107 results about "Performance comparison" patented technology

The invention provides a distributed formation control method driven by a recursive balance network under a communication attack, and relates to the technical field of cooperative control of a multi-agent system, and the method comprises the steps: constructing a distributed multi-agent system model, and constructing an RBN controller heterogeneous communication network architecture and a DoS attack model; the RBN controller design is optimized, the distributed RBN controller architecture is realized, the stability of the RBN controller is analyzed based on the shrinkage mapping theory, and the exponential convergence of the system state difference is proved by constructing a Lyapunov function; training the optimized RBN controller by adopting a difficulty priority confrontation training strategy, designing a multi-objective loss function, and dynamically adjusting the training weight of each difficulty level; and simulating the distributed multi-agent system optimized by the above steps, testing the robustness of the distributed multi-agent system under incremental DoS attack intensity, and performing comparative analysis of heterogeneous and isomorphic communication network configuration and comprehensive performance comparison of an RBN controller and a traditional MPC method.

The embodiment of the invention provides a compressor performance evaluation method and device, electronic equipment and a storage medium, and belongs to the technical field of refrigeration. The method comprises the following steps: constructing a thermodynamic cycle flow of the compressor under each preset condition according to a compression process of the compressor under a plurality of preset conditions; for each preset condition, based on the thermodynamic cycle flow of the compressor under the preset condition, a mathematical model of the compressor under the preset condition is constructed, and the mathematical model comprises a plurality of target performance parameters used for evaluating the performance of the compressor; fitting the mathematical model of the compressor under the preset condition with the thermophysical parameters of the refrigerant of the compressor to obtain the value of each target performance parameter of the compressor under the preset condition; and performing performance comparison evaluation on the values of the multiple target performance parameters of the compressor under each preset condition to obtain an evaluation result. According to the embodiment of the invention, the compressor performance can be quantitatively evaluated.

The invention discloses a building composite phase change material intelligent matching and optimizing method based on a large language model, and belongs to the technical field of building energy saving and intelligent material design. Basic thermophysical property data, building environment parameter data and user demand data are collected; a deep reinforcement learning technology is utilized to construct an intelligent model based on a deep Q network strategy, and generated data samples are integrated into a thermophysical property database; outputting a candidate material combination recommendation scheme by adopting a large language model; evaluating the candidate material combination recommendation scheme by using the semantic tag vector and a sorting engine to obtain a performance evaluation result; generating a performance evaluation report according to the simulation model; a multi-agent negotiation algorithm is adopted, cross-regional thermal control performance is optimized, a multi-dimensional performance comparison diagram and tuning suggestions are generated, correction information of designers is recorded, optimization is carried out, and feedback is provided in a self-adaptive mode. According to the method, the building composite phase change material combination is screened, cross-regional thermal control is optimized, adaptive schemes and suggestions are output, and the self-adaptive optimization capability of the system is improved.

The invention provides a performance testing method and device for a large language model and related equipment. The performance testing method comprises the steps that multiple testing platforms for testing, testing scene configuration parameters and a structured testing data set for testing are determined; on the basis of the test scene configuration parameters and the structured test data set, synchronous constraint parameters are generated and distributed to the multiple test platforms, and then a preset load generator is used for controlling the multiple test platforms to concurrently execute reasoning test tasks about the large language model; and when the test is executed, collecting multiple dimension performance indexes of each test platform in real time for data analysis, and generating a reasoning performance comparison evaluation report of the large language model in the multiple test platforms according to a data analysis result. According to the method, the cross-platform performance evaluation period is greatly shortened, and the waste of computing resources is reduced. Manual operation errors are avoided, comparability of multi-platform performance indexes is guaranteed, and more accurate and reliable multi-platform performance test results can be obtained.

The invention discloses a multi-unmanned aerial vehicle cooperative task allocation and path planning method based on a genetic algorithm, and relates to an unmanned aerial vehicle path planning method. And operating a Dijkstra algorithm to accurately solve the shortest feasible path between all airports and task points, and finally generating a global path cost matrix for quick query. A complete and safe flight path that each unmanned aerial vehicle starts from an airport, sequentially accesses task points and returns is graphically displayed, a convergence curve of an algorithm, population diversity changes and performance comparison data under different task scales, unmanned aerial vehicle numbers and obstacle densities can be output, and a parameter sensitivity analysis function is supplemented. And the effectiveness, the stability and the practical value of the proposed method in a static urban environment are comprehensively verified.

