79results about How to "Achieve continuous optimization" patented technology

The invention provides an intelligent pipeline inner wall damage identification method based on voiceprint feature extraction, and relates to the technical field of sound wave detection.The method comprises the steps that pipeline acoustic time sequence data, medium flow velocity and pressure data, medium type identification and structure parameter information are collected; performing multi-scale feature extraction on the acoustic signal, constructing a voiceprint feature expression model and a damage evaluation model based on a voiceprint feature learning network, introducing a structural voiceprint stability coefficient, a damage type voiceprint separation coefficient and a damage evolution risk coefficient, and combining a multi-level threshold judgment mechanism to determine the damage type of the acoustic signal. Graded early warning, damage type identification and evolution risk discrimination of the inner wall damage of the pipeline are realized; when an uncontrollable damage evolution risk is identified, a maintenance strategy is automatically triggered, and manual nondestructive testing and repairing are guided; and adaptive optimization is carried out on model parameters to form a closed-loop correction mechanism for pipeline inner wall damage identification and risk assessment, so that the pipeline operation safety and the intelligent level of maintenance decision are improved.

The application discloses a kind of campus safety early warning method based on multi-modal fusion and AI vision, it is related to computer vision and intelligent security technology field, comprising the following steps: S1, access control passage perception dataset is constructed;S2, based on access control passage perception dataset identification passage object entering order and passage behavior characteristics;S3, facing relationship expression, in combination with gate occupancy state and authorized behavior record, matching relationship discrimination is carried out;S4, according to gate occupancy state and accompanying behavior characteristics, risk-driven evaluation is carried out;S5, according to risk response condition, linkage control behavior is executed and passage process record and strategy adjustment are completed.The problem that the same direction accompanying borrow line under the passage peak is not associated with the opening and closing rhythm of access control, leading to the problem that the trailing mixed in is difficult to identify in advance is solved.

The application provides a die cutting and hot stamping equipment intelligent state monitoring and fault diagnosis system and method, and relates to the field of intelligent monitoring.The method comprises the following steps: collecting working parameters of multiple key parts of the die cutting and hot stamping equipment, constructing a time sequence collection window, slidingly collecting the working parameters, and obtaining characteristic information reflecting the equipment state; based on the characteristic information, constructing an anomaly detection model, performing anomaly detection on the equipment state, obtaining an anomaly score, and judging whether the equipment state is abnormal according to the anomaly score; for the characteristic information judged as abnormal, constructing a fault diagnosis model based on the fault type to which the characteristic information belongs, performing fault diagnosis on the equipment state, and generating a diagnosis result; and generating a comprehensive diagnosis and operation and maintenance decision report according to the diagnosis result.The application realizes comprehensive perception of the internal state of a closed host through multi-sensor collaborative monitoring and dynamic time sequence collection, breaks through the limitations of traditional monitoring, and provides accurate data basis for early fault warning and predictive maintenance.

The application discloses a spraying robot laser radar inertia tight coupling positioning method based on IMU vibration spectrum sensing, comprising the following steps: 1) collecting IMU original data and preprocessing, and constructing a multi-dimensional standardized ground vibration fingerprint; 2) receiving the output multi-dimensional standardized ground vibration fingerprint, and realizing integrated decision of ground state sensing and positioning risk assessment; 3) according to the output decision triple, dynamically matching a pre-established multi-dimensional mapping library of “ground type-vibration interference level-treatment strategy”; 4) fusing the output point cloud without vibration noise and distortion and IMU pre-integration data, realizing adaptive tight coupling state optimization, and outputting pose estimation residual error; 5) based on the output pose estimation residual error, evaluating the mismatch degree of the current positioning system parameters and the environment, and realizing full-link closed-loop adaptive optimization. The application realizes integrated decision of ground type identification and positioning risk, and solves the problem that ground sensing and positioning decision are disconnected.

The invention discloses a gene sequencing data management method for whole genome methylation sequencing, and relates to the technical field of gene sequencing data management.The method comprises the steps that original gene sequencing data are obtained and preprocessed, and a regional methylation matrix is generated; calculating a technical difference measure and a phenotype difference measure of each region unit based on the region methylation matrix and the sample metadata, identifying a technical methylation bias domain, and grading the technical methylation bias domain into a steady-state technical methylation bias domain and an event-driven technical methylation bias domain according to the occurrence frequency of the technical methylation bias domain in multiple batches; based on a linear adjacency relation and a co-bias relation, constructing a technical methylation bias super-domain and classifying the technical methylation bias super-domain; performing regional shielding on the methylation bias super-domain of the steady state technology, performing normalization correction on the methylation bias super-domain of the event-driven technology, and generating a corrected regional methylation matrix; calculating a sample quality index based on the corrected matrix and performing quality grading; according to the method, technical bias can be identified and corrected, and the methylation data quality and analysis reliability are improved.

