Document management method and system for logistics transportation

Abstract

The invention relates to the technical field of intelligent data management, in particular to a receipt management method and system for logistics transportation. Comprising the following steps: S1, acquiring data of N different types of receipts, extracting association information among the N different types of receipts, and acquiring association degrees among the N different types of receipts based on the association information; the associated information refers to the accurate matching degree of data fields among different receipts and the coupling relationship of business processes; the correlation degree is calculated through a correlation degree formula; s2, obtaining an abnormal correlation degree based on the correlation degree; the abnormal association degree refers to topological structure analysis. According to the invention, through the multi-dimensional correlation model and dynamic weight adjustment, abnormal receipts are accurately identified and risks are blocked, core nodes are sorted in a layered manner in combination with a PageRank algorithm, new data are rapidly embedded by using an increment strategy, cross-system collaborative optimization is realized, and the real-time performance and robustness of logistics receipt management are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent data management, and in particular to a document management method and system for logistics transportation. Background Art

[0002] As the core link of the modern supply chain, logistics and transportation involve multiple business processes such as procurement, warehousing, transportation, and settlement. The types of documents generated in the process are complex and closely related. Traditional document management systems mostly rely on manual entry and static storage, which makes it difficult to effectively capture the dynamic relationship between multiple types of documents. Especially in the scenario of cross-system and multi-node collaboration, document information is scattered in different business modules, forming data islands, resulting in frequent problems such as mismatch of key fields and broken process timing dependencies. At the same time, due to imperfect data cleaning mechanisms or abnormal operations, abnormal documents such as "ghost documents" (still referenced after physical deletion) and "zombie documents" (wrongly reused after logical deletion) often appear, further exacerbating information confusion. Although existing technologies can realize basic data storage and retrieval, they lack the ability to quantify and analyze the complex coupling relationship between documents, cannot identify abnormal interactions in real time or dynamically optimize the association network, and are difficult to support the needs of high-efficiency and high-precision logistics collaboration. With the acceleration of digital transformation in the logistics industry, there is an urgent need for a management solution that can deeply integrate business processes and data intelligence to systematically solve the problems of association governance and risk prevention and control of heterogeneous documents. Summary of the invention

[0003] In order to overcome the disadvantage of difficulty in coordinating multi-source heterogeneous documents, the present invention provides a document management method and system for logistics transportation.

[0004] The technical solution is: a document management method for logistics transportation, comprising the following steps: S1: Obtain N different types of document data, extract association information between the N different types of documents, and obtain the association degree between the N different types of documents based on the association information; the association information refers to the precise matching degree of data fields between different documents and the coupling relationship of business processes; the association degree is calculated by the association degree formula; S2: Based on the correlation, obtain an abnormal correlation; the abnormal correlation refers to quantifying the interaction density and abnormal pattern characteristics of the document with ghost documents, zombie documents, and fragmented documents through topological structure analysis; the ghost document refers to a document that is physically deleted but still referenced by multiple nodes; the zombie document is a document that is still incorrectly reused after logical deletion; the fragmented document is a document that remains after deletion fails and is not properly cleaned up; S3: Based on the correlation, N different types of documents are deeply sorted, wherein the deep sorting refers to sorting the documents from large to small according to the size of the correlation; S4: Based on the depth sorting result, the newly generated document is marked with a relevance, and then the document is regenerated.

[0005] Preferably, the acquiring of N different types of document data, extracting association information between the N different types of documents, and obtaining the degree of association between the N different types of documents based on the association information includes: Sort N different types of documents in order according to the business process; Based on the sorting results, extract data fields of N different types of documents for matching; Based on the sorting results and matching results, the correlation formula is used to calculate the correlation between N different types of documents.

[0006] Preferably, the correlation between N different types of documents is calculated based on the sorting results and the matching results using a correlation formula, including: the basic correlation formula is as follows:

[0007] in, As the basic correlation, , is the weight coefficient, is the field matching degree, It is the degree of process coupling.

