Dynamically energy regulated low power distributed sensor network

Abstract

The application provides a kind of dynamic energy regulation low-power distributed sensor network, node layer is distributed with multiple sensor nodes, responsible for collecting environmental data respectively;Network layer is connected with node layer, and is provided with dynamic energy management module, can adjust node energy according to each sensor node energy and task priority;Still be provided with distributed collaborative calibration module, use distributed algorithm, by neighborhood node collaborative mechanism, compare each node and neighborhood node data, automatically adjust calibration parameter, then upload data to cloud platform layer.Cloud platform layer analyzes, stores and assists sensor node calibration received environmental data.The application realizes the efficient monitoring and real-time calibration of each sensor under wide area environment, and overcomes the energy consumption bottleneck, insufficient measurement accuracy and low communication efficiency and other problems in the prior art.The system realizes efficient, accurate, low-power wide area monitoring through the comprehensive application of low-power sensor technology, dynamic energy regulation technology, automatic calibration technology, low-power wireless communication technology and intelligent cloud adjustment mechanism.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) technology, and in particular to a low-power distributed sensor network with dynamic energy regulation. Background Technology

[0002] In the current fields of the Internet of Things (IoT) and Wireless Sensor Networks (WSN), the application of distributed sensor networks is becoming increasingly widespread, especially in environmental monitoring, industrial automation, and smart city construction, demonstrating great potential. While existing sensor networks can meet the needs of small-scale monitoring to a certain extent, they have many shortcomings in wide-area distributed scenarios. First, sensor nodes typically rely on battery power. Although some methods have attempted to replace battery power with solar or vibration energy harvesting, the randomness and regionality of energy harvesting lead to uneven energy distribution among nodes. Furthermore, energy depletion can cause node failure, affecting the lifespan of the entire network. Moreover, current technologies struggle to achieve efficient energy utilization and dynamic energy balance among nodes, further exacerbating the energy consumption bottleneck.

[0003] Secondly, sensors operating for extended periods are susceptible to measurement drift and errors due to factors such as temperature and pressure in complex environments. Traditional centralized calibration methods are not only costly, power-consuming, and inefficient, but also ill-suited to the demands of large-scale distributed networks. Furthermore, in wide-area monitoring scenarios, the communication load between nodes is high, and existing network topology adjustment and optimization techniques struggle to address the dynamic demands arising from node failures or environmental changes, leading to high communication latency, increased energy consumption, and consequently impacting network reliability and efficiency. Simultaneously, existing monitoring systems have limited remote management capabilities for node status and lack flexible dynamic adjustment mechanisms, making it difficult to meet the real-time monitoring needs of complex scenarios. Summary of the Invention

[0004] In view of the shortcomings of the prior art described above, the purpose of this invention is to provide a low-power distributed sensor network with dynamic energy regulation, which solves the technical problems of existing sensor network technology such as high cost, high power consumption, low efficiency, difficulty in supporting wide-area monitoring, limited remote management capability of node status, lack of flexible dynamic adjustment mechanism, and difficulty in meeting the real-time monitoring and automatic calibration requirements of complex scenarios.

[0005] To achieve the above and other related objectives, this invention provides a low-power distributed sensor network with dynamic energy regulation. The network includes: a node layer, in which multiple sensor nodes are distributed and deployed to collect environmental data; a network layer, communicatively connected to the node layer, including a dynamic energy management module and a distributed collaborative calibration module; wherein the dynamic energy management module receives environmental data from each sensor node and dynamically adjusts the energy of each sensor node based on its energy and task priority; the distributed collaborative calibration module, connected to the dynamic energy management module, uses a distributed algorithm and a neighborhood node collaboration mechanism to automatically adjust calibration parameters by comparing the data of each sensor node with its respective neighboring nodes, and uploads the environmental data from each sensor node; and a cloud platform layer, communicatively connected to the network layer, analyzes and stores the uploaded environmental data, and assists the sensor nodes in calibration.

[0006] In one embodiment of the present invention, each sensor node includes: a power supply module, a multimodal low-power sensor, and a microcontroller; wherein, the power supply module is used to provide power to the sensor node; the multimodal low-power sensor is connected to the power supply module and is used to collect environmental data; wherein, the multimodal low-power sensor includes at least: a sensor for detecting carbon dioxide concentration; the microcontroller is connected to the multimodal low-power sensor and is used to perform preliminary processing and filtering of the environmental data, and transmit the processed environmental data externally.

