Traceable data resource product financial integration method and device and medium

Abstract

The invention relates to the technical field of supply chain management, in particular to a traceable data resource product finance integration method and device and a medium, and the traceable data resource product finance integration method is based on geographical location information, original feature values of objects, time records and raw material information. A region code of a geographic position is converted into a numerical form, and a unique identifier code is generated by combining an object characteristic value and a timestamp and adding a classification characteristic of raw material information. According to the method, a unique identifier is generated through geographic position codes, object feature values, time records and raw material classification, the uniqueness and traceability of data are ensured, node classification and association graph optimization data structured management and path dynamic optimization improve the tracking efficiency and accuracy, distributed storage integrates fragmentation and indexing, and the tracking efficiency and accuracy are improved. And the data reliability and cooperation capability are enhanced, security management is realized based on permission grouping, and the data credibility and circulation efficiency are comprehensively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of supply chain management, and in particular to a method, device and medium for integrating financialization of products based on traceable data resources. Background Art

[0002] The technical field of financial integration methods for traceable data resource products includes data resource management, supply chain management, financial integration and other related fields. The core content of this technical field is to ensure the integrity and authenticity of data in different links by building a traceable system for data resources, so as to achieve efficient management of data resources in different application scenarios. The overall technical field includes data collection, storage, processing and multi-party collaborative applications, focusing on how to achieve deep integration of data resources in management, business and financial applications, especially to ensure data tracking and verifiability in complex transaction scenarios, and provide a reliable basis for resource optimization and business decision-making.

[0003] Among them, supply chain management refers to the technical matters of coordinating and managing the flow of goods, services or information in the supply chain. The subject of this patent covers the collection, storage and verification methods of data resources in the supply chain. Specifically, it uses chain storage based on timestamps to achieve data traceability and immutability, and implements data structured management through coding rules. At the same time, combined with dynamic information collection during the transaction process, data flow records and process verification are achieved through collaborative communication between nodes. These technical means provide data support and tracking capabilities for supply chain management, ensuring transparent and accurate information transmission between various links in the supply chain.

[0004] The existing technology lacks unified identifier generation rules, which can easily lead to multi-node data redundancy and conflict, affecting the integrity of data flow. Data storage is mainly centralized, with limited performance and reduced query efficiency in high-concurrency scenarios, making it difficult to support multi-node collaboration needs. The dynamic tracking capabilities of time-series data are insufficient, and logistics flow information is prone to lags or loss, making it difficult to meet the high accuracy requirements of traceability. The authority management hierarchy is relatively simple, lacking detailed control over complex attribution characteristics and node authority relationships, resulting in low transparency in data access and prominent authority confusion. These problems weaken the credibility and management efficiency of data in multi-node collaboration and complex transaction scenarios. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a financial integration method, device and medium based on traceable data resource products.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: a financial integration method of data resource products based on traceability, comprising the following steps:

[0007] S1: Based on the geographic location information, the original feature value of the object, the time record and the raw material information, the regional code of the geographic location is converted into a numerical form, combined with the object feature value and timestamp, and the classification characteristics of the raw material information are added to generate a unique identifier code;

[0008] S2: Based on the unique identifier code, the geographic location information and product specification parameters are divided into node types, the geographic location information is defined as a geographic node, the product specification classification information is mapped as a specification node, the spatial relationship between the nodes and the specification dependency are marked as edges, and the node and edge parameters are combined to establish a node and edge association graph;

[0009] S3: Based on the node and edge association graph, analyze the time series attributes and logistics flow status in the node and establish records according to the time series, recursively sort the edge parameter values ​​according to the logical attributes, map the node and edge relationship into a path structure, and optimize the dynamic path relationship with the edge relationship strength to establish a full-process traceability path;

[0010] S4: Based on the full-process traceability path, the logistics flow status and time series are sharded and stored in logical blocks, the node information and time series are associated and mapped to distributed indexes, the mapping relationships are integrated and organized according to the distributed storage architecture to generate storage tables, and a distributed storage mapping index is established;

[0011] S5: Based on the distributed storage mapping index, extract the authorization file associated coding modification record and parse the data characteristics, classify according to the attribution characteristics and node authority relationship, generate authority groups and establish hierarchical access rules, output access level information after logical verification of the authority groups, and generate a hierarchical authority control structure.

[0012] As a further solution of the present invention, the steps of obtaining the unique identifier code are specifically as follows:

[0013] S101: converting the area code according to the geographical location into a numerical form, calculating the weighted center coordinates through the latitude and longitude of the geographical information, extracting the original feature value of the object and the numerical information of the timestamp, and combining the numerical area code, the object feature value and the timestamp information to generate a preliminary set;

[0014] S102: Analyze the classification characteristics of the preliminary set and the raw material information, and quantify the classification characteristics of the raw material information by combining the raw material type number and the classification weight parameter, using the formula:

[0015]

[0016] Calculating the classification characteristic value, merging the classification characteristic value with the region code, time stamp and object characteristic value in the preliminary set, and generating an intermediate set including the classification characteristic value;

[0017] Among them, C r represents the classification feature value, w i Represents the weight parameter of the i-th type of raw materials, R i represents the numerical characteristics of the classification of the i-th raw material, T represents the quantitative value of the timestamp information, and n represents the total number of raw material classifications;

[0018] S103: calling the intermediate set, combining the classification characteristic value and the time characteristic parameter for splicing, and generating a unique identifier code by weighted calculation of the joint region code and the object characteristic value.

[0019] As a further solution of the present invention, the step of obtaining the node-edge association graph is specifically:

[0020] S201: Based on the unique identifier code, extract the geographic location information and product specification parameters in the code, parse the category attributes of the geographic location information and the specification parameters, classify the data according to the parsed category attributes, classify the geographic location information into geographic nodes, classify the product specification parameters into specification nodes, and generate a node classification result;

[0021] S202: Based on the node classification result, the spatial relationship and specification dependency characteristics of the combination of geographic nodes and specification nodes are analyzed one by one, and the association relationship between nodes is constructed. The edges between nodes are marked by matching the attribute parameters of the nodes with the dependency parameters, and the relationship attribute weights of the edges are assigned according to the parameter values. The formula is used:

[0022]

[0023] Calculate edge weights, update edge weight attributes based on the calculation results, and generate node-edge relationship labeling results;

[0024] Among them, E ab represents the edge weight between geographic nodes and specification nodes, A a and A b Represent the attribute parameters of the a-th node and the b-th node, C ab represents the dependency weight of the relationship connecting the ath node and the bth node, t represents the total number of nodes in the network, and C am Represents the connection weight between the a-th node and other nodes m;

[0025] S203: Based on the node-edge relationship labeling results, all node attribute parameters and edge relationship attribute parameters are integrated, an overall network structure framework of nodes and edges is established by layer-by-layer association, and a node-edge association graph is generated by combining nodes and edges.

