Business index statistical method and device based on multiple levels, equipment and medium

Abstract

The invention provides a business index statistical method and device based on multiple levels, equipment and a medium, relates to the technical field of data processing, and is used for solving the problem that a traditional business index statistical method is relatively low in efficiency and accuracy. The method comprises the following steps: acquiring a plurality of basic business data tables from a source data layer of a civil aviation enterprise; associating a plurality of basic business data tables according to the flight identifier, and generating a data wide table of a data wide surface layer; respectively calculating a target statistical value of each atomic index in the refined index layer according to the data wide table; calculating the statistical value of each business index in the statistical layer according to the target statistical value of each atomic index in the refined index layer; and generating a user view of the user view layer according to the statistical value of each business index in the statistical layer. According to the method, the accuracy of data statistics is ensured through a multi-level design, and the efficiency of data statistics is improved through reusable atomic indexes.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and provides a method, apparatus, equipment and medium for statistical analysis of business indicators based on multiple levels. Background Technology

[0002] In the field of civil aviation data statistics, traditional indicator statistics methods calculate business indicators at the statistical layer based on data from the source data layer. With the rapid development of the civil aviation industry, the types and volume of business data based on big data platforms are increasing. At the same time, as business continues to evolve, the actual statistical business indicators need to be continuously optimized and upgraded. Traditional business indicator statistics methods suffer from problems such as unclear hierarchy, complex and time-consuming calculation logic, and redundant calculations, resulting in low efficiency and accuracy of business indicator statistics. Summary of the Invention

[0003] This application provides a multi-level business indicator statistics method, apparatus, device, and medium to address the problems of low efficiency and accuracy in traditional business indicator statistics methods.

[0004] Firstly, a multi-level business indicator statistics method is provided, including: Multiple basic business data tables are obtained from the source data layer of civil aviation enterprises; Based on the flight identifier, associate the multiple basic business data tables to generate a wide data table at the wide data layer; Based on the data wide table, the target statistical values ​​of each atomic indicator in the refined indicator layer are calculated respectively; the atomic indicator is the smallest data unit that has independent calculation meaning and can be reused. Based on the target statistical values ​​of each atomic indicator in the refined indicator layer, calculate the statistical values ​​of each business indicator in the statistical layer; Based on the statistical values ​​of each business indicator in the statistical layer, a user view of the user view layer is generated.

[0005] Optionally, before obtaining multiple basic business data tables from the source data layer of civil aviation enterprises, the method further includes: The business indicators in the statistical layer are broken down to obtain multiple atomic indicators; The frequency of occurrence of each atomic indicator is counted, and the indicators are sorted and aggregated in descending order of frequency to generate a refined indicator layer.

[0006] Optionally, the business metrics in the statistical layer can be broken down to obtain multiple atomic metrics, including: Obtain the definition text of each business indicator in the statistical layer; The definition text of each business indicator is split using a word segmenter specifically designed for the civil aviation industry to obtain word segmentation results; the word segmenter is trained based on historical business indicator definition texts. Receive user correction instructions for the word segmentation results and obtain multiple atomic indicators.

[0007] Optionally, the correction instructions may include merging incorrectly split related phrases and / or supplementing limiting constraints.

[0008] Optionally, the step of calculating the target statistical value of each atomic indicator in the refined indicator layer according to the data wide table includes: Based on the data wide table, calculate the initial statistical values ​​of each atomic index in the refined index layer; Logical verification is performed on the initial statistical values ​​of each atomic index to obtain the verification results; If the verification result is determined to be qualified, the initial statistical value of each atomic index is determined as the target statistical value of each atomic index.

[0009] Optionally, the step of performing logical verification on the initial statistical values ​​of each atomic index to obtain the verification result includes: The initial statistical values ​​of each atomic indicator are performed in chronological order, and / or the initial statistical values ​​of each atomic indicator are performed in state order according to the state conflict rule base to obtain the verification result; wherein, the state conflict rule base stores combinations of entity conflict states.

[0010] Optionally, after performing logical verification on the initial statistical values ​​of each atomic index and obtaining the verification results, the method further includes: If the verification result is unqualified, a verification log is generated; Receive the solution input by the user based on the verification log; According to the solution, the initial statistical values ​​of the non-compliant atomic indicators are corrected to obtain the target statistical values ​​of each atomic indicator.

[0011] Optionally, after statistically analyzing the frequency of occurrence of each atomic indicator, sorting and aggregating them in descending order of frequency to generate a refined indicator layer, the method further includes: Analyze the calculation logic of each atomic index and extract the feature vector of each atomic index; Calculate the similarity between each feature vector, and cluster atomic indices with similarity higher than a preset similarity into the same computational logic block.

[0012] Optionally, the feature vector includes parameter types and a data source table; the similarity calculation formula is as follows: S = α × S1 + β × S2 Where S is the similarity between the feature vectors of the two atomic indicators, S1 is the similarity between the parameter types of the two atomic indicators, α is the weight of the parameter type, S2 is the similarity between the data source tables of the two atomic indicators, β is the weight of the data source table, and α+β=1.

