305 results about "Quantitative model" patented technology

The invention relates to the technical field of industrial Internet of Things, in particular to a machine equipment online state monitoring and fault diagnosis system, which comprises the following steps of: acquiring multi-source heterogeneous sensing data through an edge computing node deployed on an equipment body, performing adaptive noise filtering and feature dimension reduction processing on original data, and acquiring multi-source heterogeneous sensing data; outputting a standardized equipment state vector set; inputting the equipment state vector set into a dynamic knowledge graph engine, constructing a fault evolution network comprising space-time correlation characteristics based on an equipment operation entropy change quantification model, and generating a graph node connection relationship with a weight coefficient; and inputting the fault evolution network into a migration reinforcement learning module, and outputting a diagnosis decision set comprising a fault type, a severity degree and an evolution path through knowledge migration of a cross-device fault mode. According to the method, the problems of edge redundancy and single feature expression in traditional rule-based atlas construction are effectively avoided, and the structuring ability and physical traceability of fault recognition are improved.

The invention discloses a road safety early warning method and system based on a mixed precision quantification visual large model, and the method comprises the steps: collecting road traffic safety videos and pictures, and carrying out the preprocessing, data enhancement and marking, thereby forming a diversified data set; a pre-trained visual large model is selected as a teacher model, after fine tuning, output layer and middle layer knowledge is extracted, key features are weighted, and meanwhile, a lightweight neural network is taken as a student model, same input is received, and prediction and middle feature maps are output. And inputting data into the two models and the student model to carry out mixing precision quantification forward propagation, constructing a total loss function containing tasks, knowledge distillation and quantification learning loss, and updating parameters through back propagation. And after training is completed, exporting a quantitative model, and deploying the quantitative model to an edge computing platform to realize safety early warning. The lightweight model can realize rapid reasoning on edge equipment such as a vehicle-mounted road side, and the problem that performance and efficiency are difficult to consider in a traditional model compression method is solved.

The invention discloses a forest-town junction region fire dynamic early warning method based on multi-source data assimilation, and the method comprises the following steps: inputting collected multi-source observation data into a fire risk prediction model for training, and outputting a fire risk grid map covering the whole forest-town junction region for monitoring a high-risk building group; constructing a fire spreading numerical model covering multi-material, multi-scale and multi-physical mechanism coupling; a fire propagation characteristic quantitative model is constructed, and the building heat influence is evaluated; and constructing a data assimilation and fire dynamic prediction model, and dynamically predicting and outputting fire spreading parameters and a spreading trend chart. According to the method, a forest-building-oriented complex scene is constructed, and propagation behaviors of flames under multiple mechanisms of convection, radiation and conduction are effectively described; through the modes of risk identification, dynamic simulation, data assimilation, visual early warning and the like, the precision, response speed and engineering applicability of forest-town boundary region fire prediction are remarkably improved.

The invention provides a target recognition model reasoning optimization method and device, and the method comprises the steps: firstly carrying out the structural analysis and sensitivity evaluation of a pre-training model, extracting the structural features of each network layer, activating the distribution features, carrying out the quantitative sensitivity scoring, and constructing a data set reflecting the hierarchical features and fault-tolerant capability; and querying a quantitative configuration knowledge base based on the data set to generate a heterogeneous quantitative strategy. Layered low-bit quantization is executed according to the strategy, and a layered weighted loss function is introduced to carry out quantization perception training, so that precision loss caused by bit width compression is effectively compensated. According to the method, through hierarchical heterogeneous quantification, the model recognition precision is preserved to the maximum extent while high compression ratio and reasoning acceleration are achieved, and particularly, the performance of a high-sensitivity layer is protected. The generated heterogeneous quantitative model remarkably reduces memory occupation and power consumption, is suitable for an edge hardware platform with limited resources, forms a set of complete automatic process from analysis and configuration to training compensation, and has good universality and engineering practical value.

