660 results about "Real time tracking" patented technology

The invention discloses an AI agent design platform decision optimization method based on big data, and particularly relates to the field of artificial intelligence, comprising multi-modal data sensing layer construction, a streaming feature calculation engine, a dynamic index fusion center and an adaptive decision matrix. According to the method, accurate synchronous monitoring of the utilization rate of hardware resources and dynamic collaborative optimization of heterogeneous computing units are achieved, and the resource scheduling efficiency in a complex computing scene is remarkably improved; knowledge system degradation caused by long-term learning is effectively prevented, and the continuous reliability of a cognitive system is ensured. The provided multi-dimensional decision credibility verification system is fused with interpretability penetration analysis, environment coupling modeling and logic drift detection technologies, the limitation of a traditional single credibility index is broken through, the risk prediction and fault-tolerant capability of the decision process is remarkably enhanced, and a full-dimensional safety decision guarantee system is constructed for an intelligent agent.

The invention relates to the field of digital twinborn modeling, and discloses a digital twinborn visual modeling method and system based on a neural network, and the method comprises the steps: collecting the multi-dimensional perception data of a target physical system, carrying out the format unification and normalization processing of the original data of a sensor through a heterogeneous data fusion module, and obtaining a data fusion module; constructing a high-dimensional input feature set for neural network modeling by combining a structured embedding algorithm; carrying out stability pre-evaluation on the constructed input feature set, and screening core features by adopting a disturbance sensitivity analysis mechanism; a dynamic residual feedback mechanism is introduced to carry out enhanced training on the preliminary twin model, and modeling is carried out on the space-time dependency relationship of different components through a graph neural network; tracking a stability index of visual representation in real time in a model training process; and according to a multi-dimensional visualization result output by the final twin model, performing interpretation in combination with an industrial scene semantic rule base. The method has the advantage of improving the practicability of the twin model in the industrial scene.

The invention provides a disaster area unmanned aerial vehicle cluster dynamic task allocation and cooperative control method and system. The method comprises the steps that S1, original data of a disaster area scene are collected in real time through ground IoT equipment; s2, performing dynamic task priority marking on the original data, and integrating multi-dimensional information of the unmanned aerial vehicle; s3, tasks are allocated to the unmanned aerial vehicle based on a hybrid allocation strategy, and an initial obstacle avoidance path is generated; s4, when the unmanned aerial vehicle executes the task, the flight path is updated in real time, temporary obstacles are avoided, path conflicts are coordinated through a conflict detection algorithm, and task redistribution or degradation is carried out if necessary; s5, periodically, globally and dynamically adjusting task priorities or correcting paths; and S6, constructing a self-adaptive closed-loop control mechanism, and tracking multi-dimensional information and task attributes of the unmanned aerial vehicle in real time. According to the method, the global resource utilization rate is maximized while the second-level response of the high-priority task is guaranteed, and finally, the robustness and the adaptive ability of the unmanned aerial vehicle cluster in a complex scene are realized.

The invention belongs to the technical field of information, and relates to a multi-level dynamic intelligent automatic data processing and information evaluation method and system. The method comprises the steps that a database environment operation data set is constructed, targeted training and fine adjustment are conducted on a large language model in combination with reinforcement learning, and full-automatic data analysis, query and information extraction of multiple tables in a database are achieved; a multi-level agent interaction and cooperation framework is established, a scheduling agent is responsible for global decision making and strategy planning, an operation agent is responsible for execution of specific tasks, and the scheduling agent and the operation agent share information and coordinate the tasks to form a highly cooperative whole; and establishing a cyclic supervision and error correction mechanism, scheduling the intelligent agent to learn a normative workflow example of the target working environment and track the state of the operation intelligent agent in real time, identifying the behavior of the operation intelligent agent deviating from a predetermined path, and taking corrective measures. According to the method, high efficiency, intelligence and automation of data processing are realized, and the method has remarkable practical value and innovative significance.

