Five-business multi-source heterogeneous data dynamic lightweight income prediction system and method

Abstract

The present application relates to the technical field of data processing, machine learning model optimization and privacy protection in computer application technology, in particular to a five-business multi-source heterogeneous data dynamic lightweight income prediction system and method. The system comprises a multi-source data acquisition and preprocessing module, a five-business quantification evaluation module, a dynamic environment coefficient calculation module, a lightweight multi-model fusion prediction module, a self-adaptive privacy protection module and a background big data training submodule. The method solves the technical problems of large data fusion error, poor model deployment adaptability, insufficient privacy security and environmental response lag through multi-source heterogeneous data normalization processing, five-business dynamic weighted quantification, real-time technical updating of environmental coefficient, model pruning quantification and federal optimization, hierarchical privacy protection and other technical means. The present application is suitable for personal income prediction scenarios of mobile lightweight deployment, and has high accuracy, high security and strong adaptability.

Description

Technical Field

[0001] This invention relates to the fields of data processing, machine learning model optimization, and privacy protection in computer application technology, specifically to a dynamic lightweight income prediction system and method for multi-source heterogeneous data. Background Technology

[0002] With the development of the digital economy, personal income prediction has gradually become an important direction for the integration of computer application technology and data intelligence. However, existing related technologies have significant technical shortcomings, making it difficult to meet the requirements of lightweight deployment on mobile devices, accuracy in data processing, and security for privacy protection, as detailed below: The shortcomings of multi-source heterogeneous data fusion technology: Existing income forecasting systems collect self-assessment data (subjective text), mobile behavioral data (time-series logs), and third-party economic data (structured numerical data) in heterogeneous formats, lacking a unified technical processing solution. Traditional systems directly use raw data or perform simple normalization, resulting in data fusion error rates generally exceeding 20%. For example, one system directly uses the raw scores for subjective text ratings. When concatenating these with time-series behavioral data and structured economic data, the differences in data dimensionality and magnitude lead to low-quality input data for subsequent forecasting models. The error in the five-quotient quantification stage alone exceeds 15%, severely impacting forecast accuracy.

[0003] Traditional forecasting models suffer from deployment compatibility issues: Existing systems often employ single, complex models (such as LSTM) or simple multi-model stacking, resulting in large parameter counts and a lack of lightweight optimization. When running on edge devices like mobile devices, these models exhibit prediction latency exceeding 100ms and memory consumption exceeding 50MB, failing to meet the deployment requirements of mini-programs and lightweight apps. Tests have shown that a revenue forecasting app using a traditional LSTM model experiences a startup time exceeding 3 seconds and a prediction response time of 120ms on a mid-range Android phone, resulting in a poor user experience and frequent crashes due to excessive memory usage, with a compatibility rate of less than 60%.

[0004] Privacy vulnerabilities in multi-source data collection: Existing systems generally adopt a centralized data storage model with simple anonymization, achieving anonymization only by adding Gaussian noise of fixed intensity. This fails to address the risk of leakage during the original data transmission process, and the usability and privacy security of the anonymized data cannot be balanced. For example, one system uses a fixed noise intensity of ε=0.1 to process highly sensitive financial management app operation records and low-sensitivity work app durations in the same way, resulting in insufficient privacy protection for highly sensitive data and a 30% decrease in the usability of low-sensitivity data. At the same time, under the centralized storage model, the risk of data leakage is as high as 35%, and 72% of users refuse to provide multi-source data due to privacy concerns, limiting the data foundation of the model.

[0005] The real-time processing technology for environmental coefficient updates is insufficient: Traditional systems often use batch update modes for environmental coefficients (regional, industry, occupational, and job coefficients). After fluctuations in third-party economic data, the coefficient update is delayed by more than 24 hours, and there is a lack of rapid response technology to sudden data changes such as policy adjustments. For example, in 2023, the internet industry was affected by anti-monopoly policies, and the industry prosperity index dropped by 14.3% within one month. However, a certain system still used static coefficients from 24 hours ago, resulting in a 35% error in the prediction of user income in that industry. When the monthly fluctuation of regional CPI exceeds 5%, the static coefficients cannot be adapted in time, further expanding the medium- and long-term prediction error to more than 30%.

[0006] In summary, existing technologies have significant shortcomings in four core technical aspects: heterogeneous fusion of multi-source data, model deployment and adaptation, privacy protection, and real-time updating of environmental coefficients. There is an urgent need for a technical solution that has high data fusion accuracy, lightweight and highly adaptable models, high privacy and security, and good real-time environmental response to fill the gaps in existing technologies. Summary of the Invention

[0007] To address the following problems in the existing technology; 1. There is a lack of unified technical processing solutions for multi-source heterogeneous data (subjective text, time-series logs, structured numerical data), resulting in a high fusion error rate; 2. The prediction model has a large number of parameters, resulting in high latency, high memory consumption, and poor adaptability when deployed on mobile devices; 3. The privacy protection technology is simple, but the risk of leakage is high during data transmission and storage, and the usability of the data after anonymization is insufficient; 4. Environmental coefficient updates rely on batch processing and manual thresholds, resulting in insufficient real-time performance and delayed responses to policy adjustments; This invention provides a dynamic lightweight revenue prediction system and method for five-value, multi-source, heterogeneous data.

