A data asset sharing method and system based on Fangyuan Cloud

Abstract

The present invention relates to the technical field of data asset sharing, and specifically provides a data asset sharing method and system based on Fangyuan Cloud, which includes the following steps: obtaining data structure information and establishing a standardized list, counting field matching and jump situations to construct a remapping list, extracting idle paths to aggregate path groups to generate a candidate list, judging priority conflicts to generate a flow splitting mapping result, and combining fields and paths for docking to execute sharing operations. In the present invention, through the combination of field name encoding and type expression, the ability to uniformly identify fields is enhanced. The remapping field pairs are screened by synthesizing the scoring of coding differences and type vector differences to improve the accuracy of field matching. The path groups are constructed by integrating path status, business attribution, and priority elements. Under path conflicts, alternative numbers and flow splitting identifiers are configured, and the fields and path numbers are combined for docking to achieve granular collaborative sharing, strengthen the correspondence between the data channel structure and access control, and improve the stability of data sharing in multiple environments.

Description

Technical Field

[0001] The present invention relates to the technical field of data asset sharing, and in particular to a data asset sharing method and system based on Fangyuan Cloud. Background Art

[0002] The technical field of data asset sharing includes technical solutions for collaborative utilization and unified allocation of distributed data resources in a multi-organization and multi-system environment. The core content is to achieve secure, efficient, and compliant circulation and use of data among different management entities, covering links such as data standardization processing, permission control, data desensitization, and transmission mechanism design. The aim is to break data islands and realize cross-system and cross-platform data discoverability, accessibility, understandability, and reusability by establishing a unified data identification system, metadata management mechanism, and exchange interface protocol. It involves the design of data management systems, data security control strategies, and the formulation of data interaction processes, and is a key supporting technology for promoting digital transformation and data element circulation.

[0003] Among them, the data asset sharing method based on Fangyuan Cloud refers to a technical solution that relies on the Fangyuan Cloud platform to achieve cross-domain sharing of multi-source heterogeneous data assets, covering matters such as data access, standard mapping, sharing strategy execution, and access control management, and is completed by constructing a data resource directory system, setting sharing levels, formulating access rules, and configuring an authentication mechanism. By establishing a data model for resource description, structured arrangement of the accessed data sources is realized, and an access request verification mechanism is used to cooperate with the authorization system for access permission management. At the same time, data cross-platform sharing is completed in combination with the data exchange scheduling process.

[0004] In the existing data asset sharing process, there is no fine-grained encoding and structural expression modeling for the accessed field character sets, and the field expression level is insufficient, resulting in unclear semantic distinction between fields in cross-domain structure docking, affecting the structure fusion efficiency; the field matching process depends on label consistency rather than quantitative analysis, lacking a numerical judgment standard for structural similarity, and prone to incorrect mapping or missing identification of field jumps; the resource path screening process does not incorporate the business attribute dimension, and the path configuration tends to be static allocation, lacking a dynamic scheduling response mechanism, and the resource usage is limited by the current availability status; in the case of path conflicts, there is no construction of alternative paths and diversion mechanisms, and it is easy to have a single-point blockage of the path, causing risks of distribution lag or interruption of high-frequency data requests; there is no construction of a binding channel structure between the path and the field, and the path allocation lacks control support for the field sharing range, easily leading to problems such as data desensitization failure or permission mismatch. For example, in a multi-service cross-deployment scenario, if the path allocation lacks business recognition capabilities and conflict alternative strategies, it is extremely easy to cause priority imbalance, reducing the stability of the sharing link and the coordination degree of system resources. Summary of the Invention

[0005] The object of the present invention is to solve the disadvantages existing in the prior art, and a data asset sharing method based on Fangyuan Cloud is proposed.

[0006] To achieve the above object, the present invention adopts the following technical solution: A data asset sharing method based on Fangyuan Cloud, comprising the following steps:

[0007] S1: Obtain the data structure information of the data source to be accessed on the Fangyuan Cloud platform, encode and map the character set of the field names, combine them with the field data type values, and establish a data structure standardization list;

[0008] S2: According to the data structure standardization list, count the unique matching quantity of the field labels corresponding to the data source and the quantity of repeated jump fields, compare the sharing threshold and the jump tolerance value, extract the candidate field pairs for remapping and perform weighted synthesis scoring, and establish a data field sharing remapping list;

[0009] S3: Obtain the data source resource request information in the sharing stage, record the business scenario, extract the idle path numbers, aggregate them into shareable path groups according to the belonging business scenarios, and obtain a sharing path allocation candidate list;

[0010] S4: Based on the sharing path allocation candidate list, retrieve the usage priority corresponding to the occupied path numbers, compare the priorities of the data sources to be accessed, and if there are priority conflicts, allocate alternative path numbers to generate a data source diversion path mapping result;

[0011] S5: According to the data field sharing remapping list and the data source diversion path mapping table, combine the field remapping field pairs and path numbers to perform data path docking, and execute the data asset sharing operation.

[0012] As a further solution of the present invention, the data structure standardization list includes field name mapping rules, field type expression models, and a standardized field identifier set. The data field sharing remapping list includes field remapping pairs, structure adaptation scores, and shared field unique identifiers. The sharing path allocation candidate list includes a path number set, a business scenario path group, and an available status identifier. The data source diversion path mapping result includes alternative path numbers, diversion identifiers, and a priority comparison table. The data asset sharing operation includes a path number field docking set, a mapped field data stream, and a shared operation execution label.

[0013] As a further solution of the present invention, the specific steps for obtaining the data structure standardization list are as follows:

[0014] S111: Obtain the first-level field label information exposed by the data source to be accessed in the Fangyuan Cloud Platform, detect the data type values corresponding to the field labels, call the mapping rules between the field label character sets and field types, perform item-by-item encoding conversion on the character sets in the field labels, and splice and integrate them with the field type information to generate a combined result of field structure encodings;

[0015] S112: According to the combined result of the field structure encodings, screen out the field items in which the repetition rate of the character set encodings in the fields exceeds the standard encoding repetition threshold, count the number of duplicate combinations existing in the corresponding field types, and obtain the distribution range interval values of the field character sets under different types to generate field encoding distribution interval data;

[0016] S113: Based on the field encoding distribution interval data, judge the degree of type consistency of each field after encoding mapping, and call the number of type mappings corresponding to the field label character sets and the length of the field structure encoding, and use the formula:

[0017] ;

[0018] Calculate the cross-type field structure encoding consistency difference value , judge whether field reconstruction is required, and establish a data structure standardization list, where represents the number of type mappings of the th field, represents the character set encoding repetition rate of the th field, represents the character set length of the th field, represents the encoding combination length of the th field, represents the total number of type mapping fields, represents the total number of encoding length fields.

[0019] As a further solution of the present invention, the steps for obtaining the data field sharing and remapping list are specifically as follows:

[0020] S211: Based on the field label information recorded in the data structure standardization list, count the matching relationship between the field labels in all the accessed data sources in the current Fangyuan Cloud Platform and the standard field labels, call the field label set and the data source field label set for item-by-item comparison, obtain the number of unique labels corresponding to the standard field labels in each data source one by one, and accumulate and count the frequency of repeated occurrences of the labels to obtain the unique matching number and repeated jump number of the field labels;

[0021] S212: Based on the unique matching quantity and the repeated jump quantity of the field labels, respectively perform interval judgment with the sharing threshold and the jump tolerance value, extract the data source fields that meet the sharing threshold, and correspondingly generate a set of field pairs that can be replaced and accessed. Call the character set encoding set of field labels and the field type vector set, calculate the number of character set encoding differences and the difference of field type expression vectors between field pairs respectively, and use the formula:

[0022] ;

[0023] Calculate the composite score of the character set difference and type expression offset of the field pair , screen the minimum score value among all field pairs, and obtain the field structure adaptation result. Among them, represents the th candidate character set encoding value of the field, represents the th standard character set encoding value of the field, represents the th candidate type expression vector value of the field, represents the th standard type expression vector value of the field, represents the number of encoding difference items, represents the type expression dimension number;

[0024] S213: Based on the field structure adaptation result, select the field pair with the minimum score according to the score value, mark it as the high-adaptation field combination, and form a field pair entry with the original field label and the standard field label to which it belongs. Combine and summarize to form a field mapping record set, and establish a data field sharing remapping list.