[0008] Preferably, the correlation between N different types of documents is calculated based on the sorting results and the matching results using a correlation formula, including: the field matching formula is as follows:

[0009] in, is the field matching degree, For the The weight of the field, For documents No. The value of the field, For documents No. The value of the field, is the matching function; the process coupling formula is as follows,

[0010] in, is the process coupling degree, , , is the weight coefficient, is the timing dependency strength, is the number of state transfers, is the shared resource ratio.

[0011] Preferably, obtaining the abnormal correlation degree based on the correlation degree includes: the abnormal correlation degree formula is as follows:

[0012] in, is the abnormal correlation, is the abnormal weight type, For documents With type The number of interactions of abnormal documents, For documents The total number of interactions, They are ghost documents, zombie documents, and fragmented documents respectively; the final correlation formula is as follows:

[0013] in, is the final correlation, As the basic correlation, For documents The abnormal correlation of For documents The abnormal correlation degree; If there is no abnormal interaction between the two documents, the final correlation is equal to the basic value; If any document has an abnormal association, the association will be reduced in proportion to the interaction density.

[0014] Preferably, the N different types of documents are deeply sorted based on the correlation, and the deep sorting refers to sorting from large to small according to the size of the correlation, including: For each document, the final correlation between the document and the remaining documents is accumulated to form a global correlation strength index, which is used to quantify the influence of the document in the overall network; The documents and their associations are modeled as a weighted graph structure. The importance score of each node is calculated based on the PageRank algorithm. The depth level is divided according to the score. The higher the importance, the higher the level. When adding a new document, the association strength and importance score of the affected nodes are efficiently adjusted through local association calculation and incremental update strategy; when the score fluctuation exceeds the preset threshold, it is automatically marked as a deep change node and the updated sorting result is output.

[0015] Preferably, the documents and document association relationships are modeled as a weighted graph structure, the importance score of each node is calculated based on the PageRank algorithm, and the depth level is divided according to the score. The higher the importance, the higher the level, including: The weighted graph uses documents as nodes, and the final correlation between two documents For edge weights, construct a directed network; Iteratively calculate node importance based on PageRank algorithm: each node distributes the current node score to adjacent nodes according to the outgoing edge weight ratio, and superimposes the damping factor to balance the random jump probability until the score converges; Finally, the node PageRank values ​​are divided into discrete depth levels according to quantiles. The higher the value, the deeper the level, which reflects the core degree in the global association network.

[0016] Preferably, when adding a new document, the association strength and importance score of the affected nodes are efficiently adjusted through local association calculation and incremental update strategy, including: When adding a new document, only the relevance between the document and the existing documents is calculated, and the directly related node set is locked; Through the incremental update strategy, newly added association edges are inserted into the graph structure, which only triggers the global association strength of related nodes to be re-accumulated, and the PageRank score is iteratively updated based on the local subgraph; The influence range of random jumps is constrained by the damping factor. When the node score change exceeds the threshold, the neighborhood is expanded and updated layer by layer until the change in the node score is less than the set convergence threshold and the iteration is stopped.

[0017] Preferably, the step of marking the newly generated document with a relevance based on the depth sorting result and then regenerating the document comprises: Based on the depth sorting results, when marking the relevance of new documents, core nodes at the same level or higher level are prioritized to establish strong associations, and association edges are generated through dynamic weight allocation rules; Based on the updated network topology, incremental document reconstruction is triggered and the global sorting is updated synchronously.

[0018] Preferably, the document management system for logistics transportation comprises: The data association analysis module extracts the matching degree between documents and the process coupling relationship based on the process sequence and field matching rules, and dynamically calculates the basic correlation degree; the coupling degree integrates the time sequence, state and resource characteristics to form a comprehensive evaluation; The anomaly detection module identifies the interaction density of abnormal documents through the topological network, quantifies the abnormal characteristics and corrects the correlation; The deep sorting module models the document as a weighted directed graph and calculates the node core degree to divide the hierarchy; when adding a new document, the node score is locally updated to control the impact range; The dynamic reconstruction module prioritizes binding to high-core layer nodes, assigns weights according to layer differences, triggers incremental reconstruction to update the topology, ensures real-time sorting and network stability, and achieves exception isolation and dynamic balance maintenance.

