Service software system and method based on artificial intelligence (AI)

Abstract

The invention belongs to the technical field of service software, and discloses an AI-based service software system and method, and the method comprises the steps: collecting multi-source data through a data collection module; the data processing module preprocesses the data; the data privacy protection module encrypts sensitive information of each link; the model construction module constructs a model according to specific application tasks and data characteristics; the model optimization module is used for optimizing the model; the algorithm strengthening module strengthens the model algorithm; the external interaction module realizes seamless joint when the AI system is integrated with the traditional database; the performance monitoring module monitors system performance in real time; and the security protection module timely repairs the found security vulnerabilities. The data quality can be improved; the model is subjected to optimization such as model compression, hardware adaptation, interpretability enhancement and generalization ability improvement through the model optimization module, the model operation efficiency can be improved, and the technical effects that the algorithm enhancement module carries out attack defense and common sense reasoning enhancement on the model algorithm are achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of service software, more particularly, to an AI service software system and method based on artificial intelligence. BACKGROUND

[0002] In today's society, AI service software systems based on artificial intelligence play an extremely important role in many fields. In the medical field, they can assist doctors in disease diagnosis and medical image analysis, improving the accuracy and efficiency of diagnosis. In the financial industry, they can be used for risk assessment and fraud detection to ensure financial security. In the transportation field, they can optimize traffic flow control and automatic driving to improve traffic efficiency and safety. AI service software systems provide intelligent solutions for various industries, helping them upgrade and transform. Through data analysis and prediction, AI service software systems can help enterprises and institutions better plan and manage resources. Based on the analysis of a large amount of data and model prediction, AI service software systems can provide strong support for decision-making, helping enterprises and government departments make more scientific and accurate decisions.

[0003] The prior art document with publication number CN116910412A provides an AI service software system based on artificial intelligence, which includes 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 reasoning module, and a specified configuration pushing module. This AI service software system based on artificial intelligence increases or reduces the number of AI reasoning servers connected with the switch according to the calculation amount of the algorithm in the AI software, which has higher flexibility. At the same time, it uses network switches to transfer information, which has faster transmission speed and lower delay. Through the big data management module, it can classify big data content according to user search content, aggregate data processing, and automatically train models for standardized data through matching and merging logic and the AI intelligent algorithm reasoning module.

[0004] The prior technical solutions have the following defects: 1. The prior art lacks effective means for real-time deviation detection of data and real-time monitoring of data distribution using data mining algorithms, which makes it difficult to guarantee data quality and makes it impossible to discover data abnormalities in time, thereby making the data provided for model construction unreliable and affecting the performance and effect of subsequent models. 2. Some current technologies have limitations in model optimization, resulting in low model running efficiency, excessive hardware resource occupation, poor model interpretability, difficulty in understanding the decision-making process, insufficient universality, and inability to run well in different scenarios. 3. The prior art lacks effective counter-attack defense mechanisms for model algorithm reinforcement, and the model is vulnerable to malicious attacks and difficult to maintain stable performance in complex and variable environments.

[0005] In view of this, an artificial intelligence AI service software system and method are proposed. SUMMARY

[0006] 1. Technical problem to be solved.

[0007] The present application aims to provide an artificial intelligence AI service software system and method, which solves the technical problems proposed in the background art, realizes pre-processing of data and real-time deviation detection by a data processing module, real-time monitoring of data distribution using data mining algorithms, improves data quality, optimizes models by model compression, hardware adaptation, interpretability enhancement and generalization ability improvement, improves model running efficiency, and realizes the technical effects of counter-attack defense and common sense reasoning enhancement of model algorithms by an algorithm reinforcement module.

[0008] 2. Technical solution.

[0009] The technical solution of the present application provides an artificial intelligence AI service software system, which comprises.

[0010] Data collection module: build a multi-channel data collection platform to collect multi-source data, including sensor data, web crawlers, user uploads and other methods. Label the data to accurately label the collected image, text and other data to improve the accuracy of model training. Implement data quality control mechanisms to ensure the quality and consistency of input data.

[0011] Data processing module: pre-process the data, perform real-time deviation detection, use data mining algorithms to monitor data distribution in real time, and start the manual review process as soon as category data distribution is found to be abnormal, or supplement data and adjust sample weights through automatic scripts.

[0012] Data privacy protection module: high-strength encryption algorithms such as AES are used to encrypt sensitive information at each link of data collection, storage, and transmission.

[0013] Model construction module: build models according to specific application tasks and data characteristics, including convolutional neural network (CNN), recurrent neural network (RNN), its variants LSTM, GRU, decision tree model, and linear regression model. Train the model through the training set, validate the model through the validation set, and test the model through the test set.

[0014] Model optimization module: optimize the model, including model compression, hardware adaptation, explainability enhancement, and generalization ability improvement. Model compression reduces model parameters and storage requirements through pruning algorithm and quantization technology, making it more suitable for edge computing. Hardware adaptation optimization develops adaptation tools for different hardware platforms to optimize the operation efficiency of deep learning models. Explainability enhancement integrates explainability algorithms by fusing model frameworks with strong explainability and neural networks, improving model decision transparency. Generalization ability improvement embeds data augmentation toolkit in data preprocessing stage to expand data quantity and diversity, and applies regularization technology in model training process to prevent overfitting and improve model generalization performance.

[0015] Algorithm reinforcement module: reinforce model algorithms, including adversarial attack defense, common sense reasoning enhancement, and model update maintenance.

[0016] Adversarial attack defense: introduce adversarial training mechanism in the model training process. Each round of training, through FGSM algorithm to generate adversarial samples, with the original sample to input the model for training, make the model learn to identify and resist the adversarial attack, improve the model robustness. Regularly perform knowledge distillation on complex models to transfer the knowledge of complex models to simple and robust distillation models. In the inference stage, use the distillation model for prediction to reduce the impact of adversarial attacks on model output.

[0017] Common sense reasoning enhancement: build a knowledge graph management platform and deeply integrate it with natural language processing, computer vision, and other models. In text understanding tasks, the model can query the knowledge graph in real time to obtain semantic relationships and common sense background information to assist text analysis and decision-making. Design a multi-task learning module to dynamically schedule multiple sub-tasks based on task relevance and difficulty. In the intelligent customer service scenario, the model learns multiple tasks such as semantic understanding, intent recognition, and knowledge question answering simultaneously, and promotes common sense reasoning ability through task cooperation.

