Multi-dimensional constraint projection method and system for dynamic analysis of civil aviation engineering file quality

Abstract

This invention discloses a method and system for dynamic analysis of civil aviation engineering archive quality based on multi-dimensional constrained projection. Applied to the field of intelligent archive analysis technology, the method includes: constructing a data indicator system for civil aviation engineering archive quality based on procedural constraints; periodically collecting raw archive data and obtaining archive analysis data; determining the entropy weight of each indicator data at the current moment; analyzing the archive quality of each decision-making unit and generating optimization objectives for each decision-making unit at the current moment through projection analysis based on the analysis results; and repeatedly acquiring archive analysis data for the next period and generating optimization objectives for different moments. This invention integrates dynamic weight calculation, SBM efficiency evaluation, and projection analysis, achieving periodic dynamic evaluation and optimization closed-loop. It truly realizes dynamic, objective, quantitative, and operable evaluation and improvement guidance, improving the objectivity and adaptability of the evaluation results.

Description

Technical Field

[0001] This invention relates to intelligent archive analysis technology, specifically to a method and system for dynamic quality analysis of civil aviation engineering archives using multi-dimensional constrained projection. Background Technology

[0002] Construction projects are characterized by large investment scale, long construction period, multiple professional categories, and complex participating units. The management of their project data directly affects project quality traceability, completion acceptance efficiency, and subsequent operation and maintenance management. According to the "Regulations for the Management of Construction Project Data for Transport Airports" (MH / T 5078.1-2024), airport construction project data must cover multiple professional fields such as airfield engineering, air traffic control engineering, visual navigation aids engineering, terminal building process flow and low-voltage electrical system engineering, and fuel supply engineering. Data management includes project progress control, quality control, and cost management, involving various units such as construction, surveying, design, supervision, construction, and testing. Generally, project archives should be formed simultaneously with the construction process and should reflect the true situation of the project. Project archives should possess completeness, accuracy, systematicness, and traceability, meeting the needs of construction, management, supervision, operation, and maintenance activities for evidence, responsibility, and information. However, in actual construction projects, there is a common phenomenon of "emphasizing construction while neglecting archives." Existing methods for evaluating archive quality mainly have the following technical problems:

[0003] (1) The evaluation dimension is singular, making it difficult to handle the evaluation relationship of multiple indicators.

[0004] Traditional archival acceptance often employs qualitative inspections or simple weighted scoring methods. For example, while some existing technologies divide evaluation indicators into four aspects—basic management, file quality, information technology construction, and archival security—they still use a fixed-weight scoring method. This approach cannot handle the complex synergistic relationships between multiple quality characteristics such as timeliness, accuracy, completeness, systematicness, and traceability. When one dimension performs exceptionally well while another dimension is deficient, the overall score fails to reflect true management performance.

[0005] (2) The weighting of indicators is highly subjective and lacks objective basis for determining the weights.

[0006] In existing archival quality assessment processes, scoring often relies on the experience and judgment of acceptance experts. However, differences in scoring standards among experts lead to insufficient consistency and objectivity in the evaluation results. The patent application filed by China Water Resources Northern Survey and Design Research Co., Ltd. in April 2025, entitled "A Multi-Dimensional Archival Quality Analysis Method and System Based on Quantitative Assessment" (Publication No. CN119941057A), attempts to reduce the influence of subjective factors through a data-driven approach. However, its core weighting still relies on expert experience. When there are numerous evaluation indicators and differences in expert perception, subjective weighting methods struggle to guarantee the objectivity and stability of the weights.

[0007] (3) Insufficient dynamic adaptability makes it difficult to match changes in the construction stage and lacks a mechanism for seamless connection throughout the entire process.

[0008] Construction project archive management generally suffers from problems such as "emphasis on reconstruction over management, delayed archiving, and incomplete collection." Existing archive quality evaluation methods mostly adopt a one-time acceptance method after completion or a fixed-cycle quarterly assessment. They fail to conduct phased evaluations based on the characteristics of archive formation at different construction stages, such as foundation treatment, pavement construction, installation of navigation aids, commissioning of low-voltage systems, and preparation for completion acceptance. Furthermore, they lack a method to effectively integrate the evaluation results of each stage into a comprehensive evaluation of the entire process.

[0009] (4) The direction of improvement is vague and lacks quantitative guidance.

[0010] Existing evaluation methods only provide a comprehensive score or grade (such as excellent, good, satisfactory, unsatisfactory), which cannot provide specific quantitative improvement directions and optimization paths for projects where the quality of archives does not meet the standards. When the quality of archives in a certain section is rated as "unsatisfactory," managers find it difficult to accurately determine whether the problem lies in insufficient timeliness, lack of accuracy, or incompleteness, and they are even less able to ascertain the specific extent to which each indicator needs improvement.

[0011] (5) Lack of effective handling of undesirable outputs

[0012] In the process of record management, negative factors such as archiving delays, document defects, and missing signatures are typical "undesirable outputs." Traditional evaluation methods usually treat them as input indicators or ignore them directly, leading to distorted evaluation results. How to handle both desired and undesirable outputs within the same framework is a technical challenge that urgently needs to be solved in this field.

[0013] To address the aforementioned issues, existing technologies already include technical solutions that combine the entropy weight method with the SBM model for application in other fields. For example, Chinese patent application number CN202510651951.3, filed by State Grid Fujian Electric Power Co., Ltd., discloses a corporate investment efficiency measurement system based on LASSO-game theory combined weighting-super-efficiency SBM, combining the entropy weight method, AHP, and super-efficiency SBM for corporate investment efficiency measurement; Chinese patent application number CN201610961816.X, filed by the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, discloses a method for evaluating the economic and environmental efficiency of urban agglomerations, using the DEA-SBM model and the entropy weight TOPSIS model to analyze the economic and environmental efficiency of urban agglomerations; and Chinese patent application number CN202410693999.6, filed by the Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, discloses a method for evaluating the efficiency of water resource utilization caused by urbanization, combining the super-efficiency SBM model with the combined weighting evaluation method for water resource utilization efficiency evaluation.