The invention belongs to the technical field of medical robots, and particularly relates to a mammary gland puncture surgical robot navigation system and device based on multi-modal image fusion. The multi-mode image data are registered and fused, high-precision three-dimensional reconstruction of the mammary gland tissue is achieved, more visual and accurate surgical navigation information is provided for doctors, the position of the puncture needle is tracked in real time, space registration is conducted on the puncture needle and the image data, and therefore the precision and safety of the surgery are improved, and the operation efficiency is improved. Meanwhile, puncture force feedback information of a puncture needle in the needle moving process is collected in real time, the implementation effectiveness of a puncture navigation path is evaluated, when it is found that the puncture path has risks, dynamic adjustment is conducted immediately, a new puncture path bypassing an obstacle area and reaching a focus is planned again, and the risk of the puncture path is avoided. And if the safe path cannot be obtained, the system automatically stops needle insertion and gives an acousto-optic early warning prompt, so that the surgical risk caused by manual misoperation in the traditional surgery is avoided.

The invention relates to the field of transportation visualization, in particular to a cold chain cargo transportation management visualization method and system. The method comprises the following steps of collecting cold chain cargo state parameters, performing cargo quality trend evaluation, and generating state data streams of a plurality of cargoes; performing dynamic positioning and tracking on the cold-chain transport vehicle, performing space-time mapping processing according to the state data flow, and constructing a real-time tracking trajectory flow; performing future aging deviation prediction on the real-time tracking trajectory flow to generate an aging deviation prediction result; carrying out transportation risk comprehensive assessment according to the aging deviation prediction result, and generating a transportation risk assessment report; and carrying out path planning calculation and client notification pushing according to the transportation risk assessment report so as to execute intelligent transportation management. According to the invention, the transportation energy consumption and time cost are reduced, the resource allocation efficiency is improved, and the cargo real-time tracking visualization effect is improved.

The invention belongs to the technical field of environmental science, and relates to a multi-source satellite remote sensing cooperative atmospheric pollution monitoring and dynamic tracing method and system. The method comprises the following steps: collecting, processing and screening multi-source data; constructing a pollutant diffusion model, and generating pollutant concentration distribution data; dynamically adjusting the diffusion coefficient and attenuation rate of the pollutant diffusion model, and generating a pollutant concentration distribution thermodynamic diagram; performing space matching, and iteratively updating the diffusion coefficient and the attenuation rate of the pollutant diffusion model until matching parameters meet set conditions; and verifying pollutant concentration distribution data in the pollutant concentration distribution thermodynamic diagram. According to the invention, parameters of the pollutant diffusion model can be dynamically adjusted and optimized based on real-time meteorological data, the accuracy of pollutant concentration distribution data generated by the pollutant diffusion model is ensured, and real-time tracking and accurate positioning of a pollution source are realized; the monitoring precision is obviously improved; the cooperative monitoring efficiency is improved, and comprehensive monitoring of atmospheric pollutants is realized.

The invention relates to the technical field of offshore area monitoring, in particular to an offshore area pollutant diffusion real-time tracking and traceability monitoring system. The method comprises the following steps: a data perception and fusion unit collects and preprocesses data, carries out space-time calibration and multi-source fusion, and outputs a standardized fusion space-time sequence; the pollutant diffusion simulation and prediction unit calculates a flow velocity field, simulates a diffusion process and predicts a trend; the pollutant traceability positioning unit tracks a pollution path and inverts pollution source parameters; the emergency decision support unit determines a risk level and generates an emergency plan. And through a space-time alignment algorithm and a multi-source fusion mechanism, the data consistency and reliability are improved, a high-quality data basis is provided for subsequent processing, and the monitoring accuracy is guaranteed.

The invention discloses an intelligent dynamic tracking and early warning method and system for carbon emission, and relates to the technical field of carbon emission management.The method comprises the steps that a digital twinborn model of a target area is constructed, grid division is conducted based on monitoring precision, and a first layer of fixed monitoring points are arranged; planning a mobile monitoring path in combination with a carbon emission attention fitting result, and configuring a second layer of mobile monitoring points; collecting data through two layers of monitoring points, and establishing a carbon emission data network; performing airflow simulation based on a digital twinborn model, performing traceability inversion in combination with a data network, and positioning an emission source; and finally, equipment-level positioning and carbon emission tracking early warning are realized through equipment matching analysis. The technical problems that in the prior art, the carbon emission monitoring coverage range is limited, the traceability is insufficient, and accurate positioning and real-time early warning of the carbon emission source are difficult to achieve are solved, and the technical effects of high-precision traceability positioning and real-time traceability early warning of the carbon emission source are achieved.

