Multi-category non-ferrous metal cargo source intelligent matching scheduling method and system

Abstract

The application discloses a multi-category non-ferrous metal cargo source intelligent matching scheduling method and system, and relates to the field of cargo source matching. The method comprises the following steps: establishing a transportation constraint rule library containing an intermetallic compatibility matrix, determining the co-loading, isolated loading or non-co-loading state based on the standard electrode potential difference value, and marking the isolation level; receiving and verifying the order, and outputting the standardized order object; executing three-level filtering, and gradually screening the feasible matching scheme based on the rule library; constructing a safety risk assessment model, and calculating the comprehensive risk score by comprehensively considering five risk factors including metal chemical properties, transportation distance, seasonal climate, vehicle condition and in-transit monitoring level; and preferentially recommending the scheme with a score lower than the threshold value through the risk-oriented priority recommendation mechanism. The system comprises a rule library management module, an order preprocessing module, a three-level filtering module, a safety risk assessment module and a risk-oriented recommendation module. The application effectively avoids the non-ferrous metal mixed loading compatibility risk, and improves the safety and intelligent level of transportation scheduling.

Description

Technical Field

[0001] This application relates to the field of non-ferrous metal supply matching and processing, specifically to a method and system for intelligent matching and scheduling of multiple types of non-ferrous metal supplies. Background Technology

[0002] Non-ferrous metal logistics transportation involves vehicle scheduling and matching of multiple types of goods. Due to the significant differences in chemical properties among different non-ferrous metals, there are strict requirements for the isolation of loading between some metal combinations, and even an absolute prohibition against co-loading. How to effectively avoid the compatibility risks between these goods in the process of traditional vehicle scheduling and matching methods has become a technical bottleneck that urgently needs to be overcome.

[0003] Common vehicle scheduling and matching methods generally construct matching rules based on general conditions such as vehicle type, load capacity, transportation route, and time window, and conduct preliminary screening and scheme recommendation for candidate vehicles. However, the entire matching decision-making process completely lacks a systematic consideration of the chemical characteristics of non-ferrous metals and their compatibility constraints. It is impossible to establish a compatibility matrix between goods to identify the requirements for separate loading and the prohibition of co-loading, and it also lacks an in-transit risk assessment mechanism to quantitatively predict the safety of mixed loading schemes. This results in hidden transportation safety hazards in the matching results. The safe scheduling of transportation of multi-category non-ferrous metal goods has become a key problem that urgently needs to be solved in the current logistics scheduling field. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method and system for intelligent matching and scheduling of multiple types of non-ferrous metal resources, which can effectively solve the problems mentioned in the background.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: In a first aspect, the present invention provides a method for intelligent matching and scheduling of multiple categories of non-ferrous metal resources, the method comprising the following steps: A non-ferrous metal transportation constraint rule base is established, which includes an inter-metal compatibility matrix. This matrix quantifies the compatibility status of any two non-ferrous metals based on the standard electrode potential difference. When the absolute value of the standard electrode potential difference is less than 0.5 volts, it is marked as a co-loading state; when it is greater than or equal to 0.5 volts but less than 1.5 volts, it is marked as a segregated loading state; and when it is greater than or equal to 1.5 volts, it is marked as a non-co-loading state. Furthermore, for those marked as segregated loading states, a segregation level requirement is specified. The segregation levels include: Level 1 segregation requiring the installation of a basic physical barrier between goods; Level 2 segregation requiring the installation of a sealed partition or sealed segregated container on top of Level 1 segregation; and Level 3 segregation requiring the configuration of a dedicated segregated compartment or independent transport container equipped with an independent temperature and humidity control unit and exhaust gas treatment device on top of Level 2 segregation. Receive shipping orders to be matched, parse and verify the order data, and output a standardized order object; A three-tiered filtering mechanism is implemented to screen candidate vehicles step by step based on the non-ferrous metal transportation constraint rule base to obtain a set of feasible matching schemes; A safety risk assessment model is constructed, and a comprehensive risk score is calculated for each feasible matching scheme in the set of feasible matching schemes based on the risk factors of metal chemical properties, transportation distance, seasonal climate, vehicle condition, and on-the-road monitoring level. The risk-oriented priority recommendation mechanism compares the comprehensive risk score with a preset risk score threshold and prioritizes recommending feasible matching schemes whose comprehensive risk score is less than or equal to the risk score threshold.

[0006] On the other hand, the present invention provides an intelligent matching and scheduling system for multiple categories of non-ferrous metal resources, including: The rule base management module is used to establish and store a non-ferrous metal transportation constraint rule base, which includes an inter-metal compatibility matrix, a protection level requirement system, and a special vehicle requirement mapping. The transportation order receiving and preprocessing module is used to receive transportation orders to be matched, parse and verify the order data, and output standardized order objects. The three-level filtering module is used to perform general condition filtering, verification filtering based on the intermetallic compatibility matrix and protection level requirement system, and sorting filtering based on multi-dimensional comprehensive scoring to obtain a set of feasible matching schemes. The safety risk assessment module is used to construct a safety risk assessment model and calculate a comprehensive risk score for each feasible matching scheme in the set of feasible matching schemes, based on the risk factors of metal chemical properties, transportation distance, seasonal climate, vehicle condition, and on-the-road monitoring level. The risk-oriented recommendation module is used to compare the comprehensive risk score with a preset risk score threshold and prioritize recommending feasible matching schemes where the comprehensive risk score is less than or equal to the risk score threshold.

[0007] In summary, this application includes at least one of the following beneficial technical effects: 1. This invention establishes a non-ferrous metal transportation constraint rule library containing an inter-metal compatibility matrix, quantifies the compatibility of metal co-loading based on standard electrode potential difference and generation enthalpy data, and provides a matching isolation level requirement and protection level system. This systematically solves the technical problem of difficulty in identifying compatibility risks when multiple types of non-ferrous metals are mixed, and avoids transportation safety accidents caused by inter-metal chemical reactions from the source.

[0008] 2. This invention employs a three-level filtering mechanism to screen candidate vehicles step by step, sequentially performing general condition filtering, deep verification based on compatibility matrix and protection level system, and multi-dimensional comprehensive scoring and ranking. This organically integrates safety constraints and business constraints, ensuring that the matching scheme meets the various requirements of the transportation order while achieving efficient and accurate reduction of the candidate vehicle range, significantly improving the computational efficiency and result quality of scheduling and matching.

[0009] 3. Construct a safety risk assessment model that covers five risk factors: metal chemical properties, transportation distance, seasonal climate, vehicle condition, and on-the-road monitoring level. Adopt a risk-oriented priority recommendation mechanism, and take safety risk as the primary decision-making basis on the basis of traditional cost and timeliness considerations. Prioritize the recommendation of low-risk solutions, while generating targeted risk warnings and mitigation measures for high-risk solutions. This realizes the transformation of transportation scheduling from passive response to proactive prevention, and comprehensively improves the safety assurance level of non-ferrous metal logistics transportation. Attached Figure Description

[0010] Figure 1 A schematic diagram of the overall technical solution for an intelligent matching and scheduling method for multiple categories of non-ferrous metal resources; Figure 2 A flowchart illustrating the three-level filtering mechanism in the intelligent matching and scheduling method for multiple categories of non-ferrous metal sources; Figure 3 A schematic diagram illustrating the principle of the safety risk assessment model in the intelligent matching and scheduling method for multiple categories of non-ferrous metal resources; Figure 4 The flowchart shows the risk-oriented priority recommendation mechanism in the intelligent matching and scheduling method for multiple categories of non-ferrous metal sources. Detailed Implementation

[0011] To make the objectives, technical solutions, and advantages of this invention clearer, the following description is provided in conjunction with the appendix. Figure 1 To be continued Figure 4 Specific embodiments are provided to further illustrate the present invention in detail.

[0012] Firstly, this embodiment details the intelligent matching and scheduling method for multiple categories of non-ferrous metal resources. This method establishes a systematic non-ferrous metal transportation constraint rule base as its foundational data, combines a three-tiered filtering mechanism to progressively screen and optimize candidate vehicles, and introduces a safety risk assessment model to quantify the risks of feasible matching schemes. Finally, it outputs the optimal matching result through a risk-oriented priority recommendation mechanism. The technical architecture of this method encompasses multiple functional modules, including a data layer, rule layer, filtering layer, evaluation layer, and recommendation layer. These modules interact and collaborate through standardized data interfaces, collectively forming a complete intelligent matching and scheduling system for multiple categories of non-ferrous metal resources. The following is a detailed explanation.

[0013] First, for step S1, a non-ferrous metal transportation constraint rule base is established. This base comprises three interconnected sub-modules: an inter-metal compatibility matrix to record the co-loading compatibility status between any two non-ferrous metals; a protection level requirement system to define the protection requirements for various non-ferrous metals in four dimensions: waterproofing, moisture-proofing, oxidation prevention, and corrosion prevention; and a special vehicle requirement mapping to establish the correspondence between non-ferrous metal types and applicable vehicle types. These three sub-modules collectively provide rule data support for the subsequent three-tiered filtering mechanism. The specific process of establishing the rule base is as follows.

