791 results about "Differential evolution algorithm" patented technology

The invention provides an anomaly detection and maintenance decision optimization method and system for automatic test equipment, and relates to the technical field of automation, and the method comprises the steps: deploying an edge calculation unit and a multi-level sensor network to collect equipment data, carrying out the fault diagnosis through a deep network model, and constructing a maintenance decision model in combination with equipment operation data. The model is input into a quantum enhancement dual-network system to calculate a reward value, and is used for training a maintenance strategy network under a federal reinforcement learning framework. And generating a maintenance resource configuration scheme through a quantum evolutionary algorithm, and generating a maintenance scheme through optimization of the multi-criterion evaluation model and the self-adaptive multi-target differential evolutionary algorithm. According to the scheme, a high-fidelity digital twin model and a multi-task deep transfer learning network are input for verification and updating, updated data are transmitted to a field control system through an industrial Internet of Things platform, and the updated data are recorded by using a block chain technology for updating a deep network model.

The invention provides a method and a system for evaluating the packaging tightness of an integrated circuit in a high-temperature environment, and relates to the technical field of circuit testing, and the method comprises the steps: obtaining the temperature, tracer gas concentration and gas pressure data of the integrated circuit, and extracting temperature nonlinear features; non-local weighted adaptive anisotropic diffusion filtering and mixed tensor decomposition are utilized to obtain concentration multi-dimensional distribution features, air pressure frequency domain features are extracted, and feature fusion and space-time dependency analysis are performed through a quantum depth auto-encoder, a multi-head cross attention network and a heterogeneous space-time diagram neural network. Time sequence features are extracted through waveform dynamic time warping and a bidirectional gating circulation attention network, sealing defect diagnosis is performed based on knowledge distillation, a failure trend is predicted by using a causal long-short term memory network, and a failure mode is matched in combination with a dynamic heterogeneous knowledge graph. And a multi-target Pareto differential evolution algorithm is adopted to evaluate the packaging reliability and generate an evaluation report.

The invention discloses an industrial equipment communication method and system based on the industrial internet, and the method comprises the following steps: S1, collecting the multi-modal data of industrial equipment, and generating a preliminary multi-modal fusion feature; s2, performing event-driven analysis on the preliminary multi-modal fusion features to generate event sensing data; s3, constructing a semantic-driven communication protocol, and generating a semantic data queue with a priority identifier; s4, compressing the semantic data queue and carrying out time consistency calibration; s5, optimizing a communication frequency and bandwidth resource allocation strategy in combination with a mixed firefly optimization algorithm and a dynamic differential evolution algorithm; s6, constructing an industrial equipment communication topology model, and predicting path delay and bandwidth occupation indexes of the communication network in combination with the space-time multi-task network; and S7, constructing a zero-sum game model to analyze the potential confrontation relationship in the communication process, and outputting a secure communication scheme. According to the invention, semantic-driven communication, a hybrid optimization algorithm and the like are adopted, and intelligent communication of industrial equipment is realized.

The technical scheme of the invention relates to the technical field of gas detection, in particular to a hazardous chemical storage leakage positioning and tracing method based on a gas array. The method comprises the following steps: collecting gas concentration, components and environmental physical field parameters through a three-dimensional heterogeneous sensor array, and generating a multi-modal data set; and matching the noise mode feature library in the knowledge base by using a transfer learning algorithm, dynamically correcting the baseline drift of the sensor, and outputting calibrated data. A closed-loop adaptive mechanism adjusts sensor parameters and network hyper-parameters through meta reinforcement learning, neural architecture searches and optimizes a model structure, and a differential evolution algorithm updates fluid mechanics boundary conditions. Through cooperation of physical constraint and data driving, the problem of signal distortion in a complex environment is solved, the leakage source positioning precision and robustness are improved, and the method is suitable for the field of hazardous chemical substance storage safety monitoring.

The invention discloses a network security threat intelligent identification and defense method based on artificial intelligence, and the method comprises the steps: S1, collecting network flow data, user behavior data and system log data from a network security system, and outputting a finally converged network feature data set; s2, performing global optimization search on the network feature data set by applying a hyper-entropy differential evolution algorithm to generate network feature data optimized by hyper-entropy differential evolution; s3, constructing a network traffic anomaly detection probability model based on variational Bayesian reasoning by using the network feature data after hyper-entropy differential evolution optimization; s4, inputting the network feature data set into the network traffic anomaly detection probability model, and evaluating the risk probability of an abnormal behavior; and S5, generating a self-adaptive defense strategy based on a detection result, and forming defense strategy data. According to the invention, the overall stability and defense capability of the network security system are improved.

