57 results about "Observation parameter" patented technology

The invention discloses an intelligent reconstruction and prediction method and system for an ocean three-dimensional flow field, and relates to the technical field of data processing, and the method comprises the steps: obtaining the multi-source observation data of an ocean through a large language model and an edge monitoring device; preprocessing and fusing the multi-source observation data to obtain fused observation data; taking the target observation parameters in the fused observation data as nodes, and taking the space-time correlation and physical quantity coupling relationship among the target observation parameters as edges to construct a space-time correlation map; reconstructing and determining reconstruction data corresponding to the multi-source observation data according to the space-time correlation atlas; the three-dimensional flow field of the ocean is obtained through prediction according to the reconstruction data, and a visual three-dimensional flow field is output. According to the method, the multi-source observation data are fused and reconstructed, and the accuracy of predicting the ocean three-dimensional flow field can be improved.

The invention discloses a water surface target tracking method and system based on multi-physical parameter fusion, and the method comprises the steps: obtaining a physical layer observation parameter of a target through a water surface monitoring radar, and constructing a multi-dimensional nonlinear observation vector; establishing a multi-mode motion model set including constant speed, variable speed and turning, and realizing dynamic switching among motion modes through a Markov chain; adjusting a process noise covariance and an observation noise covariance based on a residual covariance estimation result in the sliding window by adopting an adaptive extended Kalman filtering algorithm; distributing weights according to the measurement variance of each physical parameter, optimizing the Kalman gain through a weighted least square method, and completing the updating and estimation of a target state; dynamic switching of motion modes is achieved through a Markov chain, and when state estimation residual errors of continuous preset times exceed a preset threshold value, model mismatch is judged, and a Markov chain model switching mechanism is triggered; according to the method, the sea condition adaptability, the calculation efficiency and the engineering expandability can be improved.

The invention belongs to the technical field of ocean data processing, and discloses a GNSS-R and scatterometer combined same-orbit time sequence sea surface wind speed inversion method and system, and the method comprises the following steps: obtaining historical satellite data of ocean surface wind, carrying out the preprocessing of the historical satellite data, and obtaining the preprocessed data, constructing a training sample set in combination with the reference wind speed and the surge auxiliary data; constructing an initial wind speed inversion model by using a plurality of single-moment multi-frequency information fusion modules and multi-moment interactive attention enhancement modules, and training the initial wind speed inversion model by using the training sample set to obtain a same-orbit time sequence sea surface wind speed inversion model; and obtaining a time delay-Doppler correlation power diagram, GNSS-R observation parameters, scatterometer parameters and effective wave height of storm and surge, and completing sea surface wind speed inversion by using the same-orbit time sequence sea surface wind speed inversion model to obtain sea surface wind speed prediction data.

The invention discloses a satellite navigation positioning method and device and a storage medium. The positioning method comprises the steps that observation parameters collected by a mobile terminal in a preset epoch are obtained, the observation parameters comprise GNSS parameters and inertial navigation correlation parameters, and the GNSS parameters comprise GNSS prior parameters and GNSS posterior parameters; the observation parameters are input into a preset positioning precision evaluation model, a precision evaluation value is obtained, and the positioning precision evaluation model is obtained based on training of historical observation parameters collected by multiple epochs; determining whether the positioning information represented by the current observation parameter is accurate based on the precision evaluation value; when it is determined that the positioning information is inaccurate, a positioning result is determined based on a floating point solution of integer ambiguity; and when the positioning information is determined to be accurate, determining a positioning result based on the fixed solution of the integer ambiguity. The method has the technical effect of improving the positioning precision and the positioning reliability of the global satellite navigation system.

The invention provides a carbon emission calculation method based on adaptive Kalman filtering. According to the carbon emission calculation method based on adaptive Kalman filtering, a data matrix is generated by acquiring multi-source data, and a unified data basis is provided for subsequent calculation; a dynamic system model and an error measurement matrix are initialized, a state transition model indicates a carbon emission evolution law, an observation model establishes a mapping relation between multi-source data and carbon emission, and the error measurement matrix dynamically indicates a model effect and adaptively adjusts calculation parameters to ensure the adaptability of the model to the dynamic change of the system; a carbon emission prediction parameter and an error measurement matrix are calculated in real time through adaptive Kalman filtering, a prediction value is corrected through an observation parameter, an optimal estimation parameter and an observation residual error are obtained, meanwhile, the error measurement matrix is updated in real time, and dynamic fusion and noise suppression of multi-source data are achieved through the closed-loop process. And the precision and the real-time performance of carbon emission calculation are improved.

