A multi-dimensional security data fusion security management information early warning platform
Through a multi-dimensional security data fusion platform, cross-modal correlation analysis and dynamic risk assessment were achieved, solving the problems of high latency, high privacy risks and insufficient visualization in existing security systems. This improved the detection accuracy and management efficiency of complex threats and met compliance requirements.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- 中环低碳节能技术(北京)有限公司
- Filing Date
- 2026-03-04
- Publication Date
- 2026-05-29
AI Technical Summary
The centralized architecture of existing security systems leads to high latency, high privacy risks, static and insufficient visualization of early warnings, making it difficult to meet the real-time perception and compliance requirements of complex and multi-dimensional threats.
A multi-dimensional security data fusion platform is adopted, which realizes cross-modal correlation analysis, dynamic risk assessment and encrypted storage through multi-source data acquisition module, data fusion module, risk warning module, visual management interface module and secure storage module. Combined with adaptive attention mechanism and federated learning framework, it supports low-latency real-time fusion and privacy protection.
It significantly improves the accuracy and response speed of threat detection in complex scenarios, reduces operation and maintenance costs, meets the requirements of cybersecurity law and privacy protection, and improves the decision-making efficiency of managers and the scalability of the system.
Smart Images
Figure CN122116551A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of information-based early warning platform technology, and in particular relates to a security management information-based early warning platform that integrates multi-dimensional security data. Background Technology
[0002] With the acceleration of urbanization and the rapid development of information technology, public safety management is facing increasingly complex challenges. Traditional security systems mainly rely on single or limited data sources, such as video surveillance or independent sensors, to detect and alarm on safety and fire hazards.
[0003] However, some problems still exist in the above solutions:
[0004] Video surveillance images are susceptible to environmental factors such as insufficient lighting, inclement weather, and obstructions, leading to a decrease in the accuracy of identifying behaviors that pose safety and fire hazards. Sensor signals often only reflect local physical changes and lack global correlation. Although network logs can record the access trajectories of people or vehicles, they are difficult to directly correlate with intrusion, vandalism, or fire hazard events in physical space. The information between multiple sources is isolated and lacks effective integration, resulting in insufficient perception of complex and multi-dimensional threats such as safety and fire hazards by the system. This often leads to missed or false alarms, affecting the timeliness and reliability of security management.
[0005] Existing security management systems mostly adopt a centralized data processing architecture. All raw video images, sensor signals, and network logs need to be uploaded to a central server for analysis. This not only places high demands on network bandwidth and central computing resources, but also easily causes processing delays in scenarios with many edge devices or limited network conditions, making it difficult to meet the real-time early warning needs for security and fire hazard events. At the same time, the centralized storage and transmission of raw security data poses serious security risks. Once data is leaked or tampered with, it will cause major privacy and security risks involving sensitive information such as faces, behavioral trajectories, and access control records. With the implementation of laws and regulations such as the Cybersecurity Law, the Data Security Law, and the Personal Information Protection Law, the privacy protection requirements for sensitive security data such as face recognition and behavioral trajectories are becoming increasingly stringent, and the traditional centralized architecture can no longer meet compliance requirements.
[0006] Furthermore, existing early warning mechanisms are mostly based on fixed thresholds or simple rules, which are difficult to adapt to dynamically changing security scenarios. The evolution of security and fire hazards and other threats is often time-varying and uncertain. Static thresholds are prone to frequent false alarms during low-risk periods and serious underreporting during high-risk periods, which reduces the practicality and reliability of the system. In terms of visualization, existing systems mostly use two-dimensional planar displays with a single information hierarchy. It is difficult for managers to quickly and intuitively grasp multi-dimensional risk information and event development trends such as densely populated areas, abnormal loitering areas, suspicious trajectories, and the distribution of fire hazards, which affects the efficiency of emergency decision-making. Summary of the Invention
[0007] This invention provides a security management information early warning platform that integrates multi-dimensional security data, aiming to solve the problems of high latency, high privacy risks, static early warning and insufficient visualization caused by the centralized architecture of existing security systems.
