Aircraft production and operation unit performance evaluation method and system

Abstract

The invention discloses an aircraft production and operation unit performance evaluation method and system, and the method comprises the steps: S1, constructing an evaluation index system and historical sample evaluation data, and carrying out the data normalization processing of the historical sample evaluation data; s2, an index weight assignment model is constructed, the normalized historical sample evaluation data are input into the historical sample evaluation data, and the index weight assignment model comprises four calculation modules which respectively obtain weights A1-A4 of indexes; s3, constructing a combination evaluation model based on deviation maximization, and calculating to obtain a combination evaluation weight corresponding to the index; s4, constructing a topological network by taking the indexes as network nodes, and correcting the weights of the indexes through the topological network; and S4, evaluating based on the evaluation index system to obtain evaluation data of each index, and calculating to obtain an efficiency evaluation result. Effective evaluation of the event investigation efficiency of the aircraft production and operation unit is realized, the efficiency condition can be known conveniently, and the core investigation capability and the safety management efficiency can be improved.

Description

Technical Field

[0001] The present invention relates to the field of event investigation effectiveness evaluation, and in particular to an aircraft production and operation unit effectiveness evaluation method and system. Background Art

[0002] Safety is the lifeline of civil aviation and the eternal theme of civil aviation; aircraft production and operation units are important units for civil aircraft incident investigation and an important component of civil aviation safety management. In order to strengthen the construction of the incident investigation system of aircraft production and operation units, the Civil Aviation Administration of China issued and implemented the "Guidelines for the Construction of Aircraft Incident Investigation System for Civil Aviation Production and Operation Units" (Civil Aviation General Safety Development (2020) No. 5) in 2020. All aircraft production and operation units attach great importance to it, take immediate action and actively promote the construction of the incident investigation system, continue to carry out in-depth investigation practice, further improve the investigation, continuously improve the ability of investigators, and continuously show the effectiveness of the investigation.

[0003] It is an important task for aircraft production and operation units to conduct aircraft incident investigations. It is a complex and systematic work involving multiple departments and multiple personnel. Aircraft production and operation units need to evaluate the construction and effectiveness of the incident investigation system. Civil aviation management units also need to effectively evaluate the system effectiveness of different aircraft production and operation units. However, since the incident investigation system involves many sections and projects, generally including investigation organizations or institutions, emergency response investigation work manuals, investigator teams, investigation equipment or equipment, engineering test analysis, safety recommendation management, etc., these sections and projects each include subordinate specific projects. The incident investigation system is constructed in sequence according to the subordinate hierarchy. How to conduct system effectiveness evaluation is a major problem for management departments to perform management and supervision. Effectiveness evaluation can effectively discover weak links, make up for shortcomings and weaknesses, continuously consolidate and deepen incident investigation work, improve system maturity, and strive to build a more scientific, reasonable, and safety-prominent investigation system to better play the actual role of investigation in promoting safety. Therefore, evaluating the effectiveness of the incident investigation system of aircraft production and operation units is a current difficulty and key task. It is also an important measure to promote aircraft production and operation units to conduct safety investigations, safe production, and safe research and development at the source. It plays a positive role in promoting safety and urgently needs to carry out evaluation research. Summary of the invention

[0004] The purpose of the present invention is to solve the technical problems pointed out by the background technology, and to provide an aircraft production and operation unit performance evaluation method and system, which adopts an indicator weight assignment model, a combined evaluation model, and a topological network to process historical sample evaluation data in sequence, so as to obtain the weight of each indicator in the evaluation indicator system, and then multiply the evaluation data of the indicator by the corresponding weight to obtain the indicator evaluation result corresponding to the indicator item, and then add up all the indicator evaluation results to obtain the final performance evaluation result.

