43 results about "Combinatorial algorithms" patented technology

The invention relates to the technical field of test question generation, in particular to an intelligent test question generation method and system based on learning behavior analysis. The method comprises the following steps: acquiring interactive behavior data and score data of learners in a user online learning platform in real time; inputting the interactive behavior data and the score data into a pre-trained knowledge state analysis model to generate a user knowledge state matrix; based on the knowledge state matrix and in combination with a preset teaching target library, identifying a target knowledge point set which needs to be strengthened currently and a corresponding cognitive training type; according to the target knowledge point set needing to be strengthened and the corresponding cognitive training type, a test question element combination algorithm is called, question stems, interference items and question solving path prompts are dynamically assembled, and personalized test questions are generated. The method has the advantages that full-closed-loop intelligent teaching from behavior analysis of the user to targeted training is achieved, and personalized test questions adaptive to individual cognitive vulnerabilities are dynamically generated.

The invention discloses a GNSS (Global Navigation Satellite System) signal quality adaptive integrated navigation method, and relates to the technical field of vehicle navigation. IMU and GNSS positioning information and positioning states are read, integrated navigation is realized through an ESKF loose combination algorithm, a measurement covariance matrix is changed in real time according to the GNSS positioning state and a DOP factor, and finally, an error state value is estimated and fed back to an integrated navigation system. Position and speed information errors and attitude Euler angle errors under a navigation coordinate system are fed back to INS mechanical arrangement, angular acceleration zero offset errors of the IMU are fed back to the IMU, and updating of the state quantity is completed. A measurement covariance matrix is changed in real time according to a GNSS positioning state and a DOP factor by aiming at IMU and GNSS data through a loosely combined ESKF fusion algorithm, an error state is estimated and fed back to an integrated navigation system, and the positioning precision can be effectively improved.

The invention belongs to the technical field of packaging jet printing, and discloses an external packaging carton two-dimensional code jet printing quality online monitoring and error correction process, which comprises the following steps: building a vision, laser, spectrum and pressure four-source sensing positioning system in a preprocessing stage, comprehensively collecting carton material and form related data, and forming a pre-calibration closed loop in cooperation with microsample jet printing verification; in the jet printing process, a high-speed imaging system of self-adaptive light supplement works synchronously, after an image is preprocessed through a combinatorial algorithm, various defects such as blooming and scratches are accurately recognized through a ResNet34-Transform-GAN fusion model, and then an LSTM neural network is used for predicting the development trend of the defects and conducting early warning; a three-level parameter configuration system of cloud decision, edge execution and twin simulation is utilized, an edge calculation unit uploads data such as materials, sizes and flatness collected in real time to the cloud, historical optimal parameters of the corresponding materials are called, and then dynamic adjustment is conducted in combination with the conveying speed.

The invention provides a method for identifying the characteristics of a metal foreign body in a GIS, and the method comprises the steps: building an electric field simulation model, and setting the metal foreign body; a multi-modal sensor is adopted to collect signals generated by partial discharge of the metal foreign matter so as to obtain a multi-modal atlas database; performing multi-domain feature extraction on the multi-modal atlas database, and generating a lightweight fingerprint feature vector through feature dimension reduction; the lightweight fingerprint feature vector sequentially passes through all steps of a combinatorial algorithm to solve identification items of the metal foreign matter; and selecting a globally optimal group of identification items to be combined into a metal foreign matter feature identification scheme. According to the invention, fine analysis and processing are carried out on partial discharge signals through a signal processing and pattern recognition algorithm, so that the detection and classification precision of metal foreign matters can be effectively improved; real-time monitoring and quick response under complex working conditions can be realized, a quantitative basis can be provided for fault early warning and equipment maintenance, and the operation reliability and safety of GIS equipment are further enhanced.

