Park integrated unmanned intelligent management system based on Internet of Things
Through the multi-module collaboration mechanism of the Internet of Things, the problem of rigid protocols, data silos and policy disconnection in the park's intelligent management system is solved, unmanned intelligent management is realized throughout the time period, security and response efficiency are improved, and a reliable, flexible and self-optimized management paradigm is provided.
Patent Information
- Application Number
- CN202510838759.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-23
- Publication Date
- 2025-08-01
AI Technical Summary
The existing park intelligent management system has significant defects in protocol compatibility, data analysis depth, dynamic strategy optimization and closed-loop response efficiency. It cannot integrate high concurrent streaming data in real time, resulting in loss of key information or accumulation of errors, and the inability to adaptively adjust priority, resulting in delayed execution of high-priority tasks.
A multi-module collaboration mechanism based on the Internet of Things is adopted, including the Internet of Things device interface unit, data acquisition unit, intelligent analysis unit, management strategy generation unit, device control unit and user interaction unit. Through adaptive protocol identification, deep learning model, predictive event generation engine and real-time data flow processing module, a closed-loop feedback network is built to realize dynamic strategy optimization and device control.
It realizes unmanned intelligent management of park operations for all periods, improves security, response efficiency and user experience, eliminates lag and error accumulation in traditional systems, and ensures seamless execution of high-priority tasks and optimized resource allocation.
Smart Images
Figure CN120416293A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the field of intelligent management, and more specifically, particularly relates to an integrated unmanned intelligent management system for a park based on the Internet of Things. Background Art
[0002] Under the background of the rapid development of current Internet of Things technology, park intelligent management systems have been widely used in the unmanned operation of industrial parks, science and technology parks, and commercial complexes. Existing systems generally adopt a basic Internet of Things architecture, collect environmental data through a sensor network, generate management strategies via a central analysis platform, and drive actuators.
[0003] However, such systems have significant defects in core dimensions such as protocol compatibility, data analysis depth, policy dynamic optimization, and closed-loop response efficiency: the management strategies in the existing technology are generated based on a fixed rule library, unable to dynamically adjust priorities according to the urgency of events, and the execution results of the strategies are not feedback to the decision-making end. When there is a conflict between security events and energy consumption adjustment strategies, the system cannot adaptively re-queue, resulting in the delay of high-priority tasks;
[0004] The data acquisition layer mostly adopts a batch processing mode, unable to integrate high-concurrency stream data in real time, lacking a redundancy check mechanism during the transmission process, causing the loss of key information or error accumulation. The analysis layer relies on static threshold detection, without establishing a predictive model, lagging in the recognition of complex anomalies, making the system fall into a passive response loop. Based on this, we propose an integrated unmanned intelligent management system for a park based on the Internet of Things to specifically solve the above problems. Summary of the Invention
[0005] The purpose of the present invention is to solve the disadvantages existing in the prior art, and propose an integrated unmanned intelligent management system for a park based on the Internet of Things. Based on an innovative collaborative mechanism of multiple modules, it solves the stubborn defects of protocol rigidity, data islands, policy disconnection, and configuration complexity of the existing park management system from the source of the system architecture; by deeply integrating Internet of Things technology and adaptive computing logic, it realizes the all-time unmanned intelligent management of park operations, significantly improving security, response efficiency, and user experience, and providing a set of reliable, flexible, and self-optimizing management paradigms for modern parks.
[0006] To achieve the above purpose, the present invention provides the following technical solutions:
[0007] An integrated unmanned intelligent management system for a park based on the Internet of Things, comprising:
[0008] An Internet of Things device interface unit, a data acquisition unit, an intelligent analysis unit, a management strategy generation unit, a device control unit, a monitoring and alarm unit, and a user interaction unit;
[0009] The Internet of Things device interface unit is communicatively coupled to the data acquisition unit; the data acquisition unit sends the integrated data to the intelligent analysis unit;
[0010] The intelligent analysis unit transmits the analysis result to the management policy generation unit; the management policy generation unit triggers the device control unit to execute the policy; the intelligent analysis unit triggers the monitoring and alarm unit when detecting an anomaly;
[0011] The user interaction unit inputs preset rules to the management policy generation unit to form a closed-loop feedback mechanism; each module unit realizes two-way data transmission and collaborative work through the Internet of Things network, so as to achieve full unmanned management of the park.