[0014] However, the aforementioned patents are all applied to areas such as corporate investment efficiency, urban agglomeration economic environment, and water resource utilization, which differ from the technical field, evaluation objects, and indicator system of construction project archive quality evaluation. Construction project archive quality evaluation has a unique indicator system (timeliness, accuracy, completeness, systematicness, and traceability) and special application scenarios (dynamic changes during construction phases and the multi-stage characteristics of the archive formation process), making direct application of existing technologies impossible. Therefore, there is an urgent need for an evaluation method specifically designed for construction project archive quality evaluation, capable of deeply integrating objective weighting and efficiency evaluation, and dynamically adapting to changes in the construction phase. Consequently, there is an urgent need for an archive quality evaluation method and system that can achieve multi-dimensional indicator collaborative evaluation, reduce subjective interference, dynamically adapt to changes in the construction phase, and provide quantitative improvement paths. Summary of the Invention

[0015] In order to at least overcome the above-mentioned shortcomings in the prior art, the purpose of this application is to provide a method and system for dynamic analysis of the quality of civil aviation engineering archives based on multi-dimensional constrained projection.

[0016] Firstly, embodiments of this application provide a method for dynamic analysis of the quality of civil aviation engineering archives using multi-dimensional constrained projection, including:

[0017] A data indicator system for the quality of civil aviation engineering archives is constructed based on procedural constraints; the data indicator system includes input indicators, expected output indicators, and unexpected output indicators.

[0018] The original data of the archives of different decision-making units in the same construction stage of the target civil aviation construction project are periodically collected, and the input index data, expected output index data and unexpected output index data of the original data of the archives are obtained as archive analysis data.

[0019] The entropy weight of each indicator in the archive analysis data at the current moment is determined by the entropy weight method.

[0020] The archive quality of each decision unit is analyzed using a weighted SBM model based on the entropy weights. Projection analysis is then performed based on the analysis results. By calculating the differences between the current decision unit's various indicators and the projected target values, the optimization target of each decision unit at the current moment is generated.

[0021] The archive analysis data is repeatedly acquired for the next cycle, and optimization targets are generated at different times.

[0022] In one possible implementation, the entropy weights are obtained by:

[0023] Normalize the same indicator data for all decision-making units to generate normalized data;

[0024] Calculate the proportion of the normalized data of each decision unit in the corresponding indicator data to the normalized data of all decision units;

[0025] Calculate the information entropy of the corresponding indicator data based on the stated ratio;

[0026] The entropy weights of the corresponding indicator data are calculated based on the information entropy.

[0027] In one possible implementation, the use of a weighted SBM model to analyze archival quality includes:

[0028] Constructing a weighted SBM model:

[0029]

[0030] Constraints:

[0031]

[0032]

[0033]

[0034] In the formula, Let be the efficiency value of the k-th decision unit in the t-th sampling period, where m is the number of input indicators, q is the number of expected output indicators, and l is the number of undesirable output indicators. Let be the normalized value of the i-th input indicator of the k-th decision unit in the t-th sampling period. The normalized value of the i-th input indicator for the j-th decision unit in the t-th sampling period. Let i be the target value of the i-th input indicator. Let r be the normalized value of the expected output index of the k-th decision unit in the t-th sampling period. Let r be the normalized value of the expected output index of the j-th decision unit in the t-th sampling period. Let r be the target value of the expected output indicator. Let p be the normalized value of the p-th undesired output index of the k-th decision unit in the t-th sampling period. Let p be the normalized value of the p-th undesired output index of the j-th decision unit in the t-th sampling period. Let p be the target value of the undesired output. The entropy weight of the r-th expected output indicator is... Let the entropy weight be the value of the p-th undesirable output indicator. Let be the weight coefficient of the j-th indicator;

[0035] In one possible implementation, the optimization objective of the decision-making unit at the current moment includes:

[0036] When the efficiency value of the decision-making unit at the current moment is less than 1, the current input indicator data, the current expected output indicator data, and the current unexpected output indicator data are obtained from the archive analysis data.

[0037] The differences in input indicator data, expected output indicator data, and unexpected output indicator data are calculated as difference data; the differences in input indicator data are the differences between the current input indicator data and the target value of the input indicator; the differences in expected output indicator data are the differences between the current expected output indicator and the target value of the expected output indicator; the differences in unexpected output indicator data are the differences between the current unexpected output indicator and the target value of the unexpected output indicator; the target value is the process data output by the weighted SBM model.

[0038] The difference data are sorted from largest to smallest, and the sorting order is used as the optimization priority data for the corresponding indicators.

[0039] The optimization priority data and the difference data are used as the optimization targets at the current moment.

[0040] One possible implementation also includes:

[0041] When construction is completed, for each construction stage, the average of the efficiency values ​​obtained from multiple evaluations within that construction stage is taken as the node efficiency value of that decision-making unit in that construction stage.

[0042] The overall efficiency value of the decision-making unit in the target civil aviation construction project is obtained by weighting the efficiency values ​​of all nodes of the decision-making unit; the weighting weight is the proportion of resources invested in each construction stage in the total construction stages; and the archive quality of the corresponding decision-making unit is judged based on the overall efficiency value.

[0043] In one possible implementation, the input indicators include archival staff working hours, management costs, archiving plan duration, and investment in information technology construction; the expected output indicators include archiving timeliness rate, node response delay, random inspection pass rate, data logical consistency, signature and seal completeness rate, actual archiving rate, key document completeness rate, audio-visual material matching rate, classification accuracy rate, cataloging standardization rate, numbering continuity, signature traceability completeness rate, change closure rate, and responsibility chain completeness rate; the undesired output indicators include archiving delay days, number of document defects, and number of missing signatures.

[0044] Secondly, this application also provides a dynamic analysis system for the quality of civil aviation engineering archives based on multi-dimensional constrained projection, including:

[0045] The construction unit is configured to construct a data indicator system for the quality of civil aviation engineering archives according to procedural constraints; the data indicator system includes input indicators, expected output indicators, and unexpected output indicators.

[0046] The data collection unit is configured to periodically collect raw data from different decision-making units at the same construction stage of the target civil aviation construction project, and to obtain input indicator data, expected output indicator data and unexpected output indicator data from the raw data as data for data analysis.

