Intelligent internet-of-things dynamic collaborative monitoring method, system, equipment and medium

Abstract

The invention discloses an intelligent internet-of-things dynamic cooperative monitoring method, system and device and a medium, belongs to the technical field of internet of things, and aims to solve the technical problems of how to improve the cooperative work capability between devices, dynamically adjust the device state according to real-time data, quickly respond to environment change, optimize resource utilization and improve the device efficiency. The technical scheme is as follows: data acquisition: each piece of Internet of Things equipment is equipped with a temperature sensor, a humidity sensor, an illumination sensor and a motion sensor for acquiring environmental data in real time; data communication: performing environment data exchange between each piece of Internet of Things equipment and an Internet of Things platform through a wireless communication protocol; cloud platform data analysis and processing: processing the acquired environment data by adopting a data stream or real-time data mining mode, and identifying key change factors; performing dynamic cooperative scheduling; machine learning and intelligent prediction: establishing a data model by training historical data; and adaptive adjustment and optimization are carried out.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and in particular to a method, system, device and medium for dynamic collaborative monitoring of intelligent Internet of Things. Background Art

[0002] With the rapid development of the intelligent Internet of Things, more and more devices have achieved data exchange and monitoring through the Internet. However, existing IoT monitoring systems often rely on static monitoring rules and preset conditions to work, and are difficult to adapt to rapidly changing environments and device states. They have poor coordination and adaptability in dynamic environments, resulting in low monitoring efficiency, waste of resources, and slow response speed.

[0003] Existing technologies mainly focus on IoT device monitoring technologies based on static configurations. Most solutions can only monitor and control data through fixed rules, but these systems cannot adapt to the dynamic changes in interactions between devices and environmental conditions in real time. Devices respond slowly, resulting in data delays and inaccuracies.

[0004] Therefore, how to improve the collaborative working capabilities between devices, dynamically adjust device status according to real-time data, quickly respond to environmental changes, optimize resource utilization, and improve equipment efficiency is a technical problem that needs to be solved urgently. Summary of the invention

[0005] The technical task of the present invention is to provide a smart Internet of Things dynamic collaborative monitoring method, system, equipment and medium to solve the problem of how to improve the collaborative working ability between devices, dynamically adjust the device status according to real-time data, quickly respond to environmental changes, optimize resource utilization, and improve equipment efficiency.

[0006] The technical task of the present invention is achieved in the following way: a method for dynamic collaborative monitoring of intelligent Internet of Things, the method is as follows:

[0007] Data collection: Each IoT device is equipped with temperature sensors, humidity sensors, light sensors, and motion sensors to collect environmental data in real time;

[0008] Data communication: Each IoT device exchanges environmental data with the IoT platform through wireless communication protocols; the environmental data includes information on ambient temperature, humidity, light intensity, and device working status; the wireless communication protocols include Wi-Fi, ZigBee, and LoRa;

[0009] Cloud platform data analysis and processing: The collected environmental data is processed by means of data stream or real-time data mining to identify key change factors, and the optimal working state is deduced based on the mutual relationship between devices; moreover, the cloud platform analyzes the state changes of devices and the trend of environmental changes in real time, and dynamically adjusts the working mode of devices according to the state changes of devices and the trend of environmental changes;

[0010] Dynamic collaborative scheduling: When the environmental data collected by any Internet of Things device exceeds the preset threshold, analyze the possible impact on other Internet of Things devices of the corresponding environmental data; when the state change of the corresponding Internet of Things device causes the efficiency of other Internet of Things devices to decrease or resource waste, adjust other Internet of Things devices through the dynamic collaborative scheduling mechanism, so as to jointly respond to environmental changes;

[0011] Machine learning and intelligent prediction: By training historical data, a data model is established, and the data model is used to predict the trend of environmental changes;

[0012] Adaptive adjustment and optimization: When the state of the Internet of Things device and the environmental conditions change, automatically optimize the working parameters of the device according to the real-time data and prediction results to ensure that the device is always in the best operating state.

