7results about How to "Comprehensive consideration" patented technology

This invention provides a method for predicting surface vibration waveforms during tunnel blasting, comprising: first, deriving the vibration waveform function of a short-charge explosive charge based on Heelan theory; then, constructing a vibration calculation form suitable for tunnel cut-out blasting based on this short-charge explosive charge vibration waveform function, and calculating the surface vibration waveform function of the cut-out holes using the superposition principle; next, simplifying and correcting the formula for the waveform function of multiple-hole blasting to obtain the surface vibration waveform function of multiple-hole blasting in the tunnel; finally, determining the specific parameters in the formula by comparing with field measured data, and obtaining a surface vibration waveform function curve suitable for field use. This method can comprehensively and accurately predict the waveform time history curve of the entire process of surface vibration during tunnel blasting, rather than just predicting a single blasting peak value. It can effectively promote the field application of surface vibration waveform prediction for tunnel blasting and is of great significance for optimizing blasting parameters and ensuring the safety of various facilities and personnel around the blasting area.

This application discloses an anomaly localization method and a cloud management platform based on a cloud management platform, which can improve the efficiency of anomaly localization to a certain extent. The method includes: the cloud management platform can create a large-scale computing node cluster for tenants, which can contain multiple computing nodes. If these multiple computing nodes fail to execute a job task, the cloud management platform can first obtain the logs generated by these multiple computing nodes during the execution of the job task, and generate log space characteristics and log time characteristics of these multiple computing nodes based on the logs. Then, the cloud management platform can evaluate these multiple computing nodes based on these log space characteristics and these log time characteristics to obtain evaluation values ​​for these multiple computing nodes. Subsequently, the cloud management platform can identify computing nodes whose evaluation values ​​are greater than or equal to a preset threshold as computing nodes that have experienced an anomaly.

This application belongs to the field of battery pack technology, and particularly relates to a method for determining the aging value of a battery pack, an electronic device, and a readable storage medium. The method calculates the cycle aging value and the static aging value based on the battery pack's cycle aging parameters, a preset first parameter table, and static aging parameters, and then determines the battery pack's aging value based on these two values. This application considers both the aging effects of cyclic charging and discharging and the aging effects of static storage when determining the aging value, providing a comprehensive assessment of the factors affecting battery pack aging. This results in higher accuracy and reliability of the battery pack's aging value, thereby reducing the risk of battery pack use.

The application discloses a compressor service life prediction method and an electronic starting controller. The method comprises the following steps: obtaining working condition parameters of a compressor, performing time sequence unification and preprocessing on the working condition parameters, and generating quantitative indexes, wherein the quantitative indexes comprise long-time sequence sensitive indexes and short-time sequence fluctuation indexes; extracting physical characteristics and model automatic characteristics of the long-time sequence sensitive indexes and the short-time sequence fluctuation indexes, and obtaining a time sequence sample set; training and testing a pre-constructed fusion model by using the time sequence sample set, obtaining an evaluation result of the fusion model, and deploying based on the evaluation result. The long-period aging characteristics and high-frequency fluctuation characteristics in the compressor are processed in parallel by using the fusion model. Compared with the existing service life prediction mode, the overall precision is higher, the comprehensive consideration factors are more comprehensive, and the double paths can also adjust the hierarchical parameters to adjust the overall precision to retain stable characteristics and adapt to the dynamic working conditions of the compressor.

The present application relates to a kind of unmanned boundary scene risk assessment method, system and electronic equipment, belong to unmanned technical field, including steps: obtaining boundary scene information and vehicle speed, and determine scene type risk index, traffic density risk index and road type risk index, vehicle speed risk index, then comprehensive above each risk index obtains the risk index of unmanned boundary scene.The present application considers the risk index of quantitative characterization environment risk degree by considering boundary scene type, traffic density, road type and vehicle speed in the risk assessment method of unmanned boundary scene, comprehensively assesses potential driving risk in driving environment, provides theoretical basis for improving the safety of unmanned technology, compared with prior art, more comprehensive consideration factor, more reasonable evaluation method, provides safer, more accurate data support for further driving decision.

This invention discloses a method for predicting the time required for personnel to return to the working face after blasting mining in underground non-coal mines. It relates to the field of mine safety prediction technology. First, a negative feedback neural network model is established to correlate the personnel return time with four relevant indicators: roadway, ventilation duct, explosives, and gas. An initial model of the negative feedback neural network is configured. Sample data is collected from multiple channels. The weights and biases of the negative feedback neural network are globally optimized to obtain a prediction model for the personnel return time after blasting mining within the allowable error range. This prediction model is then trained and tested. Decision-makers use the prediction model to predict the return time and guide relevant personnel to return to the working face to continue operations. This invention achieves intelligent and accurate prediction of the personnel return time after blasting mining, effectively improving the accuracy and speed of the prediction results, making the predictions more scientific and effective, and enhancing their reliability.

The application provides a large and medium-sized oil and gas field geological reserve scale development prediction method based on multi-dimension, comprising the following steps: step 1, obtaining the residual geological resource quantity, exploration input, exploration well success rate and reserve data of the exploration unit; step 2, based on four-factor sample data, establishing a four-dimension prediction oil and gas geological reserve growth trend prediction basic equation; step 3, establishing a relationship equation between each coefficient in the four-dimension oil and gas geological reserve prediction model and the four factors; step 4, determining the four-dimension oil and gas geological reserve prediction model parameters of the prediction unit; step 5, using the four-dimension prediction oil and gas geological reserve growth trend method to predict the oil and gas geological reserve. The large and medium-sized oil and gas field geological reserve scale development prediction method based on multi-dimension can summarize the core influencing factors of reserve growth, establish a multi-control reserve growth mode, and realize accurate prediction of the oil and gas geological reserve growth trend.