[0026] As a further solution of the present invention, the steps for obtaining the full-process traceability path are specifically as follows:

[0027] S301: Based on the node-edge association graph, analyze the time series attributes and logistics flow status of multiple nodes in the graph, extract the time series and state parameters of each node, analyze the time series features in the time series, and record the node state layer by layer according to the parameter changes, summarize the state flow information of multiple nodes, and generate a time series record set;

[0028] S302: Using the time series record set, recursively sort the edge parameter values, construct edge parameter sorting rules based on the logical attribute dependency characteristics, normalize the edge logical attribute values ​​and set weight adjustment factors, using the formula:

[0029]

[0030] Calculate edge weights, optimize logical sorting edge parameters based on edge weights, and generate a logical sorting edge parameter set;

[0031] Among them, D ij represents the path dependency strength between node i and node j, V ik and V jk Represent the edge parameter values ​​from node i and node j to node k, S max is the maximum value of the edge logical attribute, S ij is the logical attribute value of edge i to edge j, n represents the total number of nodes in the network, and k is the identifier of the intermediate node involved in the calculation;

[0032] S303: Based on the logically sorted edge parameter set, the edge parameter value is mapped to the path structure, the node relationship and edge logical dependency strength are called to adjust the path weight according to the edge weight, the dynamic path relationship is mapped, all logical attributes and relationship parameters of the path dependency are integrated, and a full-process traceability path is generated.

[0033] As a further solution of the present invention, the step of obtaining the distributed storage mapping index is specifically as follows:

[0034] S401: Based on the full-process traceability path, the time series of the logistics flow status is analyzed, and the flow status is sliced ​​according to the slicing logic of the time series. The time series index and flow association relationship are extracted for each state data in the time slice, and the association data between the nodes are summarized to generate a logical block slicing record;

[0035] S402: Map the node time series to the distributed index using the logical block shard record, calculate the intersection value of the node attribute and the time series flow state, set the weight to normalize the intersection parameter, and use the formula:

[0036]

[0037] Calculate the mapping strength between nodes and time series, and generate a distributed mapping relationship between time series after updating the mapping value;

[0038] Among them, I nt represents the mapping strength value of node n and time series t, A nx Indicates the attribute association value of node n to the fragment sequence x, B xt Indicates the state value from shard sequence x to time series t, p is the total number of shard sequences, q is the total number of time series, x is the shard sequence identifier, and y is the time series identifier;

[0039] S403: Based on the time series distributed mapping relationship, the logical dependency relationship between nodes is called to optimize the storage table structure, the mapping data is multi-layered integrated under the distributed architecture, the storage table index items are organized through the hierarchical relationship of the mapping information, a distributed storage structure is constructed, and a distributed storage mapping index is generated.

[0040] As a further solution of the present invention, the step of obtaining the hierarchical authority control structure is specifically as follows:

[0041] S501: Based on the distributed storage mapping index, extract the associated code and modification record of the authorization file, parse the data characteristics of the authorization file, call the modification range and modification frequency parameters of the file, classify the data in the modification record and generate a global record table, and generate an authorization file modification record data set;

[0042] S502: using the authorization file modification record data set, analyzing the association parameters of the file type and the node authority respectively according to the file attribution characteristics and the node authority relationship, establishing the authority grouping rule according to the intersection of the file type attribution characteristics and the corresponding node authority, mapping the association between the file and the node to the grouping relationship table, and generating the authority classification and grouping relationship;

[0043] S503: Based on the permission classification and grouping relationship, parameter design is performed on the hierarchical access rules, and the normalized weight coefficient of the association between the file type and the permission between nodes is set, using the formula:

[0044]

[0045] Calculate the permission strength value of each file type in the differentiated access level, call the result to adjust the rule parameters in the permission grouping, and generate a hierarchical access rule matrix;

[0046] Among them, P uv represents the permission strength value of the u-th file type to the v-th access level, W ux Indicates the associated permission value of the u-th file type and the x-th node, C xis the file access frequency of the xth node, z is the total number of nodes, u represents the file type identifier, v represents the access level identifier, and x and y are the node identifiers respectively;

[0047] S504: Utilize the hierarchical access rule matrix to output the access level information corresponding to each permission group, integrate all grouping rule parameters into a distributed structure, call multi-level information to establish a global access rule table, and generate a hierarchical permission control structure.

[0048] Financial integration equipment based on traceable data resource products, including:

[0049] The code generation module extracts the area code value based on the area code of the geographic location information, the object feature value set, the timestamp value and the category identifier of the raw material information, and performs encryption processing on the combination with the feature value set, the timestamp value and the category identifier to generate a unique identifier code;

[0050] The association graph construction module extracts the regional code from the geographic location information as the node label to define the geographic node based on the unique identifier code, converts the product specification classification information into the specification node, marks the spatial and specification dependency relationship between the nodes, calculates the connection weight between the nodes, and establishes the node and edge association graph;

[0051] The full-process traceability module extracts the time series attributes and logistics flow status of the nodes based on the node-edge association graph, establishes time series records by timestamp sorting, sorts the edge relationship parameters, maps the node and edge dependencies into a path structure, optimizes the edge relationship weights in the path, and obtains the full-process traceability path;

[0052] The distributed storage module extracts the logistics flow status and time series information based on the full-process traceability path, divides it by time period and establishes a mapping relationship with the node information, builds logical blocks and integrates them into a distributed storage table, and creates a distributed storage mapping index;

[0053] Based on the distributed storage mapping index, the permission control module extracts the associated codes and modification records in the authorization file, parses the data category and attribution attributes, classifies the data according to the node association information, generates permission groups, establishes access level rules, and creates a hierarchical permission control structure.