[0018] Model update maintenance: Build an incremental learning platform that automatically detects changes in data categories and distributions when new data arrives. For image classification models, if new class images appear, the platform automatically triggers an incremental training process to learn new data features based on the original model, avoiding the need to retrain the entire model. Develop a model version management system to record detailed information about model parameters, training data, performance indicators, and other information for each version. Provide version comparison and rollback functions to ensure that the model update process is controllable and can quickly recover to a stable version when model performance problems occur.

[0019] External interaction module: Establish a lightweight model resource pool for high real-time scenarios to select low-complexity models; unify the feature learning platform to develop a universal framework for multi-modal data feature conversion using self-encoder to learn shared feature representations; design a fusion strategy management module to provide early, late, and intermediate fusion strategies; develop interface standards and data format specifications for AI service software systems to interface with external systems, develop middleware and adapter software libraries to support data format conversion, protocol adaptation, and communication coordination, and achieve seamless integration of AI systems with traditional databases.

[0020] Performance monitoring module: Real-time monitoring of system performance, monitoring key indicators such as model inference speed, resource occupancy, accuracy, etc. Use Prometheus and Grafana tools to build visual panels to visually present system performance changes. When performance indicators deviate from preset thresholds, automatically trigger optimization processes.

[0021] Security protection module: Use Intrusion Detection and Prevention Systems (IDS / IPS) to monitor network traffic in real-time, identify malicious attack behavior against AI systems, and block them in a timely manner. Regularly conduct security vulnerability scans using tools such as OWASP ZAP to comprehensively detect system web applications, API interfaces, and other security vulnerabilities, and promptly fix any discovered security vulnerabilities.

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

[0023] 1. Adversarial attack defense.

[0024] 1.1, Introduce the training mechanism of confrontation: before starting the model training, configure the training environment and related tools to ensure that the generation of adversarial samples and the normal operation of model training can be realized. In each round of training, algorithms such as FGSM fast gradient symbol method are used to process the original samples to generate adversarial samples. These adversarial samples are small but carefully designed perturbations of the original samples, designed to mislead the model to make wrong predictions. The generated adversarial samples are input into the model together with the original samples for training. The model learns the features of the original samples while learning how to identify and resist the interference of these adversarial samples, thereby gradually improving its robustness.

[0025] 1.2, Knowledge distillation: determine a complex and high-performance model that may be sensitive to adversarial attacks as the teacher model, and select a relatively simple and robust model as the student model (distillation model). The teacher model can be any complex model that performs well on related tasks, has rich knowledge and strong expression ability. Regularly perform knowledge distillation on the complex model. During knowledge distillation, the knowledge learned by the complex model (such as feature representation, decision boundary, etc.) is transferred to the distillation model through specific methods. Specifically, the distillation model can learn the output probability distribution of the complex model to achieve knowledge transfer. In the inference stage, use the distillation model obtained through knowledge distillation for prediction. Because the distillation model has better robustness, it can effectively reduce the impact of adversarial attacks on model output, improve the prediction accuracy and stability of the model when facing adversarial samples.

[0026] 2, Common sense reasoning enhancement.

[0027] 2.1, Build a knowledge graph management platform: plan the overall architecture of the knowledge graph management platform, including the design of data storage, data query, knowledge update and other modules. Determine the type of database used by the platform and related technical frameworks. Collect knowledge data related to natural language processing, computer vision and other tasks, and clean, organize and structure these data for storage in the knowledge graph.

[0028] Integrate the knowledge graph management platform with natural language processing, computer vision and other models to ensure that the model can query the knowledge graph in real time and obtain the required knowledge information during operation.

[0029] 2.2, Application in text understanding task: when the model performs text understanding task, the model will query the knowledge graph in real time according to the text content. Using the 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 multi-task learning module: analyze the specific application scenario, determine the relevant multiple sub-tasks, and evaluate the correlation and difficulty between these tasks. For example, in the intelligent customer service scenario, determine the semantic understanding, intent recognition, knowledge question and answer sub-tasks, and analyze their relationship and difficulty level. Build a dynamic scheduling model, design a dynamic scheduling mechanism for the multi-task learning module, and reasonably arrange the model to learn multiple sub-tasks at the same time according to the correlation and difficulty of the tasks. Some algorithms or strategies can be used to balance the training time and resource allocation of different tasks to ensure that the model can effectively learn each sub-task. In the process of the model learning multiple sub-tasks at the same time, through the cooperation between tasks, the common sense reasoning ability of the model is promoted.

[0031] 3, Model update and maintenance.

[0032] 3.1, Build incremental learning platform: when new data arrives, the incremental learning platform automatically detects the data and analyzes the category and distribution change of the data. Statistical analysis, machine learning algorithms and other methods can be used to determine whether the new data is significantly different from the original data. Trigger incremental training for applications such as image classification models, and if new category images are detected in the new data, the platform automatically triggers the incremental training process. Incremental training learns the features of new data based on the original model without retraining the entire model, saving time and computing resources. During the incremental training process, the model parameters are updated according to the new data, so that the model can adapt to the new data features and distribution, and improve the performance and generalization ability of the model.

[0033] 3.2, Develop model version management system: record detailed information of each version of the model, including model parameters, training data, performance indicators (such as accuracy, recall rate, F1 value, etc.), training time, training environment, etc. Ensure that this information can accurately reflect the characteristics and performance of each model version. Develop model version comparison function, users can easily compare the differences between different versions of the model, understand the changes of the model in the update process. By comparing performance indicators and other information, evaluate the effect of model update. Provide version rollback function, when the model has performance problems or other abnormal situations after updating, users can quickly restore the model to the previous stable version to ensure the normal operation of the model and the stability of the service.

[0034] The application provides an artificial intelligence (AI) service software use method, which comprises the following steps.

[0035] S1, the data collection module builds a multi-channel data collection platform, collects multi-source data, and covers sensor data, web crawlers, user uploads and other methods.