[0013] Preferably, the method further includes dynamic collaborative scheduling, specifically as follows:

[0014] When the environmental data collected by any Internet of Things device exceeds the preset threshold, analyze the possible impact on other Internet of Things devices of the corresponding environmental data; when the state change of the corresponding Internet of Things device causes the efficiency of other Internet of Things devices to decrease or resource waste, adjust other Internet of Things devices through the dynamic collaborative scheduling mechanism, so as to jointly respond to environmental changes.

[0015] Preferably, the method further includes feedback and response, specifically: Each Internet of Things device feeds back the corresponding working state and environment to the cloud platform, and after receiving the feedback information, re-evaluate the scheduling result to ensure that the collaborative work of the devices is in the best state.

[0016] More preferably, the key change factors identified in the process of cloud platform data analysis and processing are specifically as follows:

[0017] Data preprocessing: Perform data cleaning operations such as noise filtering, missing value processing, and outlier removal on the collected raw data, and perform normalization processing on the data collected by different sensors to make each index in the same order of magnitude for easy comparison and analysis;

[0018] Feature extraction: Extract the mean, standard deviation, maximum value, minimum value and rate of change from continuous data for feature statistics. The mean, standard deviation, maximum value, minimum value and rate of change can reflect the fluctuation of the data; and use the sliding window technique to calculate the trend, periodicity or mutation points of the data (for example, local mean change, variance increase, etc.) to capture the dynamic changes of the environmental state;

[0019] Anomaly detection and change point identification: Adopt statistical methods such as CUSUM (Cumulative Sum Control Chart) and EWMA (Exponentially Weighted Moving Average) or anomaly detection algorithms combined with machine learning to monitor mutations or deviations from expected behavior in the data stream, and use window analysis or sliding window detection data stream processing techniques to identify obvious turning points in the data, that is, changes significantly different from the historical trend;

[0020] Correlation analysis and model verification: By calculating the correlation coefficients between different data items (such as Pearson correlation coefficient, mutual information, etc.), determine the features that have a strong correlation with device performance or environmental changes, and use historical data to train a prediction model (such as decision tree, random forest, etc.) to evaluate the impact of each feature on the working state of the IoT device, so as to confirm the key change factors.

[0021] Preferably, the indicators of the key change factors include environmental parameters, device status indicators, dynamic change indicators and device interaction parameters;

[0022] Among them, the environmental parameters include temperature, humidity, light intensity and air pressure or other meteorological factors;

[0023] The device status indicators include the device working status and communication delay information; the device working status includes battery power, load and temperature, reflecting the operation status of the device itself;

[0024] Dynamic change indicators: Rate of change or amplitude of fluctuation and abnormal signals.

[0025] Preferably, the data model is established as follows:

[0026] Data collection: Collect historical data from each IoT device; among them, the historical data includes environmental parameters (temperature, humidity, light, etc.) and device operation status information;

[0027] Data preprocessing: After cleaning the collected data by removing noise data and outliers and filling in missing data, then convert the data to the same dimension for convenient subsequent analysis;

[0028] Feature extraction: Extract prediction statistical features, time series features and rate of change from the original data; among them, the statistical features include mean and variance; the time series features include trend and seasonality;

[0029] Feature selection: Determine the most critical features for predicting environmental changes through correlation analysis or dimensionality reduction methods (such as principal component analysis);

[0030] Model selection: Select a suitable algorithm according to the data characteristics; specifically: if the data has obvious time series characteristics, use time series prediction models such as ARIMA and LSTM; or, use regression models or ensemble learning methods (such as random forests and gradient boosting trees);

[0031] Model training: Use the dataset obtained through preprocessing and feature engineering to train the selected model so that the selected model can learn the laws of environmental changes from historical data;

[0032] Model validation and tuning: Use part of the historical data (test set) to verify the prediction accuracy of the selected model, and adjust the model parameters (such as learning rate, number of hidden layers, etc.) to improve the generalization ability of the model;

[0033] Model deployment and real-time update: Deploy the verified model to the cloud platform, receive new data in real time, and adjust the device status according to the prediction results; at the same time, continuously retrain and optimize the model with new data to enable the model to have the ability of continuous learning and adaptation.

