151 results about "Correlation index" patented technology

The invention belongs to the technical field of wind-solar power generation power monitoring, and particularly relates to a new energy wind-solar power data monitoring system and method. According to the method, power fluctuation characteristics and equipment state correlation indexes are extracted through abnormal value detection and time sequence analysis, so that the precision and efficiency of data processing are improved, and in the aspect of prediction, dynamic comparison between a historical operation data set and the current power fluctuation characteristics is utilized, and environmental parameter change trends are combined. A power prediction curve and a confidence interval are generated, so that the system can recognize potential abnormal operation in advance, decision support time is provided for operation and maintenance personnel, in addition, the system has corresponding fault diagnosis and optimization capabilities, when the prediction deviation degree exceeds a preset threshold value, an early warning mechanism is automatically triggered, a fault source is positioned through a fault diagnosis process, and the fault diagnosis efficiency is improved. And finally, according to a fault diagnosis result, a power prediction curve is corrected and optimized to form a closed-loop optimization mechanism, and the prediction precision and the operation efficiency of the system are continuously improved.

The present invention is applicable to the technical field of carbon emission monitoring. Provided are a carbon emission prediction method and a related apparatus. The method comprises: acquiring total carbon emission and total power consumption of each industry; calculating a correlation index of the total carbon emission and the total power consumption, so as to screen a target monitored industry; acquiring carbon emission and power consumption of the target monitored industry; on the basis of the carbon emission and the power consumption, calculating an electricity-to-carbon emission transfer coefficient based on the target monitored industry; on the basis of the target monitored industry, matching a target monitored enterprise, and on the basis of randomness, periodicity and climate factors, constructing a power consumption prediction model of the target monitored enterprise; acquiring actual power consumption of the target monitored enterprise and inputting same into the power consumption prediction model for training; matching an outputted predicted power consumption value with the electricity-to-carbon emission transfer coefficient; and generating a predicted carbon emission value. On the basis of the linear relationship between carbon emission and power consumption, the present invention uses power consumption prediction to estimate predicted carbon emission values, thus effectively improving the efficiency and accuracy of carbon emission prediction.

The invention discloses a chemical plant safety early warning and multi-dimensional correlation analysis method and device and a storage medium. By collecting equipment operation parameters, personnel operation and environment data, a reference model library is constructed to realize dynamic threshold early warning; after the early warning is triggered, calculating a first correlation index, a second correlation index and a third correlation index respectively by adopting time sequence similarity analysis, historical statistical analysis and process knowledge graph reasoning; and positioning the core inducement through multi-index weighted fusion. The problems that a traditional method is poor in early warning adaptability, weak in system collaboration and insufficient in tracing depth are solved, and accurate early warning, deep tracing and closed-loop management are achieved.

The invention provides a trade whole industry chain data intelligent matching and resource docking method, and relates to the technical field of international trade data analysis and geographic information systems.The method comprises the steps that customs declaration data, logistics tracking data, enterprise supply chain data and market public opinion data are collected in real time through a standardized interface; constructing a multi-source heterogeneous data fusion system of block chain evidence storage; dynamically modeling a whole industry chain relation network based on the knowledge graph, and quantifying the supply chain dependency, the trade flow direction and the policy influence weight; calculating an industrial chain correlation index ICRI and a resource matching priority coefficient RMPC, optimizing a logistics path in combination with a geographic space analysis algorithm, and avoiding a policy restriction area in real time; and rendering a matching result through a three-dimensional visualization platform, and iteratively updating model parameters based on user interaction feedback. According to the method, the limitation of a traditional data island is broken through, dynamic quantitative analysis and accurate resource matching are realized, and the trade data integration efficiency is remarkably improved through intelligent path planning and interactive decision support.

The invention discloses a traditional Chinese medicine diagnosis and treatment aided decision-making method and system, and relates to the technical field of traditional Chinese medicine intelligent diagnosis and treatment aided decision-making, and the method comprises the steps: carrying out the wavelet transformation analysis of pulse condition waveform time series data, extracting a pulse condition dynamic change feature sequence, inputting a tongue image color change rate and a pulse condition feature change rate into a space-time convolution network, and obtaining a time-space convolution network; generating a tongue pulse dynamic association index and outputting a preliminary syndrome classification; fourier transform analysis is carried out on the spleen channel acupoint impedance spectrum, false-true state judgment is carried out in combination with tongue pulse dynamic correlation indexes, preliminary syndrome classification is corrected, and a false-true state quantification result and corrected syndrome classification are output; and matching the corrected syndrome classification with the traditional Chinese medicine knowledge map, adjusting the dosage according to a virtual-real state quantification result, reversely verifying the drug-syndrome specificity through a curative effect database, screening an optimal treatment scheme, and outputting a personalized diagnosis and treatment scheme. According to the method, the syndrome classification precision is improved, and an objective basis is provided for personalized dose adjustment.

