Training method of trajectory tracking model and hydrous particle trajectory tracking method
By training a trajectory tracking model and using simulated information about water condensate particles to calculate their trajectories, the problem of the inability to track water condensate particles in existing technologies has been solved, thus improving the comprehensiveness and accuracy of meteorological observations.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2022-08-03
- Publication Date
- 2026-03-17
AI Technical Summary
Existing particle tracking methods cannot effectively track water particles with terminal velocities, such as raindrops, graupel, and hail, resulting in insufficient comprehensiveness and accuracy in meteorological observations.
By acquiring simulated information about water-soluble particles, including three-dimensional environmental wind information and terminal velocity of fall, a trajectory tracking model is trained to calculate the motion trajectory of the water-soluble particles, and the particle motion is represented by Lagrange field data.
It improves the comprehensiveness and accuracy of meteorological observation and can effectively track the movement trajectory of water condensate particles.
Smart Images
Figure CN115374684B_ABST
Abstract
Description
Technical Field
[0001] This application relates to particle trajectory tracking technology, and more particularly to a training method for a trajectory tracking model and a method for tracking the trajectory of hydrogel particles. Background Technology
[0002] Water condensate particles are a collective term for cloud droplets, raindrops, ice crystals, snow crystals, graupel, and hail particles in the atmosphere. Meteorological observations require tracking both air particle trajectories and water condensate particle trajectories.
[0003] Current particle estimation and tracking methods can only track the trajectories of air particles, and can only track particles that move with the airflow (such as water vapor and chemical tracers), but cannot track water condensate particles with terminal velocity (such as raindrops, graupel, hail, etc.).
[0004] How to track the trajectory of water-condensate particle swarms to improve the comprehensiveness and accuracy of gas phase observation remains a problem to be solved. Summary of the Invention
[0005] This application provides a training method for a trajectory tracking model and a method for tracking the trajectory of water condensate particles, which are used to track the trajectory of water condensate particle swarms to improve the comprehensiveness and accuracy of gas phase observation.
[0006] On the one hand, this application provides a method for tracking the trajectory of hydrogel particles, including:
[0007] Acquire training data, which includes simulated information of at least one hydrogel particle within at least a preset time period. The simulated information includes at least three-dimensional environmental wind information of the environment in which at least one hydrogel particle is located within at least a preset time period and the simulated terminal velocity of at least one hydrogel particle within at least a preset time period.
[0008] The initial trajectory tracking model is trained based on the training data to obtain the trajectory tracking model, which is used to calculate the motion trajectory information of the water-soluble particles based on the simulated information of the water-soluble particles.
[0009] In one embodiment, the initial trajectory tracking model is specifically used for:
[0010] Obtain the first position information of any hydrogel particle in the Eulerian field;
[0011] Based on the simulation information corresponding to the first position information of any given hydrogel particle, determine the position change information and the second position information after the position change of the given hydrogel particle, wherein the hydrogel particle has different simulation information corresponding to different position information;
[0012] The first position information is updated to the second position information, and the process returns to the step of determining the position change information and the second position information after the change of position of any one of the hydrogel particles based on the simulation information corresponding to the first position information, until the motion trajectory information of any one of the hydrogel particles is determined within at least a preset time period.
[0013] In one embodiment, acquiring training data includes:
[0014] At least the simulated information of at least one hydrogel particle generated by the meteorological model analysis module within a preset time period based on the analysis of real three-dimensional environmental information and information of hydrogel particles in the three-dimensional environment should be obtained.
[0015] In one embodiment, the data output by the trajectory tracking model is data in a Lagrange field.
[0016] On the other hand, this application provides a method for tracking the trajectory of hydrogel particles, including:
[0017] The simulation information of the water-condensate particles to be tracked over a period of time is obtained. The simulation information includes at least the three-dimensional environmental wind information of the environment in which the water-condensate particles to be tracked are located during the simulated period of time and the terminal velocity of the water-condensate particles to be tracked during the simulated period of time.
[0018] The simulated information of the hydrogel particles to be tracked is input into the trajectory tracking model trained according to a preset method to obtain the motion trajectory information of the hydrogel particles to be tracked within the time period. The motion trajectory information includes at least the position change information of the hydrogel particles within the time period.
[0019] In one embodiment, obtaining simulated information about the hydrogel particles to be tracked over a period of time includes:
[0020] The meteorological model analysis module outputs simulated information of the water-gel particles to be tracked over a period of time after analyzing the actual information of the water-gel particles to be tracked and the actual three-dimensional environmental information.
