407 results about "Decision tree model" patented technology

The invention provides a combined wind power prediction method suitable for a distributed wind power plant, and the method comprises the steps: collecting the real-time meteorological data and historical power data of a wind power plant cluster, carrying out the cross-wind-plant data collaborative cleaning, and generating a time-space aligned standardized data set. Constructing an adaptive spatio-temporal feature extractor, outputting a spatio-temporal feature matrix, and inputting the spatio-temporal feature matrix into the spatio-temporal adaptive neural network, the graph attention prediction model and the physical constraint decision tree model to generate three prediction sequences. And the sequences are fused through a space-time collaborative attention mechanism to generate a dynamic weighted combination prediction result. And performing physical constraint correction on the result by using a space-time residual error correction network to generate a final prediction sequence. And updating the neural network topological structure based on the prediction error distribution, and outputting a prediction result with uncertainty evaluation to a power grid dispatching system. According to the method, the precision and reliability of wind power prediction of the distributed wind power plant can be improved, and the stability and economy of power grid dispatching are improved.

The invention discloses an unstructured storage hierarchical strategy optimization method based on machine learning, and relates to the technical field of data storage management, and the method comprises the steps: monitoring a file access event in real time, generating an access log, and extracting a multi-dimensional feature data set; utilizing the trained multi-dimensional decision tree model to distribute a corresponding storage hierarchy for the storage object to obtain a storage hierarchy decision; according to a storage level decision, distributing the storage objects to different storage layers, and carrying out resource configuration and storage operation; monitoring the access condition of the storage object in the new storage hierarchy in real time, and collecting file access performance, storage cost and response time to obtain feedback data; by monitoring the file access event in real time and extracting the multi-dimensional feature data set containing the basic attribute, the access behavior and the context information, efficient response of hotspot data and reasonable utilization of resources are ensured, and the overall performance utilization rate and the cost effectiveness of storage are remarkably improved.

The invention relates to the technical field of data encryption, and discloses a data encryption method, encryption equipment and a storage medium. In the method, an encryption device obtains to-be-encrypted data uploaded by a terminal, and extracts a plurality of data features from the to-be-encrypted data, the data features including a data sensitive feature, a business risk feature and a user behavior feature; performing quantitative scoring on the plurality of data features to obtain a plurality of feature scores, and calculating a total data score according to the plurality of feature scores; inputting the total data score into a preset trained decision tree model to determine the security level of the to-be-encrypted data; according to the security level, determining a target encryption algorithm from a preset corresponding relationship between the security level and the encryption algorithm; and encrypting the to-be-encrypted data according to the target encryption algorithm and the dynamically generated encryption key. By means of the method, the problems that differential protection is difficult to achieve according to data characteristics in a related encryption method, and security risks are caused by key management staticization are solved.

The invention discloses a hydrological data model fusion analysis method based on water resource scheduling decision support, and relates to the field of water resource scheduling, and the method comprises the steps: loading an HMM-BP noise detection model, obtaining a multi-source data fusion model, and outputting a space-time grid data cube; constructing a water consumption behavior prediction unit through a TPB decision tree model, adding a water right transaction engine, and outputting a water consumption prediction value, a behavior of an intelligent contract protocol and a transaction cooperation model; an incremental method is adopted to generate N combined scene matrixes of rainfall and temperature rise correlation adjustment, an ecological stability evaluator is combined, a climate and ecological coupling model is constructed, and a scene and risk mapping table and a resource re-allocation weight are output; and constructing an intelligent rule base platform to obtain an executable decision logic tree. The system has the advantage that full-chain intelligentization of water resource scheduling is realized through modular integration. Therefore, social and market collaborative prediction, climate and ecological risk quantification and adaptive decision support are achieved.