[0054] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the financial integration method of traceable data resource products as described above.

[0055] Compared with the prior art, the advantages and positive effects of the present invention are:

[0056] In the present invention, a unique identifier is generated by combining geographic location area codes, object feature values, time records, and raw material classification characteristics, thereby achieving the uniqueness and traceability of data resources. Node classification and association graphs combine the relationships between geographic nodes, specification nodes, and edges to improve the logical association and structured management capabilities of data. Logistics time series attribute analysis and dynamic path optimization enhance the efficiency and accuracy of data tracking, ensuring the transparency and integrity of the entire process. Distributed storage improves data reliability and query efficiency through sharding, index mapping, and storage table integration, while supporting multi-node collaboration. Access control based on attribution characteristics and permission grouping ensures hierarchical security management of data access. Overall, the credibility and efficient flow of data in complex transaction scenarios are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a schematic diagram of the main steps of the present invention;

[0058] Figure 2 A flow chart of the steps for obtaining the unique identifier code of the present invention;

[0059] Figure 3 A flowchart of the steps for obtaining a node and edge association graph of the present invention;

[0060] Figure 4 A flowchart of the steps for obtaining the full-process traceability path of the present invention;

[0061] Figure 5 A flowchart of the steps for obtaining the distributed storage mapping index of the present invention;

[0062] Figure 6 This is a flow chart of the steps for obtaining the hierarchical authority control structure of the present invention. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0064] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0065] See also Figure 1 The present invention provides a technical solution: a financial integration method based on traceable data resource products, comprising the following steps:

[0066] S1: Based on the geographic location information, the original feature value of the object, the time record and the raw material information, the regional code of the geographic location is converted into a numerical form, combined with the object feature value and timestamp, and the classification characteristics of the raw material information are added to generate a unique identifier code;

[0067] S2: Based on the unique identifier code, the geographic location information and product specification parameters are divided into node types, the geographic location information is defined as the geographic node, the product specification classification information is mapped as the specification node, the spatial relationship between the nodes and the specification dependency are marked as the edge, and the node and edge association graph is established by combining the node and edge parameters;

[0068] S3: Based on the node and edge association graph, analyze the time series attributes and logistics flow status in the node and establish records according to the time series, recursively sort the edge parameter values ​​according to the logical attributes, map the node and edge relationship into a path structure, and optimize the dynamic path relationship with the edge relationship strength to establish a full-process traceability path;

[0069] S4: Based on the full-process traceability path, the logistics flow status and time series are sharded and stored in logical blocks, the node information and time series are associated and mapped to the distributed index, the mapping relationship is integrated and organized according to the distributed storage architecture to generate a storage table, and a distributed storage mapping index is established;

[0070] S5: Based on the distributed storage mapping index, extract the authorization file associated coding modification records and parse the data characteristics, classify them according to the attribution characteristics and node permission relationships, generate permission groups and establish hierarchical access rules, perform logical verification on the permission groups and output the access level information to generate a hierarchical permission control structure.

[0071] The unique identifier code specifically includes the regional code of the geographic location, object feature values, timestamp, and classification characteristics of raw material information. The node and edge association graph includes geographic nodes, specification nodes, spatial relationship edges, and specification dependency edges. The full-process traceability path includes time series attribute parsing records, logistics flow status records, path structure mapping relationships, and edge relationship strength optimization paths. The distributed storage mapping index includes logical block sharding records, time series distributed indexes, and distributed storage tables. The hierarchical permission control structure includes permission grouping, hierarchical access rules, and access level information.

[0072] See also Figure 2 , the specific steps to obtain the unique identifier code are:

[0073] S101: converting the area code according to the geographical location into a numerical form, calculating the weighted center coordinates through the latitude and longitude of the geographical information, extracting the original feature value of the object and the numerical information of the timestamp, and combining the numerical area code, the object feature value and the timestamp information to generate a preliminary set;

[0074] According to the regional code of the geographical location, the regional code is converted into numerical form, and the weighted center coordinates are calculated by the latitude and longitude of the geographical information. When extracting the geographical code, the polygon data of the geographical area is used as the basis, and the vertex coordinate information of the polygon is obtained respectively. According to the weighted calculation formula Among them A i represents the area distribution weight within the geographic region, x i ,y i The weighted center coordinates of the regions are calculated for the vertex coordinates of each region, and then the original feature values ​​of the objects are extracted, including relevant object attributes, such as production parameters, physical status and other related information. The extracted feature values ​​are standardized to ensure the comparability between different features. Finally, by obtaining the numerical information of the timestamp, the timestamp is converted into a continuous numerical form using the segmented mapping method of date and time. For example, each day is divided into 24 hours, with the hour as the smallest unit, and the numerical form is T = day × 24 + hour. Combine the numerical region code, object feature value and timestamp information to generate a preliminary set;

[0075] S102: Analyze the classification characteristics of the preliminary set and raw material information, and quantify the classification characteristics of the raw material information by combining the raw material type number and classification weight parameter, using the formula:

[0076]

[0077] Calculating the classification characteristic value, merging the classification characteristic value with the region code, time stamp and object characteristic value in the preliminary set, and generating an intermediate set including the classification characteristic value;

[0078] Among them, C r represents the classification feature value, w i Represents the weight parameter of the i-th type of raw materials, R i represents the numerical characteristics of the classification of the i-th raw material, T represents the quantitative value of the timestamp information, and n represents the total number of raw material classifications;

[0079] formula:

[0080]

[0081] The benefit of the formula is that by combining the classification characteristic weights, classification characteristic values ​​and time characteristics of raw material information for comprehensive calculation, the classification characteristic values ​​of raw material information can be accurately quantified, so that the impact of different raw material classifications on the final set has refined differences.

