Internet of Things big data system for cross-subject logistics information collaboration

Abstract

The invention relates to the technical field of logistics information cooperative processing, and particularly discloses a cross-subject logistics information cooperative Internet of Things big data system. By constructing a cross-subject logistics information collaborative internet of things big data scheme, the problem of information isolated island caused by independence of information management systems among multiple subjects and data standard difference is solved, logistics data real-time sharing and collaborative decision-making are realized, the vehicle and goods matching efficiency and the storage resource utilization rate are improved, and the overall operation level of a logistics network is improved.

Description

Technical Field

[0001] This invention relates to the field of logistics information collaborative processing technology, and in particular to an Internet of Things big data system for cross-entity logistics information collaboration. Background Technology

[0002] With the rapid development of e-commerce and supply chain management, the logistics industry is undergoing digital transformation and upgrading. The Internet of Things (IoT) and big data technologies have been widely applied to warehouse monitoring, transportation tracking, and delivery scheduling, significantly improving the operational efficiency of individual logistics entities. However, modern logistics systems involve multiple stakeholders, including cargo owners, carriers, warehousing service providers, and end customers. Each entity typically deploys independent information management systems, employing differentiated data standards and communication protocols. This results in logistics data being scattered across heterogeneous platforms, creating severe information silos. This fragmented architecture makes it difficult to share key information such as transportation status, inventory levels, and demand forecasts in real time across the supply chain, leading to inefficient vehicle-cargo matching, idle warehousing resources, and redundant scheduling. Furthermore, existing IoT data acquisition devices lack unified data semantic standards when deployed across different entities. This makes it difficult to effectively integrate and analyze massive amounts of heterogeneous data for real-time collaborative decision-making, hindering dynamic optimization and risk warning at the supply chain level and restricting the overall operational efficiency of the logistics network.

[0003] Therefore, there is an urgent need to provide a technical solution to address the above problems. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides an IoT big data system for cross-entity logistics information collaboration. The technical solution of this system is as follows: The cross-entity heterogeneous data access module is used to collect raw logistics data from the independent information management systems of multiple logistics participants and convert the raw logistics data into standard format data that conforms to the unified data semantic specification. The data semantic mapping module is used to construct a cross-entity logistics data semantic model and map the differentiated data standards of each logistics participant to the unified data semantic specification through the cross-entity logistics data semantic model. The heterogeneous stream data fusion module is used to perform streaming association, aggregation and real-time status estimation on the standard format data according to the unified data semantic specification, and generate fused logistics status information. The cross-entity collaborative decision-making module is used to perform supply chain network collaborative optimization based on the integrated logistics status information, and generate collaborative scheduling instructions based on the results of the supply chain network collaborative optimization. The collaborative scheduling instructions are used to adjust transportation resources, warehousing resources, or distribution resources. The collaborative instruction distribution module is used to convert the collaborative scheduling instructions into a communication protocol format compatible with the independent information management system of each logistics participant, and send the converted collaborative scheduling instructions to the corresponding independent information management system of the logistics participant.

[0005] The technical solution of this invention solves the problem of information silos caused by independent information management systems and differences in data standards among multiple entities by constructing an IoT big data solution for cross-entity logistics information collaboration. It realizes real-time sharing of logistics data and collaborative decision-making, improves the efficiency of vehicle-cargo matching and the utilization rate of warehousing resources, and improves the overall operation level of the logistics network.

[0006] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0007] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0008] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 This is a schematic diagram of an embodiment of an IoT big data system for cross-entity logistics information collaboration according to the present invention. Detailed Implementation

[0009] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0010] Figure 1 This diagram illustrates the structure of an embodiment of an IoT big data system for cross-entity logistics information collaboration provided by the present invention. Figure 1 As shown, the system includes: The cross-entity heterogeneous data access module 110 is used to collect raw logistics data from the independent information management systems of multiple logistics participants and convert the raw logistics data into standard format data that conforms to the unified data semantic specification.

[0011] In this context, logistics participants refer to the various independent entities involved in the supply chain of logistics activities, including cargo owners, carriers, warehousing service providers, and end customers. For example, the cargo owner might be e-commerce company A, the carrier might be express delivery company B, the warehousing service provider might be cloud warehouse company C, and the end customer might be individual consumer D. Independent information management systems refer to the information platforms independently deployed by each logistics participant to manage their own business. For example, e-commerce company A might have an internal order management system, express delivery company B might have an internal transportation management system, and cloud warehouse company C might have an internal warehouse management system. These systems operate independently and are not directly interconnected. Raw logistics data refers to unprocessed data directly collected from the independent information management systems of the logistics participants; for example, order number "ORD001", product name "smartphone", and delivery address "No. 1, XX Road, XX District, XX City" collected from the order management system of e-commerce company A; vehicle number "V001", GPS coordinates "39.90°N 116.40°E", and driving speed "45" collected from the transportation management system of express delivery company B; and inventory SKU number "X001" and inventory quantity "500" collected from the warehouse management system of cloud warehouse company C.

[0012] The unified data semantic specification refers to a set of predefined, standardized rules for describing logistics data to eliminate discrepancies in data standards among various logistics participants. For example, it stipulates that all logistics participants must use "inventory quantity" as the field name, integers as the field type, "pieces" as the unit, and unsigned decimal numbers as the encoding rule when describing inventory levels. Standard format data refers to data obtained by converting raw logistics data according to the unified data semantic specification. For example, it converts "SKU X001, quantity 500 pieces" from the original data of C Cloud Warehouse Company into the format "Inventory SKU number X001, inventory quantity 500, inventory unit piece" which conforms to the unified data semantic specification.