[0047] The weighting unit is configured to determine the entropy weight of each indicator data in the archive analysis data at the current time using the entropy weight method;

[0048] The optimization unit is configured to analyze the archive quality of each decision unit using a weighted SBM model based on the entropy weights, and perform projection analysis based on the analysis results. By calculating the differences between the current decision unit's various indicators and the projected target values, the optimization target of each decision unit at the current moment is generated.

[0049] The loop unit is configured to repeatedly acquire the archive analysis data for the next cycle and generate optimization targets at different times.

[0050] In one possible implementation, the weight unit is further configured as follows:

[0051] Normalize the same indicator data for all decision-making units to generate normalized data;

[0052] Calculate the proportion of the normalized data of each decision unit in the corresponding indicator data to the normalized data of all decision units;

[0053] Calculate the information entropy of the corresponding indicator data based on the stated ratio;

[0054] The entropy weights of the corresponding indicator data are calculated based on the information entropy.

[0055] In one possible implementation, the optimization unit is further configured as follows:

[0056] Constructing a weighted SBM model:

[0057]

[0058] Constraints:

[0059]

[0060]

[0061]

[0062] In the formula, Let be the efficiency value of the k-th decision unit in the t-th sampling period, where m is the number of input indicators, q is the number of expected output indicators, and l is the number of undesirable output indicators. Let be the normalized value of the i-th input indicator of the k-th decision unit in the t-th sampling period. The normalized value of the i-th input indicator for the j-th decision unit in the t-th sampling period. Let i be the target value of the i-th input indicator. Let r be the normalized value of the expected output index of the k-th decision unit in the t-th sampling period. Let r be the normalized value of the expected output index of the j-th decision unit in the t-th sampling period. Let r be the target value of the expected output indicator. Let p be the normalized value of the p-th undesired output index of the k-th decision unit in the t-th sampling period. Let p be the normalized value of the p-th undesired output index of the j-th decision unit in the t-th sampling period. Let p be the target value of the undesired output. The entropy weight of the r-th expected output indicator is... Let the entropy weight be the value of the p-th undesirable output indicator. Let be the weight coefficient of the j-th indicator;

[0063] The quality of archives for each decision-making unit is determined based on the efficiency value output by the weighted SBM model.

[0064] In one possible implementation, the optimization unit is further configured as follows:

[0065] When the efficiency value of the decision-making unit at the current moment is less than 1, the current input indicator data, the current expected output indicator data, and the current unexpected output indicator data are obtained from the archive analysis data.

[0066] The differences in input indicator data, expected output indicator data, and unexpected output indicator data are calculated as difference data; the differences in input indicator data are the differences between the current input indicator data and the target value of the input indicator; the differences in expected output indicator data are the differences between the current expected output indicator and the target value of the expected output indicator; the differences in unexpected output indicator data are the differences between the current unexpected output indicator and the target value of the unexpected output indicator; the target value is the process data output by the weighted SBM model.

[0067] The difference data are sorted from largest to smallest, and the sorting order is used as the optimization priority data for the corresponding indicators.

[0068] The optimization priority data and the difference data are used as the optimization targets at the current moment.

[0069] Compared with existing technologies, this invention achieves dynamic, objective, quantitative, and operable evaluation and improvement guidance, and has the following advantages and beneficial effects:

[0070] (1) It realizes the integrated application of dynamic weight calculation, SBM efficiency evaluation and projection analysis. By automatically calculating the weights based on actual data in each cycle, it avoids objective weighting bias and improves the objectivity and adaptability of the evaluation results.

[0071] (2) Periodic dynamic evaluation and optimization closed loop have been achieved. By periodically collecting data and repeatedly performing analysis, continuous monitoring and dynamic optimization of archival quality have been achieved, and the trend of changes in archival quality can be grasped in a timely manner and targeted measures can be taken.

[0072] (3) It realizes the generation of quantitative optimization targets through projection analysis. Through this method and system application, the specific extent of improvement required for each indicator can be accurately calculated, and the optimization priority can be given by ranking, providing a clear and operable guidance path for the quality of archives.

[0073] (4) Effectively handle undesirable outputs. Taking into account the negative factors of undesirable outputs will more accurately reflect the actual situation of record management.

[0074] (5) Achieve comprehensive efficiency evaluation throughout the entire process. By using the weighted method, the efficiency values ​​of each stage can be comprehensively evaluated, thus achieving an overall evaluation of the quality of the archives throughout the entire process and making up for the shortcomings of stage-based separation. Attached Figure Description

[0075] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings:

[0076] Figure 1 This is a schematic diagram of the method steps in an embodiment of this application. Detailed Implementation

[0077] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the accompanying drawings in this application are for illustrative and descriptive purposes only and are not intended to limit the scope of protection of this application. Furthermore, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of this application. It should be understood that the operations in the flowcharts may not be implemented in sequence, and steps without logical contextual relationships may be reversed or implemented simultaneously. In addition, those skilled in the art, guided by the content of this application, may add one or more other operations to the flowcharts, or remove one or more operations from the flowcharts.

[0078] Furthermore, the described embodiments are merely some, not all, of the embodiments of this application. The components of the embodiments of this application described and illustrated herein can typically be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0079] Please refer to the following: Figure 1 This is a flowchart illustrating the dynamic analysis method for the quality of civil aviation engineering archives using multi-dimensional constrained projection provided in this embodiment of the invention. Further, the dynamic analysis method for the quality of civil aviation engineering archives using multi-dimensional constrained projection may specifically include the content described in steps S1-S5.

[0080] S1: Construct a data indicator system for the quality of civil aviation engineering archives based on procedural constraints; the data indicator system includes input indicators, expected output indicators, and unexpected output indicators;

[0081] S2: Periodically collect the original data of the archives of different decision-making units in the same construction stage of the target civil aviation construction project, and obtain the input index data, expected output index data and unexpected output index data of the original data of the archives as archive analysis data;

[0082] S3: Determine the entropy weight of each indicator data in the archive analysis data at the current moment using the entropy weight method;

[0083] S4: Based on the entropy weight, the weighted SBM model is used to analyze the archive quality of each decision unit, and projection analysis is performed based on the analysis results. By calculating the difference between the current decision unit's various indicators and the projected target value, the optimization target of each decision unit at the current moment is generated.

[0084] S5: Repeat the acquisition of the archive analysis data for the next cycle and generate optimization targets at different times.

