234 results about "Critical threshold" patented technology

The invention provides an online monitoring analysis method and system for hydropower station pump equipment, and relates to the technical field of information, and the method comprises the steps: judging that a bolt group has a fatigue failure risk when a fatigue damage degree D value reaches a preset critical threshold value, and comprehensively evaluating the health state of the bolt group in combination with bolt pre-tightening force monitoring data and a loosening failure critical threshold value of the bolt group, obtaining a residual service life prediction result of the bolt group; and by analyzing the stress nephogram and the strain nephogram of the stress concentration area around the bolt hole and the residual service life prediction result, a topological optimization and parameter optimization method is adopted to carry out optimization design on the structural arrangement and the pre-tightening force application scheme of the bolt group, and optimal bolt group design parameters for prolonging the fatigue life are obtained. Accurate prediction and optimal design of the fatigue life of the bolt group are realized, and the operation reliability and safety of the pump are improved.

The invention relates to a PFC-PWM (Pulse Width Modulation) dual-mode control method for a high-voltage direct-current power supply with a wide output voltage range. The output current of the high-voltage direct-current power supply is detected in real time, the critical threshold value of the load impedance is mapped into the hysteresis interval of the output current, the theoretical boundary of mode switching is obtained, and the control mode is dynamically adjusted. In order to further guarantee smooth switching of the two control modes, hysteresis control logic is designed, and undisturbed smooth switching of the PFM mode and the PWM mode is achieved. Based on an energy equivalence principle and a frequency consistency constraint of a critical load SWL working point, a current threshold criterion and a parameter pre-synchronization strategy are constructed, seamless connection of a mode switching process in a frequency domain and an energy domain is ensured, a transient oscillation risk is remarkably reduced, and a guarantee is provided for continuous and stable output of a full-range load working condition.

The invention discloses a thunder early warning model optimization method based on a deep learning algorithm. The thunder early warning model optimization method comprises the steps that five risk early warning levels are divided according to the distance between a thunder occurrence position and a target point, namely six corresponding early warning levels are obtained; selecting lightning positioning, radar extrapolation results and atmospheric electric field instrument data around the target point from the sample data set; and risk grading preprocessing is carried out on the thunder early warning probabilities and critical thresholds of the three early warning factors. The method has the following advantages that the precision is high, analysis and modeling are carried out through comprehensive multi-source data, information related to thunder and lightning occurrence and development can be captured more comprehensively, and therefore the precision of early warning is improved. Data can be obtained and processed in real time, early warning information can be updated in time, and the requirement of a user for the real-time performance is met. Personalized customization: personalized risk grading and early warning setting can be carried out according to characteristics of different target point locations and user requirements.

The invention discloses a sleep mild wake-up method based on a user sleep curve, and belongs to the technical field of sleep monitoring, and the method comprises the steps: collecting a physiological signal of a user, generating a sleep stage curve by using a pre-trained sleep stage model, and carrying out the parallel analysis of heart rate variability and respiratory coordination to generate a mood index curve; overlapping and fusing the two curves to form a sleep-mood alignment feature set; in a preset wake-up time range, analyzing the sleep stage and psychological state of the user according to the feature set, and dynamically determining an optimal wake-up starting opportunity; when the clock arrives, an instruction is sent to the linkage alarm clock, and sound, light and touch multi-mode stimulation is triggered in sequence in a cooperative mode; in the wake-up process, the sleep depth of the user is continuously monitored, if it is detected that the depth recovery exceeds the critical threshold value, the stimulation intensity is adaptively adjusted until the depth falls back, it is ensured that the user naturally wakes up under the condition that the discomfort is the lowest, and intelligent mild wake-up is achieved.

The invention relates to a method and a system for detecting the sealing performance of a spherical underwater connector. The method comprises the following steps: injecting dry inert gas with the same working pressure into a sealing ring through a pressure measuring hole; 5-15-degree and 0.1-1Hz sinusoidal deflection motion is applied to the sphere, and the dynamic working condition that the underwater pipeline is impacted by ocean current for a long time is simulated through short-term limit periodic deflection motion; meanwhile, the pressure fluctuation amplitude delta P is recorded with the sampling frequency larger than or equal to 10 Hz, the first period delta P0 serves as the benchmark, delta Pn is measured regularly, and a delta P (t) curve is built; and determining a critical threshold value delta Pmax through an experiment, and calculating the duration of increasing the delta P0 to the delta Pmax according to the delta P (t) curve as the predicted service life. Through short-term limit dynamic loading and high-frequency monitoring, real-time state detection and long-term performance evaluation of the sealing ring are achieved, and the problem that sealing performance degradation under alternating stress cannot be predicted through traditional static detection is solved.

