A bulk logistics supply and demand matching big data scheduling system

Abstract

The application discloses a kind of bulk logistics supply and demand matching big data scheduling system, it is related to bulk logistics scheduling technical field, the system includes: cargo right information acquisition module, data docking interface module, supply and demand matching scheduling module, cargo traceability and right ownership verification module and data storage module;The application builds the real-time docking mechanism of cargo right transfer information and scheduling system, realizes the deep coupling of cargo right dynamic change information and supply and demand scheduling, dynamically adjusts the matching parameters such as transport capacity type, transport batch, solves the problem of not accurate matching caused by cargo right management and supply and demand scheduling segmentation in prior art, improves the adaptation degree and transport efficiency of bulk logistics supply and demand matching, simultaneously, by embedding cargo traceability and right ownership verification link in scheduling process, effectively guarantee the right ownership security in the process of cargo right transfer, reduce the risk of cargo right dispute, improve the reliability and safety of bulk logistics supply and demand matching scheduling.

Description

Technical Field

[0001] This invention relates to the field of bulk logistics scheduling technology, specifically a big data scheduling system for matching supply and demand in bulk logistics. Background Technology

[0002] Bulk logistics refers to logistics activities involving large quantities of goods such as coal, steel, ore, and building materials, which are transported over long distances and have relatively low added value. It serves as a crucial link between industrial production, infrastructure construction, and end-consumer spending, directly impacting the production efficiency and cost control of related industries. Bulk logistics supply-demand matching involves precisely matching cargo owners' transportation needs (including cargo type, volume, time, and delivery location) with the transportation supply provided by carriers and individual transport providers (including capacity type, carrying capacity, routes, and service prices). This achieves a rational allocation of cargo and transport resources. Big data scheduling for bulk logistics supply-demand matching leverages big data technology to integrate multi-dimensional information such as cargo sources, transport capacity, warehousing, and road conditions. Through intelligent analysis and decision-making using algorithmic models, it optimizes supply-demand matching efficiency and develops scientific scheduling plans. This scheduling method effectively breaks down information asymmetry barriers in the bulk logistics industry, improves logistics resource utilization, reduces empty-load rates and transportation costs, and ensures timely delivery.

[0003] However, existing big data scheduling technologies for matching supply and demand in bulk logistics have certain shortcomings. Current systems generally separate cargo ownership management from supply and demand scheduling, failing to fully consider the dynamic characteristics of cargo ownership transfer in bulk commodities. In actual bulk logistics scenarios, cargo ownership frequently undergoes dynamic changes such as transfers, warehouse receipt pledges, and batch pickups. However, existing scheduling systems lack an effective connection mechanism with the cargo ownership management system, making it impossible to obtain real-time dynamic information on cargo ownership. This results in mismatches between generated supply and demand matching schemes, such as capacity type selection, transportation batch planning, and delivery node settings, and the actual state of cargo ownership. This not only wastes transportation resources and reduces transportation efficiency but also easily leads to cargo ownership disputes. Furthermore, existing systems lack a ownership confirmation process for cargo ownership transfer, making it difficult to guarantee the security of ownership during cargo transportation. This affects the accuracy and reliability of bulk logistics supply and demand matching scheduling. Therefore, developing a big data scheduling system for matching supply and demand in bulk logistics is of great significance. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a big data scheduling system for matching supply and demand in bulk logistics. This system achieves deep coupling between dynamic changes in cargo ownership information and supply and demand scheduling by establishing a real-time connection mechanism between cargo ownership transfer information and the scheduling system. It dynamically adjusts matching parameters such as transport capacity type and transport batches, improving the adaptability and efficiency of bulk logistics supply and demand matching. Furthermore, by embedding cargo traceability and ownership verification processes into the scheduling process, it effectively ensures the security of ownership during the cargo ownership transfer process, reduces the risk of cargo ownership disputes, and enhances the reliability and security of bulk logistics supply and demand matching scheduling.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a big data scheduling system for matching supply and demand in bulk logistics, the system comprising: a cargo ownership information collection module, a data interface module, a supply and demand matching scheduling module, a cargo traceability and ownership verification module, and a data storage module;

[0006] The cargo ownership information collection module collects dynamic changes in cargo ownership during the circulation of bulk commodities and transmits the collected dynamic changes in cargo ownership to the data interface module. The dynamic changes in cargo ownership include the types and times of changes such as cargo ownership transfer, warehouse receipt pledge, and batch delivery, as well as the scope of goods involved and the characteristics of the subject of the change of ownership.

[0007] The data interface module establishes a communication link between the external systems related to the transfer of ownership and this system, and transmits the received dynamic change information of ownership to the supply and demand matching and scheduling module.

