Aircraft fuel weighting coefficient dynamic measurement method, system and equipment based on rolling filtering improvement and medium

Abstract

The invention relates to the technical field of aircraft fuel oil measurement, in particular to an improved aircraft fuel oil weighting coefficient dynamic measurement method, system and device based on rolling filtering and a medium. The method comprises the following steps: firstly, post-processing a fuel quantity signal according to a filter, and carrying out consistency check on multiple sampling original data to obtain an optimal sampling data set; secondly, finding a sampling data sequence with the highest support degree through the relation matrix, replacing abnormal data with measurement data of the sampling data sequence, improving the fusion accuracy of multiple pieces of dynamic data in combination with self-adaptive weighted estimation, performing self-adaptive weighted sampling data fusion, improving fuel oil quantity measurement data trend estimation under rolling filtering, and improving fuel oil quantity measurement accuracy. The state is closer to the real state, the oil level fluctuation error is eliminated, the oil quantity measurement error caused by maneuvering flight is overcome, the stability of rolling smoothing filtering measurement data is effectively improved, and the measurement precision of fuel oil is further improved.

Description

Technical Field

[0001] The invention relates to the technical field of aircraft fuel measurement, and in particular to a method, system, equipment and medium for dynamically measuring aircraft fuel weighting coefficient based on rolling filter improvement. Background Art

[0002] At present, the rolling smoothing filter algorithm of the fuel measurement system based on computer technology has greatly improved the accuracy of fuel measurement, but it has brought about the problem of poor measurement stability. In practical applications, the output fuel data will jump frequently, and even output fuel data that is far from the actual situation, which has seriously affected the pilot's correct judgment of the aircraft's working status. Due to the influence of the sensor's own accuracy and installation position, transmission error and the interference of the surrounding environment on the fuel measurement data, in the actual aircraft fuel measurement, the measurement data of the same attribute parameter of aviation fuel has different degrees of deviation. Considering the weak observability of the system, the observation data cannot be fully utilized, which in turn leads to a slow convergence speed of the software filter, a reduced estimation accuracy, and poor accuracy of the original data obtained. The shortcomings of the rolling smoothing filter are that the filter depth is not easy to determine, resulting in suboptimal filter estimation or filter divergence, inaccurate initial value estimation, and the existence of inherent defects such as hysteresis of fuel measurement data. Summary of the invention

[0003] Aiming at the problems of poor measurement stability and hysteresis of fuel quantity measurement data in the rolling smoothing algorithm of the existing fuel measurement system, the present invention proposes a dynamic measurement method, system, equipment and medium of aircraft fuel weighting coefficient improved based on rolling filtering; the method firstly performs post-processing on the fuel quantity signal according to the filter, performs consistency check on the original data sampled multiple times, and obtains the optimal sampling data set; secondly, the sampling data sequence with the highest support is found through the relationship matrix, and the abnormal data is replaced by its measurement data, and then the accuracy of multiple dynamic data fusion is improved by combining adaptive weighted estimation, and adaptive weighted sampling data fusion is performed, so as to improve the trend estimation of fuel quantity measurement data under rolling filtering, which is closer to the real state, eliminate the error of oil level fluctuation, overcome the error of fuel quantity measurement during maneuvering flight, effectively improve the stability of rolling smoothing filter measurement data, and further improve the measurement accuracy of fuel.

[0004] The specific implementation contents of the present invention are as follows:

[0005] A dynamic measurement method for aircraft fuel weighting coefficient based on rolling filter improvement specifically includes the following steps:

[0006] Step S1: Check the original fuel quantity data according to the set filter consistency to obtain a sample data set;

[0007] Step S2: Calculate the quantile dispersion according to the sampling data set, and set the invalid sampling data judgment interval according to the quantile dispersion to obtain the optimal sampling data set;

[0008] Step S3: According to the probability distribution curve, calculate the confidence distance measure and construct the confidence matrix;

[0009] Step S4: Obtain the sampling sequence with the highest support according to the relationship matrix, dynamically assign the optimal weighting factor, and obtain the sampling data fusion result.

