24results about How to "Improve predictive ability" patented technology

This invention discloses a liquid-cooled collaborative intelligent temperature control method and system based on layered PCM, relating to the field of liquid-cooled temperature control technology. The method includes: S1, real-time acquisition of liquid-cooled temperature control data and preprocessing of the data; S2, calculation of the sensible and latent heat absorption of the outer and inner PCM layers, assessment of the risk of thermal imbalance on the conductor surface, determination of the current thermal state, and output of a thermal risk warning signal; S3, assessment of the degree to which cable bending weakens thermal coupling performance, triggering a layered PCM collaborative temperature control strategy, and execution of graded control commands for the power pump, circulation pump, and three-way valve; S4, assessment of the deviation between the current heat exchange capacity and the heat load demand, correction of the cooling response coefficient, and optimization of the PCM collaborative temperature control strategy. This solves the problems of unstable thermal coupling between the coolant pipe and the heating conductor in existing liquid-cooled cables, and the tendency for pipe deformation and contact thermal resistance and localized hot spots to occur with frequent bending.

The application provides a trajectory planning method and system of a mechanical arm continuous synchronization mode, relates to the technical field of mechanical arms, and collects operation related data of a mechanical arm system; a digital twin model is used to simulate the movement process of the mechanical arm along a preset movement trajectory, so as to generate a risk prediction result of the mechanical arm in the continuous synchronization mode; the preset movement trajectory is divided into multiple trajectory segments, and each trajectory segment is optimized and integrated based on the risk prediction result and the movement characteristic parameters of the mechanical arm, so as to form a preliminary operation trajectory; operation state data is analyzed, so as to generate data anomaly information; each mechanical arm in the mechanical arm system is controlled to move according to the preliminary operation trajectory, and real-time operation data of each mechanical arm is collected; the preliminary operation trajectory is adjusted in combination with the data anomaly information and the risk prediction result, so as to obtain a target operation trajectory of the mechanical arm, and safe, efficient and self-adaptive target trajectory operation of the mechanical arm in the continuous synchronization mode is realized.

The invention discloses a biomarker for predicting severe treatment of a COVID-19 patient and application of the biomarker, and relates to the technical field of biomedicine. According to the invention, a COVID-19 patient severity prediction or auxiliary prediction model which is obtained by constructing biomarkers including Gemelia haemolsos, Streptococcus sp.A12, Fusobacterium pseudoperiodontium, Gemelia mollissima, Villonella atypical, Villonella rogosa and Prevotella melaninogena is found, the model is used for predicting and evaluating the severity of the COVID-19 patient, the sensitivity is high, and the specificity is good. The invention further discloses a method for predicting and evaluating the severity of the COVID-19 patient severity, and the method has the advantages that the method can be used for predicting and evaluating the severity of the COVID-19 patient severity;

This invention relates to the field of fall detection technology, specifically disclosing a millimeter-wave radar fall detection method that integrates HRV nonlinear features and 3D point clouds. The method includes the following steps: acquiring the inter-heart rate interval (IBI) sequence and 3D point cloud of the target in parallel based on millimeter-wave radar signals; extracting HRV nonlinear physiological features based on the IBI sequence and biomechanical features based on the 3D point cloud; aligning the HRV nonlinear physiological features and biomechanical features to a unified time axis and performing feature coupling calculations to obtain four types of coupling indices; the four types of coupling indices are: cardiac-gait phase coupling, exercise-induced heart rate response, cardiac-triggered motion variability, and joint entropy; and performing fall detection on the target based on a three-level progressive detection mechanism. This invention effectively captures abnormal physiological signals before a fall by establishing an intrinsic correlation between physiological and motion signals, effectively solving the problem of warning lag caused by traditional methods relying solely on motion features.

The application provides a quantitative analysis method for ore pore and fissure structure, and relates to the technical fields of mineral processing, metallurgical engineering and geological analysis. The method realizes accurate prediction of effective porosity of heap leaching ore by coupling on-site particle size investigation, laboratory "quasi on-site" heap construction, ore block fissure microscopic measurement and mass-volume conservation calculation. The method effectively overcomes the technical problems that the traditional method depends on single scale measurement, and is difficult to comprehensively reflect the complex structure from nanoscale pore to centimeter scale fissure, especially for the mixed ore of coarse and fine particle grades. The existing technology often processes different particle grades respectively, lacks systematic integration and mutual correction, and causes deviation of the calculation results of porosity and fissure rate.