[0013] The data semantic mapping module 120 is used to construct a cross-entity logistics data semantic model and map the differentiated data standards of each logistics participant to the unified data semantic specification through the cross-entity logistics data semantic model.

[0014] The cross-entity logistics data semantic model refers to a structured model that describes the mapping relationship between the differentiated data standards of various logistics participants and the unified data semantic specification. For example, this model defines how the "GPS coordinates" field of Express Company B is mapped to the "current location of the transport vehicle" field in the unified semantic specification, and defines the conversion rules from degree, minute, and second format to decimal format. Differentiated data standards refer to the different data definition rules formed by the independent information management systems of various logistics participants during long-term operation. For example, the order management system of E-commerce Company A uses the "order amount" field to represent the total amount of goods, while the transportation management system of Express Company B uses the "freight amount" field to represent transportation costs. The two have different definitions of the amount in terms of scope and precision.

[0015] The heterogeneous flow data fusion module 130 is used to perform streaming association, aggregation and real-time status estimation on the standard format data according to the unified data semantic specification, and generate fused logistics status information.

[0016] Among them, integrated logistics status information refers to information reflecting the overall status of the supply chain generated by associating and aggregating standard format data from multiple logistics participants. For example, by associating the order information of e-commerce company A, the vehicle location information of express delivery company B, and the inventory information of cloud warehouse company C, the overall status information "Order ORD001 currently has sufficient inventory, vehicle V001 has been assigned for delivery, and the vehicle's current location is 5 kilometers away from the delivery address" can be generated.

[0017] The cross-entity collaborative decision-making module 140 is used to perform supply chain network collaborative optimization based on the integrated logistics status information, and generate collaborative scheduling instructions based on the results of the supply chain network collaborative optimization. The collaborative scheduling instructions are used to adjust transportation resources, warehousing resources, or distribution resources.

[0018] Supply chain network collaborative optimization refers to the process of globally coordinating and optimizing resources across all links of the supply chain based on integrated logistics status information. For example, it involves calculating how to allocate vehicles to minimize overall delivery costs and maximize delivery time based on all current order information, vehicle location information, and warehouse inventory information. The result of supply chain network collaborative optimization refers to the resource allocation plan obtained after solving the supply chain network collaborative optimization problem. For example, the result after optimization calculation might be "adjust vehicle V001 from its current route to route B, and transfer 200 SKU X001 items from warehouse W001 to warehouse W002 to meet order demand in the area covered by warehouse W002." Collaborative scheduling instructions refer to executable commands generated based on the results of supply chain network collaborative optimization to guide various logistics participants in adjusting resources. For example, a scheduling instruction might be generated: "Vehicle V001 immediately departs from its current location to warehouse W001 to load 200 SKU X001 items and delivers them to warehouse W002 before 18:00 on the same day."

[0019] Transportation resources refer to various capacity elements involved in the logistics transportation process, including transport vehicles, drivers, and transport routes; for example, vehicle V001, driver Zhao, and pre-set transport route L001 owned by B Express Transportation Company. Warehousing resources refer to various warehousing elements involved in warehousing management, including warehouse locations, inventory, and warehousing equipment; for example, shelf R01, SKU X001 stored on warehouse W001 owned by C Cloud Warehouse Company, and forklift equipment F01. Delivery resources refer to various delivery elements involved in the last-mile delivery process, including delivery vehicles, delivery personnel, and delivery stations; for example, electric tricycle D001 responsible for last-mile delivery, deliveryman Qian, and regional delivery station S001.

[0020] The collaborative instruction distribution module 150 is used to convert the collaborative scheduling instruction into a communication protocol format compatible with the independent information management system of each logistics participant, and send the converted collaborative scheduling instruction to the corresponding independent information management system of the logistics participant.

[0021] The communication protocol format refers to the specific data encapsulation and transmission rules adopted by the independent information management systems of each logistics participant during data exchange. For example, E-commerce Company A's order management system uses the HTTP protocol to transmit JSON format data, while Express Delivery Company B's transportation management system uses the MQTT protocol to transmit binary format data. The converted collaborative dispatch instruction refers to the data packet obtained by encapsulating the collaborative dispatch instruction according to the communication protocol format of the target independent information management system. For example, the collaborative dispatch instruction is encapsulated into an MQTT protocol binary data packet that conforms to the requirements of Express Delivery Company B's transportation management system, containing the instruction type field "vehicle_dispatch", the vehicle number field "V001", and the route field "R002".

[0022] The technical solution in this embodiment solves the problem of information silos caused by the independent information management systems and differences in data standards among multiple entities by constructing an IoT big data solution for cross-entity logistics information collaboration. It realizes real-time sharing of logistics data and collaborative decision-making, improves the efficiency of vehicle-cargo matching and the utilization rate of warehousing resources, and improves the overall operation level of the logistics network.

[0023] In one alternative embodiment, the cross-subject heterogeneous data access module 110 is specifically used for: Each logistics participant's independent information management system is connected via a predefined interface adapter, and corresponding raw logistics data is collected from each participant's independent information management system.

[0024] Among them, the predefined interface adapter refers to: a pre-configured software interface component used to connect the independent information management systems of different logistics participants; for example, an MQTT protocol adapter configured for the transportation management system of B Express Company, which can send and receive data according to the interface specifications of the transportation management system.

[0025] The raw logistics data of each logistics participant is analyzed, and the corresponding logistics element information is identified and extracted from the raw logistics data of each logistics participant.

[0026] Among them, logistics element information refers to key data elements with business meaning extracted from raw logistics data; for example, order number "ORD001", consignee name "Li", and contact number "13800000000" extracted from the raw logistics data of E-commerce Company A.