[0019] Beneficial effects: The present invention significantly improves the accuracy and collaborative efficiency of logistics document management by constructing a multi-dimensional association network and a dynamic optimization mechanism. First, based on the dual quantitative analysis of field matching and process coupling, a dynamic association model between heterogeneous documents is established, breaking through the traditional system's dependence on static data islands and realizing deep semantic fusion across business modules. Secondly, through topological structure analysis and abnormal pattern recognition, risk sources such as ghost documents and zombie documents are accurately located, and the association weights are dynamically adjusted in combination with the interaction density characteristics to effectively block the abnormal data propagation path. Furthermore, the core nodes of the document network are hierarchically sorted by using the global association strength index and weighted graph modeling technology, and the node influence is quantified by the PageRank algorithm to ensure that key business documents are preferentially reached in process collaboration. On this basis, the incremental update strategy is combined with the local subgraph iteration mechanism to realize the rapid embedding of new documents and the adaptive adjustment of network topology, avoiding the waste of resources for full-scale calculations. Finally, a closed-loop optimization system is formed, which continuously improves the robustness of the association network through dynamic weight allocation and incremental reconstruction, so that the document system has adaptive abnormal immunity and real-time collaborative response capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a flow chart of the document management method for logistics transportation of the present invention; Figure 2 It is a schematic diagram of the structure of the document management system used for logistics transportation of the present invention. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] Embodiment 1: A document management method for logistics transportation, such as Figure 1 As shown, the following steps are included: S1: Obtain N different types of document data, extract association information between the N different types of documents, and obtain the association degree between the N different types of documents based on the association information; the association information refers to the precise matching degree of data fields between different documents and the coupling relationship of business processes; the association degree is calculated by the association degree formula; S2: Based on the correlation, obtain an abnormal correlation; the abnormal correlation refers to quantifying the interaction density and abnormal pattern characteristics of the document with ghost documents, zombie documents, and fragmented documents through topological structure analysis; the ghost document refers to a document that is physically deleted but still referenced by multiple nodes; the zombie document is a document that is still incorrectly reused after logical deletion; the fragmented document is a document that remains after deletion fails and is not properly cleaned up; S3: Based on the correlation, N different types of documents are deeply sorted, wherein the deep sorting refers to sorting the documents from large to small according to the size of the correlation; S4: Based on the depth sorting result, the newly generated document is marked with a relevance, and then the document is regenerated.

[0023] Acquiring N different types of document data, extracting association information between the N different types of documents, and obtaining the degree of association between the N different types of documents based on the association information, including: Sort N different types of documents in order according to the business process; Based on the sorting results, extract data fields of N different types of documents for matching; Based on the sorting results and matching results, the correlation formula is used to calculate the correlation between N different types of documents.

[0024] Based on the sorting results and matching results, the correlation between N different types of documents is calculated using the correlation formula, including: The basic correlation formula is as follows:

[0025] in, As the basic correlation, , is the weight coefficient, is the field matching degree, It is the degree of process coupling.

[0026] To further illustrate, suppose that in a certain enterprise supply chain system, a purchase order ( ) and the warehouse entry order ( )’s correlation analysis.

[0027] 1. Field Matching ( )calculate.

[0028] Logic: Compare the common fields of the two documents (such as order number, supplier name, material code).

[0029] If the order numbers are exactly the same, 1 point is awarded; if they are partially matched (such as the last 4 digits are the same), 0.5 points are awarded; other fields are scored according to the similarity ratio (such as 0.3 points for a supplier name match).

[0030] Assuming the field weights are evenly distributed, the total matching degree =0.1+0.33=0.43.

[0031] 2. Process Coupling Degree ( )calculate.

[0032] Logic: Analyze business process dependencies.