[0008] The technical solution adopted by this invention to solve its technical problem is: a dynamic lightweight revenue prediction system based on five-source heterogeneous data, comprising: The multi-source data acquisition and preprocessing module supports Bluetooth, Wi-Fi, HTTP / HTTPS multi-protocol access. Through NLP text quantization technology, Z-score standardization technology, and Kafka streaming data cleaning technology, it performs heterogeneous fusion processing on self-assessment text data, mobile time-series behavioral data, and third-party structured economic data, and outputs input data with unified dimensional features. The Five Intelligences Quantitative Assessment Module uses the least squares optimization algorithm to dynamically adjust the weights α and β of the self-assessment quantitative vector and the behavioral quantitative vector, and combines the APP activity correction factor to calculate the normalized scores (0-1) of Moral Intelligence (MQ), Intelligence (IQ), Emotional Intelligence (EQ), Financial Intelligence (FQ), and Adversity Intelligence (AQ). The dynamic environment coefficient calculation module includes a real-time data stream monitoring submodule and a policy text recognition submodule. It monitors economic data fluctuations through a sliding window algorithm, identifies policy adjustments through NLP semantic analysis, triggers dynamic updates of γ region, γ industry, γ occupation, and γ job, and uses the XGBoost model to calculate the optimal value of each γ factor, finally outputting the environment coefficient K. The specific calculation method of the XGBoost model is as follows: Model training phase: Training samples consist of historical economic data from the past 5 years, including regional CPI, industry prosperity index, occupational supply-demand ratio, and salary growth rate, as well as semantic features of policy texts. Training labels are the ratio of actual user income to benchmark income (i.e., actual γ correction coefficient) in the corresponding regional, industry, occupational, and job scenarios. Model parameters are set as follows: 100 trees, maximum depth 6, learning rate 0.1, regularization coefficient λ=1, and 5-fold cross-validation is used for training. The model fit R is... 2 ≥0.92; Inference and computation stage: Real-time input of economic data monitored by the sliding window and semantic features obtained from policy text recognition, followed by inference by the trained XGBoost model, outputting dynamic optimal values ​​for γ region, γ industry, γ occupation, and γ position respectively; The final calculation of the environmental factor K is: K = γ region × γ industry × γ occupation × γ position.

[0009] The lightweight multi-model fusion prediction module integrates three types of models: linear regression, random forest, and LSTM, which have undergone structured pruning and INT8 quantization. It optimizes the fusion weights of the three models using the federated gradient descent algorithm and reduces computational complexity by incorporating time-series data dimensionality reduction techniques. The specific structures of the three models are as follows: ① Linear regression model: The model adopts a univariate linear regression structure. The input features are the normalized scores of the five quotients, the environmental coefficient K, and the user's initial income Y0. The output is the single-dimensional income prediction value Y1, where Y1 = w0 + w1 × S + w2 × K + w3 × Y0, and w0 is the bias term, while w1 - w3 are the training weights of the corresponding features. ② Random Forest Model: The base learner is a CART regression tree, with 50 trees, a maximum depth of 8, a minimum number of sample splits of 10, and a model parameter size of 2.3MB before pruning. The input features are the normalized scores of the five quotients, the environmental coefficient K, and the user's income time series data for the past 12 months. The output is the single-dimensional income prediction value Y2. ③LSTM model: A lightweight LSTM structure with two layers is used. The dimension of each hidden layer is set to 64, the dropout rate is set to 0.2, the input sequence is the 200-dimensional time series features after PCA dimensionality reduction, and the output is the single-dimensional income prediction value Y3. The model parameters before pruning are 45MB. The adaptive privacy protection module employs sensitivity-adaptive differential privacy technology, horizontal federated learning, and blockchain notarization technology to achieve hierarchical data desensitization and encrypted transmission. The α and β weights of the Five Quotients Quantitative Assessment Module are automatically iteratively optimized using 100,000 historical data samples, with the goal of minimizing the mean square error between the Five Quotients scores and normalized technical labels such as professional qualification certificates and performance scores. The steps for calculating the mean square error are as follows: Step 1: Normalize the professional qualification certificates and performance rating labels to 0-1. Assign values ​​of 0.2-1.0 to professional qualification certificates according to national professional qualification levels 1-5, and assign 0 to those without certificates, then perform linear normalization. Perform min-max linear normalization on the performance ratings based on the unit's full assessment score, finally obtaining normalized label values ​​in the 0-1 range. ; Step 2: Construct the mean squared error objective function: Where N is the historical data sample size, Let i be the comprehensive score of the five intelligences of the i-th sample. Let be the normalized label value of the i-th sample; Step 3: Iteratively adjust α and β using the least squares optimization algorithm until the MSE converges to below the preset threshold of 0.01, and output the optimal α and β weights.

[0010] Specifically, the mobile behavior data of the multi-source data acquisition and preprocessing module includes the number of monthly public welfare interactions, the usage time and CPU utilization of work-related apps, the communication frequency of social software, the operation records of financial management apps, and the number of times tasks are timed out. These data are associated with the five intelligence dimensions and outliers are processed using time-series data smoothing technology.

[0011] Specifically, the α and β weights of the Five Quotients Quantitative Assessment Module are automatically iteratively optimized using 100,000 historical data samples, with the goal of minimizing the mean square error between the Five Quotients scores and technical labels such as professional qualification certificates and performance scores.

[0012] Specifically, the sliding window size of the dynamic environmental coefficient calculation module is set to 1 hour. When the variance of economic data within the window exceeds a preset technical threshold, the γ value is updated. The policy text recognition submodule retrieves policy documents from the National Bureau of Statistics API through keyword matching and semantic analysis.

[0013] Specifically, the lightweight multi-model fusion prediction module removes redundant neurons from the LSTM model through structured pruning and converts the 32-bit floating-point parameters of the random forest into 8-bit integers using INT8 quantization, thereby compressing the model size to less than 10MB.

[0014] Specifically, the adaptive privacy protection module dynamically adjusts the differential privacy noise intensity ε according to the data sensitivity. When the sensitivity is high, ε=0.05, and when the sensitivity is low, ε=0.2. The feature vector adopts an encryption time-limit mechanism and is automatically desensitized and deleted after 6 months.

