402 results about "Sensor node" patented technology

The invention relates to a flying dust monitoring data processing and classifying method based on multi-source sensing fusion, and the method specifically comprises the following steps: firstly, deploying multi-source flying dust monitoring sensor nodes in a target region to collect data, carrying out the marking, and generating a data set; performing continuous wavelet transform on the acquired data, extracting a wavelet energy spectrum and a Shannon entropy, and splicing to obtain an enhanced feature tensor; secondly, through a two-stage fusion and coding strategy, frequency band energy features are extracted through wavelet packet decomposition, multi-channel cross-correlation, statistical moment and ratio features are calculated to form time sequence mode coding features, and multi-source heterogeneous feature fusion is achieved in combination with a local time sequence feature matrix; then constructing a deep learning model containing a multi-scale time sequence feature extraction and dynamic fusion module, and inputting a fusion feature matrix for training; and finally, inputting the preprocessed new monitoring data into the trained model, and outputting a dust source and pollution level classification result. The dust monitoring data classification accuracy and the dust source identification precision can be effectively improved.

A system for self-executing enhanced interaction with a node-based logistics receptacle. The system includes a wireless accessory sensor node disposed on the node-based logistics receptacle to monitor storage receptacle components of the node-based logistics receptacle to generate sensor data. The system includes a bridge node disposed on the node-based logistics receptacle that uploads information related the sensor data, detects an external device separate from the node-based logistics receptacle, communicates with the external device to establish a smart contract based connection that provides an interaction privilege, interfaces with the external device according to the interaction privilege, and transmits an update message to the backend server that corresponds to at least a portion of the uploaded information related to the sensor data and information related to interfacing with the external device.

A dynamic learning server-based logistics system includes a node-based logistics receptacle operative to receive a delivery item as part of a logistics transaction and also includes a backend server maintaining a management profile related to operation of the node-based logistics receptacle. The node-based logistics receptacle includes a plurality of monitored receptacle components, a wireless accessory sensor node having a plurality of sensors, and a bridge node operative to retrieve event information from the wireless accessory sensor node. The backend server receives the retrieved event information, compares the retrieved event information with the management profile, identifies a threshold change condition from the comparison, dynamically revises the management profile when the threshold change condition is identified, and transmits an adjustment message to the bridge node. The adjustment message is based upon the revised management profile, and the adjustment message initiates a timing change to operation of the bridge node.

The invention relates to an underwater wireless sensor network path sensing routing method based on deep reinforcement learning, which comprises the following steps that: firstly, a node constructs and periodically updates a transmission preference model based on local and neighbor node interaction information; secondly, deploying a deep reinforcement learning model at each underwater sensor node to perform distributed routing strategy learning; and finally, generating a global guide vector by the sink node according to the routing path information of the received data packet, reversely spreading the global guide vector to the source node, fusing the global guide vector with a local transmission preference vector of the node to generate a guide reward, optimizing the deep reinforcement learning model, and updating a routing strategy. According to the method, the problems of difference and complexity of underwater transmission tasks can be solved, and the network data transmission efficiency and the overall service quality are improved in combination with local preference and global guidance while the node online learning is kept to adapt to the dynamic underwater environment.

The invention relates to a tartary buckwheat pest and disease damage dynamic monitoring method, system and device and a medium, and belongs to the technical field of agricultural intelligent monitoring, the dynamic monitoring method comprises the following steps: periodically collecting environmental parameter data through fixed sensor nodes deployed in a farmland, and obtaining leaf vibration signals and multispectral image data at the same time; performing space-time alignment on the blade vibration signal and the multispectral image data, correcting radiation distortion in the multispectral image data, and outputting a registration data set; according to the registration data set, fusing to generate a multi-modal feature vector, inputting the multi-modal feature vector into a pre-trained space-time analysis model, and outputting a risk level distribution diagram with a geographic coordinate mark; generating a control instruction set according to the risk level distribution map, and triggering execution equipment to execute pest control operation; and optimizing weight parameters of the space-time analysis model through the generative adversarial network based on the execution log of the control instruction set and the historical multi-modal feature vector. The scientificity and timeliness of pest control can be improved.