The invention discloses an urban user power utilization efficiency collaborative optimization analysis method and system based on the Internet of Things, and relates to the technical field of power analysis, and the technical scheme is characterized in that a multi-source heterogeneous data pool containing timestamp alignment is constructed; generating a user electricity consumption characteristic matrix through a space-time dynamic weight fusion algorithm; calculating a frequency modulation potential value through an entropy-TOPSIS combinatorial algorithm based on the user power consumption characteristic matrix; inputting the frequency modulation potential value into an LSTM-GAN confrontation generative network, and dynamically generating a three-dimensional efficiency parameter vector containing a user optimization degree, a good power grid contribution degree and an environmental benefit index; and based on the three-dimensional efficiency parameter vector, an optimization target of a user and a power grid is coordinated through a Stackelberg game model, and a dynamic electricity price strategy and a load adjustment strategy are output. The method has the advantages of being low in response delay, high in energy efficiency optimization rate, good in environmental benefit and the like when being used for coping with complex power consumption scenes in urban areas.

The invention discloses a cable material diameter detection method based on an eddy current impedance effect, and particularly relates to the field of cable nondestructive testing, which comprises the following steps: S1, synchronously acquiring the resistance and inductance of a coil under a low-frequency signal and the resistance and inductance of the coil under a high-frequency signal by adopting a double-frequency excitation eddy current sensor; the environment temperature and the lift-off distance between the sensor and the cable are measured; s2, denoising is carried out through a combinatorial algorithm, and temperature compensation is carried out on the resistance and inductance of a sensor coil and the conductivity of a cable core wire; s3, calculating a material sensitive factor under low frequency, calculating a line diameter sensitive factor under high frequency, and correcting when the lift-off distance deviates; s4, calculating a material deviation rate to judge the material, fitting a linear regression model, and combining with conductivity to correct the skin depth to calculate the line diameter; and S5, according to the material and the wire diameter, comprehensively determining progressive output, and triggering a corresponding alarm in case of abnormality. The problems of parameter coupling and incomplete temperature compensation are solved, and the detection precision and the environmental adaptability are improved.

The invention discloses a 5G and Beidou fused vehicle positioning navigation system. The system comprises a multi-source heterogeneous positioning module; a high-precision map and a dynamic traffic information base; a self-adaptive multi-layer map matching module; the dynamic path planning module considers steering limitation and real-time position feedback; and a central information fusion and decision unit. According to the invention, multi-source heterogeneous positioning data such as Beidou / GPS, 5G positioning, high-precision IMU, a vehicle-mounted atomic clock and a barometric altimeter are fused, and a tight combination algorithm based on ESKF or a factor graph is adopted, so that the system can realize accurate positioning in an environment with good satellite signals, partial shielding and even serious loss. Continuous, smooth and high-precision vehicle state estimation can be output, particularly when the number of visible satellites is insufficient, double-satellite positioning can be achieved by using the vehicle-mounted atomic clock to predict the clock error and combining the elevation constraint of the barometric altimeter, and the usability and robustness of positioning are improved.

The embodiment of the invention provides an MCU anti-theft authentication method and system and a storage medium, and belongs to the technical field of automobile controller safety authentication. The method comprises the following steps: setting a first identification code and a second identification code according to a unique identification code of a vehicle, and sending the first identification code and the second identification code to an MCU; combining the first identification code and the second identification code according to a combination algorithm to obtain a first combination value; calculating a hash value of the first combination value according to a hash algorithm as a third identification code and sending the third identification code to the MCU; performing first authentication according to the first identification code, the second identification code and the third identification code, and judging whether the first authentication is successful or not; under the condition that the first authentication is judged to be successful, performing second authentication and judging whether the second authentication is successful or not; and under the condition of judging that the second authentication is successful, judging that the anti-theft authentication is successful. The MCU anti-theft authentication method is high in safety and reliability.