The invention belongs to the technical field of computer artificial intelligence, and relates to a citrus intelligent planting management-oriented large model field quantification and adaptive model deployment method, which comprises the following steps of: firstly, constructing a large model calibration data set and carrying out preprocessing, and inputting a Transform model to execute forward reasoning; then carrying out left multiplication rotation on a weight matrix of a to-be-quantized layer of the model and right multiplication rotation on an activation matrix, executing GPTQ quantization, calculating a dynamic adaptive smoothing factor of each channel of the activation matrix, executing normalization and symmetric quantization according to hidden dimension grouping, and generating a quantized Transform model; then obtaining citrus industry text data to construct a fine tuning instruction set, and performing supervised training on the quantitative model to generate a full-quantitative and full-precision model; and finally, by training a task complexity classifier, selecting a full-quantization or full-precision model as a target deployment model according to a task complexity level, so that efficient quantization and intelligent task adaptation of the model are realized.

The invention discloses a source-load interaction method based on an electricity utilization information acquisition system, and relates to the field of power distribution network operation and optimization. The method comprises the following four steps: S1, power consumption data acquisition: dynamically acquiring user power consumption, distributed energy output and power grid state data through intelligent equipment, and ensuring real-time accuracy; s2, load characteristic analysis: constructing a quantitative model to analyze characteristics such as a user load elastic coefficient and adjustable potential, establishing a supply and demand balance evaluation system in combination with distributed energy fluctuation, and determining peak and valley time periods and gaps; s3, generating an interactive strategy, and generating and issuing dynamic strategies such as electricity price excitation, load regulation and energy scheduling based on an analysis result; and S4, executing and feeding back the strategy, monitoring the execution effect in real time, and optimizing the strategy through a closed loop mechanism. According to the method, the problems of staticization, single mode and low efficiency of a traditional method are solved, the power grid supply and demand balance capability, the renewable energy consumption rate and the strategy accuracy are improved, and low-carbon and intelligent operation of the power distribution network is supported.

The invention is suitable for the field of industrial safety monitoring, and provides an industrial control safety monitoring method and system, and the method comprises the steps: collecting the network flow data of an industrial control network in real time based on an industrial protocol analysis engine; synchronously acquiring operation state parameters of the key equipment by adopting an edge computing node; constructing a coupling relation quantitative model of the network flow and the equipment operation state, and calculating a dynamic correlation coefficient between the network flow and the equipment operation state through the coupling relation quantitative model; constructing reference correlation coefficient distribution in a normal production state based on historical data, and dynamically updating a safety threshold value; when the dynamic correlation coefficient exceeds a safety threshold value, triggering a multi-stage early warning mechanism; according to the method and the system, deep fusion monitoring of the industrial control network and the equipment operation state is realized, and the limitation of traditional split type monitoring is broken through. Through dynamic quantification of a coupling relationship, adaptive threshold adjustment and multi-level early warning, the timeliness and accuracy of abnormal risk identification are significantly improved, and network attacks and equipment fault hazards are effectively reduced.

The invention discloses a damping thermal insulation material performance evaluation method and system for a battery pack, and relates to the technical field of battery pack thermal management, and the method comprises the steps: extracting a calibration sample set, applying transient thermal excitation, and collecting an infrared thermal image sequence to extract thermal response features; performing industrial CT scanning to obtain defect quantitative indexes, and forming a calibration data set with the thermal response characteristics; establishing a quantitative correlation model based on the calibration data set, and determining a batch quality threshold according to model output distribution; executing the same excitation and collection on an online sample to be detected to extract the characteristics of the sample, inputting the model to obtain a defect quantitative prediction value, comparing the defect quantitative prediction value with a batch quality threshold, and outputting an evaluation result; when a preset period or a trigger condition is met, a new sample is supplemented and extracted, and newly added calibration data is obtained to update the model and the threshold value. According to the method, accurate material performance evaluation is provided by establishing a quantitative model of thermal response characteristics and defect quantitative indexes, and evaluation consistency and stability are enhanced through a dynamic updating mechanism.