[0008] The supply and demand matching and scheduling module pre-stores basic information on cargo sources, basic information on transportation capacity, transportation route information and historical scheduling data. After receiving information on dynamic changes in cargo ownership, it adjusts the supply and demand matching parameters of transportation capacity type, transportation batch and delivery node, generates a scheduling plan that adapts to the status of cargo ownership, and transmits the scheduling plan to the cargo traceability and ownership verification module and the data storage module respectively.

[0009] The cargo traceability and ownership verification module is embedded in the scheduling scheme execution process, collects data on key aspects of cargo transportation and constructs traceability files, performs real-time verification of cargo ownership, and transmits the collected data, traceability files and verification results to the data storage module. The data storage module stores various types of data and related files in categories.

[0010] Furthermore, the cargo ownership information collection module performs the following operations when collecting dynamic cargo ownership change information:

[0011] Establish data communication connections with the title management system and warehouse receipt management system, and clarify the triggering conditions for information collection and the frequency of data transmission;

[0012] When the conditions for collecting information on changes in ownership of goods are triggered, the type of change in ownership of goods and the corresponding time node of the change are captured, and the specific scope of the goods involved is defined.

[0013] Collect the identity information of the main entity before and after the change of ownership, as well as relevant authorization certificate information, and standardize the format of the collected information.

[0014] The processed information is verified for integrity. Once the verification is successful, the information is transmitted to the data interface module.

[0015] Furthermore, the supply and demand matching and scheduling module performs the following operations when performing supply and demand matching and generating scheduling schemes:

[0016] Receive dynamic changes in cargo ownership information transmitted from the data interface module and align it with pre-stored basic cargo information and basic transportation capacity information;

[0017] The impact of cargo ownership changes on transportation demand is analyzed using a built-in algorithm model to determine the direction and priority of adjustments to supply and demand matching parameters. The adjustment range of the parameters is determined using a formula. Calculate, where, For the first The adjustment range of the supply and demand matching parameters. For the first The sensitivity coefficient of the title to goods of the item parameter, For the first The intensity of the impact of changes in ownership of goods. For system dynamic correction coefficients, Based on statistical analysis of historical correlation data between different parameters and various types of changes in ownership, the following determinations were made. This is derived from the quantification of the scale of goods involved in the change of ownership and the timeliness requirements of the change. The determination is based on fitting feedback data from recent scheduling plans;

[0018] Based on the adjustment priority and the calculated adjustment range, the parameters for selecting capacity type, dividing transportation batches, and planning delivery nodes are optimized in sequence to screen out candidate capacity resources that meet the requirements of cargo ownership status.

[0019] The matching degree between candidate transportation capacity resources and cargo sources is determined by a formula. A quantitative assessment is conducted, and based on the assessment results, a final scheduling plan is determined and synchronized to relevant execution entities and data storage modules. To ensure a good overall match between candidate transport capacity and cargo sources, For the fit and suitability of the ownership of the goods, For the matching degree of transportation capacity resources, For the adaptability of the transportation process, , , The weighting coefficients are determined by analyzing the impact of various adaptation factors on the implementation effect of the scheme in historical scheduling data.

[0020] Furthermore, the cargo traceability and ownership verification module performs the following operations when performing cargo traceability and ownership verification:

[0021] The scheduling plan pre-defines key data collection nodes for goods leaving the warehouse, during transportation, transit nodes, and entering the warehouse, and clarifies the location data, status data, and ownership certificate types that need to be collected at each node;

[0022] At each key node, corresponding data and voucher information are acquired through standardized collection methods, uploaded to the system in real time, and associated with the corresponding cargo identifier;

[0023] A traceability archive is built based on the collected end-to-end data, and the data relationships are sorted out in chronological order;

[0024] Cross-verify the ownership certificate information and dynamic change information of goods ownership from the traceability archives using a formula. Calculate the ownership consistency index, verify the consistency between the identity of the entity picking up and receiving the goods and the entity owning the goods, and complete the dynamic ownership verification. This is the ownership consistency index. For the matching degree of electronic signatures on ownership certificates, To verify the consistency of the identity information of the ownership entity, To determine the consistency of cross-referencing records of changes in ownership, , , To verify the dimensional weights, they were determined based on statistical analysis of the error recognition rate of each dimension in a large number of ownership verification samples.

[0025] Furthermore, the data interface module adopts a RESTful standardized communication interface architecture, which is compatible with the cargo ownership management system and warehouse receipt management system of different manufacturers. It uses an encrypted transmission protocol to transmit dynamic cargo ownership change information and sets up a data transmission anomaly monitoring mechanism to trigger a retransmission command when transmission delay or data loss occurs.

[0026] Furthermore, the data storage module adopts a distributed storage architecture, which is divided into a cargo ownership data storage partition, a basic information storage partition, a scheduling scheme storage partition, and a traceability verification data storage partition. Each partition is managed independently and data is associated through an index, supporting fast data retrieval and batch export, and has off-site backup function.

