Intelligent monitoring and visual management system based on multi-source data fusion

Abstract

The invention discloses an intelligent monitoring and visual management system based on multi-source data fusion. The system comprises a data acquisition module, a data fusion module, an intelligent analysis module, a visualization module and a management control module, and realizes comprehensive monitoring and management of environment, equipment and personnel through a multi-source data fusion technology. The system can intelligently analyze various types of monitoring data and display the monitoring data in real time through a visual interface, so that the efficiency and accuracy of monitoring management are improved. The system is suitable for the fields of industry, traffic, security and protection and the like, and can effectively meet the monitoring requirements in a complex environment.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent monitoring and data processing, and specifically relates to an intelligent monitoring and visualization management system based on multi-source data fusion. Background Art

[0002] With the continuous improvement of social informatization and intelligence, technologies such as video surveillance, environmental monitoring, and equipment operation parameter monitoring have been widely used in various fields. However, existing monitoring systems generally have the following problems:

[0003] 1. The data island phenomenon is serious: the data formats generated by different monitoring devices are not unified, making it difficult to achieve effective integration, resulting in low data utilization efficiency.

[0004] 2. The monitoring system is not highly intelligent: Traditional monitoring systems mainly rely on manual analysis and are unable to cope with massive amounts of data and complex scenarios.

[0005] 3. Single means of visualization: The visualization display method of the existing monitoring system is relatively simple and cannot meet the diverse and personalized needs of users. Summary of the invention

[0006] The purpose of the present invention is to provide an intelligent monitoring and visualization management system based on multi-source data fusion, which can efficiently collect, process and analyze multi-source monitoring data, and display monitoring information through an intuitive visualization interface, thereby improving monitoring efficiency and intelligence level.

[0007] The technical implementation scheme of the present invention is as follows:

[0008] A multi-source data fusion intelligent monitoring system that integrates multiple modules such as data acquisition, preprocessing, fusion, intelligent analysis, visualization, background management, and system expansion and integration interface. Through real-time acquisition and fusion of multi-source data such as video surveillance, environmental monitoring, and equipment operating parameters, combined with efficient preprocessing and intelligent analysis technology, comprehensive, real-time and intelligent monitoring of monitoring scenes is achieved. At the same time, the background management system ensures data security and system reliability, while the system expansion and integration interface promotes seamless connection and resource sharing with other monitoring systems.

[0009] 1. Multi-source data fusion intelligent monitoring data acquisition module:

[0010] The data acquisition module of the present invention is the basis of the system. It can collect data in real time from various data sources, such as high-definition video surveillance cameras, environmental monitoring sensors (such as temperature, humidity, air quality sensors), equipment operation parameter sensors (such as current, voltage, vibration sensors), etc. It uses advanced data acquisition technologies, such as the Internet of Things (IoT) protocol, wireless sensor network (WSN), etc., to ensure the real-time and comprehensiveness of the data. The module also has a data pre-acquisition function, which can automatically start the data acquisition process according to preset rules or event trigger conditions.

[0011] 2. Efficient data preprocessing unit:

[0012] The unit performs preliminary processing on the collected raw data, including data cleaning (removing invalid or erroneous data), denoising (reducing noise interference in the data), calibration (ensuring data accuracy) and normalization (converting data into a unified format). It uses efficient algorithms and parallel processing technology to reduce data processing time and improve processing efficiency. The unit also has an automated data quality monitoring function that can monitor data quality in real time and automatically correct problems when they are found.

[0013] 3. Advanced multi-source data fusion processing center:

[0014] The multi-source data fusion processing center of the present invention is the core technology of the system. It uses innovative algorithms, such as image block similarity calculation, graph calculation, machine learning, etc., to deeply fuse data from different sources. It can identify and integrate relevant data from different data sources to form a unified data format and view for subsequent analysis and processing. The center also has adaptive adjustment capabilities and can dynamically adjust data fusion strategies and parameters according to changes in monitoring scenarios and analysis requirements.

[0015] 4. Intelligent monitoring and analysis engine:

[0016] The engine is the intelligent core of the system. It conducts in-depth analysis of the fused data, including intelligent analysis functions such as anomaly detection, trend prediction, and pattern recognition. It uses advanced machine learning and deep learning technologies to automatically identify abnormal behaviors in monitoring scenarios, predict potential safety hazards, and automatically adjust monitoring strategies. The engine also has the ability to self-learn and optimize, and can continuously improve the analysis model and the accuracy of the analysis based on historical data and user feedback.

[0017] 5. Intuitive visual display platform:

[0018] The platform displays the results of intelligent analysis in the form of charts, images, animations, etc. on the monitoring screen, making it easy for users to quickly understand and take action. It supports users to customize monitoring screens and alarm conditions, improving the interactivity and practicality of the monitoring system. The platform also has a multi-user collaboration function, allowing different users to view and operate monitoring data according to their own permissions and needs.

