Overall reliability evaluation method and device for field oral diagnosis and treatment vehicle

Abstract

The invention discloses an overall reliability evaluation method and device for a field oral diagnosis and treatment vehicle, a subsystem of the field oral diagnosis and treatment vehicle comprises a vehicle body chassis, a square cabin, an oral diagnosis and treatment module, an information communication module and a guarantee module, and the method comprises the following steps: acquiring a subsystem reliability measurement information set of the field oral diagnosis and treatment vehicle; preprocessing the reliability measurement index set of all subsystems to obtain a preprocessed measurement data set; and performing reliability fusion calculation processing on the preprocessed measurement data set to obtain an overall reliability evaluation value of the field oral diagnosis and treatment vehicle.

Description

Technical Field

[0001] This invention relates to the fields of industrial data processing and equipment reliability assessment, specifically to a method and apparatus for overall reliability assessment of a field dental clinic vehicle. Background Technology

[0002] As a complex piece of equipment involving multiple systems working together, the overall reliability of a mobile dental clinic depends on the reliable coupling of its various subsystems, including the chassis, the modular cabin, and the treatment modules. Existing reliability assessment technologies suffer from the following bottlenecks:

[0003] Multi-source heterogeneous data processing is inefficient: Subsystem reliability data (such as reliability, failure rate, mean time between failures, etc.) have different physical meanings and dimensions. Traditional methods only perform simple numerical comparisons or arithmetic averages, without building a unified analysis framework across indicators.

[0004] Insufficient utilization of dynamic temporal characteristics: The reliability of subsystems evolves over time (e.g., the failure rate increases due to wear of chassis components), but existing technologies mostly use static threshold assessments, ignoring the temporal patterns of data sequences (e.g., the periodic impact of seasonal environmental changes on the failure rate of information communication modules), resulting in weak early fault warning capabilities.

[0005] Lack of pattern correlation verification: The timestamps of reliability data from different subsystems may be biased (e.g., inconsistent sampling frequencies of sensors in different modules), and there is a lack of cross-validation of the rationality of data patterns (e.g., logical consistency verification between the power supply stability of the assurance module and the reliability of the diagnostic module), which can easily lead to evaluation errors due to asynchronous or abnormal data patterns.

[0006] Poor accuracy of global fusion models: Traditional fusion methods (such as weighted average) do not consider the failure correlation between subsystems (such as the decline in chassis off-road performance may simultaneously cause excessive vibration of the cabin and failure of medical equipment), and cannot quantify the dynamic contribution of each indicator to the overall reliability, resulting in the evaluation results being unable to accurately guide system optimization. Summary of the Invention

[0007] This invention mainly addresses the problems of insufficient utilization of dynamic temporal features, lack of pattern association verification, and poor accuracy of global fusion models in the existing overall reliability assessment of field dental clinics. This invention discloses an overall reliability assessment method and device for field dental clinics.

[0008] In a first aspect, this invention discloses a method for assessing the overall reliability of a field dental clinic vehicle. The field dental clinic vehicle's subsystems include a vehicle chassis, a modular cabin, a dental treatment module, an information communication module, and a support module. The method includes:

[0009] S1, Collect the subsystem reliability measurement information set of the field dental clinic vehicle; the subsystem reliability measurement information set includes the reliability measurement index set of all subsystems of the field dental clinic vehicle; the reliability measurement index set includes reliability measurement data sequence, failure rate measurement data sequence, mean time between failures measurement data sequence, failure rate measurement data sequence and reliable life measurement data sequence;

[0010] S2, preprocess the set of reliability measurement indicators for all subsystems to obtain a set of preprocessed measurement data;

[0011] S3, perform reliability fusion calculation on the preprocessed measurement data set to obtain the overall reliability assessment value of the field dental clinic vehicle.

