A cross-domain data analysis method, device, equipment and storage medium

Abstract

This invention discloses a cross-domain data analysis method, apparatus, device, and storage medium. The method includes: acquiring initial cross-domain circulation data and performing multi-stage data processing on the initial cross-domain circulation data to obtain target cross-domain circulation data; determining a full-process degradation index corresponding to the target cross-domain circulation data based on the target cross-domain circulation data and the initial cross-domain circulation data; determining the contribution parameter weights corresponding to each contribution parameter based on the full-process degradation index, and determining the data value parameters corresponding to the target cross-domain circulation data based on the contribution parameter weights. This method can determine the degradation index of data after multi-stage data processing, determine appropriate contribution parameter weights based on the degradation index, and then perform data value parameter analysis based on the contribution parameter weights. This achieves full-stage data degradation quantification in cross-domain circulation and determines appropriate parameter analysis weights based on the data degradation index, improving the accuracy of cross-domain circulation data value assessment.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of data analysis technology, and in particular to a cross-domain data analysis method, apparatus, device and storage medium. Background Technology

[0002] In the evaluation of cross-domain data circulation, existing technologies only focus on "data ownership and quantity," ignoring the core issue of data quality loss during cross-domain circulation: data undergoes multiple data processing stages from provider to receiver, each of which causes "data degradation" (i.e., a decrease in feature integrity, accuracy retention, and semantic consistency). However, no monitoring or quantification mechanism for degradation is designed, resulting in the evaluated "contribution" only reflecting the "quantity ownership" of data, which is severely disconnected from the actual usable value of the data (directly affected by the degree of degradation). For example, after cross-domain anonymization, the number of effective features of a data provider's "unique data" (weighted at 0.6) decreases from 100 to 30, significantly reducing its actual value. However, the scheme still calculates the contribution based on the original weight, resulting in unfair value distribution.

[0003] Furthermore, existing technologies for data value assessment often employ a fixed rule where "the number of ownership determines the weight" (0.6 for unique data, 0.3 for shared data, and 0.1 for shared data), failing to consider the impact of data quality differences on the weight. Data within the same ownership category can have vastly different actual values ​​due to varying degrees of degradation. Data from different ownership categories may exhibit different values ​​due to varying degrees of degradation, with "data with a low number of ownership items being more valuable than data with a high number of ownership items." Static weights prevent the solution from adapting to the value assessment of data with different degrees of degradation, limiting its applicability to "ideal scenarios with no degradation loss." Summary of the Invention

[0004] This invention provides a cross-domain data analysis method, apparatus, device, and storage medium. The technical solution of this invention solves the problem of evaluation distortion in the existing technology when evaluating the value of cross-domain circulation data. It can determine the degradation index of data after multiple stages of data processing, determine the appropriate contribution parameter weights based on the degradation index, and then perform data value parameter analysis based on the contribution parameter weights. This realizes the quantification of data degradation in all stages of cross-domain circulation, and determines the appropriate parameter analysis weights based on the data degradation index, thereby improving the accuracy of cross-domain circulation data value evaluation.

[0005] In a first aspect, embodiments of the present invention provide a cross-domain data analysis method, the method comprising: Initial cross-domain circulation data is obtained, and target cross-domain circulation data is obtained by performing multi-stage data processing on the initial cross-domain circulation data; based on the target cross-domain circulation data and the initial cross-domain circulation data, the full-process degradation index corresponding to the target cross-domain circulation data is determined; based on the full-process degradation index, the contribution parameter weight corresponding to each contribution parameter is determined, and the data value parameter corresponding to the target cross-domain circulation data is determined based on the contribution parameter weight.

[0006] Secondly, embodiments of the present invention provide a cross-domain data analysis device, the device comprising: The data acquisition module is used to acquire initial cross-domain circulation data and perform multi-stage data processing on the initial cross-domain circulation data to obtain target cross-domain circulation data; the degradation index determination module is used to determine the full-process degradation index corresponding to the target cross-domain circulation data based on the target cross-domain circulation data and the initial cross-domain circulation data; the data value analysis module is used to determine the contribution parameter weight corresponding to each contribution parameter based on the full-process degradation index, and determine the data value parameter corresponding to the target cross-domain circulation data based on the contribution parameter weight.

[0007] Thirdly, embodiments of the present invention provide a computer device, the computer device comprising: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the cross-domain data analysis method described in any embodiment.

[0008] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the cross-domain data analysis method described in any embodiment.

[0009] The technical solution provided by this invention involves acquiring initial cross-domain circulation data and processing it through multiple stages to obtain target cross-domain circulation data. Based on the target cross-domain circulation data and the initial cross-domain circulation data, a full-process degradation index corresponding to the target cross-domain circulation data is determined. Based on the full-process degradation index, the contribution parameter weights corresponding to each contribution parameter are determined, and based on the contribution parameter weights, the data value parameters corresponding to the target cross-domain circulation data are determined. This technical solution solves the problem of evaluation distortion in existing technologies for cross-domain circulation data value assessment. It can determine the degradation index of data after multi-stage data processing, determine appropriate contribution parameter weights based on the degradation index, and then perform data value parameter analysis based on the contribution parameter weights. This achieves full-stage data degradation quantification in cross-domain circulation and improves the accuracy of cross-domain circulation data value assessment by determining appropriate parameter analysis weights based on the data degradation index. Attached Figure Description

[0010] Figure 1 This is a flowchart of a cross-domain data analysis method provided by an embodiment of the present invention; Figure 2 This is a flowchart of another cross-domain data analysis method provided by an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of a cross-domain data analysis device provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0011] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. The acquisition, storage, use, and processing of data in the technical solutions of the embodiments of the present invention all comply with the relevant provisions of national laws and regulations.