A shadow generation method for a three-dimensional model, the method comprising: acquiring light source information of a virtual light source and an observation parameter of a virtual camera, wherein the light source information is used for forming an illumination effect of the virtual light source in a virtual scene, and the observation parameter is used for forming a field-of-view range of the virtual camera in the virtual scene for a three-dimensional model; on the basis of the light source information and the observation parameter, generating a multi-angle contour map corresponding to the three-dimensional model, wherein the multi-angle contour map is used for representing an initial shadow; on the basis of the light source information and the observation parameter, projecting the multi-angle contour map onto a reference plane, so as to obtain a hard shadow image corresponding to the three-dimensional model; on the basis of the light source information and material attributes of the three-dimensional model, obtaining a shadow feature of the three-dimensional model; and inputting the hard shadow image and the shadow feature into a trained shadow prediction model based on a convolutional neural network, and on the basis of the hard shadow image and the shadow feature and by means of the shadow prediction model, outputting a soft shadow image corresponding to the three-dimensional model.

The invention relates to the technical field of bamboo forest ecological environment monitoring, in particular to a bamboo forest ecological environment collaborative observation method based on multi-source Internet of Things sensing, and the method comprises the steps: synchronously collecting a multi-source collaborative observation parameter set of a bamboo forest slope through an Internet of Things sensor network, and then constructing a point-surface scale fusion bamboo forest moisture dynamic model; correcting evapotranspiration point cluster data by using a random forest gradient lifting algorithm to generate terrain weighted evapotranspiration, aggregating runoff data according to terrain weight to obtain a total amount of runoff sub-regions, then executing dynamic water balance constraint calculation, optimizing infiltration capacity in combination with a variation assimilation algorithm, redistributing infiltration capacity space proportion according to soil hydraulic conductivity when a residual error exceeds a threshold value, and calculating the total amount of the runoff sub-regions. Finally, according to the residual space distribution thermodynamic diagram, the evapotranspiration point cluster is migrated to the micro-topography representative area, and observation network adaptive optimization is achieved, the problems that in the prior art, the data scale is disjointed, the observation precision is insufficient, and the network is difficult to adapt to change are solved, and the bamboo forest ecological observation precision and reliability are improved.

The invention relates to a shadow generation method and device for a three-dimensional model, computer equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring light source information of a virtual light source and observation parameters of a virtual camera; according to the light source information and the observation parameters, a multi-view profile diagram corresponding to the three-dimensional model is generated, and the multi-view profile diagram is used for representing an initial shadow; projecting the multi-view profile diagram to a reference plane according to the light source information and the observation parameters to obtain a hard shadow image corresponding to the three-dimensional model; obtaining shadow features of the three-dimensional model according to the light source information and material attributes of the three-dimensional model; and inputting the hard shadow image and the shadow features into a trained shadow prediction model based on a convolutional neural network, and outputting a soft shadow image corresponding to the three-dimensional model through the shadow prediction model according to the hard shadow image and the shadow features. By adopting the method, the shadow of the three-dimensional model can be quickly generated.

The invention discloses a transformer thermal circuit model parameter correction method based on extended Kalman filtering, and belongs to the technical field of transformer temperature rise tests, and the method comprises the steps: S1, defining a state equation representing the change of dynamic parameters along with time in a transformer thermal circuit model according to a state transition matrix; s2, on the basis of the physical relation between the observation parameters and the dynamic parameters in the transformer thermal circuit model, defining a nonlinear equation representing the nonlinear relation between the observation parameters and the dynamic parameters; s3, linearizing the nonlinear equation based on extended Kalman filtering to obtain a linear equation; and S4, obtaining a first predicted value of the dynamic parameter based on the state equation, obtaining a second predicted value of the observation parameter based on the linear equation, and correcting the first predicted value of the dynamic parameter based on the second predicted value and the actual value of the observation parameter to obtain a final value of the dynamic parameter. The technical problem of low transformer temperature prediction accuracy caused by the fact that the temperature dependence of thermal resistance is not considered in the prior art is solved.