The application discloses an energy storage power station intelligent optimization method and system based on deep coupling learning, relates to the technical field of energy storage power station optimization, and is used for solving the problems of maximum demand capture failure caused by production plan fluctuation and invalid charging and discharging loss caused thereby, extracting strong correlation characteristics of production procedures and power mutation, constructing a time-space matrix anchor peak value inducement, outputting a latent space feature vector through an encoder by using a deep coupling multi-task network, explicitly quantifying a demand refresh risk probability, introducing the peak value risk and a battery health soft measurement quantity into a target function based on an MPC-DRL rolling optimization framework, dynamically adjusting charging and discharging sensitivity, constructing a composite feedback signal by extracting extreme samples of missed or false peak values, and realizing closed-loop evolution of attention reconstruction and decision threshold prediction; the robustness of demand management under a complex production environment is improved, the power consumption cost is reduced, and the whole life cycle optimal balance of battery life and economic benefits is realized.

The application discloses a bridge structure self-adaptive sampling monitoring method and device suitable for an extreme road environment, and belongs to the technical field of bridge structure health monitoring.The core target of the application is to solve the problems of repeated arrangement of existing monitoring equipment, road-bridge data fragmentation, and low self-adaptive sampling regulation precision.Through innovative design of a road-bridge multiplexing integrated sensing node, single-node multi-parameter and full-scene monitoring are realized, and the arrangement cost and construction difficulty are reduced; through construction of a self-adaptive sampling strategy based on fuzzy logic control, a feasible algorithm model is given, continuous dynamic regulation of sampling parameters is realized, monitoring precision and low power consumption are taken into account, deep collaborative linkage of road environment monitoring and bridge structure monitoring is realized, and the safe operation of the bridge structure under the extreme road environment is ensured.

The application provides a content review model training method and device, equipment, a storage medium and a computer program product; the method comprises the following steps: predicting compliance of at least one piece of content to be reviewed by a content review model to obtain a predicted review result of each piece of content to be reviewed; for each piece of content to be reviewed, determining a predicted difficulty score of the content to be reviewed based on the predicted review result; from at least one piece of content to be reviewed, a target review content is selected whose predicted difficulty score meets a difficulty score condition; the content review model is trained by taking the target review content as a sample and taking the actual review result of the target review content as a sample label of the sample, so as to update the content review model; by the application, the training cost of the content review model can be reduced, and the training speed and content review accuracy of the content review model can be improved.

The application provides a disaster-causing similar typhoon retrieval model acquisition method, a retrieval method, a terminal and a medium. The method comprises the following steps: acquiring spatial distribution information and meteorological field information of each path observation point in a target typhoon and related typhoons, and dividing the target typhoon into a first target typhoon and a second target typhoon, and dividing each related typhoon into a first related typhoon and a second related typhoon; based on the path similarity and the meteorological field similarity between the first target typhoon and the corresponding first related typhoon, and in combination with the fitting similarity between the second target typhoon and the corresponding second related typhoon, the weight parameters in the disaster-causing similar typhoon search model are optimized and configured to obtain a final disaster-causing similar typhoon search model; and the configured disaster-causing similar typhoon search model is used to obtain the corresponding disaster-causing similar typhoon of a typhoon to be retrieved. The application improves the prediction ability of the disaster-causing similar typhoon for the path abnormal typhoon and improves the accuracy of the disaster-causing similar typhoon search.

The invention provides a wine cross-flow filtration flow rate control method and system, a fixed terminal turbidity target value is determined according to product standards and downstream process requirements, an initial circulation flow rate reference value and an initial operation pressure are determined according to initial characteristic parameters and preset membrane module process parameters, and then cross-flow filtration is started. The real-time turbidity, the transmembrane pressure difference and the permeate flux of the filtrate are monitored synchronously, the membrane pollution development index is determined based on the rising rate of the transmembrane pressure difference and the falling rate of the permeate flux, and then the circulation flow rate is adjusted according to the comparison result of the membrane pollution development index and the preset membrane pollution threshold value. And when the real-time turbidity is continuously lower than the terminal turbidity target value and the transmembrane pressure difference and the permeate flux are in a reasonable working condition range, judging that the wine filtration is completed. By adopting the scheme of the invention, intelligent flow velocity control can be performed on wine cross-flow filtration based on the predicted pollution trend at the initial stage of filtration.

The invention discloses a decision task-oriented graph ontology relationship modeling method and device, and the method comprises the steps: carrying out the entity recognition and extraction processing of text data in the technical field of decision, and obtaining an ontology text set; the ontology text set comprises an attribute text set and a relation text set of each ontology; the attribute text set comprises various attribute texts; the relation text set comprises various kinds of relation texts; performing ontology relationship modeling processing on the ontology text set to obtain a graph ontology relationship prediction model; and processing a newly collected ontology attribute text set by using the graph ontology relationship prediction model to obtain relationship information of the ontology.

