Multi-object multi-model deployment and control early warning system

Abstract

The invention discloses a multi-object and multi-model deployment and control early warning system. The multi-object multi-model deployment and control early warning system comprises a multi-source data fusion module, an efficient algorithm and model optimization module, a real-time data processing and analysis module, a multi-model cooperation and decision module, an adaptive learning and evolution module and a security and privacy protection module. And a more comprehensive and deep judgment result is provided, so that the police officers can make more accurate decisions in the operation process. The multi-model structure of the system realizes cooperative work through the support of each module, and finally achieves the goals of accurate data, timely analysis and reliable early warning. And due to the close relationship between each module and multiple models, the system has higher decision-making ability and adaptability when facing a complex dynamic environment.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence, and in particular to a multi-object and multi-model control and early warning system. Background Art

[0002] Conventional technical content of the industry at home and abroad at this stage:

[0004] 1. Data collection and sensor deployment:

[0005] Current technologies rely on a variety of sensors, including image sensors, temperature and humidity sensors, and infrared sensors, to collect monitoring data in a distributed manner. However, the data collection process often encounters environmental interference and data delays, which affect the comprehensiveness and real-time performance of monitoring.

[0006] 2. Model construction and algorithm design:

[0007] Existing early warning systems typically use deep learning or statistical models to identify and predict object behavior. However, the complexity of different objects and scenarios increases the difficulty of model training. Furthermore, when dealing with the differences in characteristics of multiple objects and changing scenarios, the models may misjudge or miss detections, affecting the accuracy of the system.

[0008] The shortcomings of current conventional technologies mainly include:

[0009] 1. Computational complexity and resource consumption: Because the system needs to process multiple objects and models simultaneously, it places high demands on computing resources, which may lead to slower processing speeds or resource exhaustion. In addition, the complex computational process may also increase the system's energy consumption and maintenance costs.

[0010] 2. Difficulties in model selection and switching:

[0011] In practical applications, selecting the appropriate model for early warning based on the current scenario and needs is a challenge. Different models may have different advantages and disadvantages and scopes of application, and model selection and switching often require complex rules and algorithms. This increases system complexity and operational difficulty.

[0012] 3. Difficulty of data fusion and processing:

[0013] The fusion and processing of multi-source, heterogeneous data is a complex problem. Data from different sources may have different formats, quality, and timestamps, making effective fusion and processing of this data a technical challenge. Furthermore, noise and outliers in the data can also affect early warning results.

[0014] 4. Risks of false alarms and missed alarms:

[0015] Due to the complexity of multiple models and data uncertainty, the system may be subject to the risk of false positives and false negatives. False positives may arise from model inaccuracies or data noise, while false negatives may result from the model failing to cover all possible anomalies or missing data. This can negatively impact the system's early warning effectiveness. Summary of the Invention

[0016] The present application aims to solve one of the technical problems in the related art at least to a certain extent.

[0017] To this end, one of the purposes of this application is to propose a multi-object and multi-model control and early warning system to solve the problems of high computational complexity and large resource consumption in current technologies, and to improve system efficiency by optimizing algorithms, introducing distributed and parallel processing, and dynamic resource management; to enhance the flexibility of model selection and switching, and to enable the system to automatically select the optimal model according to different scenarios through adaptive model selection and expansion and update of the model library; to enhance data fusion and processing capabilities, and to develop multi-source data fusion technology, data preprocessing and real-time data processing to ensure data quality and real-time response; to reduce the risk of false alarms and missed alarms, and to ensure the reliability and stability of early warnings by improving model accuracy, multi-model verification and integration, and real-time feedback mechanisms; and to enhance the overall performance and reliability of the system through modular design, redundant fault-tolerant mechanisms, and safety protection measures, so that it has good scalability and stability, and can cope with complex and changing monitoring needs.

[0018] To achieve the above objectives, the first embodiment of the present application proposes a multi-object multi-model deployment control and warning system, which includes a multi-source data fusion module, an efficient algorithm and model optimization module, a real-time data processing and analysis module, a multi-model collaboration and decision-making module, an adaptive learning and evolution module, and a security and privacy protection module, wherein:

[0019] The multi-source data fusion module is used to comprehensively process and analyze data from different data sources with different formats and qualities to extract valuable information and features;

[0020] The efficient algorithm and model optimization module is used to improve and optimize the algorithm and model according to specific application scenarios and requirements to improve their computing efficiency and prediction accuracy;

[0021] The real-time data processing and analysis module is used to quickly process and analyze the data generated in real time to achieve timely discovery and early warning of potential risks;

[0022] The multi-model collaboration and decision-making module is used to comprehensively analyze and make decisions based on the results of multiple models to improve the reliability and stability of early warning;

[0023] The adaptive learning and evolution module is used to automatically adjust and optimize system parameters and model structure, so that the system can continuously adapt to complex and changing environments.

[0024] The security and privacy protection module is used to protect the system from malicious attacks and data leakage threats, and ensure the confidentiality of user privacy.

[0025] In one embodiment of the present application, the multi-source data fusion module includes a data acquisition unit, a data cleaning unit, a data conversion unit, a data fusion unit and a feature extraction unit, wherein:

[0026] The data acquisition unit is used to automatically capture data from various data sources through distributed data acquisition tools Kafka and / or Flume, and integrate them through interfaces, wherein:

[0027] The data includes key personnel databases, position data, portrait data, vehicle data, Wi-Fi fence data, three-dimensional trajectory data, social media data, logistics and consumption data, historical warning and event data, equipment location data and / or police base data;

[0028] The data cleaning unit processes outliers and duplicate data using the data cleaning tools OpenRefine and / or Pandas to remove redundant, abnormal, and noisy data to ensure data accuracy and consistency;

[0029] The data conversion unit standardizes and converts data in different formats through the data conversion framework Talend and / Apache Nifi to adapt it to subsequent processing requirements;

[0030] The data fusion unit integrates data by time, space or object characteristics through a Bayesian reasoning fusion algorithm to form a comprehensive view across data sources, providing a unified data foundation for subsequent analysis;

[0031] The feature extraction unit extracts core features from the fused data using the feature selection algorithm LASSO and / or decision tree for model analysis and early warning judgment, wherein:

[0032] The core features include identity information features, device identification features, biometric features, behavior trajectory features, social association features, risk scores and behavior tags.

[0033] In one embodiment of the present application, the efficient algorithm and model optimization module includes an algorithm optimization unit and a model optimization unit.

[0034] The algorithm optimization unit implements the following steps S101-S103:

[0035] S101: Select appropriate algorithms based on task requirements and data characteristics: For classification tasks, select the SVM algorithm for object classification; for clustering tasks, select the K-means algorithm for group analysis; for anomaly detection tasks, use the isolation forest algorithm to detect abnormal behaviors; for trajectory analysis tasks, use trajectory clustering and dynamic time warping (DTW) algorithms for adjoint relationship analysis. The tasks include classification, clustering, anomaly detection, and trajectory analysis.

[0036] S102: Adjust the selected algorithm to adapt to large-scale data and complex application scenarios: Split the computational tasks in the algorithm into multiple subtasks and execute them simultaneously on multiple processors or computing nodes, thereby distributing the computational burden and improving overall processing efficiency.

[0037] S103: Combining multiple algorithms to improve analysis accuracy: In the abnormal behavior detection process, the SVM algorithm and the isolation forest algorithm are combined to combine classification with anomaly detection to improve the recognition accuracy of false positives and false negatives. In the trajectory analysis task, trajectory clustering is combined with dynamic time warping (DTW) to perform adjoint relationship analysis.

