Low-power-consumption distributed sensor network with dynamic energy regulation

Abstract

The invention provides a low-power-consumption distributed sensor network for dynamic energy adjustment, and the network is characterized in that a plurality of sensor nodes are deployed in a node layer in a distributed manner, and are responsible for collecting environment data; the network layer is connected with the node layer, is provided with a dynamic energy management module, and can dynamically adjust node energy according to node energy of each sensor and task priority; the system is also provided with a distributed collaborative calibration module, compares data of each node and a neighborhood node by applying a distributed algorithm and borrowing a neighborhood node collaborative mechanism, automatically adjusts calibration parameters, and then uploads the data to the cloud platform layer. And the cloud platform layer analyzes and stores the received environment data and assists the sensor nodes in calibration. According to the invention, high-efficiency monitoring and real-time calibration of each sensor in a wide-area environment are realized, and the problems of energy consumption bottleneck, insufficient measurement precision, low communication efficiency and the like in the prior art are solved. According to the system, through comprehensive application of a low-power-consumption sensor technology, a dynamic energy adjustment technology, an automatic calibration technology, a low-power-consumption wireless communication technology and an intelligent cloud adjustment mechanism, efficient, accurate and low-energy-consumption wide-area monitoring is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of the Internet of Things, and particularly to a low-power distributed sensor network with dynamic energy regulation. Background Art

[0002] In the current fields of the Internet of Things (IoT) and wireless sensor networks (WSNs), distributed sensor networks are increasingly widely used, especially showing great potential in environmental monitoring, industrial automation, smart city construction, etc. Although existing sensor networks can meet the monitoring requirements of a small range to a certain extent, there are many deficiencies in wide-area distribution scenarios. First of all, sensor nodes usually rely on battery power supply. Although some methods attempt to replace battery power supply through solar energy or vibration energy harvesting, the randomness and regionality of energy harvesting lead to uneven energy distribution among nodes. At the same time, energy depletion will also cause node failure, affecting the life of the entire network. Moreover, it is difficult for existing technologies to achieve efficient energy utilization and dynamic energy balance among nodes, further exacerbating the energy consumption bottleneck problem.

[0003] Secondly, sensors operating for a long time are prone to measurement drift and errors under the influence of factors such as temperature and pressure in a complex environment. Traditional centralized calibration methods are not only costly, power-consuming, and inefficient, but also difficult to meet the requirements of large-scale distributed networks. In addition, in wide-area monitoring scenarios, the communication load between nodes is large, and existing network topology adjustment and optimization technologies are difficult to cope with the dynamic requirements brought about by node failure or environmental changes, resulting in high communication latency and increased energy consumption, thereby affecting the reliability and efficiency of the network. At the same time, the existing monitoring system has limited remote management capabilities for node status and lacks a flexible dynamic adjustment mechanism, making it difficult to meet the real-time monitoring requirements of complex scenarios. Summary of the Invention

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

[0005] To achieve the above object and other related objects, the present invention provides a low-power distributed sensor network with dynamic energy regulation. The network includes: a node layer, where multiple sensor nodes are distributed to collect environmental data respectively; 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 is configured to receive environmental data from each sensor node and dynamically regulate the energy of each sensor node based on the energy and task priority of each sensor node; the distributed collaborative calibration module is connected to the dynamic energy management module and is configured to use a distributed algorithm to automatically adjust calibration parameters by comparing the data of each sensor node with that of its respective neighboring nodes through a neighborhood node collaboration mechanism, and upload the environmental data of each sensor node; a cloud platform layer, communicatively connected to the network layer, for analyzing and storing the uploaded environmental data and assisting the sensor nodes in calibration.

[0006] In an embodiment of the present invention, each sensor node includes: an energy supply module, a multi-modal low-power sensor, and a microcontroller; wherein, the energy supply module is configured to supply electrical energy to the sensor node; the multi-modal low-power sensor is connected to the energy supply module and is configured to collect environmental data; wherein, the multi-modal low-power sensor at least includes: a sensor for detecting carbon dioxide concentration; the microcontroller is connected to the multi-modal low-power sensor and is configured to preliminarily process and screen the environmental data and transmit the processed environmental data outward.

