Combined low-power-consumption communication sensing device
By designing a combined low-power communication sensing device, adopting modular design and intelligent control, the problems of data acquisition accuracy and sensor configuration flexibility in the low-power mode of the existing technology are solved, and high-precision data acquisition and long battery life are achieved, which is suitable for high-reliability Internet of Things applications.
Patent Information
- Application Number
- CN202411831324.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-12
- Publication Date
- 2025-04-11
AI Technical Summary
Existing communication sensing devices are difficult to maintain high-precision data acquisition and analysis capabilities in low-power modes, and sensor configuration and replacement lack flexibility, affecting the system's response capabilities and operating efficiency.
A combined low-power communication sensing device is designed, adopting modular design and intelligent control, integrating low-power design module, modular sensor module, wireless communication module, central control module and security and privacy module. Through adaptive energy management and distributed energy consumption optimization, the equipment can operate stably for a long time at extremely low energy consumption; through intelligent sensor aggregation and self-optimization units, the acquisition frequency and sensor configuration are dynamically adjusted to adapt to different environments and task requirements.
It realizes high-precision data acquisition and analysis in low-power mode, extends the battery life of the equipment, improves the system's response capability and operation efficiency, and is suitable for high-reliability fields such as remote monitoring and environmental monitoring.
Smart Images
Figure CN119803547A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of industrial automation, and in particular to a combined low-power consumption communication sensor device. Background Art
[0002] Existing communication sensor devices play a vital role in IoT applications and are widely used in smart cities, industrial automation and other fields. These devices usually integrate multiple sensors, such as temperature, humidity, pressure, light, etc., to collect environmental data and transmit the data to the central control module or the cloud through wireless communication technology to achieve remote monitoring and management; these devices are usually designed to be low-power to extend battery life and reduce maintenance requirements, especially in remote or unattended application scenarios. In addition, most of the existing communication sensor devices have a modular design, allowing users to flexibly configure different types of sensors according to specific needs to adapt to a variety of monitoring tasks. The wireless communication module is one of the core components of these devices. They usually support multiple wireless communication protocols, such as Wi-Fi, LoRa, Zigbee or NB-IoT, to ensure that data can be transmitted to the target system stably and in a timely manner; at the same time, with the development of the Internet of Things, these devices are constantly improving in data acquisition accuracy, real-time and transmission reliability, aiming to provide accurate data support and decision-making basis for various IoT application scenarios. Through these functions, communication sensor devices have achieved comprehensive perception and intelligent management of the environment in the IoT system, promoting the widespread application and development of IoT technology.
[0003] First, conventional communication sensor devices usually need to run for a long time in IoT applications, especially in remote or unattended application scenarios, where energy supply is greatly limited. Traditional sensor devices usually have high power consumption, resulting in limited device life. Frequent battery replacement or maintenance not only increases operating costs, but may also lead to interruptions in monitoring data, affecting the overall reliability and stability of the system. In addition, high-energy consumption devices may face greater energy consumption problems when operating in harsh environments or extreme conditions, further shortening the working time of the equipment. Secondly, in low-power mode, many conventional communication sensor devices find it difficult to maintain high-performance operation, resulting in reduced data acquisition accuracy or increased acquisition delay. For environmental monitoring and industrial control application scenarios, data accuracy and real-time performance are crucial because these data directly affect the decision-making quality and response speed of the system. However, traditional low-power designs often compromise on data acquisition performance, resulting in the inability to capture key data in a timely and accurate manner, thus affecting the operation of the entire system. Finally, the application scenarios of the Internet of Things are complex and changeable. Different monitoring tasks may require different types of sensor equipment. However, conventional communication sensor devices lack flexibility in the configuration and replacement of sensors, and usually require complex operations and long downtime. Especially in some application scenarios that require frequent replacement of sensors or adjustment of sensor configurations, traditional equipment has low maintenance efficiency and cannot quickly adapt to environmental changes, resulting in the overall responsiveness and operating efficiency of the system being affected. In addition, the installation and maintenance of sensors usually require professionals, which increases operating costs and time investment. Summary of the invention
[0004] In view of the deficiencies in the prior art, the present invention provides a combined low-power communication sensor device, which solves the problem that the device cannot guarantee the accuracy and timeliness of data collection under certain conditions.
[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a combined low-power communication sensor device, comprising: A protective shell, one side of which is provided with an opening and closing plate; A docking plate 1 is installed inside the protective shell, a docking plate 2 is arranged at the bottom of the docking plate 1, and a sensor is arranged between the docking plate 1 and the docking plate 2; A docking assembly, which is installed between the first docking plate and the second docking plate and is used for quickly replacing the sensor; The PLC module is installed between the first docking plate and the second docking plate and is used for intelligent control of the equipment.
[0006] Preferably, the PLC module includes a low-power design module, a modular sensor module, a wireless communication module, a central control module and a security and privacy module; The low power design module is used to minimize the energy consumption of the device, ensuring that the device can operate for a long time under extremely low energy consumption conditions; The modular sensor module is used for flexible combination and expansion of intelligent control sensors to meet different monitoring needs; The wireless communication module is used to transmit the collected data to the central control module through a low-power wireless communication protocol; The central control module centrally receives, processes and analyzes data from each sensor node, and provides remote monitoring, data analysis and system management functions; The security and privacy module is used to prevent unauthorized access and data leakage.