[0036] S2, the data processing module pre-processes the data, performs real-time deviation detection, and uses a data mining algorithm to monitor the data distribution in real time.

[0037] S3, the data privacy protection module uses high-strength encryption algorithms such as AES to encrypt sensitive information at each link of data collection, storage, and transmission.

[0038] S4, the model construction module constructs a model according to the specific application task and data characteristics, trains the model through a training set, verifies the model through a validation set, and tests the model through a test set.

[0039] S5, the model optimization module optimizes the model, including model compression, hardware adaptation, explainability enhancement, and generalization ability improvement.

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

[0041] S7, the external interaction module establishes a lightweight model resource pool, selects low-complexity models for high real-time scenarios; a unified feature learning platform is developed to realize multi-modal data feature conversion using a self-encoder to learn shared feature representations; a fusion strategy management module is designed to provide early, late, and intermediate fusion strategies; interface standards and data format specifications are developed for AI service software systems to interface with external systems, and middleware and adapter software libraries are developed to support data format conversion, protocol adaptation, and communication coordination, enabling seamless integration of AI systems with traditional databases.

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

[0043] S9, the security protection module uses an intrusion detection and prevention system (IDS / IPS) to monitor network traffic in real time, identify malicious attack behavior against AI systems, and block them in a timely manner. Regular security vulnerability scanning is carried out using tools such as OWASP ZAP to comprehensively detect system web applications, API interfaces, etc., and timely repair of discovered security vulnerabilities.

[0044] 3. Beneficial effects.

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

[0046] 1. The data processing module pre-processes the data and performs real-time deviation detection, and uses a data mining algorithm to monitor the data distribution, which can improve the data quality, timely detect data anomalies, and provide more reliable data for model construction.

[0047] 2. The data privacy protection module uses high-strength encryption algorithms such as AES to encrypt sensitive information at each link of data collection, storage, and transmission, effectively protecting data privacy and preventing sensitive information leakage.

[0048] 3. The model optimization module optimizes the model by model compression, hardware adaptation, explainability enhancement, and generalization ability improvement, etc. to improve the model running efficiency, make it better adapt to the hardware environment, and enhance the model's understandability and versatility.

[0049] 4. The algorithm strengthening module strengthens the model algorithm by defending against adversarial attacks, enhancing common sense reasoning, and updating and maintaining the model, which can improve the security, intelligence, and continuous effectiveness of the model, making it more stable in various situations. BRIEF DESCRIPTION OF DRAWINGS

[0050] Figure 1 The overall schematic diagram of the AI service software system disclosed in the present application. DETAILED DESCRIPTION

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

[0052] REFERENCE Figure 1 The present application provides an AI service software system based on artificial intelligence, which includes.

[0053] Data collection module: build a multi-channel data collection platform to collect multi-source data, including sensor data, web crawlers, user uploads, etc. In image recognition applications, multiple types of cameras are equipped to collect image data under different lighting, angle, and shooting device conditions to avoid single data bias. The data is labeled for accurate labeling of collected image, text, and other data to improve the accuracy of model training. At the same time, a data quality control mechanism is implemented to ensure the quality and consistency of input data.

[0054] Data processing module: pre-process the data, perform real-time bias detection, and use data mining algorithms to monitor data distribution in real time. Once abnormal data distribution is found in the category data, such as unbalanced regional or occupational data in credit approval data, the artificial review process is started immediately, or the data is supplemented through automatic scripts and the sample weight is adjusted.

[0055] Data privacy protection module: high-strength encryption algorithms such as AES are used to encrypt sensitive information at each link of data collection, storage, and transmission. When the database is stored, sensitive fields such as user ID numbers and medical records are encrypted and stored. When data is transmitted over the network, the SSL / TLS (Secure Sockets Layer and Transport Layer Security) encryption protocol is used to ensure data security.

[0056] A federated learning platform is constructed to support multiple parties in model training. For example, in a medical data sharing scenario, each hospital trains a model locally and uploads the parameters, and the central server aggregates the updates to avoid the privacy risks caused by centralized transmission and storage of raw medical data.

[0057] Model construction module: Construct models according to specific application tasks and data characteristics, including convolutional neural network CNN (for image-related tasks; such as image recognition application scenarios), recurrent neural network RNN and its variants LSTM, GRU (suitable for processing sequence data, such as text classification, machine translation, etc. in natural language processing tasks), decision tree model (for classification and regression tasks) and linear regression model (suitable for regression tasks, such as predicting house prices, etc.). Train the model through the training set, validate the model through the validation set, and test the model through the test set.

[0058] Model optimization module: including model compression, hardware adaptation, explainability enhancement and generalization ability improvement; model compression reduces model parameters and storage requirements through pruning algorithm and quantization technology, making it more suitable for edge computing. Hardware adaptation optimization develops adaptation tools for different hardware platforms to optimize the operation efficiency of deep learning models. Explainability enhancement integrates explainability algorithms by fusing explainability strong model framework with neural network, improving model decision transparency. Generalization ability improvement embeds data augmentation toolkit in data preprocessing stage to expand data quantity and diversity, and applies regularization technology in model training process to prevent overfitting and improve model generalization performance.

[0059] Model compression: after model training is completed, model compression process is automatically triggered, pruning algorithm is used to remove redundant connections and neurons, and model parameters are reduced. At the same time, quantization technology is used to convert parameter data type from 32-bit floating point number to 8-bit integer, reducing storage and calculation requirements, making the model more suitable for edge computing devices. It also includes hardware adaptation optimization, explainability enhancement and generalization ability improvement.

[0060] Hardware adaptation optimization: develop model adaptation tools for different hardware platforms, such as GPU graphics processing unit and TPU tensor processing unit. When deploying deep learning models, optimize convolution, pooling and other operations according to hardware architecture characteristics to improve hardware utilization and operation efficiency.

[0061] Explainability enhancement: For hybrid model architectures, preferentially select models with strong explainability such as decision trees and linear regression as the basic framework. For complex tasks, integrate explainable neural network architectures. In financial risk prediction, use linear regression models to give preliminary predictions, and then use explainable neural networks to analyze complex factors in depth, making the model decision-making process more transparent. Integrate LIME (Local Interpretable Model-agnostic Explanations), SHAP (Shapley Additive exPlanations), and other explainability algorithms in the model reasoning service. Users can choose to view the impact of input features on the output when obtaining model prediction results, such as displaying the contribution of each pixel area to the classification decision in image classification results.

