtain a global transport path set and a path-transport mode set; Specifically, a database query tool, such as SQL Server Management Studio, can be used to execute SQL queries to retrieve transportation path data from the logistics service provider database. The query will locate relevant records based on the ID or other unique identifier of the target logistics service provider. Then, a data extraction tool, such as Python's Pandas library, is used to extract the global transportation path set and the transportation mode corresponding to each path from the database. This data can be organized into a table format that contains the starting point, end point, waypoints, and transportation mode type of the path. Finally, this data is stored as a global transportation path set and a path-transportation mode set.

[0067] Step S42: Graphically visualize the global transport path set to obtain a service provider transport network coverage map; Specifically, the global transport path set can be imported into a graphical visualization tool such as Tableau or QGIS. Then, the drawing function of these tools is used to draw the transport routes on the map based on the path data, and to distinguish different modes of transport with different colors or line styles according to the mode of transport, for example, rail is represented by one line, while sea is represented by another style of line. In addition, interactive elements such as pop-ups can be added to display detailed information on the path and mode. After the visualization is completed, a service provider transportation network overlay is generated.

[0068] Step S43: marking geographic nodes on the service provider's transportation network coverage map according to the shipping address data and the receiving address data to obtain a marked service provider's transportation network coverage map; Specifically, you can use GIS software, such as ArcGIS, to open the service provider's transportation network coverage map. Then, based on the shipping address and delivery address data, use the software's annotation tools to accurately mark these nodes on the map. You can use different icons or colors to distinguish different types of nodes, for example, use a green icon to represent the shipping location and a red icon to represent the delivery location. In addition, you can add annotation text to display the name of the node or other relevant information. After the annotation is completed, you will get the annotation service provider's transportation network coverage map.

[0069] Step S44: screening the mode and path of the transportation network coverage map of the marked service provider according to the transportation mode and path-transportation mode set of the target commodity to obtain a preliminary transportation path set; Specifically, data analysis tools such as Excel or Python can be used to load the target commodity's transportation mode data and path-transportation mode set. Then, by writing a screening script to compare the target transportation mode with each path in the path set, find the transportation path that meets the requirements. For example, if the target commodity needs to be transported by sea mode, the screening conditions will include all paths that contain sea sections. Next, use the query function of the GIS software to select the corresponding path on the marked service provider transportation network coverage map based on the screening results. Finally, these screened paths are combined into a preliminary transportation path set.

[0070] Step S45: collecting historical traffic flow data for the preliminary transport path set to obtain a historical path traffic flow data set, and identifying traffic congestion sections for the preliminary transport path set based on the historical path traffic flow data set to obtain a congestion risk section data set; Specifically, traffic flow analysis software, such as TransCAD or INRIX, can be used to extract traffic flow data within a specific time period from a historical database. These data include but are not limited to the number of vehicles, average speed, and accident reports. Then, using data visualization tools such as Tableau or Python's Matplotlib library, the traffic flow data is geospatial analyzed with the preliminary transportation route set to identify the pattern and frequency of traffic congestion. By setting a threshold, such as the average speed within a specific time period is lower than a certain value, the congestion risk section is automatically identified. Finally, this information is organized into a congestion risk section dataset.

[0071] Step S46: intelligently filter the congested sections of the preliminary transport path set according to the congestion risk section data set to obtain an optimized transport path set, and perform shortest path screening on the optimized transport path set to obtain commodity transport path data; Specifically, path optimization algorithms, such as the Dijkstra algorithm or the A* search algorithm, can be used in combination with a congestion risk section dataset to implement path filtering logic through programming. For example, Python's NetworkX library can be used to implement these algorithms and exclude paths that contain congestion risk sections. Next, the shortest path calculation is performed on the remaining paths to determine the optimal transportation route. In addition, network analysis tools in GIS software, such as ArcGIS's Network Analyst, can be used to perform shortest path analysis of the path. Finally, the path set after congestion filtering and shortest path optimization is used as the optimized transportation path set, and the shortest path selected is used as the commodity transportation path data.

[0072] Step S47: Analyze the transportation resource demand of bulk commodities according to the commodity transportation route data and the target commodity transportation mode to obtain transportation resource demand data.

