354 results about "Mapping algorithm" patented technology

The invention discloses a defect repair method based on digital twinning and friction stir welding technologies, and relates to the technical field of intelligent manufacturing and digital twinning, and the defect repair method comprises the following steps: synchronously capturing full-dimensional data of a welding area through a multi-mode sensor array integrated by an actuator; secondly, segmenting defect boundaries by adopting a deep learning algorithm, constructing a dynamic twin model in combination with thermal-force field coupling simulation, and accurately mapping defect three-dimensional features; generating a repair track according to the twinborn model, converting the repair track into a robot joint instruction through a curved surface parameterization mapping algorithm, and implanting real-time anti-collision constraint; in the repairing process, based on reinforcement learning control of the material rheological resistance and the temperature gradient, the rotating speed, the advancing speed and the down force of the tool are dynamically adjusted; and after repairing, micro-focus CT scanning is started immediately, actually measured data is compared with twinborn prediction, and when the deviation exceeds a threshold value, a re-repairing process is triggered automatically. The method solves the problems that a traditional method depends on manual intervention and the precision of a sensor is easily interfered by the environment.

The invention relates to the field of measurement, in particular to a subway tunnel secondary lining section limit measurement method based on SLAM mobile scanning, which comprises the following steps: step 1, tunnel point cloud acquisition and target clustering point cloud acquisition; step 2, CPIII control point coordinate extraction; step 3, point cloud coordinate conversion; 4, performing point cloud processing, and removing noisy points and non-secondary-lining target point cloud data in the tunnel secondary-lining point cloud; and step 5, performing three-dimensional limit deviation analysis, performing spatial comparative analysis on the point cloud under the construction coordinate system and the subway secondary lining tunnel design three-dimensional model by adopting a voxelization deviation mapping algorithm, extracting a continuous deviation region through voxel clustering, and generating a three-dimensional deviation thermodynamic diagram. According to the method, the two-lining tunnel point cloud obtained by SLAM mobile scanning is accurately converted into a construction coordinate system by combining CPIII high-precision control points in the tunnel, rapid, visual and three-dimensional contrastive analysis with design of a three-dimensional model can be achieved, and visual and reliable data support is provided for line and slope adjustment, track laying and defect improvement.

The invention belongs to the technical field of data processing, and particularly discloses a dynamic path planning method and system for an inspection robot, and the method comprises the steps: collecting field data through a sensor, processing a laser scanning point cloud and a camera image through a positioning mapping algorithm, and fusing multi-source information to generate a real-time map. Obtaining a three-dimensional environment model containing a dynamic obstacle position; according to the three-dimensional environment model, obstacle features are extracted, a deep learning model is adopted to identify moving personnel and randomly placed goods, obstacle types and movement tracks are judged, and a classified obstacle data set is obtained; through the classified obstacle data set, the distance and the relative speed between the obstacle and the current position of the robot are calculated, and if the distance is smaller than a preset threshold value and the speed is larger than zero, high-priority interference is marked; according to the invention, the problem of inaccurate dynamic obstacle identification and obstacle avoidance in a complex scene in the prior art is solved.

The present invention discloses a distributed heuristic unmanned cluster brain-like swarm intelligence fusion search and capture method, comprising: step 1: completing single target detection on the basis of a Yolov5 network, and predicting the position of a next frame of target on the basis of a DeepSort algorithm; step 2, on the basis of the distance difference and the area difference, determining, by mapping, navigation end point coordinates formula (1) of an intelligent agent in a physical space; step 3, constructing a diffusion-contraction coordinated chaotic mapping algorithm, and mapping the update of the navigation end point coordinates into a uniformly distributed chaotic space; step 4: on the basis of target detection confidence decision information and regional position information of all intelligent agents, fusing and generating global search information formula (3) of a search space formula (2); step 5: on the basis of the intelligent agent global search information, fusing various sensing data in a multi-agent system, so as to estimate target position information; and step 6: performing measurement on the basis of a real-time position of the intelligent agent and an obstacle position. The present invention has high capture efficiency and can accurately update a capture end point of the intelligent agent to complete a task of encircling a moving target.