[0019] 6. Comprehensive backend management system:

[0020] The backend management system is responsible for data storage, query, report generation, and user rights management of the entire monitoring system. It uses high-security data storage technology to ensure the security and integrity of the data. The system also includes data backup and recovery functions, as well as sensor calibration and fault detection functions to ensure the accuracy and reliability of the data.

[0021] 7. System expansion and integration interface:

[0022] The present invention also includes an interface module integrated with other systems, supporting data sharing and linkage control with other monitoring systems. It adopts standardized interface protocols and data formats to ensure compatibility and interoperability with other systems. The interface module also supports the integration of third-party applications and services, allowing users to expand and customize the functions of the monitoring system according to their own needs. Compared with the prior art, the specific beneficial effects of the present invention are as follows:

[0023] 1. Improve monitoring efficiency: By real-time collection and integration of multi-source data, the present invention can quickly respond to monitoring needs, improve monitoring efficiency, and reduce human intervention.

[0024] 2. Enhanced data analysis capabilities: The intelligent analysis engine of the present invention can extract useful information from complex data, provide a scientific basis for decision-making, and improve the intelligence level of the monitoring system.

[0025] 3. Ensure data security: The data backup and recovery functions of the background management system ensure the security of monitoring data and prevent data loss.

[0026] 4. Improve system reliability: Sensor calibration and fault detection functions ensure the accuracy of data collection and improve the reliability of the entire monitoring system.

[0027] 5. Promote system integration: Through the integrated interface, the present invention achieves seamless connection with other monitoring systems, and promotes the sharing and collaborative work of monitoring resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the embodiments of this project, the following briefly introduces the drawings required for use in the embodiments.

[0029] Figure 1 This is a system framework diagram of the intelligent monitoring and visualization management system based on multi-source data fusion provided by the present invention, which describes in detail the technical solution of this project and the relationship between the various components.

[0030] Figure 2 This is a framework diagram of the data acquisition module provided by the present invention.

[0031] Figure 3 This is a framework diagram of the data preprocessing module provided by the present invention.

[0032] Figure 4 This is a framework diagram of the intelligent analysis module provided by the present invention.

[0033] Figure 5 This is a framework diagram of the visualization module provided by the present invention.

[0034] Figure 6 This is the background management module framework diagram and the system integration and interface module framework diagram provided by the present invention.

[0035] It should be pointed out that the system framework diagram provided by the invention is only an example of an embodiment of the project and is not intended to limit the protection scope of the project. Those skilled in the art can modify, replace or improve the modules in the diagram according to specific application scenarios and requirements. These modifications and improvements are carried out without departing from the basic spirit and principles of the project, and therefore also fall within the protection scope of the project. DETAILED DESCRIPTION

[0036] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0037] In order to illustrate the technical solution of the present invention, a specific embodiment is provided below for illustration.

[0038] Example

[0039] See also Figure 1 This embodiment provides an intelligent monitoring and visualization management system based on multi-source data fusion, which includes a data acquisition module, a data preprocessing module, a multi-source data fusion module, an intelligent analysis module, a visualization display module, a background management module, and a system integration and interface module.

[0040] Implementation of data acquisition module

[0041] In this embodiment, the data acquisition module realizes real-time acquisition of video streams, environmental parameters and equipment operation data in the monitoring area by deploying high-definition cameras, environmental monitoring sensors, equipment operation parameter sensors and other equipment. The cameras are domestically produced well-known brands with night vision and high-definition functions, and the sensors are domestically produced sensors with high precision and good stability to ensure the accuracy and reliability of the data.

[0042] 1. Equipment selection and deployment

[0043] (1) HD camera deployment

[0044] Brand selection:

[0045] We use high-definition cameras from well-known domestic brands to ensure product quality and after-sales service.

[0046] Feature Request:

[0047] The camera must have the ability to capture high-definition video with a resolution of 1080P or above, and also have night vision function to ensure the need for 24-hour monitoring.

[0048] Installation location:

[0049] According to the specific conditions of the monitored area, reasonably plan the installation location of the camera to ensure coverage without blind spots while taking into account privacy protection requirements.

[0050] (2) Environmental Monitoring Sensor Deployment

[0051] Sensor selection:

[0052] Select domestic sensors with high precision and good stability, including temperature and humidity sensors, air quality sensors, light intensity sensors, etc.

[0053] Installation requirements:

[0054] Sensors should be installed at locations that best reflect environmental conditions. For example, temperature and humidity sensors should be installed in the center of the room, and light sensors should be installed near windows.

[0055] (3) Equipment operation parameter sensor deployment

[0056] Sensor Type:

[0057] Including current sensors, voltage sensors, speed sensors, etc., which are used to monitor the operating status of the equipment.

[0058] Installation Specifications:

[0059] The sensor should be directly connected to the corresponding parameter output port of the monitored device to ensure the real-time and accuracy of data collection.

