Hydrogenation safety control method and system based on adaptive robust filtering

Abstract

The invention discloses a hydrogenation safety control method and system based on adaptive robust filtering, and relates to the technical field of hydrogen energy, and the method comprises the steps: building a state prediction equation of a plurality of sensors in a hydrogenation process of a hydrogen refueling station based on Kalman filtering; constructing a filtering residual error based on the state prediction equation, and constructing a robust factor based on the filtering residual error; adjusting the initial observation noise covariance matrix of each sensor based on the robust factor to obtain an observation noise covariance matrix after robust adjustment; based on the health degree of each sensor, calculating a weight value corresponding to each sensor; dynamically adjusting the observation noise covariance matrix after the adversarial adjustment based on the weight value to obtain an observation noise covariance matrix after the weight adjustment; and based on the observation noise covariance matrix after weight adjustment and the state prediction equation, performing filtering updating on the state parameters of the plurality of sensors. The technical problem that in the prior art, the control precision and safety of a hydrogen refueling station are insufficient is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of hydrogen energy, and in particular to a hydrogenation safety control method and system based on adaptive robust filtering. Background Art

[0002] With the continuous development of hydrogen energy, more and more companies are beginning to use filtering algorithms to process various sensor data during the hydrogen refueling process. However, the existing technology of hydrogen refueling stations relies on threshold alarms and cannot predict potential risks through real-time data fusion. In addition, due to factors such as the status of the vehicle hydrogen storage tank and changes in ambient temperature during the hydrogen refueling process, the control accuracy of the fixed parameter filtering algorithm will decrease. Sensors such as pressure, temperature, and flow are easily affected by environmental noise and may also have problems such as control signal distortion. This is likely to lead to accidents not being discovered in time during the hydrogen refueling process, hydrogen refueling efficiency being reduced, or sensors giving false alarms, resulting in casualties and property losses. Therefore, it is necessary to propose an effective filtering algorithm to improve the safety and efficiency of the hydrogen refueling process.

[0003] Kalman filtering is an algorithm used to estimate the state of a system. It optimizes the estimation results by combining prediction and measurement. This method is robust to Gaussian noise and has high real-time data processing performance. It can be used to estimate the data changes of a certain sensor during the hydrogenation process. However, the reliability of different sensors varies, and the noise parameters of the traditional Kalman filtering algorithm are fixed. It is impossible to dynamically adjust the process noise covariance matrix and the observation noise covariance matrix according to the hydrogenation stage (such as pre-cooling and supercharging), and the ability to suppress sensor noise is insufficient. In the hydrogenation process, a simple weighted average or fixed priority strategy (such as prioritizing trust in temperature sensors) is used. The confidence of each sensor is not dynamically adjusted according to the reliability of the data, and the changes in the real-time health status of the sensor are ignored. If a single sensor suddenly fails, it will contaminate the overall filtering results. Summary of the Invention

[0004] In order to solve the above technical problems existing in the prior art, the embodiment of the present invention provides a hydrogenation safety control method and system based on adaptive robust filtering. The technical solution is as follows:

[0005] On the one hand, a hydrogenation safety control method based on adaptive robust filtering is provided, the method comprising: constructing a state prediction equation for multiple sensors in a hydrogenation station during the hydrogenation process based on Kalman filtering; constructing a filter residual based on the state prediction equation, and constructing a robustness factor based on the filter residual; adjusting the initial observation noise covariance matrix of each sensor based on the robustness factor to obtain an observation noise covariance matrix after robustness adjustment; calculating a weight value corresponding to each sensor based on the health of each sensor; dynamically adjusting the observation noise covariance matrix after robustness adjustment based on the weight value to obtain an observation noise covariance matrix after weight adjustment; filtering and updating the state parameters of the multiple sensors based on the observation noise covariance matrix after weight adjustment and the state prediction equation.

[0006] Optionally, the state prediction equation includes:

[0007]

[0008] Where, is the optimal predicted value of the sensor state parameter at time k-1, u k is the control system input of the hydrogen refueling station, w k is the process noise, f(·) is the nonlinear state transfer function, is the prior estimate of the state parameter at time k predicted based on time k-1.