The invention belongs to the technical field of artificial intelligence, is applied to the field of financial science and technology and the field of medical health, and discloses a rule engine configuration method, device and equipment based on machine learning and a storage medium. The method comprises the steps that historical calculation data and performance indexes in historical calculation tasks are collected, and a training data set is constructed; a machine learning model is trained, and algorithm parameters of the rule engine are dynamically optimized through the training data set; configuring an expression and a user-defined function of a rule engine, and realizing analysis and execution of scripts through JEXL (JavaScript Exchange Library); establishing a dynamic mapping relationship between the algorithm configuration library and the business variables, mapping algorithm variable names and business variable names, and generating configurable algorithm rules; and analyzing task load and resource requirements based on a pre-trained machine learning model, distributing system resources, and repairing abnormal behaviors in a calculation process. According to the invention, the problems of low processing efficiency, high maintenance cost, unreasonable resource allocation and poor expandability in the prior art are solved.

A method for surveying an environment by a movable surveying instrument with a progressional capturing of 2D-images by at least one camera and applying a visual simultaneous location and mapping algorithm (VSLAM) or a visual inertial simultaneous location and mapping algorithm (VISLAM) with a progressional deriving of a sparse evolving point cloud of at least part of the environment, and a progressional deriving of a trajectory of movement. The method comprises a progressional matching of the sparse evolving point cloud with a previously derived 3D-geometry, with a minimizing of a function configured to model a distance between the sparse point cloud and the previously derived 3D-geometry and deriving a spatial localization and orientation of the surveying instrument. At least one surveying measurement value of the environment by a spatial measurement unit is combined with the sparse point cloud or the previously derived 3D-geometry.

The invention relates to the technical field of bridge engineering structure health monitoring, in particular to a service life prediction method and system for a bridge connection part, and the method comprises the steps: collecting the strain of a grouting sleeve, a reinforcement-grouting interface ultrasonic reflection signal and environment temperature and humidity data through a distributed sensor array; a three-dimensional dynamic void rate model is generated through fusion of a spatial topological mapping algorithm, a correlation function of load circulation and void expansion is established, the dynamic void rate is input into a bond strength coupling degradation model, after strength attenuation is quantized in combination with steel bar corrosion data and bond failure early warning is triggered, stress redistribution is calculated through multi-physics coupling simulation, and the dynamic void rate is obtained. Early warning is taken as a starting node, the actual load spectrum and temperature and humidity data are combined, Monte Carlo sampling is adopted to simulate rigidity degradation and output the residual life probability, the system comprises a multi-source collaborative sensing unit, a micro-void evolution analysis unit and a life prediction decision unit, and the safety control precision of bridge connection parts is improved.

The invention provides a battery car dynamic charging optimization management system based on battery health state evaluation, and belongs to the technical field of battery car charging management. The system comprises a data acquisition module which is connected with an environment sensing device and a battery management system and acquires environment and battery parameters in real time; the data storage module is used for storing an initial training data set, a charging and discharging data set and a battery aging feature library; the model building module is used for building a battery health state evaluation model through a migration strategy and building a response model through a modeling strategy; the health state evaluation module is used for extracting associated features with a critical damage threshold value as a core through a mapping algorithm based on the battery parameters, the environmental parameters and an aging feature library; and the charging strategy generation module comprises a strategy generation unit, a reference parameter generation unit and a dynamic correction unit. The system effectively improves the adaptability of battery car charging, guarantees the efficient and stable operation of the battery, and remarkably improves the safety and prolongs the service life of the battery.

