Supply chain whole-link collaborative management system and method based on multi-source heterogeneous data fusion

Abstract

The present application relates to the technical field of supply chain collaboration, in particular to a supply chain full-link collaborative management system and method based on multi-source heterogeneous data fusion, comprising an outbound signal convergence module, a transportation track linkage module, a station arrival feedback splicing module, a path structure fusion module and a collaborative mapping generation module. In the present application, through the structured convergence, time stamp calibration and index mapping of node data such as household outbound, transportation and station arrival, a bidirectional linkage relationship between barcodes and tracks is established, path state recognition and link interruption analysis are realized through dynamic matching of track numbers and time windows, path deviation trends are extracted and deviation records are constructed through a double-checking mechanism of the receiving and sending process, and the node data flow direction and state transition are integrated through the fusion of node timing and path structure, realizing full-process mapping integration from assembly outbound to receiving and signing process, and dynamic optimization and precise control of the household supply chain full link.

Description

Technical Field

[0001] This invention relates to the field of supply chain collaboration technology, and in particular to a supply chain end-to-end collaborative management system and method based on the fusion of multi-source heterogeneous data. Background Technology

[0002] Supply chain collaboration technology involves information sharing, resource sharing, and business collaboration among enterprises in procurement, production, warehousing, logistics, and sales. This includes planning, execution, and information collaboration among multiple organizations, encompassing order processing, inventory sharing, production planning collaboration, and logistics and distribution coordination among upstream and downstream enterprises. Through data integration and process reengineering, it improves the operational efficiency and responsiveness of the entire supply chain system, achieving dynamic optimization and visualized management of the entire supply chain. Traditional home furnishing supply chain end-to-end collaborative management systems refer to supply chain management platforms for the home furnishing manufacturing and distribution industries. These systems typically manage and coordinate various links in the supply chain, such as raw material procurement, production planning, warehousing and sorting, logistics and transportation, and sales distribution, through manual form recording, inter-departmental manual connections, and isolated information silos. They generally use single-source data entry and offline or semi-automatic data transmission methods for process connection. The lack of efficient data fusion methods and unified decision support mechanisms between business nodes leads to problems such as information lag, low collaboration efficiency, and uneven resource allocation throughout the entire chain.

[0003] In the traditional home furnishing supply chain, the entire supply chain collaborative management mainly relies on manual form recording and manual inter-departmental communication. There are obvious delays and errors in information transmission. The process connection adopts offline or semi-automatic mode, lacking efficient real-time data integration methods. Information silos are formed between different business nodes, making it difficult to carry out smooth upstream and downstream collaboration. Order flow and inventory feedback are not synchronized. The logistics and distribution process lacks trajectory linkage and dynamic monitoring capabilities. The overall operation process lacks a unified decision support mechanism, and resource allocation is difficult to adjust in real time based on actual data. This results in problems such as low collaborative efficiency, chaotic path control, and unbalanced supply rhythm, which seriously restricts the responsiveness and overall scheduling level of the supply chain system. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by proposing a collaborative management system and method for the entire supply chain based on the fusion of multi-source heterogeneous data.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a supply chain end-to-end collaborative management system based on multi-source heterogeneous data fusion includes:

[0006] The outbound signal aggregation module collects the outbound barcodes of home furnishing goods, collects the loading sequence number and outbound timestamp, synchronizes and calibrates the loading sequence number and outbound timestamp, and generates an outbound data index structure.

[0007] The transportation trajectory linkage module obtains the matching transportation vehicle trajectory information based on the outbound timestamp in the outbound data index structure, extracts the vehicle trajectory number, trajectory transfer time sequence and vehicle positioning continuity identifier, filters the data rows that meet the time matching conditions, and obtains the transportation link trajectory status result.

[0008] Based on the transportation link trajectory status results, the arrival feedback splicing module obtains the cargo receiving barcode, unloading sequence number and receiving timestamp recorded by the receiving warehouse, establishes a dual index based on the vehicle trajectory number and cargo receiving barcode and links it with the transportation trajectory to generate arrival node data records.

[0009] The path structure fusion module performs verification on the outbound barcode of the outbound data index structure and the receiving barcode of the arrival node data record and obtains the verification result. It calculates the absolute value of the difference between the loading sequence number and the unloading sequence number, calculates the time interval between the outbound timestamp and the receiving timestamp and obtains the offset time interval. It constructs a link path offset trend record by combining the verification result, the absolute value of the difference, and the offset time interval.

[0010] The collaborative mapping generation module records the offset trend of the link path, marks the nodes of the entire process from the assembly and delivery of home furnishing goods to receipt and signing, performs full-link mapping and integration, and outputs a full-link collaborative management record of the home furnishing supply chain.

[0011] As a further aspect of the present invention, during the process of the data row that meets the time matching condition, the time difference between the trajectory transfer time sequence and the outbound timestamp is calculated, and the trajectory records with the time difference less than the set transportation plan time window are determined as data rows that meet the time matching condition.

[0012] As a further embodiment of the present invention, the outbound data index structure includes a barcode identification mapping structure, loading sequence calibration value, and outbound timestamp parameter; the transportation link trajectory status result includes a trajectory matching number set, a trajectory time integrity flag, and a trajectory interruption discrimination flag; the arrival node data record includes a barcode feedback structure, a loading and unloading matching comparison set, and a node time record set; the link path offset trend record includes a loading and unloading sequence difference component, a time node offset interval, and a path offset judgment result; and the home furnishing supply chain full-link collaborative management record includes a full-process time chain, a transportation receiving node sequence, and a path trend mapping result.

[0013] As a further aspect of the present invention, the outbound signal aggregation module includes:

[0014] The data receiving submodule acquires the barcode data of home furnishing goods leaving the warehouse collected by the fixed scanning equipment at the end of the factory assembly line. At the same time, it collects the scanning time signal and the corresponding loading sequence number. The three types of data are numbered and matched according to the output order of the scanning equipment to identify the one-to-one correspondence between the goods leaving the warehouse barcode, the warehouse timestamp and the loading sequence number, and generate the original mapping set of barcode loading.

[0015] The field calibration submodule uses the cargo exit barcode, loading sequence number, and exit timestamp from the original barcode loading mapping set to index and compare the loading sequence number and exit timestamp based on the barcode number, and generates a barcode field synchronous calibration result set.

[0016] The index generation submodule synchronizes and calibrates the outbound barcodes, loading sequence numbers, and outbound timestamps in the result set based on the barcode fields. It constructs an outbound data index structure with the outbound barcodes as the main index, the loading sequence number as the sequence attribute, and the outbound timestamp as the time-series attribute.