[0038] The model optimization unit includes the following steps S201-S205:

[0039] S201: Model structure adjustment:

[0040] For deep learning models, the number of layers and nodes of the neural network is adjusted to adapt to complex data. For tasks with high real-time requirements, lightweight models are used to improve computing speed.

[0041] S202: Parameter optimization:

[0042] Optimize the model's hyperparameters using grid search, random search, or Bayesian optimization to find the best parameter combination to improve the model's performance.

[0043] S203: Training method improvement:

[0044] Transfer learning: Utilize existing pre-trained models and fine-tune them to quickly adapt the model to the new data distribution, reducing training time and improving efficiency.

[0045] Online learning: When data streams are constantly updated, the model parameters are adjusted in real time to enable it to track data changes and ensure that the model maintains high accuracy and stability in a dynamic environment.

[0046] Reinforcement learning: In complex decision-making scenarios, the strategy is gradually optimized through the interaction between the model and the environment to improve the decision-making ability and adaptability of the model in unknown or dynamic environments.

[0047] S204: Feature selection and dimensionality reduction:

[0048] Use feature selection and dimensionality reduction techniques to remove redundant features, reduce the dimension of model input, reduce computational complexity, and improve model execution efficiency.

[0049] S205: Model Compression and Acceleration:

[0050] Model compression techniques such as knowledge distillation, pruning, and / or quantization can be used to reduce model size and computational complexity, thereby accelerating model inference and improving the system's real-time responsiveness.

[0051] In one embodiment of the present application, the real-time data processing and analysis module implements the following steps:

[0052] S301: Use API interfaces to connect to data sources and manage data inflow through Kafka to ensure data continuity and real-time availability.

[0053] S302: Use the stream processing framework Apache Spark Streaming to segment, filter, and transform data to achieve real-time analysis. The stream processing framework's window function is used for time-sharing processing, and regular expressions are used for data format cleaning.

[0054] S303: Use incremental logistic regression and / or KNN online learning algorithms to continuously update model parameters to adapt the model to the changing data stream.

[0055] S304: Based on the isolation forest and / or local outlier factor algorithms, detect abnormal patterns in real time, trigger early warnings, and record important data in the database to achieve efficient writing and fast query, and support traceability.

[0056] The abnormal modes include:

[0057] Abnormal behavior: monitor the target object's frequent entry and exit of sensitive areas, unusual paths or travel time,

[0058] Location anomaly: Monitor whether the target object appears in unusual geographical locations or sensitive areas, or appears in areas not usually visited.

[0059] Social association anomaly: By analyzing social relationship or accompanying relationship data, it is detected that specific high-risk individuals frequently appear with other unrecorded accompanying persons.

[0060] Equipment anomalies: Monitor equipment associated with personnel for frequent replacement or frequent contact with other high-risk objects.

[0061] Frequency and timing anomalies: The frequency of abnormal activities in sensitive areas increases at night, or multiple risk persons appear in a short period of time.

[0062] In one embodiment of the present application, the model includes:

[0063] Personnel data quality analysis model: personnel data used to aggregate key personnel information and maintain data integrity;

[0064] 3D trajectory data dynamic aggregation model: By integrating multiple trajectory data, a thematic trajectory library of personnel dynamics is formed;

[0065] Key personnel collision warning model: realizes collision comparison between key personnel and other data and triggers warning;

[0066] Key group abnormal behavior early warning model: Automatically analyze and warn of abnormal behavior of key groups at sensitive nodes;

[0067] Personnel mining analysis model: Analyze the accompanying relationships of key personnel and mine potential related personnel;

[0068] And key group trend analysis model: through trend analysis, identify the active characteristics of the group and provide support for advance warning.

[0069] In one embodiment of the present application, the model collaboration and decision module includes the following steps S601-S606:

[0070] S601: Model selection and switching:

[0071] Determine the application scenario based on data features, calculate the adaptability score of each candidate model, and dynamically select the optimal model using a multi-armed bandit algorithm. When data features change significantly, automatically switch to a model with higher adaptability.

[0072] S602: Model Integration:

[0073] Simultaneously analyze multiple models to generate different results. Weights are assigned based on model performance, and the results are fused using a weighted average method. A voting mechanism is employed in classification tasks to ensure the stability of the final decision. If the results are abnormal, a conditional trigger is used to perform secondary verification and regenerate the results to reduce false positives and false negatives.

[0074] S603: Make a final decision on the model results based on the rule base:

[0075] Set warning rules based on scenarios, use the rule engine to check data, trigger warnings when rules are met, and automatically update and optimize the rule base through feedback data;

[0076] S604: Feedback Adjustment:

[0077] Collect feedback information after early warning, adjust model weights and parameters based on the feedback, optimize performance, and update the model regularly to adapt it to the latest data.

[0078] In one embodiment of the present application, the present invention also provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that when the processor executes the program, the steps of a multi-object model deployment and warning system are implemented.

[0079] In one embodiment of the present application, the present invention further provides a computer-readable storage medium having a computer program stored thereon, characterized in that when the program is executed by a processor, the program implements the steps of a multi-object model deployment and warning system.

[0080] In one embodiment of the present application, the present invention further provides a processor, characterized in that the processor is used to run a program, wherein the program executes the steps of the multi-object model deployment and warning system when running.

[0081] Compared with the prior art, the multi-object model control and early warning system of the embodiment of the present application has the following advantages:

[0082] 1. This invention reduces data redundancy through data screening and cleaning, and uses conflict resolution strategies to improve the accuracy and reliability of fusion results and ensure the consistency of data fusion.

[0083] 2. The solution of the present invention combines audio and image features, performs early fusion of time series data through a bidirectional recurrent neural network, and fully utilizes the key information in the audio and video signals. Compared with single-modal processing, this multimodal fusion greatly improves the model's ability to understand complex video events, can maximize the use of data resources, eliminate redundancy and conflict, and provide a more reliable foundation for high-quality data modeling. In particular, it has significant advantages in the accuracy and robustness of behavior recognition.

[0084] 3. The present invention realizes the encrypted transmission and storage protection of data, and cooperates with the hierarchical management of access rights by the access permission unit, thereby effectively protecting the privacy and security of personal information and ensuring the high reliability and security of the system in data storage and access.

[0085] 4. The present invention can flexibly adjust and expand behavior categories to adapt to different application scenarios and needs. At the same time, different classification models, feature extraction algorithms and parameter tuning strategies can be selected according to specific project requirements, and it has good adaptability and scalability.

[0086] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0088] Figure 1 The figure is a flowchart of the steps of the multi-object model control and early warning system of the present invention.

[0089] Figure 2 This is a modular schematic diagram of the multi-source data fusion module of the multi-object model of the present invention.

[0090] Figure 3 It is a flow chart of the model collaboration and decision-making module of the multi-object model of the present invention.

[0091] Figure 4 Schematic diagram of the process of the adaptive learning and evolution module of the multi-object model of the present invention. DETAILED DESCRIPTION

[0092] The embodiments of the present application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application and are not to be construed as limiting the present application. On the contrary, the embodiments of the present application include all variations, modifications, and equivalents that fall within the spirit and scope of the appended claims.

[0093] The following describes the multi-object model control and warning system of the embodiment of the present application with reference to the accompanying drawings.

[0094] The multi-object model control and warning system provided in the embodiment of the present application can be applied to.