[0034] An intelligent IoT dynamic collaborative monitoring system, which is used to implement the intelligent IoT dynamic collaborative monitoring method as described above; the system includes:

[0035] The IoT device layer is used to collect environmental data (such as temperature, humidity, light, etc.) and device status information through various sensors and monitoring devices;

[0036] The communication network layer is used to transmit the collected data to the central data processing unit through a wireless or wired network;

[0037] The cloud platform is used to receive, store and process data, and use real-time data analysis algorithms and machine learning models to train and predict historical data, and give decision-making suggestions for device scheduling and status adjustment.

[0038] An electronic device, including: a memory and at least one processor;

[0039] Wherein, a computer program is stored on the memory;

[0040] The at least one processor executes the computer program stored in the memory, so that the at least one processor executes the intelligent IoT dynamic collaborative monitoring method as described above.

[0041] A computer-readable storage medium stores a computer program, and the computer program can be executed by a processor to implement the intelligent IoT dynamic cooperative monitoring method as described above.

[0042] The intelligent IoT dynamic cooperative monitoring method, system, device and medium of the present invention have the following advantages:

[0043] (1) The present invention can significantly improve the response speed of the IoT system, the cooperation ability between devices and the resource utilization efficiency; specifically, it has the following advantages:

[0044] ① Dynamically adapt to environmental changes: Through real-time data analysis and prediction, the present invention can quickly respond to environmental changes, adjust the working state of devices, and avoid the response lag problem of traditional IoT systems;

[0045] ② Improve the cooperation efficiency of devices: IoT devices can cooperate and adjust according to real-time data, realize intelligent linkage between multiple devices, and thus improve the overall monitoring efficiency;

[0046] ③ Optimize resource utilization: Through intelligent scheduling and feedback mechanisms, the present invention can achieve load balancing between different devices, avoid resource waste, and reduce energy consumption;

[0047] ④ Continuously learn and optimize: The present invention has the ability of self-learning and self-optimization, can continuously improve the working efficiency as the system runs, and reduce human intervention;

[0048] (2) The present invention conducts centralized control and real-time scheduling through a cloud platform, realizes real-time data analysis and cloud platform collaborative scheduling, and solves the cooperation problem between devices;

[0049] (3) Through intelligent prediction and continuous learning, the present invention can make timely adjustments according to environmental changes, and improve the adaptive ability of the system;

[0050] (4) The present invention also has an adaptive feedback ability. Devices can not only respond to environmental changes, but also optimize and adjust according to feedback to ensure the efficient operation of the system;

[0051] (5) Through the dynamic cooperative monitoring technology, the present invention improves the cooperative working ability between devices, enables the system to dynamically adjust the device state according to real-time data, quickly respond to environmental changes, and then optimize resource utilization and improve the overall system efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The present invention will be further described below with reference to the drawings.

[0053] Att Figure 1 is a flowchart of the intelligent IoT dynamic cooperative monitoring method. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0054] The intelligent IoT dynamic collaborative monitoring method, system, device and medium of the present invention will be described in detail below with reference to the accompanying drawings of the specification and specific embodiments.