The invention relates to a habitat restoration-oriented sediment elution critical threshold evaluation method. The method comprises the following steps: introducing form classification including an exchange state, a carbonate binding state, an iron and manganese oxide binding state, an organic binding state and a residue state; a dynamic disturbance response mechanism is fused, and the elutable proportion is re-evaluated under the conditions of flow velocity, pH, oxidation reduction potential Eh and change; calculating a potential active flux index PAFI to quantify a real releasable part of the pollutants in the bottom mud; establishing pollution flux and biological response time sequence data; depicting ecological response sensitive inflection points by using nonlinear fitting; extracting an ecological function critical damage point EBCP as an ecological constraint upper limit of the elution threshold; constructing a habitat bearing factor set comprising a water body volume ratio, submerged vegetation density and benthic organism biomass; introducing a bearing feedback coefficient CFC, and dynamically correcting a pollutant critical flux value; and outputting a loadable elution flux interval for judging whether the ecological risk red line is touched or not.

The invention discloses a high geological disaster chain risk quantitative assessment method adapted to a high-cold and high-altitude environment, and relates to the technical field of geological disaster monitoring and prevention and control, and the method comprises the following steps: S100, building an irradiation and reflection combined observation layer, collecting the dynamic intensity of direct light and reflected light, generating an exposure time sequence fingerprint baseline, and obtaining an exposure time sequence fingerprint baseline; and determining a critical threshold value of the direct light intensity according to the base line. The method comprises the following steps: constructing an irradiation and reflection combined observation layer to extract light intensity characteristics, and generating an exposure time sequence fingerprint baseline; a trigger point boundary is extracted by adopting coherent phase decomposition and anti-fact playback, and a continuous track is reconstructed; the overexposure risk is predicted based on the time-frequency score, and spectrum gating and polarization rotation are linked to regulate and control the exposure rhythm; and reverse phase traction and phase conjugate intervention are implemented in a steady-state window, so that boundary information is maintained and the risk identification precision is improved.

The invention discloses a transformer iron core vibration monitoring and fault diagnosis method based on voiceprint characteristics, and belongs to the technical field of power equipment monitoring and fault diagnosis, and the method comprises the steps: obtaining voiceprint signals of each monitoring point about the vibration of an iron core in a preset transformer, and collecting the voiceprint characteristics according to the voiceprint signals of different monitoring points, analyzing the change rule of each voiceprint signal; according to the change rule, distinguishing the change of the voiceprint signal according to light change and moderate change, and obtaining the operation state of the preset transformer under the condition of light change; the operation state comprises current parameters of a preset transformer; and giving a monitoring period according to the light change, and presetting a critical threshold value related to the vibration frequency change of the voiceprint signal in different monitoring periods. According to the invention, by monitoring and analyzing the voiceprint signal and combining the technical means of multiple monitoring points, dynamic monitoring and enhanced learning, early fault early warning and accurate diagnosis of the operation state of the transformer are realized, and the method has relatively high practicability and innovativeness.

The invention discloses an unmanned aerial vehicle self-adaptive anti-interference control system and method, and relates to the technical field of unmanned aerial vehicle communication anti-interference control, and the method comprises the steps: carrying out the fusion of an unmanned aerial vehicle communication signal and an environment interference signal which are collected in real time, generating a time-varying density distribution matrix, carrying out the time-frequency domain feature extraction, and carrying out the time-frequency domain feature extraction; generating an environment interference intensity grade and a spectrum characteristic spectrum; inputting the environmental interference intensity grade and the spectrum characteristic spectrum into a chaos pre-judgment network, calculating a Lyapunov index based on a Lorentz chaos equation set, and generating a pre-judgment result and a strategy triggering instruction when the Lyapunov index exceeds a preset critical threshold value; and inputting a pre-judgment result and a strategy triggering instruction into the constructed meta-learning model, generating an anti-interference suppression strategy through multi-task cooperative reasoning, and performing quantization coding on the anti-interference suppression strategy to convert the anti-interference suppression strategy into a dynamic anti-interference instruction. According to the method, advanced perception and microsecond-level response to time-varying interference are realized through dynamic analysis of the chaos anticipation network.