[0012] Figure 1 This is a flowchart of a cross-domain data analysis method provided by an embodiment of the present invention. The embodiment of the present invention can be applied to scenarios of value analysis of cross-domain flow data. The method can be executed by a cross-domain data analysis device, which can be implemented by software and / or hardware.

[0013] like Figure 1 As shown, the cross-domain data analysis method includes the following steps: S110. Obtain initial cross-domain circulation data, and then perform multi-stage data processing on the initial cross-domain circulation data to obtain target cross-domain circulation data.

[0014] The initial cross-domain circulation data can be unprocessed data capable of cross-domain circulation. Correspondingly, the target cross-domain circulation data can be processed data capable of cross-domain circulation. Specifically, after obtaining the initial cross-domain circulation data, multiple stages of data processing can be performed on it to obtain the target cross-domain circulation data. The technical solution of this invention can analyze the data value of the target cross-domain circulation data.

[0015] S120. Based on the target cross-domain circulation data and the initial cross-domain circulation data, determine the full-process degradation index corresponding to the target cross-domain circulation data.

[0016] The end-to-end degradation index can be used to represent the degree of data loss after going through all data processing stages. Specifically, it can analyze the differences in data characteristics between the target cross-domain circulation data and the initial cross-domain circulation data, and then determine the end-to-end degradation index corresponding to the target cross-domain circulation data based on the degree of difference.

[0017] S130. Based on the full-process degradation index, determine the contribution parameter weights corresponding to each contribution parameter, and determine the data value parameters corresponding to the target cross-domain circulation data based on the contribution parameter weights.

[0018] The contribution parameters can be those used to determine the data value assessment. The contribution parameter weights can be the weighting coefficients of the contribution parameters in determining the data value. Specifically, a mapping relationship between the full-process degradation index and the contribution parameter weights can be preset. After determining the full-process degradation index, the contribution parameter weights corresponding to each contribution parameter can be determined based on this mapping relationship. The data value parameter can represent the data value corresponding to the target cross-domain flow data. Specifically, the data value parameter can be obtained by weighted summation of all contribution parameters based on their weights.

[0019] The technical solution provided by this invention involves acquiring initial cross-domain circulation data and processing it through multiple stages to obtain target cross-domain circulation data. Based on the target cross-domain circulation data and the initial cross-domain circulation data, a full-process degradation index corresponding to the target cross-domain circulation data is determined. Based on the full-process degradation index, the contribution parameter weights corresponding to each contribution parameter are determined, and based on the contribution parameter weights, the data value parameters corresponding to the target cross-domain circulation data are determined. This technical solution solves the problem of evaluation distortion in existing technologies for cross-domain circulation data value assessment. It can determine the degradation index of data after multi-stage data processing, determine appropriate contribution parameter weights based on the degradation index, and then perform data value parameter analysis based on the contribution parameter weights. This achieves full-stage data degradation quantification in cross-domain circulation and improves the accuracy of cross-domain circulation data value assessment by determining appropriate parameter analysis weights based on the data degradation index.

[0020] Figure 2 This is a flowchart of another cross-domain data analysis method provided by an embodiment of the present invention. The embodiments of the present invention can be applied to scenarios of value analysis of cross-domain flow data. Based on the above embodiments, this embodiment further explains how to determine the full-process degradation index corresponding to the target cross-domain flow data based on the target cross-domain flow data and the initial cross-domain flow data; and how to determine the contribution parameter weight corresponding to each contribution parameter based on the full-process degradation index, and determine the data value parameter corresponding to the target cross-domain flow data based on the contribution parameter weight. This device can be implemented by software and / or hardware and integrated into a computer device with application development capabilities.

[0021] like Figure 2 As shown, the cross-domain data analysis method includes the following steps: S210. Obtain initial cross-domain circulation data, and then perform multi-stage data processing on the initial cross-domain circulation data to obtain target cross-domain circulation data.

[0022] The initial cross-domain circulation data can be unprocessed data capable of cross-domain circulation. Correspondingly, the target cross-domain circulation data can be processed data capable of cross-domain circulation. Specifically, after obtaining the initial cross-domain circulation data, multiple stages of data processing can be performed on it to obtain the target cross-domain circulation data. The technical solution of this invention can analyze the data value of the target cross-domain circulation data.

[0023] S220. For each data processing stage, based on the target cross-domain circulation data and the initial cross-domain circulation data, determine the single-stage degradation index corresponding to the data processing stage.