The invention relates to the technical field of anti-fatigue of steel parts, and discloses an anti-fatigue system and method for a high-hardness steel part based on ultrasonic-shot blasting composite strengthening. The method comprises the steps that the macroscopic geometrical morphology, the microstructure and the dynamic stress-strain data of a target steel part are collected through a multi-mode sensing system, and a multi-physics field digital twinborn model is constructed through fusion; on the basis of fatigue damage feature distribution in the model, sorting by using an enhanced decision tree model, and triggering parameter configuration by using a priority over-critical value; synchronously calculating ultrasonic field regulation and control parameters and shot blasting trajectory planning parameters through an adaptive neural network algorithm; the parameters are injected into a digital twin model to simulate material response and energy transfer, data such as a microdefect closed state are output, and a parameter correction signal is generated through multi-dimensional consistency inspection. According to the method, precise control over composite strengthening is achieved, and the anti-fatigue performance of the high-hardness steel part is effectively improved.

The invention provides a substation safety intelligent monitoring method based on infrared binocular vision, which comprises the following steps: acquiring an infrared radiation image, a visible light image and environmental parameters of equipment, and accurately inverting the real temperature of the equipment after eliminating environmental interference in combination with a depth distance calculation and physical compensation model; a historical time sequence of a real temperature is used to construct a multi-dimensional state vector fusing a thermal feature, a spatial feature and an environment feature, and intelligent classification evaluation of the security risk of the transformer substation is realized through an improved decision tree model. Meanwhile, a time sequence prediction model is introduced to predict the temperature rise trend of the equipment, the remaining time for reaching an early warning threshold value is calculated, and a dynamic grading early warning report is generated. According to the method, the temperature measurement precision and the risk identification accuracy are effectively improved, early warning and active prevention and control of the operation state of the equipment are realized, and the operation safety and the intelligent operation and maintenance level of the transformer substation are remarkably improved.

The invention discloses a multi-modal AI-based contract online analysis optimization system and method, and relates to the field of contract analysis, and the method comprises the steps: obtaining a to-be-analyzed contract file, and carrying out the standardization processing of the to-be-analyzed contract file; dividing the content features of the contract file into text content features, visual layout features and logic structure features, and synchronously extracting the features; dynamically generating an analysis process path through a lightweight decision tree model, and selecting an optimal analysis engine combination; calling at least three analysis engines for parallel processing based on multi-engine collaborative analysis; correcting a conflict analysis result generated by the parallel processing analysis engine based on an attention mechanism to generate a final analysis result; and recording the accuracy rate and time consumption index of each analysis, and dynamically adjusting an analysis process generation strategy through reinforcement learning. The method has the advantages that the text, visual and logic features in the contract are extracted based on the multi-modal AI technology, and the accuracy and efficiency of contract analysis are improved through multi-engine collaborative analysis and attention mechanism correction.

The invention belongs to the technical field of computer fault processing, and discloses an automatic fault processing method based on cooperation of a decision tree and an intelligent agent. The method comprises the following steps: constructing a dynamic decision tree model with a threshold adaptive adjustment mechanism; deploying a cooperative system composed of a diagnosis agent, a strategy generation agent and an execution verification agent; collecting real-time operation data and extracting time-frequency and statistical characteristics through a multi-dimensional sensor; a two-stage diagnosis mechanism is adopted to identify known and novel fault modes; candidate disposal strategies are generated and evaluated in combination with deep reinforcement learning; optimizing an optimal strategy through physical constraint check and state evolution simulation, and issuing and executing the optimal strategy; and finally, online updating of the model and the knowledge base is realized based on treatment effect feedback. According to the technical scheme, high precision of fault diagnosis, self-adaption of the disposal strategy and sustainable evolution of the system capability are realized, and the reliability and the automation level of operation and maintenance of industrial equipment are remarkably improved.

