246results about How to "Improve comparability" patented technology

The invention provides a power system operation state intelligent monitoring method and system, and relates to the technical field of power data monitoring, and the method comprises the steps: obtaining multi-source heterogeneous operation data, obtaining a standard operation data set, adaptively extracting high-order multi-modal feature representation through a confidence weighted fusion method, and obtaining an operation state multi-modal feature set. Constructing an intelligent monitoring cognitive map by learning topological association characteristics and semantic association rules of the running state; mapping the real-time operation data into an intelligent monitoring cognitive map, dynamically sensing an operation state spatio-temporal evolution rule through a spatio-temporal dependence modeling unit and a graph feature propagation unit in an operation state graph learning model, and generating a prediction and diagnosis result; and learning and generating an optimal control strategy of the power system through a depth deterministic strategy gradient algorithm, decoupling the optimal control strategy of the power system into a plurality of local sub-strategies, issuing the local sub-strategies to corresponding power control equipment, and obtaining a distributed cooperative control scheme through a federated learning algorithm.

The invention relates to the technical field of biological information, in particular to a method and system for online integration of multi-source single cell data. The method comprises the following steps: acquiring multi-source single cell transcriptome data; performing data quality evaluation based on gene expression quantity, mitochondrial gene proportion and cell data depth on the single cell transcriptome data to obtain initial quality control data; setting standardized filtering parameters according to the initial quality control data, and performing quality control parameter filtering on the single cell transcriptome data to obtain filtered cell data; performing gene expression characteristic analysis on the filtered cell data to generate high-variation gene expression characteristic data; and performing gene expression spatio-temporal distribution statistics according to the high-variation gene expression feature data to generate gene expression statistical feature data. According to the method, through refined quality control and batch effect correction, data from different sources can be seamlessly integrated on the premise of ensuring data quality, so that noise caused by different batches and technical platforms is eliminated.

The invention provides a traditional Chinese medicine abnormal state recognition method, system and device and a storage medium, and relates to the field of abnormal recognition. The method comprises the following steps: acquiring product information of a to-be-detected traditional Chinese medicine, and determining a data acquisition standard of the to-be-detected traditional Chinese medicine based on the product information of the to-be-detected traditional Chinese medicine and a preset detection rule; based on a data acquisition standard of the traditional Chinese medicine to be detected, inputting the image data into a preset image anomaly detection model to obtain a preliminary image anomaly region; performing feature fusion on the visual feature data and the spectral feature data to obtain an abnormal feature vector; and inputting the abnormal feature vector into a preset abnormal state judgment model, outputting abnormal state data, and performing abnormal state judgment based on the abnormal state data and a preset abnormal state judgment threshold to obtain an abnormal state detection result of the to-be-detected traditional Chinese medicine. Through the scheme, the abnormal state of the traditional Chinese medicine can be identified timely and accurately.

The invention relates to the technical field of energy storage power station management, and discloses an energy storage power station management system based on big data analysis, which comprises a data acquisition module, a preprocessing module, a performance analysis module, a comprehensive evaluation module and an early warning display unit. A foundation is laid for analysis; on the aspect of performance monitoring of the energy storage unit, the performance can be accurately evaluated, faults can be early warned in advance, replacement is reasonably guided, and stable operation of the energy storage unit is guaranteed; the overall performance of the energy storage power station is evaluated, the state can be comprehensively mastered, potential problems can be found in time, and operation and maintenance strategies can be formulated; in the aspect of operation optimization, the charging and discharging process can be optimized, the service life of equipment is prolonged, and the energy utilization efficiency is improved; in the aspects of information display and decision support, information is transmitted in real time, scientific decision is assisted, operation and maintenance personnel can conveniently and reasonably operate in time, and efficient, stable and safe operation of the energy storage power station is comprehensively guaranteed.