[0005] The purpose of the present invention is achieved through the following technical solutions:

[0006] A method for evaluating the effectiveness of an aircraft production and operation unit, the method comprising:

[0007] S1. Construct an evaluation index system including an index item library, which includes several upper and lower level indicators and corresponding evaluation criteria; construct historical sample evaluation data, which is obtained by sequentially evaluating the indicators based on the evaluation index system, and perform data normalization on the historical sample evaluation data;

[0008] S2. Construct an indicator weight assignment model, input the normalized historical sample evaluation data into the indicator weight assignment model, and the indicator weight assignment model includes four calculation modules. The first calculation module in the indicator weight assignment model uses the hierarchical analysis method to compare the indicators as factors to obtain the judgment matrix A and calculate its maximum eigenvalue λ max and normalized eigenvectors, and the weights A1 corresponding to each indicator are obtained after consistency test; the second calculation module in the indicator weight assignment model uses the priority diagram method to calculate the importance of each pair of indicators and obtain the importance matrix, and the indicator importance ratio is used as the weight A2; the third calculation module in the indicator weight assignment model uses the entropy weight method to calculate the contribution and entropy weight of the indicator, and the entropy weight is used as the weight A3 corresponding to the indicator; the fourth calculation module in the indicator weight assignment model uses the coefficient of variation method to calculate the mean and standard deviation of the indicator, and then calculates the coefficient of variation of the indicator and normalizes it to obtain the weight A4 corresponding to the indicator;

[0009] S3. Construct a combined evaluation model based on maximum deviation, take the indicator as the evaluation object and obtain the combined evaluation weight corresponding to indicator i according to the following formula where f ip represents the evaluation value corresponding to the pth calculation module of indicator i in the indicator weight assignment model, f tp It represents the evaluation value corresponding to the p-th calculation module of indicator t in the indicator weight assignment model, and n represents the total number of indicators;

[0010] S4. Use the index as the network node to build a topological network. The topological potential Φ(v j ) is expressed as: where d ij represents the difference in combined evaluation weights between indicators i and j, β i Represents the node attribute value of indicator i, the node attribute value is the combined evaluation weight of indicator i, and δ represents the control coefficient; the weight of indicator j after topological network correction is obtained according to the following formula

[0011] S5: Based on the evaluation index system, the evaluation data of each indicator is obtained, and the evaluation data of each indicator is normalized and combined with the weight obtained in step S4. The performance evaluation result is obtained by multiplying the corresponding indicators and adding up the multiplication results of all indicators.

[0012] In order to better implement the aircraft production and operation unit performance evaluation method of the present invention, in step S5, the evaluation data of each indicator before normalization processing also includes the following data updating processing method: calculating the mission completion ratio of the indicator Where P a represents the actual evaluation data of the indicator, P m Indicates the expected evaluation data set by the indicator; if the mission completion ratio is less than 1, the evaluation data of the corresponding indicator is updated to Otherwise no processing is done.

[0013] Preferably, in step S2, the judgment matrix A is expressed as follows: where a ij Indicates that index i corresponds to element x i The element x corresponding to index j j The impact ratio.

[0014] Preferably, in step S2, the importance matrix expression is as follows: where b ij Represents the relative importance value between indicator i and indicator j; add the matrix B to get the total value T n :

[0015] The weight A2 formula of a certain indicator is as follows: b i It represents the sum of the data in the row where indicator i is located in the importance matrix.

[0016] Preferably, in step S2, the third calculation module uses the entropy weight method to obtain the discriminant matrix P j : where x′ j It represents the dimensionless data of indicator j; the contribution of indicator j is E j The expression is as follows:

[0017] The weight A3 of indicator j is expressed as follows: Where D j =1-E j .

[0018] Preferably, in step S2, the fourth calculation module uses the coefficient of variation method to obtain the coefficient of variation expression of the indicator j as follows: in represents the normalized mean of index j, S j It represents the standard deviation of index j after normalization;

[0019] After normalizing the coefficients of variation of all indicators, the weight A4 expression corresponding to indicator j is obtained as follows:

[0020]

[0021] Preferably, in step S4, the weight matrix expression of the network nodes in the topological network is as follows:

[0022]

[0023] Preferably, in step S1, the data normalization processing formula of the historical sample evaluation data is as follows:

[0024] X nor Indicates that the indicator corresponds to the standardized data, X min Indicates the minimum data in the corresponding data of the indicator, X max It indicates the maximum data among the data corresponding to the indicator, and X indicates the data corresponding to the indicator.