The invention provides an antenna automatic control method adaptive to different antenna deployment modes. The antenna automatic control method comprises the steps that S1, a remote information processor reads circuit level data of an external antenna; s2, the communication module diagnoses the circuit level data of the external antenna based on an antenna in-situ identification algorithm, and judges the working state of the external antenna; step S3, based on a single-pole double-throw switch combination algorithm, evaluating a judgment result in the step S2, calculating an optimal switching scheme, and sending a corresponding switching instruction; s4, the single-pole double-throw switch receives the switching instruction and executes switching operation, the communication module verifies the switching effect through the step S2, and if switching fails, the switching instruction is sent again; and S5, setting a signal instability threshold, and comparing the signal receiving intensities before and after switching. According to the invention, a plurality of TBOX parts do not need to be varied to adapt to the selection of different antenna deployment, the flexibility of antenna deployment is improved, and the platform compatibility difficulty is reduced.

The invention discloses a five-axis numerical control machine tool machining error on-line compensation system based on deep learning, relates to the technical field of machine tool error compensation, and solves the problems that position correction difference is caused by neglecting a complex coupling relation between rotating shafts and linear shafts of different types of machine tools, and the error compensation accuracy is high. And deep learning models for machine tools with different requirement types are not designed, so that the model prediction precision is influenced. Determining the coupling relation of each axis error through the HTM homogeneous transformation matrix of the machine tool kinematic chain; aiming at kinematic chain structure differences of different types of machine tools, a differential correction strategy is designed. And constructing a zero drift time sequence model in combination with a historical drift trend term and a random error term predicted by the LSTM. Geometric error terms strongly related to machining errors are screened through grey correlation analysis, then the weight of the geometric error terms is quantified through random forest, the accuracy of key error recognition through a combinatorial algorithm is improved, and the model prediction precision is improved by training fusion models for machine tools with different requirement types.

The invention belongs to the technical field of biological carbon sequestration, and discloses a carbon sequestration pathway selection method and system based on assimilation pathway evaluation.The method comprises the steps that all reactions with carbon dioxide, carbonate and a one-carbon compound as substrates are obtained, carbon dioxide and bicarbonate radicals are used as carbon sources, and a plurality of carbon sequestration reactions are obtained through screening; carrying out pathway calculation on the carbon sequestration reaction by utilizing a combinatorial algorithm in combination with a simple flux equilibrium analysis technology, and determining a core carbon sequestration reaction according to a calculation result; based on the enzyme catalysis efficiency, simplicity, energy and reduction equivalent consumption and metabolite conversion relation in the core carbon sequestration reaction, the carbon sequestration pathway is evaluated, and the corresponding carbon sequestration pathway is selected according to the evaluation result. According to the invention, a plurality of novel carbon dioxide assimilation approaches with prospects are obtained, and a valuable alternative scheme is provided for bioconversion of industrial carbon dioxide in the future.

The invention relates to an osteoarthritis AI (osteoarthritis) prediction method based on NETs (neutrophile granulocyte extracellular traps) related key genes, and aims to develop a model universally applicable to gene diagnosis or molecular diagnosis by analyzing and disclosing a large sample data set through an advanced algorithm by utilizing ML (makeup language) to reveal genetic regulation of neutrophile granulocyte extracellular traps (NETs) in joint tissues. Therefore, the optimal algorithm needs to be compared and screened by using multiple algorithms during modeling, the gene (molecule) diagnosis efficiency is improved by combining the algorithms on the basis, key genes which can best reflect OA characteristics and are driven by NETs (key gene characteristics related to the NETs) are selected, and the disease-related genes and the relationship between the disease-related genes and disease phenotypes are identified, so that the disease-related genes are identified. Therefore, understanding of interaction of biological components is deepened, and diagnosis and treatment strategies are improved.

