400 results about "Selection algorithm" patented technology

The invention discloses a task length prediction-based large language model reasoning calculation service energy consumption optimization scheduling method, which comprises the following steps of: firstly, reasoning by taking an Alpaca-52k instruction data set as an input source and a large language model (such as Llama3-8B), counting the number of output tokens of the large language model, and labeling each piece of input data; then, a Qwen2-1. 5B large language model is finely tuned by using an Alpaca-52k instruction data set and the response length of Llama3-8B reasoning, and computing resources of the prediction method are reduced on the premise that the prediction performance is guaranteed; then, the response length of the Llama3-8B reasoning task is predicted through the fine-tuned Qwen2-1. 5B model, task balanced sorting scheduling is carried out according to the response length so as to improve the large language model reasoning speed, and finally, a deep reinforcement learning power selection algorithm is used to reduce the calculation power as much as possible on the premise that the large language model reasoning task time delay is met so as to improve the large language model reasoning efficiency. Therefore, the energy consumption of large language model reasoning calculation is reduced.

The invention relates to the technical field of wind power equipment monitoring, and discloses a wind power gear box intelligent fault early warning method and system based on machine learning. The method comprises the steps that multi-source monitoring data such as vibration signals, temperature data and oil analysis data of the wind power gear box are acquired, and multi-scale operation characteristics are extracted through time-frequency conjoint analysis; key fault sensitive features are determined through an adaptive feature selection algorithm, and a dynamic fault feature weight matrix is constructed in combination with a historical fault case library; multi-modal data fusion is adopted to generate an enhanced fault feature set, and modal decomposition is carried out on the enhanced fault feature set to obtain a trend component and a fluctuation component; a fault evolution feature space is constructed by using a deep neural network based on two components, then a fault development mode is identified by using a time sequence mode matching algorithm, and finally a graded early warning signal is generated according to a matching degree with a preset mode, so that fault features can be comprehensively captured, and safe operation of a wind power gear box is ensured.

The invention provides a digital twin hydrological station system which is characterized in that the system comprises a layered architecture system and a multi-dimensional safety protection mechanism, the layered architecture system is composed of a sensing layer, a transmission layer, a data layer, a model layer, an application layer and a display layer from bottom to top, the sensing layer comprises a heterogeneous hydrological monitoring equipment cluster, and the transmission layer comprises a heterogeneous hydrological monitoring equipment cluster. The equipment cluster is provided with a water level sensor array, a Doppler flowmeter, a spectrum water quality analyzer and an environment monitoring terminal, and total element acquisition of hydrological element data, equipment operation state data and environment data is realized; the transmission layer adopts a hybrid communication protocol stack, integrates an ultra-short wave communication module, an NB-IoT narrowband communication module and an optical fiber communication module, and constructs a multi-path redundant transmission channel, and a key data transmission channel is configured with a dynamic link selection algorithm; the data layer comprises a distributed storage architecture, adopts a mixed storage mode of a relational database cluster and an unstructured database, and is internally provided with a data cleaning engine and a spatio-temporal data fusion module.

The invention relates to a priority scheduling generation method and system for Beidou satellite short message communication, and the method comprises the steps: collecting multi-dimensional features, such as message service types, user identities, spatio-temporal information, channel states and the like, dynamically calculating the priority score of each message through a configurable weighting model, and then, carrying out the dynamic calculation of the priority score of each message; the method comprises the following steps of: abstracting a message into a conflict graph vertex, establishing an edge according to a receiving conflict relationship, distributing candidate message sets which are not conflicted with each other for each scheduling time slot by adopting an independent set selection algorithm, and performing joint optimization on a message sending sequence and power distribution by using a genetic algorithm in each time slot by taking a maximized comprehensive utility value as a target, and generating a final scheduling instruction. Through dynamic evaluation, conflict avoidance and resource collaboration, intelligence and self-adaption of a scheduling strategy are realized, and the guarantee capability of a system for key messages and the overall resource utilization efficiency are remarkably improved.