[0012] Preferably, the Internet of Things device interface unit integrates an adaptive protocol recognition sub-module and a physical security locking mechanism, which are used to automatically identify multiple Internet of Things device communication protocols and trigger security locking when detecting an abnormal connection attempt;
[0013] Among them, the adaptive protocol recognition sub-module identifies Bluetooth, Wi-Fi, and Zigbee protocols by dynamically matching device characteristics without manual configuration. The physical security locking mechanism activates the device port isolation function based on the abnormal signal strength value received by the Internet of Things device interface unit to prevent unauthorized access. By enhancing protocol compatibility and security protection, it directly links with the data acquisition unit to ensure the reliability of the data source received by the data acquisition unit, and improves the robustness and fault resistance of the overall park management system.
[0014] Preferably, the intelligent analysis unit is further configured to integrate a deep learning model and a predictive event generation engine, which are used to identify potential failure modes and generate predictive anomaly signals based on historical data;
[0015] The deep learning model mines data patterns based on the neural network architecture, and the predictive event generation engine extends the output of the intelligent analysis unit to future time window events, directly triggering the management policy generation unit to pre-generate countermeasures to avoid lagged responses. This feature collaborates with the monitoring and alarm unit to achieve preventive alarms rather than passive responses.
[0016] Preferably, the intelligent analysis unit includes a predictive event calculation module, which is used to calculate the predictive event score based on historical data and real-time feedback to achieve a predictive response to potential risks rather than passive detection; the predictive event calculation module executes the following calculation formula to generate a quantitative output of event risk:
[0017] , where P is the predictive event score, indicating the potential impact intensity of the event occurrence, is the time weight coefficient, which is dynamically adjusted based on the historical learning model of the intelligent analysis unit to strengthen the attenuation effect of the correlation between recent event data and the current time. is the time decay factor. is the data confidence weight. is the data confidence level. is the user input factor ratio, which is the scaling ratio of the user preference vector obtained from the user interaction unit. is the modulus value of the user input vector. The feedback loop coefficient; the above formula improves the accuracy of predicting security events in the park by introducing multi-dimensional weighted sum feedback elements.
[0018] Preferably, the custom rule engine configured by the user interaction unit combines intelligent silence mechanism feedback to generate dynamic rule adaptation values based on user input and environmental signals; the custom rule engine executes the following calculation formula to quantify the self-learning effect of the rules:
[0019] , where is the rule fitness index, representing the effectiveness score of the user-customized rules. is the integral of the user preference vector, collected from the input history of the user interaction unit. is the time decay exponent, where e is the natural constant and λ is the learning rate parameter. is the rule confidence scaling coefficient. is the confidence score. is the sine conversion term of the response progress. is the response progress of the intelligent silence mechanism; the above formula integrates time decay and dynamic response to improve the rule personalization adaptation degree and reduce misconfiguration in park applications.
[0020] Preferably, the device control unit integrates an adaptive fault-tolerant driver and an execution feedback loop to automatically correct the control signal deviation when driving physical devices and return the status tracking to the data acquisition unit.
[0021] The adaptive fault-tolerant driver monitors the device response delay and environmental interference, adjusts the signal strength to ensure reliable execution of commands, and the execution feedback loop transmits the operation results back to the data acquisition unit for secondary optimization by the intelligent analysis unit. This feature interacts with the monitoring and alarm unit to automatically trigger a backup plan instead of external intervention when the device fails, effectively strengthening the continuity and self-healing ability of the park's unmanned management.