[0025] As a further solution of the present invention, the specific steps for obtaining the candidate list of shared path assignments are as follows:

[0026] S311: Obtain the data source resource request information entering the sharing stage, extract the corresponding data source number and field mapping record, collect the business scenario identification items in the associated data source resource request entries, and perform label mapping on the data source resource request information according to the business scenario identification to obtain the business scenario identification mapping result;

[0027] S312: According to the business scenario identification mapping result, call the path number set, path status set and current path occupancy set in the shared path status library, extract the list of path numbers with the current path status as the allocable status and the occupancy flag as empty, detect the resource priority threshold associated with each path number, aggregate each path number according to the business scenario respectively, and obtain the group of shareable path numbers;

[0028] S313: Based on the group of shareable path numbers and combined with the business scenario identifier, integrate all the shareable path numbers under each type of business scenario into one type of shared path, and establish a candidate list for shared path allocation.

[0029] As a further solution of the present invention, the step of obtaining the data source shunt path mapping result is specifically as follows:

[0030] S411: Based on all the path numbers in the candidate list for shared path allocation, retrieve the path number items currently in the occupied state in the path status field, collect the usage priority values registered in the resource usage records associated with the path numbers, and compare the resource priority values in the resource request of the data source to be accessed. Statistically analyze all the conflicting path number sets to obtain a group of priority-conflicting path numbers;

[0031] S412: According to the group of priority-conflicting path numbers, filter out the unoccupied shareable path numbers in the same business scenario, and perform number pairing attempts with the judgment conditions that the path status is idle and the business scenario identifier is the same. Use the formula:

[0032] ;

[0033] Calculate the difference score value between the resource usage deviation degree and the priority reconstruction adaptation value for each pair of path numbers , select the path number with the smallest score value as the alternative path number, and make a marked record to generate an alternative path mapping score result, where, represents the resource request intensity of the th conflicting path, represents the resource idle level of the th alternative path, represents the load cycle value of the th alternative path, represents the priority difference of the th conflicting path, represents the cumulative occupied duration of the th path, represents the remaining available cycle number of the th path, is the number of conflicting paths, is the number of path priority difference items;

[0034] S413: According to the alternative path mapping score result, select the path number with the smallest score value corresponding to each conflicting path as the alternative path number, bind it to the original conflicting path number, and establish a shunt identification record item to establish the data source shunt path mapping result.

[0035] As a further solution of the present invention, the specific steps of the data asset sharing operation are as follows:

[0036] S511: According to the field mapping field pairs recorded in the data field sharing remapping list, obtain the corresponding relationship between the standard field and the original field in each pair of fields. Combine the set of path numbers already allocated in the data source diversion path mapping result, and perform a combined matching of the field pairs and the path numbers according to the data source number. Merge and combine the field mapping field pairs and the path numbers to obtain a field path combination comparison table;

[0037] S512: According to the field path combination comparison table, extract the field character set encoding and field type vectors in all field pairs. Call the resource type label, business scenario identifier, and path transmission capacity associated with the path number, and compare the field structure information and the path resource structure characteristics item by item to confirm the structural matching and resource accessibility between the two, and obtain the field path combination records with successful structural matching;

[0038] S513: Based on the field path combination records with successful structural matching, extract the field path combination records that meet the path access conditions as the formal connectable field data path mapping relationship. Establish a data path docking relationship according to the field mapping order and the path number, and perform the data asset sharing operation.

[0039] A data asset sharing system based on Fangyuan Cloud, comprising:

[0040] The data structure standardization module obtains field labels and type information, extracts the character set and encodes the mapping combination to uniformly express the structure, and generates a data structure standardization list;

[0041] The field mapping and screening module, based on the data structure standardization list, counts the unique matching number and the number of jump variables of the fields, determines whether the sharing threshold and the jump tolerance value are met, extracts candidate field pairs, calculates the difference in character set encoding and the difference in type expression vectors, performs weighted scoring, screens the field pairs with the best scores to establish an adaptation relationship, and generates a data field sharing remapping list;

[0042] The resource request analysis module obtains resource request information and scenario identifiers, filters idle paths in combination with path status and occupancy, and builds a shareable path group according to the business scenario to obtain a candidate list for shared path allocation;

[0043] The path priority conflict resolution module, according to the candidate list for shared path allocation, compares the data source priority and the occupied path priority. If there is a conflict, it allocates an alternative path in the same scenario to generate a data source diversion path mapping result;

[0044] The data sharing execution module combines the data field sharing remapping list and the data source shunt path mapping table to perform data path docking and execute data asset sharing operations.

[0045] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0046] In the present invention, by performing character set encoding on the accessed field names and constructing a structural expression in combination with the field data types, the clarity of field semantic expression is improved, so that the structural information has consistency and comparability, effectively avoiding ambiguities and misjudgments caused by heterogeneous fields; constructing remapped field pairs according to the field matching accuracy index, and performing quantitative evaluation in combination with the coding difference and the type expression vector difference to realize the screening and optimization of the mapping results, enhancing the discrimination and accuracy of the structural adaptation between fields; in the process of sharing path screening, aggregating path groups by integrating multiple dimensions such as path idle state, business scenario attribution, and priority threshold, breaking the single resource allocation logic, constructing a path candidate mechanism with scenario orientation, and expanding the elastic boundary of resource scheduling; configuring alternative numbers and shunt identifiers for priority conflict paths to strengthen the continuity and fault tolerance of path allocation, ensuring smooth sharing in a high-density request environment; in the path docking stage, compound-binding the field remapping pairs and path numbers to establish a shared channel structure that coordinates at the field granularity and path granularity, improving the organizational orderliness and operation accuracy of the data transmission channel, and expanding the intelligent scheduling depth and policy elastic range of data sharing in a complex environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 is the main step flowchart of the present invention;

[0048] Figure 2 is the flowchart for obtaining the data structure standardization list of the present invention;

[0049] Figure 3 is the flowchart for obtaining the data field sharing remapping list of the present invention;

[0050] Figure 4 is the flowchart for obtaining the candidate list of shared path allocation of the present invention;

[0051] Figure 5 is the flowchart for obtaining the mapping result of the data source shunt path of the present invention;

[0052] Figure 6 is the flowchart for executing the data asset sharing operation of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0053] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0054] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, and is 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 should not be construed as a limitation to the present invention. In addition, in the description of the present invention, the meaning of "a plurality" is two or more, unless otherwise specifically defined.