[0007] In one embodiment of the present invention, the multimodal low-power sensor further includes one or more of a temperature sensor, a humidity sensor, and a pressure sensor, for real-time monitoring of temperature data, humidity data, and pressure data to construct an environmental compensation model.

[0008] In one embodiment of the present invention, the energy supply module includes: a controller, a solar panel, and a battery; wherein, the controller is connected to the solar panel and the battery, and is used to determine, based on historical data and current environmental conditions, whether photovoltaic power generation can meet the power supply conditions required for the normal operation of the sensor node; if it meets the conditions, the solar panel directly supplies power to the sensor node; if it does not meet the conditions, the battery supplies power to the sensor node.

[0009] In one embodiment of the present invention, the dynamic energy management module includes: an energy prediction unit, used to use a time series analysis-based model to predict the energy distribution of each sensor node for a future time period based on the historical energy consumption and current remaining energy of each sensor node to obtain the predicted energy value of each sensor node; and a dynamic adjustment unit, connected to the energy prediction unit, used to dynamically adjust the task allocation, sampling frequency, and working mode of each sensor node based on the predicted energy value of each sensor node and the environmental data transmitted in real time by each sensor node in each adjustment cycle according to the energy adjustment strategy.

[0010] In one embodiment of the present invention, the energy regulation strategy includes: a sampling frequency adjustment strategy, including: when the current remaining energy or energy prediction value of a sensor node is less than a set threshold, activating the low-power mode of the sensor node, and reducing the sampling frequency of non-critical tasks of the sensor node based on the environmental data currently transmitted by the sensor node; and a task priority adjustment strategy, including: determining the current task priority of each sensor node based on the frequency of the environmental data currently transmitted by each sensor node, and dividing each sensor into critical nodes and auxiliary nodes in combination with the energy prediction value of each sensor node, dynamically adjusting the task allocation and working mode of critical nodes and auxiliary nodes, so that critical nodes take priority in undertaking critical tasks, and only critical nodes are allowed to transmit data when the energy demand cannot meet the data transmission of all nodes.

[0011] In one embodiment of the present invention, each sensor node shares energy through a wireless energy transfer principle based on magnetic resonance coupling.

[0012] In one embodiment of the present invention, the distributed collaborative calibration module includes: a deviation judgment module, a calibration module, and a data upload module; wherein, the deviation judgment module is used to compare the environmental data of the current sensor node with the environmental data of the neighboring nodes of the sensor node to determine whether there is a deviation; the calibration module is used to automatically calibrate using a distributed algorithm and a domain collaboration mechanism between nodes if a deviation is determined, and send the calibrated data to the deviation judgment module to determine whether there is a deviation again, until the data is without deviation; the data upload module uploads the environmental data without deviation to the cloud platform layer if no deviation is determined.

[0013] In one embodiment of the present invention, the cloud platform layer is used to receive environmental data from the network layer for deviation judgment and analysis; if it is determined that the data has a deviation, the analysis result is sent to the calibration module of the network layer so that the calibration module can assist in calibration by combining the analysis result; if it is determined that the data has no deviation, a low-power data processing algorithm is used to analyze the environmental data and store it.

[0014] In one embodiment of the present invention, the sensor node performs long-distance transmission via LoRa low-power wide area network and short-distance communication via Bluetooth low-power technology, and combines an optimized multi-hop routing algorithm to perform data hopping between nodes.

