275 results about "Feedback regulation" patented technology

The present invention relates to the technical field of circuit control, and relates to a direct-current high-voltage generator regulation and control method and system based on intelligent feedback. A fuzzy control logic operation is performed on an output current flux value and an input voltage fluctuation factor of a load experimental object, so as to obtain the disturbance amplitude of a voltage fluctuation state for a current flux change trend; if a step-up transformer can execute a PID control technology by adjusting a transformation ratio, PID real-time adjustment parameters are calculated, and a crossover mutation operation of individuals is performed on the basis of a fitness function, so as to generate a first boost regulation and control scheme; and if the step-up transformer cannot execute the PID control technology by adjusting the transformation ratio, a calibrated state feedback controller is constructed on the basis of a state variable of the current step-up transformer and an adjustment signal output amplitude, so as to generate a second boost regulation and control scheme. The present invention achieves intelligent feedback regulation and control by independently analyzing components of direct-current high-voltage generators, improving the stability and accuracy of outputting high-voltage direct current.

The invention provides a sewage treatment carbon source intelligent adding method, which comprises the following steps: collecting multi-parameter feed-forward and feedback signals of water inlet and an anoxic tank, constructing a dynamic model containing feed-forward compensation, model prediction control and feedback compensation, calculating the theoretical adding amount of a carbon source, inputting a predicted value and feedback parameters into an LSTM network for correction, and optimizing a network structure by a genetic algorithm. The adding amount is controlled in a closed-loop mode through a variable frequency pump, the LSTM weight is updated on the basis that the error is larger than 5%, and finally a control strategy is optimized by using an NSGA-II algorithm and integrating carbon source consumption, effluent total nitrogen and energy consumption. The dynamic self-adaptive carbon source adding method is constructed by fusing feedforward perception, LSTM prediction, feedback regulation and multi-objective optimization, so that quick response and accurate control on water quality fluctuation are realized, the denitrification efficiency and the carbon source utilization rate are improved, and the method has excellent engineering adaptability and popularization value.

The invention discloses a server mainboard regional distributed liquid cooling heat exchange adjusting method, which comprises the following steps of: acquiring a temperature parameter set and a power consumption parameter set of each monitoring region in real time, and generating a thermal load parameter feature vector; identifying a high heat load core area set according to the heat load parameter feature vector; generating a liquid cooling branch flow distribution instruction set based on the position distribution of the high heat load core area set; executing the liquid cooling branch flow distribution instruction set, and obtaining an adjusted temperature feedback parameter set; according to the deviation degree of the temperature feedback parameter set and the target temperature interval, dynamic feedback adjustment is carried out by applying the heat conduction compensation coefficient and the fluid viscosity-temperature compensation coefficient, and an updated liquid cooling branch flow distribution instruction set is generated. The method has the following advantages and effects that by sensing the thermal load space distribution characteristics of all areas of the mainboard in real time, cooling flow distribution is dynamically adapted, real-time compensation of the fluid viscosity-temperature effect is integrated, local hot spots are eliminated, and the heat dissipation effect is improved.

The invention relates to the technical field of biomedical engineering, and discloses an acute respiratory distress syndrome mechanical ventilation adaptive adjustment control system, which comprises a data acquisition module, a signal preprocessing module, a physiological parameter calculation module, a prediction module, a decision and control logic module and the like. Wherein the prediction module predicts a dynamic lung compliance change trend by using a long short-term memory neural network model, and the decision and control logic module generates a ventilation parameter adjustment instruction according to a prediction result and a clinical safety rule. By adopting the technical scheme, the system overcomes the hysteresis of traditional feedback regulation, reduces the risk of breathing machine related lung injury, optimizes oxygenation and carbon dioxide removal efficiency, and provides an accurate, safe and individualized mechanical ventilation treatment scheme.