[0027] The logistics element information of each logistics participant is mapped and reorganized according to the unified data semantic specification to generate standard format data that conforms to the unified data semantic specification.

[0028] Field mapping and format reorganization refers to the process of adjusting the field names and numerical formats in logistics element information according to a unified data semantic specification. For example, the field "Total order price 199.00 yuan" in the original data of Company A e-commerce is mapped to "Order amount 199.00" in the unified semantic specification, and the currency unit "yuan" is unified to the "RMB yuan" required by the specification.

[0029] Among the above-mentioned optional methods, it is even more important to achieve standardized access to the independent information management systems of different logistics participants, converting differentiated raw data into standard data in a unified format, thus providing a foundation for subsequent data processing.

[0030] In an alternative embodiment, the data semantic mapping module 120 is specifically used for: Obtain data standard definition information from the independent information management system of each logistics participant.

[0031] Among them, the data standard definition information refers to the information describing the data structure definition in the independent information management system of the logistics participants; for example, the data dictionary of the transportation management system of B Express Transportation Company defines "vehicle table containing fields: vehicle number varchar(20), GPS coordinates varchar(50), speed float".

[0032] The data standard definition information of each logistics participant is parsed to identify the data field names, data field types, data coding rules, and data unit definitions used by each logistics participant when describing logistics elements.

[0033] The data field name refers to the name used to identify data attributes in the data standard definition information; for example, "Vehicle Number," "GPS Coordinates," and "Speed" in the B Express Transportation Company's transportation management system. The data field type refers to the data type specified in the data standard definition information; for example, the "Vehicle Number" field in the B Express Transportation Company's transportation management system is a string, and the "Speed" field is a floating-point number. The data encoding rule refers to the encoding method specified in the data standard definition information; for example, the "GPS Coordinates" field in the B Express Transportation Company's transportation management system uses the WGS84 coordinate system and a degree-minute-second format. The data unit definition refers to the unit of measurement for the data field specified in the data standard definition information; for example, the "Speed" field in the B Express Transportation Company's transportation management system is in kilometers per hour.

[0034] A commonality analysis and difference comparison are conducted on the data field names, data field types, data coding rules, and data unit definitions of all logistics participants. Based on the results of the commonality analysis and difference comparison, a unified descriptive framework covering all logistics participants is abstracted.

[0035] The commonality analysis and difference comparison refer to the process of comparing the data standard definition information of all logistics participants to identify commonalities and differences. For example, comparing the data standards of e-commerce company A, express delivery company B, and cloud warehouse company C reveals that all three companies have a "time" field, but company A uses a timestamp format, company B uses a date and time format, and company C uses a string format—this is a difference. All three companies have a "number" field, and all are string types—this is a commonality. The result of the commonality analysis and difference comparison refers to the analytical conclusions obtained after performing commonality analysis and difference comparison on the data standard definition information. For example, the analysis result might be: "All participants have a time field, but the format is inconsistent; all have a number field, and the type is consistent. It is recommended to unify the time field to a timestamp format." The unified description framework refers to a unified data structure description that can cover all logistics elements of all logistics participants, abstracted from the results of the commonality analysis and difference comparison. For example, the unified description framework stipulates that logistics elements include five core dimensions: "entity number, entity type, timestamp, geographical location, and status value," each with standard data type and format requirements.

[0036] The cross-entity logistics data semantic model is constructed based on the unified description framework. The cross-entity logistics data semantic model defines unified data field names, unified data field types, unified data encoding rules, and unified data unit definitions. The unified data field names, unified data field types, unified data encoding rules, and unified data unit definitions together constitute the unified data semantic specification.

[0037] A mapping relationship is established between differentiated data standards and the unified data semantic specification for each logistics participant. The original data field names, original data field types, original data encoding rules, and original data unit definitions in the differentiated data standards of each logistics participant are respectively mapped to the corresponding unified data field names, unified data field types, unified data encoding rules, and unified data unit definitions in the unified data semantic specification.

[0038] The mapping relationship refers to the correspondence between the differentiated data standards of each logistics participant and the unified data semantic specification. For example, the "GPS coordinates" field of B Express Transportation Company is mapped to the "geographic location" field in the unified data semantic specification. The conversion rule is to convert the degree, minute and second format to the decimal format.

[0039] Among the above-mentioned optional approaches, a unified data semantic specification can be further constructed to establish a mapping relationship between differentiated data standards and unified specifications, thereby achieving semantic mutual recognition and standardized conversion of heterogeneous data.

[0040] In an alternative embodiment, the heterogeneous stream data fusion module 130 is specifically used for: The system receives standard format data from multiple logistics participants in accordance with the unified data semantics specification. The standard format data is continuously flowing streaming data.

[0041] Among them, continuously flowing streaming data refers to data that is constantly generated and transmitted into the system in real time over time; for example, the transportation management system of B Express Transportation Company sends the real-time GPS coordinate data of vehicle V001 to the system every 10 seconds, forming a continuous data stream.

[0042] The streaming data is partitioned according to the data type of logistics elements. Each partition corresponds to the same type of logistics element. The types of logistics elements include at least one of inventory quantity, transportation location, vehicle speed, order status, and delivery time.

[0043] Within each partition, the streaming data is divided into time windows according to a preset time window strategy, and each time window contains streaming data within the corresponding time interval.

[0044] The preset time window strategy refers to the pre-defined rules for dividing streaming data into time windows; for example, setting the time window length to 5 minutes, the sliding step to 1 minute, and calculating the average vehicle speed over the past 5 minutes every 1 minute.

