595results about How to "Achieve optimization" patented technology

The invention provides a power distribution network reliability cooperative control method and system under distributed power supply access, relates to the technical field of electric power, and designs a power distribution network reliability index system considering a distributed power supply based on a load demand uncertainty model, and adopts a Monte Carlo simulation method. Evaluating the reliability of the power distribution network in combination with the power distribution network reliability index system, and formulating a cooperative control strategy including distributed power supply output control, load demand response management and network topology reconstruction according to an evaluation result; a multi-target cooperative control optimization model with reliability improvement, economical efficiency optimization and environmental benefit maximization as targets is established, the multi-target cooperative control optimization model is solved by adopting an improved particle swarm algorithm, and an optimal cooperative control strategy is generated; and based on the optimal cooperative control strategy, issuing and executing a control instruction to each control object.

The invention belongs to the technical field of Linux kernel schedulers, and particularly relates to a Linux kernel scheduler parameter optimization system and method.The Linux kernel scheduler parameter optimization system comprises a data collection module, a large model training and optimization module, a kernel scheduler module, a code generation and verification module, a small model training module and a small model reasoning module; the large model is responsible for periodically providing a visual angle of global optimization, generating an initial weight and a scheduler prototype of the small model, extracting a rule from long-term accumulated data, and ensuring that an optimization strategy has a global visual field. Meanwhile, the small model serves as a local adapter to process the dynamic scheduling requirement of a kernel mode in real time. The layered optimization mechanism can balance short-term adaptability and long-term performance improvement.

The invention relates to the technical field of computer television image communication, in particular to a computer television all-in-one machine control method, system and device. The method comprises the following steps: through multi-view image acquisition and processing, generating a three-dimensional scene grid and carrying out scene element analysis to form a scene element set, then chasing limbs of a user, generating a user limb image set, restoring an environment basic tone, obtaining display content of the all-in-one machine and carrying out content semantic analysis, and finally obtaining a user limb image set. Calculating interaction intensity to obtain content interaction intensity data, speculating a user body language and performing user intention prediction in combination with the interaction intensity data, adjusting display content of the all-in-one machine based on the predicted user operation intention, generating simulation adjustment content, performing composition optimization through a scene element set, and generating updated display content. The method for controlling the computer and television all-in-one machine is more accurate and more efficient.

The invention discloses an AI-based intelligent energy management method and system for air conditioning equipment. The method comprises the following steps that indoor environment data are collected in real time through a sensor on the air conditioning equipment; cleaning, de-noising and normalizing the acquired environment data; collecting air conditioner setting preference data of the user; based on the collected data, reasoning and result output are conducted on operation of the air conditioner through an AI model in the edge computing device; according to the result output, an air conditioner control strategy adapting to the current environment and user requirements is generated; dynamically adjusting the operation mode of the air conditioner based on the real-time load and the power grid condition; the air conditioner control strategy is adjusted according to feedback data in the air conditioner operation process; according to the AI-based intelligent energy management method and system for the air conditioning equipment, a comfortable environment can be provided, efficient energy management can be achieved, the overall energy consumption is reduced, and the pressure on a power grid is reduced.

The invention relates to the technical field of building design, in particular to a low-carbon building design method based on a full life cycle. The method comprises the following steps: acquiring building material data; performing data preprocessing on the building material data to generate building material processing data; obtaining a building three-dimensional diagram and construction process data; performing material carbon emission coefficient calculation on the building material processing data to generate material carbon emission data; building a building dynamic process three-dimensional diagram by using the building three-dimensional diagram and the construction flow data, endowing material attributes by using building material data, endowing carbon emission by using material carbon emission data, and generating a three-dimensional dynamic carbon emission model; therefore, by constructing the full-life-cycle carbon emission three-dimensional dynamic model, the problem that in traditional building carbon emission evaluation, a static model is difficult to adapt to dynamic environment changes is solved, and the accuracy and real-time performance of building full-life-cycle carbon emission management are improved.

The invention provides a large-scale language model knowledge editing method and device based on pattern matching, and the method comprises the steps: obtaining target editing knowledge which is used for carrying out the knowledge updating of a large-scale language model; determining a plurality of mode matching degrees between each level in the large language model and the target editing knowledge; determining the highest mode matching degree in the plurality of mode matching degrees, and taking the hierarchy corresponding to the highest mode matching degree as a target editing layer; performing rank-one editing on the multi-layer perceptron of the target editing layer based on the target editing knowledge to obtain an edited large language model; according to the method and the device, other irrelevant data is not influenced while knowledge editing is accurately carried out.

