CN117828999B - Digital twin satellite group intelligent management system and method - Google Patents
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Abstract
The invention discloses an intelligent management system and method for a digital twin satellite group, which relate to the technical field of satellite operation management, and are characterized in that a Bp neural network is used for training to obtain satellite operation digital twin model output, the operation state of a satellite is predicted, corresponding prediction results are obtained, a plurality of prediction results are summarized to generate a state prediction set, the difference value between the satellite operation state parameter and a front value is obtained, a satellite variation index is generated, a satellite to be regulated is screened out from a plurality of satellites, and corresponding regulation priority is confirmed; and matching corresponding adjustment schemes for the states of all satellites in sequence, predicting and acquiring running state data of all satellites after the adjustment schemes are executed, establishing a motion digital twin model of the satellites according to the running state data, and performing visual processing on the motion process predicted and acquired by all satellites. By determining the adjustment priority, the adjustment process for each satellite can be orderly performed, and mutual interference among each satellite is avoided.
Description
Technical Field
The invention relates to the technical field of satellite operation management, in particular to an intelligent management system and method for a digital twin satellite group.
Background
The intelligent management of satellite groups refers to complex and accurate maneuvering operation among multiple satellites in an in-orbit satellite constellation through highly-automated intersection and approaching operation so as to realize various tasks including in-orbit service coordination, orbit upgrading and the like.
The digital twin technology is a technology for integrating simulation processes of multiple disciplines, multiple physical quantities, multiple scales and multiple probabilities based on data such as a physical model, sensor update and operation history. The digital twin technique can complete mapping in the virtual space, reflecting the full life cycle process of the corresponding physical equipment. It is considered a digital mapping system of one or more important, mutually dependent equipment systems. Due to the complexity of satellite management tasks, digital twinning techniques are also increasingly being applied in this field.
In the Chinese patent application publication No. CN105049110A, a distributed management system of microsatellite group is disclosed, which comprises a distributed management system of satellite group formed by closely networking a plurality of microsatellites, and based on the space wireless communication technology, each subsystem realizes independent data management and inter-satellite data sharing management of each microsatellite through WiFi wireless communication. The invention realizes the functions of star remote control, star remote measurement, star clock synchronization, star communication, star program control and the like through the multi-star networking.
In the application, because of the high-efficiency network self-organization and quick recovery function of the WiFi standard, each microsatellite adopts an industrial shelf type integrated circuit, the star group distributed management system has the characteristics of high instantaneity, strong flexibility, high reliability, low investment cost, reconfigurability and the like, and provides reference value for engineering application of management systems of future microsatellite groups.
However, when the number of satellites is large and the distribution of the satellites in each area is quite uneven, the position and the posture of the satellites need to be adjusted, but in the existing adjustment method, when a plurality of satellites are adjusted, adjustment is usually performed simultaneously, and certain interference can be generated between the satellites in the adjustment process, so that the adjustment result can be difficult to reach the expected value, and the actual efficiency of adjustment is low.
Therefore, the invention provides an intelligent management system and method for digital twin satellite groups.
Disclosure of Invention
(One) solving the technical problems
Aiming at the defects of the prior art, the invention provides a digital twin satellite group intelligent management system and a method, which are characterized in that the output of a satellite operation digital twin model is obtained after the Bp neural network is used for training, the operation state of the satellite is predicted, corresponding prediction results are obtained, a plurality of prediction results are summarized to generate a state prediction set, the difference value between the satellite operation state parameter and a front value is obtained, a satellite variation index is generated, a satellite to be regulated is screened out from a plurality of satellites, and corresponding regulation priority is confirmed; and matching corresponding adjustment schemes for the states of all satellites in sequence, predicting and acquiring running state data of all satellites after the adjustment schemes are executed, establishing a satellite motion model according to the running state data, and performing visual processing on the motion process predicted and acquired by all satellites. The adjustment process of each satellite can be orderly carried out by determining the adjustment priority, so that the mutual interference among the satellites is avoided, and the problems in the background technology are solved.