The invention discloses an aquaculture comprehensive guarantee method and system based on multi-modal data acquisition, and relates to the technical field of intelligent aquaculture, and the method comprises the steps: collecting aquaculture multi-modal data and an aquaculture image of an aquaculture region, carrying out the aquaculture feature capture of the aquaculture image, and outputting a visual feature group; inputting the visual feature group into a MobileNet lightweight model, and outputting a health state label and a confidence score; based on the health state label and the confidence score, outputting disease type data through a residual network structure optimized by transfer learning; constructing a graph neural network according to the disease type data, and generating a dynamic regulation and control scheme; and the control center executes the dynamic regulation and control scheme, carries out regulation and control effect detection and target comparison on the aquaculture area of the dynamic regulation and control scheme, judges whether the regulation and control effect is effective or not, and generates a feedback regulation instruction. According to the invention, through multi-modal data fusion and intelligent decision closed loop, dynamic and accurate guarantee of aquatic product health management is realized.

The invention discloses a new energy consumption multi-objective decision reasoning method and system based on a knowledge graph. The method comprises the following steps: completing data acquisition and preprocessing; extracting the relationship between the core entities and the entities to generate a structured triple, and further constructing an energy field knowledge graph; designing a loss function by taking economy and stability as optimization targets, training a GNN integrated with power grid topology perception in combination with historical scheduling data, and completing model construction; entity change and relation update in the new data are identified through an incremental learning method, and the energy field knowledge graph is automatically supplemented or corrected; according to a current scheduling demand, extracting a corresponding associated sub-graph from the updated knowledge graph, inputting the trained GNN model, and outputting a scheduling decision scheme; and accessing the scheduling decision scheme output by the GNN to a power grid digital twinborn simulation platform to complete analogue simulation and feedback regulation. And efficient and safe consumption of new energy in a dynamic environment is ensured.

The invention relates to the technical field of medical data processing and human-computer interaction, and particularly discloses a rehabilitation training feedback adjusting system based on a brain-computer interface and myoelectricity sensing. The system comprises a multi-modal physiological signal acquisition module, a signal fusion and intention decoding module, a rehabilitation state dynamic evaluation module, a self-adaptive feedback strategy generation module and a multi-channel feedback execution module. By synchronously collecting and fusing the electroencephalogram signals and the electromyographic signals to decode the motion intention, dynamically evaluate the rehabilitation state and predict the trend, a multi-sensory collaborative feedback strategy is adaptively generated, personalized and dynamic precise adjustment of rehabilitation training is achieved, and the training effect and the patient participation degree are improved. The system can autonomously switch between a reinforcement learning strategy and an auxiliary guiding strategy according to the real-time performance and the long-term trend of a patient, and dynamically optimize various feedback parameters.

The invention provides a biofeedback type electrical stimulation regulation and control device based on multi-mode physiological signal monitoring and a control method, the regulation and control device comprises an electronic component layer, a circuit layer, a man-machine interaction part and an adhesion layer, physiological signal acquisition and electrical stimulation regulation technologies are utilized, and a multi-source biosensor and an intelligent processing module are integrated, so that the regulation and control of the biofeedback type electrical stimulation regulation and control device is realized. Dynamic monitoring and real-time analysis of physiological signals are achieved, self-adaptive adjustment of personalized parameters is conducted based on signal characteristics, and a non-invasive biological feedback regulation and control system is formed. Through integrated multi-mode detection and intelligent regulation and control functions, a non-invasive and convenient regulation and control means is provided for wearers with abnormal nerve or physiological states; a portable and comfortable attached structure is adopted, the device can be worn at the Neiguan point for a long time, physiological signal continuous monitoring and electrical stimulation closed-loop feedback adjustment are achieved, and the personalized health management requirement of a user is met.