[0095] like Figure 1-4 As shown, the multi-object multi-model deployment warning system of the embodiment of the present application includes a multi-source data fusion module, an efficient algorithm and model optimization module, a real-time data processing and analysis module, a multi-model collaboration and decision-making module, an adaptive learning and evolution module, and a security and privacy protection module, wherein,

[0096] The multi-source data fusion module is used to comprehensively process and analyze data from different data sources with different formats and qualities to extract valuable information and features;

[0097] The efficient algorithm and model optimization module is used to improve and optimize the algorithm and model according to specific application scenarios and requirements to improve their computing efficiency and prediction accuracy;

[0098] The real-time data processing and analysis module is used to quickly process and analyze the data generated in real time to achieve timely discovery and early warning of potential risks;

[0099] The multi-model collaboration and decision-making module is used to comprehensively analyze and make decisions based on the results of multiple models to improve the reliability and stability of early warning;

[0100] The adaptive learning and evolution module is used to automatically adjust and optimize system parameters and model structure, so that the system can continuously adapt to complex and changing environments.

[0101] The security and privacy protection module is used to protect the system from malicious attacks and data leakage threats, and ensure the confidentiality of user privacy.

[0102] It should be noted that in a multi-object, multi-model early warning system, each module works closely with the multiple models to support the system's overall early warning performance. The following is how each module is connected to the multiple models:

[0103] Multi-source data fusion module: This is the foundation for multi-model collaboration. By integrating data from different sources and formats to form a comprehensive view, multiple models can analyze and infer based on a unified data structure. This module provides multi-dimensional and complete data support for multiple models, ensuring higher accuracy and comprehensiveness in analysis and judgment.

[0104] Efficient Algorithm and Model Optimization Module: This module improves the accuracy and execution efficiency of multiple models. Different models (such as classification, clustering, and anomaly detection) have their own strengths in specific scenarios. This module improves the performance of individual models by selecting appropriate algorithms, optimizing model structures, and adjusting parameters. It also enhances the synergy of models when processing big data, making the overall analysis more timely and reliable.

[0105] Real-time data processing and analysis module: Real-time data processing enables multiple models to quickly respond to changes in the current data stream, enabling rapid data cleaning, filtering, and segmentation, ensuring that the input data for multiple models is always up-to-date and accurate. This support enables multiple models to quickly respond, detect, and provide early warnings when risks arise.

[0106] Multi-model collaboration and decision-making module: This module directly manages and coordinates the collaboration of multiple models. By weighting, integrating, and judging the output of multiple models, it ensures that the final warning results are accurate and reliable. At the same time, the module can flexibly select, switch, or integrate models in different scenarios, fully leveraging the advantages of each model to make warnings more accurate and reduce false alarm rates.

[0107] Adaptive Learning and Evolution Module: This module enables adaptive optimization of multiple models. The system adjusts model parameters and structure through a continuous feedback mechanism and automatically updates and optimizes models using reinforcement learning and evolutionary algorithms, enabling multiple models to adapt to dynamic environments and maintain flexibility. The module ensures that multiple models can adapt to complex scenario changes over the long term and have efficient response capabilities in emergencies.

[0108] Security and privacy protection module: This module ensures the security of multiple models in data transmission and storage. Through data encryption, access control and other means, the module ensures that multiple models are protected from external attacks and data leakage risks when processing sensitive information, providing guarantees for the safe operation of the models, thereby supporting the overall stability and reliability of the system.

[0109] Overall, the system's multi-model structure achieves collaborative work through the support of each module, ultimately achieving the goals of accurate data, timely analysis, and reliable early warning. The close relationship between each module and the multiple models enables the system to have higher decision-making capabilities and adaptability when facing complex dynamic environments.

[0110] It should be noted that the multi-source data fusion module includes a data acquisition unit, a data cleaning unit, a data conversion unit, a data fusion unit and a feature extraction unit, wherein:

[0111] The data acquisition unit is used to automatically capture data from various data sources through distributed data acquisition tools Kafka and / or Flume, and integrate them through interfaces, wherein:

[0112] The data includes key personnel databases, position data, portrait data, vehicle data, Wi-Fi fence data, three-dimensional trajectory data, social media data, logistics and consumption data, historical warning and event data, equipment location data and / or police base data;

[0113] Device point data refers to the data collected by devices within a monitoring or control system. Specifically, it includes real-time data or status information collected by devices at specific locations (points). These points may be hardware locations such as sensors, controllers, and switches. Each point records specific device data, such as temperature, pressure, current, and flow.

[0114] Police base data is a collection of data used to support basic information for police work. It forms the foundational data established within public security or police systems. It includes various static and dynamic information related to police work, such as personnel information, case records, geographic location, surveillance data, and vehicle information.

[0115] The data cleaning unit processes outliers and duplicate data using the data cleaning tools OpenRefine and / or Pandas to remove redundant, abnormal, and noisy data to ensure data accuracy and consistency;

[0116] The data conversion unit standardizes and converts data in different formats through the data conversion framework Talend and / Apache Nifi to adapt it to subsequent processing requirements;

[0117] The data fusion unit integrates data according to time, space or object characteristics through a Bayesian reasoning fusion algorithm to form a comprehensive view across data sources, providing a unified data basis for subsequent analysis.

[0118] The feature extraction unit extracts core features from the fused data using the feature selection algorithm LASSO and / or decision tree for model analysis and early warning judgment, wherein the core features include identity information features, device identification features, biometric features, behavior trajectory features, social association features, risk scores and behavior tags.

[0119] It should be noted that in this embodiment, the data conversion unit feature extraction standardizes and converts data of different formats through the data conversion framework Talend and / Apache Nifi. The following describes the steps of standardization and format conversion of core features to ensure that each core feature in the system has a consistent format and content, providing a standardized data foundation for subsequent processing:

[0120] 1. Identity information feature standardization: Convert identity information from different sources (such as ID number, name, date of birth, or gender) into a unified encoding format, using a fixed character encoding (such as UTF-8) to ensure compatibility. At the same time, standardize field names and data formats to ensure that all data has consistent character length and format to facilitate subsequent matching and retrieval.

[0121] 2. Device identification feature consistency: Uniformly encode and encrypt the device's MAC address, mobile phone ID, and other features to ensure that all device identification information conforms to a unified format (such as a 12-digit MAC address format). For non-standard device data, format conversion and consistency are performed to simplify the feature extraction and correlation analysis process.

[0122] 3. Biometric formatting: Unify biometric data from different sources (such as portrait data, fingerprints, or iris images) into compatible file formats (such as JPEG or PNG), unify image resolution, and ensure the clarity and size consistency of biometric images to improve accuracy in subsequent pattern recognition and matching processes;

[0123] 4. Time and space consistency of behavioral trajectory features: Standardize the timestamps and geographic location information in the behavioral trajectory data, unify all times into UTC format, and use a unified latitude and longitude format (such as the WGS84 coordinate system) for geographic information. This ensures that the trajectory data has a consistent time and space benchmark in cross-source analysis, facilitating event synchronization and spatial correlation.

[0124] 5. Normalize social association feature labels: Unify the label encoding of the target object's social association data, such as frequency of association and common places of visit. Unify the label information into standardized fields (such as frequency of association code and association strength level) to ensure consistent labeling of data from different sources, facilitating the model's analysis and classification of social relationships.

[0125] 6. Risk scoring standardization: Convert risk scores to a standardized score range (e.g., 0-10) to ensure consistent scoring across all data sources. For data sources with different scoring calculation methods, normalize them to form a unified risk score level for subsequent risk assessment and early warning judgment of the model.