The invention relates to the technical field of intelligent power grid monitoring, in particular to an operation fault monitoring method and system for power equipment of a transformer substation, and the method comprises the steps: analyzing the equipment operation stability of regional nodes at different moments, and then analyzing the load fluctuation similarity between the regional nodes and adjacent regional nodes; obtaining a similarity coefficient corresponding to the region node; analyzing fluctuation difference conditions at adjacent moments and load fluctuation variation amplitudes at non-adjacent moments to obtain load mutation indexes of the regional nodes; and finally, combining the similar coefficient and the load sudden change index to obtain a load sudden change interference index of the regional node, and performing operation fault analysis on the power equipment in the transformer substation. Based on the power utilization monitoring data fluctuation and the regional power supply change, the power utilization fluctuation characteristics of each regional node are dynamically analyzed, the performance comparison is performed, the external load fluctuation and the equipment internal fault characteristics are distinguished, and the identification precision of the operation fault monitoring of the power equipment of the transformer substation can be effectively improved.

The invention provides a front-end development performance analysis and construction configuration reverse optimization method. Performance timeline data, including a resource loading time sequence, main thread task decomposition and key rendering performance indexes, of a browser developer tool are automatically collected in a running environment, and all-around monitoring of page running performance is achieved; based on the performance bottleneck of correlation between data identification and construction configuration, a low-efficiency mode of relying on artificial experience analysis is broken through; the performance problem type is mapped into the specific configuration parameter through the preset rule base, the optimization configuration item is automatically generated, and the problem that general configuration lacks pertinence is solved; an output strategy is further dynamically adjusted in combination with environmental parameters, and it is ensured that configuration adapts to multi-scene requirements; and finally, performance comparison data before and after configuration change is displayed through a visual report, a closed-loop feedback mechanism is formed, the developer optimization threshold is remarkably reduced, and the cross-environment performance consistency and iteration efficiency of front-end applications are improved.

The invention relates to the field of automobile financial consumption, and provides a method, a system, equipment and a medium for intelligently and automatically recommending an automobile loan scheme. According to the method, a two-dimensional portrait is constructed by obtaining user data and fund party data, the user data is processed by feature crossing and a portrait algorithm to generate a user portrait, and the fund party data is processed by normalization and feature engineering to generate a fund party portrait. The method is advantaged in that the user and fund party portraits are matched based on the machine learning model, the matching degree is calculated, the personalized car loan scheme is generated according to matching degree sorting, interest rate, deadline and approval requirements are considered, accurate matching of the user and the fund party is realized, and recommendation success rate and user satisfaction are substantially improved. Meanwhile, the method supports dynamic adjustment of the model, a matching algorithm is optimized in real time through performance comparison of the challenger model and the champion model, efficient extension and multi-fund-party access are achieved, and the method has industrial popularization value.

A system and method for quantifying the impact of investor behavioral persona on investment outcomes. A Behavioral Risk Index (BRI) is calculated based on investor behavioral factors including investor type, behavioral biases, and financial literacy. The system simulates behavior-impacted investment returns by mapping exit and re-entry points of investor groups sharing a common behavior persona during historical market events. The simulated behavior-impacted investment outcomes is quantified by comparing to buy-and-hold strategy. Personalized visualizations, including the visualization of the BRI, behavioral factor breakdowns and performance comparisons, are displayed to assist financial professionals and investors in identifying behavioral risk exposure and demonstrating the value of behavioral coaching.

The invention provides an A / B link performance comparison and strategy optimization method and system based on service flow, and belongs to the field of network performance monitoring and network strategy verification. The method comprises the following steps: firstly, performing bypass collection on real service traffic to obtain original data; copying the same copy of original data, injecting the original data into the link A and the link B at the same time, and then respectively collecting service flows of the link A and the link B; establishing a session alignment model, and judging whether the service traffic of the link A and the link B belongs to homologous traffic or not; after time drift compensation is carried out on sending timestamps of the homologous flow in the two paths, visible time sequence fields of homologous events are extracted respectively, differential indexes of the homologous events of the two paths on key time sequence parameters are calculated, and different percentile values of all the differential indexes under each parameter type in a preset time window are counted; and link performance comparison and strategy verification are carried out according to the difference index and the percentile value. According to the invention, the authenticity and efficiency of performance comparison and strategy optimization of different paths are improved.