[0010] In order to better implement the present invention, further, the step S1 specifically includes the following steps:

[0011] Step S11: setting the filter queue address, the sampling value queue length, the filter queue upper limit and the filter queue lower limit;

[0012] Step S12: removing false data according to the valid range of the fuel quantity sampling value;

[0013] Step S13: performing consistency check on the original fuel quantity data sampled multiple times according to the judgment interval of invalid sampling data to obtain the optimal sampling data set.

[0014] In order to better implement the present invention, further, the step S2 specifically includes the following steps:

[0015] Step S21: obtaining a sample data set according to the filter queue upper limit and the filter queue lower limit;

[0016] Step S22: sort the sample data set to obtain the sampling median;

[0017] Step S23: Obtaining the upper fifth score and the lower fifth score according to the sampling median;

[0018] Step S24: Calculate the quantile dispersion according to the upper fifth score and the lower fifth score;

[0019] Step S25: setting the invalid sampling data judgment interval according to the quantile dispersion, eliminating the invalid sampling data, and obtaining the optimal sampling data set.

[0020] In order to better implement the present invention, further, the step S3 specifically includes the following steps:

[0021] Step S31: Calculate the confidence distance measure according to the probability distribution curve of the sampling data and the variance of the sampling data in the optimal sampling data set;

[0022] Step S32: construct a confidence matrix based on the confidence distance measure to obtain the fusion upper limit.

[0023] In order to better implement the present invention, further, the step S4 specifically includes the following steps:

[0024] Step S41: Obtaining the relationship coefficient according to the confidence distance measurement and the set fusion upper limit;

[0025] Step S42: Calculate the support of the sampling sequence according to the relationship coefficient to obtain the sampling sequence with the highest support;

[0026] Step S43: setting an optimal weighting factor according to the minimum total mean square error of the sampled data sequence;

[0027] Step S44: dynamically assigning the optimal weighting factor to the sampling sequence with the highest support, and obtaining the sampling data fusion result.

[0028] Based on the above proposed improved aircraft fuel weighting coefficient dynamic measurement method based on rolling filter, in order to better realize the present invention, further, a improved aircraft fuel weighting coefficient dynamic measurement system based on rolling filter is proposed, which is used to execute the above proposed improved aircraft fuel weighting coefficient dynamic measurement method based on rolling filter; it includes a sampling unit, a sampling optimization unit, a confidence unit, and a fusion unit;

[0029] The sampling unit is used to check the original fuel quantity data according to the set filter consistency to obtain a sampling data set;

[0030] The sampling optimization unit is used to calculate the quantile dispersion according to the sampling data set, and set the invalid sampling data judgment interval according to the quantile dispersion to obtain the optimal sampling data set;

[0031] The confidence unit is used to calculate the confidence distance measure according to the probability distribution curve, construct the confidence matrix, and obtain the coupling upper limit;

[0032] The fusion unit is used to obtain the sampling sequence with the highest support according to the relationship matrix, dynamically allocate the optimal weighting factor, and obtain the sampling data fusion result.

[0033] Based on the above-proposed improved dynamic measurement method of aircraft fuel weighting coefficient based on rolling filtering, in order to better realize the present invention, further, an electronic device is proposed, including a memory and a processor; a computer program is stored on the memory; when the computer program is executed on the processor, the above-proposed improved dynamic measurement method of aircraft fuel weighting coefficient based on rolling filtering is realized.

[0034] Based on the above-proposed improved dynamic measurement method of aircraft fuel weighting coefficient based on rolling filtering, in order to better realize the present invention, further, a computer-readable storage medium is proposed, on which computer instructions are stored; when the computer instructions are executed on the above-mentioned electronic device, the above-proposed improved dynamic measurement method of aircraft fuel weighting coefficient based on rolling filtering is implemented.

[0035] The present invention has the following beneficial effects:

[0036] (1) The present invention performs denoising on the original fuel measurement signal before rolling filtering, thereby being able to make a better estimate of the fuel data change signal, obtain a higher sampling data fusion result, and improve the anti-interference ability; it is independent of hardware characteristics, simple and easy to implement, and highly reliable.

