Spacecraft in-orbit operation health assessment method

Abstract

The invention discloses a spacecraft in-orbit operation health assessment method, and relates to the technical field of space flight and aviation. Environmental data such as gravity gradient, radiation intensity and dust particle density of an asteroid surface are collected in real time through a multi-mode sensor array, modeling and prediction are carried out by adopting a dynamic Bayesian network in combination with Kalman filtering, and the health assessment accuracy of the spacecraft in-orbit operation is improved. The method comprises the following steps: dynamically generating environment characteristic parameters including a risk level and an environment change trend, and in health state prediction, simulating stress distribution in a low-gravitation environment through digital twinborn body and finite element analysis, predicting a fatigue state and a failure risk of a contact device, and accurately evaluating the fatigue life of a high-stress area. The quantification capability of the failure risk is improved through the risk probability model; besides, according to a health prediction result, landing parameters including a damping mode and a landing speed are dynamically optimized, and a dynamic planning method is used for minimizing impact force and stress load, so that the detector can be in stable contact in a low-gravity environment.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of aerospace technology, in particular to a spacecraft on-orbit operation health assessment method. BACKGROUND

[0002] The deep space probe performs a landing task on the surface of an asteroid, which is one of the application scenarios of spacecraft on-orbit operation. The target of this scenario includes surface sampling, geological survey and data collection, etc. Under this background, the spacecraft faces extremely complex and variable environments, such as unstable dynamics under low gravity, high radiation conditions and potential threats of dust particles to equipment. However, the traditional on-orbit operation health assessment method is mainly applied to a relatively stable on-orbit operation environment, and the effect is poor under this scenario.

[0003] The traditional scheme mainly depends on the monitoring of pre-defined thresholds and the fault detection based on physical models. When some key parameters exceed the threshold range, an alarm is triggered and a preset fault handling strategy is called. In the asteroid landing task, the gravity on the surface of the asteroid is extremely low, which easily leads to an unstable state of the landing device. The traditional scheme is difficult to predict the stress condition and fatigue risk of the contact device in real time. In addition, the high radiation environment and particle dust storm may cause abnormal or short-term failure of the sensor signal. The fixed threshold method cannot adapt to this environmental change and cannot compensate for the health status of the sensor.

[0004] To cope with the above-mentioned disadvantages, some traditional schemes increase the redundancy design and compensation through ground support, but such compensation measures increase the system complexity and mission cost, and cannot fundamentally solve the evaluation blind area in the dynamic environment. Therefore, there is an urgent need for a spacecraft on-orbit operation health assessment scheme to solve such problems. SUMMARY

[0005] In view of the above-mentioned existing problems, the present application is proposed.

[0006] The present application provides a spacecraft on-orbit operation health assessment method to solve the problem that the traditional health assessment scheme cannot judge the influence of external environmental changes on the key components of the system in real time due to the complex and dynamic changes of the asteroid surface environment, the abnormality of mechanical components caused by high-intensity radiation and low-gravity environment.

[0007] To solve the above-mentioned technical problems, the present application provides the following technical scheme:

[0008] The present application provides a spacecraft on-orbit operation health assessment method, which comprises,

[0009] Step S1, real-time acquisition of environmental data of the surface of the asteroid by using a sensor array on the probe, and generation of environmental characteristic parameters based on the environmental data; the environmental characteristics include risk level and estimated environmental change trend;

[0010] Step S2, health state prediction based on the digital twin model of the probe, input the environmental characteristic parameters generated in step S1 into the digital twin, and evaluate the fatigue state of the landing device by combining the finite element analysis method;

[0011] Step S3, according to the health state prediction result in step S2, real-time optimization of landing operation parameters of the probe;

[0012] Step S4, during the landing process, real-time state monitoring is performed on the key components of the probe, and cleaning is performed through the particle pollution compensation mechanism;

[0013] Step S5, based on the state of the cleaned components and the updated health evaluation result, dynamically adjust the task execution strategy of the probe:

[0014] Dynamically optimize the priority of the sampling task, and modify the running path of the probe.

[0015] As a preferred scheme of the spacecraft in-orbit operation health evaluation method, wherein: the environmental data includes:

[0016] Gravity gradient data of the surface of the asteroid, used to describe the distribution of the gravitational field and its local changes,

[0017] Radiation intensity measurement data, used to evaluate the potential impact of the radiation environment on the electronic equipment of the probe,

[0018] Dust particle density data, used to analyze the coverage or erosion risk of the optical system and mechanical structure;

[0019] The sensor array includes an inertial measurement unit (IMU), a radiation meter, and a particle detector.