The application provides a special-shaped character detection and recognition method, system, device and storage medium, and relates to the field of special-shaped character detection and recognition.The method comprises the following steps: (1) constructing a training set based on an existing product image, and importing the training set into a rotating target detection model for training; (2) inputting a product image to be detected into the trained rotating target detection model to output string information M of different regions in the product image to be detected; and (3) based on the string information M of different regions, adopting an optimal combination algorithm to combine characters in front and back, and forming a plurality of independent strings.The application uses a rotating target detection model for character recognition, and adopts an optimal combination algorithm for character combination on the basis of character recognition, and then detects special-shaped strings on medical devices and other products.The application solves the problem of low character detection accuracy caused by irregular string shapes in a complex scene.

The invention discloses a mechanical arm scanning track planning method based on an improved ant colony algorithm, which comprises the following steps: step 1, dividing a to-be-measured workpiece into n primitives, setting n measuring points on the to-be-measured workpiece, and setting a complete set T of the measuring points as follows: T = {T1, T2, T3,..., Tm}; 2, solving a set coverage problem by using an improved greedy algorithm to obtain an optimal selection method of a scanning point position set; 3, solving a scanning track by using an improved ant colony algorithm and a combined algorithm based on a BFS strategy; step 4, optimizing the scanning track by using a particle swarm algorithm to obtain an overall scanning track with the shortest scanning time; and step 5, obtaining an overall scanning track with the shortest scanning time by using a particle swarm algorithm. According to the method, a scanning track is solved by using an improved ant colony algorithm and a BFS-based strategy, a larger area is selected to continue to carry out strategy path search, and a smaller area is solved by using the ant colony algorithm to obtain a loop. And opening the loop, combining the loop to an original path, and solving the whole path through the BFS to obtain an optimal path.

The invention discloses an unmanned aerial vehicle security situation awareness method based on multi-source heterogeneous data fusion, and belongs to the technical field of unmanned aerial vehicles, and the method comprises the steps: obtaining the multi-source heterogeneous data of a target unmanned aerial vehicle in real time, and carrying out the preprocessing of the multi-source heterogeneous data; performing feature extraction on the preprocessed multi-source heterogeneous data according to data types to generate multi-source heterogeneous features; performing conversion processing on the multi-source heterogeneous features through a heterogeneous feature self-combination algorithm to generate fusion features, and introducing CLS nodes to generate single time step diagram structure data; cLS node vector representation is extracted from the single time step graph structure data through a graph neural network; paying attention to key data through an attention mechanism for CLS node vector representation to generate a comprehensive feature vector; and performing security situation awareness classification on the comprehensive feature vector through a CatBoost algorithm to generate a security situation awareness result. The dynamic change of the flight environment can be responded in real time, and the flight safety and the situation prediction capability of the unmanned aerial vehicle are enhanced.

The invention provides a track fusion method and system based on Kalman filtering and a convex combination theory, and the method comprises the steps: data collection: collecting ship AIS data information collected by a base station at a certain place; data processing: carrying out data cleaning, data conversion and data aggregation processing on the acquired ship AIS data information; track data generation: assuming that a ship track is in uniform linear motion, generating track data according to an object motion expression, and considering loss caused by external influence to obtain an observation model; kalman filtering processing: processing the generated track data by using a Kalman filtering algorithm, and sequentially ending the prediction of the track information of all the sensors; and performing convex combination fusion, inputting predicted trajectory information after Kalman filtering iteration of each sensor is finished, and performing flight path fusion by adopting a convex combination algorithm. The method has technical advancement in the aspects of structure processing, track association, track state estimation fusion and the like, and the effect and accuracy of track fusion can be improved.

The present invention discloses a method for extracting the periodic and trend characteristics of InSAR ground subsidence considering spatial heterogeneity. This method obtains long-time series synthetic aperture radar image data, uses the PS-InSAR technology to conduct high-precision monitoring of ground subsidence. On this basis, principal component analysis (PCA) is adopted to reduce the dimension of deformation information, and a combined algorithm integrating spatio-temporal spectral clustering and independent component analysis (ICA) of blind source signals is proposed to efficiently identify different types of subsidence characteristics, extract the periodic characteristics, trend characteristics, and random characteristics of different categories of ground subsidence, and can conduct a more comprehensive, detailed, and accurate analysis of ground subsidence, providing a more reliable and scientific basis for subsidence monitoring, risk assessment, and prevention and control decision-making, and having broad application prospects.