[0015] As described above, this invention is a low-power distributed sensor network with dynamic energy regulation, offering the following advantages: The node layer of this invention features a distributed deployment of multiple sensor nodes, each responsible for collecting environmental data. The network layer, connected to the node layer, includes a dynamic energy management module that dynamically adjusts node energy based on the energy and task priority of each sensor node. A distributed collaborative calibration module is also included, employing a distributed algorithm and a neighboring node collaboration mechanism to compare data between each node and its neighbors, automatically adjusting calibration parameters before uploading the data to the cloud platform layer. The cloud platform layer analyzes and stores the received environmental data and assists in sensor node calibration. This invention achieves efficient monitoring and real-time calibration of sensors in a wide-area environment, while overcoming existing technologies' energy consumption bottlenecks, insufficient measurement accuracy, and low communication efficiency. This system achieves efficient, accurate, and low-energy-consumption wide-area monitoring through the comprehensive application of low-power sensor technology, dynamic energy regulation technology, automatic calibration technology, low-power wireless communication technology, and an intelligent cloud-based adjustment mechanism. Attached Figure Description

[0016] Figure 1 The diagram shown is a structural schematic of a low-power distributed sensor network with dynamic energy regulation according to an embodiment of the present invention.

[0017] Figure 2 The diagram shown is a structural schematic of a sensor node according to an embodiment of the present invention.

[0018] Figure 3 The diagram shown is a schematic representation of the energy supply process in one embodiment of the present invention.

[0019] Figure 4 The diagram shown is a structural schematic of a dynamic energy management module according to an embodiment of the present invention.

[0020] Figure 5 The diagram shown is a schematic of the sensor node calibration process in one embodiment of the present invention. Detailed Implementation

[0021] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0022] It should be noted that in the following description, reference is made to the accompanying drawings, which illustrate several embodiments of the invention. It should be understood that other embodiments may also be used, and changes in mechanical composition, structure, electrical system, and operation may be made without departing from the spirit and scope of the invention. The following detailed description should not be considered limiting, and the scope of the embodiments of the invention is defined only by the claims of the published patents. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention. Spatially related terms, such as “upper,” “lower,” “left,” “right,” “below,” “below,” “lower part,” “above,” “upper part,” etc., may be used herein to illustrate the relationship between one element or feature shown in the figures and another element or feature.

[0023] Throughout this specification, when it is said that a part is "connected" to another part, this includes not only "direct connection" but also "indirect connection" by placing other elements in between. Furthermore, when it is said that a part "includes" a certain constituent element, unless otherwise stated otherwise, this does not exclude other constituent elements, but rather means that other constituent elements may also be included.

[0024] The terms "first," "second," and "third," etc., used herein are for the purpose of describing various parts, components, regions, layers, and / or segments, but are not limiting. These terms are used only to distinguish one part, component, region, layer, or segment from others. Therefore, the "first part," "component," "region," "layer," or "segment" described below may refer to a "second part," "component," "region," "layer," or "segment" without departing from the scope of this invention.

[0025] Furthermore, as used herein, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context indicates otherwise. It should be further understood that the terms “comprising,” “including,” indicate the presence of the stated feature, operation, element, component, item, kind, and / or group, but do not preclude the presence, occurrence, or addition of one or more other features, operations, elements, components, items, kinds, and / or groups. The terms “or” and “and / or” as used herein are interpreted as inclusive, or mean any one or any combination thereof. Thus, “A, B, or C” or “A, B, and / or C” means “any one of: A; B; C; A and B; A and C; B and C; A, B, and C.” Exceptions to this definition arise only when combinations of elements, functions, or operations are inherently mutually exclusive in some manner.

[0026] This invention provides a low-power distributed sensor network with dynamic energy regulation. The node layer has multiple sensor nodes distributed across the network, each responsible for collecting environmental data. The network layer, connected to the node layer, includes a dynamic energy management module that dynamically adjusts node energy based on the energy of each sensor node and its task priority. A distributed collaborative calibration module is also included, employing a distributed algorithm and a neighboring node collaboration mechanism to compare data between each node and its neighbors, automatically adjusting calibration parameters before uploading the data to the cloud platform layer. The cloud platform layer analyzes and stores the received environmental data and assists in sensor node calibration. This invention achieves efficient monitoring and real-time calibration of sensors in a wide-area environment, while overcoming the energy consumption bottlenecks, insufficient measurement accuracy, and low communication efficiency problems of existing technologies. This system achieves efficient, accurate, and low-energy-consumption wide-area monitoring through the comprehensive application of low-power sensor technology, dynamic energy regulation technology, automatic calibration technology, low-power wireless communication technology, and an intelligent cloud-based adjustment mechanism.

[0027] The present invention will now be described in detail with reference to the accompanying drawings, so that those skilled in the art can readily implement it. The present invention can be embodied in many different forms and is not limited to the embodiments described herein.

