1333results about How to "Overcome limitations" patented technology

The invention provides a control node locating protecting method of undercutting channel segment water level lowering amplitude. The method comprises the steps that a lateral erosion zone and a deep erosion zone are divided; a target channel section is subjected to meshing, a plane two-dimension water flow mathematic model is established, parameter calibration and verification are carried out; a protection project is generalized to the plane two-dimension water flow mathematic model, the arranging zone and the width of the protecting project are determined; local gradient amplification and upstream water level amplification corresponding to each working condition are obtained, mesh sequences are analyzed respectively; key nodes or other nodes which have obvious influence are selected, for lateral erosion nodes, a hidden closure dam and a beach protection part are arranged on a river branch or a bottomland, and for deep erosion nodes, a bottom protecting band or a hidden closure dam are placed in a main groove. According to the method, nodes are divided into the lateral erosion nodes and the deep erosion nodes, protection part difference of the nodes in different types is provided, by comparing the efficiency of a local channel segment arranging protecting project, the positions of the nodes are accurately located, and the optimum efficiency of the protecting project is guaranteed powerfully.

The invention relates to the technical field of intelligent traffic and Internet of Vehicles, in particular to a traffic and air pollution bidirectional coupling flow prediction method based on double-path dynamic fusion. Comprising the following steps: collecting traffic flow and air pollutant concentration data, and carrying out space-time alignment and reversible instance normalization; the data is divided into two branches, the first branch extracts time-dependent features through gated convolution and probability sparse self-attention, and the second branch obtains variable interaction features through dimension remodeling, context extraction and reversible coupling transformation; bidirectional feature interaction is carried out through cross attention, weights are dynamically generated based on channel attention, and residual connection is carried out after weighted fusion; and performing linear mapping and inverse normalization on the fused features to obtain a traffic flow predicted value. According to the method, the prediction precision and robustness in a pollution sensitive scene are remarkably improved.

The invention relates to the technical field of vehicle suspension system control, and discloses a multi-source data fused shock absorber self-adaptive regulation and control method and system.The method comprises the steps that multi-source signal data are synchronously collected through a sensor, and a preliminary signal set is generated in combination with environment change data; analyzing vibration signal peak amplitude and displacement data integral variation trend, distinguishing high-frequency small-acceleration and low-frequency large-amplitude pavement excitation characteristics, and determining signal contribution degree distribution; dynamically adjusting the signal weight and introducing temperature compensation to correct stiffness drift to generate a weighted vector; carrying out weighting processing, smoothing, noise suppression and nonlinear amplitude estimation to generate fusion data, and optimizing the weight to obtain a refined weighted vector; after secondary fusion, a cross-frequency-band transition smoothing technology and a least square method are adopted to determine damping characteristic parameters; according to the vehicle suspension system self-adaptive regulation and control method and system, the self-adaptive regulation and control accuracy and the working condition adaptability of the vehicle suspension system are improved.

The invention relates to the technical field of intelligent monitoring of power equipment, in particular to an intelligent circuit breaker state monitoring method and system. The method comprises the steps that unified time sequence modeling is carried out on multi-dimensional operation data of the circuit breaker, periodic behavior fragments with internal rhythm characteristics are extracted, state feature vectors based on historical behavior self-reference are constructed, and the state feature vectors are mapped to a continuous interpretable state space to achieve dynamic visual expression of the operation state; on the basis, the state deviation intensity and the change direction consistency are quantified, so that progressive identification and early warning from normal operation to an abnormal state are realized; and combined prediction is further carried out in combination with a state trajectory and an abnormal index, a risk quantification result and an active intervention strategy are generated, and model adaptive optimization is realized through closed-loop feedback. According to the invention, intelligent operation and maintenance closed loop from real-time monitoring and abnormal early warning to predictive maintenance is completed.

The invention relates to the field of welding seam detection, and discloses a welding seam center line real-time tracking method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining a laser tracking evaluation speed, an ultrasonic tracking evaluation speed and a tracking speed threshold value of a to-be-detected weldment; if the laser tracking evaluation speed and the ultrasonic tracking evaluation speed are both larger than the tracking speed threshold value, the weld joint center line of the to-be-detected weldment is tracked according to a laser and ultrasonic simultaneous inspection strategy; and if the laser tracking evaluation speed or the ultrasonic tracking evaluation speed is smaller than or equal to the tracking speed threshold value, the welding seam center line of the to-be-detected weldment is tracked according to a main laser and auxiliary ultrasonic strategy. According to the invention, the obvious limitation of a single laser tracking technology under the conditions of a complex surface and strong illumination is overcome, and high-precision and high-robustness real-time tracking is realized through acousto-optic fusion.

