Aircraft fuel measurement method based on multi-sensor multi-measurement dynamic fusion

Abstract

The application relates to the technical field of aircraft fuel measurement, and discloses an aircraft fuel measurement method based on multi-sensor multi-measurement value dynamic fusion, which comprises the following steps: obtaining fuel measurement values falling into a confidence interval from original fuel measurement data collected from multiple sensors through a software filter; grouping multiple fuel measurement values according to the sensors to which the multiple fuel measurement values belong, and sequentially numbering the multiple fuel measurement values in the groups; sequentially numbering the multiple sensors; combining a weighted average method and a Lagrange interpolation method to optimize a fusion filter algorithm, and constructing a multi-sensor multi-measurement value dynamic fusion model; then, iteratively processing input fuel measurement values through the multi-sensor multi-measurement value dynamic fusion model to obtain a fusion result of the multi-sensor multi-measurement value, which is used to represent a predicted value of aircraft fuel quantity. The application combines the weighted average method and the Lagrange interpolation method to optimize the fusion filter algorithm, and constructs the multi-sensor multi-measurement value dynamic fusion model, so that the model output result is closer to the real state, and the measurement precision is provided.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of aircraft fuel measurement, in particular to a multi-sensor multi-measurement value dynamic fusion aircraft fuel measurement method. BACKGROUND

[0002] At present, the fuel measurement system based on computer technology greatly improves the accuracy of oil measurement, but brings the problem of poor measurement stability. For the core problem of multi-source data fusion in multi-sensor network, it does not have universality in practical application, and lacks fault tolerance. In the case of error output oil data, it may get wrong fusion results, and even output oil data far from the actual situation, which has seriously affected the correct judgment of the pilot on the working state of the aircraft. Moreover, if the waiting time is too short in the oil data fusion process, it will cause too many invalid fusion times, data dispersion and low value density. If the waiting time is too long, the transmission delay will increase, which seriously affects the reliability and effectiveness of the analysis results. Due to the uncertainty, incompleteness and instability of sensor information, the error correction capability of the oil data fusion process is also very low, which leads to slow convergence speed of the software filter, reduces the estimation accuracy, and obtains poor precision of the original data, thereby causing the filter suboptimal estimation or divergence, inaccurate initial value estimation, oil measurement data lag and other inherent defect problems.

[0003] The Chinese invention patent with patent publication No. CN116776285A discloses a multi-sensor fuel measurement data weighted fusion method based on confidence interval, which performs weighted processing on each sensor through a general weighted average data fusion filter algorithm. In order to further reduce errors, the present application optimizes the design based on the above scheme, improves the previous weighted average data fusion filter algorithm, and then establishes a multi-sensor multi-measurement value dynamic fusion model based on Lagrange interpolation by performing Lagrange interpolation on the oil measurement value of a single sensor, so that the model output result is closer to the true state. SUMMARY

[0004] The present application provides a multi-sensor multi-measurement value dynamic fusion aircraft fuel measurement method based on the prior art, which optimizes the fusion filter algorithm by combining the weighted average method and the Lagrange interpolation method, constructs a multi-sensor multi-measurement value dynamic fusion model, and the model output result is closer to the true state, which can overcome the measurement error caused by multi-sensor multi-measurement value fusion in maneuvering flight, further improves the measurement accuracy of fuel, and has more superior prediction performance.

[0005] The application realizes the method by the following technical scheme: a kind of aircraft fuel measuring method of multi-sensor multi-measurement value dynamic fusion, set up a software filter for post-processing of fuel oil signal, obtain the oil measurement value falling into confidence interval from the original oil measurement data collected by multiple sensors through software filter;After obtaining the oil measurement value falling into confidence interval, multiple oil measurement values are grouped according to the sensor to which they belong and numbered in turn within the group, and multiple sensors are numbered in turn;Then, the weighted average method and Lagrange interpolation method are combined to optimize the fusion filter algorithm, and a multi-sensor multi-measurement value dynamic fusion model is constructed;Then, the input oil measurement value is iterated through the multi-sensor multi-measurement value dynamic fusion model, and the fusion result of the multi-sensor multi-measurement value is obtained, which is used to represent the predicted value of the aircraft fuel oil.