[0037] (2) The present invention improves the estimation of fuel quantity measurement data trend under rolling filtering, making it closer to the actual state, eliminating the error of oil level fluctuation, overcoming the error of fuel quantity measurement during maneuvering flight, effectively improving the stability of rolling smoothing filter measurement data, further improving the measurement accuracy of fuel, and having more superior performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 A schematic block diagram of the process of checking the original fuel quantity data for filter consistency provided in an embodiment of the present invention.

[0039] Figure 2 The present invention provides an overall flow chart of an improved aircraft fuel weighting coefficient dynamic measurement method based on rolling filtering according to an embodiment of the present invention. DETAILED DESCRIPTION

[0040] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. It should be understood that the described embodiments are only part of the embodiments of the present invention, not all of the embodiments, and therefore should not be regarded as limiting the scope of protection. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technical personnel in this field without making creative work are within the scope of protection of the present invention.

[0041] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "disposed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be the internal communication of two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0042] Embodiment 1:

[0043] This embodiment proposes a dynamic measurement method of aircraft fuel weighting coefficient based on rolling filter improvement, which specifically includes the following steps:

[0044] Step S1: Check the original fuel quantity data according to the set filter consistency to obtain a sample data set.

[0045] Furthermore, the step S1 specifically includes the following steps:

[0046] Step S11: setting the filter queue address, the sampling value queue length, the filter queue upper limit and the filter queue lower limit;

[0047] Step S12: removing false data according to the valid range of the fuel quantity sampling value;

[0048] Step S13: performing consistency check on the original fuel quantity data sampled multiple times according to the judgment interval of invalid sampling data to obtain the optimal sampling data set.

[0049] Step S2: Calculate the quantile dispersion according to the sampling data set, and set the invalid sampling data judgment interval according to the quantile dispersion to obtain the optimal sampling data set.

[0050] Furthermore, the step S2 specifically includes the following steps:

[0051] Step S21: obtaining a sample data set according to the filter queue upper limit and the filter queue lower limit;

[0052] Step S22: sort the sample data set to obtain the sampling median;

[0053] Step S23: Obtaining the upper fifth score and the lower fifth score according to the sampling median;

[0054] Step S24: Calculate the quantile dispersion according to the upper fifth score and the lower fifth score;

[0055] Step S25: setting the invalid sampling data judgment interval according to the quantile dispersion, eliminating the invalid sampling data, and obtaining the optimal sampling data set.

[0056] Step S3: According to the probability distribution curve, calculate the confidence distance measure and construct the confidence matrix.

[0057] Furthermore, the step S3 specifically includes the following steps:

[0058] Step S31: Calculate the confidence distance measure according to the probability distribution curve of the sampling data and the variance of the sampling data in the optimal sampling data set;

[0059] Step S32: construct a confidence matrix based on the confidence distance measure to obtain the fusion upper limit.

[0060] Step S4: Obtain the sampling sequence with the highest support according to the relationship matrix, dynamically assign the optimal weighting factor, and obtain the sampling data fusion result.

[0061] Furthermore, the step S4 specifically includes the following steps:

[0062] Step S41: Obtaining the relationship coefficient according to the confidence distance measurement and the set fusion upper limit;

[0063] Step S42: Calculate the support of the sampling sequence according to the relationship coefficient to obtain the sampling sequence with the highest support;

[0064] Step S43: setting an optimal weighting factor according to the minimum total mean square error of the sampled data sequence;

[0065] Step S44: dynamically assigning the optimal weighting factor to the sampling sequence with the highest support, and obtaining the sampling data fusion result.