[0008] This invention is implemented as follows: a multi-dimensional security data fusion security management information early warning platform, comprising:
[0009] The multi-source data acquisition module is used to acquire video surveillance images, infrared sensor signals, access control sensor signals, vibration sensor signals, and network logs of personnel and vehicles in real time.
[0010] The data fusion module is used to perform cross-modal fusion of the video surveillance images, sensor signals and network logs based on an adaptive attention mechanism, so as to realize correlation analysis and feature extraction for safety and fire hazards;
[0011] The risk warning module is used to generate warning signals for safety and fire hazards in real time by applying a dynamic risk quantification algorithm based on the fused features.
[0012] The visual management interface module is used to interactively display risk heat maps, real-time movement trajectories of suspicious targets, and historical event replays on 3D electronic maps and building models;
[0013] The secure storage module is used to encrypt and store fusion features, early warning records, and related video clips, and ensure data integrity.
[0014] Preferably, the data fusion module includes an attention weight calculation unit and a feature aggregation unit, wherein the attention weight calculation unit uses a formula... Calculate the attention weights between different data sources, the and For data feature embedding vectors, the This is a learnable parameter matrix used for dynamically allocating fusion weights.
[0015] Preferably, the feature aggregation unit employs a multi-head attention mechanism, through the formula... Aggregate multi-head outputs, where each The It is a value vector used to capture multiple data association patterns in parallel, improving the robustness and accuracy of fusion.
[0016] Preferably, the data fusion module further includes an edge preprocessing unit for performing preliminary filtering and compression processing at the data source end, reducing the central transmission load and supporting low-latency real-time fusion applications.
[0017] Preferably, the data fusion module further includes a privacy protection unit, which uses a federated learning framework to train the fusion model without sharing the original data.
[0018] Preferably, the risk warning module includes a risk calculation unit and a threshold adjustment unit, wherein the risk calculation unit uses a formula... Calculate the time-varying risk value, the The This is a feature fusion method used to achieve dynamic quantitative assessment of potential threats.
[0019] Preferably, the threshold adjustment unit employs an online learning algorithm to adaptively update the weights based on feedback data. By minimizing the false alarm rate, the warning threshold is optimized, thereby improving the long-term adaptability of the system.
[0020] Preferably, the risk warning module further includes an output interface unit for supporting voice alarms, mobile push notifications, and automated response triggering, and for integration with external emergency systems.
[0021] Preferably, the visualization management interface module includes a heat map display unit and a trajectory overlay unit, used to generate three-dimensional heat maps and real-time trajectory visualization, supporting user-defined queries and augmented reality interaction, and improving decision support capabilities.
[0022] Preferably, the secure storage module includes an encrypted storage unit and an audit verification unit, and uses a zero-knowledge proof protocol to verify data integrity and access traceability.
[0023] Compared with related technologies, the security management information early warning platform with multi-dimensional security data fusion provided by this invention has the following beneficial effects:
[0024] 1. This platform uses a multi-source data acquisition module to collect video surveillance images, infrared sensor signals, access control sensor signals, vibration sensor signals, smoke sensor signals, and personnel and vehicle access logs in real time. Combined with a data fusion module that uses an adaptive attention mechanism for cross-modal correlation analysis and feature extraction, it effectively integrates complementary information from heterogeneous data sources. This overcomes the limitations of single data sources, such as insufficient lighting, occlusion, limited viewing angles, or incomplete information. It significantly improves detection robustness, especially in complex scenarios involving security and fire hazards. The fused high-quality feature data provides reliable input for the risk warning module, enabling the platform to dynamically and accurately identify potential threats and significantly improve the accuracy of warnings. The recall rate and risk warning module, through the collaborative work of the risk calculation unit and threshold adjustment unit, achieves real-time quantitative assessment and adaptive optimization of risk levels. Based on feedback from security personnel, it automatically reduces false alarms and missed alarms, ensuring efficient threat perception capabilities even in complex scenarios such as morning and evening rush hours, inclement weather, or nighttime. Meanwhile, the visualization management interface module provides a 3D heat map display unit and trajectory overlay unit, which present the fusion results, risk heat distribution, and suspicious target movement trajectories on building models or electronic maps in an intuitive and interactive way. It supports user-defined timeline playback queries and augmented reality interaction, significantly improving managers' rapid understanding of the on-site situation and emergency decision-making efficiency.