[0025] An aircraft production and operation unit performance evaluation system includes an indicator weight assignment model, a combined evaluation model, an evaluation indicator system, historical sample evaluation data, a topological network and an efficiency evaluation module. The evaluation indicator system is constructed according to an indicator item library, and the indicator item library includes a number of upper and lower level indicators and corresponding evaluation standards; the historical sample evaluation data is obtained by sequentially evaluating the indicators based on the evaluation indicator system, and the historical sample evaluation data is subjected to data normalization processing; the indicator weight assignment model is used to receive the input normalized historical sample evaluation data and perform the following processing: the weighting model includes a hierarchical analysis calculation module, a priority diagram calculation module, an entropy weight calculation module, and a coefficient of variation calculation module, and the hierarchical analysis calculation module adopts a hierarchical analysis method to compare the indicators as factors to obtain a judgment matrix A and calculate its maximum eigenvalue λ max and normalized eigenvectors, and the weights A1 corresponding to each indicator are obtained through consistency test; the priority diagram calculation module uses the priority diagram method to calculate the importance of each pair of indicators and obtain the importance matrix, and uses the indicator importance ratio as the weight A2; the entropy weight calculation module uses the entropy weight method to calculate the contribution and entropy weight of the indicator, and uses the entropy weight as the weight A3 corresponding to the indicator; the coefficient of variation method calculation module uses the coefficient of variation method to calculate the mean and standard deviation of the indicator, and then calculates the coefficient of variation of the indicator and normalizes it to obtain the weight A4 corresponding to the indicator;

[0026] The combined evaluation model is constructed based on the maximum deviation. The combined evaluation model takes the index as the evaluation object and obtains the combined evaluation weight corresponding to the index i according to the following formula

[0027] where f ip It represents the evaluation value corresponding to the p-th calculation module of indicator i in the indicator weight assignment model, P represents the total number of calculation modules in the indicator weight assignment model, and n represents the total number of indicators;

[0028] The topological network is constructed with indicators as network nodes, and the topological potential Φ(v j ) is expressed as: where d ij represents the difference in combined evaluation weights between indicators i and j, β i Represents the node attribute value of indicator i, the node attribute value is the combined evaluation weight of indicator i, and δ represents the control coefficient; the weight of indicator j after topological network correction is obtained according to the following formula

[0029] The performance evaluation module obtains evaluation data of each indicator based on the evaluation index system, normalizes the evaluation data of each indicator and adds them to the weights. The performance evaluation result is obtained by multiplying the corresponding indicators and adding up the multiplication results of all indicators.

[0030] In order to better implement the aircraft production and operation unit performance evaluation system of the present invention, the performance evaluation module calculates the mission completion ratio of the index Where P a represents the actual evaluation data of the indicator, P m Indicates the expected evaluation data set by the indicator; if the mission completion ratio is less than 1, the evaluation data of the corresponding indicator is updated to Otherwise, no processing is done; the evaluation data of each indicator after update is normalized and combined with the weight The performance evaluation result is obtained by multiplying the corresponding indicators and adding up the multiplication results of all indicators.

[0031] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0032] (1) The present invention uses an indicator weight assignment model, a combined evaluation model, and a topological network to process the historical sample evaluation data in sequence, so as to obtain the weight of each indicator in the evaluation indicator system, and then multiply the evaluation data of the indicator by the corresponding weight to obtain the indicator evaluation result corresponding to the indicator item, and then add up all the indicator evaluation results to obtain the final performance evaluation result.

[0033] (2) The present invention constructs an evaluation index system that can scientifically evaluate the effectiveness of a complex event investigation system, achieve effective evaluation of the event investigation effectiveness of aircraft production and operation units, and obtain quantitative single indicator evaluation results and overall effectiveness evaluation results, which is convenient for understanding the effectiveness and is conducive to improving core investigation capabilities and safety management effectiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 The present invention is a flowchart of the method for evaluating the effectiveness of an aircraft production and operation unit. DETAILED DESCRIPTION

[0035] The present invention is further described in detail below in conjunction with embodiments:

[0036] Example

[0037] like Figure 1 As shown, a method for evaluating the effectiveness of an aircraft production and operation unit includes:

[0038] S1. Construct an evaluation index system including an index item library, which includes several upper and lower level indicators and corresponding evaluation standards. In this embodiment, the contents that need to be evaluated by the aircraft production and operation unit include six first-level items, namely, investigation organization or institution, emergency response investigation work manual, investigator team, investigation equipment or equipment, engineering test analysis, and safety suggestion management. Each first-level project includes several second-level projects. For example, the first-level project of the investigation organization or institution includes four second-level projects, namely, document manual, organizational structure, departmental responsibilities, and investigation mechanism. For example, the first-level project of the investigator team includes five second-level projects, namely, investigator training, selection and appointment, responsibilities and authority, investigator file and certificate management, and occupational health protection. Each second-level project includes several third-level projects. For example, the second-level project of the document manual includes three-level projects such as clarifying the responsibilities of each department in the investigation and the qualification requirements of the relevant personnel in the investigation. For example, the second-level project of selection and appointment includes three-level projects such as authorization of qualified investigators, number requirements of qualified investigators, and professional coverage of selected investigators that meet the requirements. Therefore, the indicators of the indicator library in the evaluation indicator system of the present invention are also extracted according to the content that the aircraft production and operation unit needs to evaluate and constructed according to the corresponding level, and at the same time, the corresponding evaluation standards are constructed according to the level. The evaluation indicator system constructed in this embodiment has 6 first-level indicators and 28 second-level indicators respectively, and several third-level indicators.

[0039] Construct historical sample evaluation data. The historical sample evaluation data is obtained by evaluating in sequence according to the indicators based on the evaluation index system (the historical sample evaluation data is evaluated according to the evaluation index system to obtain its corresponding evaluation value. The evaluation value is generally a score, also known as the data obtained by the evaluation corresponding to the indicator. The evaluation value of a certain indicator corresponds to the content of the corresponding level that the aircraft production and operation unit needs to evaluate), and perform data normalization on the historical sample evaluation data.

[0040] In some embodiments, the data normalization processing formula for historical sample evaluation data is as follows:

[0041] X nor Indicates that the indicator corresponds to the standardized data, X min Indicates the minimum data in the corresponding data of the indicator, X max It indicates the maximum data among the data corresponding to the indicator, and X indicates the data corresponding to the indicator.

[0042] S2. Construct an indicator weight assignment model, input the normalized historical sample evaluation data into the indicator weight assignment model, and the indicator weight assignment model includes four calculation modules. The first calculation module in the indicator weight assignment model uses the hierarchical analysis method to compare the indicators as factors to obtain the judgment matrix A and calculate its maximum eigenvalue λ max And the normalized eigenvector, the weight A1 corresponding to each indicator is obtained through consistency test; the judgment matrix A of the first calculation module in the indicator weight assignment model is expressed as follows:

[0043] where a ij Indicates that index i corresponds to element x i The element x corresponding to index j j Then find its maximum eigenvalue λ max and eigenvector W, based on the largest eigenvalue λ max A double test of the consistency index CI and the consistency ratio CR is performed. After the test is passed, the feature vector W is normalized, thereby obtaining the normalized feature vector corresponding to each index and using them as the weight A1 in turn.

[0044] In some embodiments, the maximum eigenvalue maxλ of the judgment matrix A is calculated

[0045] Calculate the consistency index CI (Consistency Index)

[0046]

[0047] Check the following table to obtain the corresponding average random consistency index RI (Random Index), as shown in the following table:

[0048] Table 3 Average random consistency index values

[0049] Matrix order n 2 3 4 5 6 7 8 9 10 … RI value 0 0.58 0.90 1.12 1.24 1.32 1.41 1.45 1.49 …

[0050] Calculate the consistency ratio CR (Consistency Ratio)

[0051]

[0052] Make a consistency judgment: When CR < 0.1, the judgment matrix A has satisfactory consistency and the analysis result is accepted; on the contrary, when CR ≥ 0.1, the judgment matrix A does not have satisfactory consistency and needs to be modified.

[0053] The above steps result in a single-layer ranking, which is the weight vector of a group of elements to an element in the previous layer. The total hierarchical ranking is to calculate the relative importance weight of the lowest factor relative to the highest factor layer by layer from top to bottom along the recursive hierarchy structure, that is, the importance weight of all factors at a certain level relative to the highest level. The total ranking weight is to synthesize the weights under the single criteria from top to bottom.