The invention relates to the field of electronic photo frames, and discloses an electronic photo frame intelligent interaction control method and system based on eye movement tracking, and the method comprises the steps: carrying out the real-time tracking of an eyeball position based on the eye image data of a user, and obtaining an eye movement track sequence according to the pupil center coordinate change and the eyelid opening and closing state change; calculating a fixation point coordinate, a fixation time length and an eye hopping frequency based on the eye movement track sequence; the current eye movement behavior type of the user is obtained through a personalized recognition model; matching with a preset eye movement pattern library based on the current eye movement behavior type of the user to obtain a candidate instruction set; and executing photo frame function operation according to the control instruction. The method has the following effect that the convenience and accuracy of man-machine interaction are improved based on non-contact intelligent control of eye movement.

The invention discloses an unmanned aerial vehicle cluster cooperative control system and method, and relates to the technical field of unmanned aerial vehicle cluster control, and the system comprises a multi-source sensing unit, an integrated laser radar, an RGB-D camera and a millimeter wave radar array, and is used for generating a centimeter-level precision three-dimensional map and tracking a dynamic obstacle in real time; the collaborative decision-making unit comprises an artificial intelligence prediction module which is used for predicting a collision point based on the movement state of the obstacle; the safety space construction module is used for generating a dynamic safety sphere according to the formation envelope size and the obstacle volume; the artificial intelligence correction module is used for constructing a safety section and generating an optimization path; and the dynamic communication unit is used for carrying out cluster data interaction by adopting hybrid networking of 5G and LoRa adaptive switching. The unmanned aerial vehicle cluster can be used as a whole to avoid obstacles, the original forms of the unmanned aerial vehicles are synchronously kept, and it is guaranteed that multiple unmanned aerial vehicles in the cluster do not collide.

The invention relates to the technical field of combustion monitoring, and discloses a combustion instability boundary prediction system based on complex environment condition analysis, and the system comprises a multi-source sensing module, a data fusion module, an anti-interference processing module, an instability feature library, an intelligent decision engine and a digital twinborn body. A dynamic environment interference compensation mechanism is constructed, spectrum correction parameters are configured according to different environment working conditions during real-time prediction of a combustion instability boundary, the problem of spectrum distortion caused by environment disturbance on combustion dynamic parameters is eliminated in real time, spectrum interference deviation of a combustion measurement position can be automatically sensed and corrected, and real-time prediction of the combustion instability boundary is realized. The accuracy of combustion instability sensitive feature extraction is guaranteed, the instability early warning error is reduced, a flame pose real-time tracking system is deployed, fuel control parameter self-adaptive adjustment is triggered immediately when it is detected that the pose is abnormal, autonomous correction of the combustion instability feature recognition position is achieved, and it is guaranteed that the feature capture space is accurately positioned.

The invention relates to the technical field of computer systems and human-computer interaction, in particular to a text travel element universe virtual role collaboration system and method based on large-space interaction. The method specifically comprises the following steps: tracking a user spatial position in real time through a multi-source positioning technology, and carrying out identity authentication and equipment legality verification by fusing multi-modal features; constructing a virtual-real fused semantic point cloud structural body, binding role interaction nodes, dynamically generating a task collaboration graph by using a graph neural network, and driving multiple roles to collaboratively execute subtasks; calling a graph attention network and a GPT module to deduce personalized plot branches in combination with user multi-modal input and a historical track, and rendering an immersive interactive scene in real time; spatial preferences and content interests are mined by adopting DBSCAN clustering and depth feature coding, a dynamic portrait is constructed, and a task path and content presentation are optimized through a weighted recommendation algorithm. According to the method, cloud rendering, edge calculation and cross-module cooperative control are integrated, and the interactivity and immersion experience of the text travel element universe are remarkably improved.