[0073] Specifically, please refer to the sub-steps of step S47 for the detailed implementation process of this embodiment.

[0074] The present invention ensures the breadth and diversity of path selection by comprehensively extracting the global transportation paths of the target logistics service providers, and at the same time uses graphic visualization technology to create an intuitive transportation network coverage map. The practicality and accuracy of the network coverage map are enhanced through precise geographic node annotation. The rationality and applicability of the transportation path are further ensured through efficient mode path screening. By intelligently identifying and optimizing sections with congestion risks, traffic delays are effectively avoided and transportation efficiency is improved. Based on the optimized path data, accurate transportation resource demand analysis ensures the efficiency of resource allocation. These measures work together to reduce transportation costs, improve transportation reliability and flexibility, and can adapt to real-time traffic conditions and demand changes.

[0075] Preferably, step S47 includes the following steps: Step S471: extracting path information from the commodity transportation path data to obtain a path key data set, wherein the path key data set includes transportation path length data and transportation transfer times; Specifically, you can use GIS software, such as ArcGIS or QGIS, to load the commodity transportation path data and use the software's measurement tools to determine the length of each path. Then, by analyzing the path data, identify the transfer points on the path and record the number of transfers. You can use network analysis tools to assist in identifying transfer points. These tools can identify intersections or specific types of nodes in the path, such as transportation hubs. Then, organize the path lengths and transfer times into a path key data set.

[0076] Step S472: Determine the transport vehicle model of the bulk commodity according to the commodity transportation weight data and the target commodity transportation mode (the target commodity transportation mode is any one of the railway transportation mode, the ocean transportation mode, the road transportation mode, the inland waterway transportation mode, the air transportation mode, the highway transportation mode, the light railway transportation mode and the short-distance ocean transportation mode) to obtain the transport vehicle model data; Specifically, Microsoft Excel or SQL Server spreadsheets and database systems can be used to manage the shipping weight data of goods. For the target commodity transportation mode, such as rail, sea, road, inland waterway transportation, and aviation, refer to the transportation industry standards and specifications in professional logistics software and online databases. Subsequently, Python's Pandas library is used for data analysis, and the script automatically matches the shipping weight with the carrying capacity of different transportation modes to screen out the appropriate transportation vehicle model. For example, for goods suitable for road transportation, the script will retrieve the appropriate truck model from the database. Further, genetic algorithms or linear programming software multi-objective optimization tools are used to find a balance between cost, time, and carrying capacity, and select the most optimized transportation vehicle. Finally, the obtained transportation vehicle model data will be screened and optimized.

[0077] Step S473: Calculate the transportation energy demand of the bulk commodity according to the transportation route length data and the transportation tool model data to obtain transportation energy demand data; Specifically, you can use transportation simulation software, such as Vehicle Routing Problem (VRP) software, to input the transportation route length and transportation vehicle model data. Then, calculate the total amount of energy required based on the energy efficiency of the transportation vehicle and the route length. For example, you can use the fuel consumption rate per unit distance multiplied by the route length to estimate the fuel demand. In addition, consider the energy type of different transportation modes, such as the electrical energy demand of electric vehicles or the fuel demand of ships. Organize the calculation results into transportation energy demand data.

[0078] Step S474: Analyze the transit resource demand of bulk commodities according to the number of transportation transits to obtain transit resource demand data, and record the transportation vehicle model data, transportation energy demand data and transit resource demand data as transportation resource demand data.

[0079] Specifically, the resources required for each transfer point, such as handling equipment, storage space, and operators, can be evaluated based on the number of transfers in the path key data set. Then, use logistics planning software or spreadsheet software to calculate and record the resource requirements for each transfer point. For example, if the number of transfers is large, more handling equipment or longer storage time is required. Next, the transportation vehicle model data, transportation energy demand data, and transfer resource demand data are integrated to form a transportation resource demand data set.