[0022] Preferably, the monitoring and alarm unit includes a multi-modal alarm distribution system and an intelligent silence mechanism to spread alarms through multiple output channels and automatically terminate the alarm according to the event resolution progress.
[0023] The multimodal alarm distribution system simultaneously activates sound alarms, LED flashing, and mobile device push notifications, covering the entire campus. The intelligent silencing mechanism is based on the abnormality release signal of the intelligent analysis unit, and gradually shuts down the alarm output after evaluating the completion of event processing.
[0024] Preferably, the data acquisition unit includes a real-time data stream processing module and a redundancy check mechanism for continuously processing high-concurrency data input and automatically correcting transmission errors;
[0025] The real-time data stream processing module is based on buffer management and sliding window algorithm, and integrates the data stream from the IoT device interface unit in batches to ensure that the data collection unit does not lose key information during peak periods. The redundancy check mechanism automatically detects duplicate or damaged records through data version comparison and triggers retransmission requests to the IoT device interface unit, enabling the data collection unit to interact efficiently with the intelligent analysis unit and reduce the source of analysis errors.
[0026] Preferably, each unit is configured as a whole to embed a self-organizing network optimization module for automatically adjusting the data transmission path and bandwidth allocation when the IoT network is congested;
[0027] The self-organizing network optimization module dynamically switches routing paths based on inter-module communication load perception, prioritizes key flows from data acquisition units to intelligent analysis units, triggers path reconfiguration through congestion analysis results generated by the intelligent analysis unit, and interacts with all module units to form a feedback loop, reducing delays and improving system responsiveness.
[0028] The technical effects and advantages of the present invention: Compared with the existing technology, the integrated unmanned intelligent management system for a park based on the Internet of Things provided by the present invention has the following effects:
[0029] The intelligent analysis unit builds a closed-loop data feedback network by embedding a real-time data stream processing module and a predictive event computing engine. This mechanism implements streaming synchronous processing from sensor data acquisition to analysis, integrating the multi-dimensional interaction of historical and real-time data to ensure deep coupling of cross-source data, eliminating the lag of traditional batch processing, and solving the problem of complex event identification by replacing passive detection with predictive response.
[0030] The priority optimization engine of the management policy generation unit establishes a dynamic policy calculation model by integrating device status feedback and policy adaptability factors. Based on real-time resource availability and policy historical effectiveness weights, this model continuously adjusts the policy sequence priority to generate driving signals, and links the device control engine to perform energy efficiency optimization calculations. Through feedback-driven closed-loop adaptation, it ensures seamless execution of high-priority tasks and optimizes resource allocation to avoid ineffective energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS
[0031] Figure 1 This is the architecture diagram of the park integrated unmanned intelligent management system based on the Internet of Things for the present invention. Detailed implementation manners
[0032] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts fall within the scope of protection of the present invention.
[0033] The present invention provides an Figure 1 architecture of a park integrated unmanned intelligent management system based on the Internet of Things as shown. Based on an innovative collaborative mechanism of multiple modules, including protocol compatibility enhancement, data flow closed-loop integration, policy dynamic optimization, alarm intelligent termination, and user rule self-learning, it solves the stubborn defects of protocol rigidity, data islands, policy disconnection, and configuration complexity of existing park management systems from the source of the system architecture. This design realizes the all-time unmanned intelligent management of park operations by deeply integrating Internet of Things technology and adaptive computing logic, significantly improving security, response efficiency, and user experience, and providing a reliable, flexible, and self-optimizing management paradigm for modern parks.