[0012] The process of preprocessing the reliability measurement index set of all subsystems to obtain a preprocessed measurement data set includes:

[0013] S21, perform data noise reduction processing on the reliability measurement index set of each subsystem to obtain the corresponding first measurement data set;

[0014] S22, perform time alignment processing on the first set of measurement data for each subsystem to obtain the corresponding second set of measurement data;

[0015] S23, perform joint mode verification on the second measurement data set of each subsystem to obtain the corresponding preprocessed set of reliability measurement indicators;

[0016] S24. Using the preprocessed reliability measurement index set of all subsystems, a preprocessed measurement data set is constructed.

[0017] The reliability fusion calculation of the preprocessed measurement data set yields the overall reliability assessment value of the field dental clinic vehicle, including:

[0018] S31, perform item-by-item fusion evaluation on the preprocessed reliability measurement index set of each subsystem in the preprocessed measurement data set to obtain the characteristic value, boundary value and dynamic value of the reliability of each subsystem;

[0019] S32 performs fusion calculations on the characteristic values, boundary values, and dynamic values ​​of the reliability of all subsystems to obtain the overall reliability assessment value of the field dental clinic vehicle.

[0020] The preprocessed reliability measurement index set for each subsystem in the preprocessed measurement data set is subjected to item-by-item fusion evaluation to obtain the characteristic values, boundary values, and dynamic values ​​of the reliability of each subsystem, including:

[0021] S311, Obtain the set of reliability standard index values ​​for each subsystem; the set of reliability standard index values ​​includes reliability standard value, failure rate standard value, mean time between failures standard value, failure rate standard value, and reliability life standard value.

[0022] S312, For each subsystem, the preprocessed set of reliability measurement indicators is compared with the corresponding set of reliability standard indicator values ​​to calculate the difference, resulting in a set of difference sequences; the set of difference sequences includes difference sequences; the difference sequences are obtained by calculating the difference between each measurement data sequence and its corresponding standard value.

[0023] S313, Perform statistical calculations on the set of differential sequences to obtain the corresponding dynamic values;

[0024] S314, Perform feature boundary calculation on the set of differential sequences to obtain the corresponding feature values ​​and boundary values.

[0025] The expression for the statistical calculation is:

[0026]

[0027] Where Td represents the dynamic value, p ji Let be the probability distribution of the j-th difference sequence in the i-th value interval of the difference sequence set. This represents the number of combinations of selecting i distinct elements from P distinct elements, where P is the total number of values ​​in the range.

[0028] The step of calculating the feature boundaries of the differential sequence set to obtain the corresponding feature values ​​and boundary values ​​includes:

[0029] S3141, using the difference sequences in the set of difference sequences as row vectors, a difference matrix is ​​constructed;

[0030] S3142, calculate the rank T of the difference matrix;

[0031] S3143, Perform singular value decomposition on the difference matrix to obtain the feature matrix;

[0032] The computational expression for the singular value decomposition process is:

[0033] X = U∑V,

[0034] Where X is the difference matrix, ∑ is the characteristic matrix, U is the left matrix, and V is the right matrix;

[0035] S3144, The feature matrix and difference matrix are calculated and processed to obtain the corresponding eigenvalues ​​and boundary values.

[0036] The expression for calculating the eigenvalue is:

[0037]

[0038] Where, x i,j and ω1 and ω2 are the elements in the i-th row and j-th column of the difference matrix and the feature matrix, respectively, ω1 and ω2 are preset weighting factors, N and M are the row dimension and column dimension, respectively, and μ is the eigenvalue;

[0039] The expression for calculating the boundary value is:

[0040]

[0041] Where θ is the boundary value, max j＝1,2,…,M This indicates that for any value j from 1 to M, the maximum value in the brackets [] is taken. Let A represent the mean of the i-th row vector of the feature matrix. j and Let represent the j-th column vector of the difference matrix and the characteristic matrix, respectively.

[0042] A second aspect of this invention discloses an overall reliability assessment device for a field dental clinic vehicle, the device comprising:

[0043] Memory containing executable program code;

[0044] A processor coupled to the memory;

[0045] The processor calls the executable program code stored in the memory to execute the overall reliability assessment method of the field dental clinic vehicle.