[0060] 2. Data collection and transmission

[0061] Video stream acquisition:

[0062] The video stream of the monitored area is collected in real time through high-definition cameras, and the continuity and stability of the video data are ensured.

[0063] Environmental parameter collection:

[0064] Environmental monitoring sensors collect environmental parameter data in real time according to the preset sampling frequency.

[0065] Equipment operation data collection:

[0066] The equipment operation parameter sensor monitors the equipment's operating status in real time and records relevant parameters.

[0067] Transmission method:

[0068] A combination of wired network transmission and wireless network transmission is adopted to ensure the reliability and flexibility of data transmission.

[0069] Data Encryption:

[0070] The transmitted data is encrypted to ensure the security of the data transmission process.

[0071] 3. System debugging and maintenance

[0072] Equipment debugging:

[0073] After the equipment is installed, debug it one by one to ensure that each device can work properly.

[0074] System joint debugging:

[0075] The entire data acquisition system is debugged to ensure that all devices, sensors and data transmission systems work together.

[0076] Regular inspection:

[0077] Regularly check cameras, sensors and other equipment to ensure they are operating properly.

[0078] Troubleshooting:

[0079] Establish a rapid response mechanism to handle faults in a timely manner.

[0080] Through the above implementation measures, the data acquisition module can realize the real-time and accurate acquisition of the video stream, environmental parameters and equipment operation data of the monitoring area, providing reliable data support for subsequent data analysis and processing. Figure 2 Implementation of the data preprocessing module

[0081] The data preprocessing module uses the leading data processing technology in China to clean, denoise and calibrate the collected data. The data cleaning process automatically identifies and removes invalid data, the denoising process uses technologies such as wavelet transform, and the data calibration is carried out according to national standards to ensure data consistency.

[0082] 1. Technology selection and standards

[0083] Data cleaning:

[0084] Adopt the domestically leading data cleaning technology, including the ability to automatically identify and eliminate invalid data.

[0085] Data denoising:

[0086] Use advanced technologies such as wavelet transform to denoise the data and improve data quality.

[0087] Data calibration:

[0088] Data calibration is carried out according to relevant national standards and industry specifications to ensure data accuracy and consistency.

[0089] 2. Implementation steps

[0090] Data cleaning

[0091] Automatic recognition:

[0092] Develop or adopt existing data cleaning algorithms to automatically identify invalid data such as missing values, outliers, and duplicate values. Eliminate invalid data:

[0093] Based on the recognition results, invalid data is automatically eliminated or data that requires manual review is marked.

[0094] Data Denoising

[0095] Wavelet Transform:

[0096] Wavelet transform technology is used to decompose the data at multiple scales and remove noise components.

[0097] Signal reconstruction:

[0098] Based on wavelet transform, the signal is reconstructed to restore the original characteristics of the data.

[0099] Data calibration

[0100] Establish calibration standards: Establish specific standards for data calibration based on national standards and industry specifications.

[0101] To perform the calibration:

[0102] The data is normalized using a calibration algorithm to ensure that the data conforms to a pre-set calibration standard.

[0103] 3. System testing and optimization

[0104] Functional testing:

[0105] Test each function of the data preprocessing module to ensure that each link works as expected.

[0106] Performance Test:

[0107] Evaluate the system's processing power, efficiency, and stability to ensure it can meet actual application needs.

[0108] Algorithm optimization:

[0109] Based on the test results, the data cleaning, denoising and calibration algorithms are optimized to improve processing efficiency and accuracy.

[0110] Process Optimization:

[0111] Adjust the data preprocessing process, simplify the operation steps, and reduce data processing time.

[0112] 4. Maintenance and Updates

[0113] Daily maintenance

[0114] Monitor system operation status: Regularly check the system operation log to ensure the stable operation of the data preprocessing module.

[0115] Rapid response: Quickly diagnose and repair any problems that arise in the system.

[0116] Technology Updates

[0117] Track new technologies: Keep an eye on the development of data processing technologies and evaluate the potential value of new technologies to existing systems. Regularly upgrade: Regularly upgrade the data preprocessing module based on technology development and business needs.

[0118] Through the above implementation measures, the data preprocessing module can effectively improve the data quality and provide a reliable data foundation for subsequent data analysis and application. Figure 3 Shown

[0119] Implementation of multi-source data fusion module

[0120] The multi-source data fusion module uses a self-developed data fusion algorithm to integrate data from different data sources to form a unified data view. The module supports multiple data formats and communication protocols, achieving seamless data fusion.

[0121] 1. System design

[0122] System architecture design:

[0123] Design a modular system architecture to ensure that the multi-source data fusion module can operate independently and collaborate with other modules.

[0124] Determine the interface specifications between the data fusion module and other subsystems (such as data acquisition, storage, analysis, visualization, etc.).