[0055] Please refer to Figure 1 , a method for sharing data assets based on Fangyuan Cloud, comprising the following steps:

[0056] S1: Obtain the first-layer field tags and corresponding data type information of the data structure of the data source to be accessed on the Fangyuan Cloud platform, extract the field name character set and field data type, combine the encoded mapping of the field name character set with the field data type value, unify the data structure expression, and establish a data structure standardization list;

[0057] S2: According to the records of each field tag in the data structure standardization list, count the unique matching number and the number of repeated jump fields of the corresponding field tags of the data sources already accessed in the current Fangyuan Cloud environment, and perform interval judgment with the sharing threshold and the jump tolerance value respectively. When the unique matching number meets the sharing threshold, extract the candidate field pairs for remapping, synthesize the weights of the encoding difference number of the candidate field pair character sets and the difference of the type expression vectors for scoring, and screen the field pair with the optimal score as the structure adaptation result to establish a data field sharing remapping list;

[0058] S3: Obtain the data source resource request information entering the sharing stage, record the business scenario identifier to which it belongs, combine the path status, current occupancy and resource priority threshold in the sharing path status library, extract the path numbers with the resource usage status being idle, aggregate them according to the business scenario to which they belong into a shareable path group, and obtain a candidate list for sharing path allocation;

[0059] S4: Based on all the path records in the shared path allocation candidate list, retrieve the usage priorities corresponding to all the path numbers in the occupied state, compare the priority of the data source to be accessed with the priorities in the current occupied state record. If there is a priority conflict for the current path, allocate an alternative path number in the same business scenario for the current requested path and establish a shunt identifier, generating a data source shunt path mapping result;

[0060] S5: According to the mapped relationship fields that have been completed in the data field shared remapping list and the path allocation numbers in the data source shunt path mapping table, combine the field remapping field pairs and the path numbers for data path docking, and perform the data asset sharing operation.

[0061] The data structure standardization list includes field name mapping rules, field type expression models, and a standardized field identifier set. The data field shared remapping list includes field remapping pairs, structure adaptation scores, and shared field unique identifiers. The shared path allocation candidate list includes a path number set, a business scenario path group, and an available status identifier. The data source shunt path mapping result includes an alternative path number, a shunt identifier, and a priority comparison table. The data asset sharing operation includes a path number field docking set, a mapped field data stream, and a shared operation execution tag.

[0062] Please refer to Figure 2 , step S1 is as follows:

[0063] S111: Obtain the first-level field label information exposed by the data source to be accessed in the Fangyuan Cloud Platform, detect the data type values corresponding to the field labels, call the field label character set and field type mapping rules, perform item-by-item encoding conversion on the character set in the field labels, and splice and integrate them with the field type information to generate a field structure encoding combination result;

[0064] Obtain the first-level field label information exposed by the data source to be connected in the Fangyuan Cloud Platform, that is, extract the field names and data types from the structured data. For example, in a certain water quality monitoring, fields such as "dissolved oxygen", "temperature", and "pH value" and their corresponding types are "floating point", "integer", and "floating point". It is necessary to detect each field label item by item to confirm whether it conforms to the character set rule. For example, the character set is restricted to be composed of English letters and underscores. During the detection, it is found that the field "dissolved_oxygen" is legal, and "pH value" is illegal. After disassembling it into "pH_zhi" and then mapping it, the mapping operation of the character set can refer to the ASCII coding table. Each character is mapped to a two-digit code, such as "_" is mapped to "5F", and "p" is mapped to "70". Further combine the fields to form a complete character coding string, and then generate a combined coding value according to its data type. For example, "pH_zhi" is "70485F7A6869", and the data type is float, and the type value setting code is "02". Finally, it is combined into "70485F7A686902". The above operations are carried out on a field-by-field basis. After generating the combined coding for all fields, it is necessary to splice the field label coding and the data type coding to facilitate subsequent judgment of the duplication rate and coding rules. The coding of the field name "Temperature" is "54656D7065726174757265", the data type is float, and the coding is "02", combined into "54656D706572617475726502". This structure is used to construct a metadata index table in the actual business scenario, such as judging the coding consistency of sensor fields in different regions in the urban pipe network real-time monitoring system. After completing the structure splicing, write all the encodings into the total field structure table and establish a mapping index to assist in subsequent identification of field duplication situations and mapping relationships, such as the combined result of the field structure coding.

[0065] S112: According to the combined result of the field structure coding, screen out the field items in the field where the duplication rate of the character set coding exceeds the standard coding duplication threshold, count the number of duplicate combinations existing in the corresponding field types, and obtain the distribution range interval values of the field character sets under different types to generate field coding distribution interval data;

[0066] Based on the combined result of field structure encoding, it is necessary to screen out the fields with a character set encoding duplication rate exceeding the threshold from the combined encoding values. First, set the encoding duplication threshold to 60%. That is, if the proportion of duplicate characters in the character encoding of any field exceeds 60% of the total length, it is considered that there is redundancy or naming conflict. Count the number of such fields and mark them. For example, the encoding of field A is "616161626364", and the proportion of the duplicate character "61" is 50%, which does not exceed the threshold and is not marked; the encoding of field B is "707070707273", and the proportion of the character "70" is 66.7%, which is marked as a duplicate field. Further, count the distribution of various data types in the duplicate fields. For example, 10 items are marked in the integer fields and 8 items are marked in the floating-point fields. Then, count the character set encoding length ranges corresponding to each field type. For example, the average length of integer fields is 8 bytes, and the average length of floating-point fields is 10 bytes. Construct the encoding length distribution intervals of fields under different data types, and classify and judge the landing points of each field within the type range to determine whether they are in the abnormal range. The specific threshold is determined by the sample mean and standard deviation. If the encoding length of a certain field falls outside the mean ± 2 times the standard deviation, it is recorded as abnormal. Use the data of a monitoring platform with a sample size of 100 to construct the standard, as shown in the following table.

[0067] Table 1 Sample Table of Field Encoding Structure:

[0068] ;

[0069] As shown in Table 1, the proportion of duplicate characters in the field "Chloride" is 0.67, exceeding the set threshold of 0.6. Therefore, it is included in the set of fields with abnormal encoding. After screening and counting all fields, combined with the distribution, draw an interval judgment graph to further obtain the field encoding distribution interval data under each type.

[0070] S113: Based on the field encoding distribution interval data, judge the type consistency degree of each field after encoding mapping, and call the type mapping quantity corresponding to the field label character set and the field structure encoding length. Use the formula:

[0071] ;

[0072] Calculate the cross-type field structure encoding consistency difference value ; judge whether field reconstruction is required, and establish a data structure standardization list. Among them, represents the th type mapping quantity of the field, represents the th character set encoding duplication rate of the field, represents the th character set length of the field, represents the The encoding combination length of a field, Indicates the total number of type mapping fields, Indicates the total number of encoding length fields;

[0073] According to the above field encoding distribution interval values, judge the consistency of type mapping between fields. If a field has corresponding fields in multiple types but the encoding differences are significant, it means that there are differences in the naming specifications of this field. It is necessary to compare the character set length and mapping quantity of the field under different types, and then calculate the structural consistency based on this.

[0074] : The number of type mappings of the field, indicating the number of different data types corresponding to the field in the system. For example, the field "temperature" is mapped to int, float, and double in 3 tables respectively, so ;

[0075] : The encoding repetition rate of the field, such as 0.64;

[0076] : The character set length of the field, such as "dissolved oxygen" is 8;

[0077] : The encoding combination length of the field, such as 12;

[0078] , ;

[0079] Substitute the sample data:

[0080] ;

[0081] ;

[0082] ;

[0083] This result shows that the structural encoding consistency difference value is 43.1, indicating that there are significant differences between the current fields. The system can judge whether it meets the access standard structure based on this. If It is considered that the standards are consistent (the standard value of 20 is set based on the average structural deviation tolerance generated by the analysis of field codes in a multi-class structured data system. In the calculation process, the conventional fluctuation ranges of the number of field mappings, the length of field codes, and the coding repetition rate are comprehensively considered, and the median deviation amount is used for truncation setting. Specifically, it is set as follows: in each data source structure, the average coding length of the field is set to 10 bytes, the average character repetition rate is 0.5, and the number of type mappings usually does not exceed 4. These three together constitute a floating interval with a difference value fluctuating between 18 and 22. Therefore, the standard consistency judgment threshold is set to 20. This threshold will show an upward trend when the field mapping scale expands and the field character complexity increases, and tends to decrease in an environment with a high degree of field standardization concentration, so as to ensure the discrimination accuracy during field coding quantization and at the same time limit the access of unreasonable field structures), otherwise, field reconstruction is required. Therefore, a data structure standardization list is finally obtained.