[0024] The single-stage degradation index can be used to represent the degree of data loss after passing through a single data processing stage. Specifically, in the target cross-domain data and the initial cross-domain data, there can be intermediate data before and after each data processing stage. For each data processing stage, the single-stage degradation index corresponding to that stage can be determined by analyzing the differences in data before and after the stage. The data processing stages include at least one of the following: encryption, de-identification, format conversion, transmission, and storage.

[0025] Furthermore, the specific processing methods in some data processing stages are described below: The encryption process is as follows: an encryption algorithm is selected based on the security requirements of the data source domain (SM4 for government affairs, RSA 2048 bits for finance, and AES 256 bits for industry), and the algorithm is automatically matched through the "algorithm selection engine" (e.g., when the data source is identified as a government department, the SM4 encryption interface is automatically called). The encryption key is distributed and stored through a blockchain key management system (KMS) to avoid single point of leakage.

[0026] The desensitization process is as follows: A desensitization rule library is pre-stored for each sector (for government sectors, "legal entity ID number and contact information" must be fully desensitized; for the financial sector, "bank card number" retains only the first 6 and last 4 digits; for the industrial sector, "equipment core parameters" are desensitized as needed). The module automatically matches desensitization rules based on data type (such as "identity information," "transaction information," and "equipment parameters"). After desensitization, the effectiveness is verified using "sensitive information detection tools" (such as regular expression matching and keyword search) to ensure that the sensitive information leakage rate is ≤0.1%). The format conversion process is as follows: (1) Built-in multi-domain protocol parsing library: pre-store the protocol specifications of GB / T35273-2020 (XML format) for the government sector, ISO / IEC 29100-2011 (JSON format) for the financial sector, and IEC62890-2018 (Protobuf format) for the industrial sector, and support automatic identification of the data source domain (identifying fields through the data header); (2) Construct dynamic semantic mapping algorithm: establish a "cross-domain field mapping table" (such as "unified credit code for enterprises" in government sector corresponding to "user_id" in financial sector, and "equipment number" in industrial sector corresponding to "asset code" in government sector), and combine it with NLP semantic similarity calculation (cosine similarity ≥ 0.95 is judged as a match) to realize intelligent mapping of non-standard fields and avoid the limitations of manually maintaining the mapping table; (3) Format conversion verification: after conversion, verify the data format through "format compliance verification script" (such as XML Schema verification, JSON Schema verification) to ensure that it meets the requirements of the target domain protocol, and the verification pass rate must be ≥ 99.9%.

[0027] Optionally, based on the target cross-domain circulation data and the initial cross-domain circulation data, a single-stage degradation index corresponding to each data processing stage is determined, including: for each data processing stage, a single-index degradation index is determined based on the data before and after the data processing stage; the single-index degradation index includes at least one of feature completeness, accuracy retention rate, and semantic consistency; the single-stage degradation index of the data processing stage is compared with the sum of the single-stage degradation indices of all data processing stages, and the ratio is used as the single-index weight; the single-index entropy value is determined based on the single-index weight of all data processing stages, and the single-index weight corresponding to each single-index degradation index is determined based on the single-index entropy value; the single-index degradation indices corresponding to each data processing stage are weighted and summed based on the single-index weight to obtain the single-stage degradation index corresponding to the data processing stage.

[0028] The single-index degradation index can be a parameter that analyzes the degree of wear before and after data processing from a single perspective. Specifically, the single-index degradation index includes at least one of feature integrity, accuracy retention rate, and semantic consistency. The calculation methods for feature integrity, accuracy retention rate, and semantic consistency are shown in Table 1. Table 1. Calculation Logic of Single-Indicator Degradation Index Optionally, since the FI, PRR, and SC parameters are all percentage data (0-100), no unit conversion is required. Only outlier removal algorithms (3σ principle) are needed to remove abnormal data caused by acquisition errors (such as unreasonable values ​​like FI=120) to ensure data validity.

[0029] Furthermore, the single-indicator weight can be the proportion of the single-indicator degradation index in a data processing stage to the total weight of all data processing stages. Specifically, for feature completeness, the feature completeness of a data processing stage can be compared with the sum of the feature completeness of all data processing stages, and the resulting ratio can be used as the single-indicator weight of that data processing stage regarding adjusted completeness. For precision retention, the precision retention rate of a data processing stage can be compared with the sum of the precision retention rates of all data processing stages, and the resulting ratio can be used as the single-indicator weight of that data processing stage regarding precision retention. For semantic consistency, the semantic consistency of a data processing stage can be compared with the sum of the semantic consistency of all data processing stages, and the resulting ratio can be used as the single-indicator weight of that data processing stage regarding semantic consistency.

[0030] Furthermore, the single-index entropy value can be the entropy value corresponding to a single-index degradation index. Specifically, for each single-index degradation index, the single-index degradation indices corresponding to all data processing stages can be substituted into the formula for calculating the entropy value to determine the single-index entropy value corresponding to that single-index degradation index. The single-index weight can be the weighting coefficient of the single-index degradation index in reflecting the degree of data wear. Specifically, for each single-index degradation index, the single-index entropy value corresponding to that single-index degradation index and the single-index entropy values ​​corresponding to all single-index degradation indices can be substituted into the formula for calculating the index weight to obtain the single-index weight corresponding to that single-index degradation index. Finally, for each data processing stage, the single-index degradation indices corresponding to the data processing stage can be weighted and summed based on the single-index weight to obtain the single-stage degradation index corresponding to the data processing stage.