The invention discloses a real-time feedback method and system for a hydrogen peroxide production process, and relates to the technical field of intelligent feedback. The real-time feedback method and system for the hydrogen peroxide production process comprises the following steps: S1, preprocessing production process data and historical process data; s2, self-adaptive time sequence prediction feedback regulation and control analysis is carried out, the rotating speed of a servo motor is adjusted, and a sampling flow error value is corrected; s3, performing regulation and control characteristic attribution analysis, and performing mixed solution transfer; s4, performing process equipment self-diagnosis for efficient emulsion separation and multi-parameter mahalanobis distance discrimination; and S5, carrying out process parameter optimization and risk early warning. The problems that an existing system is difficult to achieve automatic continuous sampling and flow self-adaptive regulation and control in the hydrogen peroxide production process, and high-precision real-time monitoring and intelligent early warning are difficult to achieve are solved.

The invention discloses an artificial intelligence-based intelligent regulation and control method and system for a performance test of a valve actuating mechanism, and belongs to the technical field of industrial equipment automation control. The method comprises the following steps: firstly, collecting multi-source time sequence data in a testing process of a valve actuating mechanism, constructing a long short-term memory (LSTM) network model, and further predicting performance index degradation of the valve actuating mechanism; secondly, test parameter dynamic optimization of reinforcement learning RL is carried out, an optimal strategy is solved, and function updating is carried out; and finally, fault early warning and diagnosis are carried out, closed-loop feedback regulation and control are realized, and a closed-loop intelligent regulation and control framework for the performance test of the valve actuating mechanism is formed. The method is realized based on a data acquisition module, an edge calculation module, an intelligent decision module and an early warning diagnosis module. According to the invention, the fault early warning and diagnosis capability of the valve actuating mechanism can be improved, and the test regulation can be adjusted in real time according to the optimal strategy generated by the valve actuating mechanism, so that the adaptive test is realized.

The invention discloses a monitoring feedback system for an embankment compaction process under high liquid limit soil based on an intelligent sensor, and relates to the technical field of high liquid limit soil roadbed construction, and the monitoring feedback system comprises an intelligent sensor node buried in embankment filling soil, a data communication module connected with the node, and a feedback control terminal for receiving data; the intelligent sensor node is characterized in that the intelligent sensor node comprises a dynamic self-calibration anchoring mechanism which is composed of a deformable memory alloy net-shaped anchoring sleeve, and the sleeve is in a contraction state during initial embedding, expands into a multidirectional barb net-shaped structure and is embedded into a soil body when the temperature is increased to a preset phase change temperature range through rolling. According to the monitoring and feedback system for the compaction process of the embankment under the high liquid limit soil based on the intelligent sensor, the problem of data distortion caused by displacement and inclination of an embedded sensor is solved; and through intelligent linkage of an anchoring program and compaction parameter feedback, full-closed-loop control of displacement suppression, data fidelity and failure repair is formed, and the accuracy of compaction state evaluation and the reliability of feedback regulation and control are ensured.

The invention discloses a wearable emotion regulation system and method integrated with an artificial intelligence algorithm, and belongs to the technical field of intelligent wearing, system hardware comprises an Arduino microcontroller, a biosensor array, an audio output module, a man-machine interaction module and a communication module; software comprises a multi-mode emotion recognition model, a self-adaptive music recommendation engine and a self-healing logic controller, implementation steps include data acquisition and preprocessing, emotion state recognition, music interaction strategies and closed-loop feedback adjustment, the system is integrated to clothes, and the system has specific sensor layout and a safety mechanism. The method can be used in the fields of depression adjuvant therapy, motion monitoring and the like.

The invention discloses a method and a device for controlling the flow rate of pressurized air flow of a bed layer of a pressure swing adsorption device, and relates to the technical field of adsorption control. The method and the device for controlling the flow rate of the pressurized air flow of the bed layer of the pressure swing adsorption device comprise the following steps: S1, acquiring pressure difference state data in real time, and preprocessing the pressure difference state data; s2, slip sheet displacement balance is carried out on the differential pressure state data; s3, throttling opening response is conducted on the pressure difference state data; s4, setting a movement boundary of the slip sheet; and S5, the pressure difference state data, the sliding vane displacement balance result and the throttling opening response result are synthesized to execute buffer energy consumption evaluation. The problems that the throttling opening is fixed, the gas flux cannot be dynamically adjusted according to the actual working condition and the adjusting rigidity and the energy efficiency are reduced due to the fact that the front and back pressure difference change of the adsorption tower cannot be sensed in real time in the throttling process and a pressure difference driven feedback adjusting mechanism is lacked are solved.