The invention relates to the technical field of power system measurement, and discloses a current transformer error dynamic monitoring method and system.The current transformer error dynamic monitoring method comprises the steps that current transformer time sequence data and a system event log are obtained; constructing a time sequence causal graph to represent the time correlation between the event and the error change; identifying potential causal links by applying a counter causal model; designing a multi-world simulation engine to generate an anti-fact scene; quantifying a causal effect by comparing actual observation with an anti-fact simulation result; establishing a monitoring mechanism to track key trigger events in real time; generating a dynamic causal interpretation report and adjusting a compensation strategy; according to the method, the limitation of traditional correlation analysis is broken through, the causal relationship and the correlation can be accurately distinguished, the real triggering factor of the error change of the current transformer can be accurately identified, the false alarm rate and the missing report rate are reduced, and the accurate dynamic monitoring of the error of the current transformer is realized.

The invention relates to the technical field of industrial platform data analysis, in particular to a renewable resource recovery data management system based on the Internet of Things, which comprises the steps of synchronously acquiring weight, spectrum and microwave characteristic data of a tested resource through a data acquisition module, executing moisture weight decoupling operation by utilizing a resource matching module, and performing data analysis; then, an inventory evolution module tracks performance loss of resources in real time by using an evolution model of an integrated long and short-term memory network, generates a resource attenuation weight, and constructs a two-dimensional decision matrix containing scheduling priority and preprocessing strength grade instructions in combination with real-time inventory saturation; and finally, the clearance scheduling module executes dynamic pruning and bidirectional optimization through a resource scheduling model, accurately allocates the loading share and the access sequence of each node, and generates a dynamic instruction set containing a delivery sequence. According to the method, feeding homogenization is realized through industrial data multi-dimensional collaborative analysis.

The invention discloses an intelligent identification and grading early warning method based on mine production abnormity, and a system and equipment thereof. The method comprises the following steps: obtaining space-time evolution data, earth surface deformation data and environment monitoring data of rock mass stress in a target area; performing abnormal mode detection on the data by using a space-time convolutional network to obtain accident risk probability distribution data; using an ultra wide band positioning system to track the movement track of the person entering the target area in real time to obtain the movement track data of the person; and constructing a three-dimensional early warning index system in combination with the accident risk probability distribution data in the target area and a dynamic early warning mechanism, performing dynamic early warning grading on different positions in the target area by using the three-dimensional early warning index system, and generating a grading response strategy matched with the personnel activity trajectory data. According to the invention, the problems of incapability of tracking a personnel movement track in real time and low timeliness of mine production abnormity early warning are solved.

The invention discloses an airport hub construction intelligent regulation and control method and system based on BIM + 5G, and relates to the technical field of construction scheduling optimization, and the method comprises the steps: collecting on-site real-time data to construct a path construction sequence model, constructing a dynamic construction state set with consistent space and time sequence through component coding and semantic attribute mapping, and constructing a dynamic construction state set with consistent time sequence; and constructing a minimum disturbance optimization algorithm of four-dimensional disturbance cost based on the construction disturbance mapping graph, outputting procedure sequence adjustment, resource rearrangement, path decoupling and environment avoidance intervention suggestions, and tracking a construction response state in real time based on an intervention execution feedback mechanism. According to the method, unified modeling of construction plans, resource allocation and green indexes is achieved by constructing a ternary structure and a construction disturbance mapping graph, the field state is dynamically collected in combination with a 5G and edge sensing system, process abnormity and resource conflicts are accurately recognized, an efficient and executable regulation and control strategy is generated based on a multi-target disturbance optimization algorithm, and the efficiency and the reliability of the system are improved. And the intelligence, responsiveness and energy-saving level of the construction process are obviously improved.