(II) technical scheme
In order to achieve the above purpose, the invention is realized by the following technical scheme:
an intelligent management method for digital twin satellite group comprises the following steps: comprising the steps of (a) a step of,
Dividing a satellite distribution area into a plurality of subareas, acquiring the distribution state and the communication state of satellites in each subarea, establishing an area density set, and generating a density index from the area density setIf the obtained density index/>If the density threshold value is exceeded, an adjustment instruction is sent out;
Monitoring the running state of each satellite, establishing a satellite running state set, performing feature extraction, constructing a model feature set, using a Bp neural network, acquiring satellite running digital twin model output after training, predicting the running state of the satellite by using the model output, acquiring corresponding prediction results, and summarizing a plurality of prediction results to generate a state prediction set;
acquiring a plurality of satellite running state parameters in a state prediction set, acquiring the difference value between the satellite running state parameters and a previous value, generating a state variable set after summarizing, and generating a satellite variation index from the state variable set By satellite variation index/>Screening out the satellite to be adjusted from a plurality of satellites, and confirming the corresponding adjustment priority;
Wherein the satellite change index The acquisition mode is as follows:
The running resistance is adjusted Proximity distance/>Attitude stability/>After dimensionless treatment, the method is according to the following formula:
，
the significance of the coefficients is: ，/> and/> ，/>、/>Is a weight coefficient;
the method comprises the steps that the motion state characteristics of the satellite are identified through the operation states of the satellite obtained through prediction, corresponding adjustment schemes are matched for the states of the satellite in sequence, the operation state data of the satellite after the adjustment schemes are executed are obtained through prediction, a motion model of the satellite is built through the operation state data, and visual processing is conducted on the motion process obtained through prediction of the satellite.
Further, determining a satellite orbit coverage area as a monitoring area, dividing the monitoring area into a plurality of sub-areas, numbering each sub-area respectively, inquiring to obtain the current distributed satellite density Sd in each sub-area, and counting the data transmission quantity of each satellite when the satellite in the current sub-area is in a communicable state to obtain the data transmission density Dn; after the data are summarized, establishing a regional density set; generating a density index from the set of region densitiesIf the obtained density index/>And if the density threshold value is exceeded, an adjustment instruction is sent out.
Further, the density indexThe acquisition mode is as follows: the satellite density Sd and the data transmission density Dn are subjected to linear normalization processing, and corresponding data values are mapped to intervals/>And then according to the following formula:
，
Wherein, Is the mean of satellite density in each sub-region,/>The standard value of the satellite density is qualified,Data transmission density in each sub-region, average value,/>Qualified standard value of data transmission density,/>For the satellite density values in the i subregion,/>A value of data transmission density within the i subregion; weight coefficient:，/> and/> ，/>N is the number of subareas and is a positive integer greater than 1.
Further, the operation states of all satellites in the monitoring area are monitored, and after operation state data are obtained, a satellite operation state set is built in a summarizing mode; preprocessing each item of data in the satellite running state set, and then extracting features to obtain data features which can be used for model training, and summarizing the data features to construct a model feature set;
Constructing an initial digital twin model by using a Bp neural network, training and testing the constructed initial digital twin model, obtaining a trained initial digital twin model, and outputting the trained initial digital twin model as a satellite operation digital twin model; and predicting satellite states in the monitoring area by using the trained satellite operation digital twin model, obtaining corresponding prediction results, and generating a state prediction set.
Further, calculating and obtaining the attitude stability Zt of the satellite, detecting and obtaining the current running resistance Fo of the satellite, and obtaining the adjacent distance Pd after determining the distance between the current satellite and the target satellite; comparing the above parameters with the predicted values respectively, and obtaining the difference values of the two parameters, respectively marked as running resistanceProximity distance/>Attitude stability/>Summarizing the parameters to generate a state variable set;
Generating satellite change index from state variable set If the acquired satellite variation indexIf the change threshold value is exceeded, determining satellites to be adjusted, and marking the corresponding satellites to be adjusted respectively; satellite variation index/>, of each satellite to be adjustedAnd comparing with the variation threshold value, acquiring the ratio of the two, sequencing a plurality of ratios from large to small, taking the sequencing as the adjustment priority of a plurality of satellites to be adjusted, and marking the corresponding satellites to be adjusted by the adjustment priority.