A machine learning device includes an image set acquiring unit to acquire an image set including images, and an image set selecting unit to select an image set similar to the acquired image set from a plurality of image sets different from the acquired image set. In addition, the machine learning device includes a performance comparison unit, and a preprocessing acquisition unit to select a learning model from a plurality of machine-learned learning models based on a performance comparison result by the performance comparison unit and to acquire preprocessing performed on an image set used for machine learning of the learning model selected. Furthermore, the machine learning device includes a model learning unit to perform the acquired preprocessing on the acquired image set and to cause a learning model that has not yet trained to perform machine learning using the preprocessed image set.

The invention discloses a cross-scale virtual-real synchronization method and system for unmanned system cluster experiment teaching, and the method comprises the steps: S1, generating a corresponding digital twinborn simulation scene through a system according to a template parameter according to an algorithm template selected by a user in a graphical interface; s2, timestamp alignment and delay compensation are carried out through a cross-scale virtual-real synchronization engine, the digital twin simulation scene is synchronously mapped to a semi-physical simulation node and a physical cluster, and state cross-scale synchronization and synchronization error detection among digital twin, semi-physical simulation and the physical cluster are realized; s3, executing an algorithm task in a real object cluster, and performing real-time monitoring and control by a dynamic security fence; and S4, after the experiment is finished, generating an experiment report through the automatic evaluation engine according to the preset performance indexes. According to the invention, seamless migration among three modes can be realized without changing codes, and rapid verification, performance comparison and teaching reproduction of algorithms such as formation, obstacle avoidance and collaborative SLAM can be completed.

The invention provides a ship refrigerated container cluster redundancy control method and system, and the method comprises the steps: deploying a plurality of temperature sensors in each refrigerated container, and obtaining high-reliability environment temperature information through employing a multi-source data fusion technology; monitoring the running state of the local main controller of the refrigerated container in real time, judging whether the local main controller fails or not, and sending out an alarm signal; the redundant system monitors and stores all key temperature data of the refrigerated container in real time, and performs simple preprocessing to form a historical temperature database; comparing the control efficiency of the local main / backup controller and the control efficiency of the redundant system, and deciding an output signal weight; an accumulated historical database is utilized to dynamically adjust and optimize an adaptive fuzzy PID control rule in the redundant system; and collecting and counting models of local main / standby controllers and comparison and selection results of controller states and efficiency, and uploading the comparison and selection results to the cloud to construct an equipment efficiency knowledge base, so that faults can be accurately identified in a short time, the global temperature field characterization precision is improved, and unified regulation and control of containers of multiple brands are realized.

The invention relates to a nanoindentation and reliability characterization technology thereof, in particular to a heterogeneous material welding reliability characterization method applied to a brush type sealing structure. By means of the nanoindentation technology, mechanical properties such as hardness, elasticity modulus and plastic deformation of the welded joint are obtained by analyzing indenter deformation and indentation morphology. Meanwhile, indentation testing is carried out on different positions of the brush type sealing welding joint, and the performance comparison condition of all areas of the welding joint is obtained. Compared with a traditional reliability characterization means, the device and the method have the advantages that various areas of the heterogeneous welding joint including a base material, a heat affected zone and a fusion zone are tested under the nanoscale, various material performance indexes such as hardness, elasticity modulus and plastic deformation are measured at the same time, and the device and the method have high-precision and multifunctional testing capacity and are suitable for popularization and application. And moreover, the joint cannot be damaged or other properties cannot be influenced, and compared with a room-temperature stretching result, the nanoindentation technology has relatively high reliability and repeatability, and an accurate characterization result is obtained.

The invention relates to a fan low-efficiency unit identification control method and a medium, and the method comprises the steps: building a multi-dimensional power curve model fusing a plurality of environmental factors based on a machine learning algorithm, deploying intelligent monitoring equipment on each fan of a wind power plant, collecting key parameters in real time, and generating a fan operation monitoring data set; according to the collected monitoring data, bin-divided BIN curve fitting is carried out on the draught fan, performance expressions of the draught fan in different wind speed and wind direction intervals are analyzed, space-time performance comparative analysis is carried out in combination with historical operation data, and by comparing performance data of the draught fan in different time periods and different wind speed intervals, a low-efficiency time period and a low-efficiency reason are determined; by constructing the multi-dimensional power curve model and combining the warehouse BIN analysis and the space-time performance comparison technology, the low-efficiency unit identification accuracy rate is 100%.