[0013] Optionally, generating the user view of the user view layer based on the statistical values ​​of each business indicator in the statistical layer includes: Obtain the statistical values ​​and preset thresholds of each business indicator in the statistical layer; the preset thresholds include warning thresholds; If the statistical values ​​of all business metrics in the statistics layer do not exceed the corresponding warning thresholds, a normal user view is generated.

[0014] Optionally, after obtaining the statistical values ​​and preset thresholds of each business indicator in the statistical layer, the method further includes: If the statistical value of any business indicator exceeds the warning threshold for that business indicator, a user view with a warning label and associated anomaly analysis entry will be generated.

[0015] Optionally, the preset threshold further includes a boundary threshold greater than the warning threshold; after generating a user view with a warning identifier and associated anomaly analysis entry if the statistical value of any business indicator exceeds the warning threshold of that business indicator, the method further includes: If the statistical value of any business indicator exceeds the critical threshold of that business indicator, an emergency operation will be automatically triggered. The emergency operation includes generating an anomaly report, pushing it to the to-do list, and locking operation permissions.

[0016] Secondly, a multi-level business indicator statistics device is provided, including: The acquisition module is used to obtain multiple basic business data tables from the source data layer of civil aviation enterprises; The association module is used to associate the multiple basic business data tables based on the flight identifier and generate a wide data table for the wide data layer. The calculation module is used to calculate the target statistical value of each atomic indicator in the refined indicator layer according to the data wide table; and to calculate the statistical value of each business indicator in the statistical layer according to the target statistical value of each atomic indicator in the refined indicator layer; wherein the atomic indicator is the smallest data unit with independent calculation meaning and reusability. The generation module is used to generate the user view of the user view layer based on the statistical values ​​of each business indicator in the statistical layer.

[0017] Thirdly, this application provides a computer device including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the multi-level business indicator statistical method described in the first aspect.

[0018] Fourthly, this application provides a computer-readable storage medium storing a computer program, on which a processor executes the computer program to implement the multi-level business indicator statistical method described in the first aspect.

[0019] Compared with the prior art, the beneficial effects of the embodiments of this application are as follows: This application provides a multi-level business indicator statistics method, which includes: obtaining multiple basic business data tables from the source data layer of a civil aviation enterprise; associating the multiple basic business data tables according to flight identifiers to generate a data wide table in the data wide table layer; calculating the target statistical value of each atomic indicator in the refined indicator layer according to the data wide table; the atomic indicator is the smallest data unit with independent calculation meaning and reusability; calculating the statistical value of each business indicator in the statistical layer according to the target statistical value of each atomic indicator in the refined indicator layer; and generating a user view in the user view layer according to the statistical value of each business indicator in the statistical layer.

[0020] This application employs a multi-layered design, managing business metrics hierarchically from the source data layer and wide table layer to the detailed metric layer, statistical layer, and user view layer. This results in a clear and easily understandable data structure, with each layer having a clear objective and defined responsibilities, avoiding confusion in data processing and calculation. Each layer depends on the results of the layer above, progressing layer by layer to ensure the accuracy and consistency of metric statistics. By linking multiple basic business data tables and generating a wide data table, the number of join operations between data tables is reduced, thereby avoiding frequent data connections and redundant data calculations and improving the efficiency of metric statistics. Furthermore, atomic metrics, as the smallest reusable data unit, ensure data accuracy and computational efficiency while avoiding overly complex and lengthy calculation processes, further improving the efficiency of metric statistics. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0022] Figure 1 A schematic diagram of the computing device structure of the hardware operating environment involved in the embodiments of this application; Figure 2 A flowchart illustrating a multi-level business indicator statistics method provided in this application embodiment; Figure 3 A schematic diagram illustrating the formation process of the refined index layer provided in this application embodiment; Figure 4 A schematic diagram of the five-layer processing framework provided in the embodiments of this application; Figure 5 A schematic diagram of the structure of a multi-level business indicator statistics device provided in this application embodiment.

[0023] The diagram is labeled as follows: 101-Processor, 102-Communication bus, 103-Network interface, 104-User interface, 105-Memory. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments of this application, other embodiments obtained by those skilled in the art without creative effort are all within the scope of protection of this application. Unless otherwise specified, the embodiments and features in the embodiments of this application can be arbitrarily combined with each other. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than that shown here.

[0025] To address the low efficiency and accuracy of traditional business indicator statistical methods, this application provides a multi-level business indicator statistical method that can be executed by a computer device. Please refer to... Figure 1 This is a schematic diagram of the computer device structure of the hardware operating environment involved in the embodiments of this application.