[0007] Preferably, the low power consumption design module includes an adaptive energy management unit and a distributed energy consumption optimization unit; The adaptive energy management unit is used to evaluate the availability of environmental energy in real time and dynamically switch energy sources; The real-time assessment uses multi-source energy sensing sensors to detect different types of environmental energy; The distributed energy consumption optimization unit is used to independently equip each sensor node with an energy consumption optimization subunit, which can adjust itself according to the priority of the current task and the remaining energy level; The regulation relies on a distributed energy consumption optimization formula, which is as follows:
[0008] in is the adjusted energy consumption level, is the adjusted sampling frequency, is the adjusted communication frequency, is the baseline energy consumption level, is the base sampling frequency, is the reference communication frequency, is the task priority, is the remaining energy level and For the environmental status.
[0009] Preferably, the modular sensor module includes an intelligent sensor aggregation and self-optimization unit and an environmental change perception and emergency response unit; The intelligent sensor aggregation and self-optimization unit is used to combine data from multiple sensors so that the device can autonomously optimize the data collection strategy; The autonomous optimization data collection strategy includes the following steps: S1. Data aggregation and analysis: Collect data from multiple sensors and perform preliminary aggregation processing. S2, weight allocation: dynamically adjust the weight of each sensor data according to the current environment status and task priority; S3. Forecast error evaluation: The system compares the current aggregated data with the historical data to evaluate the data change trend and fluctuation. It then calculates the forecast error by analyzing the difference between the current data and the historical trend. If the error is large, it means that the current environment may have changed significantly, and the data collection frequency needs to be increased to capture these changes. S4, collection frequency adjustment: starting from the baseline collection frequency, adjust the collection frequency according to the task priority and prediction error. The higher the task priority and the larger the prediction error, the higher the collection frequency. At the same time, consider the light and temperature to further adjust the collection frequency. S5. Energy level assessment and adjustment: Real-time monitoring of the remaining energy level. When the energy level is lower than the preset threshold, the system enters energy-saving mode. In energy-saving mode, the acquisition frequency is reduced to reduce energy consumption, but the most critical data is still captured. S6, Dynamic Optimization and Execution: Based on the above evaluation, dynamically adjust the sensor collection frequency and implement new data collection strategies, while continuously monitoring the environmental status, energy level and data collection effect, and make further optimization adjustments based on real-time changes; S7. Self-learning and optimization: Record the effect and related data after each adjustment, gradually optimize the decision-making model through machine learning algorithms, and improve the accuracy and effect of future adjustments; In the step S4, the most critical data include abnormal data, trend data, threshold data and priority data; In the step S7, gradually optimizing the decision model by a machine learning algorithm includes the following steps: S11, Data collection and feature extraction: Collect data from all sensor nodes and extract features related to key data, including mean, variance, maximum, minimum, trend slope and abnormal points; then annotate the data to distinguish normal data from key data; S22. Train the machine learning model: Select the random forest learning model to identify and predict key data. Use historical data to train the model. The model will learn how to identify and classify key data. Then use cross-validation to evaluate the performance of the model to ensure that the model can accurately predict key data on new data. S33, Real-time data prediction and optimization: The device obtains sensor data in real time and inputs it into the trained model for prediction. The model identifies key data in real time and marks the data as high priority data. Finally, based on the prediction results and energy consumption model, the system automatically adjusts the sampling frequency and communication frequency of the sensor; S44, Self-learning and iterative optimization: After each decision, the system uses the collected key data and actual environmental changes as feedback to update the model parameters. At the same time, the system regularly retrains the model, using new data and feedback. Through continuous learning and optimization, the system can gradually improve energy efficiency, while ensuring that key data is captured, while further reducing energy consumption; The environmental change sensing and emergency response unit is used to detect drastic changes in the environment in real time and immediately activate the emergency mode, increase the sampling frequency and data transmission rate to capture important environmental change data; The drastic changes include but are not limited to sudden temperature changes and vibrations; The important environmental change data include toxic gas monitoring data, abnormal temperature changes, equipment failure data, environmental disaster warning data and abnormal trend data.
[0010] Preferably, the wireless communication module includes a dynamic channel management and interference avoidance unit and an asynchronous transmission and data cache unit; The dynamic channel management and interference avoidance unit is used to monitor the wireless communication environment in real time and automatically switch to the optimal channel according to the channel congestion level. It also has an interference detection function. When potential wireless interference is detected, it will automatically reduce the transmission power and switch the frequency band; The asynchronous transmission and data cache unit is used to perform asynchronous transmission and cache processing on data when the communication network is unstable and the bandwidth is limited, and to send the cached data preferentially after the transmission condition is restored.