The application relates to the technical field of energy storage operation and maintenance, and discloses an energy storage operation and maintenance method and device based on reinforcement learning. The method comprises the following steps: constructing a reinforcement learning model for energy storage power station operation and maintenance; constructing an operation and maintenance knowledge base, wherein the operation and maintenance knowledge base comprises an experience pool and a policy library; acquiring operation state data of an energy storage power station; performing feature extraction on the operation state data to obtain target state features; performing similarity retrieval in the policy library based on the target state features to obtain a retrieval result; determining a generation mode of an operation and maintenance action based on the retrieval result and a preset state evaluation rule, wherein the generation mode comprises generating an operation and maintenance action based on the reinforcement learning model and generating an operation and maintenance action based on the operation and maintenance knowledge base; and generating an operation and maintenance action of the energy storage power station based on the generation mode.

The application discloses a hydropower station time-sharing power generation benefit dynamic control method fusing power grid load demand, obtains multi-dimensional operation data of a hydropower station and power grid time-sharing load and electricity price data, constructs a data correlation model based on an attention mechanism, generates a fusion feature data set through dynamic weight distribution, takes time-sharing power generation benefit maximization as an objective function, combines reservoir water balance, unit output limitation and downstream ecological flow constraints, introduces a deep reinforcement learning algorithm to construct a dynamic control model, trains the model and optimizes parameters by using historical and simulation data, then inputs real-time data to generate an optimal time-sharing power generation control scheme, issues an instruction and monitors output deviation, and realizes dynamic control; the method can accurately match power grid load and time-sharing electricity price, improves power generation benefit while meeting multiple constraints, and is suitable for various hydropower station dispatching scenarios which need to consider power grid response and benefit optimization.

This invention discloses an intelligent operation and control method and system for cold storage and subcooling cold storage. The method includes: acquiring basic parameters and real-time monitoring data of the cold storage; calculating the real-time load and short-term load change trends of the cold storage through a dynamic load prediction model; formulating an operation strategy based on load factor and time-of-use electricity price information, and intelligently selecting normal cooling, combined cooling with cold storage, or combined cooling with cold release and subcooling modes; achieving smooth switching between the three modes by adjusting the operating status of the compressor unit, electronic expansion valve, electric valve, and water pump; and continuously optimizing the load prediction model and control strategy using real-time feedback of the system's operating status. This invention achieves coordinated control of the cold storage and subcooling processes, improving the system's energy efficiency and operational economy under variable load conditions, and is applicable to the energy-saving and optimized operation of various types of cold storage and subcooling cold storage facilities.

The invention relates to the technical field of edge computing and automatic optimization algorithms, and discloses a multi-edge computing task unloading and scheduling hierarchical automatic optimization method based on large language model driving, which comprises the following steps: S1, constructing a computing system, acquiring task data and edge server data to form a data set, dividing the data set into a training set, an optimization set and a verification set; and S2, constructing a joint task unloading and scheduling optimization model taking the total completion time delay of the system as an optimization target, taking minimization of the total completion time delay of the system as an optimization target function, and taking the total completion time delay of the system obtained by the joint task unloading and scheduling optimization model as a fitness value. According to the method, efficient and automatic optimization of task unloading and scheduling problems in the multi-edge computing environment is achieved, the intelligent level and generalization ability of a computing system are improved through an algorithm evolution mechanism driven by a large language model, and the method has important theoretical significance and wide engineering application prospects.

The application belongs to the technical field of vehicle information anonymization detection, and particularly relates to a multi-type image anonymization annotation dataset construction and target coverage rate determination method. The method is based on a face and license plate image dataset with double annotation of theoretical anonymization region and anonymization features, introduces an anonymization feature extraction branch in an improved YOLOv5-L model and performs cross-modal feature fusion, combines a plurality of loss functions with theoretical anonymization region positioning loss as the core and a two-stage progressive training and difficult example mining mechanism, and realizes precise learning of the anonymization features. After normalizing the input anonymization image, the model inference obtains the theoretical anonymization region coordinates and maps them back to the original size, and through non-maximum suppression and matching of the IoU threshold, the region coverage rate is calculated to determine the positive detection, missed detection and statistical false detection rate. The application effectively overcomes the feature dependency failure and model robustness problem, and realizes high-precision, low-misjudgment anonymization detection and evaluation under various anonymization conditions.