[0006] Further, in order to better illustrate the present application, when constructing the multi-sensor multi-measurement value dynamic fusion model, the oil measurement value expression of a single sensor, an a-order Lagrange interpolation expression of a single oil measurement value are constructed respectively, the measurement variance expression of a single sensor is calculated by Lagrange interpolation method, which is used to calculate the measurement variance of each sensor;Then, the weight expression of a single sensor is constructed based on the measurement variance of a single sensor, which is used to calculate the fusion weight of each sensor;Then, the fusion result expression is constructed by weighted average method based on the fusion weight of a single sensor and the oil measurement value of a single sensor, which is used to calculate the fusion result of multi-sensor multi-measurement value, and the predicted value of aircraft fuel oil is obtained.

[0007] Compared with the prior art, the present application has the following advantages and benefits.

[0008] (1) The present application uses the output value of the software filter to post-process the fuel oil signal, improves the calculation method of the weight factor of each sensor based on the general weighted average data fusion filter algorithm, establishes a multi-sensor multi-measurement value dynamic fusion mathematical model based on Lagrange interpolation, and can overcome the measurement error caused by multi-sensor multi-measurement value fusion during maneuvering flight, further improving the measurement accuracy of fuel.

[0009] (2) The present application is a new fuel measurement method based on weighted fusion of multi-sensor multi-measurement value of aircraft under confidence interval, which can better estimate the change signal of fuel data, and is suitable for occasions with higher estimation accuracy of multi-sensor.

[0010] (3) The aircraft fuel measurement method disclosed by the present application can eliminate the error of oil surface fluctuation, overcome the limitations of general software filter which does not consider the change of aircraft attitude and the stability of fuel measurement data, further improve the measurement accuracy of fuel, and has more superior performance. BRIEF DESCRIPTION OF DRAWINGS

[0011] Figure 1A main flowchart of a multi-sensor multi-measurement value dynamic fusion aircraft fuel measurement method provided by the application. DETAILED DESCRIPTION

[0012] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by a person of ordinary skill in the art without creative work are within the protection scope of the application.

[0013] Embodiment 1

[0014] In the prior art, data redundancy of a multi-sensor network includes four cases.

[0015] (1) multiple sensors sample the same data in the confidence interval when the measurement value appears;

[0016] (2) the same sensor samples the same data at different time periods, that is, the sampling target does not change, and the oil sampling data and the sent oil data overlap;

[0017] (3) the same sensor samples data changes at different time periods, but the data change is not large, and the target feature does not change;

[0018] (4) multiple sensors sample different data of the target at different time periods.

[0019] Firstly, for the first two cases, to reduce the time consumption caused by complex operation, when the same data is sampled for the same target, only one sensor sends data, and the remaining nodes do not send. At this time, the algorithm is as follows: after the sensor collects the detection data, the neighbor node information is checked, the sampling data is compared, the data is the same, one sensor sends a data packet, and the remaining sensors do not send. The new sampling data enters, and the original sampling data is covered. If the sensor collects the same data at different times, only the time stamp of the data packet is changed, and the data is not sent.

[0020] Secondly, for the last two cases, the technical solution disclosed in the Chinese patent with the patent publication number CN116776285A uses a software filter to post-process the fuel oil signal, and uses a weighted average data fusion algorithm to calculate the fuel oil, thereby reducing the data redundancy of the multi-sensor network.

[0021] However, compared with the prior art which only uses the weighted average data fusion algorithm, the system has poor followability.

[0022] Therefore, based on the technical concept of combining the weighted average method and the Lagrange interpolation method, the existing fusion filtering algorithm is optimized to improve the calculation method of the weight factor of each sensor, and a multi-sensor multi-measurement value dynamic fusion model based on Lagrange interpolation is established. According to the input measurement values of each sensor, the optimized multi-sensor multi-measurement value dynamic fusion model can obtain the fusion result of the multi-sensor multi-measurement value through model algorithm iteration, realize the trend estimation of the aircraft fuel measurement value, make it closer to the real state, and thus improve the aircraft fuel measurement accuracy.