Further, the operation state data of each satellite in the monitoring area obtained by prediction are summarized, the operation state characteristics of each satellite are identified according to the state data, the currently existing satellite adjustment schemes are collected, an adjustment scheme library is formed after the operation state data are summarized, the operation state characteristics of each satellite are combined with the correspondence of the adjustment schemes, and the corresponding adjustment schemes are adjusted and matched for each satellite to be adjusted in sequence according to the adjustment priority.
Further, after a corresponding adjustment scheme is matched with the satellite to be adjusted with the highest adjustment priority, the adjustment scheme is executed as input, the trained satellite operation digital twin model is used for predicting the states of all satellites in the monitoring area, operation state data of all satellites are obtained, and if the execution effect of the adjustment scheme fails to reach the expectation, the satellite operation digital twin model is combined for carrying out one or more corrections on parameters of the adjustment scheme until the adjustment scheme is feasible.
Further, executing the corrected adjustment scheme, predicting the state data of each satellite after execution by using a satellite operation digital twin model, acquiring an adjustment scheme corresponding to the satellite to be adjusted with the secondary priority based on the prediction result, correcting the adjustment scheme when the adjustment scheme is difficult to reach the expectation, and iterating until the corresponding adjustment scheme is matched or corrected for all the satellites to be adjusted, wherein the adjustment scheme is used as a secondary scheme.
Further, the trained satellite operation digital twin model predicts the operation state data of each satellite after the secondary scheme is executed, so as to obtain corresponding prediction results, and a satellite operation state prediction data set is generated after summarization;
Acquiring the running state data of each satellite from a satellite running state prediction data set, matching the corresponding mathematical model or algorithm model by the data characteristics of the running state data, and building a satellite motion model after training; and converting the result obtained by calculating the mathematical digital twin model into an image or an animation by using computer graphics and a visualization technology.
An intelligent management system for a digital twin satellite constellation, comprising:
The detection unit is used for establishing a regional density set after acquiring the distribution state and the communication state of the satellites in each sub-region, generating a density index by the regional density set, and sending out an adjustment instruction if the acquired density index exceeds a density threshold value;
The model construction unit monitors the running states of all satellites, establishes a satellite running state set, acquires satellite running digital twin model output after being trained by using a Bp neural network, predicts the running states of the satellites by using the model construction unit, acquires corresponding prediction results, and gathers a plurality of prediction results to generate a state prediction set;
the evaluation unit acquires a plurality of satellite running state parameters in the state prediction set, acquires the difference value between the satellite running state parameters and the previous value, generates a state variable set after summarization, generates a satellite variation index from the state variable set, screens out satellites to be regulated, and confirms corresponding regulation priority;
The display unit is used for sequentially matching the states of the satellites with corresponding adjustment schemes, predicting and acquiring running state data of the satellites after the adjustment schemes are executed, establishing a satellite movement digital twin model according to the running state data, and performing visual processing on the movement process predicted and acquired by the satellites.
(III) beneficial effects
The invention provides an intelligent management system and method for a digital twin satellite group, which have the following beneficial effects:
1. by generating a density index The current distribution state of each satellite is evaluated, if the current distribution state of the satellite is very uneven, the state of at least part of the satellites needs to be adjusted, so that the method has pertinence when the operation posture and the operation state of the satellites are adjusted, the ineffective investment can be reduced, and the adjustment efficiency is improved.
2. The satellite state in the monitoring area is predicted by using the trained satellite operation digital twin model, the operation state of the next period of the satellite can be obtained, a plurality of satellites with poorer operation states can be screened by predicting the satellite state, and the part to be regulated or corrected in the satellite can be determined, so that the operation state of the satellite is guaranteed.