This invention belongs to the field of lighting fixture positioning, and provides a lighting fixture positioning method, device, and lighting fixture. The method includes: obtaining a first original rotation angle of a first pulley and a second original rotation angle of a differential pulley; processing the second original rotation angle using a dynamic error suppression algorithm to obtain an optimized rotation angle and outputting it; calculating the absolute rotation angle of a swaying component based on the first original rotation angle and the optimized rotation angle, and determining the current position of the swaying component based on the absolute rotation angle; and updating system parameters through intelligent self-learning calibration when preset conditions are met, and updating the current position to the historical learning position list, wherein the system parameters include at least the reset reading difference. In this embodiment of the invention, processing the second original rotation angle using a dynamic error suppression algorithm can effectively suppress random errors, and updating system parameters through intelligent self-learning calibration can automatically adapt to environmental changes and device aging, ensuring the long-term operational accuracy of the system.

The present application relates to the technical field of Pickleball, and specifically relates to an intelligent wearable system for monitoring sports posture. The system synchronously collects multi-axis acceleration and angular velocity original signals before and after the player hits the ball through the inertial measurement unit built in the wearable device, generates time series after time window interception and normalization processing; removes noise and baseline drift by wavelet transform; fuses time domain and frequency domain features to construct a high-dimensional comprehensive feature vector; realizes preliminary classification of actions through a long short-term memory network, refines similar action types by combining a convolutional neural network, and finally outputs accurate classification results through a support vector machine; dynamically optimizes model parameters based on a real-time feedback mechanism, and enhances local feature representation through a channel attention mechanism. The system can accurately identify more than fifteen Pickleball special technical actions, has high robustness and self-adaptive ability, can provide real-time posture specification guidance for athletes, and helps to improve training efficiency and prevent sports injuries.

The invention discloses a corrugated paper production line virtual debugging and operation optimization system based on digital twinning. The system comprises a digital twinning building module, a multi-dimensional model building and training module, a dominant variable recognition module and a virtual debugging and optimization module. The digital twin construction module is used for acquiring physical entity data and operation condition data of a production line and constructing a digital twin consistent with the physical production line; and the multi-dimensional model construction and training module is used for synchronously constructing a multi-physical sensitive dimension model in the digital twin for key processes of a production line, and relates to the technical field of corrugated paper production. According to the corrugated paper production line virtual debugging and operation optimization system based on digital twinning, by constructing the digital twinning body and the virtual debugging module, the production process can be debugged and optimized in advance in a virtual environment, the debugging period of a traditional production line is remarkably shortened, and the debugging cost and risk in actual production are reduced.

The present application relates to the technical field of judicial informatization and psychological health assessment, in particular to a method and device for diagnosing depression and anxiety risk of high mobility personnel in the judicial field, which comprises: obtaining an evaluation data set of the personnel to be evaluated; inputting the evaluation data set into a pre-trained fusion analysis model to output the quantitative scores of depression and anxiety of the personnel to be evaluated; the fusion analysis model comprises a feature fusion layer and a multi-task risk assessment layer; the feature fusion layer is used to convert the evaluation data set into a unified feature representation to obtain a standardized data set; the multi-task risk assessment layer comprises a plurality of learners, which learn the standardized data set from different angles and synthesize the quantitative scores through a weighted voting method. The present application adopts an analysis method combining large models, deep learning and machine learning, which can mine deep correlations and non-linear complex features between multi-source data, significantly improving the accuracy and intelligence of risk identification.

The application provides a self-evolving intelligent fund analysis method, relates to the field of data processing, and comprises the following steps: receiving a natural language analysis instruction, combining a large language model decomposition task, extracting account fund flow characteristics and matching with historical cases, and generating a weighted execution instruction; identifying a transfer path based on a fund correlation graph, extracting a priority path subgraph structure according to a user identifier, and forming a directional analysis model; performing node risk scoring on the fund correlation graph, outputting a risk account identifier and an analysis report, and realizing intelligent and accurate fund risk identification.