The invention discloses an FME-based adjacent pattern spot fusion method and system, and relates to the technical field of adjacent pattern spot fusion, and the method comprises the steps of data processing, parameter setting and rule configuration, adjacent pattern spot fusion execution, result verification and output and feedback and optimization, fusion standard and rule determination, index system unified evaluation, and spatial relationship optimization processing. Manual intervention is reduced through the automatic working process, and the processing efficiency is improved; the invention further provides an adjacent pattern spot fusion system based on FME, the adjacent pattern spot fusion system comprises a data preprocessing module, a data input module, a parameter setting and rule configuration module, a comprehensive correlation index calculation module, a pattern spot fusion module and a result verification and output module, and the adjacent pattern spot fusion method based on FME is executed.

The invention relates to the technical field of anti-seismic performance detection, in particular to an anti-seismic performance detection method and system for an assembled steel-wood composite structure, and the method comprises the steps: arranging a multidirectional acoustic emission sensor in a steel-wood structure node area, applying simulated seismic vibration, and synchronously collecting acoustic emission signals; the method comprises the following steps: preprocessing collected acoustic emission signals, dividing the acoustic emission signals into signals corresponding to microcracks and contact slippage, respectively calculating an energy attenuation rate and a phase offset, and constructing a matrix; micro-impact is applied to the steel-wood node through an electromagnetic excitation device, a response signal is collected, resonant frequency is extracted, the drift distance between the resonant frequency and a reference value is calculated, and the severity of interface damage is determined according to the value of the drift distance; the damage correlation index calculation takes the characteristic value of the interface wave propagation characteristic matrix and the resonance frequency drift distance as input, the damage correlation index is calculated through the correlation coefficient, and the damage grade is divided according to the correlation coefficient. And differential characterization of different damage types is realized through the interface wave propagation characteristic matrix.

The invention provides a low-voltage arc fault detection method and apparatus, and an electronic device. The method comprises the steps of obtaining m current data of a target low-voltage power distribution network within a preset time period; m is a positive integer greater than or equal to 1, feature extraction is performed on the m current data based on a preset feature extraction method, m first feature sets are obtained, and each first feature set comprises n first features; n is a positive integer greater than or equal to 24, performing correlation calculation on n first features in the target first feature set to obtain a correlation index scores, and performing feature selection on the target first feature set according to the a correlation index scores by adopting a preset feature selection method to obtain a target second feature set; and inputting the target second feature set into a preset arc detection model to obtain a target arc detection result. When arc fault detection is carried out, the accuracy and the stability of fault detection can be improved.

The invention discloses a chemical process anomaly type identification method based on a multi-dimensional causal association index, and belongs to the technical field of chemical process monitoring and fault diagnosis. The method comprises the following steps: firstly, carrying out offline modeling: acquiring normal working condition data, constructing a monitoring model by utilizing a trend slow feature analysis algorithm, and determining a statistic control limit; during online application, statistics of real-time data are calculated through a model, and an alarm is given and a type identification process is triggered if the statistics exceed a limit. A multi-dimensional causal association index (MCAI) is constructed by analyzing whether a quality variable deviates or not and further performing comprehensive analysis on a root variable causing an anomaly from four dimensions of a position (CPI) of the root variable in a process, an attribute (CAI) of the root variable in a causal network and an influence propagation capability (CPD) of the root variable. The problem that a traditional anomaly detection method cannot distinguish process faults from new working conditions is solved, false alarm can be effectively avoided, and a decision basis is provided for accurate maintenance.