[0007] In an embodiment of the present invention, the multi-modal 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 an 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 configured to determine whether photovoltaic power generation can meet the power supply conditions required for the normal operation of the sensor node according to historical data and current environmental conditions; if it is satisfied, the solar panel directly supplies power to the sensor node; if it is not satisfied, the battery supplies power to the sensor node.

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

[0010] In an embodiment of the present invention, the energy adjustment 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, starting 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; a task priority adjustment strategy, including: determining the current task priority of each sensor node according to the frequency of the environmental data currently transmitted by each sensor node, and classifying each sensor node into a critical node and an auxiliary node in combination with the energy prediction value of each sensor node, and dynamically adjusting the task allocation and working mode of the critical node and the auxiliary node, so that the critical node preferentially undertakes critical tasks, and only allows the critical node to transmit data when the energy demand cannot meet the data transmission of all nodes.

[0011] In an embodiment of the present invention, each sensor node performs energy sharing based on the principle of wireless energy transmission by magnetic resonance coupling.

[0012] In an 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 configured to compare the environmental data of the current sensor node with the environmental data of the neighboring nodes of the sensor node to judge whether there is a deviation; the calibration module is configured to, in the case of judging that there is a deviation, perform automatic calibration by using a distributed algorithm and the domain collaboration mechanism between nodes, and send the calibrated data to the deviation judgment module to judge again whether there is a deviation until the data has no deviation; the data upload module, if it judges that there is no deviation, uploads the environmental data judged to have no deviation to the cloud platform layer.

[0013] In an embodiment of the present invention, the cloud platform layer is configured to receive the environmental data from the network layer for deviation judgment and analysis; in the case of judging that the data is deviated, send the analysis result to the calibration module of the network layer, so that the calibration module performs assisted calibration in combination with the analysis result; in the case of judging that the data has no deviation, analyze the environmental data by using a low-power data processing algorithm and store it.

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

[0015] As described above, the present invention is a low-power distributed sensor network with dynamic energy regulation, and has the following beneficial effects: Multiple sensor nodes are distributed in a distributed manner at the node layer, responsible for collecting environmental data respectively; The network layer is connected to the node layer and is provided with a dynamic energy management module, which can dynamically adjust the energy of the nodes according to the energy and task priorities of each sensor node; A distributed collaborative calibration module is also provided, which uses a distributed algorithm and borrows the neighborhood node collaboration mechanism to compare the data of each node with that of the neighborhood nodes, automatically adjusts the calibration parameters, and then uploads the data to the cloud platform layer. The cloud platform layer analyzes and stores the received environmental data and assists in calibrating the sensor nodes. The present invention realizes the efficient monitoring and real-time calibration of each sensor in a wide-area environment, and at the same time overcomes the problems of energy consumption bottleneck, insufficient measurement accuracy, and low communication efficiency in the prior art. 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, the system realizes efficient, accurate, and low-energy wide-area monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It shows a schematic structural diagram of a low-power distributed sensor network with dynamic energy regulation in one embodiment of the present invention.

[0017] Figure 2 It shows a schematic structural diagram of a sensor node in one embodiment of the present invention.

[0018] Figure 3 It shows a schematic diagram of the energy supply process in one embodiment of the present invention.

[0019] Figure 4 It shows a schematic structural diagram of the dynamic energy management module in one embodiment of the present invention.