The present invention provides a wind power off-grid hydrogen production power supply topology and control method without a step-down transformer, including: obtaining wind speed data, using a wavelet threshold algorithm for denoising, predicting power based on an LSTM-GRU hybrid neural network, and extracting power fluctuation characteristics through multi-scale entropy analysis; obtaining load data, using Kalman filtering and recursive least squares to establish an impedance model, and extracting dynamic characteristics in combination with Hilbert transform; using a differential evolution algorithm for multi-objective optimization to generate an optimal switching sequence and power allocation strategy; achieving system control through space vector modulation, balanced control, soft switch control and harmonic compensation; achieving state estimation and disturbance suppression based on a sliding mode observer and a nonlinear observer; and achieving fault diagnosis and protection using a deep belief network. The present invention improves system efficiency and reliability and improves dynamic performance.

The invention relates to the technical field of power distribution station control, in particular to a reactive power optimization control system of a power distribution station. Comprising a power distribution station data acquisition module, a reactive prediction module, an optimization decision module, a distributed cooperative control module and a monitoring interaction module. The power distribution station data acquisition module acquires parameters such as voltage and current and guarantees data integrity; the reactive power prediction module performs modeling based on a graph neural network and generates a prediction report; the optimization decision module adopts a differential evolution algorithm to generate an optimal compensation scheme; the distributed cooperative control module realizes cooperation between devices through fractional calculus; and the monitoring interaction module realizes equipment diagnosis, aging prediction and dynamic feedback. Through an advanced algorithm and a cooperative control technology, high efficiency, reliability and intelligence of reactive power optimization control of the power distribution station are realized, and the operation efficiency and reliability of the power distribution station are improved.

The invention relates to a multi-element microphone array sound source localization method based on multistage signal preprocessing and subspace spectrum optimization, and belongs to the technical field of acoustic detection. Aiming at the problems of poor noise immunity, weak multi-sound-source resolution capability and low calculation efficiency of the existing sound source positioning technology, a triple signal preprocessing and subspace collaborative optimization scheme is provided; firstly, incoherent noise is suppressed through phase coherent filtering, a signal is reconstructed through principal component analysis, and phase deviation is calibrated through fundamental frequency; then constructing a guiding matrix and decomposing a noise subspace, and extracting a coarse positioning result; and finally, high-precision angle optimization is realized based on a chaos initialization differential evolution algorithm, and the efficiency is improved by combining a dynamic search range and an early stop mechanism. According to the method, the anti-interference capability in a low signal-to-noise ratio environment is remarkably enhanced, the problems of missing detection and false detection during dense distribution of multiple sound sources are effectively solved, meanwhile, the positioning precision and the real-time performance are considered, and the method is suitable for acoustic fault detection of complex scenes such as power transmission line inspection.

The invention provides a steam extraction energy storage and heat energy storage integrated regulation control method and system, and relates to the technical field, and the method comprises the steps: building a digital twinborn model of a steam extraction energy storage and heat energy storage integrated system, and respectively obtaining corresponding data; establishing a multi-objective optimization model based on the collected data; solving the multi-objective optimization model by using an improved multi-objective differential evolution algorithm in combination with an adaptive variation strategy and a non-dominated sorting mechanism to obtain an optimal adjustment strategy; on the basis of the optimal adjusting strategy, the opening degree of a steam extraction valve of each stage of steam extraction point is adjusted in real time through a distributed control system, and the steam extraction flow is controlled; the inflation power and the deflation power of the compressed air energy storage unit are dynamically adjusted, and meanwhile the charging and discharging power of the flywheel energy storage unit is controlled; the heat charging and discharging rate of the phase change material heat storage unit is adjusted in a self-adaptive mode, and meanwhile the heat charging and discharging flow of the fused salt heat storage unit is controlled.

The invention discloses an electromagnetic ultrasonic transducer rapid design method based on an S-SADE algorithm, and the method comprises the steps: constructing an electromagnetic ultrasonic transducer model and a joint simulation platform, obtaining initial sample data and various parameters, and initializing a population and a sample database; using samples in the database to construct an RBF proxy model; performing variation and crossover operation on the parent population individuals according to five variation strategies to obtain a filial population; using an RBF agent model to predict fitness values of offspring individuals, transmitting parameters of an optimal individual to an electromagnetic ultrasonic transducer model for simulation evaluation, and updating a database; selecting a most expected sub-population from the five sub-populations as a next-generation population; and outputting an optimal parameter design scheme. According to the adaptive differential evolution algorithm, appropriate variation strategies and control parameters can be selected in different evolution stages, the objective function values of population individuals are predicted through the proxy model instead of expensive simulation evaluation, and computing resources can be saved to a great extent.

