19 results about "Cumulative effect" patented technology

The invention relates to the technical field of electroplating process control, and discloses a vertical electroplating feeding control method and system.The method comprises the steps that anode current distribution and electrolyte parameters are monitored in real time, and the feeding rate matching degree is judged; the plating thickness uniformity risk level is evaluated based on a fuzzy control model, and the cumulative effect intensity of temperature fluctuation on anode passivation is quantified through multivariable regression analysis and a dynamic feedback mechanism; judging whether to trigger multi-stage feeding strategy recombination according to the risk level and the cumulative effect intensity; the current density and the electrolyte flow velocity are decoupled, a material consumption compensation coefficient is calculated, an adjustment instruction is generated, and iterative optimization can be carried out according to the execution effect. The system comprises a state monitoring module, a risk assessment module, an effect analysis module, a strategy decision module, a parameter decoupling module and a compensation execution module. According to the invention, the feeding control precision and the process stability are improved, and the method is suitable for precise control of the vertical electroplating process.

The invention relates to the field of fire safety operation and maintenance management, and discloses a service life prediction method and system of a fire valve and a storage medium. The method comprises the following steps: collecting operation data of a fire valve, extracting degradation characteristics, and analyzing interaction between the degradation characteristics and environmental factors to construct a torque change rate model; non-linear characteristic parameters are determined based on the torque change index fusion environment influence; when the parameter exceeds a threshold value, evaluating the linkage influence to obtain an influence coefficient; adjusting the sealed rough simulation grid and determining a dynamic evolution path; integrating the operation frequency fluctuation to update the path connection strength so as to extract an attenuation trend, and determining a degradation threshold point; generating a compensation signal to optimize a torque change rate model to obtain a nonlinear rate correction value; and quantifying the cumulative effect based on the correction value and dividing the life interval of the fire valve. The problem that performance degradation of the fire valve under the complex working condition is difficult to accurately evaluate is solved, and the service life prediction accuracy and the maintenance decision scientificity are improved.

The invention relates to the technical field of nuclear power safety, in particular to a method for regular safety evaluation and cumulative effect modification of a nuclear power plant. A nuclear power plant regular safety evaluation and modification cumulative effect method comprises the following steps: obtaining implemented modification information, and establishing a nuclear power plant modification database; and establishing a function influence matrix, a determination theory safety analysis influence matrix, a probability theory safety analysis influence matrix, a disaster influence matrix, a radiation protection influence matrix, an external radiation environment influence matrix and an equipment identification influence matrix for each modification content in the nuclear power plant modification database. And obtaining a modified cumulative effect analysis evaluation result according to one or more types of analysis in the influence matrix. According to the method, the cumulative effects of all implemented modifications are systematically combed, the direct and indirect influence of the cumulative effects generated by all historical implemented modifications on the overall safety performance of the nuclear power plant is evaluated, and the reliability or effectiveness of the overall safety function of the nuclear power plant is maintained.

The application discloses a system and method for pile foundation quality detection, and relates to the technical field of pile foundation quality detection, and solves the problems of difficulty in determining the optimal excitation energy range, deviation of damage quantization criteria, and low reliability of detection results, wherein the application randomly distributes excitation pulses with different energies to different excitation areas, ensures that each energy is excited only once in one area and the same area is not continuously used, combines with environment monitoring and correction during resetting, effectively eliminates the cumulative effect of previous excitation and environmental interference, obtains pure damage increment data, identifies the inflection point of the damage increment-energy curve, takes the inflection point energy as the critical excitation energy of the depth, takes the minimum value and the maximum value of all depths to form an adaptive detection energy window, and fundamentally solves the technical problems of difficulty in determining the excitation energy range and contradiction between detection results of the same pile under different energies.