[0014] Step S101: Collect chemical property data of non-ferrous metals. In this embodiment, chemical property data of various non-ferrous metals are collected from materials science databases, standard chemical handbooks, and transportation safety regulations. The collected data includes metal reactivity data, oxidation tendency data, corrosion characteristic data, and intermetallic chemical reaction probability data. Metal reactivity data reflects the degree to which metal atoms lose electrons and is used to determine whether spontaneous oxidation reactions will occur in non-ferrous metals during transportation.

[0015] Oxidation tendency data records the ease with which non-ferrous metals react with oxygen to form oxides. Corrosion characteristic data includes the corrosion type, corrosion rate, and physicochemical properties of corrosion products of non-ferrous metals under specific environmental conditions. Intermetallic chemical reaction probability data records the types of chemical reactions that may occur between different non-ferrous metals under direct or indirect contact conditions, including displacement reactions, alloying reactions, and the formation of electrochemical corrosion pairs. In this embodiment, the above data is classified and stored according to metal type codes to establish a basic chemical characteristic database.

[0016] Step S102: Construct an intermetallic compatibility matrix. Based on the chemical property data collected in step S101, this embodiment constructs an intermetallic compatibility matrix. The intermetallic compatibility matrix is ​​organized using a two-dimensional array structure, with rows and columns corresponding to different non-ferrous metal type codes. In this embodiment, a unique type code is assigned to each non-ferrous metal, covering both the metal's element symbol code and material subdivision code.

[0017] Each element in the intermetallic compatibility matrix represents the co-loading compatibility status between the metals in the corresponding row and column. Compatibility status includes three types: co-loading allowed, segregated loading, and non-co-loading allowed. Co-loading allowed means that the two non-ferrous metals can be mixed and loaded in the same transport space without causing safety risks or quality damage. Segregated loading means that the two non-ferrous metals must be physically separated before co-loading. Non-co-loading allowed means that the two non-ferrous metals are prohibited from being co-loaded in the same transport space.

[0018] Step S103: Fill in the compatibility state values ​​of the intermetallic compatibility matrix. In this embodiment, based on the chemical property data collected in step S101, determine the compatibility state value for each element in the intermetallic compatibility matrix. For the case where the row metal and column metal are the same, i.e., the elements on the diagonal of the matrix, the compatibility state is marked as a co-capable state.

[0019] In cases where the row metal and column metal are different, this embodiment determines the difference based on the standard electrode potential difference between the two non-ferrous metals and the enthalpy of formation of the intermetallic compound. The absolute value of the difference between the standard electrode potentials of the row metal and the column metal is calculated and denoted as ΔE. The calculation formula is as follows: in, This represents the standard electrode potential value of the metal corresponding to the matrix row, in volts. This represents the standard electrode potential value of the metal corresponding to the matrix column, in volts.

[0020] When ΔE is less than 0.5 volts, the compatibility status of the matrix element is marked as co-loadable. When ΔE is greater than or equal to 0.5 volts and less than 1.5 volts, the compatibility status of the matrix element is marked as isolated loading. When ΔE is greater than or equal to 1.5 volts, the compatibility status of the matrix element is marked as non-co-loadable.

[0021] This embodiment also refers to the enthalpy of formation data of intermetallic compounds for auxiliary determination. When two non-ferrous metals can form a thermodynamically stable intermetallic compound, and the enthalpy of formation of the reaction is negative and its absolute value is greater than 20 kJ per mole, the compatibility status of the matrix elements is marked as non-co-loading. For metals that react violently with water, including sodium and potassium, this embodiment marks their compatibility status with all aqueous substances or metals that may adsorb water as non-co-loading.

[0022] Step S104: Mark the isolation level requirements for the isolated loading status. When the compatibility status is determined to be isolated loading in step S103, this embodiment marks the corresponding isolation level requirements in the inter-metal compatibility matrix. The isolation level is divided into three progressive levels. Level 1 isolation requires the setting up of basic physical isolation barriers between goods, including laying anti-slip mats, setting up wooden partitions, or using flexible isolation nets.

[0023] Level 2 isolation requires, in addition to Level 1 isolation, the installation of sealed partitions or sealed isolation containers between goods to ensure that there is no possibility of gas or liquid exchange between the goods. The sealed partitions are made of corrosion-resistant materials and are completely sealed between their edges and the walls of the carriage.

[0024] Level 3 isolation requires, in addition to meeting all the conditions of Level 2 isolation, the configuration of a dedicated isolation compartment or independent transport container, with each isolation compartment or transport container equipped with an independent temperature and humidity control unit and exhaust gas treatment device.

[0025] Step S105: Define the grading standards for protection levels. This embodiment establishes a grading standard system for protection levels, covering four evaluation dimensions: waterproof, moisture-proof, oxidation-proof, and corrosion-proof. The waterproof level dimension evaluates the vehicle's ability to prevent moisture from penetrating the cargo area during transportation.

[0026] The moisture resistance rating assesses a vehicle's ability to control ambient humidity during transportation. The oxidation resistance rating assesses a vehicle's ability to prevent non-ferrous metals from reacting chemically with oxygen during transportation. The corrosion resistance rating assesses a vehicle's ability to prevent non-ferrous metals from corroding during transportation.

[0027] Each dimension is divided into three levels according to the level of protection, from low to high. Level 1 is the basic protection level, requiring the vehicle to have basic sealing performance and rainproof capability. Level 2 is the enhanced protection level, requiring the vehicle to have sealing performance, moisture-proof facilities, and ventilation control capability. Level 3 is the professional protection level, requiring the vehicle to have a temperature and humidity control system, an inert gas protection system, and real-time environmental monitoring equipment.

[0028] Step S106: Assign corresponding protection level requirements to each non-ferrous metal. In this embodiment, based on the chemical property data collected in step S101 and the storage conditions of the non-ferrous metals, the protection level requirements for each non-ferrous metal are determined in four dimensions: waterproof, moisture-proof, oxidation-proof, and corrosion-proof.

[0029] The chemical reactivity of non-ferrous metals is the primary factor influencing the required protection level. This embodiment determines the chemical reactivity level based on the standard electrode potential values ​​of the non-ferrous metals. Class I active metals, including calcium and potassium, with a standard electrode potential below -2.0 volts, are assigned a level 3 based on both oxidation and moisture resistance.

[0030] Class II reactive metals, including aluminum and magnesium, with standard electrode potentials between -2.0 volts and -0.5 volts, are classified as Grade 2 in terms of oxidation and moisture resistance. Metals with standard electrode potentials above -0.5 volts, including copper and silver, are classified as Grade 1 in terms of oxidation and moisture resistance.

[0031] Storage conditions for non-ferrous metals are the second factor influencing protection rating requirements. Each non-ferrous metal specifies optimal storage environment parameters in its material specifications, including temperature range, humidity range, and atmospheric composition requirements. Metals requiring storage at temperatures below 25 degrees Celsius and relative humidity below 40% are assigned a rating of 2 or 3 for water and moisture protection. Metals requiring storage in an inert atmosphere are assigned a rating of 3 for oxidation protection.

[0032] Risk factor assessment during transportation is the third factor influencing the required protection level. This embodiment obtains route information from the transportation order, extracting data on climate characteristics along the route, road condition levels, number of transfers, and transit time. When the transportation route passes through high-humidity areas, the moisture protection level requirement is increased by one level. When the transportation route passes through high-temperature areas, the oxidation protection level requirement is increased by one level.

[0033] Step S107: Establish the mapping relationship between protection level and vehicle essential facilities. In this embodiment, three protection levels are defined for four dimensions: waterproof, moisture-proof, oxidation-proof, and corrosion-proof. Based on the functional protection objectives to be achieved at each level, the facility configuration that the vehicle should have in each dimension is determined.

[0034] Waterproofing levels: Level 1 aims to prevent ordinary rainwater and road splashes from penetrating the cargo compartment, requiring the vehicle to have sealing strips and drainage channels. Level 2 prevents water from splashing from road surfaces or seeping into gaps in the cargo compartment floor due to wheel traction, adding a water-resistant structure to the cargo compartment floor and high-level drainage holes to the Level 1 standard. Level 3 prevents water from seeping in through tiny gaps in the doors under high-pressure rain, and also prevents external moisture from being drawn in due to pressure differences between the inside and outside of the cargo compartment, adding a double-sealed door structure and a pressure balancing valve to the Level 2 standard.

[0035] Moisture protection dimensions: Level 1 requires vehicles to have ventilation capabilities to prevent moisture accumulation through natural ventilation. Level 2 actively reduces the humidity inside the vehicle through physical adsorption in high external humidity environments and allows for real-time monitoring of humidity levels, adding a desiccant rack and humidity indicator to the Level 1 design. Level 3 provides an active dehumidification or complete moisture isolation atmosphere for non-ferrous metals that are prone to violent chemical reactions upon contact with water or are highly hygroscopic, adding an active dehumidification device or an inert gas replacement system to the Level 2 design.

