An artificial intelligence (AI) service software system and method

Abstract

The application belongs to the technical field of service software, and discloses an AI-based service software system and method, which comprises the following steps: a data collection module collects multi-source data; a data processing module pre-processes the data; a data privacy protection module encrypts sensitive information at each link; a model construction module constructs a model according to specific application tasks and data characteristics; a model optimization module optimizes the model; an algorithm strengthening module strengthens the model algorithm; an external interaction module realizes seamless integration of the AI system and a traditional database; a performance monitoring module monitors the system performance in real time; and a security protection module timely repairs discovered security vulnerabilities. The application can improve data quality; through model compression, hardware adaptation, explainability enhancement and generalization ability improvement of the model optimization module, the model running efficiency can be improved; and the algorithm strengthening module can realize the technical effects of counter-attack defense and common sense reasoning enhancement on the model algorithm.

Description

Technical Field

[0001] This application relates to the field of service software technology, and more specifically, to an artificial intelligence (AI) service software system and method. Background Technology

[0002] In today's society, AI-based service software systems hold a crucial position in numerous fields. In the medical field, they assist doctors in disease diagnosis and medical image analysis, improving diagnostic accuracy and efficiency. In the financial industry, they are used for risk assessment and fraud detection, ensuring financial security. In the transportation sector, they optimize traffic flow control and autonomous driving, improving traffic efficiency and safety. AI service software systems provide intelligent solutions for various industries, facilitating industry upgrading and transformation. Through data analysis and prediction, AI service software systems can help enterprises and institutions better plan and manage resources. Based on the analysis of large amounts of data and model predictions, AI service software systems can provide strong support for decision-making, helping enterprises and government departments make more scientific and accurate decisions.

[0003] Existing technology publication CN116910412A discloses an AI-based service software system, including a business software unit, a service request unit, and a service processing unit. The business software unit includes a data knowledge base module, a big data management module, an AI intelligent algorithm inference module, and a specified configuration push module. This AI-based service software system, by setting up the business software unit, service request unit, and service processing unit, increases or decreases the number of AI inference servers connected to the network switch according to the computational load of the algorithm in the AI ​​software, thus offering greater flexibility. Simultaneously, utilizing the network switch for information transmission results in faster transmission speeds and lower latency. The big data management module can classify big data content based on user search content, aggregate data, and automatically train models from standardized data through matching and merging logic and the AI ​​intelligent algorithm inference module.

[0004] While the existing technical solutions described above can achieve the relevant beneficial effects through their structure, they still have the following drawbacks: 1. Existing technologies lack effective means for real-time deviation detection of data and real-time monitoring of data distribution using data mining algorithms. This makes it difficult to guarantee data quality and detect anomalies in a timely manner, resulting in insufficient reliability of the data provided for model construction and affecting the performance and effectiveness of subsequent models. 2. Some current technologies have limitations in model optimization, leading to low model operating efficiency, excessive hardware resource consumption, poor model interpretability, difficulty in understanding the decision-making process, and insufficient versatility, making it unable to operate well in different scenarios. 3. Existing technologies lack effective adversarial attack defense mechanisms in model algorithm enhancement, making the models vulnerable to malicious attacks and unable to maintain stable performance in complex and ever-changing environments.

[0005] In view of this, we propose an AI-based service software system and method. Summary of the Invention

[0006] 1. The technical problems to be solved.

[0007] The purpose of this application is to provide an AI service software system and method that solves the technical problems mentioned in the background. It achieves data quality improvement by preprocessing data and detecting deviations in real time through a data processing module, and monitoring data distribution in real time using data mining algorithms. The model optimization module optimizes the model by performing model compression, hardware adaptation, interpretability enhancement, and generalization ability improvement, thereby improving model running efficiency. The algorithm enhancement module enhances the model algorithm by performing adversarial attack defense and common sense reasoning enhancement.

[0008] 2. Technical solution.

[0009] This application provides an AI-based service software system, including...

[0010] Data Collection Module: This module establishes a multi-channel data acquisition platform to collect data from multiple sources, including sensor data, web crawlers, and user uploads. It also annotates the collected images and text data to improve the accuracy of model training. Finally, it implements a data quality control mechanism to ensure the quality and consistency of the input data.

[0011] Data processing module: preprocesses the data, performs real-time deviation detection, uses data mining algorithms to monitor data distribution in real time, and immediately initiates a manual review process or uses automatic scripts to supplement data and adjust sample weights once abnormal distribution of category data is detected.

[0012] Data privacy protection module: Employs high-strength encryption algorithms such as Advanced Encryption Standard (AES) to encrypt sensitive information at each stage of data collection, storage, and transmission.

[0013] Model building module: This module builds models based on specific application tasks and data characteristics, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and their variants LSTM, GRU, decision tree models, and linear regression models. Models are trained on a training set, validated on a validation set, and tested on a test set.

[0014] The model optimization module optimizes the model in four main aspects: model compression, hardware adaptation, interpretability enhancement, and generalization improvement. Model compression reduces model parameters and storage requirements through pruning algorithms and quantization techniques, making it more suitable for edge computing. Hardware adaptation optimization develops adaptation tools for different hardware platforms to optimize the computational efficiency of deep learning models. Interpretability enhancement adopts a highly interpretable model framework fused with neural networks, integrating interpretive algorithms to improve the transparency of model decisions. Generalization improvement incorporates a data augmentation toolkit during the data preprocessing stage to expand the amount and diversity of data, and applies techniques such as regularization during model training to prevent overfitting and improve the model's generalization performance.

[0015] Algorithm enhancement module: Enhances the model algorithm, including adversarial attack defense, common sense reasoning enhancement, and model update and maintenance.

[0016] Adversarial Attack Defense: An adversarial training mechanism is introduced into the model training process. During each training round, adversarial examples are generated using algorithms such as FGSM and input into the model along with the original examples. This allows the model to learn to recognize and defend against adversarial attacks, improving its robustness. Knowledge distillation is performed periodically on complex models, transferring knowledge from complex models to simpler, more robust distilled models. During the inference phase, the distilled model is used for prediction, reducing the impact of adversarial attacks on the model's output.

[0017] Enhanced Commonsense Reasoning: A knowledge graph management platform is built and deeply integrated with models from natural language processing and computer vision. In text understanding tasks, the model can query the knowledge graph in real time to obtain semantic relationships and commonsense background information, assisting in text analysis and decision-making. A multi-task learning module is designed to dynamically schedule the model to learn multiple sub-tasks simultaneously based on task relevance and difficulty. In intelligent customer service scenarios, the model simultaneously learns tasks such as semantic understanding, intent recognition, and knowledge-based question answering, promoting enhanced commonsense reasoning capabilities through inter-task collaboration.

[0018] Model Update and Maintenance: An incremental learning platform is built to automatically detect changes in data categories and distributions when new data arrives. For image classification models, if a new category of image appears, the platform automatically triggers an incremental training process, learning new data features based on the existing model, avoiding retraining the entire model. A model version management system is developed to record detailed information such as parameters, training data, and performance metrics for each model version. Version comparison and rollback functions are provided to ensure the model update process is controllable and to quickly revert to a stable version if model performance issues arise.

[0019] External Interaction Module: Establish a lightweight model resource pool to select low-computational-complexity models for high-real-time scenarios; unify the feature learning platform, develop a general framework to realize multimodal data feature conversion, and use autoencoders to learn shared feature representations; design a fusion strategy management module to provide multiple fusion strategies such as early, late, and intermediate; formulate interface standards and data format specifications for the AI ​​service software system to connect with external systems, develop middleware and adapter software libraries to support data format conversion, protocol adaptation, and communication coordination, and achieve seamless integration between AI systems and traditional databases.

[0020] Performance Monitoring Module: Monitors system performance in real time, tracking key metrics such as model inference speed, resource utilization, and accuracy. Utilizing tools like Prometheus and Grafana, a visualization dashboard is built to intuitively present system performance changes. Optimization processes are automatically triggered when performance metrics deviate from preset thresholds.

