194 results about "Network formation" patented technology

The invention provides a deep learning-driven coastline change dynamic monitoring method, and belongs to the technical field of image understanding and recognizing.The deep learning-driven coastline change dynamic monitoring method comprises the steps that a time sequence attention matrix is formed by constructing a multi-source satellite data fusion network, and coastline form change features are extracted by applying a fourth-order time sequence convolutional neural network to construct a first fluctuation matrix; and performing dimension reduction processing by using a space-time attention mechanism to generate a second fluctuation matrix, and converting the second fluctuation matrix into a quantitative index through a fluctuation conversion matrix. The method comprises the steps of inputting data into a pre-trained CoastDynNet model to analyze a physical mechanism, performing fine adjustment by applying a coastline evolution optimization function, performing multi-scale decomposition by using a WaveletTempNet network to separate long and short term changes, and finally constructing a multi-model integration framework by using a Bayesian model averaging method to generate a comprehensive coastline dynamic evaluation report.

A method for quality inspection of an etching paste material based on a spectral response difference, including: identifying a position of density mutation boundary through spectral collection and density gradient analysis. Applying a specific frequency beam to obtain the resonance response parameter, generating stress field reconstruction data. Performing a reverse optical tracking to obtain a defect formation path, generating degradation prediction data. Establishing a self-organizing optical monitoring grid, to form a hierarchical spectral fingerprint library. Constructing a multi-point linked defect blocking network, to form an interconnected optical energy field. Monitoring a cooperative response to obtain network stability data, Identifying abnormal position coordinates based on a material quality grade and the network stability data, obtaining the abnormal position coordinates. Performing a phase adjustment to obtain a phase difference spectral set, conducting a phase comparison marking on the abnormal position coordinates, completing the quality inspection of the etching paste material.

The invention provides a signal optimization transmission system and method based on HPLC and HRF dual-mode communication, a signal acquisition and processing module of the signal optimization transmission system based on HPLC and HRF dual-mode communication is used for acquiring signals of two communication modes of HPLC and HRF in real time, preprocessing the signals and extracting key signal parameters; the dual-mode switching module is used for dynamically switching an HPLC mode and an HRF mode based on channel quality; the access module is used for constructing a longitudinal backbone network and a transverse Mesh network, forming a hybrid topology, optimizing a routing path and coordinating resource allocation and conflict avoidance of HPLC and HRF; the intelligent decision module is used for predicting future interference intensity according to the historical interference data and adjusting channel parameters; the central coordinator module is used for network initialization and resource allocation; according to the system and the method, the HPLC mode and the HRF mode are automatically switched according to the channel quality, the longitudinal HPLC backbone network is combined with the transverse HRF Mesh network to form a four-dimensional communication network, and the robustness in a complex scene is remarkably improved.

The invention belongs to the technical field of power system stability control, and particularly discloses an inverter network following and constructing control mode adaptive adjustment method and system, and the method comprises the steps: obtaining a current power grid state parameter in real time; a multi-scale fusion depth evaluation model is constructed and trained; inputting the current power grid state parameters into the trained multi-scale fusion depth evaluation model, and outputting current power grid state evaluation indexes; according to the current power grid state evaluation index, adaptively selecting a control mode, wherein the control mode comprises a network following type inverter dominant mode, a network constructing type inverter dominant mode and a transition mode; and according to the selected control mode, verifying whether the power grid state satisfies a constraint condition, and if not, carrying out global optimization on the control parameters of the inverter. According to the invention, the dynamic stability and disturbance response capability of the power system can be improved through the adaptive adjustment of the network-following type and network-constructing type control modes of the inverter.