[0034] The system architecture is specifically as follows:
[0035] It includes multiple module units, each module unit is configured to perform specific functions to achieve unmanned management of the park. The system is used to monitor and control physical devices in the park and is interconnected through an Internet of Things network. It is characterized in that the system includes:
[0036] An Internet of Things device interface unit, configured to connect and manage multiple Internet of Things devices, and used to collect environmental parameters and device status data from within the park. The Internet of Things device interface unit is further configured to integrate an adaptive protocol recognition sub-module and a physical security locking mechanism, used to automatically identify multiple Internet of Things device communication protocols and trigger security locking when detecting abnormal connection attempts. Among them, the adaptive protocol recognition sub-module identifies Bluetooth, Wi-Fi, and Zigbee protocols by dynamically matching device characteristics without manual configuration. The physical security locking mechanism activates the device port isolation function based on the abnormal signal strength value received by the Internet of Things device interface unit to prevent unauthorized access. This feature directly links with the data collection unit by enhancing protocol compatibility and security protection, ensuring the reliability of the data sources received by the data collection unit, thereby improving the robustness and fault resistance of the overall park management system.
[0037] The data acquisition unit is configured to receive data from the Internet of Things device interface unit, and perform preliminary cleaning and formatting on the data for integrating scattered data into a consistent format; the data acquisition unit is further configured to add a real-time data stream processing module and a redundancy check mechanism for continuously processing high-concurrency data inputs and automatically correcting transmission errors, where the real-time data stream processing module integrates data streams from the Internet of Things device interface unit in batches based on buffer management and sliding window algorithms to ensure that the data acquisition unit does not lose key information during peak periods, and the redundancy check mechanism automatically detects duplicate or corrupted records through data version comparison and triggers a retransmission request to the Internet of Things device interface unit. This feature enables the data acquisition unit to interact efficiently with the intelligent analysis unit, reduces the sources of analysis errors, and significantly improves the system's response speed in campus environmental monitoring compared to traditional batch processing methods.
[0038] The intelligent analysis unit is configured to perform real-time analysis based on the data provided by the data acquisition unit for identifying abnormal states or optimization opportunities in the campus and outputting analysis results; the intelligent analysis unit is further configured to integrate a deep learning model and a predictive event generation engine for identifying potential failure modes and generating predictive anomaly signals based on historical data, where the deep learning model mines data patterns based on a neural network architecture, and the predictive event generation engine extends the output of the intelligent analysis unit to future time window events and directly triggers the management strategy generation unit to pre-generate response strategies to avoid lagged responses. This feature collaborates with the monitoring and alarm unit to achieve preventive alarms instead of passive responses, thereby reducing operational risks in campus security and energy consumption control.
[0039] It should be noted that the intelligent analysis unit includes a predictive event calculation module for calculating a predictive event score based on historical data and real-time feedback to achieve a predictive response to potential risks rather than passive detection; the predictive event calculation module executes the following calculation formula to generate a quantitative output of event risk:
[0040] , where P is the predictive event score representing the potential impact intensity of the event occurrence, is the time weight coefficient, dynamically adjusted based on the historical learning model of the intelligent analysis unit to strengthen the attenuation effect of the correlation between recent event data and the current time, is the time decay factor, is the data confidence weight, is the data confidence level, is the user input factor ratio, the scaling ratio of the user preference vector obtained from the user interaction unit, is the user input vector modulus, the feedback loop coefficient; the above formula improves the prediction accuracy of campus security events by introducing multi-dimensional weighted sums and feedback elements.
[0041] A management strategy generation unit, configured to receive the analysis results of the intelligent analysis unit and generate management strategies based on preset rules for dynamically determining control commands; the management strategy generation unit is further configured to introduce a strategy optimization engine and a real-time priority evaluation sub-module for dynamically adjusting the strategy sequence to adapt to sudden park events, where the strategy optimization engine uses a rule tree structure to evaluate the analysis results of the intelligent analysis unit, automatically filters unreasonable commands, and the real-time priority evaluation sub-module rearranges the strategy queue according to the urgency of the event (such as security events taking precedence over energy consumption adjustment), and directly outputs it to the device control unit for execution. This feature is feedback-coupled with the user interaction unit, allowing the user to input and adjust the rule tree parameters, forming a closed-loop optimization mechanism, which significantly improves the strategy decision-making efficiency and reduces the occurrence rate of misoperations in the industrial park scenario.