The invention relates to the technical field of unmanned aerial vehicle assisted edge computing, and discloses a deep neural network task-oriented collaborative reasoning and unmanned aerial vehicle trajectory optimization algorithm, which comprises the following steps of S1, establishing a multi-unmanned aerial vehicle assisted mobile edge computing system model; s2, providing an optimal segmentation point selection algorithm, selecting an optimal segmentation point for each user task, and determining a task unloading proportion; s3, providing an unmanned aerial vehicle and ground user matching algorithm, and allocating computing resources of the unmanned aerial vehicle; and S4, modeling the unmanned aerial vehicle trajectory optimization and ground user transmitting power selection problem as a Markov decision process, providing a dynamic DNN task unloading and trajectory optimization algorithm based on deep reinforcement learning, and obtaining the optimal trajectory of the unmanned aerial vehicle and the transmitting power of the ground user in each time slot by training an intelligent agent. According to the algorithm, rewards can be continuously improved, and delay and energy consumption of DNN tasks in the MEC system are effectively reduced.

The invention provides a cue word optimization method and system based on an approximate sub-module function and continuous learning, and relates to the technical field of artificial intelligence multi-mode perception.The method comprises the steps that a candidate cue word set is constructed, a combined objective function based on the property of the approximate sub-module function is designed, and a candidate cue word set is constructed; solving the combined objective function by adopting a greedy selection algorithm combining random disturbance, multi-round iteration and a task self-adaptive mechanism, realizing optimal selection of a candidate cue word set, gradually selecting a cue word with the maximum gain from the candidate cue word set, adding the cue word into an optimal subset, and when a new cue word is selected, selecting the cue word with the maximum gain into the optimal subset. And if the target cue words are selected, carrying out local optimization once, after all the target cue words are selected, carrying out joint optimization on all the cue words in a continuous space by adopting an alternate optimization strategy, and carrying out continuous iteration until the optimized cue words are obtained. According to the method and the device, efficient self-adaptive updating of the cue words of the language model is realized, so that the generalization performance and robustness of the model in zero-sample, few-sample and concept drift scenes are improved.

The invention relates to a distributed photovoltaic ultra-short-term prediction method and system based on satellite cloud picture information, and belongs to the technical field of photovoltaic power prediction. Firstly, the correlation degree of meteorological variables and distributed photovoltaic power is measured through a Pearson correlation coefficient, and cloud layer distribution and movement characteristics thereof are analyzed and found to be key factors influencing photovoltaic output precision, so that a cloud cluster movement prediction framework under multiple time scales is constructed: a CNN-LSTM hybrid model is adopted to capture a nonlinear evolution rule of a cloud cluster; secondly, a feature cloud region dynamic selection algorithm is innovatively designed by means of a satellite cloud picture data source, an ultra-short-term distributed photovoltaic power prediction model based on the convolutional neural network is constructed by accurately quantifying a cloud layer shielding effect and combining the convolutional neural network, and the prediction precision is remarkably improved by effectively fusing cloud motion features.

The invention discloses a peasant household credit assessment method and system based on multi-modal data federated learning, and relates to the technical field of computer intelligence, and the method comprises the steps: constructing a dual-channel representation mode of land assets based on GIS space coding and property and age limit information, and generating multi-dimensional feature input of peasant household production capacity and asset conditions in combination with multi-source data; a three-level federated learning structure is constructed, data is safely exchanged through TLS communication encryption and equipment identity authentication, and differential privacy noise is injected; a Top-K gradient selection algorithm is adopted to carry out parameter sparse transmission, a sparse rate is dynamically adjusted in combination with a round increasing strategy, a grouping sparse convolution and parameter freezing mechanism is introduced, and a federal deep learning model is compressed and an adaptive edge is deployed through 8-bit distillation quantification; according to the method, multi-modal feature construction, layered federated training and a privacy enhancement mechanism are fused, and fine modeling, lightweight deployment and data security collaboration of peasant household credit assessment are realized.