The application discloses a mixed material granulation control system and method based on a multi-scale visual network, and belongs to the technical field of mixed material granulation control. Through collecting material composition, initial moisture content and discharge flow of each stock bin, batch process tracking data is established in combination with material conveying sequence, a particle size prediction model is used to predict the particle size proportion distribution of the current granulation batch, and the coarse water addition amount for advance adjustment is calculated. After the mixed material is completed and enters the subsequent conveying belt, the mixed material image is collected and input into the multi-scale visual network, the actual particle size proportion distribution is identified, the fine water addition amount for feedback correction is calculated, the coarse water addition amount and the fine water addition amount are dynamically fused and process constraint correction is performed, the water addition control amount is generated and hierarchical valve adjustment is performed, and the prediction control, visual feedback and closed-loop water addition collaborative control of the mixed material granulation process are realized.

The invention discloses a multi-modal brain function connection brain tumor postoperative post-traumatic stress disorder classification system and method. The system comprises a multi-modal data acquisition module, a multi-map brain network construction module, a multi-kernel map convolution feature extraction module, a dynamic prediction model construction module and an interpretability visualization module. Structural magnetic resonance, functional magnetic resonance and electroencephalogram multi-modal data are integrated, a functional connection network is constructed based on multi-graph division, and multi-scale features are extracted by using a multi-kernel graph convolutional network, so that high-precision PTSD classification and prediction are realized. The problems that existing brain tumor postoperative PTSD diagnosis depends on subjective evaluation and lacks objective biological markers are solved, an integrated solution of early recognition, individualized diagnosis and interpretable analysis is provided, and the diagnosis precision and clinical effectiveness of brain tumor postoperative mental complications are remarkably improved.

This application relates to the field of steam turbine cold-end optimization technology, and particularly to a method, device, equipment, and medium for coordinated control of spray cooling in air-cooled units. This method acquires environmental and unit load data in real time and predicts their changing trends during future control periods. Based on a thermodynamic characteristic model, it calculates the spray cooling amount required to achieve the target back pressure, thereby formulating a feedforward spray water volume control scheme. Then, considering the predicted operating conditions and the spray scheme, it uses a decision optimization model to optimize the air-cooled fan speed, achieving coordinated control of the fan and spray system. This method achieves proactive and precise control of spray water volume and fan speed. This coordinated optimization strategy ensures that the system can operate in an economical mode under different operating conditions. That is, while meeting back pressure constraints, it balances the power consumption of the fan and the water consumption of the spray system, significantly improving the overall economic operation level of the system while ensuring the safe and efficient operation of the unit.

The invention relates to the technical field of seed vigor detection, and discloses a rice single seed vigor index evaluation calculation method and system. According to the method, multi-dimensional information of the weight gain ratio representing substance absorption, the root length representing root development, the root number and the bud length representing germ growth is fused into one index, the physiological status and development potential of the seeds can be reflected more comprehensively, and a single seed vigor index calculation model is further constructed. Accurate quantification of vigor of a single seed is realized, and seed individuals with similar appearances but different internal vigor can be effectively distinguished. Elastic network regression is adopted in the process of constructing a single seed vital index calculation model, the defect that coefficients are unstable when traditional multiple linear regression faces strong correlation phenotypic data is effectively overcome, and the established model has higher reliability and generalization ability.

The application discloses an active phased-array radar target detection correction method and particularly relates to the field of detection correction, and comprises the following steps: S1, a control center triggers radar scanning, calculates and extracts detection data, and transmits the detection data to a data preprocessing unit; S2, a preprocessing unit extracts echo parameter, acquires an echo signal validity coefficient, judges signal validity, and processes; S3, the control center triggers a three-axis gyroscope attitude measurement unit to collect attitude data, and transmits the attitude data to a coordinate conversion unit to call a conversion matrix; and S4, the coordinate conversion unit corrects an angle, calculates relative three-dimensional coordinates, and transmits the relative three-dimensional coordinates to a multi-source data fusion unit. The application constructs a detection correction system in which a phased-array radar, a gyroscope, a GPS and multiple data processing units are cooperated, improves the cooperation and intelligent level of the whole detection correction system, and is suitable for various target detection scenes with complex carrier attitude changes, and is more practical and adaptive.