[0011] Preferably, the central control module includes an intelligent data stratification and multi-mode analysis unit and a self-optimization and learning algorithm unit; The intelligent data stratification and multi-mode analysis unit automatically performs stratification processing according to the source and type of data, and performs comprehensive analysis in combination with different data sources; The layered processing is specifically divided into a real-time data layer, an external data layer and a historical data layer; The different data sources include weather forecasts and historical data; The self-optimization and learning algorithm unit continuously optimizes its own analysis model through machine learning algorithms to improve the accuracy of predictions; The optimization step comprises the steps of: S331, Dynamic Learning and Update: Real-time Data Input: The latest sensor data and external data are input into the model to make predictions in real time; S332, error calculation: by comparing the model prediction results with the actual observation results, the prediction error E is calculated;
[0012] in, is the actual observed value, is the model prediction value, is the number of samples; S333, model update: According to the error, the model parameters are optimized and adjusted through the back propagation algorithm. The specific formula of the back propagation algorithm is as follows:
[0013] in are model parameters, is the learning rate, is the gradient of the error with respect to the parameters.
[0014] Preferably, the security and privacy module includes a distributed encryption and dynamic key generation unit and a behavior anomaly detection and response unit; The distributed encryption and dynamic key generation unit performs data encryption through a distributed architecture, and each node has an independent encryption key; The behavior anomaly detection and response unit monitors the behavior of all devices in the system in real time, and takes immediate response measures when unauthorized data access and communication behavior is detected; The response measures include isolating suspicious devices and redistributing security policies.
[0015] Preferably, the docking assembly includes a docking column 1, which is fixed at the bottom of a docking plate 1, a docking column 2 is slidably arranged inside the docking column 1, a fixed column is fixed inside the docking column 2, a spring is sleeved on the outer wall of the fixed column, one end of the spring is fixed inside the docking column 2, a magnet 2 is fixed at the other end of the spring, and the fixed column slides inside the magnet 2 and repels it.
[0016] Preferably, a sliding column is fixed on one side of the second magnet, a ball is arranged on one side of the sliding column, and a limiting block is fixed on the outer wall of the sliding column.
[0017] Preferably, an opening and closing plate is provided on one side of the protective shell, a mounting plate is fixed on the other side of the protective shell, and a magnet 1 is embedded on the top of the protective shell.
[0018] Working principle: When it is necessary to replace different sensors according to needs, the spring lock at the opening and closing plate position can be opened. At this time, the docking plate 1 and the docking plate 2 are pulled out, and the docking plate 1 is pulled at the same time. The position of the docking plate 2 remains unchanged, and the corresponding ball will be squeezed, and then through the sliding column, the magnet 2 is close to the fixed column. At the same time, the fixed column is partially immersed in the magnet 2, and a repulsive force is generated between them. The volume space occupied by the corresponding spring is reduced, so that the docking column 1 and the docking column 2 can be separated smoothly. During installation, the ball will still be squeezed by force first. However, after the docking column 1 and the docking column 2 are docked, wait for the ball to align with the position. After alignment, the spring generates repulsion through its own repulsion and the spring due to its reduction. The elastic potential energy of the device further pushes the magnet 2, thereby driving the sliding column to push the ball to engage with the empty slot in the docking column 1, so as to achieve quick docking. When the docking plate 1 and the docking plate 2 are separated, the corresponding sensor can be separated from the corresponding adapter socket on the docking plate 2, and then a different sensor can be replaced to adapt. The overall device can be installed in the corresponding required position by bolts using the mounting plate as required, or when the device needs to be installed on some devices for connection, the magnet 1 can be directly used to absorb the magnetic attraction of the device casing for connection, and through the appropriate installation position, it can also collect and supplement energy by itself through the solar panel arranged on the outer surface of the protective shell.
[0019] The present invention provides a combined low-power communication sensor device, which has the following beneficial effects: 1. The present invention enables the device to maintain high-precision data collection and analysis capabilities in low-power mode through the central control module, ensuring the accuracy and timeliness of key data. In addition, through the intelligent management and optimization of sensors, the system can adapt to different working environments and operate stably in long-life IoT application scenarios. It is widely used in remote monitoring and environmental monitoring fields with high reliability requirements.
[0020] 2. The present invention integrates an adaptive energy management unit and a distributed energy consumption optimization unit to intelligently adjust energy usage under changing environmental conditions, ensuring that the equipment can operate stably for a long time with extremely low energy consumption. This design greatly extends the battery life of the equipment, making it particularly suitable for IoT application scenarios with strict energy consumption requirements, and significantly reducing maintenance frequency and energy costs.
[0021] 3. The present invention adopts a modular design, which makes the installation and replacement process of the same type of sensors extremely simple. Users can easily replace sensors through a quick disassembly and assembly mechanism without complicated operations and tools. This not only improves the maintenance efficiency of the equipment, but also ensures that in application scenarios where frequent replacement and adjustment of sensor configurations are required, the equipment can respond quickly and maintain efficient operation. BRIEF DESCRIPTION OF THE DRAWINGS
[0022] Figure 1 A perspective view of the present invention; Figure 2 This is a schematic diagram of the internal structure of the protective shell of the present invention; Figure 3 It is a schematic diagram of the connection structure of the docking plate 1 and the docking plate 2 of the present invention; Figure 4 This is a schematic diagram of the internal structure of the docking column 2 of the present invention; Figure 5 It is a schematic diagram of a PLC module of the present invention; Figure 6 It is a schematic diagram of a low power consumption design module of the present invention; Figure 7 It is a schematic diagram of a modular sensor module of the present invention; Figure 8 is a schematic diagram of a wireless communication module of the present invention; Fig. 9 It is a schematic diagram of the central control module of the present invention; Fig.10 Schematic diagram of the security and privacy module of the present invention.