[0046] In a third aspect of this invention, a computer-storable medium is disclosed, wherein the computer-storable medium stores computer instructions, and when the computer instructions are invoked by a computer, they are used to execute the overall reliability assessment method for the field dental clinic vehicle.

[0047] In a fourth aspect of this invention, an information data processing terminal is disclosed, which is used to implement the overall reliability assessment method for the field dental clinic vehicle.

[0048] The beneficial effects of this invention are as follows:

[0049] This invention systematically addresses the core shortcomings of traditional reliability assessments in areas such as heterogeneous data fusion, temporal feature utilization, and failure correlation analysis through a comprehensive innovation process involving multi-source data spatiotemporal alignment, joint verification of failure modes, dynamic distribution feature modeling, and system-level coupled evaluation. Its technological advantages lie not only in the high-precision design of individual computational models but also in the construction of a closed-loop evaluation system encompassing "data cleaning, pattern mining, feature extraction, and global fusion." This system can accurately quantify the reliability level of mobile medical vehicles in complex field environments, providing a scientific basis for equipment design improvements and maintenance strategy formulation, and significantly enhancing the mission success rate and survivability of equipment under extreme conditions.

[0050] This invention employs a noise reduction algorithm adapted to the characteristics of reliability data (such as outlier filtering based on a sliding window) to eliminate noise such as failure rate jumps and abnormal spikes in mean fault interval time caused by transient interference from sensors, ensuring that the original data accurately reflects the actual failure patterns of the subsystem. This invention unifies heterogeneous data from different subsystems (such as vibration data sampled at the second level from the chassis and fault data recorded at the minute level from the diagnostic module) to the same time coordinate system through linear interpolation or synchronous resampling techniques, solving the time-series misalignment problem caused by differences in sampling frequencies and laying the foundation for subsequent cross-system correlation analysis. Attached Figure Description

[0051] Figure 1 This is a flowchart illustrating the implementation of the method of the present invention. Detailed Implementation

[0052] To better understand the content of this invention, an embodiment is provided here.

[0053] Figure 1 This is a flowchart illustrating the implementation of the method of the present invention.

[0054] In a first aspect, this invention discloses a method for assessing the overall reliability of a field dental clinic vehicle. The field dental clinic vehicle's subsystems include a vehicle chassis, a modular cabin, a dental treatment module, an information communication module, and a support module. The method includes:

[0055] S1, Collect the subsystem reliability measurement information set of the field dental clinic vehicle; the subsystem reliability measurement information set includes the reliability measurement index set of all subsystems of the field dental clinic vehicle; the reliability measurement index set includes reliability measurement data sequence, failure rate measurement data sequence, mean time between failures measurement data sequence, failure rate measurement data sequence and reliable life measurement data sequence;

[0056] S2, preprocess the set of reliability measurement indicators for all subsystems to obtain a set of preprocessed measurement data;

[0057] S3, perform reliability fusion calculation on the preprocessed measurement data set to obtain the overall reliability assessment value of the field dental clinic vehicle.

[0058] The process of preprocessing the reliability measurement index set of all subsystems to obtain a preprocessed measurement data set includes:

[0059] S21, perform data noise reduction processing on the reliability measurement index set of each subsystem to obtain the corresponding first measurement data set;

[0060] S22, perform time alignment processing on the first set of measurement data for each subsystem to obtain the corresponding second set of measurement data;

[0061] S23, perform joint mode verification on the second measurement data set of each subsystem to obtain the corresponding preprocessed set of reliability measurement indicators;

[0062] S24. Using the preprocessed reliability measurement index set of all subsystems, a preprocessed measurement data set is constructed.

[0063] The reliability fusion calculation of the preprocessed measurement data set yields the overall reliability assessment value of the field dental clinic vehicle, including:

[0064] S31, perform item-by-item fusion evaluation on the preprocessed reliability measurement index set of each subsystem in the preprocessed measurement data set to obtain the characteristic value, boundary value and dynamic value of the reliability of each subsystem;

[0065] S32 performs fusion calculations on the characteristic values, boundary values, and dynamic values ​​of the reliability of all subsystems to obtain the overall reliability assessment value of the field dental clinic vehicle.