The invention provides a construction method of an ocean observation and exploration large model, and belongs to the technical field of large models.Multi-mode original data are collected by constructing a multi-source ocean data collection matrix, an environment change degree vector is established, preprocessing and noise reduction are conducted on the original data by adopting a nonlinear matrix mapping algorithm based on a Gaussian kernel function, and the large model is constructed. An ocean observation and exploration large model architecture of a liquid neural network structure is constructed, different branches are made to process input data of different dimensions by means of asymmetric design, a super sparse reconstruction matrix is established, and high-dimensional original data are reconstructed from low-dimensional observation by means of a compressed sensing reconstruction mechanism. And finally, a supervised training process is executed to optimize model parameters so as to complete the construction of an ocean observation and exploration large model, and the technical problem that high-precision fusion modeling of ocean multi-modal observation data is difficult to realize under the conditions of spatial-temporal distribution sparsity and data isomerism is solved.

The invention relates to the technical field of image enhancement, in particular to a rhizosphere microflora distribution analysis method based on image enhancement, which comprises the following steps: acquiring a continuous microscopic image sequence of a rhizosphere microenvironment, converting the continuous microscopic image sequence into a gray data space, and constructing a standard input frame set for optical flow calculation. According to the method, the stability score of each feature is calculated by using the signal-to-noise ratio stability measurement function, and the long-life-cycle feature is screened and reserved, so that the random imaging noise of the short life cycle can be filtered fundamentally, and the topological structure of the target community is obtained; and mapping the topological structure back to an original image space by using an inverse persistence mapping algorithm to be fused with a continuous microscopic image sequence, and finally, remarkably enhancing weak biological signals while keeping original form details, thereby realizing high signal-to-noise ratio analysis of microflora spatial and temporal distribution in a complex rhizosphere environment.

The invention discloses a BIM model reconstruction method based on multi-source data fusion and semantic recognition, and the method comprises the steps: automatically generating an orthogonal projection snapshot image, and carrying out the component recognition and segmentation through a visual large language model, thereby effectively reducing the manual intervention, and remarkably improving the modeling efficiency; meanwhile, point cloud data and a model output result are combined, an orthogonal mapping algorithm and space intersection and clustering analysis are utilized, rapid conversion from a two-dimensional image to a three-dimensional model is achieved, the modeling process is further accelerated, accurate mapping of pixel points is ensured through the orthogonal mapping algorithm, and the geometric accuracy of the model is guaranteed; in addition, the visual large language model improves the accuracy of component recognition and segmentation, reduces human errors, and enhances semantic precision. In addition, multi-source data fusion and semantic recognition enable modeling to be more intelligent, different building requirements can be automatically met, the whole process from data acquisition and processing to model generation is automatic, the error rate is reduced, and the high standard of modern building engineering for precision and efficiency is fully met.

The invention belongs to the field of robot positioning and mapping algorithms, and provides a visual SLAM method and device for point-line feature fusion and a medium, and the method comprises the following steps: preprocessing an image, and obtaining point features and line features; matching the point features and the line features to obtain matched point features and matched line features; obtaining the initial pose of the camera through the matched point features and the matched line features, and constructing map points; constructing a three-dimensional map straight line through the matched line features; and optimizing the three-dimensional map points and the three-dimensional map straight lines. Complementary advantages are formed through extraction, matching, mapping and optimization processes of tight coupling point features and line features, and the fundamental problem of insufficient features in a weak texture environment is effectively solved.

The embodiment of the invention provides a batch retrieval method based on a verifiable bloom filter and electronic equipment, and relates to the technical field of data security. The method comprises the following steps: converting a server data set into a key value pair form, and mapping keywords in the data set in the key value pair form into equal repeated codes through an equal repeated code mapping algorithm to obtain an equal repeated code data set; determining a storage position of a second verifiable bloom filter according to a keyword in the equal-repetition-code data set, and storing a data entry corresponding to the keyword in the equal-repetition-code data set and a check code generated based on the data entry to the storage position; the client receives the query keyword set and maps the query keyword set to the first verifiable Bloom filter; and taking each bit of the first verifiable Bloom filter as a selection vector to execute an oblivious transmission protocol to obtain a return result of the server side. The embodiment provided by the invention provides a safe and efficient batch retrieval method based on the verifiable Bloom filter.