[0080] The present invention provides a detailed data basis for resource allocation by accurately extracting key information of the transportation path, such as path length and number of transfers, so that the selection of transportation tools is more reasonable and efficient matching of tools and needs is ensured. Accurate calculation of energy demand is based on these data, which optimizes energy use and helps cost control. Efficient planning of transit resources is based on a clear analysis of the number of transfers, providing a scientific basis for resource allocation in the transit link. By comprehensively analyzing the demand for transportation resources, the transportation tools, energy demand and transit resources are integrated, providing solid data support for the refined management of logistics services. The present invention effectively reduces logistics costs, improves the predictability of the transportation process, enhances the adaptability of the transportation system, and makes transportation services more flexible and efficient.

[0081] Preferably, step S5 comprises the following steps: Step S51: extracting the unit price of transportation services from the logistics service provider database according to the transportation mode of the target commodity to obtain the unit price data of transportation services; Specifically, you can access the logistics service provider database and use database query tools such as SQL Server Management Studio or MongoDB Compass to perform query operations based on the specific requirements of the target commodity transportation mode. The query will locate the data field containing the unit price information of the transportation service. Then, use data extraction tools such as Python scripts with the Pandas library to crawl and organize the unit price data of the transportation service from the database. This data includes quotes from different service providers, service types, and rates based on distance or weight.

[0082] Step S52: Collect multi-dimensional resource costs of the transportation resource demand data to obtain a transportation resource cost data set; Specifically, based on the transportation resource demand data, you can determine the cost dimensions that need to be evaluated, such as transportation tool costs, energy costs, and transit costs. Then, use cost analysis tools, such as Microsoft Excel or professional logistics cost analysis software, to collect the market price or contract price of each resource. It is necessary to obtain data from multiple data sources, including but not limited to market research reports, supplier quotations, or internal cost databases. Next, integrate the collected cost data into the transportation resource cost data set, which will contain the unit cost and demand for each resource.

[0083] Step S53: Based on the commodity transportation route data, transportation service unit price data, transportation resource demand data and transportation resource cost data set, a multi-factor cost composition model is constructed for the bulk commodity to obtain a commodity transportation cost composition model; Specifically, statistical analysis software, such as SPSS or R language, can be used to take commodity transportation path data, transportation service unit price data, transportation resource demand data and transportation resource cost data set as input variables. Then, a cost model is established based on the logic and formula of logistics cost calculation. This model contains multiple components, such as fixed costs, variable costs, distance costs, and the relationship between them and input variables. Then, optimization algorithms, such as linear programming or integer programming, are used to determine the transportation plan with the lowest cost, and finally a commodity transportation cost composition model is obtained.

[0084] Step S54: converting the commodity transportation cost composition model into a parameterized formula to obtain a transportation cost calculation formula; Specifically, mathematical modeling software, such as MATLAB or Mathematica, can be used to construct mathematical expressions based on the various cost elements and the relationships between them in the commodity transportation cost composition model. This involves determining fixed cost parameters, variable cost parameters, distance cost parameters, and defining how they interact to calculate the total cost. Next, these mathematical expressions are organized into one or more formulas that will serve as the basis for transportation cost calculation. The formulas are then verified to ensure that they accurately reflect the cost composition in different situations. Finally, these formulas are documented and embedded in the transportation cost calculation tool to prepare for the next step of building a pricing engine.

[0085] Step S55: Based on the transportation cost calculation formula, a transportation pricing engine is constructed for bulk commodities by combining the target commodity transportation mode and commodity transportation path data to obtain a dynamic transportation pricing engine.

[0086] Specifically, you can use programming languages ​​such as Python or Java to write code based on the transportation cost calculation formula to achieve the automatic cost calculation function. Then, combined with the target commodity transportation mode and commodity transportation path data, develop a user interface that allows users to enter or select these parameters and display the calculation results. You can use a web framework such as Django or Flask to create an online pricing tool, or use a desktop application framework such as Electron or Qt to create a standalone application. Next, integrate the pricing engine with the logistics information system to ensure that it can access real-time transportation data and market information to provide accurate pricing results. Use APIs to obtain necessary data such as fuel prices and the availability of transportation tools, and dynamically adjust the output of the pricing engine based on this data. Finally, conduct thorough testing, including unit testing, integration testing, and user acceptance testing, to ensure the accuracy and reliability of the pricing engine. After the test is completed, the dynamic transportation pricing engine is deployed to the production environment for use by logistics planners and customers to achieve real-time and accurate transportation cost calculation and pricing.