[0085] When implementing the embodiments of this application, the procedural constraints can adopt corresponding standards. For example, for airport construction, the "Regulations for the Management of Construction Data of Transport Airports" (MH / T 5078.1-2024) can be adopted, which constructs a data indicator system for analysis. Please refer to the table below for specific indicators:

[0086]

[0087]

[0088] It incorporates many indicators that are compatible with civil aviation construction projects. These indicators can effectively measure the quality of archives and can also be easily obtained through automated means.

[0089] In this application embodiment, the supervision of archival quality often needs to be carried out according to certain rules. Therefore, this application adopts a periodic approach to collecting raw archival data for civil aviation construction projects. This period can change with the progress of the project; for example, regular inspections can be used in the early stages of the project, while node assessments need to be added in the middle and later stages, with inspections conducted at key project nodes. Simultaneously, the archival status will differ for different construction stages, so it is necessary to extract multiple decision-making units in the same construction stage simultaneously for archival analysis to ensure the accuracy of weight extraction. It should be understood that the decision-making units mentioned in this application are generally divided according to construction units or construction sections. The corresponding indicator data can be obtained from the acquired raw archival data according to the aforementioned indicator system.

[0090] In this embodiment, when performing archival quality analysis using the weighted SBM model, it is necessary to first obtain the weight value corresponding to each indicator. Since this embodiment obtains multiple sets of archival data from the same construction phase during the aforementioned process, the weight value can be obtained based on the entropy weight method. This weight value can effectively characterize the key control indicators with the greatest data differences and strongest discriminatory power within the same construction phase, assigning them higher objective weights. This allows the analysis results to truly reflect the weaknesses in archival management at the current stage. Furthermore, although existing technologies also use the entropy weight method to obtain the weights of the SBM model, they do not employ the horizontal comparison method used in this application.

[0091] In this embodiment, after constructing a weighted SBM model using entropy weights, the efficiency value of archives for different decision-making units in the current state can be obtained. A higher efficiency value indicates higher archive quality. This quantifies the abstract concept of archive quality into an objective parameter variable for further optimization analysis. Simultaneously, in each cycle, archive analysis data needs to be acquired again to determine the corresponding optimization objectives. This allows each decision-making unit to accurately understand the direction of archive quality optimization and then perform targeted optimization to improve archive quality.

[0092] In one possible implementation, the entropy weights are obtained by:

[0093] Normalize the same indicator data for all decision-making units to generate normalized data;

[0094] Calculate the proportion of the normalized data of each decision unit in the corresponding indicator data to the normalized data of all decision units;

[0095] Calculate the information entropy of the corresponding indicator data based on the stated ratio; if the normalized value is 0, add a very small value (e.g., 10). -10 This allows the calculations to continue without affecting the final result.

[0096] The entropy weights of the corresponding indicator data are calculated based on the information entropy.

[0097] In the implementation of this application embodiment, when calculating the entropy weight, the obtained corresponding index data needs to be normalized first. The min-max normalization method can be used for standardization. The larger the positive index, the better, and the smaller the negative index, the better. After the normalization is completed, the corresponding entropy weight needs to be calculated using the entropy weight method.

[0098] In one possible implementation, the use of a weighted SBM model to analyze archival quality includes:

[0099] Constructing a weighted SBM model:

[0100]

[0101] Constraints:

[0102]

[0103]

[0104]

[0105] In the formula, Let be the efficiency value of the k-th decision unit in the t-th sampling period, where m is the number of input indicators, q is the number of expected output indicators, and l is the number of undesirable output indicators. Let be the normalized value of the i-th input indicator of the k-th decision unit in the t-th sampling period. The normalized value of the i-th input indicator for the j-th decision unit in the t-th sampling period. Let i be the target value of the i-th input indicator. Let r be the normalized value of the expected output index of the k-th decision unit in the t-th sampling period. Let r be the normalized value of the expected output index of the j-th decision unit in the t-th sampling period. Let r be the target value of the expected output indicator. Let p be the normalized value of the p-th undesired output index of the k-th decision unit in the t-th sampling period. Let p be the normalized value of the p-th undesired output index of the j-th decision unit in the t-th sampling period. Let p be the target value of the undesired output. The entropy weight of the r-th expected output indicator is... Let the entropy weight be the value of the p-th undesirable output indicator. Let be the weight coefficient of the j-th indicator;

[0106] The quality of each decision-making unit's archives is determined based on the efficiency value output by the weighted SBM model. Note: Both terms in the denominator must be less than 1. If a term is greater than 1, the reciprocal is taken, which does not affect the relative ranking. An efficiency value less than 1 indicates invalidity, while a value greater than 1 indicates validity. For cases with a small sample size (usually less than 20), where the strictly weighted super-efficiency SBM model is computationally complex, an approximate calculation method can be used to synthesize a single output index using entropy weights. This approximate calculation method retains the objectivity of the entropy weights, distinguishes the directional differences between expected and unexpected outputs, and is robust and highly interpretable in practice. It has been widely accepted and applied in academia.

[0107] Analysis of academic research results shows that the improvement direction and priority ranking of the approximate calculation method and the SBM model calculation are consistent. However, the improvement range of the approximate calculation method is slightly conservative, while the improvement range of the SBM model calculation is more accurate. The difference between the two results is less than 10%, which also meets the needs of engineering practice applications.

[0108] For example, the approximate calculation method uses the comprehensive index method to replace the complex denominator in the SBM model. After positiveizing the undesirable outputs, the joint weight of all output indicators is calculated by the entropy weight method to obtain the final comprehensive output index.

[0109] In this embodiment, the efficiency value generated here can effectively analyze the current state of the archive quality. In subsequent analysis, different archive qualities can be distinguished based on the efficiency value. For example, when the efficiency value is less than 0.6, it indicates serious non-compliance or a serious defect, requiring suspension of project payment, rectification within a specified period, and organization of a special review. When the efficiency value is greater than or equal to 0.6 but less than 0.8, it indicates obvious defects, requiring the issuance of a rectification notice and a focused review. When the efficiency value is greater than or equal to 0.8, it indicates basic compliance but requires optimization, and optimization suggestions can be provided for continuous monitoring. It should be understood that the entropy weight in this application mainly applies to expected and unexpected output indicators, and not to input indicators. The main reason for this is that, in scientific practice, the inventors have found that input indicators can generally be considered equally important, and therefore do not use entropy weighting. However, output indicators need to be considered. Indicators with greater data variation contain more information and should be given higher weights, i.e., entropy weights. In this embodiment of the application, the weight coefficient and the entropy weight are different weight coefficients. The weight coefficient is the weight coefficient that comes with the SBM model and will be automatically updated in each calculation.