The invention belongs to the technical field of energy storage power stations, and particularly relates to an energy storage power station electric quantity optimization method based on graph calculation, which comprises the following steps: S1, predicting future power generation capacity and load demand of an energy storage power station based on graph calculation: acquiring historical power generation data and load data of a distributed power station in a grid-connected area of the energy storage power station, and external influence data, constructing a time-space diagram neural network model, and obtaining prediction results of the power generation data and the load data in each hour period of the next 24 hours; s2, calculating the power and load value of each node in the power grid through power flow analysis; and according to the predicted future power generation capacity and the load demand, the electric quantity needing to be provided or consumed by the energy storage power station is calculated. Optimization of energy of the power storage station is achieved, and the cost of the power storage station is reduced.

The invention relates to the technical field of data analysis, and discloses a laboratory data analysis method and system based on a machine learning technology. The method comprises the following steps: firstly, introducing an adaptive threshold, and identifying and deleting abnormal data in original laboratory data by using an improved isolated forest algorithm to obtain a processed laboratory data set; secondly, improving the generative adversarial network to obtain an improved generative adversarial network filling model, and performing data filling on the processed laboratory data set for multiple times to obtain a processed laboratory data set; according to errors of experimental result data, an improved black-wing optimization algorithm is used for searching for the minimum error, and optimization of laboratory data is achieved; and finally, establishing a laboratory database, performing data storage on the processed laboratory data set, and completing laboratory data analysis. By analyzing and processing the laboratory data, the purpose of analyzing the laboratory data is achieved, and the method is accurate and objective.

According to the energy storage coupling heat pump steam generation method and system, the mode that the heat pump device and the energy storage device cooperatively operate is adopted, and efficient and economical steam supply is achieved. The method comprises the steps that high-temperature water is prepared and stored in the off-peak electricity period through the heat pump device, and the high-temperature water is further heated through the energy storage device to generate superheated steam for a user to use. The energy storage device comprises a plurality of energy storage modules, and a heat exchange pipeline of the energy storage device can be switched between a parallel connection mode and a series connection mode through switching, so that deep heat release and flexible regulation and control are achieved. According to the method, the energy storage capacity and heat release power of the energy storage module are dynamically adjusted, enthalpy increment distribution of the heat pump and the energy storage device is optimized in combination with load prediction, and diversified steam requirements are met. And meanwhile, preheating or dynamic supplement is conducted on the energy storage module through the heat pump device, waste heat recovery is achieved, and the energy efficiency is improved. According to the method, the operation cost is remarkably reduced, the capacity of the system for adapting to load fluctuation is enhanced, and the method is suitable for industrial steam use scenes and has the advantages of being efficient, stable and flexible.

Disclosed are a synchronous rectification control circuit, a synchronous rectification control method and a switching power supply, the synchronous rectification control circuit comprising: a volt-second setting unit for processing a volt-second product reference value into at least any two of a first volt-second product threshold to a third volt-second product threshold; the first control driving unit is used for sending a first conduction signal to conduct the synchronous rectifier tube and controlling the pulse width of the first conduction signal according to a first volt-second product threshold value; the second control driving unit is used for calculating a first volt-second product of voltages at two ends of the secondary winding within first time after the first conduction signal is generated, and controlling the synchronous rectifier tube to be conducted for follow current after the first volt-second product is greater than a second volt-second product threshold value; and the voltage judgment unit is used for calculating a second volt-second product of voltages at two ends of the secondary winding within a second time after the first conduction signal is generated, and judging the state of the input voltage according to the second volt-second product and a third volt-second product threshold value. Through the arrangement, the volt-second setting pin of the synchronous rectification control circuit can be reused to execute various functions.

The embodiment of the invention discloses a video generation method and electronic equipment. The method comprises the following steps: creating a 3D virtual film field environment based on a 3D development engine; after a video generation demand text is received, a script is generated through mutual cooperation among multiple agents based on an artificial intelligence AI large model, the multiple agents are used for simulating multiple function posts involved in the manual video production process, and the script comprises role brief introductions in the script. A line text and role positions, actions and lens types corresponding to lines; and performing structured processing on the script, and simulating a video shooting process in the 3D virtual film field environment according to the structured script so as to generate a target video. According to the embodiment of the invention, the video generation quality and the character consistency can be improved.