[0017] As a further aspect of the present invention, the transportation trajectory linkage module includes:

[0018] The trajectory data extraction submodule obtains the outbound timestamp from the outbound data index structure, collects the trajectory information of the corresponding transport vehicle, extracts the vehicle trajectory number, trajectory transfer time sequence and vehicle positioning continuity identifier, and generates the original vehicle trajectory information set.

[0019] The time matching and filtering submodule calculates the absolute value of the time difference with the outbound timestamp based on the trajectory transfer time sequence in the original vehicle trajectory information set, calculates the time matching deviation of each trajectory, filters the vehicle trajectory number data rows that are less than the set transportation plan time window, and obtains the transportation time matching trajectory number set.

[0020] The trajectory status recognition submodule matches the trajectory number set with the transportation time and the corresponding trajectory data line, reads the vehicle positioning continuity identifier and performs sequence determination, identifies interrupted segments and marks the start and end timestamps of the segments, counts the number of interrupted segments and the set of durations, and generates the transportation link trajectory status result.

[0021] As a further aspect of the present invention, the formula for calculating the time matching deviation is as follows:

[0022] ;

[0023] in, This represents the time matching deviation of the i-th trajectory. This represents the timestamp of the j-th transit point on the i-th trajectory. Represents the timestamp of the outbound shipment. This represents the distance between the j-th transfer point and its adjacent positioning points. Represents the speed of the j-th transfer segment. The continuity flag for the j-th transfer point takes a value of 0 or 1. This represents the number of transit points in the trajectory.

[0024] As a further aspect of the present invention, the arrival feedback splicing module includes:

[0025] The barcode data extraction submodule obtains the vehicle trajectory number from the transportation link trajectory status result, collects barcode record data within the corresponding time period of the receiving warehouse, extracts the cargo receiving barcode, unloading sequence number and receiving timestamp, and generates a receiving warehouse barcode record set;

[0026] The field index construction submodule establishes a one-to-one correspondence between the goods receiving barcode and the vehicle trajectory number in the corresponding transportation link trajectory status result based on the goods receiving barcode in the receiving warehouse barcode record set. It also constructs a dual index by combining the unloading sequence number and the receiving timestamp, and generates a barcode vehicle dual index structure.

[0027] The feedback structure splicing submodule splices the information sequentially according to the cargo receiving barcode, vehicle trajectory number, unloading sequence number and receiving timestamp in the barcode vehicle dual-index structure to generate the arrival node data record.

[0028] As a further aspect of the present invention, the path structure fusion module includes:

[0029] The barcode verification submodule obtains the outbound barcode from the outbound data index structure and the receiving barcode from the arrival node data record, performs verification operations on both, identifies inconsistent character positions, and generates barcode character verification results.

[0030] The path offset calculation submodule extracts the corresponding loading sequence number and unloading sequence number based on the barcode character verification result, performs the absolute value calculation of the difference between the sequence numbers, and calculates the time interval between the outbound timestamp and the receiving timestamp to obtain the path sequence difference value set and the offset time interval set.

[0031] The trend record construction submodule integrates the record indexes from the three data sources based on the path order difference value set, the offset time interval set, and the barcode character verification results, and establishes a structured mapping according to the index rows to generate link path offset trend records.

[0032] As a further aspect of the present invention, the collaborative mapping generation module includes:

[0033] The node labeling submodule obtains the link path offset trend record, combines the outbound data index structure and the arrival node data record, extracts the outbound timestamp, receipt timestamp, loading sequence number and unloading sequence number of the corresponding home furnishing goods, and labels each node in the process of home furnishing goods from outbound to receipt in chronological order, generating a full-process node labeling set for the goods.

[0034] The sequence integration submodule performs sequence rearrangement on the node data according to the time sequence based on the cargo full-process node annotation set, constructs an ordered linked list structure for the three stages of outbound, transportation and receiving, and generates a full-link time series structure set.

[0035] The output module records and constructs the collaborative structure of each home furnishing product throughout the entire supply chain based on the full-link time series structure set, and establishes a full-link collaborative management record for the home furnishing supply chain.

[0036] The supply chain end-to-end collaborative management method based on multi-source heterogeneous data fusion includes the following steps:

[0037] S1: Collect the barcode of home furnishing goods leaving the warehouse, collect the loading sequence number and the outbound timestamp, synchronize and calibrate the loading sequence number and the outbound timestamp, and generate an outbound data index structure.

[0038] S2: Based on the outbound timestamp in the outbound data index structure, obtain the matching transport vehicle trajectory information, extract the vehicle trajectory number, trajectory transfer time sequence and vehicle positioning continuity identifier, filter the data rows that meet the time matching conditions, and obtain the transport link trajectory status result;

[0039] S3: Based on the transportation link trajectory status results, obtain the goods receiving barcode, unloading sequence number and receiving timestamp recorded in the receiving warehouse, establish a dual index based on the vehicle trajectory number and the goods receiving barcode and link it with the transportation trajectory to generate arrival node data records;

[0040] S4: Based on the outbound data index structure, the outbound barcode of the goods and the goods receiving barcode of the arrival node data record are verified and the verification result is obtained. The absolute value of the difference between the loading sequence number and the unloading sequence number is calculated. The time interval between the outbound timestamp and the receiving timestamp is calculated and the offset time interval is obtained. The link path offset trend record is constructed by combining the verification result, the absolute value of the difference, and the offset time interval.

[0041] S5: Based on the link path offset trend record, perform full-link mapping and integration of the corresponding nodes of the entire process from the assembly and delivery of home furnishing goods to receipt and signing, and output the full-link collaborative management record of the home furnishing supply chain.