The invention discloses a casein-sourced multifunctional peptide, and belongs to the field of food science and nutriology. The casein is subjected to enzymolysis through pepsin, a method combining activity verification and mass spectrum identification is adopted, the novel multifunctional peptide YPE with a clear sequence is screened and verified, the highest ABTS clearance rate of the casein peptide YPE can reach 77.76%, the highest hydroxyl radical clearance rate of the casein peptide YPE can reach 40.18%, and the casein peptide YPE can be used for preparing the novel multifunctional peptide YPE. The inhibition rate of alpha-glucosidase reaches 30.47%, the inhibition rate of escherichia coli is 10.18%, and the limitation of an existing single-function peptide in application is overcome.

The invention discloses a multi-source heterogeneous data dynamic causal blood relationship tracing method and a related device, and belongs to the technical field of data blood relationship detection.The method comprises the steps that static blood relationship metadata and dynamic index time sequence data of aviation test data are collected, a dynamic causal blood relationship map is constructed, each edge of the map is associated with a causal strength weight, and the dynamic causal blood relationship is obtained; and reverse traceability is carried out through the dynamic causal blood relationship map, and root cause nodes causing abnormity are determined. Static consanguinity and dynamic indexes are collected, a dynamic causal consanguinity map with nodes as triads and edges carrying causal strength weights is constructed, and dynamic attributes of the map break through static relevance limitation, so that traceability can reveal state change and causal dynamics; the influence degree between nodes is quantized by introducing a causal strength weight, and the root cause is determined by combining a probabilistic reasoning model, so that the spanning from fuzzy association to quantitative positioning is realized, and the problems that the root cause positioning is fuzzy and is lack of quantitative analysis are solved.

The invention discloses a coal mill internal multi-source heterogeneous data fusion early warning method and system, and belongs to the technical field of industrial equipment fault diagnosis. The method aims at solving the technical problems that an existing coal mill early warning method is insufficient in multi-source data fusion, poor in working condition adaptability, low in early warning accuracy and lack of fault tracing and accurate control. According to the technical scheme, the method comprises the steps that structured process parameter data and unstructured physical field data of the coal mill are obtained; constructing a digital antigen vector representing a running state; constructing an autologous space based on the historical health data, and generating a dynamic immunodetector set covering a non-autologous space; calculating the Euclidean distance between the digital antigen vector and the detector and a danger signal index; early warning is triggered through cooperative judgment, feedback control is executed, and a fault source is positioned in combination with fault tracing. The device state can be comprehensively represented, the working condition adaptability and the early warning accuracy are improved, accurate fault positioning and control are achieved, and the device maintenance cost is reduced.

The invention provides a rainfall monitoring method and system based on unmanned aerial vehicle multi-mode noise reduction in the technical field of environment monitoring and unmanned aerial vehicle application, and the method comprises the steps: S1, collecting a visible light image and a long-wave infrared image through a multispectral vision module after an unmanned aerial vehicle takes off, and collecting a rainfall audio from an acoustic cabin through a microphone; s2, carrying out noise reduction processing on the visible light image, the long-wave infrared image and the rainfall audio; s3, extracting a raindrop size distribution histogram and a spatial density thermodynamic diagram based on the visible light image and the long-wave infrared image, and extracting acoustic MFCC features based on rainfall audio; s4, performing feature fusion operation based on the raindrop size distribution histogram, the visual density thermodynamic diagram and the acoustic MFCC features to obtain rainfall intensity; and S5, carrying out cumulant dynamic compensation based on the rainfall intensity to obtain the cumulative rainfall. The rainfall monitoring method has the advantages that the precision, robustness and practical level of rainfall monitoring are greatly improved.

The invention discloses polymer-based anti-crack concrete and a preparation method thereof, and relates to the technical field of concrete. When the polymer-based anti-crack concrete is prepared, acrylic acid is grafted on the surfaces of pretreated polypropylene particles to prepare modified polypropylene particles; coating sodium metasilicate with sodium alginate to prepare a self-repairing microcapsule; the preparation method comprises the following steps: hydrolyzing 3-glycidyl ether oxypropyl trimethoxy silane, and grafting the hydrolyzed 3-glycidyl ether oxypropyl trimethoxy silane on the surface of silicon dioxide to prepare modified silicon dioxide; mixing the modified polypropylene particles, basalt fibers and polyacrylonitrile fibers to obtain a solid functional component; cement, slag powder, silica fume, an expanding agent, a solid functional component, modified silicon dioxide, a water reducing agent, an air entraining agent, deionized water, river sand, gravel and the pre-wetted self-repairing microcapsule are mixed, and the polymer-based anti-crack concrete is prepared. The polymer-based anti-crack concrete prepared by the invention has excellent anti-crack, freeze-thaw-resistant and self-repairing properties.