Provided in the embodiments of the present disclosure are an address translation method and apparatus, and an electronic device and a storage medium. The address translation method is applied to an address translation apparatus, the apparatus comprising multiple sets of translation lookaside buffers, wherein each of the multiple sets of translation lookaside buffers is configured to store page table information for translating a virtual address into a physical address. The address translation method comprises: in response to obtaining a first virtual address translation request, determining a target translation lookaside buffer from multiple sets of translation lookaside buffers on the basis of a first mapping algorithm, wherein the first virtual address translation request comprises an object virtual address; and accessing page table information in the target translation lookaside buffer, in order to translate the object virtual address into an object physical address. The method further comprises: acquiring access frequency information for each of the multiple sets of translation lookaside buffers; on the basis of the access frequency information, determining whether the first mapping algorithm is required to be updated; and in response to the first mapping algorithm being required to be updated, updating the mapping algorithm to a second mapping algorithm.

The invention discloses a vehicle target motion state estimation method based on an unmanned aerial vehicle video, and relates to the technical field of computer vision and intelligent monitoring, and the method comprises the steps: receiving a real-time video stream of an unmanned aerial vehicle, decoding the real-time video stream to obtain an original video frame, carrying out the graying, zooming and normalization preprocessing to obtain a processed image, and synchronously adjusting the size of a vehicle bounding box; vehicle targets are detected, a unique ID is allocated for multi-target tracking, and a motion trail is maintained; determining a background feature region, detecting and tracking feature points to estimate an inter-frame transformation matrix, and performing accumulative transformation to obtain a background overall transformation relation and a historical frame-to-current frame mapping mechanism; calculating a displacement residual error based on the trajectory and the mapping, judging direction consistency, estimating a smooth speed, and updating a motion confidence coefficient to judge a vehicle motion or static state and static duration; outputting the ID, the state, the static duration and the average speed of the vehicle; the method effectively eliminates the motion interference of the unmanned aerial vehicle, improves the estimation precision, is suitable for the traffic monitoring of the unmanned aerial vehicle, and is high in real-time performance and reliability.

The invention provides a multi-cylinder synchronous accurate control method for a hydraulic jacking system of a tower crane, and belongs to the technical field of tower cranes. A pressure sensor and a displacement sensor are mounted on each hydraulic cylinder of a hydraulic jacking system of the tower crane, and a force sensor is arranged at a key position of a tower body, so that a multi-parameter real-time monitoring network is established; an improved Gaussian kernel function nonlinear matrix mapping algorithm is adopted to calculate a pressure deviation coefficient and a displacement synchronization metric value between hydraulic cylinders, the pressure adjustment processing frequency is dynamically adjusted according to the pressure deviation coefficient value, and self-adaptive load balancing control is achieved through tower body stress unbalance threshold value calculation and stress adjustment gain calculation. And finally, the flow distribution of each hydraulic cylinder is adjusted through a proportional valve, and the convergence of the system is ensured by utilizing Lyapunov stability analysis, so that the technical problem that the multi-cylinder synchronous control of the hydraulic jacking system of the tower crane is not accurate enough is solved.

The invention relates to the technical field of rehabilitation medical treatment, in particular to a hand rehabilitation training system based on virtual reality and gesture interaction, and the system comprises a gesture sensing module which collects a hand movement track, a muscle electric signal, grip pressure and a joint movement angle; the VR rehabilitation scene construction module constructs a rehabilitation training virtual scene, simulates a hand interaction behavior and a force feedback effect through an adaptive physical engine and a neural action mapping algorithm, and outputs simulation data; the intelligent evaluation center evaluates the action standard degree and the muscle activation efficiency based on an improved CNN-GRU model, identifies an action compensation mode and abnormal force generation, and outputs an evaluation report; the interactive training unit renders a motion state and a training process in real time, and generates personalized training guidance in combination with voice, visual and tactile prompts; and the personalized regulation and control unit predicts a rehabilitation trend and a stage target through a reinforcement learning algorithm, and dynamically adjusts training parameters and scene difficulty. Therefore, the problems of low interaction precision, incapability of sharing and the like in the prior art are solved.