The invention relates to the technical field of electrical digital data processing, and discloses a data analysis system for dynamic monitoring and intelligent prediction of physical parameters and an implementation method, and the method comprises the steps: setting a sensor node comprising a measurement oscillation circuit and a reference oscillation circuit, so as to obtain a differential frequency signal which is mapped by a physical agent and is subjected to temperature compensation; and the central processing unit calculates the drift rate of the differential frequency signal to predict chronic degradation, and triggers the sensor node to switch to a multi-principle electrical detection mode when the rate exceeds a threshold value so as to diagnose the cause of an acute event. The inherent contradiction between the high sensitivity and the wide temperature range operation universality of the existing monitoring technology is avoided, meanwhile, the system is endowed with the capability of performing cause discrimination on the acute sudden risk, and the monitoring precision and the decision effectiveness are remarkably improved.

The invention discloses a multi-modal sensor abstract description and packaging method and system based on software definition. Firstly, a unified sensor abstract description model based on software definition is introduced, so that hardware independence is realized, and the differences of a bottom-layer multi-mode sensor in the aspects of interfaces, protocols and data formats can be effectively shielded; secondly, abstract packaging of data is achieved through an autonomous information processing module, and high-bandwidth and high-redundancy original data is converted into low-bandwidth and high-value structured information; in addition, by means of automatic registration, discovery, scheduling and control mechanisms of the sensor management control module on sensor resources, the system realizes a real plug-and-play function. According to the invention, by deploying the autonomous information processing modules at various sensor nodes, the original sensing data is preprocessed, and standardized data packaging with semantic consistency is output, so that the operation load of the central controller is reduced, and the information processing efficiency and the system response capability are also improved.

The invention relates to the technical field of sensor networks and Internet of Things, in particular to a data center air quality intelligent early warning method and system based on a sensor network. The method comprises the following steps: constructing a self-organizing cooperative network by deploying multiple types of sensor nodes in a key area of a data center, and collecting and correcting multi-dimensional air data in real time; the data reliability is improved through inter-node dynamic weight fusion and local anomaly recognition; establishing an air parameter and machine room structure correlation model by utilizing space mapping and time sequence analysis, and identifying a micro-scale diffusion trend; a self-adaptive dynamic threshold mechanism is constructed, and threshold rolling optimization is realized in combination with historical statistics and real-time feedback; and generating a graded alarm strategy based on multi-stage early warning judgment and triggering conditions, and continuously self-optimizing early warning precision and response efficiency through closed-loop feedback. According to the invention, all-around, intelligent and high-reliability early warning and regulation and control of the air quality of the data center are realized.

The invention relates to the technical field of coal mine automation control, and discloses a coal flow windscreen wiper multi-device cooperative control method and system. The method comprises the following steps: obtaining an original environment data set of each sensor node in an underground environment, processing the original environment data set to obtain a communication quality index, calculating bandwidth demand change of each node in combination with historical transmission records of the nodes, and predicting a resource allocation proportion; when the current available network resources cannot meet the requirements, sorting the key data packets to obtain a priority transmission sequence; extracting high-priority data from the sequence, temporarily storing the high-priority data to a local cache unit, and determining the state of a buffer area; dynamically adjusting bandwidth allocation by adopting an intelligent optimization model, and updating communication resource configuration; and judging whether the resource configuration meets a load balance requirement, if so, sending a data packet through a wireless channel, determining a transmission performance index, and generating a resource management scheme of a next period. The cooperative work efficiency of multiple devices of the coal flow windscreen wiper in the high-dust and high-humidity environment is improved.

The invention discloses a distributed sensor abnormal event identification method for intelligent traffic, and particularly relates to the technical field of traffic information perception and identification, and the method comprises the following steps: generating an abnormal information initial confidence value through deploying a sensor node with a confidence value dynamic adjustment function; during fusion processing, independent identification priorities of single node anomalies are reserved; after sequence reconstruction of a unified time reference, judging whether a response condition is met or not in combination with trajectory evolution; if yes, an abnormal response process is triggered, a space-time compensation set is constructed based on historical data of the sensing blind area for verification, and finally an abnormal intervention instruction is output and a traffic scheduling system is linked; according to the method, the sensing sensitivity, the fusion accuracy and the response timeliness of the abnormal information in a complex traffic environment are improved, the problem of an identification blind area that single-point abnormity is covered is avoided, state reconstruction and supplementary verification of the sensing blind area are realized, and the rapid intervention capability of an intelligent traffic system on emergencies is enhanced.