[0055] Embodiment 1:

[0056] As shown Figure 1 in the figure, this embodiment provides an intelligent IoT dynamic collaborative monitoring method, and the method is as follows:

[0057] S1. Data collection: Each IoT device is equipped with a temperature sensor, a humidity sensor, a light sensor and a motion sensor for real-time collection of environmental data;

[0058] S2. Data communication: Each IoT device exchanges environmental data with the IoT platform through a wireless communication protocol; wherein, the environmental data includes information on environmental temperature, humidity, light intensity and device working status; the wireless communication protocols include Wi-Fi, ZigBee and LoRa;

[0059] S3. Cloud platform data analysis and processing: The collected environmental data is processed by means of data flow or real-time data mining to identify key change factors, and the optimal working state is deduced based on the mutual relationship between devices; and the cloud platform analyzes the state changes of devices and environmental change trends in real time, and dynamically adjusts the working mode of devices according to the state changes of devices and environmental change trends;

[0060] S4. Dynamic collaborative scheduling: When the environmental data collected by any IoT device exceeds the preset threshold, analyze the possible impact of the corresponding environmental data on other IoT devices; when the state change of the corresponding IoT device causes the efficiency of other IoT devices to decrease or resource waste, adjust other IoT devices through the dynamic collaborative scheduling mechanism, so as to jointly respond to environmental changes;

[0061] S5. Machine learning and intelligent prediction: By training historical data, a data model is established, and the data model is used to predict environmental change trends; for example, based on the temperature change law in the past few days, the system can predict the temperature change within the next 24 hours in advance, and accordingly adjust the operation modes of air conditioners and temperature control devices in advance; through intelligent prediction, not only can sudden environmental changes be coped with, but also the energy consumption can be reduced and the device life can be extended by adjusting the device state in advance;

[0062] S6. Adaptive adjustment and optimization: When the state of the IoT device and the environmental conditions change, automatically optimize the working parameters of the device according to the real-time data and prediction results to ensure that the device is always in the best operating state.

[0063] This embodiment also includes dynamic collaborative scheduling, which is specifically as follows:

[0064] When the environmental data collected by any Internet of Things device exceeds the preset threshold, analyze the possible impacts on other Internet of Things devices for the corresponding environmental data; when the state change of the corresponding Internet of Things device causes a decrease in the efficiency or waste of resources of other Internet of Things devices, adjust other Internet of Things devices through the dynamic collaborative scheduling mechanism, so as to jointly respond to environmental changes.

[0065] For example, if a temperature sensor detects that the temperature is abnormally high, the system can automatically start a fan or air conditioning device, and at the same time adjust the working mode of the lighting device to achieve optimized energy use and a comfortable working environment.

[0066] This embodiment also includes feedback and response, specifically: each Internet of Things device feeds back the corresponding working state and environment to the cloud platform, and after receiving the feedback information, re-evaluates the scheduling result to ensure that the collaborative work of the devices is in the best state, which can ensure that the system is always in the best operating state, reduce unnecessary energy consumption, and improve the operating efficiency of the devices.

[0067] The specific process of identifying key change factors in the data analysis and processing of the cloud platform in step S3 of this embodiment is as follows:

[0068] S301. Data preprocessing: Perform data cleaning operations such as noise filtering, missing value processing, and outlier removal on the collected raw data, and perform normalization processing on the data collected by different sensors, so that each index is in the same order of magnitude, which is convenient for comparison and analysis;

[0069] S302. Feature extraction: Extract the mean, standard deviation, maximum value, minimum value, and change rate from the continuous data for feature statistics. The mean, standard deviation, maximum value, minimum value, and change rate can reflect the fluctuation of the data; and use the sliding window technology to calculate the trend, periodicity, or mutation point of the data (for example, local mean change, variance increase, etc.) to capture the dynamic changes of the environmental state;

[0070] S303. Anomaly detection and change point identification: Adopt statistical methods such as CUSUM (Cumulative Sum Control Chart) and EWMA (Exponentially Weighted Moving Average), or anomaly detection algorithms combined with machine learning, to monitor the mutation or deviation from the expected behavior in the data stream, and use window analysis or sliding window detection data stream processing technology to identify obvious turning points in the data, that is, changes significantly different from the historical trend;

[0071] S304, Correlation Analysis and Model Validation: By calculating the correlation coefficients between different data items (such as Pearson correlation coefficient, mutual information, etc.), identify the features that have a strong correlation with device performance or environmental changes, and use historical data to train a prediction model (such as decision tree, random forest, etc.), evaluate the impact of each feature on the working state of the IoT device, so as to confirm the key change factors.

