623 results about "Adaptive matching" patented technology

The invention discloses a charge and discharge controllable system and method for a decommissioned battery, and relates to the technical field of intelligent charge control. By collecting the capacity fading rate, the internal resistance value, the cycle index and the environment temperature data of the decommissioned battery in real time, a health degree parameter is calculated by adopting a nonlinear coupling algorithm; and the future health degree evolution trend is predicted in combination with the LSTM neural network. And dynamically generating a grading label according to a preset scene threshold matrix, and matching the charging demand thermodynamic diagram with the battery grading label through a dynamic scheduling algorithm to realize intelligent distribution of charging and discharging power. And introducing a photovoltaic-battery-power grid cooperative power supply model, predicting and dynamically adjusting the power supply proportion based on the environment temperature and the photovoltaic output, and deploying to a target scene. And through a dynamic health degree evaluation and scene adaptive matching mechanism, the utilization rate of the retired battery is improved, the deployment cost of charging facilities is reduced, and the power supply reliability under multiple scenes is remarkably improved.

The invention relates to the technical field of sensors, and discloses a temperature compensation method based on a silicon resonance pressure sensor. According to the method, real-time temperature drift data is collected through a sliding time window, and a temperature sensitive area and a temperature non-sensitive area are accurately identified; establishing a coupling model of zero drift and full scale drift in the temperature sensitive area; then, piecewise linearization processing is conducted on the coupling model through a dynamic weight distribution strategy, and high-precision temperature compensation parameters are generated; and finally, according to the environment temperature value detected in real time, automatically calling the temperature compensation parameter of the corresponding segment, and carrying out real-time dynamic correction on the original output value of the sensor. According to the method, through region identification and model coupling of temperature drift characteristics, fine division and adaptive matching of a compensation strategy are realized, and the measurement precision and long-term stability of the silicon resonance pressure sensor in a wide temperature range are improved.

The invention relates to a secure communication method for an edge node and terminal equipment based on dynamic key negotiation. The method comprises the following steps: the terminal equipment sends an initial signal when initiating a communication request; the edge node generates a scenarized first key negotiation parameter and feeds back the scenarized first key negotiation parameter after passing dual identity and state verification; after the terminal device decrypts the parameter, a dynamic entropy value is collected by combining a network adaptive sensor, and a second key negotiation parameter associated with the first parameter feature is generated; the two parties calculate session keys based on an ECDH algorithm, and the consistency of the keys is ensured through dynamic entropy verification and Hash comparison; and after the key negotiation succeeds, entering a hierarchical encrypted data transmission stage, and dynamically updating a session key based on a multi-dimensional trigger mechanism. According to the method, through hardware credibility verification, a scenarized key strategy, dynamic entropy enhancement and national secret algorithm adaptation, dynamic management of keys and adaptive matching of terminal resources are achieved, attacks such as equipment counterfeiting and parameter tampering are effectively resisted, and the high-security and compliance requirements in the fields of industrial control and the like are met.

The invention relates to an intelligent interview scoring system capable of explaining decisions based on a large language model, and the system comprises a multi-mode resume analysis and feature coding unit, a resume feature adaptive matching unit, an interactive scoring and knowledge enhancement unit, and an answer quality evaluation unit. Text, image and audio features are extracted through a cross-modal attention mechanism of a multi-modal large language model, resume features are encoded into dynamic word vectors, and entity-level feature vectors are extracted; the post description text is encoded into a demand feature vector by a resume feature adaptive matching unit; calculating semantic similarity between the resume entity feature vector and the demand feature vector; the interactive scoring and knowledge enhancement unit dynamically retrieves knowledge fragments to generate a preliminary evaluation report containing a scoring basis; and the answer quality evaluation unit fuses the information density, the fluency and the integrating degree to generate a final score. And the whole-process intelligence from demand analysis to final decision making is realized.

