378results about How to "Strong real-time" patented technology

The invention relates to the technical field of intelligent traffic prediction, in particular to a time-space correlation traffic flow prediction method based on deep learning, which comprises the following steps: S1, collecting traffic flow data and related external environment data in real time, and establishing an initial traffic network topology structure; s2, calculating the dynamic weight of each node; s3, dynamically adjusting the weight of each edge in the traffic network according to the node weight change in the S2 so as to update the topological structure of the traffic network; s4, performing feature extraction on the spatial dependency relationship among the traffic nodes, and extracting time evolution features of the traffic flow data at the same time; s5, constructing an adaptive space-time fusion model; s6, traffic flow prediction is carried out, and adaptive space-time fusion model parameters are optimized; according to the method, through dynamic fusion of the spatial-temporal characteristics and adaptive adjustment of the model parameters, the precision and real-time performance of traffic flow prediction are effectively improved, and the adaptability and generalization ability of the model in a complex dynamic traffic scene are enhanced.

The invention relates to the technical field of unmanned aerial vehicle control, and discloses an unmanned aerial vehicle inspection control system and method based on data analysis, and the method comprises the steps: receiving environment feature data of a target region, and constructing a static environment model; obtaining kinetic parameters of the unmanned aerial vehicle, and calculating a feasible flight envelope in combination with the static environment model; after taking off, the unmanned aerial vehicle collects environment data in real time, and a multi-level environment topology model is constructed; acquiring state data in real time, and judging an obstacle avoidance level; if yes, local path planning is carried out, and an optimal flight path is generated; acquiring trajectory deviation and energy consumption data, dynamically calibrating the flight envelope, and optimizing the flight envelope; according to the method, environment perception, trajectory optimization and online learning are combined, the method is adaptive to a complex environment, and the intelligent level of unmanned aerial vehicle inspection is improved.

The invention discloses a ship target detection method, device and equipment and a storage medium, relates to the technical field of image recognition, and discloses a ship target detection method comprising the following steps: obtaining a preset ship image data set; training a preset target detection model based on the preset ship image data set to obtain a ship target detection model, the preset target detection model comprising a feature extraction network, a feature fusion network and a multi-scale detection head which are connected in sequence, the feature extraction network is a lightweight convolutional neural network improved based on an attention mechanism; and obtaining a to-be-detected image, and performing ship target detection on the to-be-detected image through the ship target detection model to obtain a ship target detection result. According to the scheme, the ship target detection model has high real-time performance and accuracy when processing large-scale image data, and the detection precision of ship targets of different scales is improved.

The invention relates to the technical field of geological disaster intelligent monitoring, in particular to a geological disaster intelligent monitoring system and method based on multiple sensors. The method comprises the following steps: covering a monitoring area through multiple sensors, and collecting and summarizing environment data; separating effective signals and noise by adopting a space-time adaptive decomposition method, completing data denoising by combining a signal-to-noise ratio dynamic threshold and a local weighted regression smoothing trend residual error, then aligning time and dimensions through linear interpolation and standardization, and integrating multi-sensor data based on an adaptive weighted fusion method; the fused data is subjected to generation and review of a to-be-reviewed mark, so that the data integrity is ensured; key features are extracted, a mixed model based on LSTM and a random forest is constructed to evaluate disaster risks, and a risk grade division rule is set; and finally, outputting fusion data and an analysis result, and generating an early warning signal and a decision suggestion for potential geological disasters. According to the invention, accurate monitoring and risk assessment of geological environment changes are realized.

The invention provides a multi-agent cooperation system, which comprises a multi-agent cooperation engine module, a task decomposition and distribution module and a tool calling normal form module, and is characterized in that the task decomposition and distribution module is used for decomposing tasks and distributing decomposed sub-tasks to agents; the multi-agent cooperation engine module is used for managing cooperation and communication between agents; and the tool calling module enables the intelligent agent to dynamically call different tools. According to the multi-agent collaboration system, the collaboration efficiency among the agents, the flexibility of task decomposition, the adaptivity of tool calling and the intelligent level of user interaction are remarkably improved, and remarkable technical advantages and effects are shown in complex task processing and dynamic environment processing.