The invention relates to the field of breath sound recognition, and particularly discloses a breath sound classification method and system based on a Mel spectrogram, which utilizes a breath sound classification model with a novel network structure to improve the sensitivity and specificity of breath sound classification. Particularly, the breath sound classification model with the novel network structure comprises an audio frequency spectrogram converter network, a multi-scale feature extraction network, a time sequence feature extraction network and a linear output layer network, the multi-scale feature extraction network and the time sequence feature extraction network form a feature coding and decoding architecture to carry out global feature and local feature reinforcement learning on breath sound spectrum features output by the audio frequency spectrogram converter network, so that sensitivity and specificity of breath sound classification are improved.

The invention provides a multifunctional inspection device detection system based on a distribution network mobile operation terminal, and relates to the technical field of electric power detection, and the multifunctional inspection device detection system comprises a portable main terminal of a dynamic collaborative architecture, a wearable terminal for augmented reality interaction and a distributed heterogeneous sensing cluster, which form a closed-loop data chain through an electric power dedicated low-delay wireless communication network; the portable main terminal is integrated with a heterogeneous computing unit with adaptive computing power distribution, can perform real-time fusion analysis on multi-dimensional sensing data, and outputs a visual result containing fault location and confidence; the wearable terminal superposes fault information to a real scene in a three-dimensional marking form through a virtual-real fusion positioning technology, and supports eye movement and voice collaborative interaction; multi-module collaborative acquisition of the distributed heterogeneous sensing cluster is combined with a nanosecond timestamp synchronization mechanism, so that the limitation of traditional single parameter detection is broken through, rich and synchronous basic data is provided for subsequent analysis, and the comprehensiveness of routing inspection is greatly improved.

The embodiment of the invention provides a closed-loop control method for power driving and protection, and the method comprises the steps: constructing a voltage feedback type circuit through a high-speed operational amplifier, and forming a closed-loop gain structure through a feedback network of the voltage feedback type circuit; the load current of the output end of the voltage feedback type circuit is monitored in real time, and when the load current exceeds a first set threshold value, a protection mechanism is triggered to turn off the output stage; the power-on surge current is limited through the soft start control circuit; phase information of a load reflection coefficient is obtained, an inductance / capacitance combination of the variable dynamic impedance matching network is selected according to phase information table look-up, inductance / capacitance parameters are switched through a relay, and the transmission standing-wave ratio between the power amplifier and a load is optimized; monitoring the temperature of the radiating fin of the power tube, and dynamically adjusting the resistance value of the feedback network; and performing cooperative control of short-circuit protection, over-temperature protection and surge suppression according to the priority. According to the embodiment of the invention, the performance bottleneck in a load fluctuation scene is broken through.

The invention discloses an elastic distributed Nash equilibrium search method and device under FDI attack, and relates to the technical field of non-cooperative game decision, the method comprises the following steps: constructing a multi-agent network, forming a non-cooperative game, and defining an objective function of each agent in the multi-agent network; establishing a malicious FDI attack model; the malicious FDI attack model maps false data injection attack to control input of an agent; an elastic self-adaptive distributed Nash equilibrium search algorithm is designed based on the target function, so that behaviors of other intelligent agents are estimated under the condition that the intelligent agents cannot obtain global information and attack information; the elastic self-adaptive distributed Nash equilibrium search algorithm comprises a leader-follower consistent consensus estimation-based identifier signal designed by an observer and a self-adaptive dynamic gain part. The elastic self-adaptive distributed Nash equilibrium search method solves the problem of elastic self-adaptive distributed Nash equilibrium search when the non-cooperative game is attacked by unknown FDI.

The invention discloses an intelligent non-contact structure displacement detection method based on computer vision, and belongs to the field of deep learning, and the method comprises the steps: obtaining a first image containing a displacement target, building a first recognition model based on a YOLOv7 algorithm, processing the first image through the first recognition model, and obtaining a recognition result of the displacement target; constructing a tracking network of the displacement target based on a DeepSORT algorithm, and inputting an identification result into the tracking network to form a movement track of the displacement target; a structural displacement analysis network is constructed based on an IPM-ED method, the structural displacement analysis network analyzes the motion trail, and actual structural displacement of the displacement target is generated; and forming a first time-history curve of the displacement target based on the actual structure displacement, and comparing the first time-history curve with the first measurement curve to obtain a structure displacement detection effect. According to the invention, the precision of non-contact displacement detection is improved.