The invention relates to the technical field of circuit board analysis, in particular to an integrated circuit defect analysis method and system, and the method comprises the steps: extracting and screening E-test data of a target product from a big data platform for integrated circuit manufacturing; performing defect classification modeling processing on the screened E-test test data; calculating a defect correlation index according to the classification accuracy and the correlation metric value of each candidate E-test item; determining a defect judgment threshold value and a numerical value distribution direction related to the defect based on a logic regression classifier result of the key test item, and generating a binary defect distribution diagram corresponding to each jointed board; calculating the image similarity between the bad jointed board graph and each good jointed board graph; judging whether the image similarity is higher than a preset threshold value or not, and adding the jointed boards of the good jointed board graph into a virtual bad jointed board list; and based on the virtual bad jointed board list, analyzing and comparing the manufacturing process flow data of the virtual bad jointed board and the original defective jointed board, and determining the potential root cause causing the defect. According to the method, the problems that in the E-test scene of the circuit board, the number of defect samples is extremely small, categories are extremely unbalanced, an experience threshold value is difficult to set, and root causes are difficult to trace can be solved.

The invention discloses a GIS disconnecting switch full-state quantity edge intelligent analysis system based on the Internet of Things, particularly relates to the field of online monitoring of high-voltage power equipment, and is used for solving the problem of false correlation interference caused by lack of stage anchor points and causal constraints in an existing power sequence overall synchronization method. Therefore, the problems of the jitter of the opening and closing position criterion and the inconsistency of multi-mode diagnosis can be solved. According to the method, event anchor points are extracted from a power sequence, an auxiliary contact sequence and a travel sequence on the edge side, a unified time axis and a stage tag sequence are constructed, and a multi-modal fragment stream is segmented; generating a causal mask according to the stage label, calculating a masking mutual information matrix, and outputting a peak sequence and a peak sequence index; calculating a mutual information arc length ratio and a peak sequence accumulative homomorphism in the window, assigning a trust mark based on a prototype facet area coefficient, and outputting a position sequence in the stage; and splicing the position track and generating an evidence association index to realize full state quantity intelligent analysis.

The invention discloses an intelligent calibration method and system for the electric quantity of an instrument, and relates to the technical field of electric energy metering of an electric power system.The intelligent calibration method comprises the steps that an electric meter network diagram with a main meter and sub-meters as nodes is constructed, and robust composite correlation indexes are calculated on the basis of reading increments in a sliding time window; the interference of common background load and instantaneous noise is suppressed by using a threshold clipping and main table purification mechanism, so that the depiction capability of the correlation graph on a physical connection relationship is improved fundamentally, and the phase to which the sub-table belongs can be identified more accurately in a stock area with unknown phase or incomplete topology; a graph neural network model is introduced based on the network graph, electric meter embedding representation with physical meaning is obtained through neighborhood information aggregation, phase grouping is achieved in combination with unsupervised clustering, split-phase energy balance loss is designed in the training stage, and the difference between the reading of a main meter and the sum of the reading of sub-meters in each phase group is used as a constraint. Alignment of the embedding space and the energy conservation relation is pushed in a self-supervised mode.

The invention provides an elastic monitoring alarm method and system based on multistage cooperative verification. The method comprises the step of remarkably improving the alarm accuracy through a three-level verification mechanism. The first-stage verification adopts intelligent anomaly detection, compares a current index with a historical baseline, and identifies a preliminary anomaly; in the second-stage verification, association index analysis is introduced, and pseudo anomalies caused by normal business fluctuation are filtered; and the third-stage verification performs cross-customer collaborative analysis to distinguish individual anomaly from global events. In addition, fault root causes are automatically positioned through dependency link analysis, and structured alarm information containing causes, confidence coefficients and processing suggestions is generated. And a closed-loop learning mechanism is established, operation and maintenance feedback is continuously collected, and alarm parameters are automatically adjusted by adopting Bayesian optimization. According to the method, the false alarm rate is reduced, the root cause positioning accuracy is improved, the average repair time is shortened, and the IT operation and maintenance efficiency is remarkably improved.

The embodiment of the invention discloses a power grid line state monitoring method and device based on vibration acoustics. The method comprises the following steps: acquiring a power grid power transmission line vibration signal and a sound wave signal; according to the topological structure of the power transmission line, the spatial distribution of adjacent lines and the measuring point positions of different sensors in the same line, constructing a signal propagation model, and calculating a multi-dimensional correlation index between the vibration signal and the sound wave signal, the correlation index including a time domain correlation coefficient, a frequency domain power spectral density and a phase difference feature; simulating signal propagation characteristics in a normal operation mode by using the signal propagation model, generating a reference correlation index, and identifying whether the power transmission line has an abnormal state; and carrying out accurate positioning on the abnormal state by adopting a spatial positioning algorithm, and carrying out multi-level evaluation according to the abnormal confidence coefficient and the influence range to generate a line abnormal report. According to the invention, real-time monitoring and accurate positioning of the abnormal state of the power transmission line are realized.