[0126] 7. Behavioral label standardization: The behavioral labels of objects (such as abnormal activities, visits to specific places, etc.) are coded and normalized, and all label fields use consistent naming and coding methods (such as "abnormal behavior" is marked as "ABN") to ensure efficiency and consistency in data classification and retrieval, and also facilitate the model to perform contextual analysis of label information.

[0127] Through the above-mentioned standardization and format conversion steps, various core features have a unified format and specification, ensuring the consistency and accuracy of feature extraction, behavior analysis, pattern recognition and other processing processes, thereby improving the overall analysis efficiency of the data fusion system and the reliability of early warning.

[0128] In one embodiment of the present application, the efficient algorithm and model optimization module includes an algorithm optimization unit and a model optimization unit, wherein the algorithm optimization unit implements the following steps S101-S103:

[0129] S101: Select the appropriate algorithm based on task requirements and data characteristics:

[0130] For classification tasks, the SVM algorithm is selected for object classification. For clustering tasks, the K-means algorithm is selected for group analysis. For anomaly detection tasks, the isolation forest algorithm is used to detect abnormal behavior. For trajectory analysis tasks, trajectory clustering and dynamic time warping (DTW) algorithms are used for adjoint relationship analysis. The tasks include classification, clustering, anomaly detection, and trajectory analysis.

[0131] S102: Adjust the selected algorithm to adapt to large-scale data and complex application scenarios:

[0132] Split the computational tasks in the algorithm into multiple subtasks and execute them simultaneously on multiple processors or computing nodes, thereby distributing the computational burden and improving overall processing efficiency;

[0133] S103: Combine multiple algorithms to improve analysis accuracy:

[0134] In the process of abnormal behavior detection, the SVM algorithm and the isolation forest algorithm are combined to combine classification with anomaly detection to improve the recognition accuracy of false positives and missed negatives.

[0135] In the trajectory analysis task, trajectory clustering is combined with dynamic time warping (DTW) to perform adjoint relationship analysis.

[0136] The model optimization unit includes the following steps S201-S205:

[0137] S201: Model structure adjustment:

[0138] For deep learning models, the number of layers and nodes in the neural network can be adjusted to adapt to complex data. For tasks with high real-time requirements, lightweight models can be used to improve computing speed.

[0139] S202: Parameter optimization:

[0140] Optimize model hyperparameters using grid search, random search, or Bayesian optimization to find the best parameter combination to improve model performance;

[0141] S203: Training method improvement:

[0142] Transfer learning: Utilize existing pre-trained models and fine-tune them to quickly adapt the model to the new data distribution, reducing training time and improving efficiency.

[0143] Online learning: When data streams are constantly updated, the model parameters are adjusted in real time to enable it to track data changes and ensure that the model maintains high accuracy and stability in a dynamic environment.

[0144] Reinforcement learning: In complex decision-making scenarios, strategies are gradually optimized through the interaction between the model and the environment to improve the model's decision-making ability and adaptability in unknown or dynamic environments.

[0145] It should be noted that in this embodiment, in order to improve the decision-making ability and adaptability of the model in unknown or dynamic environments, the specific steps of gradually optimizing the strategy through the interaction between the model and the environment are as follows:

[0146] 1. Environment definition and state observation: Determine the model's environment, including possible states and behaviors. By observing the current state of the environment, extract relevant features and use them as input to help the model identify key factors in decision-making. For example, in security monitoring tasks, the state may include the object's geographic location, time, and behavior patterns.

[0147] 2. Reward function design: Build a reward mechanism that is appropriate for the application scenario and provide positive or negative rewards based on the effectiveness of the model's decisions. For example, when the model successfully identifies risks in anomaly detection, it receives positive rewards; while omissions or false positives receive negative rewards. The reward function helps the model identify beneficial behaviors and guides it to gradually optimize its decision-making strategy.

[0148] 3. Policy Update and Learning Algorithm: Reinforcement learning algorithms (such as Q-learning or Deep Q Network (DQN)) are used to update the model's decision-making strategy. Through continuous exploration and experimentation, the model selects actions that maximize benefits while avoiding actions that may lead to negative consequences. Through reward feedback, the model gradually optimizes its strategy to make more accurate decisions in subsequent similar scenarios.

[0149] 4. Strategy Exploration and Exploitation Balance: Set the model's exploration and exploitation balance. In the initial stage, the model explores different strategies to accumulate experience. As training progresses, the model gradually leans towards utilizing the best strategy it has learned, thereby reducing errors and improving decision-making efficiency.

[0150] 5. Dynamic Environmental Adaptation: When the environment changes, the model adaptively adjusts its strategy through a real-time update mechanism. For example, when new risk patterns or object behaviors emerge, the model can adjust in real time based on environmental feedback to ensure that the model maintains efficient decision-making despite these changes.

[0151] 6. Multiple Iterations and Optimization: Through multiple interactions and optimizations, the model further improves the accuracy and adaptability of its strategy in each cycle. The model learns from experience in each iteration, continuously optimizes the decision-making process, and adapts to more complex or unprecedented scenarios.

[0152] S204: Feature selection and dimensionality reduction:

[0153] Use feature selection and dimensionality reduction techniques to remove redundant features, reduce the dimensions of model input, reduce computational complexity, and improve model execution efficiency;

[0154] S205: Model Compression and Acceleration:

[0155] Model compression techniques such as knowledge distillation, pruning, and / or quantization can be used to reduce model size and computational complexity, thereby accelerating model inference and improving the system's real-time responsiveness.

[0156] In one embodiment of the present application, the real-time data processing and analysis module implements the following steps:

[0157] S301: Use API interfaces to connect to data sources and manage data inflow through Kafka to ensure data continuity and real-time availability.

[0158] S302: Use the stream processing framework Apache Spark Streaming to split, filter, and transform data to achieve real-time analysis. The window function of the stream processing framework is used for time-sharing processing, and regular expressions are used for data format cleaning.

[0159] S303: Use incremental logistic regression and / or KNN online learning algorithms to continuously update model parameters to adapt the model to the changing data stream.

[0160] S304: Based on the isolation forest and / or local outlier factor algorithms, abnormal patterns are detected in real time, warnings are triggered, and important data is recorded in the database to achieve efficient writing and fast query, supporting traceability.

[0161] The abnormal modes include:

[0162] Abnormal behavior: monitor the target object's frequent entry and exit of sensitive areas, unusual paths or travel time,

[0163] Location anomaly: Monitor whether the target object appears in unusual geographical locations or sensitive areas, or appears in areas not usually visited.

[0164] Social association anomaly: By analyzing social relationship or accompanying relationship data, it is detected that specific high-risk individuals frequently appear with other unrecorded accompanying persons.

[0165] Equipment anomalies: Monitor equipment associated with personnel for frequent replacement or frequent contact with other high-risk objects.

[0166] Frequency and timing anomalies: The frequency of abnormal activities in sensitive areas increases at night, or multiple risk persons appear in a short period of time.

[0167] It should be noted that in this embodiment, in order to achieve efficient writing and fast query to support traceability, the specific steps of detecting abnormal patterns in real time, triggering warnings, and recording important data in the database based on the isolation forest and / or local outlier factor algorithms are as follows:

[0168] 1. Real-time detection of abnormal patterns: Utilizes the isolation forest algorithm or local outlier factor algorithm to perform anomaly detection and analysis on real-time data streams. The algorithm scans incoming data in real time and calculates outlier scores based on behavioral characteristics, location, and other indicators to identify potential abnormal patterns. For abnormal events that exceed the threshold, the system immediately triggers an early warning signal.