The invention provides a meteorological-power collaborative prediction method and device, and belongs to the field of new energy power systems. The method provided by the invention comprises the steps of determining condition parameters of a prediction task and a prediction site, wherein the condition parameters at least comprise multi-source meteorological observation data and new energy station operation data; according to the multi-source meteorological observation data and a pre-trained meteorological prediction large model, generating a future time sequence meteorological prediction result of the prediction site in a future time period; according to the time scale of the prediction task and historical weather conditions, adaptively matching a corresponding parameter set of the pre-trained power prediction large model; and inputting the future time sequence weather prediction result into the large power prediction model to generate a new energy power prediction result of the new energy station. According to the meteorological-power collaborative prediction method and device provided by the invention, the technical problems that the collaboration of meteorological prediction and power prediction in a new energy power system is insufficient, the precision is limited, and a complex scene is difficult to adapt can be solved.

The invention relates to the field of computer vision, in particular to an image feature recognition method based on computer vision, which comprises the following steps of: preprocessing an input image, removing image noise and correcting image gray deviation to obtain a preprocessed image; performing multi-scale feature extraction on the preprocessed image, obtaining image local texture features and global contour features under different scales, performing dynamic weight fusion, calculating dynamic fusion weight based on a feature response value and a local complexity factor, and performing dimension reduction processing on fusion features through a principal component analysis algorithm to obtain a target feature set; and matching the reference features, calculating the Euclidean distance between the target feature set and the reference features, determining an adaptive matching threshold, judging a matching result, and completing image feature recognition. According to the method, through adaptive filtering and gray level equalization preprocessing, Gaussian pyramid multi-scale feature extraction and dynamic fusion and adaptive threshold matching, feature extraction accuracy, characterization capability and recognition robustness are improved.

The invention discloses a horizontal five-axis turning and milling composite center with a self-adaptive matching system. The horizontal five-axis turning and milling composite center comprises a tool turret, a boring tool mechanism, a tool changing area and a control system. A radio frequency identification reading device is arranged in a tool changing area of the tool turret and the boring tool mechanism, and an RFID chip tag is attached to the outer surface of each tool and used for storing tool serial numbers, specifications and length compensation parameters. And the control system realizes self-adaptive matching and sequential error correction of the cutter and the working procedure through radio frequency identification. The tool changing area is provided with a visual monitoring assembly which comprises a plurality of sets of industrial cameras, an adjustable light source and a structured light stripe illumination module and is used for detecting the clamping posture, the abrasion state and the type of a tool. The system has self-learning and digital twinning simulation functions, and can simulate the motion trail of the cutter and predict the interference risk before the cutter is changed. According to the method, integrated control over automatic tool recognition, posture correction, abrasion detection and path prediction is achieved, and the tool changing precision, the operation stability and the automation level of the horizontal five-axis turning and milling composite center are remarkably improved.

The invention relates to the technical field of image processing, in particular to a communication image filtering and denoising method, which comprises the following steps of: calculating a phase consistency numerical value of each pixel point in a communication image based on the input communication image, selecting an area as a structural point according to the phase consistency numerical value, and extracting a local direction attribute of the structural point, and generating a local structure feature map. According to the method, the anisotropic wavelet basis parameters are dynamically generated based on the local structure feature map, so that the wavelet function is adaptively matched with the image region characteristics in shape, direction and scale, the high-frequency detail retention capability is improved, and the problem of frequency band aliasing of a fixed wavelet basis in a non-uniform noise scene is solved. A dynamic adjustment threshold matrix is constructed in combination with local structure features and wavelet coefficient distribution, noise and effective signals are distinguished through signal saliency evaluation, wavelet coefficient amplitude is adjusted in a targeted manner, and excessive smoothness is avoided while noise is suppressed.