[0066] The preprocessed reliability measurement index set for each subsystem in the preprocessed measurement data set is subjected to item-by-item fusion evaluation to obtain the characteristic values, boundary values, and dynamic values ​​of the reliability of each subsystem, including:

[0067] S311, Obtain the set of reliability standard index values ​​for each subsystem; the set of reliability standard index values ​​includes reliability standard value, failure rate standard value, mean time between failures standard value, failure rate standard value, and reliability life standard value.

[0068] S312, For each subsystem, the preprocessed set of reliability measurement indicators is compared with the corresponding set of reliability standard indicator values ​​to calculate the difference, resulting in a set of difference sequences; the set of difference sequences includes difference sequences; the difference sequences are obtained by calculating the difference between each measurement data sequence and its corresponding standard value.

[0069] S313, Perform statistical calculations on the set of differential sequences to obtain the corresponding dynamic values;

[0070] S314, Perform feature boundary calculation on the set of differential sequences to obtain the corresponding feature values ​​and boundary values.

[0071] The expression for the statistical calculation is:

[0072]

[0073] Where Td represents the dynamic value, p ji Let be the probability distribution of the j-th difference sequence in the set of difference sequences in the i-th value interval, which can be obtained by statistically analyzing the probability of the difference sequence in each value interval. This represents the number of combinations of selecting i distinct elements from P distinct elements, where P is the total number of values ​​in the range.

[0074] The numerator of the statistical calculation measures the deviation of the differential sequence from uniformity in each value interval by multiplying the combination weights and probability deviations (e.g., the numerator value increases when the failure rate difference is concentrated in the high-risk interval); the denominator is normalized by the standard deviation of the exponential function and the sine function to suppress the excessive influence of outliers on the dynamic value, while preserving the distribution morphology characteristics (e.g., the sine function fluctuation corresponding to periodic fluctuations), and finally outputs a comprehensive index Td that reflects the dynamic distribution characteristics of the differential sequence.

[0075] The first to fifth difference sequences are the difference sequences for reliability, failure rate, mean time between failures, failure rate, and reliable lifetime, respectively.

[0076] The step of calculating the feature boundaries of the differential sequence set to obtain the corresponding feature values ​​and boundary values ​​includes:

[0077] S3141, using the difference sequences in the set of difference sequences as row vectors, a difference matrix is ​​constructed;

[0078] S3142, calculate the rank T of the difference matrix;

[0079] S3143, Perform singular value decomposition on the difference matrix to obtain the feature matrix;

[0080] The computational expression for the singular value decomposition process is:

[0081] X = U∑V,

[0082] Where X is the difference matrix, ∑ is the characteristic matrix, U is the left matrix, and V is the right matrix;

[0083] S3144, The feature matrix and difference matrix are calculated and processed to obtain the corresponding eigenvalues ​​and boundary values.

[0084] The expression for calculating the eigenvalue is:

[0085]

[0086] Where, x i,j and ω1 and ω2 are the elements in the i-th row and j-th column of the difference matrix and the feature matrix, respectively, ω1 and ω2 are preset weighting factors, N and M are the row dimension and column dimension, respectively, and μ is the eigenvalue;

[0087] The expression for calculating the boundary value is:

[0088]

[0089] Where θ is the boundary value, max j＝1,2,…,M This indicates that for any value j from 1 to M, the maximum value in the brackets [] is taken. Let A represent the mean of the i-th row vector of the feature matrix. j and Let represent the j-th column vector of the difference matrix and the characteristic matrix, respectively.

[0090] By decomposing the difference matrix into a left matrix (subsystem failure mode), a feature matrix (Σ, indicator contribution), and a right matrix (time correlation), the key features that dominate the reliability fluctuations of the subsystem (such as the dominant influence of the largest singular value in Σ on the chassis failure rate) are extracted.