The invention discloses a multi-node sensor communication link cooperative scheduling method and system based on star flash, and relates to the technical field of new-generation wireless communication and sensors. The method comprises the following steps: constructing a star flash sensor data reporting system; calculating a node valid data volume based on parameters such as sensor types and channel numbers; estimating transmission time and distributing time slots in combination with a star flash frame structure; transmission parameters are dynamically adjusted through a real-time link quality perception and prediction model, and a data priority and preemption mechanism is supported; issuing a scheduling strategy by using the JSON configuration file; and the nodes communicate according to configuration and realize fault self-diagnosis and self-healing. The system comprises a star flash management node, a sensor node and an embedded cooperative scheduling engine. According to the method, multi-node cooperative scheduling, link adaptive optimization and system autonomous recovery are realized, and the communication reliability, the channel utilization rate and the system robustness are remarkably improved.

The invention relates to the technical field of intelligent traffic monitoring, and discloses a road surface accumulated water detection and identification method based on multi-view feature fusion. The method comprises the following steps: acquiring image data, depth information and environmental parameters through sensor nodes deployed at multiple positions of a road surface to form an original monitoring data set; performing multi-source feature extraction and fusion processing on the data set to obtain an accumulated water feature image set; performing spatial domain analysis by using the image set to generate a pavement partition consistency map; time dimension data is extracted based on the map, dynamic change evaluation is carried out, and a ponding evolution report is output; detecting an abnormal mode from the report, and identifying an abnormal ponding area; and calculating a risk index according to the abnormal region, and generating a final road surface ponding risk map. According to the method, through multi-source data fusion and spatio-temporal conjoint analysis, accurate detection, dynamic evolution tracking and risk assessment of pavement ponding are realized, and the accuracy and early warning capability of urban road ponding monitoring are remarkably improved.

The invention discloses a traffic flow prediction method for a double-layer multi-scale dynamic graph convolutional network, and relates to the technical field of traffic, and the method comprises the following steps: constructing a double-layer structure of a traffic network, which comprises a node layer composed of traffic sensor nodes and a region layer formed by clustering nodes in the node layer, node layer traffic flow data and area layer traffic flow data are extracted; respectively mapping the node layer traffic flow data and the region layer traffic flow data to a potential space to obtain a node layer initial hidden state and a region layer initial hidden state; inputting the initial hidden state of the node layer and the initial hidden state of the region layer into a sequence containing at least one space-time module for processing so as to capture a dynamic space-time dependency relationship; the method has the effect of providing more reliable decision support for traffic management and travel planning.

The invention relates to the technical field of building energy consumption management, in particular to a building heating ventilation air conditioner intelligent control method for energy efficiency optimization, and the method comprises the steps: dividing a building into a plurality of independent temperature control regions, and deploying multifunctional sensor nodes; acquiring real-time environment data, personnel data and weather forecast data; the ultra-short-term load prediction model is maintained to predict the cold / heat load demand of the area in the future 15 minutes, and a cold / heat load demand prediction value is obtained; reporting the cold / heat load demand prediction value to a central processing unit in a standardized data format; the central processing unit optimizes the energy distribution of the whole building through a multi-objective optimization algorithm based on the prediction requirements of all the regions, and obtains a resource scheduling list in the next 15-minute period; and according to the resource scheduling list, a regulation and control instruction is issued to equipment in each independent temperature control area. Therefore, the problems of response lag, extensive control and the like in the prior art are solved.

The invention discloses a substation danger area judgment method and system based on electric field gradient perception, and relates to the field of safety monitoring, and the method comprises the steps: arranging an electric field sensor array in a key equipment area of a substation; performing noise reduction processing on the data of each sensor node to obtain an electric field vector set; obtaining the electric field intensity change rate of each monitoring point through a space vector differential algorithm, and generating an electric field gradient tensor; dividing the electric field gradient value, and constructing a dynamic danger level mapping rule; performing spatial interpolation through a Delaunay triangulation algorithm to generate an electric field gradient equipotential surface; constructing an electric field gradient space distribution model, and generating a dynamic danger boundary envelope body in real time; and correcting the boundary of the dangerous area through the pre-trained gradient compensation coefficient matrix and triggering a graded early warning signal. The method has the advantages that the electric field gradient change of the transformer substation is monitored in real time through the electric field sensor array and the wavelet denoising technology, the dangerous area is dynamically evaluated, and the accuracy and real-time performance of dangerous area judgment are effectively improved.

