480 results about "Real time tracking" patented technology

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 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 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 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 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 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 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 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.

The invention discloses an electromagnetic transient simulation and mapping method and system oriented to multi-physics field coupling, and belongs to the technical field of substation digital operation and maintenance and electromagnetic simulation. Deploying multiple types of sensors in a transformer substation, preprocessing the acquired multi-physical field data, and fusing with the three-dimensional twinborn model; a coupling algorithm is adopted to carry out multi-physical field coupling analysis, multi-physical quantity display and thermodynamic diagram superposition are realized, and scene evolution change simulation under various typical working conditions is supported; a micro-service architecture is adopted to construct a digital twin system, double closed-loop verification of simulation and actual measurement is performed through actual measurement data and simulation data of a real-model test field, model optimization is completed, and real-time mapping of electromagnetic transient dynamic simulation and a transformer substation physical entity is realized. The method improves the precision of model and entity mapping, meets the requirements of real-time tracking and response of the electromagnetic transient change of the transformer substation, adapts to the digital transformation of different transformer substations, and provides a basis for the operation and maintenance of the transformer substations.

The invention relates to a global passenger flow direction real-time tracking method for rail transit station video monitoring, and belongs to the technical field of intelligent traffic, and the method comprises the following steps: S1, collecting video data and business data in a station, and carrying out the time-space synchronization and preprocessing of multi-source heterogeneous data; s2, extracting and fusing fine-grained clothing textures, carried objects and gait features of passengers in real time; s3, compressing a search space based on a station topology and a space-time reachability model, and performing global identity association by adopting a probability graph model; s4, maintaining and correcting the continuity of the track through forward-backward joint reasoning and external data verification, and finally outputting a continuous passenger track with consistent identity; s5, carrying out association and semantic annotation on the continuous passenger tracks and the business data; and S6, performing analysis based on the fused data and generating a regulation and control instruction.

The invention discloses a CO2-EGR visual monitoring method based on nuclear magnetic resonance. The CO2-EGR visual monitoring method aims at solving the problem that dynamic observation of the pore scale gas drive process is insufficient in the traditional technology. Multi-sequence nuclear magnetic resonance monitoring is synchronously executed, a T2 spectrum is obtained through a CPMG sequence to analyze CH4 content changes, a layered T2 spectrum is generated through an SE-SPI sequence to represent axial fluid distribution, a one-dimensional frequency signal is collected based on a GR-HSE sequence to track a displacement leading edge in real time, and fluid migration visualization is achieved in combination with an HSE overall imaging sequence. The system integrates a gas injection unit, a high-pressure-resistant nuclear magnetic holder, a back pressure controller and a data acquisition module, and is hermetically connected through a high-pressure-resistant pipeline to form a closed-loop experiment system. According to the method, displacement front recognition, displacement process in-situ analysis and quantitative characterization of the displacement efficiency in the non-intrusive CO2-EGR process are achieved for the first time, the CO2-EGR visualization bottleneck is broken through, and a scientific basis is provided for optimizing gas injection parameters and improving the natural gas recovery rate and the carbon sequestration efficiency.

The invention discloses a sensitive data protection-oriented generative model differential privacy leakage prevention method and system, and belongs to the technical field of artificial intelligence and data privacy protection. Constructing a forward diffusion and reverse sampling process based on sample information and a diffusion generation model framework; calculating the semantic sensitivity of the current generation state in each step of reverse sampling through a self-adaptive differential disturbance regulation algorithm, and regulating the noise intensity of the current step according to self-adaption; privacy expenditure brought by each round of disturbance is monitored through a verifiable privacy budget tracking mechanism, and real-time tracking privacy budget consumption is obtained; identifying high-similarity samples as high-risk areas in each round of generation by generating a risk perception feedback optimization strategy; and the model is guided to be far away from the privacy sensitive area by dynamically enhancing the disturbance intensity of high-similarity samples. According to the method, the privacy protection capability of the model in the sensitive field is effectively improved while high-quality data generation is realized.

The invention discloses a transcranial stimulation magnetic therapy coil navigation method, a transcranial stimulation magnetic therapy coil navigation system, a storage medium and equipment. The method comprises the following steps: acquiring three-dimensional structure information of the head of a target object, magnetic resonance imaging (MRI) data of an individual target object, or three-dimensional model data obtained by matching from a general MRI template library; real-time surface geometric information of the head of the target object is collected through an infrared optical tracking system; performing registration fusion on the three-dimensional structure information and the real-time surface geometric information, and generating an individualized three-dimensional brain model synchronized with a real space coordinate system on a navigation interface; the spatial position and posture of the optical marker fixed on the magnetic therapy coil are tracked in real time through an infrared optical tracking system and correspondingly mapped to the individualized three-dimensional brain model, and visual navigation of the magnetic therapy coil relative to the brain anatomical structure is achieved. Through the individualized three-dimensional navigation and real-time visualization technology, the problems that a traditional TMS is low in positioning precision, invisible in operation and poor in treatment repeatability are solved.