The invention provides a multi-personality decision simulation and game method for military artificial intelligence. Simulation of an AI opponent model on complex human behaviors is enhanced through a DIKWP semantic framework. The system firstly configures personality parameters based on a psychological model (such as MBTI, big five personalities), and maps the personality parameters to an AI cognitive model to construct a behavior preference model; then, a noise injection algorithm is adopted to simulate emotion and cognitive deviation, and unique deviation of different personalities in decision making is reproduced; the multi-agent self-gaming engine enables AIs with different personality characteristics to repeatedly game in a red-blue confrontation environment, optimizes a strategy and evaluates winning rate distribution. And the decision fusion analysis module summarizes the data, generates personality performance comparison and behavior pedigree diagrams under each tactical situation, and predicts opponent behaviors. The DIKWP semantic integration interface ensures that a personality simulation result can be seamlessly connected with a knowledge graph and a strategic intention module of an existing AI system. According to the method, AI opponent modeling is enriched on the semantic level, the decision simulation degree of a real commander is improved, and as a supplement of an existing AI simulation system, the ability of military AI in the aspects of strategic pre-judgment and cognitive game is enhanced.

The application discloses a kind of dynamic partitioning method and system based on data skew model, it is related to big data technology field, including collecting data and pre-processing data, establish data skew evaluation model to predict pre-processed data, according to data characteristics, the data skew degree in job processing process is predicted, determines partition optimization strategy, designs evaluation experiment to compare dynamic partitioning with static partitioning, and repeatedly optimizes dynamic partitioning.This application proposes dynamic partitioning algorithm based on data skew model, which can select suitable partitioning strategy according to the skew degree of the data being processed, use the algorithm of three optimization methods based on Spark dynamic partitioning to process data performance comparison experiment, design and verify the universality and efficiency of the above dynamic partitioning scheme, can make the load balancing of the whole computing cluster, make full use of the computing resources of cluster, more efficiently complete data processing and calculation.

The invention provides a bionic honeycomb liquid cooling micro-channel heat exchanger structure and a design method thereof, and the design method comprises the steps of heat exchanger three-dimensional modeling design, fluid dynamics optimization design calculation and comprehensive performance comparison, designing an inner channel structure of a bionic honeycomb and a heat source connection part at the bottom of the heat exchanger by utilizing three-dimensional modeling software, and importing finite element software. Dividing grids, establishing a finite element model, setting a laminar flow model, adding a heat source, cooling water flow and other boundary conditions, simulating fluid flow and heat transfer in the heat exchanger, obtaining a fluid dynamic model of the bionic honeycomb heat exchanger, and recording temperature field and flow field data. And the comprehensive performance of the bionic honeycomb liquid cooling micro-channel heat exchanger is calculated, the structural parameters are changed, the comprehensive performance of honeycomb heat exchangers of different structures is compared, and finally the bionic honeycomb liquid cooling micro-channel heat exchanger with the optimal comprehensive performance is obtained through optimization. According to the method, organic combination of design and performance is achieved, and theoretical guidance is provided for structural design of the bionic honeycomb liquid cooling micro-channel heat exchanger.

The invention relates to the technical field of formation lithology identification, in particular to a formation lithology automatic identification method based on a machine learning optimization model, which comprises the following steps: acquiring logging curves of a plurality of wells, and drawing a formation lithology distribution diagram based on the logging curves of the plurality of wells; carrying out denoising processing on the logging curve of each well by utilizing wavelet transform processing, and screening out representative data segments of various lithologic data to form a data set; performing model construction based on the data set by using a machine learning algorithm, and obtaining an automatic stratum lithology identification model through hyper-parameter optimization and performance comparison; and analyzing the contribution amount of the characteristic parameters of the stratum lithology automatic identification model by using a random forest algorithm, carrying out intersection analysis on the logging curve of each well in sequence, and determining an influence factor sequence of stratum lithology identification. According to the method, the machine learning model for automatically identifying the stratum lithology is constructed, the automatic identification of the stratum lithology can be realized, and the lithology identification efficiency is improved.

The utility model discloses a water treatment filter material performance contrast experiment device which comprises a water tank and a water inlet pipe, experiment liquid is pumped into the water tank through the water inlet pipe, the water tank is arranged in an inclined mode, a notch is formed in the bottom of the water tank, and fin grooves are formed in the outer side of the lower end of the water tank. The fin groove comprises a flow guide part opposite to the notch and a storage part mounted at the side lower end of the flow guide part, the flow guide part and the storage part form a containing chamber for containing a filter material, the side, away from the flow guide part, of the storage part is rotationally connected with the lower end of the water tank, and the flow guide part is in a fin shape. According to the utility model, relevant operation in a laboratory is facilitated, and the experiment efficiency is further improved.