[0066] Working principle: This embodiment first sets a software filter for post-processing the fuel quantity signal. In the filter, the filter queue address, sampling value queue length, upper and lower limits of the filter queue are set; then the effective range of the fuel quantity sampling value is used to exclude obvious false data; then the sampling value is judged according to the judgment interval of invalid data to obtain the optimal sampling data set; then based on the support and adaptive weighted estimation theory, the accuracy of multiple dynamic data fusion is proposed; according to the relevant data analysis, a more accurate sampling data fusion result is calculated. This embodiment is based on the sampling data of rolling filtering, and uses the output value of the filter to post-process the fuel quantity signal, and then combines the adaptive weighted algorithm to establish a mathematical model, which improves the estimation result of the traditional rolling filtering arithmetic mean, improves the accuracy of multiple dynamic data fusion, makes it closer to the real state, eliminates the oil level fluctuation error, overcomes the error of oil quantity measurement caused by rolling smoothing filtering, and further improves the measurement accuracy of fuel.

[0067] Embodiment 2:

[0068] This embodiment is based on the above embodiment 1. Figure 1 , Figure 2 As shown, a specific embodiment is described in detail, which specifically includes the following steps.

[0069] Step S1: Check the original fuel quantity data according to the set filter consistency to obtain a sample data set;

[0070] First, a software filter is set to post-process the fuel quantity signal. In the filter, the filter queue address, the sampling value queue length setting, and the upper limit Δ of the filter queue are set. upper and the lower limit of the filter queue Δ upper ; Then, before fusing the multi-sampled raw data, use Δ limit Obvious false data is rejected; the sampling value is judged according to the judgment interval of invalid data to obtain the optimal sampling data set; based on the support and adaptive weighted estimation theory, the accuracy of multiple dynamic data fusion is proposed; finally, based on the relevant data analysis, a more accurate estimation result is calculated. Among them, Δ limit S is the oil sampling value i The effective range is positive, Δ upper and Δ lower are the upper and lower limits of the filter queue respectively; if the sample value S obtained by the filter i Falling in [Δ lower ,Δ upper ] interval, record the sampling value.

[0071] The mathematical expression of the filter sampling value X is:

[0072] T={T1,...Ti,...Tn}, i=1,2,....n (1)

[0073] Where n is the length of the sampling value queue, and i represents the i-th sampling value.

[0074] Step S2: Calculate the quantile dispersion according to the sampling data set, and set the invalid sampling data judgment interval according to the quantile dispersion to obtain the optimal sampling data set.

[0075] Define the median T M for:

[0076]

[0077] Definition of Upper Five Fraction F U For the interval [T M ,T n ], the median and lower quintile F L For the interval [T1,T M] is the median. Therefore, the quantile dispersion d F It is expressed as follows:

[0078] d F =F U -F L (3)

[0079] Set the distance from the median to be greater than α·d F The sampling data is abnormal sampling data, that is, the judgment interval of invalid sampling data is:

[0080] T i -T M |>α·d F (4)

[0081] Where: α is a constant, generally 0.5, 1.0, 2.0, etc.; i is 1, 2, 3, ..., n-1, n. When the abnormal data of the detection sampling sequence is found and removed, the remaining sampling data constitutes the optimal sampling data set.

[0082] Step S3: According to the probability distribution curve, calculate the confidence distance measure and construct the confidence matrix.

[0083] In the optimal sampling data set, the sampling data X i and X j are the i-th sampling value in the optimal sampling data set, and the j-th sampling data sequence has the same optimal sampling value, and obeys the Gaussian distribution, with its probability distribution curve P i (x) and P j (x) is the characteristic function of the i-th and j-th sampling data sequences, x i , x j Denoted as X i and X j A sample observation value, σ i , σ j As its variance, the confidence distance measure is introduced to facilitate intuitive reflection of x i and x j The deviation between .

[0084]

[0085]

[0086] Among them, d ij represents the confidence distance measure between the optimal sampling value in the i-th sampling data sequence and the optimal sampling value in the j-th sampling data sequence; d ji Represents the confidence distance measure between the optimal sampling value in the j-th sampling data sequence and the optimal sampling value in the i-th sampling data sequence.