[0020] Figure 5 It shows a schematic diagram of the sensor node calibration process in one embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] The following specific examples illustrate the implementation manners 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 implementation manners. Each detail 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, without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0022] It should be noted that in the following description, reference is made to the accompanying drawings, which describe several embodiments of the present invention. It should be understood that other embodiments may also be used, and mechanical compositions, structures, electrical, and operational changes may be made without departing from the spirit and scope of the present invention. The following detailed description should not be considered restrictive, and the scope of the embodiments of the present invention is only defined by the claims of the published patent. The terms used herein are only for describing specific embodiments and are not intended to limit the present invention. Spatially related terms, such as "upper", "lower", "left", "right", "below", "beneath", "lower part", "above", "upper part", etc., may be used in the text to facilitate the description of the relationship between one element or feature shown in the figure and another element or feature.

[0023] Throughout the specification, when it is said that a certain part is "connected" to another part, this includes not only the case of "direct connection", but also the case of "indirect connection" with other elements placed therebetween. Additionally, when it is said that a certain part "includes" a certain constituent element, unless there is a particularly contrary record, it does not exclude other constituent elements, but means that other constituent elements may also be included.

[0024] The first, second, and third terms mentioned therein are used to describe various parts, components, regions, layers, and / or segments, but are not limited thereto. These terms are only used to distinguish one part, component, region, layer, or segment from other parts, components, regions, layers, or segments. Therefore, the first part, component, region, layer, or segment described below can be referred to as the second part, component, region, layer, or segment within the scope not exceeding the present 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 clearly dictates otherwise. It should be further understood that the terms "comprises", "comprising" indicate the presence of the stated features, operations, elements, components, items, kinds, and / or groups, 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 to be construed as inclusive, or meaning any one or any combination. Thus, "A, B or C" or "A, B and / or C" means "any of the following: A; B; C; A and B; A and C; B and C; A, B and C". Exceptions to this definition occur only when the combination of elements, functions or operations are mutually exclusive in some manner.

[0026] The present invention provides a low-power distributed sensor network with dynamic energy regulation. Multiple sensor nodes are distributed in the node layer, responsible for collecting environmental data respectively; the network layer is connected to the node layer and is provided with a dynamic energy management module, which can dynamically adjust the energy of the nodes according to the energy and task priorities of each sensor node; a distributed collaborative calibration module is also provided, which uses a distributed algorithm and the collaborative mechanism of neighboring nodes to compare the data of each node with that of neighboring nodes, automatically adjusts the calibration parameters, and then uploads the data to the cloud platform layer. The cloud platform layer analyzes and stores the received environmental data and assists in calibrating the sensor nodes. The present invention realizes the efficient monitoring and real-time calibration of each sensor in a wide-area environment, and at the same time overcomes the problems of energy consumption bottleneck, insufficient measurement accuracy and low communication efficiency in the prior art. The system realizes efficient, accurate and 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.

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

[0028] As Figure 1 Show a schematic structural diagram of a low-power distributed sensor network with dynamic energy regulation in an embodiment of the present invention.

[0029] The network includes:

[0030] Node layer 1, with multiple sensor nodes S1 to SN distributed; where N is a positive integer; each sensor node is used to collect environmental data respectively.

[0031] Network layer 2, communicatively connected to node layer 1, including: a dynamic energy management module and a distributed collaborative calibration module;

[0032] Among them, 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 and task priority of each sensor node.

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

[0034] The cloud platform layer 3 is communicatively connected to the network layer 2 and is used to analyze and store the uploaded environmental data and assist the sensor nodes in calibration.

[0035] In one embodiment, as Figure 2 , each sensor node includes: an energy supply module, a multi-modal low-power sensor, and a microcontroller.

[0036] Among them, the energy supply module is used to provide electrical energy for the sensor node.

[0037] The multi-modal low-power sensor is connected to the energy supply module and is used to collect environmental data; among them, the multi-modal low-power sensor adopts a high-sensitivity and low-power sensor, and at least includes: a sensor for detecting carbon dioxide concentration, which is used to monitor carbon dioxide concentration data in real time.

[0038] The microcontroller is connected to the multi-modal low-power sensor and is used to preliminarily process and screen the environmental data and transmit the processed environmental data outward, for example, perform data transmission with other nodes or the network layer 2.