[0169] 2. Data tagging and priority assignment: For detected abnormal patterns, the relevant data is marked as high-priority important data and assigned different priorities based on the system's preset rules to ensure that important data can be quickly stored and subsequently accessed. At the same time, priority tags facilitate tracking of key events in subsequent analysis;

[0170] 3. Batch writing and data caching: Use a data buffer for batch writing of data. For high-frequency abnormal data streams, temporarily accumulate data through buffering, and then perform batch writing operations to reduce database load. Batch writing can significantly improve writing efficiency and ensure that data enters the database quickly and stably.

[0171] 4. Index creation and optimization: Create specialized indexes for abnormal data to support efficient query and traceability analysis. By setting appropriate index structures (such as B-tree or hash indexes), you can accelerate database query processes, ensure that the system can quickly retrieve key data in large-scale data scenarios, and meet traceability requirements.

[0172] 5. Metadata recording: When writing data, it includes timestamps, geographic location information, detection algorithm type, and other metadata to ensure the temporal and spatial integrity of the data. By storing standardized metadata, the system can accurately locate the time and location of abnormal events, providing a basis for subsequent analysis and tracking.

[0173] 6. Database partitioning and storage optimization: In a distributed database, data is partitioned and stored by time, location, or exception type to ensure data access efficiency. Partitioning strategies can optimize data access speed and reduce query scope, supporting efficient management and processing of large-scale data.

[0174] 7. Backup and redundant storage: Abnormal data is stored synchronously in the primary database and the backup database to ensure high data availability and security. Redundant storage can avoid data loss and ensure data integrity when backtracking is required.

[0175] 8. Query and tracing support: After data is written, the system uses pre-set index structures and partitioning strategies to support fast queries and trace historical abnormal data. When tracing back within a specific time period or geographic area, the system can efficiently query the required data, providing accurate support for event analysis and decision-making.

[0176] Through the above steps, the real-time data processing and analysis module in this embodiment can achieve rapid detection, efficient writing and fast query of abnormal data, support the system's retrospective analysis needs, and provide efficient and reliable data support for monitoring and early warning.

[0177] The model includes:

[0178] 1. Quality analysis model: personnel data used to aggregate key personnel information and maintain data integrity;

[0179] 2. 3D trajectory data dynamic aggregation model: By integrating multiple trajectory data, a thematic trajectory library of personnel dynamics is formed;

[0180] 3. Key personnel collision warning model: realizes collision comparison between key personnel and other data and triggers warning;

[0181] 4. Early warning model for abnormal behavior of key groups: Automatically analyze and warn of abnormal behavior of key groups at sensitive nodes;

[0182] 5. Personnel mining analysis model: Analyze the accompanying relationship of key personnel and mine potential related personnel;

[0183] 6. Key group trend analysis model: Through trend analysis, identify the active characteristics of the group and provide support for advance warning.

[0184] Among them, the personnel data qualitative analysis model is used to collect and analyze information on various key personnel, including key personnel collected locally, key personnel pushed by provincial and ministerial levels, personnel related to local key police incidents, personnel reported on petitions and stability maintenance, and key personnel mined by the model;

[0185] The categories of information collected include those related to Xinjiang and terrorism, incidents and disasters, key petitions, extremist statements, and public investment, forming an integrated database of key personnel;

[0186] The key personnel database includes personnel ID number, name, date of birth, gender, registered police station, current address police station, personnel classification, contact information, mobile phone MAC and portrait photo;

[0187] The personnel data quality analysis model monitors the quality of personnel information in the database. Missing data such as mobile phone MAC, photos or vehicle information and other key information will be automatically pushed to local public security organs and police departments for supplementation and improvement. Those who are no longer active will be cleared to ensure the completeness, accuracy and real-timeness of the data.

[0188] The three-dimensional trajectory data dynamic aggregation model is used to integrate and process trajectory information from multiple data sources, including position data from ticketing, hotels, Internet cafes, and equipment points, mid-air data from electronic police, micro-checkpoints, and facial recognition, as well as aerial data from electronic fences and network fences.

[0189] It should be noted that, in this embodiment, the three-dimensional trajectory data dynamic aggregation model implements the following steps:

[0190] S401: Real-time monitoring and data cleaning: Based on the collected MAC addresses and photos of key individuals, the system monitors Wi-Fi fences and facial recognition data in real time, filters and cleans noise data, and extracts effective information. The cleaned data provides a reliable data source for subsequent key group movement warning models and accompanying personnel mining and analysis models;

[0191] S402: Association determination and score setting: When the MAC address of a key individual appears in a preset area, the system automatically collects other MAC addresses at that location and assigns a preliminary association score. For example, the default score for an accompanying MAC address is 1 point. After determining the sequence of devices or the proximity of locations in the same trajectory, the score for the same trajectory association is increased to 3 points to further confirm the possibility of association.

[0192] S403: Data feedback and archiving: When the score exceeds the set threshold, the photo, MAC address, timestamp and score are recorded and fed back to the key personnel status analysis model as the basis for subsequent analysis;

[0193] S404: Identification of accompanying relationships: Based on the key personnel, the six personnel before and after them are identified as associated candidates, and scores of 1-3 points are assigned in sequence. This associated information, image data, and score records are fed back to the key personnel status analysis model for subsequent accompanying personnel analysis and scoring archiving.

[0194] The key personnel collision warning model enables early warning and group movement behavior monitoring. Early warning includes: early warning at key or sensitive nodes, and the system prompts relevant units to conduct investigations and stabilization preparations for special groups or key targets involved in sensitive nodes;

[0195] Monitoring of abnormal group movement behavior includes: classifying and analyzing the dynamic trajectory data of the same key group. If any abnormal migration conditions exist, the system will automatically trigger an early warning and notify relevant units to implement stabilization measures;

[0196] It should be noted that the abnormal migration row conditions include:

[0197] 1. The number of MACs of local similar group members in the same area reaches 5 or more;

[0198] 2. During non-sensitive periods, members of the local group travel to the same city or direction, and the departure time interval is within 36 hours;

[0199] 3. During sensitive periods, members of the local group travel to the same city or direction, and the departure time interval is within 8 hours;

[0200] 4. Local members of the same group are identified more than three times within the face recognition area.

[0201] The personnel mining and analysis model analyzes the trajectory data of passengers, co-residents, or co-travelers to determine whether there is accompanying behavior. For those whose co-travel frequency reaches the upper threshold, the system will identify them as possible accompanying persons and mark them as key subjects. After manual verification, they will be added to the local key personnel database to ensure the comprehensiveness and accuracy of personnel association.

[0202] Among them, persons who meet any of the following conditions will be marked as possible accompanying persons:

[0203] 1. The time difference between the ticket collection or purchase time and that of the key personnel is no more than 5 minutes, and the two have ridden together for 3 or more times;

[0204] 2. Its portrait tag score exceeds the threshold of 5 points;

[0205] 3. Its MAC tag score exceeds the threshold of 10 points.

[0206] The key group trend analysis model is based on the accumulated data. It uses methods such as multiple linear regression to analyze the activity patterns of key groups, forming trend characteristics of their active time periods, active areas, and active personnel. The trend characteristic results are regularly pushed to relevant units for early prevention and real-time decision-making, providing trend insight support for leaders.

[0207] It should be noted that the trend characteristics of active time periods, active areas, and active people formed by the key group trend analysis model described in this embodiment are specifically implemented in the following steps: Data collection and integration:

[0208] 1. Aggregate data from multiple sources, including activity times, geographic location information, frequented locations, and accompanying individuals of key groups. Data sources include surveillance video, geofencing, social network analysis, device location data, and / or police base station data. This data is integrated into a complete picture of group activity.