[0110] When analyzing archival quality using efficiency values, the following table can be used as a reference:

[0111]

[0112] In one possible implementation, the optimization objective of the decision-making unit at the current moment includes:

[0113] When the efficiency value of the decision-making unit at the current moment is less than 1, the current input indicator data, the current expected output indicator data, and the current unexpected output indicator data are obtained from the archive analysis data.

[0114] The differences in input indicator data, expected output indicator data, and unexpected output indicator data are calculated as difference data; the differences in input indicator data are the differences between the current input indicator data and the target value of the input indicator; the differences in expected output indicator data are the differences between the current expected output indicator and the target value of the expected output indicator; the differences in unexpected output indicator data are the differences between the current unexpected output indicator and the target value of the unexpected output indicator; the target value is the process data output by the weighted SBM model.

[0115] The difference data are sorted from largest to smallest, and the sorting order is used as the optimization priority data for the corresponding indicators.

[0116] The optimization priority data and the difference data are used as the optimization targets at the current moment.

[0117] In the implementation of this application's embodiments, optimization directions can also be proposed based on the results generated by the above model. The projection theorem of this computational model is used to calculate the improvement targets and optimization paths for ineffective decision-making units. Specifically, by analyzing the gap between different indicator data and their corresponding target values, the degree of improvement for each indicator data can be effectively determined, and the content corresponding to the indicator data most deserving of optimization can be obtained after sorting. It should be understood that the output results of the SBM used in this application may have three cases: an efficiency value less than 1 indicates room for improvement; an efficiency value equal to 1 indicates that the decision-making unit is on the efficiency frontier and no improvement is needed for the time being; and an efficiency value greater than 1 indicates that the decision-making unit is super-efficient and effective, exceeding the frontier.

[0118] One possible implementation also includes:

[0119] When construction is completed, for each construction stage, the average of the efficiency values ​​obtained from multiple evaluations within that construction stage is taken as the node efficiency value of that decision-making unit in that construction stage.

[0120] The overall efficiency value of the decision-making unit in the target civil aviation construction project is obtained by weighting the efficiency values ​​of all nodes of the decision-making unit; the weighting weight is the proportion of resources invested in each construction stage in the total construction stages.

[0121] The quality of the archives of the corresponding decision-making unit is judged based on the comprehensive efficiency value.

[0122] When implementing this embodiment, after all construction stages are completed, the overall archive quality data has been collected. At this point, it is necessary to analyze the overall archive quality. Here, a weighted calculation method is used to calculate the comprehensive efficiency value. The weight is the proportion of resources occupied by a single construction stage in the total construction stages. These resources can be investment costs, time costs, or other quantifiable important indicators.

[0123] In one possible implementation, the input indicators include archival staff working hours, management costs, archiving plan duration, and investment in information technology construction; the expected output indicators include archiving timeliness rate, node response delay, random inspection pass rate, data logical consistency, signature and seal completeness rate, actual archiving rate, key document completeness rate, audio-visual material matching rate, classification accuracy rate, cataloging standardization rate, numbering continuity, signature traceability completeness rate, change closure rate, and responsibility chain completeness rate; the undesired output indicators include archiving delay days, number of document defects, and number of missing signatures.

[0124] For example, this application provides a specific use case: an expansion project of a transport airport includes three specialties: airfield pavement engineering, air traffic control engineering, and visual navigation aids engineering, divided into 8 construction sections. The total project duration is 36 months, and the division and duration of each stage are as follows:

[0125]

[0126] Typically, this model is applied in scenarios where dynamic weight calculation, SBM efficiency evaluation, and projection analysis are integrated; it allows for periodic dynamic evaluation and closed-loop optimization; it generates quantitative optimization targets through projection analysis; and it performs overall evaluation of the quality of the entire archive process using a weighted method. In this case, it is applied as follows:

[0127] I. Interim Evaluation and Projection Analysis

[0128] Based on the actual project scale, taking stage T1 as an example, there are 8 construction companies and 4 supervision companies.

[0129] (1) Data acquisition and normalization processing

[0130] Taking a certain moment in phase T1 as an example, the input and output data of 8 sections were collected, and some key data of 6 sections are illustrated.

[0131] The input indicators are shown in the table below:

[0132]

[0133] The output indicators, after normalization, are shown in the table below:

[0134]

[0135] (2) Calculation of objective dynamic entropy weight

[0136] To ensure complete objectivity of the data, the joint entropy weight method was used to calculate the information entropy, information redundancy, and entropy weight of all output indicators. The entropy weight results are as follows: Y4 data logical consistency (entropy weight 0.073), Y3 sampling pass rate (entropy weight 0.072), Y6 actual classification rate (entropy weight 0.072), B2 number of document defects (entropy weight 0.070), and Y9 classification accuracy (entropy weight 0.067) have relatively high weights, which is consistent with the characteristics of many hidden works and dense original records in the foundation treatment stage.

[0137] (3) Weighted Super-Efficiency SBM Evaluation and Early Warning Analysis

[0138] The efficiency value was calculated using the SBM model, and the results are as follows:

[0139]

[0140] In the table above, DMU1-DMU8 represent sections 1-8 respectively;

[0141] It can be seen that the efficiency values ​​of DMU3 and DMU7 are greater than 1, which is considered to be super-efficient and effective. This indicates that their T1 stage file management efficiency is better than the frontier level. In addition, it can be seen that the efficiency value of section 6 is the lowest, triggering a yellow warning. At the same time, sections with an efficiency value lower than 1 need to be improved.

[0142] (4) Projection analysis generates quantitative optimization indicators

[0143] Based on the model's calculation results, projection analysis and improvement path generation can be achieved. By accurately calculating the specific improvement range required for each indicator and prioritizing optimization through ranking, a clear and actionable guiding path for archival quality is provided. Depending on the specific case, the model's projection analysis can be extended to determine the improvement range of each tertiary indicator to establish priorities, and further analysis can be conducted to derive the improvement priorities for secondary indicators.