[0020] As a preferred scheme of the spacecraft in-orbit operation health evaluation method, wherein: the step of generating environmental characteristic parameters based on environmental data is,

[0021] Collecting environmental data and constructing a data set, defining the environmental data set as D t :

[0022] D t ={G t ,R t ,P t},

[0023] Wherein, t represents the current time step, D t represents the environmental data set at time t, G t represents the gravity gradient data, R t represents the radiation intensity measurement data, and P t represents the dust particle density data;

[0024] The environment data is modeled using a dynamic Bayesian network, which is based on a linear state space model, whose state transition equation and observation equation are:

[0025] X t = AX t-1 + BU t + ε t ,

[0026] Z t = HX t + δ t ,

[0027] where X t represents the hidden state variable at time t, represents the environment feature, X t-1 represents the hidden state variable at time t-1, U t represents the control input at time t, equals D t , ε t represents the state noise, Z t represents the observation value at time t, A represents the state transition matrix, B represents the input control matrix, H represents the observation matrix, δ t represents the observation noise;

[0028] State estimation is performed using Kalman filtering, which is divided into two stages, prediction and update,

[0029] Prediction stage:

[0030]

[0031] where X represents the hidden state prediction value at time t, represents the hidden state estimation value at time t-1, represents the prediction error covariance at time t, P t-1 represents the error covariance at time t-1, Q represents the process noise covariance,

[0032] Update stage:

[0033]

[0034] where K t represents the Kalman gain, which is the weight distribution of the control observation value and the predicted value, R represents the observation noise covariance, represents the hidden state update value at time t, and I represents the identity matrix;

[0035] The generated environment feature parameter is θ t :

[0036]

[0037] wherein θ t represents the environmental characteristic parameter set at time t, represents the risk level generated based on the classification model, T t represents the environmental change trend predicted based on Kalman filtering.

[0038] As a preferred scheme of the spacecraft on-orbit operation health assessment method, the finite element analysis method comprises,

[0039] The stress distribution of the contact device in a low-gravity environment is simulated.

[0040] The fatigue state of the stress concentration area is predicted, and the possible failure risk caused by environmental changes is estimated.

[0041] As a preferred scheme of the spacecraft on-orbit operation health assessment method, the step of inputting the environmental characteristic parameters generated in step S1 into the digital twin and combining the finite element analysis method to evaluate the fatigue state of the landing device is,

[0042] The digital twin in a low-gravity environment is constructed, and the fatigue state is evaluated in combination with the finite element analysis. The digital twin is constructed based on the finite element analysis FEM in combination with numerical simulation, and the parameter space is M:

[0043] M={m,k,c,g,μ},

[0044] wherein m represents the equivalent mass of the landing device, k represents the equivalent stiffness of the landing device, c represents the damping coefficient, g represents the gravitational acceleration of the low-gravity environment, and μ represents the friction coefficient of the contact surface,

[0045] Input the environmental characteristic parameters, and input the environmental characteristic parameters θ t as an input to update the twin,

[0046]

[0047] wherein, represents the state of the digital twin at time t, f(M,θ t ) represents a state update function determined by the digital twin parameters M and the environmental characteristic parameters θ t ;

[0048] The stress distribution of the landing device is calculated using finite element analysis, and the calculation formula is:

[0049] K·U=F,

[0050] σ(x,y,z)=D·ε(x,y,z),

[0051] Wherein, K represents the global stiffness matrix, U represents the node displacement vector, F represents the external force vector, σ(x,y,z) represents the stress distribution of a certain point, D represents the elastic matrix of the material, and ε(x,y,z) represents the strain distribution,

[0052] The fatigue life is calculated using the Basquin fatigue equation, and the calculation formula is:

[0053]

[0054] The failure probability of the high stress area is calculated through the failure risk model, and the calculation formula is:

[0055]

[0056] Wherein, N f represents the fatigue life, and represents the allowable stress cycle number, σ a represents the alternating stress amplitude, σ e represents the fatigue limit of the material, and the stress is lower than this value, b represents the fatigue strength index, and λ represents the failure risk coefficient, R f represents the failure risk probability, and represents the failure possibility of the structure under the maximum stress, σ max represents the maximum stress of the stress concentration area,

[0057] The fatigue state results are obtained by combining the finite element analysis and the fatigue life prediction, and are represented as:

[0058] Φ t ={σ max ,N f ,R f},

[0059] Wherein,

[0060] Φ t represents the fatigue state result set at time t,

[0061] σ max represents the maximum stress of the stress concentration area, N f represents the predicted fatigue life, and R f represents the failure risk probability.

