132 results about "Spatial model" patented technology

The invention discloses a rapid construction method and system for a three-dimensional space model of a multi-level building, and relates to the field of building model construction, and the method comprises the following operation steps: S1, constructing a dynamic demand-oriented detail level model; s2, automatic component modeling based on image segmentation; s3, performing multi-version difference judgment and model updating; and S4, cloud collaboration and load optimization. According to the method and the system for quickly constructing the three-dimensional space model of the multilevel building, the region is dynamically divided through the semantic weight, so that the rendering efficiency during model construction can be effectively improved, the memory can be effectively reduced during rendering, the visual fidelity can be optimized, key curvature characteristics can be forcibly reserved for a high semantic weight region, and the construction efficiency is improved. Based on this, deformation distortion caused by simplification can be avoided, a visible area in the model can be automatically identified, an invisible area can be degraded, only the contour line frame of the building is reserved, and the overall memory occupation can be greatly reduced.

The invention discloses a simulation optimization method for a digital twin factory. The simulation optimization method comprises the steps of obtaining workshop equipment data and performing classified storage; generating a task priority and resource configuration rule based on the basic operation data of the equipment; constructing a three-dimensional space model by using a 3D modeling tool, and creating a digital twinborn simulation factory model in combination with real-time production data; constructing a dynamic planning state space and a decision variable, and determining the resource use time, the resource allocation position and the specific quantity of each task; according to the resource allocation scheme and the task chain dependency relationship, evaluating a risk value of a task node in combination with historical equipment fault data; and based on the influence range report of the production fault chain, evaluating a task execution sequence and adjusting resource allocation and task time arrangement. According to the method, the influence of faults on a production plan and resource waste are reduced, the stability and adaptability of a factory system are remarkably enhanced, and the reliability and fine management level of overall production are improved.

The invention discloses a three-dimensional digital twin modeling method, system, equipment and medium taking a reservoir as an object, and relates to the technical field of reservoir monitoring, and the method comprises the steps: constructing a three-dimensional space model facing a reservoir entity, and forming a visual simulation environment; accessing multi-source data related to reservoir operation, and establishing a unified data aggregation and management mechanism; integrating a real-time operation state in the three-dimensional space model, and dynamically mapping perception information and business activities of the reservoir; and based on a set analysis model and a determination rule, carrying out intelligent processing on the reservoir operation data, identifying potential risks and generating an early warning result. According to the invention, by establishing a multi-type analysis model and a judgment rule system, real-time calculation and risk identification are carried out on key indexes, intelligent early warning of risks such as emergencies, trend anomalies and historical extreme value overrun is realized, and the risk prevention and control capability of the reservoir is effectively improved.

The invention relates to a target detection method and system based on improved YOLOv11, and aims to improve the grabbing precision and operation efficiency of a mechanical arm in a complex environment. The method combines an augmented reality technology, a target detection algorithm, a depth sensing technology and an advanced motion control system, and specifically comprises the following steps that an RGB-D camera is used for collecting data; establishing a three-dimensional space model, and performing accurate alignment processing on the depth image and the color image; based on a mercuric chloride Atlas 200I DK A2 development board, in combination with an improved YOLOv11 model, efficient detection and identification of a target are realized; a target is converted from a pixel coordinate system to a camera coordinate system through a coordinate conversion method, and then the target is further mapped to the three-dimensional space position of a mechanical arm base coordinate system; finally, grabbing operation is completed through the elephant mechanical arm MyCobot 280M5. By combining RGB-D information, a deep learning detection algorithm and a three-dimensional coordinate conversion method, efficient and accurate target grabbing of the mechanical arm under the complex environment is achieved through the system, and the system has the advantages of being high in precision and real-time performance, easy to maintain, easy to integrate and deploy and the like.