[0028] like Figure 1 This diagram illustrates the structure of a low-power distributed sensor network with dynamic energy regulation, as shown in an embodiment of the present invention.

[0029] The network includes:

[0030] Node layer 1 consists of multiple sensor nodes S1 to SN, which are distributed in a distributed manner; where N is a positive integer; each sensor node is used to collect environmental data.

[0031] Network layer 2, which communicates with node layer 1, includes: a dynamic energy management module and a distributed collaborative calibration module;

[0032] The dynamic energy management module is used to receive environmental data from each sensor node and dynamically adjust the energy of each sensor node based on the energy of each sensor node and the task priority.

[0033] The distributed collaborative calibration module is connected to the dynamic energy management module. It is used to automatically adjust the calibration parameters by comparing the data of each sensor node with its respective neighboring nodes through a distributed algorithm and a collaborative mechanism of neighboring nodes, and to upload the environmental data of each sensor node.

[0034] The cloud platform layer 3 communicates with the network layer 2 and is used to analyze and store the uploaded environmental data, as well as assist sensor nodes in calibration.

[0035] In one embodiment, such as Figure 2 Each sensor node includes: a power supply module, a multimodal low-power sensor, and a microcontroller;

[0036] The energy supply module is used to provide power to the sensor nodes.

[0037] A multimodal low-power sensor is connected to the power supply module for collecting environmental data; wherein the multimodal low-power sensor is a high-sensitivity, low-power sensor, and includes at least: a sensor for detecting carbon dioxide concentration for real-time monitoring of carbon dioxide concentration data;

[0038] The microcontroller is connected to the multimodal low-power sensor and is used to perform preliminary processing and filtering of environmental data, and to transmit the processed environmental data outward, for example, to transmit data with other nodes or network layer 2.

[0039] In one embodiment, the multimodal low-power sensor further includes one or more of a temperature sensor, a humidity sensor, and a pressure sensor, used to monitor temperature, humidity, and pressure data in real time to build an environmental compensation model and improve measurement accuracy. In actual measurements, changes in ambient temperature, humidity, and pressure can affect the measurement results. By building a compensation model, environmental interference can be effectively reduced, thereby improving measurement accuracy. The sensor dynamically adjusts the sampling frequency according to different scenarios, extending the node's battery life.

[0040] In one embodiment, the energy supply module includes a controller, a solar panel, and a battery; wherein the controller is connected to the solar panel and the battery; the solar panel is a high-efficiency monocrystalline silicon solar panel, which can work stably under different light intensities and provide sufficient power to the sensor node.

[0041] like Figure 3The controller determines, based on historical data and current environmental conditions, whether photovoltaic power generation can meet the power supply requirements for the normal operation of the sensor node. The historical data may cover the power supply status of the sensor node in different time periods and environments; the current environmental conditions include factors such as light intensity and temperature, all of which affect the effectiveness of photovoltaic power generation.

[0042] If the power supply requirements of the sensor nodes are met, the solar panels will directly power the sensor nodes. This method is not only environmentally friendly and energy-saving, but also makes full use of solar energy resources.

[0043] If the power supply requirements of the sensor nodes are not met, the battery will power the sensor nodes. For example, when the light intensity is below 100 lx (lux), it will automatically switch to backup battery power mode.

[0044] This design maximizes the use of solar energy while ensuring that sensor nodes can operate normally even when solar energy is insufficient, thus improving the reliability and stability of the system.

[0045] In one embodiment, the microcontroller of the sensor node performs long-distance transmission via LoRa Low-Power Wide Area Network and short-range communication via Bluetooth Low Energy technology; it also uses an optimized multi-hop routing algorithm for data transfer between nodes. When a node needs to send data but the target node is far away, the node will select a suitable intermediate node as the data forwarding path according to the algorithm. This multi-hop routing algorithm is optimized to dynamically adjust the routing path based on real-time network conditions, such as the remaining energy of nodes and the quality of communication links.

[0046] By integrating LoRa and BLE communication technologies and employing an optimized multi-hop routing algorithm, the system enables sensor nodes to transmit data efficiently and stably in various distance scenarios, meeting the needs of diverse application scenarios and promoting the widespread application of sensor networks in various fields.