The invention relates to the technical field of intelligent equipment evaluation and fault prediction, and discloses a dynamic equipment quality evaluation method based on multi-agent collaborative reinforcement learning, and the method comprises the steps: obtaining original data from equipment and a sensor, and carrying out the preprocessing; analyzing the preprocessed data, and identifying key factors influencing the quality of the equipment; constructing a plurality of expert models, and predicting and evaluating an analysis result; fusing the weight of each expert model to obtain a comprehensive evaluation result; and performing risk assessment on the comprehensive assessment result, extracting key influence factors and decision rules, and generating an assessment result. Efficient fusion of multi-source data and automatic identification of key factors are realized, and the accuracy and reliability of equipment quality evaluation are remarkably improved; a model fusion result is optimized through dynamic weight distribution, so that the prediction performance is improved; the problems that in the prior art, data fusion is difficult, key factor identification depends on experience, and the interpretability of an evaluation result is insufficient are solved.

The invention provides an intelligent refrigeration system efficiency optimization method, system and device based on deep learning, and relates to the technical field of intelligent refrigeration system efficiency optimization. The method comprises the following steps: firstly, collecting historical operation parameters and environmental data of the refrigeration system, preprocessing, and integrating into a sample set according to a timestamp; and based on the hybrid model of the convolutional neural network and the long-short term memory network, constructing a state prediction model for predicting the system operation state. And in combination with the state prediction model, an optimization objective function is defined for data prediction at the next future moment, and an optimal adjustment strategy for system operation is formulated by using a deep Q network model. And finally, monitoring current operation parameters and environment data in real time, calculating a comprehensive safety score, comparing the comprehensive safety score with a safety threshold value, and deciding whether to continue to optimize the efficiency or preferentially carry out troubleshooting. According to the method, through combination of deep learning and physical modeling, efficient and safe refrigeration system operation optimization is realized.

The invention discloses a high-precision laser ranging system and method based on multi-frequency modulation, and relates to the field of laser ranging, and the system comprises a multi-frequency laser transmitting module which comprises generators of different frequency bands and can be independently regulated, rapidly switched and matched with a high-precision frequency modulation unit; the laser receiving and signal conditioning module is used for processing signals by using an avalanche photodiode, a low-noise preamplifier and the like; the signal processing and analyzing module is used for calculating the distance by means of a high-speed digital signal processor in combination with a multi-frequency distance measurement algorithm; a display and control module and a temperature compensation module are further included, the method comprises the steps of multi-frequency laser emission, receiving conditioning, signal processing, display control and temperature compensation, and high-precision distance measurement is achieved through fine operation according to environment and target characteristics. The method is high in distance measurement precision, and error compensation in a complex environment exceeds 90%; the adaptability is high, and automatic adjustment is achieved in different distances and environments; the real-time performance is good, and a result can be obtained within 10 milliseconds; operation is convenient, wireless and data management is supported, and laser ranging innovation is promoted.

The invention provides an ecological hydrological simulation method based on a machine learning model, and the method comprises the steps: obtaining ecological hydrological data, carrying out the preprocessing of the obtained ecological hydrological data, obtaining a data set, building an ecological hydrological model based on an improved ConvLSTM network structure, and training the ecological hydrological model based on the data set. And obtaining a trained ecological hydrological model, inputting to-be-measured data into the trained ecological hydrological model, and performing evapotranspiration prediction. According to the method, evapotranspiration prediction is realized based on various data, advantages of remote sensing data and ground meteorological data are fully utilized, evapotranspiration prediction precision is remarkably improved, real-time data can be processed, real-time performance is high, different applications can be performed according to different areas, flexible adjustment of model input can be realized, expandability is high, and the method is suitable for popularization and application. The method can be applied to multiple fields, and provides real-time support for agricultural and ecological monitoring.

The invention discloses an interferometric phase unwrapping method based on a double-branch decoding residual network, and the method comprises the following steps: S1, constructing an interferometric phase data set which comprises an interferometric phase diagram of pixel phase wrapping and a corresponding real interferometric phase diagram; s2, constructing a double-branch decoding residual network, wherein the double-branch decoding residual network comprises an input layer, an encoder network, a double-branch decoder network comprising a first branch decoder module and a second branch decoder module, and an output layer which are connected in sequence; training the double-branch decoding residual network by using the interferometric phase data set to obtain a double-branch decoding residual network interferometric phase unwrapping model and the weight of the model; and S3, inputting the interferometric phase diagram subjected to pixel phase winding into a double-branch decoding residual network interferometric phase unwrapping model to obtain a predicted interferometric unwrapping phase diagram. According to the method, the interferometric phase unwrapping under the condition of low signal-to-noise ratio is realized, and the interferometric measurement precision of the synthetic aperture radar is improved.