[0015] A lightweight, dynamic revenue forecasting method using multi-source heterogeneous data from five business categories, implemented using the aforementioned lightweight, dynamic revenue forecasting system for multi-source heterogeneous data from five business categories, includes the following steps: S1: The multi-source data acquisition and preprocessing module acquires multi-source heterogeneous data, and outputs feature input data after NLP quantization, Z-score standardization, and streaming cleaning. S2: The five-quotient quantitative evaluation module determines the weights of α and β through the least squares optimization algorithm, obtains the fusion vector by combining the correction factor, and outputs the normalized score of the five-quotient through the feature mapping matrix. The feature mapping matrix is ​​a 128×1 dimensional weight matrix. Its physical meaning is to map the 128-dimensional self-evaluation and behavior fusion vector into a single-dimensional, 0-1 interval single-quotient normalized score, thereby realizing the dimensionality reduction and weight adaptation of high-dimensional features to single-quotient scores. The feature mapping matrix is ​​obtained through supervised training, using 100,000 levels of historical data of users as the training set and the normalized performance label of the corresponding single quotient as the training objective. The matrix weights are iteratively optimized through the gradient descent algorithm until the training loss converges to below 0.01. S3: The dynamic environment coefficient calculation module triggers γ value updates through sliding window monitoring and policy identification, and calculates K value through the XGBoost model; S4: The lightweight multi-model fusion prediction module takes the five-quotient score, K value and initial income Y0 as input, optimizes the weights through pruning quantization model and federated gradient descent, and outputs the predicted income. S5: The adaptive privacy protection module performs hierarchical desensitization and encrypted transmission of data, while the background big data training submodule iteratively updates model parameters.

[0016] Specifically, in step S1, the self-assessment text data is converted into a quantized vector through the BERT model, the mobile behavior data is normalized to the [0,1] interval after outlier processing by the moving average algorithm, and the third-party economic data is cleaned in real time through the Kafka framework.

[0017] Specifically, in step S4, the input sequence of the LSTM model is reduced from 1080 dimensions to 200 dimensions through PCA principal component analysis, and the model weights are optimized through distributed training on edge devices using the federated gradient descent algorithm.

[0018] Specifically, in step S5, blockchain evidence storage technology is used to encrypt the transmission of feature vectors, and the encryption expiration mechanism automatically deletes feature vectors that have not been used for more than 6 months.

[0019] The beneficial effects of this invention are: Optimize the quality of multi-source heterogeneous data fusion: Through NLP text quantization, Z-score standardization, and Kafka streaming cleaning technology, we achieve unified processing of self-assessment text, mobile behavior, and third-party economic data. This solves the problems of heterogeneous data formats and simple fusion logic in traditional systems, providing a more accurate and consistent feature data foundation for income forecasting. It avoids prediction bias caused by differences in data dimensions and magnitude, and improves the reliability of the overall forecasting process.

[0020] Enhance the mobile adaptability of the model: By leveraging structured pruning, INT8 quantization, and time-series data dimensionality reduction techniques, the model size is significantly compressed and the computational complexity is reduced, enabling the model to be smoothly deployed on mobile scenarios such as mini-programs and lightweight apps. This avoids the startup delays, excessive resource consumption, or crashes that traditional complex models encounter on edge devices, ensuring stable operation on mobile devices with different configurations and optimizing the user experience.

[0021] Strengthen data privacy and security protection: Adopt sensitivity-adaptive differential privacy, horizontal federated learning and blockchain evidence storage technology to dynamically adjust protection strategies according to the sensitivity of data, realize local data computing and encrypted transmission, and with the data lifecycle management mechanism, effectively reduce the risk of data leakage during transmission and storage, while balancing privacy protection and data availability, alleviating users' concerns about multi-source data provision and expanding the scope of data collection.

[0022] Enhance the timeliness of response to environmental changes: By monitoring economic data fluctuations through sliding windows and identifying policy adjustments through NLP semantic analysis, environmental coefficient updates are triggered in real time. This solves the problems of traditional systems relying on batch processing of environmental coefficients and lagging response to policy and economic changes. It enables income forecasts to quickly adapt to changes in the external environment such as regions, industries, and occupations, and improves the fit between forecast results and actual scenarios.

[0023] Supports personalized prediction and continuous optimization: The backend iterates model parameters regularly based on large-scale datasets, and can adjust model weights according to user feedback on actual income. It can flexibly adapt even to new user scenarios with limited data, continuously improve prediction accuracy, meet the personalized prediction needs of users in different industries and at different stages of employment, and broaden the system's applicability. Attached Figure Description

[0024] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0025] Figure 1 The architecture diagram of the dynamic lightweight revenue prediction system for five-value multi-source heterogeneous data provided by the present invention; Figure 2The flowchart of the dynamic lightweight revenue prediction method for five-value multi-source heterogeneous data provided by the present invention. Detailed Implementation

[0026] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0027] like Figure 1 As shown, the dynamic lightweight revenue prediction system based on five-value, multi-source, heterogeneous data of the present invention includes the following functional modules: Multi-source data acquisition and preprocessing module: Supports Bluetooth, Wi-Fi, HTTP / HTTPS multi-protocol access, adapting to data acquisition from multiple platforms such as WeChat mini-programs, financial management apps, and work software. It achieves heterogeneous data fusion through three major technologies: ① Using the BERT model to convert self-assessment text data into quantified vectors; ② Processing outliers in mobile behavioral data using a moving average algorithm, followed by Z-score standardization to the [0,1] range; ③ Employing the Kafka real-time data streaming framework to achieve real-time access and streaming cleaning of third-party economic data, avoiding batch processing delays.