[0031] For example, the specific calculation steps for the single-stage degradation index are described below: Calculate the weight of a single indicator: ( Here, j is the standardized value of the j-th indicator in the i-th stage, and n is the number of data processing stages (here, n=5, j=FI,PRR,SC). Calculate the entropy value of a single index (entropy value) The smaller the value, the higher the dispersion of the indicator. Calculate the weight of a single indicator: ; ④ Weight verification: Through verification using 100 sets of cross-domain circulation test data, the optimal weights were determined to be FI=0.4, PRR=0.3, and SC=0.3 (under these weights, the consistency between the quantitative results and the manual quality assessment is ≥95%). Single-stage degradation score calculation: Based on the weighted sum of the FI, PRR, and SC parameters for each stage, the formula is as follows: ( is the single-stage degradation index of the i-th stage, with a value ranging from 0 to 100. A higher score indicates a lighter degree of degradation.

[0032] S230. Determine the overall degradation index based on the degradation index of each individual step in all data processing steps.

[0033] The overall degradation index can be used to represent the degree of data loss after going through all data processing stages. Optionally, the overall degradation index can be obtained by summing the individual degradation indices corresponding to all data processing stages, or the average of the individual degradation indices of all data processing stages can be used as the overall degradation index; there is no limitation here.

[0034] For example, the overall degradation index can be calculated by using the "arithmetic mean method" to summarize the degradation scores of the five stages. The formula is as follows: The DDI value ranges from 0 to 100, with ≤50 defined as high loss, 51-80 as medium loss, and ≥81 as low loss. Quantitative result verification is then performed: a "consistency verification algorithm" verifies the logical rationality of the DDI and the scores of each stage (e.g., if a stage score is ≤30 but DDI ≥80, it is considered a calculation anomaly and a recalculation is triggered), ensuring DDI accuracy ≥99.9%. Furthermore, on-chain notarization is possible: the DDI, degradation scores of each stage, raw data of collected parameters, and calculation process logs are packaged into a "quantitative result data package," which is then written to a consortium blockchain (such as FISCO BCOS) via a blockchain smart contract, generating a unique notarization hash to ensure data immutability and traceability.

[0035] S240. Determine the attribution contribution weight and business contribution weight based on the data range of the whole process degradation index.

[0036] The attribution contribution weight can be the weight of the attribution contribution parameter in reflecting the data value. Similarly, the business contribution weight can be the weight of the business contribution parameter in reflecting the data value. Specifically, multiple data ranges can be preset for the end-to-end degradation index, each with its own attribution contribution weight and business contribution weight. After determining the end-to-end degradation index, the corresponding attribution contribution weight and business contribution weight can be matched based on the data range in which the end-to-end degradation index falls.

[0037] For example, the correspondence between the overall degradation index and the weights of the contribution parameters is shown in Table 2: Table 2. Correspondence between the overall degradation index and the weights of contribution parameters S250. Determine the attribution contribution parameter and business contribution parameter corresponding to the target cross-domain data. Based on the attribution contribution weight and business contribution weight, sum the attribution contribution parameter and business contribution parameter to obtain the data value parameter.

[0038] The attribution contribution parameter can reflect the degree of contribution of the data provider to the overall data. The attribution contribution parameter can be calculated by allocating basic weights according to the number of subsets to which the data belongs (0.6 for unique data, 0.3 for data shared by two parties, and 0.1 for data shared by multiple parties). =Base weight × Data size score, where the data size score is quantified based on sample size (0-100). The business contribution parameter can be used to quantify the actual value of data to downstream business (e.g., in model training scenarios, the accuracy improvement rate brought by data × 100; in decision support scenarios, the contribution rate of data to correct decisions × 100), denoted as... .

[0039] The data value parameter can represent the data value corresponding to the target cross-domain flow of data. Specifically, the data value parameter can be obtained by weighted summing of the attribution contribution parameter and the business contribution parameter based on the attribution contribution weight and the business contribution weight.

[0040] Optionally, based on the attribution contribution weight and the business contribution weight, the data value parameter is obtained by weighted summation of the attribution contribution parameter and the business contribution parameter, including: adding the product of the attribution contribution weight and the attribution contribution parameter to the product of the business contribution weight and the business contribution parameter to obtain the dynamic value parameter; determining the corresponding target compensation coefficient based on the full-process degradation index, using the sum of the target compensation coefficient and one as the target correction coefficient, and using the product of the dynamic value parameter and the target correction coefficient as the data value parameter.

[0041] The dynamic value parameter can be a value parameter determined from the perspective of business contribution. The formula for calculating the dynamic value parameter is: ( The value ranges from 0 to 100, reflecting the basic value of the data at the current level of degradation.