[0025] 2. This platform boasts significant advantages in data security and privacy protection. The data fusion module's built-in privacy protection unit employs a federated learning framework, completing the training and feature extraction of security and fire hazard behavior recognition models without sharing original video images, target feature information, or access control records. This ensures sensitive security information remains on local devices, avoiding the risk of leakage from centralized data. The secure storage module uses an encrypted storage unit to persistently store the fused face and identifier feature vectors, warning records, and related video clips. An audit verification unit uses a zero-knowledge proof protocol to verify data integrity and ensure access traceability, effectively preventing unauthorized tampering and leakage. Meeting stringent compliance requirements of laws such as the Cybersecurity Law, Data Security Law, and Personal Information Protection Law, the data fusion module's edge preprocessing unit performs preliminary detection and anomaly screening of personnel and vehicles at the camera or sensor end, significantly reducing the transmission load and computing pressure on the central server. This not only lowers the demand for network bandwidth and storage resources but also improves the system's real-time response capabilities, making it particularly suitable for deployment scenarios with limited bandwidth or a large number of edge devices. Overall, this platform achieves a balance between efficient data fusion and security protection through technological innovation, reducing the operational costs of traditional centralized security systems in terms of data transmission, storage, and privacy compliance, while improving the system's scalability and long-term stability. Attached Figure Description
[0026] Figure 1 This is a schematic diagram illustrating the overall operation of the present invention. Detailed Implementation
[0027] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used herein in the specification of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having," and any variations thereof, in the specification, claims, and foregoing drawings of this application are intended to cover non-exclusive inclusion. The terms "first," "second," etc., in the specification, claims, or foregoing drawings of this application are used to distinguish different objects, not to describe a particular order.
[0028] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.
[0029] Example
[0030] A preferred embodiment of the multi-dimensional security data fusion security management information early warning platform provided by the present invention is as follows: Figure 1 As shown: A security management information early warning platform that integrates multi-dimensional security data, comprising:
[0031] The multi-source data acquisition module is used to acquire video surveillance images, infrared sensor signals, access control sensor signals, vibration sensor signals, and network logs of personnel and vehicles in real time.
[0032] The data fusion module is used to perform cross-modal fusion of video surveillance images, sensor signals and network logs based on an adaptive attention mechanism, so as to realize correlation analysis and feature extraction for safety and fire hazards;
[0033] The risk warning module is used to generate warning signals for safety and fire hazards in real time by applying a dynamic risk quantification algorithm based on the fused features.
[0034] The visual management interface module is used to interactively display risk heat maps, real-time movement trajectories of suspicious targets, and historical event replays on 3D electronic maps and building models;
[0035] The secure storage module is used to encrypt and store fusion features, early warning records, and related video clips, and ensure data integrity.
[0036] In this embodiment, the multi-source data acquisition module of the platform first continuously acquires video images captured by cameras, real-time signals generated by various sensors, and behavior logs recorded by network devices through standardized interfaces, thereby providing a complete and timely raw data foundation for the entire system. After the acquired data enters the data fusion module, it immediately performs cross-modal correlation processing and extracts key features. The fused features are then transmitted to the risk warning module for risk assessment and rapid warning generation. Managers can view the fusion results and warning information in real time and query historical records through the visual management interface module. All key data is simultaneously encrypted and stored by the secure storage module to ensure long-term integrity and availability.
[0037] In a further preferred embodiment of the present invention, the data fusion module includes an attention weight calculation unit and a feature aggregation unit, wherein the attention weight calculation unit calculates the data weight using a formula... Calculate the attention weights between different data sources. and Embed vectors for data features. This is a learnable parameter matrix used for dynamically allocating fusion weights.