The invention discloses a method, a system and equipment for simulating a customer approval result based on historical data learning. The method comprises the steps of user data collection and data preprocessing; constructing and training a model; model evaluation: performing prediction accuracy evaluation on the trained model by using an independent test data set, and if an evaluation result is not good, adjusting and optimizing model parameters until the evaluation result meets requirements; adjusting a marketing strategy according to a simulation result; applying the strategy to a marketing operation scene, and applying the adjusted marketing strategy to actual marketing operation; effect monitoring and iterative optimization are carried out, feedback and iterative optimization are carried out on the model, the marketing strategy or the whole process according to the monitoring result, and the prediction accuracy and the marketing effect are continuously improved. According to the method, intelligent simulation and prediction of a complex approval process are realized, the accuracy and reliability of an approval result are greatly improved, a more personalized marketing strategy and service scheme are formulated, and the marketing effect and customer satisfaction are remarkably improved.

The invention discloses a monocular vision pose measurement method and system based on a circular feature target spot, and the method comprises the following steps: camera calibration, image collection, image preprocessing, and feature point detection and matching: employing Hough circle detection to extract the center two-dimensional pixel coordinate and radius of the circular feature target spot, obtaining a uniquely determined relationship between the feature target spots according to the circle center coordinates and the radius information; performing pose measurement, and obtaining a rotation matrix R and a translation vector T which are converted from a target coordinate system in each collected pose image to a camera coordinate system according to a PnP algorithm; and solving the relative pose. According to the method, a specially designed cooperative target containing five circular characteristic target spots is used as a target object, and a rapid detection algorithm realized according to target characteristics is combined, so that circle center matching sorting is realized, and the precision is improved. The method has the advantages of being simple in structure, simple in camera calibration, large in measurement view field, high in real-time performance and the like, and meanwhile the defects that the view field is small and stereo matching is difficult in stereo vision are overcome.

The invention discloses a sower operation soil compaction detection and evaluation method, and relates to the field of agriculture. The invention aims to solve the problem that soil is damaged due to soil compaction in the operation process of the seeder. The invention relates to a sower operation soil compaction detection and evaluation method. The method comprises the following steps: collecting characteristic data of a sower and carrying out standardization processing; calculating the soil compaction degree by utilizing the characteristic data of the sowing machine after standardization treatment; calculating a risk index according to the soil compaction degree; and calculating a comprehensive score according to the risk index, and realizing early warning according to the comprehensive score.

The invention provides a multi-gun-head charging pile power distribution method, medium and system, and belongs to the technical field of charging piles, and the method comprises the steps: firstly obtaining the historical operation data of a charging pile, and building a mathematical model containing a plurality of error terms, such as equations of charging power, time, battery capacity and the like; then, a hybrid model is constructed, and the hybrid model comprises a multi-branch neural network and a mathematical model so as to adapt to charging piles of different scales; and training by adopting historical data to obtain a power distribution model. In the actual charging process, real-time data are input, power distribution is adjusted, and feedback data are collected at the same time. And on the basis of real-time data and prediction errors, a lightweight Lora model is utilized to perform continuous fine adjustment on the fusion sub-network, and the main model is updated regularly. Through a mathematical modeling and machine learning modeling fusion mode, the technical problem that existing multi-gun-head charging pile power distribution is mainly carried out through a simple preset or fixed mode, and complex charging scenes and requirements cannot be well adapted is solved.

The invention relates to the technical field of language recognition, in particular to a multi-dialect voice text conversion system and processing method, and the system comprises an audio collection module which is responsible for collecting voice input of a user and supports PCM, MP3 and WAV formats and sampling rates of 16kHz and 44.1 kHz; the dialect detection module is used for recognizing a dialect type of voice input by utilizing a pre-training language classifier; the voice feature extraction module is used for converting the voice signal into feature representation, such as Mel spectrum and MFCC (Mel Frequency Cepstrum Coefficient); the multi-dialect speech recognition module is used for mapping the features into a text sequence based on a speech recognition model of multi-task learning; and the text post-processing module is used for outputting a final standardized text according to a language rule and a context optimization recognition result. The multi-dialect support covers main dialect languages and can be quickly expanded to new dialects through transfer learning.