[0062] Generalization ability improvement: In the data preprocessing stage, build-in data augmentation toolkit supports diversified transformation of image, text, audio and other multi-type data. Image data can be rotated, scaled, cropped, and noise added; text data can be synonym replaced, random words deleted and inserted, etc. to expand data volume and diversity. In the model training process, automatically apply L1, L2 regularization and Dropout techniques to prevent model overfitting. According to the task characteristics and data set size, dynamically adjust the regularization parameters to ensure that the model focuses on learning the general features in the data and improves the generalization performance.

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

[0064] Adversarial attack defense: In the model training process, introduce adversarial training mechanism. In each training round, generate adversarial samples through FGSM and other algorithms, input them into the model together with the original samples for training, so that the model learns to recognize and resist adversarial attacks, improving the model's robustness. Regularly perform knowledge distillation on complex models to transfer the knowledge of complex models to simple and robust distillation models. In the reasoning stage, use the distillation model for prediction to reduce the impact of adversarial attacks on model output.

[0065] Common sense reasoning enhancement: Build a knowledge graph management platform and deeply integrate it with natural language processing, computer vision, and other models. In text understanding tasks, the model can query the knowledge graph in real time to obtain semantic relationships and common sense background information to assist text analysis and decision-making. Design a multi-task learning module to dynamically schedule multiple sub-tasks based on task relevance and difficulty. In the intelligent customer service scenario, the model simultaneously learns semantic understanding, intent recognition, knowledge question answering, and other tasks to improve common sense reasoning ability through task collaboration.

[0066] Model update maintenance: Build an incremental learning platform that automatically detects changes in data categories and distributions when new data arrives. For image classification models, if new class images appear, the platform automatically triggers an incremental training process to learn new data features based on the original model, avoiding the need to retrain the entire model. Develop a model version management system to record detailed information about each version of the model, including parameters, training data, performance indicators, and more. Provide version comparison and rollback functions to ensure that the model update process is controllable and can quickly recover to a stable version if performance problems occur.

[0067] External interaction module: Establish a lightweight model resource pool for high real-time requirement application scenarios such as autonomous driving and industrial control, and select low-complexity model structures. In the autonomous driving perception module, use lightweight target detection models combined with hardware acceleration and parallel computing technology to improve model inference speed. Build a prediction cache system for tasks with regular inputs such as daily weather prediction and traffic flow prediction, and pre-compute and cache the results. When real-time requests arrive, directly obtain the results from the cache to reduce computation time. Unify the feature learning platform to develop a general feature representation learning framework that supports image, text, audio, and other multi-modal data feature conversion. Use techniques such as autoencoders to learn shared feature representations for different modal data, laying the foundation for subsequent fusion processing. Design a fusion strategy management module to provide early fusion, late fusion, and intermediate fusion strategies. For example, in the intelligent security scenario, automatically select the optimal fusion strategy based on video image and audio data characteristics and task requirements to improve system performance. Develop interface standards and data format specifications for AI service software systems to interface with external systems, including RESTful API, gRPC, and other common interface protocols. When integrating with enterprise ERP systems, ensure 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 format acceptable by the database, enabling seamless integration.

[0068] Performance monitoring module: Real-time monitoring of system performance, monitoring key indicators such as model inference speed, resource occupancy, and accuracy. Use Prometheus and Grafana tools to build visual panels to visually present system performance changes. When performance indicators deviate from preset thresholds, automatically trigger optimization processes. For example, if inference speed slows down, the system can dynamically adjust model parallel computing strategies or re-evaluate model compression and quantization effects for secondary optimization based on hardware resource usage.

[0069] Security protection module: Adopting Intrusion Detection and Prevention System (IDS / IPS), real-time monitoring of network traffic, identifying malicious attack behaviors against AI systems such as DDoS attacks, SQL injection, etc., and blocking in time. Regularly carry out security vulnerability scanning, use OWASP ZAP and other tools to comprehensively detect the system's Web application, API interface, etc., and timely repair the discovered security vulnerabilities.

[0070] Further, the data processing module pre-processes the data, conducts real-time deviation detection, and uses data mining algorithms to monitor data distribution in real time, including the following steps.

[0071] 1. Data preprocessing: including data cleaning, data conversion and data integration.

[0072] Data cleaning: identify and handle missing values, error values and duplicate values in data. Data can be "cleaned" by filling missing values (such as filling with mean, median or most likely value), deleting duplicate records, and correcting obvious error data.

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

[0074] Data integration: combine data from multiple data sources and store it uniformly, solve the consistency problem of data between different sources.

[0075] 2. Real-time deviation detection: identify key indicators that need to be monitored, such as mean, standard deviation, proportion of data, etc., and set reasonable deviation thresholds. For example, set a normal fluctuation range as the threshold for the proportion of regions or occupations in credit approval data. Real-time data collection and calculation, real-time data collection, and calculation according to the set indicators. For example, real-time calculation of the proportion of each region or occupation in credit approval data. Compare the real-time calculated indicator value with the pre-set threshold to determine whether there is a deviation. If the indicator value exceeds the threshold range, it is considered that a deviation has occurred.

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

[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. Abnormality handling: Perform data integrity checks to confirm whether the information related to abnormal data is complete. Evaluate whether the abnormal data is reasonable by combining historical data, market conditions, and business logic. Determine which data sources to obtain supplementary data from, such as other related business databases or external data providers. Based on the data sources and supplementary rules, write scripts to automatically obtain and insert data. For example, a script can periodically obtain the latest demographic data or economic indicator data for a region or profession from external data sources as supplementary information to add to the credit approval dataset. Run the automatic script and set up monitoring mechanisms to ensure the smooth progress of data supplementation and that the supplemented data meets quality requirements. Calculate the number of samples for each category (e.g., different regions or professions). Adjust each sample in the training data according to the calculated weights. For categories with fewer samples, increase the weight of their samples; for categories with more samples, decrease the weight of their samples, so that the model pays more attention to categories with fewer samples during training, balancing the importance of different categories.