A smart shopping cart identifies items using cameras and sensors. The cart captures images of items within its storage area and applies machine-learning models, such as a barcode detection model, an OCR model, and an image embedding model, to generate identifier predictions. These predictions are processed using an efficient selection algorithm, which may involve majority voting, weighted voting, or linear regression, to select the most accurate identifier. The cart updates its display and user interface with the identified item. The process may be performed primarily by the CPU to enhance computational efficiency, avoiding the latency associated with GPU data transfer. Additional techniques, such as circular buffers and frame skipping, are employed to further optimize resource usage.

The invention belongs to the technical field of software development and testing, and discloses an artificial intelligence-based software development automatic test case generation system, which is characterized in that an immune heuristic case self-repairing module is adopted, defects are regarded as antigens, antibody cases capable of being self-updated are generated by using a clone selection algorithm, and a gene rearrangement mechanism is automatically triggered when an interface is changed, so that the test efficiency is improved. The details of the use case are adjusted while the core detection logic is reserved; compared with a traditional method, the mechanism can realize use case dynamic adaptation without manual intervention, the maintenance workload is remarkably reduced, and the method is particularly suitable for a complex software system with frequent iteration; the space-time coupling test scene generation engine fuses dynamic scenes such as interaction and state transition of a module and short-time operation after precise coverage login by using a space-time convolutional network; the cross-dimension holographic use case synthesis module integrates multi-source data such as codes, hardware and user behaviors through tensor decomposition to generate a composite use case; functions and performance of software in a complex scene can be comprehensively verified, and test blind areas are remarkably reduced.

The invention belongs to the technical field of artificial intelligence and medical diagnosis, and particularly relates to an interpretable Transform medical diagnosis method based on prototype learning, and the method comprises the following steps: data preprocessing; extracting prototype features; constructing a model; optimizing an attention mechanism; key value pair storage: storing the key value pair as a parameterized prototype; designing a loss function; an Adam algorithm is selected to train the model, and the learning rate and batch size hyper-parameters are adjusted according to the actual situation; and evaluating and optimizing the model. According to the method, a prototype learning mechanism is introduced, the attention mechanism of the Transform model is optimized, and the innovative method overcomes the defect of insufficient model interpretation in the prior art. By combining the prototype features and the attention weight, the model can more accurately position key information, and the accuracy of medical diagnosis is improved.

The invention provides a single tree segmentation-biological parameter estimation method based on a vehicle-mounted LiDAR point cloud, and belongs to the technical field of urban landscaping intelligent monitoring. The point cloud individual tree segmentation algorithm based on tree geometric feature constraint is designed for solving the problem that individual tree segmentation is difficult due to crown overlapping in an urban scene, and the method takes a tree geometric structure as a constraint, combines point cloud reflection intensity information, a clustering algorithm, a main direction index and other means, and obtains the individual tree segmentation algorithm based on the tree geometric feature constraint. And accurate extraction of trunks and crowns in the scene point cloud is realized. Aiming at the problems of high feature redundancy, poor model interpretability and the like in an existing estimation method, a random forest model is taken as a basis, an adaptive feature selection algorithm is introduced to improve variable screening efficiency, hyper-parameters are dynamically adjusted by utilizing pigeon inspired optimization, and generalization and stability of the model are enhanced; an estimation model used for estimating biological parameters such as leaf area index, biomass and carbon reserve is designed.