[0082] Detailed explanation of the formula and the process of formula calculation and derivation:

[0083] w i Represents the weight parameter of the i-th type of raw material, which is determined by analyzing the actual contribution of the raw materials. For example, the proportion of raw materials in the total cost is used as the weight, R i It represents the classification characteristic value of the i-th type of raw materials, which is obtained by quantifying the physical or chemical properties of the raw materials. It can be specifically expressed by the formula Calculate, where P i is the measured characteristic value of the i-th type of raw material, P total is the total characteristic value of all raw materials, T represents the numerical value of the timestamp information, and the numerical method is as described in paragraph 1, through the mapping calculation of date and time, the classification characteristic value C is calculated r , assuming n = 3, the weights of the three raw materials w1 = 0.4, w2 = 0.35, w3 = 0.25, the corresponding classification characteristic values ​​R1 = 1.2, R2 = 0.9, R3 = 0.8, and the timestamp value T = 48, substitute into the formula for calculation:

[0084]

[0085] C r =48.01;

[0086] This result shows that the classification feature value C r The size of is affected by the weight, classification characteristic value and time characteristics. This value generates the quantitative characteristics of the intermediate set by combining the region, characteristic value and raw material information.

[0087] S103: calling the intermediate set, combining the classification characteristic value and the time characteristic parameter for splicing, and generating a unique identifier code by weighted calculation of the joint area code and the object characteristic value.

[0088] Call the intermediate set, combine the classification characteristic value and time characteristic parameter for further splicing, normalize the classification characteristic value to ensure that the classification characteristic value is in a comparable range, and then use the weighted calculation of the combined regional code and object characteristic value, and use the mapping relationship between the regional code and the characteristic value, and use the formula Calculate the unique identifier code, where E j represents the region coding component, F jThe components representing the object's characteristic value are calculated by multiplying the region code and the characteristic value component by component to obtain the basic code value of the identifier, and finally combined with the weighted distribution of the time characteristic value to generate a unique identifier code;

[0089] See also Figure 3 , the specific steps for obtaining the node and edge association graph are:

[0090] S201: extracting geographic location information and product specification parameters from the code based on the unique identifier code, parsing the category attributes of the geographic location information and the specification parameters, classifying the data according to the parsed category attributes, classifying the geographic location information into geographic nodes, classifying the product specification parameters into specification nodes, and generating a node classification result;

[0091] Based on the unique identifier code, the unique identifier code is first parsed, and the geographic location information and product specification parameters are extracted through the digit mark at a fixed position in the code. The extracted geographic location information is disassembled according to multiple administrative regions such as country, province, city, and district to form multi-level geographic attributes. At the same time, the key attributes in the product specification parameters are separated, such as the product's size, weight, material and other parameters. Non-numeric parameters are quantified using mapping rules. For example, the material category is marked with a code with a fixed numerical range, and a complete specification parameter table is formed in combination with the specific numerical parameters in the product production process. The parsed geographic information is further mapped to the corresponding geographic nodes according to the hierarchy, and the specification parameters are mapped to the specification nodes according to the quantification processing results. Finally, the geographic location information is classified as a geographic node, and the product specification parameters are classified as a specification node to generate a node classification result.

[0092] S202: Based on the node classification results, the spatial relationship and specification dependency characteristics of the combination of geographic nodes and specification nodes are analyzed one by one, and the association relationship between nodes is constructed. The edges between nodes are marked by matching the attribute parameters of the nodes with the dependency parameters, and the relationship attribute weights of the edges are assigned according to the parameter values. The formula is used:

[0093]

[0094] Calculate edge weights, update edge weight attributes based on the calculation results, and generate node-edge relationship labeling results;

[0095] Among them, E ab represents the edge weight between geographic nodes and specification nodes, A a and A b Represent the attribute parameters of the a-th node and the b-th node, C ab represents the dependency weight of the relationship connecting the ath node and the bth node, t represents the total number of nodes in the network, and C amRepresents the connection weight between the a-th node and other nodes m;

[0096] formula:

[0097]

[0098] The benefit of the formula is that it improves the accuracy of edge weight calculation by combining the attribute difference value of the node and the dependency weight of the edge and introducing the normalization processing of all relevant connection weights. At the same time, by combining the sum of squares and absolute values, it ensures the sensitivity of edge weights to node attribute differences and connection weights.

[0099] Detailed explanation of the formula and the process of formula calculation and derivation:

[0100] Assume that the attribute parameter A between the geographic node and the specification node a and A b 15.2 and 10.8 respectively. These values ​​are calculated by measuring the attributes or specification parameters (such as area, material density) associated with the geographical location of the node, and the weight C ab is 3.2, which is obtained by analyzing the frequency ratio of historical transaction data between nodes. The connection weight of node a and other nodes m is C am The set is [2.5, 3.2, 1.8]. These values ​​are extracted and calculated by analyzing the data exchange volume in the node connection relationship and substituted into the formula:

[0101]

[0102] The result shows that the calculated edge weight of 3.17 represents the dependency strength between the geographic node and the specification node. The higher the value, the stronger the dependency between the two nodes. This result is further used to generate the node-edge relationship labeling results;

[0103] S203: Based on the node-edge relationship labeling results, all node attribute parameters and edge relationship attribute parameters are integrated, and the overall network structure framework of nodes and edges is established through a layer-by-layer association method. The nodes and edges are combined to generate a node-edge association graph.

[0104] Based on the node-edge relationship labeling results, by integrating the attribute parameters of all nodes with the relationship parameters of the edges, the attribute parameter values ​​of the nodes are first normalized, for example, the attribute parameter values ​​of the node size, position, etc. are converted into dimensionless form for consistency processing, and then the weight parameters of the edges are graded, for example, they are divided into three levels: high, medium, and low according to the strength of the edges. When establishing the network structure framework, the nodes with association relationships are connected one by one, and the connection priority is set according to the edge weight level. Through the layer-by-layer network structure construction method, a low-level framework is first formed, and then high-priority connections are integrated to form the overall network structure. Finally, the network node set and edge set of the association graph are generated by combining the comprehensive parameters of the nodes and edges, and a node-edge association graph is generated;

[0105] See also Figure 4 , the specific steps for obtaining the full process traceability path are:

[0106] S301: Based on the node-edge association graph, analyze the time series attributes and logistics flow status of multiple nodes in the graph, extract the time series and state parameters of each node, analyze the time series features in the time series, and record the node state layer by layer according to the parameter changes, summarize the state flow information of multiple nodes, and generate a time series record set;