The invention discloses an AI-based building design scene automatic intelligent generation method and system, and relates to the technical field of building design, accurate basic data is provided for subsequent design by constructing a building related data set H, then a three-dimensional space model of an ancient building is generated by using 3D modeling and image recognition technologies, and the three-dimensional space model of the ancient building is obtained. According to the method, the to-be-repaired model area is marked, the recognition accuracy of the to-be-repaired model area is improved, in material suitability analysis, the building repair material is selected according to the material suitability score CL, the material compatibility and the repair effect are ensured, a first ancient building repair design scheme is obtained according to the AI model, meanwhile, finite element analysis is conducted, and the recognition accuracy of the to-be-repaired model area is improved. According to the method, it is ensured that the repaired historic building can bear loads and keep stability, finally, through target function optimization and simulation, an optimal historic building repair design scheme is output, a repair plan is executed, high efficiency and accuracy of the historic building repair process are ensured, and the repair quality of the historic building is improved.

The invention provides an indoor positioning method and system based on vision, geomagnetism and inertial navigation, and the method comprises the steps: obtaining inertial navigation data, and determining a local region where a target object is located in an indoor space according to the inertial navigation data; wherein the inertial navigation data is obtained by a positioning device carried by a target object; obtaining a visual image of the local area, and determining the position of the target object in a pre-constructed spatial model according to the visual image to obtain a first position; determining a target range according to the first position, and simulating an environment magnetic field around the target object under the condition that the magnetic disturbance object exists in the target range; real-time magnetic field data is obtained, the real-time magnetic field data is obtained through measurement of magnetic field measurement equipment carried by the target object, the real-time magnetic field data is matched with the environment magnetic field, the specific position of the target object in the local area is determined, and a second position is obtained. According to the invention, multiple positioning technologies are fused, and the indoor positioning accuracy can be further improved.

The invention provides an improved YOLOv8 underwater image target detection method, device, medium and program, a fine-grained feature extraction module is arranged to replace a C2f module on the basis of YOLOv8, the fine-grained feature extraction module comprises a visual state space model and a local feature extraction module which are arranged in parallel, feature distribution is adjusted through linear transformation in the visual state space model, and the local feature extraction module is used for extracting local features in the visual state space model. A multi-scale space context is extracted in combination with depth separable convolution and a SiLU activation function; in the local feature extraction module, detail information of a target is focused through a convolution kernel channel attention mechanism, and noise interference is suppressed; the two outputs are subjected to element-by-element addition fusion and then enter a multi-scale cross attention fusion module, local and global features of different scales are fused in the multi-scale cross attention fusion module through dynamic weighting, and fine-grained features are output. The improved YOLOv8 is used for underwater target detection, and the identification capability of a fuzzy target and a complex background in underwater image target detection can be remarkably improved.

The invention provides a traffic flow prediction method combining a dynamic graph neural network and a Mama mechanism. The future short-term traffic flow is predicted by using historical traffic data. According to the method, firstly, normalization preprocessing is carried out on traffic state data collected by multiple sensors, a training sample is generated by adopting a sliding window, and a traffic flow value in the next one hour is predicted according to data in the past 24 hours. On the basis of the model structure, a time modeling module composed of multiple layers of MambaBlocks is constructed and used for capturing historical time sequence dependence; constructing a spatial modeling module of dynamic graph convolution, and combining a static adjacency matrix of the road network with a learnable adaptive adjacency structure to extract spatial association; and finally, the outputs of the modules are fused, and a prediction result is obtained through a prediction output module. In the training process, a Huber loss function is used as an optimization target, and evaluation indexes such as a mean absolute error (MAE), a root mean square error (RMSE) and a mean absolute percentage error (MAPE) are used for evaluating the performance of the model. According to the method, the traffic space-time dynamic characteristics are effectively mined, and the long-range dependence modeling capability and the prediction precision are improved.