The invention relates to the technical field of ecological data analysis, in particular to a pumped storage power station slope ecological restoration effect evaluation method, which comprises the following steps: monitoring a pumped storage power station slope ecological restoration area; calculating a dry season restoration state characterization value; calculating a frozen season restoration state characterization value; calculating the representation value of the restoration state in the rainy season; outputting a corresponding repair effect evaluation prompt according to each repair level; and correcting the preset evaluation duration. Through real-time monitoring and dynamic weight adjustment, the accuracy and adaptability of an evaluation result are ensured, environmental changes can be responded in time, comprehensive ecological restoration state analysis is provided through comprehensive consideration of various environmental parameters, different restoration levels are clearly distinguished through the evaluation result, and the method has the advantages of being high in practicability and the like. Clear guidance is provided for taking targeted restoration measures, and the problem that the restoration effect assessment is inaccurate due to the fact that the adaptability to extreme climate conditions is insufficient and ecological restoration measures cannot be adjusted in time is effectively solved.

The invention relates to the technical field of biological information mining, in particular to a multi-omics data optimization method and system for intestinal flora matching and a medium. The method comprises the following steps: acquiring historical intestinal tract multi-omics data and integrating the historical intestinal tract multi-omics data to obtain host intestinal tract multi-omics data; performing multi-omics data fusion based on the host intestinal multi-omics data to obtain microbial flora fusion data; performing pattern classification according to the host intestinal tract multi-omics data to obtain host intestinal tract physiological pattern data; carrying out physiological pattern influence factor calculation on the microbial flora fusion data to obtain host intestinal tract physiological pattern influence factors; and performing host intestinal flora matching on the microbial flora fusion data and the host intestinal physiological data according to the host intestinal physiological pattern influence factor, thereby obtaining intestinal flora matching data. According to the method, the accuracy of flora matching prediction can be improved.

The invention relates to the technical field of single cell data processing, in particular to a database visualization method based on single cells. The method comprises the following steps: acquiring a single cell data set; standardizing the single cell data set to obtain standardized single cell data; performing high-dimensional feature extraction on the standardized single-cell data to obtain single-cell high-dimensional feature data; and performing nonlinear dimension reduction processing on the single-cell high-dimensional feature data to obtain single-cell dimension reduction data. Performing data clustering on the single cell dimension reduction data to obtain single cell clustering data; performing gene differential expression extraction on the single cell clustering data to obtain differential expression gene data; multi-modal data fusion analysis is carried out on single cell data through a data processing technology, a feature mapping technology and a visualization technology; and dynamic weight division is carried out on the single cell database, so that the effect of visualization of the single cell database is enhanced.

The invention discloses an outdoor environment monitoring method and system, a storage medium and a program product, and the method comprises the steps: obtaining a plurality of initial detection sub-regions; performing preliminary sampling at the central point of the initial detection sub-region to obtain initial environment data; constructing an environment parameter space distribution model; calculating a variation coefficient, and determining a high-variation sub-region; judging whether the area of the high-variation sub-region is greater than a preset minimum area or not; if yes, the minimum sub-region is divided, and the center point serves as a sampling point; if not, increasing a preset number of sampling points; controlling the detection unmanned aerial vehicle to perform secondary sampling at the sampling points to obtain initial environment data, and updating the environment parameter spatial distribution model; calculating an updated overall prediction error, and judging whether the updated overall prediction error is greater than a preset error; and if not, determining the initial environment data with the maximum similarity, and determining the region corresponding to the sampling point as a candidate experiment region. According to the invention, the accuracy of the environmental parameters detected in different outdoor environments is improved.