[0036] Oxidation prevention dimensions: Level 1 aims to prevent slow oxidation caused by external dust and airborne oxidizing particles contacting the cargo surface, requiring vehicles to have standard sealing performance. Level 2 uses an airtight compartment structure to control the oxygen concentration inside the compartment below a preset safety threshold and monitors oxygen concentration changes in real time, adding a sealed compartment and oxygen concentration monitor to Level 1. Level 3 aims to create an oxygen-free transportation environment for non-ferrous metals with extremely strong oxidizing properties that can rapidly oxidize or even burn in air at normal temperature and pressure, maintaining the oxygen concentration continuously below 0.5%, adding an inert gas protection system to Level 2, including inert gas storage tanks, gas delivery pipelines, and gas concentration sensors.

[0037] Corrosion Protection Dimensions: Level 1 aims to prevent contact corrosion of the cargo with corrosive chemicals left over from previous transport on the vehicle's floor and corners, requiring no corrosive residues inside the vehicle compartment. Level 2 aims to prevent corrosion of the vehicle's metal structure from accidental leakage of small amounts of corrosive substances during transport and to effectively collect leaked substances to prevent their spread. This level adds a corrosion-resistant coating and a leak collection device to Level 1. Level 3 aims to prevent chemical corrosion damage to high-purity, high-surface-quality non-ferrous metal materials caused by volatile acidic or alkaline gaseous media from coexisting cargo or corrosive industrial atmospheres in the external environment. This level adds an independent sealed compartment and acid / alkalinity monitoring sensors to Level 2.

[0038] Step S108: Define a classification system for special vehicle types. In this embodiment, special vehicle types are divided into five categories. Temperature-controlled vehicles are equipped with a precise temperature control system and are suitable for transporting temperature-sensitive non-ferrous metals, including low-melting-point alloys. Enclosed vehicles have a fully sealed compartment structure and are suitable for transporting metal products requiring dust and moisture protection, including powdered metals and precision-machined metal parts.

[0039] The partitioned wagon type features adjustable partition mounting rails and securing devices inside the wagon compartment, suitable for the combined transport of various metal types requiring segregated loading. The reinforced wagon type has enhanced design in terms of wagon structure and securing devices, suitable for transporting high-density metal ingots or heavy metal components. The standard freight wagon type is a standard-configuration general-purpose transport vehicle, suitable for transporting conventional metal goods without special requirements.

[0040] Step S109 defines the essential facility configurations for each special vehicle type. Essential facilities for a temperature-controlled vehicle include a high-precision temperature sensor, a refrigeration unit, a heating unit, a temperature controller, and an insulated compartment structure. The temperature sensor's measurement accuracy is no less than ±0.5 degrees Celsius. The temperature controller employs a closed-loop control algorithm, adjusting the cooling or heating output power in real time based on the temperature sensor feedback signal. The thermal conductivity of the insulated compartment structure does not exceed 0.03 W / (m·K).

[0041] Essential features of enclosed vehicles include a sealed body, airtight doors, a drainage system, and a moisture-proof mat. The joints of the sealed body are sealed by welding or filling with sealant. Airtight doors are equipped with multiple sealing strips, and the gap between the door frame and the door leaf does not exceed 2 mm. The moisture-proof mat material has a water absorption rate of no more than 5%.

[0042] Essential features of a bulkhead car include a rail-mounted bulkhead installation system, bulkhead fixing clips, and a buffer pad. The rail-mounted bulkhead installation system has tracks along the side walls and floor of the car, within which the bulkheads slide and lock into any position. The buffer pad material has both insulating and cushioning properties.

[0043] Essential features of reinforced vehicles include a strengthened cargo box frame, anti-collision buffer devices, heavy-duty cargo anchoring points, and a shock-absorbing suspension system. The cross-sectional dimensions of the reinforced cargo box frame are more than 30% larger than those of a standard cargo box. The single-point load-bearing capacity of each heavy-duty cargo anchoring point is no less than 5 tons.

[0044] Step S110: Establish a mapping relationship between non-ferrous metal types and required vehicle types. In this embodiment, based on the protection level requirements determined in step S106, the isolation level requirements marked in step S104, and the physical characteristics of non-ferrous metals, each non-ferrous metal is mapped to the corresponding vehicle type.

[0045] The first group of highly reactive chemical metals, including calcium and potassium, require a level 3 rating for both oxidation and moisture protection, which corresponds to the type of vehicle equipped with an inert gas protection system.

[0046] Metal powders that are prone to moisture absorption and deliquescence, including magnesium powder and aluminum powder, require a moisture resistance rating of Level 3 and must be in powder form. This corresponds to the type of enclosed vehicle, which is additionally equipped with explosion-proof electrical equipment and electrostatic grounding devices.

[0047] High-value non-ferrous metals, including titanium and zirconium alloys, are mapped to enclosed or temperature-controlled vehicle types and equipped with advanced security monitoring systems, including video surveillance cameras, door magnetic switch alarms, and satellite positioning trackers.

[0048] The combination of multiple metal types involving isolation levels one or two is mapped to the type of partition vehicle.

[0049] High-density heavy metal components, including copper and nickel ingots, are mapped onto rugged vehicle types.

[0050] All protection level requirements are Level 1 and do not involve the segregation of metal products, which are mapped to the ordinary truck type. The mapping results are stored in the rule base in the form of a data table.

[0051] Step S1 establishes the non-ferrous metal transportation constraint rule base. The intermetallic compatibility matrix provides quantitative co-load compatibility judgment rules based on standard electrode potential difference values ​​and intermetallic compound formation enthalpy data. The protection level requirement system establishes grading standards from four dimensions and forms verifiable judgment criteria through mapping of vehicle essential facilities. The special vehicle requirement mapping transforms the protection and isolation requirements of non-ferrous metals into vehicle selection rules. The three sub-library modules together constitute a complete rule support system, providing a data foundation for the three-level filtering mechanism in the subsequent step S3.

[0052] The next step, S2, involves receiving the shipping orders to be matched. The shipping order receiving module establishes a communication connection with the external order management system or user terminal, and receives the shipping order data to be matched via a network transmission protocol. This embodiment uses Hypertext Transfer Protocol (HTTP) or Transmission Control Protocol (TCP) as the data transmission protocol, and the order data is encapsulated in JavaScript Object Notation (SIN) or Extensible Markup Language (EXPLAIN) format. The order data includes core data fields such as order number, shipper information, consignee information, cargo information, route information, time requirements, weight requirements, and volume constraints. The data preprocessing submodule parses and verifies the received order data. The specific processing procedure is as follows.

[0053] Step S201: Establish a communication connection and receive order data. The transportation order receiving module listens on a preset network port, waiting for connection requests initiated by the external order management system or user terminal. After the connection is established, the external order management system or user terminal encapsulates the transportation order data to be matched according to the agreed data format and sends it to the transportation order receiving module. The transportation order receiving module performs integrity verification on the received complete data packet. After the verification passes, the order data is submitted to the data preprocessing submodule.

[0054] Step S202: Parse and verify the cargo information. The data preprocessing submodule parses the cargo information field from the order data. The cargo information specifically includes the type of non-ferrous metal to be transported, the batch quantity, the packaging form, the weight of each piece, the dimensions of each piece, and any special storage requirements for the cargo.

[0055] Non-ferrous metal type information is identified using metal classification codes. These codes encompass two levels: element symbol codes and material sub-codes. Element symbol codes use the symbols defined by the International Union of Pure and Applied Chemistry (IUPAC). Material sub-codes use two digits to represent the purity level, alloy grade, or heat treatment state for the same element. The combination of element symbol codes and material sub-codes ensures that each specific non-ferrous metal material can be uniquely identified and accurately matched.

[0056] The data preprocessing submodule performs format and validity checks on the cargo information. Format checks include: whether the metal classification code format conforms to the coding standard and whether the batch quantity is a positive integer. Validity checks include: whether the weight of a single item is a positive number, whether the dimensions of a single item are positive numbers, and whether the packaging form is a valid value within the enumerated range. Valid enumerated values ​​for packaging form include bagged, drummed, boxed, palletized, and unpackaged.

[0057] Step S203: Parse and verify the transportation route information. The data preprocessing submodule parses the transportation route information field from the order data. The transportation route information includes the detailed address of the origin, the detailed address of the destination, the route preference settings, and the conditions and constraints for whether transfers are allowed.

[0058] The data preprocessing submodule converts the detailed addresses of the origin and destination into geographic coordinate data. The conversion process is as follows: the data preprocessing submodule concatenates the structured address into a complete address string, then sends a query request to the geocoding service interface. The geocoding service interface returns the longitude and latitude values ​​corresponding to that address. The origin longitude and latitude values ​​are recorded as origin coordinates, and the destination longitude and latitude values ​​are recorded as destination coordinates.