[0107] Based on the node and edge association graph, the time series attributes and logistics flow status of each node in the graph are analyzed, the time series data and logistics state values ​​of the nodes are extracted, and the trend changes of the time series data are analyzed one by one. By judging the continuity and change range of the logistics state value at each time point, the state points with abnormal changes are screened out, and a time series of data points is established. The fluctuation range and trend change difference of the key points in the time series are used to conduct a horizontal comparative analysis of the logistics state of different nodes. The change rules and characteristic points of the logistics state of each node are extracted through data analysis tools, and these key points and characteristic points are classified as the state records of the corresponding nodes, forming a step-by-step mapping of the time series relationship between nodes, sorting out all node records and establishing a complete state flow set containing time series features, and generating a time series record set;

[0108] S302: Using the time series record set, recursively sort the edge parameter values, build edge parameter sorting rules based on the logical attribute dependency characteristics, normalize the edge logical attribute values ​​and set weight adjustment factors, using the formula:

[0109]

[0110] Calculate edge weights, optimize logical sorting edge parameters based on edge weights, and generate a logical sorting edge parameter set;

[0111] Among them, D ij represents the path dependency strength between node i and node j, V ik and V jkRepresent the edge parameter values ​​from node i and node j to node k, S max is the maximum value of the edge logical attribute, S ij is the logical attribute value of edge i to edge j, n represents the total number of nodes in the network, and k is the identifier of the intermediate node involved in the calculation;

[0112] formula:

[0113]

[0114] The benefit of the formula is that it combines the normalized calculation of path dependency strength with the logarithmic adjustment of edge logic attribute values ​​to comprehensively express path dependency and logic relationship strength, thus optimizing the accuracy and flexibility of dynamic path dependency calculation;

[0115] Detailed explanation of the formula and the process of formula calculation and derivation:

[0116] V ik and V jk They represent the edge parameter values ​​from node i and node j to node k, which are acquired through real-time monitoring by edge parameter acquisition tools. Assume that there are 5 nodes in a network, and the edge parameter values ​​are V ik =3,5,7,6,2 and V jk =2,6,8,4,1, then calculate |V ik -V jk | We get 1,1,1,2,1, and sum it up to get Logical attribute value S max It represents the maximum value of all edge logical attributes, which is 10 according to the collected data analysis. ik is the logical attribute value of the edge from node i to node j, which is calculated by the normalization analysis tool to obtain S ik =5, substitute into the formula:

[0117]

[0118] The result shows that the dependency strength between paths is 0.050, which reflects the closeness between nodes i and j in terms of logical relationship and dynamic dependency. The result can further optimize the logical sorting edge parameters and generate a set of logical sorting edge parameters;

[0119] Formula parameter explanation: D lj represents the path dependency strength between node i and node j, V ik and V jk Represent the edge parameter values ​​from node i and node j to node k, S max is the maximum value of the edge logical attribute, S ij is the logical attribute value of edge i to edge j, n represents the total number of nodes in the network, and k is the identifier of the intermediate node involved in the calculation;

[0120] S303: Based on the logically sorted edge parameter set, the edge parameter value is mapped to the path structure, the node relationship and edge logical dependency strength are called to adjust the path weight according to the edge weight, the dynamic path relationship is mapped, all logical attributes and relationship parameters of the path dependency are integrated, and the full-process traceability path is generated.

[0121] Based on the logical sorting edge parameter set, the edge logical attribute value and weight adjustment factor are called, the edge parameter value and the logical attribute strength are comprehensively adjusted, mapped to the path structure one by one, and the optimal path is selected according to the dynamic path logical dependency strength. The path weight and the logical attribute are combined to form a dynamic path set. The logical sorting result of the path set is further optimized to select a path subset with a higher weight and a logical attribute value that meets the preset conditions. The path subset is dynamically adjusted and expanded to the logical model of the full path. According to the dynamic path relationship of the model, a path set containing a complete node mapping is generated to generate a full-process traceability path.

[0122] See also Figure 5 , the specific steps for obtaining the distributed storage mapping index are:

[0123] S401: Based on the full-process traceability path, the time series of logistics flow status is analyzed, and the flow status is sliced ​​according to the slicing logic of the time series. The time series index and flow association relationship are extracted for each state data in the time slice, and the association data between nodes is summarized to generate a logical block slicing record;

[0124] Based on the whole process traceability path, the sharding logic of logistics flow status and time series is used to slice the flow status according to the time dimension. By parsing the time range and associated flow data of each slice, the flow status of each node is matched with the corresponding time segment, and the logistics node information and flow path between nodes within the time range are extracted. Then, the logistics status data in the slice is quantified, and the non-numeric status is mapped to a specific value in a serialized manner. For example, the status "waiting" can be mapped to 1, "transportation" can be mapped to 2, and the status of completed flow can be mapped to 3. By digitizing each state data of the slice, it can be ensured that the subsequent It can continue to operate on the time series, extract the node dependency between the flow states, quantify the connection strength between the paths, quantify the dependency weight of each pair of nodes according to the logistics dependency between the nodes (for example, the frequency of the same goods passing through multiple nodes in the logistics path is used as the dependency basis), further record the combined data of the slice state and the node dependency strength, generate the basic information set of the logical block, and then store each piece of record information of the logical block in slices, generate an index based on the node connection relationship within the slice, bind the flow state and the time series to the slice storage, and complete the generation of logical block slices and generate logical block slice records through such processing.

[0125] S402: Map the node time series to the distributed index using the logical block shard record, calculate the intersection value of the node attribute and the time series flow state, set the weight to normalize the intersection parameter, and use the formula:

[0126]

[0127] Calculate the mapping strength between nodes and time series, and generate a distributed mapping relationship between time series after updating the mapping value;

[0128] Among them, I nt represents the mapping strength value of node n and time series t, A nx Indicates the attribute association value of node n to the fragment sequence x, B xt Indicates the state value from shard sequence x to time series t, p is the total number of shard sequences, q is the total number of time series, x is the shard sequence identifier, and y is the time series identifier;

[0129] formula:

[0130]

[0131] The benefit of the formula is that by performing numerical intersection analysis on the node attribute parameters and the time series status and normalizing the weight parameters, the accuracy and logical correlation of the node and time series mapping strength calculation are enhanced, avoiding the mapping deviation problem caused by uneven parameter weights in traditional methods.