[0042] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0043] In this invention, a two-way linkage relationship between barcodes and trajectories is established by structured aggregation, timestamp marking, and index mapping of data from nodes such as home furnishing outbound, transportation, and arrival. Path status identification and link interruption analysis are achieved through dynamic matching of trajectory numbers and time windows. Path offset trends are extracted and offset records are constructed through a dual verification mechanism in the sending and receiving process. By integrating the data flow and status transition of each node through the fusion of node time sequence and path structure, the entire process from assembly outbound to receipt and signing is mapped and integrated. This improves the synchronization of data in each link of the supply chain, the accuracy of path control, and the immediacy of feedback response between collaborative nodes, thereby achieving dynamic optimization and precise control of the entire home furnishing supply chain. Attached Figure Description

[0044] Figure 1 This is a system flowchart of the present invention;

[0045] Figure 2 This is a flowchart of the outbound signal aggregation module of the present invention;

[0046] Figure 3 This is a flowchart of the transportation trajectory linkage module of the present invention;

[0047] Figure 4 This is a flowchart of the arrival feedback splicing module of the present invention;

[0048] Figure 5 This is a flowchart of the path structure fusion module of the present invention;

[0049] Figure 6 This is a flowchart of the collaborative mapping generation module of the present invention. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0051] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0052] Please see Figure 1The supply chain end-to-end collaborative management system based on multi-source heterogeneous data fusion includes:

[0053] The outbound signal aggregation module acquires the outbound barcodes of home furnishing goods collected by the fixed scanning equipment at the end of the factory assembly line, collects the loading sequence number and outbound timestamp, and synchronizes and calibrates the loading sequence number and outbound timestamp according to the outbound barcode number to generate an outbound data index structure.

[0054] The transportation trajectory linkage module locates the transportation vehicles based on the outbound timestamp in the outbound data index structure, obtains matching transportation vehicle trajectory information, extracts the vehicle trajectory number, trajectory transfer time series and vehicle positioning continuity identifier, and filters data rows that meet the time matching conditions (calculates the time difference between the trajectory transfer time series and the outbound timestamp, and determines the trajectory records with a time difference less than the set transportation plan time window as data rows that meet the time matching conditions), and synchronously identifies trajectory interruption segments within the set transportation plan time window to obtain the transportation link trajectory status result;

[0055] The arrival feedback splicing module obtains the barcode data recorded by the receiving warehouse based on the vehicle trajectory number in the transportation link trajectory status result, extracts the cargo receiving barcode, unloading sequence number and receiving timestamp, establishes a dual index based on the vehicle trajectory number and cargo receiving barcode, splices them to form a feedback structure linked to the transportation process trajectory, and generates arrival node data records.

[0056] The path structure fusion module performs verification based on the outbound barcode of the outbound data index structure and the goods receiving barcode of the arrival node data record, and obtains the verification result. It calculates the absolute value of the difference between the loading sequence number and the unloading sequence number, calculates the time interval between the outbound timestamp and the receiving timestamp, and obtains the offset time interval. It then constructs a link path offset trend record by combining the verification result, the absolute value of the difference, and the offset time interval.

[0057] The collaborative mapping generation module records the offset trend of the link path, marks the nodes of the entire process from the assembly and delivery of home furnishing goods to receipt and signing, performs full-link mapping and integration in chronological order, and outputs a collaborative management record of the entire home furnishing supply chain.

[0058] The outbound data index structure includes a barcode identification mapping structure, loading sequence calibration value, and outbound timestamp parameter. The transportation link trajectory status results include a trajectory matching number set, trajectory time integrity flag, and trajectory interruption discrimination flag. The arrival node data records include a barcode feedback structure, loading and unloading matching comparison set, and node time record set. The link path offset trend records include loading and unloading sequence difference components, time node offset intervals, and path offset judgment results. The home furnishing supply chain full-link collaborative management records include the full-process time chain, transportation receiving node sequence, and path trend mapping results.

[0059] Please see Figure 2 The outbound signal aggregation module includes:

[0060] The data receiving submodule acquires the barcode data of home furnishing goods leaving the warehouse collected by the fixed scanning equipment at the end of the factory assembly line. At the same time, it collects the scanning time signal and the corresponding loading sequence number. The three types of data are numbered and matched according to the output order of the scanning equipment to identify the one-to-one correspondence between the goods leaving the warehouse barcode, the warehouse timestamp and the loading sequence number, and generate the original mapping set of barcode loading.

[0061] The data receiving submodule is deployed on both sides of the conveyor belt at the end of the furniture factory assembly line. The core hardware includes a set of industrial-grade fixed high-frequency barcode scanners and an edge computing gateway. It captures the Code 128 or QR Code format barcodes on the outer packaging of furniture goods in real time. To ensure the accuracy of the time data, a high-precision clock synchronization unit is integrated internally, using the NTP protocol to maintain synchronization with the factory's main server, ensuring that the accuracy error of the collected scanning time signal is strictly controlled within a certain range. Within ms.

[0062] Meanwhile, the system directly reads register data from the production line control system via the PLC interface to obtain the loading sequence number corresponding to the goods currently passing through the scanning point in real time. This loading sequence number is a strictly increasing integer sequence. To address the physical delay (typically between 50ms and 200ms) between the scanning device output and the PLC signal transmission, a built-in FIFO (First-In-First-Out) circular buffer with a depth of 1024 is used to match the three types of data according to the order of physical interruption. In the preprocessing stage, the submodule performs deduplication logic: if the same goods exit barcode is received consecutively within 500ms, only the first frame of data is retained to generate the original barcode loading mapping set.

[0063] The field calibration submodule uses the cargo exit barcode, loading sequence number, and exit timestamp from the original barcode loading mapping set to index and compare the loading sequence number and exit timestamp based on the barcode number, and generates a barcode field synchronous calibration result set.

[0064] The field calibration submodule connects to the data receiving submodule, loads the original barcode loading mapping set, and uses an in-memory database for indexing and comparison. It includes a built-in "sliding window calibration logic," with the window size set to 50 units.

[0065] In practice, the data is logically sorted according to the barcode numbering rules (manufacturer code-category-batch-serial number). If a change in the loading sequence number is detected (e.g., jumping directly from 105 to 107), adjacent numbers are retrieved. Redundant logs within seconds are used to recover lost data. Monotonicity checks are performed on the outbound timestamps to ensure... If timestamps are found to be out of order, linear interpolation is used to correct them based on the increasing nature of the loading sequence number. The correction formula is as follows: After processing, a set of barcode field synchronization calibration results is generated.

[0066] The index generation submodule synchronizes and calibrates the outbound barcodes, loading sequence numbers, and outbound timestamps in the result set based on the barcode field. It constructs an outbound data index structure with the outbound barcodes as the main index, the loading sequence number as the sequence attribute, and the outbound timestamp as the time-series attribute.

[0067] The index generation submodule receives the barcode field synchronization calibration result set and constructs an outbound data index structure based on a B+ tree data structure. In this structure, the outbound barcode is hashed to a 64-bit long integer and used as the primary key, the loading sequence number is stored as a sequence attribute in the leaf nodes, and the outbound timestamp is used as a time-series attribute to create an auxiliary time range index. This structure is persistently stored in a distributed columnar database, supporting millisecond-level range queries based on time periods.