[0072] The indicators of the key change factors in this embodiment include environmental parameters, device status indicators, dynamic change indicators, and device interaction parameters;

[0073] Among them, the environmental parameters include temperature, humidity, light intensity, air pressure, or other meteorological factors;

[0074] The device status indicators include the device working state and communication delay information; the device working state includes battery power, load, and temperature, reflecting the operating condition of the device itself;

[0075] Dynamic change indicators: change rate or fluctuation range and abnormal signals.

[0076] The establishment of the data model in step S5 of this embodiment is specifically as follows:

[0077] S501, Data Collection: Collect historical data from each IoT device; among them, the historical data includes environmental parameters (temperature, humidity, light, etc.) and device operation status information;

[0078] S502, Data Preprocessing: After cleaning the collected data by removing noise data and outliers and filling in missing data, then convert the data to the same dimension for convenient subsequent analysis;

[0079] S503, Feature Extraction: Extract the prediction statistical features, time series features, and change rate from the original data; among them, the statistical features include mean and variance; the time series features include trend and seasonality;

[0080] S504, Feature Selection: Determine the most critical features for environmental change prediction through correlation analysis or dimensionality reduction methods (such as principal component analysis);

[0081] S505, Select Model: Select a suitable algorithm according to the data characteristics; specifically: if the data has obvious time series characteristics, use time series prediction models such as ARIMA and LSTM; or, use regression models or ensemble learning methods (such as random forest, gradient boosting tree);

[0082] S506, Train Model: Use the data set obtained by preprocessing and feature engineering to train the selected model so that the selected model can learn the laws of environmental changes from historical data;

[0083] S507. Model Verification and Tuning: Use part of the historical data (test set) to verify the prediction accuracy of the selected model, adjust the model parameters (such as learning rate, number of hidden layers, etc.), and improve the generalization ability of the model;

[0084] S508. Model Deployment and Real-time Update: Deploy the verified model to the cloud platform, receive new data in real time, and adjust the device status according to the prediction results; at the same time, continuously retrain and optimize the model with new data to enable the model to have the ability of continuous learning and adaptation.

[0085] Embodiment 2:

[0086] This embodiment provides an intelligent IoT dynamic collaborative monitoring system, which is used to implement the intelligent IoT dynamic collaborative monitoring method in Embodiment 1; the system includes:

[0087] The IoT device layer is used to collect environmental data (such as temperature, humidity, light, etc.) and device status information through various sensors and monitoring devices;

[0088] The communication network layer is used to transmit the collected data to the central data processing unit through a wireless or wired network;

[0089] The cloud platform is used to receive, store and process data, and use real-time data analysis algorithms and machine learning models to train and predict historical data, and give decision-making suggestions for device scheduling and status adjustment.

[0090] Embodiment 3:

[0091] This embodiment also provides an electronic device, including: a memory and a processor;

[0092] Wherein, the memory stores computer execution instructions;

[0093] The processor executes the computer execution instructions stored in the memory, so that the processor executes the intelligent IoT dynamic collaborative monitoring method in any embodiment of the present invention.

[0094] The processor may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0095] The memory can be used to store computer programs and / or modules. By running or executing the computer programs and / or modules stored in the memory and invoking the data stored in the memory, the processor realizes various functions of the electronic device. The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store the operating system, application programs required for at least one function, etc.; the data storage area can store data created according to the use of the terminal, etc. In addition, the memory may also include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash memory card, at least one magnetic disk storage period, a flash memory device, or other volatile solid-state storage devices.