The invention provides a QLC NAND interference compensation control method and system, and the method comprises the steps: collecting adjacent unit voltage offset data of a target storage unit through an on-chip reference unit array integrated on a QLC NAND flash memory chip; performing feature extraction on the data to obtain interference feature parameters including at least one of voltage drift amplitude, drift rate and drift fluctuation frequency, and inputting the interference feature parameters into a pre-trained lightweight decision tree model to output an interference trend prediction result, a programming parameter optimization strategy is generated based on the result, the pulse width and the step voltage increment are optimized, meanwhile, fine adjustment of model parameters is triggered by collecting programming verification data after each time of optimization, dynamic accurate compensation of memory cell interference under the high write-in load of the QLC NAND flash memory is achieved, programming voltage drift is effectively restrained, and the reliability of the QLC NAND flash memory is improved. The adaptive capacity of the writing algorithm is improved, and the overall service life of the flash memory is prolonged.

The invention discloses an execution time aware computing power network heterogeneous GPU cluster deep learning task node allocation method and system. The method comprises the following steps: firstly, based on a deep learning task, extracting and preprocessing task features and available node features; secondly, a sampler equally divides new tasks without historical data to available nodes, and each node performs mixed sampling on the tasks until all the tasks estimate execution time data; taking execution time data as a training set, taking the task features and the node features as a test set, and using a regression decision tree model to predict the execution time of the task on each node; performing task allocation on each node by using a cost search algorithm and a short job total JCT priority strategy; and finally, periodically monitoring node resources released in the cluster to obtain an optimal node allocation result. According to the method, task delay and total task JCT are remarkably reduced, cluster node resource changes are monitored in real time, and the resource utilization rate is increased.

The invention belongs to the technical field of medical treatment, and discloses a wearable intelligent method and device for monitoring the mental health of community old people, and the method comprises the steps: collecting the physiological indexes of the community old people in real time through an intelligent chest card, and achieving the dynamic collection of multi-dimensional data; data are transmitted to the cloud platform through Bluetooth or Wi-Fi, the equipment supports a data caching mechanism, and key data are temporarily stored when connection is abnormal; a dynamic evaluation model is constructed based on AI learning, and a multi-factor decision tree model and a natural language processing NLP algorithm are combined; a group psychological health trend and an environment risk thermodynamic diagram are presented in real time through a chest card APP, main factors influencing psychological health of the old are analyzed, and community managers are assisted in making an intervention plan. By making and applying the community old people mental health monitoring and early warning model, a unified mental health assessment system is provided for old people in different states, the applicability, universality and compliance of mental assessment are improved, home rehabilitation is achieved, and the overall medical cost is reduced.

The invention discloses a voltage transformer state influence factor analysis method, system and device and a medium, and belongs to the technical field of voltage fault analysis, and the method comprises the steps: collecting state parameters of a voltage transformer, carrying out the preprocessing of the state parameters, and constructing a nonlinear coupling relation between multidimensional feature tensor capture features; analyzing voltage fluctuation characteristics of the voltage transformer, and automatically correcting the insulation state evaluation model; and inputting abnormal feature vectors obtained by monitoring into a recursive attribution decision tree model, carrying out multi-dimensional abnormal clustering and causal analysis, generating a tracing report, and carrying out multi-dimensional evaluation and verification on an analysis result. According to the invention, continuous dynamic full-coverage monitoring of the equipment insulation health condition is realized, the accuracy and robustness of anomaly detection are improved, the phenomena of missing detection and false alarm are reduced, the scientificity of anomaly tracing analysis and the interpretation of early warning decision are improved, and the reliability of the system is improved. And the intelligent level of the monitoring and early warning system is improved by continuously adapting to new abnormal types and complex operation environments.

The invention relates to the technical field of artificial intelligence, and discloses a business process configuration method based on Activiti and AI decision, and the method comprises the steps: collecting the historical execution data of a business process through a process instance monitoring module, generating a process behavior feature data set, carrying out the training processing of an AI decision model based on the process behavior feature data set, and carrying out the analysis of the AI decision model. A business process decision tree model is constructed, AI decision routing labels are configured for decision nodes in an Activiti process engine, non-intrusive intelligent decision integration is achieved, and when a process instance is executed to a key decision node, a system automatically calls the pre-trained business process decision tree model to conduct real-time path analysis and generate an optimal dynamic routing instruction. The problem of decision stiffness caused by the fact that a traditional workflow depends on artificial experience configuration is solved, the accuracy and adaptability of flow branch selection in a complex service scene are improved, and meanwhile the compatibility and stability of an original flow engine are guaranteed.