[0059] Route preference settings include prioritizing highways, shortest distance, lowest toll fees, or best road conditions. The condition for allowing transit is a Boolean value; a true value indicates that the order is allowed to transfer goods at a transit point, while a false value indicates that direct transport is required.

[0060] The data preprocessing submodule performs format and rationality checks on the transportation route information. Format checks include: whether the detailed addresses of the origin and destination contain complete address hierarchy elements, and whether the geographic coordinate transformation successfully obtains valid longitude and latitude values. Rationality checks include: whether a reasonable path exists between the origin and destination, and whether the constraints allowing transshipment are Boolean values.

[0061] Step S204: Parse and validate the time window parameters. The data preprocessing submodule parses the time requirement field from the order data. The time window parameters include expected loading time, expected unloading time, latest loading time, earliest unloading time, and time window flexibility range. Expected loading time, expected unloading time, latest loading time, and earliest unloading time are all expressed using Coordinated Universal Time (UTC) timestamps. The time window flexibility range records the allowed buffer time for loading and unloading times to be brought forward or delayed; the unit of the time window flexibility range is minutes.

[0062] The data preprocessing submodule performs a logical consistency check on the time window parameters. The logical consistency check includes: whether the expected loading time is earlier than the latest loading time, whether the latest loading time is earlier than the expected unloading time, whether the expected unloading time is earlier than the earliest unloading time, and whether the time window flexibility range is a non-negative value.

[0063] Step S205: Parse and verify load and volume constraints. The data preprocessing submodule parses the load requirement field and volume constraint field from the order data.

[0064] The load requirement is expressed as a numerical range. This range includes a lower limit and an upper limit. The lower limit records the minimum total weight of the non-ferrous metals to be transported, and the upper limit records the maximum total weight. The load requirement field also includes a subfield for maximum weight per unit, which records the maximum weight of a single unit of non-ferrous metals to be transported, with the unit being kilograms.

[0065] Volume constraints are expressed as numerical ranges. These ranges include a lower limit and an upper limit. The lower limit records the minimum total volume of the non-ferrous metals to be transported, while the upper limit records the maximum total volume. The volume constraint field also includes a subfield for maximum single-item size, which records the maximum dimension of a single shipment of non-ferrous metals to be transported, in millimeters. The maximum single-item size includes three dimensions: maximum length, maximum width, and maximum height.

[0066] The data preprocessing submodule performs a rationality check on the load requirement and volume constraints. The rationality check includes: whether the lower limit of the load requirement is a positive number and not greater than the upper limit of the load requirement; whether the maximum weight of a single piece is a positive number; whether the lower limit of the volume constraint is a positive number and not greater than the upper limit of the volume constraint; and whether the maximum length, width, and height of a single piece are all positive numbers.

[0067] Step S206: Perform comprehensive validation and output standardized order objects. The data preprocessing submodule summarizes the validation results of each field. The comprehensive summary checks for cross-field logical conflicts. The cross-field logical conflict check includes the following:

[0068] Check for conflicts between the allowable transshipment constraints and the time window parameters in the transportation route information. If transshipment is not allowed and the time required for direct transportation between the origin and destination exceeds the difference between the expected unloading time and the expected loading time, it is considered a logical conflict.

[0069] Check whether the load requirements and volume constraints meet physical consistency. If the density value of the non-ferrous metal to be transported multiplied by the lower limit of the total volume is significantly greater than the lower limit of the total weight and the difference exceeds the preset density tolerance threshold, then the cargo information is determined to be physically inconsistent.

[0070] If a cross-field logical conflict is found during the comprehensive check, the data preprocessing submodule generates the corresponding error code and error description, and returns the verification failure result to the shipping order receiving module. The shipping order receiving module encapsulates the verification failure result into a response message and returns it to the external order management system or user terminal through the original communication connection, requesting the external order management system or user terminal to correct the order data and resubmit it.

[0071] If all comprehensive checks pass, the data preprocessing submodule assembles the parsed results of each field into a standardized order object. The standardized order object includes basic order information fields, complete cargo information fields, complete route information fields, time constraint fields, load constraint fields, and volume constraint fields. The standardized order object is organized using a unified data structure, facilitating direct reading and processing by the subsequent first-level general conditional filtering module. The data preprocessing submodule then passes the standardized order object to the input interface of the general conditional filtering module, entering the three-level filtering process in step S3.

[0072] Step S2 completes the reception and preprocessing of the transportation orders to be matched. The transportation order receiving module is responsible for establishing the communication connection and receiving raw data, while the data preprocessing submodule is responsible for parsing and verifying cargo information, transportation route information, time window parameters, load requirements, and volume constraints. The standardized order object output after successful verification provides input parameters for the first-level general condition filtering module to perform various judgments.

[0073] For step S3, a three-level filtering mechanism is used to progressively screen candidate vehicles. In this embodiment, after order receipt and data preprocessing are completed, a three-level filtering mechanism is used to progressively screen candidate vehicles based on the non-ferrous metal transportation constraint rule base established in step S1. The three-level filtering mechanism follows a coarse-to-fine and easy-to-difficult filtering strategy, gradually narrowing down the range of candidate vehicles through a progressively advancing screening process, and finally retaining the set of feasible matching schemes that meet all constraints. The specific processing procedures for the three levels are as follows.

[0074] Step S301: Read the basic information of candidate vehicles and perform the first-level general condition filtering. In this embodiment, the basic information data of candidate vehicles is read from the vehicle management database. The basic information data of candidate vehicles includes the vehicle's unique identifier, vehicle type code, internal dimensions of the cargo compartment, effective volume of the cargo compartment, rated load capacity, actual available load capacity, current location coordinates, and available time window range. In this embodiment, the origin coordinates, destination coordinates, expected loading time window, upper limit of load capacity requirement, and upper limit of volume constraint are also read from the standardized order object output in step S2.

[0075] In this embodiment, the following four general condition judgment logics are executed in parallel for each candidate vehicle.

[0076] Route compatibility assessment: Calculate the spatial distance between the current location coordinates of the candidate vehicle and the order's origin coordinates. If the spatial distance exceeds the preset maximum dispatch radius, the candidate vehicle is excluded. The maximum dispatch radius is preset based on the transportation network coverage, ranging from 50 km to 500 km.

[0077] Time window coverage judgment: The estimated arrival time of the candidate vehicle at the origin is compared with the expected loading time window of the order. If the estimated arrival time at the origin is later than the end of the expected loading time window, the candidate vehicle is excluded.

[0078] Sufficient load capacity determination: The rated load capacity of the candidate vehicle is compared with the upper limit of the order's load capacity requirement. If the rated load capacity is less than the upper limit of the load capacity requirement, the candidate vehicle is excluded.

[0079] Sufficient volume space determination: The effective volume of the candidate vehicle's cargo compartment is compared with the upper limit of the order volume constraint. If the effective volume of the cargo compartment is less than the upper limit of the volume constraint, the candidate vehicle is excluded.

[0080] Candidate vehicles that pass all four judgment logics are retained to form the first-level filtering result set. For scenarios with a large candidate vehicle database, this embodiment adopts a multi-threaded parallel computing approach, dividing the candidate vehicle set into multiple subsets and then executing the above four judgment logics separately.

[0081] Step S302: Query the compatibility status and perform isolation level verification. In this embodiment, the metal classification code of the non-ferrous metal to be transported is obtained from the standardized order object output in step S2. This embodiment queries the record of the cargo currently loaded on the candidate vehicle. The record of the cargo currently loaded on the candidate vehicle is stored in the loading status table of the vehicle management database. The loading status table includes the metal classification code of the loaded non-ferrous metal, the loading location, and the loading quantity.

[0082] For each type of non-ferrous metal already loaded on the candidate vehicle, this embodiment uses the metal classification code of the loaded type and the metal classification code of the non-ferrous metal to be transported as indexes to query the inter-metal compatibility matrix established in step S1 to obtain the compatibility status between the loaded type and the type to be transported.

[0083] When the compatibility status is "cannot be shared", this embodiment excludes candidate vehicles.

[0084] When the compatibility status is "isolated loading," this embodiment queries the intermetallic compatibility matrix for the corresponding isolation level requirements. Isolation level requirements are categorized as Level 1, Level 2, or Level 3 isolation. This embodiment reads the isolation facility configuration parameters of candidate vehicles, including whether the vehicle compartment is equipped with isolation partitions, the sealing level of the partitions, the type of corrosion-resistant material used for the partitions, the layout of the isolation space, and the number of independent isolation compartments. This embodiment compares the isolation facility configuration parameters of candidate vehicles with the isolation level requirements. If the isolation facility configuration parameters of a candidate vehicle do not meet the isolation level requirements, the candidate vehicle is excluded.

[0085] When the compatibility status is "can be shared", this embodiment determines that the candidate vehicle passes the compatibility check.