The invention discloses a cross-chain transaction-oriented smart contract collaboration method, and belongs to the technical field of cross-chain contract collaboration, and the method specifically comprises the steps: deploying a log collection assembly at each cross-chain transaction smart contract node, and recording log information in real time; based on a cross-chain collaborative typical risk scene, various risks are converted into quantifiable detection rules, and a risk rule model reads node log streams in real time; when the risk rule model detects an exception, forming a complete cross-chain calling graph according to the transaction association identifier in the node log; extracting operation features of nodes in the cross-chain calling atlas, comparing the operation features with a normal node feature library, and screening out suspected fault nodes of which the operation features deviate from a threshold value; the suspected fault node abnormal data, the graph fragments and the log information are integrated, a unique responsibility affirmation abstract is generated, and the unique responsibility affirmation abstract and complete materials are stored in a shared evidence storage network to form a non-tampering certificate; and after the voucher is generated, calling a responsibility investigation intelligent contract, and executing cross-chain responsibility investigation operation according to the fault node identifier and the responsibility terms.

A control device may be configured to form a network at a unique coordinated startup time. The control device may identify a role assigned to the control device in a previously-formed network that the control device was attached to. The control device may determine a unique coordinated startup time for the control device based on the role assigned to the control device in the previously-formed network. The control device may initiate a network formation procedure at the unique coordinated startup time for the device. For example, the network formation procedure may cause the control device to attach to another control device in the network. The network formation procedure is configured to enable the control device to assume the role assigned to the control device in the previously-formed network in the new network.

A control device may be configured to delay an attachment procedure while attachment messages are being transmitted over the network. The control device may be configured to initiate an attachment procedure with a router device on a network at the end a back-off period of time. The attachment procedure may include transmitting attachment messages (e.g., parent request messages) that enable the control device to transmit and receive messages over the network through the router device. During the back-off period of time, the control device may determine an attachment message is received from another control device on the network. And, if an attachment message (e.g., a parent request messages and / or a link request message) is received from another control device, the control device may increase the back-off period of time (e.g., delaying when the control device initiates its attachment procedure).

The invention discloses a networking new energy cluster wide area state perception and data fusion method and system, and the method comprises the steps: carrying out the multi-level data collection of all stations of a new energy cluster, and obtaining the multi-level multi-source heterogeneous data; performing multi-time-scale data preprocessing on the multi-level multi-source heterogeneous data to obtain fused data; constructing a multi-level adaptive state sensing model based on the fused data, and performing state sensing based on the multi-level adaptive state sensing model; and constructing a multi-domain associated data mapping mechanism based on the fused data and the state sensing result, so as to realize a closed loop from sensing to evaluation and from evaluation to optimization based on the multi-domain associated data mapping mechanism. According to the method, a technical system of'multi-level acquisition, multi-time-space fusion, adaptive perception and multi-domain mapping 'is constructed, an end-to-end closed loop of a new energy cluster from perception to optimization is realized, and deep collaboration and accurate interaction between new energy and a power grid are realized through technical innovation and fusion application.

The invention discloses a category incremental target detection method and related equipment, and the method comprises the steps: firstly extracting the multi-scale features of an input image, coding a known category text label to generate an embedded feature, calculating an average feature, and then generating an unknown category pseudo text embedded feature; a cross-modal feature pyramid is formed through a cross-modal fusion network, so that a known target is predicted and an unknown target is positioned; self-supervised training is carried out on an unknown target to generate a pseudo label to finish category fine tuning; and finally, model expansion is realized through incremental updating of a low-rank matrix and a shared matrix. According to the scheme, through pseudo-text embedding features and cross-modal fusion, the model is endowed with the unknown target recognition capability, and the application limitation of the open world is broken; by means of self-supervised false label fine tuning and matrix increment updating, full-category retraining is avoided, computing resource consumption and training duration are reduced, interference on original category weights is prevented, and stability and reliability of a target detection system during category expansion are guaranteed.