The invention provides a key test factor determination method and device based on a Bayesian causal network, and relates to the technical field of reason tracing. The method comprises the following steps: acquiring a multi-dimensional observation parameter of a preset intelligent system, and generating an initial data set based on the multi-dimensional observation parameter; based on an improved Bayesian causal structure learning algorithm and the initial data set, determining a causal structure diagram between an input factor and a target output in the initial data set; based on the causal structure diagram, performing intervention operation on an input factor pointing to the target output to determine an average causal influence degree of the input factor on the target output; and calculating importance degree scores of the input factors according to a preset weight fusion mode and the average causal influence degree, and determining the input factors corresponding to the importance degree scores meeting a preset key factor selection condition as key test factors. In the intelligent system, the real causal-driven effect between the variables is effectively determined, and the accuracy of the key test factor is improved.

A method includes obtaining satellite information, including a plurality of navigation satellite identifiers, and satellite data, observed values of an observation parameter, and weight values that are separately associated with the plurality of navigation satellite identifiers; determining that an inaccurate observed value exists in a set of the observed values; determining, based on the satellite information, a check vector representing accuracy of the set, and a plurality of eigenvectors representing accuracy of an observed value of the observation parameter that is associated with a corresponding navigation satellite identifier; performing cluster analysis on the check vector and the plurality of eigenvectors, and determining a target navigation satellite identifier corresponding to an eigenvector that is of a same type as the check vector; and performing selection in the plurality of navigation satellite identifiers and / or adjusting a weight value associated with the target navigation satellite identifier based on the target navigation satellite identifier.

The invention discloses a sea surface wind speed inversion method and system based on a CYGNSS and Willoughby model, and belongs to the technical field of ocean remote sensing. The method comprises the following steps: firstly, extracting a cyclone parameter from IBTrACS data through a Willoughby model, and encrypting the cyclone parameter to 1 second for sampling by utilizing segmented three times of Hermite interpolation; screening CYGNSS mirror reflection points and constructing a data set in combination with a space matching algorithm; processing CYGNSS L1-level data and extracting observation parameters; establishing a basic GMF (Gaussian Mixture Function) according to incidence angle grouping, and optimizing the weight through nonlinear fitting; the Willoughby model wind speed and the basic GMF are fused to generate an enhanced GMF, the DDM signal-to-noise ratio is dynamically adjusted in combination with minimum variance estimation, and the precision is improved. According to the method, the GNSS-R technology is utilized to overcome the defects of high cost, limited coverage and the like of a traditional method, the sea surface wind speed monitoring reliability under the extreme weather is enhanced through collaborative optimization of a physical model and actually measured data, and the method has the advantages of low cost, wide coverage and interference resistance and is suitable for refined marine meteorological monitoring.

This invention discloses a crop-adaptive agricultural meteorological observation system, belonging to the field of agricultural meteorological observation technology. It integrates multiple devices to collaboratively measure various meteorological variables to indicate weather conditions, deploys them in a grid-like layout, and collects and transmits data regularly. It collects key growth parameters according to crop growth stages and synchronously correlates them with meteorological data; optimizes data and accurately matches it to construct a fusion dataset; establishes a correlation model to identify key meteorological factors; adjusts observation parameters according to crop needs; sets two-level early warnings and pushes information through multiple channels; retrieves data from multiple dimensions and securely stores it; and provides visual operation and parameter setting functions. This invention improves the accuracy of meteorological data through multi-variable collaborative observation, dynamically adapts to the needs of different crop growth stages and varieties, provides comprehensive regional collaborative observation coverage, timely and targeted early warnings, supports linkage with external devices, promotes the refinement and intelligence of agricultural meteorological observation and planting management, and helps ensure crop growth.

This invention discloses a method for inverting sea surface wind speed from GNSS reflected signals based on mask perception and image-based Mamba-CNN. The method includes: performing spatiotemporal matching and interpolation processing on multi-source spaceborne GNSS-R observation data and ERA5 reanalysis data; gridding the processed observation parameters according to geographical location to obtain a multi-channel grid image and acquiring a binarized occupancy mask; inputting the multi-channel grid image and the binarized occupancy mask into a mask perception Mamba-CNN model; calibrating the grid features through a mask conditional encoder and a feature linear modulation layer, and performing spatial modeling using feature extraction blocks combining convolutional and Mamba state space paths to obtain the wind speed inversion result; aggregating the extracted features using center-perception pooling, and outputting the final sea surface wind speed inversion image through a regression network. This invention can significantly reduce computational complexity, effectively reconstruct the continuous wind field structure, and improve the inversion accuracy and robustness in high-wind-speed areas.

