Intelligent monitoring management system based on Internet of Things

Abstract

The invention discloses an intelligent monitoring management system based on Internet of Things, which relates to the technical field of intelligent monitoring management and comprises an intelligent monitoring terminal module, an alarm processing module and a central management server, wherein the intelligent monitoring terminal module collects urban environment information, identifies abnormal behaviors or events, and keeps real-time data exchange with the central management server and the alarm processing module through a wireless network; the alarm processing module processes the received information and performs layered early warning according to the identified abnormal behavior or event; and the central management server is used for executing analysis, early warning and response measures. According to the invention, an intelligent monitoring management system based on the Internet of Things, which has the advantages of comprehensive coverage and quick response, is constructed, and a safer living environment is provided for citizens.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent monitoring management, and in particular to an intelligent monitoring management system based on the Internet of Things. Background Art

[0002] The Internet of Things technology refers to connecting various physical devices, sensors, actuators, etc. through the Internet to achieve data exchange and collaborative work between devices. In the intelligent monitoring and management system, the application of the Internet of Things technology enables the system to collect and process a large amount of environmental data in real time, improving the accuracy and real-time nature of monitoring.

[0003] "Smart Eye" is a comprehensive intelligent monitoring and management system that uses Internet of Things technology, big data analysis, artificial intelligence algorithms and other means to achieve all-round, all-weather monitoring and management of the urban environment.

[0004] However, although the IoT-based intelligent monitoring and management system has performed well in improving urban security and management efficiency, it also has some significant shortcomings and challenges. The following are some of the main shortcomings: Data quality issues: Sensor devices may be affected by environmental factors, resulting in inaccurate or distorted data collection. Poor data quality will affect the analysis and decision-making capabilities of the IoT's intelligent monitoring management system.

[0005] Data processing errors: Errors may occur during big data processing and analysis, such as incomplete data cleaning and improper algorithm selection, which will affect the performance and reliability of the IoT's intelligent monitoring and management system.

[0006] Poor practicality: Traditional path planning algorithms are prone to falling into local optimal solutions, especially in complex urban environments, and it is difficult to find the global optimal solution. In complex urban environments, sensor data may be interfered with, resulting in reduced accuracy of path planning. As a result, path planning and emergency avoidance are too complicated and less accurate, resulting in poor practicality of the intelligent monitoring and management system of the Internet of Things.

[0007] In view of this, it is necessary to improve the existing intelligent monitoring and management system of the Internet of Things. Summary of the invention

[0008] The present invention aims to solve at least one of the technical problems in the prior art to a certain extent, by proposing an intelligent monitoring management system based on the Internet of Things, which is composed of intelligent monitoring terminal modules deployed in various parts of the city, an alarm processing module and a central management server, to solve the technical problems existing in the above-mentioned prior art.

[0009] To achieve the above objectives, in a first aspect, the present application provides, including An intelligent monitoring and management system based on the Internet of Things, comprising: Intelligent monitoring terminal module, alarm processing module and central management server; The intelligent monitoring terminal module collects urban environment information, identifies abnormal behaviors or events, and maintains real-time data exchange with the central management server and the alarm processing module through a wireless network; The alarm processing module processes the received information and issues layered warnings based on the identified abnormal behaviors or events; The central management server is used to perform analysis, early warning and response measures to ensure the safe operation of the city.

[0010] Furthermore, the intelligent monitoring terminal module includes: Environmental sensing unit, used to monitor information about the urban environment; Anomaly detection module, which analyzes information about the urban environment and identifies threats or abnormal behavior; The first wireless transceiver module is used for data transmission between the central management server and the alarm processing module.

[0011] Furthermore, the anomaly detection module uses a pre-stored deep learning model to identify abnormal behaviors or events based on information about the urban environment.

[0012] Furthermore, the deep learning model is any one or more of a convolutional neural network, a recurrent neural network, a long short-term memory network, and a generative adversarial network.

[0013] Furthermore, the environment perception unit integrates one or more sensors of camera, sound, air quality and traffic flow.

[0014] Furthermore, the alarm processing module includes: The preliminary judgment unit, when the intelligent monitoring terminal module identifies a threat or abnormal behavior, checks whether the sensor is working properly. If so, it starts the alarm processing program. If not, it determines that it is a false alarm caused by sensor failure; The first-layer processing unit obtains historical data of the source of threats or abnormal behaviors and determines whether the current situation is reasonable by comparing the historical data. If it is not certain, it enters the next layer of processing; The second-layer processing unit requests the user end to confirm the threat or abnormal behavior. If the confirmation is correct, the alarm is weighted and a highly suspected real alarm is obtained; if not, the alarm is lifted; The third layer processing unit: uploads the information to the central management server and requests the central management server to make a judgment or manual review; The fourth-level processing unit automatically notifies the corresponding department of highly suspected real alarms.