[0087] Since the optimal sampling value obeys Gaussian distribution, we can get:

[0088]

[0089] Assuming that there are k data in the optimal sampling data set, there is a confidence distance measure d ij (i, j = 1, 2, ..., k) constitute the confidence matrix D k :

[0090]

[0091] Step S4: Obtain the sampling sequence with the highest support according to the relationship matrix, dynamically assign the optimal weighting factor, and obtain the sampling data fusion result.

[0092] Given the fusion upper limit θ ij , then let the relationship coefficient r ij The value of is:

[0093]

[0094] When ij =1, indicating that the i-th sampling data sequence supports the j-th sampling data sequence, and vice versa when r ij = 0, it means that the i-th sampling data sequence does not support the j-th sampling data sequence. ij =1 and r ji =1 means that the i-th sampling data sequence and the j-th sampling data sequence support each other. After data fusion for N-time sampling data sequences, the parameter accuracy can be improved to the accuracy of a single sampling sequence. times.

[0095] Through the relationship matrix R k The sampling sequence with the highest support can be found. The measured sampling data is called super data. The super data is used to replace the removed abnormal data, and finally a dynamic weighted fusion estimation is performed. The relationship matrix R k as follows:

[0096]

[0097] That is, the relationship matrix R k The sum of the mth column of is the support of the mth sampling data sequence supported by other sampling sequences. The calculation formula for the sampling sequence support is:

[0098]

[0099] Based on the idea of ​​adaptive weighted fusion estimation algorithm, the optimal weighting factor W corresponding to each sampling sequence is dynamically assigned according to the real-time measured data each time.i (i=1,2,…,n), where i represents the number of weighting factors, which is equal to the number of sampled data. If the variance of a certain sampled data is larger, the corresponding weight assigned is smaller, so as to obtain the final fusion estimate Closer to the true value X. Assumption: The same characteristic parameter of the aircraft fuel quantity is measured using n sampling data sequences, σ1, σ2, …, σ n is the variance of the n-times sampling data sequence, the theoretical value of the sampling measurement is X, and the detection sampling data each time is X i (i=1,2,…,n), where i represents the number of sampled data. Each detection sampled data is independent of each other and is an unbiased estimate of X, so the estimated parameters are the final fusion result. and W i Must meet:

[0100]

[0101]

[0102] The total mean square error σ is derived from equations (13) and (14): 2 :

[0103]

[0104] According to the multivariate function extreme value theory, the total mean square error σ is obtained 2 The minimum condition, the weight corresponding to each detection sampling data is W i (i=1,2,…,n), when the variance is smaller, the corresponding weight is larger and the minimum total mean square error is for:

[0105]

[0106] It corresponds to the weighting factor W of each detection sampling data i for:

[0107]

[0108] The corresponding weight W of the sampled data detected multiple times is calculated by formula (17): i Calculate and then substitute into formula (13) to get the final sampling data fusion result

[0109] Working principle: Figure 1As shown, in the filter, there are provided a filter queue address, a sampling value queue length setting, a filter limit, a filter queue upper limit, a filter queue lower limit, and a detection sample value sequence. The filter queue address defines the storage starting position of the sampled data, which is convenient for finding the initial value of the filter. The sampling value queue length setting defines the response speed and queue length of the filter, which is convenient for finding the number of data to be filtered. The filter limit defines the value range of the sampled value, which is used to eliminate obviously false data. The filter queue upper limit and the filter queue lower limit define the value range of the filter sequence, which is used to distinguish the sampling value range. The detection sample value sequence defines the output interface of the sampled data.

[0110] Its working principle is to first use the filtering limit Δ limit Remove obvious false data; then use the upper and lower limits of the filter queue Δ upper and Δ lower To distinguish the credibility of the sampled data, a detection sampling value sequence is obtained; finally, the software filter outputs the detection sampling value. limit S is the oil sampling value i The effective range is positive, Δ upper and Δ lower They are the upper and lower limits of the filter queue respectively.

[0111] Since the actual fuel consumption of the aircraft is reduced at a relatively stable fuel consumption rate, the detection sampling value sequence x i Changes within a certain range. When it changes downward, detect the sampling value sequence x i The minimum relative base oil volume is not less than Δ lower , can also change upward, but the maximum cannot exceed Δ upper .