The invention belongs to the field of agricultural technology and ecological monitoring, and particularly discloses a Hainan island wild tea tree growth model construction method and system.The method comprises the steps that firstly, a microenvironment competition factor is obtained by quantifying a local competition index based on diameter-level distribution in a fixed quadrat around a target tea tree; selecting a preset cubic function growth mechanism model to calculate a theoretical growth index value according to a vegetation vertical zone corresponding to the altitude of the tea tree; then fusing the local competition index, the theoretical growth index value, the altitude, the canopy density and the growth environment type to construct a feature vector so as to train a gradient lifting decision tree model, and establishing a multi-factor growth state prediction model; and finally, predicting the growth state of an unknown tea tree by using the model. According to the method, the ecological mechanism and machine learning are effectively fused, the growth prediction precision is remarkably improved, and a quantitative basis is provided for precise conservation.

The invention discloses an electric power marketing metering equipment abnormity early warning method and system based on edge computing, and relates to the technical field of electric power system monitoring, and the method comprises the steps: collecting the original multi-mode operation data flow of target electric power marketing metering equipment in real time during the operation of the system; performing sliding window segmentation and parallel feature extraction on the data stream to obtain a mixed feature vector; inputting the mixed feature vector into an isolated forest model and a gradient boosting decision tree model in an integrated anomaly detection engine, and calculating a comprehensive anomaly confidence score; generating a local early warning event or cache feature data according to the confidence score, and triggering an adaptive communication scheduling process; on the collaborative decision-making platform side, receiving and aggregating reported information, carrying out Bayesian network reasoning by combining the multi-source data of the power grid, and completing global anomaly verification and final decision-making; and starting a model evolution process according to a global verification result, executing incremental learning and generating a parameter updating package.

The invention relates to the technical field of power distribution network toughness demand response, in particular to a power distribution network toughness improvement optimization method, system and device based on response prediction and a storage medium. Training an integrated decision tree model based on historical data to predict a response intention value of the cooling and heating load user, and determining a temperature regulation boundary and a response frequency upper limit according to the response intention value; generating an initial scene set through source load uncertainty sampling, extracting a typical scene by adopting a transportation distance scene reduction method, and introducing an overall offset constraint and a single-point extreme value constraint to perform two-dimensional limitation on scene probability distribution; a scene probability combination enabling the load recovery value to be minimum is searched in a probability distribution domain, and a scheduling scheme enabling the key load recovery value to be maximum is solved under the condition that the power flow constraint, the cold and heat power balance constraint and the temperature regulation boundary constraint are met; and carrying out probability weighting on the load recovery values of different scenes to obtain a toughness evaluation value, and carrying out sensitivity analysis.

The invention discloses an unmanned aerial vehicle cluster area sealing control method and system in a dynamic game scene. The method comprises the following steps: in a region exploration stage, realizing coverage and detection search of a task region based on a collaborative search algorithm of multi-agent partition perception and path planning; in the collaborative interception stage, based on the established target motion prediction model and the self-adaptive allocation mechanism of the enemy target, dynamic allocation is realized, the optimal interception path of the unmanned ship is planned, and meanwhile, energy charging and secondary flying of the unmanned ship are carried out in time; in the confrontation game stage, based on a decision tree model, in combination with multi-dimensional battlefield evaluation data, decision execution and evaluation are carried out, attack, avoidance or other tactical instructions are generated, and an optimal instruction and global target distribution are waited and cyclically made; and after an attack instruction is sent out, performing a safe predefined tactical attack in combination with tactical action execution logic. According to the method, the problems of poor unmanned cluster cooperative perception, difficulty in resource scheduling and untimely decision response in a complex dynamic environment are effectively solved, and the interception efficiency and comprehensive combat capability of regional sealing and control are improved.