[0039] In one embodiment, the multi-modal low-power sensor further includes one or more of a temperature sensor, a humidity sensor, and a pressure sensor, which are used to monitor temperature data, humidity data, and pressure data in real time to construct an environmental compensation model and improve the measurement accuracy. In actual measurement, the changes in environmental temperature, humidity, and pressure will affect the measurement results. By constructing a compensation model, the interference of environmental factors can be effectively reduced, thereby improving the measurement accuracy. The sensor dynamically adjusts the sampling frequency according to different scenarios to extend the battery life of the node.

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

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

[0042] If the power supply demand of the sensor node is met, the solar panel will directly supply power to the sensor node. This method is not only environmentally friendly and energy-saving, but also can make full use of solar energy resources.

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

[0044] This design can make the most of solar energy, and at the same time ensure that the sensor node can operate normally even when solar energy is insufficient, improving the reliability and stability of the system.

[0045] In one embodiment, the microcontroller of the sensor node performs long-distance transmission through the LoRa low-power wide area network and short-distance communication through the low-power Bluetooth technology; combined with an optimized multi-hop routing algorithm for data hopping 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 and can dynamically adjust the routing path according to the real-time conditions of the network, such as the remaining energy of the nodes and the quality of the communication link.

[0046] By integrating two communication technologies, LoRa and BLE, and applying an optimized multi-hop routing algorithm, the sensor node can transmit data efficiently and stably in different distance scenarios, meeting the requirements of diverse application scenarios and promoting the wide application of sensor networks in various fields.

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

[0048] An energy prediction unit for using a model based on time series analysis to obtain the energy prediction values of each sensor node according to the historical energy consumption of each sensor node and the current remaining energy for the energy distribution prediction result in a future time period;

[0049] The energy prediction module uses a model based on time series analysis (such as the ARIMA model) to predict the future energy level of the node. The data input to the model includes: Historical energy consumption of the node: It contains the energy consumption of the sensor node over a past period of time, which is reflected by different past light intensities and different external environmental factors. By analyzing these historical data, the patterns and trends of the node's energy can be discovered. The current remaining energy is reflected by the current light intensity and the current environmental factors; the light intensity directly affects the power generation efficiency of the solar panel, and thus affects the energy supply of the sensor node. Therefore, the light intensity is one of the important factors for predicting energy. Environmental factors: such as temperature, task load, etc. Temperature affects the performance of the battery and the power consumption of the sensor, and the task load directly determines the energy consumption of the sensor node.

[0050] Assume that the energy state of the node is represented by a time series, and its energy change can be predicted by the following formula:

[0051]

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

[0053] The dynamic energy management module of the network layer can, based on the energy prediction value, identify in advance the nodes that may have low energy and formulate corresponding resource scheduling strategies, such as adjusting energy distribution, optimizing task arrangements, etc., to avoid node failure due to insufficient energy.

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

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

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

[0057] The frequency adjustment formula is:

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

[0059] In the formula, f′ NC is the reduced sampling frequency, that is, the adjusted sampling frequency; f NC is the sampling frequency before adjustment, and α is the adjustment coefficient, which is dynamically adjusted according to the remaining energy of the node. When the remaining energy of the node is less, the value of α will be larger, so that the reduced sampling frequency f′ NC is lower to further save energy.

[0060] The task priority adjustment strategy includes: The dynamic adjustment unit will determine the current task priority according to the frequency of the environmental data currently transmitted by each sensor node. Usually, the node with a high data transmission frequency has a higher task priority, because this may mean that the node is performing a very important task for the system.

[0061] Combining the predicted energy value of each sensor node and the task priority, the dynamic adjustment unit will divide all sensor nodes into critical nodes and auxiliary nodes. Critical nodes are usually those nodes with relatively sufficient energy and high task priority. They play a core role in the system. They can stably undertake critical tasks to ensure the normal operation of the core functions of the system; Auxiliary nodes are nodes with relatively less energy or lower task priority. Their main role is to assist critical nodes to complete some tasks when their own energy allows. According to the different types of allocated nodes, the energy of each sensor node is adjusted by dynamically adjusting the task allocation type, task allocation ratio and working mode of critical nodes and auxiliary nodes.