The invention discloses an electric power monitoring system based on artificial intelligence, and relates to the technical field of electric power monitoring, comprising: extracting a behavior disturbance unit from communication, operation and configuration logs of an electric power system; constructing a disturbance sequence by the behavior disturbance units according to the dependency relationship between the time sequence and the power system module; training an anti-fact generation network by using the disturbance sequences to form anti-fact tracks in one-to-one correspondence with the disturbance sequences; performing node-by-node comparison on the disturbance sequence and the anti-fact trajectory, extracting deviation points and combining the deviation points into an attack evolution chain; constructing a multi-dimensional action prediction tensor based on the attack evolution chain, wherein the multi-dimensional action prediction tensor is used for reasoning next-step action probability distribution of an attacker; and dynamically adjusting defense resources of the power monitoring system according to an action prediction tensor output result, and implementing right limiting, cheating or steering operation with specific granularity to interrupt attack evolution chain extension. According to the method, the attack path can be gradually restored from the detected abnormal behaviors, and the possible next action of an attacker is predicted.

The invention discloses a digital media content element accurate screening method based on artificial intelligence image recognition, and relates to the technical field of digital media, and the method comprises the steps: building a distributed capture network to form a dynamic content pool, and building a metadata index database; calling a multi-modal image perception engine to generate a double-layer characteristic spectrum containing dominant and recessive elements; constructing a distributed recognition model cluster based on federated learning; converting the user demand into a screening parameter set and generating a decision tree; screening and secondarily verifying an output result through a double-path matching mechanism; and constructing a reinforcement learning reward function based on user behaviors, and driving the model and the decision tree to co-evolve. Multi-source heterogeneous content full-dimension analysis is achieved, the recognition comprehensiveness and depth are improved, knowledge barriers and privacy risks are solved, the screening accuracy and flexibility are improved, the system is endowed with the continuous optimization capacity, and the method is suitable for efficient and accurate digital media content screening scenes.

The invention discloses a large-scale unmanned aerial vehicle cluster regional cooperative positioning method based on a network formation game, which comprises the following steps: firstly, carrying out regional cooperative navigation on a large-scale unmanned aerial vehicle cluster, then finding a cooperative unmanned aerial vehicle set with the maximum utility through a game rule, selecting an optimal connection strategy network structure, and carrying out cooperative navigation on the large-scale unmanned aerial vehicle cluster. The low-precision unmanned aerial vehicle can adaptively select the high-precision unmanned aerial vehicle in the region for cooperative positioning; and finally, positioning the low-precision unmanned aerial vehicle by using a belief propagation method according to the cooperative unmanned aerial vehicle set updated each time. According to the method, the scale of the unmanned aerial vehicle cluster is expanded, the large-scale cluster is subjected to region division, and the cooperative unmanned aerial vehicle set of the to-be-positioned unmanned aerial vehicles in each region is optimized through a network formation game method, so that the to-be-positioned unmanned aerial vehicles only need to receive navigation information in the cooperative region and adaptively select the optimal cooperative unmanned aerial vehicle; therefore, the network connection complexity is reduced, the effectiveness of the unmanned aerial vehicle is maximized, and the positioning precision of the unmanned aerial vehicle cluster is ensured.

According to the combined phase estimation and defocusing fuzzy data set generation method provided by the invention, matching is carried out after a full-bright image is projected, so that the shadow defect can be avoided, and the depth estimation of the shadow part of the projected image is correct; structured light projection fringes are improved, Gray codes and phase shift codes are combined, the moire phenomenon is avoided, and accurate edge parallax is obtained. The invention also provides a DPD-based joint phase estimation and defocusing fuzzy network forming method, and the method comprises the steps: setting an independent parallax estimation module, and obtaining an accurate parallax result; according to the parallax image, an image alignment module is added, the deblurring performance is improved, and parallax estimation is more accurate. The invention further provides a QPD-based combined phase estimation and defocusing fuzzy network forming method, parallax fusion and feature fusion are carried out by fully utilizing parallax information and image feature information according to the imaging principle of a QPD image sensor, and a clearer deblurring effect and a more accurate depth estimation effect are obtained.