The invention discloses a locating and sizing planning method and system based on improved multi-objective differential evolution, and relates to the technical field of electric power system planning and optimizing.The method comprises the steps that electric power system data are collected and processed, and the electric power system data comprise network topology, line parameters, load characteristics and generator set parameters; the method comprises the following steps: establishing a static var compensator locating and sizing planning model based on power system data, setting an objective function, and formulating a constraint condition set; a self-adaptive variation strategy and an elitism strategy are introduced, and a multi-target differential evolution algorithm is improved; according to the method, a self-adaptive variation strategy and an elitism strategy are introduced, and the improved multi-target differential evolution algorithm is combined, so that the searching capability and the convergence of the algorithm are greatly improved, and the optimal locating and sizing scheme is obtained. And the optimal solution can be more efficiently searched in the complex solution space.

The invention discloses a novel power system multi-target active and reactive power coordination optimization method and device, and relates to the technical field of novel power system optimization scheduling, and the method comprises the steps: building a multi-target active and reactive power coordination optimization model with the lowest total operation converted power loss and the optimal static stability index of a system as optimization targets, determining an electric network constraint condition; water energy storage active output, thermal power generating unit active output, wind power actual grid-connected power, photovoltaic actual grid-connected power, reactive compensation equipment capacity and converter reactive capacity are used as populations, and the lowest total operation converted power loss and the optimal static stability index of the system are used as population fitness. And generating upper and lower limits of population generation and upper and lower limits of population scheduling change in front and back time periods by using an electric network constraint condition, and solving a multi-target active and reactive coordinated optimization model by using a multi-target differential evolution algorithm. According to the method, the converted power loss and the static stability in a novel power system scheduling decision can be considered.

A rail area extraction method based on laser point cloud data is provided, including: preprocessing collected laser point cloud data; screening and clustering the laser point cloud data based on a fixed distance segmentation method, and representing laser point cloud data of reference objects by a main laser point cloud data cluster; projecting laser point cloud data of reference objects to a horizontal plane, fitting reference curves based on an improved differential evolution algorithm with a train left side reference curve as an upper boundary and a train right side reference curve as a lower boundary; selecting a target boundary line from upper and lower boundaries based on a laser point cloud data amount-density two-step decision method; calculating a rail area center line based on the target boundary line; and selecting a rail area boundary line extension method or rail area center line extension method to calculate the rail area.

The invention discloses an intelligent optimization decision-making method and system for an oilfield water injection system, and belongs to the technical field of oilfield development engineering. The method comprises the following steps: constructing a pipe network digital twinborn model and carrying out actual measurement verification; executing hydraulic-thermal simulation based on the model to generate a pressure-flow map; optimizing water distribution in combination with an injection allocation scheme and matching a pump set combination; generating a control instruction and executing a pressurization scheme; monitoring the state of the pipe network in real time and correcting model parameters. Through the digital twin technology and the differential evolution algorithm, global optimization decision of water distribution and pump set combination of the water injection system is achieved, and the problems that a traditional system is high in energy consumption and low in efficiency are solved.

The invention discloses a hydrogen energy system safety assessment method based on deep learning, and the method comprises the following steps: S1, collecting and preprocessing the dynamic operation parameters of a hydrogen energy system, and generating an input feature matrix; s2, inputting the feature matrix into a multi-resolution feature sensing module to generate a high-dimensional feature map; s3, inputting the high-dimensional feature map into a dynamic time bending network to generate a parameter incidence matrix; s4, optimizing feature weight distribution based on an adaptive differential evolution algorithm; s5, abnormal distribution is captured through the dynamic graph convolutional network, and a dynamic abnormal graph is generated; s6, simulating an anomaly propagation path, and generating a cascade anomaly analysis result in combination with a dynamic traceability mechanism; s7, a cascade anomaly analysis result is optimized through a grey wolf-firefly hybrid optimization algorithm; and S8, dynamic operation parameters are adjusted based on a final cascade risk prediction result, and hydrogen energy system safety assessment is completed. According to the method, deep learning, a hybrid optimization algorithm and the like are combined, and safety evaluation of abnormal detection and risk prediction of the hydrogen energy system is realized.