[0091] The eigenvalue expression integrates the local deviation of exponential decay (reflecting the closeness of data points to the mean) with the global correlation of the sine logarithm (characterizing the coordinated fluctuation characteristics between indicators), and balances local stability and global coupling through the weighting factor ω1 / ω2 to quantify the comprehensive characteristics of the reliability of the subsystem.

[0092] Boundary value expressions, based on the maximum deviation and directional consistency of column vectors (vector dot product normalization term), determine the safety boundary of subsystem reliability (e.g., when a column vector deviates from the mean of the feature matrix by more than the boundary value, the indicator is determined to enter the risk range).

[0093] The difference calculation can be a subtraction calculation, that is, subtracting the data sequence in the preprocessed reliability measurement index set of each subsystem from the standard value in the corresponding reliability standard index value set to obtain the corresponding difference sequence;

[0094] The expression for the difference calculation can also be:

[0095]

[0096] in, Let α be the i-th element of the differential sequence, and α0 be the corresponding standard value. i Let i be the i-th element of the measurement data sequence.

[0097] The reliability is the probability that a subsystem will complete its intended function within a specified time.

[0098] The failure rate is the probability that a subsystem fails per unit of time.

[0099] The mean time between failures (MTBF) is the average time interval between two consecutive failures of a subsystem.

[0100] The failure rate is the frequency of failure of a subsystem per unit time.

[0101] The reliable lifetime is the time it takes for the reliability of a subsystem to decrease to a specified value R.

[0102] In the overall reliability fusion stage, a reliability model considering multi-system coupling is constructed by fusing the three dimensions of subsystem eigenvalues ​​(reflecting their own reliability level), boundary values ​​(safety thresholds), and dynamic values ​​(distribution fluctuations). Subsystems with low eigenvalues ​​and high dynamic values ​​(such as chassis with large reliability fluctuations) are given higher weights to highlight their negative impact on overall reliability. Boundary values ​​are used to cross-validate the failure propagation path between subsystems (such as when the vibration of the cabin exceeds the limit boundary value, the reliability boundary value of the diagnostic and treatment module decreases synchronously), thus upgrading from "single system assessment" to "system-level failure chain analysis".

[0103] The joint verification process for the patterns includes:

[0104] S231, for the second measurement data set of each subsystem, a corresponding measurement matrix is ​​constructed; the row vector of the measurement matrix is ​​the measurement data sequence in the second measurement data set;

[0105] S232, for each measurement data sequence corresponding to the measurement time series of the second measurement data set of each subsystem, a time matrix is ​​constructed by taking the row vector;

[0106] The measurement time series corresponding to the measurement data sequence is a sequence formed by using the measurement time of each measurement data in the measurement data sequence.

[0107] S233, Based on the time matrix and measurement matrix, a pattern matching model is constructed;

[0108] S234, Solve the pattern matching model to obtain the matching matrix for each subsystem;

[0109] S235, calculate the set of singular values ​​of the matching matrix;

[0110] S236, Using the set of singular values ​​as the coefficients of the polynomial, and using the total number of singular values ​​in the set of singular values ​​minus 1 as the order of the matching polynomial, a matching polynomial is constructed;

[0111] Using the set of singular values ​​as the coefficients of the polynomial means that the singular value corresponding to the first singular value vector in the set of singular values ​​is the coefficient of the highest order of the polynomial, and so on, with the singular value corresponding to the second singular value vector in the set of singular values ​​being the coefficient of the second highest order of the polynomial, and the singular value corresponding to the last singular value vector in the set of singular values ​​being the constant term of the polynomial.

[0112] S237, for each element of each measurement data sequence in the second measurement data set, with the measurement time of the element as the independent variable, input the matching polynomial to calculate the corresponding matching value, calculate the absolute value of the difference between the element and the corresponding matching value, and determine whether the absolute value of the difference is less than or equal to a preset threshold value. If it is not less than, delete the element from the measurement data sequence.

[0113] S238, for each element of each measurement data sequence in the second measurement data set of a subsystem, execute S237 to obtain the preprocessed reliability measurement index set of the subsystem.