[0062] As a preferred scheme of the spacecraft on-orbit operation health assessment method, wherein: the optimization of step S3 comprises,

[0063] Adjusting the damping mode of the contact device to alleviate the load of the instantaneous impact and the high stress area;

[0064] Controlling the landing speed to realize stable and controllable surface contact under low gravity conditions.

[0065] As a preferred scheme of the spacecraft on-orbit operation health assessment method, the step of optimizing the landing operation parameters of the probe in real time according to the health state prediction result in step S2 is,

[0066] According to the fatigue state result generated in step S2, the damping coefficient c of the damping device is adjusted opt , and the calculation formula is:

[0067]

[0068] wherein c opt represents the optimized damping coefficient, c0 represents the initial damping coefficient, σ max represents the maximum stress of the high stress area, σ y represents the yield stress of the material,

[0069] The landing velocity is optimized by a dynamic programming method of controlling energy and damping consumption, and the optimization target is to minimize the landing energy E, and the formula is:

[0070]

[0071] wherein v opt represents the optimized landing velocity, m represents the equivalent mass of the landing device, g represents the gravitational acceleration in the low gravity environment, k opt represents the equivalent stiffness, c opt represents the optimized damping coefficient,

[0072] The operation mode of the contact device is optimized, and the optimization formula is:

[0073] F c =-C opt v opt -k opt Δx,

[0074] wherein F c represents the control force applied by the contact device, v opt represents the optimized landing velocity, and Δx represents the compression amount of the contact device,

[0075] The optimized landing operation parameters include:

[0076] Ψ t ={c opt ,v opt ,F c},

[0077] wherein,

[0078] Ψ t represents the optimized parameter set at time t,

[0079] c opt denotes the optimized damping coefficient, v opt denotes the optimized landing speed, F c denotes the control force of the contact device.

[0080] As a preferred scheme of the spacecraft on-orbit health assessment method, the key components include optical equipment and contact devices.

[0081] The compensation mechanism includes:

[0082] The dust particles on the optical equipment and the contact devices are removed by using a miniature ultrasonic device,

[0083] After cleaning, the affected sensors are recalibrated.

[0084] As a preferred scheme of the spacecraft on-orbit health assessment method, the step of cleaning by the particle pollution compensation mechanism is,

[0085] Using a fault detection algorithm F d The state of the key components is monitored, and the state monitoring model is:

[0086]

[0087] wherein S comp denotes the state index of the key components, denotes the real-time observation value of the current component, denotes the reference reference value of the component, F d denotes the state comparison function, which is calculated based on residual analysis method,

[0088] An ultrasonic cleaning device is introduced, and the vibration intensity is:

[0089]

[0090] wherein I clean denotes the ultrasonic cleaning intensity, μ c denotes the ultrasonic transmission efficiency, f c denotes the vibration frequency of the cleaning device, A c denotes the vibration amplitude,

[0091] After cleaning, the affected sensors are recalibrated by using a recalibration algorithm C cal The affected sensors are calibrated, and the calculation formula is:

[0092]

[0093] wherein, represents the calibrated sensor value, represents the original sensor value, C cal represents the sensor calibration function,

[0094] The state of the key components after cleaning is:

[0095]

[0096] wherein, S clean represents the set of state results after cleaning, S comp represents the state indicator of the key components, represents the calibrated sensor value.

[0097] As a preferred scheme of the spacecraft on-orbit operation health assessment method, wherein: the step of dynamically adjusting the task execution strategy of the probe based on the state of the components after cleaning and the updated health assessment result is,

[0098] by priority optimization function P opt dynamically adjust the order of the sampling task, the function is:

[0099]

[0100] wherein, P opt represents the optimized task priority, w1, w2 represents the weight factor, which represents the relative importance of the risk level and the fatigue life, represents the risk level, N f represents the fatigue life,

[0101] Based on the path planning algorithm to regenerate the operation path, the goal is to minimize the influence of high stress area and high risk area in the path, and the path optimization formula is:

[0102]

[0103] wherein, Γ t represents the optimized operation path,

[0104] σ max represents the maximum stress value of the path point, σ y represents the yield stress of the material, represents the risk level of the path point, ds represents the differential segment length on the path;

[0105] The dynamic adjustment result includes the optimized sampling task priority and the operation path:

[0106] Θ t ={P opt , Γ t},

[0107] wherein, Θ t denotes the adjustment result set of the task execution strategy, P opt denotes the optimized task priority, Γ t denotes the optimized running path.