[0021] In one embodiment, the method further includes:
[0022] Repeat the steps described above to obtain the simulated information of the water-coated particles to be tracked until the motion trajectory information of each water-coated particle to be tracked in the water-coated particle swarm is obtained in the corresponding time period, thus obtaining the motion trajectory information of the water-coated particle swarm.
[0023] In one embodiment, the trajectory information of the hydrogel to be tracked during the time period and the trajectory information of the hydrogel particle swarm are both data in a Lagrange field.
[0024] On the other hand, this application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;
[0025] The memory stores computer-executed instructions;
[0026] The processor executes computer execution instructions stored in the memory to implement the trajectory tracking model training method as described in the first aspect, or the hydrogel particle trajectory tracking method as described in the second aspect.
[0027] On the other hand, this application provides a computer-readable storage medium storing computer-executable instructions that, when executed, cause a computer to perform a training method for a trajectory tracking model as described in the first aspect, or a water-gel particle trajectory tracking method as described in the second aspect.
[0028] In summary, embodiments of this application provide a method for training a trajectory tracking model. This method acquires training data, which includes simulated information of at least one hydrophobic particle over at least a preset time period. The simulated information includes at least three-dimensional environmental wind information of the environment in which the at least one hydrophobic particle is located during the preset time period, and the simulated terminal velocity of the at least one hydrophobic particle during the preset time period. Based on the training data, an initial trajectory tracking model is then trained to obtain the trajectory tracking model. This trajectory tracking model is used to calculate the motion trajectory information of the hydrophobic particle based on the simulated information of the hydrophobic particle. In other words, the estimation tracking model provided in this embodiment can be used to track the motion trajectory of hydrophobic particles, thereby improving the comprehensiveness and accuracy of gas phase observation. Attached Figure Description
[0029] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.
[0030] Figure 1 A schematic diagram illustrating an application scenario of the training method for the trajectory tracking model provided in this application;
[0031] Figure 2 A flowchart illustrating a training method for a trajectory tracking model provided in one embodiment of this application;
[0032] Figure 3 Position information of hydrogel particles in an Eulerian field provided for one embodiment of this application;
[0033] Figure 4 The trajectory information of any hydrogel particle provided in one embodiment of this application;
[0034] Figure 5 A flowchart illustrating a method for tracking the trajectory of hydrogel particles provided in one embodiment of this application;
[0035] Figure 6 A schematic diagram of a training apparatus for a trajectory tracking model provided in one embodiment of this application;
[0036] Figure 7 A schematic diagram of a hydrogel particle trajectory tracking device provided in one embodiment of this application;
[0037] Figure 8 A schematic diagram of an electronic device provided for one embodiment of this application.
[0038] The accompanying drawings have illustrated specific embodiments of this disclosure, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concepts of this disclosure to those skilled in the art through reference to particular embodiments. Detailed Implementation
[0039] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.
[0040] In the description of this application, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.
[0041] Water condensate particles are a collective term for cloud droplets, raindrops, ice crystals, snow crystals, graupel, and hail particles in the atmosphere. Meteorological observations require tracking both air particle trajectories and water condensate particle trajectories.
[0042] Current particle estimation and tracking methods can only track the trajectories of airborne particles, specifically those moving with airflow (such as water vapor and chemical tracers). They cannot track condensate particles with terminal velocities (such as raindrops, graupel, and hail). How to track the trajectories of condensate particle swarms to improve the comprehensiveness and accuracy of gas phase observations remains a problem to be solved.
[0043] Based on this, this application provides a training method for a trajectory tracking model and a method for tracking the trajectory of condensate particles. The training method acquires training data, which includes simulated information of at least one condensate particle over at least a preset time period. This simulated information includes at least three-dimensional environmental wind information of the environment in which the at least one condensate particle is located during the simulated time period, and the simulated terminal velocity of the at least one condensate particle during the simulated time period. The initial trajectory tracking model is then trained based on this training data to obtain the trajectory tracking model. This trajectory tracking model is used to calculate the motion trajectory information of the condensate particles based on the simulated information of the condensate particles. In other words, the estimation tracking model provided in this embodiment can be used to track the motion trajectory of condensate particles, thereby improving the comprehensiveness and accuracy of gas phase observation.
