Cross-domain data value intelligent grading evaluation method based on multi-dimensional dynamic weight optimization

Abstract

The invention discloses a cross-domain data value intelligent grading evaluation system and method based on multi-dimensional dynamic weight optimization, and solves the problems of high field limitation, poor dynamic adaptability and single dimension in the prior art. The system comprises a cross-domain self-adaptive grading engine which matches a special template through a domain adapter and dynamically optimizes the weight; a multi-dimensional value dynamic fusion model is adopted, a four-dimensional evaluation matrix is constructed, and an LSTM network is adopted to calculate the weight; a value entropy polymerization algorithm is combined with PageRank to correct dimension relevance so as to generate a grading result; and the business strategy executor is used for converting the result into an API instruction driving decision. The method achieves cross-domain reuse, improves the classification accuracy, improves the decision timeliness, and is suitable for the fields of traffic transportation, cultural relic protection, finance and the like.

Description

Technical Field

[0001] This invention belongs to the field of interdisciplinary technology of artificial intelligence and data governance. Specifically, it relates to a cross-domain intelligent hierarchical evaluation system and method for data value based on multi-dimensional dynamic weight optimization, which is applicable to the quantitative management of data value in multiple fields such as transportation, cultural relic protection, and finance. Background Technology

[0002] Current data grading technologies have significant drawbacks:

[0003] Domain limitations: For example, the patent CN202411110376.8 of DPtech is only applicable to medical data, relies on domain-specific features, and cannot be transferred to other fields such as transportation and finance. Cross-domain reuse requires redevelopment, which is costly and inefficient.

[0004] Static assessment: China Construction Bank's patent CN120106958A uses fixed threshold rules (such as fixed weights and preset thresholds), which cannot dynamically respond to changes in data status (such as improvements in employee behavior, the discovery of new cultural relics, etc.), resulting in the classification results lagging behind actual business needs.

[0005] Limited Dimensions: The evaluation dimensions are limited to a single feature (such as sensitivity), ignoring key dimensions such as business value and timeliness value, making it difficult to fully reflect the comprehensive value of the data. Summary of the Invention

[0006] Technical problems to be solved

[0007] Break through the technical barriers in vertical fields to achieve cross-industry reuse of hierarchical models and avoid redundant development;

[0008] Establish a multi-dimensional dynamic value assessment system to solve the problem of rigid static rules and respond to data changes in real time;

[0009] Establish a real-time linkage mechanism between hierarchical results and business decisions to improve decision-making efficiency.

[0010] Technical solution

[0011] The technical solution of the present invention is as described in claims 1-8, and its core innovation includes:

[0012] Cross-domain adaptive hierarchical engine

[0013] Domain adapter: By recognizing the structural fingerprint of the input data (such as field type and business tag), it matches the corresponding domain template library (transportation, cultural relic protection, finance).

[0014] Example of a domain template (Transportation): Includes initial weight values ​​[Business value 0.4, Timeliness value 0.3, Safety risk 0.3], attenuation coefficient λ_traffic = 0.05, and feature set ["Violation frequency", "Training completion rate", "Assessment score"].

[0015] The weight dynamic optimization module: It incorporates an LSTM neural network to collect data state change signals in real time, outputs a weight correction vector ΔW, and dynamically updates the weights (pseudocode: [~, ΔW] = ...).

[0016] lstm(net,data_stream);W_new=W_base+η×ΔW);

[0017] Edge computing interface: Supports lightweight deployment of mobile terminals (such as vehicle terminals and cultural relic inspection equipment) to reduce data transmission costs.

[0018] Multi-dimensional value dynamic fusion model

[0019] Construct a four-dimensional evaluation matrix: D = [Business Value, Timeliness Value, Relevance Value, Security Risk], to comprehensively cover the characteristics of data value;

[0020] Timeliness value dimension: The time decay function β(t)=e^(-λ·t) is adopted, where λ_traffic=0.05 in the transportation field and λ_culture=0.01 in the cultural relics protection field, to adapt to the timeliness decay characteristics of data in different fields;

[0021] Associated Value Dimension: Using the industry association tree algorithm, in the financial field, funds are traced down to the Nth layer of associated entities, and in the cultural relics field, the spatiotemporal association strength with cultural relics of the same category is calculated.