[0081] Further, the model construction module: construct the model according to the specific application task and data characteristics, including the following steps.

[0082] 1. Determine the specific application task and data characteristics: Determine the type of task, whether it is an image-related task (such as image recognition, image segmentation, etc.), a sequence data processing task (such as text classification, sentiment analysis, machine translation, time series prediction, etc.), a classification task or a regression task, etc.

[0083] Study data characteristics: including 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 numerical values, discrete numerical values), or other types.

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

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

[0087] Data features: For image data, consider image size, resolution, color channel, etc.; for text data, consider vocabulary size, text length, presence of special symbols, etc.

[0088] 2. Model construction: Construct different models according to different tasks.

[0089] Image-related tasks: If the task is image recognition, image classification, etc., prefer to use convolutional neural networks (CNN). For example, in a handwritten digit recognition task, you can choose classic CNN model architectures such as LeNet, AlexNet, VGGNet, etc., and adjust and optimize them according to specific requirements and data size. For image segmentation tasks, you can choose specialized CNN architectures such as U-Net, Mask R-CNN, etc., which can accurately segment and label different regions in images.

[0090] Sequence data processing tasks: For text classification, sentiment analysis, and other tasks in natural language processing, recurrent neural networks (RNN) and their variants, long short-term memory networks (LSTM), and gated recurrent units (GRU), are commonly used. For machine translation tasks, an encoder-decoder structure is usually used, combined with LSTM, GRU, or Transformer models. For time series prediction tasks such as stock price prediction and temperature prediction, LSTM and GRU can effectively handle long-term dependencies in time series, and you can also consider using specialized time series models such as ARIMA, Prophet, etc., in combination with deep learning models.

[0091] Classification and regression tasks: For simple classification and regression tasks, choose decision tree models. They are easy to understand and interpret, and can visually display the relationship between features and target variables. For example, in a classification task to predict whether a customer will churn, a decision tree can make decisions based on customer age, consumption amount, purchase frequency, and other features. For regression tasks, if the data shows a linear relationship, choose a linear regression model.

[0092] 3. Data preprocessing: Preprocess image data, including normalization and data augmentation. Data augmentation expands training data through rotation, flipping, cropping, scaling, etc., increasing data diversity and improving model generalization.

[0093] Preprocess text data, including tokenization, word embedding, padding, and truncation.

[0094] Preprocess numerical data, including normalization and discretization. Discretization is a process of discretizing continuous numerical data into discrete numerical data according to certain rules.

[0095] 4、Model training: Divide the original data into training set, validation set and test set. According to the type of model and task requirements, set appropriate model parameters, such as learning rate, iteration number, hidden layer neuron number, etc. According to the type of task, select appropriate loss function. For classification tasks, common loss functions include cross-entropy loss function; for regression tasks, common loss functions include mean square error loss function, etc. Select appropriate optimizer to update model parameters, such as stochastic gradient descent (SGD), Adam, Adagrad, etc. Use the training set to train the model. During the training process, the model calculates the error between the predicted value and the true value according to the loss function, and updates the model parameters through the optimizer, constantly adjusts the weights of the model to minimize the loss function. Record the loss value and other indicators (such as accuracy, recall rate, etc.) during the training process to monitor the training progress and performance of the model.

[0096] 5、Model validation: During the training process, use the validation set to evaluate the model regularly. Input the validation set into the trained model to calculate the loss value and other indicators of the model on the validation set. According to the evaluation results of the validation set, adjust the parameters of the model. Save the models at different training stages during the training process, and select the best-performing model as the final model according to the evaluation results of the validation set.

[0097] 6、Model testing: Input the test set into the final selected model, calculate the loss value and other indicators of the model on the test set, such as accuracy, recall rate, F1 value (for classification tasks), root mean square error (RMSE), mean absolute error (MAE) (for regression tasks), etc. According to the evaluation results of the test set, comprehensively evaluate the performance of the model, and judge whether the model meets the requirements of practical application. If the model performance is not ideal, further analyze the reasons, such as data problems, improper model selection, unreasonable parameter setting, etc., and make corresponding improvements and optimizations.

[0098] Further, the model optimization module involves four aspects of model compression, hardware adaptation, explainability enhancement and generalization ability improvement. Including the following steps.

[0099] 1、Model compression.

[0100] 1.1、Trigger mechanism: When the signal of model training completion is detected, automatically start the model compression process. By setting a training completion flag in the training code, when the number of training rounds reaches the preset value or the loss function converges to a certain extent, the flag is set to true, thereby triggering the compression process.

[0101] 1.2, Pruning algorithm application: Evaluate the importance of connections and neurons, using methods such as weight-based evaluation, for each connection weight in the neural network, calculate its absolute value. Connections and neurons with smaller weight absolute values have relatively smaller impact on model output and can be considered redundant. Gradient-based evaluation methods can also be used to calculate the gradient of connections and neurons on the loss function, and smaller gradients indicate lower importance. According to the evaluation results, set a threshold, disconnect the connections with weight absolute values less than the threshold, and remove the corresponding neurons from the network. After removal, recompile and initialize the model to update the model structure.

[0102] 1.3, Quantization implementation: Perform parameter data type conversion, traverse all parameters of the model, and convert the original 32-bit floating-point type parameters to 8-bit integer type. Update the storage structure of the model and store the converted 8-bit integer parameters. In the calculation process of the model, adjust the calculation logic accordingly, use 8-bit integer operation rules for convolution, matrix multiplication, etc. operations to reduce storage and calculation requirements.

[0103] 2, Hardware adaptation 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 of GPU, memory bandwidth, TPU tensor processing unit structure, etc.), computing power (such as floating-point operation capability, integer operation capability) and storage capacity, etc.

[0105] 2.2, Develop adaptation tools: According to the characteristics of different hardware platforms, develop special model adaptation tool libraries. For example, for GPU platforms, use CUDA or OpenCL programming interfaces to develop adaptation functions to optimize parallel computing of convolution, pooling, etc. 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 when deployed.