The invention discloses a multi-device state sensing driven intelligent storage abnormity early warning method, and relates to the field of storage cargo management, and the method comprises the steps: 1, collecting multi-source data through a multi-source sensor node, including odor intensity, volatile organic compound concentration, carbon dioxide or carbon monoxide content, and environment temperature and humidity, meanwhile, physical coordinates of the multi-source sensor nodes are obtained; 2, acquiring historical multi-source data in advance, constructing a dynamic threshold model based on the historical multi-source data, calculating odor fluctuation, a gas combination abnormal index and a temperature and humidity coupling risk value according to the multi-source data, and introducing time evolution trend analysis to obtain a gas combination abnormal index and a temperature and humidity coupling risk value; and a dynamic threshold model is combined to identify risk signals in the odor fluctuation degree, the gas combination anomaly index and the temperature and humidity coupling risk value, a space attenuation model is adopted to generate a three-dimensional anomaly thermodynamic diagram based on the physical coordinates of the multi-source sensor node and the risk signals, and a high-risk area is positioned.

The present invention relates to the field of wireless environmental monitoring. Provided are a wireless environmental monitoring method and system based on the Internet of Things. The wireless environmental monitoring method based on the Internet of Things comprises: S1, deploying a plurality of wireless sensor nodes for collecting environment parameters, which comprise temperature, humidity, and air quality; S2, the sensor nodes transmitting collected data to a gateway device by means of low-power wireless communication technology; and S3, the gateway device performing preliminary processing on the received data, and transmitting the data to a cloud server by means of the Internet. The stability and anti-interference capability of data transmission are ensured by using Zigbee communication technology, adaptive frequency-hopping technology, and a dual-antenna system. In terms of data processing, the system uses edge computing technology to perform partial data processing at the gateway device, thereby reducing the computational pressure on the cloud server. Moreover, the cloud server uses a distributed computing architecture, thereby achieving efficient processing of large-scale data and providing good scalability.

Disclosed is a system for providing dynamic spectrum access, the system comprising: one or more sensor nodes configured to collect environmental data; a plurality of Internet of things (IoT) gateways arranged in data or signal communication with the one or more sensor nodes via a network protocol, each IoT gateway comprising at least one spectrum channel; a distributed ledger configured for secure data processing between each of the plurality of IoT gateways and the one or more sensor nodes; and a dynamic spectrum access module arranged in data or signal communication with the plurality of IoT gateways, the dynamic spectrum access module configured to implement a deep learning algorithm, the deep learning algorithm configured to obtain the environmental data as input, parse the environmental data to obtain spectrum environment parameters, predict one or more use states of the at least one spectrum channel of each of the plurality of IoT gateways; and provide a spectrum access action based on the predicted one or more use states.

The invention discloses an intelligent metasurface auxiliary directional charger deployment method based on a heterogeneous graph neural network and deep reinforcement learning, and belongs to the field of wireless energy supplementation of the Internet of Things. The method aims at solving the problems that at present, related work is mostly one-time static geometric deployment, the coverage range is limited, flexibility is insufficient, the optimization granularity is coarse, high-quality charging full coverage is difficult to achieve under the cost constraint, and the overall charging effectiveness of a network is difficult to maximize. According to the method, a heterogeneous network graph fusing the relation of sensor nodes, directional chargers and intelligent super surfaces (RIS) is constructed, a heterogeneous graph neural network is deployed in a base station in a centralized mode to extract the network structure and energy state characteristics, and a near-end strategy optimization algorithm is further combined. And joint optimization of a directional charger deployment position and a charging direction as well as an RIS position and reflection configuration is realized. According to the method, the charging coverage rate, the charging utility and the deployment cost are taken as a comprehensive target, the optimal deployment strategy can be intelligently generated, the charging coverage is expanded, the charging utility is improved and the node failure rate is reduced on the premise of controllable cost, so that the purpose of prolonging the service life of the network is achieved.

A multimode cabinet intelligent management terminal for narrowband Internet of Things communication comprises an environment monitoring module, a load sensing module, a protocol switching module and a bandwidth scheduling module. The environment monitoring module obtains key indexes of network signal strength, delay and packet loss rate and calculates environment data; the load sensing module collects CPU usage rate and memory usage condition load data, evaluates the load state of an Internet of Things sensor node, and generates load data and environment data. The protocol switching module dynamically evaluates network environment and equipment load changes, predicts a network environment change trend by adopting a weighted decision method, and screens an optimal protocol based on a protocol switching strategy; and the bandwidth scheduling module performs optimal allocation on the bandwidth, automatically adjusts bandwidth resources, and performs release and reallocation by monitoring the network condition in real time to obtain bandwidth resource information. The invention provides a multimode cabinet intelligent management terminal for narrowband Internet of Things communication. Accurate point prediction and reliable interval prediction and probability prediction of wind power are realized.