The invention discloses a product full life cycle carbon emission tracking method, which is embedded in a product carbon footprint management service platform, and specifically comprises the following steps: S1, formulating a product definition boundary and a truncation standard based on a carbon emission calculation formula; s2, data acquisition and accounting are completed according to local conditions, and real-time monitoring and data acquisition are carried out on all links in the product production process. According to the method, the total emission amount of greenhouse gases directly or indirectly generated by a product in the whole life cycle of the product is calculated, including the emission in the extraction, production, transportation, use and waste processes of raw materials, so that the calculation and result traceability of the carbon footprint in the whole life cycle of the product are realized. Accurate carbon emission data are provided for enterprises, the green level of the production process and technical equipment is improved, and the transformation of the enterprises and the society to low-carbon economy is promoted.

The invention relates to the technical field of medical care, in particular to a tumor prognosis prediction method based on tumor database text recognition, which comprises the following steps: S1, data collection and preprocessing: collecting medical data related to tumors from a plurality of tumor databases; s2, text information extraction: performing information extraction on clinical text data; s3, predicting a tumor metastasis path: predicting the metastasis path by utilizing the graph neural network model, predicting a potential metastasis path of the tumor, and analyzing the influence of different metastasis paths on the prognosis of the patient; s4, prognosis evaluation and risk prediction: predicting the tumor prognosis risk in combination with the medical data of the patient, and comprehensively evaluating the five-year survival rate and the recurrence risk of the patient. According to the method, the medical institution can be assisted in optimizing resource allocation, a scientific personalized diagnosis and treatment scheme is formulated, and the life quality and the treatment effect of the patient are remarkably improved.

The invention discloses a fermentation strain yield prediction method based on artificial intelligence, particularly relates to the field of biological fermentation production, and is used for solving the problems that in the prior art, due to the fact that the yield is difficult to directly map in concentration prediction, monitoring indexes are normal, the final yield deviates from expectation, and reliable strain comparison and production management decision cannot be supported. A historical batch process observation sequence and a material account sequence are aligned to generate a conversion efficiency characterization sequence to form a yield learning sample training yield prediction model, and the conversion efficiency characterization sequence is generated on batches and input into the model to obtain a yield prediction value. And calculating an accounting volume trajectory consistency parameter and a conversion contribution steady-state proportion parameter, inputting the parameters into a coefficient analysis model to obtain a decision credible coefficient, generating a yield risk judgment result for a yield prediction value based on the decision credible coefficient, and realizing a new-batch online prediction record.

The invention discloses a photovoltaic array fault detection method based on IV characteristics and a data model, and relates to the technical field of fault detection.The method comprises the steps that firstly, the collection frequency Cp is determined by comprehensively considering the dynamic characteristics, historical faults and the occurrence speed, it is ensured that key fault information is not missed, and then by means of second derivative analysis and squareness calculation of an IV curve, the fault information is obtained; and a data model is combined to accurately identify a local shadow fault, and the situation that previous fault judgment depends on a single index is changed. In the aspect of energy conversion efficiency optimization, a local maximum power point is determined according to a judgment factor Pz and an IV curve, photovoltaic array output and inverter input are dynamically matched, and compared with a traditional fixed matching mode, the power generation efficiency is further improved, and energy waste is reduced. By starting a model verification mechanism, the accuracy and stability of the model can be verified in different scenes and time spans.

The present invention relates to the technical field of sleep diagnosis, and in particular to a sleep staging method based on deep learning, the method comprising: collecting a number of target physiological signals, preprocessing each target physiological signal to obtain a number of standard physiological signals; synchronously aligning and integrating each standard physiological signal in turn to obtain a standard sleep data set; determining the actual correlation between each physiological signal to be integrated in the standard sleep data set, and determining an actual signal combination strategy according to the actual correlation; generating an actual sleep data set according to the actual signal combination strategy, and extracting features from the actual sleep data set to generate actual feature parameters; inputting the actual feature parameters into a deep learning model to determine the actual sleep state; analyzing the actual sleep state, and performing corresponding sleep intervention according to the analysis results. The present invention realizes low-cost non-intrusive sleep monitoring, thereby improving people's physical health.