[0045] The streaming data within each time window is aggregated and calculated to obtain the intermediate state value of the logistics elements corresponding to each time window.

[0046] Aggregation calculation refers to the process of performing statistical calculations on multiple data points within a time window to obtain characteristic values ​​representing that window. For example, aggregating the 30 GPS coordinates reported by vehicle V001 within a 5-minute time window yields the average driving speed within that window. Intermediate state values ​​of logistics elements refer to values ​​representing the state of a single time window obtained by aggregating streaming data within that window. For example, the average speed of vehicle V001 within the 09:00 to 09:05 time window is calculated to be 45 kilometers per hour.

[0047] The intermediate state values ​​of logistics elements in different zones are correlated and calculated according to preset cross-element association rules to generate the fused logistics state information.

[0048] Among them, the preset cross-element association rules refer to the pre-defined rules used to describe the relationship between different logistics elements; for example, the rule stipulates that "when the inventory level is lower than the safety stock and the order volume increases, the association triggers a replenishment warning."

[0049] Among the above-mentioned optional methods, multi-entity streaming data can be further partitioned and aggregated with time windows to achieve cross-factor correlation calculations and generate integrated logistics status information that reflects the overall status of the supply chain.

[0050] In one alternative approach, the heterogeneous streaming data fusion module 130 performs aggregation calculations on the streaming data within each time window, including: Obtain the time series sequence of streaming data within each time window, wherein the time series sequence contains multiple data points arranged in chronological order.

[0051] Among them, the time sequence of streaming data refers to the data sequence formed by arranging streaming data in chronological order; for example, the GPS coordinates reported by vehicle V001 between 09:00 and 09:05 are arranged in chronological order as (09:00, coordinate A), (09:01, coordinate B), (09:02, coordinate C)...(09:05, coordinate F).

[0052] The position weight of each data point within the current time window is determined based on the timestamp of each data point, and the position weight is negatively correlated with the time difference between the data point and the current time.

[0053] Among them, position weight refers to the computational weight value assigned to a data point based on its timestamp being close to the current time. For example, the closer a data point is to the current time, the greater its position weight and the greater its contribution to the calculation of the current state.

[0054] The following formula is used to perform weighted aggregation calculations on the streaming data within each time window to obtain the intermediate state value of the logistics element corresponding to the current time window.

[0055]

[0056] in, This indicates the intermediate state value of the logistics element corresponding to the current time window. This indicates the total number of data points within the current time window. This represents the value of the i-th data point within the current time window. This represents the timestamp of the i-th data point. Indicates the current moment. Represents the time decay coefficient and , This represents the time decay weight factor for the i-th data point.

[0057] It should be noted that the weighted aggregation formula based on time decay is constructed by assigning a weight factor to each data point within the current time window, which decays exponentially as the time difference between its timestamp and the current moment increases. The weighted sum of all data points is then divided by the sum of the weight factors, thereby achieving smooth aggregation of streaming data. The above formula makes the data closer to the current moment contribute more to the aggregation result, thus accurately reflecting the real-time dynamic characteristics of logistics elements within the time window.

[0058] Among the above-mentioned optional methods, a time decay weighting strategy is further adopted to aggregate and calculate the data points within the window, so that the data points closer to the current time are given higher weights, thereby improving the real-time performance and accuracy of the intermediate state values ​​of logistics elements.

[0059] In one optional approach, the heterogeneous flow data fusion module 130 performs correlation calculations on the intermediate state values ​​of logistics elements in different partitions according to preset cross-element correlation rules, including: Obtain the intermediate state value of the first logistics element corresponding to the first partition and the intermediate state value of the second logistics element corresponding to the second partition. The first logistics element and the second logistics element belong to different logistics element types.

[0060] The first partition refers to one of the partitions obtained after dividing streaming data according to the type of logistics element. This partition corresponds to a specific type of logistics element. For example, if the vehicle speed data in the streaming data is assigned to the speed partition, this speed partition is the first partition. The first logistics element intermediate state value refers to the intermediate state value of the logistics element obtained after aggregating and calculating the streaming data within the first partition. For example, the average speed of 45 kilometers per hour obtained after aggregating and calculating the speed data of vehicle V001 within the speed partition during the time window from 09:00 to 09:05 is the first logistics element intermediate state value.

[0061] The second partition refers to another partition obtained after partitioning the streaming data according to the type of logistics element. This partition corresponds to a different type of logistics element than the first partition. For example, if the vehicle location data in the streaming data is assigned to the location partition, this location partition is the second partition. The intermediate state value of the second logistics element refers to the intermediate state value of the logistics element obtained after aggregating and calculating the streaming data within the second partition. For example, the center point coordinates (116.40°E, 39.90°N) obtained after aggregating and calculating the location data of vehicle V001 within the location partition during the time window from 09:00 to 09:05 are the intermediate state value of the second logistics element.

[0062] The type of association between the first logistics element and the second logistics element is determined based on a preset association rule base. The type of association includes causal relationship, synergistic relationship, or constraint relationship.

[0063] The pre-defined association rule base refers to a database that pre-stores rules governing the relationships between various logistics elements. For example, the association rule base stores rules such as "the association between speed data and location data can calculate road segment traffic efficiency" and "the association between inventory data and order data can calculate inventory turnover rate." The type of association relationship refers to the specific classification of the relationship between two logistics elements, including causal relationships, synergistic relationships, or restrictive relationships. For example, transportation speed and delivery timeliness are causal, with increased transportation speed leading to shorter delivery times; inventory quantity and order fulfillment rate are synergistic, with sufficient inventory resulting in a high order fulfillment rate; and warehouse capacity and inbound quantity are restrictive, with limited warehouse capacity limiting inbound quantity.