The invention relates to the technical field of tunnel and underground engineering, in particular to a method for dynamically calculating the jacking resistance of a jacking pipe based on mud real-time sensing data driving. Comprising the following steps: S1, constructing a pipe-slurry-soil interface friction force database; s2, calculating the real-time detected jacking resistance of the mud; s3, optimizing a friction resistance database based on data driving; and S4, the oil cylinder thrust is calculated in real time based on the jacking resistance. According to the method, the pipe-slurry-soil contact state is directly judged by detecting the thickness and distribution of resistance reduction slurry in real time, and real-time, accurate and unitized calculation of the jacking resistance is realized by calling a dynamically optimized interface friction database; a machine learning technology is utilized, so that database parameters can be self-learned and self-adaptively corrected according to field measured data, and the long-term accuracy of a calculation model is ensured; based on resistance distribution calculated and predicted in real time, an intelligent control system is constructed, and self-adaptive matching and regulation of thrust of the pushing oil cylinder and the hinged oil cylinder are achieved.

The invention discloses a computer microcontroller low-power-consumption control method based on an RISC-V framework, and relates to the field of microcontrollers. According to the computer microcontroller low-power-consumption control method based on the RISC-V architecture, a core state cycle monitoring module, a sleep duration prediction unit, a dynamic voltage regulation unit, a multi-stage threshold cycle judgment module, a wake-up interference filtering unit and a closed-loop execution regulation module are configured. According to the computer microcontroller low-power-consumption control method based on the RISC-V architecture, a sleep duration prediction unit calculates a predicted sleep duration based on historical task data, a dynamic voltage regulation unit adaptively matches a core sleep voltage according to the predicted duration, hierarchical verification is performed on a core operation parameter through a multi-level threshold cycle judgment module, and the core operation parameter is subjected to low-power-consumption control based on the RISC-V architecture. And the wake-up interference filtering unit performs progressive filtering on an external wake-up signal, and controls the core to sleep / wake up in combination with the closed-loop execution adjusting module.

The invention provides an energy storage electrical intelligent system of a self-adaptive control strategy, and relates to the technical field of electric power grids. According to the invention, multi-source operation data of a power grid, a load, new energy and an energy storage system are collected in real time through the sensing layer, the decision-making layer realizes accurate synchronization and updating of the system operation state by using a digital twin model, and distributed optimization of swarm intelligence and extreme scene testing of confrontation deduction are fused. Dynamically generating and screening out an optimal control instruction which shows robustness under various working conditions; and finally, the execution layer accurately controls the energy storage system to form a sensing-decision-execution-feedback adaptive closed loop, so that the control strategy of the energy storage system can be automatically and accurately adjusted along with the fluctuation of the running state of the power grid, the stiffness defect of a fixed strategy is overcome, the capacity of the power grid for dealing with source load fluctuation and sudden faults is enhanced, and the energy storage system is ensured to be more stable. The self-adaptive matching of the energy storage system control strategy and the power grid operation state is realized, and the dynamic stability and the operation safety of the power grid are improved.

The invention provides a semantic feature adaptive matching and error correction method and system for construction project cost declaration. The method comprises the following steps: constructing a parameterization + tense enterprise exclusive material knowledge graph; constructing a multi-level title cost ID tree; the map fusion cost tree is matched with semantics; and performing expert auditing and adaptive optimization driven by multi-dimensional confidence. According to the method, the parameterized knowledge graph and the super vector fusing the hierarchy and the graph context are constructed, so that the core parameters of the material can be deeply understood, non-standard filling and reporting are accurately matched, and the accuracy of automatic matching is greatly improved; according to the method, a multi-dimensional confidence evaluation model is designed, ambiguous points in a matching result can be actively recognized, experts are guided through a comparative problem, and past time-consuming whole reading is converted into efficient parameter-level fixed-point error correction; every error correction can be captured by the system through a man-machine closed-loop mechanism, written back to the knowledge graph in the form of incremental knowledge and used as a training data optimization model.

The invention aims to disclose an image anaphora segmentation method based on multilevel feature fusion and dual-channel information enhancement. The method comprises the following steps: obtaining and extracting visual features to be segmented and language features in an anaphora text; self-adaptively matching visual features of different levels by using language features, and performing spectrum enhancement on the visual features by using Fourier transform; through cross-modal fusion of visual and language features of different hierarchies, multi-level visual features with language perception are constructed; constructing visual language fusion features by using dual-channel information enhancement; and generating a segmentation mask. According to the method, the advantages of a large model, adaptive dynamic matching and feature enhancement are fused, automatic generation of the segmentation mask is realized, the efficiency and accuracy are improved, and the method has wide application and popularization values.