The invention discloses an unmanned aerial vehicle non-cooperative environment autonomous landing method and system based on three-dimensional visual semantic information, and relates to the technical field of unmanned aerial vehicle landing recognized.The method comprises the steps that during emergency landing, a semantic segmentation model is used for generating a two-dimensional semantic segmentation map and obtaining semantic tags, and a landing area is automatically screened and tracked; fusing vision and inertia information through an airborne binocular camera and an IMU (Inertial Measurement Unit), outputting an accurate pose and depth map, and inputting a map construction algorithm to reconstruct a landform map of a landing area; projecting the semantic tag to a three-dimensional space and fusing the semantic tag with a terrain map to generate an evaluation map; and finally, an optimal landing point is determined by a landing point selection algorithm, and autonomous landing is completed through a flight control system. The method has the characteristics of high precision and fast reasoning, gets rid of dependence on external signals, significantly improves the landing capability under complex terrains, and guarantees the safety and reliability of autonomous landing.

The invention relates to the technical field of remote sensing data processing and earth surface parameter inversion, and discloses an earth surface matrix parameter hyperspectral data inversion method and system based on machine learning. Comprising the following steps: constructing a multi-source heterogeneous hyperspectral data set; performing feature screening on the preprocessed hyperspectral data set based on an adaptive band selection algorithm, constructing a dynamic weight matrix by calculating mutual information entropy and inter-class distance measurement between spectral bands to realize intelligent screening of key feature bands, and combining spectral derivative conversion and spectral index calculation to generate an enhanced feature vector; and a multi-task transfer learning neural network model is constructed, and an output layer realizes multi-parameter collaborative inversion based on a multi-task learning architecture. And performing preprocessing and feature enhancement operation which is the same as that of the training data on the hyperspectral image data of the to-be-inverted region, inputting the trained neural network model, and outputting a surface matrix parameter inversion result.

The invention provides a distributed photovoltaic data acquisition method and device based on adaptive encryption communication and multi-link redundancy, and the method comprises the steps: collecting real-time power generation data, equipment state data and environment parameters of a photovoltaic system through a multi-source sensor, and forming original data; an MQTT over TLS adaptive encryption communication protocol is adopted to perform end-to-end encryption on original data, and dynamic key management is combined to ensure transmission security; based on a dynamic link selection algorithm, encrypted data is transmitted to a power master station through 4G and LoRaWAN double-link redundancy, so that the transmission reliability is improved; and the master station side decrypts the data and then integrates the data to a database to support power grid dispatching and predictive analysis. According to the invention, the problems of safety and reliability in distributed photovoltaic data acquisition are solved, and the method is suitable for a smart power grid monitoring scene with high reliability and high real-time performance requirements.

The invention provides an end-side cloud collaborative approximate redundancy removal system and method for multi-camera video analysis, and belongs to the technical field of end-side cloud collaborative optimization of an internet application layer. According to the invention, an end-side cloud collaborative cache architecture is provided, the cloud cache provides more storage resources with lower cost, the edge cache is constructed through a maximum coverage cache selection algorithm, the edge cache is located at a place closer to the camera, and the delay is shorter. The camera is connected to the edge server and continuously transmits captured video streams to the edge server, and the system quickly and accurately identifies a target result by combining the advantages of the two caches. The same target is only identified once by the system, and then the features and the identification result of the target are stored in the cache. And when the target appears again, the system can query the corresponding identification result from the cache without calling the model for repeated calculation, so that the calculation resources are greatly saved.

The invention provides a multi-system redundant computer processing method and system and electronic equipment, and belongs to the technical field of computers, and the method comprises the following steps: S101, obtaining eight different nodes; s102, establishing a communication channel, encrypting the communication channel, and establishing an on-off channel; s103, acquiring a task feature vector, acquiring a resource feature vector, performing a dynamic clone selection algorithm, acquiring an optimal resource scheduling scheme, and performing resource scheduling; s104, detecting the computer computing node and the computer total node, and if any one node fails, starting a redundancy switching mechanism; s105, the real-time resource use condition of each computer computing node is monitored in real time, and when the load of at least one computer computing node reaches 85%, a redundant resource scheduling mechanism is started; s106, during operation, recording the related information in real time by the backup database; according to the method, resource allocation is more reasonable, and the data processing speed is increased.