The invention relates to an anti-multipath Beidou antenna design method based on reconfigurable polarization, and belongs to the technical field of satellite navigation and positioning. The method comprises the following steps: fusing data through a distributed sensing and generative adversarial network, and identifying direct and reflected signals by using a decision tree model; constructing a polarization decision model driven by reinforcement learning, and dynamically switching the polarization state of the antenna through a digital phase shifter array, so that the polarization state is matched with a direct signal and mismatched with a reflected signal, thereby realizing multi-path suppression of a polarization domain; a reconfigurable phased array and a digital beam forming technology are combined, a multipath incoming wave direction is predicted, and dynamic null is constructed for spatial filtering; signal tracking and decoupling are carried out by adopting particle filtering and a long-short-term memory network, a vision-inertial navigation cooperation mechanism is introduced, combined suppression and compensation of a dynamic multipath source are realized, and an anti-multipath positioning result is output. And low-cost and high-precision indoor Beidou positioning is realized.

The invention relates to the cross technical field of biological medicine and image processing, and discloses a mesenchymal stem cell aging detection method based on image processing. The method comprises the following steps: automatically collecting bright field and multi-channel fluorescence images in an integrated cell culture monitoring device; after background correction and illumination homogenization, segmenting the cells by using a U-Net network and extracting single cell contours; calculating characteristics such as cell area, roundness, cytoplasmic ratio, nuclear form irregularity index, lysosome fluorescence intensity mean value and distribution entropy; and inputting a gradient boosting decision tree model to judge the unicellular aging state, and evaluating population aging with a 20% positive rate threshold. According to the invention, label-free, non-invasive and high-flux accurate detection is realized, and the method is superior to manual interpretation.

The invention belongs to the technical field of building construction and digital twinning, and relates to an intelligent matching method and system based on artificial intelligence. According to the method, construction environment data are collected in real time, risk marks are identified, the deformation amount of the component is calculated in combination with a time sequence prediction model, and a BIM model is corrected to generate a virtual component for environment compensation; carrying out geometric matching analysis by adopting a three-dimensional point cloud registration technology, and judging a hole alignment error and an interface gap; dynamically adjusting the installation sequence based on the dynamic decision tree model by integrating the geometric mismatch degree, the environmental risk and the construction logic; and carrying out incremental updating on the model according to the actual installation deviation. According to the method, the technical problem of installation mismatch caused by component deformation due to environmental factors such as temperature and humidity in the construction process is effectively solved, dynamic adaptation and precision control of the construction process are achieved, the matching success rate and construction efficiency of component assembly are improved, and meanwhile the adaptability and reliability of the system are optimized through continuous learning.

The invention relates to the field of radio interference intelligent identification and positioning, in particular to a multi-source data fusion water radio interference identification and positioning method and system, and the method comprises the following steps: collecting multi-source data; performing multi-source data fusion processing; constructing a rule base, and screening out suspected abnormal signals from the frequency spectrum situation map through threshold judgment and rule matching; a support vector machine model is called, multi-dimensional static signal features are used as input, a classification hyperplane is constructed through a radial basis kernel function, and normal signals and abnormal signals are distinguished from suspected abnormal signals; calling a decision tree model, and performing scene judgment on the suspected abnormal signal to identify the abnormal signal; calling a convolutional neural network model, converting the time domain signal into a spectrogram through short-time Fourier transform, and extracting texture features through multilayer convolution to identify an abnormal signal; through the method and the system, real-time identification and high-precision positioning of interference signals can be realized.