The invention discloses an intelligent authentication system based on a gateway, and relates to the technical field of intelligent authentication. The system comprises a strategy integration module, an intelligent analysis module, a strategy decision module, a dynamic signature module and an authentication execution module. According to the invention, heterogeneous authentication strategies are uniformly managed through the strategy integration module, the intelligent analysis module carries out real-time risk scoring on access requests, the strategy decision module dynamically generates authentication strategy identifiers according to the risk scoring, the client is driven to generate signatures containing algorithm identifiers through the dynamic signature module, and finally the authentication execution module verifies the signatures and routes according to the signatures. According to the method, the adaptive matching of the authentication logic and the risk context is realized, and the problems that the adaptive capacity of an authentication mechanism in a dynamic risk environment is insufficient and intelligent and gradient authentication response cannot be realized in the prior art are solved.

The invention discloses an oil chromatography data trend classification method and system based on depth feature enhancement and an attention mechanism, and the method comprises the steps: carrying out numeralization conversion, deletion detection and grouping trend calculation on oil chromatography original gas component data, and generating a basic feature vector; executing multi-scale sliding statistics, change rate and subsequence feature enhancement, and calculating comprehensive similarity and attention weight based on a template library to generate a weighted similarity vector; splicing the enhanced feature and the weighted similarity vector into a time sequence input sequence, and outputting an oil chromatogram trend classification result after attention expansion and long and short term memory network processing. According to the method, structured processing and basic trend extraction of data are realized, adaptive matching and weighted aggregation of historical operation modes are realized, and a multi-dimensional dependency relationship and time sequence dynamic change are captured, so that accurate classification of oil chromatogram trends is realized.

The invention relates to the technical field of data processing, in particular to an intelligent rehabilitation nursing management system based on data analysis, and the system comprises a motion state collection module which collects original electromyographic signals and joint torque and angle values, a physiological feature analysis module which extracts a frequency domain median frequency point sequence and constructs an electromyographic fatigue gradient; the motion compensation calculation module calculates the tail end driving torque and generates a motion compensation coefficient, and the self-adaptive control execution module calculates the resistance increment based on the fatigue gradient and calculates virtual wall parameters to construct a control strategy when the compensation coefficient exceeds the standard. According to the method, the fatigue gradient is constructed in combination with frequency domain analysis, the compensation ratio is calculated by utilizing the dynamic model, the resistance is adjusted according to the compensation coefficient and the fatigue state, and the virtual wall constraint is constructed, so that quantitative analysis of the physiological features and the motion state is realized, and the optimal strategy is adaptively matched under the condition that the safety is ensured; and the training intelligence level and accuracy are improved.

The invention provides an efficient task scheduling strategy recommendation method and system for a distributed network surveying and mapping scene, and relates to the crossing field of network space surveying and mapping, network security, distributed scheduling and intelligent optimization recommendation algorithms. Analyzing the task request to extract a task type, a task target network address set, a regional distribution feature, a task execution timeliness requirement and calculation and bandwidth resources required by estimation, and forming a task feature vector; and performing state acquisition and capability evaluation on the distributed surveying and mapping nodes based on task resource requirements defined by the task feature vectors, and performing normalization processing to form node capability vectors. Through multi-source information vectorization and closed-loop optimization, adaptive matching of task scheduling and resource dynamics is realized.