The invention discloses a short-term traffic flow prediction method based on a dynamic multi-dimensional global perception graph convolutional network, and belongs to the field of traffic flow prediction. The method comprises the following steps: acquiring an input dynamic graph feature sequence, and generating space-time element parameters; carrying out feature enhancement on the dynamic graph feature sequence, fusing the dynamic graph feature sequence with the space-time element parameters, and then respectively carrying out space-time feature coding; fusing the spatio-temporal characteristics obtained by coding the spatio-temporal characteristics; and carrying out space-time trend alignment decoding on the fused space-time features. According to the method, the defects of limited modeling precision, insufficient dynamic adaptability, incomplete spatial-temporal feature capture and the like in short-term traffic flow prediction are overcome, and the accuracy and the applicability of short-term traffic flow prediction are improved.

The invention relates to the technical field of the intelligent power grid industry, in particular to a power distribution and utilization ring network fault intelligent troubleshooting method and system. According to the method, a looped network situation primitive is generated through multi-source heterogeneous data fusion. The primitives are input into synchronous compression transformation to extract transient fault initial features, and the transient fault initial features are mapped into fault propagation path features through a graph convolutional network considering topological structure constraints; then, inputting the features into a space-time correlation prediction model, integrating defect type judgment and trend pre-judgment features, and generating a fault diagnosis evidence set; and starting an investigation mechanism based on the evidence set to determine an interval to be investigated, and performing closed-loop verification by constructing a fusion judgment matrix based on multi-modal physical sensing information to realize physical space locking of a fault defect point. According to the method, deep learning diagnosis and multi-mode positioning are integrated, and the detection precision and the positioning efficiency of looped network defects and the identification capability of transient hidden faults are effectively improved.

The invention relates to the technical field of image processing, in particular to a method and system for detecting surface defects of an insulating layer of a polyethylene insulated cable, and the method comprises the steps: obtaining a surface image of the insulating layer; the defect response index of each pixel in the surface image is calculated, and the defect response index is in positive correlation with the product of the gradient amplitude of each pixel in the corresponding local window and is in positive correlation with the absolute value of the difference value of the gray average values of all the pixels in the local window. According to the method, a defect response index is constructed by introducing a gradient direction angle variance, the defect response index and the defect response index are distinguished by utilizing the physical characteristics that the gradient direction of a highlight region is consistent and a defect region is disordered, and a defect significance enhancement factor is constructed according to the physical characteristics to dynamically adjust the Gaussian kernel scale. According to the method, large-kernel smooth interference is automatically adopted in a highlight area, small-kernel reserved details are adopted in a defect area, the limitation of a traditional fixed parameter algorithm is effectively overcome, and the defect detection precision under the complex reflective background is remarkably improved.

The invention relates to a BERT and multi-module fusion-based track circuit fault label classification method and device and a medium, and the method comprises the steps: obtaining a to-be-classified track circuit fault text, and carrying out the cleaning and standardization processing of the to-be-classified track circuit fault text; performing word segmentation and vectorization processing on the track circuit fault text to generate a lexical symbol sequence conforming to BERT encoder input specifications; extracting a context semantic vector representation of the input lexical symbol sequence through a BERT encoder; performing feature extraction through a TextCNN module and a self-attention module which are parallel to obtain local features and weighted global features, and splicing the local features and the weighted global features to obtain a comprehensive feature vector; and synchronously generating a multi-label classification result of a fault phenomenon, a fault reason and a solution measure corresponding to the current fault text through a multi-label classification head based on the comprehensive feature vector. Compared with the prior art, the method has the advantages of high discrimination, high accuracy, high practicability and the like.