The invention provides a digital clock remote metering method based on network delay suppression, which comprises the following steps of: constructing a layered synchronous architecture, deploying a main clock source, a plurality of regional clock servers and a plurality of terminal devices, connecting the plurality of regional clock servers with the main clock source through optical fiber special lines to obtain reference time, each regional clock server is connected with a plurality of terminal devices through a 5G / industrial Ethernet to form a three-level synchronous network; rTT data of the terminal device and a regional server are obtained through bidirectional timestamp interaction, and message screening is carried out according to a preset rule; calculating a final time delay compensation value by adopting a mixed time delay compensation algorithm, and calibrating a terminal clock by adopting the final time delay compensation value; and adopting a time selection algorithm to obtain an optimal reference source for data synchronous analysis. According to the invention, the time deviation problem between the current time indicating value of the digital clock and the standard time and the data synchronization analysis problem can be solved.

The invention discloses a quantum network path selection and resource allocation method and system for realizing high throughput, and the method comprises the steps: initializing global variables and network parameters at least comprising a network topology structure, a request number and the maximum capacity of edges through building a quantum network model; setting the initial capacity of each edge, adjusting the capacity according to the fidelity of the edge, and removing the edge of which the fidelity is less than 0.5; designing a path selection algorithm, and selecting an optimal path by using the characteristics of the quantum state; constructing a resource allocation strategy based on quantum entanglement and quantum parallelism, and dynamically allocating network resources; and verifying the validity of the path selection and resource allocation method through the quantum network simulator. The resource allocation sequence is optimized, redundant operation in the resource allocation process is reduced, and therefore consumption of computing resources is reduced.

The invention discloses a path planning method and system for power grid engineering construction machinery transportation, and a medium. The method comprises the following steps: constructing a construction environment model and a grid adjacency list according to path planning data; processing the construction environment model and the grid adjacency list based on a path connectivity algorithm to obtain a weighted grid connectivity graph; analyzing the weighted grid connected graph based on a path analysis algorithm to obtain a transportation route combination graph; on the basis of a path selection algorithm, selecting a route communicating all construction nodes of the construction area in the transportation route combination diagram as a temporary transportation route; and detecting all the temporary transportation routes based on a route detection algorithm to obtain a target transportation route. Therefore, the shortest and most economical transportation planning path is found through the adaptive path algorithm and the deep reinforcement learning technology.

The invention discloses an optimized node selection method based on a BGP-iSec protocol partial deployment scene, which comprises the following steps of: in a first stage, calculating three static topological characteristics of nodes according to a key node selection algorithm, weighting the three static topological characteristics and AS node levels in proportion, calculating a mixed weight of the nodes, and selecting the three static topological characteristics according to the mixed weight; preliminarily determining candidate nodes; and in the second stage, a greedy algorithm is adopted, the node with the maximum gain for the current path coverage rate is gradually selected from the candidate nodes according to the priority and added into the to-be-deployed set until the path coverage rate is stable or the number of the nodes reaches the specified deployment cost. According to the method, the key AS node selection strategy is formulated, so that the defense effect of the path manipulation / prefix hijacking attack is optimized under the limited deployment cost.

The invention provides a block chain-based distributed federated learning Internet of Vehicles knowledge sharing method, and relates to the field of block chains and Internet of Vehicles. Comprising the following steps: firstly, adopting a neural factor decomposition machine (NFM) as a prediction network, and selecting a vehicle node most suitable for participating in learning in combination with a dynamic adaptive node selection algorithm; secondly, the vehicle node transmits the learning result as transaction data to a roadside unit, and the roadside unit generates candidate blocks and ensures transparency and non-tampering of the data through a consensus mechanism based on knowledge proof; thirdly, global model updating is carried out in the edge cloud service by utilizing a knowledge distillation technology, and the model performance is optimized; and finally, constructing a non-cooperative game excitation model of vehicle nodes and roadside units, performing reasonable excitation distribution by adopting a deep Q network (DQN), and encouraging the nodes to actively participate in learning and contribute. Through the block chain technology and the distributed federated learning mechanism, the efficiency and security of knowledge sharing in the Internet of Vehicles environment are significantly improved.