The invention discloses a cement-based telegraph pole defect detection method based on big data analysis, and the method comprises the steps: arranging a distributed optical fiber sensing network to collect the stress data of a target telegraph pole, and obtaining a stress feature set through denoising and feature extraction; constructing a structure damage feature library based on the attributes of the telegraph pole and the material characteristics, and screening key damage features through a self-adaptive matching threshold value; and in combination with concrete carbonization characteristics and steel bar corrosion characteristics, carrying out weighted analysis by adopting a multi-modal fusion model and an attention mechanism, and outputting an optimally matched defect detection result. According to the method, through multi-source data fusion and dynamic threshold optimization, the recognition precision of typical damages such as cracks and spalling is remarkably improved, a graded early warning and maintenance scheme can be automatically generated according to a detection result, and intelligent monitoring and operation and maintenance of the health state of the telegraph pole are achieved; the method has the characteristics of high detection accuracy and strong environmental adaptability, and is suitable for long-term safety monitoring of the electric power infrastructure.

The invention discloses a method and a system for intelligently pushing a live program by a set top box, and relates to the technical field of intelligent pushing. The method comprises the following steps: registering a remote controller input event capturer on an operating system layer to collect and classify user operation events, extracting behavior characteristic data and carrying out standardized preprocessing; a behavior state data set and a situation data set are formed, a remote control behavior anxiety index anx is constructed through the behavior state data set, behavior state evaluation is carried out, the regularity of user operation is evaluated, the influence of the current environment of a user on an operation behavior is analyzed in combination with a situation adaptation matching index cfs, and then an intervention activation comprehensive index sis is calculated. The method is used for judging whether pushing of the pacifying type live program is triggered or not. According to historical statistics, a double intervention threshold is set, a dynamic response type push decision is carried out on the emotional state of the user, identification of potential anxiety behaviors and content intervention control are effectively achieved, and user experience and content adaptability are improved.

The invention discloses a numerical simulation method for aero-engine combustion chamber spray combustion, and belongs to the technical field of aero-engine combustion chamber spray combustion simulation. According to the numerical simulation method, external circulation is carried out based on a self-adaptive matching iteration solution control mechanism of liquid drops and flow field motion scales; the method comprises the following steps: starting from the definition of a mixed fraction and component transportation, deducing a mixed fraction source item and a component transportation equation source item under different definitions, establishing a coupling relationship between fuel evaporation and combustion, and having a flow-spray-combustion high-fidelity coupling simulation capability; an RCCE method is introduced to establish a flame surface database, a large number of components in a chemical reaction mechanism are mapped to a limited number of constraint variables for dimensionality reduction solution, a constraint equilibrium state is obtained according to a maximum entropy principle, and finally the concentration of each component is reconstructed by using constraint potential energy. The generation efficiency of the complex fuel chemical reaction database is improved while the key chemical thermodynamic coupling precision is ensured.

The invention discloses a power grid panorama self-driving control method, system, equipment and medium based on source-grid-load-storage cooperation, and belongs to the technical field of power grid intelligent control, and the method comprises the steps: collecting multi-source heterogeneous data of four sides of a source, a grid, a load and a storage, carrying out the preprocessing, carrying out the calculation through a state estimation model, and generating a power grid panorama real-time operation situation; prediction is carried out through a multi-resource adaptive evaluation model, the combined adjustment capability of the source network load storage is obtained, and the adjustable reserve capacity is evaluated in combination with the operation situation; a first algorithm is adopted to calculate and output risk indexes, then a second algorithm is adopted to calculate to obtain operation risk values, and the operation risk values are graded; and solving through an optimization model with risk mitigation as a target to generate an adaptive scheduling instruction. According to the invention, through a dynamic scheduling mechanism of source-grid-load-storage coordination, in combination with risk level self-adaptive matching scheduling resources, the defects that a traditional scheduling mode lacks collaboration and risks are passively handled are overcome, and the operation safety and economy of a power grid are improved.