[0132] Detailed explanation of the formula and the process of formula calculation and derivation:

[0133] A nx It represents the attribute association value of node n to the fragment sequence x. Through the correlation analysis of the logistics parameters of node n (such as transportation frequency and cargo quantity) and the fragment sequence in the time series, it is quantized to 0.6 based on the normalized attribute value;

[0134] B xt It represents the state value of the fragment sequence x to the time series t. By analyzing the connection strength between the logistics state of the flow node x in the time series and the subsequent time series, B is calculated. xt is 0.8;

[0135] p is the total number of shard sequences, set to 5, extracted through logical block shard records;

[0136] q is the total number of time series, which is set to 3 and obtained by dividing the time series;

[0137] Substitute the above parameters into the formula:

[0138]

[0139] Calculation steps:

[0140] 1. Calculate the molecular part:

[0141] 2. Calculate the denominator:

[0142]

[0143] 3. Sum of denominators: 1.34 + 1.39 = 2.73;

[0144] 4. Calculate the mapping intensity:

[0145] The result shows that the mapping strength between node n and time series t is 0.879, indicating that the two are highly correlated in the logistics flow state and can be used for subsequent distributed index mapping generation.

[0146] S403: Based on the time series distributed mapping relationship, the logical dependency relationship between nodes is called to optimize the storage table structure, the mapping data is multi-layered integrated under the distributed architecture, the storage table index items are organized through the hierarchical relationship of the mapping information, a distributed storage structure is constructed, and a distributed storage mapping index is generated.

[0147] Based on the distributed mapping relationship of time series, the logical dependency relationship between nodes is called to optimize the storage table structure. By analyzing the time series mapping intensity data, the nodes and time series corresponding to the high-intensity mapping values ​​are selected for storage priority sorting to ensure that the hierarchical relationship of the mapping logic is optimized. A hierarchical indexing mechanism is used for high-intensity mapping relationships, and the node relationships with lower mapping intensity in the logical dependency relationship are hierarchically processed. The hierarchical weight of the mapping index is used to distribute the node relationship. At the same time, the logical index mapping of each node and time series is integrated into a unified table index structure. Finally, the index table is integrated in the logical block, and the mapping data is stored in each logical storage area in a hierarchical distribution manner to generate a distributed storage mapping index.

[0148] See also Figure 6 ,The specific steps for obtaining the hierarchical authority control structure are:

[0149] S501: Based on the distributed storage mapping index, extract the associated code and modification record of the authorization file, parse the data characteristics of the authorization file, call the modification range and modification frequency parameters of the file, classify the data in the modification record and generate a global record table, and generate an authorization file modification record data set;

[0150] Based on the distributed storage mapping index, the associated code and modification record of the authorized file are extracted, and the association and scope of each record in the authorized file are analyzed. First, the associated code of the authorized file is split into two parts: the basic code and the extended code. The file's permission scope and grouping are determined by the extended code. Then, the time tag and modification type are extracted by the modification record. The modification operation frequency in the record is counted, and the time tag is converted into time series data and grouped by time period. The coverage of the modification range is analyzed according to the attribution attribute of the file type. By analyzing all records within the coverage range, it is determined whether there is a conflicting code. The proportion of conflicting codes is calculated and the part whose value exceeds the average modification frequency is extracted as an abnormal record. By quantifying the characteristics of the abnormal records, the modification records with the same attribution characteristics are screened out and grouping rules are established. These data are indexed according to the node attribution of the record. The scope of file modification and the frequency of permission use are evaluated by the permission attributes of the attribution node. Key information is extracted by combining the characteristics of the index and the node to generate an authorized file modification record data set.

[0151] S502: using the authorization file modification record data set, analyzing the association parameters of the file type and the node authority respectively according to the file attribution characteristics and the node authority relationship, establishing the authority grouping rule according to the intersection of the file type attribution characteristics and the corresponding node authority, mapping the association between the file and the node to the grouping relationship table, and generating the authority classification and grouping relationship;

[0152] Using the authorized file modification record data set, the file attribution characteristics and node permission relationships are classified and processed respectively. First, the file type and node permission attributes in the record are parsed, and the file type is quantified into an attribution characteristic matrix. Each node permission data is matched to the attribution characteristic matrix. By comparing the coverage of the attribution characteristic matrix and the intersection value of the node permission, the node set consistent with the attribution characteristic is extracted, and these nodes are grouped and the permission range is marked. Then, for the grouped nodes, the impact value of each group of nodes on the file permission is evaluated. The intersection calculation model is used to quantify the size of the intersection of the file type and the node permission, and it is determined whether the node permission value exceeds the standard deviation range of the attribution characteristic weight. By normalizing the matrix after the intersection calculation, the permission influence range of each group of nodes is calculated and the grouping rules are established. The relationship between the file type and the node permission is integrated to generate a mapping matrix. Finally, the mapping relationship between files and nodes is organized into a permission grouping table to generate permission classification and grouping relationship.

[0153] S503: Based on the permission classification and grouping relationship, the hierarchical access rules are parameterized and the normalized weight coefficients of the association between the file type and the permission between nodes are set. The formula is:

[0154]

[0155] Calculate the permission strength value of each file type in the differentiated access level, call the result to adjust the rule parameters in the permission grouping, and generate a hierarchical access rule matrix;

[0156] Among them, P uv represents the permission strength value of the u-th file type to the v-th access level, W ux Indicates the associated permission value of the u-th file type and the x-th node, C x is the file access frequency of the xth node, z is the total number of nodes, u represents the file type identifier, v represents the access level identifier, and x and y are the node identifiers respectively;

[0157] formula:

[0158]

[0159] The benefit of the formula is that by introducing the node-associated authority value W ux and node access frequency C x , which quantifies the strong correlation between file type and node authority, and reduces the influence of extreme values ​​through normalization operation, thereby improving the accuracy and robustness of classification rule calculation;

[0160] Detailed explanation of the formula and the process of formula calculation and derivation:

[0161] W ux =0.8, indicating the permission association value between the first file type and node 1, which is obtained through the historical records of node permissions;

[0162] C x ={50,30,20}, indicating the file access frequencies of nodes 1, 2, and 3, respectively, obtained from the file access log statistics;

[0163] Substitute into the formula:

[0164]

[0165] Step by step calculation:

[0166] P uv =(0.8 0.5)+(0.8 0.3)+(0.8 0.2);

[0167] P uv =0.4+0.24+0.16=0.8;

[0168] The result shows that the permission strength between file type and access level is 0.8, which means that the permission correlation between files and nodes is high. The generated hierarchical access rule matrix will use this result as a basis to adjust the permission grouping and access level;

[0169] S504: Using the hierarchical access rule matrix, output the access level information corresponding to each permission group, integrate all grouping rule parameters into a distributed structure, call multi-level information to establish a global access rule table, and generate a hierarchical permission control structure.