[0015] Furthermore, the central management server includes: The data analysis unit conducts a comprehensive evaluation of the information uploaded by the third-layer processing unit and, based on the evaluation results, executes a response of directly clearing the alarm, providing a pre-set response, or transferring to manual review; The positioning unit locates the incident location based on the assessment results, obtains the positioning information, and dispatches the nearest emergency force to the scene; The risk avoidance unit provides users with safety tips and emergency risk avoidance guidance plans based on the assessment results and positioning information.

[0016] Furthermore, the risk avoidance unit comprises: The escape path generation subunit determines the threat or anomaly type based on the evaluation results, and generates an escape path from the user's current location to the nearest safe place using a path planning algorithm based on the threat or anomaly type and location information; The real-time traffic condition acquisition unit dynamically adjusts the risk avoidance path based on the real-time traffic condition.

[0017] Furthermore, the path planning algorithm includes: S100: Randomly generate the initial population in N is the population size; Define the upper and lower bounds of the search space ; Initialize the maximum number of iterations to T and the current number of iterations to t=0; S200: For each population individual X i , calculate its fitness value f(X i ), where the fitness function is the path length or time cost; S300: Update location; S400: For each population individual X i , with a preset probability P levy To perform a Levi flight: ; Among them, α is the step size factor, μ and ν are random variables that satisfy the normal distribution and Cauchy distribution, β is the Levy index, and rand is a random number in the interval [0, 1]; S500: Dynamically adjust the step size factor α: ; in, and are the initial and final step factors respectively, and t is the current number of iterations; S600: Select the first k elite individuals in the current population, and generate a reverse solution for each elite individual: ; In the formula, An elite individual. is the reverse solution; S700: Reverse solution Join the population, recalculate the fitness value, and select the optimal solution; S800: Determine the number of iterations. If the current number of iterations t reaches the maximum number of iterations T, stop the iteration and output the optimal solution; otherwise, update the number of iterations t and return to S200.

[0018] Further, the step S300: updating the location includes: S310: Update the position of each individual according to the current optimal solution: ; ; Where A and C are coefficient vectors, defined as follows: ; ; Among them, a decreases linearly from 2 to 0, and is a random number between [0, 1]; S320: Perform a spiral search: ; ; S330: Perform random search: ; If p < 0.5, a prey encirclement or spiral search is performed; otherwise, an individual is randomly selected as a reference point: ; ; In the formula, is a randomly selected individual, A and C are coefficient vectors.

[0019] Beneficial effects of the invention: Compared with the prior art, the invention provides an intelligent monitoring and management system based on the Internet of Things, which is composed of intelligent monitoring terminal modules, alarm processing modules and a central management server deployed in various parts of the city. It can effectively improve the safety and convenience of the city by integrating multiple sensors and intelligent algorithms; by optimizing hardware configuration and software algorithms, it can improve the analysis and decision-making capabilities of the intelligent monitoring and management system of the Internet of Things, ensure the performance and reliability of the intelligent monitoring and management system of the Internet of Things, and at the same time effectively generate the optimal risk avoidance path from the user's current location to the nearest safe place, improve the search efficiency and convergence speed of the algorithm, and increase the practicality of the intelligent monitoring and management system of the Internet of Things. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a functional block diagram of the system of the present invention. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] Example 1, please refer to Figure 1 As shown, the present application provides an intelligent monitoring and management system based on the Internet of Things, including: Intelligent monitoring terminal module, alarm processing module and central management server; Among them, the intelligent monitoring terminal module collects urban environmental information, identifies abnormal behaviors or events, and maintains real-time data exchange with the central management server and alarm processing module through the wireless network; The alarm processing module processes the received information and issues layered warnings based on the identified abnormal behaviors or events; The central management server is used to perform analysis, early warning and response measures to ensure the safe operation of the city.

[0023] In the specific implementation process, the intelligent monitoring terminal module includes: Environmental sensing unit, used to monitor information about the urban environment; Anomaly detection module, which is used to analyze information about the urban environment and identify threats or abnormal behaviors (such as fire, theft, traffic accidents, etc.); The first wireless transceiver module is used for data transmission between the central management server and the alarm processing module to ensure the timeliness and accuracy of information.

[0024] In the specific implementation process, the anomaly detection module uses a pre-stored deep learning model to identify abnormal behaviors or events based on information about the urban environment.