[0047] In one embodiment, Figure 4 The dynamic energy management module includes:

[0048] The energy prediction unit is used to obtain the predicted energy value of each sensor node by using a time series analysis-based model to predict the energy distribution of each sensor node for a future time period based on the node's historical energy consumption and current remaining energy.

[0049] The energy prediction module uses time-series analysis-based models (such as the ARIMA model) to predict the future energy levels of nodes. The model input data includes: historical node energy consumption: This includes the energy consumption of the sensor node over a past period, reflected by different light intensities and external environmental factors. Analyzing this historical data reveals patterns and trends in node energy consumption. Current remaining energy is reflected by the current light intensity and environmental factors; light intensity directly affects the power generation efficiency of solar panels, thus affecting the energy supply of the sensor node. Therefore, light intensity is one of the important factors in predicting energy. Environmental factors: Such as temperature and workload. Temperature affects battery performance and sensor power consumption, while workload directly determines the energy consumption of the sensor node.

[0050] Assuming the energy state of a node is represented by a time series, its energy changes can be predicted using the following formula:

[0051]

[0052] In the formula, E i (t+Δt) represents the predicted energy value of the i-th sensor node at time t+Δt; E i The remaining energy of the i-th sensor node is obtained from the current light intensity and current environmental factors. …are model parameters; ∈ t This is the random error term.

[0053] The dynamic energy management module at the network layer can identify nodes that may experience low energy based on energy prediction values ​​and formulate corresponding resource scheduling strategies, such as adjusting energy allocation and optimizing task arrangements, to prevent nodes from failing due to insufficient energy.

[0054] The dynamic adjustment unit is used to dynamically adjust the task allocation, sampling frequency, and working mode of each sensor node based on the energy prediction value of each sensor node obtained by the energy prediction unit and the environmental data transmitted in real time by each sensor node in each adjustment cycle, according to the energy adjustment strategy.

[0055] In one embodiment, the energy regulation strategy includes:

[0056] Sampling frequency adjustment strategy: When the current remaining energy or predicted energy value of a sensor node is less than a preset threshold, the system will activate the low-power mode of that sensor node. This threshold is to ensure that the node can take timely measures when energy is insufficient, preventing failure due to energy depletion. In low-power mode, based on the environmental data currently transmitted by the sensor node, the sampling frequency of non-critical tasks of that node is reduced. Non-critical tasks refer to tasks that have a minimal impact on the overall system functionality; reducing their sampling frequency can effectively reduce the node's energy consumption without affecting the system's main functions.

[0057] The frequency adjustment formula is:

[0058] f′ NC =f NC (1-α), α∈(0,1); (2)

[0059] In the formula, f′ NC The reduced sampling frequency, i.e., the adjusted sampling frequency; f NC This is the sampling frequency before adjustment, and α is an adjustment coefficient that is dynamically adjusted based on the remaining energy of the node. The less energy a node has remaining, the larger the value of α will be, thus increasing the reduced sampling frequency f′. NC Lower, to further save energy.

[0060] The task priority adjustment strategy includes: the dynamic adjustment unit determines the current task priority of each sensor node based on the frequency of environmental data transmitted. Typically, nodes with higher data transmission frequencies have higher task priorities, as this may indicate that the node is performing a task critical to the system.

[0061] Based on the predicted energy values ​​and task priorities of each sensor node, the dynamic adjustment unit categorizes all sensor nodes into critical nodes and auxiliary nodes. Critical nodes are typically those with relatively abundant energy and high task priority; they play a core role in the system, reliably undertaking critical tasks and ensuring the normal operation of core system functions. Auxiliary nodes, on the other hand, have relatively less energy or lower task priority; their main role is to assist critical nodes in completing some tasks, provided their own energy allows. By dynamically adjusting the task allocation type, weighting, and operating mode of critical and auxiliary nodes, the energy of each sensor node is regulated according to the assigned node type.

[0062] When adjusting the task allocation type and weight of key nodes and auxiliary nodes, the system prioritizes the execution of high-priority tasks and first allocates key tasks to key nodes. The workload of key nodes will be dynamically adjusted according to the load balancing strategy.