The invention discloses a debris flow intelligent monitoring and early warning system. The system comprises a sensing layer which is responsible for deploying a multi-parameter sensor matrix, constructing a space-air-ground integrated monitoring system, collecting debris flow formation environment key parameters in real time, performing preliminary data preprocessing, generating an encrypted compressed data packet and sending the encrypted compressed data packet to a transmission layer; the transmission layer is used for receiving the encrypted and compressed data packet through a 5G and Beidou short message dual-channel redundancy transmission technology and forwarding the encrypted and compressed data packet to the analysis layer; and the analysis layer is responsible for constructing a hybrid early warning model driven by a physical mechanism and data, fusing decrypted and decompressed data, dynamically capturing a coupling relationship among rainfall, soil humidity and terrain gradient parameters, learning a multi-factor evolution mode of a historical debris flow event, generating a dynamically adjusted critical threshold curved surface, and performing multi-parameter joint early warning. The early warning information is output to the application layer; and the application layer is responsible for triggering graded early warning and driving multiple departments to cooperatively respond based on the early warning information.

The invention discloses a geotechnical engineering catastrophe dynamic real-time intelligent early warning method and system based on displacement monitoring, and the method comprises the steps: collecting the real-time displacement data and related parameters of a geotechnical engineering region through displacement monitoring equipment, and transmitting the real-time displacement data and related parameters to a bank slope stability evolution analysis platform to establish a multi-dimensional database; calling a fractured rock mass effective stress analysis model to calculate rock mass effective stress distribution characteristics, and inputting a multi-grid sliding surface stress calculation algorithm to obtain a potential sliding surface stress state and a critical threshold value; inputting the sliding surface stress parameters and the displacement data into a multi-field coupling analysis learning prediction model to obtain a displacement evolution trend and a catastrophe risk coefficient; and finally, real-time early warning information is generated and fed back through the catastrophe dynamic real-time intelligent early warning module. The method improves the real-time performance and accuracy of early warning, can effectively guarantee the safety of geotechnical engineering, and is suitable for catastrophe early warning scenes of geotechnical engineering such as bank slopes and foundation pits.

The invention provides an electronic current transformer operation data calculation system, and relates to the technical field of data processing, and the system comprises a data processing module which is used for carrying out the sliding time window scanning of an equipment dependence adjacency matrix, calculating the local reachable density under a dynamic neighbor topological distance through a local outlier factor algorithm, if it is detected that the harmonic total distortion rate gradient enters a statistical hypothesis test rejection domain or the winding temperature rise rate exceeds a material thermal aging critical threshold, extracting a multi-dimensional abnormal feature vector set; the risk quantitative index generation module is used for generating a risk quantitative index group from the multi-dimensional abnormal feature vector set through a pre-trained fault propagation probability model; and the processing instruction set generation module is used for establishing a multi-objective optimization function based on the risk quantification index group, performing non-dominated sorting evolution on a processing instruction chromosome by adopting an NSGA-II genetic algorithm, screening a dynamic optimization instruction sequence matched with a real-time load fluctuation coefficient through a Pareto frontier solution set, and generating a structured processing instruction set. The operation reliability of the power system is improved.

The invention, which relates to the technical field of power grid stability testing, discloses a stability boundary evaluation method for a network-following type and network-constructing type hybrid wind power plant, comprising the following steps: S1, collecting system data of the network-following type and network-constructing type hybrid wind power plant in real time, and calculating a composite index by using the collected data; s2, setting a critical threshold value, performing preliminary evaluation on the overall stability of the system by using the critical threshold value and the composite index, and if the preliminary evaluation result is stable, entering the step S3; s3, outputting fast, medium and slow scale index characteristics in real time by using a multi-time scale monitoring module, and calculating multi-scale indexes by using the fast, medium and slow scale index characteristics and a dynamic weight distribution mechanism; and S4, performing secondary judgment on the stability of the system by using a multi-scale index. According to the method, a collaborative decision-making mechanism based on a composite stability index and multi-time scale monitoring is adopted, namely, a double-layer criterion system is adopted, the dimension limitation of a single criterion is broken through, and the identification precision of the instability critical point of the hybrid wind power plant is remarkably improved.