[0125] 2. Data fusion module development

[0126] Data preprocessing

[0127] Develop data cleaning functions to automatically identify and mark invalid data by setting data validity rules, such as data range, format, type, etc., use outlier detection algorithms, such as box plots, Z scores, etc., to discover potential erroneous data, and automatically correct them through algorithms or prompt manual review, and use data deduplication technology, such as hash algorithms, to identify and delete duplicate data records to reduce the waste of storage space and computing resources.

[0128] Implement data standardization to ensure that all data use the same unit of measurement for easy comparison and analysis. Use methods such as minimum-maximum standardization and Z-score standardization to scale the data to a fixed range and eliminate the dimensional effects between different data sources.

[0129] Self-developed algorithm

[0130] (1) Spatiotemporal alignment algorithm:

[0131] To achieve spatiotemporal alignment of the data, we developed the following formula:

[0132] T(S i ,t j )=f(S i ,t j ,θ)

[0133] Among them, T(S i ,t j ) represents the spatial position S i and time point t j The function f is responsible for adjusting the data according to the spatial and temporal parameters θ, ensuring that the information collected by different sensors or data sources can be matched in a unified spatiotemporal coordinate system. In the development of the spatiotemporal alignment algorithm, we also considered the following factors:

[0134] Timestamp correction technology is used to ensure that the time synchronization error of different sensors is within an acceptable range.

[0135] Develop coordinate conversion algorithms to convert data in different coordinate systems into a unified coordinate system for spatial analysis. Coordinate conversion algorithms are used to convert data from one coordinate system to another. This is especially important when dealing with geospatial data because different systems and devices may use different coordinate systems (such as WGS 84, UTM, etc.). The main functions and steps of the coordinate conversion algorithm include:

[0136] Coordinate system identification: Identify the coordinate system in which the data is currently located.

[0137] Transformation model selection: Select the appropriate transformation model based on the source coordinate system and the target coordinate system. Common models include:

[0138] Affine transformation: A linear transformation that applies to a plane coordinate system.

[0139] Helmert transformation: used for conversion between earth coordinate systems, involving translation, rotation and scaling.

[0140] Molodensky transformation: It is also a method used for conversion between earth coordinate systems.

[0141] This process is crucial for time series analysis of events in surveillance systems. For example, in public safety monitoring, the same event captured by different cameras needs to be aligned in the correct spatiotemporal coordinates for coherent event reconstruction and analysis.

[0142] (2) Data fusion rules:

[0143] In the rule-based approach, we define the following formula to describe the fusion process:

[0144] D fusion =R(D1,D2,...,D n )

[0145] Among them, D fusion is the fused data set, D1,D2,...,D n are data sets from different sources. Fusion rules R may include strategies such as data priority, weight assignment, and conflict resolution. When defining data fusion rules, we also considered the following strategies: Dynamic weight adjustment: According to the quality and reliability of the data source, the weight assignment is adjusted in real time to improve the accuracy of the fused data. Conflict detection and resolution: Develop conflict detection algorithms to identify inconsistencies in the data fusion process and use preset conflict resolution strategies to handle them.

[0146] Conflict detection algorithms are used to identify and resolve data inconsistencies that occur during the fusion process of different data sources. These inconsistencies may occur due to a variety of reasons, such as sensor errors, data transmission delays, system failures, etc. The main functions and steps of conflict detection algorithms include:

[0147] Compare data from different data sources and identify discrepancies.

[0148] Determine which data is inconsistent, i.e., where conflicts exist.

[0149] Conflicts are divided into resolvable ones (such as temporary inconsistencies caused by time delays) and unresolvable ones (such as data errors caused by hardware failures).

[0150] Resolve or mark conflicts based on preset rules or algorithms. Common strategies include: Select the data provided by the majority of data sources as the correct data. Take a data source that is considered the most reliable as the standard. Assign weights based on the reliability of the data source and obtain fused data after weighted average. Compare with historical data and select the data that best matches historical trends.

[0151] For example, when processing environmental monitoring data from multiple sensors, some sensors may be more reliable than others, so their readings are given higher weight in the fusion process.

[0152] (3) Data integration:

[0153] Data integration quality assessment:

[0154] During the data integration process, we use a multi-dimensional quality assessment system to ensure that the integrated data set meets the following key standards:

[0155] Completeness: We check whether the integrated data set contains all necessary information and no missing values. Through data cleaning and filling strategies, we ensure the comprehensiveness of the data and provide a complete information basis for analysis and decision-making.

[0156] Uniqueness: We ensure the uniqueness of each record in the integrated data set through deduplication algorithms and the setting of unique identifiers. This eliminates the analysis bias that may be caused by duplicate data and improves the consistency and reliability of the data.

[0157] Consistency: We ensure that the integrated data is logically consistent and complies with business rules and standards. By comparing and verifying data from different data sources, we resolve possible conflicts and inconsistencies.

[0158] Accuracy: We verify the authenticity and correctness of the integrated data, and ensure data accuracy through data verification and error correction processes, which is crucial for subsequent analysis and decision-making.