The invention discloses a medicine sales management method and system based on information monitoring, and relates to the technical field of medicine sales management, and the method comprises the steps: collecting medicine sales time sequence data, carrying out the cleaning and structural processing, and generating standardized sales data; based on the standardized sales data, constructing a sales behavior time sequence causal network, embedding virtual sales intervention nodes, and generating a time sequence causal graph; the intervention strategy is coded to the virtual intervention node in the time sequence causal atlas, the graph neural network is utilized to simulate time sequence propagation of the intervention strategy, and a sales prediction result is obtained; and combining the sales prediction result with the real-time inventory, constructing a multi-level distributed sales dependency network, and forming the sales dependency network. According to the method, the preset intervention strategy is embedded into the causal structure in the node form, so that dynamic modeling of the multivariable causal relationship in the sales behavior is realized, and the prediction result not only reflects the historical trend, but also can respond to the possible influence of different intervention strategies.

The invention provides an unmanned aerial vehicle distribution scheduling method and system applied to a communication interruption region, and the method comprises the steps: determining unmanned aerial vehicle information used for the communication interruption region, and carrying out the air networking of all unmanned aerial vehicles according to the unmanned aerial vehicle information; after networking succeeds, defining a coordinate set of all the unmanned aerial vehicles participating in networking, constructing a logarithmic path loss model according to the coordinate set, and obtaining the signal propagation intensity of any position point of the communication interruption region according to the path loss model; constructing a signal coverage rate objective function according to the signal propagation intensity, and constructing a network average link quality objective function and a node energy consumption balance objective function to obtain a comprehensive objective function; and taking the maximization of the comprehensive objective function as a target, and solving by adopting an improved particle swarm optimization algorithm to obtain the deployment positions of all the unmanned aerial vehicles in the communication interruption region. According to the invention, the deployment efficiency and reliability of the disaster area communication system can be significantly improved.

The invention belongs to the technical field of communication, and particularly relates to a Bluetooth-based multi-user ad hoc network talkback implementation method, which comprises the following steps that: equipment to be networked initiates a networking request; the to-be-networked device sends a broadcast data packet to the surrounding device in a broadcast or scanning mode, and continuously scans the surrounding device; screening the equipment of which the parameters of the surrounding equipment accord with a preset connection condition as target networking equipment; establishing a communication channel connection between the to-be-networked device and the target networking device, wherein the communication channel comprises a data transmission channel and a voice transmission channel; performing connection quality evaluation on the communication channel by adopting a connection quality evaluation mechanism, generating an evaluation result, and adjusting networking connection according to the evaluation result; according to the scheme, through an innovative equipment discovery and connection strategy, an efficient networking protocol and topological structure, optimized voice transmission quality and dynamic connection management and optimization, the convenient, efficient and high-quality requirements of multi-person real-time intercom communication can be met.

The invention belongs to the technical field of computer hardware, and discloses a self-adaptive voltage regulation method for dynamic power consumption management of a notebook computer, which comprises the following steps: collecting multi-source data, generating a state label, and constructing to obtain a four-dimensional situation map; task association strength and load types are analyzed through space-time association, and double tags are generated in combination with user behaviors; component security boundaries are divided, multi-type credits are distributed, and a DAO smart contract set is formed by constructing a component node network; a state circulation rule is formulated in combination with the real-time load characteristics of the components; in combination with the V-F curve, a voltage regulation parameter table is generated in the sub-regulation scene, a synchronous regulation group is reconstructed, a synchronous regulation group configuration table is generated, then component voltage regulation and state circulation are executed, and a state circulation completion signal is generated; generating a parameter optimization scheme through three-level feedback, and deducing and quantifying the expected effect of the scheme to form an evaluation report; and executing a parameter optimization scheme, and synchronously updating to a preorder link as a new reference.