The invention relates to the technical field of data analysis, and discloses an ecological cycle agriculture data acquisition and analysis method and system based on the Internet of Things. The method comprises the following steps: clustering sensor nodes through an LMCO algorithm to obtain a heterogeneous device sensing network; performing predictive data acquisition according to the sensing network and the RSSI signal of the unmanned aerial vehicle to obtain a space-time coupling data set; the cloud node performs causal mapping processing on the data set to obtain causal chain data; performing influence mining on the causal chain data by a cyclic effect propagation algorithm to obtain a propagation rule and a synergy parameter; and performing intelligent feedback regulation and control according to the synergy parameters and the acquisition strategy to obtain an optimized decision scheme. The problem that in the prior art, causal relationship analysis between ecological cycle agricultural links and an intelligent feedback regulation and control mechanism based on the cycle effect cannot be established is solved.

The invention discloses a robot intelligent management and control system for energy conservation and efficiency improvement of a cold end of an indirect cooling auxiliary unit, and belongs to the technical field of intelligent management and control, the robot intelligent management and control system comprises an initial evaluation module, a dynamic planning module and a feedback monitoring module, obtaining a scaling thickness distribution diagram and a scaling coverage rate, and generating a fin flow channel matrix model; the dynamic planning module is used for dividing virtual operation blocks, constructing a scaling planning path in combination with a cleaning index, carrying out rapid rough sweeping, carrying out cavitation early warning, and processing cavitation early warning through a hierarchical control architecture; and the feedback monitoring module is used for constructing a quality deviation set, screening out substandard virtual operation blocks, calculating and correcting priorities, sorting and grading, optimizing and correcting parameters by utilizing a genetic algorithm, and performing feedback regulation and control, so that the cold end heat exchange efficiency is improved to achieve an energy-saving target, a fin structure is protected, and the service life of equipment is prolonged.

The invention discloses a heat dissipation control method and device for a dustproof and waterproof charging pile and a medium, belongs to the technical field of charging pile heat dissipation, and aims to solve the problems that a fan heat dissipation mode of an existing waterproof and dustproof charging pile is single, power change adjustment is difficult to perform according to the actual temperature in the charging pile, and the charging cost is low. Energy waste is easily caused; and fan fault detection is difficult to realize. The method comprises the following steps: collecting module power data and module temperature data of a started charging pile; according to the module power data and the module temperature data, PID fuzzy control is conducted on the running state of the cooling fan, and a fan speed regulation control strategy is obtained; performing pulse width frequency conversion processing on a driving circuit of the cooling fan through a fan speed regulation control strategy to obtain a dynamic fan driving strategy; and through a dynamic fan driving strategy and based on the collected cooling fan airflow feedback data, feedback regulation control over the fan rotating speed of the cooling fan is completed, so that cooling control over the charging pile is achieved.

The invention relates to the technical field of intelligent control, in particular to an intelligent control system for efficient ozone generation, which comprises the following steps: a driving distribution module, a gas ratio regulation and control module, a state recognition module, a leakage judgment module and a feedback regulation module. According to the method, through linkage judgment of the real-time fluctuation trend of the temperature and humidity and the response priority sequence, dynamic sorting of parameters and timely recognition of dominant factors are achieved, and by combining dry and wet gas ratio regulation and control in an ozone reaction efficiency interval and grading analysis of a current curve and discharge stability characteristics in a discharge period, the ozone reaction efficiency is improved. Multi-condition risk judgment of a synchronous relation between ozone concentration and airflow change is introduced, screening of stable sections and progressive comparison of rhythm configuration are performed by applying multi-period concentration parameters and key operation parameters, and an automatic adjustment strategy based on multi-source working condition participation items is formed. A closed-loop control structure of multi-dimensional condition collaborative recognition, dynamic adjustment and real-time feedback circulation is constructed, and self-adaptive optimization of gas path, discharge and adjustment actions is achieved.