The present application relates to the technical field of equipment maintenance, and more particularly to an intelligent predictive equipment maintenance system, comprising a data acquisition module, a data processing module, a maintenance decision module, a maintenance execution module, a user interaction module and a closed-loop optimization module. The data acquisition module collects equipment operating parameters through the deployment of a sensor array to generate standardized data streams; the data processing module extracts time sequence features and performs state deduction through a virtual model to output fault analysis results; the maintenance decision module calls enterprise resource information for multi-objective optimization to generate a dynamic maintenance plan; the maintenance execution module assists in guiding the execution of maintenance through augmented reality and records data; the user interaction module dynamically displays equipment status and risk information; and the closed-loop optimization module updates model parameters based on feedback data. The system realizes predictive maintenance under data driving through module collaboration, effectively reduces unplanned downtime and resource waste, and improves fault diagnosis accuracy and operation and maintenance efficiency.

The invention discloses a financial time series data multi-scale feature analysis and prediction method and system, and belongs to the technical field of financial data processing and deep learning, and the method comprises the steps: obtaining and preprocessing multi-source financial time series data; performing multi-scale decomposition by adopting discrete wavelet transform and empirical mode decomposition to generate a trend component, a periodic component and a noise component; respectively extracting time dependence characteristics of each component through a time sequence Transform coding module; multi-scale features are subjected to adaptive weighted fusion through a multi-scale attention fusion module; outputting a predicted value and a confidence interval through a probability prediction module; and the closed-loop optimization module adjusts decomposition parameters and attention weights according to prediction error feedback, so that different frequency components of financial time series data can be effectively separated, a multilevel time dependency relationship is captured, and adaptive feature fusion and online optimization are realized.

The invention discloses a device cooperative control method and system based on a distributed system, a terminal and a storage medium, and the method comprises the steps: obtaining the operation state data and communication link information of a plurality of physical devices, mapping each physical device into a node in a dynamic topological graph, and carrying out the cooperative control of the plurality of physical devices based on the operation state data and the communication link information; constructing feature representation of the distributed system; inputting the feature representation into a collaborative decision-making model for topological adaptive processing and inter-device long-range dependency relationship modeling, and outputting collaborative decision-making instructions corresponding to the physical devices; distributing the collaborative decision instruction to corresponding physical equipment for execution, and collecting system-level efficiency feedback generated when the collaborative decision instruction is executed; and dynamically optimizing the collaborative decision model according to the efficiency feedback and a preset system-level resource constraint so as to continuously adapt to the topology and state change of the distributed system. According to the method, the self-adaption to the dynamic change network topology is realized, and accurate collaborative decision and efficient resource control can be carried out.

The application belongs to the technical field of point cloud processing, and discloses a multi-modal semantic mapping method and system based on a master-slave architecture. The method comprises the following steps: S1, extracting feature points in point cloud data and calculating the covariance matrix of the neighborhood point cloud of each feature point; S2, solving the optimal pose corresponding to the minimum of the target function; S3, converting the obtained point cloud data into a world coordinate system using the optimal pose, fusing all frame point cloud data in the world coordinate system to form a three-dimensional point cloud map, projecting the three-dimensional point cloud map to a camera coordinate system, and inputting the three-dimensional point cloud map projected to the camera coordinate system into a semantic segmentation neural network to obtain the semantic label corresponding to each point cloud in the three-dimensional point cloud map, thereby achieving semantic mapping. Through the application, the problems of weak dynamic adaptability, insufficient model generalization ability and long-term running precision decay of the traditional three-dimensional semantic mapping method in a dynamic complex scene are solved.

The application discloses a medical institution hospital infection protection measure evaluation method and system based on cost-benefit analysis, relates to the technical field of data processing, and comprises the following steps: collecting medical insurance policy release information and disease control early warning information; the application realizes adaptive identification and segmented modeling of continuous multi-breakpoint impact scenarios by constructing multiple homogeneous period models and organizing the models into a model library in time sequence, solves the problem that a single-breakpoint detection algorithm in the prior art cannot handle multi-breakpoint continuous impact and subsequent impact is forcibly classified into a segmented fragment, leading to model parameter mismatch, and guarantees the parameter accuracy of the evaluation model under different environmental stages; the discriminator outputs a probability value based on anchor data, and the output results of the previous homogeneous period model and the subsequent homogeneous period model are weighted and fused based on the probability value, smooth transition of the evaluation output in the junction window period is realized, and the continuity and reliability of the decision basis in the junction window period are ensured.