A non-contact real-time monitoring system of physiological signs based on millimeter-wave radar includes a millimeter-wave radar and multiple modules for processing radar signals. The millimeter-wave radar is configured to continuously transmit electromagnetic wave signals and simultaneously receive echo signals, perform frequency mixing processing on the echo signals to obtain an intermediate frequency signal, and process the intermediate frequency signal to obtain a radar four-dimensional data matrix. Human body physiological signs are monitored by analyzing body thoracic cavity micro-motion information in signals through the modules; a target echo is processed by adopting a constant false alarm rate detection algorithm, and invalid signals are filtered. A self-adaptive range cell selection algorithm based on short-time stability of respiratory signals is adopted to capture radar echoes reflecting physiological movement. Mixed human body physiological sign signals are processed by using a VMD algorithm, and key parameters in VMD are optimized by using a GWO algorithm.

Disclosed are techniques for wireless communication. In an aspect, a method of wireless communication performed by a first pedestrian user equipment (PUE) includes performing a ranging operation to a set of UEs, the set including at least a second PUE, and providing ranging data to a third entity, the third entity comprising a vehicle user equipment (VUE) or a road-side unit (RSU). The set of UEs may be randomly selected or selected using a selection algorithm. The set of UEs may be selected by the PUE or by the third entity. The ranging data may include the location of the first PUE, and may include a report on the battery status of the first PUE. The third entity may use the ranging data to update estimated positions of the first PUE and the set of UEs.

A method for previewing the terrain in front of an emergency rescue vehicle in a geometric feature degradation scene relates to the technical field of road surface recognition, realizes accurate alignment of LiDAR point cloud and IMU data through timestamp synchronization and linear interpolation, and combines a spherical projection model and a degradation perception complementary feature selection algorithm to realize the previewing of the terrain in front of the emergency rescue vehicle. Converting the three-dimensional point cloud into a robust intensity image and extracting gradient significant features; based on a double-observation secondary filter frame, a motion state is predicted by utilizing IMU forward propagation, a point cloud geometric residual error and an intensity image luminosity residual error are synchronously fused, timestamp deviation is compensated through back propagation, and a multi-source observation model under a global coordinate system is constructed. Finally, the error state is iteratively optimized to realize collaborative output of the high-precision odometer and the three-dimensional terrain map, and the problems of data asynchronism, feature degradation and dynamic interference in a complex scene are effectively solved.

The invention discloses an Internet of Vehicles low-delay federated learning method based on semi-asynchronous communication, and the method comprises the steps: constructing a vehicle-road cloud cooperative federated learning framework and a system performance model, building a federated learning training time delay optimization model, minimizing the overall training time delay, and meeting the vehicle energy consumption upper limit and global model precision constraint conditions; a multi-dimensional priority dynamic vehicle selection mechanism is realized, and probability sampling is carried out by using a roulette selection algorithm; a vehicle-RSU federated learning strategy based on semi-asynchronous communication and knowledge distillation optimizes the problem of lagging behind and the problem of model obsolessness; a cloud model aggregation opportunity is optimized based on a model difference metric and an adaptive cloud aggregation strategy of a dynamic threshold trigger mechanism. According to the method, collaborative optimization is carried out on multiple levels of equipment selection, intra-layer communication aggregation, old model processing, inter-layer aggregation triggering and the like, so that the overall training time delay of the Internet of Vehicles federated learning system can be effectively reduced on the premise of satisfying model precision and equipment energy consumption constraints.