[0084] The operation logic of this formula is designed based on the comprehensive measurement method of structural coding differences in different dimensions. First, two sets of parameter sets are introduced in the numerator part. One set is the product term of the number of field type mappings and the coding repetition rate, which represents the number of redundant structure mappings of each field in multiple data types, and the square root of the coding repetition rate is taken to moderately amplify the weight of highly repetitive fields and at the same time control the non-linear impact of extremely high repetition values on the overall calculation; the other set is the product of the field character set length and the coding combination length, which reflects the complexity of the field code itself in the character dimension and the structure dimension. The two respectively represent the structural extensibility and redundancy characteristics. Therefore, through the difference between these two sets of values, the structural inconsistency degree between the field mapping quantity and the coding complexity is reflected. And in the final calculation, the denominator is normalized, representing the combined count of the number of field samples, ensuring the comparability of structural differences under different sample scales. Taking the absolute value in the overall structure is to avoid interference caused by the difference direction to the result interpretation, so that stably expresses the structural deviation intensity across fields.

[0085] Please refer to Figure 3 , and the steps of S2 are as follows:

[0086] S211: Based on the field label information recorded in the data structure standardization list, count the matching relationship between the field labels in all the connected data sources in the current Fangyuan Cloud platform and the standard field labels. Call the field label set and the data source field label set for item-by-item comparison, obtain the number of unique labels corresponding one-to-one to the standard field labels in each data source, and accumulate the frequency of repeated occurrences of the labels to obtain the number of unique matches and the number of repeated jumps of the field labels;

[0087] Based on the field label content recorded in the data structure standardization list, obtain the standard field name, character set encoding value, and field type in this list as the matching benchmarks. Extract the field label sets in the connected data sources item by item, and call the character set encoding values of each field label for comparison operations. Use the same character set encoding values for one-to-one matching. If the character sets are exactly the same and the field types are the same, it is regarded as the only matching field, record its position index in the current data source, and at the same time call the occurrence frequency of each field label in different data sources. Accumulate the number of times the same label appears repeatedly in multiple sources as the jump number. The following example is used to illustrate: The standard field label is "customer_id", the character set encoding is "CU123", and the field type is "integer". There is a field "cust_id" in data source A, its encoding is "CU123", and the type is "integer", so it is the only matching field, and record the index position of this field as A1. Repeated jump fields such as the field "region_code" exist in data sources B, C, and D, with encodings of "RC001", "RC002", and "RC003" respectively. The field types are the same but the encodings are different, and the cumulative jump number is 2. Then calculate the number of unique matches and the number of jump fields. Suppose there are a total of 30 standard fields, and 50 data source field labels are extracted on the current platform, among which the number of matching fields is 18, and the jump field count is 9. According to the set sharing threshold of 50% of the number of standard fields, that is, 15, and the jump tolerance value of 10, it is judged that the number of unique matches 18 > 15 and the jump number 9 < 10, which meets the field remapping determination condition, and obtain the number of unique matches and the number of repeated jumps of the field labels.

[0088] S212: According to the number of unique matches and the number of repeated jumps of the field labels, perform interval judgments with the sharing threshold and the jump tolerance value respectively. Extract the data source fields that meet the sharing threshold, and correspondingly generate a set of field pairs that can be replaced and connected. Call the character set encoding set of the field labels and the field type vector set, and calculate the number of character set encoding differences and the field type expression vector differences between the field pairs respectively. Use the formula:

[0089] ;

[0090] Calculate the combined score of the character set difference and type expression offset of the field pairs , screen the minimum score among all field pairs, and obtain the field structure adaptation result. Among them, represents the candidate character set encoding value of the th field, represents the standard character set encoding value of the th field, represents the candidate type expression vector value of the th field, represents the The vector value of the standard type of a field Indicates the number of encoding difference items Indicates the number of dimensions of the type expression

[0091] According to the unique matching quantity and repeated jump quantity of field labels, after satisfying field sharing and jump tolerance, all remappable field pairs are extracted from the matching field set, and the corresponding character set encoding value and type expression vector value of the field pair are called. Suppose field pair D1 is the standard field "order_amount" and the data source field "ord_amt", and the character set encodings are "OA456" and "OA467" respectively. Then the ASCII code differences between the corresponding positions of each character form a character set difference value array {0, 0, 0, 1, 1, 0}. After taking the absolute value and summing, the difference quantity is 2. The type expression vector is assumed to be {1, 0, 0, 1} for the standard field and {1, 1, 0, 0} for the data source field. The vector difference is {0, -1, 0, 1}, and the sum of squares is 2. Taking the square root gives a result of approximately 1.414. The same operation is performed on each candidate field pair, and calculations are carried out using the formula.

[0092] Suppose the character set encoding difference value array in the current field pair is {2, 1, 0, 1, 0, 0}, then , and the average difference is , the dimension of the type expression vector is 4, and the sum of squares of differences is 2, then the vector distance is , then:

[0093] ;

[0094] After collecting the scores of all field pairs, the field pair with the minimum score is selected as the structure adaptation field group, and then the field structure adaptation result is obtained.

[0095] Table 2 lists the encoding differences and type vector values of example field pairs:

[0096] Table 2 Field Pair Adaptation Parameter Table:

[0097] ;

[0098] As shown in Table 2, the score of the field pair "order_amount - ord_amt" is 1.374, which is better than other field pairs and is selected as the adapted field.

[0099] The operation logic of this formula is based on two core dimensions: the difference in field character set encoding and the offset degree of field type expression vectors. The former calculates the sum of the absolute values of the encoding differences at each character position and takes the average to reflect the proximity of field labels at the character level, which is suitable for detecting whether there is structural similarity in field naming among different data sources. The latter obtains the Euclidean distance by taking the square root of the sum of the squares of the vector differences, which is used to characterize the similarity of fields in semantic or type expression, thus reflecting their compatibility at the functional dimension. The two parts measure the matching quality from the structural and semantic perspectives respectively, and use an additive structure to weight and fuse them to form a comprehensive adaptation score. The use of absolute value and average operations in the character set part is to weaken the fluctuation impact of field length on the score, while the use of square root after square difference in the vector part is to conform to the standard geometric representation of distance measurement in vector space. The entire formula structure realizes dimension unification through a simple linear combination method, enabling the differences at the character encoding level and the semantic level to be comprehensively expressed under the same dimension, and further providing a clearer and more complete adaptation evaluation basis in field remapping evaluation.

[0100] S213: Based on the field structure adaptation results, select the field pairs with the minimum scores according to the score values, mark them as high-adaptation field combinations, and form field pair entries by combining their original field labels and standard field labels. Combine and summarize them to form a field mapping record set, and establish a data field sharing remapping list;

[0101] According to the field structure adaptation results, sort all candidate field pair results in ascending order and select the field group with the lowest score as the high-adaptation field combination. Form paired records from the corresponding data source field labels and standard field labels, and aggregate information such as field labels, matching sources, and score values into a set of mapping entries. Classify and label them according to the field group dimension in the list structure to generate a structured list, forming a data field sharing remapping list. For example, the standard field "customer_email" and the data source field "cust_mail" form a field pair, the matching source is data source C, and the adaptation score is 1.092. The combined result is written into the shared remapping list table. The list entry format is as follows: ["customer_email", "cust_mail", "source: C", "score: 1.092"]. Store all field pair combination records as shared remapping content for subsequent structural access and call, and finally establish a shared and recognizable remapping path and output it to a list file in JSON or CSV format for unified recognition.