[0108] The present application has the beneficial effects that: the present application collects environmental data such as gravity gradient, radiation intensity and dust particle density on the surface of an asteroid in real time through a multi-modal sensor array, and adopts a dynamic Bayesian network combined with Kalman filtering for modeling and prediction, dynamically generates environmental characteristic parameters including risk level and environmental change trend, in health state prediction, simulates stress distribution in a low-gravity environment through a digital twin combined with finite element analysis, predicts fatigue state and failure risk of the contact device, accurately assesses fatigue life in high-stress areas, and further improves the quantitative ability of failure risk through a risk probability model; in addition, according to the health prediction result, dynamically optimizes landing parameters including shock absorption mode and landing speed, minimizes impact force and stress load using a dynamic programming method, so that the probe realizes stable contact in a low-gravity environment, and at the same time, in order to cope with the influence of high radiation and dust particles on optical equipment and mechanical structure, a particle pollution compensation mechanism is introduced, the failure probability of key components is reduced through ultrasonic cleaning and sensor re-calibration; based on the state after cleaning and the health assessment result, dynamically adjusts the sampling task priority and the probe path, and the optimization goal is to reduce the residence time in high-risk areas, improve task efficiency and success rate. BRIEF DESCRIPTION OF DRAWINGS

[0109] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creating laborious work.

[0110] Figure 1 The flowchart of the on-orbit operation health assessment method of the spacecraft of the present application. DETAILED DESCRIPTION

[0111] In order to make the above-mentioned purposes, features and advantages of the present application more apparent and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings of the specification.

[0112] In the following description, many specific details are set forth in order to provide a thorough understanding of the present application, but the present application can also be implemented in other ways different from those described herein, and those skilled in the art can make similar generalizations without departing from the connotation of the present application, therefore the present application is not limited to the specific embodiments disclosed below.

[0113] Second, the term "one embodiment" or "an embodiment" as may be used herein means a specific implementation, or example, that can include features that are, but are not required to be, included in at least one implementation of the present disclosure. This use of "in one embodiment" or "an embodiment" is simply used to provide a concrete example of features included in at least one implementation of the present disclosure. It can be used in the description of a feature, structure, or characteristic as being included in one or more implementations of the disclosure, and each variation thereof is a candidate for implementation. Failure to describe such features, structures, or characteristics in "in one embodiment" or "an embodiment" should not be regarded as preventing such features, structures, or characteristics from being claimed.

[0114] Embodiment 1, Reference Figure 1 The embodiment provides a spacecraft on-orbit operation health assessment method, comprising:

[0115] Step S1, real-time acquisition of environment data on the surface of the asteroid by the sensor array on the probe, and generation of environment feature parameters based on the environment data; the environment features include risk level and estimated environment change trend;

[0116] The environment data includes:

[0117] Gravity gradient data on the surface of the asteroid, used to depict the distribution of the gravitational field and its local changes,

[0118] Radiation intensity measurement data, used to assess the potential impact of the radiation environment on the electronic equipment of the probe,

[0119] Dust particle density data, used to analyze the coverage or erosion risk of the optical system and mechanical structure;

[0120] The sensor array includes an inertial measurement unit (IMU), a radiometer, and a particle detector;

[0121] The step of generating environment feature parameters based on environment data is,

[0122] Acquisition of environment data and construction of a data set, defining the environment data set as D t :

[0123] D t = {G t , R t , P t},

[0124] Wherein, t represents the current time step, D t represents the environment data set at time t, G t represents the gravity gradient data, R t represents the radiation intensity measurement data, and P t represents the dust particle density data;

[0125] Dynamic Bayesian networks are used to model the environment data, and the dynamic Bayesian networks are based on a linear state space model, whose state transition equation and observation equation are:

[0126] X t = AX t-1+BU t +ε t ,

[0127] Z t =HX t +δ t ,

[0128] Among them, X t represents the hidden state variable at time t, represents the environmental characteristics, X t-1 represents the hidden state variable at time t-1, U t represents the control input at time t, which is equal to D t , ε t represents the state noise, Z t represents the observation value at time t, A represents the state transfer matrix, B represents the input control matrix, H represents the observation matrix, δ t represents the observation noise;

[0129] Using Kalman filtering for state estimation is divided into two stages: prediction and update.