The invention discloses an elevator operation and maintenance retrieval enhanced question and answer method, device and equipment and a storage medium. According to the method, by introducing the parameterized hyperedge, the limitation of a binary relationship of a common knowledge graph is solved, the method can adapt to a multi-entity and multi-constraint elevator operation and maintenance retrieval enhanced question-answer scene, and the accuracy of elevator operation and maintenance retrieval enhanced question-answer and elevator maintenance work orders is improved. Besides, the comprehensive score of each parameterized hyperedge in the hyperedge candidate set is obtained by integrating the semantic similarity score and the consistency score, and K target parameterized hyperedges of which the comprehensive scores are in the front are determined, so that the semantic similarity between the elevator maintenance consultation text and text items of the target parameterized hyperedges is ensured, and the comprehensive score of each parameterized hyperedge in the hyperedge candidate set is determined. And the working condition represented by the constraint set of the field observation parameters and the target parameterized hyperedge is matched, so that the accuracy of the generated elevator maintenance work order is improved.

The invention provides an accelerator parameter selection method and device, equipment, a storage medium and a program product, and relates to the field of hardware design, core design parameters of target accelerator design and preset weights thereof can be determined according to priori knowledge, and in a design space of the target accelerator design, the number of candidate values of the core design parameters is used as the number of clusters, and the cluster number is calculated. According to the cluster number and the distance between the parameter points in the design space, parameter point clustering is carried out, parameter point clustering is carried out under the guidance of priori knowledge, and a parameter point cluster under each type of core design parameter configuration is obtained. Then, high-value candidate parameter points can be screened from each parameter point cluster. Then, observation parameter points can be sampled from the candidate parameter points, accelerator design simulation is performed on the observation parameter points to obtain real index values, and Bayesian optimization processing is performed on the candidate parameter points in an iteration manner according to the observation parameter points and the real index values thereof to obtain target design points of target accelerator design, so that the parameter point selection effect can be improved.

The invention relates to a small watershed multi-element cooperative monitoring method, device and equipment and a medium. The method comprises the following steps: acquiring satellite remote sensing data, unmanned aerial vehicle observation data and ground observation data of a target drainage basin; preprocessing the satellite remote sensing data, the unmanned aerial vehicle observation data and the ground observation data to obtain multi-element observation parameters; carrying out basin live inversion according to the multi-element observation parameters to obtain land utilization grids, water body boundary-area data, runoff data and sediment data; and according to the land utilization grid, the water body boundary-area data, the runoff data and the sediment data, constructing a rainfall-runoff-sediment-water quality chain response relationship, and obtaining drainage basin multi-element scale coordination time sequence data, an element influence map and land utilization influence evaluation. By adopting the method, multi-element data space-time cooperative monitoring can be realized, and the full-chain demand of small-watershed ecological hydrological management is supported.

The application discloses a method and device for inverting typhoon wind speed by using synthetic aperture radar, equipment, medium and product, and relates to the field of ocean remote sensing and application. The method comprises the following steps: obtaining a group of observation data of the synthetic aperture radar when the synthetic aperture radar inverts the typhoon wind speed; obtaining an inversion value of the typhoon wind speed based on the weight of each observation parameter and the group of observation data of the synthetic aperture radar when the synthetic aperture radar inverts the typhoon wind speed; the determination process of the weight is as follows: constructing a target function for inverting the typhoon wind speed by using the synthetic aperture radar based on elastic network regularization; obtaining the weight of each observation parameter by solving the target function for inverting the typhoon wind speed by using the synthetic aperture radar based on elastic network regularization according to a training data set; and the training data set comprises a plurality of groups of observation data of the synthetic aperture radar and corresponding typhoon wind speeds of each group of observation data when the synthetic aperture radar monitors sample typhoon wind speed. The application can realize high-precision inversion of the typhoon wind speed.

The invention discloses a GNSS-R soil humidity inversion method and device, a storage medium and computer equipment. Comprising the following steps: inputting a delay-Doppler graph obtained by detecting a target area by a satellite-borne GNSS-R into a feature extraction model corresponding to the target area, extracting a global dependency relationship of a feature sequence of the delay-Doppler graph through the feature extraction model, and outputting global scattering feature data of the target area based on the global dependency relationship; inputting the ground environment data of the target area and the observation parameters of the satellite-borne GNSS-R into a geographically weighted regression model corresponding to the target area, performing fitting calculation on the spatial correlation of the ground environment data and the observation parameters through the geographically weighted regression model, and outputting prior inversion data of the soil humidity of the target area; and performing nonlinear transformation and feature fusion on the global scattering feature data, the prior inversion data and the observation parameters based on a soil humidity model to obtain a soil humidity inversion result. Therefore, the adaptability and inversion accuracy of the model in different geographical environments are improved.