[0102] Please refer to Figure 4 , and the steps of S3 are as follows:

[0103] S311: Obtain the data source resource request information entering the sharing stage, extract the corresponding data source number and field mapping records, collect the business scenario identification items in the associated data source resource request entries, and perform labeled mapping on the data source resource request information according to the business scenario identification to obtain the business scenario identification mapping result;

[0104] To obtain the data source resource request information entering the sharing stage, it is necessary to first clarify the data source number and field mapping records included in the field structure adaptation result. For the data source number, it can be directly extracted through the source system unique identification field attached in the resource request record. For example, in the resource request record table, the field "source_id" is "DS202404", indicating that it comes from the system with the data source number DS202404. After extracting this number, the "source_id" field in the field mapping record table can be called for joint matching to obtain the detailed field mapping corresponding to this data source, and further extract the involved field names, field standard labels, and field adaptation levels to form a set of field adaptation details; To collect the business scenario identification items in the associated data source resource request entries, it is necessary to locate the "business_tag" field in the data source resource request record. Through the data recorded in this field, such as "BIZ-CUST", it is judged that it belongs to the business scenario of customer information classification. If there are multiple business scenarios, each record needs to be independently marked and then classified; In the process of performing labeled mapping on the data source resource request information according to the business scenario identification, it is necessary to perform an aggregation mapping operation on the data source number and the business scenario identification. For example, in a resource request record, source_id is DS202404 and business_tag is BIZ-CUST, an aggregation key "DS202404_BIZ-CUST" can be constructed, and all data source request records can be classified through this constructed aggregation identifier to achieve resource request grouping at the business scenario level.

[0105] In actual operation, if the collected resource request records are as shown in the following table:

[0106] Table 3 Resource Request Record Table:

[0107] ;

[0108] As shown in Table 3, they respectively represent three business scenarios of customer, finance, and operation. When constructing the mapping, they are respectively identified as "DS202404_BIZ-CUST", "DS202405_BIZ-FIN", and "DS202406_BIZ-OPS". In this way, the business channels involved in the subsequent resource path allocation can be classified, and finally the business scenario identification mapping result can be obtained.

[0109] S312: According to the mapping result of the business scenario identifier, call the path number set, path status set, and current path occupancy set in the shared path status library, extract the list of path numbers with the current path status being the allocable status and the occupancy flag being empty, detect the resource priority thresholds associated with each path number, aggregate each path number by business scenario respectively, and obtain the group of shareable path numbers;

[0110] According to the mapping result of the business scenario identifier, it is necessary to call the path number set, path status set, and current path occupancy set from the shared path status library. The path number set is read through the field "path_id", such as "PATH_01". In the status set, the value of the field "status" is "allocable" or "occupied". In the current occupancy set, "occupy_flag" is a boolean value indicating whether it is occupied; in the process of extracting the list of path numbers with the current path status being the allocable status and the occupancy flag being empty, two screening conditions need to be met simultaneously, that is, the value of the status field is "allocable" and the value of the occupy_flag field is False. For example, if a path number is PATH_07, status is "allocable", and occupy_flag is False, then this path can enter the next judgment; when detecting the resource priority thresholds associated with each path number, it is necessary to extract the priority field "priority_threshold". For example, the priority threshold corresponding to a path PATH_07 is 75, and this value represents that the allocation priority requirement needs to be higher than 75 to enter the sharing process; make a difference judgment and quantity calculation on the resource priority threshold and indicators such as the current request intensity, idle duration, cumulative occupancy times, and total request volume. Among them, the current request intensity is determined by the resource request frequency of each type of business scenario. For example, if the number of resource requests for the business scenario BIZ-CUST in the recent 5 days is 20 times, then its request intensity value can be set to 20. The idle duration is the time interval since the last allocation, in hours. If the last use time of PATH_07 was 48 hours ago, then the idle duration is 48 hours. The cumulative occupancy times are read through the field "use_count", such as 32 times. The total request volume field "req_total" is the cumulative value of recent request data, such as 65 times. Perform difference or relative quantification processing according to such indicators and uniformly use the standardized method for subsequent grouping and screening; in the process of aggregating each path number by business scenario respectively, use the business scenario as the index field to aggregate all path number sets that meet the above conditions. For example, if the set of allocable path numbers under the business scenario "BIZ-CUST" is [PATH_03, PATH_07, PATH_09], then this set is the path candidate numbers for this scenario, and finally obtain the group of shareable path numbers.

[0111] S313: Based on the shareable path number group, combined with the business scenario identifier of the belonging, integrate all the shareable path numbers under each type of business scenario into a type of shared path access, and establish a shared path allocation candidate list;

[0112] When reading the shareable path number group, it is necessary to clarify the path number array aggregated under each business scenario, and perform horizontal integration processing in combination with the business scenario identifier field. For example, for the "BIZ-CUST" scenario, its corresponding path number set is [PATH_03, PATH_07, PATH_09], then this array can be regarded as the available path pool for this scenario in the follow-up; in the process of integrating all the shareable path numbers under each type of business scenario into a type of shared path access, it is necessary to perform normalization identification processing on the path number array and construct an access identifier field. For example, use "SHR-BIZ-CUST" to identify the shared path sets PATH_03, PATH_07, PATH_09, and similarly "SHR-BIZ-FIN" is used to identify PATH_04, PATH_08, etc.; when generating the path number record table that can be used for shared allocation for each type of business scenario, it is necessary to construct a structured output table or record file, and the fields need to include business scenario identifier, shared path number, path status identifier and other fields. In the process of data structure arrangement, if there are no allocable paths in a certain scenario, the shared path access under the business scenario identifier of this item is empty, to avoid cross-scenario resource mixing; finally, integrate all scenario identifiers and their shareable path number combinations, and establish a shared path allocation candidate list.

[0113] Please refer to Figure 5 , the steps of S4 are as follows:

[0114] S411: Based on all the path numbers in the shared path allocation candidate list, retrieve the path number items that are currently in the occupied state in the path status field, collect the usage priority values registered in the resource usage records associated with the path numbers, and compare the resource priority values in the resource request of the data source to be accessed, and count all the conflicting path number sets to obtain the priority conflict path number group;

[0115] When obtaining all path numbers in the shared path allocation candidate list and retrieving the path number items that are currently in the occupied state, it is first necessary to extract all path records from the shared path list, perform a retrieval operation on the status field of each path, and identify the path entries marked as "occupied". For example, if the statuses of path numbers P01, P03, and P07 are all "occupied", then these three numbers are extracted for subsequent analysis. Next, for each occupied path number, query its resource usage record field, and extract its usage priority through the priority record value in the field. For example, the priority of P01 is 3, P03 is 2, and P07 is 5. Here, the priority value is numerical, and the larger the value, the higher the priority. These priority values are used as reference indicators for the occupied state of existing resources. Subsequently, extract the priority information corresponding to the data source to be accessed currently. Assume that a data source access number is DS001, and its resource priority is 4. At this time, it is necessary to compare the priority of DS001 with the priorities of the above path numbers item by item. For example, the comparison result with the priority 3 of P01 is a conflict (because 4>3), and the comparison result with P07 is non-conflict (because 4<5). From this, it is judged that DS001 has a priority conflict with P01 and P03, but there is no conflict with P07. Output the conflict path numbers P01 and P03 as the priority conflict path set to obtain the priority conflict path number group. In this process, the judgment basis for priority conflict can set the priority conflict determination reference value to 1, that is, when the difference between the access data source priority value and the occupied path priority value is greater than or equal to 1, it is determined as a conflict. This set threshold comes from the system's unified priority suppression standard. Table 4 lists the status and priority of some paths for reference.