[0023] Among them, 1. Protective shell; 2. Opening and closing plate; 3. Mounting plate; 4. Magnet 1; 5. Docking plate 1; 6. Docking plate 2; 7. Docking column 1; 8. Docking column 2; 9. Fixed column; 10. Spring; 11. Magnet 2; 12. Sliding column; 13. Limit block; 14. Ball; 15. Sensor; 16. PLC module. DETAILED DESCRIPTION
[0024] The following will be combined with the drawings in the specification 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.
[0025] Please see attached Figure 1 , Attachment Figure 3 and attached Figure 5 , an embodiment of the present invention provides a combined low-power communication sensor device comprising: A protective shell 1, one side of which is provided with an opening and closing plate 2; A docking plate 1 5 is installed inside the protective shell 1, a docking plate 2 6 is arranged at the bottom of the docking plate 1 5, and a sensor 15 is arranged between the docking plate 1 5 and the docking plate 2 6; A docking assembly, which is installed between the docking plate 1 5 and the docking plate 2 6 and is used for quickly replacing the sensor 15; PLC module 16, which is installed between docking plate 1 5 and docking plate 2 6, and is used for intelligent control of the equipment; The PLC module 16 includes a low power design module, a modular sensor module, a wireless communication module, a central control module, and a security and privacy module; The low-power design module is used to minimize the energy consumption of the device, ensuring that the device can operate for a long time under extremely low energy consumption conditions; The modular sensor module is used for flexible combination and expansion of intelligent control sensors 15 to meet different monitoring requirements; The wireless communication module is used to transmit the collected data to the central control module through a low-power wireless communication protocol; The central control module centrally receives, processes and analyzes data from each sensor node, providing remote monitoring, data analysis and system management functions; The Security and Privacy module is used to prevent unauthorized access and data leakage.
[0026] Specifically, the protective shell 1 provides overall physical protection, and facilitates users to quickly access internal components through the opening and closing plate 2. The docking assembly between the docking plate 1 5 and the docking plate 2 6 allows the user to quickly replace the sensor 15 to ensure that the device can adapt to different monitoring needs. The PLC module 16 serves as the core control unit and integrates a low-power design module, a modular sensor module, a wireless communication module, a central control module and a security and privacy module. Among them, the low-power design module ensures that the device operates stably for a long time under low energy consumption by dynamically adjusting the power management strategy; the modular sensor module intelligently controls the combination and expansion of different types of sensors 15 according to the requirements of the current task and environmental conditions to achieve a flexible monitoring layout; the wireless communication module is responsible for transmitting the data collected by the sensor 15 in a low-power manner The data is transmitted to the central control module to ensure the reliability and efficiency of data transmission. The central control module centrally manages the data from each sensor 15, and provides accurate remote monitoring and data analysis through intelligent data stratification and multi-mode analysis functions, combined with historical data and real-time environmental information. At the same time, it uses self-optimization and learning algorithms to continuously improve the system's prediction accuracy and responsiveness. The security and privacy module uses multi-layer encryption and access control mechanisms to ensure data security and prevent unauthorized access and potential data leakage risks. In the entire system, the data collection, transmission, processing and storage processes form an efficient closed loop. All modules work together to ensure that the system can operate stably and reliably in a complex environment, and achieve optimal performance in energy utilization, data management and security assurance.
[0027] Please see attached Figure 6 ,The low power consumption design module includes an adaptive energy management unit and a distributed energy consumption optimization unit; The adaptive energy management unit is used to evaluate the availability of environmental energy in real time and dynamically switch energy sources; Real-time assessment uses multi-source energy sensing sensors to detect different types of ambient energy; The distributed energy consumption optimization unit is used to independently equip each sensor node with an energy consumption optimization sub-unit, which can adjust itself according to the priority of the current task and the remaining energy level; The regulation relies on the distributed energy consumption optimization formula, which is as follows:
[0028] in is the adjusted energy consumption level, is the adjusted sampling frequency, is the adjusted communication frequency, is the baseline energy consumption level, is the base sampling frequency, is the reference communication frequency, For task priority 1 to 10, is the remaining energy level percentage and For the environmental status.
[0029] Specifically, the adaptive energy management unit first detects the sources of light energy, heat energy, and vibration energy in the environment in real time through multi-source energy sensing sensors, and dynamically selects and switches energy sources according to the detected environmental energy availability, ensuring that the system can maximize the use of available energy under different environmental conditions. At the same time, the distributed energy consumption optimization unit operates independently at each sensor node, relying on the distributed energy consumption optimization formula to dynamically adjust the energy consumption level, sampling frequency, and communication frequency of each node according to the priority of the current task, the remaining energy level, and the environmental status. Specifically, during the operation of the system, the adjusted energy consumption level is calculated by the formula. Through this distributed energy consumption optimization strategy, each sensor node can autonomously adjust its working state according to its specific environment and task requirements to achieve the optimal energy consumption of the overall system, while ensuring the reliability and efficiency of data collection and communication during the execution of key tasks. The coordination between adaptive energy management and distributed energy consumption optimization enables the system to not only adapt to the changing external environment, but also significantly extend the battery life of the equipment while ensuring the stability of system operation and data integrity.