[0038] In this embodiment, when multi-source data enters the data fusion module, the attention weight calculation unit first converts video, sensor, and log data into feature embedding vectors. Then, it uses a learnable parameter matrix to calculate the similarity between each data source and generate attention weights, thereby dynamically highlighting the most relevant information sources in the current scene and ensuring that the fusion process always focuses on key modalities. The feature aggregation unit then performs weighted integration of features based on these weights to form a high-quality unified feature representation, avoiding information redundancy and improving the efficiency of subsequent analysis. The entire process runs in real time, thereby providing accurate and reliable fusion data support for the risk warning module.
[0039] In a further preferred embodiment of the present invention, the feature aggregation unit employs a multi-head attention mechanism, through a formula... Aggregate multi-head outputs, where each , It is a value vector used to capture multiple data association patterns in parallel, improving the robustness and accuracy of fusion.
[0040] In this embodiment, after the attention weight calculation unit generates the weights, the feature aggregation unit immediately starts the multi-head attention mechanism, which divides the feature embedding vector into multiple independent heads and calculates their respective weighted value vectors in parallel. Then, the outputs of all heads are aggregated by concatenation to form a comprehensive feature representation. This parallel processing method enables the platform to simultaneously capture the spatial correlation between video and sensors, the temporal correlation between logs and sensors, and the complex cross-relationships among the three. Even under noise interference or partial data loss, it can maintain a stable fusion effect, thereby significantly improving the robustness and accuracy of cross-modal correlation analysis and providing a more comprehensive and reliable feature input for the risk warning module.
[0041] In a further preferred embodiment of the present invention, the data fusion module further includes an edge preprocessing unit, which is used to perform preliminary filtering and compression processing at the data source end to reduce the central transmission load and support low-latency real-time fusion applications.
[0042] In this embodiment, when raw data is generated by cameras, sensors, or network devices, the edge preprocessing unit immediately performs noise filtering, outlier removal, and preliminary feature compression locally, uploading only the simplified and effective data to the central server. This significantly reduces network transmission volume and central computing pressure, effectively avoiding transmission congestion in scenarios with numerous edge devices or limited network bandwidth. It ensures that the data fusion module can complete fusion processing within milliseconds, supporting true real-time cross-modal analysis and feature extraction, and providing low-latency assurance for subsequent risk warning.
[0043] In a further preferred embodiment of the present invention, the data fusion module further includes a privacy protection unit, which uses a federated learning framework to train the fusion model without sharing the original data.
[0044] In this embodiment, during the initial deployment or model update phase of the platform, the privacy protection unit initiates a federated learning framework. Each edge device independently trains a local model using local video, sensor, and log data, and only uploads the model parameter gradients to the central server. After the central server completes parameter aggregation, it sends the global model back to each device. This process is repeated iteratively until the model converges. Throughout the entire training process, the original security data remains on the local device and never leaves the data source, thereby completely avoiding the privacy leakage risk caused by centralized transmission. At the same time, it maintains the high accuracy of the fusion model, ensuring that the data fusion module can achieve efficient cross-modal correlation analysis in actual operation while fully complying with privacy compliance requirements.
[0045] In a further preferred embodiment of the present invention, the risk warning module includes a risk calculation unit and a threshold adjustment unit, wherein the risk calculation unit uses a formula... Calculate the time-varying risk value. This is a feature fusion method used to achieve dynamic quantitative assessment of potential threats.
[0046] In this embodiment, after the fused features enter the risk warning module, the risk calculation unit immediately calculates the time-varying risk value in real time based on the current fused features, fully considering the intensity changes of the threat over time, thereby accurately quantifying the development trend of abnormal events. The threshold adjustment unit simultaneously monitors the difference between the calculation results and the actual feedback. When the risk value exceeds the current threshold, an early warning signal is immediately generated and pushed to the visual management interface module. The entire process runs dynamically and continuously, ensuring that the platform can promptly detect and assess potential threats, avoiding missed or false alarms caused by traditional static methods.