[0064] Based on the association relationship type, the intermediate state values ​​of the first logistics element and the second logistics element are associated and fused to generate the fused logistics element state information.

[0065] Among them, the integrated logistics element status information refers to the new information obtained by integrating the intermediate status values ​​of logistics elements from multiple zones according to their correlation relationships; for example, after integrating vehicle speed data and vehicle location data, the road segment efficiency information of "vehicle V001 has an average travel speed of 45 kilometers per hour on road segment X" is obtained.

[0066] The merged logistics element status information is combined with the intermediate status values ​​of other partitioned logistics elements that have not participated in the fusion to form the merged logistics status information.

[0067] Among the above-mentioned optional methods, further identification of causal, synergistic, or restrictive relationships between different logistics elements and related fusion calculations are performed to form multi-dimensional fused logistics status information to support supply chain collaborative decision-making.

[0068] In an alternative embodiment, the cross-agent collaborative decision-making module 140 is specifically used for: The fused logistics status information is obtained from the heterogeneous flow data fusion module. The fused logistics status information includes the real-time inventory status, real-time transportation status, real-time order status, and real-time delivery status of each logistics participant.

[0069] Real-time inventory status refers to dynamic information reflecting the current inventory situation at each warehouse node; for example, at the current moment, warehouse W001 has 500 units of SKU X001 in stock, and warehouse W002 has 200 units of SKU X001 in stock. Real-time transportation status refers to dynamic information reflecting the current transportation status of each transport vehicle; for example, at the current moment, vehicle V001 is located at coordinates (116.40°E, 39.90°N), traveling east at a current speed of 45 km / h, and is expected to arrive at warehouse W001 in 30 minutes. Real-time order status refers to dynamic information reflecting the current processing progress of each order; for example, order ORD001 is currently in the "picked" status, and order ORD002 is currently in the "transporting" status. Real-time delivery status refers to dynamic information reflecting the current execution status of each delivery task; for example, delivery task DEL001 is currently in the "delivery person has departed" status and is expected to arrive at the customer's address in 20 minutes.

[0070] The current inventory level and available storage capacity of each warehousing node are determined based on the real-time inventory status in the integrated logistics status information. The on-the-way location and estimated arrival time of each transport vehicle are determined based on the real-time transportation status in the integrated logistics status information. The current processing progress of each order is determined based on the real-time order status in the integrated logistics status information. The current execution status of each delivery task is determined based on the real-time delivery status in the integrated logistics status information.

[0071] The current inventory level refers to the current inventory quantity at each storage node; for example, the current inventory level of warehouse W001 is 500 units, and the current inventory level of warehouse W002 is 200 units. Available storage capacity refers to the remaining space available for storing goods at each storage node; for example, warehouse W001 has a total capacity of 1000 units, with 500 units already stored, resulting in an available storage capacity of 500 units; warehouse W002 has a total capacity of 500 units, with 200 units already stored, resulting in an available storage capacity of 300 units. En route location refers to the current geographical location of the transport vehicle during transportation; for example, vehicle V001's current en route location is (116.40°E, 39.90°N). Estimated arrival time refers to the estimated time when the transport vehicle will arrive at its destination; for example, vehicle V001 is expected to arrive at warehouse W001 in 30 minutes, at 15:30. Current processing progress refers to the percentage of processes or the current stage of processing an order; for example, order ORD001 has completed picking and packing and is currently in the pending shipment stage, with a processing progress of 60%. Current execution status refers to the execution status and completion degree of a delivery task at the current moment; for example, delivery task DEL001 has been picked up by the delivery person and is en route to the customer's address, with 70% of the journey completed.

[0072] A supply chain network collaborative optimization model is constructed based on the current inventory level of each warehousing node, the on-the-way location of each transport vehicle, the current processing progress of each order, and the current execution status of each delivery task. The supply chain network collaborative optimization model takes minimizing the total cost of the supply chain network as the objective function and uses warehousing capacity constraints, transportation capacity constraints, and order timeliness constraints as constraints.

[0073] The supply chain network collaborative optimization model refers to a mathematical model built based on integrated logistics status information to solve for the optimal allocation of supply chain resources. For example, a linear programming model is constructed with the objective of minimizing the total delivery cost of all orders, and constraints such as the inventory capacity of each warehouse, the transportation capacity of each vehicle, and the timeliness requirements of each order. The total cost of the supply chain network refers to the sum of all costs incurred during the operation of the supply chain, including transportation costs, warehousing costs, and delay costs. For example, the current total cost of the supply chain network is 5000 yuan for transportation costs, 2000 yuan for warehousing costs, and 300 yuan for delay costs, totaling 7300 yuan. The warehousing capacity constraint means that the inventory of each warehousing node in the supply chain network collaborative optimization model cannot exceed its available warehousing capacity. For example, the inventory of warehouse W001 cannot exceed its available warehousing capacity of 500 units, and the inventory of warehouse W002 cannot exceed its available warehousing capacity of 300 units. Transportation capacity constraints refer to the following: In the supply chain network collaborative optimization model, the transportation workload of each vehicle cannot exceed its transportation capacity limit; for example, if vehicle V001 has a maximum load capacity of 5 tons, the total weight of the transportation tasks assigned to this vehicle must not exceed 5 tons. Order timeliness constraints refer to the following: In the supply chain network collaborative optimization model, the completion time of each order must meet the order timeliness requirements; for example, if order ORD001 requires delivery before 18:00 on the same day, the estimated completion time of this order in the model must be earlier than 18:00.