3. Generating satellite change index from state variable setBy satellite variation index/>The prediction states of the satellites are evaluated so as to screen out parts needing to be adjusted from the satellites, the priority of the satellites during adjustment is determined, the workload of adjustment can be reduced when the satellites need to be adjusted, the pertinence during screening is improved, the adjustment process of the satellites can be orderly carried out by determining the adjustment priority, the mutual interference among the satellites is avoided, and efficient management and intelligent management of satellite operation are realized.
4. Corresponding adjustment schemes are matched for each satellite, targeted adjustment is achieved, simulation analysis is conducted on each adjustment scheme on the basis of combining a satellite operation digital twin model, and correction effects are achieved, so that scheme effectiveness is guaranteed; on the basis of establishing a motion digital twin model, the running state and the path of the satellite are visually displayed, and when the running state of the satellite is predicted and adjusted, the satellite is convenient to observe and further adjust, and the management efficiency is improved as a whole.
Drawings
FIG. 1 is a schematic flow chart of a digital twin satellite group intelligent management method of the invention;
FIG. 2 is a schematic diagram of the digital twin satellite constellation intelligent management system according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1 and 2, the present invention provides an intelligent management method for digital twin satellite group, comprising:
dividing a satellite distribution area into a plurality of subareas, after acquiring the distribution state and the communication state of satellites in each subarea, establishing an area density set, and generating a density index from the area density set If the obtained density index/>If the density threshold value is exceeded, an adjustment instruction is sent out;
The first step comprises the following steps:
Step 101, determining a satellite orbit coverage area as a monitoring area, dividing the monitoring area into a plurality of sub-areas, numbering each sub-area respectively, inquiring to obtain the current distributed satellite density Sd in each sub-area, and counting the data transmission quantity of each satellite when the satellite in the current sub-area is in a communicable state to obtain the data transmission density Dn; after the data are summarized, establishing a regional density set;
step 102, generating a density index from the regional density set The acquisition mode is as follows: the satellite density Sd and the data transmission density Dn are subjected to linear normalization processing, and corresponding data values are mapped to intervals/>And then according to the following formula:
，
Wherein, Is the mean of satellite density in each sub-region,/>The standard value of the satellite density is qualified,Data transmission density in each sub-region, average value,/>Qualified standard value of data transmission density,/>For the satellite density values in the i subregion,/>A value of data transmission density within the i subregion; weight coefficient:，/> and/> ，/>N is the number of subareas and is a positive integer greater than 1.
Presetting a density threshold value according to historical data and the expectation of satellite operation states, and if the acquired density indexAnd if the density threshold value is exceeded, an adjustment instruction is sent out.
In use, the contents of steps 101 and 102 are combined:
Generating a density index based on satellite density Sd and data transmission density Dn in each sub-area by querying satellite states in each sub-area The current distribution state of each satellite is evaluated, if the current distribution state of the satellite is very uneven, the state of at least part of the satellites needs to be adjusted, so that the method has pertinence when the operation posture and the operation state of the satellites are adjusted, the ineffective investment can be reduced, and the adjustment efficiency is improved.