The invention discloses a foggy day real-time target detection method and system based on Fourier transform image defogging, and relates to the technical field of image target detection. The method comprises the following steps: preprocessing data, normalizing the size of an image, implementing a multi-dimensional image enhancement strategy, and designing a self-adaptive amplitude enhancement module based on fast Fourier transform to carry out defogging processing on the image; feature extraction: inputting the preprocessed image into a backbone network to generate feature maps with different scales, and enhancing the spatial modeling capability of the feature maps by a spatial modeling SimAM module by using a SimAM attention mechanism; feature fusion: adding a weight convolution pyramid module into the neck network to form a feature pyramid architecture, and performing multi-scale feature fusion operation on different-scale feature maps to obtain multi-scale semantic features; information post-processing decoding and prediction are carried out, after the multi-scale semantic features are decoupled through a detection head, two independent tensors of classification and positioning are generated, and tensor information is analyzed to obtain a detection result. The accuracy of the detection model is improved.

The invention discloses a smart park environment space monitoring method and system based on digital twinning, and relates to the technical field of digital twinning. Park environment space data is collected in real time by using a data information collection technology, and a digital model is established according to the park environment space data; setting an update transmission channel network, and performing digital mapping on the digital model through the update transmission channel network based on digital twinning to obtain a park environment space model; obtaining newest park environment data based on the park environment space model, performing data processing on the newest park environment data to obtain specific area environment data, and further obtaining decision park environment data according to the specific area environment data; establishing a park space decision-making model according to the decision-making park environment data; a park space decision is received according to the park space decision model; the intelligent decision-making capability of the model is effectively improved.

The invention relates to an urban green land high-resolution remote sensing monitoring method. The method comprises the following steps: data acquisition and preprocessing: synchronously acquiring point cloud data, hyperspectral data and multi-temporal remote sensing data; feature engineering: constructing a multi-dimensional feature space, and optimizing the multi-dimensional feature space through feature selection; model training: on the basis of the optimized multi-dimensional feature space, performing filling, differential normalization processing and regularization improvement on a U-net + model through pixel values of the edges of mirror images in sequence for urban green land features, and establishing the urban green land-oriented and multi-dimensional feature-based U-net + model, so that the urban green land-oriented and multi-dimensional feature-based U-net + model is obtained; adding the multi-dimensional feature data of the training sample into the U-net + model for training, predicting the spatial distribution of the urban green land after the training is completed, obtaining a prediction result of the U-net + model, and combining a mixed loss function with an adaptive training strategy; and post-processing and result generation: applying adaptive CRFs to optimize a U-net + model prediction result, generating a high-resolution urban green land distribution map, and outputting a precision evaluation report.

The invention relates to an inshore water quality day-by-day kilometer-level inversion method, system, device and medium, and belongs to the technical field of water quality monitoring, the method comprises the following steps: obtaining remote sensing image data, meteorological reanalysis data and ground water quality site measured data, preprocessing, carrying out space-time matching fusion, using the fusion data as a sample to carry out model training, and obtaining a model training model; obtaining a space model; a spatial model offshore water quality spatial distribution diagram; and constructing a CNN-BiLSTM mixed time sequence model, and training the CNN-BiLSTM mixed time sequence model by taking historical water quality data of a ground water quality station and the offshore water quality spatial distribution map as samples to obtain a prediction model. The method is suitable for red tide early warning, pollution tracing and marine ecological management, breaks through monthly monitoring limitation, and realizes day-by-day kilometer-level water quality dynamic tracking; remote sensing large-range coverage and site high-precision data are fused, and the problem of inshore environment heterogeneity is solved; the time-space double-model cascade architecture significantly improves the generalization ability under complex hydrological conditions.

The invention discloses a fire-fighting equipment control system and method based on the Internet of Things, and relates to the technical field of the Internet of Things. An indoor scene environment monitoring network is constructed to collect real-time environment data and scene image data of a monitored scene; indoor scene image data is called to construct a pixel point decomposition model, comprehensive evaluation is performed on chromaticity information of each pixel point in the image data based on the decomposition model, and abnormal point location analysis is performed on each pixel point based on pixel point chromaticity evaluation data; based on the scene image data pixel abnormal point analysis data, constructing a spatial model to determine a scene abnormal area; performing steady-state fire abnormality index analysis on the indoor scene abnormal area according to the space abnormal area, and performing abnormality judgment on the scene fire based on analysis data; outputting the indoor scene monitoring data through an Internet of Things monitoring port, and labeling a scene abnormal area and feeding back fire data; according to the invention, the conditions of low precision and high false alarm rate of the traditional fire extinguishing system are improved.