[0021] Security Protection Module: Employs an Intrusion Detection and Prevention System (IDS / IPS) to monitor network traffic in real time, identify malicious attacks targeting the AI ​​system, and block them promptly. Regular security vulnerability scans are conducted using tools such as OWASP and ZAP to comprehensively inspect the system's web applications and API interfaces, and to promptly patch any discovered vulnerabilities.

[0022] Furthermore, the algorithm enhancement module strengthens the model algorithm, including adversarial attack defense, common sense reasoning enhancement, and model update and maintenance; including the following steps.

[0023] 1. Countering attacks and defenses.

[0024] 1.1 Introducing an Adversarial Training Mechanism: Before starting model training, configure the training environment and related tools to ensure the generation of adversarial examples and the normal operation of model training. In each training round, algorithms such as the Fast Gradient Sign Method (FGSM) are used to process the original samples, generating adversarial examples. These adversarial examples are small but carefully designed perturbations to the original samples, intended to mislead the model into making incorrect predictions. The generated adversarial examples are then input into the model along with the original samples for training. While learning the features of the original samples, the model learns how to identify and resist the interference of these adversarial examples, thereby gradually improving its robustness.

[0025] 1.2 Knowledge Distillation: A complex, high-performance model, but potentially sensitive to adversarial attacks, is selected as the teacher model. Simultaneously, a relatively simple, robust model is chosen as the student model (distillation model). The teacher model can be any complex model that performs well on the relevant task, possesses rich knowledge, and has strong expressive power. Knowledge distillation is performed periodically on the complex model. During knowledge distillation, specific methods are used to transfer the knowledge learned by the complex model (such as feature representations and decision boundaries) to the distilled model. Specifically, knowledge transfer can be achieved by having the distilled model learn the output probability distribution of the complex model. In the inference phase, the distilled model is used for prediction. Because the distilled model has better robustness, it can effectively reduce the impact of adversarial attacks on the model output, improving the prediction accuracy and stability when facing adversarial examples.

[0026] 2. Enhanced common sense reasoning.

[0027] 2.1 Building a Knowledge Graph Management Platform: Plan the overall architecture of the knowledge graph management platform, including the design of modules such as data storage, data query, and knowledge update. Determine the type of database and related technical framework to be used by the platform. Collect knowledge data related to tasks such as natural language processing and computer vision, and clean, organize, and structure this data so that it can be stored in the knowledge graph.

[0028] The knowledge graph management platform is deeply integrated with models such as natural language processing and computer vision to ensure that the models can query the knowledge graph in real time during operation and obtain the required knowledge information.

[0029] 2.2 Applications in Text Understanding Tasks: When the model performs text understanding tasks, it queries the knowledge graph in real time based on the text content. Utilizing information obtained from the knowledge graph, the model can more accurately analyze the meaning of the text and make more reasonable decisions.

[0030] 2.3 Design a Multi-Task Learning Module: Analyze specific application scenarios, identify multiple relevant sub-tasks, and evaluate the relevance and difficulty among these tasks. For example, in an intelligent customer service scenario, identify sub-tasks such as semantic understanding, intent recognition, and knowledge-based question answering, and analyze their connections and difficulty levels. Construct a dynamic scheduling model and design a dynamic scheduling mechanism for the multi-task learning module. Based on the relevance and difficulty of the tasks, rationally arrange the model to learn multiple sub-tasks simultaneously. Algorithms or strategies can be used to balance the training time and resource allocation for different tasks, ensuring the model can effectively learn each sub-task. During the process of the model learning multiple sub-tasks simultaneously, the synergistic effect between tasks promotes the improvement of the model's common-sense reasoning ability.

[0031] 3. Model update and maintenance.

[0032] 3.1 Building an Incremental Learning Platform: When new data arrives, the incremental learning platform automatically detects and analyzes changes in data categories and distribution. Statistical analysis and machine learning algorithms can be used to determine if the new data differs significantly from the existing data. Incremental training is then triggered. For applications like image classification models, if a new category of image is detected in the new data, the platform automatically initiates the incremental training process. Incremental training learns the features of the new data based on the existing model, without needing to retrain the entire model, thus saving time and computational resources. During incremental training, the model parameters are updated based on the new data, enabling the model to adapt to new data features and distributions, improving model performance and generalization ability.

[0033] 3.2 Develop a model version management system: This system will record detailed information for each model version, including model parameters, training data, performance metrics (such as accuracy, recall, F1 score, etc.), training time, and training environment. This ensures that this information accurately reflects the characteristics and performance of each model version. A model version comparison function will be developed, allowing users to easily compare the differences between different model versions and understand the changes during the update process. The effectiveness of model updates can be evaluated by comparing performance metrics and other information. A version rollback function will be provided, allowing users to quickly restore the model to a previous stable version if performance issues or other anomalies occur after an update, ensuring the normal operation of the model and the stability of the service.

[0034] This invention provides a method for using AI-based service software, comprising the following steps.

[0035] S1. The data collection module builds a multi-channel data acquisition platform to collect data from multiple sources, including sensor data, web crawlers, and user uploads.

[0036] S2, the data processing module preprocesses the data, performs real-time deviation detection, and uses data mining algorithms to monitor the data distribution in real time.

[0037] S3, the data privacy protection module, adopts high-strength encryption algorithms such as the Advanced Encryption Standard (AES) to encrypt sensitive information in all stages of data collection, storage, and transmission.

[0038] S4. The model building module builds models based on specific application tasks and data characteristics, trains the models using the training set, validates the models using the validation set, and tests the models using the test set.

[0039] S5, the model optimization module, optimizes the model, including model compression, hardware adaptation, enhanced interpretability, and improved generalization ability.

[0040] S6, the algorithm enhancement module, strengthens the model algorithm, including adversarial attack defense, common sense reasoning enhancement, and model update and maintenance.

[0041] S7. The external interaction module establishes a lightweight model resource pool to select low-computational-complexity models for high-real-time scenarios; it unifies the feature learning platform, develops a general framework to realize multimodal data feature conversion, and uses autoencoders to learn shared feature representations; it designs a fusion strategy management module to provide multiple fusion strategies such as early, late, and intermediate; it formulates interface standards and data format specifications for the AI ​​service software system to connect with external systems, develops middleware and adapter software libraries to support data format conversion, protocol adaptation, and communication coordination, and achieves seamless integration between the AI ​​system and traditional databases.

[0042] S8, the performance monitoring module, monitors system performance in real time, and monitors key indicators such as model inference speed, resource utilization, and accuracy.

[0043] The S9 security protection module employs an intrusion detection and prevention system (IDS / IPS) to monitor network traffic in real time, identify malicious attacks targeting the AI ​​system, and block them promptly. Regular security vulnerability scans are conducted using tools such as OWASP and ZAP to comprehensively inspect the system's web applications and API interfaces, and to promptly patch any discovered security vulnerabilities.

[0044] 3. Beneficial effects.

[0045] One or more technical solutions provided in this application have at least the following technical effects or advantages.

[0046] 1. This invention preprocesses data and detects deviations in real time through a data processing module, and uses data mining algorithms to monitor data distribution in real time, which can improve data quality, detect data anomalies in a timely manner, and provide more reliable data for model construction.

[0047] 2. The data privacy protection module adopts high-strength encryption algorithms such as the Advanced Encryption Standard (AES) to encrypt sensitive information at each stage of data collection, storage, and transmission, effectively protecting data privacy and preventing the leakage of sensitive information.

[0048] 3. The model optimization module performs optimizations on the model, such as model compression, hardware adaptation, interpretability enhancement, and generalization ability improvement. This can improve the model's running efficiency, make it better adaptable to the hardware environment, and enhance the model's understandability and versatility.