[0086] Step S303: Perform protection level requirement verification and vehicle type matching verification. In this embodiment, based on the metal classification code of the non-ferrous metal to be transported, retrieve the corresponding protection level requirement parameters of the non-ferrous metal to be transported from the protection level requirement system established in step S1. The protection level requirement parameters include four dimensions: waterproof level requirement, moisture-proof level requirement, oxidation-proof level requirement, and corrosion-proof level requirement. The value of each dimension is level 1, level 2, or level 3.

[0087] This embodiment reads the actual protection capability configuration parameters of the candidate vehicle. The actual protection capability configuration parameters of the candidate vehicle include the vehicle's sealing performance level, heat insulation performance level, moisture resistance performance level, temperature and humidity control accuracy level, and whether it is equipped with an inert gas protection system.

[0088] This embodiment compares the protection level requirement parameters with the actual protection capability configuration parameters of the candidate vehicles dimension by dimension. If the actual protection capability level of a candidate vehicle in any dimension is lower than the protection level requirement in that dimension, the candidate vehicle is excluded.

[0089] In this embodiment, based on the metal classification code of the non-ferrous metal to be transported, the recommended vehicle type corresponding to the non-ferrous metal to be transported is retrieved from the special vehicle requirement mapping established in step S1. The recommended vehicle types include temperature-controlled trucks, enclosed trucks, partitioned trucks, reinforced trucks, or ordinary freight trucks.

[0090] This embodiment reads the actual vehicle type code of the candidate vehicle. If the actual vehicle type of the candidate vehicle matches the recommended vehicle type, the candidate vehicle passes the vehicle type matching check. If the actual vehicle type of the candidate vehicle does not match the recommended vehicle type, this embodiment queries the compatibility and substitution relationship table. The compatibility and substitution relationship table is pre-established based on the actual functional configuration of various types of vehicles, and records the vehicle type pairs that are allowed to substitute for each other and their substitution conditions. If there is a compatibility and substitution relationship between the actual vehicle type of the candidate vehicle and the recommended vehicle type, the candidate vehicle passes the vehicle type matching check. If the two are neither consistent nor have a compatibility and substitution relationship, the candidate vehicle is excluded.

[0091] Step S304: Read the feasible matching schemes that have passed the second-level filtering. In this embodiment, the candidate vehicles that have passed the second-level filtering are paired with the transportation orders to form feasible matching schemes. This embodiment reads the basic information of the candidate vehicles, the transportation order constraint parameters, and the matching analysis report generated during the second-level filtering process for each feasible matching scheme. The matching analysis report records the verification results between the candidate vehicles and the transportation orders in three dimensions: compatibility matching, protection level matching, and vehicle type matching.

[0092] Step S305: Calculate the multi-dimensional comprehensive score. In this embodiment, for each feasible matching scheme, calculate the quantitative evaluation values ​​for transportation cost, transportation timeliness, safety risk, and vehicle utilization. The quantitative evaluation values ​​for each indicator are dimensionless values, ranging from 0 to 100.

[0093] The quantitative evaluation value of transportation cost indicators is calculated based on transportation distance cost, fuel consumption cost, toll cost, loading and unloading cost, and special handling cost. Transportation distance cost is calculated by multiplying the transportation distance from the origin to the destination by the unit mileage transportation fee rate. Fuel consumption cost is calculated by multiplying the transportation distance by the vehicle's fuel consumption per unit mileage, and then by the fuel price per unit. Toll cost is calculated based on the toll standards and toll mileage of the highways traversed along the transportation route. Loading and unloading cost is calculated by multiplying the number of batches of goods and the weight of each piece by the loading and unloading fee rate. Special handling cost is calculated based on whether non-ferrous metals require temperature control, inert gas protection, or segregated loading.

[0094] The quantitative evaluation value of the transportation timeliness index is calculated based on the estimated time deviation of the vehicle's arrival at the loading point, the degree of conformity between the actual transportation time and the expected transportation time, and the predicted on-time delivery rate. The estimated time deviation of the vehicle's arrival at the loading point is calculated based on the difference between the estimated travel time from the candidate vehicle's current location to the origin and the start time of the expected loading time window.

[0095] The quantitative evaluation value of the safety risk indicator is directly derived from the comprehensive risk score output by the safety risk assessment model in step S4 for feasible matching solutions.

[0096] The quantitative evaluation value of the vehicle utilization rate index is calculated based on the vehicle load utilization rate, the cargo compartment volume utilization rate, and the matching probability of return freight. The vehicle load utilization rate is calculated by dividing the order load requirement by the rated load capacity of the candidate vehicle. The cargo compartment volume utilization rate is calculated by dividing the order volume constraint by the effective volume of the candidate vehicle's cargo compartment.

[0097] This embodiment uses a weighted summation model to calculate the comprehensive score of each feasible matching scheme. The formula for calculating the comprehensive score is as follows: in, This represents the overall score of feasible matching solutions. It is a dimensionless numerical value, ranging from 0 to 100. The weighting coefficients representing transportation cost indicators. It is a dimensionless value greater than 0 and less than 1. This represents a quantitative evaluation value for transportation cost indicators. It is a dimensionless numerical value, ranging from 0 to 100. The weighting coefficients representing transportation timeliness indicators. It is a dimensionless value greater than 0 and less than 1. This represents the quantitative evaluation value of the transportation timeliness indicator. It is a dimensionless numerical value, ranging from 0 to 100. The weighting coefficients of the safety risk indicators. It is a dimensionless value greater than 0 and less than 1. This represents the quantitative evaluation value of the safety risk indicator. It is a dimensionless numerical value, ranging from 0 to 100. The weighting coefficients for the vehicle utilization rate index are indicated. It is a dimensionless value greater than 0 and less than 1. This represents a quantitative evaluation value for the vehicle utilization rate indicator. This is a dimensionless numerical value, ranging from 0 to 100. The sum of the four weighting coefficients equals 1.

[0098] This embodiment is based on a comprehensive score. Sort all feasible matching solutions in descending order and output a list of recommended solutions. The list of recommended solutions includes the ranking number and overall score of each feasible matching solution. Quantitative evaluation value of transportation cost indicators Quantitative evaluation value of transportation timeliness indicators Quantitative evaluation value of safety risk indicators Quantitative evaluation value of vehicle utilization rate index The system includes unique identifiers for candidate vehicles and detailed parameters for matching schemes. These parameters include route matching results, time window matching results, load matching results, volume matching results, compatibility matching results, protection level matching results, and vehicle type matching results between candidate vehicles and transportation orders. The recommended scheme list is output to the risk-oriented priority recommendation mechanism module in step S5.

[0099] Step S3 completes the three-tiered filtering mechanism for progressive screening. The first tier achieves rapid initial screening through four general conditional judgments. The second tier performs in-depth verification through compatibility status query, isolation level verification, protection level verification, and vehicle type matching verification. The third tier calculates a comprehensive score using a weighted summation model and outputs it in a sorted manner. The three tiers progressively narrow down the range of candidate vehicles, and the final output list of recommended solutions provides candidate solution data for step S5.

[0100] For step S4 in the method of this embodiment, a safety risk assessment model is constructed to perform on-the-road risk quantification prediction for each feasible matching scheme output in step S3. The safety risk assessment model covers five risk factor dimensions: metal chemical properties risk factor, transportation distance risk factor, seasonal climate risk factor, vehicle condition risk factor, and on-the-road monitoring level risk factor. The following are the quantification assessment methods for each risk factor and the calculation process of the comprehensive risk score.

[0101] Step S401: Establish quantitative assessment rules for risk factors of metal chemical properties. In this embodiment, for the non-ferrous metal to be transported in the feasible matching scheme, obtain the metal activity level data, oxidation tendency intensity data and intermetallic reactivity data of the non-ferrous metal to be transported.

[0102] Metal activity levels are classified based on the standard electrode potential values ​​of the non-ferrous metal to be transported. Metals with a standard electrode potential below -2.0 volts are assigned a activity level of 3. Metals with a standard electrode potential between -2.0 volts and -0.5 volts are assigned a activity level of 2. Metals with a standard electrode potential above -0.5 volts are assigned a activity level of 1.

[0103] The oxidation tendency intensity is classified based on the standard Gibbs free energy change of the oxidation reaction of the non-ferrous metal to be transported under normal temperature and pressure conditions. Metals with an oxidation tendency intensity below -400 kJ / mol are assigned a value of 3. Metals with an oxidation tendency intensity between -400 kJ / mol and -200 kJ / mol are assigned a value of 2. Metals with an oxidation tendency intensity above -200 kJ / mol are assigned a value of 1.

[0104] Intermetallic reactivity is determined based on the possibility of a chemical reaction between the non-ferrous metal to be transported and the non-ferrous metal already loaded in the candidate vehicle. If a displacement reaction or alloying reaction exists between the non-ferrous metal to be transported and the non-ferrous metal already loaded in the candidate vehicle, the intermetallic reactivity value is assigned to 1. If there is no possibility of any chemical reaction between the non-ferrous metal to be transported and the non-ferrous metal already loaded in the candidate vehicle, the intermetallic reactivity value is assigned to 0.