[0170] Using the hierarchical access rule matrix, the permission strength of each file type in the matrix is ​​compared and analyzed with the access level. The permission range data and access frequency parameters of all nodes in the group are called, the permission strength values ​​of all groups are extracted and summarized by level, the strength of the permission level is sorted and priority rules are established, the conflicting data is identified through cross-validation of the permission rules and its weight parameters are readjusted, the hierarchical information in the matrix is ​​called to further filter high-intensity permission data and organize it into a unified rule table, each file type permission in the group is mapped to a specific access level, the access rules and permission level relationship tables of all groups are integrated to generate a hierarchical permission control structure.

[0171] Financial integration equipment based on traceable data resource products, including:

[0172] The code generation module extracts the area code value based on the area code of the geographic location information, the object feature value set, the timestamp value and the category identifier of the raw material information, and performs encryption processing on the combination with the feature value set, the timestamp value and the category identifier to generate a unique identifier code;

[0173] The association graph construction module is based on the unique identifier encoding, extracts the regional code from the geographic location information as the node label to define the geographic node, transforms the product specification classification information into the specification node, marks the spatial and specification dependency between nodes, calculates the connection weight between nodes, and establishes the node and edge association graph;

[0174] The full-process traceability module extracts the time-series attributes and logistics flow status of nodes based on the node-edge association graph, establishes time series records by timestamp sorting, sorts edge relationship parameters, maps node and edge dependencies into path structures, optimizes edge relationship weights in the path, and obtains the full-process traceability path;

[0175] The distributed storage module extracts the logistics flow status and time series information based on the full-process traceability path, divides it by time period and establishes a mapping relationship with the node information, builds logical blocks and integrates them into distributed storage tables, and creates a distributed storage mapping index;

[0176] The permission control module is based on the distributed storage mapping index, extracts the associated codes and modification records in the authorization file, parses the data category and attribution attributes, classifies the data according to the node association information, generates permission groups, establishes access level rules, and creates a hierarchical permission control structure.

[0177] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A financial integration method based on traceable data resource products, characterized in that: The following steps are involved: S1: Based on the geographic location information, the original feature value of the object, the time record and the raw material information, the regional code of the geographic location is converted into a numerical form, combined with the object feature value and timestamp, and the classification characteristics of the raw material information are added to generate a unique identifier code; S2: Based on the unique identifier code, the geographic location information and product specification parameters are divided into node types, the geographic location information is defined as a geographic node, the product specification classification information is mapped as a specification node, the spatial relationship between the nodes and the specification dependency are marked as edges, and the node and edge parameters are combined to establish a node and edge association graph; S3: Based on the node and edge association graph, analyze the time series attributes and logistics flow status in the node and establish records according to the time series, recursively sort the edge parameter values ​​according to the logical attributes, map the node and edge relationship into a path structure, and optimize the dynamic path relationship with the edge relationship strength to establish a full-process traceability path; S4: Based on the full-process traceability path, the logistics flow status and time series are sharded and stored in logical blocks, the node information and time series are associated and mapped to distributed indexes, the mapping relationships are integrated and organized according to the distributed storage architecture to generate storage tables, and a distributed storage mapping index is established; S5: Based on the distributed storage mapping index, extract the authorization file associated coding modification record and parse the data characteristics, classify according to the attribution characteristics and node authority relationship, generate authority groups and establish hierarchical access rules, output access level information after logical verification of the authority groups, and generate a hierarchical authority control structure.

2. The financial integration method of data resource products based on traceability according to claim 1 is characterized in that: The steps for obtaining the unique identifier code are specifically as follows: S101: converting the area code according to the geographical location into a numerical form, calculating the weighted center coordinates through the latitude and longitude of the geographical information, extracting the original feature value of the object and the numerical information of the timestamp, and combining the numerical area code, the object feature value and the timestamp information to generate a preliminary set; S102: Analyze the classification characteristics of the preliminary set and the raw material information, and quantify the classification characteristics of the raw material information by combining the raw material type number and the classification weight parameter, using the formula: Calculating the classification characteristic value, merging the classification characteristic value with the region code, time stamp and object characteristic value in the preliminary set, and generating an intermediate set including the classification characteristic value; Among them, C r represents the classification feature value, w i Represents the weight parameter of the i-th type of raw materials, R i represents the numerical characteristics of the classification of the i-th raw material, T represents the quantitative value of the timestamp information, and n represents the total number of raw material classifications; S103: calling the intermediate set, combining the classification characteristic value and the time characteristic parameter for splicing, and generating a unique identifier code by weighted calculation of the joint region code and the object characteristic value.

3. The financial integration method of data resource products based on traceability according to claim 2 is characterized in that: The steps of obtaining the node and edge association graph are specifically as follows: S201: Based on the unique identifier code, extract the geographic location information and product specification parameters in the code, parse the category attributes of the geographic location information and the specification parameters, classify the data according to the parsed category attributes, classify the geographic location information into geographic nodes, classify the product specification parameters into specification nodes, and generate a node classification result; S202: Based on the node classification result, the spatial relationship and specification dependency characteristics of the combination of geographic nodes and specification nodes are analyzed one by one, and the association relationship between nodes is constructed. The edges between nodes are marked by matching the attribute parameters of the nodes with the dependency parameters, and the relationship attribute weights of the edges are assigned according to the parameter values. The formula is used: Calculate edge weights, update edge weight attributes based on the calculation results, and generate node-edge relationship labeling results; Among them, E ab represents the edge weight between geographic nodes and specification nodes, A a and A b Represent the attribute parameters of the a-th node and the b-th node, C ab represents the dependency weight of the relationship connecting the ath node and the bth node, t represents the total number of nodes in the network, and C am Represents the connection weight between the a-th node and other nodes m; S203: Based on the node-edge relationship labeling results, all node attribute parameters and edge relationship attribute parameters are integrated, an overall network structure framework of nodes and edges is established by layer-by-layer association, and a node-edge association graph is generated by combining nodes and edges.