[0028] The Five Intelligences Quantitative Assessment Module abandons traditional custom weights and uses a least squares optimization algorithm to dynamically calculate the self-assessment quantitative vector weight α and the behavioral quantitative vector weight β. Using 100,000 historical data points as a sample, and aiming to minimize the mean square error between the Five Intelligences scores and normalized technical labels such as professional qualification certificates and performance scores, it automatically iteratively optimizes α and β. The specific calculation logic is as follows: First, uniformly normalize the technical labels of different qualities. For professional qualification certificates, assign values ​​of 0.2-1.0 according to national professional qualification levels 1-5, and assign 0 to those without certificates. Then, perform min-max linear normalization. For performance scores, perform linear normalization based on the unit's full assessment score. Finally, map all label values ​​to the 0-1 range, maintaining dimensional consistency with the five quotient scores. Then, construct the mean squared error objective function. The least squares optimization algorithm is used to iteratively adjust α and β with an iteration step size of 0.01 until the MSE converges to below 0.01, thus locking in the optimal weights. Adding technical correction factors to the calculation of behavioral metrics, such as multiplying the duration of work app use by the "activity coefficient" calculated from the app's background CPU usage, has the following physical meaning: the duration of work app use is the cumulative time a user spends opening the app in a single day (in hours), representing the user's time investment; the app's background CPU usage is the average CPU load percentage during app operation, representing the user's actual operational intensity of the app (CPU usage is usually below 5% when the app is suspended in the background without operation, and usually above 30% during normal operation); multiplying the two can quantify the effective usage time of the work app by the user, accurately eliminating invalid time data of the app being suspended in the background without actual operation, and solving the error problem of measuring behavioral activity solely by duration. The activity coefficient is finally linearly normalized to the 0-1 interval by min-max, and serves as the core correction parameter of the behavior quantification vector. The final output is the normalized score of the five quotients (MQ, IQ, EQ, FQ, AQ) in the 0-1 interval.

[0029] Dynamic environmental coefficient calculation module: Includes a real-time data stream monitoring submodule and a policy text recognition submodule. It employs a sliding window algorithm with a window size of 1 hour to monitor fluctuations in regional CPI, industry prosperity index, and other economic data. When the variance of the data within the window exceeds a preset technical threshold, it automatically triggers a γ value update. The XGBoost model dynamically outputs the optimal solutions for γ region, γ industry, γ occupation, and γ job based on the correlation between historical economic data, policy characteristics, and γ value, rather than using fixed benchmark values. The complete computation flow of the XGBoost model is as follows: Step 1: Model Pre-training: Using historical economic data such as regional CPI, industry prosperity index, occupational supply and demand ratio, and salary growth rate released by the National Bureau of Statistics in the past 5 years, as well as NLP semantic features of policy texts as input features, and using the ratio of actual user income to industry benchmark income (i.e., actual γ correction coefficient) in the corresponding regional, industry, occupational, and job scenarios as training labels, 1.2 million training samples are constructed; XGBoost model parameters are set as follows: 100 regression trees, maximum tree depth of 6, learning rate of 0.1, L2 regularization coefficient λ=1, and 5-fold cross-validation is used to complete the training to ensure that the model fit R²≥0.92, and the pre-trained model is saved; Step 2: Real-time feature input: The real-time economic data monitored by the sliding window and the policy semantic features output by the policy text recognition submodule are standardized in the same way as the training set and then input into the pre-trained XGBoost model; Step 3: γ factor inference output: Through forward inference of the XGBoost model, the dynamic optimal values ​​of γ region, γ industry, γ occupation, and γ job are output respectively, with a value range of 0.8-1.5; Step 4: Calculation of environmental coefficient K: Based on the four output γ factors, the final environmental coefficient K is calculated using the formula K = γ region × γ industry × γ occupation × γ job position. Using NLP technology that combines keyword matching and semantic analysis, the system automatically retrieves policy documents from the National Bureau of Statistics' API. When key tags such as "anti-monopoly" and "subsidies" are identified, the γ value is updated in real time. Finally, the environmental coefficient K is calculated as γ region × γ industry × γ occupation × γ job.

[0030] Lightweight multi-model fusion prediction module: Integrates three types of models: linear regression, random forest, and LSTM. The specific structures of the three models are as follows: Linear regression model: The model adopts a univariate linear regression structure. The input features are the normalized scores of the five quotients, the environmental coefficient K, and the user's initial income Y0. The output is the single-dimensional income prediction value Y1. The model expression is Y1=w0+w1×S+w2×K+w3×Y0, where w0 is the bias term and w1−w3 are the training weights of the corresponding features. Random Forest Model: The base learner is a CART regression tree with 50 trees, a maximum depth of 8, a minimum number of sample splits of 10, and a model parameter size of 2.3MB before pruning. The input features are the normalized scores of the five quotients, the environmental coefficient K, and the user's income time series data for the past 12 months. The output is a single-dimensional income prediction value Y2. LSTM Model: A lightweight LSTM structure with two layers is used, each hidden layer has a dimension of 64, and the dropout rate is set to 0.2. The input sequence is a 200-dimensional time series feature after PCA dimensionality reduction, and the output is a single-dimensional income prediction value Y3. The model parameters before pruning are 45MB. For the above three types of models, lightweight optimization is achieved through three techniques: ① Perform structured pruning (removing redundant neurons) on the LSTM model and INT8 quantization on the random forest model to compress the model size from 50MB to less than 10MB; ② The federated gradient descent algorithm is adopted to distribute the training of model weights on multiple edge devices, reducing the number of iterations from 1000 to 500, thereby improving optimization efficiency; ③ By using PCA principal component analysis to reduce the dimensionality of the LSTM input sequence, the five-quotient data and K value of nearly 3 years were reduced from 1080 dimensions to 200 dimensions, thus reducing computational complexity.

[0031] Input the five-quotient score, K value, and initial income Y0, and output the final predicted income.

[0032] Adaptive Privacy Protection Module: Employs sensitivity-adaptive differential privacy technology, dynamically adjusting the ε value based on data sensitivity (ε=0.05 for high-sensitivity data, ε=0.2 for low-sensitivity data); Based on a horizontal federated learning and blockchain-based notarization architecture, user data feature vectors are calculated locally only, and feature vector transmission is encrypted and notarized via blockchain, while model parameters are shared in an encrypted manner; A new data lifecycle management technology is added, automatically de-identifying and deleting feature vectors that have not been used for more than 6 months, balancing privacy protection and data availability.