[0105] In this embodiment, the metal activity level, oxidation tendency intensity, and intermetallic reactivity are added together to obtain a quantitative evaluation value for the metal chemical property risk factor, denoted as . . These are dimensionless numerical values, ranging from 0 to 7. A higher value indicates a higher risk level for feasible matching schemes in terms of metal chemical properties.

[0106] Step S402: Establish quantitative assessment rules for transportation distance risk factors. In this embodiment, the transportation mileage value between the coordinates of the origin and the destination is read from the feasible matching schemes. The unit of the transportation mileage value is kilometers. The transportation mileage value serves as the basic quantitative evaluation value of the transportation distance risk factor, and the unit of measurement for the transportation mileage value is hundreds of kilometers.

[0107] This embodiment reads the road classification information of the roads traversed by the transportation route. The road classification information is divided into four levels: expressway, national highway, provincial highway, and county road. The road condition value for expressway is assigned 0, for national highway it is assigned 1, for provincial highway it is assigned 2, and for county road it is assigned 3.

[0108] This embodiment reads environmental characteristic data along the transportation route. The environmental characteristic data includes altitude variation, geological hazard susceptibility level, and the number of climate zone crossings. Altitude variation records the altitude difference between the origin and destination of the transportation route, with the unit of altitude variation being meters. Geological hazard susceptibility level is determined by querying geological hazard zoning maps of the areas along the route, classifying them into three levels: low-susceptibility, medium-susceptibility, and high-susceptibility, assigned values ​​of 0, 1, and 2 respectively. The number of climate zone crossings records the number of different climate zones traversed along the transportation route.

[0109] In this embodiment, the quantitative evaluation value of the transportation distance risk factor is calculated according to the following rules, denoted as: The value obtained by dividing the transport mileage by 100 is added to the road condition value, then added to the value obtained by dividing the altitude change by 1000, then added to the geological hazard susceptibility level value, and finally added to the number of climate zone crossings. The result is used as the... . The value is dimensionless. A higher value indicates a higher risk level for the feasible matching scheme in terms of transportation distance.

[0110] Step S403: Establish quantitative assessment rules for seasonal climate risk factors. In this embodiment, the expected loading and unloading times filled in the transportation order are obtained to determine the seasonal period in which the transportation task takes place. The seasonal period is divided into four categories: spring, summer, autumn, and winter.

[0111] This embodiment queries the corresponding climate parameter baseline values ​​based on the seasonal period. The climate parameter baseline values ​​include the average temperature range, average humidity range, average number of days with precipitation per month, and average wind speed. The climate parameter baseline values ​​are obtained from historical meteorological data statistical reports of the areas along the target transportation route.

[0112] In this embodiment, the quantitative evaluation value of the seasonal climate risk factor is calculated according to the following rules, denoted as: The temperature risk score is determined based on the difference between the seasonal average maximum temperature and 25 degrees Celsius; for every 5 degrees Celsius increase in the difference, the temperature risk score increases by 1. The humidity risk score is determined based on the difference between the seasonal average relative humidity and 60%; for every 10% increase in the difference, the humidity risk score increases by 1. The precipitation risk score is determined based on the seasonal average number of rainy days; for every 5 days of rain in the monthly average, the precipitation risk score increases by 1. The wind risk score is determined based on the seasonal average wind force level; for every level increase in the average wind force level, the wind risk score increases by 1. In this embodiment, the temperature risk score, humidity risk score, precipitation risk score, and wind risk score are added together to obtain... . The value is dimensionless. A higher value indicates a higher risk level for feasible matching schemes in terms of seasonal climate.

[0113] Step S404: Establish quantitative assessment rules for vehicle condition risk factors. In this embodiment, vehicle condition data of candidate vehicles is read from the vehicle management database. Vehicle condition data includes vehicle age, cumulative mileage, maintenance frequency, fault repair history, safety facility configuration list, and vehicle annual inspection status.

[0114] Vehicles less than 3 years old have an age risk score of 0. Vehicles 3 years or older but less than 8 years old have an age risk score of 1. Vehicles 8 years or older have an age risk score of 2.

[0115] Vehicles with a cumulative mileage of less than 100,000 kilometers have a mileage risk score of 0. Vehicles with a cumulative mileage of 100,000 kilometers or more but less than 300,000 kilometers have a mileage risk score of 1. Vehicles with a cumulative mileage of 300,000 kilometers or more have a mileage risk score of 2.

[0116] Vehicles requiring maintenance 4 times or more per year have a maintenance risk score of 0. Vehicles requiring maintenance 2 times or more but less than 4 times per year have a maintenance risk score of 1. Vehicles requiring maintenance less than 2 times per year have a maintenance risk score of 2.

[0117] The fault repair history includes the number of faults a vehicle has experienced in the past 3 years. A vehicle with 0 faults has a fault risk score of 0. A vehicle with 1 fault has a fault risk score of 1. A vehicle with 2 or more faults has a fault risk score of 2.

[0118] The safety equipment configuration list records whether the vehicle is equipped with an anti-lock braking system (ABS), electronic stability control system (ESC), tire pressure monitoring system (TPMS), and reversing radar. For each missing safety feature among these three systems, the safety equipment risk score increases by 1.

[0119] Vehicles that pass the annual inspection have an inspection risk score of 0. Vehicles that fail the annual inspection but have been repaired have an inspection risk score of 1.

[0120] This embodiment adds up the vehicle age risk score, mileage risk score, maintenance risk score, fault risk score, safety facility risk score, and inspection risk score to obtain a quantitative evaluation value for the vehicle condition risk factor, denoted as . . These are dimensionless numerical values, ranging from 0 to 11. A higher value indicates a higher risk level for the feasible matching solution in terms of vehicle condition.

[0121] Step S405: Establish quantitative assessment rules for on-the-road monitoring level risk factors. In this embodiment, on-the-road monitoring configuration data of candidate vehicles is read from the vehicle management database. On-the-road monitoring configuration data includes the configuration status of real-time positioning monitoring devices, video surveillance coverage, environmental parameter sensor configuration list, emergency communication equipment status, professional escort personnel configuration plan, and emergency rescue response time commitment.

[0122] Vehicles equipped with GPS trackers have a location monitoring score of 0. Vehicles without GPS trackers have a location monitoring score of 1.

[0123] For vehicles with video surveillance coverage of 80% or more, the video surveillance score is 0. For vehicles with video surveillance coverage of 50% or more but less than 80%, the video surveillance score is 1. For vehicles with video surveillance coverage of less than 50%, the video surveillance score is 2.

[0124] The environmental parameter sensor configuration list records whether the vehicle is equipped with a temperature sensor, humidity sensor, and vibration sensor. For each missing environmental parameter sensor (temperature, humidity, or vibration), the environmental sensor score increases by 1.

[0125] Vehicles equipped with emergency communication equipment will have a communication equipment score of 0. Vehicles without emergency communication equipment will have a communication equipment score of 1.

[0126] For vehicles equipped with professional escort personnel, the escort personnel score is assigned 0. For vehicles without professional escort personnel, the escort personnel score is assigned 2.

[0127] Vehicles with a promised emergency response time of 2 hours or less receive a rescue time score of 0. Vehicles with a promised emergency response time of 2 hours or less receive a rescue time score of 1. Vehicles with a promised emergency response time of more than 6 hours receive a rescue time score of 2.

[0128] This embodiment adds up the scores for location monitoring, video surveillance, environmental sensors, communication equipment, escort personnel, and rescue timeliness to obtain a quantitative evaluation value for the on-the-spot monitoring level risk factor, denoted as . . These are dimensionless numerical values, ranging from 0 to 9. A higher value indicates a weaker on-the-go monitoring capability of the feasible matching scheme.

[0129] Step S406: Calculate the comprehensive risk score. In this embodiment, the weight coefficients of each risk factor are determined based on the statistical analysis results of historical non-ferrous metal transportation accident data. The statistical analysis method is as follows: collect non-ferrous metal transportation accident cases from at least three years, and statistically analyze the proportion of accidents caused by incompatible metal chemical properties, excessive transportation distance, severe seasonal weather, vehicle technical failure, and lack of on-the-road supervision. After normalizing the proportion of each accident cause, use it as the initial weight of each risk factor. Then, combine it with the experience score of non-ferrous metal transportation safety experts for weighted correction, and finally determine the weight coefficients of each factor. After the above statistical analysis and expert correction, the weight coefficient of the metal chemical property risk factor is denoted as […]. [,] [Set to 0.30.]