The invention relates to the field of polyolefin plastics, in particular to a glass fiber reinforced modified polyolefin plastic and a preparation method thereof, which are used for solving the problem that the application of the existing glass fiber reinforced modified polyolefin plastic in some high-performance fields is limited due to poor mechanical strength and flame-retardant and fireproof performance of the existing glass fiber reinforced modified polyolefin plastic. According to the preparation method, the organic phosphorus-nitrogen modified glass fiber is added into the polyolefin resin to serve as a reinforcing agent, so that the polyolefin plastic not only is obviously improved in mechanical property, but also can keep relatively good performance in a high-temperature environment, and the flame retardant property and the fireproof grade are obviously improved; and the fireproof door can meet the fireproof standard with higher requirements. Meanwhile, according to the preparation method, a reasonable production process and technical means are adopted, and good combination of the glass fiber and the polyolefin material is ensured, so that comprehensive optimization of the plastic performance is realized, the performance and the application range of the polyolefin plastic are remarkably improved, and the requirement of high-performance application under various complex environmental conditions is met.

The invention discloses a signal timing scheme automatic generation system based on multiple algorithms, and the system comprises an index data statistics module which is used for carrying out the statistics of a plurality of preset index data in obtained traffic big data in any traffic region which is delimited in advance, and obtaining a statistical result of the index data; the index algorithm model management module is used for performing result representation on any index in the statistical result by using a corresponding specific index algorithm model; the optimization model generation module is used for constructing a multi-algorithm signal timing optimization model for a plurality of index algorithm models corresponding to the plurality of indexes; and the problem solving module is used for solving the optimization model and generating a signal timing scheme of the target traffic area according to a solving result. According to the invention, through integration of the multi-algorithm model, automatic generation of the traffic signal timing scheme is realized, the efficiency and effect of traffic management are improved, and powerful technical support is provided for sustainable development of urban traffic.

The invention provides a lepidolite roasting process optimization method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a plurality of pieces of sample data, each piece of sample data including a target parameter value corresponding to a decision parameter variable in a lepidolite roasting process and index data corresponding to a preset index, the preset indexes comprise at least one of the following indexes: lepidolite transformation rate, energy consumption intensity and carbon emission intensity; constructing a mapping function between the decision parameter variable and a preset index according to the multiple pieces of sample data; and by taking an index target corresponding to the preset index as an optimization target, optimizing the decision parameter variable based on a preset optimization algorithm and the mapping function to obtain a target parameter set corresponding to the decision parameter variable. According to the embodiment provided by the scheme, the technological parameters in the roasting process of the lepidolite rotary kiln can be optimized, the reliability of the technological parameters in the roasting process of the lepidolite rotary kiln is improved, and then the roasting stability of the lepidolite is improved.

The invention relates to the technical field of cable safety, in particular to a cable repairing method based on hierarchical path planning and related equipment, and the method comprises the steps: obtaining an original point cloud of a target cable, carrying out the preprocessing of the original point cloud, and obtaining a damaged region point cloud; according to the method, the damaged area point cloud is subjected to voxelization processing, the damaged area point cloud subjected to voxelization processing is subjected to internal voxel filling, the zigzag path of the target cable is generated according to the voxelization damaged point cloud, the target cable is repaired based on the zigzag path, the method can adapt to the complex form of the damaged area, and the repairing integrity and the repairing quality are improved.

The invention provides a welding machine current adjusting system and method based on high-effect chopping control, and relates to the technical field of current control, and the welding machine current adjusting system comprises the steps that a welding positioning modeling module carries out modeling to obtain a welding positioning model; the welding data extraction module is used for identifying welding seam change nodes and extracting welding area data to obtain a plurality of welding material parameter sets; the chopping analysis module is used for carrying out chopping control analysis on the welding material parameter sets and outputting a plurality of chopping modulation parameters; the welding track fitting module carries out switching stability analysis and welding track fitting and outputs K welding tracks; the modulation parameter assembling module is used for assembling a plurality of chopping modulation parameters to obtain K chopping modulation sequences; the welding sequence analysis module is connected with K welding tracks to generate an optimized welding track; and the current control module performs dynamic current control. The technical problem that according to a traditional adjusting method, the welding current cannot be rapidly adjusted according to changes of welding conditions in the welding process is solved, and the welding precision and the welding quality are improved.