[0033] If the purchase order must trigger the warehouse order after it is generated (timing dependency), the coupling strength =0.8; Two documents share the same warehouse resources (resource dependency), coupling strength =0.2; Assuming sub-weight =0.7, =0.3, then =0.7×0.8+0.3×0.2=0.62.

[0034] 3. Calculation of comprehensive correlation.

[0035] Parameters: Set =0.6 (focus on field matching), =0.4 (focusing on process coupling).

[0036] result: =0.6×0.43+0.4×0.62=0.506.

[0037] Precise association: quantify the association strength between two documents (0.506). When it is higher than the threshold (such as 0.5), the system automatically marks it as a "strongly associated pair"; Process optimization: Identify the timing dependency (0.8) of the procurement-warehousing process, trigger automatic verification rules, and reduce manual verification; Abnormal warning: If the field matching degree is lower than the threshold (assuming the optimal field matching degree threshold is 0.2), but the process coupling degree is higher than the threshold (assuming the optimal process coupling degree threshold is 0.8), the risk of "document information misalignment" is prompted.

[0038] Based on the sorting results and matching results, the correlation formula is used to calculate the correlation between N different types of documents, including: The field matching formula is as follows:

[0039] in, is the field matching degree, For the The weight of the field, For documents No. The value of the field, For documents No. The value of the field, is the matching function; the process coupling formula is as follows,

[0040] in, is the process coupling degree, , , is the weight coefficient, is the timing dependency strength, is the number of state transfers, is the shared resource ratio.

[0041] Further explanation is that matching functions include exact field formulas, numeric field formulas, and text field formulas. The exact field formula is as follows,

[0042] The value is 0 or 1. Applicable scenarios: Boolean, enumeration, or unique identification field; Example: The order status field is all "Received" → =1; if one party is "in transit" → =0; The formula for the numeric field is as follows,

[0043] Applicable scenarios: Numeric fields that need to quantify the degree of difference; Characteristics: When completely equal =1, the greater the difference Approaching 0; sensitive to outliers (e.g. 1vs100→ =0.01); Example: The weight field values ​​are 80kg and 100kg respectively → =1-20 / 100=0.8; The text field formula is as follows,

[0044] Applicable scenarios: semantic similarity matching of text descriptions, addresses, etc. Prerequisite: The text needs to be converted into a numerical vector; Features: The value range is [-1, 1], usually taking the absolute value or normalizing to [0, 1]; ignoring the length of the text and focusing on the direction similarity; Example: "Zhangjiang Road, Pudong New Area" vs "Shanghai Zhangjiang High-Tech Park" → vectorized calculation yields =0.72; The field matching formula is as follows:

[0045] Numerator: Traverse the common fields of the two documents (such as order number, supplier), and each field is matched according to the degree of matching (through Function calculation) are multiplied by their weights and then accumulated.

[0046] Denominator: The sum of all weights, used for normalization to ensure that the result is in the range [0,1].

[0047] Example: Purchase Order ( ) and the warehouse entry order ( ) has 3 fields: order number (weight 0.5), material code (weight 0.3), supplier (weight 0.2).

[0048] The order number exactly matches ( =1), the material code partially matches ( = 0.5), supplier mismatch ( =0).

[0049] calculate: =0.5×1+0.3×0.5+0.2×00.5+0.3+0.2=0.65 / 1=0.65 The process coupling formula is as follows:

[0050] Timing dependency ( ): If there is a strict order in the process (such as the purchase order can be created only after the warehouse entry order is generated), the strength is high (for example: =0.8).

[0051] State transfer ( ): The number of status linkages between documents (such as the purchase order "paid" triggers the warehouse receipt "pending receipt"), the more times, the higher the coupling degree.

[0052] Shared Resources ( ): The proportion of two documents sharing the same resource (such as warehouse, fund account) (for example: sharing the same warehouse accounts for 50%, =0.5).

[0053] Weight ( , , ): reflects the importance of different factors (e.g. =0.6 focuses on timing, =0.3 focuses on status, =0.1 focuses on resources).