[0116] Table 4 Path Status and Priority Table:

[0117] ;

[0118] As shown in Table 4, by comparing the current priority of the path with the data source priority value, it is possible to effectively identify the priority conflict path numbers. In actual implementation, such item-by-item comparison needs to be performed for each access judgment. The "priority" field during the execution process is generally obtained from the system initialization resource weight configuration, and the conventional setting range is between 1 and 10. The higher the value, the stronger the priority. This priority field does not directly calculate using weight parameters, but is based on a static hierarchical system predefined by the system scheduling strategy and is dynamically associated and marked through resource occupancy information.

[0119] S412: According to the priority conflict path number group, screen the shareable path numbers that are not occupied in the same business scenario, and perform number pairing attempts with the judgment conditions that the path status is idle and the business scenario identification belongs to the same. Use the formula:

[0120] ;

[0121] Calculate the difference score between the resource usage deviation degree and the priority reconstruction adaptation value for each pair of path numbers , select the path number with the smallest score value as the alternative path number, record it, and generate the alternative path mapping score result, where, Indicates the The resource request intensity of the conflicting paths, Indicates the The resource slack level of the alternative paths, Indicates the Duty cycle values for alternative paths, Indicates the The priority difference of the conflicting paths, Indicates the The cumulative occupied time of each path, Indicates the The number of remaining available cycles for each path, is the number of conflicting paths, is the number of path priority difference items;

[0122] When selecting unoccupied path numbers in the same business scenario for diversion and replacement allocation based on the priority conflict path number group, first, based on the path numbers P01 and P03 obtained in the previous paragraph and their business scenario identification information, for example, they all belong to the business scenario "BIZ_A", search all path numbers with a path status of "idle" and a business identification of "BIZ_A" in the shared path allocation candidate list, such as path numbers P05 and P08. Then, determine whether these candidate paths meet the replacement conditions. Here, it is necessary to calculate and compare parameters such as resource usage intensity, idle level, and load cycle. Assume that the resource request intensity of P01 is 65, the idle level of P05 is 80, and the load cycle is 4, that is:

[0123] , , ;

[0124] , , ;

[0125] , , ;

[0126] , , ;

[0127] Substitute the formula into the calculation as follows:

[0128] Part 1:

[0129] ;

[0130] ;

[0131] ;

[0132] Part Two:

[0133] ;

[0134] ;

[0135] ;

[0136] Substituting as a whole gives:

[0137] ;

[0138] The final score value is 24.2912. Similarly, perform the same calculation process for other paths such as P08, and select the one with the smallest score value as the alternative path. The smaller this value is, the weaker the overall offset and priority conflict, and it is more appropriate as the alternative number configuration. The benefit of the formula is that by introducing the dynamic ratio structure between the resource request intensity and the resource idle level, and at the same time superimposing the path load cycle adjustment factor and the priority difference quantitative accumulation, the adaptation and difference control of the conflict path to the service equivalent idle path are realized. Finally, the alternative path mapping score result is obtained.

[0139] The operation logic of the formula is to uniformly measure and quantitatively evaluate the resource request characteristics and path availability characteristics, so as to realize the differential scoring of the adaptability of the alternative path. The formula structure is divided into two parts. First, the left summation term is used to measure the relative offset between the resource request intensity of the conflict path and the idle level of the candidate path. The offset is attenuated by the square root value of the path load cycle to introduce the adjustment coefficient of the time dimension and avoid the extreme influence of the short-cycle fluctuation of the path on the judgment. And the right summation term represents the alternative path in the priority difference and its cumulative occupied duration and the remaining available cycle Comprehensive adjustability evaluation under certain conditions, where the absolute value operation processes the priority direction to avoid the cancellation of positive and negative errors. At the same time, addition is used to integrate the priority adaptation error and the path stability index. Finally, the absolute value of the difference between the scores in two directions is obtained to get the overall stability deviation of the alternative path adaptation. This logic integrates multi-dimensional indicators through addition, realizes the constraint control of cycle factors through division, and realizes the non-linear scaling of volatility factors through square root extraction, so as to establish a multi-factor fusion and adaptation mechanism that takes into account resource load differences, priority contradiction degrees, and path availability trends.

[0140] S413: According to the alternative path mapping score results, select the path number with the smallest score corresponding to each conflict path as the alternative path number, bind it to the original conflict path number, establish a shunt identification record item, and establish the data source shunt path mapping result;

[0141] After obtaining the alternative path mapping score value, select the path number with the smallest score and bind it to the original conflict path number for configuration, form a mapping pair and register the shunt identification. This operation needs to traverse all conflict paths and correspond to their optimal alternative paths in terms of scores. For example, the score of P01 is 24.2912, and the corresponding alternative path is P05. If the path with the smallest score for P03 is P08, then establish the alternative binding relationships of P01→P05 and P03→P08 respectively. At the same time, write the shunt status identification field to P05 and P08, and the identification status bit is "1", which is used to distinguish the direct allocation path and the shunt path in subsequent scheduling. Finally, generate a mapping entry set according to the data source number. For example, DS001 contains two binding relationships of P01→P05 and P03→P08, and integrate them to form the data source shunt path mapping result. In this process, there is no need to repeatedly judge the resource status and priority, and only call the score result sorting and execute the binding operation. This result shows that the shunt path forms an optimal docking through quantitative scoring in both resource status determination and alternative path matching, and can effectively realize the structural mapping reconstruction between the data source and the path resources.

[0142] Please refer to Figure 6 , the steps of S5 are as follows:

[0143] S511: According to the field mapping field pairs recorded in the data field sharing and remapping list, obtain the corresponding relationship between the standard field and the original field in each pair of fields. Combine the set of path numbers already allocated in the data source shunt path mapping result, and perform joint matching of the field mapping field pairs and the path numbers according to the data source number, and merge and combine the field mapping field pairs and the path numbers to obtain a field-path combination comparison table;

[0144] According to the path numbers in the data source shunt path mapping result corresponding to the field mapping field pairs recorded in the data field sharing remapping list, it is necessary to combine the field pairs and path numbers and establish a field-path combination comparison table. First, extract each pair of field names in the field pairs, and identify their source fields and target fields. Identify the path number corresponding to the field in the field hierarchy structure. By querying the path number set, establish the association relationship between the field and the path. For example, if the original field name is "Customer Number" and the target field is "Customer ID", and the source data source is A1 and the assigned path number is P13, then a mapping item of "Customer Number - Customer ID - P13" is formed. Then, summarize multiple mapping items into a field-path combination set. During this process, it is also necessary to further analyze the table structure to which each field belongs and the transmission type label of the path number. If the table to which the field belongs is the "Customer Information Table" and the path transmission type is the "High-Speed Shared Path", then this path label needs to be added to the combined information to complete the pairing identification of the field and the path transmission attribute. The actual processing example is that if the field group [User ID, Transaction Amount, Transaction Time] in the A1 data source is mapped to the target fields [UID, Amount, Timestamp], and their path numbers are P101, P102, and P103, then three groups of field-path records are combined, such as "User ID - UID - P101", "Transaction Amount - Amount - P102", and "Transaction Time - Timestamp - P103". To further refine the corresponding relationship between the path number and the field in this combined table, it is necessary to call the shared path allocation record, compare each source field in the field mapping table item by item in the path allocation record, and verify whether there is a direct association with the path number. Determine whether the combination is successfully established through boolean matching. The judgment criterion is that the source field name exists in the corresponding data source field list in the path allocation record and has a one-to-one mapping relationship with the target field. Under the condition that this condition is met, write the three items of the source field, target field, and path number into the field-path combination table, as shown in Table 5.