The invention provides a medical emergency processing method, system and device based on big data and a medium, and belongs to the technical field of medical information.The method comprises the steps that a medical emergency event is responded, and structured event description data is generated; dynamically collecting and cleaning data, and constructing a data graph; performing illness condition criticality grading on the wounded, predicting illness condition development trend by combining a medical image analysis result of the wounded, and outputting an injury condition grading report; based on an improved VRPTW model and a genetic algorithm model, generating a scheduling scheme from ambulance initial assignment to path planning to medical resource pre-allocation; the resource execution state is tracked in real time, when the response time exceeds the set amplitude of the expected response time, the reinforcement learning model is started to dynamically adjust the scheduling scheme based on the data graph, and an effect evaluation report is generated. According to the invention, through multi-mode signal receiving, data integration, intelligent injury assessment, resource scheduling optimization and dynamic adjustment, rapid, accurate and efficient medical emergency response is realized.

The invention relates to the technical field of image recognition, in particular to a horticultural product fungal disease image recognition system and method, and the system comprises an image collection module, an image segmentation module, an anomaly detection module, a feature extraction module, a disease recognition module and a decision output module. In the prior art, the intensity of a light supplementing lamp is manually adjusted, and instantly changing illumination conditions (such as cloud layer shielding and tree shadow shaking) are difficult to deal with in an open-air plantation, so that leaf scab details are often submerged by strong light or dim light is blurred; according to the scheme, a closed-loop linkage system of ambient light perception and an annular LED is innovatively deployed, and the light supplementing intensity is reversely adjusted in a millisecond level by tracking ambient brightness in real time, so that purple gray hyphae of downy mildew in mist in the morning and anthracnose black spots under the noon burning sun present completely consistent color saturation and texture definition in imaging; and the problem of misdiagnosis caused by illumination fluctuation in the traditional method is thoroughly solved.

The invention belongs to the technical field of data processing, and particularly relates to a power supply load prediction method and system for a new energy vehicle, and the method comprises the steps: obtaining real-time operation parameters and a historical power supply load data set of the new energy vehicle; for any moment, determining the load fluctuation sensitivity according to the offset of the power supply load relative to the historical power supply load in the same period and the difference of the power supply load relative to the previous moment; determining the load demand potential by combining the difference between the residual electric quantity of the on-line controlled vehicle and the environment temperature; and determining a prediction weight based on the two indexes, extracting a long or short period data set in combination with a magnitude relationship between the prediction weight and a preset weight threshold, and realizing prediction by using an exponential smoothing method. According to the method, the problems of response lag and insufficient precision of a traditional prediction algorithm when a charging scene is highly dynamic are effectively solved, and high-precision real-time tracking of unsteady state load characteristics is realized.

The invention discloses a radar sounding-based hail grading early warning method and system for a convective monomer hazard index and jump increase, and relates to the technical field of severe convective weather early warning. According to the method, monomer segmentation is carried out and a severe convection monomer hazard model is constructed based on regional multi-weather radar data and air sounding data, a discrimination threshold is determined based on local air sounding data, and segmented convection monomers are screened for three times to obtain classification types of the convection monomers; and on the basis, tracking a hazard index track of a convective monomer core area in real time, and when a monomer hazard index shows a remarkable jump characteristic in a set time window, dynamically judging the occurrence probability and grade of hail, and pushing a digital alarm. The classification, digitization and progressive algorithm of the invention fuses multi-source data collaborative analysis, dynamic threshold determination and real-time evolution tracking, and significantly improves the timeliness and accuracy of the turning point of the heavy rainfall hail severe convection weather process and hail classification early warning identification.

The invention discloses an optical and neural feedback multi-modal data analysis method for a nerve regulation target, and belongs to the technical field of cranial nerve treatment, and the method specifically comprises the steps: receiving a head movement instruction of a patient through an interactive interface, pausing transcranial magnetic stimulation after receiving the instruction, and switching to a head movement monitoring state; an infrared optical navigation device is combined with a magnetic resonance image to construct an individualized brain three-dimensional model, head position changes are tracked in real time, and autonomic nerve feedback signals are collected at the same time; synchronously aligning the head coordinates with the neural feedback signals to generate a multi-modal data set, and analyzing relevance to obtain real-time regulation and control parameters; the positioning and stimulation intensity of the transcranial magnetic stimulation coil are adaptively adjusted according to the parameters, and related information is displayed on a treatment interface; after the head of the patient finishes moving and is stable, a rapid re-calibration process is automatically triggered, high-precision monitoring is recovered, and magnetic stimulation output is reactivated; according to the invention, the treatment stability and adaptability are improved.