The application discloses an application method of a Transformer architecture based on a biological regulation mechanism, S1, original data is divided into basic units token, and then each discrete token is mapped into a continuous high-dimensional vector through a word embedding layer; S2, a prototype activation unit identifies an initial semantic core from the input embedded token, and scores an activation value based on the embedded vector of the token; S3, a multi-scale prototype aggregation module performs multi-scale pattern aggregation on the prototype candidate, and forms a prototype vector across tokens; S4, a regulation path diverter determines a main / auxiliary path distribution strategy of each token according to the context semantic weight and the prototype responsiveness of the token; S5, the main path performs a standard QKV calculation process; S6, a biological regulation loop module performs feedback regulation on the auxiliary path token by using the aggregated prototype vector in the S3 step; S7, two token expression streams generated by the main path and the auxiliary path are integrated, so that the final output of the Transformer layer is generated.

The invention provides an iron-making production ore blending process method based on multi-target intelligent optimization and dynamic feedback regulation. The method comprises the steps that ore components, price and production data are collected, a quality and cost prediction model is built, a multi-target optimization model is built with molten iron quality, cost, blast furnace smooth operation and environmental protection as targets, and a Pareto optimal ore blending scheme is solved through an evolutionary algorithm. And real-time data is monitored on line, model predictive control is triggered to automatically adjust ore blending when setting is deviated, a sintering-pelletizing-blast furnace collaborative simulation model is built to optimize cross-process parameters, solid waste classified recovery and resource utilization are synchronously implemented, and intelligent low-carbon ironmaking is achieved. The method can improve the quality of molten iron, reduce the production cost, improve the operation efficiency of a blast furnace, reduce pollutant discharge, realize resource utilization of solid wastes, and improve the comprehensive benefits and environmental protection level of ironmaking production.

The invention discloses an autistic child emotion regulation system integrating electroencephalogram feedback and art, and relates to the technical field of medical intelligent rehabilitation. An emotional state decoding module; a dynamic generation type art interaction guiding module; a closed-loop feedback regulation and control module; and an adaptive optimization module. According to the method, deep fusion of physiological signal monitoring and artistic emotion expression is realized, scientificity and effectiveness of emotion regulation intervention of the autistic children are remarkably improved, participation willingness and compliance of the children are effectively improved, personalized iteration and accurate regulation of the intervention process are realized, limitation of traditional single-mode intervention is broken through, and the method is suitable for popularization and application. Not only is the objectivity of emotion recognition and the pertinence of art guidance enhanced, but also an individualized rehabilitation path is formed through long-term memory map construction, and an intelligent, quantifiable and sustainable new emotion regulation normal form is provided for autistic children.

The invention relates to an output voltage control system for an intelligent annunciator, a driving system comprises a power supply module, a feedback regulation module and a driving module, the power supply module adopts a two-stage inversion structure, the power supply module comprises a boost converter and an inverter, the feedback regulation module is in communication connection with the boost converter and the inverter, and the driving module is in communication connection with the feedback regulation module. The power supply module is electrically connected with the driving module, the boost converter is electrically connected with the inverter, the driving module is electrically connected with the inverter, and the feedback regulation module adopts fuzzy PI control and is used for regulating the output voltage of the inverter. According to the method, the fuzzy control strategy is adopted, proportional-integral control is combined, the stability of traditional PI control and the adaptive capacity of fuzzy logic are combined, and the precision and robustness of voltage control can be remarkably improved.