The invention relates to the technical field of agricultural production resource big data processing, and discloses an agricultural production resource configuration optimization and management system based on deep learning, which realizes deep fusion of macroscopic remote sensing and microscopic sensing data by constructing a space-time heterogeneous graph neural network, effectively solves the problem of mismatching of space-time scales of multi-source agricultural data, and improves the accuracy of agricultural production resource configuration optimization and management. By using the domain adversarial training and meta-learning technology, the system can extract environment-independent general crop characteristics from source domain historical data, and by combining physical mechanism constraints, the rapid self-adaption of the model can be completed in a target new farm only by using a very small number of samples, so that the data acquisition cost and the model deployment period are remarkably reduced, and the model deployment efficiency is improved. In addition, a physically guided loss function ensures that a prediction result accords with a biological law, and accurate on-demand distribution of water and fertilizer resources is realized in combination with a reverse resource mapping algorithm, so that agricultural production resources are greatly saved while the crop yield is increased, and the robustness and generalization ability of an intelligent system in a complex and changeable environment are enhanced.

The invention relates to the technical field of aircraft path planning, and particularly provides an autonomous path planning method based on multi-rotor multi-aircraft cooperative hoisting, which comprises the following steps: constructing a dynamic node network through an asymmetric topological mapping algorithm based on real-time deformation feedback data of a hoisting target object, generating a self-adaptive bearing frame, and carrying out multi-rotor multi-aircraft cooperative hoisting on the basis of the self-adaptive bearing frame; the internal stress gradient distribution is converted into an initial potential energy field for path planning; based on the characteristics of the initial potential energy field of the bearing frame, a vortex interference mechanism between rotor wing groups is introduced, and dynamic track seed points are embedded into the initial potential energy field; generating a trajectory probability cloud with a non-uniform density feature; regulating and controlling phase parameters of a topological structure elastic modal unit of the derivative field; and implanting a bipolar feedback channel in the trajectory probability cloud, triggering trajectory solidification through an energy difference threshold, and generating a collaborative path group meeting dynamic balance. According to the invention, full-autonomous dynamic path planning and real-time adaptive adjustment of the multi-rotor cluster in hoisting operation are realized.

The invention provides a graph data communication intensity mapping method and system based on optical transport network topology. According to the method, an adjacent matrix is established by extracting graph data structure information, vertex centrality and a clustering coefficient are calculated, and a graph data communication feature matrix is generated; oTN network equipment information and optical link topology are collected, link parameters and wavelength resource states are measured, and an OTN physical network feature matrix is obtained; establishing a double-layer mapping relation matrix through a resource mapping algorithm; integrating the communication frequency, the time delay sensitivity, the bandwidth requirement, the wavelength resource and the topological matching degree, and calculating a communication strength weight matrix; generating a candidate layout scheme, performing multi-dimensional evaluation, and determining an optimized data layout scheme; and dynamically adjusting the mapping relation by adopting an incremental updating algorithm. According to the invention, the efficient collaboration of the graph computing task and the optical transport network resource is realized, and the performance and resource utilization efficiency of the distributed graph computing system are obviously improved.