[0130] Prediction stage:

[0131]

[0132] in, represents the predicted value of the hidden state at time t, represents the estimated value of the hidden state at time t-1, represents the prediction error covariance at time t, P t-1 represents the error covariance at time t-1, Q represents the process noise covariance,

[0133] Update phase:

[0134]

[0135] Among them, K t represents the Kalman gain, which controls the weight distribution between observations and predictions, and R represents the observation noise covariance. represents the hidden state update value at time t, and I represents the identity matrix;

[0136] The generated environmental characteristic parameter is θ t :

[0137]

[0138] Among them, θ t represents the set of environmental characteristic parameters at time t, represents the risk level generated based on the classification model, T trepresenting the trend of environmental changes based on Kalman filter prediction;

[0139] Specifically, the dynamic Bayesian network is the core here, which models the environmental data by combining Kalman filter and performs dynamic estimation; through the double-layer structure of state transition equation and observation equation, the time series data is converted into hidden state, and the prediction accuracy is improved through updating to generate environmental characteristic parameters.

[0140] Step S2, health state prediction based on the digital twin model of the probe, input the environmental characteristic parameters generated in step S1 into the digital twin, and evaluate the fatigue state of the landing device by combining the finite element analysis method;

[0141] The finite element analysis method includes,

[0142] Stress distribution simulation of the contact device in low gravity environment;

[0143] Predict the fatigue state of the stress concentration area and estimate the possible failure risk caused by environmental changes;

[0144] The step of inputting the environmental characteristic parameters generated in step S1 into the digital twin and evaluating the fatigue state of the landing device by combining the finite element analysis method is,

[0145] Constructing a digital twin in a low gravity environment and evaluating the fatigue state by combining finite element analysis, the digital twin is constructed based on finite element analysis FEM combined with numerical simulation, and its parameter space is M:

[0146] M={m,k,c,g,μ},

[0147] Wherein, m represents the equivalent mass of the landing device, k represents the equivalent stiffness of the landing device, c represents the damping coefficient, g represents the gravity acceleration of the low gravity environment, and μ represents the friction coefficient of the contact surface,

[0148] Input the environmental characteristic parameters θ t as input to update the twin,

[0149]

[0150] Wherein, represents the state of the digital twin at time t, f(M,θ t ) represents the state update function, which is determined by the digital twin parameters M and the environmental characteristic parameters θ t ;

[0151] Use finite element analysis to calculate the stress distribution of the landing device, and the calculation formula is:

[0152] K·U=F,

[0153] σ(x,y,z)=D·ε(x,y,z),

[0154] Among them, K represents the global stiffness matrix, U represents the node displacement vector, F represents the external force vector, σ(x,y,z) represents the stress distribution at a certain point, D represents the elastic matrix of the material, and ε(x,y,z) represents the strain distribution.

[0155] The fatigue life is calculated using the Basquin fatigue equation, which is:

[0156]

[0157] The failure probability of the high stress area is calculated using the failure risk model. The calculation formula is:

[0158]

[0159] Among them, N f represents fatigue life, represents the number of allowable stress cycles, σ a represents the alternating stress amplitude, σ e It represents the fatigue limit of the material. When the stress is lower than this value, there is no fatigue failure. b represents the fatigue strength index. λ represents the failure risk coefficient. R f represents the failure risk probability, which indicates the failure possibility of the structure under maximum stress, σ max represents the maximum stress in the stress concentration area,

[0160] Combining finite element analysis and fatigue life prediction, the fatigue state results are obtained, which are expressed as:

[0161] Φ t ={σ max ,N f ,R f},

[0162] in,

[0163] Φ t represents the fatigue state result set at time t,

[0164] σ max Indicates the maximum stress in the stress concentration area, N f is the expected fatigue life, R f represents the probability of failure risk;

[0165] Specifically, based on the Basquin fatigue life formula and combined with the finite element analysis method, the stress distribution simulation and fatigue state evaluation of the landing device structure are carried out to accurately evaluate the structural life in a low-gravity environment.

[0166] Step S3, according to the health state prediction result in step S2, real-time optimization of the landing operation parameters of the probe;

[0167] The optimization of step S3 includes,

[0168] Adjusting the damping mode of the contact device to alleviate the instantaneous impact and the load of the high stress area;

[0169] Controlling the landing speed to achieve stable and controllable surface contact under low gravity conditions;

[0170] The step of real-time optimization of the landing operation parameters of the probe according to the health state prediction result in step S2 is,

[0171] According to the fatigue state result generated in step S2, adjusting the damping coefficient c of the damping device opt The calculation formula is:

[0172]

[0173] Wherein, c opt represents the optimized damping coefficient, c0 represents the initial damping coefficient, σ max represents the maximum stress of the high stress area, σ y represents the yield stress of the material,

[0174] Optimizing the landing speed, solving by dynamic programming method of controlling energy and damping consumption, the optimization goal is to minimize the landing energy E, the formula is:

[0175]