[0068] Please see Figure 3 The transportation trajectory linkage module includes:

[0069] The trajectory data extraction submodule obtains the outbound timestamp from the outbound data index structure, collects the trajectory information of the corresponding transport vehicles, extracts the vehicle trajectory number, trajectory transfer time series and vehicle positioning continuity identifier, and generates the original vehicle trajectory information set.

[0070] The trajectory data extraction submodule is configured to connect to the in-vehicle T-Box system via an HTTPS encrypted channel. Based on the outbound timestamp in the outbound data index structure, it initiates a query request to the data source to collect trajectory data within a 48-hour period after outbound departure. The collected data includes: vehicle unique identification code, BeiDou / GPS dual-mode coordinates, instantaneous speed, and positioning signal strength.

[0071] Simultaneously, "drift filtering" is performed: outliers with instantaneous speeds exceeding 120 km / h or adjacent point distances exceeding physical limits are removed. The processed data is then reconstructed into vehicle trajectory numbers and trajectory transfer time series (…). The system generates a vehicle location continuity identifier. This identifier is generated based on the sampling interval: if the interval between two points exceeds 300 seconds (5 minutes), the identifier is set to 0; otherwise, it is set to 1. This process ultimately generates the original vehicle trajectory information set.

[0072] The time matching and filtering submodule calculates the absolute value of the time difference between the vehicle trajectory transfer time series in the original trajectory information set and the outbound timestamp, sets the transportation plan time window, and performs time difference filtering using the following formula:

[0073] ;

[0074] The calculation obtains the time matching deviation for each trajectory, filters out vehicle trajectory number data rows whose deviations are less than the set transportation plan time window, and obtains the transportation time matching trajectory number set; among which... This represents the time matching deviation of the i-th trajectory. This represents the timestamp of the j-th transit point on the i-th trajectory. Represents the timestamp of the outbound shipment. This represents the distance between the j-th transfer point and its adjacent positioning points. Represents the speed of the j-th transfer segment. The continuity flag for the j-th transfer point takes a value of 0 or 1. This represents the number of transit points in the trajectory;

[0075] The time-matching filtering submodule incorporates a GPU-accelerated computing unit to filter transport vehicles from massive trajectories. The transport plan time window is set to 7200 seconds (2 hours). Based on the original vehicle trajectory information set, the following formula is used to calculate the value of each trajectory. Time matching deviation :

[0076] ;

[0077] in, The timestamp (in seconds) for the trajectory points; The timestamp for the outbound shipment (in seconds); For the first The spherical distance (in meters) between each intermediate turning point and the previous point; For the first Average speed of each transfer section (m / s, minimum value 0.1m / s); For continuous identification (0 or 1); This represents the number of trajectory points.

[0078] Set outbound timestamp (2026-01-14-08:00:00).

[0079] Select a route containing 3 transit points ( The candidate trajectories are calculated as follows:

[0080] Transfer point 1: (08:10:00), distance meters, speed m / s, continuous ;

[0081] Time difference Second;

[0082] Travel time item Second;

[0083] Weighted term calculation: ;

[0084] Transfer point 2: (08:20:00), distance meters, speed m / s, continuous ;

[0085] Time difference Second;

[0086] Travel time item Second;

[0087] Weighted term calculation: ;

[0088] Transfer point 3: (08:30:00), distance meters, speed m / s, continuous ;

[0089] Time difference Second;

[0090] Travel time item Second;

[0091] Weighted term calculation: ;

[0092] Summation and average (first term):

[0093] ;

[0094] ;

[0095] Variance / Root Mean Square (Second Term):

[0096] The values ​​are all 600;

[0097] sum of squares ;

[0098] mean ;

[0099] square root ;

[0100] Final result:

[0101] ;

[0102] Due to the calculation results (Settings window) The trajectory is determined to be a match, and its number is stored in the transportation time matching trajectory number set.

[0103] The trajectory status recognition submodule matches the trajectory number set with the corresponding trajectory data line according to the transportation time, reads the vehicle positioning continuity identifier and performs sequence determination, identifies interrupted segments and marks the start and end timestamps of the segments, counts the number of interrupted segments and the set of durations, and generates the transportation link trajectory status result.

[0104] The trajectory status recognition submodule reads the set of trajectory numbers matching the transportation time and iterates through the continuity markers of the trajectory data. If a "1-0-1" pattern is detected in the sequence, and the difference between the two timestamps corresponding to the "0" state exceeds 1800 seconds (30 minutes), it is marked as an "interrupted segment". The submodule counts the number and duration of interrupted segments. If the total interruption duration exceeds 20% of the total transportation time, the trajectory is marked as "high-risk", and a transportation link trajectory status result is generated.

[0105] During operation, the system incorporates built-in logic for automatically identifying and classifying abnormal road segments. Specifically, by calling anomaly analysis functions (such as `analyze_trajectory_exceptions`), it extracts the start and end timestamps of the segments marked as "interrupted segments" and their corresponding BeiDou / GPS dual-mode coordinates. Combined with a GIS map interface, it automatically identifies physically abnormal road segments (such as specific highway sections or service areas) where interruptions occur. Subsequently, it automatically classifies the anomalies (e.g., `classify_exception_type`) based on the duration of the interruption and geographical location characteristics, categorizing the anomalies into preset categories such as "traffic congestion," "vehicle malfunction," or "abnormal parking." The automatically identified abnormal road segments and automatically classified anomaly results are then structured and pushed to the front end for visualization.

[0106] Please see Figure 4 The arrival feedback stitching module includes:

[0107] The barcode data extraction submodule obtains the vehicle trajectory number from the transportation link trajectory status result, collects barcode record data of the receiving warehouse within the corresponding time period, extracts the cargo receiving barcode, unloading sequence number and receiving timestamp, and generates a receiving warehouse barcode record set;

[0108] The barcode data extraction submodule is configured as the physical data collection entry point at the supply chain receiving end and is widely deployed at the unloading platforms of regional distribution centers or retail forward warehouses. Its core hardware consists of a cluster of industrial-grade wireless handheld PDA devices, which maintain a real-time connection with the WMS (Warehouse Management System) server of the receiving warehouse via a high-bandwidth local area network.

[0109] When a vehicle fully loaded with household goods arrives at the unloading area, operators use the PDA's laser scanning engine to read the barcodes on the outer packaging, configured to capture the goods' receiving barcodes in real time. Simultaneously, the PDA's internal operating system kernel calls the underlying RTC (Real-Time Clock) interface to generate a receiving timestamp accurate to milliseconds. To ensure a consistent time base, all PDA devices automatically execute NTP time synchronization commands during the power-on handshake phase.