[0044] The trajectory tracking model training method provided in this application is applied to electronic devices, such as computers, backend servers, etc. Figure 1 This diagram illustrates the application of the training method for the trajectory tracking model provided in this application. In the diagram, the electronic device acquires training data, which includes simulated information about at least one hydrogel particle over at least a preset time period. This simulated information includes at least three-dimensional environmental wind information of the environment in which the at least one hydrogel particle is located during the simulated preset time period, and the simulated terminal velocity of the at least one hydrogel particle during the simulated preset time period. Based on this training data, an initial trajectory tracking model is trained to obtain the trajectory tracking model. This trajectory tracking model is used to calculate the motion trajectory information of the hydrogel particles based on the simulated information of the hydrogel particles.
[0045] Please see Figure 2 One embodiment of this application provides a training method for a trajectory tracking model, comprising:
[0046] S210, acquire training data, which includes simulation information of at least one water-based particle within at least a preset time period. The simulation information includes at least three-dimensional environmental wind information of the environment in which at least one water-based particle is located within at least a preset time period and the terminal velocity of at least one water-based particle within at least a preset time period.
[0047] The meteorological model analysis module is used to generate simulated three-dimensional environmental wind information based on the analysis of real three-dimensional environmental information. This three-dimensional environmental wind information includes simulated wind direction, wind speed, and wind force. The meteorological simulation analysis module is also used to generate the terminal velocity of at least one water-condensate particle over at least a preset time period based on the analysis of water-condensate particle information. This water-condensate particle information includes, for example, the terminal velocity of the water-condensate particle under previously simulated conditions close to the real three-dimensional environmental information.
[0048] In an optional embodiment, the training data can be obtained from the output of the weather model analysis module. That is, when obtaining the training data, at least the simulated information of at least one hydrogel particle generated by the weather model analysis module within at least a preset time period based on the analysis of real three-dimensional environmental information and information of hydrogel particles in the three-dimensional environmental information is obtained.
[0049] The more extensive and complex the training data, the better the training effect of the initial trajectory tracking model.
[0050] The training data includes simulated information of at least one hydrogel particle within a preset time period, which is data from an Eulerian field. An Eulerian field measures the change in velocity (spatial change divided by temporal change) at a given location, studying the spatial distribution of velocities of different particles at the same location at the moment they reach that location. For example, if we compare hydrogel particles to students, and we want to know what the students in a classroom are doing, an Eulerian field is like installing a monitor in the classroom, observing only what the people in their seats are doing, without needing to study their positions.
[0051] S220, The initial trajectory tracking model is trained based on the training data to obtain the trajectory tracking model, which is used to calculate the motion trajectory information of the water-soluble particles based on the simulated information of the water-soluble particles.
[0052] This initial trajectory tracking model is specifically used to track the trajectory of any hydrogel particle.
[0053] like Figure 3 As shown, Figure 3 Figure (a) shows the first position information of any hydrogel particle in the Eulerian field (l0(x0, y0, z0) shown in the figure). This first position information can be understood as the initial position of the trajectory seed point planted at time t0.
[0054] The initial trajectory tracking module is specifically used to obtain the first position information of any given hydrogel particle in the Eulerian field, and then determine the position change information and the second position information after the change of position of the given hydrogel particle based on the simulation information corresponding to the first position information.
[0055] When determining the positional change information of any given hydrogel particle based on the simulation information corresponding to its initial position, it actually predicts the particle's trajectory and distance of movement based on that simulation information. Three-dimensional environmental wind affects the particle's trajectory and distance of movement; for example, strong winds cause the particles to move along the wind direction. Combining this with the particle's terminal velocity, the particle's trajectory and distance of movement can be predicted. This predicted trajectory and distance constitute the particle's positional change information.
[0056] After determining the positional change information of any given hydrogel particle, the second positional information after the positional change can be determined based on the positional change information of that given hydrogel particle and the first positional information. For example... Figure 3 The second position information identified in Figure (b) is l1(x1, y1, z1).
[0057] Trajectory tracking is a continuous process that requires constantly determining the position information of the water-soluble particles. Therefore, after determining the second position information, it is also necessary to determine the third position information after the position changes from the second position information, the fourth position information after the position changes from the third position information, and so on, until all position information within the entire time window (preset time period) is determined, so as to obtain the motion trajectory information of the water-soluble particles.