The invention discloses a multi-objective optimization scheduling method and system for an electricity-hydrogen collaborative micro-grid, and the equipment unit in the electricity-hydrogen collaborative micro-grid comprises a photovoltaic output unit, a fan output unit, a battery energy storage unit, a diesel generator unit, a hydrogen energy storage unit, and a flexible load demand side management unit. The method comprises the following steps: constructing a multi-target optimization scheduling model of the power-hydrogen collaborative micro-grid based on an operation model of an equipment unit in the power-hydrogen collaborative micro-grid; and solving the established multi-target optimization scheduling model of the electricity-hydrogen collaborative micro-grid to obtain an optimization scheduling scheme. According to the method, an emerging hydrogen energy technology is incorporated into a micro-grid system, a hydrogen energy storage unit in the micro-grid is incorporated into an optimal scheduling scheme, a hydrogen power generation and electric hydrogen production system is integrated with traditional battery energy storage and renewable energy sources, and the diversity and flexibility of micro-grid scheduling are improved.

The invention relates to the technical field of safety helmet positioning and ranging, and provides an intelligent safety helmet positioning optimization method and system based on a positioning precision evaluation index, and the method comprises the steps: obtaining a to-be-analyzed intelligent safety helmet positioning and ranging algorithm; setting a positioning precision evaluation index and constructing a positioning base station coordinate simulation environment; performing simulation analysis on the to-be-analyzed intelligent safety helmet positioning ranging algorithm by using the positioning precision evaluation index and the positioning base station coordinate simulation environment to obtain insufficient positioning precision of the to-be-analyzed intelligent safety helmet positioning ranging algorithm; and obtaining an intelligent safety helmet positioning and ranging optimization algorithm according to the insufficient positioning precision. The method can achieve the optimization of the positioning and ranging algorithm of the intelligent safety helmet, facilitates the improvement of the positioning and ranging accuracy of the intelligent safety helmet, and provides a basis for the stable and efficient use of the intelligent safety helmet.

The invention discloses a mica material production optimization method and system based on big data analysis, and relates to the field of mica production technologies, and the method comprises the steps: obtaining a mica processing product type and a historical mica processing product type; acquiring initial equipment parameters of all mica processing equipment; acquiring mica processing data and mica historical processing data according to multiple types of sensors preset in mica processing equipment; screening out core process parameters in the initial equipment parameters; calculating a core process standard value corresponding to the core process parameter; calculating the loss rate of the mica raw material in the mica processing process; screening and outputting local correction factors and global correction factors; and correcting the initial equipment parameters to obtain optimal equipment parameters. The mica processing technology is optimized, and the utilization rate of mica processing raw materials and the quality of mica processing products are improved.

The invention provides an in-vehicle noise optimization method and device, equipment and a medium. The method comprises the steps that at least one acoustic package strategy for an electric drive assembly of the vehicle is obtained; on the basis of a virtual acoustic environment created for the electric drive assembly in advance, the sound insulation amount of all the acoustic package strategies for noise generated by the electric drive assembly is determined; according to the data of each acoustic packet strategy on each of at least one preset dimension, determining a target acoustic packet strategy in the at least one acoustic packet strategy, the at least one preset dimension comprising the sound insulation amount; and according to the target acoustic package strategy, performing sound insulation treatment on the electric drive assembly. According to the method and the device, the acoustic package optimization based on the electric drive assembly can be quickly realized under the condition of shortage of resources and periods in the early stage of a project, the efficiency of in-vehicle noise optimization is remarkably improved, and the cost of in-vehicle noise optimization can be remarkably reduced.

The invention provides a data transmission method and device for ocean perception data and electronic equipment, and relates to the technical field of communication networks. In the application, through the at least one sensing node, the intermediate node and the computing node included in the node set corresponding to the target sea area, a multi-source target ocean sensing network can be constructed, higher requirements of ocean meteorological prediction on data transmission and processing are met, and a future intelligent ocean meteorological prediction scene is enabled. Moreover, at least one data transmission link meeting the preset data transmission link condition is selected from the target ocean sensing network, so that the optimization of link resources in the target ocean sensing network is realized, the link configuration in the target ocean sensing network is dynamically adjusted, the transmission delay of the ocean sensing data is reduced, and the transmission efficiency of the ocean sensing network is improved. Therefore, the overall transmission efficiency of the target ocean sensing network is improved. The rationality and timeliness of ocean sensing data transmission can be improved, and the performance and real-time forecasting capability of an ocean sensing network are further improved.