[0025] In the specific implementation process, the deep learning model is any one or more of a convolutional neural network, a recurrent neural network, a long short-term memory network, and a generative adversarial network.

[0026] In the specific implementation process, Convolutional Neural Networks (CNNs) are used in image recognition and classification; their specific uses are face recognition for identity authentication and personnel tracking; or object detection to identify specific targets in surveillance videos, such as vehicles, pedestrians, etc.

[0027] Abnormal behavior detection is to detect abnormal behaviors such as falls and fights by analyzing the action patterns in the video stream.

[0028] In the specific implementation process, Recurrent Neural Networks (RNNs) are used in the application scenario: time series analysis. Specific uses include: traffic flow prediction, predicting future traffic flow based on historical traffic data, optimizing traffic light control, environmental parameter prediction, predicting the changing trends of environmental parameters such as air quality and temperature, and taking countermeasures in advance.

[0029] In the specific implementation process, the Long Short-Term Memory (LSTM) network is used in the long-term dependency modeling scenario. The specific uses include: event prediction, combining historical data and real-time data to predict possible future events, such as fires, traffic accidents, etc. User behavior analysis, analyzing user activity patterns and predicting user behavior trends, such as the daily activity patterns of the elderly.

[0030] In the specific implementation process, Generative Adversarial Networks (GANs) are used in image generation and enhancement. The specific uses include: Image enhancement: improving the clarity of surveillance videos, especially in low-light conditions. Virtual scene generation: generating simulated surveillance scenes for training and testing other deep learning models.

[0031] Specifically, the models provided above are all existing programs and existing models. The models provided in this embodiment can be used in combination to form a multi-level and multi-dimensional intelligent analysis system. The above CNN can be used for image recognition, LSTM for time series prediction, and RL for dynamic resource scheduling, thereby achieving all-round intelligent monitoring and management.

[0032] In a specific implementation, the environment perception unit integrates one or more sensors including cameras, sound, air quality, and traffic flow.

[0033] In the specific implementation process, the alarm processing module includes: The preliminary judgment unit, when the intelligent monitoring terminal module identifies a threat or abnormal behavior, checks whether the sensor is working properly. If so, it starts the alarm processing program. If not, it determines that it is a false alarm caused by sensor failure; Specifically, when the intelligent monitoring terminal module identifies a threat or abnormal behavior, it first checks whether the sensor is working properly. If it is not a false alarm caused by a sensor failure, the alarm processing procedure is started.

[0034] The first-layer processing unit obtains historical data of the source of threats or abnormal behaviors and determines whether the current situation is reasonable by comparing the historical data. If it is not certain, it enters the next layer of processing; Specifically, the system will try to determine whether it is a false alarm through simple logical judgment. It will also judge whether the current situation is reasonable by comparing historical data. If it cannot be determined, it will proceed to the next level of processing.

[0035] The second-layer processing unit requests the user end to confirm the threat or abnormal behavior. If the confirmation is correct, the alarm is weighted and a highly suspected real alarm is obtained; if not, the alarm is lifted; Specifically, nearby citizens or staff are required to confirm the situation through the user interface. If the confirmation is correct, the alarm will continue to escalate; otherwise, the alarm will be lifted.

[0036] The third layer processing unit: uploads the information to the central management server and requests the central management server to make a judgment or manual review; Specifically, all relevant information collected (including but not limited to video recordings, environmental parameters, etc.) is uploaded to the central management server, and the data analysis module conducts a comprehensive evaluation. Based on the evaluation results, the alarm may be directly lifted, pre-set safety recommendations may be provided, or the information may be transferred to manual review.

[0037] The fourth-level processing unit automatically notifies the corresponding department of highly suspected real alarms.

[0038] Specifically, for highly suspected real alarms, the system will automatically notify relevant departments (such as the police station, fire department, etc.) and dispatch professionals to the scene to handle them if necessary. At the same time, the system will also record the entire process of the incident for subsequent analysis and improvement.

[0039] In the specific implementation process, the central management server includes: The data analysis unit conducts a comprehensive evaluation of the information uploaded by the third-layer processing unit and, based on the evaluation results, executes a response of directly clearing the alarm, providing a pre-set response, or transferring to manual review; The positioning unit locates the incident location based on the assessment results, obtains the positioning information, and dispatches the nearest emergency force to the scene; The risk avoidance unit provides users with safety tips and emergency risk avoidance guidance plans based on the assessment results and positioning information.