[0159] Validity: We assess whether the integrated data meets business requirements and intended use. Through the application of business rules and the suitability of data, we ensure the validity of the data so that it can support specific business goals.

[0160] Timeliness: We focus on the timeliness of the integrated data to ensure that the data is up to date and reflects the current business situation. Through regular updates and real-time data processing, we maintain the timeliness of the data to support the rapidly changing business environment.

[0161] We use the following formula to evaluate the quality of the integrated data:

[0162]

[0163] The Q in this formula reflects the overall quality of the fused dataset. i The deviation of the quality score from the average quality score μ and normalized it using a logistic function, we can get a quality score between 0 and 1. The parameter k controls the steepness of the function, thus affecting the sensitivity of the quality score. This evaluation result is crucial for the monitoring system because it is directly related to the accuracy of subsequent analysis and decision-making.

[0164] 3. Module function implementation

[0165] Data mapping rules:

[0166] The process of data mapping can be expressed by the following formula:

[0167]

[0168] Here, M(D i ) is the original data D i The result after mapping to the unified data model. The mapping function g is the core of the data mapping rule. It is not just a simple data conversion, but a multi-step, multi-level conversion process. Specifically, the implementation of function g includes the following aspects:

[0169] Data format conversion:

[0170] Convert the original data Di from its original format to the format required by the unified data model. This may involve data type conversion (such as string conversion to date format), data structure adjustment (such as list conversion to table), etc.

[0171] Unit Standardization:

[0172] Since different data sources may use different units of measurement, the mapping function g needs to unify all data into a standard unit system for comparison and analysis.

[0173] Semantic alignment:

[0174] At the semantic level, the mapping function g needs to handle the terminology differences between different data sources.

[0175] The parameter θ is an important part of the mapping function g, which includes the following:

[0176] Transformation ruleset:

[0177] A set of predefined rules to guide how to transform the raw data Di into a unified data representation. These rules may include steps such as data cleaning, formatting, and normalization.

[0178] Mapping parameters:

[0179] These parameters provide specific values ​​for the conversion rules, such as conversion coefficients, benchmark values, classification mapping tables, etc. For example, crime reports from different regions may have different names for the same type of crime, and the mapping rules need to unify these differences into a common semantic framework. If crime reports from different regions need to be mapped to a unified classification system, the parameter θ will contain a mapping table that details the correspondence between each region's classification and the unified classification system. In public security monitoring, crime reports from different regions may use different classification systems, and the mapping rules can unify these reports into the same classification framework to facilitate cross-regional analysis.

[0180] 4. Testing and Optimization

[0181] Algorithm efficiency evaluation:

[0182] To quantify the algorithm optimization effect, we use the following formula:

[0183]

[0184] Among them, E represents the relative improvement percentage of processing time before and after algorithm optimization. By comparing T old And after optimization T new By comparing the processing time of the monitoring system to the processing time of the monitoring system, we can intuitively see the degree of performance improvement. This evaluation is crucial to ensure that the monitoring system can maintain efficient operation when processing large amounts of real-time data. Performance optimization not only improves the system's response speed, but also reduces the consumption of hardware resources, thereby improving the overall reliability and economy of the system.

[0185] Implementation of intelligent analysis module

[0186] The intelligent analysis module integrates machine learning and deep learning algorithms, and can perform anomaly detection, trend prediction and pattern recognition. The anomaly detection algorithm uses a statistical learning-based method, trend prediction uses time series analysis technology, and pattern recognition is achieved through convolutional neural networks.

[0187] 1. Anomaly Detection Algorithm

[0188] Anomaly detection algorithms use statistical learning-based methods, the core of which is to establish behavioral patterns of normal data and identify anomalies by comparing the differences between new data points and these patterns.

[0189] (1) Data preprocessing

[0190] The input data is cleaned, including removing noise, filling missing values ​​and handling outliers, to ensure data quality.

[0191] Noise removal: Use filtering techniques such as sliding average and median filtering to reduce random noise in the data.

[0192] Missing value handling: fill in missing data through interpolation, using model prediction, or similarity-based filling.

[0193] Outlier processing: In the preprocessing stage, potential outliers are initially identified and processed to prevent them from interfering with model training.

[0194] (2) Feature extraction

[0195] Extract features from the data that help in anomaly detection. These features should be able to distinguish between normal and abnormal behavior.

[0196] Feature selection: Select the features most relevant to anomaly detection through methods such as correlation analysis and information gain.

[0197] Feature transformation: Apply dimensionality reduction techniques such as principal component analysis (PCA) and t-SNE to extract the essential features of the data.

[0198] (3) Model training

[0199] By training an anomaly detection model using a normal dataset, the model will learn the distribution of normal data.

[0200] Normal behavior modeling: Use clustering, density estimation and other techniques to establish the behavior patterns of normal data.

[0201] Model validation: Use methods such as cross-validation to ensure that the model has good generalization ability.