The invention provides a transformer internal partial discharge detection method, device and equipment and a storage medium, and belongs to the field of transformer monitoring, and the method comprises the steps: obtaining an ultrahigh frequency electric signal, an optical signal and an acoustic signal generated by transformer internal partial discharge through an ultrahigh frequency sensor, an optical sensor and an optical fiber Fabry-Perot ultrasonic sensor; a multi-state quantity integrated sensing SoC chip on the circuit board is used for conditioning and digitizing the signals to generate a digitized signal flow; inputting the signal flow into a state sensing special edge computing NPU chip, processing the signal flow by using a graph attention network algorithm, calculating an attention coefficient between sensor nodes, and fusing the multi-modal signals to generate a fusion signal; and determining the partial discharge type according to the fusion signal. According to the invention, three sensors are internally integrated, signal conditioning digitalization is carried out by using the SoC chip, multi-mode signals are fused through the NPU chip, high-precision and high-reliability identification is realized, and the difficulty of an external sensor is overcome.

The invention discloses a nuclear power plant graph attention network diagnosis method based on fault causal guidance, and relates to the technical field of nuclear power plant fault diagnosis. In order to solve the technical defect that a nuclear power plant fault diagnosis method in the prior art does not fully utilize causal information in a knowledge-driven method, the technical scheme provided by the invention comprises the following steps: acquiring operation data and high-precision simulation data of a preset number of subsystems of a nuclear power plant; carrying out fault state labeling on the simulation data; establishing a causal propagation path between the sensor nodes; extracting time sequence features of the sensor data, and constructing a time-space fused fault propagation graph; hierarchical aggregation of fault features is realized; feature extraction is carried out on the sensor data, and local modes and key fault features in the data are captured; optimizing the connection weight of the same-order neighbor nodes in the fault propagation graph; and calculating hidden variable representation, and outputting a fault diagnosis result. The method is suitable for nuclear power plant operation monitoring, intelligent fault diagnosis, safety early warning and the like.

The invention provides a multi-source sensor monitoring method and device, electronic equipment and a storage medium, and the method comprises the steps: configuring a heterogeneous sensor network according to a monitoring target and environment; a sensor node topological relation graph is established, a unique space-time identifier is distributed, and timestamp synchronous correction is carried out; processing data of different sampling frequencies and then extracting core feature vectors to form a multi-dimensional feature matrix; constructing a dynamic graph structure, learning a space-time dependency relationship between sensors, and generating a node embedding vector; establishing a space-time propagation model to predict a data evolution trend, constructing a normal working model, and calculating a reconstruction error threshold value; when abnormality is detected, triggering a sensor network adaptive reconfiguration mechanism, and adjusting the sampling frequency and sensitivity parameters of the key sensor; and constructing a hierarchical decision tree, fusing the monitoring result, generating confidence evaluation, outputting a differential early warning signal according to the risk level, and generating a monitoring report. According to the invention, the monitoring precision and the abnormal detection and fault diagnosis capability are improved.

Embodiments of the present application provide a detector device including a photon detector and a multilayer reflector. The photon detector includes a substrate, an isolation structure in a first side of the substrate, a guard ring adjacent to the isolation structure and in the first side of the substrate, a sensor node in the first side of the substrate, the guard ring between the sensor node and the isolation structure, a shared node in the first side of the substrate, the shared node between the guard ring and the isolation structure, an isolation extension structure in a second side of the substrate opposite the first side, the isolation extension structure extending from the second side to the isolation structure. The multilayer reflector is on the first side of the substrate. Using the multilayer reflector can improve collection of photons in an avalanche region.

This invention discloses an adaptive adjustment method for intelligent display equipment used in digital exhibitions, specifically relating to the field of digital exhibition technology. The method involves collecting multi-source data such as ambient light, visitor distribution, and sound through distributed lightweight sensor nodes; performing semantic fusion using a lightweight neural network to construct a dynamic exhibition hall context graph; instantiating an intelligent agent for each display device; generating resource scheduling proposals based on a shared reward mechanism and a multi-agent collaborative decision-making algorithm; an arbitrator detecting and resolving resource competition and effect offsetting conflicts; outputting a globally consistent set of control instructions; ensuring smooth execution of instructions by the devices; dynamically generating adapted display content based on contextual semantics; and continuously optimizing the decision-making model through online learning based on visitor behavior feedback. This invention achieves collaborative scheduling of equipment resources and adaptive content generation, improving the exhibition experience and resource utilization efficiency.