[0087] The present invention provides a clear pricing reference for the logistics service market through transparent transportation service pricing, reducing pricing uncertainty. By fully considering multi-dimensional resource costs, the comprehensiveness of cost calculation is ensured, and at the same time, an accurate multi-factor cost composition model is constructed to improve the accuracy of cost estimation. Through the construction of a dynamic pricing engine, the automation and real-time nature of cost calculation is achieved, and the optimization and efficiency of resource allocation are improved. The present invention effectively reduces logistics costs and improves the cost-effectiveness of services. In addition, the accurate cost calculation process enhances customer trust in logistics services and supports flexible adjustment of pricing strategies according to market changes.

[0088] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0089] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for constructing a pricing engine based on a bulk commodity transportation cost calculation formula, characterized in that: The following steps are involved: Step S1: Building a logistics service provider database; Data collection and attribute labeling of bulk commodities are performed to obtain a commodity attribute metadata set, wherein the commodity attribute metadata set includes commodity name data, commodity transportation weight data, shipping address data, and receiving address data; Step S2: Analyze the transportation conditions of bulk commodities according to commodity name data, commodity transportation weight data, shipping address data and receiving address data to obtain commodity transportation condition data; Step S3: selecting a transportation mode for the bulk commodity according to the commodity transportation condition data and the commodity transportation weight data to obtain a target commodity transportation mode; matching and selecting a logistics service provider for the bulk commodity according to the commodity transportation condition data and the target commodity transportation mode based on a logistics service provider database to obtain target logistics service provider data; Step S4: Based on the logistics service provider database and according to the target commodity transportation mode and the target logistics service provider data, the transportation path analysis of the bulk commodity is performed to obtain the commodity transportation path data; based on the commodity transportation path data, the transportation resource demand analysis of the bulk commodity is performed to obtain the transportation resource demand data; Step S5: construct a transportation cost calculation formula for bulk commodities according to the target commodity transportation mode, commodity transportation route data and transportation resource demand data to obtain the transportation cost calculation formula; construct a transportation pricing engine for bulk commodities based on the transportation cost calculation formula to obtain a dynamic transportation pricing engine.

2. The method for constructing a pricing engine based on a bulk commodity transportation cost calculation formula according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Design an attribute table for the logistics service provider to obtain the service provider attribute table; Step S12: constructing a database architecture for the logistics service provider according to the service provider attribute table to obtain a provider database architecture; Step S13: collecting attribute data of the logistics service provider according to the service provider attribute table to obtain a service provider attribute data set; Step S14: performing data mapping and filling on the service provider attribute table based on the service provider attribute data set to obtain a filled service provider attribute table; Step S15: Based on the filled service provider attribute table, the logistics service provider database is integrated according to the provider database architecture to obtain a logistics service provider database; Step S16: Data collection and attribute labeling are performed on bulk commodities to obtain a commodity attribute metadata set, wherein the commodity attribute metadata set includes commodity name data, commodity transportation weight data, shipping address data, and receiving address data.

3. The method for constructing a pricing engine based on a bulk commodity transportation cost calculation formula according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: Identify the commodity category of the bulk commodity according to the commodity name data to obtain commodity category data, wherein the commodity category data is any one of energy category data, basic raw material category data and agricultural product category data; Step S22: if the commodity category data is energy category data, then the bulk commodity is identified as an energy commodity according to the commodity name data to obtain energy commodity type data, wherein the energy commodity type data is any one of liquid energy, gaseous energy and solid energy; otherwise, directly execute step S28; Step S23: Analyze the types of potential transportation safety accidents of bulk commodities according to the commodity name data to obtain data on the types of potential transportation safety accidents; Step S24: Determine the safety device requirements for bulk commodities according to the data on the types of potential transportation safety accidents, and obtain commodity transportation safety device requirement data; Step S25: If the energy commodity type data indicates that the bulk commodity is liquid energy or gaseous energy, the corrosion characteristics of the bulk commodity are collected to obtain commodity corrosion characteristic data, and corrosion-resistant containers are selected for the bulk commodity according to the commodity corrosion characteristic data to obtain corrosion resistance data of the transport container; otherwise, step S26 is directly executed; Step S26: determining the transport container type of the bulk commodity according to the corrosion resistance data of the transport container to obtain first transport container type data; Step S27: determining the sealing requirement of the bulk commodity according to the commodity name data, obtaining container sealing data, and recording the commodity transportation safety device requirement data, the first transportation container type data and the container sealing data, or the first transportation container type data and the container sealing data as commodity transportation condition data; Step S28: Analyze the transportation conditions of the bulk commodities according to the commodity name data and the commodity category data to obtain commodity transportation condition data.