The invention discloses an environmental protection data monitoring method and system, and relates to the technical field of environmental protection data monitoring, and the method comprises the steps: constructing a monitoring network in which a fixed monitoring station and a mobile monitoring unit cooperatively work, and achieving the comprehensive collection of real-time data of air quality; multi-source data is transmitted to a cloud platform by using a high-speed data transmission network formed by low-power wireless communication LPWAN and a 5G network, and a high-resolution thermodynamic diagram is generated through a data puzzle algorithm, so that the defects of insufficient space coverage and data updating lagging of traditional monitoring are effectively overcome. According to the method, a pollutant component quantitative model is further constructed by adopting a big data analysis and stoichiometric method, real-time data of PM2.5, VOC and sulfur dioxide are mapped to each pollution source, and accurate quantification of contribution rates of pollution sources such as industrial emission, traffic tail gas and building dust is realized through automatic normalization of a soft maximization function.

The invention belongs to the technical field of biomedical signal processing, and discloses an SEEG attack detection method based on a time-frequency space-dynamic graph attention network, which comprises the following steps: constructing an epilepsy brain network sequence, defining SEEG lead points as nodes of a graph, and calculating directed connection edge weights among the nodes through a multiband directed transfer function; constructing a node feature matrix based on epilepsy induced epilepsy index features; arranging the multiband epilepsy brain network according to a time sequence to form a dynamic sequence; extracting space-spectrum correlation by using a GATv2 dynamic attention mechanism, and dynamically weighting node connection weight through a learnable attention matrix; and in combination with a time sequence convolutional network and a time attention mechanism, key time sequence features are extracted, identified and focused. According to the method, a good dynamic propagation characteristic learning capability is obtained, an SEEG pathological connection mode is disclosed, a model mechanism corresponds to an epileptic seizure propagation mechanism, and the method has a good application prospect in the aspects of epileptic seizure detection and epileptic seizure auxiliary positioning.

The invention relates to the technical field of data analysis, in particular to a traffic flow prediction method and system based on an interpretable graph and a multi-scale time trunk. The method comprises the following steps: processing historical traffic flow information of a target road network according to a prediction model to obtain traffic flow prediction information of the target road network in a target time period; the prediction model is a composite model formed by fusing a space backbone network and a time backbone network, the space backbone network comprises a graph neural network, the time backbone network comprises an improved PatchTST model, and the improved PatchTST model comprises a multi-time scale patch. The multi-time scale patch comprises a scale patch corresponding to short-term fluctuation, a scale patch corresponding to intra-day rhythm and a scale patch corresponding to cross-day trend. According to the method, the real constraint of each node in the road network can be combined, the complex time sequence characteristics of the historical traffic flow information of the road network under each time dimension can be fully mined, and more accurate and reliable traffic flow prediction information can be obtained.

The invention discloses an unmanned aerial vehicle complex airspace cooperative passage method based on a graph neural network, and the method comprises the steps: collecting the airspace data of an unmanned aerial vehicle complex airspace, and generating standardized airspace data; constructing a mixed queuing network, and forming arrival, queue length and service capability state of each service node and service link; mapping to obtain graph nodes, graph edges and graph features, and constructing an airspace graph; inputting the airspace graph into the graph neural network, and carrying out constraint updating on the mixed queuing network; integrating the service capability state, the queue length state and the congestion propagation parameters to form a network passing cost relationship; and executing a Frank-Wolfe algorithm, and generating a cooperative passing instruction for each unmanned aerial vehicle. According to the invention, by introducing the mixed queuing network and the Frank-Wolfe algorithm, efficient and stable cooperative passage scheduling of multiple unmanned aerial vehicles in a complex airspace environment is realized.