The invention discloses a trapezoidal sail multi-target size optimization method based on an NSGA-II algorithm, and relates to the technical field of unmanned sailboats. According to the method, the aspect ratio, the taper ratio and the camber ratio are selected as design parameters of the trapezoidal sail, after multiple sets of design parameters are sampled and obtained within the value range of the design parameters based on a Latin hypercube sampling method, a trapezoidal sail model is constructed by utilizing fluid simulation analysis software for each set of design parameters, and the trapezoidal sail model is obtained. Performance evaluation parameters corresponding to each group of design parameters are obtained through simulation, a database is constructed, the database is utilized to train a Kriging agent model for replacement, multiple groups of trapezoidal sail design parameters to be preferentially selected are selected, and multi-target size optimization is performed on the trapezoidal sail design parameters based on an NSGA-II algorithm and the trained Kriging agent model. And determining optimal trapezoidal sail design parameters and verifying the accuracy of the optimal trapezoidal sail design parameters. According to the method, trapezoidal sail design parameters and performance evaluation parameters are combined, and a basis is provided for optimization of a sail design scheme by using an NSGA-II method and a Kriging agent model.

The invention relates to the technical field of phototherapy instruments, in particular to a light control system and method for a phototherapy module, and the system comprises a plurality of assembly carriers, a plurality of line concentration assemblies, a plurality of phototherapy assemblies, a control assembly, a gravity sensing assembly, and a temperature sensing assembly. The line concentration assemblies are sequentially connected in series, the phototherapy assemblies are connected with the line concentration assemblies, the line concentration assemblies are transversely arranged on the assembly carrier, the assembly carrier is provided with an assembly groove, the phototherapy assemblies are arranged on the assembly groove, and the control assembly comprises a phototherapy control module, a gravity receiving module, a temperature sensing receiving module and a power supply module. The phototherapy control module is connected with the phototherapy assembly through the line concentration assembly so as to control the phototherapy assembly. The number of components can be flexibly increased or decreased, and the layout can be adjusted to adapt to wider treatment scenes and individual requirements.

The invention discloses an automatic optimization control method for resistance spot welding, which relates to the technical field of resistance spot welding, and comprises the following steps of: acquiring welding object parameters and material information, selecting a proper electrode cap, and calculating an ideal welding current value; a historical database and a prediction model are established to predict the optimal combination state of current and pressure, and the welding process is optimized; ideal parameters are integrated into control parameters, and spot welding equipment is controlled to automatically complete spot welding work; a centering test is carried out before welding, so that the abnormal welding condition is avoided; through an intelligent algorithm and historical data analysis, welding parameters in the resistance spot welding process are automatically adjusted, efficiency and stability are improved, defective products are reduced, energy is saved, and higher-quality welding and higher production efficiency are achieved.

One or more embodiments of the invention disclose a heuristic algorithm-based receiving and transportation cooperative scheduling method and apparatus, and an electronic device. The method comprises the steps of obtaining attribute parameters and task parameters related to a collection and transportation cooperation task between the harvester and the grain transport vehicle, wherein the task parameters are parameters describing the collection and transportation cooperation task; based on a preset optimization target, using the attribute parameters and the task parameters to construct a collection and transportation cooperation model used for solving a target grain unloading strategy of cooperative grain unloading of the harvester and the grain transport vehicle, the target grain unloading strategy at least comprises a transfer path for the grain transporting vehicle to move to a grain unloading point in the harvesting path after receiving a grain unloading request; based on the harvesting path of the harvester, using a heuristic algorithm to solve the harvesting and transporting cooperation model to obtain a target grain unloading strategy; and executing the target grain unloading strategy to schedule the grain transport vehicle to assist the harvester to unload grains based on the transfer path.