[0027] Furthermore, the supply and demand matching and scheduling module pre-stores basic information about the cargo sources, including cargo categories, transportation volume, transportation origin, destination, and transportation timeliness requirements; basic information about the transportation capacity, including the transportation entity, carrying capacity, transportation route coverage, and transportation cost standards; and historical scheduling data, including past matching schemes, execution effect feedback, and ownership adaptation cases.

[0028] Furthermore, the ownership verification method of the cargo traceability and ownership verification module includes electronic signature verification of ownership certificates, online verification of the identity information of the ownership subject, and cross-comparison of cargo ownership change records. When the verification result shows that the ownership is inconsistent, an early warning instruction is triggered and the execution of the relevant transportation links is suspended.

[0029] Furthermore, the data interface module is equipped with an interface adaptation and adjustment unit, which dynamically adjusts the interface parameter configuration according to the communication protocol type and data format requirements of the external interface system, adapting to data interaction with external systems of different architectures without the need for additional customized development.

[0030] Furthermore, the data storage module sets lifecycle management rules for various types of stored data, archives historical scheduling data that has exceeded the preset retention period and has no subsequent query needs, permanently stores cargo ownership data and traceability files, and supports multi-dimensional data retrieval based on cargo identification, time range, and ownership entity.

[0031] Compared with existing technologies, this big data scheduling system for matching supply and demand in bulk logistics has the following advantages:

[0032] This invention establishes a real-time connection mechanism between cargo ownership transfer information and the scheduling system, achieving deep coupling between dynamic changes in cargo ownership information and supply and demand scheduling. It dynamically adjusts matching parameters such as transport capacity type and transport batches, solving the problem of inaccurate matching caused by the separation of cargo ownership management and supply and demand scheduling in existing technologies. This improves the adaptability of supply and demand matching and transportation efficiency in bulk logistics. At the same time, by embedding cargo traceability and ownership verification links into the scheduling process, it effectively ensures the security of ownership during the cargo ownership transfer process, reduces the risk of cargo ownership disputes, and improves the reliability and security of supply and demand matching scheduling in bulk logistics. This provides technical support for the intelligent and safe development of the bulk logistics industry.

[0033] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0034] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0035] Figure 1 This is a schematic diagram of the structure of a big data scheduling system for matching supply and demand in bulk logistics.

[0036] Figure 2 A flowchart of a big data scheduling system for matching supply and demand in bulk logistics;

[0037] Figure 3 This is a flowchart of the cargo ownership information collection module during the collection of dynamic changes in cargo ownership information. Detailed Implementation

[0038] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0039] This invention addresses the problems of disconnect between cargo ownership management and supply-demand scheduling, and lack of security for ownership rights in existing systems. It proposes a big data scheduling system for matching supply and demand in bulk logistics, achieving deep coupling between dynamic cargo ownership and scheduling, and ensuring secure control throughout the entire transportation process. (See also...) Figure 1 and Figure 2 The technical solution is as follows:

[0040] The system consists of a cargo ownership information collection module, a data interface module, a supply and demand matching and scheduling module, a cargo traceability and ownership verification module, and a data storage module, forming a complete technical solution.

[0041] The cargo ownership information collection module is responsible for collecting dynamic changes in cargo ownership during the circulation of bulk commodities, including the types and timing of changes in cargo ownership transfers, warehouse receipt pledges, and batch delivery, the scope of goods involved, and the characteristics of the subject of ownership changes. It establishes a communication connection with the cargo ownership management system and warehouse receipt management system, clearly defining the collection trigger conditions and transmission frequency. After capturing relevant information, it performs format standardization processing and integrity verification. Once verification is successful, the information is transmitted to the next module.

[0042] The data interface module adopts a RESTful standardized communication interface architecture to establish communication links with relevant external systems, ensuring compatibility with systems from different vendors. It transmits dynamic changes in ownership information via encrypted transmission protocols, and includes a data transmission anomaly monitoring mechanism to trigger retransmissions in case of delays or data loss. Furthermore, it allows for dynamic adjustment of interface parameter configurations to adapt to data interaction with external systems of different architectures.

[0043] The supply and demand matching and scheduling module pre-stores basic information on cargo sources, basic information on transportation capacity, transportation route information, and historical scheduling data. After receiving information on dynamic changes in cargo ownership, it associates and aligns with the pre-stored basic data, analyzes the impact of changes in cargo ownership on transportation demand, adjusts matching parameters such as transportation capacity type, transportation batch, and delivery node, screens candidate transportation capacity resources and quantitatively evaluates their suitability, generates a scheduling plan that matches the cargo ownership status, and synchronizes it to relevant execution entities and other modules.