[0054] Assume that the previous layer A contains A1, A2, ..., A m There are m factors in total, and the hierarchical weights are a1, a2, ..., a m , and its next layer B contains n factors B1, B2, ..., B n , about A j The hierarchical single sorting weights are b 1j , b 2j , ..., b rj . Use the weight synthesis method to find the total hierarchical ranking weights b1, b2, ..., b of each factor in layer B. n ,Right now The calculation method is as follows:

[0055] Table 4. Hierarchical total ranking weight synthesis method

[0056]

[0057] The weight vector obtained by the total hierarchical sorting still needs to be checked for consistency to verify whether the result is satisfactory. Suppose the consistency index based on the jth factor of the k-1th layer is The average random consistency index is Then the comprehensive indicators of the kth layer are:

[0058]

[0059] If CR (k)<0.1, it is considered that the judgments above the kth level of the hierarchy have overall satisfactory consistency and the analysis results are accepted; on the contrary, if CR (k) When ≥0.1, it is considered that the judgments above the kth level of the hierarchy do not have overall satisfactory consistency, and the judgment matrix needs to be corrected. In practical applications, if the single-level sorting has been tested to have satisfactory consistency, there is no need to perform a satisfactory consistency test on the whole, because it is not only very difficult to consider the whole, but also very difficult to adjust the overall judgment matrix.

[0060] The second calculation module in the indicator weight assignment model uses the priority diagram method to calculate the importance of each pair of indicators and obtain the importance matrix, with the indicator importance ratio as the weight A2. The importance matrix expression of the second calculation module in the indicator weight assignment model is as follows: where b ij Represents the relative importance value between indicator i and indicator j; add the matrix B to get the total value T n : The weight A2 formula of a certain indicator (a certain indicator calculated by the second calculation module, such as indicator i) is as follows: b i It represents the sum of the data in the row where indicator i is located in the importance matrix.

[0061] The third calculation module in the indicator weight assignment model uses the entropy weight method to calculate the contribution and entropy weight of the indicator, and uses the entropy weight as the weight A3 corresponding to the indicator. The third calculation module uses the entropy weight method to obtain the discriminant matrix P j : where x′ j It represents the dimensionless data of indicator j; the contribution of indicator j is E j The expression is as follows:

[0062] The weight A3 of indicator j is expressed as follows: Where D j =1-E j ,,0<W j <1, and ∑Wj ＝ 1.

[0063] The fourth calculation module in the indicator weight assignment model uses the coefficient of variation method to find the mean and standard deviation of the indicator, and then calculates the coefficient of variation of the indicator and normalizes it to obtain the weight A4 corresponding to the indicator. The fourth calculation module uses the coefficient of variation method to obtain the coefficient of variation expression of indicator j as follows: in represents the normalized mean of index j, S j It represents the standard deviation of index j after normalization;

[0064] After normalizing the coefficients of variation of all indicators, the weight A4 expression corresponding to indicator j is obtained as follows:

[0065]

[0066] S3. Construct a combined evaluation model based on maximum deviation, take the indicator as the evaluation object and obtain the combined evaluation weight corresponding to indicator i according to the following formula where f ip represents the evaluation value corresponding to the pth calculation module of indicator i in the indicator weight assignment model, f tp It represents the evaluation value corresponding to the pth calculation module of indicator t in the indicator weight assignment model, and n represents the total number of indicators.

[0067] S4. Use the index as the network node to build a topological network. The topological potential Φ(v j ) is expressed as: where d ij represents the difference in combined evaluation weights between indicators i and j, β i = represents the node attribute value of index i, the node attribute value is the combined evaluation weight of index i (the present invention uses the combined evaluation weight of index i as the node attribute value of index i; that is, the topological network uses the index as the network node, and the node attribute value of its network node directly adopts the combined evaluation weight corresponding to the index), δ represents the control coefficient (in this embodiment, the value is 1); the weight of index j after topological network correction is obtained according to the following formula The weight matrix expression of the network nodes in the topological network is as follows:

[0068]

[0069] This example takes the effectiveness evaluation of the incident investigation system of COMAC aircraft production and operation units as an example, and lists the weights of some indicators as follows:

[0070]

[0071] S5: Based on the evaluation index system, the evaluation data of each indicator is obtained, and the evaluation data of each indicator is normalized and combined with the weight obtained in step S4. Multiply the corresponding indicators and add up the multiplication results of all indicators to get the performance evaluation result. First, normalize the evaluation data of indicator j and the weight of indicator j. The result j is obtained by multiplying them together (the multiplication of the index of the present invention and the corresponding weight of the index constitutes the index evaluation result corresponding to the index), and the results j of all the indicators are added together to obtain the performance evaluation result.