[0106] 2.3, Operation optimization: According to the characteristics of hardware architecture, optimize convolution operations. For example, use block convolution algorithm on GPU to decompose large convolution operations into multiple small convolution blocks to improve memory access efficiency; on TPU, rearrange and group the convolution kernel to reduce computation based on its tensor operation advantages. Optimize the implementation of pooling operations, such as using parallel pooling algorithms to utilize multi-threading to process different regions of pooling operations simultaneously on GPU to improve operation speed.

[0107] 3, Explainability enhancement.

[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 π 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 is 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 in which the loss function changes most rapidly in the input space. W(θ) is a weight matrix related to the model parameters θ, which adjusts the direction and size of the gradient according to the value of the model parameters, so that the perturbation direction is more in line with the characteristics of the model. For example, W(θ) can be a diagonal matrix, whose diagonal elements are determined according to some statistical quantities of θ, such as the variance, mean, etc. of the parameters. (A(x) is a dynamic adjustment matrix related to the characteristics of the sample x, which can adjust the effect of perturbation in different feature dimensions according to the distribution of the feature vector of the sample x. For example, A(x) can be constructed according to the feature importance score of the sample x, so that the perturbation on important features is relatively small, while the perturbation on unimportant features is relatively large. x represents the original input sample, which is the normal input data of the model. Taking the image recognition task as an example, x can be a picture with specific pixel values; in the text classification task, x can be a piece of text data. y represents the true label of the sample, which is the correct classification or target value known.

[0122] 1.2, Knowledge distillation: Determine a complex, high-performance but possibly more sensitive to adversarial attacks model as a teacher model, and select a relatively simple structure, better robustness model as a student model (distillation model). The teacher model can be any complex model that performs well on related tasks, has rich knowledge and strong expression ability, such as large deep neural network models, such as ResNet (Residual Network) in the image field, VGGNet, etc., BERT (Bidirectional Encoder Representations from Transformers) in natural language processing field, GPT (Generative Pretrained Transformer) etc. Regularly perform knowledge distillation on complex models. In the process of knowledge distillation, the knowledge learned by the complex model (such as feature representation, decision boundary, etc.) is transferred to the distillation model through specific methods. Specifically, the distillation model can learn the output probability distribution of the complex model to achieve knowledge transfer. In the inference stage, the distillation model obtained by knowledge distillation is used for prediction. Because the distillation model has better robustness, it can effectively reduce the influence of adversarial attacks on model output, improve the prediction accuracy and stability of the model when facing adversarial samples.

[0123] 2, Common sense reasoning enhancement.

[0124] 2.1, Build a knowledge graph management platform: Plan the overall architecture of the knowledge graph management platform, including the design of data storage, data query, knowledge update, etc. Determine the type of database used by the platform (such as graph database) and related technical framework. Collect knowledge data related to natural language processing, computer vision, etc. such as semantic relationships, common sense background information, etc. and clean, organize and structure these data for storage in the knowledge graph.

[0125] Integrate the knowledge graph management platform with natural language processing, computer vision and other models to ensure that the model can query the knowledge graph in real time during operation and obtain the required knowledge information.

[0126] 2.2, Application in text understanding tasks: When the model performs text understanding tasks, the model will query the knowledge graph in real time according to 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 analyzing and understanding the text.

[0127] Using the information obtained from the knowledge graph, the model can more accurately analyze the meaning of the text and make more reasonable decisions. For example, when judging the sentiment tendency of a text, the model can improve the accuracy of sentiment analysis by combining the semantic relationships of sentiment words and common sense knowledge in the knowledge graph.

[0128] 2.3, Design multi-task learning module: Analyze specific application scenarios, determine relevant sub-tasks, and evaluate the relevance and difficulty of these tasks. For example, in the intelligent customer service scenario, determine semantic understanding, intent recognition, knowledge question and answer, etc. sub-tasks and analyze their relationship and difficulty level. Build a dynamic scheduling model and design a dynamic scheduling mechanism for the multi-task learning module. According to the relevance and difficulty of the tasks, reasonably arrange the model to learn multiple sub-tasks at the same time. Some algorithms or strategies can be used to balance the training time and resource allocation of different tasks to ensure that the model can effectively learn each sub-task.

[0129] During the process of the model learning multiple sub-tasks at the same time, through the cooperation between tasks, the common sense reasoning ability of the model is improved. For example, the semantic understanding task can provide more accurate text information for the intent recognition task, while the intent recognition task can guide the knowledge question and answer task to more accurately obtain relevant knowledge, thereby improving the common sense reasoning ability of the model.

[0130] 3, Model update and maintenance.

[0131] 3.1, Build an incremental learning platform: When new data arrives, the incremental learning platform automatically detects the data, analyzes the category and distribution changes of the data. Statistical analysis, machine learning algorithms and other methods can be used to determine whether the new data is significantly different from the original data. Trigger incremental training, for applications such as image classification models, if new class images are detected in the new data, the platform automatically triggers the incremental training process. Incremental training learns the features of new data based on the original model without retraining the entire model, saving time and computing resources. During the incremental training process, the model's parameters are updated according to the new data, allowing the model to adapt to new data features and distributions, improving the model's performance and generalization ability.

[0132] 3.2, Develop a model version management system: Detailed records of each version of the model, including model parameters, training data, performance indicators (such as accuracy, recall, F1 value, etc.), training time, training environment, etc. Ensure that this information accurately reflects the characteristics and performance of each model version. Develop a model version comparison function, allowing users to easily compare the differences between different versions of the model and understand the changes in the model during the update process. By comparing performance indicators and other information, evaluate the effectiveness of the model update. Provide version rollback function, when the model after the update appears performance problems or other abnormal situations, users can quickly restore the model to the previous stable version, to ensure the normal operation of the model and the stability of the service.

[0133] Further, the data privacy protection module uses high-strength encryption algorithms such as AES to encrypt sensitive information at each link of data collection, storage, and transmission. Including the following steps.

[0134] 1, Data collection encryption: Before data collection, determine which data belongs to sensitive information, such as user's ID number, contact information, medical records, financial transaction information, etc. Use high-strength encryption algorithms such as AES to encrypt sensitive information. Configure the corresponding encryption key management system to ensure the secure generation, storage and use of the key. When sensitive information is collected, immediately use the selected encryption algorithm to encrypt the data. Ensure the accuracy and integrity of the encryption process, record the relevant logs of the encryption operation, including encryption time, encrypted data identification, etc.