[0044] The cargo traceability and ownership verification module is embedded in the scheduling scheme execution process. It pre-defines key data collection nodes such as transit points and warehousing during outbound transportation, clearly defining the location data, status data, and ownership certificate types to be collected at each node. Data is collected and uploaded using standardized methods to construct a traceability archive. Real-time verification is achieved through electronic signature verification of ownership certificates, online verification of cargo ownership change records, and cross-comparison. If verification fails, an alert is triggered and the transportation process is suspended; otherwise, execution continues.

[0045] The data storage module adopts a distributed storage architecture, divided into multiple storage partitions. Each partition is managed independently and linked by an index, supporting fast retrieval and batch export, and featuring off-site backup capabilities. Data lifecycle management rules are also set up to archive historical scheduling data and permanently store ownership data and traceability files.

[0046] This system improves the matching and adaptation of supply and demand in bulk logistics and transportation efficiency through the collaboration of various modules, ensures the security of cargo ownership transfer, reduces the risk of disputes, and provides technical support for the intelligent and safe development of the industry.

[0047] Example 1

[0048] This embodiment applies to the bulk logistics scenario of coal procurement in large steel enterprises. In this scenario, coal transportation involves large volumes, long distances, and frequent changes in ownership, often resulting in dynamic changes such as ownership transfers, warehouse receipt pledges, and partial pickups. Traditional dispatching systems, lacking effective integration with the ownership management system, cannot obtain real-time dynamic information on ownership, leading to a mismatch between transport capacity matching and ownership status. This not only wastes transportation resources but also frequently triggers ownership disputes. This embodiment utilizes a big data dispatching system for matching supply and demand in bulk logistics to achieve deep collaboration between dynamic changes in ownership and supply and demand scheduling. Simultaneously, it strengthens ownership verification throughout the entire transportation process, ensuring the accuracy and security of logistics transportation.

[0049] See Figure 1 and Figure 2The specific implementation process of this embodiment is as follows: The cargo ownership information collection module, as the core of the system data input, primarily establishes data communication connections with the external cargo ownership management system and warehouse receipt management system, clarifies the triggering conditions for information collection and the frequency of data transmission, and ensures real-time capture of dynamic changes in cargo ownership. In the application scenario of this embodiment, the cargo ownership change information collection condition is triggered when steel companies and coal suppliers complete the signing of cargo ownership transfer agreements, warehouse receipt pledge registration, or submit applications for partial delivery. See also... Figure 3 At this point, the module will accurately capture the type of change in ownership and the corresponding time point of the change, clearly define the specific scope of coal cargo involved, and avoid subsequent scheduling deviations due to ambiguity in the scope of cargo.

[0050] Subsequently, the module collects the identity information of the entities before and after the ownership change, including the qualification information of the transferor, transferee, pledgee, and other relevant entities, as well as the corresponding authorization certificate information, to ensure the legality of the entities involved in the change of ownership. After collection, the various types of information are standardized in format to ensure smooth data interaction between subsequent modules. Finally, the processed information is verified for completeness. After confirming that no critical information is missing, the dynamic change of ownership information is transmitted to the data interface module to provide accurate data support for subsequent scheduling.

[0051] The data interface module adopts a RESTful standardized communication interface architecture, successfully establishing communication links with the existing title management system and warehouse receipt management system of steel enterprises. This achieves compatibility with systems from different vendors, eliminating the need for additional customized development. During data transmission, the module uses an encrypted transmission protocol to encrypt dynamic title change information, preventing data tampering or leakage during transmission. Simultaneously, the module incorporates a data transmission anomaly monitoring mechanism to monitor data transmission status in real time. In the event of transmission delays or data loss, the system automatically triggers retransmission commands, ensuring the complete and timely transmission of dynamic title change information to the supply and demand matching and scheduling module. Furthermore, the module has a built-in interface adaptation and adjustment unit that dynamically adjusts interface parameter configurations according to the communication protocol type and data format requirements of external systems, adapting to the data interaction needs of external systems with different architectures and ensuring data transmission compatibility and stability.

[0052] The supply and demand matching and scheduling module pre-stores basic information on cargo sources, transportation capacity, transportation routes, and historical scheduling data. The basic cargo source information includes coal type, transportation volume, origin, destination, and delivery time requirements. The basic transportation capacity information includes the entity owning the capacity, carrying capacity, route coverage, and transportation cost standards. The historical scheduling data includes past matching schemes, execution feedback, and ownership adaptation cases. Upon receiving dynamic changes in cargo ownership information from the data interface module, the module first aligns this information with the pre-stored basic cargo source and transportation capacity information to ensure effective linkage between cargo ownership information and cargo and transportation capacity data.