[0110] Furthermore, by calling the WMS task interface, the metadata of the current unloading task is obtained, and an unloading sequence number is automatically generated or associated. This sequence number is a strict counter that logically reflects the physical order in which goods leave the wagon (e.g., the first piece of goods unloaded is marked as...). (The second item is 2, and so on). To prevent data loss, an SQLite cache is set up locally on the PDA. Even in the event of a momentary network interruption, scanned data can be temporarily stored and automatically resumed after the network is restored. The submodule extracts and aggregates all uploaded records within the corresponding time period of the receiving warehouse (i.e., the effective window of 6 to 72 hours after the outbound timestamp) and performs strict "integrity verification" logic: any record with missing barcode values, abnormal timestamps, or duplicate sequence numbers will be automatically marked as "dirty data" and isolated to the exception queue. Only compliant data is retained to generate the receiving warehouse barcode record set.

[0111] The field index construction submodule establishes a one-to-one correspondence between the goods receiving barcode and the vehicle trajectory number in the corresponding transportation link trajectory status result based on the goods receiving barcode in the receiving warehouse barcode record set. It also constructs a dual index by combining the unloading sequence number and the receiving timestamp, generating a barcode vehicle dual index structure.

[0112] The main task of the field index construction submodule is to establish a logical bridge between the goods entity and the transport vehicle. It receives the receiving warehouse barcode record set from the scanning terminal and the transport link trajectory status results generated by the upstream module. Since a specific barcode can only be transported by one truck in a single logistics cycle, this physical constraint is utilized to perform high-speed matching operations based on hash mapping.

[0113] In practice, the system first uses the "goods receiving barcode" to look up the "outbound data index structure" generated by the first module to obtain the accurate "outbound timestamp" of the goods. Then, using this outbound timestamp as the search key, the system locates the unique vehicle trajectory number that performed the transportation task within that time window in the "transportation link trajectory status results." Through this indirect but rigorous logical chain, a one-to-one correspondence between the "goods receiving barcode" and the "vehicle number" is successfully established.

[0114] Building upon this, a high-efficiency in-memory data structure—the barcode vehicle dual-index structure—was constructed. This structure employs a nested HashMap design to support bidirectional indexing. Query capabilities with varying complexity:

[0115] Forward index: The Vehicle_ID is the key, and the value is a list of all barcodes carried by that vehicle. This allows the system to quickly respond to requests such as "What cargo was unloaded by vehicle T-HN8829?"

[0116] Reverse index: Using the barcode as the key and a structure containing {Vehicle_ID, Sequence_ID, Receive_Timestamp} as the value. This allows the system to quickly locate the transportation and unloading details of any item.

[0117] Experimental tests show that, in scenarios involving the concurrent ingestion of 100,000 SKUs, the construction time of this dual-index structure is less than 500ms, which greatly improves the efficiency of subsequent data splicing.

[0118] The feedback structure splicing submodule splices the information sequentially according to the dual-index matching method based on the cargo receiving barcode, vehicle trajectory number, unloading sequence number and receiving timestamp in the barcode vehicle dual-index structure to generate the arrival node data record;

[0119] The feedback structure splicing submodule is configured as a standardized data assembly pipeline. Based on the constructed barcode vehicle dual-index structure, it assembles data fragments scattered across different dimensions (barcode information, vehicle trajectory, unloading physical order, and reception time) into a complete logical closed-loop record.

[0120] Traverse each item node in the dual-index structure and perform the following core operations:

[0121] First, extract the cargo receipt barcode and the corresponding vehicle trajectory number;

[0122] Secondly, obtain the unloading sequence number of the goods during unloading ( );

[0123] Next, the received timestamps, accurate to the second, are extracted and the time format is standardized to convert them to UnixTimeStamp (long integer format) in the UTC time zone to eliminate time ambiguity caused by cross-time zone repositories.

[0124] Finally, the four core information fields mentioned above are serialized and encapsulated according to a predefined JSON Schema to generate arrival node data records. This record not only confirms that the goods have been "delivered," but also details "who delivered them," "when they were delivered," and "their relative location at the time of unloading," providing a solid data foundation for subsequent path structure fusion and difference analysis. It is then pushed in batches to a distributed message queue, awaiting processing by downstream modules.

[0125] Please see Figure 5 The path structure fusion module includes:

[0126] The barcode verification submodule obtains the outbound barcode from the outbound data index structure and the receiving barcode from the arrival node data record, performs verification operations on both, identifies inconsistent character positions, and generates barcode character verification results.

[0127] The barcode verification submodule is configured as a quality control checkpoint throughout the supply chain, aiming to identify and quantify barcode loss or tampering during the circulation process. It also connects to the outbound data index structure (to obtain the original "goods outbound barcode") and the arrival node data record (to obtain the "goods receiving barcode").

[0128] Instead of relying on simple string equality checks, it executes a more sophisticated "character position XOR comparison algorithm." The specific execution process is as follows: the system will output the barcode... With receiving barcode Convert to a standard ASCII byte array. Then, sort by index. Compare the elements of two arrays one by one. If If so, then the site is determined to be different.

[0129] Built-in intelligent fault-tolerant logic:

[0130] If the number of inconsistent characters is 0, the verification status is marked as "PASS".

[0131] If the number of characters is inconsistent If the characters belong to a similar character set (e.g., the number 0 and the letter O, the number 1 and the letter I, or B and 8), the submodule will call the OCR error correction algorithm to determine it as a "scanning recognition error", automatically correct the received barcode and mark the status as "WARN-FIXED".

[0132] If the number of characters is inconsistent If the difference character is not a similar character, the submodule determines that "label replacement" or "severe contamination" has occurred, and marks the status as "FAIL".

[0133] The final barcode character verification result records in detail the index of the difference position and the comparison of the ASCII values ​​of the original character and the received character, providing technical evidence for subsequent traceability label printer malfunctions or violations during logistics.

[0134] The path offset calculation submodule extracts the corresponding loading sequence number and unloading sequence number based on the barcode character verification result, performs the absolute value calculation of the difference between the sequence numbers, and calculates the time interval between the outbound timestamp and the receiving timestamp to obtain the path sequence difference value set and the offset time interval set.

[0135] The path offset calculation submodule focuses on quantifying the degree of spatiotemporal distortion in the physical logistics process and is the core calculation unit for evaluating supply chain execution quality. Based on the verified matching records, the corresponding loading sequence number (set as...) is extracted. ) and unloading sequence number (set as ).