The invention discloses an electric carrying equipment drive-by-wire chassis multi-domain collaborative anti-rollover control method, which comprises the following steps of: 1, constructing a rollover risk assessment and control strategy selection method, and calculating key parameters such as yaw velocity and lateral acceleration of a vehicle in real time; step 2, based on the rollover risk index model and the rollover critical threshold in the step 1, establishing a multi-domain collaborative rollover prevention control model, and performing collaborative scheduling on independent steering, hub motor differential steering and an ESP brake control system; step 3, based on the multi-domain collaborative anti-rollover control model in the step 2; and 4, based on the priority dynamic allocation and control strategy execution result in the step 3, the central control unit coordinates the complex interaction relationship among the subsystems by using a fuzzy logic control algorithm. The invention provides a reliable control strategy priority and use frequency method, and provides a more reliable anti-rollover solution for the electric carrying equipment under a complex dynamic working condition.

The invention discloses an F-number-based crystallizer slag entrapment fault judgment method, which comprises the following steps of: acquiring process data in real time in a slab continuous casting production process, and acquiring crystallizer data and submersed nozzle data of continuous casting production, thereby acquiring a crystallizer liquid level fluctuation function F number and a ton steel heat extraction R number in a crystallizer at corresponding moments; the slag entrapment critical threshold value is determined according to historical production data, then the ratio of the crystallizer liquid level fluctuation function F number to the ton steel heat extraction R number in the crystallizer is compared with the slag entrapment critical threshold value, whether the slab has the slag inclusion defect or not is judged accordingly, and judgment of the crystallizer slag entrapment fault is achieved. According to the method, real-time judgment and forecasting of the slag entrapment fault of the crystallizer in field production are achieved, and smooth operation of the continuous casting process and treatment before rolling guidance are facilitated.

The invention relates to the technical field of geotechnical engineering and geological disaster monitoring, in particular to a rockburst tendency dynamic discrimination method, and aims to solve the limitation caused by the fact that a dynamic evolution process is simplified into a quasi-static attribute in traditional rockburst tendency evaluation. According to the method, rockburst tendency is defined as a hidden state variable evolved along with time, a physical constraint state space model containing a hidden state kinetic equation and a multi-modal observation equation is constructed, continuous stress-strain and discrete acoustic emission data are fused, hidden state probability distribution is estimated in real time through online Bayesian inference, and the probability distribution of the hidden state is estimated in real time. And in combination with adaptive information weight updating and physical consistency constraint, predicting a future state trajectory, calculating a risk probability exceeding a critical threshold, and outputting a dynamic rockburst tendency level. According to the scheme, continuous, dynamic and prospective judgment of the rockburst tendency is realized, and the accuracy and reliability of early warning are improved.

The invention discloses a food safety monitoring system and a monitoring method thereof, and relates to the technical field of food safety monitoring, and the method comprises the following steps: building a sealing integrity real-time acoustic monitoring link, obtaining an acoustic signal generated by trace leakage through a phase locking algorithm, and building a pressure differential matrix based on the acoustic signal; and inputting the pressure differential matrix into a constructed gas chromatography digital twinborn model, deducing a dynamic evolution process of a carrier gas flow field, and extracting an instantaneous flow velocity critical threshold under the current working condition. According to the invention, real-time prediction and regulation of gas chromatography flow velocity disturbance are realized through acoustic monitoring and digital twinborn modeling, magnetic fluid adjustment and temperature compensation are combined, retention time drift is inhibited, and detection stability and robustness are improved. Meanwhile, a self-adaptive peak position tracking and dynamic window adjusting mechanism is introduced, a chromatographic peak position is accurately recognized, errors are fed back to a front-end sensing link, a closed-loop optimization process is constructed, and the accuracy and reliability of food additive detection are guaranteed.