[0145] Table 5 Field-Path Combination Record Table:

[0146] ;

[0147] As shown in Table 5, the field-path combination record table clearly lists the one-to-one correspondence between the field mapping and the path number, reflecting the basic information structure of the field channel connection. The path number needs to come from the actual available path number, and the field pairs are extracted according to the data field sharing remapping list. Only some field-path combination entries are listed in this table, and all combined data needs to be dynamically generated in the system and then batch recorded into the database. The field-path combination comparison table is used as the basic input for subsequent field structure verification and shared docking, and has high stability and adjustable structure.

[0148] S512: Extract the field character set encoding and field type vector from all field pairs based on the field path combination comparison table, call the resource type label, business scenario identifier, and path transmission capacity associated with the path number, compare the field structure information with the path resource structure characteristics item by item, confirm the structural match and resource accessibility between the two, and obtain the field path combination record with a successful structural match;

[0149] According to the field path correspondence items listed in the field path combination comparison table, in order to verify their adaptation between the structure and resources, it is necessary to call the character set encoding and field type vector in the field pair one by one, parse the character set encoding length and encoding rules of each field pair at the field level, obtain its character type (such as ASCII, UTF-8, etc.), and count the dimension and unit definition of the field type vector. For example, the field "Customer ID" uses UTF-8 encoding, the character set length is 10, the field type is "string", and the corresponding type vector dimension is 1, while the field "Amount" is a floating point type with a vector dimension of 2. The corresponding structure type must match the resource label and path capacity value defined by the path number. The path number information is provided by the shared path allocation record. The field docking path number is P101, the label is "Financial Numerical Channel", and the capacity is 100Mbps. Structural adaptation must meet two conditions: first, the field type must be consistent with the path label, such as when a numeric field is docked with a financial channel, and second, the path capacity must be no less than the field data rate requirement. For example, the size of a single record in the field "Transaction Amount" is 8 bytes, processing 1000 per second, the required bandwidth is 8×1000=8000 bytes, that is, 64kbps, which is much smaller than the path capacity and can be determined as a match. The field "transaction time" uses the timestamp encoding format, the size is 6 bytes, the rate is the same as above, the required bandwidth is 6×1000=6000 bytes, that is, 48kbps, which also meets the path transmission capacity. Then, the field character set encoding value is compared with the path number bandwidth capacity value in parallel. If the difference is within the allowable range (set to the maximum transmission difference threshold of 10%), it is determined to be a match. The encoding length of the segment "UID" is 8, the capacity of path P101 is 150Mbps, and the field data transmission rate is 64kbps, accounting for 0.0426%, which is far lower than 10%. It can be judged that the structure adaptation is successful, and then the field path record of the successful adaptation is registered in the combination information. This process needs to traverse all combination items and judge the compatibility of the field and path structure item by item. If any inconsistency is found, it will be marked as "unable to connect" in the record table, and the abnormal path will be reconstructed subsequently. The structural path adaptation value is finally returned to the system as the field connection judgment result.

[0150] S513: Based on the field path combination records that have been successfully matched, extract the field path combination records that meet the access conditions and use them as the officially connectable field data path mapping relationship. Establish a data path connection relationship with the path number according to the field mapping order, and perform data asset sharing operations.

[0151] After obtaining the record of the field path combination with successful structure matching, extract all the field path combination records marked as "successful structure adaptation", bind and combine the corresponding field pairs with the path numbers, and establish an official field data path mapping relationship table. This table shall include three items: field source, field target, and bound path number, and be sorted in the order of the business tables where the fields are located to ensure the maintainability of field consistency when data is exported and docked with business modules. The binding order adopts the field serial number of the original field in the source system table. For example, the field "User ID" has a field number of 1 in the user table, the corresponding target field is "UID", its path number is P101, and it is sorted as the first record. The field "Transaction Amount" has a field number of 2 and a path of P102, and is sorted as the second record. Build the field path docking table structure accordingly. Subsequently, generate a data asset sharing value, which is a numerical data recording the number of successfully mapped field paths. It is statistically calculated by counting the number of successfully docked fields. In this example, the number of successfully docked fields is 3, so the data asset sharing value is 3. This value is used as the measurement basis for the shared resource allocation comparison module in the subsequent system. If this value is lower than the allowable lower limit of resource allocation (such as set to 2), the system will automatically abort the shared path allocation process and re-evaluate the field matching scheme. Otherwise, it can be considered that the data path configuration of the current data source is effective. Finally, enter the data sharing stage to perform data asset sharing operations.

[0152] A data asset sharing system based on Fangyuan Cloud, comprising:

[0153] The data structure standardization module obtains field label and type information, extracts the character set and encodes and maps the combination to unify the structure expression, and generates a data structure standardization list;

[0154] Based on the data structure standardization list, the field mapping and screening module counts the number of unique field matches and the number of jump variables, judges whether the sharing threshold and jump tolerance value are met, extracts candidate field pairs, calculates the difference in character set encoding numbers and the difference in type expression vectors, scores by weight, screens the field pairs with the best scores to establish an adaptation relationship, and generates a data field sharing and re-mapping list;

[0155] The resource request analysis module obtains resource request information and scenario identifiers, screens idle paths in combination with path status and occupancy, and builds a shareable path group according to the business scenario to obtain a candidate list for shared path allocation;

[0156] Based on the candidate list for shared path allocation, the path priority conflict resolution module compares the data source priority and the occupied path priority. If there is a conflict, it allocates an alternative path in the same scenario and generates a data source diversion path mapping result;

[0157] The data sharing execution module combines the data field sharing remapping list and the data source shunt path mapping table to conduct data path docking and execute data asset sharing operations.

[0158] The above are only the preferred embodiments of the present invention, and the present invention is not limited to other forms. Any person skilled in the art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A data asset sharing method based on Fangyuan Cloud, characterized in that, It includes the following steps: S1: Obtain the data structure information of the data source to be connected in the Fangyuan Cloud Platform, combine the encoded mapping of the field name character set with the field data type value, and establish a data structure standardization list; S2: According to the data structure standardization list, count the number of unique matching field tags and the number of repeated jump fields corresponding to the data source, compare the sharing threshold and the jump tolerance value, extract the candidate field pairs for remapping and perform weighted synthesis scoring, and establish a data field sharing and remapping list; S3: Obtain the data source resource request information in the sharing stage, record the business scenario, extract the idle path number, aggregate it into a shareable path group according to the affiliated business scenario, and obtain a candidate list for shared path allocation; S4: Based on the candidate list for shared path allocation, retrieve the usage priority corresponding to the path number in the occupied state, compare the priority of the data source to be connected, and if there is a priority conflict, allocate an alternative path number to generate a data source diversion path mapping result; S5: According to the data field sharing and remapping list and the data source diversion path mapping table, combine the field remapping field pairs and the path number to perform data path docking and execute the data asset sharing operation; The specific steps for obtaining the data source diversion path mapping result are as follows: S411: Based on all the path numbers in the candidate list for shared path allocation, retrieve the path number items that are currently in the occupied state in the path status field, collect the usage priority values registered in the resource usage records associated with the path number, and compare the resource priority values in the resource request of the data source to be connected, and count the set of all conflicting path numbers to obtain a priority conflict path number group; S412: According to the priority conflict path number group, filter the shareable path numbers that are not occupied in the same business scenario, and perform number pairing attempts with the judgment conditions that the path status is idle and the affiliated business scenario identifiers are the same. Use the formula: ； Calculate the difference score value between the resource usage deviation degree and the priority reconstruction adaptation value for each pair of path numbers , select the path number with the smallest score value as the alternative path number, and make a marked record to generate the alternative path mapping score result, where represents the resource request intensity of the th conflict path represents the resource idle level of the th alternative path represents the load cycle value of the th alternative path represents the priority difference of the th conflict path represents the cumulative occupation duration of the th path represents the remaining available cycle number of the th path is the number of conflict paths is the number of path priority difference items; S413: According to the alternative path mapping score result, select the path number with the smallest score value corresponding to each conflict path as the alternative path number, bind it to the original conflict path number correspondingly, and establish a diversion identifier record item to establish a data source diversion path mapping result.