[0062] For adjusting the task allocation type and task allocation ratio of critical nodes and auxiliary nodes, the system gives priority to ensuring the execution of high-priority tasks. First, critical tasks are assigned to critical nodes. For critical nodes, the amount of tasks they share will be dynamically adjusted according to the load balancing strategy;

[0063] Among them, the task sharing 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 is the task load currently borne by the node; T crit is the threshold of the critical task load; β is the sharing ratio coefficient, which determines the proportion of the node sharing the tasks of other nodes; T j represents the new task load of the node after sharing the tasks.

[0066] If the task load T i of the node is lower than the critical task load threshold T crit , then the node has the ability to share the tasks of other nodes. The amount of shared tasks is determined by β · (T i - T crit ). The task load T j after sharing is the original task load T j plus the amount of shared tasks. And the auxiliary node shares the remaining tasks when the energy allows. Through the real-time scheduling algorithm, the system dynamically adjusts the task ratio of the two types of nodes to ensure efficient operation.

[0067] For adjusting the working modes of the critical node and the auxiliary node, it adjusts whether the sensor node transmits data, and only allows the critical node to transmit data when the energy demand cannot meet the data transmission of all nodes.

[0068] In one embodiment, in many sensor networks or distributed device systems, the energy consumption of each node is not balanced. Some nodes may have their energy depleted quickly due to heavy tasks or location factors, etc., while some nodes may have energy remaining. The introduction of wireless energy sharing technology aims to solve this problem of energy imbalance. The energy is transmitted from the nodes with surplus energy to the nodes with insufficient energy in a wireless manner, thereby improving the operation efficiency and service life of the entire system. Combining with the wireless energy sharing technology, each sensor node realizes efficient energy sharing within a medium distance range through wireless energy transmission based on magnetic resonance coupling. Magnetic resonance coupling forms a magnetic field resonance through the resonant coils at the transmitting end and the receiving end, enabling the energy to be efficiently transferred from the nodes with sufficient energy to the 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] Among them, 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 timely detect abnormal data that may occur in a single sensor node, provide a basis for subsequent data calibration, and avoid system decision-making errors caused by incorrect data entering the subsequent processing link.

[0071] The calibration module is used to perform automatic calibration using a distributed algorithm and the domain collaboration mechanism between nodes in the case of judging a deviation. The distributed algorithm integrates the data of neighboring nodes for data fusion, calculates the weighted average, etc. based on the node credibility to calibrate the deviation data, monitors the node status to achieve fault detection and isolation, and can also dynamically adjust the calibration strategy according to the real-time network status. Nodes interact with each other through the domain collaboration mechanism to exchange data and status information, and jointly participate in the calibration calculation. Trust evaluation is carried out on neighboring nodes based on factors such as historical performance, and the data of high-trust nodes is preferentially used to assist in calibration, so as to achieve efficient and accurate automatic calibration, ensure the accuracy and reliability of the sensor network data, and then send the calibrated data to the deviation judgment module to judge again whether there is a deviation until the data has no deviation.

[0072] The data upload module, if it judges that there is no deviation, uploads the environmental data judged to have no deviation to the cloud platform layer 3.

[0073] In an embodiment, the cloud platform layer 3 is used to receive the environmental data from the network layer 2 for deviation judgment and analysis; in the case of judging that the data has a deviation, the analysis result is sent to the calibration module of the network layer 2 to assist in calibration by the calibration module in combination with the analysis result; in the case of judging that the data has no deviation, a low-power data processing algorithm is used to analyze and store the environmental data. The low-power data processing algorithms adopted, such as edge computing and machine learning technologies, can quickly analyze and store the uploaded data.