[0176] Wherein, v opt represents the optimized landing speed, m represents the equivalent mass of the landing device, g represents the gravity acceleration of the low gravity environment, k opt represents the equivalent stiffness, c opt represents the optimized damping coefficient,

[0177] Optimizing the operation mode of the contact device, the optimization formula is:

[0178] F c =-C opt v opt -k opt Δx,

[0179] Wherein, F c represents the control force applied by the contact device, v opt represents the optimized landing speed, and Δx represents the compression amount of the contact device,

[0180] The optimized landing operation parameters include:

[0181] Ψ t = {c opt ,v opt ,F c},

[0182] wherein,

[0183] Ψ t denotes the set of optimization parameters at time t,

[0184] c opt denotes the optimized damping coefficient, v opt denotes the optimized landing speed, F c denotes the control force of the contact device;

[0185] Specifically, in this step, the fatigue state prediction results are analyzed in real time, the damping coefficient and the landing speed are adjusted using the dynamic programming method, the control mode of the contact device is optimized, and the probe can still stably contact in a low-gravity environment, effectively relieving the instantaneous impact and reducing the load risk of the high-stress area.

[0186] Step S4, during the landing process, real-time state monitoring is performed on the key components of the probe, and cleaning is performed through a particle pollution compensation mechanism;

[0187] The key components include optical equipment and a contact device;

[0188] The compensation mechanism includes:

[0189] using a miniature ultrasonic device to remove dust particles on the optical equipment and the contact device,

[0190] After cleaning is completed, the affected sensors are recalibrated;

[0191] The step of cleaning through the particle pollution compensation mechanism is,

[0192] using a fault detection algorithm F d The state monitoring of the key components is performed, and the state monitoring model is:

[0193]

[0194] wherein, S comp denotes the state index of the key components, denotes the real-time observation value of the current component, denotes the reference reference value of the component, F d denotes the state comparison function, which is calculated based on the residual analysis method,

[0195] An ultrasonic cleaning device is introduced, and the vibration intensity is:

[0196]

[0197] wherein I clean represents the ultrasonic cleaning intensity, μ c represents the ultrasonic transmission efficiency, f c represents the vibration frequency of the cleaning device, A c represents the vibration amplitude,

[0198] After cleaning is completed, the recalibration algorithm C cal calibrates the affected sensors, and the calculation formula is:

[0199]

[0200] wherein, represents the calibrated sensor value, represents the original sensor value, C cal represents the sensor calibration function,

[0201] The state of the key components after cleaning is:

[0202]

[0203] wherein S clean represents the state result set after cleaning, S comp represents the state index of the key components, represents the calibrated sensor value;

[0204] Specifically, in step S4, the state of the key components is monitored and cleaned, an ultrasonic cleaning model is introduced, and the probability of the optical equipment and the contact device being affected by particle pollution is reduced.

[0205] Step S5, based on the state of the components after cleaning and the updated health assessment results, dynamically adjust the task execution strategy of the probe:

[0206] Dynamically optimize the priority of the sampling task, and modify the running path of the probe;

[0207] The step of dynamically adjusting the task execution strategy of the probe based on the state of the components after cleaning and the updated health assessment results is,

[0208] By the priority optimization function P opt dynamically adjust the order of the sampling task, and the function is:

[0209]

[0210] wherein P opt represents the optimized task priority, w1, w2 represents the weight factor, and represents the relative emphasis on the risk level and the fatigue life, represents the risk level, N f represents the fatigue life,

[0211] The running path is regenerated based on the path planning algorithm, aiming to minimize the influence of high stress areas and high risk areas in the path, and the path optimization formula is:

[0212]

[0213] wherein, Γ t represents the optimized running path,

[0214] σ max represents the maximum stress value of the path point, σ y represents the yield stress of the material, represents the risk level of the path point, and ds represents the differential segment length on the path;

[0215] The dynamic adjustment result includes the optimized sampling task priority and the running path:

[0216] Θ t ={P opt ,Γ t},

[0217] wherein, Θ t represents the adjustment result set of the task execution strategy, P opt represents the optimized task priority, and Γ t represents the optimized running path,

[0218] Specifically, step S5 adjusts the sampling task priority and the probe running path in real time based on the cleaned component state and the health assessment result through the priority optimization function and the path planning algorithm, so as to improve the task execution efficiency and reduce the potential risk.

[0219] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical solutions of the present application, and they should be covered in the scope of the claims of the present application.