[0072] In some embodiments, step S5 can be completely replaced by: obtaining evaluation data of each indicator based on the evaluation indicator system, and calculating the mission completion ratio of the indicator. Where P a represents the actual evaluation data of the indicator, P m Indicates the expected evaluation data set by the indicator (that is, the expected target data set by the indicator, that is, the expected target achieved by the indicator); if the mission completion ratio is less than 1, the evaluation data of the corresponding indicator is updated to Otherwise, no processing is performed. The evaluation data of each indicator after update is normalized and then compared with the weight obtained in step S4. Multiply the corresponding indicators and add up the multiplication results of all indicators to get the performance evaluation result. First, update the evaluation data after normalization of indicator j and the weight of indicator j The result j is obtained by multiplying (the multiplication of the index of the present invention and the corresponding weight of the index constitutes the index evaluation result corresponding to the index), and the results j of all the indicators are added to obtain the performance evaluation result. This embodiment takes the performance evaluation of the incident investigation system of the aircraft production and operation unit of COMAC as an example, and finally obtains the performance evaluation result of 95.5344. The incident investigation system of COMAC is evaluated using the invention method, and the performance evaluation method has been fully verified and tested. For the performance evaluation work in related fields, by establishing the performance evaluation method of the incident investigation system of aircraft production and operation units, the evaluation calculation results can scientifically reflect the maturity of the system construction and the actual performance, provide a basis for the evaluation of the system construction, and provide a reference for clarifying the direction of system improvement.

[0073] An efficiency evaluation system for an aircraft production and operation unit includes an indicator weight assignment model, a combined evaluation model, an evaluation indicator system, historical sample evaluation data, a topological network and an efficiency evaluation module. The evaluation indicator system is constructed according to an indicator item library, and the indicator item library includes a number of upper and lower level indicators and corresponding evaluation standards; the historical sample evaluation data is obtained by sequentially evaluating the indicators based on the evaluation indicator system, and the historical sample evaluation data is subjected to data normalization processing; the indicator weight assignment model is used to receive the input normalized historical sample evaluation data and perform the following processing: the weighting model includes a hierarchical analysis calculation module, a priority diagram calculation module, an entropy weight calculation module, and a coefficient of variation calculation module. The hierarchical analysis calculation module adopts a hierarchical analysis method to compare the indicators as factors to obtain a judgment matrix A and calculate its maximum eigenvalue λ maxand normalized eigenvectors, and the weights A1 corresponding to each indicator are obtained through consistency test; the priority diagram calculation module uses the priority diagram method to calculate the importance of each pair of indicators and obtain the importance matrix, with the indicator importance ratio as the weight A2; the entropy weight calculation module uses the entropy weight method to calculate the contribution and entropy weight of the indicator, and uses the entropy weight as the weight A3 corresponding to the indicator; the coefficient of variation calculation module uses the coefficient of variation method to calculate the mean and standard deviation of the indicator, and then calculates the coefficient of variation of the indicator and normalizes it to obtain the weight A4 corresponding to the indicator;

[0074] The combined evaluation model is constructed based on the maximum deviation. The combined evaluation model takes the indicator as the evaluation object and obtains the combined evaluation weight corresponding to the indicator i according to the following formula:

[0075] where f ip It represents the evaluation value corresponding to the pth calculation module of indicator i in the indicator weight assignment model, P represents the total number of calculation modules in the indicator weight assignment model, and n represents the total number of indicators.

[0076] The topological network is constructed with indicators as network nodes. The topological potential Φ(v j ) is expressed as: where d ij represents the difference in combined evaluation weights between indicators i and j, β i Represents the node attribute value of index i, and the node attribute value is the combined evaluation weight of index i (the present invention uses the combined evaluation weight of index i As the node attribute value of index i), δ represents the control coefficient (in this embodiment, the value is 1); the weight of index j after topological network correction is obtained according to the following formula

[0077] The performance evaluation module obtains the evaluation data of each indicator based on the evaluation index system, normalizes the evaluation data of each indicator and adds them to the weights. Multiply the corresponding indicators and add up the multiplication results of all indicators to get the performance evaluation result (first normalize the evaluation data of indicator j and the weight of indicator j Multiply them to get the result j, and add the results j of all indicators to get the performance evaluation result).