[0026] like Figure 1As shown, the computer device may include: a processor 101, such as a central processing unit (CPU), a communication bus 102, a user interface 104, a network interface 103, and a memory 105. The communication bus 102 is used to enable communication between these components. The user interface 104 may include a display screen and an input unit such as a keyboard; the user interface 104 may include standard wired and wireless interfaces. The network interface 103 may include standard wired and wireless interfaces (such as a Wi-Fi interface). The memory 105 may be high-speed random access memory (RAM) or stable non-volatile memory (NVM), such as a disk drive. Optionally, the memory 105 may also be a storage device independent of the aforementioned processor 101.

[0027] Those skilled in the art will understand that Figure 1 The structure shown does not constitute a limitation on the computer device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0028] like Figure 1 As shown, the memory 105, which serves as a storage medium, may include an operating system, a network communication module, a user interface module, and a multi-level business indicator statistics device.

[0029] exist Figure 1 In the computer device shown, the network interface 103 is mainly used for data communication with the network server; the user interface 104 is mainly used for data interaction with the user; the processor 101 and the memory 105 in the computer device of the present invention can be set in the computer device, and the computer device calls the multi-level business indicator statistics device stored in the memory 105 through the processor 101, and executes the multi-level business indicator statistics method provided in the embodiments of this application.

[0030] based on Figure 1 The computer equipment shown below, in conjunction with Figure 2 The present application provides a multi-level business indicator statistics method, the steps of which are as follows: S201. Obtain multiple basic business data tables from the source data layer of civil aviation enterprises.

[0031] In practical implementation, basic business data tables refer to core data tables directly related to the daily operations of civil aviation. These include several basic business data tables such as the Airport Support Plan Data Table (FPLA), the Flight Dynamics Data Table (FODC), and the Departing Flight Support Node Information Table (FPDI).

[0032] The Airport Support Plan Data Table primarily records various data points within the airport support plan, such as aircraft identification number (IIN), globally unique flight identifier (GUID), aircraft registration number, planned arrival time, planned departure time, planned arrival time, and planned departure or destination airport. The Flight Dynamics Data Table records real-time flight operation status, including actual departure time, actual landing time, and actual departure or landing airport. The Departure Flight Support Node Information Table records key time points during ground support operations, such as flight identification, start and end of refueling, start and end of boarding, calculation of wheel chock removal time, calculation of takeoff time, actual door closing time, or variable taxiing time.

[0033] S202. Based on the flight identifier, associate multiple basic business data tables to generate a wide data table at the wide data layer.

[0034] In practical implementation, the flight key is a critical field used to uniquely identify a flight, typically consisting of the airline code (two letters) and the flight number (usually 3-4 digits). In SQL, a JOIN operation is usually used to join multiple basic business data tables, and these tables are combined together using the flight key. For example, a wide data table is shown in Table 1.

[0035] Table 1. Wide Data Table

[0036] S203. Based on the wide data table, calculate the target statistical value of each atomic indicator in the refined indicator layer.

[0037] Within the refined indicator layer, there are multiple atomic indicators. An atomic indicator is the smallest, indivisible data unit with independent computational meaning. The characteristics of atomic indicators are as follows: Fundamental: Atomic indicators are the most basic units of measurement and cannot be further broken down into other indicators.

[0038] Specificity: Each atomic indicator directly reflects the result of a specific business process.

[0039] They cannot be simplified: they are direct representations of the original data and are measures that cannot be further broken down.

[0040] For example, the atomic indices are shown in Table 2: Table 2 Atomic Indicators

[0041] The following explains how to generate the detailed indicator layer: In one possible implementation, the business indicators in the statistical layer are broken down to obtain multiple atomic indicators; the frequency of occurrence of each atomic indicator is counted, and the indicators are sorted and aggregated in descending order of frequency to generate a refined indicator layer.

[0042] In the specific implementation process, the statistical layer has multiple business indicators, such as flight delay indicators, flight regularity statistics, or airport departure regularity statistics. These business indicators are manually sorted and analyzed, and then broken down according to the minimum condition that allows for independent calculation, resulting in multiple atomic indicators. Since different business indicators may be broken down into the same atomic indicator, the decomposed atomic indicators will appear multiple times in the entire statistical data. The frequency of these atomic indicators can be counted, and they can be sorted and aggregated in descending order of frequency to form refined indicator items.

[0043] Reference Figure 3 This is a schematic diagram illustrating the formation process of the refined index layer provided in an embodiment of this application. From Figure 3 As can be seen, the statistical layer has multiple business indicators, including business indicator 1, business indicator 2, etc. Business indicator 1 is broken down into condition 1, condition 2, and condition 3, and business indicator 2 is broken down into condition 2 and condition 4. Conditions 1, 2, 3, and 4 are sorted and aggregated in descending order of frequency to generate a refined indicator layer.