[0202] (4) Abnormality score

[0203] For new data points, the model outputs an anomaly score that reflects how much the data point deviates from normal behavior. If the score exceeds a preset threshold, the data point is considered an anomaly.

[0204] Threshold setting: Dynamically set the threshold of anomaly scores based on statistical methods or machine learning techniques.

[0205] Anomaly confirmation: The results of anomaly detection are verified through subsequent expert review or other algorithms.

[0206] 2. Trend prediction algorithm

[0207] Trend forecasting uses time series analysis techniques to predict future data trends.

[0208] (1) Data preprocessing:

[0209] Convert time series data to a stationary series, which may include techniques such as differencing, smoothing, etc.

[0210] Data cleaning: Identify and handle problems such as outliers and missing values ​​in time series.

[0211] Data transformation: Apply methods such as time series decomposition and seasonal adjustment to make the data stable.

[0212] (2) Model construction

[0213] Choose appropriate models such as ARIMA (Autoregressive Integrated Moving Average) or LSTM (Long Short-Term Memory) which are able to capture trends and seasonality in time series data.

[0214] Model selection: Select an appropriate model based on the characteristics of the data and forecasting requirements, such as SARIMA, exponential smoothing, etc.

[0215] Parameter optimization: Use grid search, Bayesian optimization and other techniques to find the optimal model parameters.

[0216] (3) Training Model

[0217] The model is trained using historical time series data and the prediction error is minimized by adjusting the model parameters.

[0218] Model diagnosis: Check the effectiveness of the model through residual analysis, autocorrelation function graph, etc.

[0219] Model improvement: Adjust the model structure or parameters based on the diagnosis results.

[0220] (4) Predict future trends

[0221] Use the trained model to predict future data points and provide a basis for decision making.

[0222] Prediction Interval: Provides confidence intervals for predicted values ​​to help users understand the uncertainty of the prediction.

[0223] Forecast Updates: Regularly update models and forecasts as new data arrives.

[0224] 3. Pattern Recognition Algorithms

[0225] Pattern recognition is achieved through convolutional neural networks (CNNs), which are suitable for feature learning and classification of image or sequence data.

[0226] (1) Data preprocessing

[0227] Normalize and augment input data to improve the generalization ability of the model.

[0228] Data normalization: Ensure that the input data has zero mean and unit variance to speed up the model training process.

[0229] Data augmentation: Expand the data set by rotation, scaling, cropping, etc. to improve model robustness.

[0230] (2) Network design:

[0231] Design the structure of CNN, including multiple convolutional layers and pooling layers, as well as fully connected layers for classification.

[0232] Layer design: Design the appropriate number of layers and the number of neurons in each layer according to the complexity of the task.

[0233] Activation function: Select activation functions such as ReLU and Sigmoid to introduce nonlinear characteristics.

[0234] (3) Model training:

[0235] CNN is trained using a large number of labeled data sets and the weights of the network are optimized through the back-propagation algorithm.

[0236] Loss function: Choose a loss function such as cross entropy or mean squared error to quantify the prediction error.

[0237] Optimization algorithm: Apply optimization algorithms such as Adam and SGD to adjust network weights.

[0238] (4) Pattern recognition:

[0239] Apply the trained model to new data to identify specific patterns or categories in the data.

[0240] Model evaluation: Use indicators such as accuracy, recall, and F1 score to evaluate model performance.

[0241] Model deployment: Deploy the trained model to actual application scenarios for real-time or batch pattern recognition.

[0242] Algorithm framework such as Figure 4 Shown

[0243] Implementation of the visual display module

[0244] The visualization module uses advanced graphics rendering technology to intuitively display the analysis results on the monitoring screen in the form of charts, images, animations, etc. This module supports interactive operations, and users can customize the display content and method according to their needs.

[0245] 1. Analysis and Design

[0246] System Design:

[0247] Design the user interface (UI) and user experience (UX) to ensure the interface is user-friendly and the operation is intuitive.

[0248] Determine the data source and format for visualization, as well as the interface with the data analysis module.

[0249] 2. Technology selection and development

[0250] Graphics rendering technology selection:

[0251] Select appropriate graphics rendering technology according to system requirements, such as WebGL, OpenGL, DirectX, etc.

[0252] Decide on the visualization library or framework to use, such as D3.js, Three.js, ECharts, etc.

[0253] Module Development:

[0254] Data access: Develop data access interfaces to ensure that analysis results can be seamlessly transferred to the visualization module.

[0255] Chart and Image Rendering:

[0256] Realize the rendering of charts, including line charts, bar charts, pie charts, scatter charts, etc.

[0257] Develop rendering logic for images and animations, such as heat maps, 3D models, dynamic trajectories, etc.

[0258] Interactive Design:

[0259] Implement user interaction functions, such as zooming, dragging, and clicking events.

[0260] Develop custom display functions to allow users to select displayed data dimensions, time range, etc.