The invention discloses a data aggregation processing method and device, computer equipment and a storage medium, belongs to the technical field of big data, and is applied to data aggregation in the financial field. The method comprises the following steps: firstly, grouping dimension fields by utilizing a mapping rule and a hierarchical clustering algorithm; and secondly, dynamic grouping mapping is established, one-time scanning is performed on the source data table, and each data record is directly attributed to a corresponding grouping structure, so that the data processing efficiency is improved. Meanwhile, a correlation index of the index and the data partition is constructed based on a Hash mapping algorithm. Under the support of an indexed data structure, the aggregation calculation function can efficiently execute operations such as summation and averaging, and rapid aggregation of multi-dimensional data is realized. Generally speaking, according to the method, the correctness of an aggregation result is ensured, meanwhile, the calculation and storage pressure of the system is remarkably reduced, the timeliness and expandability of data analysis are improved, and the method is particularly suitable for real-time or quasi-real-time multi-dimensional analysis requirements in a large-scale business scene.

The invention discloses a hierarchical optimization search underwater acoustic Doppler estimation method and system, and the method comprises the steps: S1, a receiving end receives an OFDM signal frame, carries out the preliminary frequency offset scanning, and obtains a preliminary Doppler frequency offset value f corresponding to the maximum value of a correlation index and a coarse Doppler estimation interval [f-1, f + 1]; s2, randomly generating a group of initial particles in the coarse Doppler estimation interval [f-1, f + 1], each initial particle representing a candidate frequency offset value; and S3, calculating the amplitude of the mutual ambiguity function of each initial particle as a fitness function for continuous iteration until a convergence condition is met, and obtaining a fine Doppler frequency offset f1. The problem of high calculation complexity can be effectively solved under the condition that Doppler estimation precision is guaranteed.

The invention relates to the technical field of digital twin and multi-source heterogeneous data fusion, and discloses a method for loading and rendering massive homologous heterogeneous data of a digital twin platform. The method aims at solving the problems of low loading efficiency, large rendering fluctuation, data semantic segmentation and the like in the prior art. The technical scheme comprises the steps of preprocessing data and establishing a correlation index; during initialization, a network state is adapted to load basic data; constructing unified abstraction layer standardized layer management; on-demand loading and multi-level caching are realized through the intelligent scheduler; a fusion rendering pipeline is configured to guarantee stable output of the 60FPS; operating a service binding engine to realize bidirectional linkage; measurement, model import, file preview, layer linkage and data maintenance are supported. The mass data loading efficiency is improved, memory overflow is avoided, service collaboration and system stability are enhanced, and the method is suitable for scenes such as urban planning, industrial simulation and emergency command.

The invention provides an online car-hailing order allocation method and system based on artificial intelligence, and a storage medium, and relates to the technical field of order allocation. The method comprises the following steps: constructing a complete order feature set by combining a starting point, a terminal point and an order type of an order, arrival time required by a passenger and historical scores of the passenger to a driver with current weather category and traffic flow information, constructing collected driver features, order features and environment features into feature vectors, and obtaining an order feature set based on a supervised learning method. Corresponding driver correlation indexes, order correlation indexes and environment correlation indexes are calculated, the model is trained, ordering is carried out according to order receiving probabilities, orders are preferentially recommended to the driver with the highest order receiving probability, and therefore the order matching success rate is increased, and the waiting time of passengers is shortened.

The present application relates to a kind of chemical instrument follow-up analysis system, including instrument model construction subsystem, experimental data analysis subsystem and instrument model correction subsystem;According to the type of analysis instrument, an instrument model is constructed, and the corresponding correlation index is established with instrument characteristic data and analysis type data to form the neural network of model, on the one hand, the experimental project information can be analyzed using instrument model, the corresponding adaptable and suggested analysis instrument is analyzed further, and the information of corresponding analysis instrument is presented, on the other hand, according to actual experimental result, instrument characteristic data in instrument model is corrected, instrument model is updated, and instrument characteristic data can be updated according to experimental result to improve the precision of system to provide accurate instrument report information, to assist experimenter to further judge.