[0114] The pattern matching model is expressed as follows:

[0115] min|TA-D|,

[0116] subject to AA T =I A ,

[0117] Among them, I A Let T represent the identity matrix with the row dimension of matrix A as its dimension, where matrix A represents the matrix to be solved, T represents the time matrix, and D represents the measurement matrix.

[0118] This invention constructs a pattern matching model using a measurement matrix (data sequence) and a time matrix (timestamp sequence). It extracts the spatiotemporal key features of the data (such as seasonal fluctuations in chassis failure frequency) using singular value decomposition, and fits the mapping relationship between time and data using a matching polynomial (e.g., time as the independent variable and reliability prediction as the dependent variable). Based on the matching polynomial, it calculates the theoretical matching value for each time point. By judging the absolute value of the difference (e.g., deleting outliers if they exceed a preset threshold), it identifies and eliminates "pseudo-reasonable data" that violates physical laws (e.g., contradictory data where the reliability of the diagnostic module remains 1 even when the power supply to the protection module is interrupted), ensuring that the data pattern conforms to the subsystem failure logic.

[0119] The algorithm for solving the pattern matching model can be a genetic algorithm or a particle filter algorithm.

[0120] The data noise reduction process can be implemented using Gaussian filtering and mean filtering algorithms.

[0121] The time alignment process unifies different types of measurement data onto the same time reference, which can be achieved using a time registration algorithm. The time registration algorithm can employ methods such as extrapolation / extrapolation or Lagrange three-point interpolation.

[0122] The measurement data sequence for each indicator is a sequence of measurement data obtained at several measurement times for that indicator.

[0123] The support module is used to provide water, power and disinfection support for the field dental clinic vehicle.

[0124] A second aspect of this invention discloses an overall reliability assessment device for a field dental clinic vehicle, the device comprising:

[0125] Memory containing executable program code;

[0126] A processor coupled to the memory;

[0127] The processor calls the executable program code stored in the memory to execute the overall reliability assessment method of the field dental clinic vehicle.

[0128] In a third aspect of this invention, a computer-storable medium is disclosed, wherein the computer-storable medium stores computer instructions, and when the computer instructions are invoked by a computer, they are used to execute the overall reliability assessment method for the field dental clinic vehicle.

[0129] In a fourth aspect of this invention, an information data processing terminal is disclosed, which is used to implement the overall reliability assessment method for the field dental clinic vehicle.

[0130] The above description is merely an embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the present invention should be included within the scope of the claims of the present invention.

Claims

1. A method for assessing the overall reliability of a mobile dental clinic in the field, characterized in that, The subsystem of the field dental clinic vehicle includes a vehicle chassis, a modular cabin, a dental treatment module, an information communication module, and a support module. The method includes: S1, Collect the subsystem reliability measurement information set of the field dental clinic vehicle; the subsystem reliability measurement information set includes the reliability measurement index set of all subsystems of the field dental clinic vehicle; the reliability measurement index set includes reliability measurement data sequence, failure rate measurement data sequence, mean time between failures measurement data sequence, failure rate measurement data sequence and reliable life measurement data sequence; S2, preprocess the set of reliability measurement indicators for all subsystems to obtain a set of preprocessed measurement data; S3, perform reliability fusion calculation on the preprocessed measurement data set to obtain the overall reliability assessment value of the field dental clinic vehicle.

2. The method for assessing the overall reliability of a field dental clinic vehicle as described in claim 1, characterized in that, The process of preprocessing the reliability measurement index set of all subsystems to obtain a preprocessed measurement data set includes: S21, perform data noise reduction processing on the reliability measurement index set of each subsystem to obtain the corresponding first measurement data set; S22, perform time alignment processing on the first set of measurement data for each subsystem to obtain the corresponding second set of measurement data; S23, perform joint mode verification on the second measurement data set of each subsystem to obtain the corresponding preprocessed set of reliability measurement indicators; S24. Using the preprocessed reliability measurement index set of all subsystems, a preprocessed measurement data set is constructed.