[0074] Solving the supply chain network collaborative optimization model yields the resource optimization allocation scheme among the various logistics participants.

[0075] Among them, the resource optimization allocation scheme refers to the optimal allocation scheme of each logistics resource obtained after solving the supply chain network collaborative optimization model; for example, the scheme obtained after solving the model is "vehicle V001 is responsible for transporting orders ORD001 and ORD002. After loading goods from warehouse W001, ORD001 is delivered first. The 200 pieces of SKU X001 in warehouse W002 are reserved for tomorrow's orders".

[0076] Based on the resource optimization allocation scheme, collaborative scheduling instructions are generated, including transportation resource scheduling instructions, warehousing resource scheduling instructions, and distribution resource scheduling instructions.

[0077] Among them, transportation resource scheduling instructions refer to specific instructions generated based on resource optimization allocation schemes to adjust transportation resources; for example, generating a transportation resource scheduling instruction: "Vehicle V001 immediately proceeds to warehouse W001 to load goods for orders ORD001 and ORD002, and after loading, delivers them according to route L002." Warehousing resource scheduling instructions refer to specific instructions generated based on resource optimization allocation schemes to adjust warehousing resources; for example, generating a warehousing resource scheduling instruction: "Warehouse W001 transfers 200 items from SKU X001 to the shipping temporary storage area, preparing for loading; Warehouse W002 reserves 200 items in stock until 9:00 AM tomorrow." Delivery resource scheduling instructions refer to specific instructions generated based on resource optimization allocation schemes to adjust delivery resources; for example, generating a delivery resource scheduling instruction: "Deliveryman Qian is responsible for the last-mile delivery of order ORD001. After picking up the package from delivery station S001, he delivers it according to route R003, expected to arrive before 5:00 PM."

[0078] Among the above-mentioned optional methods, a supply chain network collaborative optimization model is further constructed and solved based on the integrated logistics status information to generate transportation, warehousing and distribution resource scheduling instructions, thereby realizing the optimal allocation of supply chain resources.

[0079] In one optional approach, the cross-entity collaborative decision-making module 140 constructs a supply chain network collaborative optimization model based on the current inventory level of each warehousing node, the en route location of each transport vehicle, the current processing progress of each order, and the current execution status of each delivery task, including: Get the current inventory level of each storage node. and available storage capacity , where k represents the warehouse node number.

[0080] Obtain the on-route location coordinates of each transport vehicle. and estimated arrival time , where v represents the transport vehicle number.

[0081] Get the current processing progress of each order and order timeliness requirements , where o represents the order number.

[0082] Get the current execution status of each delivery task. and delivery route information , where l represents the delivery task number.

[0083] Construct an objective function based on the following formula, aiming to minimize the total cost of the supply chain network:

[0084] in, This represents the total cost of the supply chain network. This indicates the index of transport vehicles. This represents the unit distance transportation cost coefficient for the v-th transport vehicle. This indicates the current position of the v-th transport vehicle. With the target location The transportation distance between them Indicates the index of the storage node. This represents the cost coefficient per unit of idle storage capacity for the k-th storage node. This represents the available storage capacity of the k-th storage node. This represents the current inventory level of the k-th storage node. Indicates the order index. This represents the unit delay cost coefficient for the o-th order. This indicates the current estimated completion time for the 0th order. This indicates the timeliness requirement for the o-th order.

[0085] It should be noted that the objective function for minimizing the total cost of the supply chain network is constructed by accumulating the product of the unit distance transportation cost of all transport vehicles and the travel distance, the product of the unit idle storage capacity cost of all warehousing nodes and the available storage capacity minus the current inventory level, and the product of the unit delay cost of all orders and the positive difference between the current estimated completion time of the order and the order's timeliness requirement. This unified and quantified the transportation cost, storage idle cost, and order delay cost into the total cost of the supply chain network. The above formula provides a clear optimization objective for the supply chain network collaborative optimization model, driving the resource scheduling scheme to solve in the direction of minimizing cost.

[0086] Among the above-mentioned optional approaches, a multi-constraint optimization model is further established with the goal of minimizing the total cost of the supply chain network. This model comprehensively considers transportation distance, idle warehouse capacity, and order delay costs to achieve optimal resource allocation.

[0087] In an alternative embodiment, the cooperative instruction distribution module 150 is specifically used for: The collaborative scheduling instruction is received from the cross-entity collaborative decision-making module 140. The collaborative scheduling instruction includes an instruction identifier, an instruction type, instruction parameters, and the identifier of the target logistics participant.

[0088] Based on the identifier of the target logistics participant, the communication protocol type and interface address of the corresponding target independent information management system are queried in the pre-configured communication protocol mapping table.

[0089] Based on the queried communication protocol type, the collaborative scheduling instruction is encapsulated into a protocol data packet compatible with the target independent information management system.

[0090] The protocol data packet is sent to the interface address of the target independent information management system through the interface adapter corresponding to the target logistics participant.

[0091] In the above-mentioned optional methods, the collaborative scheduling instructions are further converted into a communication protocol format compatible with the target independent information management system and distributed for execution, so as to ensure the accurate transmission and implementation of instructions between different systems.

[0092] In an alternative embodiment, the system further includes a semantic model dynamic extension module, which is used to: The system monitors new logistics participants and, when a new logistics participant is detected joining, obtains the data standard definition information of the new logistics participant's independent information management system.

[0093] The data standard definition information of the newly added logistics participants is parsed to identify the new data field names, new data field types, new data encoding rules, and new data unit definitions used by the new logistics participants when describing logistics elements.