[0049] 4. The algorithm enhancement module strengthens the model algorithm by enhancing its adversarial attack defense, common sense reasoning, and model update and maintenance. This improves the model's security, intelligence, and continuous effectiveness, making it more stable in the face of various situations. Attached Figure Description

[0050] Figure 1 This is an overall schematic diagram of the AI-based service software system disclosed in this application. Detailed Implementation

[0051] The present application will be further described in detail below with reference to the accompanying drawings.

[0052] Reference Figure 1 This application provides an AI service software system, including...

[0053] Data Collection Module: This module establishes a multi-channel data acquisition platform to collect data from multiple sources, including sensor data, web crawlers, and user uploads. In image recognition applications, it equips multiple types of cameras to collect image data under varying lighting, angles, and shooting equipment conditions, avoiding bias due to data homogeneity. Data annotation is performed to accurately label collected images and text, improving the accuracy of model training. Simultaneously, a data quality control mechanism is implemented to ensure the quality and consistency of input data.

[0054] Data processing module: Preprocesses data, performs real-time deviation detection, and uses data mining algorithms to monitor data distribution in real time. Once abnormal distribution of category data is detected, such as an imbalance in the proportion of regional or occupational data in credit approval data, a manual review process is immediately initiated, or data is supplemented and sample weights are adjusted through automatic scripts.

[0055] Data privacy protection module: Employing high-strength encryption algorithms such as the Advanced Encryption Standard (AES), sensitive information is encrypted at each stage of data collection, storage, and transmission. During database storage, sensitive fields such as user ID numbers and medical records are encrypted; during network transmission, data security is ensured through SSL / TLS (Secure Sockets Layer and Transport Layer Security) encryption protocols.

[0056] A federated learning platform can be built to support multi-party participation in model training. For example, in a medical data sharing scenario, each hospital trains its model locally and uploads the parameters, which are then aggregated and updated by a central server, avoiding the privacy risks associated with the centralized transmission and storage of raw medical data.

[0057] Model building module: This module constructs models based on specific application tasks and data characteristics, including Convolutional Neural Networks (CNNs) for image-related tasks, such as image recognition applications, Recurrent Neural Networks (RNNs) and their variant LSTM, GRUs (suitable for processing sequence data, such as text classification and machine translation in natural language processing), decision tree models (for classification and regression tasks), and linear regression models (suitable for regression tasks, such as predicting house prices). Models are trained using a training set, validated using a validation set, and tested using a test set.

[0058] The model optimization module comprises four main aspects: model compression, hardware adaptation, interpretability enhancement, and generalization improvement. Model compression reduces model parameters and storage requirements through pruning algorithms and quantization techniques, making it more suitable for edge computing. Hardware adaptation optimization develops adaptation tools for different hardware platforms to optimize the computational efficiency of deep learning models. Interpretability enhancement employs a highly interpretable model framework fused with neural networks, integrating interpretive algorithms to improve the transparency of model decisions. Generalization improvement incorporates a data augmentation toolkit during the data preprocessing stage to expand data volume and diversity, while applying regularization and other techniques during model training to prevent overfitting and improve model generalization performance.

[0059] Model compression: After model training is complete, a model compression process is automatically triggered, using pruning algorithms to remove redundant connections and neurons, reducing model parameters. Simultaneously, quantization techniques are employed to convert parameter data types from 32-bit floating-point numbers to 8-bit integers, reducing storage and computational requirements and making the model more adaptable to edge computing devices. This also includes hardware adaptation optimization, enhanced interpretability, and improved generalization capabilities.

[0060] Hardware adaptation and optimization: Develop model adaptation tools for different hardware platforms, such as GPU graphics processing units and TPU tensor processing units. When deploying deep learning models, optimize operations such as convolution and pooling based on the characteristics of the hardware architecture to improve hardware utilization and computational efficiency.

[0061] Enhanced Interpretability: For hybrid model architectures, highly interpretable models such as decision trees and linear regression are prioritized as the foundational framework. For complex tasks, interpretable neural network architectures are integrated with them. In financial risk prediction, a linear regression model is used to provide initial predictions, followed by in-depth analysis of complex factors using an interpretable neural network, making the model's decision-making process more transparent. Interpretive algorithms such as LIME (Locally Interpretable Model-Independent Interpretation) and SHAP (Shapley Additive Interpretation) are integrated into the model inference service. When users obtain model prediction results, they can choose to view the degree of influence of input features on the output, such as displaying the contribution of each pixel region to the classification decision next to the image classification results.

[0062] Enhanced Generalization Ability: During the data preprocessing stage, a built-in data augmentation toolkit supports diverse transformations of various data types, including images, text, and audio. Image data can be rotated, scaled, cropped, and noise added; text data can be processed with synonym replacement, random deletion, and word insertion, expanding the data volume and diversity. During model training, L1 and L2 regularization and Dropout techniques are automatically applied to prevent overfitting. Regularization parameters are dynamically adjusted based on task characteristics and dataset size to ensure the model focuses on learning common features in the data, improving generalization performance.

[0063] Algorithm enhancement module: Enhances the model algorithm, including adversarial attack defense, common sense reasoning enhancement, and model update and maintenance.

[0064] Adversarial Attack Defense: An adversarial training mechanism is introduced into the model training process. During each training round, adversarial examples are generated using algorithms such as FGSM and input into the model along with the original examples. This allows the model to learn to recognize and defend against adversarial attacks, improving its robustness. Knowledge distillation is performed periodically on complex models, transferring knowledge from complex models to simpler, more robust distilled models. During the inference phase, the distilled model is used for prediction, reducing the impact of adversarial attacks on the model's output.

[0065] Enhanced Commonsense Reasoning: A knowledge graph management platform is built and deeply integrated with models from natural language processing and computer vision. In text understanding tasks, the model can query the knowledge graph in real time to obtain semantic relationships and commonsense background information, assisting in text analysis and decision-making. A multi-task learning module is designed to dynamically schedule the model to learn multiple sub-tasks simultaneously based on task relevance and difficulty. In intelligent customer service scenarios, the model simultaneously learns tasks such as semantic understanding, intent recognition, and knowledge-based question answering, promoting enhanced commonsense reasoning capabilities through inter-task collaboration.

[0066] Model Update and Maintenance: An incremental learning platform is built to automatically detect changes in data categories and distributions when new data arrives. For image classification models, if a new category of image appears, the platform automatically triggers an incremental training process, learning new data features based on the existing model, avoiding retraining the entire model. A model version management system is developed to record detailed information such as parameters, training data, and performance metrics for each model version. Version comparison and rollback functions are provided to ensure the model update process is controllable and to quickly revert to a stable version if model performance issues arise.

[0067] External Interaction Module: A lightweight model resource pool is established, employing low-computational-complexity model structures for applications with high real-time requirements, such as autonomous driving and industrial control. In the autonomous driving perception module, a lightweight object detection model is used, combined with hardware acceleration and parallel computing technologies to improve model inference speed. A prediction caching system is built to pre-calculate and cache results for tasks with regular input, such as daily weather forecasting and traffic flow prediction. When real-time requests arrive, results are retrieved directly from the cache, reducing computation time. A unified feature learning platform is developed, featuring a general feature representation learning framework that supports feature conversion for multimodal data such as images, text, and audio. Technologies such as autoencoders are used to learn shared feature representations of different modalities, laying the foundation for subsequent fusion processing. A fusion strategy management module is designed, providing various strategies such as early fusion, late fusion, and intermediate fusion. For example, in intelligent security scenarios, the optimal fusion strategy is automatically selected based on the characteristics of video images and audio data and task requirements to improve system performance. Interface standards and data format specifications for the AI ​​service software system to interface with external systems are defined, covering commonly used interface protocols such as RESTful API and gRPC. When integrating with enterprise ERP systems, ensure the compatibility and stability of data transmission and interaction. Develop middleware and adapter software libraries to support data format conversion, protocol adaptation, and communication coordination. When integrating AI systems with traditional databases, use middleware to convert AI model output data into a database-acceptable format for seamless integration.