[0042] The management strategy generation unit is further configured to include a strategy priority optimization engine for generating an adaptive management strategy sequence to avoid rigid rule tree execution; the strategy priority optimization engine executes the following calculation formula to dynamically calculate the strategy priority score:
[0043] , is the strategy priority score, representing the quantitative output of the urgency of strategy execution, used to replace the fixed priority queue to reduce response latency, β is a dynamic scaling factor, which is adjusted in real time based on the output of the intelligent analysis unit, is the event criticality index, directly receiving the abnormal signal strength from the intelligent analysis unit, reflecting the degree of harm of the event to the park, represents the resource availability indication, is the strategy adaptability counter, accumulating the historical successful execution times of the same type of strategy, is the system mode factor, is the user intervention coefficient, extracted from the user interaction unit; the above formula integrates resource feedback and user intervention, improving the strategy decision-making speed in industrial park applications.
[0044] A device control unit, configured to receive and execute the management strategies of the management strategy generation unit for generating control signals to drive the physical devices in the park; the device control unit is further configured to integrate an adaptive fault-tolerant driver and an execution feedback loop for automatically correcting the control signal deviation and returning the status tracking to the data acquisition unit when driving the physical devices, where the adaptive fault-tolerant driver monitors the device response latency and environmental interference, adjusts the signal strength to ensure reliable command execution, and the execution feedback loop transmits the operation result back to the data acquisition unit for secondary optimization by the intelligent analysis unit. This feature interacts with the monitoring and alarm unit, automatically triggering backup solutions instead of external intervention when the device fails, effectively strengthening the sustainability and self-healing ability of the park's unmanned management.
[0045] The monitoring alarm unit is configured to issue an alarm in response to the abnormal signal of the intelligent analysis unit, and is used to monitor the security of the campus in real time. The monitoring alarm unit is further configured to include a multimodal alarm distribution system and an intelligent silencing mechanism, which are used to disseminate alarms through multiple output channels and automatically terminate the alarm according to the progress of event resolution. The multimodal alarm distribution system simultaneously activates sound alarms, LED flashing and mobile device push, covering the entire campus. The intelligent silencing mechanism is based on the abnormal release signal of the intelligent analysis unit, and gradually shuts down the alarm output after evaluating the completion of event processing. This feature is seamlessly integrated with the user interaction unit, supporting users to customize the silence threshold, thereby reducing false alarm noise and improving the efficiency and optimization effect of the campus security experience.
[0046] Furthermore, the intelligent silencing mechanism configured in the monitoring alarm unit further cooperates with the customized rule engine to calculate the alarm silencing threshold based on the incident resolution progress to automatically terminate the alarm; the intelligent silencing mechanism performs the following calculation to determine the silencing trigger condition:
[0047] ,in, The silence threshold score. When the alarm output intensity is lower than this value, silence is triggered. It is used to replace the fixed countdown shutdown mechanism to accurately control the silence timing. is the initial alarm scaling constant, is the initial alarm intensity, is the exponential time decay factor, is an exponential function, is the attenuation rate, The time after the alarm is activated is used to simulate the natural weakening of the alarm to avoid redundant noise. is the user sensitivity factor, Feedback on successful operation comes from the execution completion report of the equipment control unit. To solve the state ratio.
[0048] The user interaction unit is configured to interact with the user and is used to receive input configuration information and present query data; the user interaction unit is further configured to integrate a natural language processing panel and a customized rule engine to support voice input and output and dynamically generate user preset rules, wherein the natural language processing panel parses the user's verbal commands and translates them into configuration instructions, and the customized rule engine compiles the input rules into an executable format for the management policy generation unit, which is applied in real time through the Internet of Things network. This feature allows the user interaction unit and the intelligent analysis unit to collaboratively self-learn user preferences.