[0096] Embodiment 4:

[0097] This embodiment also provides a computer-readable storage medium, in which multiple instructions are stored. The instructions are loaded by the processor to cause the processor to execute the intelligent IoT dynamic collaborative monitoring method in any embodiment of the present invention. Specifically, a system or device equipped with a storage medium can be provided. On the storage medium, software program codes for implementing the functions in any one of the above embodiments are stored, and the computer (or CPU or MPU) of the system or device reads and executes the program codes stored in the storage medium.

[0098] In this case, the program code read from the storage medium itself can implement the functions in any one of the above embodiments. Therefore, the program code and the storage medium storing the program code constitute a part of the present invention.

[0099] Embodiments of the storage medium for providing program codes include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RYM, DVD-RW, DVD+RW), magnetic tapes, non-volatile memory cards, and ROMs. Optionally, the program code can be downloaded from a server computer through a communication network.

[0100] In addition, it should be clear that not only can the functions in any one of the above embodiments be realized by executing the program codes read by the computer, but also by causing an operating system or the like operating on the computer based on the instructions of the program codes to complete part or all of the actual operations.

[0101] In addition, it can be understood that the program code read from the storage medium is written into the memory provided in the expansion board inserted into the computer or into the memory provided in the expansion unit connected to the computer. Subsequently, based on the instructions of the program code, the CPU or the like installed on the expansion board or the expansion unit is caused to execute part and all of the actual operations, thereby realizing the functions in any one of the above embodiments.

[0102] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent IoT dynamic collaborative monitoring method, characterized in that, The method is as follows: Data collection: Each Internet of Things device is equipped with a temperature sensor, a humidity sensor, a light sensor, and a motion sensor to collect environmental data in real time; Data communication: Each Internet of Things device exchanges environmental data with the Internet of Things platform through a wireless communication protocol; among them, the environmental data includes information on environmental temperature, humidity, light intensity, and device working status; the wireless communication protocols include Wi-Fi, ZigBee, and LoRa; Cloud platform data analysis and processing: The collected environmental data is processed by means of data flow or real-time data mining to identify key change factors, and the optimal working state is deduced based on the mutual relationship between devices; moreover, the cloud platform analyzes the state changes of devices and the environmental change trend in real time, and dynamically adjusts the working mode of devices according to the state changes of devices and the environmental change trend; Dynamic collaborative scheduling: When the environmental data collected by any Internet of Things device exceeds the preset threshold, analyze the possible impact of the corresponding environmental data on other Internet of Things devices; when the state change of the corresponding Internet of Things device causes the efficiency of other Internet of Things devices to decrease or resource waste, adjust other Internet of Things devices through the dynamic collaborative scheduling mechanism, and then jointly respond to environmental changes; Machine learning and intelligent prediction: By training historical data, a data model is established, and the data model is used to predict the environmental change trend; Adaptive adjustment and optimization: When the state of the Internet of Things device and the environmental conditions change, automatically optimize the working parameters of the device according to the real-time data and prediction results to ensure that the device is always in the best operating state.

2. The intelligent IoT dynamic collaborative monitoring method according to claim 1, wherein The method also includes dynamic collaborative scheduling, which is as follows: When the environmental data collected by any Internet of Things device exceeds the preset threshold, analyze the possible impact of the corresponding environmental data on other Internet of Things devices; when the state change of the corresponding Internet of Things device causes the efficiency of other Internet of Things devices to decrease or resource waste, adjust other Internet of Things devices through the dynamic collaborative scheduling mechanism, and then jointly respond to environmental changes.

3. The intelligent IoT dynamic collaborative monitoring method according to claim 1, wherein The method also includes feedback and response, specifically: Each Internet of Things device feeds back the corresponding working state and environment to the cloud platform, and re-evaluates the scheduling result after receiving the feedback information to ensure that the collaborative work of the devices is in the best state.