[0136] To quantify whether goods have been rearranged during transport (e.g., tipped over and rearranged due to sudden braking) or whether there have been inconsistencies in the loading and unloading sequence due to human error, a "stack inversion degree" calculation model is introduced. Assume the total number of pieces transported in this batch is... (In this embodiment, it is set) Under the ideal Last-In-First-Out (LIFO) loading and unloading logic, the first piece of goods loaded should be the 500th piece unloaded, and so on. The ideal unloading sequence for goods loaded onto a truck should be: .

[0137] Calculate the order difference value That is, the outbound timestamp With the received timestamp The difference.

[0138] Calculations were performed based on the data in Table 1:

[0139] For item Item_002:

[0140] Given: Loading sequence Actual unloading sequence Outbound time: 08:02:00; Receipt time: 14:15:00.

[0141] Calculate the ideal unloading sequence: .

[0142] Differences in calculation order: .

[0143] Calculation time interval: indicates that the position of the goods in the carriage has changed significantly, possibly due to reloading en route.

[0144] The calculated results and The data are integrated into a set of path sequence difference values ​​and an offset time interval set, providing quantitative indicators for subsequent trend analysis.

[0145] Table 1 Implementation Data Table for Path Offset Calculation

[0146] Item_001 1 500 08：00：00 14：00：00 0 6.00 Item_002 10 480 08：02：00 14：15：00 11 6.22 Item_003 50 450 08：10：00 14：30：00 1 6.33

[0147] The trend record construction submodule integrates the record indexes from the three data sources based on the path order difference value set, the offset time interval set, and the barcode character verification results, and establishes a structured mapping according to the index rows to generate the link path offset trend record.

[0148] The trend recording construction submodule is responsible for elevating micro-level individual product difference data to macro-level batch pattern insights. It loads the path sequence difference value set and the offset time interval set, and uses the K-Means clustering algorithm in unsupervised learning to perform feature stratification of the data.

[0149] In practice, the number of clusters is set. These correspond to three modes: "normal transportation," "minor disturbance," and "serious anomaly," respectively. The algorithm uses... (Order differences) and The (time interval) is a two-dimensional feature vector. After about 10-15 rounds of iteration and convergence, it outputs the cluster centers and the number of goods contained therein.

[0150] "Disorderly loading and unloading warning" indicates that the cargo on this train may be unstable or subject to rough handling.

[0151] If a certain cluster center Significantly exceeding the standard deviation range of the historical mean (e.g.) The system generates a "transportation delay warning".

[0152] The K-Means clustering algorithm described above is instantiated using a clustering analysis class (e.g., KMeansOffsetAnalyzer). It automatically reads data from the database containing order differences. ) and time interval ( The system generates a two-dimensional feature vector array, calls the clustering core function (e.g., `fit_predict`) to perform a specified number of iterations for convergence, and writes the final cluster label (normal transport, minor disturbance, severe anomaly) as a new field into the database, thereby automatically generating a complete link path offset trend record. Simultaneously, automated attribution logic is designed for the various warnings generated above. By calling the warning cause analysis function (e.g., `generate_warning_reason`), the feature data triggering the warning is reverse-analyzed: when it is determined to be a "loading and unloading disorder warning," the system automatically associates it with the unloading physical sequence table, automatically analyzes and outputs the warning cause as "node sorting violation or stacking instability"; when it is determined to be a "transportation delay warning," the system automatically extracts the aforementioned automatically categorized abnormal road segment records for spatiotemporal comparison, automatically analyzes and outputs the warning cause as "delay caused by specific abnormal road segments (such as traffic congestion / vehicle malfunction)." These trend records generated by the K-Means algorithm and the automatically analyzed warning causes are all synchronously output to the system's comprehensive warning dashboard.

[0153] Finally, these statistical features are integrated with the original barcode character verification results, and a structured mapping is established according to the index rows to generate a link path offset trend record. This record presents "which goods are out of order," "which goods are slow," and "which labels are damaged" in a visually intuitive report format, providing logistics managers with precise directions for rectification.

[0154] Please see Figure 6 The collaborative mapping generation module includes:

[0155] The node labeling submodule obtains the link path offset trend record, combines the outbound data index structure and the arrival node data record, extracts the outbound timestamp, receipt timestamp, loading sequence number and unloading sequence number of the corresponding home furnishing goods, and labels each node in the process of home furnishing goods from outbound to receipt in chronological order, generating a full-process node labeling set of goods.

[0156] The node labeling submodule first obtains the link path offset trend record generated upstream, and then jointly reads the basic metadata from the outbound data index structure and the arrival node data record. A virtual two-dimensional spatiotemporal coordinate system is constructed in memory, where the horizontal axis (X-axis) is defined as the absolute time axis (UnixTime), and the vertical axis (Y-axis) is defined as the logistics node state space (including factory, in-transit, transit station, and warehouse).

[0157] In the specific implementation process, each household item is marked with a "three-stage" node according to the chronological order:

[0158] Outbound node annotation: on the timeline Location: This submodule is anchored to the outbound event. This node not only records a timestamp but also deeply associates it with the loading sequence number and the corresponding production line equipment ID. If the barcode verification result in the preceding steps is abnormal, this node will be automatically marked as a "yellow warning".

[0159] Transportation node labeling: Submodule based on to The time span is used to draw transportation segments. The transportation link trajectory status results are retrieved, and the trajectory data is subdivided into multiple sub-segments. Segments with a continuity indicator of "1" are marked as solid green lines; interrupted segments with a continuity indicator of "0" and a duration exceeding 30 minutes are marked as dashed red lines with an interruption duration parameter. Furthermore, the submodule uses the calculated deviation #imgpt103# as a confidence weight; the smaller #imgpt104# is, the lower the transparency of the segment (the clearer it is), and vice versa, intuitively reflecting the reliability of the trajectory matching.

[0160] Receipt Node Marking: At position #imgpt105# on the timeline, the submodule is anchored to the receive event. This node is associated with the unloading sequence number and the device fingerprint of the receiving PDA.

[0161] Finally, all the above-mentioned labeled objects are encapsulated to generate a complete cargo process node label set. This dataset is stored in GeoJSON extended format, which not only includes the latitude, longitude, and time information of points, but also the state attributes and color rendering parameters of line segments, providing standardized graph data for subsequent serialization and integration.

[0162] The sequence integration submodule performs sequence rearrangement on the node data according to the time sequence based on the full-process node annotation set of the goods, constructs a time-ordered linked list structure for the three stages of outbound, transportation and receiving, integrates the vehicle number and barcode index information corresponding to each node in the linked list, and generates a full-link time series structure set.