Monitoring the running states of all satellites, establishing a satellite running state set, performing feature extraction, constructing a model feature set, using a Bp neural network, acquiring satellite running digital twin model output after training, predicting the running states of the satellites by using the Bp neural network, acquiring corresponding prediction results, and summarizing a plurality of prediction results to generate a state prediction set;
The second step comprises the following steps:
step 201, monitoring the running state of each satellite in a monitoring area, at least acquiring data such as the position, orbit height, running speed and resistance of Wei Xingyun time, the current dip angle and the distance between the current dip angle and an adjacent satellite, and after acquiring the running state data, summarizing and establishing a satellite running state set;
Preprocessing each item of data in the satellite running state set, and then extracting features to obtain data features which can be used for model training, and summarizing the data features to construct a model feature set;
Step 202, constructing an initial digital twin model after selecting a corresponding network architecture by using a Bp neural network, extracting part of characteristic data from a model characteristic set as a training set and a testing set to train and test the constructed initial digital twin model, acquiring a trained initial digital twin model, and outputting the trained initial digital twin model as a satellite operation digital twin model;
predicting satellite states in a monitoring area by using a trained satellite operation digital twin model, obtaining corresponding prediction results after a prediction period, for example, after a day, and generating a state prediction set after integrating a plurality of prediction results;
In use, the contents of steps 201 and 202 are combined:
On the basis of the current running state of the satellite, the running state of each satellite is predicted, a satellite running state set is obtained, a Bp neural network is used for training and obtaining a satellite running digital twin model, at the moment, the satellite state in a monitoring area is predicted by using the trained satellite running digital twin model, so that the running state of the next period of the satellite can be obtained, a plurality of satellites with poor running states can be screened by predicting the satellite state, the part to be regulated or corrected in the satellite running state is determined, and the running state of the satellite is guaranteed.
Step three, acquiring a plurality of satellite running state parameters in a state prediction set, acquiring the difference value between the satellite running state parameters and a previous value, generating a state variable set after summarizing, and generating a satellite variation index from the state variable setBy satellite variation index/>Screening out the satellite to be adjusted from a plurality of satellites, and confirming the corresponding adjustment priority;
the third step comprises the following steps:
step 301, calculating the attitude stability Zt of the acquired satellite, and the specific manner may refer to the following:
The calculation of the attitude stability of the satellite usually needs to be carried out by means of a specific algorithm and model, and the following two modes are more common: parameters such as angular velocity, acceleration, magnetic field intensity and the like of the satellite are calculated and acquired by utilizing data of attitude sensors such as a gyroscope, an accelerometer and the like through technologies such as data fusion, filtering and the like, and the stability of the attitude of the satellite is judged according to the stability of the parameters;
or simulating the attitude motion of the satellite in orbit by establishing a dynamic model, and evaluating the stability of the satellite attitude according to the comparison of the actual attitude data and the simulation data.
Then detecting and acquiring the current running resistance Fo of the satellite, and acquiring the adjacent distance Pd after determining the distance between the current satellite and the target satellite; comparing the above parameters with the predicted values respectively, and obtaining the difference values of the two parameters, respectively marked as running resistanceProximity distance/>Attitude stability/>Summarizing the parameters to generate a state variable set;
step 302, generating satellite change index from state variable set The acquisition mode is as follows: running resistance/>Proximity distance/>Attitude stability/>After dimensionless treatment, the method is according to the following formula:
，
the significance of the coefficients is: ，/> and/> ，/>、/>The specific value of the weight coefficient can be set by user adjustment or obtained by mathematical analysis software through simulation analysis;
Presetting a change threshold according to satellite state operation data and the expectation of the satellite operation state, and if the acquired satellite change index If the change threshold value is exceeded, determining satellites to be adjusted, and marking the corresponding satellites to be adjusted respectively;
step 303, the satellite variation index of each satellite to be adjusted Comparing with a variation threshold value, obtaining the ratio of the two, sequencing a plurality of ratios from large to small, taking the sequencing as the adjustment priority of a plurality of satellites to be adjusted, and marking the corresponding satellites to be adjusted by the adjustment priority;
In use, the contents of steps 301 to 303 are combined:
Generating satellite variation index from state variable set based on obtaining prediction result By satellite variation index/>The prediction states of the satellites are evaluated so as to screen out parts needing to be adjusted from the satellites, and further, the priority of the satellites during adjustment is determined, at the moment, through screening, when the satellites need to be adjusted, the workload of adjustment can be reduced, the pertinence during screening is improved, in addition, the adjustment process of the satellites can be orderly carried out through determining the adjustment priority, the mutual interference among the satellites is avoided, and efficient management and intelligent management of satellite operation are realized.