[0049] The custom rule engine configured in the user interaction unit is combined with intelligent silencing mechanism feedback to generate dynamic rule adaptation values based on user input and environmental signals. The custom rule engine performs the following calculation to quantify the effect of rule self-learning:
[0050] , where is the rule fitness index, representing the effectiveness score of the user-customized rule. is the integral of the user preference vector, collected from the input history of the user interaction unit. is the time decay exponent, where e is the natural constant and λ is the learning rate parameter. is the rule confidence scaling coefficient. is the confidence score. is the sine conversion term of the response progress. is the response progress of the intelligent silence mechanism; the above formula integrates time decay and dynamic response, improving the rule personalization adaptation degree and reducing misconfiguration occurrence in the park application.
[0051] Among them, the Internet of Things device interface unit is communicatively coupled to the data acquisition unit; the data acquisition unit sends the integrated data to the intelligent analysis unit; the intelligent analysis unit transmits the analysis result to the management policy generation unit; the management policy generation unit triggers the device control unit to execute the policy; the intelligent analysis unit triggers the monitoring and alarm unit when detecting an anomaly; the user interaction unit inputs a preset rule to the management policy generation unit to form a closed-loop feedback mechanism; each module unit realizes two-way data transmission and collaborative work through the Internet of Things network, so as to achieve the full unmanned management of the park.
[0052] Above, each module unit is further configured to embed a self-organizing network optimization module, which is used to automatically adjust the data transmission path and bandwidth allocation when the Internet of Things network is congested. Among them, the self-organizing network optimization module dynamically switches the routing path based on the communication load perception between modules (such as giving priority to the key flow from the data acquisition unit to the intelligent analysis unit), and triggers path reconfiguration through the congestion analysis result generated by the intelligent analysis unit. This feature forms a feedback loop with all module units, reducing latency and improving the system response ability. Especially in the high-density park scenario, the seamless integrated management effect is better than the fixed network structure method, and the value is prominent.
[0053] As an option in this embodiment:
[0054] The intelligent analysis unit is further configured to embed a cross-domain anomaly coupling analysis module, which is used to fuse multi-source data to detect compound anomalies; the cross-domain anomaly coupling analysis module executes the following calculation formula to output the cross-domain anomaly coupling index:
[0055] , where is the cross-domain anomaly coupling index, is the feedback weight factor, is the norm of the real-time sensor reading matrix, is the norm of the standard environment baseline matrix, is the device normalization scalar, is the time decay term, is the decay coefficient, is the same type of anomaly time interval, is the user tolerance factor, is the confidence feedback integral; The above formula integrates the hyperbolic transformation and the device closed-loop feedback, improving the accuracy in the compound risk identification of industrial parks.
[0056] In summary, the present invention has the following mechanisms:
[0057] Mechanism for eliminating protocol rigidity and security vulnerabilities: Through the adaptive protocol identification subunit and physical security locking mechanism in the modular design, the system realizes the dynamic protocol detection and matching ability at the device access layer; When a new device is connected, the system automatically scans its communication protocol, such as Wi-Fi, Bluetooth or Zigbee, and generates a compatibility map, avoiding manual intervention; At the same time, the hardware-level port locking function physically isolates the port when detecting abnormal connection attempts, building a double security protection barrier; This mechanism overcomes the multi-protocol conflict risk in traditional systems and significantly strengthens the data security boundary;
[0058] Mechanism for breaking data islands and optimizing response latency: The intelligent analysis unit constructs a data closed-loop feedback network by embedding a real-time data stream processing module and a predictive event calculation engine; This mechanism realizes streaming synchronous processing during the process of sensor data collection and analysis, and integrates the multi-dimensional interaction of historical and real-time data, such as risk prediction output based on event scoring, to ensure the deep coupling of cross-source data; This not only eliminates the lag of traditional batch processing, but also solves the problem of compound event identification by predicting responses instead of passive detection;