[0209] 2. Data preprocessing and cleaning: Before data analysis, preprocess the raw data, use data cleaning tools to filter out noisy data, and fill in missing values ​​to ensure data consistency and integrity. In addition, standardize the temporal and spatial information in the data for subsequent analysis.

[0210] 3. Multiple linear regression modeling: Based on the accumulated activity data, a multiple linear regression model is constructed to analyze the activity trends of key groups. By selecting variables such as activity time, activity area, and number of people, the regression model can identify the key factors affecting group activity and their relationships. For example, the model can identify the peak activity of a group in a certain time period or area.

[0211] 4. Time and regional feature extraction: Extract features from the active time periods and regions of key groups, using time series analysis methods to identify the group's active cycles, such as daily activity peaks and weekend gathering trends. Combined with geographic information system (GIS) data, extract activity area features and divide the group's high-frequency activity areas and important locations.

[0212] 5. Active Personnel Analysis: In group trend analysis, we identify key active personnel. By counting those with high frequency of appearance and high influence, we mark them as core members and conduct in-depth analysis of their activity patterns. This personnel analysis can help identify potential organizations or centers of influence.

[0213] 6. Abnormal trend detection: The model detects unusual patterns in group behavior and identifies unusual activity compared to historical data, such as sudden high-frequency activity or frequent visits to new areas. The system then sends warnings to relevant departments so they can take preventative measures.

[0214] 7. Trend feature result push: The trend features of group activity periods, regions, and individuals generated by model analysis are regularly pushed to relevant units. The analysis results are displayed through automatic reports or platform push, helping relevant departments obtain real-time trend insights and providing reference for risk prevention and decision-making;

[0215] 8. Dynamic feedback and model updates: Based on feedback results, the model is dynamically adjusted and optimized. The model will regularly receive feedback data from actual scenarios and optimize the multivariate regression model and feature extraction methods to ensure that trend analysis can follow the latest changes in group behavior and continuously provide highly accurate trend insights.

[0216] Through the above steps, the key group trend analysis model can dynamically and accurately analyze the activity patterns of the group. The generated trend characteristics provide data support for the decision-making and early prevention of relevant units, greatly improving the prediction and response capabilities in a changing environment.

[0217] In one embodiment of the present application, the model collaboration and decision module includes the following steps S601-S606:

[0218] S601: Model selection and switching:

[0219] Determine the application scenario based on data features, calculate the adaptability score of each candidate model, and dynamically select the optimal model using a multi-armed bandit algorithm. When data features change significantly, automatically switch to a model with higher adaptability.

[0220] S602: Model Integration:

[0221] Simultaneously analyze multiple models to generate different results. Weights are assigned based on model performance, and the results are fused using a weighted average method. A voting mechanism is employed in classification tasks to ensure the stability of the final decision. If the results are abnormal, a conditional trigger is used to perform secondary verification and regenerate the results to reduce false positives and false negatives.

[0222] S603: Make a final decision on the model results based on the rule base:

[0223] Set warning rules based on scenarios, use the rule engine to check data, trigger warnings when rules are met, and automatically update and optimize the rule base through feedback data;

[0224] S604: Feedback Adjustment:

[0225] Collect feedback information after early warning, adjust model weights and parameters based on the feedback, optimize performance, and update the model regularly to adapt it to the latest data.

[0226] In one embodiment of the present application, the adaptive learning and evolution module includes a reinforcement learning unit, an evolutionary algorithm unit, an adaptive parameter adjustment unit, an adaptive parameter adjustment unit, a historical data feedback unit, and a model retraining and reoptimization unit;

[0227] The reinforcement learning unit uses Q-learning and deep reinforcement learning (DQN) algorithms to continuously update strategies and model parameters based on real-time data feedback, enhancing the system's response in different scenarios.

[0228] The evolutionary algorithm unit applies evolutionary algorithms such as genetic algorithms and differential evolution to perform operations such as selection, crossover, and mutation on model parameters. The optimized parameters generated in each generation retain excellent characteristics while exploring new solutions to improve overall performance and stability.

[0229] The adaptive parameter adjustment unit adjusts the model's learning rate and exploration rate based on Bayesian optimization or adaptive control methods to optimize the model's performance under real-time data;

[0230] The historical data feedback unit records the model’s input, output, and decision results in the database and improves data quality and availability through pre-processing steps such as data cleaning and feature extraction;

[0231] The model retraining and reoptimization unit uses batch data or incremental data to retrain the model, allowing the system to adapt to the dynamic environment in continuous learning and maintain the effectiveness and accuracy of its predictions.

[0232] It should be noted that in this embodiment, in order to improve data quality and availability, the historical data feedback unit records the model input, output, and decision results in the database, and performs pre-processing steps such as data cleaning and feature extraction. The specific implementation is as follows:

[0233] 1. Data collection and recording:

[0234] Real-time recording: The system records the input data, model output and decision results of each decision in real time during the model operation, including timestamp, environment status, input features, output prediction value and action information.

[0235] Structured storage: This data is stored in a structured manner in the database and partitioned by dimensions such as time and type to facilitate subsequent query and analysis, establish appropriate index structures, and accelerate data retrieval.

[0236] 2. Data cleaning:

[0237] Noise filtering: Perform noise filtering on the recorded historical data to remove obvious erroneous data and irrelevant information, such as abnormally high or low values, duplicate records, and obvious outliers.

[0238] Missing value processing: For missing values ​​in the data, interpolation or context-based filling methods are used to process them to ensure data integrity. At the same time, the data that cannot be filled are marked for reference in subsequent analysis.

[0239] Anomaly detection and processing: Use statistical methods or anomaly detection algorithms (such as Z-score and IQR) to identify and process abnormal data, mark the detected abnormal data, and decide whether to correct or eliminate it based on business logic;

[0240] 3. Feature extraction:

[0241] Variable selection: Use feature selection methods (such as LASSO, recursive feature elimination, etc.) to select important variables related to model performance from the cleaned data, remove redundant features, and reduce computational complexity.

[0242] Feature Engineering: Based on domain knowledge and data exploration, new features are constructed, such as temporal features (activity frequency, time period features), spatial features (activity area, trajectory similarity), etc., to more comprehensively describe data characteristics.

[0243] Feature transformation and encoding: Encode categorical features (such as one-hot encoding and label encoding) and standardize or normalize numerical features to ensure that data is comparable and has a uniform scale across different dimensions;

[0244] 4. Data verification and quality assessment:

[0245] Consistency check: Perform consistency check on the processed data to verify the consistency of data format, range and type to ensure that the data meets the requirements of model input.

[0246] Quality assessment: Evaluate the processed data using data quality indicators (such as completeness, accuracy, and uniqueness), generate data quality reports regularly, and record problems and processing results during the data processing process;

[0247] 5. Data storage and version control:

[0248] Version storage: The cleaned and feature-extracted data is stored in the database and version control is implemented. A new version is generated each time the data is updated, and historical versions are retained for easy tracing and comparative analysis.

[0249] Metadata management: Storing metadata of data (such as data source, processing method, and update time) in the database to provide detailed background information and processing history for subsequent data use;

[0250] 6. Data update and feedback loop:

[0251] Incremental update: The system processes new data streams in real time and updates the results incrementally to the database to maintain the real-time and continuity of the data.

[0252] Feedback loop: The results of data processing and quality assessment are fed back to the model optimization unit to adjust the data cleaning and feature extraction strategies. At the same time, the model performance and data processing effects are recorded to facilitate continuous optimization of the data processing process.