The invention provides a power load prediction method, device and equipment based on a meteorological cumulative effect, and relates to the technical field of power load prediction. The method comprises the following steps: based on a meteorological cumulative effect extraction model, respectively carrying out meteorological feature extraction fusion on a meteorological sequence of a current day and meteorological sequences of a plurality of historical days to obtain meteorological cumulative effect characterization vectors of the current day and each historical day; wherein the meteorological sequence is a sequential sequence of multiple meteorological elements; calculating a similarity score between the current day and each historical day based on each meteorological cumulative effect representation vector and a trained similarity calculation model, and selecting a similar day from the plurality of historical days based on the similarity scores; and constructing a support set corresponding to the current day based on the meteorological cumulative effect representation vector of each similar day and the power load, and inputting the trained meta-learning prediction model to obtain a load prediction value of the prediction day. The method can improve the load prediction precision in a complex and changeable meteorological environment.

The invention discloses a method for creating a tumor personalized treatment simulation model based on overlapped microwave ablation in the technical field of biomedical engineering and computer simulation, and the method comprises the steps: constructing a three-dimensional geometric model containing a liver and a tumor based on medical image data of a patient; and establishing an overlapping microwave ablation finite element model coupling electromagnetic wave propagation and biological tissue heat transfer according to the three-dimensional geometric model. According to the method, the overlapping ablation process is dispersed into a plurality of continuous'ablation-cooling-re-ablation 'stages, the thermal field of the previous stage is inherited as the initial condition of the next stage, and the tissue characteristics changed due to temperature change are dynamically updated, so that accurate simulation of the overlapping thermal field cumulative effect is realized, and the accuracy of the thermal field cumulative effect is improved. Therefore, the prediction precision of the form of the final solidification area of overlapping ablation of the liver tumor is improved, and a doctor can clearly know the possible effect of ablation treatment according to the predicted visual solidification area and the quantitative evaluation index.

The present application belongs to the technical field of tailings thickening process control, and discloses a tailings thickening machine underflow concentration automatic detection and control system and method; through collecting multi-point turbidity data in the radial direction of the overflow area of the feedwell, early identification of the disturbance of the feed properties is realized; a layered settling model is constructed based on the mud layer interface height and the thickening machine geometric parameters, and the material residence time is accurately calculated; a medicament effect propagation descriptor is created, and the dynamic propagation process of the flocculating agent in the system is tracked; combined with the in-transit cumulative effect evaluation and future concentration prediction, feedforward-feedback composite control is realized; through lagging prediction error analysis, the model parameters are dynamically corrected, and the system performance is continuously optimized. The present method realizes predictive control and accurate adjustment of the underflow concentration, effectively solves the problem of excessive flocculating agent adjustment, and improves the stability and reliability of the tailings treatment process.

The invention provides an industrial control method and system for a glyceryl triacetate production process, and relates to the technical field of industrial control, and the method comprises the steps: obtaining a current control variable of glyceryl triacetate; outputting a prediction state variable under the current control variable by using an LSTM model with a decoupling cumulative effect; determining a state space of each current control variable; determining an action space of the current control variable adjustment amount in combination with a data-driven Lyapunov function; establishing an optimization target under the conditions that the predicted acid value reaches the expected acid value and the predicted glyceryl triacetate product concentration reaches the expected glyceryl triacetate product concentration in the shortest reaction time; in combination with the state space and the action space, solving the optimization target by using a random selection strategy to obtain an optimal decision variable about the current control variable adjustment amount; and correcting the current control variable according to the optimal decision variable. And the production speed is increased while the production quality is ensured.

The invention relates to the technical field of machine vision and oil product analysis, and discloses a full-automatic oil product acidity detection system based on machine vision. According to the system, detection is realized through links of initial acquisition, feature conversion, time sequence segmentation, environment accumulation, anomaly judgment and result synthesis. The initial acquisition link synchronously acquires an oil product image and temperature and illumination data; in the feature conversion link, color and light transmittance features are analyzed and normalized, and an acidity feature value is output; in the time sequence segmentation link, the change rate of adjacent samples of the sequence is calculated, and stable and variable sections are divided; the environment accumulation link performs integral operation on the temperature and illumination trend in the stable section, and quantifies the accumulation effect; in the abnormity judgment link, an environment accumulation result and variation section information are integrated to verify abnormal points; and a result synthesis link generates a final report. According to the system, by analyzing the acidity dynamic change trend and quantifying the environmental cumulative influence, oil product deterioration and environmental interference are distinguished, and the detection accuracy and reliability are improved.