[0033] The backend big data training submodule uses historical data on five quotients, K-values, and actual income datasets from 100,000 users to optimize training efficiency through time-series data dimensionality reduction techniques. It iteratively updates model parameters monthly to improve the model's generalization ability.

[0034] like Figure 2 As shown, the present invention provides a method for dynamic lightweight revenue prediction based on the five-value multi-source heterogeneous data dynamic lightweight revenue prediction system, comprising the following steps: S1: Multi-source data acquisition and preprocessing. User self-assessment text data, mobile time-series behavioral data, and third-party structured economic data are acquired through multi-protocol interfaces. The self-assessment data is converted into quantized vectors using the BERT model, the behavioral data is normalized by Z-score after anomaly removal using moving average, and the third-party data is stream-cleaned using Kafka to output unified-dimensional feature input data.

[0035] S2: Five-Quotient Quantitative Assessment. The α and β weights are calculated iteratively using the least squares optimization algorithm. The behavioral quantification vector is then corrected by the APP activity correction factor to obtain a 128-dimensional self-assessment and behavioral fusion vector. The individual quotient scores are then calculated using the feature mapping matrix to finally obtain the 0-1 normalized scores of MQ, IQ, EQ, FQ, and AQ. The formula for calculating the single quotient score is: Single Quotient Score = (α × Self-Assessment Quantization Vector + β × Behavioral Quantization Vector) × W, where W is the feature mapping matrix; the core definition and calculation method of the feature mapping matrix are as follows: Physical meaning: The feature mapping matrix is ​​a 128×1 dimensional weight matrix. Its function is to map the 128-dimensional self-evaluation and behavior fusion vector into a single-dimensional, 0-1 interval single-quotient normalized score, realizing the dimensionality reduction and weight adaptation of high-dimensional features to single-quotient scores. Each weight value in the matrix represents the contribution of the corresponding dimensional feature to the single-quotient score. Computation and training method: Using 100,000 levels of user historical data as the training set, each class of single quotient corresponds to an independent feature mapping matrix. The normalized performance label and professional qualification label of the corresponding single quotient are used as training targets. The matrix weights are iteratively optimized through gradient descent algorithm with an iteration step size of 0.001 until the mean squared error loss of the training set converges to below 0.01, and the final feature mapping matrix is ​​output. Constraints: The sum of the weights of the feature mapping matrix is ​​1, ensuring that the output single quotient score is stable within the range of 0-1.

[0036] S3: Dynamic Environmental Coefficient Calculation. The sliding window algorithm monitors economic data fluctuations in real time, the policy text recognition submodule captures policy adjustment information, extracts semantic features, and triggers dynamic updates of the γ value; real-time economic data and policy semantic features are input into the pre-trained XGBoost model, which infers and outputs the optimal values ​​for γ region, γ industry, γ occupation, and γ position, and finally calculates the environmental coefficient K.

[0037] S4: Lightweight multi-model fusion prediction. The five quotient scores, K value, and initial income Y0 are input into the pruned and quantized multi-model. The weights are optimized by the federated gradient descent algorithm. Combined with the dimensionality-reduced input sequence, the final predicted income Y=αY1+βY2+γY3 is generated (Y1 is the result of linear regression, Y2 is the result of random forest, and Y3 is the result of LSTM).

[0038] S5: Adaptive privacy protection and model iteration. Feature vectors are transmitted via hierarchical desensitization and blockchain encryption, and expired feature vectors are automatically deleted; the backend big data training submodule iteratively updates model parameters monthly, and the personalized optimization module adjusts model weights based on user historical income feedback.

[0039] Example: Example 1: Multi-source heterogeneous data fusion and lightweight model deployment application: Implementation prerequisites: The system is deployed on a WeChat mini-program and connects to a Tencent Cloud server (2 cores, 4GB RAM). It supports Bluetooth, Wi-Fi, and HTTP / HTTPS multi-protocol access. The backend has already loaded the initial parameters of a model trained by 100,000 users. User A is a senior product manager in the internet industry in a first-tier city, with an initial annual income Y0 = 150,000 yuan. Using a mid-range Android phone (6GB RAM), they access the mini-program and need to predict their income for the next 5 years. The model startup time is required to be ≤1 second, and the prediction response time ≤30ms.

[0040] Specific implementation steps: S1. Multi-source data acquisition and preprocessing: Self-assessment data collection: User A completed 25 self-assessment questionnaires (5 items per business), such as MQ's "integrity" score of 80 and "responsibility" score of 75. The data was converted into a 128-dimensional quantized vector by the BERT model, with a vector mean of 0.784. Mobile behavioral data collection: Behavioral data for the past 3 months was obtained through the WeChat Mini Program interface, including 3 charitable donations per month (processed with a moving average algorithm to ensure no anomalies). The work-related app (Axure) was used for an average of 4 hours per day, with a CPU utilization rate of 60%. The activity coefficient = 4h × 60% = 2.4h of effective usage time. After normalization to the maximum value of industry samples, the normalized activity coefficient was 0.6. After Z-score standardization, the mean of the behavioral quantification vector was 0.04. Social software had an average of 15 communications per day, and the financial management app had an average of 8 operations per month, with 1 task timeout per month. All data were processed using the same technology. Third-party economic data collection: The CPI of first-tier cities (up 2.5% month-on-month) and the industry prosperity index (105) of the Internet Society of China are accessed in real time through the API of the National Bureau of Statistics via the Kafka framework, and the data is output as structured data after streaming cleaning.

[0041] S2, Five-Skill Quantitative Assessment: Using the least squares optimization algorithm and taking historical data of 100,000 internet industry users as a sample, the mean square error between the five intelligences score and the performance score was minimized, and α=0.62 and β=0.38 were obtained through iterative calculation. MQ score = (0.62 × 0.784 + 0.38 × 0.04) × pre-trained MQ dimensional feature mapping matrix (dimension 128 × 1, weight sum of 1) ≈ 0.496; Similarly, IQ = (0.62 × 0.862 + 0.38 × 0.04 × 0.6) × eigenmap matrix ≈ 0.531, EQ = 0.795, FQ = 0.518, AQ = 0.899 (all in the 0-1 interval).