[0058] In terms of execution steps, the first position information is updated to the second position information, and the execution steps are returned to determine the position change information and the second position information after the change of position of any water-soluble particle based on the simulation information corresponding to the first position information, until the motion trajectory information of any water-soluble particle within at least a preset time period is determined.
[0059] Based on the functionality of the initial trajectory tracking model, after collecting a large amount of training data to train the initial trajectory tracking model, the trajectory tracking model can be obtained. In an optional embodiment, the trajectory tracking model can be obtained by stopping training when the training reaches a termination condition, such as the training time reaching a preset time or the difference between the data output by the trajectory tracking module and the reference data falling below a preset difference.
[0060] As described above, the more training data there is and the more complex it is, the more accurate the motion trajectory information output by the trajectory tracking module will be when it is applied.
[0061] It should be noted that the data output by this trajectory tracking module is in a Lagrange field. A Lagrange field studies the spatial variation of a single particle over time, where spatial position is the dependent variable and time is the independent variable. This is reflected in the trajectory equation, which is why the trajectory differential equation has an additional dt term compared to the streamline equation. For example... Figure 4 The image shows the trajectory information of any hydrogel particle, which is formed based on positional change information with time information.
[0062] In summary, this embodiment provides a training method for a trajectory tracking model. The method acquires training data, which includes simulated information of at least one hydrophobic particle over at least a preset time period. This simulated information includes at least three-dimensional environmental wind information and the terminal velocity of the simulated hydrophobic particle over the preset time period. Based on this training data, an initial trajectory tracking model is then trained to obtain the current trajectory tracking model. This model is used to calculate the trajectory information of the hydrophobic particle based on the simulated information. In other words, the estimation tracking model provided in this embodiment can be used to track the trajectory of hydrophobic particles, thereby improving the comprehensiveness and accuracy of gas phase observation.
[0063] Please see Figure 5 An embodiment of this application also provides a method for tracking the trajectory of hydrogel particles, comprising:
[0064] S510, acquire simulation information of the water-condensate particles to be tracked over a period of time. The simulation information includes at least the three-dimensional environmental wind information of the environment in which the water-condensate particles to be tracked are located during the simulated period of time and the terminal velocity of the water-condensate particles to be tracked during the simulated period of time.
[0065] The simulated information of the water-condensate particles to be tracked can be obtained from the meteorological model analysis module. That is, the simulated information of the water-condensate particles to be tracked over a period of time is obtained from the meteorological model analysis module after analyzing the information of the real water-condensate particles to be tracked and the real three-dimensional environment.
[0066] S520, the simulated information of the water-gel particle to be tracked is input into the trajectory tracking model trained according to the preset method to obtain the motion trajectory information of the water-gel particle to be tracked within a certain period of time. The motion trajectory information includes at least the position change information of the water-gel particle within a certain period of time.
[0067] The preset method can be the training method of the trajectory tracking model provided in any of the above embodiments. This trajectory tracking model is used to calculate the motion trajectory information of the water-gel particles to be tracked based on the simulated information of the water-gel particles. Specifically, it is used to calculate the motion trajectory information of the water-gel particles to be tracked over a period of time. This period of time can be selected by the tester according to actual needs, and this embodiment does not limit it. For example, the period of strongest wind can be selected to track the motion trajectory information of the water-gel particles during the period of strongest wind.
[0068] To obtain the trajectory information of a group of water-soluble particles to be tracked, it is necessary to track the trajectory of each or most of the water-soluble particles in the group. That is, repeat step S510 until the trajectory information of each water-soluble particle to be tracked within the corresponding time period is obtained, thus obtaining the trajectory information of the water-soluble particle group. The corresponding time for each water-soluble particle to be tracked can be the time period described in steps S510 and S520, or it can be a time set by the tester according to actual needs.
[0069] The trajectory information of the hydrogel to be tracked during this time period and the trajectory information of the hydrogel particle group are both data in a Lagrange field. As described above, the Lagrange field refers to the study of the spatial changes of a single particle over time, where spatial position is the dependent variable and time is the independent variable, which can be reflected in the path equation. Therefore, the path differential equation has an additional dt compared to the streamline equation.