The application discloses a kind of intelligent diagnosis and decision support method of coronary heart disease angina TCM syndrome factor based on HIS, it is related to medical information and TCM diagnosis and treatment technical field, the method will coronary heart disease angina TCM syndrome factor diagnostic criteria be deconstructed into standardization diagnostic item, build machine-readable weight rule base containing score, threshold and severity grading rule, response syndrome differentiation operation generates electronic collection form, receives structured data storage to the TCM syndrome factor fact table associated with patient main index and medical record, after structured data is submitted, trigger intelligent computing engine verification essential item, total score is accumulated, determine the establishment of syndrome factor and severity, through TCM diagnosis and treatment knowledge graph push personalized treatment or nursing suggestion in clinical business node, and continuously update rule base and knowledge graph through clinical data feedback.The application integrates corresponding function module to realize and deeply fuse with hospital information system, improves the standardization and efficiency of diagnosis and treatment, and has strong practicality.

The invention provides an icing situation sensing method and system based on time-space fusion, and the method comprises the steps: obtaining sensing data which comprehensively reflects the formation and spatial distribution of icing from different angles through a multi-point monitoring mode, and extracting key parameters, namely multi-source characteristic parameters, representing the icing situation from the sensing data, the method comprises the following steps: carrying out weighted fusion on icing data change rate, temperature deviation value and maximum icing difference of icing data on a blade based on an attention mechanism, adaptively highlighting key features, weakening the weight of secondary features, and obtaining fusion features; and then, on the basis of a related topological graph constructed by fusion features, a graph neural network and a Transform network are utilized to capture spatial distribution features and time evolution laws of icing, so that the limitation that spatial correlation is difficult to describe only depending on a time sequence model traditionally is overcome, spatial features and time sequence features are effectively extracted, final spatial-temporal features are obtained, and the spatial distribution features and the time evolution laws of icing are extracted. And prediction is carried out based on the spatial-temporal characteristics so as to improve the precision of icing situation perception.

The application discloses a green electricity consumption type honeycomb micro-grid system based on distance adaptive aggregation, relates to the technical field of distributed energy and smart grid, and comprises a configuration module, a power transmission module, an aggregation module and a control module. The distributed power supply is configured to a centralized load side unit and a decentralized load side unit, and a honeycomb unit is constructed. The honeycomb unit comprises one centralized load side unit and n decentralized load side units. The power transmission module is used for connecting the centralized load side unit and the decentralized load side unit through a booster station and a power transmission line suitable for different voltage grades, so as to form a honeycomb micro-grid architecture. The aggregation module links the honeycomb units through a virtual power plant or a physical link, so as to form a honeycomb micro-grid architecture. The control module collects power data, regulates and controls the power flow direction and connects with a public grid.

The application discloses a voice enhancement method based on a dynamic voiceprint sample pool and specifically relates to the technical field of voice processing, and comprises the following steps: maintaining a dynamic voiceprint sample pool for each speaker, associating audio segments with speech recognition text and comprehensive quality scores; collecting clone reference candidates from the sample pool, taking a segment with a text length within a preset interval as a candidate, and performing greedy merging segmentation on an overlong segment based on punctuation perception; sorting all candidates in descending order of text length and quality score, and selecting an optimal candidate as original reference material; when there are two candidates with similar quality and good length, generating a fusion audio through waveform weighted fusion; and performing neural network voice enhancement processing on the selected reference audio to obtain an enhanced clone reference audio. The application realizes automatic selection and continuous evolution of the clone reference without recording, and effectively improves the quality of timbre cloning and user experience.

This application belongs to the field of industrial internet security technology and relates to a method for constructing an industrial internet security risk knowledge graph, aiming to solve the problems of missing industrial features, poor dynamism, and risk assessment being detached from process in existing technologies. The method includes: acquiring and preprocessing multi-source heterogeneous data to generate equipment association identifiers, vulnerability association identifiers, and process node identifiers, forming a standardized dataset; extracting initial triples based on the standardized dataset and performing a two-stage entity merging; validating the merged triples according to process rules to obtain valid triples; writing the valid triples into a graph database and writing runtime sequence data into a time-series database; establishing a mapping between entities and runtime sequence data through equipment association identifiers to generate an industrial internet security risk knowledge graph. This application achieves unified data association, accurate entity fusion, and graph-time-series collaborative storage, improving the accuracy of graph construction and risk assessment capabilities.