The invention relates to the technical field of robot algorithm control, in particular to a robot control method and system based on a blind privilege information implicit alignment algorithm, equipment and a medium, and the method mainly comprises the steps that real-time observation parameters and historical observation parameters of real-time operation of a robot are obtained; obtaining privileged observation parameters, wherein the privileged observation parameters comprise a second parameter obtained when the robot runs; and inputting the real-time observation parameters, the historical observation parameters and the privileged observation parameters into an ActorCritic model to obtain a joint action actcion and a corresponding action evaluation value. According to the method, vae is not adopted, the training process is more stable, and the convergence speed is higher; the topographic information estimated by the polyae is more accurate and explainable; end-to-end training does not exist, no complex two-stage training process exists, distillation is carried out while learning is carried out, the phenomenon that only the experience of'successful 'distillation is carried out in a teacher-student framework can be avoided, and the policy is more robust and stable.

The present invention discloses a method and program product for dynamic control of wellbore pressure in natural gas hydrate drilling, which establishes an original matrix of observation parameters and an original matrix of state parameters, performs data normalization processing of hysteresis regression and missing completion on the observation parameters based on adjacent well and drilling data; combines dual gradient drilling technology to establish a dynamic control model of wellbore pressure in drilling; adopts a particle filter algorithm to predict state parameters, and uses statistical errors to evaluate the prediction effect; establishes dual constraints of drilling safety density window and overflow safety evaluation standard to verify the safety of state parameters, outputs data if the conditions are met, and updates the original matrix of state parameters to a sequence matrix of state parameters. The present invention can predict the wellbore pressure profile at any time during the natural gas hydrate drilling process, improves the calculation accuracy of multiphase flow under complex constraints, and can increase the maximum drilling depth of a single opening by optimizing the observation parameters, thereby ensuring safe and efficient drilling.

The invention relates to a parameter adjustment method for large-scale mooring buoy seaborne comparative measurement, which comprises the following steps: establishing connection with a data acquisition system of a target buoy in a wireless communication mode, acquiring an observation element type and a sampling frequency parameter of the target buoy, and adjusting the observation element type and the sampling frequency parameter of the target buoy based on the acquired parameter; performing observation parameter configuration on each sensor arranged in the comparison measurement device, synchronizing the comparison measurement device with a system clock of the target buoy, monitoring the drifting direction and speed of a water sail at the bottom of the comparison measurement device, and determining the drifting speed of the target buoy according to the collected wave observation data and the height-corrected wind speed and wind direction data. And calculating the flow velocity and the flow direction of the surface ocean current through a preset algorithm model.

A method for authenticating map elements of safety-critical driving functions via a control device is disclosed. After a mapping step is performed, at least one observation parameter for at least one mapping step of at least one map element is obtained via at least one monitoring function, and the observation parameter is compared with an expected value. Based on the comparison of the observation parameter with the expected value for at least one mapping step, at least one result value is calculated via the monitoring function, and the at least one result value is stored as a certificate and associated with the at least one map element. Furthermore, a control device, a computer program, and a computer-readable storage medium are disclosed.

The present application relates to the technical field of safety monitoring, in particular to a hoist safety performance evaluation method, the method comprising: taking hoist motor current and rope tension as observation parameters, recording data under differentiated working states, identifying dynamic characteristics of the hoist through analyzing the data, and generating dynamic parameter record results. In the present application, dynamic parameter recording allows real-time capture of subtle changes of the hoist under differentiated working states, providing a basis for accurately identifying dynamic characteristics of the equipment, and the application of performance fluctuation indicators further utilizes dynamic recording, identifies fluctuations and deviations outside the normal range through analyzing data changes, provides a basis for judging the severity of fluctuations and taking corresponding adjustment measures, improves the monitoring capability and fault prevention efficiency of the hoist operating state, can identify and take measures in time before potential risks develop into serious faults, reduces the probability of accidents, and improves the safety and efficiency of operations.

A tumor treating field system and a regulation and control method therefor, relating to the technical field of medical instruments. The tumor treating field system comprises: an observation module, configured to acquire observation parameters; a regulation module, configured to periodically acquire at least part of observation parameters as an observation parameter set, generate a target parameter set on the basis of the observation parameter set, a preset system index set, and a preset optimization algorithm, generate a regulation weight matrix on the basis of the target parameter set, the observation parameter set, a preset regulation parameter set, and a preset control algorithm, and generate a regulation instruction set on the basis of the regulation weight matrix and the regulation parameter set; and an execution module, configured to regulate an alternating electric field applied to a target tissue on the basis of the regulation and control instruction set. By achieving self-adaptive regulation and control of the tumor treating field system, the flexibility of a treatment scheme of the tumor treating field system is improved, thereby improving the therapeutic effect.