[0068] Performance Monitoring Module: This module monitors system performance in real time, tracking key metrics such as model inference speed, resource utilization, and accuracy. It utilizes tools like Prometheus and Grafana to create visual dashboards that intuitively present changes in system performance. When performance metrics deviate from preset thresholds, optimization processes are automatically triggered. For example, if inference speed slows down, the system can dynamically adjust the model parallel computing strategy based on hardware resource usage, or re-evaluate the effectiveness of model compression and quantization for secondary optimization.

[0069] Security Protection Module: Employs an Intrusion Detection and Prevention System (IDS / IPS) to monitor network traffic in real time, identify malicious attacks targeting the AI ​​system, such as DDoS attacks and SQL injection, and block them promptly. Regular security vulnerability scans are conducted using tools such as OWASP and ZAP to comprehensively inspect the system's web applications and API interfaces, and promptly patch any discovered security vulnerabilities.

[0070] Furthermore, the data processing module preprocesses the data, performs real-time deviation detection, and uses data mining algorithms to monitor data distribution in real time, including the following steps.

[0071] 1. Data preprocessing: This includes data cleaning, data transformation, and data integration.

[0072] Data cleaning: Identifying and handling missing, erroneous, and duplicate values ​​in data. Data can be "cleaned" by filling in missing values ​​(e.g., using the mean, median, or most probable value), removing duplicate records, and correcting obvious errors.

[0073] Data transformation: Performing format conversion and standardization on data, such as converting categorical data into a form that machine learning algorithms can understand (e.g., one-hot encoding), discretizing continuous variables, or transforming data of different magnitudes and ranges to a common standard (e.g., min-max normalization, Z-score normalization).

[0074] Data integration: Combining data from multiple data sources and storing it uniformly to solve the problem of data consistency across different sources.

[0075] 2. Real-time Deviation Detection: Identify key indicators to be monitored, such as the mean, standard deviation, and proportion of the data, and set reasonable deviation thresholds. For example, for the proportion of a region or occupation in credit approval data, set a normal fluctuation range as a threshold. Perform real-time data collection and calculation, collecting data in real time and performing calculations based on the set indicators. For example, calculate the proportion of each region or occupation in the credit approval data in real time. Compare the real-time calculated indicator values ​​with the preset thresholds to determine if there is a deviation. If the indicator value exceeds the threshold range, a deviation is considered to have occurred.

[0076] 3. Use data mining algorithms to monitor data distribution in real time: Based on the characteristics of the data and the monitoring objectives, select appropriate data mining model algorithms, such as cluster analysis, principal component analysis, and decision trees. For example, use cluster analysis algorithms to cluster data according to characteristics such as region or occupation, and observe the distribution of different categories of data.

[0077] Historical data is used to train the selected algorithm, establishing a data distribution model. As new data continuously flows in, the model is updated periodically or in real-time to ensure it accurately reflects the latest data distribution. The trained model is then used to analyze real-time data, detecting any abnormal changes in data distribution. For example, if the model detects a significant difference between the distribution of credit approval data for a particular region or occupation and previous patterns, it may indicate an anomaly in the category data distribution. The objective function J of the data distribution model is...

[0078] J=Σ k i=1 Σ(w x ||xu i ||) 2 +λΣ(1 / [|c i ||(c i -1)|]Σ(w x w y ||xy|| 2 ).

[0079] x∈C i ;y∈C i ;x≠y; In the formula, λ is an adjustment parameter (λ≥0). The larger λ is, the more emphasis is placed on the influence of the relative distance between samples within a cluster on the clustering results; the smaller λ is, the more emphasis is placed on the effect of the distance from the sample to the centroid on the clustering results. k represents the number of clusters, that is, the number of categories to which all samples are to be divided. C i Let represent the i-th cluster, i = 1, 2, ..., k, which is a set containing multiple samples. x and y are samples. w x w y These are the weights of samples x and y, respectively, used to represent the importance of different samples in the clustering process. Samples with larger weights have a relatively greater impact on the clustering results. i is the centroid of the i-th cluster, and is the mean vector of all samples within that cluster, representing the center of the cluster. ||xu i || represents the distance from sample x to the i-th cluster centroid u i The Euclidean distance is used to measure the similarity between a sample and its centroid. The smaller the distance, the more similar the sample is to the centroid, and the closer the sample is to the center of the cluster. ||xy|| represents cluster C. i The Euclidean distance between in-cluster samples x and y measures the similarity between different samples within a cluster. A smaller distance indicates greater similarity between the two samples; a larger distance indicates greater difference between the two samples. i | represents the number of samples in the i-th cluster, i.e., cluster C. i The number of samples included.

[0080] 4. Anomaly Handling: Conduct data integrity checks to confirm the completeness of information related to abnormal data. Evaluate the reasonableness of the abnormal data by considering historical data, market conditions, and business logic. Identify which data sources to obtain supplementary data from, such as other relevant business databases or external data providers. Based on the data sources and supplementation rules, write scripts to automatically acquire and insert data. For example, the script can periodically obtain the latest demographic data and economic indicators for a region or occupation from external data sources and add them to the credit approval dataset as supplementary information. Run the automated scripts and set up monitoring mechanisms to ensure the smooth progress of the data supplementation process and that the supplemented data meets quality requirements. Calculate the number of samples for each category (e.g., different regions or occupations). Adjust each sample in the training data based on the calculated weights. Increase the weight of samples from categories with fewer samples; decrease the weight of samples from categories with more samples, thus allowing the model to focus more on categories with fewer samples during training and balancing the importance of different categories.

[0081] Furthermore, the model building module: builds models based on specific application tasks and data characteristics, including the following steps.

[0082] 1. Determine the specific application task and data characteristics: Clarify the type of task, such as image-related tasks (e.g., image recognition, image segmentation, etc.), sequence data processing tasks (e.g., text classification, sentiment analysis, machine translation, time series prediction, etc. in natural language processing), classification tasks, or regression tasks, etc.

[0083] The characteristics of the research data include data type, data size, data distribution, and data features.

[0084] Data type: Determine whether the data is image data (such as RGB images, grayscale images, etc.), text data (word sequences, sentence sequences, etc.), numerical data (continuous values, discrete values), or other types.

[0085] Data size: The number of samples in the training data, validation data, and test data.

[0086] Data distribution: Analyzing the distribution of different categories or numerical ranges in the data.

[0087] Data features: For image data, consider features such as image size, resolution, and color channels; for text data, consider features such as vocabulary size, text length, and the presence of special symbols.

[0088] 2. Model building: Build different models according to different tasks.

[0089] Image-related tasks: For tasks such as image recognition and classification, Convolutional Neural Networks (CNNs) are preferred. For example, in handwritten digit recognition tasks, classic CNN model architectures such as LeNet, AlexNet, and VGGNet can be selected, and adjusted and optimized according to specific needs and data scale. For image segmentation tasks, specialized CNN architectures such as U-Net and Mask R-CNN can be used. These models can accurately segment and label different regions in an image.

[0090] For sequence data processing tasks, recurrent neural networks (RNNs) and their variants, long short-term memory networks (LSTMs) and gated recurrent units (GRUs), are commonly used choices for tasks such as text classification and sentiment analysis in natural language processing. For machine translation tasks, encoder-decoder structures are typically used, combined with models such as LSTMs, GRUs, or Transformers. For time series prediction tasks, such as stock price prediction and temperature prediction, LSTMs and GRUs can effectively handle long-term dependencies in time series data. Alternatively, specialized time series models such as ARIMA and Prophet can be combined with deep learning models.

[0091] Classification and Regression Tasks: For simple classification and regression tasks, choose a decision tree model. It is easy to understand and interpret, and can visually demonstrate the relationship between features and the target variable. For example, in a classification task predicting whether a customer will churn, a decision tree can make decisions based on features such as the customer's age, spending amount, and purchase frequency. For regression tasks, if the data exhibits a linear relationship, choose a linear regression model.