The invention provides an abnormal blood sampling inspection data evaluation processing method and system of a blood sampling system. Wherein the diffusion track dynamic state of an anticoagulant-blood contact surface in a blood sampling tube is tracked in real time, dielectric response waveform distortion characteristics on the two sides are synchronously collected through a capacitance sensor array, and diffusion form parameters are generated; curvature sudden change is analyzed to trigger an acoustic fluid device to modulate audio frequency and flow velocity, and a composite disturbance field is formed; performing capacitance axial scanning on the disturbed mixed fluid, extracting phase angle offset and gradient abrupt change point coordinates, and constructing a dielectric anomaly three-dimensional map; matching a standard dielectric model, positioning a coordinate set of a concentration gradient imbalance area and a coordinate set of an eddy current attenuation area, and generating an anticoagulant abnormal index and a mixed defect matrix in combination with phase angle offset difference; and establishing a data anomaly degree evaluation function based on the anomaly index and the defect matrix, dynamically associating a clinical error threshold, and outputting a test evaluation result. According to the invention, anticoagulant diffusion can be monitored in real time, and the mixing quality error can be accurately evaluated.

The invention relates to the technical field of medical information, and discloses a chronic kidney disease intelligent follow-up visit management system and method based on multi-source data. According to the method, a multi-source heterogeneous data set is formed by collecting an electronic health record of a patient, real-time wearable equipment monitoring data and active feedback information of the patient. A deep learning model is utilized, and a time sequence data analysis and cross-modal feature alignment module is combined, so that multi-source data collaborative analysis is realized, and the kidney function state of a patient is accurately evaluated. And according to the evaluation result and the individual difference of the patient, a personalized follow-up visit strategy is dynamically generated and comprises a follow-up visit period, a necessary inspection item and a risk intervention priority. The system automatically executes follow-up visit tasks, tracks execution states in real time and feeds back the execution states to the evaluation model, closed-loop iterative optimization is formed, and the chronic kidney disease management efficiency is remarkably improved.

The invention discloses a ship target detection and tracking method and system based on video image processing, and the method comprises the steps: 1), collecting a channel panoramic video stream through a high-definition camera, and extracting a continuous image sequence; 2) constructing a dynamic channel background model by using a Gaussian mixture model (GMM), and updating the background in real time to eliminate interference of water surface fluctuation and illumination variation; (3) enhancing the data obtained in the step (1) by adopting a Mosaic enhancement method, and establishing a data set; 4) optimizing a Yov7 model, and introducing a lightweight CBAM attention mechanism to carry out model training; (5) the trained weight model is experimented on the verification set, and target detection of the ship is achieved; and 6) adopting a kernel correlation filtering algorithm to realize real-time tracking of the moving ship. According to the method, the technology in the field of computer vision is utilized, the real-time video image number of the large-flow channel is effectively utilized, multi-target recognition and real-time tracking are achieved, and the method has high accuracy and robustness.

The invention discloses an e-commerce supply chain full-process traceability anti-counterfeiting method and platform based on the Internet of Things, and the method comprises the steps: encoding commodity basic information through a two-dimensional code encryption algorithm to generate an initial two-dimensional code, collecting and processing a commodity transportation track through a GPS track fusion model, and dynamically updating the two-dimensional code in combination with e-commerce supply chain parameters; and the authenticity and the circulation compliance of the commodity are judged through decryption verification at the point-of-sale terminal, and a result is fed back. A corresponding platform comprises six modules including a multi-dimensional information encryption coding module and a spatio-temporal trajectory sensing processing module, all the modules operate cooperatively, information encryption, real-time tracking, dynamic updating, accurate verification and closed-loop management of commodities in the whole process from production to sales are achieved, the problems that in the prior art, information management is outdated, and dynamic monitoring is lacked are effectively solved, and the service life of the commodities is prolonged. A reliable traceability anti-counterfeiting solution is provided for the e-commerce supply chain, and the supply chain management level and the commodity safety are improved.