[0042] Furthermore, the target compensation coefficient can be a weighted coefficient used to compensate for the data value of the target cross-domain circulating data. Specifically, a correlation is established between the "degree of degradation and the intensity of compensation" to achieve "the greater the loss, the greater the compensation," thus balancing the loss costs for the data provider. For example, a mapping relationship between the full-process degradation index and the compensation coefficient can be preset, and subsequently, the compensation coefficient matched to the full-process degradation index can be used as the target compensation coefficient based on this mapping relationship. The mapping relationship between the full-process degradation index and the compensation coefficient is shown in Table 3. Table 3. Mapping Relationship between Overall Degradation Index and Compensation Coefficient In addition, after determining the compensation coefficient, a "cost coverage check" can also be performed. Verify the rationality; if the verification fails, trigger a fine-tuning of the coefficient (e.g., adjust DCC from 0.3 to 0.32).

[0043] The target correction coefficient can be used to compensate and correct dynamic value parameters. Specifically, the sum of the target compensation coefficient and the target correction coefficient can be used as the target correction coefficient. Finally, the product of the dynamic value parameter and the target correction coefficient can be used as the data value parameter to complete the data value compensation process for the dynamic value parameter.

[0044] The formula for calculating data value parameters is: ( This serves as the benchmark for data participation in value allocation, encompassing both basic value and loss compensation.

[0045] Optionally, if preset triggering conditions are met, the object revenue parameters corresponding to each data participant can be determined based on the data value parameters and preset allocation ratios; the revenue corresponding to the object revenue parameters can be transferred to the target participant's account through the blockchain transfer interface.

[0046] The preset trigger conditions can be conditions that trigger the distribution of revenue for target cross-domain circulating data. For example, the specific conditions of the preset trigger conditions are: ① DDI has been notarized on the chain; ② The data recipient confirms that the data is available (through on-chain signature); ③ V has been finally calculated. These three conditions are used to avoid premature or erroneous triggering.

[0047] Data participants are those who participate in providing the target cross-domain data (which may include data providers and cross-domain data transfer service providers). The allocation ratio of participant roles is as follows: Data Providers (accounting for...) 70% includes basic revenue and loss compensation), cross-domain circulation service providers (accounting for 70%) 30% of the cost covers adaptation, storage, and operation and maintenance costs.

[0048] Optionally, exception handling steps can also be set: if the data recipient does not confirm the data is available within 24 hours, the contract will automatically trigger the "data return + recalculation" process; if an on-chain failure occurs during the allocation process, the contract supports breakpoint continuation to ensure that the allocation is not interrupted.

[0049] Optionally, a revenue distribution record table can be generated based on the object revenue parameters of all data participants, and the revenue distribution record table can be stored in a preset blockchain storage area; wherein, the revenue distribution record table includes at least one of the following: data participants, object revenue parameters, data value parameters, and revenue distribution time.

[0050] For example, the specific contract execution process is as follows: The contract reads the DDI stored on the blockchain. , data; ② Calculate the share of revenue for each participant according to the allocation ratio (e.g., data provider's share = ...). (×70%) ③ Transfer the profits to the on-chain accounts of each participant through the blockchain transfer interface (supports legal digital currency or points). ④ Generate allocation records (including participants, amounts, times, and DDI) and simultaneously write them to the blockchain for evidence storage; Efficiency and security guarantees: The contract is developed using the Solidity language, formally verified (ensuring no logical vulnerabilities), has an execution latency of ≤200ms, supports concurrent allocation of ≥500 transactions / second, and meets the needs of large-scale cross-domain data circulation.