[0023] Specifically, the embodiment provides a multi-sensor multi-measurement value dynamic fusion aircraft fuel measurement method, a software filter for post-processing of the fuel quantity signal is arranged, and the software filter is used to obtain fuel quantity measurement values falling within a confidence interval from original fuel quantity measurement data collected by multiple sensors; after the fuel quantity measurement values falling within the confidence interval are obtained, the multiple fuel quantity measurement values are grouped according to the sensors to which the fuel quantity measurement values belong and are sequentially numbered within the groups, and the multiple sensors are sequentially numbered; then, the fusion filtering algorithm is optimized by combining the weighted average method and the Lagrange interpolation method, and a multi-sensor multi-measurement value dynamic fusion model is constructed; then, the input fuel quantity measurement values are iterated through the multi-sensor multi-measurement value dynamic fusion model, and a fusion result of the multi-sensor multi-measurement value is obtained, which is used to represent a predicted value of the aircraft fuel quantity.

[0024] Compared with the technical solution disclosed in the Chinese invention patent with the patent publication number CN116776285A, the embodiment is a new fuel measurement method based on weighted fusion of multiple sensors and multiple measurement values of an aircraft under a confidence interval, and the optimized algorithm model can better estimate the fuel data change signal, and is suitable for occasions with higher estimation accuracy of multiple sensors.

[0025] Embodiment 2

[0026] The embodiment is described in detail based on the embodiment 1.

[0027] The aircraft fuel measurement method, as shown in Figure 1 includes steps S1-S3.

[0028] Step S1, obtaining fuel quantity measurement values;

[0029] Specifically, a software filter for post-processing of the fuel quantity signal is arranged, and the software filter is used to obtain fuel quantity measurement values falling within a confidence interval from original fuel quantity measurement data collected by multiple sensors.

[0030] Step S2, constructing a multi-sensor multi-measurement value dynamic fusion model;

[0031] Specifically, after obtaining the fuel quantity measurement values falling into the confidence interval, the multiple fuel quantity measurement values are grouped according to the sensors to which the measurement values belong and are sequentially numbered in the groups, and the multiple sensors are sequentially numbered; then, a multi-sensor multi-measurement value dynamic fusion model is constructed by combining a weighted average method and a Lagrange interpolation method to optimize and fuse the filtering algorithm.

[0032] Step S3, obtaining a predicted value of the fuel quantity of the airplane;

[0033] Specifically, the input fuel quantity measurement values are iterated by the multi-sensor multi-measurement value dynamic fusion model to obtain a fusion result of the multi-sensor multi-measurement values, which is used to represent the predicted value of the fuel quantity of the airplane.

[0034] The other parts of the embodiment are the same as those of Embodiment 1, and thus will not be described again.

[0035] Embodiment 3

[0036] The embodiment is based on Embodiment 2, and Step S1 is described in detail.

[0037] Step S1 specifically includes the following steps.

[0038] Step S11, setting a software filter for post-processing of the fuel quantity signal.

[0039] Step S12, setting a filter queue address, a confidence interval length, an effective range of the fuel quantity measurement values, an upper limit of the filter queue, and a lower limit of the filter queue in the software filter.

[0040] Step S13, judging each fuel quantity measurement value of each sensor by using the effective range of the fuel quantity measurement values, the upper limit of the filter queue, and the lower limit of the filter queue, analyzing the credibility relationship between the fuel quantity data collected by the multiple sensors, and obtaining the fuel quantity measurement values.

[0041] The filter queue address is used to define a storage starting position of the collected data.

[0042] The confidence interval length is used to define a response speed and an interval length of the filtering.

[0043] The effective range of the fuel quantity measurement values, also referred to as a filter limit Δ limit , is used to define a value range of the airplane fuel measurement data sampling values; and false airplane fuel measurement sampling data is judged according to the value range.

[0044] The upper limit Δ upper of the filter queue and the lower limit Δ lower of the filter queue are used to determine a confidence interval range of the filter queue, and the credibility of the airplane fuel measurement data sampling values is distinguished by the confidence interval range.

[0045] Since the actual fuel quantity of the aircraft is reduced at a relatively stable fuel consumption rate, the concept of a reliable measurement value is introduced here: the reliable measurement value varies within a certain range, and when it is decreasing, the minimum reliable measurement value relative to the reference fuel quantity is not less than Δ lower , and when it is increasing, the maximum reliable measurement value cannot exceed Δ upper .