The invention discloses a general cargo transportation background business management system and method and electronic equipment, the system comprises a data acquisition module, an information management module, a position tracking module and a scheduling management module, and automatic acquisition of cargo position, attribute and safety inspection information is realized through an integrated sensor, an RFID tag technology and an image identification device; the GPS positioning and local wireless network complementary correction technology is utilized to break through the positioning precision limitation in the indoor and outdoor complex environment; based on a distributed database and a real-time data processing engine, high-concurrency query and dynamic information updating are supported; in combination with cargo state and position data, an optimization scheduling instruction is generated through an algorithm, empty driving waiting of loading and unloading equipment is reduced, the method covers intelligent scheduling processes of multi-source data acquisition, standardized processing, real-time tracking and cloud driving, full-life-cycle automatic management of cargos is realized, and electronic equipment realizes full-life-cycle automatic management through a software and hardware collaborative architecture. An existing port management system is compatible, and deployment cost is reduced.

The invention discloses a multi-target tracking method, system and device, a medium and a product, and relates to the technical field of computer graphic vision, and the method comprises the steps: based on a plurality of single-view two-dimensional images, employing a lightweight key point detection model to extract human body two-dimensional key point coordinates under different views; based on the camera parameters corresponding to different visual angles and the human body two-dimensional key point coordinates under different visual angles, performing three-dimensional reconstruction on the human body key points by using a triangulation method to obtain a three-dimensional reconstruction model; projecting the three-dimensional reconstruction model, and determining a projection frame corresponding to each target in the tracked area; and a tracking algorithm and Kalman filtering are combined to complete real-time tracking of a plurality of targets in the tracked area. According to the method, the three-dimensional target tracking problem is reduced into the two-dimensional target tracking problem based on the projection frame of the three-dimensional reconstruction model relative to the horizontal plane, the accuracy and robustness of the model can be improved in a multi-target tracking scene, and meanwhile, the calculation amount is reduced.

The invention discloses an industrial PCB cooperative control method based on open-source and Feiteng CPUs, particularly relates to the technical field of industrial automatic production line control, and is used for solving the problem that a quality control instruction and a board card physical flow in an existing PCB production line are difficult to accurately synchronize, so that an adjustment instruction is lagged or mistakenly acted. A unique identifier is generated for each printed circuit board, the position of each printed circuit board is tracked in real time, and when a board card enters a synchronous control area, a Feiteng CPU obtains the state of each execution device; selecting a data query strategy based on state consistency, and collecting node state feedback and quality data; according to the method, the consensus convergence rate and the phase-frequency characteristic distortion index of the system are calculated, the triggering time point of a control instruction is dynamically adjusted when the index is abnormal, and a correction instruction is sent to execution equipment through an open source gap distributed soft bus, so that high-precision cooperative control of the instruction and board card flow is realized, and the real-time performance and reliability of closed-loop quality control are improved.

The invention relates to the technical field of rectal cancer prognosis prediction, in particular to a rectal cancer prognosis prediction method and device based on a multi-modal image and electronic equipment. The method comprises the following steps: firstly, acquiring an MRI image and a pathological image of a patient as original input data; respectively preprocessing the MRI image and the pathological image by adopting a double-flow heterogeneous feature extractor; then, by establishing a dynamic association mechanism, the change of the mapping relation between the two kinds of modal feature data is tracked in real time, the dynamic association mechanism can adjust the feature weight in a self-adaptive mode, and key changes in the disease progress process are effectively captured; and finally, based on the obtained dynamic association feature data, applying a pre-trained prediction model to obtain a prognosis prediction result. The MRI image and the pathological image are effectively integrated, and the accuracy of predicting the disease progress and the prediction repeatability are improved.

The invention provides a steel roof full-period deformation monitoring method and system based on multi-source sensor fusion, and belongs to the technical field of monitoring structural deformation or stress, and the method comprises the steps: laying a multi-source sensor network for synchronously collecting data; determining sliding block stress distribution data of the steel structure in the sliding process, and determining a local buckling risk area in combination with the dynamic vibration data; the steel roof is divided into a plurality of asymmetric sliding blocks, and a sliding path correction instruction is generated in combination with the geometric state data of the sliding track; compensating the geometric deviation of the slip track in real time, and predicting a critical deformation threshold value of a subsequent construction stage by combining an initial gap parameter of the pre-embedded support structure; and after sliding is completed, steel column falling track data are tracked in real time, the unloading rate is dynamically regulated and controlled in combination with the critical deformation threshold value, and the unloading path is calibrated through the compensated sliding track geometric state data till the deformation error is converged within the preset tolerance range. And the construction precision and safety are effectively improved.