[0047] In a further preferred embodiment of the present invention, the threshold adjustment unit employs an online learning algorithm to adaptively update the weights based on feedback data. By minimizing the false alarm rate, the warning threshold is optimized, thereby improving the long-term adaptability of the system.
[0048] In this embodiment, during the continuous operation of the risk warning module, the threshold adjustment unit receives real-time confirmation or false alarm feedback from management personnel regarding the warning signal. It then uses an online learning algorithm to gradually adjust the risk calculation weights based on this feedback data, enabling the risk assessment model to gradually adapt to changes in lighting, personnel flow patterns, and seasonal threat characteristics in specific security scenarios. As the operating time increases, the false alarm rate continues to decrease, and the warning accuracy continuously improves. The platform can achieve long-term self-optimization without manual reconfiguration, thus maintaining reliable dynamic risk assessment and warning services under different time periods and environmental conditions.
[0049] In a further preferred embodiment of the present invention, the risk warning module further includes an output interface unit for supporting voice alarms, mobile push notifications and automated response triggering, and for integration with external emergency systems.
[0050] In this embodiment, once the risk calculation unit detects that the risk value exceeds the threshold, the output interface unit immediately activates multiple early warning methods, including on-site voice alarm broadcast, real-time push notification to the administrator's mobile phone, and automatic triggering of access control locking or light warning. At the same time, it realizes data interaction with the external response system through the standard interface to improve the response efficiency in abnormal situations and achieve linkage response. The entire process from risk identification to multi-channel output takes only seconds, ensuring that threats can be dealt with quickly in the early stage, significantly shortening the emergency response time and improving the overall security management efficiency.
[0051] In a further preferred embodiment of the present invention, the visualization management interface module includes a heat map display unit and a trajectory overlay unit, used to generate a three-dimensional heat map and real-time trajectory visualization, supporting user-defined queries and augmented reality interaction, and improving decision support capabilities.
[0052] In this embodiment, after logging in, the heat map display unit generates a three-dimensional risk heat map in real time, which intuitively shows the threat density distribution within the monitored area. The trajectory overlay unit simultaneously overlays the real-time movement trajectory of suspicious targets on the map. Users can zoom, rewind the timeline, and query with custom conditions via touch screen or mobile device. When using augmented reality mode, managers only need to point their mobile phones at the scene to see the virtual overlaid heat map and trajectory information. The entire interface is responsive and rich in information hierarchy, helping users quickly understand complex situations and make accurate decisions.
[0053] In a further preferred embodiment of the present invention, the secure storage module includes an encrypted storage unit and an audit verification unit, and uses a zero-knowledge proof protocol to verify data integrity and access traceability.
[0054] In this embodiment, the fusion data, early warning records, and operation logs generated during platform operation are first encrypted and persistently stored by the encrypted storage unit using a high-strength algorithm. When any module needs to read the data, the audit verification unit uses a zero-knowledge proof protocol to verify that the data has not been tampered with without exposing the data content. At the same time, it automatically records the visitor's identity, time, and operation content, forming a complete audit chain. Administrators can query the access history at any time to ensure that all data remains intact and reliable throughout the entire lifecycle, completely eliminating the risk of unauthorized modification or leakage.
[0055] In summary, the working principle of the multi-dimensional security data fusion security management information early warning platform is based on the real-time collection, intelligent fusion, dynamic risk assessment, and security visualization management of multi-source heterogeneous data. It enables comprehensive monitoring, accurate analysis, and timely response to complex security scenarios such as security and fire hazards. The platform mainly consists of a multi-source data collection module, a data fusion module, a risk early warning module, a visualization management interface module, and a secure storage module. These modules work closely together to form a complete security management information closed-loop system.