The invention discloses an ammonia spraying real-time feedback regulation method based on an SCR (Selective Catalytic Reduction) denitration system, which comprises the following steps: dividing a cross section partition on an outlet flue of the SCR denitration system, and synchronously collecting a flue gas sample on the cross section partition; dynamically adjusting the flow of a cooling medium of a gas cold air coil pipe through a self-adaptive fuzzy PID control algorithm, and cooling and dehumidifying each path of collected flue gas sample to obtain a dry flue gas sample; the concentration of each path of dry flue gas sample is analyzed in real time, and the optimal ammonia spraying valve opening strategy of each cross section partition is predicted by combining the ammonia spraying amount of each path, the flue gas flow of the SCR denitration system, the unit load and the ammonia escape concentration and adopting a multivariable model prediction control algorithm; and according to the predicted optimal ammonia injection valve opening strategy of each cross section partition, the ammonia injection valves of the corresponding cross section partitions are adjusted, and real-time feedback adjustment of ammonia injection is achieved.

The invention provides an electroplating wastewater dosing feedback regulation method based on online water quality detection, and relates to the technical field of environmental chemical analysis and water quality monitoring, the electroplating wastewater dosing feedback regulation method comprises the following steps: collecting chromium ion concentration parameters, pH value parameters and oxidation-reduction potential parameters of electroplating wastewater, and combining a trained chromium ion reduction model to carry out real-time calculation and analysis; and dynamic correction and closed-loop feedback adjustment of the dosage are realized. Therefore, the accuracy and the response speed of dosing control can be remarkably improved, the problem of excessive dosing or insufficient dosing of the medicament is effectively reduced, and meanwhile, the stability and the standard-reaching rate of wastewater treatment are ensured, so that the economical efficiency and the environmental protection property of wastewater treatment are improved.

The invention discloses a tea mixing ratio automatic adjusting method and system and a medium, and the method comprises the steps: obtaining a visible light image and a near infrared spectrum image of a tea raw material, extracting color, texture, integrity, water content and phenol-ammonia ratio feature information through a transfer learning model, and generating raw material parameter information through a PLS regression model; generating target parameter information based on the proportioned tea information, calculating initial proportioning parameters in combination with a multi-target optimization algorithm, and mixing; in the mixing process, a stirring resistance value and a stirring image are collected in real time, the matching parameters are dynamically corrected according to feedback information, and dynamic matching of the stirring state and the target matching is achieved through circulating adjustment till mixing is completed. According to the method, through multi-mode spectral feature extraction and real-time stirring feedback regulation and control, the problem of mismatching in the static setting and dynamic mixing process of matching parameters is solved, and the mixing uniformity and the quality stability are improved.

The invention relates to the technical field of air purification, in particular to an intelligent regulation and control method based on air purification, which comprises the steps of multi-source data acquisition, preprocessing, multi-modal fusion analysis, dynamic response optimization, reinforcement learning adaptive optimization, environment adaptation judgment and the like. Multi-dimensional real-time information is acquired through a distributed sensor array, a deep neural network and a feedback regulation module are utilized to generate a precise regulation and control instruction, and adaptive optimization is realized in combination with a reinforcement learning framework. The intelligent level and the purification efficiency of the air purification system can be remarkably improved, and the efficient regulation and control requirement in a complex scene can be met.

The invention discloses a plate forming defect risk perception and blank holder force self-feedback regulation and control system and method, relates to the technical field of plate forming, and aims to solve the technical problems that in the prior art, integral blank holder force regulation and control cannot restrain the defects of wrinkling, thinning and cracking caused by local material flow unbalance in real time. The regulation and control system comprises a support; the male die is mounted on the bracket; the hydraulic devices are connected with the bracket; the blank holder is divided into a plurality of blank pressing sections, each blank pressing section is connected with the movable end of one hydraulic device, and the plate is arranged on the blank holder; the female die is mounted on the bracket in a sliding manner, is arranged above the plate and the male die, can move towards the male die, compresses the outer edge of the plate together with the blank holder, and forms the plate under the combined action of the female die and the male die; according to the plate forming defect risk perception and blank holder force self-feedback regulation and control system, the blank holder force is independently regulated and controlled in a partitioned mode, and the technical bottlenecks that due to response lag of a traditional blank holder force regulation mode, defect control is difficult, and the forming quality is unstable are thoroughly broken through.