[0253] Through the above steps, the historical data feedback unit effectively improves the quality and availability of data, ensuring that the data in the database is always of high quality, and providing a solid data foundation for model retraining, re-optimization, and the long-term stable operation of the system.

[0254] In one embodiment of the present application, the security and privacy protection module includes a personal information encryption unit and an access permission unit:

[0255] The personal information encryption unit uses the symmetric encryption AES algorithm and the asymmetric encryption RSA algorithm to encrypt, transmit and store personal identity information and behavioral data information.

[0256] The access authority unit is based on hierarchical management of user permissions and improves the security of system access through password, fingerprint, iris and / or face recognition authentication mechanisms.

[0257] It should be noted that, in this embodiment, the personal information encryption unit uses the symmetric encryption AES algorithm and the asymmetric encryption RSA algorithm to encrypt, transmit and store personal identity information and behavioral data to ensure the security of the information. The specific implementation steps are as follows:

[0258] 1. Key generation and management:

[0259] AES key generation: The system generates an AES key based on symmetric encryption requirements. This key is used to encrypt data content during transmission. After the key is generated, it is securely stored and managed by the key management system to ensure that the key is not leaked.

[0260] RSA public and private key generation: Generates an RSA key pair (public and private keys) for encrypted transmission of AES keys. The public key can be used publicly, while the private key is stored only in a secure server environment to ensure that only authorized users can decrypt the AES key.

[0261] 2. Data encryption transmission:

[0262] AES encryption of data content: Before transmission, personal identity information and behavioral data are encrypted using AES encryption. This step ensures that the transmitted data is ciphertext, and even if it is intercepted during transmission, the data content cannot be read.

[0263] AES key encryption: Use the RSA public key to encrypt the AES key and transmit the encrypted AES key along with the data. In this way, even if the transmitted key is intercepted, the original AES key cannot be restored without decryption with the private key.

[0264] Encrypted data transmission: The system transmits AES-encrypted data and RSA-encrypted AES keys through secure transmission protocols (such as HTTPS, SSL / TLS) to further enhance the security of the transmission process;

[0265] 3. Data decryption and access control:

[0266] AES key decryption: At the data receiving end, the RSA private key is used to decrypt the AES key in transmission, recover the original AES key, and use it to decrypt the received data content.

[0267] AES data decryption: The system uses the decrypted AES key to decrypt the received encrypted data and restore the original personal identity information and behavioral data information.

[0268] Access control: After decryption, according to the system permission settings, ensure that only authorized users can access the decrypted sensitive information, and prevent unauthorized users from reading the decrypted data.

[0269] 4. Data encryption storage:

[0270] AES encrypted storage: Personal information and behavior data are encrypted with AES before being stored in the database. The encrypted data is stored in the database to ensure the security of the data at the storage layer.

[0271] Partitioned storage and hierarchical encryption: Different types of sensitive information (such as personal identity data and behavioral data) are partitioned and stored according to the data sensitivity level. A multi-layer encryption structure is used for highly sensitive data to improve storage security.

[0272] Key protection: Stored AES keys are isolated and managed through a dedicated key management service, which regularly rotates and backs up keys to further enhance data storage security.

[0273] 5. Key rotation and management mechanism

[0274] Regular key rotation: The system sets a key rotation cycle and regularly replaces AES and RSA keys to ensure the security of keys in long-term operation.

[0275] Key backup and recovery: The system establishes a backup mechanism for important encryption keys to deal with key damage or loss. Key backup files are double-encrypted and stored in a secure environment. Only administrators with specific permissions can perform key recovery.

[0276] 6. Logging and Auditing:

[0277] Encryption operation records: Each data encryption and decryption operation is recorded in the log, including operation time, operator, operation type and other information, to facilitate security audits.

[0278] Audit tracking: Regularly conduct security audits on the system's encryption operations, track and analyze potential security risks, and take timely remedial measures to ensure the stable operation of the encryption module.

[0279] Through the above steps, the personal information encryption unit can efficiently and securely encrypt, transmit and store data, ensuring the security and privacy protection of data throughout its entire life cycle.

[0280] Multi-object, multi-model deployment and early warning technology is an innovative monitoring solution. By integrating multi-source data and enabling the coordinated operation of multiple models, the system achieves enhanced warning accuracy, adaptability, and reliability in complex environments. First, the technology integrates data from diverse sources, formats, and quality levels, extracting key features through data fusion to form a comprehensive monitoring perspective. The coordinated application of multiple models complements each other's strengths and verifies false positives, effectively reducing false positives and missed negatives, and improving the comprehensiveness and accuracy of early warnings. Second, the technology boasts powerful real-time data processing capabilities. Using efficient algorithms, it processes large-scale data streams in real time, rapidly identifying potential risks and triggering early warnings, significantly reducing response time. Furthermore, the system's adaptive learning and evolutionary capabilities enable it to automatically adjust model structure and parameters as the environment changes. It optimizes decisions based on real-time feedback and historical data, enhancing flexibility and adaptability in various scenarios. Combined with redundant design, fault-tolerance mechanisms, and hierarchical security protections, the system maintains stable operation and continuity even in the face of failures or external interference. In summary, multi-object and multi-model deployment warning technology has significant advantages in ensuring data processing efficiency, improving warning reliability and maintaining system robustness, meeting the modern early warning monitoring needs for efficiency, accuracy and comprehensiveness.

[0281] The proposed early warning and control system boasts comprehensive data integration, precise algorithm optimization, efficient real-time processing, adaptive learning capabilities, and rigorous security protections. It provides powerful support for multi-dimensional, multi-source data analysis, risk detection, and trend forecasting. This ensures exceptional stability and decision-making efficiency in dynamic environments, laying a solid foundation for the accuracy and reliability of early warning and monitoring.

[0282] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and deform the above embodiments within the scope of the present application.

Claims

1. A multi-object multi-model control and early warning system, characterized in that: The multi-object multi-model control and early warning system includes a multi-source data fusion module, an efficient algorithm and model optimization module, a real-time data processing and analysis module, a multi-model collaboration and decision-making module, an adaptive learning and evolution module, and a security and privacy protection module, wherein: The multi-source data fusion module is used to comprehensively process and analyze data from different data sources with different formats and qualities to extract valuable information and features; The efficient algorithm and model optimization module is used to improve and optimize the algorithm and model for specific application scenarios and requirements to improve their computing efficiency and prediction accuracy; The real-time data processing and analysis module is used to quickly process and analyze the data generated in real time to achieve timely discovery and early warning of potential risks; The multi-model coordination and decision-making module is used to comprehensively analyze and make decisions based on the results of multiple models to improve the reliability and stability of early warning; The adaptive learning and evolution module is used to automatically adjust and optimize system parameters and model structure, so that the system can continuously adapt to complex and changing environments. The security and privacy protection module is used to protect the system from malicious attacks and data leakage threats, and to ensure the confidentiality of user privacy.

2. The control and early warning system according to claim 1, characterized in that: The multi-source data fusion module includes a data acquisition unit, a data cleaning unit, a data conversion unit, a data fusion unit and a feature extraction unit, wherein: The data collection unit is used to automatically capture data from various data sources through distributed data collection tools Kafka and / or Flume, and integrate them through interfaces, wherein: The data includes key personnel database, position data, portrait data, vehicle data, WIFI fence data, three-dimensional trajectory data, social media data, logistics and consumption data, historical warning and event data, equipment location data and / or police base data; The data cleaning unit processes outliers and duplicate data through data cleaning tools OpenRefine and / or Pandas to remove redundant, abnormal and noisy data to ensure the accuracy and consistency of the data; The data conversion unit standardizes and converts data in different formats through the data conversion framework Talend and / Apache Nifi to adapt it to subsequent processing requirements; The data fusion unit integrates the data by time, space or object characteristics through a Bayesian reasoning fusion algorithm to form a comprehensive view across data sources, providing a unified data basis for subsequent analysis; The feature extraction unit extracts core features from the fused data through feature selection algorithms LASSO and / or decision trees for model analysis and early warning judgment, wherein: The core features include identity information features, device identification features, biometric features, behavior trajectory features, social association features, risk scores and behavior tags.