[0044] In this embodiment, the business indicators of the statistical layer are decomposed in reverse to generate a refined indicator layer sorted by frequency. A high frequency indicates that the atomic indicator is used in multiple statistical business indicators, and has a higher priority and a higher degree of reuse. Therefore, high-frequency atomic indicators (such as "flight delay duration" and "passenger load factor") are prioritized to avoid lengthy searches on each access, and the system can hit these commonly used atomic indicators more quickly.

[0045] In one possible embodiment, the step of breaking down the various business metrics in the statistical layer to obtain multiple atomic metrics includes: The system retrieves the definition text of each business indicator in the statistics layer; it then splits the definition text of each business indicator using a word segmenter specifically designed for the civil aviation industry to obtain the word segmentation results; the word segmenter is trained based on historical business indicator definition texts; and it receives user correction instructions for the word segmentation results to obtain multiple atomic indicators.

[0046] In the implementation process, the definition text of each business indicator in the statistics layer can first be obtained from a database, application programming interface (API), or files. These definition texts are usually based on industry standards or internal company definitions and cover specific descriptions of different business indicators. For example, the definition text of the flight regularity statistics indicator is as follows: A flight is considered on time if it meets one of the following conditions: (1) Take off within the airport ground taxiing time specified after the planned cabin door closing time, and without any abnormal situations such as returning to the airport or diverting to another airport; (2) Landing no later than 20 minutes after the planned hatch opening time.

[0047] Secondly, the definition text can be preprocessed, such as removing irrelevant characters, standardizing the format (e.g., language conversion), removing stop words or common meaningless words (e.g., "is", "of"), etc., while retaining important business terms.

[0048] Next, the segmenter can be trained based on historical business indicator definition text. Through techniques such as cross-validation, the parameters of the segmenter can be adjusted to obtain a segmenter specifically for the civil aviation field. The preprocessed definition text can then be input into the segmenter for initial splitting to obtain the segmentation results.

[0049] Finally, the system will provide a user interface or API interface that allows users to correct the word segmentation results. Users can input correction commands based on their business scenarios, and the system will correct the word segmentation results according to the correction commands to form the final refined indicator layer.

[0050] The correction instructions include merging incorrectly segmented related phrases and / or supplementing limiting constraints. For example, the results of segmentation using a word segmenter and manual correction are shown in Table 3: Table 3. Results of segmentation using a word segmenter and manual correction.

[0051] As shown in Table 3, the definition text of the flight regularity statistical indicators was split using a word segmenter, resulting in the following word segmentation results: "planned door closing time", "airport ground taxiing time", "return", "diversion", and "planned door opening time". "Planned door closing time" and "airport ground taxiing time" were manually combined to obtain "whether the takeoff occurred within the airport ground taxiing time after the planned door closing time". Further constraints were added to "planned door opening time" to obtain "landing 20 minutes later than the planned door opening time".

[0052] In this embodiment, the civil aviation-specific word segmenter can combine industry-specific terminology and standards to more accurately understand and segment the definition text of business indicators. Receiving user correction instructions on the segmentation results, based on automatic word segmentation, ensures that the segmentation results more closely reflect actual business needs. User corrections may be to rectify situations where the word segmenter cannot handle accurately, or to adjust the expression of indicators according to actual needs, thereby improving the accuracy of atomic indicators.

[0053] In one possible embodiment, after counting the frequency of occurrence of each atomic indicator, sorting and aggregating them in descending order of frequency, and generating a refined indicator layer, the method further includes: parsing the calculation logic of each atomic indicator, extracting the feature vector of each atomic indicator; calculating the similarity between each feature vector, and clustering atomic indicators with similarity higher than a preset similarity into the same calculation logic block.

[0054] In practice, each atomic indicator has a set of calculation logic (e.g., their parameter types, data source tables, etc.). By analyzing the calculation logic of each atomic indicator, these key features can be extracted, and these features can be represented by a feature vector. The feature vector includes the parameter type and the data source table. The parameter type refers to the specific data type involved in the atomic indicator, such as numeric, time-based, or Boolean. The data source table refers to the data source or related data table for the atomic indicator. For example, some indicators may come from a "flight table," while others may come from a "passenger table."

[0055] Furthermore, for each pair of atomic indices, the similarity between them is calculated based on their feature vectors. The formula is as follows: S = α × S1 + β × S2 Where S is the similarity between the feature vectors of the two atomic indicators, S1 is the similarity between the parameter types of the two atomic indicators, α is the weight of the parameter type, S2 is the similarity between the data source tables of the two atomic indicators, β is the weight of the data source table, and α+β=1.

[0056] It should be noted that the default settings are α=0.5 and β=0.5, but α and β can be flexibly adjusted according to the actual application requirements.

[0057] The following sections describe how to calculate the similarity S1 between the parameter types of two atomic indices and the similarity S2 between the data source tables: If the parameter types of two atomic indices are exactly the same, for example, both are numeric, then S1 = 1. If the parameter types of two atomic indices are completely mismatched, for example, one is numeric and the other is Boolean, then S1 = 0.