A transparent visual power grid operation and maintenance system comprises a data acquisition and preprocessing module which is composed of a sensor network and SCADA system docking unit and a data preprocessing unit; the data processing and analysis module is composed of a fault prediction and diagnosis unit, an operation and maintenance strategy optimization unit and a three-dimensional modeling and dynamic simulation unit; the visual display and user interaction module is composed of an AR / VR interface development unit and a user-defined interface and report generation unit; and the system integration and communication module is composed of a standardized API interface and cloud platform unit and a remote access and cooperation unit, and the scheme of the invention solves the problem that the complexity of power grid operation and maintenance system data causes that detection information is difficult to provide visually, thereby affecting the operation and maintenance work quality and efficiency.

The invention discloses a quantitative evaluation method for the action effect of a lithium ion cell fire extinguishing agent, which comprehensively evaluates the fire extinguishing effect, the cooling effect and the smoke toxicity inhibition effect of the fire extinguishing agent, and overcomes the defect that the overall performance of the fire extinguishing agent cannot be fully reflected under a single evaluation dimension in the existing method. Meanwhile, by introducing the weight of each index, the problem that evaluation of each dimension is independent and lack of association in a traditional method is solved; besides, by correcting the temperature when the fire extinguishing agent is sprayed, the objectivity and credibility of the evaluation result are enhanced, and it is ensured that the evaluation results under different experiment conditions have higher consistency and comparability.

The invention discloses a reference gene related to regulation and control of stress-resistant gene expression of arabidopsis thaliana and application of the reference gene, and relates to the technical field of genetic engineering, the reference gene is LHCB5 and / or LHCB4.1, the nucleotide sequence of the LHCB5 is shown as SEQ ID NO.1, and the nucleotide sequence of the LHCB4.1 is shown as SEQ ID NO.2. The invention further discloses a preparation method of the reference gene related to the regulation and control of stress-resistant gene expression of arabidopsis thaliana and application of the reference gene related to the regulation and control of stress-resistant gene expression of arabidopsis thaliana. Various analysis methods are used for analyzing and finding that reference genes LHCB5 and LHCB4.1 show consistent stability under the high-temperature stress condition of arabidopsis thaliana, and when the LHCB5 and the LHCB4.1 are independently used as reference genes and the combination of the LHCB5 and the LHCB4.1 is used as the reference genes for relative quantification, the relative expression quantity of the HSFA2 gene under the high-temperature stress condition is increased and has a consistent trend; it is verified that both the LHCB5 and the LHCB4.1 and the combination of the LHCB5 and the LHCB4.1 can be used as reference genes for qPCR analysis of the arabidopsis thaliana under high temperature stress, a reliable reference standard is provided for gene expression research of the arabidopsis thaliana under high temperature stress in the future, and the accuracy and comparability of experimental data can be improved.

The invention relates to an automatic business information collection method, system and device and a medium, and the method comprises the steps: determining a collection object of business information according to the demands of a user for the business information; controlling the automatic tool to capture business information from each information detailed page of the collection object; performing structured processing and data format conversion on the business information to obtain multi-field structured data of the business information; according to the multi-field structured data, iterative training is conducted on the selected base model, an SQL processing enhancement model is obtained, and the SQL processing enhancement model can push corresponding structured business information to a user side based on query requirements input by a user. According to the invention, the business information in the industry can be captured in a directional manner, and the dimension information of the business field desired by the user can be obtained in an inquiry manner, so that the business information can be automatically collected and sorted without a specially-assigned person, the cost is reduced, and the timeliness, comprehensiveness and accuracy of business information collection are improved.