The application provides an aero-engine high-altitude test bed intake and exhaust system, and belongs to the technical field of intake and exhaust systems, and comprises the following steps: obtaining a flowchart of preset test conditions and an intake and exhaust system, and selecting a suitable test cabin as a first test cabin based on the preset test conditions; performing data analysis based on the preset test conditions to obtain a target working condition, and calculating the operation of each valve based on the target working condition; monitoring the actual operation working condition of the test cabin in real time, and obtaining feedback information based on the real-time detection result; inputting the feedback information into a feedback-regulation scheme mapping table, determining different regulation schemes corresponding to different feedback information, and adjusting the operation working condition based on the regulation scheme. The test cabin provides different temperature and flow rate intake conditions for the engine or engine components in the test cabin, so that different test working conditions are simulated, and the simulation demand is further met.

The invention discloses a regulation and control system and a regulation and control method, the regulation and control system acts on a rock mass sample, the regulation and control system comprises a stress loading mechanism, a tunneling mechanism, an acoustic emission mechanism and a data acquisition and control mechanism, the stress loading mechanism comprises a first power assembly and a pressure head assembly, and the pressure head assembly is connected with the first power assembly; the pressure head assembly is used for applying stress to the surface of the rock mass sample along three mutually orthogonal directions of the rock mass sample under the driving of the first power assembly. And the data acquisition and control mechanism is respectively connected with the stress loading mechanism, the tunneling mechanism and the acoustic emission mechanism. The tunneling mechanism and the acoustic emission mechanism are arranged on the pressure head assembly, the interior of a rock mass sample is tunneled while pressure is applied, acoustic emission data of internal damage are collected and calculated in real time, the data collection and control mechanism analyzes the acoustic emission data, the tunneling speed of the tunneling mechanism is dynamically adjusted, and the acoustic emission data of the internal damage are acquired and calculated in real time. Feedback adjustment of the tunneling speed is achieved, and meanwhile the stability of the rock mass sample in the tunneling process is improved.

The invention relates to the technical field of laser control, and discloses an excimer laser energy detection and feedback regulation system, which comprises an energy acquisition module configured to be used for receiving part of light beams of an excimer laser and converting optical signals into analog current signals. For a strong electromagnetic interference (EMI) environment generated by high-voltage discharge of an excimer laser, nanosecond transient pulses are locked as quasi-direct current signals through a peak holding circuit, and the bandwidth requirement is reduced; the optical fiber transmission module is used for realizing electrical full isolation and cutting off common-mode interference; a truncation mean filtering algorithm is introduced at a source end, and discharge peak noise is accurately eliminated. Compared with the traditional technical scheme of directly collecting only depending on a V / F chip and having no MCU digital processing, the measurement error under the severe working condition is reduced to be within + / -3% from + / -8%, and the detection precision and the anti-interference capability are remarkably improved.

The invention provides a resin composition for a halogen-free flame-retardant cable sheath and a preparation method of the resin composition, and belongs to the technical field of polymer composites for cables, the preparation method comprises the following steps: S1, raw material grading pretreatment and core parameter accurate determination; s2, gradient pre-dispersion and uniformity feedback regulation and control; s3, step-by-step grafting compatibility and grafting rate feedback regulation and control; s4, carrying out dynamic flame-retardant regulation and control melt blending; and S5, gradient cooling granulation and finished product performance verification. In two key links of gradient pre-dispersion and dynamic melt blending, an improved fruit fly optimization algorithm-adaptive fuzzy neural network algorithm and an improved sparrow search algorithm-kernel extreme learning machine algorithm are respectively embedded, and the two algorithms are bidirectionally interactively fused, so that the problems that traditional process parameters are set according to experience and the performance fluctuation is large are solved, and the dynamic melt blending method is suitable for industrial production. The synergistic improvement of the uniformity, the flame retardant property and the mechanical property of the resin composition is realized.