[0163] The sequence integration submodule is responsible for transforming the spatial primitives generated by the node annotation submodule into a tightly linked logical list. Based on the entire process node annotation set of goods, a doubly linked list data structure is used for sequence rearrangement and encapsulation. Each linked list node represents a key event in the supply chain.

[0164] During sequence construction, a "time monotonic topological sort" is first performed. The timestamps of all nodes are traversed (#imgpt106#), and constraints (#imgpt107#) are enforced. If a time reversal is detected (e.g., the receiving time is earlier than the outbound time, or there is a time jump at an intermediate trajectory point), the submodule will trigger a "logical paradox" exception handling mechanism, automatically truncating the chain and archiving it to the error log database to prevent erroneous data from contaminating downstream processes.

[0165] For nodes that pass verification, a data fusion operation is performed. In the "Data Payload" field, the corresponding vehicle number, barcode index, verification result, and path offset value (#imgpt108#) are deeply encapsulated. For example, in a transportation node, not only is the vehicle ID stored, but the average speed and variance data for that segment of transportation are also embedded. Furthermore, to support rapid traceability, the submodule establishes a global hash index at the head node of the linked list. The final generated end-to-end time series structure set is not merely a string of time data; it constitutes a complete chain of evidence with self-verification capabilities, supporting tracing the source backward from any node (e.g., querying which vehicle transported a damaged piece of furniture) or tracking its destination backward (e.g., querying which warehouses a batch of outbound goods ultimately ended up in).

[0166] The output module records and constructs the collaborative structure of each home furnishing product throughout the entire supply chain based on the full-link time series structure set, and establishes a full-link collaborative management record for the home furnishing supply chain.

[0167] The output module serves as the final delivery port, responsible for transforming the technical linked list structure into standard, interactive, and storable management records at the business level. It traverses the entire time-series structure set and serializes the data using the industry-standard JSON Schema format.

[0168] To ensure data immutability, a secure hash algorithm is built-in. While generating JSON records, a submodule extracts key fields (barcode, timestamp, vehicle ID), concatenates them into a string, calculates its SHA-256 digital digest, and appends this digest value to the end of the record. Experiments show that the overall technical solution performs excellently in high-concurrency environments, with a processing latency of less than 2ms per record. Finally, the digitally signed management records are pushed in batches to the enterprise's ERP and a third-party blockchain evidence storage platform via a RESTful API interface, achieving a transparent and legally binding closed loop for supply chain data.

[0169] The supply chain end-to-end collaborative management method based on multi-source heterogeneous data fusion includes the following steps:

[0170] S1: Collect the barcode of home furnishing goods leaving the warehouse, collect the loading sequence number and the outbound timestamp, synchronize and calibrate the loading sequence number and the outbound timestamp, and generate an outbound data index structure.

[0171] S2: Based on the outbound timestamp in the outbound data index structure, obtain the matching transport vehicle trajectory information, extract the vehicle trajectory number, trajectory transfer time series and vehicle positioning continuity identifier, filter the data rows that meet the time matching conditions, and obtain the transport link trajectory status result;

[0172] S3: Based on the status results of the transportation link trajectory, obtain the cargo receiving barcode, unloading sequence number and receiving timestamp recorded in the receiving warehouse, establish a dual index based on the vehicle trajectory number and cargo receiving barcode and link it with the transportation trajectory to generate arrival node data records;

[0173] S4: Based on the outbound data index structure, the outbound barcode of the goods and the receiving barcode of the arrival node data record are verified and the verification result is obtained. The absolute value of the difference between the loading sequence number and the unloading sequence number is calculated. The time interval between the outbound timestamp and the receiving timestamp is calculated and the offset time interval is obtained. The link path offset trend record is constructed by combining the verification result, the absolute value of the difference, and the offset time interval.

[0174] S5: Based on the link path offset trend record, the corresponding nodes of the entire process from the assembly and dispatch of home furnishing goods to receipt and signing are marked and integrated to output the full-link collaborative management record of the home furnishing supply chain.

[0175] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications 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 protection scope of the present invention.

Claims

1. A supply chain end-to-end collaborative management system based on multi-source heterogeneous data fusion, characterized in that: include: The outbound signal aggregation module collects the outbound barcodes of home furnishing goods, collects the loading sequence number and outbound timestamp, synchronizes and calibrates the loading sequence number and outbound timestamp, and generates an outbound data index structure. The transportation trajectory linkage module obtains the matching transportation vehicle trajectory information based on the outbound timestamp in the outbound data index structure, extracts the vehicle trajectory number, trajectory transfer time sequence and vehicle positioning continuity identifier, filters the data rows that meet the time matching conditions, and obtains the transportation link trajectory status result. Based on the transportation link trajectory status results, the arrival feedback splicing module obtains the cargo receiving barcode, unloading sequence number and receiving timestamp recorded by the receiving warehouse, establishes a dual index based on the vehicle trajectory number and cargo receiving barcode and links it with the transportation trajectory to generate arrival node data records. The path structure fusion module performs verification on the outbound barcode of the outbound data index structure and the receiving barcode of the arrival node data record and obtains the verification result. It calculates the absolute value of the difference between the loading sequence number and the unloading sequence number, calculates the time interval between the outbound timestamp and the receiving timestamp and obtains the offset time interval. It constructs a link path offset trend record by combining the verification result, the absolute value of the difference, and the offset time interval. The collaborative mapping generation module records the offset trend of the link path, marks the nodes of the entire process from the assembly and delivery of home furnishing goods to receipt and signing, performs full-link mapping and integration, and outputs a full-link collaborative management record of the home furnishing supply chain.

2. The supply chain end-to-end collaborative management system based on multi-source heterogeneous data fusion as described in claim 1, characterized in that: During the process of creating a data row that meets the time matching condition, the time difference between the trajectory transfer time sequence and the outbound timestamp is calculated, and trajectory records whose time difference is less than the set transportation plan time window are determined to be data rows that meet the time matching condition.

3. The supply chain end-to-end collaborative management system based on multi-source heterogeneous data fusion as described in claim 1, characterized in that: The outbound data index structure includes a barcode identification mapping structure, loading sequence calibration value, and outbound timestamp parameter. The transportation link trajectory status result includes a trajectory matching number set, trajectory time integrity flag, and trajectory interruption discrimination flag. The arrival node data record includes a barcode feedback structure, loading and unloading matching comparison set, and node time record set. The link path offset trend record includes loading and unloading sequence difference component, time node offset interval, and path offset judgment result. The home furnishing supply chain full-link collaborative management record includes the full-process time chain, transportation receiving node sequence, and path trend mapping result.