[0094] The data standard definition information of the newly added logistics participants is compared with the existing cross-entity logistics data semantic model to determine whether the existing unified data semantic specification fully covers all logistics elements of the newly added logistics participants.

[0095] If the existing unified data semantic specification does not fully cover all logistics elements of the newly added logistics participants, the cross-entity logistics data semantic model is extended according to the data standard definition information of the newly added logistics participants. New unified data field names, new unified data field types, new unified data encoding rules, or new unified data unit definitions are added to the cross-entity logistics data semantic model to form the extended unified data semantic specification.

[0096] Establish a mapping relationship between the differentiated data standards of the newly added logistics participants and the expanded unified data semantic specification, and store the mapping relationship in the mapping relationship library.

[0097] Among the above-mentioned optional methods, dynamic access of new logistics participants is further supported. By expanding the cross-entity logistics data semantic model and establishing mapping relationships, the system can achieve adaptive support for the expansion of the supply chain network.

[0098] The above description is merely a preferred embodiment of the present invention and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this invention is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-disclosed concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this invention.

[0099] It should be noted that the terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and do not imply a specific order or sequence. Where appropriate, the order of use for similar objects can be interchanged so that the embodiments of this application described herein can be implemented in an order other than that shown or described.

[0100] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A cross-entity logistics information collaboration Internet of Things big data system, characterized in that, The system includes: The cross-entity heterogeneous data access module is used to collect raw logistics data from the independent information management systems of multiple logistics participants and convert the raw logistics data into standard format data that conforms to the unified data semantic specification. The data semantic mapping module is used to construct a cross-entity logistics data semantic model and map the differentiated data standards of each logistics participant to the unified data semantic specification through the cross-entity logistics data semantic model. The heterogeneous stream data fusion module is used to perform streaming association, aggregation and real-time status estimation on the standard format data according to the unified data semantic specification, and generate fused logistics status information. The cross-entity collaborative decision-making module is used to perform supply chain network collaborative optimization based on the integrated logistics status information, and generate collaborative scheduling instructions based on the results of the supply chain network collaborative optimization. The collaborative scheduling instructions are used to adjust transportation resources, warehousing resources, or distribution resources. The collaborative instruction distribution module is used to convert the collaborative scheduling instructions into a communication protocol format compatible with the independent information management system of each logistics participant, and send the converted collaborative scheduling instructions to the corresponding independent information management system of the logistics participant.

2. The IoT big data system for cross-entity logistics information collaboration according to claim 1, characterized in that, The cross-entity heterogeneous data access module is specifically used for: Each logistics participant's independent information management system is connected via a predefined interface adapter, and corresponding raw logistics data is collected from each participant's independent information management system. The raw logistics data of each logistics participant is analyzed, and the corresponding logistics element information is identified and extracted from the raw logistics data of each logistics participant. The logistics element information of each logistics participant is mapped and reorganized according to the unified data semantic specification to generate standard format data that conforms to the unified data semantic specification.

3. The IoT big data system for cross-entity logistics information collaboration according to claim 2, characterized in that, The data semantic mapping module is specifically used for: Obtain the data standard definition information from the independent information management system of each logistics participant; The data standard definition information of each logistics participant is parsed to identify the data field names, data field types, data coding rules, and data unit definitions used by each logistics participant when describing logistics elements; A commonality analysis and difference comparison are conducted on the data field names, data field types, data coding rules, and data unit definitions of all logistics participants. Based on the results of the commonality analysis and difference comparison, a unified description framework covering all logistics participants is abstracted. The cross-entity logistics data semantic model is constructed based on the unified description framework. The cross-entity logistics data semantic model defines unified data field names, unified data field types, unified data encoding rules, and unified data unit definitions. The unified data field names, unified data field types, unified data encoding rules, and unified data unit definitions together constitute the unified data semantic specification. A mapping relationship is established between differentiated data standards and the unified data semantic specification for each logistics participant. The original data field names, original data field types, original data encoding rules, and original data unit definitions in the differentiated data standards of each logistics participant are respectively mapped to the corresponding unified data field names, unified data field types, unified data encoding rules, and unified data unit definitions in the unified data semantic specification.

4. The IoT big data system for cross-entity logistics information collaboration according to claim 3, characterized in that, The heterogeneous stream data fusion module is specifically used for: According to the unified data semantics specification, standard format data from multiple logistics participants is received, and the standard format data is continuously flowing streaming data; The streaming data is partitioned according to the data type of logistics elements. Each partition corresponds to the same type of logistics element. The types of logistics elements include at least one of inventory quantity, transportation location, vehicle speed, order status, and delivery time. Within each partition, the streaming data is divided into time windows according to a preset time window strategy, and each time window contains streaming data within a corresponding time interval; The streaming data within each time window is aggregated and calculated to obtain the intermediate state value of the logistics elements corresponding to each time window. The intermediate state values ​​of logistics elements in different zones are correlated and calculated according to preset cross-element association rules to generate the fused logistics state information.

5. The IoT big data system for cross-entity logistics information collaboration according to claim 4, characterized in that, The heterogeneous streaming data fusion module performs aggregation calculations on streaming data within each time window, including: Obtain the time series sequence of streaming data within each time window, wherein the time series sequence contains multiple data points arranged in chronological order; The position weight of each data point within the current time window is determined based on the timestamp of each data point, and the position weight is negatively correlated with the time difference between the data point and the current time. The following formula is used to perform weighted aggregation calculations on the streaming data within each time window to obtain the intermediate state value of the logistics element corresponding to the current time window: in, This indicates the intermediate state value of the logistics element corresponding to the current time window. This indicates the total number of data points within the current time window. This represents the value of the i-th data point within the current time window. This represents the timestamp of the i-th data point. Indicates the current moment. Represents the time decay coefficient and , This represents the time decay weight factor for the i-th data point.