Step four, identifying the motion state characteristics of the satellite by the predicted and acquired satellite operation states, sequentially matching corresponding adjustment schemes for each satellite state, predicting and acquiring the operation state data of each satellite after executing the adjustment schemes, establishing a satellite motion model by the operation state data, and carrying out visual processing on the motion process predicted and acquired by each satellite;
The fourth step comprises the following steps:
step 401, summarizing the operation state data of each satellite in the monitoring area obtained by prediction, identifying the operation state characteristics of each satellite according to the state data, collecting the currently existing satellite adjustment schemes, forming an adjustment scheme library after summarizing, and sequentially adjusting and matching the corresponding adjustment schemes for each satellite to be adjusted according to the adjustment priority by combining the correspondence of the operation state characteristics of each satellite and the adjustment schemes;
Step 402, after a corresponding adjustment scheme is matched for the satellite to be adjusted with the highest adjustment priority, the adjustment scheme is used as input, the trained satellite operation digital twin model is used for predicting the state of each satellite in the monitoring area, the operation state data of each satellite is obtained, the target state data of each satellite operation is preset to be used as adjustment expectation, if the execution effect of the adjustment scheme fails to reach the expectation, the satellite operation digital twin model is combined for carrying out one or more corrections on the parameters of the adjustment scheme until the adjustment scheme is feasible;
In use, in combination with the contents of steps 401 and 402,
On the basis of the obtained prediction results, identifying and obtaining the running state characteristics of each satellite, so that corresponding adjustment schemes are matched for each satellite, and targeted adjustment is realized; meanwhile, on the basis of combining a satellite operation digital twin model, simulation analysis is carried out on each adjustment scheme, so that the correction effect is realized, and the effectiveness of the scheme can be ensured.
Step 403, executing the revised adjustment scheme, predicting each satellite state data after execution by a satellite operation digital twin model, acquiring an adjustment scheme corresponding to the satellite to be adjusted with the secondary priority based on the prediction result, revising the adjustment scheme when the adjustment scheme is difficult to reach the expectation, and iterating until the corresponding adjustment scheme is matched or revised for all the satellites to be adjusted, and taking the adjustment scheme as a secondary scheme;
Step 404, predicting the running state data of each satellite after executing the secondary scheme by the trained satellite running digital twin model to obtain corresponding prediction results, and generating a satellite running state prediction data set after summarizing;
acquiring the running state data of each satellite from a satellite running state prediction data set, matching the corresponding mathematical model or algorithm model by the data characteristics of the running state data, and building a satellite motion model after training and correction; converting the result obtained by calculating the mathematical model into an image or an animation by utilizing computer graphics and a visualization technology;
for example, software such as OpenGL or Unity is used to generate three-dimensional graphics and animation, and in order to obtain more realistic and smooth animation effects, optimization of the generated animation is required, including adjusting the frame rate of the animation, smoothing the track of motion, adding physical effects, and the like.
In use, the contents of steps 403 to 404 are combined:
The method comprises the steps of obtaining an adjustment scheme aiming at each satellite to be adjusted after correction for a plurality of times, obtaining satellite running state data after each adjustment scheme is executed through prediction, and visually displaying the running state and the path of the satellite on the basis of establishing a motion model, so that the method is more convenient to observe and further adjust when the running state of the satellite is predicted and adjusted, and the management efficiency is improved as a whole.
Referring to fig. 1 and 2, the present invention provides an intelligent management system for digital twin satellite group, comprising:
The detection unit is used for establishing a regional density set after acquiring the distribution state and the communication state of the satellites in each sub-region, generating a density index by the regional density set, and sending out an adjustment instruction if the acquired density index exceeds a density threshold value;
The model construction unit monitors the running states of all satellites, establishes a satellite running state set, acquires satellite running digital twin model output after being trained by using a Bp neural network, predicts the running states of the satellites by using the model construction unit, acquires corresponding prediction results, and gathers a plurality of prediction results to generate a state prediction set;
the evaluation unit acquires a plurality of satellite running state parameters in the state prediction set, acquires the difference value between the satellite running state parameters and the previous value, generates a state variable set after summarization, generates a satellite variation index from the state variable set, screens out satellites to be regulated, and confirms corresponding regulation priority;
The display unit is used for sequentially matching the states of the satellites with corresponding adjustment schemes, predicting and acquiring the running state data of the satellites after the adjustment schemes are executed, establishing a satellite motion model according to the running state data, and performing visual processing on the motion process predicted and acquired by the satellites.