[0022] Value Entropy Aggregation Algorithm

[0023] Construct a dimensional relationship graph G = (V, E): nodes represent value dimensions, and edge weights reflect the correlation between dimensions;

[0024] The dimension influence weight wi' is calculated using the PageRank algorithm, and the original weight is corrected: wi_final=0.7×wi+0.3×PR(vi) (PR(vi) is the node PageRank value), which solves the defect of traditional weighted summation ignoring the dimension correlation.

[0025] The comprehensive value scoring formula is: V=Σ(wi·Di)+γ·max(Di), where γ is the domain-related gain factor (e.g., γ=0.2 in the field of cultural relic protection).

[0026] Business strategy executor

[0027] The data value level is mapped based on the comprehensive value score V and preset thresholds (such as V1=80, V2=90, V3=30);

[0028] The system converts the levels into API instruction sets and outputs them to external business systems (such as financial systems and cultural relic management systems) to drive strategy execution (such as precision marketing and blockchain evidence storage).

[0029] Beneficial effects

[0030] Cross-domain versatility: Domain adapters and dedicated templates enable direct reuse of models across domains, improving transfer efficiency by 10 times and eliminating the need for retraining;

[0031] Dynamic adaptability: The LSTM network updates weights in real time with a response latency of <50ms to data changes, which is 5-8 times better than static methods;

[0032] Comprehensive assessment: By integrating four dimensions and correcting correlations, the accuracy of classification is improved by 90%;

[0033] Business synergy: The latency from hierarchical classification to decision-making has been reduced from hours to seconds, improving decision-making efficiency by 200%. Detailed Implementation

[0034] Example 1: Transportation Sector

[0035] Data input: employee violation records (number of violations in the past 3 months), training data (training completion rate of 90%);

[0036] Domain adaptation: The domain adapter matches the transportation template with initial weights [0.4, 0.3, 0.3] and λ_traffic = 0.05;

[0037] Dynamic weight optimization: If an employee has no violations for 3 consecutive months (t = 90 days), the risk dimension weight decays: w_risk' = 0.3 × e^(-0.02 × 90) ≈ 0.05;

[0038] Value aggregation: Overall score V = 25 (≤V3 level);

[0039] Business collaboration: Push customized training courses and restrict scheduling permissions.

[0040] Example 2: Cultural Relics Protection

[0041] Data input: 3D scan data of cultural relics (0.1mm accuracy), 120 academic citations;

[0042] Domain Adaptation: Matches the cultural relics protection template, with initial weights of [0.5, 0.3, 0.2].

[0043] λ_culture = 0.01;

[0044] Dynamic weight optimization: When a related cultural relic was detected, the cultural value weight was adjusted: w_culture'=0.5+0.2×3=1.1;

[0045] Value aggregation: Overall score V = 95 (≥V2 level);

[0046] Business linkage: Triggers instructions for blockchain-based evidence storage and 3D digital derivative development.

[0047] Example 3: Financial Sector

[0048] Data input: Corporate credit fund flow (penetrating down to the third-level related entity);

[0049] Domain Adaptation: Matches financial templates, with initial weights of [0.6, 0.4].

[0050] Value aggregation: Overall score V = 88 (≥V1 level);

[0051] Business Integration: Generate precise marketing strategy instructions to increase credit by 2 million. Attached Figure Description

[0052] Figure 1 The system architecture diagram shows the connection relationships and data flow of the cross-domain adaptive hierarchical engine, the multi-dimensional value dynamic fusion model, and the business strategy executor.

[0053] Figure 2 : Dynamic weight calculation flowchart, showing the interaction logic of LSTM network, time decay function, and domain feature input;

[0054] Figure 3 A business strategy linkage diagram illustrating the mapping relationship between value levels and API commands.

[0055] Industrial applicability

[0056] This invention has been applied in the following scenarios:

[0057] The intelligent education cloud platform for transportation safety manages 300,000 practitioners and achieves precise training through dynamic grading, resulting in a 35% reduction in the violation rate.

[0058] Key cultural relics protection unit security data management system: covering 7,745 national-level protected units, with a 100% blockchain-based data storage rate for top-level data, reducing security risks by 40%.