The embodiment of the invention provides a form generation method, device and system based on AI enhancement and a storage medium, and relates to the technical field of form generation. The method comprises the steps of generating form structure metadata according to a natural language processing technology, a reinforcement learning algorithm and / or a type security verification rule; according to the form structure metadata, the equipment sensor data and / or the user visual behavior data, the rendering priority of each field is determined; and generating a target form according to the rendering priority and the equipment parameters. By generating the form structure metadata which is reasonable in structure and conforms to the type safety specification, the automation degree and reliability of form design are improved. By fusing form structure metadata, device sensor data and user visual behavior data, a rendering strategy can reflect device characteristics and user attention distribution. The target form is generated by combining the rendering priority with the equipment parameters, so that the form layout is adaptively matched with the terminal equipment, and the loading performance and the interaction experience of the form are improved.

The invention belongs to the technical field of computational fluid mechanics and artificial intelligence crossing, and discloses an anisotropic turbulence modeling method and system based on scale adaptive physical convolution. Comprising a physical anchor point expansion layer used for improving a physical constraint coverage range, a physical information neural network basic full-connection layer, a convolution layer comprising a feature scale convolution kernel generation mechanism and a loss calculation process. According to the method, a physical anchor point expansion layer is introduced, additional physical calculation points are generated by setting a physical point expansion coefficient on the basis of training data points of a traditional physical information neural network, and the additional physical calculation points are combined with an original data point set to form a physical constraint calculation point set which is wider in coverage range and more reasonable in distribution; a convolution kernel size self-adaptive generation mechanism based on a turbulence characteristic scale is utilized, the turbulence characteristic scale is obtained through vorticity field analysis and energy spectrum calculation, the size of a convolution kernel is determined according to the turbulence characteristic scale, convolution operation can be matched with multi-scale physical characteristics of turbulence in a self-adaptive mode, and pertinence is higher.

The invention discloses a video data transmission method and system based on an FPGA, and relates to data transmission. The method comprises the steps that a video data stream is acquired, and a time sequence signal of the video data stream is analyzed; writing the parallel data into an external memory to form a frame buffer area; packaging the read video data into a data packet meeting a USB3.2 transmission protocol; packaging the data packet based on a USB3.2 link layer protocol; and converting the USB3.2 protocol transmission frame into a differential signal, and transmitting video data in the USB3.2 protocol transmission frame according to the differential signal. In view of data loss caused by mismatching of a video input rate and a USB output rate in the prior art, by establishing an annular buffer structure and adopting a dynamic pointer management and waterline threshold monitoring mechanism, rate adaptive matching of a video data stream is realized, and continuous and stable transmission of video data is ensured.

The invention relates to the technical field of artificial intelligence, in particular to an AI interview evaluation report generation system and method based on a competency model, and the system comprises a multi-modal data synchronization module, a feature coding module, a behavior pattern screening module, a competency feature mapping module and an evaluation result generation module. According to the method, the dynamic synchronization of the voice image and the behavior sequence is realized by constructing a multi-source signal time alignment mechanism, the signal matching precision is improved by adopting a time compensation and window division strategy, so that the sequential relationship between expression change and intonation fluctuation is stably presented, and quantifiable behavior vectors are generated through multi-dimensional feature coding; according to the method, semantic and emotion changes are kept continuous and consistent through similarity screening, competency weight mapping is established in the feature fusion stage, behavior features and ability dimension self-adaptive matching is achieved, finally, a multi-dimensional evaluation result is output in a score and text description fusion mode, and the objectivity, interpretability and reliability of the evaluation result are enhanced.

The invention discloses a real estate surveying and mapping system and method based on a geographic information system, and the system comprises an unmanned aerial vehicle surveying and mapping unit which is responsible for the collection of large-range images and laser point clouds in the air, and carries out the self-adaptive adjustment of measurement parameters according to the type of real estate. The laser measurement unit correspondingly completes measurement and supplementary measurement of a ground high-precision boundary point and can identify a shielding object in a measurement path and trigger a supplementary measurement signal, and the positioning enhancement unit provides anti-interference multi-source positioning support for the whole system so as to ensure continuous stability of positioning in a complex environment. The data fusion calibration unit unifies coordinates of cross-medium data, synchronizes timestamps and corrects data fusion deviation, the GIS processing unit undertakes core tasks of geographic information management, data processing and surveying and mapping result generation, and the linkage verification unit realizes real-time linkage verification of surveying and mapping data and real estate registration data and identifies ownership boundary conflicts. The system has the advantages of multi-source cooperation, self-adaptive matching, enhanced anti-interference and linkage calibration.