The invention relates to the technical field of intelligent evaluation of garment flatness, and particularly discloses an automatic evaluation method of a non-ironing suit seam flatness grade, and the method comprises the steps: obtaining image data and physical parameter data of a seam region in response to a detection trigger signal of the seam region of a non-ironing suit; and performing key scale screening based on texture contribution degree on the image data, calculating a relevance index between feature distribution and flatness grade probability distribution under different texture scales, and determining the texture scale of which the relevance index meets a preset contribution degree condition as a target key scale. According to the method, the entropy screening mechanism based on the texture contribution degree is introduced, so that the key scale which is most effective for flatness representation can be locked from the multi-source data in a self-adaptive manner, and the refinement and pertinence of feature extraction are realized.

The present application relates to the technical field of cloth winding machine control, and specifically to a cloth winding machine control method and system for the production of automotive interior fabrics. The method comprises: determining a correlation index by analyzing the correlation between the average level of all speed data in each speed data window in each time period and the fluctuation anomaly value; respectively analyzing the difference between the speed data and the correlation index and the corresponding fitting value, constructing a first and a second difference degree to determine the anomaly coefficient at the current moment; determining the tension complexity by analyzing the complexity of all tension data between the start of the fabric winding process and the current moment, and determining the tension anomaly value in combination with the anomaly coefficient to control the speed of the cloth winding roller during the fabric winding process at the current moment. The present application improves the accuracy of tension adjustment during the fabric winding process by reducing the interference of mechanical wear of the cloth winding machine on the speed adjustment during the fabric winding process.

The present disclosure provides a domain dictionary constructing method and apparatus, the method including: segmenting a domain corpus sample to obtain a first character segment set, where the first character segment set includes at least one first character segment; calculating an inter-character correlation index of the at least one first character segment; determining a second character segment set according to the correlation index, where the second character segment set includes a first character segment of which the correlation index is greater than or equal to a preset threshold; determining a third character segment set according to the second character segment set, where the third character segment set includes the second character segment set and second character segments of the domain corpus; constructing a domain dictionary according to the third character segment set.

The present invention discloses a furniture panel structural performance detection system and detection method, comprising: a multi-source acquisition module: collecting panel structural data and environment-related data, extracting a first correlation parameter from the collected panel structural parameters, and extracting a second correlation parameter from the related environment parameters; an association acquisition module: performing cross-modal fusion analysis on the first correlation parameter and the second correlation parameter to obtain an optimized correlation index, and obtaining trend data from the optimized correlation index; a data correction module: collecting panel characteristic data, and optimizing the mutual correlation coefficient between the panel characteristic data and the trend data, performing correlation analysis based on the optimized mutual correlation coefficient to determine an attenuation mode, performing attenuation compensation on the collected real-time panel characteristic data based on the attenuation mode, obtaining corrected panel characteristic data, and ensuring that the corrected strength data is closer to the actual performance under the actual environment.

The application discloses a kind of processing method, device, equipment and storage medium of the test data of fuel cell, specifically, establish data stack and sequentially store test data into data stack;Load test standard operating condition data, test standard operating condition data is used to indicate the parameter characteristics of effective data;The correlation index of the data in the data stack in the sliding window and test standard operating condition data is calculated;The data that meets the threshold range requirement of correlation index in data stack is output;The threshold range of correlation index is used to indicate that the data in data stack is effective data.The application realizes the continuous processing of test data by the way of sequentially storing test data into data stack, no longer needs manual splicing test data, improves the processing efficiency of test data, and the process of processing test data is based on the effective data obtained by test standard operating condition and strict threshold range of correlation index, improves the credibility of the effective data obtained.

The application provides a base station detection method and device, the method comprises the following steps: obtaining a first strong correlation index pair of a to-be-detected base station; inputting the first strong correlation index pair into at least two supervised learning models respectively to obtain a first detection result output by each supervised learning model; and determining whether the to-be-detected base station has a hidden problem according to all the first detection results. The base station detection method and device provided by the application first obtain a strong correlation index pair of a to-be-detected base station, then input the strong correlation index pair into at least two supervised learning models respectively to obtain a first detection result output by each supervised learning model, and finally determine whether the to-be-detected base station has a hidden problem according to all the first detection results, which does not depend on expert experience and improves the accuracy of detection of hidden problems of base stations.