[0063] The task distribution formula in the load balancing strategy is as follows:

[0064] T j =T j +β·(T i -T crit ),T i <T crit (3)

[0065] In the formula, T i The current task load of the node; T crit β is the threshold for critical task load; β is the sharing ratio coefficient, which determines the proportion of tasks that a node shares with other nodes; T j This indicates the new task load of the node after the task is assigned.

[0066] If the node's task load T i Below the critical task load threshold T crit Then this node has the ability to share the tasks of other nodes. The amount of task shared is determined by β·(T) i -T crit The task load T after distribution will be determined. j The original task load T j In addition to the shared workload, auxiliary nodes also take on the remaining tasks, provided energy permits. Through a real-time scheduling algorithm, the system dynamically adjusts the task ratio between the two types of nodes to ensure efficient operation.

[0067] The adjustment of the operating modes of critical nodes and auxiliary nodes determines whether sensor nodes transmit data. When energy demand cannot meet the data transmission needs of all nodes, only critical nodes are allowed to transmit data.

[0068] In one embodiment, in many sensor networks or distributed device systems, the energy consumption of each node is uneven. Some nodes may deplete their energy rapidly due to heavy workloads or location factors, while others may have surplus energy. The introduction of wireless energy sharing technology aims to solve this energy imbalance problem by wirelessly transferring energy from surplus nodes to energy-deficient nodes, thereby improving the overall system's operating efficiency and lifespan. Combined with wireless energy sharing technology, sensor nodes achieve efficient energy sharing over a medium range through wireless energy transmission based on magnetic resonance coupling. Magnetic resonance coupling creates magnetic field resonance through the resonant coils at the transmitting and receiving ends, enabling energy to be efficiently transferred from energy-sufficient nodes to low-energy nodes, achieving local energy balance.

[0069] In one embodiment, the distributed collaborative calibration module includes: a deviation judgment module, a calibration module, and a data upload module;

[0070] The deviation judgment module is used to compare the environmental data of the current sensor node with the environmental data of the neighboring nodes of the sensor node to determine whether there is a deviation. Through this comparison method, it is possible to detect data anomalies that may occur in a single sensor node in a timely manner, providing a basis for subsequent data calibration and avoiding system decision-making errors caused by erroneous data entering subsequent processing stages.

[0071] The calibration module is used to perform automatic calibration when deviations are detected, utilizing distributed algorithms and inter-node domain collaboration mechanisms. The distributed algorithm integrates data from neighboring nodes, calculates weighted averages based on node trustworthiness, and calibrates the deviation data. Simultaneously, it monitors node status for fault detection and isolation, and dynamically adjusts the calibration strategy based on real-time network conditions. Nodes interact with each other through the neighborhood collaboration mechanism, exchanging data and status information and collaboratively participating in calibration calculations. Trust assessments are conducted on neighboring nodes based on historical performance and other factors, prioritizing data from high-trust nodes to assist calibration. This achieves efficient and accurate automatic calibration, ensuring the accuracy and reliability of sensor network data. The calibrated data is then sent to the deviation detection module for further evaluation until the data is error-free.

[0072] If the data upload module determines that there is no deviation, it will upload the environmental data that has been determined to be without deviation to the cloud platform layer 3.

[0073] In one embodiment, the cloud platform layer 3 is used to receive environmental data from the network layer 2 for deviation judgment and analysis. If the data is determined to be biased, the analysis result is sent to the calibration module of the network layer 2 for calibration assistance based on the analysis result. If the data is determined to be unbiased, a low-power data processing algorithm is used to analyze the environmental data and store it. The low-power data processing algorithm used, such as edge computing and machine learning technology, can quickly analyze and store the uploaded data.

[0074] The network layer 2 and cloud platform layer 3 jointly implement the calibration process, which is as follows: Figure 5 As shown, the specific steps include:

[0075] Step S1: The deviation judgment module compares the environmental data of the current sensor node with the environmental data of its neighboring nodes to determine if there is a deviation. If there is a deviation, proceed to step S2; if there is no deviation, proceed to step S3.