The application discloses a programmable Josephson quantum voltage standard AC output characteristic comparison test system and method. The application generates synchronous step waves through homologous triggering of two sets of PJVS systems, directly collects difference signals of the two step waves by using a differential sampler, and realizes high-precision comparison of AC output characteristics of the two sets of PJVS systems by combining difference correction and effective value calculation. The application saves a complex intermediate transmission standard, simplifies the comparison process, and improves test efficiency and result accuracy. The application considers theoretical differences caused by parameters such as a PJVS junction parameter, a step number, a bias combination algorithm and a working microwave frequency difference, and further improves test precision by correction, and is suitable for comparison scenes of quantum voltage standard systems of different manufacturers and different parameter configurations.

The invention discloses a regional photovoltaic output data enhancement and ultra-short-term power prediction method based on a dynamic graph attention network combinatorial algorithm, and the method comprises the following steps: firstly, decomposing a photovoltaic power sequence into a plurality of subsequences with stable trends through employing a variation mode decomposition model optimized by a Bayesian algorithm, combining and constructing a feature matrix; secondly, constructing a combined model based on a dynamic graph attention network and Transform, representing a space-time dynamic association relationship of a feature matrix, and then predicting and reconstructing each sub-sequence at a missing moment to obtain a complete photovoltaic data set; and finally, introducing a period-trend component decoupling prediction model based on a variational inference principle, decomposing the photovoltaic power into a periodic component and a trend component, respectively predicting the periodic component and the trend component, and reconstructing the periodic component and the trend component, thereby improving the photovoltaic prediction precision. According to the invention, the authenticity and prediction precision of photovoltaic data can be effectively improved, and powerful support is provided for new energy consumption and safe operation of a power system.

The invention discloses a foundation pit engineering emergency disposal system combined with safety system evaluation and a response method, the system comprises a multi-source data acquisition module, a safety system evaluation module, an intelligent emergency disposal module and a recovery optimization module, and each module forms a closed-loop architecture. The multi-source data acquisition module acquires full-cycle data; the safety system evaluation module calculates a comprehensive weight and a risk value through a combinatorial algorithm, and quantifies a safety level; the intelligent emergency disposal module starts a grading scheme based on an evaluation result and optimizes a decision based on a BIM model; and the recovery optimization module optimizes the parameters of the feedback system. According to the response method, efficient disposal is achieved through data collection, grade judgment, grading response and redisk updating. The method improves the risk identification efficiency and judgment accuracy, optimizes the resource allocation, reduces the disposal cost, adapts to a complex foundation pit scene, and guarantees the safety of construction and the surrounding environment.

The invention provides a collaborative optimization method for working parameters of a wheel loader, which comprises the following steps of: firstly, dividing the running distance of the loader in a stepping manner to ensure that the distance of each step is equal, then accurately calculating a single-stepping cost function and a stepping time length in each stepping state by adopting a dynamic programming-Brench combinatorial algorithm and combining an initial weight factor, and finally calculating the working parameters of the wheel loader according to the single-stepping cost function and the stepping time length. And accumulating and calculating the total running time of the loader. And if the time length is different from the preset time length, dynamically adjusting the weight factor through a rooting algorithm until a preset condition is met. According to the process, collaborative optimization of the driving speed and the energy management strategy is achieved, the charging and discharging rate and frequency of the battery are effectively reduced, and therefore battery aging is slowed down. Compared with an existing electro-hydraulic parallel hybrid drive system, the energy utilization efficiency is remarkably improved, the service life of a battery is greatly prolonged, and the overall economical efficiency and environmental protection performance of the loader are improved.