Claims

1. A method for assessing the health of a spacecraft on-orbit, characterized by: include, Step S1, using the sensor array on the probe to collect environmental data on the asteroid surface in real time, and generating environmental characteristic parameters based on the environmental data; Environmental characteristics include risk levels and estimated environmental change trends; Step S2: Predicting the health status of the detector based on its digital twin model, inputting the environmental characteristic parameters generated in step S1 into the digital twin, and evaluating the fatigue status of the landing gear using a finite element analysis method. Step S3, optimizing the landing operation parameters of the probe in real time based on the health status prediction result in step S2; Step S4: During the landing process, the key components of the probe are monitored in real time and cleaned using a particle contamination compensation mechanism; Step S5: Based on the status of the cleaned components and the updated health assessment results, dynamically adjust the detector's task execution strategy: Dynamically optimize the priority of sampling tasks and modify the detector operation path.

2. The method for assessing the on-orbit health of a spacecraft according to claim 1, wherein: The environmental data includes: Gravity gradient data on the asteroid surface is used to characterize the distribution of the gravitational field and its local changes. Radiation intensity measurement data, used to assess the potential impact of the radiation environment on detector electronics, Dust particle density data to analyze the risk of dust particles covering or corroding optical systems and mechanical structures; The sensor array includes an inertial measurement unit (IMU), a radiometer, and a particle detector.

3. The method for assessing the on-orbit health of a spacecraft according to claim 2, wherein: The step of generating environmental characteristic parameters based on environmental data is: Collect environmental data and construct a data set, defining the environmental data set as D t : D t ={G t ,R t ,P t }, Among them, t represents the current time step, D t represents the environmental data set at time t, G t Represents gravity gradient data, R t Indicates radiation intensity measurement data, P t Indicates dust particle density data; The dynamic Bayesian network is used to model the environmental data. The dynamic Bayesian network is based on the linear state space model, and its state transfer equation and observation equation are: X t =AX t-1 +BU t +ε t , Z t =HX t +δ t , Among them, X t represents the hidden state variable at time t, represents the environmental characteristics, X t-1 represents the hidden state variable at time t-1, U t represents the control input at time t, which is equal to D t , ε t represents the state noise, Z t represents the observation value at time t, A represents the state transfer matrix, B represents the input control matrix, H represents the observation matrix, δ t represents the observation noise; Using Kalman filtering for state estimation is divided into two stages: prediction and update. Prediction stage: in, represents the predicted value of the hidden state at time t, represents the estimated value of the hidden state at time t-1, represents the prediction error covariance at time t, P t-1 represents the error covariance at time t-1, Q represents the process noise covariance, Update phase: Among them, K t represents the Kalman gain, which controls the weight distribution between observations and predictions, and R represents the observation noise covariance. represents the hidden state update value at time t, and I represents the identity matrix; The generated environmental characteristic parameter is θ t : Among them, θ t represents the set of environmental characteristic parameters at time t, represents the risk level generated based on the classification model, T t Represents the environmental change trend predicted by Kalman filtering.

4. The method for assessing the on-orbit health of a spacecraft according to claim 3, wherein: The finite element analysis method includes: Simulate stress distribution of contact devices in low-gravity environments; Predict the fatigue state of areas of stress concentration and estimate the possible failure risk caused by environmental changes.

5. The method for assessing the on-orbit health of a spacecraft according to claim 4, wherein: The step of inputting the environmental characteristic parameters generated in step S1 into the digital twin and evaluating the fatigue state of the landing device in combination with the finite element analysis method is as follows: Construct a digital twin in a low-gravity environment and combine it with finite element analysis to evaluate fatigue status. The digital twin is constructed based on finite element analysis (FEM) combined with numerical simulation, and its parameter space is M: M={m,k,c,g,μ}, Among them, m represents the equivalent mass of the landing device, k represents the equivalent stiffness of the landing device, c represents the damping coefficient, g represents the gravitational acceleration of the low gravity environment, and μ represents the friction coefficient of the contact surface. Input the environmental characteristic parameters and convert the environmental characteristic parameters θ generated in step S1 into t Update the twin as input, in, represents the state of the digital twin at time t, f(M, θ t ) represents the state update function, which is composed of the digital twin parameter M and the environmental characteristic parameter θ t Decide; Finite element analysis is used to calculate the stress distribution of the landing device. The calculation formula is: K·U=F, σ(x,y,z)=D·ε(x,y,z), Among them, K represents the global stiffness matrix, U represents the node displacement vector, F represents the external force vector, σ(x, y, z) represents the stress distribution at a certain point, D represents the elastic matrix of the material, ε(x, y, z) represents the strain distribution, The fatigue life is calculated using the Basquin fatigue equation, which is: The failure probability of the high stress area is calculated using the failure risk model. The calculation formula is: Among them, N f represents fatigue life, represents the number of allowable stress cycles, σ a represents the alternating stress amplitude, σ e It represents the fatigue limit of the material. When the stress is lower than this value, there is no fatigue failure. b represents the fatigue strength index. λ represents the failure risk coefficient. R f represents the failure risk probability, which indicates the failure possibility of the structure under maximum stress, σ max represents the maximum stress in the stress concentration area, Combining finite element analysis and fatigue life prediction, the fatigue state results are obtained, which are expressed as: F t ={σ max ,N f ,R f }, in, Φ t represents the fatigue state result set at time t, σ max Indicates the maximum stress in the stress concentration area, N f is the expected fatigue life, R f Represents the probability of failure risk.