The invention relates to an electricity-carbon-green evidence fusion multi-market collaborative service equilibrium decision-making method. The method comprises the following steps: step 1, analyzing electricity-carbon-green evidence benefits under a multi-coupling mechanism; step 2, based on the electricity-carbon-green evidence benefit analysis result under the multi-coupling mechanism in the step 1, constructing a system operation optimization model under the multi-coupling mechanism; step 3, training and calibrating the system operation optimization model under the multi-coupling mechanism constructed in the step 2; 4, performing optimization verification on the system operation optimization model under the multi-coupling mechanism after training and calibration in the step 3; and step 5, using the system operation optimization model under the multi-coupling mechanism after optimization verification in the step 4 to output a daily optimal decision strategy to participate in market decision execution. According to the method, collaborative optimization of electric quantity distribution, carbon emission reduction accounting and green certificate configuration can be realized, and the green power consumption accuracy and the multi-market collaborative operation efficiency are improved.

The invention relates to the technical field of flood control dispatching, and discloses a reservoir flood control water level dynamic control and flood recycling method and system, and the method comprises the steps: obtaining a surface rainfall forecast value of a reservoir basin based on real-time rainwater condition data and numerical weather forecast; a hydrodynamics and hydrology coupled drainage basin runoff model is driven, and an in-reservoir flood hydrograph in a predicted period is generated; judging the flood scale and grade based on the hydrograph, and when the flood is medium and small flood, calculating the dynamic flood control storage capacity of a downstream flood control object by adopting an equivalent flood control effect algorithm taking into account the pre-discharge scheduling capability; according to the dynamic flood control storage capacity, a dynamic flood control water level control value is obtained through backstepping of a water level-storage capacity relation curve of the reservoir; and finally, generating and executing a reservoir dispatching instruction based on the control value, and storing the water level of the reservoir to not exceed the dynamic value. The problems that a fixed flood control water level method is low in water resource utilization rate and inflexible in dispatching are solved, and flood resource efficient utilization on the premise of flood control safety is achieved.

The invention discloses a multi-modal behavior data processing system, which comprises a data acquisition module, a feature extraction layer, a dynamic attention weight layer, a multi-modal fusion layer and a downstream task decision-making layer, the data acquisition module guides human-computer interaction through an international neurological and mental interview tool matched with a DSM-5 standard and acquires audio and video stream data; the feature extraction layer extracts a video feature vector (including facial action unit activation intensity and the like), an audio feature vector (including Mel frequency cepstrum coefficient and the like) and a text feature vector (generated by a deep language model after automatic speech recognition transcription) in parallel; the dynamic attention weight layer is combined with data quality, symptomatic priori knowledge and cross-modal correlation to generate a dynamic fusion weight; weighting, splicing and dimensionality reduction are carried out on the multi-modal fusion layer to obtain a fusion feature vector; and the downstream task decision-making layer completes evaluation and generates a multi-modal behavioral index evaluation report. The system is deployed in a non-intrusive manner, the risk is controllable, and the evaluation robustness and accuracy can be improved.

The invention discloses a pipeline defect detection method and system, and belongs to the technical field of pipeline defect detection. The pipeline defect detection method comprises the following steps: acquiring a preprocessed magnetic flux leakage signal and an ultrasonic guided wave signal; performing feature extraction on the signal to obtain a first feature subjected to multi-scale enhancement processing and a second feature subjected to convolution and attention mechanism processing; fusing by adopting a strategy including mutual information calculation and adaptive weight to obtain cross-modal fusion features; and performing multi-task detection and correction based on the fusion features to obtain a detection result indicating the type, the position and the size of the defect. According to the method, the limitation of single-mode detection can be overcome, and the accuracy of defect type identification, the positioning precision and the size quantification reliability are remarkably improved through dynamic weight distribution and physical constraint verification.

The invention discloses an intensive culture precise feeding control method based on fuzzy-MPC control, and relates to the technical field of aquaculture intelligent control, and the method comprises the steps: employing a sensing system, collecting multi-source sensing data including fish activity images, healthy growth states and water quality environment data in real time, and forming a comprehensive state data set; and based on the acquired multi-source sensing data, constructing an MTL-LSTM-SAT multi-task learning model. According to the method, the multi-task learning model fusing the LSTM and the soft attention mechanism is constructed, the long-term dependency relationship and dynamic association among the water quality, the feeding and the fish state can be captured, the attention mechanism enables the model to be automatically focused on a key time step, the perception ability of the water quality fluctuation and the feeding influence stage is enhanced, and the accuracy of the water quality fluctuation and feeding influence stage is improved. Meanwhile, double-task collaborative prediction of the growth speed and the disease proportion is achieved through the shared feature layer, a prospective state sequence of the future 7 days is output, and a quantitative and reliable prediction basis is provided for feeding decision making.