The invention relates to the technical field of traffic transportation environment engineering and carbon emission evaluation, in particular to a construction method and equipment of a plateau gradient zone vehicle CO2 emission factor calculation model and a medium. Through the method, a route-level CO2 emission factor distribution map is obtained, and different quantitative calculation models are formed for different altitude intervals. And a specific and quantitative design basis is provided for low-carbon highway design. It is revealed for the first time that the CO2 emission rate of the plateau gradient zone has a remarkable altitude segmentation effect through empirical data, and a method for constructing a calculation model in a partition mode is innovatively provided. According to the method, the defects that a traditional single model is poor in adaptability and misaccurate in prediction in a complex plateau environment are overcome, and differentiated quantitative models (such as a low-altitude quadratic function and a high-altitude S-type function) are established for different altitude intervals; the finally formed partition model provides a core theoretical tool and decision basis for plateau highway carbon emission accurate accounting, low-carbon route optimization and green traffic construction.

The invention relates to the technical field of spectral analysis and concentration quantitative detection, and discloses an LIBS quantitative analysis method, device and system based on a thin plate spline regression algorithm. The method comprises the steps that multiple sets of sample data are obtained, and each set of sample data comprises corresponding target sample concentration, laser energy and spectral intensity; constructing a concentration quantitative model about the concentration, the laser energy and the spectral intensity based on a spline function; determining an optimal penalty parameter by adopting a cross validation method; based on the optimal penalty parameter, solving parameters of the concentration quantitative model through a minimization objective function to obtain a constructed concentration quantitative model; and performing quantitative analysis on a sample with unknown concentration by using the constructed concentration quantitative model. Thus, the output energy of the laser is incorporated into the core model, and an I-E-C nonlinear coupling relationship is constructed, so that the problem of calibration deviation caused by laser energy fluctuation is reduced, and the precision improvement of concentration quantification in a complex scene is realized.

The invention relates to the field of gas concentration detection, and particularly discloses a real-time feedback and adaptive learning method for natural gas infrared spectrum measurement, which comprises the following steps: S1, acquiring infrared spectrum information of a natural gas body by using an infrared spectrometer, and constructing a historical sample set; s2, preprocessing the spectral data of the historical sample set; s3, selecting an optimal algorithm and a hyper-parameter by adopting XGBoost and Bayesian optimization; s4, constructing a qualitative model to identify gas types and match data; s5, calculating the similarity between a field sample and a historical sample through a Siamese network, and setting a threshold value to screen local data; s6, improving the KNN to construct a local dynamic quantitative model to predict the concentration; s7, processing low-similarity abnormal data by the global dynamic model, and improving the reliability by combining moving average and abnormal calibration; and S8, introducing reinforcement learning and online gradient descent to adjust parameters in real time to optimize the precision. According to the technical scheme, high-accuracy natural gas detection can be carried out in a complex environment.

The invention discloses an intelligent business decision support system based on multi-modal data and a knowledge graph, and relates to the technical field of data analysis. In order to solve the problems of weak data integration capability and poor model adaptability in existing business analysis, the adopted scheme comprises a multi-modal data processing module used for collecting multi-modal data and constructing a knowledge graph after preprocessing and feature extraction; the model training and optimizing module is used for taking a large-scale pre-training language model as a core, acquiring external knowledge from the atlas in combination with a retrieval enhancement generation technology, performing parameter fine tuning, optimizing a training process and generating an optimized model at the same time; the intelligent decision generation module is used for generating a preliminary suggestion based on the optimization model and generating a report through logic deviation evaluation and correction; the dynamic feedback module is used for constructing evaluation system quantitative model performance by taking the report as input and combining real-time business data; and meanwhile, user feedback is collected to form an incremental sample dynamic optimization model. The accuracy of business decision making can be improved.