The invention relates to the technical field of tea processing, and discloses a tea processing auxiliary method and system based on intelligent decision making. The method comprises the following steps: acquiring a multi-source sensor data stream of tea processing equipment in real time, and identifying a critical time point of processing state switching; intercepting time window data by taking a critical point as a center, performing empirical mode decomposition on channel data of each sensor, extracting an intrinsic mode function component, calculating a sample entropy value, and constructing a multi-channel entropy value characteristic matrix; inputting the matrix into a graph attention network, learning a spatial dependency relationship between sensor channels, and outputting a graph embedding representation of a processing state; calculating an abnormal score and generating a processing quality deviation index; analyzing the influence of processing parameter adjustment on a quality index by adopting a gradient lifting decision tree model, and determining the contribution weight of a key sensor channel; high-weight sensor data are integrated, dynamic segmentation is carried out, timing sequence characteristics are fused by applying a gating circulation unit network, and an accurate processing control decision is output.

The invention belongs to the field of network communication, and provides a network fault automatic processing method and system, and the method comprises the steps: collecting a multi-source monitoring signal, and constructing a space-time matrix used for representing a network state; training the decision tree model by using a reinforcement learning algorithm, and constructing a reward function according to the root cause positioning accuracy and the repair effect; when the decision tree model determines a fault root cause, calling a software defined network (SDN) controller interface, generating a corresponding rerouting strategy according to the fault root cause, and issuing the rerouting strategy to network equipment for path adjustment; applying the rerouting strategy in a simulation environment, collecting QoS (Quality of Service) indexes before and after adjustment, and verifying the validity of a repair strategy; and feeding back a repair verification result to a training process of the decision tree model, and updating parameters of the reinforcement learning algorithm. According to the invention, the accuracy of network fault automatic processing can be improved.

The invention discloses an abnormal traffic detection and response method and system based on a honey hole. The method comprises the following steps: respectively deploying a first type of monitoring nodes to collect camouflage service traffic and a second type of monitoring nodes to collect real-time service traffic at two sides of a target network core data path; preprocessing the two types of traffic to generate a first traffic feature set and a second traffic feature set with a unified structure; performing reliability detection on the feature set through a decision tree model, and screening reliable traffic feature data; predicting a traffic anomaly probability and an attack type by using a specifically trained neural network model, and generating a threat score and a real-time detection result; and when high-confidence abnormal traffic is detected, triggering automatic defense measures, and feeding back abnormal features and response results to the decision tree and the neural network model for iterative optimization. According to the method, through double-path flow collaborative analysis and model dynamic optimization, the false alarm rate is remarkably reduced, and the real-time detection and response capability to hidden attacks is improved.

The invention relates to the technical field of tumor radiotherapy, and discloses a radiotherapy parameter intelligent recommendation method based on disease species, target area volume and dose characteristics, and the method comprises the steps: firstly obtaining historical helical tomography radiotherapy data containing different disease species and dose schemes, and carrying out the standardization; then, non-iterative forward dose calculation based on full-factor experimental design is executed, and treatment simulation plans are generated in batches; then, dosimetry and efficiency indexes are extracted, and an optimization rule base containing a response surface model and a Pareto optimal solution set is constructed; and finally, processing new case features by using a decision tree model, and outputting a recommended collimator mode, a screw pitch, a modulation factor value and an expected index range. According to the method, data, a model and a decision closed loop are constructed, so that the defects that the traditional plan design depends on artificial experience, the trial and error cost is high and multiple targets are difficult to balance are overcome, and the efficiency and the quality consistency of the radiotherapy plan design are remarkably improved.

The invention provides a wounded person treatment situation simulation method, system and device, and relates to the technical field of wounded person treatment situation simulation, and the method mainly comprises the steps: setting evaluation indexes and weights, including an inspection evaluation index and a treatment evaluation index; defining a Markov state, a transition probability and an influence parameter based on the injury condition of the wounded, the evaluation index and the weight; presetting an initial wounded state proportion of a Markov state, determining a transition probability value, and constructing and training a simulation model; and collecting the number of wounded persons on site, the number of first death persons and influence parameters under different treatment conditions, inputting into the simulation model, and iteratively outputting wounded person treatment situation results under different influence parameters. According to the scheme, the Markov process and the decision tree model are combined, and the wounded treatment situation can be dynamically simulated, so that the wounded treatment scheme can be efficiently and scientifically planned; evaluation indexes and weights are ingeniously designed, and the wounded treatment situation result can be more comprehensive and objective.