[0046] That is, if the fuel quantity sampling value of a certain aircraft falls within the interval [Δ lower , Δ upper ], it is considered to be reliable, and multiple measurement cycles are performed until the filter queue is filled, and the fuel quantity sampling value falling within the confidence interval range is taken as the reliable measurement value, i.e., the fuel quantity measurement value. If an extremely large value is sampled, it is considered to be invalid and is rejected.

[0047] In summary, the working principle of the software filter is as follows: after the software filter starts running, the measurement value obtained must be filled into the data queue for n measurement cycles as the basis for subsequent comparison. On the basis of filling the queue, the filter limit Δ limit is used to remove the obviously false data in the sampled data, and the obviously false data is rejected. Then, the upper limit Δ upper of the filter queue and the lower limit Δ lower of the filter queue are used to distinguish the reliability of the sampled data to obtain the reliable measurement value; finally, the software filter outputs the reliable measurement value.

[0048] Therefore, the fuel quantity measurement value input into the multi-sensor multi-measurement value dynamic fusion model for post-processing satisfies the following conditions: less than the filter limit Δ limit and within the interval [Δ lower , Δ upper ], i.e., within the confidence interval.

[0049] The other parts of this embodiment are the same as those of Embodiment 2, and thus will not be described again.

[0050] Embodiment 4:

[0051] This embodiment is based on Embodiments 2 and 3, and step S2 is described in detail.

[0052] Step S2 specifically includes the following steps.

[0053] After step S21, the multiple fuel quantity measurement values are grouped according to the sensors to which they belong and are sequentially numbered within the group, and the multiple sensors are sequentially numbered.

[0054] Assumption 1: The fuel measurement system is provided with n sensors; the n sensors are sequentially numbered, and the number of the sensor is denoted as k.

[0055] Assumption 2: There are m oil quantity measurements corresponding to a single sensor; the m oil quantity measurements are numbered sequentially and i, j, and s are used to represent the individual oil quantity measurements with different sensor numbers.

[0056] Based on the above assumptions, if the fuel quantity measurements collected by the same sensor are grouped together, then all fuel quantity measurements are divided into n groups; at this time, the fuel quantity measurement value of the k-th sensor is denoted as d. k k = 1, 2, ..., n.

[0057] The fuel quantity measurement value d of the kth sensor k The mathematical expression is:

[0058] d k ={d1,...d k ,...d n}, k=1,2,....n (1)

[0059] Where d is a general symbol for the set of all fuel quantity measurements from a single sensor, and its subscript indicates the sensor number; that is, d1 represents the fuel quantity measurement of the first sensor. k d represents the fuel quantity measurement value of the k-th sensor; n This represents the oil level measurement value of the nth sensor.

[0060] The fuel quantity measurement value d of the kth sensor k ,satisfy:

[0061] d k =D+ε k (2)

[0062] Where, d k Satisfying E(d) k ) = D, and they are independent of each other. E(·) represents the mean function.

[0063] D represents the true value of the oil quantity measurement corresponding to the k-th sensor.

[0064] ε k It is the random error of the k-th sensor during measurement, which is independent of each other and follows a normal distribution. This represents the measurement variance of n sensors;

[0065] The expression for the fusion result is as follows:

[0066]

[0067] W k satisfy:

[0068]

[0069] wherein, represents the fusion result of multi-sensor multi-measurement values.

[0070] d k represents the oil measurement value of a single sensor.

[0071] W k represents the fusion weight of a single sensor.

[0072] k represents the number of sensors, k = 1, 2, …, n; n represents the number of sensors; d k , W k The value of subscript k represents the number value of the sensor.

[0073] The total mean square error expression of the fusion result is as follows:

[0074]

[0075] wherein, J represents the total mean square error of the fusion result.

[0076] E(·) represents the average function.

[0077] D represents the measurement true value corresponding to the oil measurement value of the kth sensor.

[0078] represents the fusion result of multi-sensor multi-measurement values.

[0079] d i represents the fusion result corresponding to the i-th oil measurement value of a single sensor.

[0080] W i represents the fusion weight corresponding to the i-th oil measurement value of a single sensor.

[0081] σ i represents the measurement variance corresponding to the i-th oil measurement value of a single sensor.

[0082] The fusion weight expression of a single sensor is as follows:

[0083]

[0084] wherein, W k represents the fusion weight of a single sensor, and specifically represents the fusion weight of the kth sensor.