[0261] 3. System integration and testing

[0262] System Integration:

[0263] Integrate the visualization display module with other parts of the system, such as the data analysis module and the data fusion module.

[0264] Ensure that data flows smoothly between various modules of the system.

[0265] Functional testing:

[0266] Perform unit testing on the visual display module to ensure that each functional point is correct.

[0267] Perform integration testing to verify the module's ability to work with other systems.

[0268] 4. Deployment and Optimization

[0269] deploy:

[0270] Deploy the visualization module to the target environment, including server configuration, network settings, etc.

[0271] Conduct performance tests in actual environments to ensure stable system operation.

[0272] optimization:

[0273] The visualization module is optimized based on user feedback and performance test results.

[0274] Adjust graphics rendering parameters to improve rendering efficiency and display effects.

[0275] 5. User training and maintenance

[0276] User Training:

[0277] Provide training to users on how to use the visualization module for monitoring and analysis.

[0278] Create user manuals and help documents to facilitate user self-study and inquiry.

[0279] Maintenance and Upgrades:

[0280] Regularly inspect and maintain the system to ensure the normal operation of the visualization module.

[0281] The modules are upgraded and their functions expanded according to user needs and technological development.

[0282] Module framework such as Figure 5 Shown

[0283] ·Implementation of backend management module

[0284] The backend management module includes functions such as data storage, query, report generation and user authority management. The data storage uses a domestic database system to ensure data security and stability. The user authority management adopts the RBAC model to achieve refined authority control.

[0285] 1. System planning and preparation

[0286] Business process streamlining:

[0287] Communicate with the business department to sort out the business processes that need to be supported by backend management.

[0288] Determine the data flow path and key business nodes.

[0289] Technical environment construction:

[0290] Prepare servers and storage devices to ensure sufficient hardware resources.

[0291] Install domestic database system and conduct preliminary performance tuning.

[0292] 2. System architecture design

[0293] Modular Design:

[0294] The backend management module is subdivided into sub-modules such as data management, reporting system, authority center and log audit.

[0295] Define the interfaces and interaction protocols between sub-modules.

[0296] Security Architecture:

[0297] Design data encryption solutions to ensure the security of data transmission and storage.

[0298] Determine access control policies to ensure only authorized users can access sensitive data.

[0299] 3. Function development and implementation

[0300] Data Management:

[0301] Develop data import and export tools to support batch data processing.

[0302] Design data backup and recovery mechanisms to ensure data integrity. For example, the key point of the management interface design is the password. Only administrators have the authority to this interface, and administrators can control passwords and other information to achieve a level of superior management of subordinates. In administrator account management, only the account and password are particularly important, and other users are not allowed to enter. The advantage of this design is that if a new manager enters the company, he can simply add him to the system without wasting too much time and manpower. For example, managing customer information can realize the import and export of the entire table. Make it more intelligent and convenient for data storage and adjustment.

[0303] Reporting system:

[0304] Use chart libraries such as ECharts to visualize data.

[0305] Developed custom report generators to allow users to generate reports as per their requirements.

[0306] Permission Center:

[0307] Implement permission management based on the RBAC model and develop role and permission allocation functions.

[0308] Design a permission verification mechanism to ensure that user operations are performed within the scope of permission.

[0309] Log audit:

[0310] Record user operation logs for subsequent auditing and problem tracking.

[0311] Develop log analysis tools to help administrators monitor the system's operating status.

[0312] 4. System integration and testing

[0313] Interface integration:

[0314] Integrate the backend management module with the front-end interface and other business systems.

[0315] Ensure data can flow smoothly between different systems.

[0316] Stress Test:

[0317] Stress test the system to simulate system performance in high-concurrency scenarios.

[0318] Optimize the system based on the test results to improve performance and stability.

[0319] 5. Deployment and continuous improvement

[0320] Progressive deployment:

[0321] Adopt a progressive deployment strategy, first test it online on a small scale, and then gradually expand it to all users.

[0322] Monitor problems during the launch process and make timely adjustments.

[0323] User feedback collection:

[0324] Collect user feedback during use and identify system problems and improvement points.

[0325] Update the system regularly to optimize user experience and system performance.

[0326] Module framework such as Figure 6 Shown

[0327] ·System integration and implementation of interface modules

[0328] The system integration and interface module is responsible for data exchange and linkage control with other monitoring systems. This module complies with domestic data exchange standards and realizes data sharing and system linkage through API interfaces.

[0329] (1) Standard research and confirmation

[0330] First, we conducted in-depth research on the relevant standards for data exchange, including GB / T (national standard), SJ / T (electronic industry standard) and other series of standards to ensure that the design and implementation of the interface module complies with national regulations and industry best practices.

[0331] The specific data formats, communication protocols and security specifications that need to be followed are determined. These specifications are selected based on the actual needs of the system, while taking into account possible future expansion and upgrades.

[0332] (2) Interface design

[0333] API Design:

[0334] Design API interfaces that comply with standards, including RESTful APIs or other suitable interface types.