A machine learning device is configured to include: an image set acquisition unit (1) that acquires an image set including one or more images; and an image set selection unit (6) that selects an image set similar to the image set acquired by the image set acquisition unit (1) from a plurality of image sets different from the image set acquired by the image set acquisition unit (1). Furthermore, the machine learning device is provided with: a performance comparison unit (10) that acquires, as the performance of a plurality of machine-learned learning models, the performance of a learning model in which the image sets selected by the image set selection unit (6) are subjected to a plurality of mutually different pre-processing, and machine learning is performed using the respective pre-processed image sets; comparing the performances of the plurality of learning models after machine learning; and a preprocessing acquisition unit (11) that selects an arbitrary learning model from the plurality of machine-learned learning models on the basis of the result of comparison of the performance by the performance comparison unit (10), and acquires preprocessing for an image set used in machine learning of the selected learning model. Furthermore, the machine learning device is provided with a model learning unit (12) that performs preprocessing, which is acquired by the preprocessing acquisition unit (11), on the image set acquired by the image set acquisition unit (1), and performs machine learning on the learning model, which has not been learned, using the preprocessed image set.

The application discloses a kind of composite insulation coated metal soft magnetic powder and coating method and application.By system process flow implementation and performance comparison analysis, the soft magnetic powder composite insulation coating technology provided by the application shows significant advantages.Three-layer composite shell structure (flexible buffer layer+insulation layer+nanometer layer) effectively solves the problem of shell microcrack, insulation degradation and high dielectric loss under high frequency, high pressure and high temperature environment in traditional coating.The optimized key data: MLD / Al2O3 / [Al2O3 / HfO2] shell continuity rate reaches 99.8%, bulk resistivity is 5.2*10 9 Ω•cm, 100kHz loss is reduced by 67.1%, salt spray weight loss is only 38% of the control, fully meets the demand of high frequency magnetic device.The application provides a reliable material basis for the miniaturization and high efficiency of high frequency power electronic device.

The application relates to the technical field of intelligent coal mining, in particular to a coal mine fully-mechanized coal mining face intelligent operation online evaluation method based on dynamic correction of geological conditions, which comprises the following steps: collecting geological data of a working face and operation data of key equipment of the working face, obtaining key equipment automatic operation monitoring indexes of the fully-mechanized coal mining face, and a fully-mechanized coal mining face automatic operation index; correcting the key indexes under a standard condition based on geological conditions; calculating the key indexes under the influence of geological conditions based on the corrected key indexes, and calculating the fully-mechanized coal mining face automatic operation index based on the key indexes, the automatic operation monitoring indexes and the fully-mechanized coal mining face automatic operation index; correcting the key indexes under the standard condition and the key equipment automatic operation monitoring indexes of the fully-mechanized coal mining face, optimizing the fully-mechanized coal mining face automatic operation index, grading the operation level of the intelligent fully-mechanized coal mining face, and feeding back the evaluation result. The application can automatically quantify and strip the influence of geological condition differences, and realizes fair performance comparison between working faces.

The invention discloses a kafka theme partition creation method and device, electronic equipment, a medium and a program product, and belongs to the technical field of message middleware. The method comprises the following steps: acquiring index data of at least one node in a kafka cluster on at least one dimension; inputting the index data of at least one node into a trained Kafka cluster node performance comparison model to obtain an output result output by the Kafka cluster node performance comparison model, and determining a node of which the performance is higher than a preset value in the at least one node as a target node according to a performance sorting result of the at least one node in the output result; and creating a kafka theme partition on the target node. Therefore, the node of which the performance is higher than the preset value is selected as the target node for creating the kafka theme partition, so that the accuracy of partition distribution can be improved, and the occurrence of overhigh load of a single node and network congestion can be avoided.

Systems and methods are described for comparing execution of two or more artificial intelligence (AI) AI agents. A platform can provide a user interface (UI) that allows for selection or creation of multiple AI agents. The AI agents can utilize different agent objects, such as different prompts, datasets, or models. The AI agents can be displayed on a single UI screen, where execution of the AI agents is simultaneously simulated. The same inputs can be provided to the multiple AI agents, and the corresponding outputs can display on screen. The platform can also vectorize and compare the semantic similarity of the outputs, presenting an indication of the semantic similarity on the same UI screen.