[0074] The network layer 2 and the cloud platform layer 3 jointly implement the calibration process, and the process is as Figure 5 shown, and 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 the neighboring nodes of the sensor node to determine whether there is a deviation; if there is a deviation, step S2 is executed; if there is no deviation, step S3 is executed:

[0076] Step S2: The calibration module performs automatic calibration using a distributed algorithm and the domain collaboration mechanism between nodes, and returns the calibrated data to step S1;

[0077] Step S3: The unbiased environmental data is uploaded to the cloud platform layer 3 through the data upload module. The cloud platform layer 3 performs deviation judgment and analysis to further monitor whether there is any deviation in the data. If there is a deviation, step S4 is executed; if there is no deviation, step S5 is executed.

[0078] Step S4: The analysis result is sent to the calibration module in the network layer 2 through the cloud platform layer to assist in calibration, and the calibration data is returned to step S1.

[0079] Step S5: The cloud platform layer uses a low-power data processing algorithm to analyze and store the environmental data.

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

[0081] 1. Significantly improved energy consumption optimization

[0082] Through the dynamic energy regulation technology, the present invention balances the solar power supply and the battery power supply. At the same time, the energy management algorithm is used to adjust the energy distribution between high-energy nodes and low-energy nodes, avoiding the failure of nodes due to energy exhaustion, and thus extending the service life of the entire network.

[0083] 2. High-precision and low-error performance

[0084] Through the distributed algorithm and the neighborhood cooperation mechanism between nodes, the present invention dynamically adjusts the carbon dioxide concentration data and corrects the errors at the same time, ensuring the consistency and accuracy of the data throughout the network, realizing automatic calibration, and effectively solving the problem of the accuracy decline of traditional sensors.

[0085] 3. Reduced communication and processing power consumption

[0086] The present invention integrates LoRa low-power wide area network (LPWAN) and low-power Bluetooth (BLE) technologies. When the distance between nodes is far, LoRa is used for wide area transmission; when the distance is short, BLE is used to reduce power consumption. At the same time, the intelligent cloud adjustment mechanism reduces the data transmission volume, and the edge computing shares the cloud computing pressure.

[0087] 4. Efficient remote monitoring and management

[0088] Through the remote adjustment ability of the cloud platform, users of the present invention can realize real-time monitoring, data analysis and optimization management of the entire network, providing function support such as environmental governance, industrial detection and disaster warning.

[0089] In summary, for the low-power distributed sensor network with dynamic energy regulation of the present invention, multiple sensor nodes are distributed at the node layer and are responsible for collecting environmental data respectively; the network layer is connected to the node layer and is provided with a dynamic energy management module, which can dynamically adjust the node energy according to the energy and task priorities of each sensor node; a distributed collaborative calibration module is also provided, which uses a distributed algorithm and borrows the neighborhood node collaboration mechanism to compare the data of each node with that of the neighborhood nodes, automatically adjusts the calibration parameters, and then uploads the data to the cloud platform layer. The cloud platform layer analyzes and stores the received environmental data and assists in calibrating the sensor nodes. The present invention realizes the efficient monitoring and real-time calibration of each sensor in a wide-area environment, and at the same time overcomes problems such as energy consumption bottlenecks, insufficient measurement accuracy, and low communication efficiency in the prior art. 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, the system realizes efficient, accurate, and low-power wide-area monitoring. Therefore, the present invention effectively overcomes various shortcomings in the prior art and has high industrial utilization value.

[0090] The above embodiments merely illustrate the principles and effects of the present invention by way of example, rather than limiting the present invention. Any person familiar with this technology can modify or change the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or changes completed by those with ordinary knowledge in the technical field without departing from the spirit and technical ideas disclosed by 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: A node layer, where multiple sensor nodes are distributed in a decentralized manner and are used to collect environmental data respectively; A network layer, which is communicatively connected to the node layer and includes: a dynamic energy management module and a distributed collaborative calibration module; Among them, 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 and task priority of each sensor node; The distributed collaborative calibration module, which is connected to the dynamic energy management module, is used to use a distributed algorithm and, through the collaborative mechanism of neighboring nodes, compare the data of each sensor node with that of its respective neighboring nodes to automatically adjust the calibration parameters and upload the environmental data of each sensor node; A cloud platform layer, which is communicatively connected to the network layer and is used to analyze and store the uploaded environmental data and assist the sensor nodes in calibration.