The invention discloses a wrist joint spasm assessment method and a wrist joint spasm assessment device based on multi-source data fusion. The rotating speed data, the rotating angular speed data and the angle change data of the wrist joint, the pressure change data of the whole palm, the acceleration change data of the palm, the torque change data of the wrist joint and the electromyographic change data of related muscles in the movement process are measured, and scoring is carried out in combination with an expert system. The evaluation of wrist spasm is realized by combining a multiple linear regression algorithm with a differential evolution algorithm; and based on the multi-source data fusion, establishing an evaluation model by using a Bi-LSTM network so as to realize spasm evaluation. The wrist serves as a spasm detection part, and compared with traditional spasm detection equipment, a more convenient data acquisition mode is achieved while the evaluation precision is guaranteed.

The invention relates to the technical field of data processing, and discloses a flue gas treatment intelligent regulation and control method and system based on artificial intelligence and a medium. The method comprises the following steps: collecting parameter data of a flue gas system through a sensor network and processing to obtain preprocessed data; using unsupervised clustering to identify working condition features and categories; constructing an emission concentration error active switching control model according to the working condition information; different working condition control parameters are optimized by adopting a differential evolution algorithm; optimized parameters are input into a controller, and modes are dynamically switched according to pollutant errors. The control strategy can be automatically switched according to the working condition characteristics of the flue gas treatment system, the complex and changeable industrial production environment can be effectively dealt with, and the system operation cost is optimized while stable and up-to-standard emission of pollutants is guaranteed.

The invention relates to the technical field of carbon emission treatment, in particular to a building full-life-cycle carbon emission calculation method based on multiple linear regression. Comprising the following steps: acquiring carbon emission data of each stage of a building through a BI model, and extracting key characteristic variables such as a material carbon emission coefficient and energy consumption intensity by adopting a Latin hypercube sampling method and a fuzzy clustering algorithm; modeling carbon emission by using a multiple linear regression model, and constructing a carbon emission prediction model; optimizing model parameters through a differential evolution algorithm; and finally, calculating the total amount of carbon emission, analyzing key influence factors in a high-carbon emission stage through a causal reasoning algorithm, proposing an optimization strategy, generating an analysis report, and realizing accurate prediction and effective optimization of the carbon emission in the whole life cycle of the building. According to the method, the carbon emission of the whole life cycle of the building can be accurately evaluated, and powerful support can be provided for optimization decision-making of the carbon emission.

The invention relates to the technical field of reservoir numerical simulation, and discloses a polymer flooding reservoir rapid simulation optimization method considering sedimentary facies constraints, which comprises the following steps: identifying sedimentary facies boundaries according to distribution characteristics of sedimentary facies, and establishing a connected network model considering the sedimentary facies constraints on the basis of the sedimentary facies boundaries, well positions and various sedimentary facies attributes; carrying out one-dimensional grid division on the connected network model, converting the connected network model into a grid connection format of a universal simulator, and solving the connected network model by adopting the universal simulator to obtain pressure and saturation solutions; establishing an automatic historical fitting mathematical model based on sedimentary facies and oil reservoir constraint conditions; the economic net present value is used as an index, the water injection rate, the liquid production rate and the polymer injection rate of each well are used as optimization variables, the differential evolution algorithm is used, the total polymer injection amount and the oil reservoir pressure are restrained, and an oil reservoir injection and production optimization mathematical model is established. According to the method, the oil reservoir is optimized by comprehensively considering the characteristics of the oil reservoir after water flooding is converted into polymer flooding, so that rapid decision-making of the oil field and guidance of oil field construction are realized.

The invention discloses a method, a system and equipment for compiling a power generation plan of a water-wind-light complementary system, and relates to the crossing field of renewable energy utilization and reservoir scheduling. Adopting a multi-dimensional synchronous back substitution reduction algorithm to determine a typical scene and an occurrence probability thereof; in the second stage, the first stage is used as a boundary condition, the minimum operation risk, the optimal peak regulation performance and the maximum power generation amount and residual energy storage of the water-wind-light complementary system are used as objective functions, and the construction of a water-wind-light multi-energy complementary risk-benefit balance multi-objective scheduling model is completed according to the required constraint condition; and solving the model by using a non-dominated sorting differential evolution algorithm, and screening an equilibrium solution by using a multi-attribute decision-making method to obtain a monthly typical daily power generation plan of the water-wind-light complementary system considering the uncertainty of the system. The method can provide a basis for scientific formulation of the power transmission curve of the water-wind-light complementary system.