The invention provides a transient voltage stability control method and system based on transmission and distribution cooperation. The method comprises the following steps: constructing a power distribution network reactive power support capability quantification model; quantizing a reactive power support capability boundary in real time by solving a power distribution network reactive power support capability quantification model, and determining a reactive power support power feasible region capable of being provided; and based on a quantitative evaluation result, dynamically adjusting a reactive power interaction value between the power transmission and distribution networks by adopting an improved droop control strategy, and realizing power transmission network transient voltage stability control based on active support of the power distribution network. Power distribution is carried out according to the dynamic reactive power margin of the reactive power regulation equipment by considering the transient voltage support requirement of the power transmission network and the reactive power transmission loss of the power distribution network; and obtaining a reactive power regulation instruction, and coordinating reactive power resources at the power distribution network side according to transient voltage stability control and power distribution of the power transmission network to realize cooperative control. Based on the method, the invention further provides a transient voltage stability control system based on transmission and distribution cooperation. The optimal transient voltage support is provided for the power transmission network.

The invention provides a model quantization implementation method, a model and computer equipment, and the method comprises the steps: obtaining a target floating point model and a training data set, and determining the quantization configuration corresponding to each neural network layer contained in the target floating point model; the quantization configuration corresponding to different neural network layers is related to the change degree of the performance of the corresponding neural network layer compared with the target floating point model after the corresponding neural network layer is quantized, and according to the quantization configuration corresponding to each neural network layer, performing pseudo quantization on parameters of the corresponding neural network layer in the target floating point model to obtain a first pseudo quantization model; and based on the target floating point model and the training data set, performing quantitative perception training on the first pseudo-quantitative model to obtain a target quantitative model, and deploying the target quantitative model to a terminal device to execute a calculation task.

The invention relates to the technical field of talent management and data analysis, in particular to a multi-source data driven scientific and technological talent ability portrait accurate matching method. The method comprises the following steps: collecting and preprocessing multi-source talent data, and mining text information by applying a natural language processing technology; constructing a talent knowledge structure graph by identifying the relationship between the key entities and the extracted entities; determining a plurality of capability dimensions, and establishing a quantitative model or rule for each dimension to measure the capability; calculating scores of all dimensions by comprehensively considering personal differences of talents and working situation factors, and generating a visual ability portrait; a demand standard is determined by analyzing the situation of a demand side, the matching degree between talents and the demand standard is calculated after the talents are screened from a talent pool, the recommended talents are ranked according to the matching degree, and related parameters and rules are optimized according to feedback of the demand side. The talent ability can be comprehensively and accurately described, efficient and accurate matching is achieved, the method adapts to dynamic changes, and talent configuration in the science and technology service field is effectively optimized.

The invention relates to an instruction-level man-machine collaborative driving method and device, and the method comprises the steps: designing an uncertainty quantitative model for the problem of hybrid uncertainty in a man-machine fusion process, and proposing a human instruction optimal approximation strategy based on safety constraints, and guaranteeing that when man-machine decisions are inconsistent, the human instruction optimal approximation strategy is not consistent with the human instruction optimal approximation strategy. Safety and human intention are considered; a human intention prediction model is constructed based on a learning and interaction model, so that the efficiency and effectiveness of man-machine fusion intelligent decision making are improved. Therefore, the problems that control right switching of a traditional cooperation mode lacks smooth transition, and the movement freedom degree of a driver is limited are solved.

The invention provides a PC (Personal Computer) end quantitative model reasoning method. The method comprises the following steps: S1, loading input information; s2, deserializing the model; s3, operator calculation is carried out; and S4, storing the data, and selecting a dump data result. The quantitative model reasoning on the PC end is used for simply reasoning the model, verifying the result of the model and obtaining the data of the network layer of each stage reasoned in the model, and the PC end reasoning is used for correcting the data, assisting the quantitative model, reasoning the float model, post-quantitative model and training the quantitative model and verifying the correctness of the data. According to the method, model reasoning is convenient and effective, model data verification is facilitated, and data information of a quantitative model is provided.