[0051] Compared with existing technologies, this application addresses the core shortcomings of "no degradation quantization, static weights, no cross-domain capability, and no compensation mechanism" by introducing an innovative technical solution that offers five key technological advantages, as detailed below: 1. Achieve data degradation and quantification across all stages of cross-domain data flow, solving the problem of "inability to assess data quality and value" in existing technologies. Existing technology has the following drawbacks: it only focuses on data ownership and quantity, completely ignoring the quality degradation in cross-domain circulation processes such as encryption, desensitization, and format conversion, resulting in a disconnect between the evaluation results and the actual usable value of the data; The technical advantages of this application are: it can accurately quantify the degree of data degradation in all stages of cross-domain circulation, clearly reflect the impact of data quality loss on value, and ensure that the value assessment matches the actual usable value; Technical support: A three-dimensional quantification model of "feature integrity - accuracy retention rate - semantic consistency" is adopted, combined with the entropy method to objectively weight and generate the degradation index (DDI), covering 5 core links of cross-domain circulation, with a quantification error of ≤3%. 2. Construct a dynamic weight adjustment system to address the problem of existing technologies having "static and singular weights that are disconnected from data quality". Existing technology has the following drawbacks: It only allocates fixed weights based on the "number of subsets to which the data belongs" (e.g., 0.6 for unique data and 0.3 for data shared by both parties), without considering differences in data quality, resulting in an imbalance in weights between low-quality and high-quality data. The advantages of this application are: It can dynamically adjust the weights according to the degree of data degradation, realize the value assessment of "quality first and multi-dimensional linkage", and improve the scientificity and adaptability of weight allocation. Technical support: Based on the Degradation Index (DDI) as the core adjustment basis, the proportions of "attribution contribution weight" and "business contribution weight" are set differently (e.g., the attribution weight of high loss data is increased to 0.5, while the attribution weight of low loss data is maintained at 0.3), forming a multi-dimensional dynamic weight system. 3. Possesses multi-domain and cross-domain adaptability, solving the problem that existing technologies are "only suitable for the same domain and cannot be implemented across domains". Existing technology has the following drawbacks: it only supports data transactions between multiple entities within the same domain, and cannot be compatible with protocol formats, security specifications, and identity authentication requirements of different domains, making it completely unusable in cross-domain scenarios; The advantages of this application are: it enables seamless cross-domain data flow across multiple fields such as government affairs, finance, and industry, is compatible with technical standards in different fields, and breaks through the limitations of cross-domain application scenarios; Technical support includes: a "multi-protocol conversion module" to adapt to different data formats (XML / JSON / Protobuf); a "distributed identity authentication (DID) module" to achieve cross-institutional identity verification; and a "multi-standard security adaptation module" to be compatible with encryption / de-identification rules across various fields, with cross-domain adaptation error ≤1%. 4. Establishing a loss compensation and automated allocation mechanism to address the problem of existing technologies lacking loss compensation and relying on manual value allocation. Existing technology has the following drawbacks: it only outputs contribution results without any loss compensation design, and the value allocation needs to be performed manually, which results in low efficiency, high error, and easy disputes. The advantages of this application are: it can provide differentiated compensation for data degradation and loss, while realizing the automation and transparency of value distribution, balancing the interests of data providers and improving distribution efficiency; Technical Support: A tiered compensation coefficient system based on the Degradation Index (DDI) is designed (high-loss DCC=0.3, medium-loss 0.15, low-loss 0.05). The entire process of "quantification-calculation-compensation-distribution" is solidified through blockchain smart contracts, automatically executing revenue allocation and on-chain notarization, with an allocation error ≤0.5% and a processing time ≤200ms. 5. The introduction of blockchain trust support addresses the problems of existing technologies such as "lack of notarization mechanism and difficulty in overcoming cross-domain trust barriers." Existing technical shortcomings: There is no data storage and trust guarantee design. In cross-domain scenarios, participants have doubts about the authenticity of contribution calculation and value distribution results, and the trust barrier is difficult to overcome. The technical advantages of this application are: it enables the immutable storage and traceability of key data in cross-domain transactions, significantly improving the trust among cross-domain participants and reducing cooperation disputes; Technical support: Degradation quantification data (DDI, scores at each stage), value allocation data (weights, compensation coefficients, final value), and execution results (allocation amount, role, timestamp) are all uploaded to the consortium blockchain for evidence storage, providing a real-time query and audit interface to ensure the authenticity and traceability of the data.

[0052] The technical solution provided by this invention obtains initial cross-domain circulation data and performs multi-stage data processing on the initial cross-domain circulation data to obtain target cross-domain circulation data. For each data processing stage, based on the target cross-domain circulation data and the initial cross-domain circulation data, a single-stage degradation index is determined for each data processing stage. The overall degradation index is determined based on the single-stage degradation indices of all data processing stages. The attribution contribution weight and business contribution weight are determined based on the data range of the overall degradation index. The attribution contribution parameter and business contribution parameter corresponding to the target cross-domain circulation data are determined, and the data value parameter is obtained by weighted summation based on the attribution contribution weight and business contribution weight. This technical solution solves the problem of evaluation distortion in existing technologies for cross-domain circulation data value assessment. It can determine the degradation index of data after multi-stage data processing, determine appropriate contribution parameter weights based on the degradation index, and then perform data value parameter analysis based on the contribution parameter weights. This achieves full-stage data degradation quantification in cross-domain circulation and determines appropriate parameter analysis weights based on the data degradation index, thereby improving the accuracy of cross-domain circulation data value assessment.

[0053] Figure 3 This is a schematic diagram of a cross-domain data analysis device provided in an embodiment of the present invention. The embodiment of the present invention can be applied to scenarios of value analysis of cross-domain flow data. The device can be implemented by software and / or hardware and integrated into a computer device with application development capabilities.

[0054] like Figure 3 As shown, the cross-domain data analysis device includes: a data acquisition module 310, a degradation index determination module 320, and a data value analysis module 330.

[0055] The data acquisition module 310 is used to acquire initial cross-domain circulation data and perform multi-stage data processing on the initial cross-domain circulation data to obtain target cross-domain circulation data; the degradation index determination module 320 is used to determine the full-process degradation index corresponding to the target cross-domain circulation data based on the target cross-domain circulation data and the initial cross-domain circulation data; and the data value analysis module 330 is used to determine the contribution parameter weight corresponding to each contribution parameter based on the full-process degradation index, and determine the data value parameter corresponding to the target cross-domain circulation data based on the contribution parameter weight.

[0056] The technical solution provided by this invention involves acquiring initial cross-domain circulation data and processing it through multiple stages to obtain target cross-domain circulation data. Based on the target cross-domain circulation data and the initial cross-domain circulation data, a full-process degradation index corresponding to the target cross-domain circulation data is determined. Based on the full-process degradation index, the contribution parameter weights corresponding to each contribution parameter are determined, and based on the contribution parameter weights, the data value parameters corresponding to the target cross-domain circulation data are determined. This technical solution solves the problem of evaluation distortion in existing technologies for cross-domain circulation data value assessment. It can determine the degradation index of data after multi-stage data processing, determine appropriate contribution parameter weights based on the degradation index, and then perform data value parameter analysis based on the contribution parameter weights. This achieves full-stage data degradation quantification in cross-domain circulation and improves the accuracy of cross-domain circulation data value assessment by determining appropriate parameter analysis weights based on the data degradation index.