[0030] Please see attached Figure 7 ,The modular sensor module includes an intelligent sensor aggregation and ,self-optimization unit and an environmental change perception and emergency response unit; The intelligent sensor aggregation and self-optimization unit is used to combine the data of multiple sensors 15 so that the device can autonomously optimize the data collection strategy; The autonomous optimization data collection strategy includes the following steps: S1. Data aggregation and analysis: Collect data from multiple sensors 15 and perform preliminary aggregation processing. S2, weight allocation: dynamically adjust the weight of each sensor data according to the current environment status and task priority; S3. Forecast error evaluation: The system compares the current aggregated data with the historical data to evaluate the data change trend and fluctuation. It then calculates the forecast error by analyzing the difference between the current data and the historical trend. If the error is large, it means that the current environment may have changed significantly, and the data collection frequency needs to be increased to capture these changes. S4, collection frequency adjustment: starting from the baseline collection frequency, adjust the collection frequency according to the task priority and prediction error. The higher the task priority and the larger the prediction error, the higher the collection frequency. At the same time, consider the light and temperature to further adjust the collection frequency. S5. Energy level assessment and adjustment: Real-time monitoring of the remaining energy level. When the energy level is lower than the preset threshold, the system enters energy-saving mode. In energy-saving mode, the acquisition frequency is reduced to reduce energy consumption, but the most critical data is still captured. S6, dynamic optimization and execution: according to the above evaluation, dynamically adjust the acquisition frequency of the sensor 15 and implement a new data acquisition strategy, while continuously monitoring the environmental status, energy level and data acquisition effect, and make further optimization adjustments according to real-time changes; S7. Self-learning and optimization: Record the effect and related data after each adjustment, gradually optimize the decision-making model through machine learning algorithms, and improve the accuracy and effect of future adjustments; In step S4, the most critical data include abnormal data, trend data, threshold data, and priority data; In step S7, gradually optimizing the decision model through the machine learning algorithm includes the following steps; S11, Data collection and feature extraction: Collect data from all sensor nodes and extract features related to key data, including mean, variance, maximum, minimum, trend slope and abnormal points; then annotate the data to distinguish normal data from key data; S22. Train the machine learning model: Select the random forest learning model to identify and predict key data. Use historical data to train the model. The model will learn how to identify and classify key data. Then use cross-validation to evaluate the performance of the model to ensure that the model can accurately predict key data on new data. S33, real-time data prediction and optimization: the device obtains sensor data in real time and inputs it into the trained model for prediction. The model identifies key data in real time and marks the data as high priority data. Finally, based on the prediction results and energy consumption model, the system automatically adjusts the sampling frequency and communication frequency of the sensor 15; S44, Self-learning and iterative optimization: After each decision, the system uses the collected key data and actual environmental changes as feedback to update the model parameters. At the same time, the system regularly retrains the model, using new data and feedback. Through continuous learning and optimization, the system can gradually improve energy efficiency, while ensuring that key data is captured, while further reducing energy consumption; The environmental change perception and emergency response unit is used to detect drastic changes in the environment in real time and immediately activate the emergency mode, increasing the sampling frequency and data transmission rate to capture important environmental change data; Severe changes include, but are not limited to, sudden temperature changes and vibrations; Important environmental change data include toxic gas monitoring data, abnormal temperature changes, equipment failure data, environmental disaster warning data and abnormal trend data.
[0031] Specifically, first, the intelligent sensor aggregation and self-optimization unit collects data from multiple sensors 15 through the data aggregation and analysis step S1, and obtains a comprehensive environmental state through weighted averaging and data fusion methods. In the weight allocation step S2, the weight of the data of each sensor 15 is dynamically adjusted according to the current environmental state and the priority of the task to ensure that the data importance of the key sensor 15 in a specific task is given priority. Subsequently, in the prediction error evaluation step S3, the system compares the current aggregated data with the historical data and calculates the prediction error. If the error is large, the system will increase the collection frequency in the collection frequency adjustment step S4, especially for abnormal data, trend data, threshold data and priority data, to capture important environmental changes. At the same time, in the energy level evaluation and adjustment step S5, the system monitors the remaining energy in real time. When the energy is lower than the preset threshold, it automatically enters the energy-saving mode and reduces the collection frequency, but still ensures the capture of key data. In the dynamic optimization and execution step S6, the system According to the changes in environmental status and energy levels, the collection frequency is dynamically adjusted, and the optimized data collection strategy is executed. At the same time, the decision model S7 is continuously optimized through the machine learning algorithm. This process includes data collection and feature extraction S11, training machine learning models S22, real-time data prediction and optimization S33, and self-learning and iterative optimization S44, and finally forms an efficient data collection and processing system capable of self-learning and self-optimization. In this process, the environmental change perception and emergency response unit monitors the drastic changes in the environment in real time, such as sudden temperature changes and vibrations. Once these changes are detected, the system immediately activates the emergency mode, increases the sampling frequency and data transmission rate, and ensures that important environmental change data is captured in a timely manner, including toxic gas detection data, abnormal temperature changes, equipment failure data, environmental disaster warning data and abnormal trend data. The close cooperation between the two units enables the system to operate efficiently in a dynamic environment, ensuring the timeliness, accuracy and completeness of the data while maximizing energy savings.