[0085] represents the measurement variance of a single sensor, and specifically represents the measurement variance of the kth sensor.

[0086] k represents the number of sensors, k = 1, 2, …, n; n represents the number of sensors.

[0087] To eliminate the influence of errors and maximize the estimation accuracy of the measurement value detected by a single sensor, the measurement variance of the sensor is estimated by interpolation of the oil quantity measurement value of a single sensor, without relying on the measurement values ​​of other sensors. According to equation (2), suppose a certain sensor measures a total of m oil quantity values, and the oil quantity measurement values ​​are:

[0088] d(x i )=D(x i )+ε(x i (7)

[0089] Where d(x) i ) represents the fuel quantity measurement value of a single sensor, specifically the fused measurement value corresponding to the i-th fuel quantity measurement value of a single sensor. i This represents the i-th oil quantity measurement value corresponding to a single sensor. Here, d(x) i ) and x i Although both are parameters representing measured oil volume, d(x) i ) uses a single sensor as the description object, x i It uses a single oil quantity measurement value sampled by the sensor as the description object, therefore d(x) i ) is equivalent to a set, x i Equivalent to an element in a set.

[0090] D(x i ) represents the true value of the measured oil quantity of a single sensor for the i-th oil quantity measurement.

[0091] ε(x i ) represents the random error of the i-th oil quantity measurement value of a single sensor.

[0092] d(x i ), D(x i ), ε(x) i ) in x i It is the parameter symbol representing a single oil quantity measurement value, x i The subscript i is the number used to represent a single oil quantity measurement value, i = 1, 2, ..., m.

[0093] In this invention, x i x j x s Similarly, when the subscript is not assigned a specific value, it serves as a general expression for a single fuel quantity measurement; when the subscript is assigned a specific value, it serves as the fuel quantity measurement corresponding to a specific sensor number. For example, when x... i When the index i is not assigned a specific value, x i As a general expression for a single oil quantity measurement; when x iThe subscript i is assigned a specific numerical value, x i represents the ith oil quantity measurement value of a single sensor.

[0094] According to the Lagrange interpolation theorem, the a-order Lagrange interpolation of the ith oil quantity measurement value of a single sensor is obtained, and the expression is as follows:

[0095]

[0096] wherein L(x i ) represents the a-order Lagrange interpolation of the ith oil quantity measurement value of a single sensor. a represents the order set in the Lagrange interpolation method, a is an even number, and a < m.

[0097] x i , x j represent the ith oil quantity measurement value and the jth oil quantity measurement value of a single sensor, the values of i and j represent the number values of the oil quantity measurement values in a single sensor, i = 1, 2, …, m, j = 1, 2, …, m, i ≠ j; m represents the number of oil quantity measurement values collected by a single sensor.

[0098] d(x j ) represents the fusion measurement value corresponding to the jth oil quantity measurement value of a single sensor.

[0099] ω(x j ) represents the ratio coefficient of the difference values of two groups of measurements in a single sensor.

[0100] The ratio coefficient of the difference values of two groups of measurements in a single sensor ω(x j ) is a (a-1) order polynomial, and the expression is as follows:

[0101]

[0102] wherein x i , x j , x s represent the ith oil quantity measurement value, the jth oil quantity measurement value, and the sth oil quantity measurement value of a single sensor, the values of i, j, and s represent the number values of the oil quantity measurement values in a single sensor, i = 1, 2, …, m, j = 1, 2, …, m, s = 1, 2, …, m, i ≠ j ≠ s; m represents the number of oil quantity measurement values collected by a single sensor. The difference values x i -x s and x j -x s in formula (9) represent the difference values between two groups of oil quantity measurement values of a certain sensor.

[0103] The difference between the fusion measurement value of a certain sensor and the interpolation value can be calculated from formula (7) and formula (8). At this time, the difference between the fusion measurement value of a single oil quantity measurement value and the Lagrange interpolation is expressed as follows:

[0104]

[0105] where Δ(x i ) represents the difference between the fusion measurement value of the i-th oil quantity measurement value of a single sensor and the Lagrange interpolation.

[0106] d(x i ) represents the fusion measurement value corresponding to the i-th oil quantity measurement value of a single sensor.