The invention relates to the technical field of concrete electric pole manufacturing, and discloses an optimized steel fiber anti-crack ultra-high performance concrete centrifugal electric pole manufacturing method and system. The method comprises the following steps: dividing an electric pole manufacturing process into a plurality of manufacturing units according to production batches, and collecting material component data, process setting data and online detection data in each manufacturing unit to form a multi-source production data set; calculating an anti-cracking performance index and a strength performance index based on a multi-source production data set, and dynamically outputting an initial process correction value through a dual-threshold monitoring mechanism and a performance adjustment model; performing adaptive adjustment on the initial process correction value of each manufacturing unit according to the production correlation between the adjacent manufacturing units to generate an adjusted process correction value; and carrying out serialization processing on the adjusted process correction value, and outputting a production optimization instruction which is applied to a manufacturing execution process. According to the method, electric pole manufacturing whole-process data integration and dynamic process optimization are achieved, and the production efficiency and the product quality stability can be improved.

The invention discloses a structure sensor optimal arrangement method and system based on distributed deep reinforcement learning. The method comprises the following steps: constructing a deep reinforcement learning agent based on a non-decreasing quantile function network; constructing an interactive environment of sensor optimization arrangement based on a reverse order decision theory, and defining a state, an action, a reward function and a termination constraint; establishing a structural discrete state space equation based on a modal coordinate transformation theory, and calculating a trace of an estimation error covariance matrix by using a three-step recursive estimation algorithm; based on the trace and a sensor deployment cost function, constructing an optimization function aiming at minimizing an estimation error; and finally, training an intelligent agent by adopting a distribution reinforcement learning algorithm based on Huber quantile regression, and searching an optimal sensor arrangement scheme meeting cost and modal observability constraints by maximizing accumulated rewards. According to the invention, the optimal arrangement of multiple types of sensors under the constraint of fixed deployment cost is realized, and the precision and efficiency of structural state estimation are improved.

The invention discloses an IDC machine room fault point prediction method and system based on artificial intelligence and the Internet of Things, relates to the technical field of computers, and aims to solve the problems that hidden risks cannot be identified before temperature exceeds a limit, fault response is passive, and fault cause interpretation ability is lacked in the aspects of heat dissipation and airflow organization management of an existing IDC machine room. According to the method, data of a PDU, a UPS, an air conditioner, a temperature and humidity probe, a BMC, an entrance guard and the like are analyzed and standardized through data access of the Internet of Things, and a unified time axis is constructed to carry out quality marking and credible normal fragment screening; based on cabinet topology and an air supply partition map, effective air volume and theoretical temperature rise are estimated, a cabinet-level air supply temperature field is solved under anchoring constraint and spatial smooth constraint, and the short-circuit air return proportion and a vertical hot spot height section are further inverted; therefore, early prediction, accurate positioning and explainable output of fault points are realized, and the stability and the operation and maintenance efficiency of the machine room are improved.

The invention belongs to the technical field of photovoltaic power station power generation, and particularly relates to a single-crystal assembly photovoltaic power station power generation data monitoring and analyzing system, which comprises a multi-modal sensing acquisition and real-time analysis layer, a single-crystal assembly power generation data analysis layer, a single-crystal assembly power generation data analysis layer and a single-crystal assembly power generation data analysis layer, and constructing a component-level multi-physics field coupling model, wherein the model fusion method comprises the following steps: adaptively adjusting the monitoring acquisition frequency of a corresponding region, focusing key data acquisition in a fault incubation period, and keeping the conventional monitoring intensity when the component state is stable. And meanwhile, the cooperative control module for carbon flow monitoring, analysis and energizing generates a precise control instruction based on a dynamic optimization result, so that the problems of one-way open loop and collection strategy stiffness of traditional system monitoring and analysis are solved, the pertinence of fault diagnosis is improved, unnecessary monitoring resource consumption is reduced, and the system monitoring energy efficiency is improved by more than or equal to 40%.

The embodiment of the invention discloses a fault self-healing operation determination method and device, electronic equipment, a medium and a product. The method comprises the following steps: acquiring alarm monitoring data of a target service, and constructing a knowledge graph based on the alarm monitoring data; wherein the knowledge graph comprises entity nodes, relation edges, weights of the relation edges and feature vectors of the entity nodes; performing fault root cause node positioning processing on the knowledge graph to obtain a fault root cause node corresponding to the target service; wherein the fault root cause node is used for indicating an entity node which is easy to cause a cascading fault in the target service; determining a self-healing operation set corresponding to the fault root cause node; wherein the success rate of the self-healing operation in the self-healing operation set is greater than a preset success rate threshold; and determining the action value of each self-healing operation in the self-healing operation set, and determining the self-healing operation with the maximum action value as the target self-healing operation of the fault root cause node.