[0009] Optionally, constructing a filter residual based on the state prediction equation and constructing a robustness factor based on the filter residual includes: calculating the filter residual of the state parameter of each sensor based on the state prediction equation; calculating the robustness factor based on the Mahalanobis distance of the filter residual; wherein,

[0010] The calculation formula of the filtering residual includes:

[0011]

[0012] Where r k is the filter residual at time k, z k is the actual observation value of the multiple sensors at time k, and H is the measurement matrix of the multiple sensors;

[0013] The calculation formula of the Mahalanobis distance of the filtering residual includes:

[0014]

[0015] S k =HP k|k-1 H T +R k

[0016] Where, d k is the Mahalanobis distance of the filter residual at time k, S k is the covariance matrix of the filter residual at time k, P k|k-1 is the covariance matrix of the prior estimate of the state parameters at time k, R k is the observation noise covariance matrix at time k;

[0017] The calculation formula of the robustness factor includes:

[0018]

[0019] Where, ρ k is the robustness factor at time k, is the chi-square distribution threshold, a is the confidence level, and m is the sensor number.

[0020] Optionally, the observation noise covariance matrix after the robustness adjustment includes:

[0021] R′ k =ρ k *R0

[0022] Where R′ k is the observation noise covariance matrix after robustness adjustment at time k, and R0 is the initial observation noise covariance matrix.

[0023] Optionally, calculating a weight value corresponding to each sensor based on the health of each sensor includes: calculating the health of each sensor based on a recent mean absolute error and a self-test signal of each sensor; and calculating a weight value corresponding to each sensor based on the health; wherein,

[0024] The calculation formula of the health degree includes:

[0025]

[0026] Where h i is the health of the i-th sensor, MAE i is the recent mean absolute error of the i-th sensor, α is the weight coefficient, S self-test,i is the self-test signal of the i-th sensor;

[0027] The calculation formula of the weight value includes:

[0028]

[0029] Where w i is the weight value of the i-th sensor, and n is the number of sensors.

[0030] Optionally, the observation noise covariance matrix after the weight adjustment includes:

[0031] R″ k =diag(w1R′ 1,k ,…,w n R′ n,k )

[0032] Where R″ k is the observation noise covariance matrix after weight adjustment at time k, R′ i,k is the observation noise covariance matrix of the i-th sensor at time k after robustness adjustment.

[0033] Optionally, based on the observation noise covariance matrix after the weight adjustment and the state prediction equation, the state parameters of the multiple sensors are filtered and updated, including: calculating the Kalman gain of the state prediction equation based on the observation noise covariance matrix after the weight adjustment; and filtering and updating the state parameters of the multiple sensors based on the Kalman gain.

[0034] Optionally, the method further includes: if the weight value is detected to be lower than a preset weight threshold, an alarm signal is issued for the sensor corresponding to the weight value lower than the preset weight threshold; if the filtering residual is higher than the preset residual threshold, a fault safety linkage is executed.

[0035] Optionally, the preset residual threshold includes a first residual threshold, a second residual threshold and a third residual threshold; and fault safety linkage is executed, including: if the filter residual is greater than the first residual threshold, a warning signal is issued; if the filter residual is greater than the second residual threshold, a speed reduction operation is performed on the hydrogenation process of the hydrogenation station; if the filter residual is greater than the third residual threshold, an emergency stop operation is performed on the hydrogenation process of the hydrogenation station.

[0036] On the other hand, a hydrogen refueling safety control system based on adaptive robust filtering is also provided, which is used to implement the method provided by an embodiment of the present invention; it includes: a first building module, a second building module, a robustness adjustment module, a calculation module, a weight adjustment module, and a prediction module; wherein, the first building module is used to construct a state prediction equation for multiple sensors in a hydrogen refueling station during the hydrogen refueling process based on Kalman filtering; the second building module is used to construct a filter residual based on the state prediction equation, and to construct a robustness factor based on the filter residual; the robustness adjustment module is used to adjust the initial observation noise covariance matrix of each sensor based on the robustness factor to obtain the observation noise covariance matrix after robustness adjustment; the calculation module is used to calculate the weight value corresponding to each sensor based on the health of each sensor; the weight adjustment module is used to dynamically adjust the observation noise covariance matrix after robustness adjustment based on the weight value to obtain the observation noise covariance matrix after weight adjustment; the prediction module is used to filter and update the state parameters of the multiple sensors based on the observation noise covariance matrix after weight adjustment and the state prediction equation.