The invention relates to the technical field of automatic driving, in particular to an open type automatic driving safety management system and method supporting multi-benefit-party strategy arbitration, and the system comprises an open type safety strategy interface module which is used for defining a standardized strategy description specification; the strategy plug-in management module is used for receiving and loading the strategy plug-ins conforming to the strategy description specifications; the perception and fusion module is used for collecting the vehicle state data, the environment perception data and the service context data and fusing the data to generate a unified context model; the multi-strategy evaluation module is used for screening a candidate strategy set according to a unified context model and a trigger condition when an abnormal event is detected, and evaluating and selecting an optimal strategy; and the strategy execution and monitoring module is used for executing the action template corresponding to the optimal strategy. According to the invention, the automatic driving vehicle can select adaptive degradation strategies according to different operation scenes and business requirements, and the adaptability of the system to diversified business scenes is improved.

The invention provides a power load management simulation system, method and device and a medium. The power load management simulation system comprises a digital simulator, hardware and simulation software under a unified specification standard. The hardware comprises a calculation unit, an actual physical device and a simulation interface under a unified specification standard; the simulation software is used for providing a digital model and a simulation environment required by simulation; the calculation unit is used for acquiring the operation data of the actual physical device according to the digital model and processing the data; the digital simulator is used for performing power load management simulation on the processed operation data in combination with a digital model; the simulation system provides hardware and simulation software which are unified in specification and standard, compatibility and stability of the system can be guaranteed, integration complexity is reduced, and real-time performance and accuracy of simulation results are improved. In the simulation method, a simulation link sequence and an implementation method of each link under a standard are provided, so that the correctness and credibility of a power load management simulation result are ensured.

The invention relates to the technical field of medical data processing, in particular to a tumor nursing data processing system based on cloud computing, and the system comprises a multi-scale feature analysis module which obtains tumor nursing data, splits the tumor nursing data into a plurality of time dimensions, extracts multi-scale features of the tumor nursing data, and sends the multi-scale features to a cloud computing module; and carrying out importance sorting on the multi-scale features of the tumor nursing data to obtain a preliminary tumor nursing data feature alignment result. According to the method, feature decomposition and analysis of multiple time scales are carried out on the tumor nursing data, feature changes in different time dimensions are effectively captured, and the comprehensiveness of feature recognition is enhanced. According to the method, important features are sorted, the matching relation is dynamically adjusted, feature combinations with high relevance are subjected to weighted fusion, meanwhile, the large-scale data processing efficiency is improved through distributed optimization, and the accuracy and adaptability of data integration are remarkably improved.

The invention discloses a method and a system for processing comprehensive health assessment data of old people based on digital twinning, and relates to the technical field of data processing, the method comprises the following steps: fusing multi-source health data of a target old people group, establishing a health knowledge graph, and storing the health knowledge graph; constructing a digital twinborn health model based on the health knowledge graph, performing a simulation test in the constructed digital twinborn health model, and identifying health risks existing in the health data; calculating the health feature similarity between the target elderly group and the reference group by using a similarity algorithm; and analyzing health problem risks existing in the target elderly group by using the health feature similarity, and making a comprehensive health assessment report based on an analysis result. According to the method, the health knowledge graph is established by fusing the multi-source health data, and the digital twinborn health model is constructed, so that the quality and comparability of the data are improved, and a solid foundation is provided for subsequent simulation tests and risk identification.

The invention discloses a blue-green algae identification and quantification method and device based on unmanned aerial vehicle remote sensing images and deep learning and a medium, and relates to the technical field of information data processing. Combining meteorological data and water quality monitoring data to construct an adaptive dynamic environment algorithm to extract EXIF metadata including camera parameters and attitude angle information, and establishing a geometric projection model to perform coarse orthographic correction on an original aerial image; the method comprises the following steps: extracting a multi-scale cyanobacterial bloom image feature map by stages based on a ResNet architecture and in combination with a feature pyramid network FPN fused with an attention mechanism, and obtaining an instance mask of cyanobacterial bloom through an anchor-free region proposal network, ROI Align and a head network; based on an improved GIS space projection and deep learning algorithm, carrying out high-precision area measurement and calculation on a binary mask image converted from the instance mask; according to the method, the influence of irrelevant interference on blue-green algae identification can be reduced, and the accuracy and comparability of blue-green algae identification and area calculation are improved.