[0054] Example: Process coupling between purchase order and incoming order.

[0055] Timing dependency strength =0.8, number of state transfers =2 times (corresponding to =0.3), shared warehouse ratio =0.5.

[0056] calculate: =0.6×0.8+0.3×2+0.1×0.5=0.48+0.6+0.05=1.13, Note: If the sum of weights exceeds 1, normalization is required.

[0057] Total correlation between two documents = + ,like =0.7, =0.3, then R=0.7×0.65+0.3×1.13=0.794. When it is higher than the threshold of 0.7, it is judged as a strong correlation and the automatic review process is triggered.

[0058] Anomaly detection: If the field matching degree is high but the process coupling degree is low, it indicates the risk of "data island", otherwise it will warn of "process breakpoint".

[0059] Based on the correlation, an abnormal correlation is obtained, including: the abnormal correlation formula is as follows,

[0060] in, is the abnormal correlation, is the abnormal weight type, For documents With type The number of interactions of abnormal documents, For documents The total number of interactions, They are abnormal types of ghost documents, zombie documents, and fragmented documents. The final correlation formula is as follows:

[0061] in, is the final correlation, As the basic correlation, For documents The abnormal correlation of For documents The abnormal correlation degree; If there is no abnormal interaction between the two documents, the final correlation is equal to the basic value; If any document has an abnormal association, the association will be reduced in proportion to the interaction density.

[0062] Further explanation is that the abnormal correlation formula is as follows:

[0063] Objective: Quantify documents With abnormal documents (such as ghost documents , Zombie Invoices , fragmented documents ) of the interaction risk.

[0064] Numerator: Statistics Number of interactions with a certain type of abnormal documents , multiplied by its risk weight (e.g. zombie documents have a higher weight).

[0065] Denominator: Total number of interactions , used for normalization (proportion of abnormal interactions).

[0066] Formula essence: If the document frequently interacts with high-risk abnormal documents (such as High and large), then the abnormal correlation Significant increase.

[0067] Example: Purchase Order The total number of interactions is 20, including: With ghost receipts ( ) Interact 5 times ( =0.6), With zombie documents ( ) Interact 2 times ( =0.3), With fragmented documents ( ) Interact 1 time ( =0.1).

[0068] calculate: =0.6*5 / 20+0.3*2 / 20+0.1*1 / 20=0.15+0.03+0.005=0.185 The final correlation formula is as follows:

[0069] Internal logic: Basic correlation correction: If there is no abnormal interaction between the two documents ( =0), = ; If there is an anomaly, reduce the correlation by the ratio of the average anomaly level of the two documents.

[0070] Risk suppression mechanism: The higher the density of abnormal interactions (e.g. =0.3), the final correlation decay is more obvious (the multiplier approaches 0.7).

[0071] Example: Purchase Order With the warehouse receipt The basic correlation =0.8, Abnormal correlation =0.185, Abnormal correlation =0.05.

[0072] calculate: =0.8*(1−0.185+0.052)=0.8*(1−0.1175)=0.8*0.8825=0.706, Dynamic risk adjustment: The correlation between normal documents remains stable (e.g. =0.8); If the document Frequently associated zombie documents ( =0.3), then =0.8*0.85=0.68, which triggers manual review when it is lower than the threshold of 0.7.

[0073] Abnormal location: high The interaction of zombie documents will significantly reduce the correlation and directly expose the "zombie" risk nodes in the supply chain.

[0074] Based on the relevance, N different types of documents are deeply sorted. The deep sorting refers to sorting from large to small according to the relevance, including: For each document, the final correlation between the document and the remaining documents is accumulated to form a global correlation strength index, which is used to quantify the influence of the document in the overall network; The documents and their associations are modeled as a weighted graph structure. The importance score of each node is calculated based on the PageRank algorithm. The depth level is divided according to the score. The higher the importance, the higher the level. When adding a new document, the association strength and importance score of the affected nodes are efficiently adjusted through local association calculation and incremental update strategy; when the score fluctuation exceeds the preset threshold, it is automatically marked as a deep change node and the updated sorting result is output.