[0092] 3. Data preprocessing: Image data is preprocessed, including normalization and data augmentation. Data augmentation expands the training data through operations such as rotation, flipping, cropping, and scaling, increasing the diversity of the data and improving the model's generalization ability.

[0093] Preprocessing of text data includes word segmentation, word embedding, padding, and truncation.

[0094] Numerical data preprocessing includes normalization and discretization; discretization is the process of converting continuous numerical data into discrete numerical values ​​according to certain rules.

[0095] 4. Model Training: Divide the original data into training, validation, and test sets. Set appropriate model parameters based on the model type and task requirements, such as learning rate, number of iterations, and number of hidden layer neurons. Select a suitable loss function based on the task type. For classification tasks, commonly used loss functions include cross-entropy loss; for regression tasks, mean squared error loss is commonly used. Choose a suitable optimizer to update the model parameters, such as stochastic gradient descent (SGD), Adam, Adagrad, etc. Train the model using the training set. During training, the model calculates the error between the predicted and true values ​​based on the loss function and updates the model parameters through the optimizer, continuously adjusting the model weights to minimize the loss function. Record the loss value and other metrics (such as accuracy and recall) during training to monitor the model's training progress and performance.

[0096] 5. Model Validation: During training, the model is periodically evaluated using a validation set. The validation set is input into the trained model, and the loss value and other metrics are calculated on the validation set. The model parameters are adjusted based on the validation set evaluation results. During training, models from different training stages are saved, and the best-performing model is selected as the final model based on the validation set evaluation results.

[0097] 6. Model Testing: Input the test set into the final selected model and calculate the model's loss value and other metrics on the test set, such as accuracy, recall, F1 score (for classification tasks), root mean square error (RMSE), and mean absolute error (MAE) (for regression tasks). Based on the evaluation results of the test set, comprehensively evaluate the model's performance to determine if it meets the needs of the practical application. If the model performance is unsatisfactory, further analyze the reasons, such as data issues, inappropriate model selection, or unreasonable parameter settings, and make corresponding improvements and optimizations.

[0098] Furthermore, the model optimization module involves four main aspects: model compression, hardware adaptation, enhanced interpretability, and improved generalization ability. It includes the following steps.

[0099] 1. Model compression.

[0100] 1.1 Triggering Mechanism: The model compression process is automatically initiated when a signal indicating model training completion is detected. This is achieved by setting a training completion flag in the training code. When the number of training epochs reaches a preset value or the loss function converges to a certain extent, the flag is set to true, thus triggering the compression process.

[0101] 1.2 Application of Pruning Algorithms: To evaluate the importance of connections and neurons, methods such as weight-based evaluation are used. For each connection weight in the neural network, its absolute value is calculated. Connections and neurons with smaller absolute weight values ​​have a relatively small impact on the model output and can be considered redundant. Alternatively, gradient-based evaluation methods can be used, calculating the gradient of connections and neurons with respect to the loss function; smaller gradients indicate lower importance. Based on the evaluation results, a threshold is set, and connections with absolute weight values ​​less than this threshold are disconnected, and the corresponding neurons are removed from the network. After removal, the model is recompiled and initialized to update the model structure.

[0102] 1.3 Quantization Implementation: Perform parameter data type conversion, iterating through all parameters of the model and converting the original 32-bit floating-point parameters to 8-bit integers. Update the model's storage structure to store the converted 8-bit integer parameters. During the model's computation, adjust the computation logic accordingly, using the 8-bit integer arithmetic rules for operations such as convolution and matrix multiplication to reduce storage and computational requirements.

[0103] 2. Hardware compatibility optimization.

[0104] 2.1 Hardware Platform Analysis: Determine the hardware platform to be deployed for the model, such as GPU or TPU. Collect relevant information about the hardware platform, including hardware architecture characteristics (such as the number of CUDA cores and memory bandwidth of GPUs, and the tensor processing unit structure of TPUs), computing power (such as floating-point arithmetic power and integer arithmetic power), and storage capacity.

[0105] 2.2 Develop Adaptation Tools: Develop specialized model adaptation tool libraries based on the characteristics of different hardware platforms. For example, for GPU platforms, use programming interfaces such as CUDA or OpenCL to develop adaptation functions to optimize parallel computation of operations such as convolution and pooling; for TPU platforms, develop adaptation tools according to TPU programming specifications to achieve efficient tensor operations. Integrate the developed adaptation tools into the deep learning framework so that the model can automatically call the adaptation tools for optimization during deployment.

[0106] 2.3 Computational Optimization: Based on the characteristics of the hardware architecture, convolutional operations are optimized. For example, on GPUs, block convolution algorithms are used to decompose large convolutional operations into multiple smaller convolutional blocks, improving memory access efficiency; on TPUs, the advantages of tensor operations are utilized to rearrange and group convolutional kernels, reducing computational load. The implementation of pooling operations is optimized, such as using parallel pooling algorithms, where multiple threads on the GPU simultaneously process pooling operations in different regions, improving computational speed.

[0107] 3. Enhanced interpretability.

[0108] 3.1 Base Model Selection: Determine the task type and analyze the specific application task, such as classification, regression, and prediction. For simple tasks, prioritize interpretable models such as decision trees and linear regression as the base framework. Integrate complex models: For complex tasks, integrate interpretable neural network architectures (such as interpretable convolutional neural networks and interpretable recurrent neural networks) with the base model. For example, in financial risk prediction, first use a linear regression model to provide preliminary prediction results, and then input the results into an interpretable neural network for in-depth analysis.

[0109] 3.2 Integration of Interpretive Algorithms: Integrate interpretive algorithms such as LIME or SHAP into the model inference service. LIME explains the predictions of complex models by fitting an interpretable model to a local range; SHAP, based on the Shapley value in game theory, calculates the contribution of each input feature to the output. In the model's user interface, provide an option for users to view the degree of influence of input features on the output when obtaining model prediction results. For example, next to image classification results, use the SHAP algorithm to calculate the contribution of each pixel region to the classification decision and display it to the user in a visual manner.

[0110] Furthermore, an improved LIME (Locally Interpretable Model-Independent Interpretation) model is adopted, and the model is as follows.

[0111] L(f,g,π x )=Σ{a xz ×π x (z)×d(x,z)×[f(z)-g(z)] 2}

[0112] d(x,z)=1 / [1+dist(x,z)]; z∈Z; where, L(f,g,π x `g(z)` is the loss function used to measure the difference between the complex model `f(x)` and the interpretable model `g(z)` within a local range. Minimizing this loss function allows the interpretable model `g(z)` to better approximate the predictions of the complex model `f(x)`. `f(x)` represents the complex model, i.e., the original model that needs to be explained. It receives input `x` (which can be sample data, feature vectors, etc.) and outputs the corresponding prediction. `g(z)` represents the interpretable model fitted within a local range, used to approximate the behavior of the complex model `f(x)`. It receives input `z` (which can be sample data, feature vectors, etc.) and outputs the prediction. x (z) is the local weight of sample z relative to x. It measures the importance of sample z in the local region centered at x. The larger the weight, the greater the proportion of sample z in the local explanation, and the greater its influence on the fit of the interpretable model g(z). xz`d(x,z)` is a dynamic adjustment factor with a value range of (0-1). It dynamically changes based on certain characteristics of samples x and z (such as the cosine similarity between feature vectors) to adjust the importance of sample z in the loss function calculation. `d(x,z)` is the distance decay function. It is a function of some distance metric (such as Euclidean distance, Manhattan distance, etc.) between samples x and z. This function adjusts the weight of sample z in the loss function calculation based on the distance between samples x and z. Generally, the greater the distance, the smaller the value of `d(x,z)`, thus reducing the proportion of samples farther from the target sample x in the loss calculation. `dist(x,z)` is the distance metric between samples x and z. `z` is a sample element in the local sample set Z, used as input to the interpretable model `g(z)` when calculating the loss function; Z is the local sample set, which contains a series of samples selected within a local region centered at x.