[0058] If the two atomic metrics have the same data source table, then S2 = 1. If the two atomic metrics have different data source tables, then S2 = 2 × the number of identical fields / the total number of fields in both data source tables. If the two atomic metrics do not have any identical fields in their data source tables, then S2 = 0.

[0059] Finally, after calculating the similarity between each feature vector, atomic indicators with similarity scores higher than a preset similarity score (e.g., 0.8) can be clustered into the same computational logic block. For example, the three atomic indicators of landing 20 minutes later than the planned hatch opening time, delay time, and current delay time all have time-type parameters and similar computational logic, so these three atomic indicators can be clustered into one computational logic block.

[0060] In this embodiment, clustering based on the relevance of computational logic allows for more centralized and unified management of a group of atomic metrics with similar computational logic. When adding new related business metrics or modifying computational logic later, it is easier to locate problems and maintain them uniformly. Furthermore, by comprehensively considering multiple dimensions (parameter type, data source), a more comprehensive similarity assessment can be obtained, rather than relying solely on a single dimension. This avoids overlooking other potential similarities due to a single factor, thereby improving the overall accuracy of clustering.

[0061] In one possible embodiment, the specific steps of S203 include: calculating the initial statistical value of each atomic indicator in the refined indicator layer according to the data wide table; performing logical verification on the initial statistical value of each atomic indicator to obtain the verification result; if the verification result is determined to be qualified, then determining the initial statistical value of each atomic indicator as the target statistical value of each atomic indicator.

[0062] In the specific implementation process, firstly, based on the information in the wide data table, the initial statistical value of each atomic indicator in the refined indicator layer can be calculated to provide basic data for subsequent verification. Then, a logic validator can be used to perform time-series verification, state verification, etc., on the initial statistical values ​​of each atomic indicator to obtain the verification results. If the verification result is determined to be qualified, it indicates that the logic between each atomic indicator in the refined indicator layer is consistent, and the initial statistical value of each atomic indicator can be directly determined as the target statistical value of each atomic indicator.

[0063] In this embodiment of the application, logical verification can ensure that the statistical values ​​of atomic indicators are logically acceptable, effectively identify and correct non-compliant data, reduce deviations caused by erroneous data, and help improve the accuracy of subsequent business indicator statistics.

[0064] In one possible embodiment, the initial statistical values ​​of each atomic index are logically verified to obtain the verification results. There are three specific implementation methods for this, which are described below: The first method involves performing time-series verification on the initial statistical values ​​of each atomic index in chronological order to obtain the verification results.

[0065] Specifically, the purpose of time-series verification is to validate the consistency and reasonableness of data along the timeline. For example, if there is a conflict between the departure and arrival times of a flight, the verification result will be unqualified.

[0066] The second method involves performing state verification on the initial statistical values ​​of each atomic index based on the state conflict rule base to obtain the verification results.

[0067] Specifically, the state conflict rule base stores combinations of contradictory entity states, defining which combinations of states are not allowed to occur simultaneously. For example, "delayed" and "not delayed" cannot coexist, as can "not taken off" and "landed." If both the "not taken off" and "landed" states of a flight are true, the validation result is invalid.

[0068] The third method involves performing time-series verification on the initial statistical values ​​of each atomic indicator according to the time sequence to obtain the time-series verification result, performing state verification on the initial statistical values ​​of each atomic indicator according to the state conflict rule base to obtain the state verification result, and combining the time-series verification result and the state verification result to obtain the verification result.

[0069] Specifically, if both the timing verification result and the status verification result are qualified, then the verification result is qualified; if either the timing verification result or the status verification result is unqualified, then the verification result is unqualified.

[0070] In this embodiment of the application, the initial statistical values ​​of each atomic indicator are subjected to time-series verification and state verification. Time-series verification can help discover time errors (such as unreasonable order of events), while state verification can ensure that there are no logical conflicts between data states, thereby ensuring the accuracy of the data and improving the accuracy of subsequent business indicator statistics.

[0071] In one possible embodiment, after performing state verification on the target statistical values ​​of each atomic index in the refined index layer according to the state conflict rule base and obtaining the state verification result, the method further includes: If the timing verification result or the status verification result is unqualified, a verification log is generated; the solution input by the user based on the verification log is received; based on the solution, the initial statistical values ​​of the unqualified atomic indicators are corrected to obtain the target statistical values ​​of each atomic indicator.

[0072] During implementation, when the timing or status verification result is unqualified, the system automatically records the unqualified information, including which atomic indicators failed the verification and the reason for the failure. This information is organized into a verification log. Users can view the contents of the verification log and input corresponding solutions. For example, users may choose to manually modify the initial statistical values ​​of certain atomic indicators or adjust the data calculation method. Based on the solution, the system will adjust the initial statistical values ​​of the unqualified atomic indicators, such as changing the timestamp, correcting the status combination, or adjusting the data calculation process, thereby obtaining the target statistical values ​​of the atomic indicators.