[0032] Please see attached Figure 8 -Attached Fig.10 ,The wireless communication module includes a dynamic channel management and interference avoidance unit and an asynchronous transmission and data caching unit; The dynamic channel management and interference avoidance unit is used to monitor the wireless communication environment in real time and automatically switch to the optimal channel according to the channel congestion level. It also has an interference detection function. When potential wireless interference is detected, it will automatically reduce the transmission power and switch the frequency band. The asynchronous transmission and data cache unit is used to perform asynchronous transmission and cache processing on data when the communication network is unstable and the bandwidth is limited, and after the transmission conditions are restored, the cached data is sent first; The central control module includes an intelligent data stratification and multi-mode analysis unit and a self-optimization and learning algorithm unit; The intelligent data stratification and multi-mode analysis unit automatically performs stratification processing according to the source and type of data, and conducts comprehensive analysis in combination with different data sources; Layered processing is specifically divided into real-time data layer, external data layer and historical data layer; Different data sources include weather forecasts and historical data; The self-optimization and learning algorithm unit continuously optimizes its own analysis model through machine learning algorithms to improve the accuracy of predictions; The optimization step includes the following steps: S331, Dynamic Learning and Update: Real-time Data Input: The latest sensor data and external data are input into the model to make predictions in real time; S332, error calculation: by comparing the model prediction results with the actual observation results, the prediction error E is calculated;
[0033] in, is the actual observed value, is the model prediction value, is the number of samples; S333, model update: According to the error, the model parameters are optimized and adjusted through the back propagation algorithm. The specific formula of the back propagation algorithm is as follows:
[0034] in are model parameters, is the learning rate, is the gradient of the error with respect to the parameter; The security and privacy module includes a distributed encryption and dynamic key generation unit and a behavior anomaly detection and response unit; Distributed encryption and dynamic key generation units encrypt data through a distributed architecture, and each node has an independent encryption key; The behavior anomaly detection and response unit monitors the behavior of all devices in the system in real time and takes immediate response measures when unauthorized data access and communication behavior is detected; Response measures include isolating suspicious devices and reassigning security policies.
[0035] Specifically, the dynamic channel management and interference avoidance unit in the wireless communication module first automatically identifies channel congestion and potential interference problems by real-time monitoring of the wireless communication environment, and quickly switches to the optimal channel and reduces the transmission power to maintain communication stability. When the communication network is unstable and the bandwidth is limited, the asynchronous transmission and data cache unit will start the asynchronous transmission mode, temporarily store the data in the local cache, and give priority to transmitting these cached data after the network conditions are restored to ensure the integrity and timeliness of the data. After these data are transmitted to the central control module, the intelligent data stratification and multi-mode analysis unit automatically performs stratification processing according to the source and type of the data, and divides the data into real-time data layer, external data layer and historical data layer, and conducts comprehensive analysis in combination with weather forecasts and historical data to provide the system with comprehensive environmental monitoring results; in this process, through dynamic The model is continuously adjusted in step S331 of dynamic learning and updating. According to the result of step S332 of error calculation, the model parameters are optimized through the back propagation algorithm in step S333 to ensure that the prediction accuracy of the model is continuously improved. At the same time, the security and privacy module encrypts the data in a distributed manner through the distributed encryption and dynamic key generation unit. Each node has an independent encryption key to ensure the security of the data during transmission. When the behavior anomaly detection and response unit detects abnormal behavior of any device in the system, especially unauthorized data access and communication behavior, the system will immediately take response measures, including isolating suspicious devices and reallocating security policies to maintain the overall security of the system. This multi-module collaboration enables the system to maintain efficient operation in a dynamic and complex environment, ensuring the reliability of data transmission, the accuracy of data analysis and the robustness of system security.
[0036] Please see attached Figure 2 and attached Figure 4 The docking assembly includes a docking column 7, which is fixed at the bottom of a docking plate 5. A docking column 8 slides inside the docking column 7, a fixed column 9 is fixed inside the docking column 8, and a spring 10 is sleeved on the outer wall of the fixed column 9. One end of the spring 10 is fixed inside the docking column 8, and a magnet 11 is fixed on the other end of the spring 10. The fixed column 9 slides inside the magnet 11 and repels it. A sliding column 12 is fixed on one side of the magnet 11, and a ball 14 is arranged on one side of the sliding column 12. A limiting block 13 is fixed on the outer wall of the sliding column 12. An opening and closing plate 2 is arranged on one side of the protective shell 1, and a mounting plate 3 is fixed on the other side of the protective shell 1. A magnet 4 is embedded on the top of the protective shell 1.