The invention relates to the technical field of handbag material surface defect image detection, in particular to an environment-friendly handbag material surface defect image detection method and system, and the method comprises the following steps: obtaining image data of the surface of an environment-friendly handbag material; dividing the image data into a plurality of local areas; for each local area, extracting image features representing visual features of the local area; establishing a normal feature range of the local area, and continuously adjusting the normal feature range in combination with the environment information; comparing the current image feature with the continuously adjusted normal feature range to identify an abnormal region deviating from the continuously adjusted normal feature range; and according to the image feature mode of the abnormal region, natural textures and defects are distinguished, and defect information is output. According to the method, natural textures and real defects on the surface of the environment-friendly handbag material can be effectively distinguished, potential defects with low contrast and fuzzy edges can be identified, and the accuracy and reliability of detection are improved.

The application belongs to the technical field of polymer material recycling, and particularly relates to a high-efficiency enzymatic hydrolysis process of polycaprolactam. The high-efficiency enzymatic hydrolysis process of polycaprolactam provided by the application comprises the steps of acid pretreatment, enzymatic catalytic depolymerization and product post-treatment. The process combines sulfuric acid and a multi-enzyme system to treat PA6 with high substrate concentration. The physical form and chemical accessibility of PA6 are significantly changed through sulfuric acid pretreatment, which creates conditions for subsequent enzymatic hydrolysis reaction, and realizes efficient, deep and green degradation of PA6.

The application relates to the technical field of data analysis, and discloses a karst fissure identification-based open-pit mine blasting parameter optimization method and system. The method comprises the following steps: obtaining karst fissure distribution characteristic data through multi-source detection, establishing a three-dimensional fissure network topology structure, analyzing static breaking agent inflation stress propagation rules, performing multi-objective optimization solving based on stress propagation direction and intensity distribution to obtain an initial blasting parameter combination, and obtaining dynamic adjustment blasting parameters through real-time tracking and regulation according to fissure dynamic evolution in the blasting process. The application solves the problem that static blasting parameters cannot be real-time regulated according to the dynamic evolution state of the fissure network in the blasting process, resulting in deviation between the blasting effect and the expected target.

The present application relates to the technical field of fault detection, in particular to a chemical production process abnormal condition identification method and system, comprising the following steps: collecting reactor temperature concentration and flow and aligning and denoising to generate a multi-source time sequence set, extracting spatial measurement point temperature difference and differentiating symbols to generate a temperature difference direction sequence, calculating conversion amount based on concentration and flow and calculating rate gradient and direction sequence inversion comparison to generate heat release inversion sequence, obtaining inversion time and spatial coordinates and misaligning to align to form a lag mismatch set, combining time window temperature change and mismatch frequency to input a support vector machine classification to obtain abnormal condition identification results, in the present application, a multi-source synchronous time sequence set is constructed to extract temperature difference direction, spatial gradient is calculated to obtain inversion characteristics, time and space misalignment is implemented to align to capture physical conduction lag law, mismatch frequency is used to rely on a support vector machine to perform classification, single point limitations in dealing with conduction lag are overcome, the technical problem that hidden faults are easily covered is solved, and accurate identification of abnormal variations in complex conditions is realized.

This invention discloses a method and system for calculating the exploitable reserves of multi-layered gas reservoirs, belonging to the field of oil and gas development technology. The method includes the following steps: evaluating the dynamic reserves of multi-layered syndicated gas wells; calculating the contribution ratio of stratified production at different production times of the gas well; based on the contribution ratio of stratified production, and according to the monthly single-point stratified production splitting coefficient, calculating the daily production splitting coefficient through linear interpolation, and combining it with the daily production of the gas well to calculate the daily production of each sub-layer, and accumulating the cumulative production of each sub-layer; based on the cumulative production of each sub-layer, calculating the cumulative gas production ratio of each stratum, and combining it with the dynamic reserves calculation results of the gas well, determining the exploitable reserves of the strata. This method provides a prediction method for the exploitable reserves of multi-layered gas reservoirs. Based on the dynamic and static parameters of the gas well sub-layers and the stratified production capacity, this method calculates the exploitable reserves of multi-layered syndicated gas wells, and can effectively guide the prediction of the exploitable reserves of multi-layered gas reservoirs.