The invention discloses a precipitation prediction method based on a GRU and an LSTM neural network. The precipitation prediction method comprises the following steps: selecting meteorological station data continuously monitored for a long time; removing abnormal values of the meteorological station data, and complementing missing values; selecting first 85% of historical data for model parameter training, and selecting last 15% of recent data for model generalization ability test; normalizing the three precipitation index dimensions of the training set and the test set respectively; combining an algorithm model by using GRU and LSTM components; inputting the preprocessed training set data into the network, using a mean square error as a loss function, adopting an Adam algorithm to optimize the loss function, and iteratively updating network parameters until the model converges; data to be predicted are input into the trained model after being preprocessed in the first step, and a precipitation prediction result is output. Sequence data information is processed through multiple influence factor data and the superposed GRU and LSTM combination, the gradient disappearance problem is avoided, the prediction effect is good, precision is high, and applicability is good.

The invention relates to the technical field of unmanned operation, and provides an unmanned operation target identification method and device based on a construction site, and the method comprises the steps: obtaining a to-be-detected target image in an unmanned operation process; for a to-be-detected target image, a target recognition result is obtained through the target recognition model; wherein the target recognition model adopts a convolutional neural network and a transformer module, a position, where the transformer module is inserted, in a neural network structure is optimized by using a vulture-ant colony combinatorial algorithm, and the vulture-ant colony combinatorial algorithm uses an ant colony algorithm to improve two exploration strategies in an exploration stage of the vulture algorithm. And the hunger degree of the vulture algorithm is improved by adopting the recognition time and the recognition precision of the neural network, and the exploration stage and the mining stage of entering the vulture algorithm are determined according to the improved hunger degree. The unmanned operation target recognition accuracy of the target detection neural network is improved, and the calculation efficiency is also considered.

The invention discloses a lung adenocarcinoma prognosis prediction method based on machine learning. RNA transcription data and clinical information of a lung adenocarcinoma patient are collected from a database, and a data set is constructed; performing gene expression data preprocessing and difference analysis to obtain differential expression genes; sequencing the risk coefficients of the differential genes by using single-factor COX analysis; carrying out survival related feature recognition on the screened genes by virtue of eight machine learning algorithms related to survival analysis to obtain related genes with prognosis values; a machine learning combinatorial algorithm is utilized to train multiple prognosis models, C-index average values of the prognosis models are calculated and compared, and an optimal StepCox-RSF algorithm is selected to carry out patient layering and prognosis prediction model construction according to feature genes; according to the risk score of the patient, predicting the total survival rate and the risk grouping condition of the patient; according to the method, the reflecting capacity of the model to the immune microenvironment is remarkably improved, the prediction precision is optimized, and a scientific basis is provided for personalized treatment of lung adenocarcinoma patients.

The present invention provides a transmission parameter optimization method and system based on a combined intelligent algorithm, wherein the transmission ratios of each gear distributed in a geometric progression are obtained according to the number of gears in the transmission, the maximum transmission ratio and the minimum transmission ratio; the transmission ratios of each gear distributed in a geometric progression are subjected to a certain value range and a value step length to obtain a combination scheme of the transmission ratios of the transmission gearbox; according to the obtained combination scheme of the transmission ratios of the transmission gearbox and a preset dynamic performance model and a fuel economy model, a multi-objective optimization is performed based on a combined algorithm of an ant colony algorithm and an artificial fish swarm algorithm to determine a Pareto optimal solution, and the best transmission ratio scheme of the transmission gearbox is obtained according to different selections of requirements; the present invention combines the ant colony algorithm that can quickly converge to a local optimal value and the artificial fish swarm algorithm that can obtain a wide range of robustness advantages, so as to form a combined intelligent algorithm, take dynamic performance and fuel economy into consideration, realize more accurate and rapid multi-objective optimization, and obtain the optimal matching scheme of the transmission gearbox efficiently and accurately.