[0037] The embodiment of the present invention provides a hydrogen refueling safety control method and system based on adaptive robust filtering, which introduces an anti-error mechanism and a multi-sensor fusion strategy on the basis of traditional Kalman filtering, suppresses the influence of sensor wild values ​​on the filtering results by dynamically adjusting the observation noise covariance matrix, improves redundancy and reliability by dynamically allocating weights to multiple sensors, and improves the safety and efficiency of hydrogen refueling stations during the hydrogen refueling process, alleviating the technical problems of insufficient control accuracy and safety of hydrogen refueling stations in the existing technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0039] Figure 1 This is a flow chart of a hydrogenation safety control method based on adaptive robust filtering provided by an embodiment of the present invention;

[0040] Figure 2 Schematic diagram of a hydrogenation safety control system based on adaptive robust filtering provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0041] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0042] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0043] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0044] Figure 1 FIG is a flow chart of a hydrogenation safety control method based on adaptive robust filtering according to an embodiment of the present invention. Figure 1 As shown, the method specifically includes the following steps:

[0045] Step S102: constructing state prediction equations for multiple sensors in the hydrogen refueling station during the hydrogen refueling process based on Kalman filtering.

[0046] Step S104: constructing a filter residual based on the state prediction equation, and constructing a robustness factor based on the filter residual.

[0047] Step S106 : adjusting the initial observation noise covariance matrix of each sensor based on the robustness factor to obtain an observation noise covariance matrix after robustness adjustment.

[0048] Step S108: Calculate the weight value corresponding to each sensor based on the health of each sensor.

[0049] Step S110 , dynamically adjusting the observation noise covariance matrix after the error adjustment based on the weight value to obtain the observation noise covariance matrix after the weight adjustment.

[0050] Step S112 : filtering and updating the state parameters of the multiple sensors based on the weight-adjusted observation noise covariance matrix and the state prediction equation.

[0051] Specifically, the state prediction equation includes:

[0052]

[0053] Where, is the optimal predicted value of the sensor state parameter at time k-1, u k is the control system input of the hydrogen refueling station, w k is the process noise, f(·) is the nonlinear state transfer function, is the prior estimate of the state parameter at time k predicted based on time k-1.

[0054] Optionally, the sensors include pressure sensors, temperature sensors, flow sensors and hydrogenation gun angle sensors; the state parameters include pressure P, temperature T, flow F and hydrogenation gun angle θ. The control system input u of the hydrogenation station k =[V valve ,f compressor ] T , where V valve is the valve opening, f compressor is the compressor frequency. Process noise w k It obeys a Gaussian distribution with a mean of 0, and the covariance matrix of the process noise is Q k .

[0055] Specifically, step S104 further includes the following steps:

[0056] Step S1041, calculating the filter residual of the state parameter of each sensor based on the state prediction equation;

[0057] Step S1042, calculating the robustness factor based on the Mahalanobis distance of the filter residual; wherein,

[0058] The calculation formula for filtering residual includes:

[0059]

[0060] Where r k is the filter residual at time k, z k is the actual observation value of multiple sensors at time k, and H is the observation matrix of multiple sensors;

[0061] The calculation formula of the Mahalanobis distance of the filter residual includes:

[0062]

[0063] S k =HP k|k-1 H T +R k

[0064] Where, d k is the Mahalanobis distance of the filter residual at time k, S k is the covariance matrix of the filter residual at time k, P k|k-1 is the covariance matrix of the prior estimate of the state parameters at time k, R k is the observation noise covariance matrix at time k;

[0065] The calculation formula of the robustness factor includes:

[0066]

[0067] Where, ρ k is the robustness factor at time k, is the chi-square distribution threshold, a is the confidence level, for example, a=0.95; m is the sensor number. It is judged as an outlier, and by increasing R k , to reduce the weight of the current sensor observation value and avoid outliers contaminating the filtering results.

[0068] Specifically, the observation noise covariance matrix after robustness adjustment includes:

[0069] R′ k =ρ k *R0

[0070] Where R′ k is the observation noise covariance matrix after robustness adjustment at time k, and R0 is the initial observation noise covariance matrix, obtained through sensor calibration. For example, Among them, σ P is the initial noise covariance matrix of the pressure sensor, σ T is the initial noise covariance matrix of the temperature sensor, σ F is the initial noise covariance matrix of the flow sensor, σ θ is the initial noise covariance matrix of the hydrogenation gun angle sensor.