6. The IoT big data system for cross-entity logistics information collaboration according to claim 5, characterized in that, The heterogeneous flow data fusion module performs correlation calculations on the intermediate state values ​​of logistics elements in different partitions according to preset cross-element correlation rules, including: Obtain the intermediate state value of the first logistics element corresponding to the first partition and the intermediate state value of the second logistics element corresponding to the second partition, wherein the first logistics element and the second logistics element belong to different logistics element types; The type of association between the first logistics element and the second logistics element is determined based on a preset association rule base. The type of association includes causal relationship, synergistic relationship, or constraint relationship. The heterogeneous flow data fusion module performs correlation and fusion calculations on the intermediate state values ​​of the first logistics element and the second logistics element according to the correlation relationship type, and generates fused logistics element state information. The merged logistics element status information is combined with the intermediate status values ​​of other partitioned logistics elements that have not participated in the fusion to form the merged logistics status information.

7. The IoT big data system for cross-entity logistics information collaboration according to claim 6, characterized in that, The cross-entity collaborative decision-making module is specifically used for: The fused logistics status information is obtained from the heterogeneous flow data fusion module. The fused logistics status information includes the real-time inventory status, real-time transportation status, real-time order status, and real-time delivery status of each logistics participant. The current inventory level and available storage capacity of each warehousing node are determined based on the real-time inventory status in the integrated logistics status information. The on-the-way location and estimated arrival time of each transport vehicle are determined based on the real-time transportation status in the integrated logistics status information. The current processing progress of each order is determined based on the real-time order status in the integrated logistics status information. The current execution status of each delivery task is determined based on the real-time delivery status in the integrated logistics status information. A supply chain network collaborative optimization model is constructed based on the current inventory level of each warehousing node, the on-the-way location of each transport vehicle, the current processing progress of each order, and the current execution status of each delivery task. The supply chain network collaborative optimization model takes minimizing the total cost of the supply chain network as the objective function and uses warehousing capacity constraints, transportation capacity constraints, and order timeliness constraints as constraints. Solving the supply chain network collaborative optimization model yields the resource optimization allocation scheme among the various logistics participants; Based on the resource optimization allocation scheme, collaborative scheduling instructions are generated, including transportation resource scheduling instructions, warehousing resource scheduling instructions, and distribution resource scheduling instructions.

8. The IoT big data system for cross-entity logistics information collaboration according to claim 7, characterized in that, The cross-entity collaborative decision-making module constructs a supply chain network collaborative optimization model based on the current inventory level of each warehousing node, the on-the-way location of each transport vehicle, the current processing progress of each order, and the current execution status of each delivery task, including: Get the current inventory level of each storage node. and available storage capacity , where k represents the warehouse node number; Obtain the on-route location coordinates of each transport vehicle. and estimated arrival time , where v represents the transport vehicle number; Get the current processing progress of each order and order timeliness requirements , where o represents the order number; Get the current execution status of each delivery task. and delivery route information , where l represents the delivery task number; Construct an objective function based on the following formula, aiming to minimize the total cost of the supply chain network: in, This represents the total cost of the supply chain network. This indicates the index of transport vehicles. This represents the unit distance transportation cost coefficient for the v-th transport vehicle. This indicates the current position of the v-th transport vehicle. With the target location The transportation distance between them Indicates the index of the storage node. This represents the cost coefficient per unit of idle storage capacity for the k-th storage node. This represents the available storage capacity of the k-th storage node. This represents the current inventory level of the k-th storage node. Indicates the order index. This represents the unit delay cost coefficient for the o-th order. This indicates the current estimated completion time for the 0th order. This indicates the timeliness requirement for the o-th order.

9. The IoT big data system for cross-entity logistics information collaboration according to claim 8, characterized in that, The collaborative instruction distribution module is specifically used for: The collaborative scheduling instruction is received from the cross-entity collaborative decision-making module. The collaborative scheduling instruction includes an instruction identifier, an instruction type, instruction parameters, and the identifier of the target logistics participant. Based on the identifier of the target logistics participant, the communication protocol type and interface address of the corresponding target independent information management system are queried in the pre-configured communication protocol mapping table; Based on the queried communication protocol type, the collaborative scheduling instruction is encapsulated into a protocol data packet compatible with the target independent information management system; The protocol data packet is sent to the interface address of the target independent information management system through the interface adapter corresponding to the target logistics participant.

10. The IoT big data system for cross-entity logistics information collaboration according to claim 9, characterized in that, The system also includes a semantic model dynamic expansion module, which is used for: Monitor new logistics participants; when a new logistics participant is detected to be connected, obtain the data standard definition information of the independent information management system of the new logistics participant. The data standard definition information of the newly added logistics participants is parsed to identify the new data field names, new data field types, new data coding rules, and new data unit definitions used by the new logistics participants when describing logistics elements. The data standard definition information of the newly added logistics participants is compared with the existing cross-entity logistics data semantic model to determine whether the existing unified data semantic specification fully covers all logistics elements of the newly added logistics participants. If the existing unified data semantic specification does not fully cover all logistics elements of the newly added logistics participants, the cross-entity logistics data semantic model is extended according to the data standard definition information of the newly added logistics participants. New unified data field names, new unified data field types, new unified data coding rules, or new unified data unit definitions are added to the cross-entity logistics data semantic model to form the extended unified data semantic specification. Establish a mapping relationship between the differentiated data standards of the newly added logistics participants and the expanded unified data semantic specification, and store the mapping relationship in the mapping relationship library.

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