The embodiment of the invention discloses an electrical stimulation parameter determination method, device and equipment, a medium and a medical system. The method comprises the following steps: acquiring a brain three-dimensional model corresponding to a target user of which the brain is implanted with at least one stimulation electrode; determining at least one electrode contact combination corresponding to the target nucleus simulation model according to the first spatial position information of the target nucleus simulation model and the second spatial position information of the electrode simulation model; determining at least one group of candidate electrical stimulation parameters of the electrode contact combination according to the electrode contact combination and a target nucleus simulation model; determining the total power consumption of the stimulator of the candidate electrical stimulation parameters according to a preset total power consumption quantitative model of the stimulator and the candidate electrical stimulation parameters; and determining at least one group of target recommended electrical stimulation parameters from the plurality of groups of candidate electrical stimulation parameters according to the total power consumption of the plurality of stimulators. According to the technical scheme, the effect of accurately screening the recommended electrical stimulation parameters with low energy consumption and efficient stimulation effect according to the electrical stimulation power consumption is achieved.

The invention discloses a meta-model decision-making-based intelligent prediction method for inclusion quality in an electroslag remelting process. The method comprises the following steps of: constructing a sample data set containing process and component characteristics and target variables; obtaining a first-layer basic model based on an SHAP value cumulative contribution rate screening method; a heterogeneous learner is adopted to construct a first layer structure of the stacked integrated learning model, and Bayesian is adopted to carry out adjustment and optimization; adopting logistic regression as a meta-learner to construct a second-layer structure of the stacked integrated learning model; training by adopting a five-fold cross validation strategy, and predicting the performance by using a multi-index quantitative model; and deploying the D-type inclusion prediction model in the electroslag remelting process to an actual process, collecting process parameters as input data in real time by using a multi-sensor group, performing D-type inclusion risk prediction of a corresponding heat, and outputting an inclusion risk prediction result. According to the method, the accuracy and reliability of D-type inclusion prediction can be remarkably improved.

The invention discloses a method for heterogeneous scheduling of computing power under a large model training reasoning framework, relates to the technical field of computing power scheduling, and solves the technical problem that task interruption or resource waste is easily caused by incomplete task migration, splitting and recovery mechanisms when computing power is insufficient or equipment is abnormal. According to the method, parameters such as the load rate, the residual computing power and the temperature of the CPU, the GPU and the FPGA are dynamically collected, the calculation amount, the duration and the delay requirement of the task are combined, a quantitative model is established, the resource utilization rate is increased, a temperature prediction model is established in stages, a dynamic threshold value is set in combination with the equipment type, the environment temperature and the health degree, temperature early warning and migration in the task execution process are achieved, and the task execution efficiency is improved. The frequency reduction risk caused by overheating of equipment is reduced, the task interruption loss is ensured to be reduced by more than 50% through task splitting, intelligent migration and pause recovery mechanisms aiming at insufficient computing power or equipment abnormity, and the stability of the system during peak flow or hardware failure is improved.

The invention provides an intelligent substation dynamic regulation and control method and system based on RIS and a medium, and belongs to the technical field of substation communication regulation and control. The method comprises the following steps: giving a risk weight according to the type of equipment, constructing a substation equipment risk grading quantitative model based on the risk weight, and obtaining a risk index of the equipment based on the substation equipment risk grading quantitative model and real-time data; constructing a space electromagnetic fingerprint map of the intelligent substation based on the electromagnetic field data; acquiring a propagation shielding scene in the intelligent substation according to the signal propagation path, acquiring a corresponding signal transmission compensation mode according to the propagation shielding scene, adjusting the RIS unit according to the signal transmission compensation mode and the risk index, and generating an initial optimization phase matrix; and optimizing the initial optimization phase matrix based on a power grid state constraint condition, and generating a working phase matrix of the RIS unit. The communication quality is improved through the signal transmission compensation mode and the risk index, and meanwhile the power grid safety is guaranteed by meeting the unit constraint condition.