[0053] Subsequently, the impact of cargo ownership changes on transportation demand is analyzed using a built-in algorithm model to determine the adjustment direction and priority of supply and demand matching parameters. In the specific implementation of this embodiment, the parameter adjustment range is determined using a formula. Calculation, where For the first The adjustment range of the supply and demand matching parameters. For the first The sensitivity coefficient of the item parameter to changes in ownership is determined based on statistical analysis of historical correlation data between different parameters and various types of ownership changes. It can reflect the sensitivity of different matching parameters to changes in ownership. For the first The impact of changes in ownership of goods is quantified by the scale of the goods involved and the timeliness requirements of the change. The system's dynamic correction coefficients are determined by fitting feedback data from recent scheduling plans, allowing for real-time correction of parameter adjustment deviations. Based on adjustment priorities and calculated adjustment ranges, the module sequentially optimizes capacity type selection, transportation batch division, and delivery node planning parameters. For example, when ownership is transferred and the cargo volume is large, capacity type parameters are adjusted first to screen candidate capacity resources that meet the requirements. If there are strict time requirements for cargo ownership changes, the focus is on optimizing delivery node planning parameters to ensure timely delivery of goods.

[0054] After selecting candidate transport capacity resources that meet the cargo ownership status requirements, the suitability between the candidate transport capacity resources and the cargo sources is quantitatively evaluated. In the specific implementation of this embodiment, the suitability evaluation uses a formula. Calculation, where To ensure a good overall match between candidate transport capacity and cargo sources, For cargo ownership fit, it is used to measure whether candidate transport capacity meets the requirements of the current cargo ownership status. To assess the matching degree of transportation capacity resources, this reflects the degree to which the carrying capacity, transportation routes, and other aspects of candidate transportation capacity match the demand for goods. The transportation process adaptability reflects the compatibility between the transportation process of candidate transport capacity and the cargo ownership transfer process. , , The weighting coefficients are determined by analyzing the impact of various adaptation factors on the execution effect of the plan in historical scheduling data. Based on the evaluation results, the module determines the final scheduling plan and synchronizes it with relevant carriers, warehousing companies, and other implementing entities, as well as the data storage module.

[0055] The cargo traceability and ownership verification module is embedded throughout the entire scheduling process. The scheduling plan pre-defines key data collection nodes such as cargo outbound, during transportation, transit nodes, and warehousing, and specifies the location data, status data, and ownership certificate types to be collected at each node. In the coal outbound process, data such as outbound documents, transport vehicle information, outbound location, and time are collected. During transportation, vehicle location data is collected in real time through a positioning system, while simultaneously recording status data such as cargo temperature and humidity. At transit nodes, cargo transit handover documents, transit location, and time information are collected. In the warehousing process, warehousing documents, cargo acceptance status data, and ownership certificate information are collected.

[0056] Data and documentation information at each key node are acquired through standardized collection methods, uploaded to the system in real time, and linked to the unique identifier of the corresponding coal cargo to ensure accurate data-cargo matching. Based on the collected end-to-end data, the module organizes data relationships chronologically to build a complete cargo traceability file, enabling full traceability of cargo transportation. In the ownership verification stage, the module uses three methods for real-time verification: electronic signature verification of ownership documents, online verification of ownership entity identity information, and cross-comparison of cargo ownership change records.

[0057] In the specific implementation of this embodiment, the formula is used. Calculate the ownership consistency index, where This is the ownership consistency index. For the matching degree of electronic signatures on ownership certificates, To verify the consistency of the identity information of the ownership entity, To determine the consistency of cross-referencing records of changes in ownership, , , To verify the dimensional weights, the error recognition rates of each dimension were statistically analyzed from a large number of ownership verification samples. This formula is used to quantitatively assess ownership consistency, verifying the consistency between the identity of the picking-up and receiving entities and the ownership entity, thus completing dynamic ownership verification. When the verification result shows an inconsistency in ownership, the system immediately triggers an early warning command and suspends the execution of the relevant transportation links. Transportation resumes only after the problem is investigated and resolved. If the verification passes, the transportation process continues, and the collected data, traceability files, and verification results are transmitted to the data storage module.

[0058] The data storage module adopts a distributed storage architecture, dividing the received data and related files into four partitions: ownership data storage partition, basic information storage partition, scheduling plan storage partition, and traceability verification data storage partition. Each partition is managed independently and linked through indexes to ensure the orderliness and efficiency of data management. The module supports fast data retrieval and batch export, and also has off-site backup capabilities to effectively guarantee the security and integrity of data storage.

[0059] In terms of data lifecycle management, the module has established clear management rules. Historical scheduling data that exceeds the preset retention period and has no subsequent query needs is archived to release storage resources. Cargo ownership data and traceability files are permanently stored to provide a basis for subsequent cargo ownership tracing and dispute resolution. In addition, the module supports multi-dimensional data retrieval based on cargo identification, time range, and ownership entity, making it convenient for steel companies, carriers, regulatory agencies, and other relevant parties to quickly query the data they need and improve data utilization efficiency.