[0040] In the specific implementation process, the hedging unit includes: The escape path generation subunit determines the threat or anomaly type based on the evaluation results, and generates an escape path from the user's current location to the nearest safe place using a path planning algorithm based on the threat or anomaly type and location information; The real-time traffic condition acquisition unit dynamically adjusts the risk avoidance path based on the real-time traffic condition.

[0041] In the specific implementation process, in order to generate the optimal risk avoidance path from the user's current location to the nearest safe place, this embodiment adopts an improved whale optimization algorithm (WOA). This improved WOA introduces a random Levy flight strategy based on cosine evolution and an elite reverse learning strategy to improve the algorithm's search efficiency and convergence speed. The path planning algorithm includes: S100: Randomly generate the initial population ,in N is the population size; Define the upper and lower bounds of the search space ; Initialize the maximum number of iterations to T and the current number of iterations to t=0; S200: For each population individual X i , calculate its fitness value f(X i ), where the fitness function is the path length or time cost; S300: Update location; S400: For each population individual X i , with a preset probability P levy To perform a Levi flight: ; Among them, α is the step size factor, μ and ν are random variables that satisfy the normal distribution and Cauchy distribution, β is the Levy index, and rand is a random number in the interval [0, 1]; Specifically, in programming languages, a random number generation function is usually used to generate random numbers such as rand, allowing the algorithm to explore more extensively in the search space.

[0042] S500: Dynamically adjust the step size factor α: ; in, and are the initial and final step factors respectively, and t is the current number of iterations; S600: Select the first k elite individuals in the current population, and generate a reverse solution for each elite individual: ; In the formula, An elite individual. is the reverse solution; S700: Reverse solution Join the population, recalculate the fitness value, and select the optimal solution; S800: Determine the number of iterations. If the current number of iterations t reaches the maximum number of iterations T, stop the iteration and output the optimal solution; otherwise, update the number of iterations t and return to S200.

[0043] In a specific implementation process, S300: updating the location includes: S310: Update the position of each individual according to the current optimal solution: ; ; Where A and C are coefficient vectors, defined as follows: ; ; Among them, a decreases linearly from 2 to 0, and is a random number between [0, 1]; S320: Perform a spiral search: ; ; S330: Perform random search: ; If p < 0.5, a prey encirclement or spiral search is performed; otherwise, an individual is randomly selected as a reference point: ; ; In the formula, is a randomly selected individual, A and C are coefficient vectors.

[0044] In the specific implementation process, the central management server has an HTTP service interface and a standard API interface.

[0045] In the specific implementation process, in the smart city monitoring and management system, path planning is a key link, especially in the risk avoidance recommendation scenario, in order to generate the optimal risk avoidance path from the user's current location to the nearest safe place.

[0046] In the specific implementation process, the intelligent monitoring and management system based on the Internet of Things provided in this embodiment can be applied to the following scenarios: Fire evacuation: In the event of a fire, the system provides users with the nearest safe exits and shelter locations, guiding users to evacuate quickly.

[0047] Earthquake evacuation: When an earthquake occurs, the system recommends users to go to the nearest open area or shelter, and provides detailed evacuation routes and safety instructions.

[0048] Flood avoidance: Before a flood comes, the system provides users with the nearest high ground or safe area and guides users to move safely.

[0049] Traffic accident avoidance: When a traffic accident occurs, the system provides users with the location of the nearest hospital or emergency station and guides users to seek medical treatment quickly.

[0050] Specifically, through the above technical solutions, it can be seen that the present invention provides an intelligent monitoring and management system based on the Internet of Things with comprehensive coverage and rapid response, which can not only improve the overall safety level of the city, but also provide more convenient services for citizens and improve their quality of life.

[0051] Through the description of the above implementation methods, the embodiments of the present invention can be provided as methods, systems or computer program products. Based on such an understanding, the above technical solutions can be essentially or partly contributed to the prior art in the form of software products, which can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and include several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0052] In the embodiments provided in the present application, it should be understood that the disclosed system or method can be implemented in other ways. The embodiments described above are merely schematic. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of systems, modules and units can be electrical, mechanical or other forms.

[0053] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An intelligent monitoring and management system based on the Internet of Things, characterized in that: include: Intelligent monitoring terminal module, alarm processing module and central management server; The intelligent monitoring terminal module collects urban environment information, identifies abnormal behaviors or events, and maintains real-time data exchange with the central management server and the alarm processing module through a wireless network; The alarm processing module processes the received information and issues layered warnings based on the identified abnormal behaviors or events; The central management server is used to perform analysis, early warning and response measures.