The invention discloses an incremental real-time data synchronization gateway system and method, and the system comprises a database drive agent module which carries out the agent, interception and analysis of SQL statements and binding parameters, recognition of data changes, and construction of an internal model for describing the data changes for JDBC drives of various database types; the drive loader module is used for loading database drive agents corresponding to different database types; the data change identification module is used for further analyzing the internal model from the database driving agent module and identifying specific data change; the data storage adaptation module is used for converting the identified specific data change into a format supported by corresponding target storage; and the data change asynchronous sending module is used for sending the converted specific data change to target storage in an asynchronous mode. According to the system and the method, newly adding, deleting and updating operations of database data are captured in real time, and changed data are synchronized to a message queue or Elasticsearch and the like in real time for a downstream system to use.

The invention discloses a fuel cell fault-tolerant control method and device based on reinforcement learning in the field of fuel cells, and the method comprises the steps: obtaining monitoring parameters of a fuel cell in an operation state, and constructing a state space based on the monitoring parameters; obtaining an executable operation in the running state of the fuel cell, and constructing an action space based on the executable operation; constructing a reward function based on the state space and the action space by taking control cost minimization as a target; constructing a fault-tolerant control strategy model based on a DDPG algorithm, and training and verifying the fault-tolerant control strategy model through a state space, an action space and a reward function; and fuel cell fault-tolerant control is carried out through the trained and verified fault-tolerant control strategy model. According to the method, the DDPG algorithm is introduced, target state parameters are monitored in real time in the operation process, a control strategy is automatically adjusted by using reinforcement learning when a fault occurs, it is ensured that the system can still keep stable power output under the fault condition, and optimal control is achieved.

The invention discloses an intelligent power conversion cabinet control system and a power conversion cabinet, and relates to the technical field of power conversion cabinet control, and the system comprises a user interaction module, a touch screen and voice recognition convenient operation, and multilingual interaction; the battery management module is used for accurately monitoring parameters, quickly early warning abnormity and intelligently regulating and controlling charging and discharging; the battery replacement control module is high in identity verification speed and stable, accurate and fast in battery replacement operation; the data storage and analysis module is used for mass data storage and deep mining; the communication module is used for realizing dual-mode stable transmission and encrypted cache; the cabinet body comprises a plurality of battery cabins, the protection is comprehensive, the heat dissipation is intelligent, the modular design is beneficial to expansion, and the high-speed bus is strongly connected. The system is convenient in interaction and efficient in battery replacement. The battery management is accurate, the service life is prolonged, the data-driven operation is optimized, the communication is safe and stable, the remote monitoring is convenient, the cabinet body design is reasonable, the protection and heat dissipation are good, the modularization is flexible, the battery replacement service experience and operation benefits are powerfully improved, and the development of the intelligent battery replacement field is promoted.

The invention belongs to the technical field of ship and ocean engineering, and discloses a ship seakeeping intelligent forecasting method and system based on a physical information neural network. According to the method, a CFD-RAO method is combined with PINNs, and rapid and high-precision prediction of ship seakeeping is realized by constructing a coupling model of a physical equation and a neural network loss function. According to the invention, rapid and high-precision prediction of ship seakeeping is realized. The method not only can effectively cope with multi-degree-of-freedom coupling motion under complex sea conditions, but also has high real-time performance and adaptability, and provides more scientific and reliable technical support for ship design and navigation safety.

The invention relates to an automobile automatic obstacle avoidance system based on real-time 3D target detection, the system comprises a point cloud data collection module, an image data collection module, a LoGoNet network and an early warning and feedback module, and the LoGoNet network is used for realizing real-time target detection of an obstacle, detecting whether there is an obstacle, and judging the category of the obstacle. The system is a local-to-global multi-modal data fusion network, can carry out real-time 3D target detection automobile automatic obstacle avoidance, solves the inherent limitation of a single mode, carries out local-to-global multi-modal fusion by using the complementary characteristics of an image and a laser radar, provides a more complete three-dimensional environment perception capability through multi-source fusion, and improves the real-time 3D target detection efficiency. And the vehicle is helped to better perceive the surrounding environment.