[0071] Specifically, step S108 further includes the following steps:

[0072] Step S1081, calculating the health of each sensor based on the recent mean absolute error and self-test signal of each sensor;

[0073] Step S1082: Calculate the weight value corresponding to each sensor based on the health level;

[0074] The calculation formula for health includes:

[0075]

[0076] Where h i is the health of the i-th sensor, MAE i is the recent mean absolute error of the i-th sensor, α is the weight coefficient, for example, it can be set to α = 0.7 according to experience; S self-test,i is the self-test signal of the i-th sensor, 1 indicates normal and 0 indicates abnormal;

[0077] The calculation formula for the weight value includes:

[0078]

[0079] Where w i is the weight value of the i-th sensor, and n is the number of sensors.

[0080] Specifically, the observation noise covariance matrix after weight adjustment includes:

[0081] R″ k =diag(w1R′ 1,k ,…,w n R′ n,k )

[0082] Where R″ k is the observation noise covariance matrix after weight adjustment at time k, R′ i,k is the observation noise covariance matrix after robustness adjustment at the i-th sensor k moment. Weight value w i The higher it is, the lower the proportion of the noise covariance matrix of the corresponding sensor in fusion, which means that the measurement value of the sensor is more trusted.

[0083] Specifically, step S112 further includes the following steps:

[0084] Step S1121, calculating the Kalman gain of the state prediction equation based on the observation noise covariance matrix after weight adjustment;

[0085] Step S1122: Filter and update the state parameters of the multiple sensors based on the Kalman gain.

[0086] Specifically, the calculation formula of Kalman gain is as follows:

[0087]

[0088] p k|k-1 =FP k-1 F T +Q k

[0089] Where, P k-1 is the covariance matrix of the best predicted value at time k-1, k k is the Kalman gain at time k.

[0090] Filter Update: is the best prediction value of the model at time k, that is, the final output value. k As the value increases, k k The value becomes smaller, The proportion of the actual observation value of the sensor decreases, that is, the system trusts the predicted value of the model more.

[0091] The method provided by the embodiment of the present invention also includes redundancy check. Specifically, if the weight value is detected to be lower than the preset weight threshold, the sensor corresponding to the weight value lower than the preset weight threshold will send an alarm signal. i If it is continuously less than 0.1, the safety personnel will be reminded to conduct timely inspections.

[0092] The method provided by the embodiment of the present invention further includes fail-safe linkage. Specifically, if the filtering residual is higher than a preset residual threshold, fail-safe linkage is performed.

[0093] In an optional implementation provided by an embodiment of the present invention, the preset residual threshold includes a first residual threshold, a second residual threshold, and a third residual threshold; and performing fail-safe linkage includes:

[0094] If the filter residual is greater than the first residual threshold, an early warning signal is issued; for example, when r k When it is >10%, the HMI interface displays "Local abnormality, please check" and the yellow LED light flashes, and the buzzer sounds at intervals of one second;

[0095] If the filter residual is greater than the second residual threshold, the hydrogenation process of the hydrogenation station is decelerated; for example, when r k >40% for more than 2 seconds, the red LED is always on, set V valve ×0.5, f compressor ×0.7, the buzzer sounds continuously;

[0096] If the filter residual is greater than the third residual threshold, the hydrogenation process of the hydrogenation station is emergency stopped. For example, when r k >70%, the red LED light flashes quickly, set V valve =0, the pressure relief valve is activated and the buzzer gives a high-frequency alarm.

[0097] From the above description, it can be seen that an embodiment of the present invention provides a hydrogenation safety control method based on adaptive robust filtering, which uses filter residuals to achieve operating condition adaptation, that is, three-level response. It introduces a robustness mechanism into the Kalman filter for the first time, uses the Mahalanobis distance to quantify anomalies, and dynamically adjusts the observation noise covariance matrix through the robustness factor to suppress sensor wild values ​​in real time. Therefore, the method provided by the present invention can enhance noise suppression compared to traditional Kalman filtering.