[0113] Furthermore, an improved SHAP (Shapely additive interpretation) model is adopted, and the model is as follows.

[0114] φ i =Σ{β S ×[(|S|!(|N|-|S|-1)!]×[v(S∪{i})-V(S)] / (|N|!)}.

[0115] S∈N\{i};where, φ i This represents the SHAP value of the i-th feature. It reflects the influence factor β when considering feature subsets. S β represents the contribution of the i-th feature to the final output of the model. A larger value indicates a greater impact of the feature on the model output. S represents a feature subset, which is a subset of the entire feature set N after removing the i-th feature (i.e., (N\{i})). Different combinations of S represent different feature combinations. N represents the set of all features, containing all features of the model input. i represents the index of the target feature for which the SHAP value is to be calculated. S This is the feature subset influence factor, ranging from 0 to 1. It is determined based on factors such as the size of the feature subset S and the correlation between features. |S| represents the number of features contained in the feature subset S. |N| represents the total number of features contained in all feature sets N. v(S) represents the output value of the model when the model input contains only the features from the feature subset S. v(S∪{i}) represents the output value of the model when the model input contains the features from the feature subset S and the i-th feature.

[0116] 4. Improved generalization ability.

[0117] 4.1 Data Augmentation in the Data Preprocessing Stage: Based on the type of input data (e.g., images, text, audio, etc.), select appropriate data augmentation operations. For image data, use operations such as rotation, scaling, cropping, and adding noise; for text data, perform synonym replacement, random deletion, and word insertion. Apply data augmentation toolkits to batch process the training data. For example, for image data, set parameters such as rotation angle range and scaling ratio range, and the toolkit will automatically perform corresponding transformations on the images, expanding the data volume and diversity.

[0118] 4.2 Model Parameter Tuning and Optimization: During model training, L1 and L2 regularization, as well as Dropout, are automatically applied. L1 regularization adds the sum of the absolute values ​​of the parameters to the loss function, while L2 regularization adds a portion of the sum of squared parameters to the loss function. Regularization parameters and Dropout probabilities are dynamically adjusted based on task characteristics and dataset size. For example, for small datasets, regularization parameters and Dropout probabilities are appropriately increased to prevent overfitting; for large datasets, these parameters can be appropriately decreased to allow the model to better learn the features of the data. During training, performance metrics (such as accuracy and loss function values) of the model on the training and validation sets are monitored, and regularization parameters and Dropout probabilities are adjusted promptly based on performance changes to ensure the model focuses on learning the general features of the data and improves generalization performance.

[0119] Furthermore, the algorithm enhancement module strengthens the model algorithm, including adversarial attack defense, common sense reasoning enhancement, and model update and maintenance; including the following steps.

[0120] 1. Countering attacks and defenses.

[0121] 1.1 Introducing an Adversarial Training Mechanism: Before starting model training, configure the training environment and related tools to ensure the generation of adversarial examples and the normal operation of model training. In each training round, algorithms such as the Fast Gradient Sign Method (FGSM) are used to process the original samples, generating adversarial examples. These adversarial examples are small but carefully designed perturbations to the original samples, intended to mislead the model into making incorrect predictions. The generated adversarial examples are then input into the model along with the original samples for training. While learning the features of the original samples, the model learns how to identify and resist the interference of these adversarial examples, thereby gradually improving its robustness. Adversarial examples x are generated using the Fast Gradient Sign Method (FGSM). adv The model is: x adv =x+e×sign[W(θ)×▽ x L(θ,x,y)×A(x)]; where e is the perturbation intensity, controlling the degree of perturbation of the adversarial example relative to the original example. sign(.) is the sign function, used to determine the direction of the perturbation. ▽x L(θ,x,y) is the gradient of the loss function with respect to the input x, representing the direction of the fastest change in the loss function in the input space. W(θ) is a weight matrix related to the model parameters θ. It adjusts the direction and magnitude of the gradient according to the values ​​of the model parameters, making the perturbation direction more consistent with the characteristics of the model. For example, W(θ) can be a diagonal matrix, whose diagonal elements are determined based on certain statistics of θ (such as the variance, mean, etc. of the parameters). A(x) is a dynamically adjusted matrix related to the features of sample x. It can adjust the effect of the perturbation on different feature dimensions according to the distribution of the feature vector of sample x. For example, A(x) can be constructed based on the feature importance score of sample x, so that the perturbation on important features is relatively small, and the perturbation on unimportant features is relatively large. x represents the original input sample, which is the normal input data of the model. Taking image recognition tasks as an example, x may be an image with specific pixel values; in text classification tasks, x may be a piece of text data. y represents the true label of the sample, which is the known correct classification or target value.

[0122] 1.2 Knowledge Distillation: A complex, high-performance model, but potentially sensitive to adversarial attacks, is selected as the teacher model. Simultaneously, a relatively simple, robust model is chosen as the student model (distilled model). The teacher model can be any complex model that performs well on the relevant task, possesses rich knowledge, and strong expressive power. Examples include large deep neural network models such as ResNet (Residual Network) and VGGNet in image processing, and BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pretrained Transformer) in natural language processing. Knowledge distillation is performed periodically on the complex model. During knowledge distillation, the knowledge learned by the complex model (such as feature representations and decision boundaries) is transferred to the distilled model using specific methods. Specifically, knowledge transfer can be achieved by having the distilled model learn the output probability distribution of the complex model. In the inference phase, the distilled model is used for prediction. Because the distilled model has better robustness, it effectively reduces the impact of adversarial attacks on the model's output, improving the model's prediction accuracy and stability when facing adversarial examples.

[0123] 2. Enhanced common sense reasoning.

[0124] 2.1 Building a Knowledge Graph Management Platform: Plan the overall architecture of the knowledge graph management platform, including the design of modules for data storage, data querying, and knowledge updating. Determine the type of database used by the platform (as shown in the figure) and the related technical framework. Collect knowledge data related to tasks such as natural language processing and computer vision, such as semantic relationships and common sense background information, and clean, organize, and structure this data so that it can be stored in the knowledge graph.

[0125] The knowledge graph management platform is deeply integrated with models such as natural language processing and computer vision to ensure that the models can query the knowledge graph in real time during operation and obtain the required knowledge information.

[0126] 2.2 Applications in Text Understanding Tasks: When the model performs text understanding tasks, it queries the knowledge graph in real time based on the text content. For example, when performing text classification or sentiment analysis, the model can obtain relevant semantic relationships and common-sense background information from the knowledge graph to assist in the analysis and understanding of the text.

[0127] By leveraging information from knowledge graphs, models can more accurately analyze the meaning of text and make more reasonable decisions. For example, when determining the sentiment of a text, combining semantic relationships of sentiment-related words in the knowledge graph with common sense knowledge can improve the accuracy of sentiment analysis.

[0128] 2.3 Design a multi-task learning module: Analyze specific application scenarios, identify multiple relevant sub-tasks, and evaluate the relevance and difficulty among these tasks. For example, in an intelligent customer service scenario, identify sub-tasks such as semantic understanding, intent recognition, and knowledge-based question answering, and analyze their connections and difficulty levels. Construct a dynamic scheduling model and design a dynamic scheduling mechanism for the multi-task learning module. Based on the relevance and difficulty of the tasks, rationally arrange the model to learn multiple sub-tasks simultaneously. Algorithms or strategies can be used to balance the training time and resource allocation for different tasks, ensuring that the model can effectively learn each sub-task.

[0129] As the model learns multiple sub-tasks simultaneously, the synergistic effect between tasks enhances the model's common-sense reasoning ability. For example, the semantic understanding task can provide more accurate textual information for the intent recognition task, while the intent recognition task can guide the knowledge question answering task to more accurately acquire relevant knowledge, thereby jointly improving the model's common-sense reasoning ability.