The invention discloses an early warning method and system for dangerous chemical substance risk operation behaviors, and relates to the field of dangerous chemical substance risk control. The method is applied to a risk early warning system, and the method comprises the following steps: obtaining various risk index data of hazardous chemicals in a production management process, the various risk index data including video data, sensor monitoring data and an operation log; performing semantic recognition on the multiple risk index data to obtain multiple risk operation events; calculating a risk equivalent value corresponding to each of the plurality of risk operation events and a risk increment value cooperatively caused by the plurality of risk operation events; determining a comprehensive risk value according to the risk equivalent values and the risk increment values corresponding to the plurality of risk operation events; and when the comprehensive risk value is greater than or equal to a risk threshold value, triggering risk early warning. By implementing the technical scheme provided by the invention, the problem that a traditional early warning mode cannot effectively quantify the cumulative effect and the synergistic effect of risk operation is solved.

The invention discloses a real-time motion error correction method and device and a computer readable storage medium, and the method comprises the steps: obtaining an initial skeleton key point coordinate of a user in a preset calibration posture, and determining the initial length of each virtual elastic connection unit as a personalized zero load reference based on a preset virtual biomechanical constraint model; calculating an elastic deformation load index of each unit relative to the reference in real time, and performing weighted aggregation to generate regional aggregation pressure of the joint region; calculating an instantaneous fatigue generation rate by using a nonlinear mapping function representing the tolerance characteristic of the biological tissue, and updating an accumulated injury risk value based on a difference value between the rate and a preset physiological recovery rate through discrete integral operation of a time dimension; and finally generating hierarchical causal feedback containing a load source identifier or a risk warning. According to the method, the hidden compensation risk of shape-to-force error can be accurately identified, and the nonlinear cumulative effect of exercise fatigue can be quantified.

The present application belongs to the technical field of tailings thickening process control, and discloses a tailings thickening machine underflow concentration automatic detection and control system and method; through collecting multi-point turbidity data in the radial direction of the overflow area of the feedwell, early identification of the disturbance of the feed properties is realized; a layered settling model is constructed based on the mud layer interface height and the thickening machine geometric parameters, and the material residence time is accurately calculated; a medicament effect propagation descriptor is created, and the dynamic propagation process of the flocculating agent in the system is tracked; combined with the in-transit cumulative effect evaluation and future concentration prediction, feedforward-feedback composite control is realized; through lagging prediction error analysis, the model parameters are dynamically corrected, and the system performance is continuously optimized. The present method realizes predictive control and accurate adjustment of the underflow concentration, effectively solves the problem of excessive flocculating agent adjustment, and improves the stability and reliability of the tailings treatment process.

The present application relates to machine vision and oil analysis technical field, disclose a kind of oil acidity full-automatic detection system based on machine vision.The system includes through initial acquisition, feature conversion, time sequence segmentation, environmental accumulation, abnormality determination and result synthesis link to realize detection.Initial acquisition link synchronously acquires oil image and temperature, illumination data;Feature conversion link analyzes color and transmittance characteristics and normalizes, exports acidity characteristic value;Time sequence segmentation link calculates the change rate of adjacent samples in sequence, and divides stable and variation section;Environmental accumulation link integrates temperature and illumination trend in stable section, quantifies its cumulative effect;Abnormality determination link verifies abnormal point by integrating environmental accumulation result and variation section information;Result synthesis link generates final report.The system distinguishes oil itself deterioration and environmental disturbance by analyzing acidity dynamic change trend and quantifying environmental cumulative effect, improves the accuracy and reliability of detection.

The invention discloses a rock creep disturbance induced instability and post-disturbance residual life prediction method. The method comprises the following steps: constructing a creep damage factor and a disturbance damage factor based on irreversible deformation; the coupling effect of creep damage and disturbance damage is comprehensively considered, a theoretical formula capable of analyzing rock creep response in a complex disturbance environment is obtained, and the problem of nonlinear response prediction of the rock creep process under the disturbance effect is creatively solved. According to the method, the disturbance aftereffect in the rock creep process can be scientifically represented, and disturbance-induced instant instability and lag instability can be predicted; according to the method, the combined elements with irreversible disturbance deformation are described in series, nonlinear correction is carried out, damage evolution and element parameters are dynamically coupled, a composite element model considering the historical disturbance cumulative effect is established, and the memory characteristic of rock mass deformation on the historical disturbance effect can be described.