[0107] L(x i ) represents the a-order Lagrange interpolation of the i-th oil quantity measurement value of a single sensor.

[0108] D(x i ) represents the measurement true value of the i-th oil quantity measurement value of a single sensor.

[0109] ε(x i ) represents the random error of the i-th oil quantity measurement value of a single sensor.

[0110] D(x j ) represents the measurement true value of the j-th oil quantity measurement value of a single sensor.

[0111] ε(x j ) represents the random error of the j-th oil quantity measurement value of a single sensor.

[0112] ω(x j ) represents the ratio coefficient of the two sets of measurement differences in a single sensor.

[0113] The variance of Δ(x i ) can be calculated from formula (10) as follows:

[0114]

[0115] where Var[Δ(x i )] represents the variance of Δ(x i ); Var(·) represents the variance function.

[0116] Var(x i ) represents the variance of the oil quantity value measured by a single sensor.

[0117] According to formula (11), from the definition of statistical variance, the variance expression of Δ(x i ) is as follows:

[0118]

[0119] In practical calculations, boundary measurements sampled by sensors cannot be used to calculate variance, and the a-order Lagrange interpolation algorithm requires the values ​​on both sides of the interpolation point. There are 10 data points, therefore the number of interpolation measurements that can be used for variance calculation is (ma-2); and because d(x i ) and L(x i Both are unbiased estimates of the measured oil volume, therefore their expected values ​​are equal to the measured oil volume, thus yielding Δ(x). i The mathematical expectation of ) is as follows:

[0120] E[Δ(x i )]=E[d(x i )]-E[L(x i )]=D(x i )-D(x i )=0 (13)

[0121] Substituting equation (13) into equation (12), we get:

[0122]

[0123] Then, by comparing equation (14) and equation (11) with respect to Δ(x) i The variance can be calculated to obtain the variance of a single sensor. The value is:

[0124]

[0125] Substituting equation (15) into equation (6) will allow us to calculate the weights of each sensor.

[0126] Finally, by combining equation (3) and the weights corresponding to each sensor obtained from the above derivation, the weighted fusion result of the multi-sensor multi-measurement data of the aircraft is obtained. The fusion result of multiple sensors and measurements is the predicted value of aircraft fuel quantity.

[0127] In this embodiment, Equation (3) provides an expression for the fusion result of multiple sensors and multiple measurements; Equation (6) provides an expression for the weight of each sensor; Equation (8) provides an expression for constructing an a-order Lagrange interpolation of the i-th oil quantity value of a certain sensor; the weight of each sensor can be calculated according to Equations (9)-(15), and then the weight can be substituted into Equation (3) to obtain the fusion result of multiple sensors and multiple measurements.

[0128] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Any simple modifications or equivalent changes made to the above embodiments based on the technical essence of the present invention shall fall within the protection scope of the present invention.

Claims

1. A method for measuring aircraft fuel quantity using dynamic fusion of multiple sensors and measurements, comprising setting up a software filter for post-processing the fuel quantity signal, and obtaining fuel quantity measurement values ​​falling within a confidence interval from the raw fuel quantity measurement data collected by multiple sensors through the software filter, characterized in that: After obtaining the fuel quantity measurements that fall within the confidence interval, the multiple fuel quantity measurements are grouped according to their respective sensors and numbered sequentially within each group. Simultaneously, the multiple sensors are numbered sequentially. Then, the fusion filtering algorithm is optimized by combining the weighted average method and the Lagrange interpolation method to construct a multi-sensor, multi-measurement dynamic fusion model. Finally, the input fuel quantity measurements are iterated through this multi-sensor, multi-measurement dynamic fusion model to obtain the fusion result, which is used to characterize the predicted value of the aircraft's fuel quantity. When constructing a dynamic fusion model of multiple sensors and multiple measurements, expressions for the oil quantity measurement value of a single sensor and an a-order Lagrange interpolation expression for a single oil quantity measurement value are constructed respectively. The measurement variance expression of a single sensor is calculated by the Lagrange interpolation method, which is used to calculate the measurement variance of each sensor. Then, based on the measurement variance of a single sensor, a weight expression for the single sensor is constructed, which is used to calculate the fusion weight of each sensor; Then, based on the fusion weights of individual sensors and the fuel quantity measurement values ​​of individual sensors, a fusion result expression is constructed using the weighted average method to calculate the fusion result of multiple sensors and multiple measurements, thereby obtaining the predicted value of aircraft fuel quantity. The measurement variance expression for a single sensor is as follows: in, Δ(x) represents the measurement variance of a single sensor. i ) represents the difference between the fused value and the Lagrange interpolation of a single oil quantity measurement; ω(x) j ) represents the ratio coefficient of the difference between two sets of measurements within a single sensor; a represents the order set in the Lagrange interpolation method, where a is an even number, and a <m;x i x j The values ​​of i and j represent the i-th and j-th oil quantity measurements corresponding to a single sensor. The values ​​of the subscripts i and j represent the numbers of the oil quantity measurements in a single sensor, i = 1, 2, ..., m, j = 1, 2, ..., m, i ≠ j; m represents the number of oil quantity measurements collected by a single sensor.