[0057] In one optional implementation, the degradation index determination module 320 is specifically used to: for each data processing stage, determine the single-stage degradation index corresponding to the data processing stage based on the target cross-domain circulation data and the initial cross-domain circulation data; wherein, the data processing stage includes at least one of: encryption stage, desensitization stage, format conversion stage, transmission stage and storage stage; and determine the full-process degradation index based on the single-stage degradation index of all data processing stages.

[0058] In an optional implementation, the degradation index determination module 320 includes a single-stage degradation index determination module 320 unit, configured to: determine the single-index degradation index for each data processing stage based on the data before and after the data processing stage; the single-index degradation index includes at least one of feature integrity, accuracy retention rate, and semantic consistency; compare the single-stage degradation index of the data processing stage with the sum of the single-stage degradation indices of all data processing stages, and use the ratio as the single-index weight; determine the single-index entropy value based on the single-index weights of all data processing stages, and determine the single-index weight corresponding to each single-index degradation index based on the single-index entropy value; and perform a weighted summation of the single-index degradation indices corresponding to the data processing stage based on the single-index weights to obtain the single-stage degradation index corresponding to the data processing stage.

[0059] In an optional implementation, the data value analysis module 330 is specifically used to: determine the attribution contribution weight and business contribution weight based on the data range in which the full-process degradation index is located; determine the attribution contribution parameter and business contribution parameter corresponding to the target cross-domain circulation data; and, based on the attribution contribution weight and business contribution weight, weightedly sum the attribution contribution parameter and business contribution parameter to obtain the data value parameter.

[0060] In an optional implementation, the data value analysis module 330 includes: a data value unit, configured to: add the product of the attribution contribution weight and the attribution contribution parameter to the product of the business contribution weight and the business contribution parameter to obtain a dynamic value parameter; determine the corresponding target compensation coefficient based on the full-process degradation index; use the sum of the target compensation coefficient plus one as the target correction coefficient; and use the product of the dynamic value parameter and the target correction coefficient as the data value parameter.

[0061] In one optional implementation, the cross-domain data analysis device further includes a data value parameter application module, used to: determine the object revenue parameter corresponding to each data participant based on the data value parameter and the preset allocation ratio when a preset trigger condition is met; and transfer the revenue corresponding to the object revenue parameter to the target participant's account through a blockchain transfer interface.

[0062] In one optional implementation, the data value parameter application module includes: a revenue distribution record unit, used to: generate a revenue distribution record table based on the object revenue parameters of all data participants, and store the revenue distribution record table in a preset blockchain storage area; wherein, the revenue distribution record table includes at least one of: data participants, object revenue parameters, data value parameters, and revenue distribution time.

[0063] The cross-domain data analysis device provided in the embodiments of the present invention can execute the cross-domain data analysis method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method execution.

[0064] Figure 4 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention. Figure 4 A block diagram of an exemplary computer device 12 suitable for implementing embodiments of the present invention is shown. Figure 4 The computer device 12 shown is merely an example and should not be construed as limiting the functionality or scope of the embodiments of the present invention. The computer device 12 can be any terminal device with computing capabilities and can be configured in a cross-domain data analysis device.

[0065] like Figure 4 As shown, the computer device 12 is represented in the form of a general-purpose computing device. The components of the computer device 12 may include, but are not limited to: one or more processors or processing units 16, system memory 28, and bus 18 connecting different system components (including system memory 28 and processing unit 16).

[0066] Bus 18 can be one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. For example, these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0067] Computer device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by computer device 12, including volatile and non-volatile media, removable and non-removable media.

[0068] System memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache 32. Computer device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be used to read and write non-removable, non-volatile magnetic media (… Figure 4 Not shown; usually referred to as a "hard drive"). Although Figure 4Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. System memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of the present invention.

[0069] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in system memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 42 typically perform the functions and / or methods described in the embodiments of the present invention.

[0070] Computer device 12 can also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), and with one or more devices that enable a user to interact with the computer device 12, and / or with any device that enables the computer device 12 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed through input / output (I / O) interface 22. Furthermore, computer device 12 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 20. Figure 4 As shown, network adapter 20 communicates with other modules of computer device 12 via bus 18. It should be understood that, although... Figure 4 As not shown, it can be used in conjunction with computer device 12 with other hardware and / or software modules, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0071] Processing unit 16 executes various functional applications and data processing by running programs stored in system memory 28, such as implementing the cross-domain data analysis method provided in this embodiment of the invention, which includes: Initial cross-domain circulation data is obtained, and target cross-domain circulation data is obtained by performing multi-stage data processing on the initial cross-domain circulation data; based on the target cross-domain circulation data and the initial cross-domain circulation data, the full-process degradation index corresponding to the target cross-domain circulation data is determined; based on the full-process degradation index, the contribution parameter weight corresponding to each contribution parameter is determined, and the data value parameter corresponding to the target cross-domain circulation data is determined based on the contribution parameter weight.