The invention discloses a method for solving a feedback set of a network, which relates to the technical field of software engineering, logic circuit design and social networking services, and comprises the following steps of: for a graph G, searching a cut edge of the graph G by using a depth-first search algorithm, and if the graph G has the cut edge, deleting the cut edge; if the maximum degree of the graph G is 0, ending the process, and outputting a feedback set; if the B is an isolated point, deleting vertexes in the B, and if the maximum degree of the B is at least 2, firstly putting a vertex v with the maximum degree in the B into a feedback set; according to the method, the feedback set of the network is directly obtained by alternately deleting the vertex with the maximum degree of the network and deleting the cut edge by utilizing the structural characteristics of the graph, and compared with the traditional solving modes such as a linear programming algorithm and a polyhedron combination algorithm, the solving method does not need to construct an adjacent vector and a feedback set polyhedron firstly, so that the solving efficiency is improved. The problem is converted into a linear programming problem, a feedback set result is directly obtained, solving is simpler and more convenient, and the result is more visual.

The present invention relates to the fields of semi-supervised learning and online learning, and particularly to a semi-supervised algorithm in the scenario of arbitrary online data streams. The algorithm framework mainly includes four parts: constructing arbitrary data streams, learning latent rules through Gaussian connection GC, learning geometric structure features of data through local density peak Local-DPC, and an online combination algorithm for accelerating convergence. The construction of arbitrary data streams is for datasets with two situations of mixture and missingness that occur in real online application scenarios; learning latent rules through GC is to construct a latent data feature space by using the observed data space through the marginal distribution function; learning the geometric structure of the data feature space through Local-DPC to construct pseudo-labels for missing labels. Finally, an online combination algorithm for accelerating convergence is constructed for models under different data distribution spaces. The semi-supervised algorithm model in the scenario of mixed online data streams not only effectively solves the problem of filling missing data, but also solves the problem of missing labels for missing data.

This invention discloses a method for selecting characteristic wavelengths in the near-infrared spectrum of turmeric vinegar solution based on a combinatorial algorithm, belonging to the fields of chemometrics and online detection of explosives. This invention utilizes a differential algorithm to obtain an initial solution. After initialization, mutation, crossover, and selection operations, a set of characteristic wavelengths for the differential optimization algorithm is obtained, reducing the time spent blindly searching in the initial stage of the Cuckoo Algorithm due to the large amount of information, thus improving the efficiency of selecting characteristic wavelengths in the near-infrared spectrum of turmeric vinegar solution. This invention uses the preprocessed spectrum corresponding to the characteristic wavelength set of the differential optimization algorithm as the initial variable, and uses the Cuckoo Algorithm to select characteristic wavelengths for the initial variable, effectively reducing dimensionality while maximizing the provision of effective information. The near-infrared quantitative analysis model for hexamethylenetetramine concentration in turmeric vinegar solution established by this invention using characteristic wavelengths selected by the combinatorial algorithm can reduce model complexity and improve model prediction accuracy and robustness.

The application discloses a kind of based on multi-sensor fusion's monitoring equipment intelligent early warning and linkage control system, belong to the technical field of abnormal state alarm system.System includes sensing perception layer, data fusion processing layer, intelligent early warning layer, linkage control layer and operation and maintenance management platform;Sensing perception layer acquires multidimensional data and standardization processing;Data fusion processing layer realizes double-layer fusion of multi-source heterogeneous data by improving evidence theory and combination algorithm, exports equipment state evaluation and fault location result;Intelligent early warning layer is based on improved clustering algorithm and completes fault classification early warning;Linkage control layer is matched with repair strategy by fault repair decision tree algorithm intelligent;Operation and maintenance management platform carries ARIMA time series prediction algorithm and realizes fault prevention beforehand.This application realizes monitoring equipment whole state accurate perception, fault early warning, and is suitable for snow bright project, wisdom traffic, safe city and other scenes.