The invention discloses a power transmission line insulator defect detection method based on deep learning, and the method comprises the steps: S1, obtaining an aerial image, carrying out the preprocessing of the aerial image, and generating an insulator defect detection data set; s2, based on a YOLOv8n model, introducing a space object attention module (RCS-OSA), and improving the feature extraction capability under a complex background; s3, adding an extrusion excitation network (SENet) in the neck network of the model to enhance the feature expression ability between channels; s4, constructing a small target detection layer, and adding a high-resolution feature map (160 * 160) to capture small defect target information; s5, optimizing a detection head by using multi-scale feature fusion and a dynamic label distribution strategy, and improving the recognition precision of the model on insulator defects under a complex background; and S6, outputting defect positions, types and confidence coefficients based on a detection result, and generating an electric power inspection report. According to the method, the accuracy and robustness of insulator defect detection under the complex background of the power transmission line are effectively improved, and the method has relatively high applicability and real-time performance.

The invention discloses a meta-cognition level prediction method based on voiced thinking driven retrieval enhancement, which is characterized in that an information extraction method in a deep learning technology is adopted to extract answer features, a voiced thinking response analysis library is constructed, a correlation mode between various answer contents and corresponding meta-cognition scores is established, and a prediction result is obtained. The method comprises the following steps: establishing a mapping model, bridging the internal relation between the answer content and the meta-cognitive level by using the established mapping model, matching the sound thinking answer with the existing answer sample in a sound thinking response analysis library to realize the prediction of the meta-cognitive level, and specifically comprising the steps of data collection, data enhancement, big five personality correction, mapping model establishment, matching, retrieval and the like. Compared with the prior art, the method has the advantages that a more scientific and targeted problem design scheme can be provided on the basis of real-time evaluation of the meta-cognition level of the student when the type of problems proposed to the student is determined, so that the method has remarkable advantages in the aspects of education evaluation and personalized learning support.

The embodiment of the invention provides an electric control clutch semi-combination point position self-learning method and device, and relates to the technical field of clutch control, and the method comprises the steps: controlling a motor to carry out zero position self-learning, and recording a mechanical zero position; determining that the vehicle meets a first vehicle state condition, and performing clutch semi-joint point self-learning; a semi-combination point position obtained through self-learning is stored in a memory, and current odometer data is recorded; determining whether the clutch is completely run-in; through self-learning, change of a clutch semi-joint point caused by long-time work of the clutch and semi-joint point deviation caused by manufacturing and assembling differences of the clutch are corrected, and torque transmission capacity reduction caused by fast change of the semi-joint point when running-in of the clutch in the initial life cycle is not complete is prevented. Therefore, the torque transmission accuracy of the clutch is ensured.

The invention discloses a brick-concrete structure explosion damage reconstruction and visualization method based on deep learning, and the method comprises the steps: obtaining a damage data set of a brick-concrete structure under multi-scene explosion through numerical simulation, and building a training database through data processing and conversion, and building a two-stage brick-concrete structure damage prediction model based on a graph neural network. Error evaluation and parameter optimization are carried out on the model, the prediction capacity of the damage prediction model is improved, and according to node displacement and node stress predicted by the two-stage brick-concrete structure damage prediction model, visualization of the damage effect of the brick-concrete structure under various explosion impacts is achieved. According to the method, the damage results of the shock waves under different equivalents and different explosion positions can be quickly obtained, the explosion damage effect is visualized, and the calculation efficiency is greatly improved while the calculation precision is ensured.

The invention discloses a long-distance rail identification and obstacle detection method and system, and the method comprises the steps: combining a long-focus optical system with an ultra-high-speed industrial camera at a hardware level, and building a high-collection-frame-rate and long-distance optical detection system; on the software level, a region of interest for obstacle detection is delimited according to recognition and visual segmentation of left and right rail lines of a rail, advanced visual tasks such as a rail obstacle intrusion detection algorithm based on target detection are executed, and finally high-frame-rate recognition and detection of long-distance rails and rail obstacles are achieved. According to the invention, a Schmidt-Cassegrain optical system with a focal length of 2000mm is used, and an ultra-high-speed industrial camera with a resolution of 1920 * 1080 and 3000 frames per second is combined to build a long-distance high-frame-rate optical detection platform which is erected on a driving position of a train to detect railway conditions within a range of 6km in real time; serious accidents caused by overlong braking distance due to high-speed running of the train can be prevented. Compared with a traditional train driver eye observation and laser radar-based scanning imaging mode, a farther detection distance, better detection and recognition robustness and a higher frame rate can be realized through the long-distance optical observation platform and the rear-end real-time detection system.