The invention discloses an auction and multi-agent deep reinforcement learning-based electric vehicle charging scheduling method, and relates to the technical field of electric vehicle shared charging reservation scheduling. The method comprises the steps of providing an auction-based shared charging reservation system model; a sharing charging optimal user selection problem is formalized by taking maximization of social welfare as a target; an auction mechanism is introduced, and a new appointment user selection incentive mechanism is provided to calculate a winner set and the payment prices of winner; the system is further expanded, and the reservable time of the charging pile is divided into a plurality of time periods; a charging period selection algorithm based on a multi-agent deep reinforcement learning network is provided, and the objective is to maximize the utility of the charging of the electric vehicle user. According to the invention, the problems of too long waiting time and waste of charging pile resources caused by the fact that the electric vehicle users are gathered in the charging peak period in the existing charging reservation system can be solved, and meanwhile, a multi-period reservation and anti-strategy scheduling mechanism can be provided for the electric vehicle users.

The invention discloses a 10kV power distribution network multi-scene fault research and judgment intelligent fusion method and system, and belongs to the technical field of power distribution network fault analysis, and the method comprises the steps: judging whether a starting condition is satisfied or not based on the change of zero-sequence voltage, and executing a feature extraction step when the starting condition is satisfied; extracting characteristic data related to the fault, wherein the characteristic data comprises a characteristic reflecting a voltage and current phase relation, a characteristic reflecting an energy characteristic and a characteristic reflecting a current energy ratio; according to a preset feature selection rule, determining features used for fault judgment from the feature data; determining whether the fault is located in the monitoring section based on the features for fault determination; and updating starting parameters for subsequent judgment based on the voltage and current data during the fault period. According to the method, the fault judgment result is given through the judgment result of one of the three algorithms selected through feature quantity self-recognition and self-judgment, the algorithms are selected in a targeted mode for multiple complex scenes on site, and the fault judgment accuracy is improved.

The invention provides a channel reconstruction method, device and equipment based on a multi-probe microwave anechoic chamber and a storage medium, and the method comprises the steps: building a target channel three-dimensional spherical power spectrum model based on an azimuth angle power spectrum and a pitch angle power spectrum; generating a target channel spatial correlation matrix based on the three-dimensional spherical power spectrum model and the position vector of each antenna pair; calculating a transmission matrix between the probe position and the antenna pair; determining a target probe based on the target channel spatial correlation matrix, the transmission matrix and a progressive selection algorithm; iterating the probe weight of the target probe through a preset probe weighting algorithm based on mirror image descent to obtain a target probe weight; obtaining the spatial correlation of a simulation channel based on the position of the selected probe and the obtained weight of the target probe, and completing channel reconstruction; according to the invention, the problems of low efficiency and difficulty in meeting high-precision requirements in the channel reconstruction process can be solved; and the channel reconstruction precision and efficiency are improved.

The invention discloses an intelligent autonomous networking monitoring method and system based on optimal channel selection, and a medium, a gateway selects an optimal channel from a plurality of communication channels as a data channel, and divides each time slot of the data channel into a communication segment and a broadcast segment; performing data communication with the networked monitoring node corresponding to the current time slot in the communication section; broadcast information of non-networking monitoring nodes is received in a broadcast segment, and a networking information packet is generated and replied; after being powered on, each monitoring node is switched to a default broadcast channel to send broadcast information to a gateway to request networking, and after receiving a networking information packet, the monitoring nodes which are not networked analyze the networking information packet to obtain networking information, calculate the next wake-up time of the monitoring nodes according to the networking information, and wake up the monitoring nodes when the monitoring nodes sleep to the next wake-up time; the networked monitoring nodes are switched to a data channel to perform data communication with the gateway after being awakened, and the nodes are timed and synchronously awakened with the gateway through a dynamic channel selection algorithm and a time-sharing synchronous communication mechanism, so that the power consumption is reduced.