[0060] In summary, this embodiment, through the complete implementation of a big data scheduling system for matching supply and demand in bulk logistics, successfully achieved real-time integration of cargo ownership transfer information with the scheduling system, deeply coupling dynamic changes in cargo ownership with supply and demand scheduling. By dynamically adjusting matching parameters such as capacity type, transportation batches, and delivery nodes, it solved the problem of inaccurate matching caused by the separation of cargo ownership management and supply and demand scheduling in traditional systems, significantly improving the adaptability and transportation efficiency of bulk logistics supply and demand matching, and effectively reducing the waste of transportation resources.

[0061] Example 2

[0062] This embodiment applies to the logistics scenario of bulk building materials such as cement and sand in a large building materials group. In this scenario, the transportation of building materials involves multiple production bases and distribution nodes, and the ownership of the goods needs to be frequently transferred between production bases, regional distributors, and end-construction parties. Moreover, some building materials are often financed through warehouse receipt pledges, and the ownership status is significantly affected by the pledge registration and release process. Traditional dispatching systems have failed to adapt to the dynamic changes in ownership of building materials flowing through multiple nodes, resulting in poor coordination between transportation plans and ownership, distribution needs, and problems such as capacity mismatch and transportation delays. At the same time, the lack of ownership verification during the warehouse receipt pledge period can easily lead to financing disputes and the risk of misappropriation of goods. This embodiment uses a big data dispatching system for matching supply and demand in bulk logistics to adapt to the characteristics of ownership transfer in building materials involving multiple entities and multiple scenarios, achieving precise coordination between dispatching plans and ownership status and distribution needs, and strengthening ownership control during the pledge period.

[0063] See Figure 1 and Figure 2The specific implementation process of this embodiment is as follows: Based on the aforementioned embodiment, the cargo ownership information collection module further expands the data collection dimensions. In addition to establishing communication connections with the cargo ownership management system and the warehouse receipt management system, it also synchronously connects with the production management system and distribution management system of the building materials group. It clarifies that the information collection trigger conditions cover scenarios such as cargo ownership transfer, warehouse receipt pledge and release, batch delivery, and changes in distribution nodes. The data transmission frequency is dynamically adjusted according to the production rhythm and distribution cycle of building materials.

[0064] The data collection conditions are triggered when the production base completes the delivery of building materials, the distributor and the end-user construction party sign a transfer agreement for the ownership of the goods, and the financial institution completes the procedures for pledging or releasing warehouse receipts. (See also...) Figure 3 The module accurately captures the type and time of ownership changes, defines the production batches, quantities and flow range of building materials involved, and collects related information such as outbound vouchers from production bases, inbound records from distributors, and registration certificates from pledge institutions. It cross-verifies the qualification documents and authorization letters of each entity before and after the ownership change, and after completing the format standardization and integrity verification, it transmits the integrated dynamic ownership change information to the data docking interface module.

[0065] The data interface module adopts a RESTful standardized communication interface architecture. Building upon compatibility with the aforementioned title management and warehouse receipt management systems, it further adapts to the industrial communication protocols of the production management system and the commercial data protocols of the distribution management system. Through an interface adaptation and adjustment unit, parameter configurations are dynamically adjusted to achieve seamless integration with multiple types of external systems without requiring additional custom development. During data transmission, an encrypted transmission protocol ensures information security. The established anomaly monitoring mechanism not only addresses transmission delays and data loss but also identifies data format conflicts between different systems, triggering format adaptation correction commands to ensure the complete and accurate transmission of dynamic title change information and related production and distribution data to the supply and demand matching and scheduling module.

[0066] The supply and demand matching and scheduling module pre-stores basic information about the goods, including building material-specific attributes such as cement grade, sand and gravel particle size, moisture-proof protection requirements, and production batch, as well as production capacity and inventory data of each production base. Basic transportation capacity information covers vehicle type information, carrying capacity, moisture-proof configuration, and distribution areas covered by transportation routes, such as tank trucks and enclosed trucks suitable for building material transportation. Historical scheduling data includes transportation loss cases of different building materials, connection schemes for multiple distribution nodes, and records of transportation restrictions during the pledge period.

[0067] After receiving the data, the module correlates and aligns the dynamic changes in ownership with pre-stored basic data and production and distribution data. It then analyzes the specific impact of ownership changes on building material transportation using a built-in algorithm model to determine the direction and priority of supply and demand matching parameter adjustments. In the specific implementation of this embodiment, the parameter adjustment range is determined using a formula. Calculations were performed to optimize the selection of transport capacity types based on the adjustment results. Priority was given to transport capacity with moisture-proof and spill-proof features. Transport batches were divided according to distribution areas and pickup batches. Delivery nodes were planned in conjunction with the construction period requirements of the end-user construction parties. Candidate transport capacity resources meeting the requirements of ownership status and building material transportation were screened. Subsequently, a formula was used... The adaptability is quantitatively evaluated to determine the final scheduling plan and synchronize it to the relevant execution entities and data storage modules.