It should be noted that, as an alternative illustration, the above formulas are all the formulas with dimensions removed and their numerical values calculated, and the formulas are formulas with a large amount of collected data being simulated by software to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application.
Claims (7)
1. An intelligent management method for a digital twin satellite group is characterized by comprising the following steps of: comprises the following steps:
Dividing a satellite distribution area into a plurality of subareas, acquiring the distribution state and the communication state of satellites in each subarea, establishing an area density set, and generating a density index from the area density set If the obtained density index/>If the density threshold value is exceeded, an adjustment instruction is sent out;
Monitoring the running state of each satellite, establishing a satellite running state set, performing feature extraction, constructing a model feature set, using a Bp neural network, acquiring satellite running digital twin model output after training, predicting the running state of the satellite by using the model output, acquiring corresponding prediction results, and summarizing a plurality of prediction results to generate a state prediction set;
acquiring a plurality of satellite running state parameters in a state prediction set, acquiring the difference value between the satellite running state parameters and a previous value, generating a state variable set after summarizing, and generating a satellite variation index from the state variable set By satellite variation indexScreening out the satellite to be adjusted from a plurality of satellites, and confirming the corresponding adjustment priority;
Wherein the satellite change index The acquisition mode is as follows: running resistance/>Proximity distance/>Attitude stability/>After dimensionless treatment, the method is according to the following formula:
，
the significance of the coefficients is: ，/> and/> ，/>、/>Is a weight coefficient;
Identifying the motion state characteristics of the satellite by the operation states of the satellite obtained by prediction, sequentially matching corresponding adjustment schemes for the states of the satellites, predicting and obtaining the operation state data of the satellites after the adjustment schemes are executed, establishing a motion digital twin model of the satellite by the operation state data, and carrying out visual processing on the motion process obtained by the satellite prediction;
Determining a satellite orbit coverage area as a monitoring area, dividing the monitoring area into a plurality of sub-areas, inquiring and obtaining the current distributed satellite density Sd in each sub-area, and counting the data transmission quantity of each satellite when the satellite in the current sub-area is in a communicable state, so as to obtain the data transmission density Dn; after the data are summarized, a regional density set is established, and a density index is generated from the regional density set If the obtained density index/>If the density threshold value is exceeded, an adjustment instruction is sent out;
The density index The acquisition mode is as follows: the satellite density Sd and the data transmission density Dn are subjected to linear normalization processing, and corresponding data values are mapped to intervals/>And then according to the following formula:
，
Wherein, Is the mean of satellite density in each sub-region,/>Is a qualified standard value of the satellite density,Data transmission density in each sub-region, average value,/>Qualified standard value of data transmission density,/>For the satellite density values in the i subregion,/>A value of data transmission density within the i subregion; weight coefficient: /(I)，/>And/>，/>N is the number of subareas and is a positive integer greater than 1;
Calculating attitude stability Zt of the acquired satellite, detecting and acquiring current running resistance Fo of the satellite, acquiring an adjacent distance Pd after determining the distance between the current satellite and a target satellite, comparing the parameters with pre-prediction values respectively, acquiring difference values of the parameters and the pre-prediction values, and marking the difference values as running resistance respectively Proximity distance/>Attitude stability/>Summarizing the parameters to generate a state variable set;
Generating satellite change index from state variable set If the acquired satellite variation index/>If the change threshold value is exceeded, determining satellites to be adjusted, and marking the corresponding satellites to be adjusted respectively; satellite variation index/>, of each satellite to be adjustedAnd comparing with the variation threshold value, acquiring the ratio of the two, sequencing a plurality of ratios from large to small, taking the sequencing as the adjustment priority of a plurality of satellites to be adjusted, and marking the corresponding satellites to be adjusted by the adjustment priority.