[0056] The workflow begins with the multi-source data acquisition module, which is responsible for real-time acquisition of video surveillance images, infrared sensor signals, access control sensor signals, vibration sensor signals, smoke sensor signals, and network logs of personnel and vehicles. Specifically, this includes video image sequences captured by cameras, intrusion or damage signals generated by infrared and vibration sensors, fire hazard signals generated by smoke sensors, and access records and behavior logs generated by access control systems and network devices. The multi-source data acquisition module connects to various devices through a unified standardized interface to ensure the timeliness and completeness of data acquisition, providing a reliable raw data foundation for subsequent cross-modal analysis of security and fire hazards.
[0057] The collected multi-source data then enters the data fusion module for deep processing. This module, based on an adaptive attention mechanism, performs cross-modal fusion of video surveillance images, sensor signals, and network logs to achieve correlation analysis and feature extraction for security and fire hazards. The attention weight calculation unit dynamically evaluates the importance of each data source in the current security scenario and assigns fusion weights accordingly. The feature aggregation unit uses a multi-head attention mechanism to capture various correlations in parallel (such as spatial intrusion correlation between video images and infrared sensors, tailing correlation between video images and access control logs, and destructive behavior correlation between vibration sensors and network logs), thereby generating high-quality fused feature representations. This module also includes an edge preprocessing unit, which performs preliminary detection and anomaly screening of personnel and vehicle targets at the camera or sensor end, significantly reducing the transmission load to the center and supporting millisecond-level real-time fusion. In addition, the privacy protection unit adopts a federated learning framework to complete model training without sharing the original video images, target feature information, or access control records, ensuring that sensitive security information always remains local and meeting the strict privacy protection requirements involving facial recognition, behavioral trajectory, and other information.
[0058] The fused feature data is transmitted to the risk warning module, which performs dynamic risk assessment based on the fusion results. The risk calculation unit quantifies the threat level of safety and fire hazards in real time, while the threshold adjustment unit continuously optimizes the assessment parameters based on the historical confirmation and false alarm feedback from security personnel. By minimizing the false alarm rate, the accuracy of the warning is improved. Once a risk is detected to exceed the threshold, the output interface unit immediately triggers multiple forms of warnings, including on-site voice alarms, mobile push notifications with video clips, execution of preset linkage controls, or activation of sound and light alarms. It also achieves seamless integration with external management platforms or emergency response platforms to ensure that threats are dealt with quickly in the early stages.
[0059] To support efficient decision-making by managers, the visual management interface module provides intuitive interactive functions. The heat map display unit generates risk heat maps on 3D building models or electronic maps, clearly showing densely populated areas, areas of abnormal loitering, and the distribution density of fire hazards. The trajectory overlay unit realizes the visualization of the real-time movement trajectory of suspicious targets. This module supports users to customize the query timeline to replay intrusion paths or abandoned events, and provides augmented reality interaction methods. It presents the fusion results, early warning information, and historical event analysis in a multi-dimensional form, greatly improving managers' perception of the on-site situation and emergency decision-making capabilities.
[0060] All critical data generated during platform operation is managed by the secure storage module. The encrypted storage unit encrypts and persistently stores the fused face and identifier feature vectors, early warning records, and related video clips. The audit and verification unit uses a zero-knowledge proof protocol to verify data integrity and records access traces to achieve traceable auditing and prevent unauthorized tampering and leakage.
[0061] Through the organic collaboration of multi-source data acquisition modules, data fusion modules, risk warning modules, visual management interface modules, and secure storage modules, this platform achieves closed-loop management of the entire process from data acquisition to fusion analysis, risk warning, and visual decision-making. It has significant advantages such as rapid real-time response, accurate and reliable anomaly detection, strict privacy protection, and a user-friendly interface. It can be effectively applied to complex scenarios such as urban comprehensive security, critical infrastructure protection, and smart park management, providing efficient information-based early warning and management capabilities for events such as fire hazards.
[0062] It is worth noting that the circuits, electronic components, and modules involved in this invention are all existing technologies, which can be fully implemented by those skilled in the art, and need not be elaborated upon. The content protected by this invention does not involve improvements to the software and methods.