The invention discloses an attention mechanism and residual network fused industrial user carbon emission monitoring method and device. The method comprises the following steps: S01, acquiring electrical load sequence data of a to-be-detected user and state data of all corresponding key carbon emission equipment within a specified duration, and taking the data as training sample data; s02, the training sample data are input into a network model formed by fusing an attention mechanism and a residual network for training, an equipment state recognition model is obtained, and the network model is obtained by embedding a channel attention mechanism and a BAM attention module into a contraction residual network; s03, acquiring real-time data and inputting the real-time data into the equipment state recognition model to obtain a recognition result; and S04, obtaining carbon emission factors corresponding to different equipment states, and calculating real-time carbon emission according to a real-time state identification result and the corresponding carbon emission factors. The method has the advantages of being simple in implementation method, low in cost, high in efficiency and precision, high in real-time performance and robustness and the like.

The invention relates to a subgrade slope stability intelligent prediction method based on deep learning, and belongs to the technical field of data processing, and the method comprises the following steps: 1, constructing a multi-field coupling data collection network, and forming a dynamic coupling data set with synchronous time response; 2, a meta learning-dynamic graph Transform hybrid model is constructed, rapid adaptation of a new slope scene is achieved through meta learning, the dynamic graph Transform updates rock mass unit mechanical association in real time, the dynamic graph Transform captures multi-field coupling data features, and a Hoek-Brown criterion and a damage evolution equation are embedded to serve as double physical constraint layers; 3, a causal discovery algorithm is introduced to identify key stability factors, a multi-objective evolutionary algorithm is adopted to optimize meta-learning-dynamic graph Transform hybrid model hyper-parameters, and a dynamic loss function is constructed by using project full life cycle risk cost; 4, inputting real-time monitoring data, outputting data, and cooperatively updating multi-engineering data; the method has the beneficial effects that the causal effect in a group is quantified, hyper-parameter optimization is guided, and the prediction performance is improved.

The invention discloses a bare rock slope ecological restoration scheme demonstration method based on VR simulation, and the method comprises the steps: obtaining the multi-source data of a bare rock slope, and carrying out the fusion processing, and generating a three-dimensional digital model and an ecological parameter distribution diagram. Key nodes in the slope are identified, a connection network between the key nodes is constructed, and a preliminary resource allocation scheme and a network topology structure are formed; water resource flowing and vegetation growth processes are simulated by means of a VR simulation technology, and a resource allocation scheme and a water flow distribution prediction map are optimized. And obtaining an optimal solution of resource allocation in combination with a vegetation coverage target and a matrix material characteristic. According to the distribution table of the key nodes, a visual model of the slope restoration process is generated through three-dimensional rendering and dynamic simulation, ecological response data is analyzed to optimize a restoration scheme, and finally a complete slope ecological restoration scheme is formed. According to the method, intelligent generation and optimization of the bare rock slope ecological restoration scheme are realized, and the restoration efficiency and accuracy are improved.

The present application belongs to the technical field of computer hardware, and discloses a self-adaptive voltage regulation method for dynamic power management of a notebook computer, comprising: collecting multi-source data, generating a state label, and constructing a four-dimensional situation map; analyzing the task correlation strength and load type through space-time correlation, generating double labels in combination with user behavior; dividing the component safety boundary and allocating multiple types of credit, then forming a DAO smart contract set by constructing a component node network; and formulating state flow rules in combination with the real-time load characteristics of the components; generating a voltage regulation parameter table in combination with a V-F curve, reconstructing a synchronous regulation group and generating a synchronous regulation group configuration table, then executing component voltage regulation and state flow, and generating a state flow completion signal; generating a parameter optimization scheme through three-level feedback, and deducing the expected effect of the quantization scheme to form an evaluation report; and executing the parameter optimization scheme and synchronously updating it to the previous link as a new benchmark.