4. The financial integration method of data resource products based on traceability according to claim 3 is characterized in that: The steps for obtaining the full-process traceability path are specifically as follows: S301: Based on the node-edge association graph, analyze the time series attributes and logistics flow status of multiple nodes in the graph, extract the time series and state parameters of each node, analyze the time series features in the time series, and record the node state layer by layer according to the parameter changes, summarize the state flow information of multiple nodes, and generate a time series record set; S302: Using the time series record set, recursively sort the edge parameter values, construct edge parameter sorting rules based on the logical attribute dependency characteristics, normalize the edge logical attribute values ​​and set weight adjustment factors, using the formula: Calculate edge weights, optimize logical sorting edge parameters based on edge weights, and generate a logical sorting edge parameter set; Among them, D ij represents the path dependency strength between node i and node j, V ik and V jk Represent the edge parameter values ​​from node i and node j to node k, S max is the maximum value of the edge logical attribute, S ij is the logical attribute value of edge i to edge j, n represents the total number of nodes in the network, and k is the identifier of the intermediate node involved in the calculation; S303: Based on the logically sorted edge parameter set, the edge parameter value is mapped to the path structure, the node relationship and edge logical dependency strength are called to adjust the path weight according to the edge weight, the dynamic path relationship is mapped, all logical attributes and relationship parameters of the path dependency are integrated, and a full-process traceability path is generated.

5. The financial integration method of data resource products based on traceability according to claim 4 is characterized in that: The steps of obtaining the distributed storage mapping index are specifically as follows: S401: Based on the full-process traceability path, the time series of the logistics flow status is analyzed, and the flow status is sliced ​​according to the slicing logic of the time series. The time series index and flow association relationship are extracted for each state data in the time slice, and the association data between the nodes are summarized to generate a logical block slicing record; S402: Map the node time series to the distributed index using the logical block shard record, calculate the intersection value of the node attribute and the time series flow state, set the weight to normalize the intersection parameter, and use the formula: Calculate the mapping strength between nodes and time series, and generate a distributed mapping relationship between time series after updating the mapping value; Among them, I nt represents the mapping strength value of node n and time series t, A nx Indicates the attribute association value of node n to the fragment sequence x, B xt Indicates the state value from shard sequence x to time series t, p is the total number of shard sequences, q is the total number of time series, x is the shard sequence identifier, and y is the time series identifier; S403: Based on the time series distributed mapping relationship, the logical dependency relationship between nodes is called to optimize the storage table structure, the mapping data is multi-layered integrated under the distributed architecture, the storage table index items are organized through the hierarchical relationship of the mapping information, a distributed storage structure is constructed, and a distributed storage mapping index is generated.

6. The financial integration method of data resource products based on traceability according to claim 5 is characterized in that: The steps for obtaining the hierarchical authority control structure are specifically as follows: S501: Based on the distributed storage mapping index, extract the associated code and modification record of the authorization file, parse the data characteristics of the authorization file, call the modification range and modification frequency parameters of the file, classify the data in the modification record and generate a global record table, and generate an authorization file modification record data set; S502: using the authorization file modification record data set, analyzing the association parameters of the file type and the node authority respectively according to the file attribution characteristics and the node authority relationship, establishing the authority grouping rule according to the intersection of the file type attribution characteristics and the corresponding node authority, mapping the association between the file and the node to the grouping relationship table, and generating the authority classification and grouping relationship; S503: Based on the permission classification and grouping relationship, parameter design is performed on the hierarchical access rules, and the normalized weight coefficient of the association between the file type and the permission between nodes is set, using the formula: Calculate the permission strength value of each file type in the differentiated access level, call the result to adjust the rule parameters in the permission grouping, and generate a hierarchical access rule matrix; Among them, P uv represents the permission strength value of the u-th file type to the v-th access level, W ux Indicates the associated permission value of the u-th file type and the x-th node, C x is the file access frequency of the xth node, z is the total number of nodes, u represents the file type identifier, v represents the access level identifier, and x and y are the node identifiers respectively; S504: Utilize the hierarchical access rule matrix to output the access level information corresponding to each permission group, integrate all grouping rule parameters into a distributed structure, call multi-level information to establish a global access rule table, and generate a hierarchical permission control structure.

7. Financial integration equipment based on traceable data resource products, characterized by: The financial integration device based on traceable data resource products is used to execute the financial integration method based on traceable data resource products according to any one of claims 1 to 6, and the device includes: The code generation module extracts the area code value based on the area code of the geographic location information, the object feature value set, the timestamp value and the category identifier of the raw material information, and performs encryption processing on the combination with the feature value set, the timestamp value and the category identifier to generate a unique identifier code; The association graph construction module extracts the regional code from the geographic location information as the node label to define the geographic node based on the unique identifier code, converts the product specification classification information into the specification node, marks the spatial and specification dependency relationship between the nodes, calculates the connection weight between the nodes, and establishes the node and edge association graph; The full-process traceability module extracts the time series attributes and logistics flow status of the nodes based on the node-edge association graph, establishes time series records by timestamp sorting, sorts the edge relationship parameters, maps the node and edge dependencies into a path structure, optimizes the edge relationship weights in the path, and obtains the full-process traceability path; The distributed storage module extracts the logistics flow status and time series information based on the full-process traceability path, divides it by time period and establishes a mapping relationship with the node information, builds logical blocks and integrates them into a distributed storage table, and creates a distributed storage mapping index; Based on the distributed storage mapping index, the permission control module extracts the associated codes and modification records in the authorization file, parses the data category and attribution attributes, classifies the data according to the node association information, generates permission groups, establishes access level rules, and creates a hierarchical permission control structure.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the financial integration method of traceable data resource products according to any one of claims 1 to 6 are implemented.

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