The invention discloses a reasoning method based on a quantitative model, the quantitative model is obtained based on a trained model, the precision of at least part of parameters of the quantitative model is smaller than the parameter precision of the trained model, and the method comprises the following steps: obtaining input task data; inputting the input task data into the quantitative model to obtain an output result; the quantitative model comprises multiple layers of neuron sub-models, and for a first sub-model and a second sub-model of adjacent layers, the output activation value of the first sub-model is used as the input of the second sub-model; under the condition that a second sub-model of a specific adjacent layer of the quantitative model comprises a fusion weight matrix parameter, a specific rotation matrix is controlled to act on an output activation value of the first sub-model to obtain a modified activation value, and the modified activation value is smaller than the output activation value; the fusion weight matrix parameter is obtained by the specific rotation matrix acting on the second initial weight parameter of the second sub-model of the specific adjacent layer corresponding to the trained model.

The invention discloses a wing load uncertainty quantitative model correction method based on credible Bayesian updating. The method adopts a non-probability interval mode to quantify the uncertainty of some parameters of the wing in a flight condition, establishes an aircraft wing load prediction finite element model, and combines uncertainty propagation influence research to obtain the load uncertainty of the aircraft wing. A credible Bayesian theory is utilized, new sample points obtained by a simulation experiment are introduced to update interval parameters, a Markov chain Monte Carlo method is utilized to obtain a relational expression between a non-probability quantization set and credibility, and a load uncertainty quantization model of a typical wing structure is established and corrected. The method has remarkable advantages in processing small sample and poor information problems in engineering practice, the credibility is utilized to describe the reliability degree of the reliability obtained through calculation, and a solution is provided for the balance problem between conservative property and credibility of the interval model.

The invention discloses a special vehicle driving task performance and sitting posture comfort coupling-oriented test evaluation system and method, and the system comprises a data collection module which is used for obtaining multi-modal driving data; the index calculation module is used for extracting and quantifying characteristic indexes from the multi-modal driving data to obtain a driving task performance evaluation index set and a driving sitting posture comfort evaluation index set; the comprehensive evaluation and time domain modeling module constructs a driving task performance-sitting posture comfort coupling-oriented comprehensive quantitative model based on the driving task performance quantitative model and the driving sitting posture comfort quantitative model; and finally, comprehensively scoring the state of the driver by using the comprehensive quantitative model, and establishing a nonlinear mapping relationship between the driver state comprehensive score and the task time to obtain a dynamic change trend of the driver state comprehensive score.

The invention provides a reasoning acceleration method, electronic equipment, a readable storage medium and a program product. The reasoning acceleration method comprises the steps that an input text is received, a quantitative model is called, in the quantitative model, all tensors comprise m-bit tensors expressed by m bits and n-bit tensors expressed by n bits, and m is larger than n; decomposing the input text into input lexical elements; calling an m-bit tensor or an n-bit tensor in the quantitative model to calculate the input lexical elements to obtain key vectors and value vectors of the input lexical elements, and storing the key vectors and the value vectors in a key value cache; calling an n-bit tensor in the quantization model, and generating an output lexical element according to a key vector and a value vector in the key value cache; and obtaining a reasoning result based on the output lexical elements.

The invention discloses an intelligent automobile longitudinal control method based on dynamic feature learning optimization, and the method comprises the steps: collecting speed and acceleration data under different passenger capacities in real time through a vehicle-mounted sensor, constructing a speed and acceleration correlation spectrogram, building a quantitative model of the passenger capacity and dynamic response delay characteristics, and calculating the dynamic response delay characteristics of the passenger capacity and the dynamic response delay characteristics. Laplace characteristic decomposition is carried out on the speed and acceleration correlation spectrogram, and an inertia sudden change characteristic mode of the intelligent automobile when the passenger capacity changes is extracted. According to the method, continuous learning and updating of the state and action mapping relation can be achieved, the problems that a traditional PID and model prediction method is high in dependence on model precision and fixed in parameter are solved, strategy parameters are pre-trained through a migration simulation experiment, the initial performance of the intelligent automobile controller is effectively improved, the online training convergence time is shortened, and the training efficiency is improved. And the control effect under low data is improved.