[0098] Furthermore, the traditional Kalman filter algorithm noise parameter setting needs to be manually set by the operator based on experience, while the present invention introduces a multi-sensor fusion strategy, which realizes dynamic weight adaptive adjustment by fusing the weight values ​​of the temperature, pressure, flow, and hydrogenation gun angle sensor values, thereby avoiding the hydrogenation machine emergency stop caused by an error in a certain sensor, thereby improving the efficiency and safety of the hydrogenation process; in addition, the present invention adaptively activates different levels of early warning mechanisms through the size of the filtering residual, thereby improving the robustness of the system.

[0099] Figure 2 Schematic diagram of a hydrogenation safety control system based on adaptive robust filtering according to an embodiment of the present invention, which is used to implement the method provided by an embodiment of the present invention. Figure 2 As shown, the system includes: a first construction module 10, a second construction module 20, a robustness adjustment module 30, a calculation module 40, a weight adjustment module 50, and a prediction module 60.

[0100] Specifically, the first building module 10 is used to build state prediction equations for multiple sensors in the hydrogen refueling process at the hydrogen refueling station based on Kalman filtering.

[0101] The second construction module 20 is used to construct a filter residual based on the state prediction equation, and to construct a robustness factor based on the filter residual.

[0102] Specifically, the second building module 20 is further configured to: calculate the filtering residual of the state parameter of each sensor based on the state prediction equation; and calculate the robustness factor based on the Mahalanobis distance of the filtering residual.

[0103] The robustness adjustment module 30 is configured to adjust the initial observation noise covariance matrix of each sensor based on the robustness factor to obtain an observation noise covariance matrix after robustness adjustment.

[0104] The calculation module 40 is configured to calculate a weight value corresponding to each sensor based on the health of each sensor.

[0105] Specifically, the calculation module 40 is further configured to calculate the health of each sensor based on the recent mean absolute error and self-test signal of each sensor; and calculate the weight value corresponding to each sensor based on the health.

[0106] A weight adjustment module 50 is used to dynamically adjust the observation noise covariance matrix after the error adjustment based on the weight value to obtain the observation noise covariance matrix after the weight adjustment;

[0107] The prediction module 60 is used to filter and update the state parameters of multiple sensors based on the observation noise covariance matrix and the state prediction equation after weight adjustment.

[0108] Specifically, the prediction module 60 is further configured to: calculate the Kalman gain of the state prediction equation based on the observation noise covariance matrix after weight adjustment; and filter and update the state parameters of the multiple sensors based on the Kalman gain.

[0109] Specifically, if Figure 2 As shown, the system provided by the embodiment of the present invention also includes a fault safety linkage module 70, which is used to: if the weight value is detected to be lower than the preset weight threshold, then an alarm signal is issued for the sensor corresponding to the weight value lower than the preset weight threshold; if the filtering residual is higher than the preset residual threshold, then a fault safety linkage is executed.

[0110] Specifically, the preset residual thresholds include a first residual threshold, a second residual threshold, and a third residual threshold; the fail-safe linkage module 70 is further configured to:

[0111] If the filtering residual is greater than the first residual threshold, an early warning signal is issued;

[0112] If the filter residual is greater than a second residual threshold, a speed reduction operation is performed on the hydrogenation process of the hydrogenation station;

[0113] If the filtering residual is greater than the third residual threshold, an emergency stop operation is performed on the hydrogenation process of the hydrogenation station.

[0114] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A hydrogenation safety control method based on adaptive robust filtering, characterized in that: The method comprises: Based on Kalman filtering, a state prediction equation for multiple sensors in the hydrogen refueling process at a hydrogen refueling station is constructed; Constructing a filter residual based on the state prediction equation, and constructing a robustness factor based on the filter residual; Adjusting the initial observation noise covariance matrix of each sensor based on the robustness factor to obtain an observation noise covariance matrix after robustness adjustment; Based on the health of each sensor, calculate the weight value corresponding to each sensor; Dynamically adjusting the observation noise covariance matrix after the robustness adjustment based on the weight value to obtain the observation noise covariance matrix after the weight adjustment; Based on the weight-adjusted observation noise covariance matrix and the state prediction equation, the state parameters of the multiple sensors are filtered and updated.

2. The method according to claim 1, characterized in that The state prediction equation includes: Where, is the optimal predicted value of the sensor state parameter at time k-1, u k is the control system input of the hydrogen refueling station, w k is the process noise, f(·) is the nonlinear state transfer function, is the prior estimate of the state parameter at time k predicted based on time k-1.