The invention discloses a virtual power plant capacity optimization configuration method and system considering carbon tax, and belongs to the field of power markets. The invention discloses a virtual power plant capacity optimization configuration method considering carbon tax. The method comprises the following steps: analyzing virtual power plant capacity cost and operation income considering carbon tax and market transaction; constructing a virtual power plant internal resource capacity optimization configuration double-layer model based on the analysis of the virtual power plant capacity cost and the operation benefit; generating a spectral clustering typical scene based on an information gap decision theory considering source load uncertainty; and solving the virtual power plant internal resource capacity optimization configuration double-layer model by adopting a differential evolution algorithm to obtain a capacity optimization configuration scheme.

The invention discloses a ship-based radar and AIS data fusion method and device based on the satellite internet, and relates to the technical field of ship information. The method comprises the following steps: acquiring self-motion, AIS and radar data recorded by a plurality of ships in real time through a low-orbit satellite, and performing target trajectory tracking and data filtering by adopting a first-order linear ship motion model and a self-adaptive extended Kalman filter; the method comprises the following steps: establishing an error model containing a radial error coefficient, an azimuth angle error and an effective detection distance for dynamic system errors of a shipborne radar, and realizing radar-AIS target matching and error correction based on an adaptive differential evolution algorithm and a Hungary algorithm; and finally, performing fusion processing on unknown targets sensed by multiple nodes through a multi-hypothesis tracking method to form a globally unified target observation result. According to the method, shipborne radar data and AIS data can be effectively associated, real-time monitoring of ship targets in a wide-area sea area is achieved, and reliable technical support is provided for marine supervision, safety early warning and track backtracking.

The invention provides an AGC hydropower station intelligent control method based on multi-source data fusion. Constructing a control feature vector of the multi-dimensional feature; performing spatial-temporal feature modeling on the control feature vector, and extracting a time sequence dependency relationship between power grid load change and hydraulic dynamic response and a spatial coupling effect between units; establishing a multi-objective optimization function, and dynamically adjusting the weight coefficient of each objective through fuzzy logic according to the current working condition; a self-adaptive differential evolution algorithm is adopted to carry out on-line optimization on an active power distribution coefficient of a unit and PID parameters of a speed regulator, and the requirements of guide vane opening change rate constraint and water hammer effect avoidance are met. According to the method, multi-source heterogeneous data such as power grid, hydraulic engineering and equipment states can be effectively fused, multi-target dynamic optimization control is realized through the space-time attention model and the adaptive differential evolution algorithm, and the control precision, the response speed and the equipment operation safety of the hydropower station AGC system are remarkably improved.

The invention discloses a permanent magnet synchronous motor temperature monitoring method based on digital-analog hybrid drive, and the method specifically comprises the steps: dividing a motor into a plurality of heat source nodes, including a stator winding, a stator tooth part, a rotor core and the like, connecting the nodes through a thermal resistance and thermal capacity network, building a thermal network model of the motor, and carrying out the thermal network model; calculating the initial values of thermal resistance and thermal capacity of each component of the motor, and optimizing parameters in a thermal network model by using a differential evolution algorithm based on the lumped parameter thermal network model and in combination with a data-driven strategy. Furthermore, temperature rise prediction is dynamically adjusted according to motor state data (such as stator current, rotating speed and the like) collected in real time so as to realize real-time temperature estimation. The method is high in temperature estimation precision, does not affect the performance of the motor, is simple in calculation, is good in real-time performance, and has physical interpretability. The temperature rise of the motor can be predicted in real time under different working conditions, early warning is given out, motor faults caused by too high temperature rise are effectively avoided, and the reliability and safety of the motor are improved.

The invention relates to the field of laser spectrum parameter extraction, and discloses a DFB laser spectrum parameter extraction method comprising the following steps: constructing a deep learning model, and training a corresponding relation between spectrum parameters and spectrum extraction information; testing a DFB laser of which parameters are to be extracted by utilizing a spectrograph, and extracting information by taking a tested spectrum as a target spectrum; inputting the initialized spectrum parameters into a learning model to predict spectrum extraction information, performing error analysis on the spectrum extraction information output by the model and target spectrum extraction information, performing population evolution by using a differential evolution algorithm, and inputting the evolved parameters into the model until target precision or iteration times are reached, and completing the extraction of spectrum parameters. According to the method disclosed by the invention, the parameter extraction time is greatly shortened, and the calculation cost is reduced; spectral parameters can be rapidly extracted, and the situation of local optimum is avoided; and batch extraction of the spectrum parameters of the DFB laser can be realized in a short time.