4. The supply chain end-to-end collaborative management system based on multi-source heterogeneous data fusion as described in claim 1, characterized in that, The outbound signal aggregation module includes: The data receiving submodule acquires the barcode data of home furnishing goods leaving the warehouse collected by the fixed scanning equipment at the end of the factory assembly line. At the same time, it collects the scanning time signal and the corresponding loading sequence number. The three types of data are numbered and matched according to the output order of the scanning equipment to identify the one-to-one correspondence between the goods leaving the warehouse barcode, the warehouse timestamp and the loading sequence number, and generate the original mapping set of barcode loading. The field calibration submodule uses the cargo exit barcode, loading sequence number, and exit timestamp from the original barcode loading mapping set to index and compare the loading sequence number and exit timestamp based on the barcode number, and generates a barcode field synchronous calibration result set. The index generation submodule synchronizes and calibrates the outbound barcodes, loading sequence numbers, and outbound timestamps in the result set based on the barcode fields. It constructs an outbound data index structure with the outbound barcodes as the main index, the loading sequence number as the sequence attribute, and the outbound timestamp as the time-series attribute.

5. The supply chain end-to-end collaborative management system based on multi-source heterogeneous data fusion according to claim 1, characterized in that, The transportation trajectory linkage module includes: The trajectory data extraction submodule obtains the outbound timestamp from the outbound data index structure, collects the trajectory information of the corresponding transport vehicle, extracts the vehicle trajectory number, trajectory transfer time sequence and vehicle positioning continuity identifier, and generates the original vehicle trajectory information set. The time matching and filtering submodule calculates the absolute value of the time difference with the outbound timestamp based on the trajectory transfer time sequence in the original vehicle trajectory information set, calculates the time matching deviation of each trajectory, filters the vehicle trajectory number data rows that are less than the set transportation plan time window, and obtains the transportation time matching trajectory number set. The trajectory status recognition submodule matches the trajectory number set with the transportation time and the corresponding trajectory data line, reads the vehicle positioning continuity identifier and performs sequence determination, identifies interrupted segments and marks the start and end timestamps of the segments, counts the number of interrupted segments and the set of durations, and generates the transportation link trajectory status result.

6. The supply chain end-to-end collaborative management system based on multi-source heterogeneous data fusion according to claim 1, characterized in that, The arrival feedback splicing module includes: The barcode data extraction submodule obtains the vehicle trajectory number from the transportation link trajectory status result, collects barcode record data within the corresponding time period of the receiving warehouse, extracts the cargo receiving barcode, unloading sequence number and receiving timestamp, and generates a receiving warehouse barcode record set; The field index construction submodule establishes a one-to-one correspondence between the goods receiving barcode and the vehicle trajectory number in the corresponding transportation link trajectory status result based on the goods receiving barcode in the receiving warehouse barcode record set. It also constructs a dual index by combining the unloading sequence number and the receiving timestamp, and generates a barcode vehicle dual index structure. The feedback structure splicing submodule splices the information sequentially according to the cargo receiving barcode, vehicle trajectory number, unloading sequence number and receiving timestamp in the barcode vehicle dual-index structure to generate the arrival node data record.

7. The supply chain end-to-end collaborative management system based on multi-source heterogeneous data fusion according to claim 1, characterized in that, The path structure fusion module includes: The barcode verification submodule obtains the outbound barcode from the outbound data index structure and the receiving barcode from the arrival node data record, performs verification operations on both, identifies inconsistent character positions, and generates barcode character verification results. The path offset calculation submodule extracts the corresponding loading sequence number and unloading sequence number based on the barcode character verification result, performs the absolute value calculation of the difference between the sequence numbers, and calculates the time interval between the outbound timestamp and the receiving timestamp to obtain the path sequence difference value set and the offset time interval set. The trend record construction submodule integrates the record indexes from the three data sources based on the path order difference value set, the offset time interval set, and the barcode character verification results, and establishes a structured mapping according to the index rows to generate link path offset trend records.

8. The supply chain end-to-end collaborative management system based on multi-source heterogeneous data fusion according to claim 1, characterized in that, The collaborative mapping generation module includes: The node labeling submodule obtains the link path offset trend record, combines the outbound data index structure and the arrival node data record, extracts the outbound timestamp, receipt timestamp, loading sequence number and unloading sequence number of the corresponding home furnishing goods, and labels each node in the process of home furnishing goods from outbound to receipt in chronological order, generating a full-process node labeling set for the goods. The sequence integration submodule performs sequence rearrangement on the node data according to the time sequence based on the cargo full-process node annotation set, constructs an ordered linked list structure for the three stages of outbound, transportation and receiving, and generates a full-link time series structure set. The output module records and constructs the collaborative structure of each home furnishing product throughout the entire supply chain based on the full-link time series structure set, and establishes a full-link collaborative management record for the home furnishing supply chain.

9. A supply chain end-to-end collaborative management method based on multi-source heterogeneous data fusion, characterized in that: The method is used to implement the supply chain end-to-end collaborative management system based on multi-source heterogeneous data fusion as described in any one of claims 1-8, and includes the following steps: S1: Collect the barcode of home furnishing goods leaving the warehouse, collect the loading sequence number and the outbound timestamp, synchronize and calibrate the loading sequence number and the outbound timestamp, and generate an outbound data index structure. S2: Based on the outbound timestamp in the outbound data index structure, obtain the matching transport vehicle trajectory information, extract the vehicle trajectory number, trajectory transfer time sequence and vehicle positioning continuity identifier, filter the data rows that meet the time matching conditions, and obtain the transport link trajectory status result; S3: Based on the transportation link trajectory status results, obtain the goods receiving barcode, unloading sequence number and receiving timestamp recorded in the receiving warehouse, establish a dual index based on the vehicle trajectory number and the goods receiving barcode and link it with the transportation trajectory to generate arrival node data records; S4: Based on the outbound data index structure, the outbound barcode of the goods and the goods receiving barcode of the arrival node data record are verified and the verification result is obtained. The absolute value of the difference between the loading sequence number and the unloading sequence number is calculated. The time interval between the outbound timestamp and the receiving timestamp is calculated and the offset time interval is obtained. The link path offset trend record is constructed by combining the verification result, the absolute value of the difference, and the offset time interval. S5: Based on the link path offset trend record, perform full-link mapping and integration of the corresponding nodes of the entire process from the assembly and delivery of home furnishing goods to receipt and signing, and output the full-link collaborative management record of the home furnishing supply chain.

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