The invention belongs to the field of visual tracking, and relates to a multi-target visual tracking method based on cooperation of a dynamic neural field and a neural network, which takes a cross-modal cooperation architecture as a core and comprises a dynamic neural field module based on multi-target trajectory maintenance and shielding matching and an improved MoESDQ neural network module. Meanwhile, a collaborative decision-making mechanism is designed, when the activation peak value of the dynamic neural field is attenuated to a preset threshold value, neural network feature matching is triggered, and disappearance target reproduction correlation is achieved based on cosine similarity. The objective of the invention is to solve the visual tracking capability under the condition of scene and target motion change in a monitoring range, for example, under an intelligent traffic intersection scene. The problems of high ID switching rate, multi-target misassociation and low tracking precision under a real-time tracking background caused by scene change or frequent shielding of vehicles and pedestrians, similar target appearances, transient disappearance and reproduction of the targets and sudden illumination change are solved.

The invention provides a bridge performance evaluation and life prediction method based on big data analysis, and relates to the technical field of bridge preventive maintenance in highway engineering, and the method comprises the steps: obtaining three-dimensional scanning data of an old bridge and a new bridge under a common service condition, and combining with complex service conditions such as dynamic load, temperature difference, creep shrinkage, etc. And the dynamic response of the connection part in an actual use environment can be accurately simulated and evaluated. Especially under the condition of common service of new and old bridges, the degradation and damage of the connection part are particularly significant, and by combining with a big data technology, the progress of material aging is tracked in real time, and possible hidden damage of the connection part is helped to be predicted. By constructing the aging factor, the damage index and the corrosion index and associating the aging factor, the damage index and the corrosion index with the connection coordination index graph, a specific rigidity loss factor can be provided for each connection unit, the performance degradation degree of the bridge connection part is reflected, and a scientific basis is provided for subsequent reinforcement and maintenance strategies.

The invention provides a building engineering data information intelligent management platform which comprises an intelligent sensing module, an edge calculation module, a data management module, an AI decision module, a business application module and a safety standard module. Through the intelligent sensing module, the edge calculation module, the data management module, the AI decision module, the service application module and the safety standard module, the position of a constructor and the equipment state can be tracked in real time, personnel are prevented from entering a dangerous area by mistake and the mechanical overload risk is prevented, operation safety is guaranteed, AI quality inspection and fault diagnosis are locally processed, cloud dependence is reduced, and the working efficiency is improved. According to the method, the system and the device, real-time early warning of potential safety hazards can still be ensured when the network is interrupted, BIM, IoT and service data can be integrated in a standardized manner, information islands are eliminated, and cross-stage quality tracing and responsibility definition are realized.

The invention discloses an all-optical three-dimensional scanning confocal fluorescent microscopic imaging device and an implementation method thereof. According to the invention, through a deep learning driven adaptive regulation and control method, CNN is adopted to process spatial distribution data and dynamically regulate and control a phase hologram and the light intensity and phase compensation of a laser, so that the focal point of exciting light is subjected to aberration-free axial displacement, a bidirectional parallel optical scanning track of a two-dimensional scanning system is optimized, and all-optical three-dimensional scanning is realized; lSTM and TCN are combined to obtain a time sequence dependency relationship, a laser light source, an adjustable diaphragm, an electric focus-adjustable lens and a photoelectric detector are integrally controlled, efficient synchronization and automatic operation and high-speed axial focusing adjustment are realized, errors and time sequence mismatch are eliminated, optical characteristics of different samples and environmental interference are automatically adapted, and high-quality imaging is kept. The robustness and the applicable scene range of the system are improved; the method is used for model biological embryo real-time tracking, intracellular signal molecule dynamic visualization, cell membrane protein migration and aggregation observation and intracellular organelle interaction tracking.