The invention provides a watermark-based model security distribution and authentication method and system, and relates to the technical field of data processing, and the method comprises the steps: distributing a watermark-containing model which is verified to be qualified to a requester authorization device through an encryption channel, the watermark generation parameters, the distribution context information and the timestamps are used as evidence storage data to be written into a secure evidence storage library; running the distributed watermark-containing model on authorized equipment, and obtaining a watermark bearing signal from output data of the model in a model running process; the watermark bearing signal is analyzed, watermark information is extracted, and an original feature vector is recovered through an inverse mapping algorithm; similarity matching and legality verification are carried out on the recovered feature vectors and records of the security evidence storage library, and model source authentication, use permission control and accurate source tracing of infringement behaviors are achieved according to verification results. According to the invention, the comprehensiveness and accuracy of anomaly detection are improved.

The invention belongs to the technical field of electric power, and discloses an industrial chain electricity consumption relation analysis method, system and device based on a convergence cross-mapping algorithm and a medium, and the method comprises the following steps: obtaining electricity consumption time sequence data of each industry; decomposing the time sequence data through a wavelet decomposition method to obtain multi-band power consumption data; performing multi-band causal analysis on the multi-band power consumption data through a convergence cross mapping method, and constructing a global causal relationship network matrix; constructing a power consumption conduction directed graph according to the global causal relationship network matrix to represent the power demand conduction relationship of each industry; and taking the electricity consumption conduction directed graph as input, and calculating actual electricity consumption according to the electricity demand conduction relation of each industry to obtain an industry chain electricity consumption evaluation result.

The invention discloses an electric power engineering cost visualization method based on big data processing, and relates to the technical field of electric power engineering, and the method comprises the steps: obtaining electric power engineering multi-source data, and loading a pre-constructed electric power engineering three-dimensional model as a visualization carrier; based on the electric power engineering multi-source data, key entities and cost association relationships are extracted according to project types to construct a cost knowledge graph; according to the cost knowledge graph and the industry specification database, generating a cost analysis result through graph association mining and specification verification; through a hierarchical dynamic mapping algorithm, performing semantic structuring processing on the cost analysis result according to a plurality of hierarchies of a project population, sub-project and equipment parts, and binding with spatial positions of components of corresponding hierarchies in the three-dimensional model of the electric power project to generate a visual mapping result; and in response to the multi-modal interaction instruction, positioning a target component in the three-dimensional model of the electric power engineering based on the sight line of the user, and displaying a cost analysis result constructed with the target component.

The invention discloses a data aggregation processing method and device, computer equipment and a storage medium, belongs to the technical field of big data, and is applied to data aggregation in the financial field. The method comprises the following steps: firstly, grouping dimension fields by utilizing a mapping rule and a hierarchical clustering algorithm; and secondly, dynamic grouping mapping is established, one-time scanning is performed on the source data table, and each data record is directly attributed to a corresponding grouping structure, so that the data processing efficiency is improved. Meanwhile, a correlation index of the index and the data partition is constructed based on a Hash mapping algorithm. Under the support of an indexed data structure, the aggregation calculation function can efficiently execute operations such as summation and averaging, and rapid aggregation of multi-dimensional data is realized. Generally speaking, according to the method, the correctness of an aggregation result is ensured, meanwhile, the calculation and storage pressure of the system is remarkably reduced, the timeliness and expandability of data analysis are improved, and the method is particularly suitable for real-time or quasi-real-time multi-dimensional analysis requirements in a large-scale business scene.

The invention relates to the technical field of defect detection imaging, and discloses a high-precision three-dimensional imaging method and system based on continuous magnetic flux leakage data. Collecting surface magnetic flux leakage signals of a measured target to form an initial signal set, and extracting feature vectors by using a convolutional neural network model; separating three-dimensional space data, and obtaining initial three-dimensional position coordinates of the defect through an iterative mapping algorithm; mining spatial geometric features, determining potential geometric boundaries, and fusing data to construct a refined position model; extracting a detection subset, correcting a shape error, constructing a space reconstruction grid, filling holes, and obtaining a complete three-dimensional shape representation; and through precision calibration and signal matching verification, a high-precision three-dimensional image is output. According to the method, the defect positioning and shape reconstruction precision can be effectively improved, the imaging integrity and accuracy are guaranteed, the problems of fuzzy positioning and shape distortion of traditional magnetic flux leakage imaging are solved, and the defect detection requirements of a complex detected target are met.