The invention relates to the technical field of multi-source data processing, in particular to an intelligent power distribution cabinet remote diagnosis and maintenance system based on multi-source information fusion. According to the system, the stability index is determined by evaluating the change stability condition of data of data source parameters in a local interval; comparing the stability of each data source parameter time sequence local area, and obtaining a stable confidence time period through the stability condition after the local area is expanded; a relevance index is obtained by analyzing the stability approximation condition and the value change consistency condition of every two data sources; and analyzing the fault evaluation index according to the deviation change of the subsequent relevance to obtain a fault evaluation result. According to the method, the historical stable time period is determined through multi-source information data fusion analysis, and the fault condition is evaluated in combination with the correlation trend change among the multi-source data, so that the system can find the fault problem of the intelligent power distribution cabinet more timely and accurately, timely and reliable maintenance is guaranteed, and the power distribution cabinet can operate safely and reliably.

The invention discloses an intelligent ship integrated control system, a data vertical correlation method, a storage medium and electronic equipment, and the system comprises a data correlation analysis module, a data collection module based on data correlation preprocessing, a ship business data calling module, and a data sharing storage module. Wherein the data correlation analysis module is used for calculating correlation indexes of vertical field data according to the similarity of the data and / or the time continuity of the data and / or the causal correlation of the data and establishing a data vertical correlation model according to the correlation indexes; the data acquisition module performs pre-association grouping on sensor data based on a data vertical association model; and the ship service data calling module calculates a ship service display result according to the indication data and the pre-associated grouped data. According to the method, the problem of low ship intelligence degree caused by poor timeliness of mass ship data analysis, service module splitting and lack of service collaboration and global optimization in the existing intelligent ship technology is solved.

The application discloses a sunspot region correlation evaluation method based on cross-validation, and comprises the following steps: 1, obtaining a flare dataset, and performing dataset segmentation according to sunspot region numbers to obtain each data subset; 2, performing machine learning algorithm training on each data subset to obtain a machine learning model of the data subset; 3, calculating a corresponding AUC index for each machine learning model to obtain an A matrix; 4, performing correlation analysis by using the A matrix to obtain a correlation index Corr; and 5, judging the correlation index Corr to obtain a given region correlation determination conclusion. The application provides a calculation method of the correlation index, which can simplify the process of establishing a prediction algorithm to a certain extent, and in the process of using federal transfer learning, can aggregate algorithms of sunspot regions with high similarity to speed up the training process and then improve the prediction efficiency.

The invention relates to the technical field of transformer substation operation and maintenance, and particularly discloses a transformer substation fault diagnosis and early warning system.The upper computer obtains equipment parameters collected by a monitoring unit and executes the following steps to achieve efficient and accurate diagnosis and early warning. Generating an alarm message; s2, carrying out noise filtering and normalization processing on the original alarm flow; s3, generating normalized feature vectors according to various normalized parameters, inputting the normalized feature vectors into a pre-trained XGBoost classifier, and outputting classification root alarm information and fault types; performing association rule mining on the effective alarm information set to generate an association rule and an association index; s4, obtaining rule root alarm information of the effective alarm message according to the association rule and the association index of the effective alarm message; and S5, performing multi-source fusion on the classification root alarm information and the rule root alarm information based on a D-S evidence theory to obtain a final fault risk rating for reference for response.

This invention provides a method, system, device, and readable storage medium for determining stress monitoring points on a offshore platform jacket structure, belonging to the technical field of offshore platform structural safety monitoring. The method includes: establishing a finite element simulation analysis model of the offshore platform jacket structure; performing scenario sampling to obtain finite element stress simulation results for candidate monitoring points; calculating the correlation index and sensitivity index of the candidate monitoring points to extract a preliminary sample set of monitoring points; generating multiple vectors of the same dimension as the number of target monitoring points to obtain multiple monitoring point combination schemes; calculating the fitness of the monitoring point combination schemes; iterating according to a set search strategy, continuously updating the fitness after iteration until the iteration is complete; and determining the location of the target monitoring point based on the fitness after the iteration. This invention, through iterative optimization of the stress monitoring point layout scheme, can improve the targeting of sensor placement and save sensor placement costs.