The invention relates to a normalized region automatic monitoring and updating method and system. The automatic monitoring and updating method for the normalized area comprises the following steps that S101, observation parameters, configured by a user, of a normalized observation area are received and stored, and the observation parameters comprise the area range, the shooting period and the filing address; s102, on the basis of the shooting period, an observation task is automatically generated at regular time; step S103, automatically issuing the generated observation task to a satellite operation unit to execute shooting; according to the normalized region automatic monitoring and updating method and system, automatic operation of the system can be driven through a small amount of configuration, a traditional manual operation mode is greatly replaced, task dynamic generation and efficient circulation from a reporting party to a satellite party are achieved, fundamental transformation of a remote sensing service mode from passive response to active pushing is promoted, and the remote sensing effect is improved. And the overall remote sensing data service capability and the intelligent level are improved.

To suppress degradation of prediction accuracy of a prediction model configured to use, as an input, an interpolated value of observed values acquired in an environment in which time series data of an observed parameter cannot be stably secured.SOLUTION: Time series data TS including multiple observed values of an observed parameter that are acquired at different time points from a subject for obtaining physiological information of the subject is received. By inputting the time series data TS into a prediction model, a range within which values interpolating the multiple observed values may fall is output as a prediction result. A first interpolation data set IT1 including at least one first interpolated value is generated by interpolating the multiple observed values with a first method. A second interpolation data set IT2 including at least one second interpolated value is generated by interpolating the multiple observed values with a second method that is different from the first method. A range RG defined between a maximum value MX and a minimum value MN among the first interpolated value and the second interpolated value is identified as the prediction result.SELECTED DRAWING: Figure 2

The invention discloses a method and a system for correcting hygroscopic parameters of non-ideal mixed aerosol and a medium, and belongs to the technical field of atmospheric measurement. The method comprises the following steps: acquiring instrument observation data; estimating instrument observation data through a ZSR ideal mixing criterion, and generating ZSR hygroscopic observation parameters; obtaining an input feature data set according to the ZSR hygroscopic observation parameters, the instrument observation data and inversion hygroscopic observation parameters generated by inversion of the instrument observation data; inputting the input feature data set into a pre-constructed deviation prediction model, and outputting a non-ideal mixed deviation item; calculating a main effect value of the input feature data set, and generating empirical correction terms based on key features according to the main effect value; and obtaining a moisture absorption parameter correction result according to the ZSR moisture absorption observation parameter, a pre-constructed seasonal correction coefficient and an empirical correction item based on the key characteristics. According to the method, the deviation part can be explained, and the contribution degree of each influence factor is quantitatively explained.

The invention discloses an object inspection method and device and electronic equipment. The method comprises the steps that an inspection instruction is received, and the inspection instruction carries a target object identifier to be inspected; in response to the inspection instruction, target space coordinates of the target object identifier and preset space coordinates and view parameters corresponding to the multiple initial cameras are determined, and the view parameters are parameters used for representing view ranges of the corresponding initial cameras; determining a plurality of candidate cameras from the plurality of initial cameras according to the target space coordinates and preset space coordinates and view parameters corresponding to the plurality of initial cameras; determining a target camera from the plurality of candidate cameras according to the observation parameters corresponding to the plurality of candidate cameras; and controlling the target camera to inspect to obtain an inspection result. According to the invention, the technical problem that the camera cannot be quickly positioned in non-preset position inspection in the related art is solved.

To prevent a reduction in the accuracy of inference of an inference model that infers biological information, even in an environment in which a sufficient number of time-series data are difficult to be secured for observation target parameters.SOLUTION: A learning data TR is generated, which is used for machine learning of an inference model 23 that infers biological information. An original data set OR is received, which includes a plurality of observation values acquired from a living body at a plurality of time points for observation target parameters for obtaining the biological information. A first interpolation data set IT1 is generated by interpolating at least one value of the observation target parameters with a first method in a period of the plurality of time points. A second interpolation data set IT2 is generated by interpolating the at least one value of the observation target parameters in the period of the plurality of time points with a second method different from the first method. The learning data TR is generated to include the first interpolation data set IT1 and the second interpolation data set IT2.SELECTED DRAWING: Figure 2