[0076] Step S2: Automatic calibration is performed using a distributed algorithm and inter-node domain collaboration mechanism through the calibration module, and the calibration data is returned to step S1;

[0077] Step S3: Upload the environmental data that is determined to be without deviation to the cloud platform layer 3 through the data upload module. The cloud platform layer 3 will then perform deviation judgment and analysis to further monitor whether there is any deviation in the data. If there is a deviation, proceed to step S4; if there is no deviation, proceed to step S5.

[0078] Step S4: Send the analysis results to the calibration module of network layer 2 through the cloud platform layer to assist in calibration, and return the calibration data to step S1;

[0079] Step S5: Analyze and store environmental data using low-power data processing algorithms at the cloud platform layer.

[0080] The present invention has the following advantages over the prior art:

[0081] 1. Significant energy consumption optimization effect

[0082] This invention uses dynamic energy regulation technology to balance solar power and battery power, and uses energy management algorithms to regulate the energy distribution between high-energy nodes and low-energy nodes, preventing nodes from failing due to energy depletion, thereby extending the lifespan of the entire network.

[0083] 2. High precision and low error performance

[0084] This invention dynamically adjusts carbon dioxide concentration data and performs error correction through distributed algorithms and a neighborhood cooperation mechanism between nodes, ensuring the consistency and accuracy of data across the entire network, achieving automatic calibration, and effectively solving the problem of decreased accuracy of traditional sensors.

[0085] 3. Reduce communication and processing power consumption

[0086] This invention integrates LoRa Low-Power Wide-Area Network (LPWAN) and Bluetooth Low Energy (BLE) technologies. LoRa is used for wide-area transmission when the distance between nodes is long, while BLE is used for short distances to reduce power consumption. Simultaneously, an intelligent cloud-based adjustment mechanism reduces data transmission volume and utilizes edge computing to alleviate the computational burden on the cloud.

[0087] 4. Efficient remote monitoring and management

[0088] This invention, through the remote adjustment capabilities of a cloud platform, allows users to achieve real-time monitoring, data analysis, and optimized management of the entire network, providing functional support for environmental governance, industrial monitoring, and disaster early warning.

[0089] In summary, the low-power distributed sensor network with dynamic energy regulation of this invention features a node layer with multiple sensor nodes distributed across the node layer, each responsible for collecting environmental data. The network layer, connected to the node layer, includes a dynamic energy management module that dynamically adjusts node energy based on the energy and task priority of each sensor node. A distributed collaborative calibration module is also included, employing a distributed algorithm and a neighboring node collaboration mechanism to compare data between each node and its neighbors, automatically adjusting calibration parameters before uploading the data to the cloud platform layer. The cloud platform layer analyzes and stores the received environmental data and assists in sensor node calibration. This invention achieves efficient monitoring and real-time calibration of sensors in a wide-area environment, while overcoming the energy consumption bottlenecks, insufficient measurement accuracy, and low communication efficiency of existing technologies. This system achieves efficient, accurate, and low-energy-consumption wide-area monitoring through the comprehensive application of low-power sensor technology, dynamic energy regulation technology, automatic calibration technology, low-power wireless communication technology, and an intelligent cloud-based adjustment mechanism. Therefore, this invention effectively overcomes the various shortcomings of existing technologies and has high industrial application value.