The invention belongs to the technical field of storm surge disaster early warning, and relates to a coastal town storm surge disaster intelligent prevention and control system and method.The system comprises an internet-of-things sensing layer, a data resource platform and an application supporting platform, the application supporting platform comprises a forecasting module, and the forecasting module is based on a multi-layer nested storm surge model; through nesting calculation of an open sea grid and a near-shore grid, the dam overtopping water flow and the water level and range of a submerged area are predicted; the early warning module is used for dynamically analyzing hydrology and water quality data in combination with a risk threshold value, generating an early warning signal and visually issuing the early warning signal through a G IS engine; the rehearsal module is used for carrying out real-time spatial model rendering on a prediction result of the prediction module by adopting an OpenGLES underlying graphic API and a GPU rendering pipeline optimization technology so as to realize restoration of dynamic change of a physical world; according to the system, the precision and the real-time performance are considered, a forecasting, early warning, rehearsing and pre-planning full-linkage module is constructed, and the decision response speed is improved.

The invention discloses a binocular stereoscopic vision electric power operation safety distance measurement method and system, relates to the technical field of image processing, and solves the problem that target identification is difficult to carry out according to a binocular image; secondly, feature extraction is difficult to carry out according to a target recognition result, and feature matching is difficult to carry out; thirdly, a three-dimensional space model is difficult to reconstruct after a disparity map is difficult to construct according to a feature matching result; and finally, the technical problems of difficulty in analyzing and calculating the safety distance among the electric power operation personnel, the electrified equipment and the large-scale high-altitude operation vehicle and difficulty in comprehensively analyzing and evaluating the safety of the electric power operation site are solved. According to the method, feature extraction and feature matching are carried out after electric power operation personnel, live-line equipment and a large-scale high-altitude operation vehicle in a binocular image are identified; the safety distances among the electric power operating personnel, the live-line equipment and the large overhead working vehicle are measured by constructing a disparity map and reconstructing a three-dimensional space model.

The invention provides a cultivated land non-cultivation data processing and analysis system and method. The system comprises a non-cultivation rate influence factor analysis module and a non-cultivation rate prediction module. The non-farming rate influence factor analysis module integrates a geographic detector model, a panel model, a space model, a space-time geographic weighted regression model and other modules to obtain a significant influence factor result of non-farming in a target area; and the non-farming rate prediction module comprises a panel model prediction module and a time t prediction module to predict the non-farming rate corresponding to each administrative region in the next year. Based on the technical scheme of the invention, multi-source heterogeneous data such as land utilization, climate environment and the like are integrated; index classification is more detailed and comprehensive, so that an analysis result is more effective; time and space factors are fused, and variables of different regions are analyzed more accurately; the factor identification accuracy is remarkably improved, the space prediction precision is remarkably improved, and the time sequence prediction error is remarkably reduced.

The invention discloses a hybrid office efficiency optimization system based on digital twinning and space calculation, and relates to the technical field of computer science and technology, and the system comprises a data collection module which captures the station layout parameters, the personnel movement track, the equipment use state and the environment parameters of an entity office space in real time through an Internet of Things sensor array, and transmits the parameters to a server; the sensor array comprises a positioning base station, a thermal imager, an intelligent electric meter and an acoustic sensor. The hybrid office efficiency optimization system provided by the invention can accurately and comprehensively map the office environment through the real-time data acquisition and multi-source fusion technology, solves the problem of single data acquisition of the traditional system, and improves the efficiency of the office by utilizing the space calculation and three-dimensional modeling technology. According to the system, a three-dimensional space model comprising an interactive station unit, an equipment entity and a personnel virtual avatar is constructed, a basis is provided for dynamic modeling and intelligent optimization, and a multi-objective optimization algorithm and a genetic algorithm are combined, so that the system can be under complex constraint conditions.