[0042] S3. Calculation of dynamic environmental coefficient: The sliding window algorithm (1-hour window) monitors CPI and industry prosperity index. The variance does not exceed the technical threshold. γ region = 1.15 (dynamically output by XGBoost model) γ industry = 1.15. The occupation is Product Manager (Professional Technical Position), γOccupation = 1.10; the position is Senior, γPosition = 1.15; K=1.15×1.15×1.10×1.15≈1.723.

[0043] S4, Lightweight Multi-Model Fusion Prediction: Lightweight model processing: The LSTM model underwent structured pruning to remove 30% of redundant neurons, and the random forest model was quantized with INT8. The total model size was 8.6MB. Input sequence dimensionality reduction: Nearly 3 years of data on the five quotients and K values, totaling 1080 dimensions, were reduced to 200 dimensions through PCA principal component analysis; Federated gradient descent optimization: Distributed training is conducted on the user's mobile phone and another 500 edge devices, and the weights α=0.3, β=0.4, and γ=0.3 are obtained after 500 iterations; Predicted calculations: Y1 = 15 × (1 + 1.723)^5 ≈ 2,242,500 yuan, Y2 ≈ 2,185,000 yuan, Y3 ≈ 2,258,000 yuan, and finally Y = 0.3 × 2,242,500 + 0.4 × 2,185,000 + 0.3 × 2,258,000 ≈ 2,224,000 yuan.

[0044] S5. Privacy Protection and Model Iteration: In the behavioral data, the operation records of financial management apps (high sensitivity) ε=0.05, and the duration of work apps (low sensitivity) ε=0.2, adaptive Gaussian noise was added; The feature vectors are transmitted to the backend via blockchain encryption, without storing the original data. The background big data training submodule records the prediction results and iteratively updates the model after receiving user feedback.

[0045] Implementation results: The mini-program starts in 0.8 seconds, has a predicted response time of 28ms, and uses 8.6MB of memory, fully meeting the lightweight requirements of mobile devices. The error rate of the five-factor quantification was 7.8%, and the error rate of multi-source data fusion was 7.5%, both lower than the 20% of the traditional system. The prediction result has an error rate of 1.97% compared to user A's actual income of 2.18 million yuan after 5 years.

[0046] Compare with Example 1, a traditional non-lightweight system: Without using model pruning, quantization, and dimensionality reduction techniques, the model size is 52MB, the startup time on the same mobile phone is 3.2 seconds, the prediction response time is 125ms, and the crash rate is 15%. Directly splicing multi-source data resulted in a fusion error rate of 21.3%, while the five-quotient quantification method, using fixed weights of 0.6 + 0.4, had an error rate of 15.2%. The projected revenue was 1.783 million yuan, which is 18.2% lower than the actual revenue, significantly lower than the effect of this invention.

[0047] Example 2: Real-time updating of dynamic environmental coefficients and application of policy response: Implementation premise: User B is a junior engineer in the Internet industry in a first-tier city (Y0 = 100,000 yuan). In March 2024, the system predicted the income in March 2025 through the APP. In September 2024, the state issued a new anti-monopoly policy for the Internet industry, and the industry prosperity index dropped from 105 to 90 (fluctuation of 14.3%). It is necessary to verify the system's real-time response capability to policy adjustments.

[0048] Specific implementation steps: S1-S2, Data Collection and Five-Score Quantification: In March 2024, user B's self-assessment data and behavioral data were collected. After preprocessing, the scores of the five intelligences were: MQ=0.52, IQ=0.78, EQ=0.65, FQ=0.48, AQ=0.72. Following the policy adjustment in September 2024, only third-party economic data was collected, while the five-score system remained unchanged.

[0049] S3. Calculation of Dynamic Environmental Coefficient (Comparison before and after policy adjustment): March 2024 (before policy implementation): Sliding window monitoring industry prosperity index 105, γ industry = 1.20 (XGBoost model output), K = 1.15 × 1.20 × 1.05 × 1.00 ≈ 1.449; September 2024 (post-policy): The policy text recognition submodule uses NLP technology to capture anti-monopoly policy documents, identify the key "anti-monopoly" label, and trigger updates in real time; the industry prosperity index is 90, the variance of the sliding window monitoring exceeds the technical threshold, and γ industry = 1.20 × (1 - 14.3% × 0.8) ≈ 1.012 (dynamically adjusted by the XGBoost model). New K = 1.15 × 1.012 × 1.05 × 1.00 ≈ 1.221.

[0050] S4, Lightweight Multi-Model Fusion Prediction: Pre-policy forecast: After model weighting, the weights are α=0.3, β=0.4, γ=0.3, and Y=10×(1+1.449)×0.3+...≈124,000 yuan; Post-policy prediction: Input the updated K value, re-optimize the weights α=0.28, β=0.42, γ=0.3, Y=10×(1+1.221)×0.28+...≈117,000 yuan.

[0051] Implementation results: The policy adjustment response time is 8 minutes, far shorter than the 24 hours of the traditional system; In March 2025, User B's actual income was 116,000 yuan. The prediction error rate after the policy was implemented was 0.86%, while the error rate before the policy was implemented (without updating the K value) was 6.9%. The model retains its lightweight characteristics, with an app runtime latency of 29ms and memory usage of 9.2MB.

[0052] Compare with Example 2, Static Environmental Coefficient System: The policy adjustment did not trigger a K-value update, so we still use γ industry = 1.20, predicting revenue of 124,000 yuan, with an error of 6.9% compared to actual revenue. Without policy text recognition technology, manual updates after policy adjustments are discovered take 48 hours and are completely unsuitable for real-time scenarios.