The invention provides an enterprise innovation capability evaluation and prediction device and method based on a neural network, and is based on a three-level index system for evaluating enterprise innovation capability. Determining an enterprise type i according to the category to which the to-be-evaluated enterprise belongs, and quantifying the second-level indexes of the to-be-evaluated enterprise through the KAN neural network and the normalized data of all the third-level indexes forming the second-level indexes; and determining the quantized value of the first-level index according to the proportion of the radar map area defined by the quantized values of all the second-level indexes of the enterprise to be evaluated relative to the maximum possible area to serve as the quantized value of the enterprise innovation ability, and evaluating the innovation ability of the enterprise to be evaluated according to the quantized value. Training a bidirectional long-short-term memory network model combined with an attention mechanism through the enterprise innovation ability of the to-be-evaluated enterprise over the years; and a Dropout method is combined to predict the future enterprise innovation capability of the to-be-evaluated enterprise, and a 95% confidence interval is constructed, so that the future innovation development trend of the to-be-evaluated enterprise is predicted.

The invention relates to the technical field of high-voltage transmission line fault positioning, and discloses a transmission line sound wave positioning method based on wavelet analysis, comprising the following steps: S1, installing acoustic sensors at two ends of a transmission line; s2, decoupling processing is carried out on the collected current signals; s3, performing Mallat algorithm decomposition on the current line mode component to obtain a wavelet coefficient of an approximate part and a wavelet coefficient of a detail part; s4, further de-noising processing is carried out on the current line mode component # imgabs0 #; and S5, obtaining the time when the fault sound wave arrives at the measuring end. According to the method, denoising processing is carried out on the wavelet coefficient # imgabs 1 # of the detail part, misjudgment information introduced by environmental noise in the signal is reduced, the wavelet coefficient is mapped to a unified range [0, 1] through normalization processing, and a variable # imgabs 2 # is introduced to carry out dynamic filtering on the signal, so that residual misjudgment information can be effectively filtered out, and the accuracy of the signal processing is improved. And the beneficial effect of accurately positioning the fault is achieved.

The invention provides a method and system for evaluating the influence of setting off fireworks and crackers on PM2.5 concentration, and relates to the technical field of environmental atmospheric pollutant evaluation, and the method comprises the steps: obtaining evaluation data of each time point in a setting off period of a monitoring station, including metal ion concentration, meteorological conditions and spatial-temporal characteristics; based on the evaluation data, the PM2.5 concentration under the firework setting-off scene and the PM2.5 concentration under the firework-free setting-off scene are predicted through a firework scene model and a background reference model; and calculating the contribution rate of the set-off fireworks and crackers to the PM2.5 concentration from two aspects of a single time point and a whole set-off time period by utilizing the predicted value of the PM2.5 concentration. According to the method, based on the firework scene model and the background reference model, efficient and accurate quantitative analysis on the influence of the set-off fireworks and crackers on the PM2.5 concentration is carried out from the two aspects of the single time point and the whole set-off time period.

This invention discloses a method for measuring the grounding resistance of transmission towers based on multi-circuit high-frequency synchronous excitation, belonging to the field of power equipment testing and grounding resistance measurement technology. The method is implemented using a multi-circuit high-frequency signal excitation transmission device. First, a multi-circuit measurement calibration parameter library is established, a non-contact coupling unit is installed and the measurement parameters are initialized, then multiple synchronous high-frequency excitation signals are generated, amplified and dynamically impedance matched, and then non-contactly coupled to the grounding down conductor. Subsequently, multiple feedback signals are synchronously acquired and signal characteristics are extracted. Based on the feedback signals, multi-circuit synchronous closed-loop calibration is performed, the grounding impedance and grounding resistance measurements of each circuit are calculated, and anomalies are eliminated and weighted fusion is performed on the multiple measurement values ​​to obtain the final grounding resistance value. This invention eliminates the need to split the grounding down conductor, improving measurement efficiency, synchronization stability, and anti-interference capability.