The invention provides a simulated aging test method for an antifouling coating. The simulated aging test method comprises the following steps: S1, constructing a natural aging performance model of the antifouling coating; s2, constructing an accelerated aging model of the antifouling coating and acquiring an acceleration ratio N of natural aging to accelerated aging; s3, based on an accelerated aging model, acquiring an acceleration period Mi corresponding to a critical threshold R (temporary i) of a copper ion exudation rate, wherein the predicted life Lp (temporary i) of the antifouling coating is equal to Mi * N; and S4, based on a natural aging model, obtaining a predicted copper ion exudation rate R < pre-i > corresponding to the predicted life Lp < pre->, judging whether R < pre-i >-R < critical i > / R < critical i > is less than or equal to 10%, if so, outputting a near threshold R < critical i > and the predicted life Lp < pre-i >, and if not, adjusting the critical threshold of the copper ion exudation rate to R < critical i + 1 > and returning to the step S3. According to the method, the performance failure threshold value of the antifouling coating is taken as a boundary condition, the performance of the antifouling coating is rapidly evaluated, the service life of the antifouling coating is quantitatively predicted, the prediction result is subjected to cooperative cross validation through linkage double models, and the reliability is improved.

The invention discloses a nonparametric prediction method for the fatigue life of a steel-FRP composite bar, and belongs to the field of fatigue performance evaluation of civil engineering materials and structures, and the nonparametric prediction method comprises the following steps: initializing component material attributes, load conditions and initial states of the steel-FRP composite bar, the component materials comprising an inner core steel bar and an FRP layer; according to an equivalent stiffness distribution principle, fatigue stresses of the component materials are calculated respectively; according to the fatigue stress of the component material, theoretical fatigue life of the component material is obtained in combination with the S-N curve relation, and cyclic jumping is executed; calculating accumulated fatigue damage of the component materials respectively based on results after cyclic jumping; and circularly updating the dynamic stiffness and the equivalent cycle number of the component material until the accumulated fatigue damage of any component material reaches a critical threshold value, judging that the component material is invalid, and outputting the fatigue life. According to the method, the fatigue life of the steel-FRP composite rib can be efficiently and accurately predicted, and the test cost is remarkably reduced.

The invention discloses a digital project management method and system based on artificial intelligence, and relates to the technical field of project management, and the method comprises the steps: constructing a project digital portrait and multi-dimensional feature analysis framework, determining an analysis domain boundary of intelligent auditing, and automatically generating an intelligent analysis grid adaptive to multi-source heterogeneous data; and performing grid division on project progress data according to an analysis domain boundary, performing adaptive partition on a project grid through an unsupervised clustering algorithm, dynamically determining a critical threshold value of risk early warning based on a project digital portrait and intelligent analysis grid and comprehensive risk stress analysis, and outputting a risk early warning decision management conclusion containing the critical threshold value. According to the management system, an intelligent analysis grid adaptive to multi-source heterogeneous data is generated by constructing a project digital portrait and a multi-dimensional feature analysis framework, and a risk early warning critical threshold value is dynamically determined, so that real-time perception, accurate analysis and intelligent decision support of the full life cycle state of a project are realized.

The invention relates to the technical field of textile processing automation control, in particular to a plc-based textile processing progress automatic tracking system, which comprises a critical threshold determination module used for determining a critical compaction tension threshold according to a preset digital twinning model; the risk index calculation module is used for calculating a prospective risk potential energy index according to a preset weight coefficient; the state evaluation module is used for combining the critical compaction tension threshold value determined by the critical threshold value determination module and the mechanical tension obtained in real time to calculate a safety margin index; the self-adaptive control module is used for generating a compensated and adjusted temperature target value based on the safety margin index calculated by the state evaluation module, the prospective risk potential energy index calculated by the risk index calculation module and the received planned speed adjustment instruction; the defect that traditional static process parameters cannot adapt to material state changes is overcome, and the accuracy of the safety benchmark is remarkably improved.

The invention relates to the technical field of automatic control of a dirty oil treatment process, in particular to a dirty oil digital operation control circulation system based on a PLC (programmable logic controller), which comprises a data acquisition module used for acquiring the real-time density and the real-time dielectric constant of feeding dirty oil in real time as well as the set temperature and the actual measurement temperature of the system; the disturbance quantification module is used for calculating an instantaneous deviation degree; determining a disturbance intensity index in combination with the change rate of the instantaneous deviation degree; the control output module is used for calculating a predictive feed-forward component and a feedback component; generating a total control output; the execution decision module is used for triggering constraint reconstruction logic when the total control output exceeds the preset maximum output power of the actuator and the disturbance intensity index exceeds a preset critical threshold value; under other working conditions, the master control output is directly output; according to the invention, the response speed and stability of temperature control are obviously improved, temperature overshoot and undershoot are effectively inhibited, and the accuracy of the process is ensured.