The invention discloses a transform-based video behavior recognition algorithm. The algorithm comprises the following steps: S1, carrying out skeleton key point feature extraction, 3D convolution and down-sampling on video frames in an input video clip; s2, performing image pre-training vit block construction on a feature layer obtained by down-sampling, and performing local and global modeling through a corresponding space unification module and a time sequence unification module; s3, performing multi-level fusion on space and time sequence information features, and then sending the space and time sequence information features and skeleton key point feature information extracted by the pose formerV2 into a multi-feature fusion module for feature information fusion; and S4, the fused feature information passes through a non-training pruning module. According to the training-free pruning unified convolution and self-attention Transformer model framework, Vits space interaction capability is pre-trained, so that the Vits space interaction capability can be fully utilized and reserved in a space unified module, and the space modeling capability of PSUformer is enhanced; the hierarchical time sequence interaction in the efficient time sequence unification module can be flexibly used for enhancing the time sequence modeling capability.

The invention provides a charging fault real-time diagnosis system based on edge calculation, and relates to the technical field of charging fault diagnosis. The method comprises the following steps: constructing a three-dimensional space model of a target charging area, and dividing the target area into M monitoring sub-areas; collecting charging fault data of each monitoring sub-region; preprocessing the charging fault data; constructing a fault risk assessment model by using an echo state network, and optimizing model hyper-parameters by using a pollen propagation algorithm; deploying a fixed edge node in a high-risk area in the fault risk level of each monitoring sub-area for continuous monitoring, planning an inspection path of a mobile edge node, and executing monitoring according to a dynamic period; receiving monitoring data of fixed and mobile edge nodes in real time, and constructing a fault prediction model based on a support vector machine to obtain a fault probability value; and the fault probability value is compared with a preset multi-level early warning threshold value, the fault level is judged, and the early warning information is output, so that the safety and the reliability of the charging process are ensured.

The invention discloses homogenization equipment for rubber asphalt processing, and relates to the technical field of rubber asphalt processing equipment, the homogenization equipment comprises an intelligent control system, the intelligent control system comprises a data analysis module, a homogenization analysis module, a compensation analysis module and an execution judgment module; the data analysis module is used for receiving and processing the sensing data sent by the sensor group to obtain a statistical index of any parameter in the sensing data; performing environment analysis on the data preprocessed by the sensor group in combination with a statistical index to obtain a homogeneous analysis result; and the homogenization analysis module is used for evaluating the mixing quality in the stirring process based on the homogenization analysis result. By arranging the multi-dimensional sensor group in the mixing box and constructing the three-dimensional space model, real-time three-dimensional monitoring and intelligent analysis of temperature, viscosity and concentration parameters in the rubber asphalt mixing process are realized, parameter abnormal points and trend changes can be identified, and the problems that traditional equipment is lagged in monitoring and cannot capture spatial distribution characteristics are solved.

The present invention discloses a tag positioning method based on RFID technology, which mainly eliminates the positioning position interference problem caused by a single tag being read by multiple antennas. The method adopts the steps of designing a three-dimensional spatial model of the positioning area and combining it with a positioning algorithm for judgment. The interference values ​​are eliminated in sequence by using the advantages and disadvantages of the antenna spatial points, an RSSI ranking table combined with a tag data information table, and the weighted total score of the three major influencing factors of the tag data information. The method realizes the precise positioning of multiple tags in the application scenario, further reduces the misjudgment results, and improves the positioning accuracy of the tags.