The invention relates to the technical field of engineering material monitoring, and discloses an SHCC material full-life monitoring system for tunnel engineering and an evaluation method. According to the system, through cooperative work of an initial data acquisition module, a material state modeling module, a service environment simulation module, a damage evolution tracking module, a performance degradation evaluation module and a service life prediction module, the whole life cycle of a material is monitored. The method comprises the following steps: acquiring internal microstructure data of a material, constructing a dynamic response map, tracking a microcrack evolution path in real time by implanting a mark point in a simulated tunnel environment, and finally establishing a performance degradation evaluation matrix to complete life prediction. According to the system, internal correlation analysis from microscopic damage to macroscopic performance is realized through two core technologies of dynamic map construction and microcrack mark tracking, and the accuracy of life prediction and the reliability of engineering durability evaluation are improved.

The invention provides a multi-parameter cooperative control method and system for a chemical product production workshop, and relates to the technical field of intelligent control, and the method comprises the steps: constructing a multi-parameter sparse coupling topology according to the dynamic mutual information amount between parameters; performing topology path sensitivity evaluation, identifying a key coupling chain which has the greatest influence on the reaction stability, and tracking the collaborative change trend of parameters on the chain in real time; establishing a parameter prediction model, and executing the adjustment priority automatic rearrangement of the intra-chain parameters based on a constraint migration mechanism; constructing a self-evolution cooperative control strategy generator, and generating a cooperative regulation and control instruction of the reaction process through online evolution; and the materials are delivered to corresponding heating equipment, cooling equipment, pressure discharging devices and feeding equipment. The technical problems that in the prior art, adjustment is generally conducted on a single parameter, deep analysis on interaction among multiple parameters is lacked, the system cannot be kept stable when the multiple parameters fluctuate, and then equipment failures or safety accidents are caused are solved.

The invention relates to the technical field of environmental monitoring, in particular to a measurement data acquisition method based on ecological detection platform construction, which comprises the following steps: monitoring surface change based on remote sensing and patrol data, identifying ground feature features and dividing sections, analyzing image resolution fluctuation and marking low-efficiency nodes, and optimizing monitoring point location and resource allocation. And scheduling the acquisition equipment to complement the measurement data, generating monitoring output and synchronously labeling the control table. According to the method, the surface change is identified by combining the remote sensing image and patrol data, the dynamic sensing capability of ground feature distribution is improved, node efficiency sorting is realized through path and equipment state analysis, patrol coverage balance is guaranteed, high-frequency data supplementary collection is performed on key areas by using spatial positioning and a layer tool, and the continuous recording capability of the ground feature change is enhanced; and a period monitoring result is integrated to identify a missing section and mark abnormity, so that linkage of data synchronous correction and mark switching is realized, and the real-time tracking and abnormity response capability of ecological changes is improved.

The invention provides an archive management model based on digital twinning, and relates to the technical field of archive management. The archive management model based on digital twinning comprises a physical layer, the physical layer is connected with a data layer, the data layer is connected with a digital twinning layer, and the digital twinning layer is connected with an intelligent control layer. According to the technology, an archive management model is constructed by utilizing digital twinning, a virtual mirror image of an entity storehouse is created through a building information model and an internet of things data stream, millimeter-level precision three-dimensional modeling is realized, archive management is more accurate and intuitive, a weight sensor can monitor the weight of the archive in real time, the deviation can be compared during returning, mistakes and omissions are avoided, and the integrity of the archive is guaranteed; the intelligent control layer generates a three-dimensional navigation path, combines a virtual reality terminal, simplifies the archive searching and returning process, reduces manual errors, tracks an administrator and identifies archives in real time through an ultra wide band positioning module and a radio frequency identification reader-writer, optimizes borrowing and returning efficiency, achieves intelligent management of an archive repository, reduces cost and improves efficiency.

The invention discloses a real-time tracking irradiation method and device for a radiotherapy target region, a medium and equipment, and the method does not limit each actuator to track the target region according to a fixed mode, but determines a position deviation component suitable for the actual condition of each actuator for each actuator according to the state of each actuator, so that the real-time tracking irradiation of the radiotherapy target region is realized. With the change of the state of each actuator, the proportion of the position deviation component which should be counteracted by each actuator is changed, so that various conditions can be flexibly handled in the radiotherapy process, and the radiotherapy efficiency is remarkably improved.