The invention discloses an expressway traffic flow control method, device and system, and the method comprises the steps: extracting a preset key feature from obtained historical road traffic data, and constructing a feature vector; sending the feature vector to a pre-trained traffic state prediction model, and predicting a current traffic state; the hyper-parameters of the traffic state prediction model are obtained through PSO algorithm optimization; sending the predicted current traffic state to a pre-trained control strategy model to obtain an optimal control strategy; the control strategy model is established and obtained by taking maximization of traffic flow efficiency, minimization of congestion degree and minimization of accident risk as targets. According to the method, the generalization ability and the prediction precision of the model can be improved, so that a solid technical support is provided for active control of the highway traffic flow.

The invention relates to a bioelectrical impedance spectroscopy noninvasive blood glucose detection method, and relates to the technical field of noninvasive blood glucose detection. The method comprises the following steps: firstly, acquiring a bioelectrical impedance spectrum of the forearm of a human body by using a bioelectrical impedance sensor, then, reducing the difference of bioelectrical impedance between different subjects and sessions by using a proposed baseline correction method, and finally, expanding and balancing a sample based on an Optuna hyper-parameter optimization algorithm. Frequency and feature selection and regression model establishment are integrated into one evaluation index, and overall optimization of hyper-parameters is achieved. Specifically, the problems of small sample size and large blood glucose extreme value estimation deviation are simultaneously solved by using a data generation method based on K nearest neighbor in combination with a Clark error grid evaluation index and sample density weighting. Secondly, according to prior information of a data structure, using sparse group minimum absolute shrinkage with a weight proportion threshold value and a selection operator to simultaneously select frequency and characteristics which contribute to modeling to the greatest extent; and finally, an ensemble learning algorithm XGBoost is selected and used to establish a final model. The overall optimization method improves the robustness of the model and the accuracy of the estimation result.

The invention relates to the technical field of data processing, and discloses a limit scooter structure design management method and system and a limit scooter. The method comprises the following steps: performing intelligent acquisition and preprocessing on multi-source heterogeneous data to obtain a multi-dimensional comprehensive data set; performing multi-level index construction and nonlinear calculation on the multi-dimensional comprehensive data set to obtain a limit scooter structure design quantitative index system; performing spatio-temporal multi-scale feature analysis on the limit scooter structure design quantitative index system to obtain a spatio-temporal distribution feature map; performing high-order correlation analysis on the spatial-temporal distribution characteristic spectrum to obtain a multi-dimensional correlation intensity distribution matrix among frame geometric parameters, wheel set configuration and a damping system, performing key factor identification and sensitivity analysis to obtain an influence factor importance ranking table, and performing parameterized structure design scheme optimization; and obtaining a multi-target structure design scheme combination and a performance evaluation index set. According to the invention, the efficiency and accuracy of limit scooter structure design management are improved.

The invention discloses a model parameter optimization method and device for an optical model, a storage medium and a product. The method comprises the steps that a layout file corresponding to a to-be-tested graph is constructed based on preset model parameters of the optical model; executing optical proximity correction and photoetching rule check based on the layout file to obtain a corresponding check result; under the condition that the inspection result does not meet the detection passing condition, adjusting model parameters of the optical model according to the inspection result to obtain adjusted model parameters; and constructing a layout file corresponding to the to-be-tested graph by utilizing the adjusted model parameters, and returning to execute optical proximity correction and photoetching rule inspection based on the layout file until an inspection result meets a detection passing condition. According to the method and the device, the model parameters can be iteratively optimized based on the inspection results of the OPC and the LRC, the problem that the layout subjected to adaptive adjustment of the optical model is difficult to correct and optimize in the OPC process is solved, and the layout adjustment efficiency, the tape-out efficiency and the yield are improved.

The invention discloses a concrete slump detection method based on deep learning and a stirring pump truck, and the method comprises the following steps: S1, collecting historical concrete slump data and slump process data, and carrying out preprocessing; s2, extracting raw material features and mix proportion features, calculating identification feature vectors, and constructing a historical concrete slump database; s3, calculating a concrete slump sequence in the pre-processed slump process data; s4, extracting initial concrete slump and concrete slump sequence characteristics, and calculating first concrete slump; and S5, collecting the raw material data of the concrete slump detection, calculating the identification feature vector of the raw material data, and performing second correction on the first concrete slump by using the identification feature vector in the historical concrete slump database to obtain a second concrete slump. The problems that a traditional concrete detection method is high in uncertainty and difficult to deal with the influence of raw material fluctuation on the concrete slump can be solved.