[0037] Specifically, the replacement process of the sensor 15 is designed to be simple and quick to adapt to different monitoring needs and ensure the efficient operation of the entire system. When the sensor 15 needs to be replaced, the user can first open the opening and closing plate 2 and release the spring lock in position. This operation allows the entire docking plate 1 5 to be pulled out together with the docking plate 2 6, and at the same time pull the docking plate 1 5, while the docking plate 2 6 remains in place. During this process, the separation process of the docking column 1 7 and the docking column 2 8 is initiated. Specifically, as the docking plate 1 5 is pulled, the corresponding part of the ball 14 of the docking component will be squeezed from the inside of the system, and then through the transmission mechanism of the sliding column 12, the magnet 2 11 is forced to approach the fixed column 9. The fixed column 9 is partially immersed in the interior of the magnet 2 11 during this process. Repulsion is generated between the two due to magnetic repulsion. This repulsive force further compresses the volume space occupied by the spring 10, so that the docking column 1 7 and the docking column 2 8 can be separated smoothly; in the sensor 1 During the replacement process of sensor 15, the adapter socket on the sensor 15 and the docking plate 2 6 will also be detached as the docking plate is separated, so that the old sensor 15 can be easily taken out and the new sensor 15 can be installed. During the whole operation process, the design of the mechanical structure ensures the smoothness of docking and separation, making the replacement process of the sensor 15 efficient and seamless. During the installation of the new sensor 15, when the docking column 1 7 and the docking column 2 8 are docked in place, the ball 14 will still be squeezed by force during the assembly process. However, as the docking proceeds, when the docking column 1 7 and the docking column 2 8 are fully docked in place, the ball 14 in the system will automatically align to the preset position; at this time, the spring 10 uses its inherent elastic potential energy and the repulsive force generated by the magnet 2 11 to push the sliding column 12 and thereby push the ball 14 into the empty groove inside the docking column 7. This design greatly improves the adaptability and installation convenience of the equipment, ensuring that fast and reliable installation and sensor 15 replacement can be achieved in various complex environments.
[0038] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.
Claims
1. A combined low-power communication sensor device, characterized in that: include: A protective shell (1), one side of which is provided with an opening and closing plate (2); A docking plate 1 (5) is installed inside the protective shell (1), a docking plate 2 (6) is arranged at the bottom of the docking plate 1 (5), and a sensor (15) is arranged between the docking plate 1 (5) and the docking plate 2 (6); A docking assembly, which is installed between the first docking plate (5) and the second docking plate (6) and is used for quickly replacing the sensor (15); A PLC module (16) is installed between the first docking plate (5) and the second docking plate (6) and is used for intelligent control of the equipment.
2. A combined low-power communication sensor device according to claim 1, characterized in that: The PLC module (16) includes a low power design module, a modular sensor module, a wireless communication module, a central control module and a security and privacy module; The low power design module is used to minimize the energy consumption of the device, ensuring that the device can operate for a long time under extremely low energy consumption conditions; The modular sensor module is used for flexible combination and expansion of intelligent control sensors (15) to meet different monitoring requirements; The wireless communication module is used to transmit the collected data to the central control module through a low-power wireless communication protocol; The central control module centrally receives, processes and analyzes data from each sensor node, and provides remote monitoring, data analysis and system management functions; The security and privacy module is used to prevent unauthorized access and data leakage.
3. A combined low-power communication sensor device according to claim 2, characterized in that: The low power consumption design module includes an adaptive energy management unit and a distributed energy consumption optimization unit; The adaptive energy management unit is used to evaluate the availability of environmental energy in real time and dynamically switch energy sources; The real-time assessment uses multi-source energy sensing sensors to detect different types of environmental energy; The distributed energy consumption optimization unit is used to independently equip each sensor node with an energy consumption optimization subunit, which can adjust itself according to the priority of the current task and the remaining energy level; The regulation relies on a distributed energy consumption optimization formula, which is as follows: in is the adjusted energy consumption level, is the adjusted sampling frequency, is the adjusted communication frequency, is the baseline energy consumption level, is the base sampling frequency, is the reference communication frequency, is the task priority (1 to 10), is the remaining energy level (percentage) and For the environmental status.