The invention relates to a mobile phone video customer service interaction method and device, electronic equipment and a storage medium, and the method comprises the steps that a video customer service request is sent to a proxy call session control function network element, and the proxy call session control function network element forwards the video customer service request to a home subscriber server. And when the corresponding user information is queried, sending a session initiation request to the service call session control function network element through an SIP protocol. And performing service logic query in response to the session initiation request through the service call session control function network element, and forwarding the session initiation request to the proxy call session control function network element through the application server after the service logic is queried. And sending the user video stream to a human body action recognition unit for action intention recognition through the proxy call session control function network element. The user intention recognition result is fed back to the application server, and the application server drives the digital customer service to operate based on the user intention recognition result so as to establish an interaction channel between the digital customer service and the user.

The invention provides an effective signal filtering and noise reduction method and system for a micro-seismic event of a tunnel water and mud inrush disaster, and relates to the technical field of signal processing. Comprising the following steps: acquiring original signals of water-inrush and mud-inrush micro-seismic events; performing modal decomposition on the original signal by using a variational modal decomposition method to obtain a plurality of modal components; calculating a cross correlation coefficient between the original signal and each modal component; based on the center frequency of each modal component and in combination with the cross correlation coefficient, performing primary noise reduction on the plurality of modal components; performing de-trend fluctuation analysis on the modal components after the primary noise reduction screening to obtain scale indexes of the modal components after the primary noise reduction screening, and performing secondary noise reduction based on the scale indexes; and reconstructing the modal component after the secondary noise reduction screening to obtain a micro-seismic signal after noise reduction filtering. According to the method, the problem that noise and effective signals in part of modal components are mixed is solved, and the weak signal extraction capacity is improved.

The invention belongs to the technical field of dioxane separation, and mainly relates to a separation device for dioxane, the separation device comprises a heating sleeve, an evaporation sleeve, a central shaft and a driving part, the central shaft is provided with a blanking plate, an adjusting plate and a scraper assembly; the circumferential edge of the discharging plate is connected with the evaporation sleeve, a plurality of discharging holes are formed in the position, close to the circumferential edge of the discharging plate, of the discharging plate, and adjusting holes are formed in the adjusting plate. The scraper assembly comprises a central sleeve and a plurality of material scrapers, and a plurality of circumferential tension springs are arranged between the central sleeve and the central shaft; a transmission structure is arranged between the central sleeve and the adjusting plate, the size of an overlapped area of the discharging hole and the adjusting hole can be automatically adjusted according to the size of resistance borne by the material scraping plate, it is guaranteed that the amount of materials entering the evaporation sleeve in unit time is matched with the resistance borne by the material scraping plate, and it is guaranteed that a material film on the inner wall of the evaporation sleeve is evenly distributed; the separation effect of the dioxane is improved.

The invention discloses a massage chair real-time control method and system based on electromyographic signals and a computer medium. The method comprises the steps that the electromyographic signals of a current massage user are obtained; extracting time domain features, frequency domain features and geometric features of the electromyographic signals; obtaining a real-time muscle state according to the time domain feature, the frequency domain feature and the geometric feature of the electromyographic signal; and controlling the massage strength and rhythm of the massage chair according to the real-time muscle state and the historical massage data of the current massage user. By analyzing the long-term muscle state and massage record of the user, an instant personalized massage scheme is generated for each user. Therefore, the actual requirements of the user can be better met, the system can provide more accurate and personalized services for the user, and the optimal user experience can be achieved.

The invention belongs to the technical field of workability detection, regulation and control of fresh concrete, and discloses a workability detection, regulation and control method and system for concrete in a mixer truck, and the method comprises the following steps: installing a rheological shearing device and a temperature testing device in a barrel of the mixer truck, and driving a shearing rod piece to shear in the concrete by utilizing the rotation of the barrel, the rheological property of the concrete can be directly tested, and the working property is obtained through the relationship between the rheological property and the working property. The test is started before the concrete mixer leaves the factory, the concrete rheological property is continuously measured in the transportation process, the change degree and trend of the concrete rheological property are evaluated, an evaluation result is compared with a preset limit value, and when the evaluation result reaches and exceeds the limit value, an alarm is immediately given and an admixture is automatically supplemented for regulation and control. The rheological property of the concrete can be continuously monitored, finely adjusted and pre-adjusted in real time in the whole transportation process, so that the concrete can be kept in a required set interval range. According to the invention, the directness, real-time performance and accurate regulation and control of workability detection of concrete during transportation can be realized.