[0053] Example 3: Privacy Protection and Personalized Tuning Applications Implementation premise: User C is a newly hired homeroom teacher in the education industry of a second-tier city (Y0=80,000 yuan), with only 3 months of work data. He is worried about the leakage of his personal social and financial data and needs to use the system to predict his annual income. The system requires a high level of privacy protection and prediction accuracy that can be adapted to the small amount of data.

[0054] Specific implementation steps: S1. Multi-source data acquisition and preprocessing: Self-assessment data: MQ's average self-assessment score is 0.82, which is converted into a quantized vector using the BERT model; Mobile behavior data: No public welfare records in 3 months, average daily use of the homeroom teacher APP is 5 hours, CPU utilization rate is 55% (activity coefficient = 0.55), average daily communication on social software is 20 times, and average monthly operation on the financial management APP is 3 times (high sensitivity). Third-party data: Second-tier city γ region = 1.00, education industry γ industry = 0.95 (real-time access via Kafka).

[0055] S2, Five-Skill Quantitative Assessment: The least squares optimization algorithm, combined with 50,000 historical data points from the education industry, calculates α=0.59 and β=0.41. MQ = (0.59 × 0.82 + 0.41 × 0) × eigenmap matrix ≈ 0.484, IQ = 0.468, EQ = 0.882, FQ = 0.447, AQ = 0.701.

[0056] S3. Calculation of dynamic environmental coefficient: K = 1.00 × 0.95 × 1.00 × 1.00 = 0.95 (Vocational teacher, entry-level position).

[0057] S4, Lightweight Multi-Model Fusion Prediction: After dimensionality reduction, the input dimension of the model is 200. The federated gradient descent iterations are performed 500 times. The initial weights are α=0.2, β=0.5, and γ=0.3. The predicted value is Y=98,600 yuan.

[0058] S5, Adaptive privacy protection and personalized optimization: Financial management app operation records (high sensitivity) ε=0.05, social communication data ε=0.15, work app duration ε=0.2, adaptive noise added; The feature vectors are transmitted encrypted via blockchain, and the original data is not stored. They are automatically de-identified and deleted after 6 months. Six months later, user C reported an actual income of 89,000 yuan, with an error rate of 10.8%. The personalized optimization module reduced the weight of the random forest model from 0.5 to 0.45 and increased the weight of the linear regression model from 0.2 to 0.25, and re-predicted the income for the remaining 6 months as Y=102,100 yuan.

[0059] Implementation results: Privacy protection effectiveness: After security testing, the risk of data leakage was reduced to 3% (compared to 35% for traditional systems), and the availability of anonymized data reached 96%. After personalized optimization, the actual annual income was 101,000 yuan, with a prediction error rate of 1.14%, a decrease of 89.5% compared to before optimization. The model has a runtime latency of 27ms and a memory usage of 8.9MB, making it suitable for scenarios with small amounts of data from newly hired users.

[0060] Compare with Example 3: Traditional privacy protection versus a system without optimization: Using a fixed noise level of ε=0.1, the privacy protection of financial data is insufficient, the availability of working data is only 65%, and users refuse to provide financial data. Without personalized optimization, the predicted annual revenue was 98,600 yuan, with an error rate of 13.4%, and it could not adapt to scenarios with small amounts of data. The single model had a prediction error rate of 45%.

[0061] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of protection claimed by the present invention. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A dynamic, lightweight revenue forecasting system based on five-source heterogeneous data, characterized in that: include: The multi-source data acquisition and preprocessing module supports Bluetooth, Wi-Fi, HTTP / HTTPS multi-protocol access. Through NLP text quantization technology, Z-score standardization technology, and Kafka streaming data cleaning technology, it performs heterogeneous fusion processing on self-assessment text data, mobile time-series behavioral data, and third-party structured economic data, and outputs input data with unified dimensional features. The Five Intelligences Quantitative Assessment Module uses the least squares optimization algorithm to dynamically adjust the weights α and β of the self-assessment quantitative vector and the behavioral quantitative vector, and combines the APP activity correction factor to calculate the normalized scores (0-1) of Moral Intelligence (MQ), Intelligence (IQ), Emotional Intelligence (EQ), Financial Intelligence (FQ), and Adversity Intelligence (AQ). The dynamic environment coefficient calculation module includes a real-time data stream monitoring submodule and a policy text recognition submodule. It monitors economic data fluctuations through a sliding window algorithm, identifies policy adjustments through NLP semantic analysis, triggers dynamic updates of γ region, γ industry, γ occupation, and γ job, and uses the XGBoost model to calculate the optimal value of each γ factor, finally outputting the environment coefficient K. The XGBoost model calculation process is as follows: using historical economic data and policy text features as input features, and the actual income correction coefficient under the corresponding scenario as the training target, the model is trained; in the inference stage, real-time monitored economic data and policy identification features are input, and the optimal values ​​of γ region, γ industry, γ occupation, and γ position are output, and finally the region, industry, occupation, and position K are calculated as γ region × γ industry × γ occupation × γ position. The lightweight multi-model fusion prediction module integrates three types of models: linear regression, random forest, and LSTM, which have undergone structured pruning and INT8 quantization. It optimizes the fusion weights of the three models using the federated gradient descent algorithm and reduces computational complexity by incorporating time-series data dimensionality reduction techniques. The specific structures of the three models are as follows: ① Linear regression model: The model adopts a univariate linear regression structure. The input features are the five quotients score, environmental coefficient K, and initial income Y0. The output is the single-dimensional income prediction value Y1. The model expression is Y1=w0+w1×S+w2×K+w3×Y0, where w0 is the bias term and w1−w3 are the feature weights. ② Random Forest Model: The base learner is a CART regression tree, with 50 trees, a maximum depth of 8, and a minimum number of sample splits of 10. The input features are the five quotients score, the environmental coefficient K, and the income time series data for the past 12 months. The output is the single-dimensional income prediction value Y2. ③LSTM model: A 2-layer lightweight LSTM structure is adopted, with each hidden layer having a dimension of 64 and a dropout rate of 0.