[0073] In this embodiment, although most of the verification and correction processes are automated, the opportunity for user intervention is still retained when facing complex problems. Users can provide customized solutions based on the actual situation. The verification log provides detailed records for subsequent analysis and correction. Through the verification and correction process, the accuracy and consistency of data are improved, and the risks caused by data errors are reduced.

[0074] S204. Based on the target statistical values ​​of each atomic indicator in the refined indicator layer, calculate the statistical value of each business indicator in the statistical layer.

[0075] For example, based on the atomic indicators in Table 2, the statistical values ​​of the flight delay indicators can be calculated as shown in Table 4.

[0076] Table 4 Statistical values ​​of flight delay indicators

[0077] The flight status types in Table 4 are determined by combining multiple atomic indicators from Table 2, such as: If a flight is cancelled on the same day, the flight status will be cancelled. If a return flight occurs, the flight status type will be "Return Flight". If a diversion occurs and the conditions for diversion within the same city are met, the flight status type is diversion; if a delay occurs, the flight status type is delay. If the flight takes off within the scheduled airport ground taxiing time after the planned cabin door closing time, or if no abnormal situations such as returning to the origin or diverting to another airport occur, the flight status is normal.

[0078] S205. Generate the user view of the user view layer based on the statistical values ​​of each business indicator in the statistics layer.

[0079] In one possible embodiment, the specific steps of S205 include: obtaining the statistical values ​​and preset thresholds of each business indicator in the statistics layer; if the statistical values ​​of all business indicators in the statistics layer do not exceed the corresponding warning thresholds, then generating a normal user view.

[0080] In practice, each business metric has a corresponding preset threshold, which includes a warning threshold and a critical threshold. The critical threshold is greater than the warning threshold. After obtaining the statistical values ​​of each business metric in the statistics layer, they can be compared with the corresponding warning thresholds. If the statistical values ​​of all business metrics are within the warning thresholds, a normal user view is generated, indicating that the status of all business metrics is within the normal range.

[0081] The user view includes the name, statistical value, and explanatory information for each business metric. This explanatory information helps end users quickly understand the meaning of each metric. Charts (bar charts, line charts, pie charts, etc.) can also be used to display different business metrics in the user view. Users can also interact with the user view, such as selecting different time intervals and filtering metrics based on specific dimensions.

[0082] In one possible embodiment, after obtaining the statistical values ​​and preset thresholds of each business indicator in the statistics layer, if the statistical value of any business indicator exceeds the warning threshold of that business indicator, a user view with a warning icon and associated with the anomaly analysis entry is generated.

[0083] In practice, if the statistical value of a certain business indicator exceeds the corresponding warning threshold, it indicates a potential risk, and a user view with a warning indicator can be generated. This user view includes not only the name, statistical value, and descriptive information of the business indicator, but also clearly marks the "warning" status for business indicators that exceed the warning threshold. It is usually highlighted in red or yellow to alert users to the anomaly of certain business indicators and provides an entry point for anomaly analysis for users to view further detailed information.

[0084] In this embodiment, when the statistical value of a certain business indicator exceeds a low warning threshold, the system can instantly generate an early warning view, providing early warnings in the early stages of a problem, preventing it from escalating, and enabling the enterprise to take timely measures to reduce operational risks and prevent significant losses. Furthermore, through the anomaly analysis portal, users can directly access in-depth analysis to view more detailed background information, historical data, and possible causes, thereby accelerating the problem identification process. This not only helps solve current problems but also provides data support for future preventative measures.

[0085] In one possible embodiment, after generating a user view with a warning label and associated anomaly analysis entry if the statistical value of any business indicator exceeds the warning threshold of that business indicator, an emergency operation is automatically triggered if the statistical value of that business indicator exceeds the critical threshold of that business indicator.

[0086] In practice, if the statistical value of a certain business indicator exceeds the warning threshold and further exceeds the critical threshold, the system will automatically trigger a series of emergency operations. These emergency operations include generating an anomaly report, pushing it to the to-do list, and locking operation permissions. These are described in detail below: 1. Generate an anomaly report Generate a detailed anomaly report, recording the abnormal business metrics and their causes, such as showing the difference between the current statistical value and the critical threshold.

[0087] 2. Push to to-do list The anomaly report will be pushed to the to-do list of the relevant responsible personnel, prompting them to handle the anomaly immediately. After receiving the anomaly report, the responsible personnel can immediately analyze the cause and take necessary actions.

[0088] 3. Lock operation permissions The system may suspend certain system functions or restrict certain user operations. For example, if there is an abnormal surge in order volume and fraud is involved, the system may lock payment-related functions to prevent further losses.