[0144] The improvement rates for each indicator in section 6 of phase T1 are calculated as follows:

[0145]

[0146] Note: The current standardized values ​​are the values ​​of each output indicator, which reflect the current relative performance.

[0147] The target standardized value is the target value produced by the SBM model. It is the process data in the SBM model calculation and reflects the ideal performance that should be achieved when reaching the frontier.

[0148] Improvement range = target standardized value - current standardized value, with positive values ​​indicating the required improvement range.

[0149] It can also be used for subsequent analysis to obtain the improvement magnitude of the five evaluation indicators, as shown in the table below:

[0150]

[0151] In the table above, the current comprehensive value is obtained by summarizing the original values ​​of the expected output indicators based on the actual values ​​of the current period, after standard normalization and by dimension; while the target comprehensive value is the target value obtained by projecting the SBM model, also after standard normalization and by dimension. The improvement margin is equal to the difference between the target comprehensive value and the current comprehensive value, and they are sorted in descending order of improvement margin to obtain the optimization priority.

[0152] As can be seen from the table above, Section 6 should prioritize improvements in accuracy, systematicness, and completeness.

[0153] When conducting projection analysis, further analysis of indicators such as marginal contribution and contribution degree can be requested for analyzing more complex situations. In practical engineering applications, the number of construction sections involved is limited, and the depth of analysis in this case is sufficient to guide practical engineering applications; therefore, no further complex and in-depth analysis is required.

[0154] The above mainly refers to the phased evaluation of the file quality of a single construction unit. It can also be used to evaluate the file quality managed by the supervision unit, so as to quantitatively assess the management effectiveness of the supervision unit.

[0155] Based on the case study, Supervision Section 1 oversees earthwork construction units 1 and 2; Supervision Section 2 oversees earthwork construction units 3 and 4; Supervision Section 3 oversees earthwork construction units 5 and 6; and Supervision Section 4 oversees earthwork construction units 7 and 8. The efficiency value of each earthwork unit is weighted according to its resource proportion (such as duration and investment scale) to obtain the archive quality efficiency under each supervision unit's jurisdiction. This case study simplifies the process, assuming that each construction unit has the same duration proportion. The resulting efficiency values ​​are shown in the table below:

[0156]

[0157] As can be seen from the table above, the archive quality efficiency value under the jurisdiction of Supervision Section 4 is the highest, while that of Supervision Section 3 is the lowest. Corresponding rewards and punishments can be carried out in accordance with the labor competition fund stipulated in the contract.

[0158] II. Periodic dynamic evaluation and optimization closed loop

[0159] By periodically collecting data and repeatedly performing analysis, continuous monitoring and dynamic optimization of archival quality are achieved, enabling timely understanding of changes in archival quality trends and the implementation of targeted measures.

[0160] In phase T1, the records of 8 construction units were inspected 5 times. The efficiency value of each inspection was repeatedly calculated, and the changes in management quality of each decision-making unit at different stages were analyzed. The results are shown in the table below:

[0161]

[0162] The table shows that the archive quality of sections 3 and 7 is relatively good. In addition, the archive quality of sections 1, 2, 4, 5, 6 and 8 continues to improve and gradually approaches the optimal level.

[0163] Early Warning and Rectification: Based on the periodic dynamic efficiency evaluation, decision-making units with an efficiency value below 0.6 are given special attention and a red alert is triggered, which can suspend project payments, require rectification within a specified period, and organize special re-inspections. This situation does not exist in this case. Decision-making units with an efficiency value between 0.6 and 0.8 are given a yellow alert, which can issue rectification notices, such as the first inspection of section 6 and the first three inspections of section 4. Decision-making units with an efficiency value between 0.8 and 1 are provided with optimization suggestions and continuously tracked. Most sections in this case meet this category, and they can be given special attention based on the improvement of each indicator of each decision-making unit.

[0164] III. Node Efficiency Evaluation

[0165] In this case study, considering that in actual engineering scenarios, the number and types of construction units vary at each construction stage, we will demonstrate the process using the example of 8 construction units undergoing 5 document checks in stage T1. The calculated node efficiency is shown in the table below:

[0166]

[0167] As can be seen from the table above, the archive quality management of sections 3 and 7 was the best throughout the entire process and should be given priority in the next stage of cooperation; the archive management quality of sections 4, 6, and 8 was lagging behind, and this aspect should be the focus of the next stage of cooperation or corresponding penalty provisions should be made in advance in the contract.

[0168] IV. Comprehensive Efficiency Evaluation of the Entire Process

[0169] Furthermore, it enables comprehensive efficiency evaluation throughout the entire process. By using a weighted method, the efficiency values ​​of each stage can be comprehensively evaluated, achieving an overall assessment of the quality of the entire process archives and overcoming the shortcomings of stage-based fragmentation. The weighting can be the resource proportion of each stage, such as construction duration, investment scale, or other quantifiable important indicators.

[0170] For the supervision unit, the overall efficiency value of the entire process can be obtained by weighting the node efficiency values ​​of the construction units under its jurisdiction at each stage. For the construction unit, the overall efficiency value of the entire process can be obtained by weighting the node efficiency values ​​of each stage, with the weights being the resource proportions such as the duration or investment scale of each stage. This allows for an overall evaluation of the quality of the entire construction project archives.

[0171] By obtaining the node efficiency values ​​at each stage and the overall efficiency value throughout the process, the quality of file management by construction and supervision units can be evaluated. This allows for the establishment of a performance credit evaluation system, which can then prioritize future collaborations, assign bonus points in bidding, or incorporate corresponding reward and penalty mechanisms into contracts. Furthermore, the overall file quality evaluation results for the construction unit can provide a reference for evaluating the management capabilities of its internal managers.

[0172] Secondly, this application also provides a dynamic analysis system for the quality of civil aviation engineering archives based on multi-dimensional constrained projection, including:

[0173] The construction unit is configured to construct a data indicator system for the quality of civil aviation engineering archives according to procedural constraints; the data indicator system includes input indicators, expected output indicators, and unexpected output indicators.

[0174] The data collection unit is configured to periodically collect raw data from different decision-making units at the same construction stage of the target civil aviation construction project, and to obtain input indicator data, expected output indicator data and unexpected output indicator data from the raw data as data for data analysis.