[0335] Define the API's functions, parameters, return data format, and error handling mechanism.

[0336] Interface development:

[0337] Write API interface code to implement data sending, receiving and processing logic.

[0338] Ensure that the interface can handle compatibility of different data formats and communication protocols.

[0339] Data exchange protocol implementation:

[0340] Implement data exchange protocols according to standards, including data encryption, signing and verification processes.

[0341] Develop data parsing and packaging functions to ensure correct data transmission.

[0342] (3) System Integration

[0343] The interface module was integrated with the existing monitoring system to ensure the correct transmission of data flow and control instructions. This step involved a complex configuration and debugging process to ensure seamless connection between the systems.

[0344] Realize data synchronization and real-time update with other monitoring systems.

[0345] (4) Test Optimization

[0346] Joint debugging test:

[0347] Conduct joint debugging tests with other systems to verify the connectivity of interfaces and the accuracy of data exchange.

[0348] Simulate various data exchange scenarios to ensure that the interface can work stably in various situations.

[0349] Safety Testing:

[0350] Perform security tests on interfaces, including prevention of common security threats such as SQL injection and cross-site scripting attacks.

[0351] Implement access controls to ensure that only authorized systems can access the API interface.

[0352] Performance optimization:

[0353] Perform performance testing on interfaces to optimize response time and concurrent processing capabilities.

[0354] Adjust server resource configuration and interface implementation logic based on test results.

[0355] (5) Documentation

[0356] Write API interface documentation, including interface definition, usage examples, and error code descriptions.

[0357] Provide system integration guides to help other developers or operation and maintenance personnel understand and use the interfaces.

[0358] (6) User training and support:

[0359] Provide interface usage training for system operators.

[0360] Establish technical support channels to answer questions users encounter when using the interface.

[0361] Module framework such as Figure 6 The above embodiments show the specific implementation methods of the technical solution of the present invention in practical applications. Through the collaborative work of the above seven modules, the system realizes intelligent analysis and visual display of monitoring data, and improves monitoring efficiency and safety. This embodiment is only a preferred example of the present invention and should not be regarded as a limitation of the present invention. Any modification, equivalent replacement or improvement made in accordance with the basic spirit and principles of the present invention should be regarded as falling within the protection scope of the present invention.

Claims

1. An intelligent monitoring and visualization management system based on multi-source data fusion, characterized in that: The system includes: a) Data acquisition module, used to collect monitoring data from multiple data sources such as video surveillance, environmental monitoring sensors, and equipment operation parameter sensors; b) Data preprocessing module, which performs preprocessing operations such as cleaning, denoising, calibration and normalization on the collected data; c) a multi-source data fusion module, which fuses the pre-processed data to form a unified data format and view, wherein the fusion process includes image block similarity calculation based on a specific method and double noise similarity calculation of image blocks using the original noise map and the map of the iterative process; d) Intelligent analysis module, which performs intelligent analysis such as anomaly detection, trend prediction, and pattern recognition on the fused data, and processes similar image blocks based on the low-rank approximation method to estimate more accurate monitoring data; e) Visual display module, which displays the analysis results on the monitoring screen in the form of charts, images, animations, etc., and provides real-time and dynamic monitoring images and data information; f) Backend management module, which receives data transmitted by each module and performs operations such as data storage, query, report generation and user authority management.

2. The system according to claim 1, characterized in that The multi-source data fusion module also includes performing spatiotemporal analysis on the fused data to identify spatiotemporal relationships and events in the monitoring scene.

3. The system according to claim 1, characterized in that The intelligent analysis module also includes a machine learning algorithm for performing operations such as classification, clustering, and association rule mining on monitoring data to improve the accuracy of anomaly detection and trend prediction.

4. The system according to claim 1, characterized in that The visualization display module also includes an interactive function, allowing the user to view detailed data, adjust the monitoring screen or set alarm conditions through operations such as clicking and dragging.

5. The system according to claim 1, characterized in that The background management module also includes data backup and recovery functions to ensure the security and integrity of data.

6. The system according to claim 1, characterized in that The data acquisition module also includes sensor calibration and fault detection functions to ensure the accuracy and reliability of the collected data.

7. The system according to claim 1, characterized in that The system also includes an interface module with other systems for realizing data sharing and linkage control with other monitoring systems.

8. The system according to claim 1, further characterized in that: The system also includes an advanced analytics module that uses deep learning technology for image and video analysis, as well as behavior recognition and prediction to improve monitoring efficiency and accuracy.

9. The system according to claim 1, further characterized in that: The system has an adaptive optimization function, which can automatically adjust system parameters according to the characteristics of the monitoring scenario, and optimize the data processing flow and analysis model through machine learning algorithms.

10. The system according to claim 1, further characterized in that: The system has data encryption and privacy protection functions to ensure the security of data transmission and storage, and protect the personal privacy of the monitored object during data processing.

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