[0075] Further explanation is that the document influence is quantified through global association strength (cumulative association degree), and the importance of nodes is evaluated and divided into levels based on PageRank after constructing a weighted graph; when a new document is added, only the association network is partially updated, and the score is dynamically adjusted through incremental calculation. When the fluctuation exceeds the threshold, the deep change node is marked to achieve efficient and adaptive network structure maintenance. The document association is mapped into a dynamic graph model, and the levels are dynamically divided according to the importance score of the node (such as PageRank value) (such as dividing the core level by quantile), and the hierarchical structure is adjusted in real time through the incremental update strategy to balance the calculation efficiency and accuracy.

[0076] The documents and their associations are modeled as a weighted graph structure. The importance score of each node is calculated based on the PageRank algorithm. The depth level is divided according to the score. The higher the importance, the higher the level, including: The weighted graph uses documents as nodes, and the final correlation between two documents For edge weights, construct a directed network; Iteratively calculate node importance based on PageRank algorithm: each node distributes the current node score to adjacent nodes according to the outgoing edge weight ratio, and superimposes the damping factor to balance the random jump probability until the score converges; Finally, the node PageRank values ​​are divided into discrete depth levels according to quantiles. The higher the value, the deeper the level, which reflects the core degree in the global association network.

[0077] Further explanation is that the weighted graph uses documents as nodes, and the final correlation between two documents is For point to 's directed edge weights (ignored if the correlation is lower than the threshold), and a directed network is constructed; the node importance is iteratively calculated based on the PageRank algorithm: the score of each node is initialized to 1 / N (N is the total number of nodes), and in each iteration, the node distributes the current score to the adjacent nodes according to the outgoing edge weight ratio, and superimposes a damping factor (usually 0.85) to simulate the random jump probability until the node score change reaches the maximum number of iterations (such as 100 times), and convergence is determined; finally, the node PageRank value is divided into discrete depth levels (such as 4 layers) according to the quartile method, and the highest level (such as Top25%) corresponds to the node with the largest PageRank value, representing its core hub status in the global network, such as the key documents that frequently drive multi-level interactions in the supply chain.

[0078] When adding a new document, the association strength and importance score of the affected nodes are efficiently adjusted through local association calculation and incremental update strategy, including: When adding a new document, only the relevance between the document and the existing documents is calculated, and the directly related node set is locked; Through the incremental update strategy, newly added association edges are inserted into the graph structure, which only triggers the global association strength of related nodes to be re-accumulated, and the PageRank score is iteratively updated based on the local subgraph; The influence range of random jumps is constrained by the damping factor. When the node score change exceeds the threshold, the neighborhood is expanded and updated layer by layer until the change in the node score is less than the set convergence threshold and the iteration is stopped.

[0079] A further explanation is that when a new document is added, only its final correlation with the existing documents is calculated. (such as similarity or causal strength), when the correlation exceeds the preset threshold (assuming that the optimal correlation threshold is >0.3), it is considered a highly associated node; through the incremental update strategy, the new document and its associated edges are inserted into the original graph, and only the global association strength (such as the sum of in-degree weights) of the affected nodes (the new nodes and the direct neighbors of the new nodes) is re-accumulated, and the PageRank score is iteratively updated in the local subgraph (such as the 2-layer neighborhood): each round only adjusts the node scores in the subgraph, distributes them according to the edge weight ratio, and superimposes a damping factor (such as 0.85) to limit the score diffusion range; when the change in the node score Δ exceeds the threshold (assuming that the node score change threshold is Δ> ), the update is extended to the outer neighborhood (such as layer 3) until the score changes of the entire image are all lower than the threshold or the maximum number of iterations (such as 20) is reached to ensure efficient convergence.

[0080] Based on the depth sorting result, the newly generated document is marked with a relevance, and then the document is regenerated, including: Based on the depth sorting results, when marking the relevance of new documents, core nodes at the same level or higher level are prioritized to establish strong associations, and association edges are generated through dynamic weight allocation rules; Based on the updated network topology, incremental document reconstruction is triggered and the global sorting is updated synchronously.