[0175] The weighting unit is configured to determine the entropy weight of each indicator data in the archive analysis data at the current time using the entropy weight method;

[0176] The optimization unit is configured to analyze the archive quality of each decision unit using a weighted SBM model based on the entropy weights, and perform projection analysis based on the analysis results. By calculating the differences between the current decision unit's various indicators and the projected target values, the optimization target of each decision unit at the current moment is generated.

[0177] The loop unit is configured to repeatedly acquire the archive analysis data for the next cycle and generate optimization targets at different times.

[0178] In one possible implementation, the weight unit is further configured as follows:

[0179] Normalize the same indicator data for all decision-making units to generate normalized data;

[0180] Calculate the proportion of the normalized data of each decision unit in the corresponding indicator data to the normalized data of all decision units;

[0181] Calculate the information entropy of the corresponding indicator data based on the stated ratio;

[0182] The entropy weights of the corresponding indicator data are calculated based on the information entropy.

[0183] In one possible implementation, the optimization unit is further configured as follows:

[0184] Constructing a weighted SBM model:

[0185]

[0186] Constraints:

[0187]

[0188]

[0189]

[0190] In the formula, Let be the efficiency value of the k-th decision unit in the t-th sampling period, where m is the number of input indicators, q is the number of expected output indicators, and l is the number of undesirable output indicators. Let be the normalized value of the i-th input indicator of the k-th decision unit in the t-th sampling period. The normalized value of the i-th input indicator for the j-th decision unit in the t-th sampling period. Let i be the target value of the i-th input indicator. Let r be the normalized value of the expected output index of the k-th decision unit in the t-th sampling period. Let r be the normalized value of the expected output index of the j-th decision unit in the t-th sampling period. Let r be the target value of the expected output indicator. Let p be the normalized value of the p-th undesired output index of the k-th decision unit in the t-th sampling period. Let p be the normalized value of the p-th undesired output index of the j-th decision unit in the t-th sampling period. Let p be the target value of the undesired output. The entropy weight of the r-th expected output indicator is... Let the entropy weight be the value of the p-th undesirable output indicator. Let be the weight coefficient of the j-th indicator;

[0191] The quality of archives in each decision-making unit is determined based on the efficiency value output by the weighted SBM model. Note: Both terms in the denominator must be less than 1; if a term is greater than 1, the reciprocal is taken. An efficiency value less than 1 indicates invalidity, while a value greater than 1 indicates validity and does not affect the relative ranking.

[0192] In one possible implementation, the optimization unit is further configured as follows:

[0193] When the efficiency value of the decision-making unit at the current moment is less than 1, the current input indicator data, the current expected output indicator data, and the current unexpected output indicator data are obtained from the archive analysis data.

[0194] The differences in input indicator data, expected output indicator data, and unexpected output indicator data are calculated as difference data; the differences in input indicator data are the differences between the current input indicator data and the target value of the input indicator; the differences in expected output indicator data are the differences between the current expected output indicator and the target value of the expected output indicator; the differences in unexpected output indicator data are the differences between the current unexpected output indicator and the target value of the unexpected output indicator; the target value is the process data output by the weighted SBM model.

[0195] The difference data are sorted from largest to smallest, and the sorting order is used as the optimization priority data for the corresponding indicators.

[0196] The optimization priority data and the difference data are used as the optimization targets at the current moment.

[0197] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0198] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices or units, or may be electrical, mechanical or other forms of connection.

[0199] The units described as separate components may or may not be physically separate. As will be apparent to those skilled in the art, the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0200] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0201] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or grid device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0202] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0203] Furthermore, the application methods and technical means of this case can be extended to any engineering industry and any field according to actual needs. It can be any engineering industry such as municipal engineering, building engineering, highway engineering, water conservancy engineering, railway engineering, etc., or any field such as on-site engineering management, safety management, investment management, schedule management, etc.

Claims

1. A method for dynamic analysis of civil aviation engineering archive quality based on multi-dimensional constrained projection, characterized in that, include: A data indicator system for the quality of civil aviation engineering archives is constructed based on procedural constraints; the data indicator system includes input indicators, expected output indicators, and unexpected output indicators. The original data of the archives of different decision-making units in the same construction stage of the target civil aviation construction project are periodically collected, and the input index data, expected output index data and unexpected output index data of the original data of the archives are obtained as archive analysis data. The entropy weight of each indicator in the archive analysis data at the current moment is determined by the entropy weight method. The archive quality of each decision unit is analyzed using a weighted SBM model based on the entropy weights. Projection analysis is then performed based on the analysis results. By calculating the differences between the current decision unit's various indicators and the projected target values, the optimization target of each decision unit at the current moment is generated. The archive analysis data is repeatedly acquired for the next cycle, and optimization targets are generated at different times.

2. The method for dynamic analysis of civil aviation engineering archive quality based on multi-dimensional constrained projection according to claim 1, characterized in that, The acquisition of entropy weights includes: Normalize the same indicator data for all decision-making units to generate normalized data; Calculate the proportion of the normalized data of each decision unit in the corresponding indicator data to the normalized data of all decision units; Calculate the information entropy of the corresponding indicator data based on the stated ratio; The entropy weights of the corresponding indicator data are calculated based on the information entropy.

3. The method for dynamic analysis of civil aviation engineering archive quality based on multi-dimensional constrained projection according to claim 1, characterized in that, The analysis of archival quality using the weighted SBM model includes: Constructing a weighted SBM model: Constraints: In the formula, Let be the efficiency value of the k-th decision unit in the t-th sampling period, where m is the number of input indicators, q is the number of expected output indicators, and l is the number of undesirable output indicators. Let be the normalized value of the i-th input indicator of the k-th decision unit in the t-th sampling period. The normalized value of the i-th input indicator for the j-th decision unit in the t-th sampling period. Let i be the target value of the i-th input indicator. Let r be the normalized value of the expected output index of the k-th decision unit in the t-th sampling period. Let r be the normalized value of the expected output index of the j-th decision unit in the t-th sampling period. Let r be the target value of the expected output indicator. Let p be the normalized value of the p-th undesired output index of the k-th decision unit in the t-th sampling period. Let p be the normalized value of the p-th undesired output index of the j-th decision unit in the t-th sampling period. Let p be the target value of the undesired output. The entropy weight of the r-th expected output indicator is... Let the entropy weight be the value of the p-th undesirable output indicator. Let be the weight coefficient of the j-th indicator; The quality of archives for each decision-making unit is determined based on the efficiency value output by the weighted SBM model.