4. The intelligent IoT dynamic collaborative monitoring method according to any one of claims 1-3, characterized in that The specific process of identifying key change factors in the cloud platform data analysis and processing is as follows: Data preprocessing: Perform data cleaning operations such as noise filtering, missing value processing, and outlier removal on the collected raw data, and normalize the data collected by different sensors to make all indicators in the same order of magnitude; Feature extraction: Extract the mean, standard deviation, maximum value, minimum value, and change rate from the continuous data for feature statistics. The mean, standard deviation, maximum value, minimum value, and change rate can reflect the fluctuation of the data; and use the sliding window technology to calculate the trend, periodicity, or mutation point of the data to capture the dynamic changes of the environmental state; Anomaly Detection and Change Point Identification: Adopt statistical methods such as CUSUM and EWMA or anomaly detection algorithms combined with machine learning to monitor mutations or deviations from expected behavior in the data stream, and use data stream processing techniques such as window analysis or sliding window detection to identify obvious turning points in the data, that is, changes significantly different from historical trends. Correlation Analysis and Model Verification: Determine features that have a strong correlation with device performance or environmental changes by calculating the correlation coefficients between different data items, and use historical data to train a prediction model to evaluate the impact of each feature on the working state of the IoT device, thereby identifying key change factors.

5. The intelligent IoT dynamic collaborative monitoring method according to claim 4, wherein The indicators of key change factors include environmental parameters, device status indicators, dynamic change indicators, and device interaction parameters. Among them, environmental parameters include temperature, humidity, light intensity, air pressure, or other meteorological factors. Device status indicators include the working state of the device and communication delay information; the working state of the device includes battery power, load, and temperature, reflecting the operating conditions of the device itself. Dynamic change indicators: change rate or fluctuation range and anomaly signals.

6. The intelligent IoT dynamic collaborative monitoring method according to claim 1, wherein, The establishment of the data model is as follows: Data collection: Collect historical data from each IoT device; among them, historical data includes environmental parameters and device operation status information. Data preprocessing: After cleaning the collected data by removing noise data and outliers and filling in missing data, then convert the data to the same dimension for convenient subsequent analysis. Feature extraction: Extract prediction statistical features, time series features, and change rates from the original data; among them, statistical features include mean and variance; time series features include trend and seasonality. Feature selection: Determine the most critical features for predicting environmental changes through correlation analysis or dimensionality reduction methods. Select a model: Select a suitable algorithm according to the data characteristics; specifically: if the data has obvious time series characteristics, adopt time series prediction models such as ARIMA and LSTM; or, adopt regression models or ensemble learning methods. Train the model: Use the data set obtained from preprocessing and feature engineering to train the selected model so that the selected model can learn the laws of environmental changes from historical data. Model verification and tuning: Use part of the historical data to verify the prediction accuracy of the selected model, adjust the model parameters, and improve the generalization ability of the model. Model deployment and real-time update: Deploy the verified model to the cloud platform, receive new data in real time, and adjust the device status according to the prediction results; at the same time, continuously retrain and optimize the model with new data to enable the model to have the ability of continuous learning and self-adaptation.

7. An intelligent IoT dynamic collaborative monitoring system, characterized in that, This system is used to implement the intelligent IoT dynamic collaborative monitoring method described in any one of claims 1 to 6; this system includes: The IoT device layer is used to collect environmental data and device status information through various sensors and monitoring devices. The communication network layer is used to transmit the collected data to the central data processing unit through a wireless or wired network. The cloud platform is used to receive, store, and process data, and use real-time data analysis algorithms and machine learning models to train and predict historical data, and give decision-making suggestions for device scheduling and status adjustment.

8. An electronic device, characterized in that, Include: A memory and at least one processor; Wherein, a computer program is stored on the memory; The at least one processor executes the computer program stored in the memory, so that the at least one processor executes the intelligent Internet of Things dynamic collaborative monitoring method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, A computer program is stored in the computer-readable storage medium, and the computer program can be executed by a processor to implement the intelligent Internet of Things dynamic collaborative monitoring method according to any one of claims 1 to 6.

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