The embodiment of the invention discloses a camera arrangement evaluation method and device based on operation data and a medium. The method comprises the steps that a three-dimensional space model of a to-be-evaluated area is acquired and visually displayed, and a virtual monitoring scene is generated; in response to an adding operation of camera point locations, displaying a visible range of each camera in the virtual monitoring scene in an overlapping manner; in response to an import operation of historical operation data, superposing and displaying a thermodynamic diagram of historical people flow density and / or work order density in the virtual monitoring scene; according to a superposition display result, determining a coverage condition of the camera on the people flow density and / or the work order density; and evaluating the point location layout of the camera according to the coverage condition. According to the embodiment of the invention, intelligent and precise camera arrangement evaluation is realized.

The invention discloses an anti-collision detection method and device for a cantilever crane, equipment and a storage medium. The method comprises the following steps: acquiring first point cloud data of the boom; performing curve fitting on the first point cloud data to obtain a boom curve of the boom; determining a target curvature of the boom based on the boom curve; based on the target curvature, target modeling parameter values are determined, and the target modeling parameter values are parameter values of modeling parameters used for spatial modeling; performing spatial modeling on the first point cloud data based on the target modeling parameter value to obtain a boom curvature boom spatial model; anti-collision detection is carried out based on the boom space model, an anti-collision detection result of the boom is generated, and the anti-collision detection result is used for indicating the obstacle having the collision risk with the boom. Therefore, the detection precision of the anti-collision detection of the cantilever crane can be improved.

The invention discloses a deep hidden variable state space model bearing degradation prediction method and system, and the method comprises the steps: correcting the simulation degradation data and real degradation data of a bearing through a data transformation function Box-Cox, fusing a plurality of time domain degradation features to obtain a degradation health index, and obtaining a degradation state representing the degradation rate of the bearing through differential transformation; constructing a prediction model based on a deep hidden variable state space model under a state space model framework; performing feature extraction, fusion and difference on the simulation degradation data and the real data to obtain degradation states of the simulation degradation data and the real data; the method comprises the following steps: acquiring a degradation state pre-training depth hidden variable state space model by using simulation degradation data, initializing the weight of the space model, predicting the degradation state obtained by real data by using a prediction model based on the depth hidden variable state space model, and accumulating the predicted degradation state to obtain a bearing degradation prediction value. The technical problems that an existing process is complex, and a large amount of time and resources are consumed are solved.

The invention discloses an urban historical mountain landscape database construction method and platform based on GIS and BIM. The method comprises the following steps: acquiring historical geographic information and historical surveying and mapping photography data of a target city; performing type division and attribute labeling on the spatial information of the target city based on historical geographic information through a GIS system platform, and generating a spatial information data total library; the GIS system platform and the BIM system platform are combined to carry out three-dimensional modeling on the building and mountain environment of the target city based on historical surveying and mapping photography data, and a total spatial model data library is generated; generating an associated data graph layer based on a GIS system platform for the target city; and constructing an urban historical mountain landscape database of the target city based on the spatial information data general library, the spatial information data general library and the associated data layer. According to the invention, three-dimensional modeling is carried out on the urban building and mountain environment by combining the GIS and the BIM system platform, and the modeling precision of the small and medium-sized building environment and the low mountain can be improved.

The present invention discloses a method for constructing a three-dimensional spatial model for power transmission line dismantling, which belongs to the technical field of power transmission line, and specifically comprises: obtaining a target area for grid division; obtaining aerial images and point cloud data in real time from a surveying point; identifying the aerial images and marking the grid area containing the power tower image as the target grid area; if the area of ​​the area where the power tower exists is outside the standard area interval, re-surveying and obtaining a second aerial image; generating a three-dimensional model of the target area; correcting the three-dimensional model according to the second aerial image to obtain a three-dimensional real-scene model, determining the spatial range of power lines between adjacent power towers and extracting corresponding point cloud data, generating power line modeling and mapping the power line modeling to the three-dimensional real-scene model to obtain a final three-dimensional model. The present invention greatly improves the efficiency and accuracy of three-dimensional modeling of power transmission lines.