[0070] In an optional embodiment, after obtaining the trajectory information of the condensate particle swarm to be tracked and the simulation information of each condensate particle swarm, the final motion information of the condensate particle swarm to be tracked can be output by combining the trajectory information of each condensate particle swarm and the simulation information of each condensate particle swarm. That is, while tracking the motion of the condensate particles, the source terms of cloud microphysical processes (Sou) along the trajectory of the condensate particle swarm are output, including environmental wind, air pressure, temperature, humidity, specific mass, terminal velocity of the particle swarm, and cloud microphysical process conversion (Sou) of the condensate particle growth. x ) and Sin x Source terms include, for example, the conversion of two raindrops into one raindrop, and sink terms include, for example, the evaporation of raindrops. Environmental factors affecting the trajectory of condensate particle swarms include wind, air pressure, temperature, humidity, specific mass, terminal velocity of the swarm, and cloud microphysical processes that contribute to condensate particle growth. x ) and Sin x All of these are output by the meteorological model analysis module.
[0071] In summary, this embodiment provides a method for tracking the trajectory of water-borne particles. The method first acquires simulated information about the water-borne particles to be tracked. This simulated information includes at least the three-dimensional environmental wind information of the environment in which the particles are located during the simulated time period and the terminal velocity of the particles during the simulated time period. The simulated information of the water-borne particles is then input into a trajectory tracking model to obtain the trajectory information of the particles during that time period. The method provided in this embodiment can track the trajectory of water-borne particles, thereby improving the comprehensiveness and accuracy of gas phase observation.
[0072] Please see Figure 6 An embodiment of this application also provides a training device 10 for a trajectory tracking model, comprising:
[0073] The acquisition module 11 is used to acquire training data, which includes simulation information of at least one water-based particle within at least a preset time period. The simulation information includes at least three-dimensional environmental wind information of the environment in which at least one water-based particle is located within at least a preset time period and the terminal velocity of at least one water-based particle within at least a preset time period.
[0074] Training module 12 is used to train the initial trajectory tracking model based on the training data to obtain the trajectory tracking model. The trajectory tracking model is used to calculate the motion trajectory information of the water condensate particles based on the simulated information of the water condensate particles.
[0075] This initial trajectory tracking model is specifically used for:
[0076] Obtain the first position information of any hydrogel particle in the Eulerian field;
[0077] Based on the simulation information corresponding to the first position information of any given hydrogel particle, determine the position change information and the second position information after the position change of the given hydrogel particle. The hydrogel particle has different simulation information corresponding to different position information.
[0078] The first position information is updated to the second position information, and the execution steps are returned. The position change information and the second position information after the change of position of any water-soluble particle are determined according to the simulation information corresponding to the first position information, until the motion trajectory information of any water-soluble particle within at least a preset time period is determined.
[0079] The acquisition module 11 is specifically used to acquire at least the simulated information of at least one hydrogel particle generated by the meteorological model analysis module based on the analysis of real three-dimensional environmental information and information of hydrogel particles in the three-dimensional environment within at least a preset time period.
[0080] The data output by this trajectory tracking model is data in a Lagrange field.
[0081] Please see Figure 7 An embodiment of this application also provides a hydrogel particle trajectory tracking device 20, comprising:
[0082] The acquisition module 21 is used to acquire simulated information of the water condensate particles to be tracked over a period of time. The simulated information includes at least the three-dimensional environmental wind information of the environment in which the water condensate particles to be tracked are located during the simulated period of time and the terminal velocity of the water condensate particles to be tracked during the simulated period of time.
[0083] Processing module 22 is used to input the simulated information of the water-soluble particles to be tracked into a trajectory tracking model trained by a preset method, so as to obtain the motion trajectory information of the water-soluble particles to be tracked within a certain period of time. The motion trajectory information includes at least the position change information of the water-soluble particles within a certain period of time. The preset method is, for example, the training method of the trajectory tracking model provided in any of the above embodiments.
[0084] The acquisition module 21 is specifically used to acquire the simulated information of the water-condensate particles to be tracked over a period of time, which is output by the meteorological model analysis module after analyzing the information of the real water-condensate particles to be tracked and the real three-dimensional environment.
[0085] The processing module 22 is also used to repeatedly execute the step of obtaining the simulation information of the water-coated particles to be tracked until the motion trajectory information of each water-coated particle to be tracked in the water-coated particle group within the corresponding time period is obtained, so as to obtain the motion trajectory information of the water-coated particle group.
[0086] The trajectory information of the hydrogel to be tracked during this period and the trajectory information of the hydrogel particle group are both data in a Lagrange field.