2. The data asset sharing method based on Fangyuan Cloud according to claim 1, wherein The data structure standardization list includes field name mapping rules, field type expression models, and a standardized field identifier set. The data field sharing and remapping list includes field remapping pairs, structure adaptation scores, and shared field unique identifiers. The candidate list for shared path allocation includes a path number set, a business scenario path group, and an available status identifier. The data source diversion path mapping result includes an alternative path number, a diversion identifier, and a priority comparison table. The data asset sharing operation includes a path number field docking set, a mapped field data stream, and a shared operation execution label.

3. The data asset sharing method based on Fangyuan Cloud according to claim 1, characterized in that The specific steps for obtaining the data structure standardization list are as follows: S111: Obtain the first-level field label information exposed by the data source to be connected in the Fangyuan Cloud Platform, detect the data type values corresponding to the field labels, call the mapping rules between the field label character sets and field types, perform item-by-item encoding conversion on the character sets in the field labels, and splice and integrate them with the field type information to generate the field structure encoding combination result; S112: According to the field structure encoding combination result, screen out the field items in the field whose character set encoding repetition rate exceeds the standard encoding repetition threshold, count the number of repeated combinations existing in the corresponding field types, and obtain the distribution range interval values of the field character sets under different types to generate the field encoding distribution interval data; S113: Based on the field encoding distribution interval data, judge the type consistency degree of each field after encoding mapping, and call the type mapping quantity corresponding to the field label character set and the field structure encoding length, using the formula: ； Calculate the consistency difference value of cross-type field structure encoding , determine whether field reconstruction is required, and establish a data structure standardization list, where represents the number of type mappings of the th field, represents the character set encoding repetition rate of the th field, represents the character set length of the th field, represents the encoding combination length of the th field, represents the total number of type mapping fields, represents the total number of encoding length fields.

4. The data asset sharing method based on Fangyuan Cloud according to claim 1, wherein The specific steps for obtaining the data field sharing remapping list are as follows: S211: Based on the field label information recorded in the data structure standardization list, count the matching relationship between the field labels in all the connected data sources in the current Fangyuan Cloud Platform and the standard field labels, call the field label set and the data source field label set for item-by-item comparison, obtain the number of unique labels corresponding to the standard field labels in each data source one by one, and accumulate and count the frequency of repeated occurrences of the labels to obtain the field label unique matching quantity and the repeated jump quantity; S212: According to the field label unique matching quantity and the repeated jump quantity, perform interval judgment with the sharing threshold and the jump tolerance value respectively, extract the data source fields that meet the sharing threshold, and correspondingly generate a set of field pairs that can be replaced and connected. Call the field label character set encoding set and the field type vector set, and calculate the number of character set encoding differences and the field type expression vector differences between the field pairs respectively, using the formula: ； Calculate the combined score of character set difference and type expression offset for calculation fields , filter the minimum score among all field pairs to obtain the field structure adaptation result, where represents the candidate character set encoding value of the th field, represents the standard character set encoding value of the th field, represents the candidate type expression vector value of the th field, represents the standard type expression vector value of the th field, represents the number of encoding difference items, represents the type expression dimension number; S213: Based on the field structure adaptation result, select the field pairs with the minimum score according to the score value ranking, mark them as high-adaptation field combinations, and form field pair entries with their original field labels and standard field labels, and combine and summarize them to form a field mapping record set to establish a data field sharing remapping list.

5. The method for sharing data assets based on Fangyuan Cloud according to claim 1, wherein, The specific steps for obtaining the candidate list of shared path allocation are as follows: S311: Obtain the data source resource request information entering the sharing stage, extract the corresponding data source number and field mapping record, collect the business scenario identification items in the associated data source resource request entries, and perform label mapping on the data source resource request information according to the business scenario identification to obtain the business scenario identification mapping result; S312: According to the business scenario identification mapping result, call the path number set, path status set and current path occupancy set in the shared path status library, extract the list of path numbers with the current path status being allocable and the occupancy flag being empty, detect the resource priority threshold associated with each path number, aggregate each path number by business scenario respectively, and obtain the group of shareable path numbers; S313: Based on the group of shareable path numbers and combined with the business scenario identifier of the belonging, integrate all the shareable path numbers under each type of business scenario into a type of shared path access, and establish a candidate list for shared path allocation.

6. The data asset sharing method based on Fangyuan Cloud according to claim 1, characterized in that The specific implementation steps of the data asset sharing operation are as follows: S511: According to the field mapping field pairs recorded in the data field sharing remapping list, obtain the corresponding relationship between the standard field and the original field in each pair of fields. Combine the set of path numbers already allocated in the data source diversion path mapping result, and perform joint matching of the field pairs and path numbers according to the data source number. Merge and combine the field mapping field pairs and path numbers to obtain a field-path combination comparison table; S512: According to the field-path combination comparison table, extract the field character set encoding and field type vector in all field pairs. Invoke the resource type label, business scenario identifier, and path transmission capacity associated with the path number, and compare the field structure information and the access path resource structure characteristics item by item to confirm the structural matching and resource accessibility between the two, and obtain the field-path combination records with successful structural matching; S513: Based on the field-path combination records with successful structural matching, extract the field-path combination records that meet the access conditions of the access path as the formal connectable field data path mapping relationship, establish a data path docking relationship according to the field mapping order and the path number, and perform the data asset sharing operation.

7. A data asset sharing system based on Fangyuan Cloud, characterized in that, The system is used to implement the data asset sharing method based on Fangyuan Cloud described in any one of claims 1-6, including: The data structure standardization module obtains the field label and type information, extracts the character set and encodes and maps the combination to unify the structural expression, and generates a data structure standardization list; The field mapping screening module, based on the data structure standardization list, counts the unique matching number and jump number of fields, judges whether it meets the sharing threshold and jump tolerance value, extracts candidate field pairs, calculates the difference number of character set encoding and the difference value of type expression vectors, performs weight scoring, screens the field pairs with the best scores to establish an adaptation relationship, and generates a data field sharing remapping list; The resource request analysis module obtains the resource request information and scenario identifier, filters the idle paths in combination with the path status and occupancy, and builds a group of shareable access paths according to the business scenario to obtain a candidate list for shared path allocation; The path priority conflict resolution module, according to the candidate list for shared path allocation, compares the data source priority and the occupied path priority. If there is a conflict, it allocates an alternative path in the same scenario to generate a data source diversion path mapping result; The data sharing execution module combines the data field sharing remapping list and the data source diversion path mapping table to perform data path docking and execute the data asset sharing operation.

Images