Claims

1. A cross-domain data value intelligent hierarchical evaluation method based on multi-dimensional dynamic weight optimization, characterized in that, Includes the following steps: (1) Receive the input data stream through the domain adapter, parse the structural features of the data source, and load the value dimension weight template of the corresponding domain; the domain includes at least transportation, cultural relic protection, and finance. The weight coefficients for each dimension are dynamically calculated using an LSTM neural network, where: The business value dimension introduces an economic return factor α, which is calculated as follows: α = k1 × ln(current business size / benchmark size) + k2 × (expected revenue growth rate), where k1 and k2 are domain coefficients; The timeliness value dimension is loaded with the time decay function β(t): β(t)=e^(-λ·t), where λ is the domain decay coefficient, and λ_traffic in the transportation domain is greater than λ_culture in the cultural relics protection domain; (3) Generate a comprehensive value score V based on the value entropy aggregation algorithm: V=Σ(wi·Di)+γ·max(Di), where wi is the dynamic weight, Di is the quantized value of the i-th dimension, and γ is the domain association gain factor; (4) Output the data value level according to the preset hierarchical threshold mapping table and trigger the business strategy optimization instruction set.

2. The method according to claim 1, characterized in that, The dynamic weight calculation in step (2) includes: Real-time acquisition of data state change signals, and output of weight correction vector ΔW through LSTM network; When the discovery of related cultural relics is detected in the field of cultural relic protection, the weight of the cultural value dimension of the related dataset is automatically increased. The correction formula is: w_culture'=w_culture+η×(archaeological evidence level index), where η is the correction coefficient; When the number of consecutive violation-free periods for employees in the transportation sector exceeds the threshold T, the risk dimension weight is reduced: w_risk'=w_risk×e^(-μ·t), where μ is the behavior improvement factor and t is the duration of violation-free periods.

3. The method according to claim 1, characterized in that, The value entropy aggregation in step (3) adopts the following method: Construct a dimensional relationship graph G = (V, E), where nodes represent value dimensions and edge weights reflect the correlation between dimensions; The dimension influence weight wi' is calculated using the PageRank algorithm, and the original weight wi is corrected: wi_final=0.7×wi+0.3×wi'.

4. The method according to claim 1, characterized in that, The business strategy optimization in step (4) includes: Automatically generate a set of precise marketing strategies for high-value capital flows in the financial sector (V≥V1 level); For top-level cultural relic protection data (V≥V2 level), trigger blockchain notarization and 3D digital derivative development instructions; For data on high-risk personnel in the transportation sector (V≤V3 level), customized training courses will be pushed to them and their dispatching privileges will be restricted.

5. A cross-domain adaptive hierarchical engine, applied to the method described in any one of claims 1-4, characterized in that... include: The domain feature extraction module is configured to: identify the fingerprint of the input data structure and match it with the domain template library; Extracting characteristics such as the frequency of traffic violations and training completion rate of employees in the transportation sector; In the field of cultural relic protection, characteristics such as the age of cultural relics, their preservation integrity, and the amount of academic citations are extracted. The weight dynamic optimization module has a built-in LSTM network to generate weight vectors in real time. Edge computing interface, supporting lightweight deployment on mobile terminals.

6. The engine according to claim 5, characterized in that, The domain template library includes: Transportation template: Initial weight w = [Business value 0.4, Timeliness value 0.3, Safety risk 0.3]; Cultural relic protection template: w = [Cultural value 0.5, Urgency of restoration 0.3, Related value 0.2]; Financial sector template: w = [fund penetration depth 0.6, industry relevance 0.4].

7. A multi-dimensional value dynamic fusion model, applied to the method described in any one of claims 1-4, characterized in that: Construct a four-dimensional evaluation matrix: D = [Business Value, Timeliness Value, Relevance Value, Security Risk]; The calculation of the associated value dimension adopts the industry association tree algorithm: in the financial field, funds are traced down to the Nth layer of associated entities; in the cultural relics field, the spatiotemporal association strength with cultural relics of the same category is calculated.

8. A cross-domain data value intelligent hierarchical evaluation system based on multi-dimensional dynamic weight optimization, characterized in that... include: The cross-domain adaptive hierarchical engine as described in claims 5-6; The multi-dimensional value dynamic fusion model as described in claim 7; The business strategy executor is configured to convert hierarchical results into API instruction sets and output them to external business systems.

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