[0130] The weighting coefficient of the transportation distance risk factor is denoted as […]. [,] [Set to 0.15. The weighting coefficient for the seasonal climate risk factor is denoted as] [,] [Set to 0.15. The weighting coefficient for the vehicle condition risk factor is denoted as] [,] [Set to 0.25. The weighting coefficient for the in-transit monitoring level risk factor is denoted as] [,] [Set to -0.15. Among them, the metal chemical properties risk factor has the highest weight because it directly determines the compatibility risk between goods; the weight coefficient of the in-transit monitoring level risk factor is negative because the stronger the in-transit monitoring capability, the greater the offsetting effect on the overall risk.]

[0131] In this embodiment, the quantitative evaluation values ​​of the five risk factors calculated in steps S401 to S405 are read respectively, and a weighted summation model is used to calculate the comprehensive risk score of the feasible matching scheme. The formula for calculating the comprehensive risk score is as follows: in, This represents the overall risk score of feasible matching solutions. It is a dimensionless numerical value. The weighting coefficients representing the risk factors of metal chemical properties. The value is 0.30. This represents a quantitative evaluation value of the risk factor for the chemical properties of metals. It is a dimensionless numerical value, ranging from 0 to 7. The weighting coefficients representing the transportation distance risk factor. The value is 0.15. This represents the quantitative evaluation value of the transportation distance risk factor. It is a dimensionless numerical value. The weighting coefficients representing seasonal climate risk factors. The value is 0.15. This represents the quantitative evaluation value of seasonal climate risk factors. It is a dimensionless numerical value. This represents the weighting coefficient of the vehicle condition risk factor. The value is 0.25. This represents a quantitative evaluation value of the vehicle condition risk factor. It is a dimensionless numerical value, ranging from 0 to 11. The weighting coefficient of the risk factor representing the level of in-transit monitoring is denoted as: , It is set to 0.15. Among them, A higher value indicates a weaker ability to monitor during transit. Positive weights are used so that the overall risk score increases as the on-the-go monitoring capacity decreases. This represents the quantitative evaluation value of the risk factor for the level of in-transit supervision. It is a dimensionless numerical value, ranging from 0 to 9.

[0132] This embodiment will incorporate risk scoring. The calculation results are output to the risk-oriented priority recommendation mechanism module in step S5. This embodiment also considers the comprehensive risk score. The numerical value generates a corresponding risk level label. When When the value is less than or equal to 3, the risk level label is low risk. When the value is greater than 3 and less than or equal to 6, the risk level label is medium risk. A score greater than 6 indicates a high-risk level. (Comprehensive Risk Score) The specific numerical values ​​and risk level labels together serve as the core basis for the scheme ranking and recommendation decision in step S5.

[0133] Steps S401 to S405 complete the quantitative assessment of the five risk factors, and obtain the comprehensive risk score through weighted summation. The risk level label provides a basis for risk assessment in step S5.

[0134] Finally, step S5, the risk-oriented priority recommendation mechanism, receives the list of recommended solutions output in step S3 and the comprehensive risk score of each solution output in step S4, and outputs the final matching recommendation result through the risk-oriented priority recommendation mechanism. This mechanism takes safety risk as the primary consideration for matching and ranking, divides the matching solutions into different risk levels by setting risk thresholds, and implements differentiated recommendation strategies based on the differences in risk levels.

[0135] Step S501: Set a risk scoring threshold. In this embodiment, the risk scoring threshold is set according to the safety management requirements for non-ferrous metal transportation and the enterprise's risk tolerance, denoted as... . It is a dimensionless numerical value. The setting method is as follows: The statistical distribution of each risk factor dimension in historical transportation accident data is read. The comprehensive risk score corresponding to the inflection point of the accident rate is used as the initial threshold, which is then adjusted according to the enterprise's safety management level. Enterprise safety management levels are divided into three levels: Level 3, Level 2, and Level 1. Level 3 represents the highest safety management requirements, corresponding to a 20% reduction in the initial threshold. Level 2 represents medium safety management requirements, corresponding to keeping the initial threshold unchanged. Level 1 represents average safety management requirements, corresponding to an 20% increase in the initial threshold.

[0136] Step S502: Classify the risk level of the solutions. In this embodiment, the set of feasible matching solutions output in step S3 and the comprehensive risk score corresponding to each feasible matching solution output in step S4 are received. This embodiment assigns a comprehensive risk score to each feasible matching scheme. Compared with the risk scoring threshold set in step S501 Comparison. When the comprehensive risk score of feasible matching solutions... Less than or equal to the risk score threshold In this embodiment, feasible matching solutions are marked as low-risk solutions. When the comprehensive risk score of a feasible matching solution... Greater than the risk score threshold In this embodiment, feasible matching schemes are marked as high-risk schemes. This embodiment records the risk level marking results for each feasible matching scheme, providing data for the subsequent execution of differentiated recommendation strategies.

[0137] Steps S501 to S502 complete the setting of risk scoring thresholds and the classification of project risk levels. Risk scoring thresholds Based on historical transportation accident data and safety management levels, each plan is determined according to a comprehensive risk score. and The comparison results are labeled as low-risk or high-risk options, providing a classification basis for subsequent differentiated recommendation strategies.

[0138] Step S503: Execute the low-risk solution priority recommendation process. In this embodiment, check whether there is a low-risk solution in the feasible matching solution set. If there is no low-risk solution in the feasible matching solution set, this embodiment jumps to the high-risk solution special handling process in step S504. If there is a low-risk solution in the feasible matching solution set, this embodiment further counts the total number of low-risk solutions.

[0139] When the total number of low-risk solutions is equal to 1, this embodiment directly outputs the low-risk solution as the final recommendation result.

[0140] When the total number of low-risk solutions is greater than 1, this embodiment reads the comprehensive score of each low-risk solution calculated in step S3. Overall score This is the evaluation value calculated in step S3 by weighting and summing the transportation cost index, transportation timeliness index, safety risk index, and vehicle utilization rate index. This embodiment uses a comprehensive scoring method. Sort all low-risk options from highest to lowest risk, and output the list of low-risk options as recommendations in the order of sorting.

[0141] Step S504: Execute the special handling procedure for high-risk solutions. If, in step S503, it is determined that there is no low-risk solution in the set of feasible matching solutions, this embodiment enters the special handling procedure for high-risk solutions. This embodiment follows a comprehensive risk score. All high-risk options are sorted from lowest to highest risk, and the list of high-risk options is output in the order of sorting as the recommendation result.

[0142] This embodiment simultaneously generates risk warning information and appends it to the recommendation result data package for output. The risk warning information includes: the current recommended solution is marked as high-risk; and the recommended solution exceeds the risk scoring threshold. The risk factor dimensions are explained, including metal chemical property risk factors. Transportation distance risk factors Seasonal climate risk factors Vehicle condition risk factors and the risk factors of the level of guardianship during transit The specific items that exceed the preset sub-dimension threshold; the risk mitigation measures recommended for risk factors that exceed the threshold; and the risk acceptance confirmation prompt, prompting the user to confirm whether they accept the risk level of the high-risk solution.

[0143] Step S5 completes the execution of the differentiated recommendation strategy. The low-risk solution priority recommendation process is based on a comprehensive score. The recommended solutions list is output after sorting. A special handling procedure for high-risk solutions is implemented based on the comprehensive risk score. After sorting, a list of recommended solutions is output, along with risk warning information. The final output includes a list of solutions and risk warning information, providing users with a complete basis for matching and recommendation decisions.

[0144] On the other hand, the intelligent matching and scheduling system for multiple categories of non-ferrous metal resources disclosed in this invention includes: The rule base management module is used to establish and store the non-ferrous metal transportation constraint rule base, which includes the inter-metal compatibility matrix, protection level requirement system and special vehicle requirement mapping. The transportation order receiving and preprocessing module is used to receive transportation orders to be matched, parse and verify the order data, and output standardized order objects. The three-level filtering module is used to perform general condition filtering, verification filtering based on intermetallic compatibility matrix and protection level requirement system, and sorting filtering based on multi-dimensional comprehensive scoring to obtain a set of feasible matching solutions. The safety risk assessment module is used to build a safety risk assessment model. For each feasible matching scheme in the set of feasible matching schemes, a comprehensive risk score is calculated based on the risk factors of metal chemical properties, transportation distance, seasonal climate, vehicle condition, and on-the-road monitoring level. The risk-oriented recommendation module compares the comprehensive risk score with a preset risk score threshold and prioritizes recommending feasible matching schemes whose comprehensive risk score is less than or equal to the risk score threshold.

[0145] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention. Therefore, the embodiments should be regarded as exemplary and non-limiting in all respects.