The invention provides a self-adaptive volume-variable intelligent air compressor and a control system thereof, and relates to the technical field of air compressors. The control system comprises a multi-source heterogeneous sensing fusion module, a dynamic manifold optimization control module, a quantum probability fault pre-judgment module, a cross-domain energy cooperative scheduling module, a distributed self-organizing communication module and a self-adaptive meta-evolution module. And a grey Markov chain model and a wavelet decomposition-support vector machine are combined to accurately predict the real-time electricity price and the factory gas consumption load. And according to the predictions, the operation mode of the air compressor is intelligently adjusted through dynamic game equilibrium and a multi-target differential evolution algorithm. In the low ebb period of the electricity price, the operation load is automatically increased by 30%-40%, and meanwhile the gas storage capacity is increased by 25%-35%; and in the peak period of electricity price, stored gas is reasonably released, and the operation load is reduced by 20%-30%. And energy waste caused by unreasonable operation is effectively avoided.

The invention provides an ultrasonic echo signal joint noise reduction method based on improved EKF and wavelet packet cooperation, belongs to the technical field of ultrasonic detection, and solves the problems that the noise reduction effect is poor in a traditional ultrasonic signal noise reduction method and noise reduction and feature retention are difficult to balance in the wavelet packet noise reduction process. Modeling the ultrasonic echo signal and the noise signal to obtain an ultrasonic echo signal with noise; extracting a target ultrasonic echo signal by using an improved extended Kalman filter to obtain an observation equation; predicting and updating on the basis of the observation equation to obtain ultrasonic echo signal estimation; optimizing the noise covariance matrix and the observation noise covariance matrix by adopting a differential evolution algorithm to obtain an optimal parameter, executing extended Kalman filter filtering based on the optimal parameter, and generating an ultrasonic echo signal estimated value after preliminary denoising; and carrying out wavelet packet threshold denoising to obtain a final denoised ultrasonic echo signal.

The invention discloses an oil reservoir inversion method based on a differential evolution particle swarm fusion algorithm, and belongs to the technical field of oil reservoir inversion, and the method comprises the steps: carrying out the oil reservoir history fitting, and determining an objective function of the oil reservoir history fitting; establishing a particle swarm optimization algorithm; establishing a differential evolution algorithm; and based on a differential evolution particle swarm fusion algorithm, solving the oil reservoir history fitting objective function to obtain an oil reservoir inversion optimal result. By adopting the above method, through fusion of the two algorithms, the algorithms can maintain the exploration capability for the global optimal solution in the search process, and can rapidly converge to the vicinity of the optimal solution, thereby improving the precision and reliability of oil reservoir inversion.

The invention provides a poison gas leakage inversion method, device and equipment, a medium and a product. According to one example of the application, the method may include: using a stochastic simulation algorithm to determine a simulation position and simulation intensity where a poison gas concentration error is minimum, the poison gas concentration error being an error between a sampled poison gas concentration of a sampling point and a stochastic simulated poison gas concentration; based on the simulation position and the simulation intensity, constructing a position constraint condition and an intensity constraint condition of the poison gas leakage source; calculating a leakage source position of a poison gas leakage source by adopting a differential evolution algorithm and a position constraint condition; and calculating the leakage source intensity of the poison gas leakage source by adopting a quasi-Newton optimization algorithm and an intensity constraint condition.

The invention provides a task resource scheduling algorithm taking user experience quality as an optimization target, and belongs to the technical field of resource scheduling. The task resource scheduling algorithm taking the user experience quality as the optimization target takes an improved differential evolution algorithm as a core; task information, hardware performance information and system resource information of a current node are acquired through a scheduling information acquisition module, and an initial task resource allocation scheme is generated; in the optimization process, a heuristic scheduling unit predicts a task execution result in combination with a KAN model, evaluates a candidate solution, and dynamically adjusts a resource allocation scheme by taking user experience quality as a fitness function. And the finally output optimal solution allocates resources to a task execution control module so as to improve the task execution efficiency and the user experience quality. According to the invention, through a double-strategy variation mechanism and a task execution prediction model, an optimal resource allocation scheme can be rapidly converged, and maximization of user experience under limited computing resources is ensured.