4. The method for constructing a pricing engine based on a bulk commodity transportation cost calculation formula according to claim 3, characterized in that: Step S28 includes the following steps: Step S281: If the commodity category data is basic raw material category data, physical and chemical property data of the bulk commodity are collected to obtain raw material physical property data and raw material chemical property data; Step S282: Determine the characteristics of the bulk commodity transport container according to the physical characteristic data and the chemical characteristic data of the raw materials to obtain the commodity transport container characteristic data; Step S283: Perform environmental sensitivity analysis on bulk commodities according to the chemical property data of raw materials to obtain commodity environmental sensitivity data; Step S284: determining the meteorological conditions for safe transportation of bulk commodities according to the commodity environmental sensitivity data, and obtaining meteorological conditions data for safe transportation; Step S285: selecting the type of bulk commodity transport container according to the commodity transport container characteristic data and the commodity environmental sensitivity data, obtaining second transport container type data, and recording the safe transport meteorological condition data and the second transport container type data as commodity transport condition data; Step S286: If the commodity category data is agricultural product category data, the transportation condition analysis of the bulk commodity is performed according to the commodity name data to obtain commodity transportation condition data.

5. The method for constructing a pricing engine based on a bulk commodity transportation cost calculation formula according to claim 4, characterized in that: Step S286 includes the following steps: Step S2861: if the commodity category data is agricultural product category data, then collect agricultural product basic data for the bulk commodity according to the commodity name data to obtain agricultural product basic attribute data, wherein the agricultural product basic attribute data includes variety data and harvest date data; Step S2862: measuring the physical properties of agricultural products for bulk commodities to obtain the physical property data of agricultural products, wherein the physical property data of agricultural products at least includes temperature sensitivity data, humidity adaptability data and gas exchange rate data; Step S2863: Analyze the chemical properties of bulk commodities to obtain chemical property data of agricultural products; Step S2864: Conduct agricultural product microbial testing on bulk commodities to obtain agricultural product microbial community data; Step S2865: Based on the preset preservation technology library and according to the physical property data of agricultural products, the chemical property data of agricultural products and the microbial community of agricultural products, the preservation technology parameters of the transportation process are obtained; Step S2866: simulate and predict the preservation effect of bulk commodities according to the agricultural product basic attribute data, agricultural product physical property data, agricultural product chemical property data, agricultural product microbial community data and transportation process preservation technical parameters to obtain preservation effect prediction data; Step S2867: selecting a transport fresh-keeping device for the bulk commodity according to the fresh-keeping effect prediction data to obtain transport fresh-keeping device data, wherein the transport fresh-keeping device data at least includes fresh-keeping device type data, temperature control parameters, and humidity control parameters; Step S2868: Select the transport container type for the bulk commodity according to the transport freshness-keeping device data, obtain the third transport container type data, and record the transport freshness-keeping device data and the third transport container type data as commodity transport condition data.

6. The method for constructing a pricing engine based on a bulk commodity transportation cost calculation formula according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: If the commodity transportation weight data is greater than or equal to the preset bulk transportation mass threshold, the railway transportation mode, the sea transportation mode, the road transportation mode and the inland waterway transportation mode are used as the initial commodity transportation mode set; Step S32: If the commodity transportation weight data is less than the preset bulk transportation mass threshold, the air transportation mode, highway transportation mode, light rail transportation mode and short-distance shipping mode are used as the initial commodity transportation mode set; Step S33: screening the initial commodity transportation mode set according to the commodity transportation condition data to obtain the target commodity transportation mode; Step S34: Based on the logistics service provider database, the logistics service provider is matched and selected for the bulk commodities according to the shipping address data, the receiving address data, the commodity transportation condition data and the target commodity transportation mode to obtain the target logistics service provider data.