[0078] In some embodiments, the performance evaluation module obtains evaluation data of each indicator based on the evaluation indicator system, and the performance evaluation module calculates the mission completion ratio of the indicator. Where P a represents the actual evaluation data of the indicator, P mIndicates the expected evaluation data set by the indicator (that is, the expected target data set by the indicator, that is, the expected target achieved by the indicator); if the mission completion ratio is less than 1, the evaluation data of the corresponding indicator is updated to Otherwise, no processing is done; the performance evaluation module normalizes the evaluation data of each indicator after update and adds them to the weights. Multiply the corresponding indicators and add up the multiplication results of all indicators to get the performance evaluation result (first update the evaluation data after normalization of indicator j and the weight of indicator j Multiply them to get the result j, and add the results j of all indicators to get the performance evaluation result).

[0079] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for evaluating the performance of an aircraft production and operation unit, characterized by: The methods include: S1. Construct an evaluation index system including an index item library, which includes several upper and lower level indicators and corresponding evaluation criteria; construct historical sample evaluation data, which is obtained by sequentially evaluating the indicators based on the evaluation index system, and perform data normalization on the historical sample evaluation data; S2. Construct an indicator weight assignment model, input the normalized historical sample evaluation data into the indicator weight assignment model, and the indicator weight assignment model includes four calculation modules. The first calculation module in the indicator weight assignment model uses the hierarchical analysis method to compare the indicators as factors to obtain the judgment matrix A and calculate its maximum eigenvalue λ max and normalized eigenvectors, and the weights A1 corresponding to each indicator are obtained after consistency test; the second calculation module in the indicator weight assignment model uses the priority diagram method to calculate the importance of each pair of indicators and obtain the importance matrix, and the indicator importance ratio is used as the weight A2; the third calculation module in the indicator weight assignment model uses the entropy weight method to calculate the contribution and entropy weight of the indicator, and the entropy weight is used as the weight A3 corresponding to the indicator; the fourth calculation module in the indicator weight assignment model uses the coefficient of variation method to calculate the mean and standard deviation of the indicator, and then calculates the coefficient of variation of the indicator and normalizes it to obtain the weight A4 corresponding to the indicator; S3. Construct a combined evaluation model based on maximum deviation, take the indicator as the evaluation object and obtain the combined evaluation weight corresponding to indicator i according to the following formula where f ip represents the evaluation value corresponding to the pth calculation module of indicator i in the indicator weight assignment model, f tp It represents the evaluation value corresponding to the p-th calculation module of indicator t in the indicator weight assignment model, and n represents the total number of indicators; S4. Use the index as the network node to build a topological network. The topological potential Φ(v j ) is expressed as: where d ij represents the difference in combined evaluation weights between indicators i and j, β i Represents the node attribute value of indicator i, the node attribute value is the combined evaluation weight of indicator i, and δ represents the control coefficient; the weight of indicator j after topological network correction is obtained according to the following formula S5: Based on the evaluation index system, the evaluation data of each indicator is obtained, and the evaluation data of each indicator is normalized and combined with the weight obtained in step S4. The performance evaluation result is obtained by multiplying the corresponding indicators and adding up the multiplication results of all indicators.

2. The method for evaluating the effectiveness of an aircraft production and operation unit according to claim 1, characterized in that: In step S5, the evaluation data of each indicator before normalization processing also includes the following data updating processing method: calculating the mission completion ratio of the indicator Where P a represents the actual evaluation data of the indicator, P m Indicates the expected evaluation data set by the indicator; if the mission completion ratio is less than 1, the evaluation data of the corresponding indicator is updated to Otherwise no processing is done.

3. The method for evaluating the effectiveness of an aircraft production and operation unit according to claim 1, characterized in that: In step S2, the judgment matrix A is expressed as follows: where a ij Indicates that index i corresponds to element x i The element x corresponding to index j j The impact ratio.

4. The method for evaluating the effectiveness of an aircraft production and operation unit according to claim 1, characterized in that: In step S2, the importance matrix expression is as follows: where b ij Represents the relative importance value between indicator i and indicator j; add the matrix B to get the total value T n : The weight A2 formula of a certain indicator is as follows: b i It represents the sum of the data in the row where indicator i is located in the importance matrix.