4. A combined low-power communication sensor device according to claim 2, characterized in that: The modular sensor module includes an intelligent sensor aggregation and self-optimization unit and an environmental change perception and emergency response unit; The intelligent sensor aggregation and self-optimization unit is used to combine data from multiple sensors (15) so that the device can autonomously optimize the data collection strategy; The autonomous optimization data collection strategy includes the following steps: S1. Data aggregation and analysis: Collect data from multiple sensors (15) and perform preliminary aggregation processing. S2, weight allocation: dynamically adjust the weight of each sensor data according to the current environment status and task priority; S3. Forecast error evaluation: The system compares the current aggregated data with the historical data to evaluate the data change trend and fluctuation. It then calculates the forecast error by analyzing the difference between the current data and the historical trend. If the error is large, it means that the current environment may have changed significantly, and the data collection frequency needs to be increased to capture these changes. S4, collection frequency adjustment: starting from the baseline collection frequency, adjust the collection frequency according to the task priority and prediction error. The higher the task priority and the larger the prediction error, the higher the collection frequency. At the same time, consider the light and temperature to further adjust the collection frequency. S5. Energy level assessment and adjustment: Real-time monitoring of the remaining energy level. When the energy level is lower than the preset threshold, the system enters energy-saving mode. In energy-saving mode, the acquisition frequency is reduced to reduce energy consumption, but the most critical data is still captured. S6, dynamic optimization and execution: based on the above evaluation, dynamically adjust the data collection frequency of the sensor (15) and implement a new data collection strategy, while continuously monitoring the environmental status, energy level and data collection effect, and make further optimization adjustments based on real-time changes; S7. Self-learning and optimization: Record the effect and related data after each adjustment, gradually optimize the decision-making model through machine learning algorithms, and improve the accuracy and effect of future adjustments; In the step S4, the most critical data include abnormal data, trend data, threshold data and priority data; In the step S7, gradually optimizing the decision model by a machine learning algorithm includes the following steps: S11, Data collection and feature extraction: Collect data from all sensor nodes and extract features related to key data, including mean, variance, maximum, minimum, trend slope and abnormal points; then annotate the data to distinguish normal data from key data; S22. Train the machine learning model: Select the random forest learning model to identify and predict key data. Use historical data to train the model. The model will learn how to identify and classify key data. Then use cross-validation to evaluate the performance of the model to ensure that the model can accurately predict key data on new data. S33, real-time data prediction and optimization: the device obtains sensor data in real time and inputs it into the trained model for prediction. The model identifies key data in real time and marks the data as high priority data. Finally, based on the prediction results and energy consumption model, the system automatically adjusts the sampling frequency and communication frequency of the sensor (15); S44, Self-learning and iterative optimization: After each decision, the system uses the collected key data and actual environmental changes as feedback to update the model parameters. At the same time, the system regularly retrains the model, using new data and feedback. Through continuous learning and optimization, the system can gradually improve energy efficiency, while ensuring that key data is captured, while further reducing energy consumption; The environmental change sensing and emergency response unit is used to detect drastic changes in the environment in real time and immediately activate the emergency mode, increase the sampling frequency and data transmission rate to capture important environmental change data; The drastic changes include but are not limited to sudden temperature changes and vibrations; The important environmental change data include toxic gas monitoring data, abnormal temperature changes, equipment failure data, environmental disaster warning data and abnormal trend data.
5. A combined low-power communication sensor device according to claim 2, characterized in that: The wireless communication module includes a dynamic channel management and interference avoidance unit and an asynchronous transmission and data cache unit; The dynamic channel management and interference avoidance unit is used to monitor the wireless communication environment in real time and automatically switch to the optimal channel according to the channel congestion level. It also has an interference detection function. When potential wireless interference is detected, it will automatically reduce the transmission power and switch the frequency band; The asynchronous transmission and data cache unit is used to perform asynchronous transmission and cache processing on data when the communication network is unstable and the bandwidth is limited, and to send the cached data preferentially after the transmission condition is restored.
6. A combined low-power communication sensor device according to claim 2, characterized in that: The central control module includes an intelligent data stratification and multi-mode analysis unit and a self-optimization and learning algorithm unit; The intelligent data stratification and multi-mode analysis unit automatically performs stratification processing according to the source and type of data, and performs comprehensive analysis in combination with different data sources; The layered processing is specifically divided into a real-time data layer, an external data layer and a historical data layer; The different data sources include weather forecasts and historical data; The self-optimization and learning algorithm unit continuously optimizes its own analysis model through machine learning algorithms to improve the accuracy of predictions; The optimization step comprises the steps of: S331, Dynamic Learning and Update: Real-time Data Input: The latest sensor data and external data are input into the model to make predictions in real time; S332, error calculation: by comparing the model prediction results with the actual observation results, the prediction error E is calculated; in, is the actual observed value, is the model prediction value, is the number of samples; S333, model update: According to the error, the model parameters are optimized and adjusted through the back propagation algorithm. The specific formula of the back propagation algorithm is as follows: in are model parameters, is the learning rate, is the gradient of the error with respect to the parameters.
7. A combined low-power communication sensor device according to claim 2, characterized in that: The security and privacy module includes a distributed encryption and dynamic key generation unit and a behavior anomaly detection and response unit; The distributed encryption and dynamic key generation unit performs data encryption through a distributed architecture, and each node has an independent encryption key; The behavior anomaly detection and response unit monitors the behavior of all devices in the system in real time, and takes immediate response measures when unauthorized data access and communication behavior is detected; The response measures include isolating suspicious devices and redistributing security policies.
8. A combined low-power communication sensor device according to claim 1, characterized in that: The docking assembly comprises a docking column (7), wherein the docking column (7) is fixed at the bottom of a docking plate (5), a docking column (8) is slidably mounted inside the docking column (7), a fixing column (9) is fixed inside the docking column (8), a spring (10) is sleeved on the outer wall of the fixing column (9), one end of the spring (10) is fixed inside the docking column (8), a magnet (11) is fixed at the other end of the spring (10), and the fixing column (9) slides inside the magnet (11) and repels it.
9. A combined low-power communication sensor device according to claim 8, characterized in that: A sliding column (12) is fixed on one side of the second magnet (11), a ball (14) is provided on one side of the sliding column (12), and a limiting block (13) is fixed on the outer wall of the sliding column (12).
10. The combined low-power communication sensor device according to claim 1, characterized in that: An opening and closing plate (2) is provided on one side of the protective shell (1), a mounting plate (3) is fixed on the other side of the protective shell (1), and a magnet 1 (4) is embedded on the top of the protective shell (1).