[0063] It should be understood that the disclosed apparatus can be implemented in other ways, given the several embodiments provided in this application. For example, the apparatus embodiments described above are merely illustrative; the division of units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or communication connections shown or discussed may be through some interfaces; the indirect coupling or communication connections between devices or units may be telecommunications or other forms.
[0064] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit the scope of protection of the invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on these embodiments, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art can still combine, add, delete, or otherwise adjust the features of the various embodiments of the present invention according to the circumstances without conflict or creative effort, thereby obtaining different technical solutions that do not fundamentally depart from the concept of the present invention. These technical solutions also fall within the scope of protection of the present invention.
Claims
1. A security management information early warning platform that integrates multi-dimensional security data, characterized in that, include: The multi-source data acquisition module is used to acquire video surveillance images, infrared sensor signals, access control sensor signals, vibration sensor signals, and network logs of personnel and vehicles in real time. The data fusion module is used to perform cross-modal fusion of the video surveillance images, sensor signals and network logs based on an adaptive attention mechanism, so as to realize correlation analysis and feature extraction for safety and fire hazards; The risk warning module is used to generate warning signals for safety and fire hazards in real time by applying a dynamic risk quantification algorithm based on the fused features. The visual management interface module is used to interactively display risk heat maps, real-time movement trajectories of suspicious targets, and historical event replays on 3D electronic maps and building models; The secure storage module is used to encrypt and store fusion features, early warning records, and related video clips, and ensure data integrity.
2. The security management information early warning platform with multi-dimensional security data fusion as described in claim 1, characterized in that, The data fusion module includes an attention weight calculation unit and a feature aggregation unit, wherein the attention weight calculation unit uses a formula... Calculate the attention weights between different data sources. and Embed vectors for data features. This is a learnable parameter matrix used for dynamically allocating fusion weights.
3. The security management information early warning platform with multi-dimensional security data fusion as described in claim 2, characterized in that, The feature aggregation unit employs a multi-head attention mechanism, through the formula... Aggregate multi-head outputs, where each , It is a value vector used to capture multiple data association patterns in parallel, improving the robustness and accuracy of fusion.
4. The security management information early warning platform with multi-dimensional security data fusion as described in claim 2, characterized in that, The data fusion module further includes an edge preprocessing unit, which performs preliminary filtering and compression processing at the data source end to reduce the central transmission load and support low-latency real-time fusion applications.
5. The security management information early warning platform with multi-dimensional security data fusion as described in claim 2, characterized in that, The data fusion module also includes a privacy protection unit, which uses a federated learning framework to train the fusion model without sharing the original data.
6. The security management information early warning platform with multi-dimensional security data fusion as described in claim 1, characterized in that, The risk warning module includes a risk calculation unit and a threshold adjustment unit, wherein the risk calculation unit uses a formula... Calculate the time-varying risk value. This is a feature fusion method used to achieve dynamic quantitative assessment of potential threats.
7. The security management information early warning platform with multi-dimensional security data fusion as described in claim 6, characterized in that, The threshold adjustment unit employs an online learning algorithm to adaptively update the weights based on feedback data. By minimizing the false alarm rate, the warning threshold is optimized, thereby improving the long-term adaptability of the system.
8. The security management information early warning platform with multi-dimensional security data fusion as described in claim 7, characterized in that, The risk warning module further includes an output interface unit for supporting voice alarms, mobile push notifications, and automated response triggering, and for integration with external emergency systems.
9. The security management information early warning platform for multi-dimensional security data fusion as described in claim 1, characterized in that, The visualization management interface module includes a heat map display unit and a trajectory overlay unit, which are used to generate three-dimensional heat maps and real-time trajectory visualization, support user-defined queries and augmented reality interaction, and improve decision support capabilities.
10. The security management information early warning platform for multi-dimensional security data fusion as described in claim 1, characterized in that, The secure storage module includes an encrypted storage unit and an audit verification unit, and uses a zero-knowledge proof protocol to verify data integrity and access traceability.