[0081] A further explanation is that based on the depth level (divided by the quartiles of the PageRank value), new documents are preferentially associated with nodes at the same or higher level (such as Top50%), and weights are dynamically allocated according to the level difference (such as the weight decays by 30% for each level difference) to generate strongly associated edges; if the number of new edges is ≥3 or the total weight changes by >10%, incremental reconstruction is triggered: only the PageRank values ​​of the affected nodes (including new documents, nodes directly associated with new documents, and direct neighbors of these nodes) are recalculated, and the sorting is updated through local iterations (such as damping factor 0.85, convergence threshold 1e-5) to ensure global level synchronization and avoid recalculation of the entire graph.

[0082] Embodiment 2: Based on Embodiment 1, a document management system for logistics transportation, such as Figure 2As shown, including: The data association analysis module extracts the matching degree between documents and the process coupling relationship based on the process sequence and field matching rules, and dynamically calculates the basic correlation degree; the coupling degree integrates the time sequence, state and resource characteristics to form a comprehensive evaluation; The anomaly detection module identifies the interaction density of abnormal documents through the topological network, quantifies the abnormal characteristics and corrects the correlation; The deep sorting module models the document as a weighted directed graph and calculates the node core degree to divide the hierarchy; when adding a new document, the node score is locally updated to control the impact range; The dynamic reconstruction module prioritizes binding to high-core layer nodes, assigns weights according to layer differences, triggers incremental reconstruction to update the topology, ensures real-time sorting and network stability, and achieves exception isolation and dynamic balance maintenance.

[0083] The above is a detailed introduction to the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A document management method for logistics transportation, characterized in that: The following steps are involved: S1: Obtain N different types of document data, extract association information between the N different types of documents, and obtain the association degree between the N different types of documents based on the association information; the association information refers to the precise matching degree of data fields between different documents and the coupling relationship of business processes; the association degree is calculated by the association degree formula; S2: Based on the correlation, obtain an abnormal correlation; the abnormal correlation refers to quantifying the interaction density and abnormal pattern characteristics of the document with ghost documents, zombie documents, and fragmented documents through topological structure analysis; the ghost document refers to a document that is physically deleted but still referenced by multiple nodes; the zombie document is a document that is still incorrectly reused after logical deletion; the fragmented document is a document that remains after deletion fails and is not properly cleaned up; S3: Based on the correlation, N different types of documents are deeply sorted, wherein the deep sorting refers to sorting the documents from large to small according to the size of the correlation; S4: Based on the depth sorting result, the newly generated document is marked with a relevance, and then the document is regenerated.

2. A document management method for logistics transportation according to claim 1, characterized in that: The step of acquiring N different types of document data, extracting association information between the N different types of documents, and obtaining the degree of association between the N different types of documents based on the association information includes: Sort N different types of documents in order according to the business process; Based on the sorting results, extract data fields of N different types of documents for matching; Based on the sorting results and matching results, the correlation formula is used to calculate the correlation between N different types of documents.

3. A document management method for logistics transportation according to claim 2, characterized in that: Based on the sorting results and the matching results, the correlation between N different types of documents is calculated using the correlation formula, including: the basic correlation formula is as follows: in, As the basic correlation, , is the weight coefficient, is the field matching degree, It is the degree of process coupling.

4. A document management method for logistics transportation according to claim 3, characterized in that: Based on the sorting results and the matching results, the correlation between N different types of documents is calculated using the correlation formula, including: the field matching formula is as follows: in, is the field matching degree, For the The weight of the field, For documents No. The value of the field, For documents No. The value of the field, is the matching function; the process coupling formula is as follows, in, is the process coupling degree, , , is the weight coefficient, is the timing dependency strength, is the number of state transfers, is the shared resource ratio.