The invention is suitable for the technical field of contactor detection, and provides a multi-channel direct current contactor control detection system, which comprises a plurality of programmable controllers, a data switch and an industrial personal computer, each programmable controller is connected with any direct current contactor and is used for acquiring opening and closing time data of the connected direct current contactor; the data switch is connected with each programmable controller and is used for transmitting each opening and closing time data; and the industrial personal computer is connected with the data switch and is used for carrying out data statistical analysis on the opening and closing time data and determining a performance detection result of each direct-current contactor so as to realize performance comparison of each direct-current contactor. According to the technical scheme provided by the invention, accurate detection and full-period statistics of the multi-channel opening and closing time interval of the direct-current contactor are realized, and real-time detection under the same aging test condition can be carried out on multiple direct-current contactors of different models at the same time; the method is used for comparing parameters such as service life and stability of direct current contactors produced by different processes.

The invention discloses an intelligent driving vehicle ADAS performance evaluation method, device, equipment and medium, and relates to the technical field of vehicle performance test.The method comprises the steps that multiple ADAS test scenes are created, multiple intelligent driving vehicles are tested to obtain test results, and the ADAS test scenes and the corresponding test results are stored in a scene test result database; defining performance evaluation rules and indexes based on ADAS performance evaluation requirements of the current vehicle so as to screen a corresponding ADAS test scene from the scene test result database, and setting scene evaluation parameters so as to calculate and obtain an ADAS performance score of the current vehicle; creating performance evaluation rules and indexes used for carrying out ADAS performance comparison with similar vehicles, and obtaining ADAS performance comparison scores of the similar vehicles; and obtaining a comprehensive scoring result of the ADAS performance of the current vehicle in combination with a weight calculation mode. The performance evaluation result is more accurate, and the cost is lower.

The present disclosure relates to a method for generating an object feature extraction model and an object recognition method. The method comprises: obtaining a plurality of to-be-integrated models from a plurality of object feature extraction models; performing feature extraction performance testing on the plurality of to-be-integrated models to obtain a first test result corresponding to each of the plurality of to-be-integrated models; selecting a current integrated model corresponding to a target test result from the plurality of to-be-integrated models; performing model integration processing on the current integrated model and a preset integrated model to obtain a target integrated model; performing extraction performance comparison processing on the target integrated model and the preset integrated model to obtain a target comparison result; and generating a target object feature extraction model based on the preset integrated model in a case where the target comparison result indicates that the object feature extraction performance of the preset integrated model is superior to the object feature extraction performance of the target integrated model. The embodiments of the present disclosure can improve the object feature extraction performance of the target object feature extraction model and improve the model deployment efficiency.

The invention belongs to the technical field of alloy fatigue life prediction, and discloses an alloy fatigue life prediction method fused with an information mining technology, which comprises the following steps: introducing key physical quantities into a feature set by combining a fatigue theory; mining inherent information in the data by adopting a symbolic regression algorithm driven by genetic programming, and adding the inherent information into a feature set as derivative features so as to construct a fatigue life prediction model of the alloy; a prediction model without derivative features is constructed for performance comparison, so that the improvement effect of fusion information mining technology aided modeling on prediction precision is evaluated; derivative feature expansion is applied to fatigue life prediction of a multi-component alloy system, and the universality of mined information is verified. According to the method, the information mining technology is adopted, hidden information in the data is extracted to assist modeling, and compared with a traditional machine learning method, more universal and high-precision fatigue life prediction is achieved, so that more reliable prediction support is provided for risk assessment of structural components.

The invention relates to the technical field of formation temperature and pressure analysis, in particular to a formation temperature and pressure machine learning analysis method based on sparrow search algorithm optimization, and the method comprises the following steps: obtaining a logging curve, formation temperature data and formation pressure data; through analysis of a logging data scatter diagram matrix and a correlation thermodynamic diagram, the correlation relation between a logging curve and the formation temperature and the formation pore pressure is determined; and based on the correlation, establishing a machine learning model for quickly calculating the formation temperature / pressure by using the logging curve and the formation temperature / pressure data to obtain a formation temperature / pressure prediction model, and optimizing the parameters of the formation temperature prediction model by using a sparrow search algorithm. According to the method, a complete analysis process including data correlation analysis, multi-model training, SSA optimization and performance comparison and screening is constructed, and it is ensured that the selected optimal model has strong generalization ability through final evaluation on the first prediction set and the second prediction set.