2. The low-power distributed sensor network with dynamic energy regulation according to claim 1, wherein Each sensor node includes: an energy supply module, a multi-modal low-power sensor, and a microcontroller; Among them, the energy supply module is used to supply electrical energy to the sensor node; The multi-modal low-power sensor, which is connected to the energy supply module, is used to collect environmental data; among them, the multi-modal low-power sensor at least includes: a sensor for detecting carbon dioxide concentration; The microcontroller, which is connected to the multi-modal low-power sensor, is used to preliminarily process and screen the environmental data and transmit the processed environmental data outward.

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

4. The dynamically energy-regulated low-power distributed sensor network according to claim 3, characterized in that, The energy supply module includes: a controller, a solar panel, and a battery; Among them, the controller, which is connected to the solar panel and the battery, is used to determine whether photovoltaic power generation can meet the power supply conditions required for the normal operation of the sensor node according to historical data and current environmental conditions; 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.

5. The dynamically energy-regulated low-power distributed sensor network according to claim 4, wherein, The dynamic energy management module includes: An energy prediction unit, which is used to use a model based on time series analysis to predict the energy distribution in a future time period according to the historical energy consumption and current remaining energy of each sensor node to obtain the energy prediction value of each sensor node; A dynamic adjustment unit, which is connected to the energy prediction unit, is used to, within each adjustment period, dynamically adjust the task allocation, sampling frequency, and working mode of each sensor node based on the energy adjustment strategy according to the energy prediction value of each sensor node and the environmental data transmitted in real time by each sensor node.

6. The dynamically energy-adjusted low-power distributed sensor network according to claim 5, characterized in that The energy adjustment strategy includes: A sampling frequency adjustment strategy, which includes: when the current remaining energy or energy prediction value of a sensor node is less than a set threshold, start the low-power mode of the sensor node and reduce the sampling frequency of non-critical tasks of the sensor node based on the environmental data currently transmitted by the sensor node; The task priority adjustment strategy includes: determining the current task priorities of each sensor node according to the frequencies of the environmental data currently transmitted by each sensor node, classifying each sensor into a critical node and an auxiliary node in combination with the energy prediction values of each sensor node, and dynamically adjusting the task allocation and working modes of the critical nodes and the auxiliary nodes, so that the critical nodes preferentially undertake critical tasks, and only critical nodes are allowed to transmit data when the energy demand cannot meet the data transmission of all nodes.

7. The low-power distributed sensor network with dynamic energy regulation according to claim 6, characterized in that, Each sensor node shares energy through the principle of wireless energy transmission based on magnetic resonance coupling.

8. The dynamically energy-adjusted low-power distributed sensor network according to claim 1, wherein The distributed collaborative calibration module includes: a deviation judgment module, a calibration module, and a data upload module; Among them, 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 the domain cooperation mechanism between nodes in the case of judging a deviation, and send the calibrated data to the deviation judgment module to judge again whether there is a deviation until the data has no deviation; The data upload module, if it judges that there is no deviation, uploads the environmental data judged to have no deviation to the cloud platform layer.

9. The dynamically energy-regulated low-power distributed sensor network according to claim 9, characterized in that, The cloud platform layer is used to receive the environmental data from the network layer for deviation judgment and analysis; in the case of judging that the data has a deviation, send the analysis result to the calibration module of the network layer for assisted calibration by the calibration module in combination with the analysis result; in the case of judging that the data has no deviation, analyze the environmental data using a low-power data processing algorithm and store it.

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

Images