2. The intelligent management method for digital twin satellite constellation according to claim 1, wherein:
Monitoring the running states of all satellites in a monitoring area, and after acquiring running state data, summarizing to establish a satellite running state set; preprocessing each item of data in the satellite running state set, and then extracting features to obtain data features which can be used for model training, and summarizing the data features to construct a model feature set;
Constructing an initial digital twin model by using a Bp neural network, training and testing the constructed initial digital twin model, obtaining a trained initial digital twin model, and outputting the trained initial digital twin model as a satellite operation digital twin model; and predicting satellite states in the monitoring area by using the trained satellite operation digital twin model, obtaining corresponding prediction results, and generating a state prediction set.
3. The intelligent management method for digital twin satellite constellation according to claim 1, wherein:
summarizing the operation state data of each satellite in the monitoring area obtained by prediction, identifying the operation state characteristics of each satellite according to the state data, collecting the currently existing satellite adjustment schemes, forming an adjustment scheme library after summarizing, and sequentially adjusting and matching the corresponding adjustment schemes for each satellite to be adjusted according to the adjustment priority by combining the correspondence of the operation state characteristics of each satellite and the adjustment schemes.
4. A digital twin satellite constellation intelligent management method according to claim 3, wherein:
After matching the corresponding adjustment scheme for the satellite to be adjusted with the highest adjustment priority, taking the adjustment scheme as input, predicting the state of each satellite in the monitoring area by using the trained satellite operation digital twin model, acquiring the operation state data of each satellite, and if the execution effect of the adjustment scheme fails to reach the expectation, carrying out one or more corrections on the parameters of the adjustment scheme by combining the satellite operation digital twin model until the adjustment scheme is feasible.
5. The intelligent management method for digital twin satellite constellation according to claim 4, wherein:
And executing the revised adjustment scheme, predicting the state data of each satellite after execution by using a satellite operation digital twin model, acquiring an adjustment scheme corresponding to the satellite to be adjusted with the secondary priority based on the prediction result, revising the adjustment scheme when the adjustment scheme is difficult to reach the expectation, and iterating until the corresponding adjustment scheme is matched or revised for all the satellites to be adjusted, and taking the adjustment scheme as a secondary scheme.
6. The intelligent management method for digital twin satellite constellation according to claim 5, wherein:
predicting the running state data of each satellite after the secondary scheme is executed by the trained satellite running digital twin model to obtain a corresponding prediction result, and generating a satellite running state prediction data set after summarizing;
acquiring operation state data of each satellite from a satellite operation state prediction data set, matching corresponding mathematical models by data characteristics of the operation state data, and building a satellite motion model after training; the result of the mathematical model calculation is converted into an image or an animation by using computer graphics and a visualization technology.
7. A digital twin satellite constellation intelligent management system, to which the method of any one of claims 1 to 6 is applied, characterized in that: comprising the following steps:
The detection unit is used for summarizing and establishing a regional density set after acquiring the distribution state and the communication state of the satellites in each sub-region, generating a density index by the regional density set, and sending out an adjustment instruction if the acquired density index exceeds a density threshold value;
The model construction unit monitors the running states of all satellites, establishes a satellite running state set, acquires satellite running digital twin model output after being trained by using a Bp neural network, predicts the running states of the satellites by using the model construction unit, acquires corresponding prediction results, and gathers a plurality of prediction results to generate a state prediction set;
the evaluation unit acquires a plurality of satellite running state parameters in the state prediction set, acquires the difference value between the satellite running state parameters and the previous value, generates a state variable set after summarization, generates a satellite variation index from the state variable set, screens out satellites to be regulated, and confirms corresponding regulation priority;
The display unit is used for sequentially matching the states of the satellites with corresponding adjustment schemes, predicting and acquiring the running state data of the satellites after the adjustment schemes are executed, establishing a satellite motion model according to the running state data, and performing visual processing on the motion process predicted and acquired by the satellites.
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