[0089] In this embodiment, when the statistical value of a business indicator exceeds a high critical threshold, it signifies a potential major anomaly or risk. Immediately triggering emergency actions helps the company respond quickly, identify the severity of the problem, and resolve it as early as possible. The generation of anomaly reports and the push of to-do lists allow relevant personnel to immediately understand the urgency of the situation and take appropriate measures. The mechanism of locking operational permissions prevents further decisions or actions from exacerbating the problem, giving relevant personnel more time to investigate and fix the issue, and avoiding greater losses due to hasty decisions or inappropriate measures.

[0090] In summary, this application also provides a multi-level business indicator statistics method, constructing a five-layer processing framework consisting of a source data layer, a wide data table layer, a detailed indicator layer, a statistics layer, and a user view layer. Figure 4 As shown, this makes the data structure clear and easy to understand. Each layer has a clear objective and responsibilities, avoiding confusion in data processing and calculation. Each layer depends on the results of the previous layer, progressing step by step to ensure the accuracy and consistency of data calculations. By linking multiple basic business data tables and generating a wide data table, the number of join operations between data tables is reduced, thereby avoiding frequent data connections and redundant data calculations, and improving the efficiency of indicator statistics. Furthermore, atomic indicators, as the smallest reusable data unit, ensure the accuracy of data and the efficiency of calculations, while avoiding overly complex and lengthy calculation processes, further improving the efficiency of indicator statistics. This solves the problems of unclear hierarchy, poor scalability, difficult later maintenance, complex and time-consuming calculation logic, redundant calculations, and inconsistent calculation logic in traditional indicator statistics methods.

[0091] Based on the same inventive concept, such as Figure 5 As shown in the figure, this application embodiment also provides a multi-level business indicator statistics device, including: The acquisition module is used to obtain multiple basic business data tables from the source data layer of civil aviation enterprises; The association module is used to associate multiple basic business data tables based on flight identifiers and generate a wide data table at the wide data layer. The calculation module is used to calculate the target statistical values ​​of each atomic indicator in the refined indicator layer based on the data wide table; and to calculate the statistical values ​​of each business indicator in the statistical layer based on the target statistical values ​​of each atomic indicator in the refined indicator layer; an atomic indicator is the smallest data unit that has independent calculation meaning and can be reused. The generation module is used to generate user views for the user view layer based on the statistical values ​​of various business indicators in the statistics layer.

[0092] It should be noted that each module in the multi-level business indicator statistics device in this embodiment corresponds one-to-one with each step in the multi-level business indicator statistics method in the aforementioned embodiment. Therefore, the specific implementation of this embodiment can refer to the implementation of the aforementioned multi-level business indicator statistics method, and will not be repeated here.

[0093] Furthermore, in one embodiment, this application also provides a computer device, the computer device including a processor, a memory and a computer program stored in the memory, the computer program being executed by the processor to implement the aforementioned multi-level business indicator statistics method.

[0094] In addition, in one embodiment, this application also provides a computer storage medium storing a computer program, which is executed by a processor to implement the aforementioned multi-level business indicator statistics method.

[0095] In some embodiments, the computer-readable storage medium may be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface memory, optical disk, or CD-ROM; or it may be a device including one or any combination of the above-mentioned memories. The computer may be a variety of computing devices, including smart terminals and servers.

[0096] In some embodiments, executable instructions may take the form of a program, software, software module, script, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a standalone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0097] As an example, executable instructions may, but do not necessarily, correspond to files in the file system. They may be stored as part of a file that holds other programs or data, for example, in one or more scripts in a Hyper Text Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple collaborative files (e.g., a file that stores one or more modules, subroutines, or code sections).

[0098] As an example, executable instructions can be deployed to execute on a single computing device, or on multiple computing devices located in one location, or on multiple computing devices distributed across multiple locations and interconnected via a communication network.

[0099] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0100] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0101] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory / random access memory, magnetic disk, optical disk) and includes several instructions to cause a multimedia terminal device (which may be a mobile phone, computer, television receiver, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0102] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A multi-level business indicator statistical method, characterized in that, include: Multiple basic business data tables are obtained from the source data layer of civil aviation enterprises; Based on the flight identifier, associate the multiple basic business data tables to generate a wide data table at the wide data layer; Based on the data wide table, calculate the target statistical values ​​of each atomic indicator in the refined indicator layer; The atomic index is the smallest data unit that has independent computational meaning and can be reused; Based on the target statistical values ​​of each atomic indicator in the refined indicator layer, calculate the statistical values ​​of each business indicator in the statistical layer; Based on the statistical values ​​of each business indicator in the statistical layer, a user view of the user view layer is generated.

2. The multi-level business indicator statistical method as described in claim 1, characterized in that, Before obtaining multiple basic business data tables from the source data layer of civil aviation enterprises, the method further includes: The business indicators in the statistical layer are broken down to obtain multiple atomic indicators; The frequency of occurrence of each atomic indicator is counted, and the indicators are sorted and aggregated in descending order of frequency to generate a refined indicator layer.