[0130] 3. Model update and maintenance.

[0131] 3.1 Building an Incremental Learning Platform: When new data arrives, the incremental learning platform automatically detects and analyzes changes in data categories and distribution. Statistical analysis and machine learning algorithms can be used to determine if the new data differs significantly from the existing data. Incremental training is then triggered. For applications like image classification models, if a new category of image is detected in the new data, the platform automatically initiates the incremental training process. Incremental training learns the features of the new data based on the existing model, without needing to retrain the entire model, thus saving time and computational resources. During incremental training, the model parameters are updated based on the new data, enabling the model to adapt to new data features and distributions, improving model performance and generalization ability.

[0132] 3.2 Develop a model version management system: This system will record detailed information for each model version, including model parameters, training data, performance metrics (such as accuracy, recall, F1 score, etc.), training time, and training environment. This ensures that this information accurately reflects the characteristics and performance of each model version. A model version comparison function will be developed, allowing users to easily compare the differences between different model versions and understand the changes during the update process. The effectiveness of model updates can be evaluated by comparing performance metrics and other information. A version rollback function will be provided, allowing users to quickly restore the model to a previous stable version if performance issues or other anomalies occur after an update, ensuring the normal operation of the model and the stability of the service.

[0133] Furthermore, the data privacy protection module employs high-strength encryption algorithms such as the Advanced Encryption Standard (AES) to encrypt sensitive information at each stage of data collection, storage, and transmission. This includes the following steps.

[0134] 1. Encryption during data acquisition: Before data acquisition, identify which data constitutes sensitive information, such as user ID numbers, contact information, medical records, and financial transaction information. Employ high-strength encryption algorithms such as Advanced Encryption Standard (AES) to encrypt sensitive information. Configure a suitable encryption key management system to ensure the secure generation, storage, and use of keys. Upon acquisition of sensitive information, immediately encrypt the data using the selected encryption algorithm. Ensure the accuracy and integrity of the encryption process, and record relevant encryption operation logs, including encryption time and encrypted data identifiers.

[0135] 2. Data Storage Encryption: Configure the database system with encryption to ensure support for encrypted storage of sensitive fields. For example, set appropriate encryption rules for database tables storing sensitive fields such as user ID numbers and medical records. Before storing sensitive data in the database, encrypt each sensitive field using an encryption algorithm. Employ appropriate encryption modes (such as CBC, ECB, etc.) and properly safeguard encryption keys to prevent leakage. Regularly update encryption keys and establish key backup and recovery mechanisms. Implement strict access control for keys; only authorized personnel can access and manage keys.

[0136] 3. Data Transmission Encryption: Ensure the use of SSL / TLS (Secure Sockets Layer and Transport Layer Security) encryption to guarantee data security during network transmission. Configure relevant certificates and keys to ensure correct protocol operation. At the data sending end, encrypt the data to be transmitted before transmitting it through an encrypted channel. At the data receiving end, decrypt the received encrypted data to verify its integrity and accuracy. Monitor the data transmission process in real time to detect abnormal traffic or security vulnerabilities. Conduct regular audits to check the usage of the encryption protocol and the security of data transmission.

[0137] This invention provides a method for using AI-based service software, comprising the following steps.

[0138] S1. The data collection module builds a multi-channel data acquisition platform to collect data from multiple sources, including sensor data, web crawlers, and user uploads.

[0139] S2, the data processing module preprocesses the data, performs real-time deviation detection, and uses data mining algorithms to monitor the data distribution in real time.

[0140] S3, the data privacy protection module, adopts high-strength encryption algorithms such as the Advanced Encryption Standard (AES) to encrypt sensitive information in all stages of data collection, storage, and transmission.

[0141] S4. The model building module builds models based on specific application tasks and data characteristics, trains the models using the training set, validates the models using the validation set, and tests the models using the test set.

[0142] S5, the model optimization module, optimizes the model, including model compression, hardware adaptation, enhanced interpretability, and improved generalization ability.

[0143] S6, the algorithm enhancement module, strengthens the model algorithm, including adversarial attack defense, common sense reasoning enhancement, and model update and maintenance.

[0144] S7. The external interaction module establishes a lightweight model resource pool to select low-computational-complexity models for high-real-time scenarios; it unifies the feature learning platform, develops a general framework to realize multimodal data feature conversion, and uses autoencoders to learn shared feature representations; it designs a fusion strategy management module to provide multiple fusion strategies such as early, late, and intermediate; it formulates interface standards and data format specifications for the AI ​​service software system to connect with external systems, develops middleware and adapter software libraries to support data format conversion, protocol adaptation, and communication coordination, and achieves seamless integration between the AI ​​system and traditional databases.

[0145] S8, the performance monitoring module, monitors system performance in real time, and monitors key indicators such as model inference speed, resource utilization, and accuracy.

[0146] The S9 security protection module employs an intrusion detection and prevention system (IDS / IPS) to monitor network traffic in real time, identify malicious attacks targeting the AI ​​system, and block them promptly. Regular security vulnerability scans are conducted using tools such as OWASP and ZAP to comprehensively inspect the system's web applications and API interfaces, and to promptly patch any discovered security vulnerabilities.

[0147] The working principle of this invention's AI service software system is as follows: A data collection module builds a multi-channel data acquisition platform to collect data from multiple sources, including sensor data, web crawlers, and user uploads. A data processing module preprocesses the data, performs real-time deviation detection, and uses data mining algorithms to monitor data distribution in real time. A data privacy protection module employs high-strength encryption algorithms such as AES (Advanced Encryption Standard) to encrypt sensitive information at each stage of data collection, storage, and transmission. A model building module constructs models based on specific application tasks and data characteristics, trains the models using a training set, validates them using a validation set, and tests them using a test set. A model optimization module optimizes the models; an algorithm enhancement module strengthens the model algorithms, including adversarial attack defense, common sense reasoning enhancement, and model update and maintenance; and an external interaction module enables seamless integration between the AI ​​system and traditional databases. A performance monitoring module monitors system performance in real time, tracking key indicators such as model inference speed, resource utilization, and accuracy. A security protection module uses an intrusion detection and prevention system (IDS / IPS) to monitor network traffic in real time, identify malicious attacks targeting the AI ​​system, and block them promptly. Regularly conduct security vulnerability scans, using tools such as OWASP and ZAP to comprehensively inspect the system's web applications, API interfaces, etc., and promptly fix any security vulnerabilities discovered.

[0148] This invention employs a data processing module to preprocess data and perform real-time deviation detection, while utilizing data mining algorithms to monitor data distribution in real time. This improves data quality, promptly identifies data anomalies, and provides more reliable data for model construction. The model optimization module performs optimizations such as model compression, hardware adaptation, enhanced interpretability, and improved generalization ability, improving model operating efficiency, better adapting to hardware environments, and enhancing the model's understandability and versatility. The algorithm enhancement module strengthens the model algorithm through adversarial attack defense, common-sense reasoning enhancement, and model update and maintenance, improving the model's security, intelligence, and continuous effectiveness, making it more stable in various situations.