[0112] After the filter starts running, the sample value S i , it is necessary to fill the data queue after n measurement cycles as the basis for subsequent comparison. On the basis of the queue being filled, firstly, S i With Δ limit Comparison can exclude obvious false data; sort according to the original order, if S i Less than Δ limit And in [Δ lower ,Δ upper ] interval, it is considered to be credible and enters the sampling data queue.

[0113] like Figure 2 As shown, if the condition falls within the range, the median T is defined according to formula (2) M ; Then define the upper quintile F according to formulas (3) and (4) U and the lower quintile F L, from which the quantile dispersion d is derived F Secondly, set the distance from the median to be greater than α·d F The sampling data of is abnormal sampling data. When the abnormal data of the detection sampling sequence is found and removed, the remaining sampling data constitutes the optimal sampling data set. Combining formula (5) and formula (6), the confidence distance measure is obtained and the confidence matrix D is calculated. k , giving the fusion upper limit θ ij According to the above relevant data analysis, through the relationship matrix R k Finally, based on the idea of ​​adaptive weighted fusion estimation algorithm, the optimal weighting factor W corresponding to each sampling sequence is dynamically assigned according to the real-time measured data each time. i , and then the corresponding weights W of the sampled data of multiple detections i Calculate and get the final sampling data fusion result

[0114] Parameter selection: The parameters in formula (4), formula (10) and formula (13)-(17) have a direct impact on the tracking and stability of the measurement system. Proper parameter selection is the key to the success of the system.

[0115] Among all the above parameters, the constant α, the fusion upper limit θ ij The theoretical value X of the sampling measurement and the variance σ of the sampling data sequence have the most obvious influence on the tracking and stability of the system. Appropriate values ​​can only be obtained through detailed analysis of test flight data and theoretical reasoning on the basis of meeting the specified rolling filter response time.

[0116] The other parts of this embodiment are the same as those of the above-mentioned embodiment 1, and thus will not be described in detail.

[0117] Embodiment 3:

[0118] Based on any one of the above-mentioned embodiments 1 to 2, this embodiment proposes a dynamic measurement system of aircraft fuel weighting coefficient based on rolling filter improvement, which is used to execute the above-mentioned dynamic measurement method of aircraft fuel weighting coefficient based on rolling filter improvement; it includes a sampling unit, a sampling optimization unit, a confidence unit, and a fusion unit;

[0119] The sampling unit is used to check the original fuel quantity data according to the set filter consistency to obtain a sampling data set;

[0120] The sampling optimization unit is used to calculate the quantile dispersion according to the sampling data set, and set the invalid sampling data judgment interval according to the quantile dispersion to obtain the optimal sampling data set;

[0121] The confidence unit is used to calculate the confidence distance measure according to the probability distribution curve, construct the confidence matrix, and obtain the coupling upper limit;

[0122] The fusion unit is used to obtain the sampling sequence with the highest support according to the relationship matrix, dynamically allocate the optimal weighting factor, and obtain the sampling data fusion result.

[0123] This embodiment also proposes an electronic device, including a memory and a processor; the memory stores a computer program; when the computer program is executed on the processor, the above-mentioned improved dynamic measurement method of aircraft fuel weighting coefficient based on rolling filtering is implemented.

[0124] This embodiment further proposes a computer-readable storage medium, on which computer instructions are stored; when the computer instructions are executed on the above-mentioned electronic device, the above-mentioned improved dynamic measurement method of aircraft fuel weighting coefficient based on rolling filtering is implemented.

[0125] The other parts of this embodiment are the same as any of the above-mentioned embodiments 1 to 2, and thus will not be described in detail.

[0126] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Any simple modification or equivalent change made to the above embodiment based on the technical essence of the present invention shall fall within the protection scope of the present invention.