[0087] Please see Figure 8 One embodiment of this application also provides an electronic device 30, including a processor 31 and a memory 32 communicatively connected to the processor 31. The memory 32 stores computer-executable instructions, and the processor 31 executes the computer-executable instructions stored in the memory 32 to implement the training method of the trajectory tracking model provided in any of the above embodiments, or the water condensate particle trajectory tracking method provided in any of the above embodiments.
[0088] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed, cause the computer-executable instructions to be executed by a processor to implement a training method for a trajectory tracking model as provided in any of the above embodiments, or a water condensate particle trajectory tracking method as provided in any of the above embodiments.
[0089] This application also provides a computer program product, including a computer program that, when executed by a processor, implements a training method for a trajectory tracking model as provided in any of the above embodiments, or a water condensate particle trajectory tracking method as provided in any of the above embodiments.
[0090] It should be noted that the aforementioned computer-readable storage media can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic random access memory (FRAM), flash memory, magnetic surface memory, optical disc, or compact disc read-only memory (CD-ROM), etc. It can also be various electronic devices that include one or any combination of the above-mentioned memories, such as mobile phones, computers, tablet devices, personal digital assistants, etc.
[0091] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.
[0092] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.
[0093] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.
[0094] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0095] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0096] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0097] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.
Claims
1. A method of tracking a trajectory of a hydrate particle, characterized by, The method comprises the following steps: Step 1: training a trajectory tracking model obtaining training data, the training data comprising simulation information of at least one hydrometeor particle in at least one preset time, the simulation information comprising at least three-dimensional environmental wind information of an environment in which the at least one hydrometeor particle is located and a terminal falling speed of the at least one hydrometeor particle in the at least one preset time simulated; training an initial trajectory tracking model based on the training data to obtain the trajectory tracking model, the trajectory tracking model being configured to calculate motion trajectory information of a hydrometeor particle according to simulation information of the hydrometeor particle; Step 2: performing trajectory tracking by using the trajectory tracking model obtaining simulation information of a hydrometeor particle to be tracked in a time period, the simulation information comprising at least three-dimensional environmental wind information of an environment in which the hydrometeor particle to be tracked is located and a terminal falling speed of the hydrometeor particle to be tracked in the time period simulated; inputting the simulation information of the hydrometeor particle to be tracked into the trajectory tracking model to obtain motion trajectory information of the hydrometeor particle to be tracked in the time period, the motion trajectory information comprising at least position change information of the hydrometeor particle in the time period; wherein the motion trajectory information of the hydrometeor to be tracked in the time period and the motion trajectory information of the hydrometeor particle group are both data in a Lagrangian field.
2. The method of claim 1, wherein, The simulation information of the hydrometeor particle to be tracked in a time period is obtained by: obtaining simulation information of a hydrometeor particle to be tracked in a time period output by a meteorological model analysis module based on analysis of real information of the hydrometeor particle to be tracked and real three-dimensional environmental information.
3. The method of claim 1, wherein, The method further comprises: repeatedly performing the step of obtaining simulation information of a hydrometeor particle to be tracked in a time period until motion trajectory information of each hydrometeor particle to be tracked in a corresponding time period in a hydrometeor particle group is obtained to obtain motion trajectory information of the hydrometeor particle group.
4. The method of claim 1, wherein, The initial trajectory tracking model is specifically configured to: obtain first position information of an arbitrary hydrometeor particle in an Euler field; determine position change information and second position information after a change in position of the arbitrary hydrometeor particle based on simulation information corresponding to the first position information of the arbitrary hydrometeor particle, wherein the hydrometeor particle has different simulation information corresponding to different position information; update the first position information to the second position information, and return to perform the step of determining the position change information and the second position information after the change in position of the arbitrary hydrometeor particle based on the simulation information corresponding to the first position information of the arbitrary hydrometeor particle until motion trajectory information of the arbitrary hydrometeor particle in at least one preset time is determined.
5. The method of claim 1, wherein, The training data is obtained by: obtaining simulation information of at least one hydrometeor particle in at least one preset time generated by a meteorological model analysis module based on analysis of real three-dimensional environmental information and information of a hydrometeor particle in a three-dimensional environment.
6. The method of claim 1, wherein, The data output by the trajectory tracking model is data in a Lagrangian field.
7. An electronic device, comprising: The method comprises the following steps: a processor and a memory connected in communication with the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method for tracking trajectories of particles of a hydrate according to any one of claims 1 to 6.
8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions which, when executed, cause a computer to perform the method for tracking trajectories of particles of a hydrate according to any one of claims 1 to 6.
Citation Information
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