This application relates to an IoT-based intelligent land remediation monitoring method and system. The monitoring method includes: acquiring satellite remote sensing elevation data and historical soil composition data of the land area to be monitored; dividing the land area to be monitored into multiple grid units and assigning grid identifiers; deploying fixed soil sensor nodes and mobile drone nodes to generate a sensor network topology; collecting real-time soil parameters of each grid unit and performing noise filtering and anomaly detection to generate a soil composition dataset; inputting the soil composition dataset and historical soil composition data into a cloud-based prediction model to output a land degradation risk score and remediation strategy; controlling the execution equipment to perform land remediation operations and monitoring changes in soil parameters after execution in real time; updating the parameters of the cloud-based prediction model and optimizing the sensor network topology based on the deviation between the changes in soil parameters after execution and the expected values. This application improves agricultural production efficiency and soil health.

The invention relates to a Citrus Huanglongbing early collaborative sensing method based on Internet of Things, in particular to the field of Internet of Things, by performing confidence-driven local collaborative filtering at a sensor node, environmental noise and individual difference interference are effectively inhibited, the signal-to-noise ratio of early weak disease features is remarkably improved, and the accuracy of the Citrus Huanglongbing early collaborative sensing method is improved. A dynamic space-time association graph constructed by a convergence gateway accurately depicts space-time association of an abnormal mode, a cloud heterogeneous graph neural network further excavates a deep mode which is robust to global interference and sensitive to local coupling anomaly from the graph, and finally, a collaborative training mechanism based on federated learning is utilized to improve the robustness of the abnormal mode on the premise of protecting data privacy. And the group intelligence of distributed data is gathered, and a lightweight diagnosis model capable of reasoning in real time on the edge side is trained, so that the early-stage, accurate, low-power-consumption and sustainable-evolution intelligent perception of the citrus huanglongbing is realized.

This invention provides a miniature general-purpose electrical control device and method, including a power adapter module, a PLC programming control unit, a central coordination controller, and an integrated control module. The invention acquires input power signals, identifies their type and amplitude range, and generates electrical state identifiers. Based on these identifiers, it selects the corresponding rectifier topology, converting the input power into a stable intermediate DC bus voltage, forming a reconfigurable DC energy pool. It acquires multi-channel load state data through miniature sensor nodes, generating a multi-dimensional load feature vector. Using an improved fuzzy PID coordination controller, it integrates load characteristics with a preset channel priority strategy, calculates energy demand weights, and generates a dynamic power allocation matrix. Based on this matrix, it adjusts the operating mode and power flow of the bidirectional DC / DC converter. This invention significantly improves power adaptability and energy utilization in multi-input, multi-output power supply scenarios, effectively addressing complex operating conditions such as grid fluctuations and uneven load distribution.

The invention provides a cultivated land quality dynamic monitoring and precise fertilization regulation and control method based on the Internet of Things, and the method comprises the following specific steps: firstly, collecting soil physical and chemical data in real time through sensor nodes deployed in a field, periodically obtaining crop growth data through an unmanned plane remote sensing platform, carrying out the preprocessing and time-space fusion of multi-source data, and carrying out the real-time monitoring of the cultivated land quality; forming a unified data set; thirdly, constructing a cultivated land quality evaluation model based on machine learning, and generating a cultivated land quality index spatial distribution map; introducing a crop growth model, a target yield and meteorological data, performing fertilization partitioning and nutrient demand calculation through a decision engine, and finally generating a precise fertilization prescription map; and finally, driving a variable fertilization mechanism equipped with a positioning system, and executing variable fertilization operation according to the prescription map. Dynamic perception, accurate evaluation and intelligent regulation and control of the cultivated land quality are achieved, and the cultivated land resource utilization efficiency and the fertilizer and water management level are effectively improved.

This invention provides an energy-saving clustering routing method for wireless sensor networks based on improved multi-target ant colony optimization, comprising: Step 1, initialization; Step 2, determining the set of active sensor nodes in the current round; Step 3, completing network clustering; Step 4, maintaining the Pareto optimal path solution set; Step 5, repeating steps 2 to 4 until the maximum number of iterations is reached or the solution set converges, outputting the Pareto optimal path solution set as the routing scheme for network data transmission. This invention improves network energy efficiency and extends network lifetime by constructing a globally collaborative energy management framework that integrates sleep scheduling, cluster head election, and routing optimization, while ensuring the real-time performance and reliability of data transmission.