[0059] Mechanism for solving policy rigidity and execution disconnection: The priority optimization engine of the management policy generation unit establishes a dynamic policy calculation model by integrating device status feedback and policy adaptability factors; Based on the real-time resource availability and the weight of policy historical effectiveness, this model continuously adjusts the priority of the policy sequence to generate a driving signal, and links the device control engine to execute energy efficiency optimization calculations; This mechanism ensures the seamless execution of high-priority tasks and optimizes resource allocation to avoid ineffective energy consumption through feedback-driven closed-loop adaptation;
[0060] Mechanism for reducing alarm noise and simplifying configuration: The monitoring and alarm unit and the user-customized rule engine work together to embed the intelligent silence mechanism into the alarm management process; This mechanism dynamically calculates the silence threshold according to the progress of event resolution, such as adjusting the alarm termination condition based on user input and environmental feedback, and provides user interaction support through the natural language interface module; This mechanism realizes rule self-learning and precise alarm control, eliminates the redundant interference of traditional timing mechanisms, and significantly reduces the user configuration complexity at the same time;
[0061] Mechanism for enhancing the overall system coordination: Design cross-layer feedback loops among multiple modules. For example, the output of the intelligent analysis unit drives the generation of the driving strategy, while the quantization coefficient executed by the device affects the strategy priority, forming an adaptive closed-loop system architecture. This mechanism strengthens the global resource sharing and self-adjustment capabilities, solves the one-way conduction problem in the background technology, and thus realizes an efficient management response without manual intervention.
[0062] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions recorded in the foregoing embodiments or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.
Claims
1. An integrated unmanned intelligent management system for a park based on the Internet of Things, characterized in that, include: IoT device interface unit, data acquisition unit, intelligent analysis unit, management strategy generation unit, device control unit, monitoring and alarm unit, and user interaction unit; The Internet of Things device interface unit is communicatively coupled to the data acquisition unit; the data acquisition unit sends the integrated data to the intelligent analysis unit; The intelligent analysis unit transmits the analysis result to the management strategy generation unit; the management strategy generation unit triggers the device control unit to execute the strategy; The intelligent analysis unit triggers the monitoring alarm unit when an abnormality is detected; The user interaction unit inputs preset rules to the management strategy generation unit to form a closed-loop feedback mechanism; each module unit realizes two-way data transmission and collaborative work through the Internet of Things network, thereby achieving comprehensive unmanned management of the park.
2. The integrated unmanned intelligent management system for a park based on the Internet of Things according to claim 1, characterized in that, The IoT device interface unit integrates an adaptive protocol identification submodule and a physical security locking mechanism to automatically identify multiple IoT device communication protocols and trigger a security lock when an abnormal connection attempt is detected; The adaptive protocol identification submodule identifies Bluetooth, Wi-Fi, and Zigbee protocols by dynamically matching device features without manual configuration. The physical security locking mechanism activates the device port isolation function based on abnormal signal strength values received by the IoT device interface unit to prevent unauthorized access. By enhancing protocol compatibility and security protection, it directly interacts with the data acquisition unit to ensure that the data source received by the data acquisition unit is reliable, thereby improving the robustness and fault resistance of the overall park management system.
3. The integrated unmanned intelligent management system for a park based on the Internet of Things according to claim 1, characterized in that, The intelligent analysis unit is further configured to integrate a deep learning model and a predictive event generation engine to identify potential failure modes and generate predictive abnormal signals based on historical data; The deep learning model mines data patterns based on a neural network architecture, and the predictive event generation engine extends the output of the intelligent analysis unit to future time window events, directly triggering the management strategy generation unit to pre-generate response strategies to avoid delayed reactions. This feature works in conjunction with the monitoring and alarm unit to achieve preventive alarms rather than passive responses.