The invention provides a fast charger based on deep learning and a fast charging multi-protocol adaptive method thereof, and the fast charger comprises a signal sensing module which is used for carrying out the multi-mode sensing of an original signal of an access device, and obtaining a sensing vector; the perception decision module is used for inputting the perception vector into a lightweight deep learning model and outputting a perception decision, and the perception decision comprises protocol probability distribution, recommended power gears and safety suggestions; and the protocol matching module is used for carrying out self-adaptive matching of the fast charging protocol according to the perception decision. According to the fast charger based on deep learning and the fast charging multi-protocol adaptive method thereof, the lightweight deep learning model is adopted to adaptively output the protocol probability distribution, the method does not depend on a preset rule base and a sequence attempt mechanism, the protocol matching efficiency is higher, and in addition, the fast charging efficiency is improved. Decision factors such as recommended power gears and safety suggestions are introduced to form constraint conditions, the charging speed and the battery safety are balanced, and the suitability is better.

The invention belongs to the technical field of shield tunnel construction, particularly relates to an intelligent preparation control method for shield backfill grouting slurry, and aims to solve the problems of poor slurry preparation adaptability, unstable quality and the like in the prior art. The method comprises the following steps: constructing a slurry performance prediction model, combining geological parameters acquired and preprocessed in real time, and utilizing a control algorithm to dynamically generate an adjusting signal, so that the grain composition, the moisture content and the admixture mixing amount of semi-finished slurry are accurately adjusted. Meanwhile, slurry performance is continuously monitored in the preparation and grouting process, data are fed back to the model to achieve parameter self-correction, and a closed-loop intelligent control system is formed. According to the invention, the self-adaptive matching of slurry performance and complex geological conditions is realized, and the grouting quality and the construction efficiency are obviously improved.

The invention discloses an inference large model thinking chain compression method, which comprises the following steps of: firstly, generating answer sets with different detailed degrees by utilizing multiple rounds of sampling of a basic large model, and adaptively selecting an inference chain length by adopting a dynamic quantile algorithm based on task difficulty; secondly, performing diversity rewriting and compression on the reasoning step through KL divergence constraint, and generating the shortest expression on the premise of ensuring semantic consistency; constructing positive and negative samples to guide the model to learn simple expression, and training by adopting a composite loss function including supervised learning and length perception preference optimization; according to the method, external annotation data or a teacher model is not needed, adaptive matching of the reasoning depth and the problem difficulty can be achieved, the semantic integrity is guaranteed, meanwhile, the reasoning efficiency is remarkably improved, high expandability and good cross-task migration ability are achieved, and the method is particularly suitable for large-model lightweight deployment in a low-computing-power environment.

The invention belongs to the technical field of laser vibration measurement, and provides a self-focusing laser Doppler vibration measurement method and system capable of resisting self-vibration of an optical fiber, and the method comprises the steps: adding a compensation light path which is used for sensing and generating reference light which is equal to an interference signal in amplitude and is opposite to the interference signal in real time in a Doppler vibration measurement light path, and taking the minimum mean square error as a self-adaptive matching criterion; interference phases obtained by synchronous demodulation of the compensation light path and the main light path are subjected to adaptive matching and cancellation in amplitude and phase dimensions, residual errors of vibration of the vibration measurement system and other environmental interferences are suppressed below noise of the vibration measurement system, and the purpose of balancing and weakening optical fiber vibration interferences in an instrument is achieved. The influence of the vibration factor of the instrument is reduced, and the interference influence is accurately eliminated through amplitude and phase matching by a demodulation algorithm corresponding to a compensation light path; meanwhile, by dynamically observing and updating the return light intensity threshold, low-frequency disturbance is resisted, and accurate automatic focusing is achieved.