[0068] The cargo traceability and ownership verification module is embedded throughout the entire scheduling scheme. Building upon the aforementioned key data collection nodes, a new distribution node handover process is added as a core data collection node. This module specifies that each node must collect data such as moisture-proof testing data for building materials, packaging integrity information, production batch numbers, and distribution handover documents. After acquiring data through standardized collection methods, the unique cargo identifier and production batch number are linked and uploaded in real time. Data relationships are then established chronologically and linked to the flow nodes to construct a traceability archive encompassing the entire production, transportation, and distribution chain.

[0069] The ownership verification process employs electronic signature verification of ownership certificates, online verification of the ownership entity's identity information, and cross-comparison of ownership change records. Simultaneously, it uses production batch numbers and pledge registration information for auxiliary verification. In the specific implementation of this embodiment, a formula is used. The system calculates the ownership consistency index to verify the consistency between the entity's identity and the ownership entity at each circulation node. If the verification result shows an inconsistency in ownership, the system immediately triggers an early warning command, suspends the relevant transportation links, and simultaneously notifies the production base, distribution nodes, and pledge institutions to jointly verify the information. If the verification passes, the transportation process continues, and the collected data, traceability files, and verification results are transmitted to the data storage module.

[0070] The data storage module adopts a distributed storage architecture, adding a production batch data storage partition on top of the aforementioned storage partitions. This partition categorizes and stores ownership data, basic information, scheduling plans, traceability verification data, and production batch data. Each partition is linked through an index, supporting rapid data retrieval and batch export, and includes off-site backup functionality. In data lifecycle management, historical scheduling data exceeding its retention period and without retrieval needs is archived. Ownership data, traceability files, production batch data, and distribution handover data are permanently stored. The module supports multi-dimensional data retrieval based on cargo identification, time range, ownership entity, production batch, and distribution region, facilitating queries and verification by building materials groups, distributors, construction companies, financial institutions, and other relevant parties.

[0071] In summary, this embodiment, through the targeted implementation of a big data scheduling system for matching supply and demand in bulk logistics, effectively adapts to the complex characteristics of the building materials industry, characterized by multiple production bases, multiple distribution nodes, and complex transfer of ownership. Building upon the technologies of the aforementioned embodiments, it achieves deep collaboration between dynamic ownership and production and distribution processes. By dynamically optimizing and adapting the transportation capacity and scheduling parameters for building materials, it solves the problems of poor transportation connections and capacity mismatch caused by multi-node ownership transfers, significantly improving the accuracy of supply and demand matching and transportation efficiency for bulk building materials.

[0072] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A big data scheduling system for matching supply and demand of bulk logistics, characterized in that, The system includes: a cargo ownership information collection module, a data interface module, a supply and demand matching and scheduling module, a cargo traceability and ownership verification module, and a data storage module; The cargo ownership information collection module collects dynamic changes in cargo ownership during the circulation of bulk commodities and transmits the collected dynamic changes in cargo ownership to the data interface module. The dynamic changes in cargo ownership include the types and times of changes such as cargo ownership transfer, warehouse receipt pledge, and partial delivery, the scope of goods involved, and the characteristics of the subject of the change of ownership. The data interface module establishes a communication link between the external systems related to the transfer of ownership and this system, and transmits the received dynamic change information of ownership to the supply and demand matching and scheduling module. The supply and demand matching and scheduling module pre-stores basic information on cargo sources, basic information on transportation capacity, transportation route information and historical scheduling data. After receiving information on dynamic changes in cargo ownership, it adjusts the supply and demand matching parameters of transportation capacity type, transportation batch and delivery node, generates a scheduling plan that adapts to the status of cargo ownership, and transmits the scheduling plan to the cargo traceability and ownership verification module and the data storage module respectively. The cargo traceability and ownership verification module is embedded in the scheduling scheme execution process, collects data on key aspects of cargo transportation and constructs traceability files, performs real-time verification of cargo ownership, and transmits the collected data, traceability files and verification results to the data storage module. The data storage module stores the received data and related files in categories. The supply and demand matching and scheduling module performs the following operations when performing supply and demand matching and generating scheduling schemes: Receive dynamic changes in cargo ownership information transmitted from the data interface module and align it with pre-stored basic cargo information and basic transportation capacity information; The impact of cargo ownership changes on transportation demand is analyzed using a built-in algorithm model to determine the direction and priority of adjustments to supply and demand matching parameters. The adjustment range of the parameters is determined using a formula. Calculate, where, For the first The adjustment range of the supply and demand matching parameters. For the first The sensitivity coefficient of the title to goods of the item parameter, For the first The intensity of the impact of changes in ownership of goods. For system dynamic correction coefficients; Based on the adjustment priority and the calculated adjustment range, the parameters for selecting capacity type, dividing transportation batches, and planning delivery nodes are optimized in sequence to screen out candidate capacity resources that meet the requirements of cargo ownership status. The matching degree between candidate transportation capacity resources and cargo sources is determined by a formula. A quantitative assessment is conducted, and based on the assessment results, a final scheduling plan is determined and synchronized to relevant execution entities and data storage modules. To ensure a good overall match between candidate transport capacity and cargo sources, For the fit and suitability of the ownership of the goods, For the matching degree of transportation capacity resources, For the adaptability of the transportation process, , , These are the weighting coefficients; The cargo traceability and ownership verification module performs the following operations when performing cargo traceability and ownership verification: The scheduling plan pre-sets key data collection nodes for goods leaving the warehouse, during transportation, transit nodes, and entering the warehouse, and clarifies the location data, status data, and ownership certificate types that need to be collected at each node; At each key node, corresponding data and voucher information are acquired through standardized collection methods, uploaded to the system in real time, and associated with the corresponding cargo identifier; A traceability archive is built based on the collected end-to-end data, and the data relationships are sorted out in chronological order; Cross-verify the ownership certificate information and dynamic change information of goods ownership from the traceability archives using a formula. Calculate the ownership consistency index, verify the consistency between the identity of the entity picking up and receiving the goods and the entity owning the goods, and complete the dynamic ownership verification. This is the ownership consistency index. For the matching degree of electronic signatures on ownership certificates, To verify the consistency of the identity information of the ownership entity, To determine the consistency of cross-referencing records of changes in ownership, , , To verify the dimensional weights.