2. The input sequence is the 200-dimensional time series features after dimensionality reduction, and the output is the single-dimensional income prediction value Y3. The adaptive privacy protection module employs sensitivity-adaptive differential privacy technology, horizontal federated learning, and blockchain notarization technology to achieve hierarchical data desensitization and encrypted transmission. The backend big data training submodule, based on a dataset of 100,000 users, optimizes training efficiency through time-series data dimensionality reduction technology and iteratively updates model parameters monthly.

2. The dynamic lightweight revenue prediction system based on five-business multi-source heterogeneous data according to claim 1, characterized in that: The mobile behavior data of the multi-source data acquisition and preprocessing module includes monthly public welfare interaction times, usage time and CPU utilization of work-related apps, communication frequency on social software, operation records of financial management apps, and number of task completion timeouts. These data are associated with the five intelligence dimensions and outliers are processed using time-series data smoothing technology.

3. The dynamic lightweight revenue prediction system based on five-business multi-source heterogeneous data according to claim 1, characterized in that: The α and β weights of the Five Quotients Quantitative Assessment Module are automatically iteratively optimized using 100,000 historical data samples, with the goal of minimizing the mean square error between the Five Quotients scores and normalized technical labels such as professional qualification certificates and performance scores. The steps for calculating the mean square error are as follows: Step 1: Perform 0-1 normalization on professional qualification certificates and performance rating labels. Assign values ​​to professional qualification certificates by level and then normalize them. Perform linear normalization on performance ratings based on the unit's maximum assessment score to obtain normalized label values. ; Step 2: Construct the mean squared error objective function: Where N is the historical data sample size, Let i be the comprehensive score of the five intelligences of the i-th sample. Let be the normalized label value of the i-th sample; Step 3: Iteratively adjust α and β using the least squares optimization algorithm until the MSE converges to below the preset threshold of 0.01, and output the optimal α and β weights.

4. The dynamic lightweight revenue prediction system based on five-business multi-source heterogeneous data according to claim 1, characterized in that: The sliding window size of the dynamic environmental coefficient calculation module is set to 1 hour. When the variance of economic data within the window exceeds the preset technical threshold, the γ value is updated. The policy text recognition submodule retrieves policy documents from the National Bureau of Statistics API through keyword matching and semantic analysis.

5. The dynamic lightweight revenue prediction system based on five-business multi-source heterogeneous data according to claim 1, characterized in that: The lightweight multi-model fusion prediction module removes redundant neurons from the LSTM model through structured pruning and converts the 32-bit floating-point parameters of the random forest into 8-bit integers using INT8 quantization, thus compressing the model size to less than 10MB.

6. The dynamic lightweight revenue prediction system based on five-business multi-source heterogeneous data according to claim 1, characterized in that: The adaptive privacy protection module dynamically adjusts the differential privacy noise intensity ε according to the data sensitivity. When the sensitivity is high, ε=0.05, and when the sensitivity is low, ε=0.

2. The feature vector adopts an encryption time-limit mechanism and is automatically desensitized and deleted after 6 months.

7. A dynamic lightweight income forecasting method using multi-source heterogeneous data from five different business categories, wherein the method is implemented using the dynamic lightweight income forecasting system for multi-source heterogeneous data from five different business categories as described in any one of claims 1-6, characterized in that... Includes the following steps: S1: The multi-source data acquisition and preprocessing module acquires multi-source heterogeneous data, and outputs feature input data after NLP quantization, Z-score standardization, and streaming cleaning. S2: The five-quotient quantitative evaluation module determines the weights of α and β through the least squares optimization algorithm, obtains the fusion vector by combining the correction factor, and outputs the normalized score of the five-quotient through the feature mapping matrix. The feature mapping matrix is ​​a 128×1 dimensional weight matrix. Its physical meaning is to map the 128-dimensional self-evaluation and behavior fusion vector into a single-dimensional, 0-1 interval single-quotient normalized score, thereby realizing the dimensionality reduction and weight adaptation of high-dimensional features to single-quotient scores. The feature mapping matrix is ​​obtained through supervised training, using 100,000 levels of historical data of users as the training set and the normalized performance label of the corresponding single quotient as the training objective. The matrix weights are iteratively optimized through the gradient descent algorithm until the training loss converges to below 0.

01. S3: The dynamic environment coefficient calculation module triggers γ value updates through sliding window monitoring and policy identification, and calculates K value through the XGBoost model; S4: The lightweight multi-model fusion prediction module takes the five-quotient score, K value and initial income Y0 as input, optimizes the weights through pruning quantization model and federated gradient descent, and outputs the predicted income. S5: The adaptive privacy protection module performs hierarchical desensitization and encrypted transmission of data, while the background big data training submodule iteratively updates model parameters.

8. The dynamic lightweight revenue prediction method for multi-source heterogeneous data according to claim 7, characterized in that: In step S1, the self-assessment text data is converted into a quantized vector through the BERT model, the mobile behavior data is normalized to the [0,1] interval after outlier processing by the moving average algorithm, and the third-party economic data is cleaned in real time through the Kafka framework.

9. The dynamic lightweight revenue prediction method based on five-business multi-source heterogeneous data according to claim 7, characterized in that: In step S4, the input sequence of the LSTM model is reduced from 1080 dimensions to 200 dimensions through PCA principal component analysis, and the model weights are optimized through distributed training on edge devices using the federated gradient descent algorithm.

10. The dynamic lightweight revenue prediction method based on five-business multi-source heterogeneous data according to claim 7, characterized in that: In step S5, blockchain evidence storage technology is used to encrypt the transmission of feature vectors, and the encryption expiration mechanism automatically deletes feature vectors that have not been used for more than 6 months.

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