[0135] 2. Data storage encryption: Encrypt the database system configuration to ensure support for encrypted storage of sensitive fields. For example, for database tables that store sensitive fields such as user ID numbers, medical records, etc., set up corresponding encryption rules. Before storing sensitive data in the database, apply encryption algorithms to each sensitive field. Use appropriate encryption modes (such as CBC, ECB, etc.), and properly manage encryption keys to prevent key leakage. Regularly update encryption keys, establish key backup and recovery mechanisms. Strictly control access to keys, only authorized personnel can access and manage keys.

[0136] 3. Data transmission encryption: Determine to use SSL / TLS (Secure Sockets Layer and Transport Layer Security) encryption protocol to ensure data security during network transmission. Configure related certificates and keys to ensure the correct operation of the protocol. In the data sending end, encrypt the data to be transmitted, and then transmit it through the encrypted channel. In the data receiving end, decrypt the received encrypted data to verify the integrity and accuracy of the data. Monitor the data transmission process in real time to detect abnormal traffic or security vulnerabilities. Regularly audit to check the use of encryption protocols and the security of data transmission.

[0137] The application provides an artificial intelligence (AI) service software use method, including the following steps.

[0138] S1, a multi-channel data collection platform is built by a data collection module, and multi-source data is collected, including sensor data, web crawlers, user uploads, etc.

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

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

[0141] S4, the model construction module constructs a model according to the specific application task and data characteristics, trains the model through a training set, verifies the model through a validation set, and tests the model through a test set.

[0142] S5, the model optimization module optimizes the model, including model compression, hardware adaptation, explainability enhancement, and generalization ability improvement.

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

[0144] S7, the external interaction module establishes a lightweight model resource pool, selects a low computational complexity model for a high real-time scenario, unifies a feature learning platform, develops a general framework to realize multi-modal data feature conversion, learns a shared feature representation with a self-encoder, designs a fusion strategy management module to provide early, late, intermediate and other fusion strategies, formulates interface standards and data format specifications for the docking of the AI service software system and external systems, develops middleware and adapter software libraries to support data format conversion, protocol adaptation and communication coordination, and realizes seamless integration of 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 occupancy, accuracy, etc.

[0146] S9, the security protection module uses an intrusion detection and defense system (IDS / IPS) to monitor network traffic in real time, identify malicious attack behaviors against the AI system, and block them in a timely manner. Periodically carry out security vulnerability scanning, use tools such as OWASP ZAP to comprehensively detect the system's web applications, API interfaces, etc., and timely repair the discovered security vulnerabilities.

[0147] The working principle of the AI service software system based on artificial intelligence is as follows: the data collection module builds a multi-channel data collection platform to collect multi-source data; sensor data, web crawlers, user uploads and other methods are covered. The data processing module pre-processes the data, performs real-time deviation detection, and uses data mining algorithms to monitor data distribution in real time. The data privacy protection module uses high-intensity encryption algorithms such as AES to encrypt sensitive information at each link of data collection, storage, and transmission. The model construction module constructs a model according to the specific application task and data characteristics, trains the model through a training set, verifies the model through a validation set, and tests the model through a test set. The model optimization module optimizes the model; the algorithm strengthening module strengthens the model algorithm, including adversarial attack defense, common sense reasoning enhancement, and model update and maintenance; the external interaction module realizes seamless integration of the AI system and traditional databases. The performance monitoring module monitors system performance in real time, and monitors key indicators such as model inference speed, resource occupancy, accuracy, etc. The security protection module uses an intrusion detection and defense system (IDS / IPS) to monitor network traffic in real time, identify malicious attack behaviors against the AI system, and block them in a timely manner. Periodically carry out security vulnerability scanning, use tools such as OWASP ZAP to comprehensively detect the system's web applications, API interfaces, etc., and timely repair the discovered security vulnerabilities.

[0148] The application can improve data quality, discover data abnormalities in time, and provide more reliable data for model construction by pre-processing data and real-time deviation detection through a data processing module, real-time monitoring of data distribution by using a data mining algorithm. The model optimization module can improve model running efficiency, make it better adapt to hardware environment, and enhance the intelligibility and versatility of the model by optimizing the model in terms of model compression, hardware adaptation, explainability enhancement, and generalization ability improvement. The algorithm strengthening module can enhance the security, intelligence, and continuous effectiveness of the model by strengthening the model algorithm in terms of adversarial attack defense, common sense reasoning enhancement, and model update maintenance, so that the model can perform more stably in the face of various situations.

[0149] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements for some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for using AI-based service software, characterized in that, Includes the following steps: S1. The data collection module builds a multi-channel data collection platform to collect data from multiple sources. S2, the data processing module preprocesses the data, detects deviations in real time, and monitors data distribution; S3. The data privacy protection module encrypts sensitive information at each stage; S4. The model building module builds models based on specific application tasks and data characteristics. S5, the model optimization module optimizes the model; S6. The algorithm enhancement module enhances the model algorithm; S7, the external interaction module enables seamless integration between the AI ​​system and traditional databases; S8, the performance monitoring module monitors system performance in real time, and monitors key indicators such as model inference speed, resource utilization and accuracy. The S9 security protection module monitors network traffic in real time, identifies malicious attacks against the AI ​​system, and blocks them in a timely manner, while also promptly fixing any security vulnerabilities discovered.

2. The method for using AI service software based on claim 1, characterized in that: Step S2 includes the following steps: S21. Data preprocessing: including data cleaning, data transformation, and data integration; S22. Real-time deviation detection: Collect data in real time and calculate based on the set indicators; compare the real-time calculated indicator values ​​with the preset thresholds to determine whether there is a deviation. S23. Real-time monitoring of data distribution using data mining algorithms: Based on the characteristics of the data and the monitoring objectives, select a data mining model algorithm, train the selected algorithm using historical data, and establish a data distribution model; analyze real-time data to detect any abnormal changes in data distribution. S24. Anomaly Handling: Conduct data integrity checks, and assess the reasonableness of abnormal data by combining historical data, market conditions, and business logic.