Claims

1. A dynamic measurement method of aircraft fuel weighting coefficient based on rolling filter improvement, characterized in that: The specific steps include: Step S1: Check the original fuel quantity data according to the set filter consistency to obtain a sample data set; Step S2: Calculate the quantile dispersion according to the sampling data set, and set the invalid sampling data judgment interval according to the quantile dispersion to obtain the optimal sampling data set; Step S3: According to the probability distribution curve, calculate the confidence distance measure and construct the confidence matrix; Step S4: Obtain the sampling sequence with the highest support according to the relationship matrix, dynamically assign the optimal weighting factor, and obtain the sampling data fusion result.

2. The method for dynamic measurement of aircraft fuel weighting coefficient based on rolling filter improvement according to claim 1 is characterized in that: The step S1 specifically includes the following steps: Step S11: setting the filter queue address, the sampling value queue length, the filter queue upper limit and the filter queue lower limit; Step S12: removing false data according to the valid range of the fuel quantity sampling value; Step S13: performing consistency check on the original fuel quantity data sampled multiple times according to the judgment interval of invalid sampling data to obtain the optimal sampling data set.

3. The method for dynamic measurement of aircraft fuel weighting coefficient based on rolling filter improvement according to claim 2 is characterized in that: The step S2 specifically includes the following steps: Step S21: obtaining a sample data set according to the filter queue upper limit and the filter queue lower limit; Step S22: sort the sample data set to obtain the sampling median; Step S23: Obtaining the upper fifth score and the lower fifth score according to the sampling median; Step S24: Calculate the quantile dispersion according to the upper fifth score and the lower fifth score; Step S25: setting the invalid sampling data judgment interval according to the quantile dispersion, eliminating the invalid sampling data, and obtaining the optimal sampling data set.

4. The method for dynamic measurement of aircraft fuel weighting coefficient based on rolling filter improvement according to claim 3 is characterized in that: The step S3 specifically comprises the following steps: Step S31: Calculate the confidence distance measure according to the probability distribution curve of the sampling data and the variance of the sampling data in the optimal sampling data set; Step S32: construct a confidence matrix based on the confidence distance measure to obtain the fusion upper limit.

5. The method for dynamic measurement of aircraft fuel weighting coefficient based on rolling filter improvement according to claim 4 is characterized in that: The step S4 specifically comprises the following steps: Step S41: Obtaining the relationship coefficient according to the confidence distance measurement and the set fusion upper limit; Step S42: Calculate the support of the sampling sequence according to the relationship coefficient to obtain the sampling sequence with the highest support; Step S43: setting an optimal weighting factor according to the minimum total mean square error of the sampled data sequence; Step S44: dynamically assigning the optimal weighting factor to the sampling sequence with the highest support, and obtaining the sampling data fusion result.

6. A dynamic measurement system of aircraft fuel weighting coefficient based on rolling filter improvement, used to execute the dynamic measurement method of aircraft fuel weighting coefficient based on rolling filter improvement as claimed in claim 1; characterized in that: Including sampling unit, sampling optimization unit, confidence unit and fusion unit; The sampling unit is used to check the original fuel quantity data according to the set filter consistency to obtain a sampling data set; the sampling optimization unit is used to calculate the quantile dispersion according to the sampling data set, and set the invalid sampling data judgment interval according to the quantile dispersion to obtain the optimal sampling data set; The confidence unit is used to calculate the confidence distance measure according to the probability distribution curve, construct the confidence matrix, and obtain the coupling upper limit; The fusion unit is used to obtain the sampling sequence with the highest support according to the relationship matrix, dynamically allocate the optimal weighting factor, and obtain the sampling data fusion result.

7. An electronic device, characterized in that: It comprises a memory and a processor; the memory stores a computer program; when the computer program is executed on the processor, the dynamic measurement method of aircraft fuel weighting coefficient based on rolling filtering improvement as described in any one of claims 1 to 5 is implemented.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions; when the computer instructions are executed on the electronic device as claimed in claim 7, the improved dynamic measurement method of aircraft fuel weighting coefficient based on rolling filtering as claimed in any one of claims 1 to 5 is implemented.

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