4. The method for dynamic analysis of civil aviation engineering archive quality based on multi-dimensional constrained projection according to claim 3, characterized in that, The optimization objectives of the decision-making unit at the current moment include: When the efficiency value of the decision-making unit at the current moment is less than 1, the current input indicator data, the current expected output indicator data, and the current unexpected output indicator data are obtained from the archive analysis data. The differences in input indicator data, expected output indicator data, and unexpected output indicator data are calculated as difference data; the differences in input indicator data are the differences between the current input indicator data and the target value of the input indicator; the differences in expected output indicator data are the differences between the current expected output indicator and the target value of the expected output indicator; the differences in unexpected output indicator data are the differences between the current unexpected output indicator and the target value of the unexpected output indicator; the target value is the process data output by the weighted SBM model. The difference data are sorted from largest to smallest, and the sorting order is used as the optimization priority data for the corresponding indicators; The optimization priority data and the difference data are used as the optimization targets at the current moment.

5. The method for dynamic analysis of civil aviation engineering archive quality based on multi-dimensional constrained projection according to claim 1, characterized in that, Also includes: When construction is completed, for each construction stage, the average of the efficiency values ​​obtained from multiple evaluations within that construction stage is taken as the node efficiency value of that decision-making unit in that construction stage. The overall efficiency value of the decision-making unit in the target civil aviation construction project is obtained by weighting the efficiency values ​​of all nodes of the decision-making unit; the weighting weight is the proportion of resources invested in each construction stage in the total construction stages. The quality of the archives of the corresponding decision-making unit is judged based on the comprehensive efficiency value.

6. The method for dynamic analysis of civil aviation engineering archive quality based on multi-dimensional constrained projection according to claim 1, characterized in that, The input indicators include archival staff working hours, management costs, archiving plan duration, and investment in information technology construction; the expected output indicators include archiving timeliness rate, node response delay, random inspection pass rate, data logical consistency, signature and seal completeness rate, actual archiving rate, key document completeness rate, audio-visual material matching rate, classification accuracy rate, cataloging standardization rate, numbering continuity, signature traceability completeness rate, change closure rate, and responsibility chain completeness rate; the undesirable output indicators include archiving delay days, number of document defects, and number of missing signatures.

7. A dynamic analysis system for the quality of civil aviation engineering archives based on multi-dimensional constrained projection, characterized in that, include: The construction unit is configured to construct a data indicator system for the quality of civil aviation engineering archives according to procedural constraints; the data indicator system includes input indicators, expected output indicators, and unexpected output indicators. The data collection unit is configured to periodically collect raw data from different decision-making units at the same construction stage of the target civil aviation construction project, and to obtain input indicator data, expected output indicator data and unexpected output indicator data from the raw data as data for data analysis. The weighting unit is configured to determine the entropy weight of each indicator data in the archive analysis data at the current time using the entropy weight method; The optimization unit is configured to analyze the archive quality of each decision unit using a weighted SBM model based on the entropy weights, and perform projection analysis based on the analysis results. By calculating the differences between the current decision unit's various indicators and the projected target values, the optimization target of each decision unit at the current moment is generated. The loop unit is configured to repeatedly acquire the archive analysis data for the next cycle and generate optimization targets at different times.

8. The multi-dimensional constrained projection-based dynamic analysis system for civil aviation engineering archive quality according to claim 7, characterized in that, The weighting unit is further configured as follows: Normalize the same indicator data for all decision-making units to generate normalized data; Calculate the proportion of the normalized data of each decision unit in the corresponding indicator data to the normalized data of all decision units; Calculate the information entropy of the corresponding indicator data based on the stated ratio; The entropy weights of the corresponding indicator data are calculated based on the information entropy.

9. The multi-dimensional constrained projection-based dynamic analysis system for civil aviation engineering archive quality according to claim 7, characterized in that, The optimization unit is further configured to: Constructing a weighted SBM model: Constraints: In the formula, Let be the efficiency value of the k-th decision unit in the t-th sampling period, where m is the number of input indicators, q is the number of expected output indicators, and l is the number of undesirable output indicators. Let be the normalized value of the i-th input indicator of the k-th decision unit in the t-th sampling period. The normalized value of the i-th input indicator for the j-th decision unit in the t-th sampling period. Let i be the target value of the i-th input indicator. Let r be the normalized value of the expected output index of the k-th decision unit in the t-th sampling period. Let r be the normalized value of the expected output index of the j-th decision unit in the t-th sampling period. Let r be the target value of the expected output indicator. Let p be the normalized value of the p-th undesired output index of the k-th decision unit in the t-th sampling period. Let p be the normalized value of the p-th undesired output index of the j-th decision unit in the t-th sampling period. Let p be the target value of the undesired output. The entropy weight of the r-th expected output indicator is... Let the entropy weight be the value of the p-th undesirable output indicator. Let be the weight coefficient of the j-th indicator; The quality of archives for each decision-making unit is determined based on the efficiency value output by the weighted SBM model.

10. The multi-dimensional constrained projection-based dynamic analysis system for civil aviation engineering archive quality according to claim 9, characterized in that, The optimization unit is further configured to: When the efficiency value of the decision-making unit at the current moment is less than 1, the current input indicator data, the current expected output indicator data, and the current unexpected output indicator data are obtained from the archive analysis data. The differences in input indicator data, expected output indicator data, and unexpected output indicator data are calculated as difference data; the differences in input indicator data are the differences between the current input indicator data and the target value of the input indicator; the differences in expected output indicator data are the differences between the current expected output indicator and the target value of the expected output indicator; the differences in unexpected output indicator data are the differences between the current unexpected output indicator and the target value of the unexpected output indicator; the target value is the process data output by the weighted SBM model. The difference data are sorted from largest to smallest, and the sorting order is used as the optimization priority data for the corresponding indicators; The optimization priority data and the difference data are used as the optimization targets at the current moment.

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