3. The method according to claim 2, characterized in that Constructing a filter residual based on the state prediction equation, and constructing a robustness factor based on the filter residual, including: Calculating the filtering residual of the state parameter of each sensor based on the state prediction equation; Based on the Mahalanobis distance of the filtering residual, a robustness factor is calculated; wherein, The calculation formula of the filtering residual includes: Where r k is the filter residual at time k, z k is the actual observation value of the multiple sensors at time k, and H is the measurement matrix of the multiple sensors; The calculation formula of the Mahalanobis distance of the filtering residual includes: S k =HP k|k-1 H T +R k Where, d k is the Mahalanobis distance of the filter residual at time k, S k is the covariance matrix of the filter residual at time k, P k|k-1 is the covariance matrix of the prior estimate of the state parameters at time k, R k is the observation noise covariance matrix at time k; The calculation formula of the robustness factor includes: Where, ρ k is the robustness factor at time k, is the chi-square distribution threshold, a is the confidence level, and m is the sensor number.

4. The method according to claim 3, characterized in that The observation noise covariance matrix after the robustness adjustment includes: R′ k =ρ k *R0 Where R′ k is the observation noise covariance matrix after robustness adjustment at time k, and R0 is the initial observation noise covariance matrix.

5. The method according to claim 1, wherein Based on the health of each sensor, the weight value corresponding to each sensor is calculated, including: Calculate the health of each sensor based on its recent mean absolute error and self-test signal; Calculate the weight value corresponding to each sensor based on the health; wherein, The calculation formula of the health degree includes: Where h i is the health of the i-th sensor, MAE i is the recent mean absolute error of the i-th sensor, α is the weight coefficient, S self-test,i is the self-test signal of the i-th sensor; The calculation formula of the weight value includes: Where w i is the weight value of the i-th sensor, and n is the number of sensors.

6. The method according to claim 5, characterized in that The observation noise covariance matrix after the weight adjustment includes: R″ k =diag(w1R′ 1,k ,…,w n R′ n,k ) Where R″ k is the observation noise covariance matrix after weight adjustment at time k, R′ i,k is the observation noise covariance matrix of the i-th sensor at time k after robustness adjustment.

7. The method according to claim 1, characterized in that Filtering and updating the state parameters of the multiple sensors based on the weight-adjusted observation noise covariance matrix and the state prediction equation, including: Calculating the Kalman gain of the state prediction equation based on the observation noise covariance matrix after the weight adjustment; The state parameters of the multiple sensors are filtered and updated based on the Kalman gain.

8. The method according to claim 1, characterized in that The method further comprises: If the weight value is detected to be lower than a preset weight threshold, an alarm signal is issued for the sensor corresponding to the weight value lower than the preset weight threshold; If the filtering residual is higher than a preset residual threshold, a fail-safe linkage is executed.

9. The method according to claim 8, characterized in that The preset residual threshold includes a first residual threshold, a second residual threshold and a third residual threshold; Execute fail-safe linkage, including: If the filtering residual is greater than the first residual threshold, an early warning signal is issued; If the filtering residual is greater than the second residual threshold, a speed reduction operation is performed on the hydrogenation process of the hydrogenation station; If the filtering residual is greater than the third residual threshold, an emergency stop operation is performed on the hydrogenation process of the hydrogenation station.

10. A hydrogenation safety control system based on adaptive robust filtering, used to implement the method according to any one of claims 1 to 9; characterized in that: include: A first building block, a second building block, a robustness adjustment module, a calculation module, a weight adjustment module, and a prediction module; wherein: The first building module is used to build a state prediction equation for multiple sensors in the hydrogen refueling station during the hydrogen refueling process based on Kalman filtering; The second construction module is used to construct a filter residual based on the state prediction equation, and to construct a robustness factor based on the filter residual; The robustness adjustment module is configured to adjust the initial observation noise covariance matrix of each sensor based on the robustness factor to obtain an observation noise covariance matrix after robustness adjustment; The calculation module is used to calculate the weight value corresponding to each sensor based on the health of each sensor; The weight adjustment module is used to dynamically adjust the observation noise covariance matrix after the robustness adjustment based on the weight value to obtain the observation noise covariance matrix after the weight adjustment; The prediction module is used to filter and update the state parameters of the multiple sensors based on the observation noise covariance matrix after the weight adjustment and the state prediction equation.

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