3. The control and early warning system according to claim 2, characterized in that: The efficient algorithm and model optimization module includes an algorithm optimization unit and a model optimization unit. The algorithm optimization unit implements the following steps S101-S103: S101: Select appropriate algorithms according to task requirements and data characteristics: For classification tasks, select the SVM algorithm to classify objects; for clustering tasks, select the K-means algorithm to implement group analysis; for anomaly detection tasks, use the isolation forest algorithm to detect abnormal behaviors; for trajectory analysis tasks, use trajectory clustering and dynamic time warping DTW algorithms to perform adjoint relationship analysis. The tasks include classification, clustering, anomaly detection, and trajectory analysis. S102: Adjust the selected algorithm to adapt to large-scale data and complex application scenarios: Split the computing tasks in the algorithm into multiple subtasks and execute them simultaneously on multiple processors or computing nodes, thereby dispersing the computing burden and improving the overall processing efficiency. S103: Combine and apply multiple algorithms to improve analysis accuracy: In the abnormal behavior detection process, combine the SVM algorithm and the isolation forest algorithm, combine classification with anomaly detection, improve the recognition accuracy of false positives and false negatives, and in the trajectory analysis task, combine trajectory clustering with dynamic time warping DTW to perform adjoint relationship analysis; The model optimization unit includes the following steps S201-S205: S201: Model structure adjustment: For deep learning models, the number of layers and nodes of the neural network is adjusted to adapt to complex data. For tasks with high real-time requirements, lightweight models are used to improve computing speed. S202: Parameter optimization: Optimize the model's hyperparameters using grid search, random search, or Bayesian optimization to find the best parameter combination to improve the model's performance. S203: Training method improvement: Transfer learning: Use existing pre-trained models and fine-tune them to quickly adapt the model to the new data distribution, reduce training time and improve efficiency. Online learning: As data streams are constantly updated, model parameters are adjusted in real time to enable it to track data changes and ensure that the model maintains high accuracy and stability in a dynamic environment. Reinforcement learning: In complex decision-making scenarios, the strategy is gradually optimized through the interaction between the model and the environment to improve the decision-making ability and adaptability of the model in unknown or dynamic environments. S204: Feature selection and dimensionality reduction: Use feature selection and dimensionality reduction techniques to remove redundant features, reduce the dimension of model input, reduce computational complexity, and improve model execution efficiency. S205: Model compression and acceleration: Model compression techniques such as knowledge distillation, pruning, and / or quantization are used to reduce model size and computational complexity, thereby accelerating model reasoning and improving the real-time responsiveness of the system.

4. The control and early warning system according to claim 3, characterized in that: The real-time data processing and analysis module implements the following steps: S301: Use the API interface to connect to the data source and manage data inflow through Kafka to ensure data continuity and real-time. S302: Use the stream processing framework Apache Spark Streaming to segment, filter and transform data to achieve real-time analysis, where the window function of the stream processing framework is used for time-sharing processing, and regular expressions are used for data format cleaning. S303: Use incremental logistic regression and / or KNN online learning algorithms to continuously update model parameters to adapt the model to the changing data stream. S304: Based on the isolation forest and / or local outlier factor algorithm, detect abnormal patterns in real time, trigger warnings, and record important data into the database to achieve efficient writing and fast query, support traceability, The abnormal modes include: Abnormal behavior: monitor the target's frequent entry and exit of sensitive areas, unusual paths or travel times, Location anomalies: Monitor whether the target object appears in unusual geographical locations or sensitive areas, or appears in areas that are not usually visited. Social association anomaly: by analyzing social relationship or companion relationship data, it is detected that specific high-risk individuals frequently appear with other unrecorded companions. Equipment anomalies: Monitor equipment associated with personnel that is frequently replaced or frequently contacts other high-risk objects. Frequency and timing anomalies: The frequency of abnormal activities in sensitive areas at night increases, or multiple risk persons appear in a short period of time.

5. The control and early warning system according to claim 4, characterized in that: The model includes: Personnel data quality analysis model: personnel data used to aggregate key personnel information and maintain data integrity; Three-dimensional trajectory data dynamic aggregation model: by integrating multiple trajectory data to form a dynamic thematic trajectory library of personnel; Key personnel collision warning model: realizes collision comparison between key personnel and other data and triggers warning; Early warning model for abnormal behavior of key groups: automatic analysis and early warning of abnormal behavior of key groups at sensitive nodes; Personnel mining analysis model: Analyze the accompanying relationships of key personnel and mine potential related personnel; And key group trend analysis model: through trend analysis, identify the active characteristics of the group and provide support for advance warning.

6. The control and early warning system according to claim 5, characterized in that: The model coordination and decision-making module includes the following steps S601-S606: S601: Model selection and switching: Determine the application scenario based on data features, calculate the adaptability score of each candidate model, and use the multi-armed bandit algorithm to dynamically select the optimal model. When data features change significantly, automatically switch to a model with higher adaptability. S602: Model Integration: Use multiple models to analyze at the same time and generate different results. Assign weights based on model performance and use weighted average method to fuse the results. Use voting mechanism in classification tasks to ensure the stability of the final decision. When the result is in abnormal mode, conditional triggering is used to perform secondary verification and regenerate the result to reduce false positives and false negatives. S603: Make a final decision on the model results based on the rule base: Set warning rules based on scenarios, use the rule engine to check data, trigger warnings when rules are met, and automatically update and optimize the rule base through feedback data; S604: Feedback adjustment: Collect feedback information after early warning, adjust model weights and parameters based on feedback, optimize performance, and update the model regularly to adapt it to the latest data.

7. The control and early warning system according to claim 6, characterized in that: The adaptive learning and evolution module includes a reinforcement learning unit, an evolutionary algorithm unit, an adaptive parameter adjustment unit, an adaptive parameter adjustment unit, a historical data feedback unit, and a model retraining and re-optimization unit; The reinforcement learning unit continuously updates strategies and model parameters based on real-time data feedback by using Q-learning and deep reinforcement learning DQN algorithms to enhance the system's response in different situations. The evolutionary algorithm unit selects, crosses, and mutates model parameters by applying evolutionary algorithms such as genetic algorithms and differential evolution. The optimized parameters generated in each generation explore new solutions while retaining excellent characteristics, thereby improving overall performance and stability. The adaptive parameter adjustment unit adjusts the learning rate and exploration rate of the model based on Bayesian optimization or adaptive control methods to optimize the performance of the model under real-time data; The historical data feedback unit records the model’s input, output, and decision results in the database, and improves data quality and availability through preprocessing steps such as data cleaning and feature extraction; The model retraining and reoptimization unit uses batch data or incremental data to retrain the model, allowing the system to adapt to the dynamic environment in continuous learning and maintain the effectiveness and accuracy of its predictions.

8. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the module according to any one of claims 1 to 7 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the module described in any one of claims 1 to 7 are implemented.

10. A processor, characterized in that: The processor is used to run a program, wherein the program executes the method of any module of claims 1 to 7 when running.

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