6. The method for assessing the on-orbit health of a spacecraft according to claim 5, wherein: The optimization of step S3 includes, Adjust the shock absorption mode of the contact device; Control landing speed.

7. The method for assessing the on-orbit health of a spacecraft according to claim 6, wherein: The step of optimizing the landing operation parameters of the probe in real time according to the health status prediction result in step S2 is: According to the fatigue state result generated in step S2, the damping coefficient c of the shock absorption device is adjusted. opt , the calculation formula is: Among them, c opt represents the optimized damping coefficient, c0 represents the initial damping coefficient, σ max represents the maximum stress in the high stress area, σ y represents the yield stress of the material, The landing speed is optimized by the dynamic programming method that controls energy and damping consumption. The optimization goal is to minimize the landing energy E. The formula is: Among them, v opt represents the optimized landing speed, m represents the equivalent mass of the landing device, g represents the gravitational acceleration of the low gravity environment, k opt represents the equivalent stiffness, c opt represents the optimized damping coefficient, Optimize the operating mode of the contact device, the optimization formula is: F c =-c opt in opt -k opt Δx, Among them, F c represents the control force exerted by the contact device, v opt represents the optimized landing speed, Δx represents the compression of the contact device, The optimized landing operation parameters include: Ψ t ={c opt ,v opt ,F c }, in, Ψ t represents the set of optimized parameters at time t, c opt represents the optimized damping coefficient, v opt Indicates the optimized landing speed, F c Indicates the control force of the contact device.

8. The method for assessing the on-orbit health of a spacecraft according to claim 7, wherein: Key components include optics and contact devices; The compensation mechanism includes: Use micro ultrasonic equipment to remove dust particles from optical equipment and contact devices. After cleaning is complete, recalibrate the affected sensor.

9. The method for assessing the on-orbit health of a spacecraft according to claim 8, wherein: The steps of cleaning by the particle contamination compensation mechanism are: Using the fault detection algorithm F d The condition monitoring model for key components is as follows: Among them, S comp Indicates the status indicators of key components, Represents the real-time observation value of the current component, Indicates the reference value of the component, F d represents the state contrast function, which is calculated based on the residual analysis method. An ultrasonic cleaning device is introduced with a vibration intensity of: Among them, I clean Indicates the ultrasonic cleaning intensity, μ c represents the ultrasonic transmission efficiency, f c Indicates the vibration frequency of the cleaning device, A c represents the vibration amplitude, After cleaning is completed, the recalibration algorithm C cal Calibrate the affected sensor using the following formula: in, Represents the sensor value after calibration, Represents the raw sensor value, C cal represents the sensor calibration function, The status of key components after cleaning is: Among them, S clean Represents the state result set after cleaning, S comp Indicates the status indicators of key components, Indicates the sensor value after calibration.

10. The method for evaluating the on-orbit health of a spacecraft according to claim 9, wherein: The step of dynamically adjusting the detector's task execution strategy based on the cleaned component status and updated health assessment results is as follows: The order of sampling tasks is dynamically adjusted through the priority optimization function Popt, which is: Among them, P opt represents the priority of the optimized task, w1 and w2 represent weight factors, which indicate the relative importance of risk level and fatigue life. Indicates the risk level, N f represents fatigue life, The operation path is regenerated based on the path planning algorithm. The goal is to minimize the impact of high stress areas and high risk areas in the path. The path optimization formula is: Among them, Γ t Represents the optimized running path, σ max Indicates the maximum stress value at the path point, σ y represents the yield stress of the material, It represents the risk level of the path point, and ds represents the length of the micro-segment on the path; The dynamic adjustment results include the optimized sampling task priority and running path: I t ={P opt ,C t }, Among them, Θ t Represents the set of adjustment results of task execution strategy, P opt represents the optimized task priority, Γ t Indicates the optimized running path.

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