2. According to the intelligent monitoring and management system based on the Internet of Things according to claim 1, it is characterized in that: The intelligent monitoring terminal module includes: Environmental sensing unit, used to monitor information about the urban environment; Anomaly detection module, which analyzes information about the urban environment and identifies threats or abnormal behavior; The first wireless transceiver module is used for data transmission between the central management server and the alarm processing module.

3. According to claim 2, the intelligent monitoring and management system based on the Internet of Things is characterized in that: The anomaly detection module uses pre-stored deep learning models to identify abnormal behaviors or events based on information about the urban environment.

4. The intelligent monitoring and management system based on the Internet of Things according to claim 3 is characterized in that: The deep learning model is any one or more of a convolutional neural network, a recurrent neural network, a long short-term memory network, and a generative adversarial network.

5. The intelligent monitoring and management system based on the Internet of Things according to claim 2 is characterized in that: The environment perception unit integrates one or more sensors of camera, sound, air quality and traffic flow.

6. The intelligent monitoring and management system based on the Internet of Things according to claim 1 is characterized in that: The alarm processing module comprises: The preliminary judgment unit, when the intelligent monitoring terminal module identifies a threat or abnormal behavior, checks whether the sensor is working properly. If so, it starts the alarm processing program. If not, it determines that the false alarm is caused by sensor failure; The first-layer processing unit obtains historical data of the source of threats or abnormal behaviors and determines whether the current situation is reasonable by comparing the historical data. If it is not certain, it enters the next layer of processing; The second-layer processing unit requests the user end to confirm the threat or abnormal behavior. If the confirmation is correct, the alarm is weighted and a highly suspected real alarm is obtained; if not, the alarm is lifted; The third layer processing unit: uploads the information to the central management server and requests the central management server to make a judgment or manual review; The fourth-level processing unit automatically notifies the corresponding department of highly suspected real alarms.

7. The intelligent monitoring and management system based on the Internet of Things according to claim 6 is characterized in that: The central management server comprises: The data analysis unit conducts a comprehensive evaluation of the information uploaded by the third-layer processing unit and, based on the evaluation results, executes a response of directly clearing the alarm, providing a pre-set response, or transferring to manual review; The positioning unit locates the incident location based on the assessment results, obtains the positioning information, and dispatches the nearest emergency force to the scene; The risk avoidance unit provides users with safety tips and emergency risk avoidance guidance plans based on the assessment results and positioning information.

8. The intelligent monitoring and management system based on the Internet of Things according to claim 7 is characterized in that: The hedging unit comprises: The escape path generation subunit determines the threat or anomaly type based on the evaluation results, and generates an escape path from the user's current location to the nearest safe place using a path planning algorithm based on the threat or anomaly type and location information; The real-time traffic condition acquisition unit dynamically adjusts the risk avoidance path based on the real-time traffic condition.

9. The intelligent monitoring and management system based on the Internet of Things according to claim 8 is characterized in that: The path planning algorithm includes: S100: Randomly generate the initial population ,in N is the population size; Define the upper and lower bounds of the search space ; Initialize the maximum number of iterations to T and the current number of iterations to t=0; S200: For each population individual X i , calculate its fitness value f(X i ), where the fitness function is the path length or time cost; S300: Update location; S400: For each population individual X i , with a preset probability P levy To perform a Levi flight: ； Among them, α is the step size factor, μ and ν are random variables that satisfy the normal distribution and Cauchy distribution, β is the Levy index, and rand is a random number in the interval [0, 1]; S500: Dynamically adjust the step size factor α: ； in, and are the initial and final step factors respectively, and t is the current number of iterations; S600: Select the first k elite individuals in the current population, and generate a reverse solution for each elite individual: ； In the formula, An elite individual. is the reverse solution; S700: Reverse solution Join the population, recalculate the fitness value, and select the optimal solution; S800: Determine the number of iterations. If the current number of iterations t reaches the maximum number of iterations T, stop the iteration and output the optimal solution; otherwise, update the number of iterations t and return to S200.

10. The intelligent monitoring and management system based on the Internet of Things according to claim 9 is characterized in that: The step S300: updating the location includes: S310: Update the position of each individual according to the current optimal solution: ； ； Where A and C are coefficient vectors, defined as follows: ； ； Among them, a decreases linearly from 2 to 0, and is a random number between [0, 1]; S320: Perform a spiral search: ； ； S330: Perform random search: ； If p < 0.5, a prey encirclement or spiral search is performed; otherwise, an individual is randomly selected as a reference point: ； ； In the formula, is a randomly selected individual, A and C are coefficient vectors.

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