5. The method for evaluating the effectiveness of an aircraft production and operation unit according to claim 1, characterized in that: The third calculation module uses the entropy weight method to obtain the discriminant matrix P j : where x′ j It represents the dimensionless data of indicator j; the contribution of indicator j is E j The expression is as follows: The weight A3 of indicator j is expressed as follows: Where D j =1-E j .

6. The method for evaluating the effectiveness of an aircraft production and operation unit according to claim 1, characterized in that: The fourth calculation module uses the coefficient of variation method to obtain the coefficient of variation expression of indicator j as follows: in represents the normalized mean of index j, S j It represents the standard deviation of index j after normalization; After normalizing the coefficients of variation of all indicators, the weight A4 expression corresponding to indicator j is obtained as follows:

7. The method for evaluating the effectiveness of an aircraft production and operation unit according to claim 1, characterized in that: The weight matrix expression of the network nodes in the topological network is as follows:

8. The method for evaluating the effectiveness of an aircraft production and operation unit according to claim 1, characterized in that: In step S1, the data normalization processing formula of the historical sample evaluation data is as follows: X nor Indicates that the indicator corresponds to the standardized data, X min Indicates the minimum data in the corresponding data of the indicator, X max It indicates the maximum data among the data corresponding to the indicator, and X indicates the data corresponding to the indicator.

9. An aircraft production and operation unit performance evaluation system, characterized by: It includes an indicator weight assignment model, a combined evaluation model, an evaluation indicator system, historical sample evaluation data, a topological network and an efficiency evaluation module. The evaluation indicator system is constructed according to an indicator item library, and the indicator item library includes several upper and lower level indicators and corresponding evaluation standards; the historical sample evaluation data is obtained by sequentially evaluating the indicators based on the evaluation indicator system, and the historical sample evaluation data is normalized; the indicator weight assignment model is used to receive the input normalized historical sample evaluation data and perform the following processing: the weighting model includes a hierarchical analysis calculation module, a priority diagram calculation module, an entropy weight calculation module, and a coefficient of variation calculation module. The hierarchical analysis calculation module uses a hierarchical analysis method to compare the indicators as factors to obtain a judgment matrix A and calculate its maximum eigenvalue λ max and normalized eigenvectors, and the weights A1 corresponding to each indicator are obtained through consistency test; the priority diagram calculation module uses the priority diagram method to calculate the importance of each pair of indicators and obtain the importance matrix, and uses the indicator importance ratio as the weight A2; the entropy weight calculation module uses the entropy weight method to calculate the contribution and entropy weight of the indicator, and uses the entropy weight as the weight A3 corresponding to the indicator; the coefficient of variation method calculation module uses the coefficient of variation method to calculate the mean and standard deviation of the indicator, and then calculates the coefficient of variation of the indicator and normalizes it to obtain the weight A4 corresponding to the indicator; The combined evaluation model is constructed based on the maximum deviation. The combined evaluation model takes the index as the evaluation object and obtains the combined evaluation weight corresponding to the index i according to the following formula where f ip It represents the evaluation value corresponding to the p-th calculation module of indicator i in the indicator weight assignment model, P represents the total number of calculation modules in the indicator weight assignment model, and n represents the total number of indicators; The topological network is constructed with indicators as network nodes, and the topological potential Φ(v j ) is expressed as: where d ij represents the difference in combined evaluation weights between indicators i and j, β i Represents the node attribute value of indicator i, the node attribute value is the combined evaluation weight of indicator i, and δ represents the control coefficient; the weight of indicator j after topological network correction is obtained according to the following formula The performance evaluation module obtains evaluation data of each indicator based on the evaluation index system, normalizes the evaluation data of each indicator and adds them to the weights. The performance evaluation result is obtained by multiplying the corresponding indicators and adding up the multiplication results of all indicators.

10. The aircraft production and operation unit effectiveness evaluation system according to claim 9, characterized in that: The performance evaluation module calculates the mission completion ratio of the indicator Where P a represents the actual evaluation data of the indicator, P m Indicates the expected evaluation data set by the indicator; if the mission completion ratio is less than 1, the evaluation data of the corresponding indicator is updated to Otherwise, no processing is done; the evaluation data of each indicator after update is normalized and combined with the weight The performance evaluation result is obtained by multiplying the corresponding indicators and adding up the multiplication results of all indicators.

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