[0146] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A method for intelligent matching and scheduling of multiple categories of non-ferrous metal resources, characterized in that, include: A non-ferrous metal transportation constraint rule base is established, which includes an inter-metal compatibility matrix. This matrix quantifies the compatibility status of any two non-ferrous metals based on the standard electrode potential difference. When the absolute value of the standard electrode potential difference is less than 0.5 volts, it is marked as a co-loading state; when it is greater than or equal to 0.5 volts but less than 1.5 volts, it is marked as a segregated loading state; and when it is greater than or equal to 1.5 volts, it is marked as a non-co-loading state. Furthermore, for those marked as segregated loading states, a segregation level requirement is specified. The segregation levels include: Level 1 segregation requiring the installation of a basic physical barrier between goods; Level 2 segregation requiring the installation of a sealed partition or sealed segregated container on top of Level 1 segregation; and Level 3 segregation requiring the configuration of a dedicated segregated compartment or independent transport container equipped with an independent temperature and humidity control unit and exhaust gas treatment device on top of Level 2 segregation. Receive shipping orders to be matched, parse and verify the order data, and output a standardized order object; A three-tiered filtering mechanism is implemented to screen candidate vehicles step by step based on the non-ferrous metal transportation constraint rule base to obtain a set of feasible matching schemes; A safety risk assessment model is constructed, and a comprehensive risk score is calculated for each feasible matching scheme in the set of feasible matching schemes based on the risk factors of metal chemical properties, transportation distance, seasonal climate, vehicle condition, and on-the-road monitoring level. The risk-oriented priority recommendation mechanism compares the comprehensive risk score with a preset risk score threshold and prioritizes recommending feasible matching schemes whose comprehensive risk score is less than or equal to the risk score threshold.

2. The method according to claim 1, characterized in that, The establishment of the non-ferrous metal transportation constraint rule base also includes: Establish a protection level requirement system, and for each non-ferrous metal, determine the protection level requirements in four assessment dimensions: waterproof, moisture-proof, oxidation-proof, and corrosion-proof, and establish a mapping relationship between the protection level of each dimension and the necessary facilities of the vehicle. A special vehicle requirement mapping is established. Based on the protection level requirements, the isolation level requirements, and the physical characteristics of non-ferrous metals, a correspondence is established between non-ferrous metal types and the required vehicle types. The vehicle types include temperature-controlled vehicles, enclosed vehicles, partitioned vehicles, reinforced vehicles, and ordinary trucks.

3. The method according to claim 2, characterized in that, The establishment of the intermetallic compatibility matrix also includes: Collect chemical property data of various non-ferrous metals, including data on metal activity, oxidation tendency, corrosion characteristics, and the probability of intermetallic chemical reactions; When two non-ferrous metals can form a thermodynamically stable intermetallic compound, and the enthalpy of formation of the reaction is negative and its absolute value is greater than 20 kJ per mole, the compatibility state of the matrix elements is marked as an incompatible state.

4. The method according to claim 1, characterized in that, The three-level filtering mechanism, based on the non-ferrous metal transportation constraint rule base, performs step-by-step screening of candidate vehicles, including: At the first level, based on the transportation route, time window, upper limit of load requirement, and upper limit of volume constraint in the standardized order object, candidate vehicles are filtered by general conditions to exclude candidate vehicles that do not meet any of the conditions of route, time, load, or volume. At the second level, for candidate vehicles that have passed the first level of filtering, the inter-metal compatibility matrix is ​​queried to obtain the compatibility status between the non-ferrous metal to be transported and the non-ferrous metal already loaded on the candidate vehicle. When the compatibility status is "cannot be co-loaded," the candidate vehicle is excluded. When the compatibility status is "isolated loading," the isolation facility configuration parameters of the candidate vehicle are checked to see if they meet the isolation level requirements. If they do not meet the requirements, the candidate vehicle is excluded. Furthermore, the actual protection capability configuration parameters of the candidate vehicle are checked to see if they meet the protection level requirements of each dimension in the protection level requirement system, and the actual vehicle type of the candidate vehicle is checked to see if it matches the corresponding vehicle type in the special vehicle requirement mapping. Candidate vehicles that do not meet the verification conditions are excluded. In the third level, for the feasible matching schemes that have passed the second level of filtering, a multi-dimensional comprehensive score is calculated, including transportation cost indicators, transportation timeliness indicators, safety risk indicators, and vehicle utilization rate indicators, and the feasible matching schemes are ranked according to the comprehensive score.

5. The method according to claim 1, characterized in that, In the construction of the safety risk assessment model, the quantitative assessment of the risk factors of metal chemical properties includes: Obtain the standard electrode potential value of the non-ferrous metal to be transported, and assign the metal activity level to 3, 2, and 1 respectively based on the standard electrode potential value being below -2.0 volts, between -2.0 volts and -0.5 volts, and above -0.5 volts; Obtain the standard Gibbs free energy change value of the oxidation reaction of the non-ferrous metal to be transported under normal temperature and pressure conditions. Based on the standard Gibbs free energy change value of the oxidation reaction being lower than -400 kJ / mol, between -400 kJ / mol and -200 kJ / mol, and higher than -200 kJ / mol, assign oxidation tendency intensity values ​​of 3, 2, and 1 respectively. If there is a displacement reaction or alloying reaction between the non-ferrous metal to be transported and the non-ferrous metal already loaded in the candidate vehicle, the intermetallic reactivity is assigned a value of 1; otherwise, it is assigned a value of 0. The quantitative evaluation value of the metal chemical property risk factor is obtained by adding the metal activity level assignment, the oxidation tendency intensity assignment, and the intermetallic reactivity assignment. .

6. The method according to claim 1, characterized in that, The comprehensive risk score is calculated using a weighted summation model, specifically as follows: Comprehensive Risk Score: in, For the comprehensive risk score, These are the weighting coefficients of the metal chemical property risk factors. This is the quantitative evaluation value of the risk factor for the chemical properties of the metal. The weighting coefficients for the transportation distance risk factor are as follows: This is the quantitative evaluation value of the transportation distance risk factor. These are the weighting coefficients for the aforementioned seasonal climate risk factors. This is the quantitative evaluation value of the aforementioned seasonal climate risk factor. The weighting coefficients for the vehicle condition risk factors are as follows: This is the quantitative evaluation value of the vehicle condition risk factor. The weighting coefficients for the risk factors of the in-transit monitoring level are as follows: This is the quantitative evaluation value of the risk factor for the level of in-transit monitoring; in, The score is calculated by adding up the scores of the candidate vehicles in six dimensions: real-time location monitoring device configuration status, video surveillance coverage, environmental parameter sensor configuration list, emergency communication equipment status, professional escort personnel configuration plan, and emergency rescue response time commitment. A higher value indicates a weaker ability to monitor during transit. The value is 0.

15.

7. The method according to claim 4, characterized in that, The calculation includes a multi-dimensional comprehensive score, including security risk indicators, specifically using a weighted summation model. The security risk indicators are directly derived from the comprehensive risk score output by the security risk assessment model. Furthermore, the weighting coefficient of the safety risk indicator is the highest among the four weighting coefficients: transportation cost indicator, transportation timeliness indicator, safety risk indicator, and vehicle utilization rate indicator.

8. The method according to claim 1, characterized in that, The risk-oriented priority recommendation mechanism also includes: When there is no feasible matching solution with a comprehensive risk score less than or equal to the risk score threshold, all feasible matching solutions are output in ascending order of comprehensive risk score, and a risk warning message is generated that includes a description of the risk factor dimensions that exceed the risk score threshold and suggested risk mitigation measures.

9. The method according to claim 1, characterized in that, The quantitative assessment of the risk factors for the level of in-transit supervision specifically includes: The candidate vehicles were scored in six dimensions: real-time location monitoring device configuration status, video surveillance coverage, environmental parameter sensor configuration list, emergency communication equipment status, professional escort personnel configuration plan, and emergency rescue response time commitment. Among them, when a candidate vehicle is not equipped with a GPS tracker, the video surveillance coverage is less than 50%, it lacks any of the following: temperature sensor, humidity sensor, or vibration sensor, it is not equipped with emergency communication equipment, it is not equipped with professional escort personnel, or the emergency rescue response commitment time is greater than 6 hours, the risk score will be increased in the corresponding dimension. The scores from the six dimensions are summed to obtain the quantitative evaluation value of the in-transit monitoring level risk factor. .

10. A multi-category non-ferrous metal supply intelligent matching and scheduling system, characterized in that, The system for implementing the method as described in any one of claims 1 to 9 includes: The rule base management module is used to establish and store a non-ferrous metal transportation constraint rule base, which includes an inter-metal compatibility matrix, a protection level requirement system, and a special vehicle requirement mapping. The transportation order receiving and preprocessing module is used to receive transportation orders to be matched, parse and verify the order data, and output standardized order objects. The three-level filtering module is used to perform general condition filtering, verification filtering based on the intermetallic compatibility matrix and protection level requirement system, and sorting filtering based on multi-dimensional comprehensive scoring to obtain a set of feasible matching schemes. The safety risk assessment module is used to construct a safety risk assessment model and calculate a comprehensive risk score for each feasible matching scheme in the set of feasible matching schemes, based on the risk factors of metal chemical properties, transportation distance, seasonal climate, vehicle condition, and on-the-road monitoring level. The risk-oriented recommendation module is used to compare the comprehensive risk score with a preset risk score threshold and prioritize recommending feasible matching schemes where the comprehensive risk score is less than or equal to the risk score threshold.

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