3. The method for assessing the overall reliability of a field dental clinic vehicle as described in claim 1, characterized in that, The reliability fusion calculation of the preprocessed measurement data set yields the overall reliability assessment value of the field dental clinic vehicle, including: S31, perform item-by-item fusion evaluation on the preprocessed reliability measurement index set of each subsystem in the preprocessed measurement data set to obtain the characteristic value, boundary value and dynamic value of the reliability of each subsystem; S32 performs fusion calculations on the characteristic values, boundary values, and dynamic values ​​of the reliability of all subsystems to obtain the overall reliability assessment value of the field dental clinic vehicle.

4. The method for assessing the overall reliability of a field dental clinic vehicle as described in claim 3, characterized in that, The preprocessed reliability measurement index set for each subsystem in the preprocessed measurement data set is subjected to item-by-item fusion evaluation to obtain the characteristic values, boundary values, and dynamic values ​​of the reliability of each subsystem, including: S311, Obtain the set of reliability standard index values ​​for each subsystem; the set of reliability standard index values ​​includes reliability standard value, failure rate standard value, mean time between failures standard value, failure rate standard value, and reliability life standard value. S312, For each subsystem, the preprocessed set of reliability measurement indicators is compared with the corresponding set of reliability standard indicator values ​​to calculate the difference, resulting in a set of difference sequences; the set of difference sequences includes difference sequences; the difference sequences are obtained by calculating the difference between each measurement data sequence and its corresponding standard value. S313, Perform statistical calculations on the set of differential sequences to obtain the corresponding dynamic values; S314, Perform feature boundary calculation on the set of differential sequences to obtain the corresponding feature values ​​and boundary values.

5. The method for assessing the overall reliability of a field dental clinic vehicle as described in claim 4, characterized in that, The expression for the statistical calculation is: Where Td represents the dynamic value, p ji Let be the probability distribution of the j-th difference sequence in the i-th value interval of the difference sequence set. This represents the number of combinations of selecting i distinct elements from P distinct elements, where P is the total number of values ​​in the range.

6. The method for assessing the overall reliability of a field dental clinic vehicle as described in claim 4, characterized in that, The step of calculating the feature boundaries of the differential sequence set to obtain the corresponding feature values ​​and boundary values ​​includes: S3141, using the difference sequences in the set of difference sequences as row vectors, a difference matrix is ​​constructed; S3142, calculate the rank T of the difference matrix; S3143, Perform singular value decomposition on the difference matrix to obtain the feature matrix; The computational expression for the singular value decomposition process is: X = U∑V, Where X is the difference matrix, ∑ is the characteristic matrix, U is the left matrix, and V is the right matrix; S3144, The feature matrix and difference matrix are calculated and processed to obtain the corresponding eigenvalues ​​and boundary values.

7. The method for assessing the overall reliability of a field dental clinic vehicle as described in claim 4, characterized in that, The expression for calculating the eigenvalue is: Where, x i,j and ω1 and ω2 are the elements in the i-th row and j-th column of the difference matrix and the feature matrix, respectively, ω1 and ω2 are preset weighting factors, N and M are the row dimension and column dimension, respectively, and μ is the eigenvalue; The expression for calculating the boundary value is: in, For boundary values, max j＝1,2,…,M This indicates that for any value j from 1 to M, the maximum value in the brackets [] is taken. Let A represent the mean of the i-th row vector of the feature matrix. j and Let represent the j-th column vector of the difference matrix and the characteristic matrix, respectively.

8. A device for evaluating the overall reliability of a mobile dental clinic, characterized in that, The device includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the overall reliability assessment method for the field dental clinic vehicle as described in any one of claims 1 to 7.

9. A computer-storable medium, characterized in that, The computer storage medium stores computer instructions, which, when invoked by the computer, are used to execute the overall reliability assessment method for the field dental clinic vehicle as described in any one of claims 1 to 7.

10. An information data processing terminal, characterized in that, The information data processing terminal is used to implement the overall reliability assessment method for the field dental clinic vehicle as described in any one of claims 1 to 7.

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