2. The aircraft fuel measurement method based on dynamic fusion of multiple sensors and measurements according to claim 1, characterized in that: The fusion result expression is as follows: W k satisfy: in, This represents the fusion result of multiple sensors and multiple measurements; d k This represents the fuel level measurement value from a single sensor; W k d represents the fusion weights of a single sensor; k represents the sensor number, k = 1, 2, ..., n; n represents the number of sensors; k W k The value of the subscript k represents the sensor number.

3. The aircraft fuel measurement method based on dynamic fusion of multiple sensors and measurements according to claim 2, characterized in that: The expression for the oil quantity measurement value of a single sensor is as follows: d k =D+e k Where, d k This represents the fuel quantity measurement value of a single sensor; D represents the true value of the fuel quantity data collected by the sensor; ε k d represents the random error of a single sensor. k ε k The value of the subscript k represents the sensor number.

4. The aircraft fuel measurement method based on dynamic fusion of multiple sensors and measurements according to claim 3, characterized in that: The fusion weight expression for a single sensor is as follows: Among them, W k Represents the fusion weights of a single sensor; The value represents the measurement variance of a single sensor; k represents the sensor number, k = 1, 2, ..., n; n represents the number of sensors; W k , The value of the subscript k represents the sensor number.

5. The aircraft fuel measurement method based on dynamic fusion of multiple sensors and measurements according to claim 4, characterized in that: The difference Δ(x) between the merged measurement and the Lagrange interpolation of a single oil quantity measurement. i The expression is as follows: Where d(x) i L(x) represents the fused measurement value corresponding to the i-th oil quantity measurement value from a single sensor; i D(x) represents the a-th order Lagrange interpolation of the i-th oil quantity measurement value from a single sensor; i ) represents the true value of the i-th oil quantity measurement from a single sensor; ε(x) i D(x) represents the random error of the i-th oil quantity measurement value from a single sensor; j ) represents the true value of the j-th oil quantity measurement from a single sensor; ε(x) j ) represents the random error of the j-th oil quantity measurement value from a single sensor; ω(x) j ) represents the ratio coefficient of the difference between two sets of measurements within a single sensor.

6. The aircraft fuel measurement method based on dynamic fusion of multiple sensors and measurements according to claim 5, characterized in that: The coefficient ω(x) of the difference between two sets of measurements within a single sensor j The expression is as follows: Where, x i x j x s Let i, j, and s represent the i-th, j-th, and s-th oil quantity measurements corresponding to a single sensor. The values ​​of i, j, and s all represent the numbers of the oil quantity measurements in a single sensor, i = 1, 2, ..., m, j = 1, 2, ..., m, s = 1, 2, ..., m, i ≠ j ≠ s; m represents the number of oil quantity measurements collected by a single sensor.

7. The aircraft fuel measurement method based on dynamic fusion of multiple sensors and measurements according to any one of claims 1-6, characterized in that: The software filter obtains fuel quantity measurements that fall within the confidence interval from the raw fuel quantity measurement data collected by multiple sensors. Specifically, it involves setting the filter queue address, confidence interval length, effective range of fuel quantity measurements, upper limit of the filter queue, and lower limit of the filter queue in the software filter; then, it uses the effective range of fuel quantity measurements, upper limit of the filter queue, and lower limit of the filter queue to judge each fuel quantity measurement value of each sensor and obtain the fuel quantity measurement value.

Images