The present application relates to the technical field of carbon footprint accounting and low-carbon management, and specifically provides a warehouse carbon emission factor dynamic calibration and adaptive allocation method and system, which comprises the following steps: collecting multi-dimensional data affecting warehouse carbon emission in a target warehouse in real time and quantifying the data to obtain a dynamic warehouse feature vector; generating an initial carbon emission factor from a pre-constructed carbon emission base factor library, and performing Bayesian calibration on the initial carbon emission factor to obtain a precise carbon emission factor; allocating carbon emission at the cargo level according to the data availability and business scenarios of the target warehouse; and then updating the precise carbon emission factor to the carbon emission base factor library based on the preset update trigger condition. The present application can realize scenario-based precise generation, dynamic calibration and cargo-level adaptive allocation of warehouse carbon emission factors, and significantly improve the accuracy and flexibility of warehouse carbon accounting.

The invention relates to the technical field of communication, and particularly discloses a data communication method based on a highway electromechanical system, and the method comprises the steps: synchronously collecting strain and temperature parameters along an optical fiber, converting the strain and temperature parameters into transmission time delay variations in a segmented manner according to preset physical characteristics, and generating real-time time delay dynamic data of each communication path through path integration; establishing and continuously updating a time delay change file containing a trend component and a disturbance margin index for each access unit; the communication local side dynamically corrects the reference round-trip delay of each unit according to the file, determines a dynamic protection interval in combination with a trend change rate and a disturbance index, and redistributes an uplink time slot window with a protection band; through comparison between periodic actual measurement sampling and a predicted value, adaptive optimization is realized on the basis of a conversion relation from a historical credibility weighted reverse correction physical parameter to a time delay parameter for an error; according to the invention, time delay drift caused by dynamic deformation can be accurately predicted and compensated, and synchronous and reliable transmission of uplink data is ensured.

This invention discloses an intelligent control method for a power lithium battery thermal management system, relating to the field of lithium battery technology. The method includes the following steps: collecting operating data of the power lithium battery through a multi-physics field real-time sensing system; performing dynamic thermodynamic modeling and anomaly detection on the operating data based on a digital twin model and an AI large-scale model to generate a real-time control strategy; employing a hierarchical control strategy to perform precise control of the thermal management system, including temperature difference control, condensation quality hierarchical control, and travel condition prediction control; optimizing the control strategy through a vehicle-cloud collaborative computing architecture; and dynamically updating thermal management parameters and fault diagnosis thresholds based on the control results. This invention solves the problems of low control accuracy, high condensation risk, and insufficient energy efficiency optimization in traditional thermal management systems due to their single control strategy and lag response, through multi-physics field sensing, digital twin and AI fusion modeling, hierarchical control, and vehicle-cloud collaboration.

The invention discloses an enterprise digital operation real-time data analysis and early warning method based on big data, and relates to the technical field of data processing, and the method comprises the following steps: S1, collecting multi-source data during enterprise operation in real time, constructing an operation data set, and preprocessing the operation data set; s2, feature extraction and fusion are carried out based on the preprocessed operation data set, and a feature model representing the comprehensive operation state of the enterprise is constructed. According to the enterprise digital operation real-time data analysis and early warning method based on the big data, multiple regulation and control strategies are pre-evaluated and optimally selected through online simulation deduction, and an executable instruction is automatically generated to directly act on a production chain, a supply chain and a service system; therefore, accurate service flow adjustment is implemented before the risk dominance or at the initial stage of spreading, and the active defense capability and intervention timeliness of an enterprise to the operation risk are effectively improved.

The application provides a deep coal seam fluidization mining lower supercritical CO2 fracturing process determination method, relates to the technical field of supercritical CO2 fracturing process determination method, and comprises the following steps: weighting and fusing the number of microseismic events and temperature disturbance values, and generating a comprehensive reconstruction intensity value of each depth unit; when performing a short-term pump test, continuously detecting the bottom hole pressure, drawing a bottom hole pressure-time square root relationship curve, and performing linear fitting by using a square root time method to obtain a dynamic filtration coefficient; constructing an effective displacement channel index, and drawing an effective displacement channel index change curve with cumulative injection amount; and weighting and fusing the microseismic events and temperature disturbance data on the depth unit, generating a comprehensive reconstruction intensity value, and providing a double confirmation index which can reflect both the crack initiation position and the CO2 diffusion range; by tracing the comprehensive reconstruction intensity value change curve with the cumulative injection amount, the black box fracturing process is converted into a visual profile management.