5. A document management method for logistics transportation according to claim 1, characterized in that: The abnormal correlation degree is obtained based on the correlation degree, including: the abnormal correlation degree formula is as follows, in, is the abnormal correlation, is the abnormal weight type, For documents With type The number of interactions of abnormal documents, For documents The total number of interactions, They are abnormal types of ghost documents, zombie documents, and fragmented documents. The final correlation formula is as follows: in, is the final correlation, As the basic correlation, For documents The abnormal correlation of For documents The abnormal correlation degree; If there is no abnormal interaction between the two documents, the final correlation is equal to the basic value; If any document has an abnormal association, the association will be reduced in proportion to the interaction density.

6. A document management method for logistics transportation according to claim 1, characterized in that: Based on the correlation, N different types of documents are sorted in depth. The deep sorting refers to sorting from large to small according to the size of the correlation, including: For each document, the final correlation between the document and the remaining documents is accumulated to form a global correlation strength index, which is used to quantify the influence of the document in the overall network; The documents and their associations are modeled as a weighted graph structure. The importance score of each node is calculated based on the PageRank algorithm. The depth level is divided according to the score. The higher the importance, the higher the level. When adding a new document, the association strength and importance score of the affected nodes are efficiently adjusted through local association calculation and incremental update strategy; when the score fluctuation exceeds the preset threshold, it is automatically marked as a deep change node and the updated sorting result is output.

7. A document management method for logistics transportation according to claim 6, characterized in that: The document and its relationship are modeled as a weighted graph structure, and the importance score of each node is calculated based on the PageRank algorithm. The depth level is divided according to the score. The higher the importance, the higher the level, including: The weighted graph uses documents as nodes, and the final correlation between two documents For edge weights, construct a directed network; Iteratively calculate node importance based on PageRank algorithm: each node distributes the current node score to adjacent nodes according to the outgoing edge weight ratio, and superimposes the damping factor to balance the random jump probability until the score converges; Finally, the node PageRank values ​​are divided into discrete depth levels according to quantiles. The higher the value, the deeper the level, which reflects the core degree in the global association network.

8. A document management method for logistics transportation according to claim 6, characterized in that: When adding a new document, the association strength and importance score of the affected nodes are efficiently adjusted through local association calculation and incremental update strategy, including: When adding a new document, only the relevance between the document and the existing documents is calculated, and the directly related node set is locked; Through the incremental update strategy, newly added association edges are inserted into the graph structure, which only triggers the global association strength of related nodes to be re-accumulated, and the PageRank score is iteratively updated based on the local subgraph; The influence range of random jumps is constrained by the damping factor. When the node score change exceeds the threshold, the neighborhood is expanded and updated layer by layer until the change in the node score is less than the set convergence threshold and the iteration is stopped.

9. A document management method for logistics transportation according to claim 1, characterized in that: The step of marking the newly generated document with a relevance degree based on the depth sorting result and then regenerating the document comprises: Based on the depth sorting results, when marking the relevance of new documents, core nodes at the same level or higher level are prioritized to establish strong associations, and association edges are generated through dynamic weight allocation rules; Based on the updated network topology, incremental document reconstruction is triggered and the global sorting is updated synchronously.

10. A document management system for logistics transportation, used to implement a document management method for logistics transportation as claimed in any one of claims 1 to 9, characterized in that: include: The data association analysis module extracts the matching degree between documents and the process coupling relationship based on the process sequence and field matching rules, and dynamically calculates the basic association degree; The coupling degree integrates timing, status and resource characteristics to form a comprehensive assessment; The anomaly detection module identifies the interaction density of abnormal documents through the topological network, quantifies the abnormal characteristics and corrects the correlation; The deep sorting module models the document as a weighted directed graph and calculates the node core degree to divide the hierarchy; when adding a new document, the node score is locally updated to control the impact range; Dynamic reconstruction module, which prioritizes binding to high-core layer nodes, assigns weights according to layer differences, triggers incremental reconstruction to update the topology, and ensures real-time sorting and network stability; Realize abnormal isolation and dynamic balance maintenance.

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