2. The big data scheduling system for matching supply and demand in bulk logistics according to claim 1, characterized in that, The cargo ownership information collection module performs the following operations when collecting dynamic cargo ownership change information: Establish data communication connections with the title management system and warehouse receipt management system, and clarify the triggering conditions for information collection and the frequency of data transmission; When the conditions for collecting information on changes in ownership of goods are triggered, the type of change in ownership of goods and the corresponding time node of the change are captured, and the specific scope of the goods involved is defined. Collect the identity information of the main entity before and after the change of ownership, as well as relevant authorization certificate information, and standardize the format of the collected information. The processed information is verified for integrity. Once the verification is successful, the information is transmitted to the data interface module.

3. The big data scheduling system for matching supply and demand in bulk logistics according to claim 1, characterized in that, The data interface module adopts a RESTful standardized communication interface architecture, which is compatible with the cargo ownership management system and warehouse receipt management system of different manufacturers. It uses an encrypted transmission protocol to transmit dynamic cargo ownership change information and sets up a data transmission anomaly monitoring mechanism to trigger a retransmission command when transmission delay or data loss occurs.

4. The big data scheduling system for matching supply and demand in bulk logistics according to claim 1, characterized in that, The data storage module adopts a distributed storage architecture, which is divided into a cargo ownership data storage partition, a basic information storage partition, a scheduling scheme storage partition, and a traceability verification data storage partition. Each partition is managed independently and data is associated through an index.

5. A big data scheduling system for matching supply and demand in bulk logistics according to claim 1, characterized in that, The supply and demand matching and scheduling module pre-stores basic information about the cargo sources, including cargo categories, transportation volume, transportation origin and destination, and transportation timeliness requirements. Basic information about the transportation capacity includes the entity to which the transportation capacity belongs, carrying capacity, transportation route coverage, and transportation cost standards. Historical scheduling data includes past matching schemes, execution effect feedback, and ownership adaptation cases.

6. The big data scheduling system for matching supply and demand in bulk logistics according to claim 1, characterized in that, The ownership verification method of the cargo traceability and ownership verification module includes electronic signature verification of ownership certificates, online verification of the identity information of the ownership subject, and cross-comparison of cargo ownership change records. When the verification result shows that the ownership is inconsistent, an early warning instruction is triggered and the execution of the relevant transportation links is suspended.

7. A big data scheduling system for matching supply and demand in bulk logistics according to claim 1, characterized in that, The data interface module is equipped with an interface adaptation and adjustment unit, which dynamically adjusts the interface parameter configuration according to the communication protocol type and data format requirements of the external interface system to adapt to data interaction with external systems of different architectures.

8. A big data scheduling system for matching supply and demand in bulk logistics according to claim 1, characterized in that, The data storage module sets lifecycle management rules for various types of stored data, archives historical scheduling data that has exceeded the preset retention period and has no subsequent query needs, permanently stores cargo ownership data and traceability files, and supports multi-dimensional data retrieval based on cargo identification, time range, and ownership entity.

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