7. The method for constructing a pricing engine based on a bulk commodity transportation cost calculation formula according to claim 6, characterized in that: Step S34 includes the following steps: Step S341: performing condition matching screening on the logistics service provider database according to the commodity transportation condition data and the target commodity transportation mode to obtain a preliminary matching service provider list; Step S342: Defining the transportation area of ​​the bulk commodity according to the shipping address data and the receiving address data to obtain target transportation range data; Step S343: searching the service scope of the logistics service providers in the preliminary matching service provider list based on the logistics service provider database to obtain a service provider service scope data set; Step S344: screening the service provider range of the preliminary matching service provider list according to the target transportation range data and the service provider service range data set to obtain a screened service provider list; Step S345: performing service site distribution statistics on the logistics service providers in the screened service provider list based on the logistics service provider database to obtain a service site distribution data set; Step S346: Optimize the distance of service providers according to the preset service site distance threshold, shipping address data, service site distribution data set and the screening service provider list to obtain target logistics service provider data.

8. The method for constructing a pricing engine based on a bulk commodity transportation cost calculation formula according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: extracting global transport paths from the logistics service provider database according to the target logistics service provider data, and extracting transport modes corresponding to the transport paths to obtain a global transport path set and a path-transport mode set; Step S42: Graphically visualize the global transport path set to obtain a service provider transport network coverage map; Step S43: marking geographic nodes on the service provider's transportation network coverage map according to the shipping address data and the receiving address data to obtain a marked service provider's transportation network coverage map; Step S44: screening the mode and path of the transportation network coverage map of the marked service provider according to the transportation mode and path-transportation mode set of the target commodity to obtain a preliminary transportation path set; Step S45: collecting historical traffic flow data for the preliminary transport path set to obtain a historical path traffic flow data set, and identifying traffic congestion sections for the preliminary transport path set based on the historical path traffic flow data set to obtain a congestion risk section data set; Step S46: intelligently filter the congested sections of the preliminary transport path set according to the congestion risk section data set to obtain an optimized transport path set, and perform shortest path screening on the optimized transport path set to obtain commodity transport path data; Step S47: Analyze the transportation resource demand of bulk commodities according to the commodity transportation route data and the target commodity transportation mode to obtain transportation resource demand data.

9. The method for constructing a pricing engine based on a bulk commodity transportation cost calculation formula according to claim 8, characterized in that: Step S47 includes the following steps: Step S471: extracting path information from the commodity transportation path data to obtain a path key data set, wherein the path key data set includes transportation path length data and transportation transfer times; Step S472: determining the transport vehicle model of the bulk commodity according to the commodity transportation weight data and the target commodity transportation mode, and obtaining the transport vehicle model data; Step S473: Calculate the transportation energy demand of the bulk commodity according to the transportation route length data and the transportation tool model data to obtain transportation energy demand data; Step S474: Analyze the transit resource demand of bulk commodities according to the number of transportation transits to obtain transit resource demand data, and record the transportation vehicle model data, transportation energy demand data and transit resource demand data as transportation resource demand data.

10. The method for constructing a pricing engine based on a bulk commodity transportation cost calculation formula according to claim 1, characterized in that: Step S5 includes the following steps: Step S51: extracting the unit price of transportation services from the logistics service provider database according to the transportation mode of the target commodity to obtain the unit price data of transportation services; Step S52: Collect multi-dimensional resource costs of the transportation resource demand data to obtain a transportation resource cost data set; Step S53: Based on the commodity transportation route data, transportation service unit price data, transportation resource demand data and transportation resource cost data set, a multi-factor cost composition model is constructed for the bulk commodity to obtain a commodity transportation cost composition model; Step S54: converting the commodity transportation cost composition model into a parameterized formula to obtain a transportation cost calculation formula; Step S55: Based on the transportation cost calculation formula, a transportation pricing engine is constructed for bulk commodities by combining the target commodity transportation mode and commodity transportation path data to obtain a dynamic transportation pricing engine.

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