The invention discloses a real-time monitoring and early warning method and system for a port engineering construction ship machine, and relates to the technical field of intelligent early warning, and the method comprises the steps: obtaining the geographic space information of a target port, constructing a three-dimensional geographic space model, fusing construction ship machine equipment and a port facility model, and generating a comprehensive port space model; the method comprises the steps of receiving real-time task data and construction progress information, locking a key monitoring area, performing regional centralized monitoring, obtaining a multi-source monitoring index set, performing dual-channel real-time risk assessment in combination with a comprehensive port space model, generating a comprehensive risk assessment result, and performing risk early warning management. The technical problems that an existing construction ship machine monitoring means cannot be adaptively adjusted according to the construction task progress, so that monitoring is not targeted, and the accuracy of risk assessment is affected are solved, and the purposes of dynamically adjusting the monitoring strategy according to the construction progress, accurately locking the key area, extracting the monitoring index and improving the risk assessment accuracy are achieved. And the accuracy of risk assessment is improved.

The invention relates to the technical field of data acquisition, and discloses a three-dimensional data acquisition management system and method, and the method comprises the following steps: S1, obtaining a three-dimensional space model of a to-be-acquired region, and discretizing the space into a plurality of sampling units to form a voxel grid; s2, identifying a shielding area in the three-dimensional space model, and constructing an effective and reachable three-dimensional acquisition space; s3, generating an initial layout point location according to the equipment view field parameters; s4, acquiring parameters and sampling unit positions based on the equipment; s5, constructing a joint optimization objective function; and S6, under the constraint condition, solving an optimal equipment layout point location and path control function through an optimization method. According to the method, the optimal equipment layout scheme and path control strategy are obtained by constructing the three-dimensional space acquisition model and adopting a plurality of factors such as joint optimization of the objective function, dynamic optimization of equipment layout points and paths, energy consumption and shielding area punishment, and the acquisition efficiency and the reasonability of path planning are improved.

The present invention discloses a digital management system for drainage pipe network operation and maintenance data, which relates to the field of digital management and includes an operation and maintenance management center, in which a pipe network management module, a data acquisition module, a data analysis module, a data collaboration module and an operation and maintenance management module are provided; the pipe network management module is used to construct a three-dimensional spatial model of the pipe network and set an operation and maintenance management node; the data acquisition module is used to collect pipeline drainage data; the data analysis module is used to obtain pipeline load data at various positions and set drainage pipe links; the data collaboration module is used to set collaborative drainage information at the operation and maintenance management node according to the pipeline load data and the drainage pipe link; the operation and maintenance management module is used to optimize the collaborative drainage information, obtain operation and maintenance decision information, and adjust the pipeline flow according to the operation and maintenance decision information; the present invention improves the collaboration and rationality in the operation and maintenance process of the drainage pipe network.

The invention belongs to the technical field of industrial monitoring and data visualization, and particularly discloses a prefabricated T-beam intelligent steam-curing visualization method and system based on a three-dimensional temperature field, and the method comprises the steps: calculating the size of a prefabricated T-beam to be subjected to steam-curing, the size of an internal space of a steam-curing chamber, and the size of an external space of the steam-curing chamber; generating a three-dimensional space model in the steam curing chamber loaded with the prefabricated T-shaped beam according to the position coordinates of the prefabricated T-shaped beam after the prefabricated T-shaped beam is transferred to the steam curing chamber through the movable pedestal; generating a corresponding three-dimensional wind speed field and a three-dimensional humidity field according to the three-dimensional space model; and according to the three-dimensional wind speed field and the three-dimensional humidity field, generating a visual three-dimensional temperature field corresponding to the three-dimensional space of the steam curing chamber through a harmonic interpolation algorithm and a digital twinning method. According to the method, the three-dimensional space model of the steam-curing room based on the type of the prefabricated T-shaped beam is constructed, the real-time environment data is integrated to construct the three-dimensional temperature field, the problem of local over-steaming or insufficient curing is avoided, and meanwhile, the visual temperature field can visually track the microenvironment change of each area in the steam-curing room.