The invention relates to the field of building material detection, in particular to a quality evaluation method based on low-water-binder-ratio concrete, which comprises the following steps: acquiring a low-water-binder-ratio concrete sample, confirming a sample detection environment based on the low-water-binder-ratio concrete sample, performing cutting operation on the low-water-binder-ratio concrete sample by using a table-type cutting machine, and determining the quality of the low-water-binder-ratio concrete sample. Calculating an intensity value by using a drying tester and a burning tester; calculating a penetration value by using a penetration controller and a penetration tester; starting an ultrasonic sensor; calculating a loss value by using a sending voltage and a receiving voltage; and constructing a concrete quality evaluation model based on a regression analysis result, substituting the strength value, the permeation value and the hydration value into the concrete quality evaluation model to calculate an evaluation value, and completing quality evaluation of the concrete with the low water-binder ratio. According to the invention, the detection speed and accuracy of the low-water-binder-ratio concrete can be improved.

The application relates to the technical field of bimetal part manufacturing, and particularly discloses an aluminum-magnesium interface intermediate layer high-flux screening device and method for quantitatively delivering magnesium droplets, which comprises an inert atmosphere experiment cavity; a bearing platform arranged in the cavity and used for fixing an aluminum alloy plate, wherein the bottom of the bearing platform is connected with a two-dimensional moving mechanism and is provided with a heating element; a wire-powder coaxial composite device, which comprises a laser emitter, a beam deflection mirror, a focusing lens and a plurality of independently controlled powder device slots, and a laser beam acts on the surface of the aluminum alloy plate after passing through the focusing lens; a magnesium wire spool and a wire feeding roller, which feeds the magnesium wire from the magnesium wire spool to the heating area of the laser emitter. The device uses a plurality of powder device slots to sequentially deposit a plurality of intermediate layer coatings on the same aluminum alloy plate, and realizes the quantitative delivery of magnesium droplets by combining the quantitative wire feeding and laser heating, so that a large number of aluminum-magnesium interface preparations of different intermediate layers can be completed on the same substrate in parallel, and the screening efficiency and data comparability are effectively improved.

The invention relates to the technical field of digital geological detection, in particular to a Danxia landform corrosion detection method and system.The Danxia landform corrosion detection method mainly comprises the steps that an initial collection interval is set, weather data of each group of basic data is obtained, and the basic data are grouped according to the weather data of the basic data; obtaining a plurality of groups of basic data sets of different weather data; and establishing a neural network prediction model, obtaining a plurality of neural network prediction model output values, and outputting the future corrosion degree of the Danxia landform through the comprehensive evaluation model according to the plurality of neural network prediction model output values. According to the method, the influence of weather is taken as a consideration factor, and different weather data are grouped, so that the data of different weather conditions are analyzed in the same group, detection errors caused by weather factors are reduced as much as possible, and meanwhile, the neural network model is adopted to analyze the basic data, so that the detection accuracy is improved. And the accuracy of the prediction result based on the basic data is further improved.

The invention provides a post-retinal detachment reduction body position management method and system and wearable equipment, and relates to the technical field of medical monitoring. The method comprises the following steps: continuously acquiring head motion data through an inertial measurement unit integrated on head-mounted equipment, resolving the data through an embedded processor to obtain a real-time head posture angle, and comparing the real-time head posture angle with a preset rehabilitation position angle range to generate a posture comparison result; meanwhile, neck strain signals are collected through a flexible strain sensor array, and biomechanical characteristic signals are obtained through signal conditioning; and finally, the two types of data are fused, and a judgment result of the consistency of the overall body position of the user is generated through a classification algorithm, so that multi-modal data fusion analysis is realized, the postoperative body position state can be accurately monitored in real time, immediate feedback is provided, and the accuracy of body position management and patient compliance are effectively improved.