The invention relates to the technical field of image processing, in particular to a gland structure irregularity quantitative analysis method and system based on a gastroscope image, and the method comprises the steps: carrying out the color correction and image contrast enhancement of an obtained original gastroscope image, and constructing a corresponding image pyramid; inputting the images in the image pyramid into an improved deep learning model for learning by adopting a multi-channel fusion input mode, and obtaining a gland binary mask after morphological processing; based on three dimensions of morphology, structural arrangement and complexity, quantitative features of a single gland structure are extracted from the gland binary mask; the extracted multi-dimensional quantitative features are trained through a gradient lifting decision tree model, an irregularity index is output, feature importance analysis is carried out, and the gland structure in the gastroscope image can be accurately recognized and quantized.

The invention discloses a wafer defect detection method and a computer program product, and relates to the technical field of semiconductor measurement. The wafer defect detection method comprises the following steps: acquiring a defect image sample with a real defect label or a false positive defect label; the method comprises the following steps: firstly, respectively performing morphological structure analysis on areas to be detected in a defect image sample to determine defect morphological characteristics, performing frequency domain transformation and energy analysis to determine defect texture characteristics and performing boundary line gray gradient analysis to determine defect boundary characteristics, and then integrating the characteristics of the three types of areas to be detected into multi-dimensional characteristics; and learning a mapping relationship between the multi-dimensional features and the defect tags through a decision tree generation algorithm in combination with the defect tags so as to construct a target decision tree model. By means of the mode that the multi-dimensional features cooperatively describe the physical and structural essence of the defects and the decision tree model is combined for classified learning, the real defects and the false positive defects can be effectively distinguished, and the accuracy of wafer defect detection is improved.

The invention relates to the technical field of digital information transmission, and discloses a Morse code encoding and decoding method and system based on artificial intelligence, and the method comprises the steps: converting text information into an initial dot symbol sequence according to a standard Morse code mapping table, collecting channel characteristic parameters and inputting the channel characteristic parameters into the lightweight decision tree model to determine a time sequence characteristic weight vector; optimizing the initial dot-drawn symbol sequence to generate an optimized dot-drawn symbol sequence; calculating an absolute transmission time length, dynamically adjusting a transmission power and a waveform envelope parameter in combination with a multipath fading coefficient of a channel, and generating a coded signal; and demodulating the received signal to obtain a time sequence feature weight vector used by a sending end, performing envelope detection on the baseband signal, segmenting according to the time sequence feature weight vector to obtain a dot symbol sequence, and converting according to a standard Morse code mapping table to obtain decoded text information. According to the method, the encoding and decoding problems of the Morse code in a dynamic environment are solved, and the method can be operated on edge equipment.

The invention relates to the technical field of data processing, and discloses a gateway processing method and device based on heterogeneous calculation and dynamic energy efficiency, and the method comprises the steps: executing data analysis operation on real-time data of a target gateway, obtaining data analysis information, and determining a processing acceleration component according to a random forest decision tree model and task type information; generating a gateway model based on the task modeling parameters, determining parallel segmentation points in the gateway model through a critical path analysis algorithm, and determining at least one subtask set of the target gateway; and determining each corresponding heterogeneous computing unit and determining a computing processing parameter based on the processing acceleration component and each subtask set so as to generate a gateway processing parameter corresponding to the target gateway. Visibly, the industrial gateway data can be intelligently processed, the intelligence and efficiency of industrial gateway data processing can be improved, and the accuracy and reliability of industrial gateway data processing can be improved.