The invention discloses a meter box topology identification method based on a high-speed broadband power line carrier technology. According to the method, a master station issues a ranging task to a concentrator, a carrier communication link is established with a plurality of terminal nodes through the concentrator, ranging data of the terminal nodes are collected, and signal strength data and signal-to-noise ratio data are received. According to background noise, normal noise, periodic impulse noise and sudden impulse noise existing in the low-voltage power line, noise analysis, frequency domain processing and filtering suppression are carried out on collected signals, so that effective data capable of reflecting communication link characteristics can be obtained. And after abnormal value detection and standardization processing are carried out on multiple rounds of measurement results, weights of different characteristics are determined based on correlation between communication characteristics and an actual topological relation, and a weighted linear regression model is constructed and used for predicting meter box affiliation of a terminal node or a relative position of the terminal node in a topology. And judging a topological structure of the terminal node according to a model output result, and verifying an identification effect through a model evaluation index.

The invention relates to the technical field of administrative management and supervision, and discloses a pilot multi-dimensional ability collaborative assessment method based on a multi-modal heterogeneous graph and decoupling prediction, and the method comprises the steps: obtaining a multi-modal original data flow in an assessment task; loading a standard operation program knowledge base and converting the standard operation program knowledge base into a logic reference map; constructing a heterogeneous information graph model fused with causal logic; calculating a manifold deviation value between the model and the logic reference diagram, and modulating node weights according to the manifold deviation value to generate enhanced feature flow; using a decoupling prediction system to separate the enhanced feature flow into independent dimension specificity representation vectors; according to the method, the standard operation instruction constraint is embedded into the bottom layer of the graph model, the system is forced to learn the causal mapping between the operation behavior and the supervision standard, and the interference between the physiological signal and the flight dynamics characteristic is eliminated.

The application discloses a model predictive control-based unmanned aerial vehicle gust disturbance suppression method and system, which comprises the following steps: taking an equivalent disturbance vector as a known input, explicitly introducing the equivalent disturbance vector into a discrete state prediction equation of MPC, and constructing a disturbance prediction sequence; at each sampling time, obtaining an optimal control sequence based on a current state estimation, a reference trajectory and the disturbance prediction sequence; and based on the equivalent disturbance vector, explicitly calculating a feedforward compensation amount according to an unmanned aerial vehicle dynamics model. The gust disturbance estimated by a nonlinear disturbance observer is explicitly introduced into a prediction model of model predictive control, the feedforward compensation amount and the nominal control amount are superposed based on the dynamics model to output, so that the controller can predict the disturbance influence in a prediction time domain and plan the control in advance; by constructing a zero-order hold disturbance prediction sequence and introducing the control amount amplitude, the control increment and the state quantity constraint into an optimization problem, the control instruction can always satisfy the motor saturation and the safe flight envelope limit.

The application relates to the technical field of data processing, in particular to a financial management method and system based on big data, which comprises the following steps: S1, performing semantic-visual fusion cleaning and standardization processing on unstructured financial data of a small and medium-sized enterprise, and outputting a standardized financial data matrix; and S2, based on the standardized financial data matrix, combining ERP data and external supply chain credit data, and performing cash flow health degree prediction and supply chain payment strategy linkage; when the application is used, semantic-visual fusion cleaning is used to reduce the workload of manual arrangement of unstructured data, improve the data accuracy, and solve the pain points of difficult data entry, many errors and omissions of the small and medium-sized enterprise; through real-time cash flow health degree prediction, the small and medium-sized enterprise can identify the fund gap risk 7-30 days in advance, and passive supply interruption can be avoided; through a dynamic payment strategy, the overdue payment probability is reduced, the cooperation stability of the core supplier is improved, an advance payment discount can be obtained, and the procurement cost is reduced.

The invention provides a resource effect prediction method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining candidate structured configuration information and semantic description information of a target resource; according to the candidate structured configuration information, evaluating a target service index of the target resource under the candidate structured configuration information to obtain a first predicted value of the target service index; according to the semantic description information, evaluating a target business index of the target resource under the candidate structured configuration information to obtain a second predicted value of the target business index; and determining a target prediction result of the target business index according to the first prediction value and the second prediction value. According to the method, semantic features contained in numerical configuration parameters and unstructured text contexts are effectively fused, the comprehensiveness and accuracy of target business index prediction are improved, the reasonability of resource configuration decisions can be enhanced, and the pre-judgment ability and configuration efficiency of a system before resources are online can also be improved.

The application discloses a kind of based on distributed access's service process data analysis system and method, it is related to big data analysis technical field, the present application is determined by analyzing historical abnormal record characteristic service node;Setting training period, construct characteristic index library and association library;Abnormal record is collected, and service score is calculated, and curve chart is drawn, and abnormal time is predicted;Real-time service process is collected, and real-time service score is calculated, and abnormal time is predicted, reduce invalid analysis range, improve abnormal detection efficiency and accuracy, significantly improve the depth and accuracy of abnormal diagnosis, effectively reduce artificial monitoring workload, improve operation efficiency.