[0090] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A low-power distributed sensor network with dynamic energy regulation, characterized in that, The network includes: The node layer consists of multiple sensor nodes deployed in a distributed manner, used to collect environmental data separately. The network layer, which communicates with the node layer, includes: a dynamic energy management module and a distributed collaborative calibration module; The dynamic energy management module is used to receive environmental data from each sensor node and dynamically adjust the energy of each sensor node based on the energy of each sensor node and the task priority. The distributed collaborative calibration module is connected to the dynamic energy management module. It is used to automatically adjust the calibration parameters by comparing the data of each sensor node with its respective neighboring nodes through a distributed algorithm and a collaborative mechanism of neighboring nodes, and to upload the environmental data of each sensor node. The cloud platform layer communicates with the network layer to analyze and store uploaded environmental data and assist sensor nodes in calibration. The energy supply module includes a controller, a solar panel, and a battery. The controller, connected to the solar panel and the battery, is used to determine, based on historical data and current environmental conditions, whether photovoltaic power generation can meet the power supply requirements for the normal operation of the sensor node. If the requirements are met, the solar panel directly supplies power to the sensor node; if not, the battery supplies power to the sensor node. The dynamic energy management module includes: The energy prediction unit is used to predict the energy distribution of each sensor node in the future time period by using a time series analysis-based model, based on the node's historical energy consumption and current remaining energy. Assuming the energy state of a node is represented by a time series, its energy changes can be predicted using the following formula: ； In the formula, To represent the i-th sensor node in The predicted energy value at that moment; The remaining energy of the i-th sensor node is obtained from the current light intensity and current environmental factors. , ... are model parameters; This is the random error term; The dynamic adjustment unit, connected to the energy prediction unit, is used to dynamically adjust the task allocation, sampling frequency and working mode of each sensor node based on the energy prediction value of each sensor node and the environmental data transmitted in real time by each sensor node in each adjustment cycle, according to the energy adjustment strategy. The energy regulation strategy includes: The sampling frequency adjustment strategy includes: when the current remaining energy or energy prediction value of the sensor node is less than a set threshold, activating the low-power mode of the sensor node, and reducing the sampling frequency of the non-critical tasks of the sensor node based on the environmental data currently transmitted by the sensor node. The frequency adjustment formula is: ； In the formula, This refers to the reduced sampling frequency, i.e., the adjusted sampling frequency. This is the sampling frequency before adjustment. It is an adjustment coefficient that is dynamically adjusted based on the remaining energy at the node. The task priority adjustment strategy includes: determining the current task priority of each sensor node based on the frequency of the environmental data currently transmitted by each sensor node, and dividing each sensor into key nodes and auxiliary nodes based on the energy prediction value of each sensor node, dynamically adjusting the task allocation and working mode of key nodes and auxiliary nodes, so that key nodes take the lead in undertaking key tasks, and only key nodes are allowed to transmit data when the energy demand cannot meet the data transmission of all nodes.

2. The low-power distributed sensor network with dynamic energy regulation according to claim 1, characterized in that, Each sensor node includes: a power supply module, a multimodal low-power sensor, and a microcontroller; The energy supply module is used to provide electrical energy to the sensor nodes. The multimodal low-power sensor is connected to the power supply module and is used to collect environmental data; wherein, the multimodal low-power sensor includes at least: a sensor for detecting carbon dioxide concentration; The microcontroller is connected to the multimodal low-power sensor and is used to perform preliminary processing and filtering of environmental data, and to transmit the processed environmental data to the outside.

3. The low-power distributed sensor network with dynamic energy regulation according to claim 2, characterized in that, The multimodal low-power sensor also includes one or more of a temperature sensor, a humidity sensor, and a pressure sensor, used to monitor temperature data, humidity data, and pressure data in real time to build an environmental compensation model.

4. The low-power distributed sensor network with dynamic energy regulation according to claim 1, characterized in that, Each sensor node shares energy through a wireless energy transfer principle based on magnetic resonance coupling.

5. The low-power distributed sensor network with dynamic energy regulation according to claim 1, characterized in that, The distributed collaborative calibration module includes: a deviation judgment module, a calibration module, and a data upload module; The deviation judgment module is used to compare the environmental data of the current sensor node with the environmental data of the neighboring nodes of the sensor node to determine whether there is a deviation. The calibration module is used to automatically calibrate the data when a deviation is detected, using a distributed algorithm and a domain collaboration mechanism between nodes. The calibrated data is then sent to the deviation detection module to check for deviation again until the data is free of deviation. If the data upload module determines that there is no deviation, it will upload the environmental data that is determined to be without deviation to the cloud platform layer.

6. The low-power distributed sensor network with dynamic energy regulation according to claim 5, characterized in that, The cloud platform layer is used to receive environmental data from the network layer for deviation judgment and analysis; if the data is determined to be biased, the analysis results are sent to the calibration module of the network layer so that the calibration module can assist in calibration by combining the analysis results; if the data is determined to be unbiased, a low-power data processing algorithm is used to analyze the environmental data and store it.

7. The low-power distributed sensor network with dynamic energy regulation according to claim 1, characterized in that, The sensor nodes use LoRa low-power wide area network for long-distance transmission and Bluetooth low-power technology for short-distance communication, and combine an optimized multi-hop routing algorithm to perform data hopping between nodes.

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