The invention discloses a battery health state prediction and fault judgment method and system in the technical field of lithium battery management, and the method comprises the steps: collecting multi-dimensional original monitoring data in real time, and calculating derived characteristic parameters; constructing a state evolution trajectory of the battery system and calculating a local curvature at each state point; comparing the local curvature with a critical threshold value to obtain PID (Proportion Integration Differentiation); identifying all unstable state points, defining a set of unstable state points which are adjacent on state space coordinates and continuous on a time sequence as a DAC (Digital-to-Analog Converter), and calculating a chain scale; calculating a monitoring GER; and continuously monitoring a local instability condition, a cross-regional association condition and an energy triggering condition, and generating a corresponding early warning signal according to the satisfaction condition of the conditions. According to the method, a multi-level and layered physical criterion system composed of PDI, DAC to GER is constructed, and extremely high reliability close to zero false alarm is achieved while sufficient early warning advance is provided.

The invention discloses an aviation part quality optimization management method and system based on data analysis, and relates to the technical field of quality management, and the method comprises the steps: collecting an acoustic emission signal and an eddy current signal in the SLM forming process of an aviation part through a multi-modal sensor, and generating a multi-modal data set; key features are extracted from the multi-modal data set, multi-dimensional principal component feature vectors are obtained through fusion, a phase space is constructed, dynamic feature parameters are calculated, and a nonlinear dynamic model is constructed; inputting a multi-modal data set acquired in real time into the nonlinear dynamic model, predicting a dynamic characteristic parameter evolution trajectory, triggering quality abnormality early warning in combination with a critical threshold, and determining a quality risk level according to a supercritical threshold amplitude; and calling a pre-stored laser parameter regulation and control scheme according to the quality risk level, adjusting the mode and power of the laser beam, and monitoring the regulation and control effect in real time until the state of the molten pool is recovered to be stable, so that high-precision quality optimization and active control in the SLM forming process of the aviation parts are realized.

The invention discloses a grassland original ecology safety dynamic evaluation and early warning system, belongs to the field of grassland safety monitoring, and aims to solve the problem that an existing dynamic evaluation and early warning system is difficult to monitor and early warn the self-restoration capacity of a grassland. Through the vegetation monitoring unit, the soil monitoring unit, the livestock monitoring unit and the restoring force monitoring unit, and through a multi-scene simulation platform establishment module of the restoring force monitoring unit, interference scenes of drought and overload grazing are preset and visually displayed based on a vegetation productivity model; a dynamic ecological model is constructed in combination with vegetation, soil and livestock grazing data collected in real time, and deduction of the self-recovery state after grassland degradation is achieved; cA-Markov is utilized to dynamically simulate variation trends of vegetation productivity, grazing intensity and the like, critical threshold values of key indexes such as livestock carrying rate and the like are accurately identified, and the anti-interference capability of an ecological system is quantified; and the decision support module integrates the DPSIR model to generate a grazing forbidding strategy, and forms a whole-process restoration force evaluation chain.

The invention discloses an on-line prediction method for slab slag entrapment defects, which comprises the following steps: collecting process data in real time in a slab continuous casting production process, and obtaining submersed nozzle data and crystallizer data, thereby obtaining the maximum flow velocity of the liquid level in a crystallizer and the pulling heat per ton of steel at the corresponding moment; the slag entrapment critical threshold value is determined according to the occurrence frequency of the slab slag entrapment defect in historical production data, then the ratio of the maximum liquid level flow rate to the heat extraction amount per ton of steel is compared with the slag entrapment critical threshold value, and accordingly whether the slab has the slag entrapment defect or not is judged. According to the invention, real-time judgment and forecast of the slag entrapment fault of the crystallizer in field production can be realized, and smooth operation of a continuous casting process and guidance of treatment before rolling are facilitated.