4. The Internet of Things-based integrated unmanned intelligent management system for a park according to claim 1, wherein, The intelligent analysis unit includes a predictive event calculation module that calculates a predictive event score based on historical data and real-time feedback to enable proactive response to potential risks rather than passive detection. The predictive event calculation module performs the following calculation to generate a quantitative output of event risk: , where P is the predictive event score, representing the potential impact intensity of the event occurrence, is the time weight coefficient, dynamically adjusted based on the historical learning model of the intelligent analysis unit to strengthen the decay effect of the relevance between the recent event data and the current time, is the time decay factor, is the data confidence weight, is the data confidence level, is the user input factor ratio, the scaling ratio of the user preference vector obtained from the user interaction unit, is the modulus value of the user input vector, Feedback loop coefficient; the above formula improves the prediction accuracy of the park security events by introducing multi-dimensional weighted sum and feedback elements.
5. The integrated unmanned intelligent management system for a park based on the Internet of Things according to claim 1, characterized in that, The customized rule engine configured by the user interaction unit is combined with the intelligent silencing mechanism feedback to generate dynamic rule adaptation values based on user input and environmental signals; the customized rule engine performs the following calculation formula to quantify the rule self-learning effect: , where is the rule fitness index, representing the effectiveness score of the user-customized rule, is the integral of the user preference vector, collected from the input history of the user interaction unit, is the time decay exponent, where e is the natural constant and λ is the learning rate parameter, is the rule confidence scaling coefficient, is the confidence score, is the sine conversion term of the response progress, is the response progress of the intelligent silence mechanism; the above formula integrates time decay and dynamic response, improving the rule personalization adaptation degree and reducing the occurrence of misconfiguration in the park application.
6. The integrated unmanned intelligent management system for a park based on the Internet of Things according to claim 1, wherein The device control unit integrates an adaptive fault-tolerant driver and an execution feedback loop for automatically correcting control signal deviations when driving the physical device and returning status tracking to the data acquisition unit; The adaptive fault-tolerant drive monitors device response delays and environmental interference, adjusts signal strength to ensure reliable command execution, and executes a feedback loop that transmits the operation results back to the data acquisition unit for secondary optimization by the intelligent analysis unit. This feature interacts with the monitoring and alarm unit, automatically triggering backup plans when equipment fails rather than external intervention, effectively enhancing the continuity and self-healing capabilities of unmanned management in the park.
7. The integrated unmanned intelligent management system for a park based on the Internet of Things according to claim 1, characterized in that The monitoring alarm unit includes a multimodal alarm distribution system and an intelligent silencing mechanism for disseminating alarms through multiple output channels and automatically terminating alarms based on the progress of incident resolution; The multimodal alarm distribution system simultaneously activates sound alarms, LED flashing, and mobile device push notifications, covering the entire campus. The intelligent silencing mechanism is based on the abnormality release signal of the intelligent analysis unit, and gradually shuts down the alarm output after evaluating the completion of event processing.
8. The integrated unmanned intelligent management system for a park based on the Internet of Things according to claim 1, characterized in that The data acquisition unit includes a real-time data stream processing module and a redundancy check mechanism for continuously processing high-concurrency data input and automatically correcting transmission errors; The real-time data stream processing module is based on buffer management and sliding window algorithm, and integrates the data stream from the IoT device interface unit in batches to ensure that the data collection unit does not lose key information during peak periods. The redundancy check mechanism automatically detects duplicate or damaged records through data version comparison and triggers retransmission requests to the IoT device interface unit, enabling the data collection unit to interact efficiently with the intelligent analysis unit and reduce the source of analysis errors.
9. The integrated unmanned intelligent management system for a park based on the Internet of Things according to claim 1, characterized in that, Each unit is configured as a whole to embed a self-organizing network optimization module, which is used to automatically adjust the data transmission path and bandwidth allocation when the IoT network is congested; The self-organizing network optimization module dynamically switches routing paths based on inter-module communication load perception, prioritizes key flows from data acquisition units to intelligent analysis units, triggers path reconfiguration through congestion analysis results generated by the intelligent analysis unit, and interacts with all module units to form a feedback loop, reducing delays and improving system responsiveness.