3. The method for using AI service software based on claim 1, characterized in that: Step S4 includes the following steps: S41. Determine the specific application task and data characteristics: Clarify the type of task and study the data characteristics, including data type, data scale, data distribution, and data features; S42. Model Building: Build different models according to different tasks; S43. Data preprocessing: Preprocessing image data, including normalization and data augmentation; preprocessing text data, including word segmentation, word embedding, padding and truncation; preprocessing numerical data, including normalization and discretization. S44. Model Training: Set model parameters according to the model type and task requirements, and select the loss function according to the task type; train the model using the training set. S45. Model Validation: Evaluate the model using a validation set; S46. Model Testing: Test the model using a test set, and comprehensively evaluate and optimize the model's performance based on the evaluation results of the test set.

4. The method for using AI service software based on artificial intelligence according to claim 1, characterized in that: Step S5 includes the following steps: S51, Model Compression; S52. Hardware Adaptation and Optimization: Develop specialized model adaptation tool libraries based on the characteristics of different hardware platforms; integrate the developed adaptation tools into the deep learning framework so that the model can automatically call the adaptation tools for optimization during deployment; optimize convolution operations based on the characteristics of the hardware architecture. S53, Enhanced interpretability; S54, Improved generalization ability.

5. The method for using AI service software based on claim 4, characterized in that: Step S51 includes the following steps: S51.1 Triggering Mechanism: When the signal indicating that the model training is complete is detected, the model compression process is automatically started; S51.2 Application of pruning algorithm: The absolute value of weights is used to quantify the value of connections and neurons, a threshold is set for filtering, low-value connections and corresponding neurons are removed, and the network topology is simplified; the pruned model is recompiled and initialized to achieve lightweighting while maintaining the ability to express key features and improve inference efficiency. S51.3 Quantization Implementation: Convert parameter data types, update the model's storage structure, adjust the computation logic during the model's computation process, and achieve model lightweighting.

6. The method for using AI service software based on claim 4, characterized in that: Step S53 includes the following steps: S53.1 Basic Model Selection: Determine the task type and analyze the specific application task; S53.2, Integration of Explanatory Algorithms: Integrate explanatory algorithms into the model inference service.

7. The method for using AI service software based on artificial intelligence according to claim 1, characterized in that: Step S3 includes the following steps: S31. Encryption during data acquisition: When sensitive information is acquired, the data is immediately encrypted using an encryption algorithm. S32. Encryption of data storage: Configure the database system with encryption to ensure that it supports encrypted storage of sensitive fields; S33. Encryption during data transmission: Ensure that SSL / TLS encryption protocol is used to protect data security during network transmission.

8. The method for using AI service software based on claim 4, characterized in that: Step S54 includes the following steps: S54.1 Data augmentation in the data preprocessing stage: Select appropriate data augmentation operations according to the type of input data; apply data augmentation toolkits to process training data in batches; S54.2 Model Parameter Adjustment and Optimization: During training, monitor the model's performance metrics on the training and validation sets, and adjust the regularization parameters in a timely manner according to performance changes to ensure that the model focuses on learning the general features in the data and improves generalization performance.

9. The method for using AI service software based on claim 1, characterized in that: Step S6 includes the following steps: S61, Counter-attack defense: S61.1 Introducing an adversarial training mechanism: In each round of training, the original samples are processed using the Fast Gradient Sign Method (FGSM) to generate adversarial samples; the generated adversarial samples and the original samples are then input into the model for training. S61.2 Knowledge Distillation: Select a high-performance complex model as the teacher model and pair it with a lightweight robust model as the student model; perform knowledge distillation on the complex model periodically. S62, Enhanced Common Sense Reasoning; S63, Model update and maintenance.

10. The method for using AI service software based on claim 9, characterized in that: Step S63 includes the following steps: S63.1 Constructing an incremental learning platform: When new data arrives, the incremental learning platform automatically detects the data, analyzes the changes in the data categories and distribution, triggers incremental training, and updates the model parameters based on the new data during the incremental training process, enabling the model to adapt to new data features and distributions, thereby improving the model's performance and generalization ability. S63.2 Develop a model version management system: record detailed information on each version of the model and develop a model version comparison function.

11. The method for using AI service software based on claim 9, 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 the related technical framework; collect knowledge data related to the task, and clean, organize and structure the data; deeply integrate the knowledge graph management platform with natural language processing and computer vision models. S62.2 Application in text understanding tasks: When the model performs text understanding tasks, it queries the knowledge graph in real time based on 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 a multi-task learning module: Analyze specific application scenarios, identify multiple related sub-tasks, and evaluate the correlation and difficulty between these tasks; construct a dynamic scheduling model, design a dynamic scheduling mechanism for the multi-task learning module, and reasonably arrange the model to learn multiple sub-tasks simultaneously according to the correlation and difficulty of the tasks.

12. The method for using AI service software based on claim 11, characterized in that: As the model learns multiple sub-tasks simultaneously, the synergistic effect between tasks enhances the model's common-sense reasoning ability.

13. An artificial intelligence (AI) service software system, comprising: The module comprises a data collection module, a data processing module, a data privacy protection module, a model building module, a model optimization module, an algorithm enhancement module, an external interaction module, a performance monitoring module, and a security protection module; its features are: Data collection module: Build a multi-channel data collection platform to collect data from multiple sources, including sensor data, web crawlers, and user uploads; Data processing module: preprocesses the data, performs real-time deviation detection, and uses data mining algorithms to monitor data distribution in real time; Data privacy protection module: Employs high-strength encryption algorithms to encrypt sensitive information at each stage; Model building module: Builds models based on specific application tasks and data characteristics; Model optimization module: Optimizes the model, including model compression, hardware adaptation, interpretability enhancement, and generalization capability improvement; Algorithm enhancement module: Enhances the model algorithm, including adversarial attack defense, common sense reasoning enhancement, and model update and maintenance; External interaction module: 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; Performance monitoring module: Monitors system performance in real time, focusing on key indicators such as model inference speed, resource utilization, and accuracy. Security protection module: Real-time monitoring of network traffic, identification of malicious attacks against the AI ​​system, and timely blocking; timely patching of discovered security vulnerabilities.

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