The invention relates to a mineral resource prediction method and system for a shallow-hidden fluorite mine, and the method comprises the steps: carrying out the metallogenic characteristic analysis of a typical fluorite deposit, and constructing a prospecting prediction conceptual model according to an analysis result; performing evaluation analysis on the resource potential, the mineralization favorable degree and the prospecting prediction matching degree of the prospecting area to obtain an optimal mineralization prediction geological unit; on the basis of the prospecting prediction conceptual model, a prospecting prediction quantitative model is obtained in combination with geological information of the typical fluorite deposit, and fluorite mine quantitative prediction is conducted on the optimal metallogenic prediction geological unit through the prospecting prediction quantitative model, so that fluorite mine prediction quantity is obtained; and under the condition that the fluorite mine prediction amount reaches a set threshold value, fluorite mine resource evaluation analysis is carried out according to the fluorite mine prediction amount in combination with the obtained geological exploration data of the ore searching area, and fluorite mine resource distribution data is obtained. From the perspective of green prospecting, the method can quickly and accurately carry out prospecting prediction on shallow hidden or deep fluorite mines.

The invention relates to the technical field of chemical production safety, in particular to a chemical production safety risk intelligent reasoning method and system and a storage medium, and the method comprises the steps: building a multi-level flow model according to a chemical production flow chart, and constructing a fault tree analysis qualitative model for the multi-level flow model through a danger and operability analysis technology; fusing and correcting the multiple groups of evidence sources according to the D-S evidence theory to obtain the initial abnormal probability of each basic event; and establishing a dynamic Bayesian network quantitative model in combination with a logic gate rule, and performing forward reasoning and backward reasoning. According to the invention, forward reasoning and backward reasoning can be carried out on abnormal event nodes in the chemical production process, causal traceability is completed, an abnormal propagation path is explored, and an effective management means is provided for safe production in the chemical process.

The invention relates to a fault prediction method and system for parameter fine tuning and adaptive structure pruning based on business influence driving, and belongs to the technical field of artificial intelligence, time series data analysis and intelligent operation and maintenance. Comprising the following steps: S1, business influence data modeling: integrating multi-source operation and maintenance data and historical business fault event data, and constructing a business influence quantitative model; s2, efficient fine tuning of service influence guide parameters: loading the pre-training fault prediction model, and performing efficient fine tuning of the parameters on the basis of service influence signals; s3, business influence driven adaptive structure pruning; S4, dynamic reasoning and alarm generation: realizing a dynamic reasoning mechanism of a pruned fault prediction model, and generating a structured and business value oriented fault prediction alarm according to a reasoning result; and S5, performing closed-loop optimization and continuous evolution. According to the invention, the configuration accuracy of the operation and maintenance resources is improved.

The invention discloses a smart city planning method and system based on digital twinning, relates to the technical field of city planning, and solves the problems that the scheme is not feasible due to dependence on manual interpretation of regulation texts, lack of automatic generation and matching of index constraint lists and possible neglect of key constraint conditions in planning suggestions, and meanwhile, the scheme is high in subjectivity and low in cost. And a unified quantitative model is lacked. Through the NLP technology, planning suggestions and regulation terms are compared in real time, conflict indexes are automatically marked, planning conditions and preliminary index calculation results are combined, the generated project planning suggestions strictly follow a policy framework, and manual interpretation errors or violation risks are avoided. A collaborative quantitative evaluation model of the single building and the regional landscape is established, and the landscape effect of the planning suggestion is evaluated from the dimensions of spatial layout, visual vestibule, skyline, color coordination and the like. The fusion degree of the project and the surrounding environment is visually reflected through quantitative scoring, and the deviation of subjective evaluation is avoided.