The invention discloses an energy-saving optimization system for energy consumption characteristics of a carrier, and belongs to the field of automobiles, the energy-saving optimization system for the energy consumption characteristics of the carrier comprises an endurance detection processing module used for continuously monitoring a marked endurance mileage displayed by an instrument panel of an electric automobile, and when it is detected that the marked endurance mileage is lower than a preset critical threshold value, the endurance detection processing module is used for continuously monitoring the marked endurance mileage displayed by the instrument panel of the electric automobile; automatically activating an energy-saving optimization mode; the high-energy-consumption driving limiting module is used for recognizing high-energy-consumption driving behaviors in real time in the energy-saving optimization mode, and the high-energy-consumption driving behaviors comprise rapid acceleration, emergency braking and continuous high-speed driving; for high-energy-consumption driving behaviors, compared with the prior art, the method has the beneficial effects that the high-energy-consumption driving behaviors are limited by detecting the driving working condition of the electric vehicle, common scenes capable of decelerating and recycling energy are recognized, energy is effectively recycled, energy consumption is reduced, the difference between the marked endurance and the actual endurance is reduced, and the driving safety of the electric vehicle is improved. The use experience of drivers is improved.

The invention relates to the technical field of soil engineering model tests, and discloses a test system and method for simulating water and mud bursting of a subsea tunnel under the action of vibration, and the system comprises a multi-degree-of-freedom servo feedback vibration module which is used for providing a multidirectional vibration loading platform and feeding back a motor current signal in real time; the visual rock-soil water inrush simulation and multi-field loading module is mounted above the vibration table and is used for accommodating a sample and applying confining pressure and water pressure load; the hierarchical multi-source data acquisition module is communicated with the discharge port and the observation window, and is used for acquiring the outburst mixture and monitoring physical field data; and the intelligent control and risk assessment analysis module is connected with each module to regulate and control loading parameters and invert a catastrophe state based on feedback data. According to the method, a self-adaptive optimization loading mechanism based on the improved particle swarm optimization algorithm is constructed, so that collaborative progressive regulation and control of pore water pressure and vibration frequency parameters are realized, and a critical threshold point of water inrush and mud inrush catastrophe can be accurately captured.

The invention discloses a state optimization method of a solid-sealed polar pole for a primary and secondary fusion circuit breaker. The invention aims to solve the problem of circuit breaker misoperation caused by a traditional fixed threshold criterion. According to the method, the temperature gradient, the mechanical vibration frequency, the strain distribution and the electric field intensity data of the solid-sealed polar pole are collected in real time through a multi-physics field coupling monitoring network, and the comprehensive degradation index (CDI) is constructed by combining spatial-temporal feature extraction of partial discharge signals. The feature weight is dynamically optimized by adopting an improved particle swarm algorithm, the degradation trend is predicted through a bidirectional long-short-term memory network (LSTM), and a dynamic early warning threshold value (DWT) and a critical threshold value (CWT) are generated. Efficient data processing and model iterative optimization are realized, and meanwhile, mechanical strain, electric field distortion and a discharge evolution path are visualized through a three-dimensional digital twin model. Actual measurement shows that the fault misjudgment rate is reduced to be lower than 5%, the early warning response time is shortened to be within 10 seconds, and the local discharge capacity is greatly reduced through the silver nanowire self-healing technology.

The invention relates to the technical field of cable detection, in particular to a high-altitude anti-falling cable detection method and device, and the method comprises the steps: obtaining a coding waveform of a cable based on a radar array, and constructing an observation matrix; constructing a high-dimensional feature space based on the observation matrix and calculating embedded data; mapping the embedded data into the curved surface topological structure, calculating to obtain a critical threshold value, and obtaining a characteristic value of the critical threshold value; acquiring a change index of the characteristic value to establish a defect characteristic spectrum, and inputting the defect characteristic spectrum into a pulse neural network for time sequence coding to generate a defect probability cloud picture; extracting spatial features and time features in the defect probability cloud picture to form a defect type tensor; evolution processing is conducted on the defect type tensor, an evolution process is calculated based on a numerical solution to obtain a repair strategy matrix, and the repair strategy matrix comprises adjustment schemes of repair measures on different space points and time. Cable faults can be effectively prevented and handled in time, and potential safety hazards are reduced.