Some disclosed methods involve obtaining extended reality (XR) discomfort indication data and classifying a user as having either a cumulative effects XR discomfort profile or a refractory XR discomfort profile. The cumulative effects XR discomfort profile may correspond with increased XR discomfort during successive instances of XR content consumption, increased XR discomfort during time intervals between successive instances of XR content consumption, or both. The refractory XR discomfort profile may correspond with reduced XR discomfort during time intervals between successive instances of XR content consumption and with reduced XR discomfort during at least some successive instances of XR content consumption. Some methods may involve outputting, by the control system, a XR discomfort profile classification, implementing, based at least in part on a user classification, one or more discomfort mitigation procedures associated with an XR presentation, or both.

The application discloses a forest fire risk prediction method based on time sequence characteristics and an integrated model, and relates to the field of forest fire risk prediction. From the data level, the training set random downsampling algorithm is adopted; from the algorithm level, the more minority class prediction model (BRF) and the setting of each model super parameter are adopted; from the evaluation level, the more minority class prediction evaluation index is selected, and the effectiveness of the fire risk prediction model under the unbalanced data is comprehensively evaluated. By extracting the average value and trend value time sequence dynamic characteristics of the meteorological and combustible materials within 16 days, the cumulative effect of the fire risk is effectively described, and the identification accuracy of the potential high-risk fire area is improved. Through the base model combination of XGBoost, LightGBM and balanced random forest and the fusion of the logistic regression meta model, the advantages of different models are integrated, and compared with a single model, the prediction accuracy is improved.

The application discloses a fish feeding behavior recognition method and system, and particularly relates to the technical field of intelligent monitoring of aquaculture, and is used for solving the problems of fuzzy feature extraction and low behavior judgment accuracy caused by the complexity of underwater environment and the instantaneousness of feeding action of the existing visual recognition method; through obtaining underwater continuous video frames, fish head movement continuity parameters are extracted and candidate key frames are screened; nonlinear dynamics characteristics of mouth deformation displacement are analyzed to generate time sequence correlation parameters, and the coupling strength of head and mouth movement is dynamically weighted and fused; finally, the cumulative effect and distribution mode statistics of the motion coupling strength are used to determine the feeding behavior state; through the multi-dimensional time sequence feature fusion and dynamic weight distribution mechanism, the application realizes accurate capture and reliable discrimination of the transient feeding action of fish in a complex underwater scene, and provides effective technical support for optimizing the feeding strategy of aquaculture.

The invention relates to a method, a device and equipment for predicting the residual life of a contact line. The method comprises the following steps: acquiring tension fluctuation data during operation of the contact line and a multispectral image of an abrasion area of the contact line, and processing the tension fluctuation data to obtain a first parameter set of the contact line; processing the multispectral image to obtain a second parameter set of the contact line; feature fusion is carried out on the first parameter set and the second parameter set to generate a multi-dimensional feature vector, and the multi-dimensional feature vector represents the mechanical fatigue degree and the geometric loss degree of the contact line; and predicting the residual life of the contact line according to the multi-dimensional feature vector. By adopting the method, the cumulative effect and geometric damage of the fatigue damage of the material can be more accurately captured, and the residual life of the contact line can be more accurately evaluated.

A fault managed power system (FMPS) and method therefor can limit the cumulative effects of repeated current pulses on the human body during a fault without changing the amplitudes of the current pulses. Upon detection of a fault, the fault managed power system progressively reduces the durations of the current pulses (i.e., ON intervals) instead of the amplitudes to limit the cumulative effects of the current pulses. The fault managed power system can perform the progressive pulse width reductions in increments or steps that limit the cumulative effects of the current pulses to below a predefined energy level. In some embodiments, the predefined level is a level that could prevent let-go or cause ventricular fibrillation in a human body. Such a fault managed power system advantageously provides a simple and efficient way to limit the energy delivered into a fault without having to change the amplitudes of the current pulses.