[0149] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An artificial intelligence (AI) service software usage method based on, characterized by, Comprise the following steps: S1, the data collection module builds a multi-channel data collection platform, collects multi-source data; S2, the data processing module pre-processes the data, real-time deviation detection, monitors the data distribution; S3, the data privacy protection module encrypts sensitive information at each link; S4, the model construction module constructs the model according to the specific application task and data characteristics; S5, the model optimization module optimizes the model; S6, the algorithm strengthening module strengthens the model algorithm; S7, the external interaction module realizes the seamless integration of the AI system and the traditional database when integrated; S8, the performance monitoring module monitors the system performance in real time, and monitors the key indicators of model reasoning speed, resource occupancy and accuracy; S9, the security protection module monitors network traffic in real time, identifies malicious attack behaviors against the AI system, and blocks them in time, and timely repair the discovered security vulnerabilities; Step S5 includes the following steps: S51, model compression; S52, hardware adaptation optimization: according to the characteristics of different hardware platforms, develop special model adaptation tool library; integrate the developed adaptation tools into the deep learning framework, so that the model can automatically call the adaptation tools for optimization when deployed; according to the characteristics of hardware architecture, optimize convolution operation; S53, explainability enhancement; S54, generalization ability improvement; Step S53 includes the following steps: S53.1, basic model selection: determine the type of task, analyze the specific application task; S53.2, explainability algorithm integration: integrate explainability algorithms in model inference service; In the model inference service, the LIME explanation algorithm is integrated, an improved LIME local explainable model independent explanation model is used, and the model is: L(f,g,pi x )=Σ{a xz ×pi x (z)×d(x,z)×[f(z)-g(z)] 2}; d(x,z) = 1 / [1+dist(x,z)]; z e Z; wherein, L(f,g,π x ) is a loss function for measuring the degree of difference between the complex model f(x) and the interpretable model g(z) in the local range; f(x) represents the complex model; g(z) represents the interpretable model fitted in the local range; π x (z) is the local weight of the sample z relative to x; a xz is a dynamic adjustment factor; d(x,z) is a distance decay function; dist(x,z) is a distance measure between samples x and z; z is a sample element in the local sample set Z; Z is the local sample set. Step S6 includes the following steps: S61, defense against adversarial attacks: S61.1, introduce an adversarial training mechanism: in each round of training, use FGSM fast gradient symbol method to process the original sample to generate an adversarial sample; input the generated adversarial sample and the original sample into the model for training; S61.2, knowledge distillation: select a high-performance complex model as a teacher model, and match a lightweight robust model as a student model; periodically perform knowledge distillation operation on the complex model; S62, common sense reasoning enhancement; S63, model update and maintenance. 2.The AI service software usage method based on artificial intelligence (AI) according to claim 1, characterized in that: Step S2 includes the following steps: S21, data preprocessing: including data cleaning, data conversion and data integration; S22, real-time deviation detection: real-time data acquisition, and calculation according to the set index; compare the index value calculated in real time with the preset threshold value to determine whether there is deviation; S23, use data mining algorithm to monitor data distribution in real time: according to the characteristics of data and the purpose of monitoring, select data mining model algorithm, use historical data to train the selected algorithm, and establish data distribution model; analyze real-time data to detect whether the data distribution has changed abnormally; S24, exception handling: perform data integrity check, evaluate whether the abnormal data is reasonable in combination with historical data, market situation and business logic. 3.The AI service software usage method based on artificial intelligence (AI) according to claim 1, characterized in that: Step S4 includes the following steps: S41, determine the specific application task and data characteristics: determine the type of task, study the characteristics of data, including data type, data size, data distribution and data features; S42, model construction: different models are constructed according to different tasks; S43, data preprocessing: image data is preprocessed, including normalization and data enhancement; text data is preprocessed, including word segmentation, word embedding, padding and truncation; numerical data is preprocessed, including normalization and discretization; S44, model training: according to the type of model and task demand, set model parameters, select loss function according to task type; use training set to train model; S45, model verification: use validation set to evaluate the model; S46, model testing: use test set to test the model, and according to the evaluation result of test set, comprehensively evaluate and optimize the performance of the model. 4.The AI service software usage method based on artificial intelligence (AI) according to claim 3, characterized in that: Step S51 includes the following steps: S51.1, trigger mechanism: when the signal of model training is detected, automatically start the model compression process; S51.2, pruning algorithm application: adopt weight absolute value method to quantify the value of connection and neuron, set threshold value filter, remove low value connection and corresponding neuron, simplify network topology; recompile and initialize the pruned model to realize lightweight while maintaining key feature expression ability and improve inference efficiency; S51.3, quantization implementation: perform parameter data type conversion, update the storage structure of the model, adjust the calculation logic in the calculation process of the model, and realize the lightweight of the model. 5.The artificial intelligence (AI) service software-based using method according to claim 1, wherein: Step S3 includes the following steps: S31, data acquisition link encryption: when sensitive information is collected, immediately use encryption algorithm to encrypt the data; S32, data storage link encryption: encrypt the database system to ensure that the sensitive fields can be encrypted and stored; S33, data transmission link encryption: determine to use SSL / TLS encryption protocol to ensure the security of data in network transmission process. 6.The AI service software usage method based on artificial intelligence (AI) according to claim 1, characterized in that: Step S54 includes the following steps: S54.1, data enhancement in data preprocessing stage: according to the type of input data, select appropriate data enhancement operation; apply data enhancement toolkit to batch process training data; S54.2, model parameter adjustment and optimization: monitor the performance indicators of the model on the training set and validation set during training, adjust the regularization parameter in time according to the performance change, ensure that the model focuses on learning the general characteristics in the data, and improve the generalization performance. 7.The artificial intelligence (AI) service software-based using method according to claim 1, wherein: Step S63 includes the following steps: S63.1, build incremental learning platform: when new data arrives, the incremental learning platform automatically detects the data, analyzes the category and distribution change of the data; trigger incremental training; in the process of incremental training, update the parameters of the model according to the new data, so that the model can adapt to the new data characteristics and distribution, improve the performance and generalization ability of the model; S63.2, develop model version management system: record the version information of each model in detail, and develop model version comparison function. 8.The AI service software usage method based on artificial intelligence (AI) according to claim 1, characterized in that: Step S62 includes the following steps: S62.1, build a knowledge graph management platform: plan the overall architecture of the knowledge graph management platform, determine the type of database used by the platform and related technical framework; collect knowledge data related to the task, and clean, organize and structure the data, integrate the knowledge graph management platform with natural language processing and computer vision models; S62.2, application in text understanding task: when the model performs text understanding task, the model queries the knowledge graph in real time according to the text content; using the information obtained from the knowledge graph, the model accurately analyzes the meaning of the text and makes more reasonable decisions; S62.3, design multi-task learning module: analyze specific application scenarios, determine related multiple subtasks, and evaluate the relevance and difficulty between these tasks; build a dynamic scheduling model, design a dynamic scheduling mechanism for the multi-task learning module, and reasonably arrange the model to learn multiple subtasks at the same time according to the relevance and difficulty of the tasks. 9.The AI service software usage method based on artificial intelligence (AI) according to claim 1, characterized in that: In the process of the model learning multiple subtasks at the same time, the collaborative effect between tasks promotes the improvement of the model's common sense reasoning ability.

10. An artificial intelligence (AI) service software system using the method of any one of claims 1-9, comprising: Data collection module, data processing module, data privacy protection module, model construction module, model optimization module, algorithm strengthening module, external interaction module, performance monitoring module and security protection module; characterized in that: Data collection module: build a multi-channel data collection platform to collect multi-source data, including sensor data, web crawlers and user upload methods; Data processing module: pre-process the data, perform real-time bias detection, and use data mining algorithms to monitor data distribution in real time; Data privacy protection module: use high-strength encryption algorithms to encrypt sensitive information at each stage; Model construction module: build models according to specific application tasks and data characteristics; Model optimization module: optimize the model, including model compression, hardware adaptation, explainability enhancement and generalization ability improvement; Algorithm strengthening module: strengthen the model algorithm, including adversarial attack defense, common sense reasoning enhancement and model update maintenance; External interaction module: develop middleware and adapter software library to support data format conversion, protocol adaptation and communication coordination, and realize seamless integration of AI system and traditional database when integrated; Performance monitoring module: real-time monitoring of system performance, monitoring of model inference speed, resource occupancy and accuracy key indicators; Security protection module: real-time monitoring of network traffic, identification of malicious attack behavior against AI system, and timely blocking; timely repair of discovered security vulnerabilities.

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