The invention discloses an intelligent mattress control method based on height, an intelligent mattress and related products. The method comprises the steps of obtaining height parameters of a human body; according to the height parameters, through a preset micro-partition mapping algorithm, to-be-activated micro-partitions, corresponding to all the body parts of the human body, on the mattress are determined, a preset number of micro-partitions are pre-divided in the length direction of the mattress, and a plurality of air bags are distributed in each micro-partition; activating the air bag of the micro-partition to be activated to obtain a current activated micro-partition, and collecting a plurality of pressure values of the micro-partition through a pressure sensor array of the mattress; according to a preset air pressure adjusting strategy, the air pressure of the air bag in the current activation differential area is adjusted according to the pressure value. According to the method, the air bag supporting subareas can be dynamically adjusted according to the heights of the users, the problem that traditional mattress subareas are fixed and are difficult to adapt to the users with different heights and weights is solved, accurate supporting and personalized adjustment are achieved, and the supporting effect and sleep comfort are improved.

The invention discloses a virtual loop generation method and system based on template library matching, and relates to the technical field of power system automation, and the method comprises the steps: matching a corresponding target subscription template from a preset subscription equipment virtual terminal template library based on the equipment type and interval identification of subscription equipment; dynamically retrieving a target publishing template in a preset publishing equipment virtual terminal template library based on the demand characteristics of the publishing equipment; based on a preset topological relation constraint rule and a semantic matching mapping algorithm, generating matched virtual terminal topological data and a communication parameter group; constructing a virtual loop instance configuration file of the intelligent substation based on the virtual terminal topological data and the communication parameter group; and respectively performing grammar layer verification, communication parameter compliance verification, virtual terminal connection attribute verification and logic layer verification on the virtual loop instance configuration file. The technical problems that in the prior art, the working efficiency of a mode of manually configuring the virtual loop is low, and mismatching of the virtual loop is prone to occurring are solved.

The invention discloses a remote sewage treatment system based on a big data model. The remote sewage treatment system comprises an information collection module (100), a data processing module (200), a data prediction module (300) and a visualization module (400), the information collection module (100) comprises a sensing network unit (101) and an edge signal calculation unit (102), the sensing network unit (101) comprises a sensor arranged at the position of a sewage treatment facility, and the edge signal calculation unit (102) is used for preprocessing collected data; the data processing module (200) verifies the causal relationship among the water quality parameters by adopting a convergent cross-mapping algorithm; and the data prediction module (300) is used for establishing a bidirectional neural network and predicting an expected water quality index. The method has the advantages that the method has better effects in the aspects of data acquisition density, feature interpretation, prediction precision, operation and maintenance efficiency and the like compared with a traditional system, and especially under the dynamic working condition, the error of the bidirectional neural network model is controlled at a low numerical value and is obviously improved compared with a traditional method.

The invention discloses a coarse-grained reconfigurable architecture cyclic mapping method and device based on a graph neural network, and relates to the technical field of computer reconfigurable computing. The method comprises the following steps: based on a priority prediction model, performing list scheduling according to a data flow diagram and hardware information to obtain a list scheduling table; performing modular scheduling according to the data flow diagram and the hardware information; pre-scheduling the data flow diagram on the basis of a list scheduling table according to the modular data flow diagram and the time extension coarse-grained array to obtain a time step attribute data flow diagram; obtaining a mapping result by using a mapping algorithm based on pattern diagram matching according to the time step attribute data flow diagram on the basis of the time extension coarse-grained array; based on the hardware information, processing unit isomorphism verification is carried out according to the mapping result; and compiling the verified mapping result to obtain an executable configuration information file. The invention provides an efficient and accurate coarse-grained reconfigurable architecture cyclic mapping method based on a graph neural network.