[0072] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the cross-domain data analysis method as provided in any embodiment of the present invention, including: Initial cross-domain circulation data is obtained, and target cross-domain circulation data is obtained by performing multi-stage data processing on the initial cross-domain circulation data; based on the target cross-domain circulation data and the initial cross-domain circulation data, the full-process degradation index corresponding to the target cross-domain circulation data is determined; based on the full-process degradation index, the contribution parameter weight corresponding to each contribution parameter is determined, and the data value parameter corresponding to the target cross-domain circulation data is determined based on the contribution parameter weight.

[0073] The computer storage medium of this invention can be any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0074] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0075] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0076] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as C, Java, Smalltalk, C++, C#, and Python, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0077] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computing device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0078] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. A cross-domain data analysis method, characterized in that, include: The initial cross-domain circulation data is obtained, and the target cross-domain circulation data is obtained after multi-stage data processing of the initial cross-domain circulation data. Based on the target cross-domain circulation data and the initial cross-domain circulation data, determine the full-process degradation index corresponding to the target cross-domain circulation data; Based on the full-process degradation index, the contribution parameter weights corresponding to each contribution parameter are determined, and the data value parameters corresponding to the target cross-domain circulation data are determined based on the contribution parameter weights.

2. The method according to claim 1, characterized in that, The step of determining the full-process degradation index corresponding to the target cross-domain circulation data based on the target cross-domain circulation data and the initial cross-domain circulation data includes: For each data processing stage, based on the target cross-domain circulation data and the initial cross-domain circulation data, a single-stage degradation index corresponding to the data processing stage is determined; wherein, the data processing stage includes at least one of the following: encryption stage, desensitization stage, format conversion stage, transmission stage, and storage stage; The overall degradation index is determined based on the degradation index of each individual step in the entire data processing process.

3. The method according to claim 2, characterized in that, The step of determining the single-stage degradation index corresponding to the data processing stage based on the target cross-domain circulation data and the initial cross-domain circulation data includes: For each data processing stage, the single-index degradation index is determined based on the data before and after the data processing stage; the single-index degradation index includes at least one of feature integrity, accuracy retention rate and semantic consistency. The degradation index of a single step in the data processing stage is compared with the sum of the degradation indices of all single steps in the data processing stage, and the resulting ratio is used as the weight of a single indicator. The entropy value of a single indicator is determined based on the proportion of single indicators in all data processing stages, and the weight of a single indicator corresponding to the degradation index of each single indicator is determined based on the entropy value of the single indicator. Based on the single index weight, the degradation index of the single index corresponding to the data processing step is weighted and summed to obtain the single-step degradation index corresponding to the data processing step.

4. The method according to claim 1, characterized in that, The process of determining the contribution parameter weights for each contribution parameter based on the full-process degradation index, and determining the data value parameters corresponding to the target cross-domain circulation data based on the contribution parameter weights, includes: The attribution contribution weight and business contribution weight are determined based on the data range of the full-process degradation index. Determine the attribution contribution parameter and business contribution parameter corresponding to the target cross-domain circulation data. Based on the attribution contribution weight and business contribution weight, the attribution contribution parameter and business contribution parameter are weighted and summed to obtain the data value parameter.

5. The method according to claim 4, characterized in that, The step of obtaining the data value parameter by weighted summation of the attribution contribution parameter and the business contribution parameter based on the attribution contribution weight and the business contribution weight includes: The dynamic value parameter is obtained by adding the product of the attribution contribution weight and the attribution contribution parameter to the product of the business contribution weight and the business contribution parameter. Based on the full-process degradation index, a corresponding target compensation coefficient is determined. The sum of the target compensation coefficient plus one is used as the target correction coefficient, and the product of the dynamic value parameter and the target correction coefficient is used as the data value parameter.

6. The method according to claim 1, characterized in that, The method further includes: Under the condition of meeting the preset triggering conditions, the object benefit parameters corresponding to each data participant are determined based on the data value parameters and the preset allocation ratio. The revenue corresponding to the object's revenue parameters is transferred to the target participant's account via a blockchain transfer interface.

7. The method according to claim 6, characterized in that, The method further includes: A revenue distribution record table is generated based on the object revenue parameters of all data participants, and the revenue distribution record table is stored in a preset blockchain storage area; The revenue distribution record table includes at least one of the following: data participants, object revenue parameters, data value parameters, and revenue distribution time.

8. A cross-domain data analysis device, characterized in that, The device includes: The data acquisition module is used to acquire initial cross-domain circulation data and perform multi-stage data processing on the initial cross-domain circulation data to obtain target cross-domain circulation data. The degradation index determination module is used to determine the full-process degradation index corresponding to the target cross-domain circulation data based on the target cross-domain circulation data and the initial cross-domain circulation data. The data value analysis module is used to determine the contribution parameter weight corresponding to each contribution parameter based on the full-process degradation index, and to determine the data value parameter corresponding to the target cross-domain circulation data based on the contribution parameter weight.

9. A computer device, characterized in that, The computer device includes: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the cross-domain data analysis method as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the cross-domain data analysis method as described in any one of claims 1-7.

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