3. The multi-level business indicator statistical method as described in claim 2, characterized in that, The business metrics in the statistical layer are broken down to obtain multiple atomic metrics, including: Obtain the definition text of each business indicator in the statistical layer; The definition text of each business indicator is split using a word segmenter specifically designed for the civil aviation industry to obtain word segmentation results; the word segmenter is trained based on historical business indicator definition texts. Receive user correction instructions for the word segmentation results and obtain multiple atomic indicators.

4. The multi-level business indicator statistical method as described in claim 3, characterized in that, The correction instructions include merging incorrectly split related phrases and / or supplementing limiting constraints.

5. The multi-level business indicator statistical method as described in claim 1, characterized in that, The step of calculating the target statistical values ​​of each atomic indicator in the refined indicator layer based on the data wide table includes: Based on the data wide table, calculate the initial statistical values ​​of each atomic index in the refined index layer; Logical verification is performed on the initial statistical values ​​of each atomic index to obtain the verification results; If the verification result is determined to be qualified, the initial statistical value of each atomic index is determined as the target statistical value of each atomic index.

6. The multi-level business indicator statistical method as described in claim 5, characterized in that, The logical verification of the initial statistical values ​​of each atomic index to obtain the verification result includes: The initial statistical values ​​of each atomic indicator are performed in chronological order, and / or the initial statistical values ​​of each atomic indicator are performed in state order according to the state conflict rule base to obtain the verification result; wherein, the state conflict rule base stores combinations of entity conflict states.

7. The multi-level business indicator statistical method as described in claim 5, characterized in that, After performing logical verification on the initial statistical values ​​of each atomic index and obtaining the verification results, the method further includes: If the verification result is unqualified, a verification log is generated; Receive the solution input by the user based on the verification log; According to the solution, the initial statistical values ​​of the non-compliant atomic indicators are corrected to obtain the target statistical values ​​of each atomic indicator.

8. The multi-level business indicator statistical method as described in claim 2, characterized in that, After statistically analyzing the frequency of occurrence of each atomic indicator, sorting and aggregating them in descending order of frequency to generate a refined indicator layer, the method further includes: Analyze the calculation logic of each atomic index and extract the feature vector of each atomic index; Calculate the similarity between each feature vector, and cluster atomic indices with similarity higher than a preset similarity into the same computational logic block.

9. The multi-level business indicator statistical method as described in claim 8, characterized in that, The feature vector includes parameter types and a data source table; the similarity calculation formula is as follows: S = α × S1 + β × S2 Where S is the similarity between the feature vectors of the two atomic indicators, S1 is the similarity between the parameter types of the two atomic indicators, α is the weight of the parameter type, S2 is the similarity between the data source tables of the two atomic indicators, β is the weight of the data source table, and α+β=1.

10. The multi-level business indicator statistical method as described in claim 1, characterized in that, The step of generating the user view of the user view layer based on the statistical values ​​of each business indicator in the statistical layer includes: Obtain the statistical values ​​and preset thresholds of each business indicator in the statistical layer; the preset thresholds include warning thresholds; If the statistical values ​​of all business metrics in the statistics layer do not exceed the corresponding warning thresholds, a normal user view is generated.

11. The multi-level business indicator statistical method as described in claim 10, characterized in that, After obtaining the statistical values ​​and preset thresholds of each business indicator in the statistical layer, the method further includes: If the statistical value of any business indicator exceeds the warning threshold corresponding to that business indicator, a user view with a warning label and associated anomaly analysis entry will be generated.

12. The multi-level business indicator statistical method as described in claim 11, characterized in that, The preset threshold also includes a boundary threshold greater than the warning threshold; after generating a user view with a warning identifier and associated anomaly analysis entry if the statistical value of any business indicator exceeds the warning threshold of that business indicator, the method further includes: If the statistical value of any business indicator exceeds the critical threshold of that business indicator, an emergency operation will be automatically triggered. The emergency operation includes generating an anomaly report, pushing it to the to-do list, and locking operation permissions.

13. A multi-level business indicator statistical device, characterized in that, include: The acquisition module is used to obtain multiple basic business data tables from the source data layer of civil aviation enterprises; The association module is used to associate the multiple basic business data tables based on the flight identifier and generate a wide data table for the wide data layer. The calculation module is used to calculate the target statistical values ​​of each atomic indicator in the refined indicator layer based on the data wide table. Based on the target statistical values ​​of each atomic indicator in the refined indicator layer, calculate the statistical values ​​of each business indicator in the statistical layer; The atomic index is the smallest data unit that has independent computational meaning and can be reused; The generation module is used to generate the user view of the user view layer based on the statistical values ​​of each business indicator in the statistical layer.

14. A computer device, characterized in that, The computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the multi-level business indicator statistical method as described in any one of claims 1-12.

15. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and the processor executes the computer program to implement the multi-level business indicator statistical method as described in any one of claims 1-12.

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