Efficiency analysis processing method and device based on dynamic weight, equipment and medium

Abstract

The invention relates to the technical field of performance evaluation, and discloses a dynamic weight-based performance analysis processing method and device, equipment and a medium. The method comprises the steps of obtaining a dynamic weight corresponding to an air route feature through a dynamic weight model based on the air route feature; identifying a label of the target object, and determining an evaluation index of the target object; calculating a comprehensive index completion rate according to the dynamic weight; and generating a corresponding processing strategy based on a preset rule, the comprehensive index completion rate and the evaluation index. According to the method and the device, the technical problems of insufficient evaluation accuracy, low evaluation matching efficiency and evaluation linkage lagging caused by index solidification in the process of evaluating the post efficiency in the prior art are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of performance evaluation, in particular to a performance analysis processing method and device based on dynamic weights, equipment and medium. BACKGROUND

[0002] At present, there are many existing technologies for performance improvement, such as revenue management post (RM) incentive evaluation. There are mainly two kinds of current industry mainstream schemes: (1) the performance trend is analyzed, the evaluation index is specified by using the same increase and decrease, the evaluation index is sorted, and the corresponding weight is given, for example, the evaluation index with high ranking in the sorting is increased in weight, and the evaluation index with low ranking is decreased in weight; (2) the task is taken, the department total task, the regional task and the route task are taken layer by layer, and the performance is evaluated according to the task quantity and the completion condition.

[0003] However, the current scheme has three technical defects: 1) Index solidification: most systems use fixed weights (such as a revenue index accounting for 30%), which cannot adapt to the performance characteristics of different routes (such as the sensitivity of the revenue index being higher than that of the sales index in the off-season route), resulting in insufficient evaluation accuracy (error rate > 15%); 2) Low efficiency of evaluation matching: relying on manual judgment of evaluation levels (such as evaluation dimensions when the regional person in charge manages the route), it is easy to appear "index mismatch" (such as using regional index to evaluate single route performance), and manual calculation time consumption accounts for more than 60%; 3) Evaluation linkage lag: the connection between the evaluation result and the corresponding processing strategy needs to be triggered manually, which has strong lag and weak timeliness.

[0004] Therefore, there is an urgent need for a performance analysis processing strategy to reduce evaluation error rate and improve response time to meet the performance improvement demand. SUMMARY

[0005] In order to solve the above technical problems, the present application provides a performance analysis processing method and device based on dynamic weights, equipment and medium.

[0006] In order to achieve the above purpose, the present application provides the following technical scheme: A performance analysis processing method based on dynamic weights, the method comprises: Based on the route characteristics, the dynamic weight corresponding to the route characteristics is obtained through a dynamic weight model; Identify the label of the target object, determine the evaluation index of the target object; According to the dynamic weight, the comprehensive index completion rate is calculated; Based on the preset rule, the comprehensive index completion rate and the evaluation index, a corresponding processing strategy is generated.

[0007] Based on the same inventive concept, the embodiment of the present application also provides a dynamic weight-based efficiency analysis processing device, the device comprises: The weight adjustment module is configured to obtain a dynamic weight corresponding to the route feature based on the route feature through a dynamic weight model; The label identification module is configured to identify the label of the target object and determine the evaluation index of the target object; The index completion rate calculation module is configured to calculate a comprehensive index completion rate according to the dynamic weight; The processing strategy generation module is configured to generate a corresponding processing strategy based on a preset rule, the comprehensive index completion rate and the evaluation index.

[0008] Based on the same inventive concept, the embodiment of the present application also provides an electronic device, comprising a memory and a processor; the processor is used to read and execute a computer program stored in the memory to realize the steps of the foregoing dynamic weight-based efficiency analysis processing method.

[0009] Based on the same inventive concept, the embodiment of the present application also provides a computer storage medium, the computer storage medium stores computer executable instructions, and the computer executable instructions realize the steps of the foregoing dynamic weight-based efficiency analysis processing method when executed.

[0010] The technical effects and advantages of the present application are as follows: Based on the route feature, the dynamic weight corresponding to the route feature is obtained through a dynamic weight model, the index weight is adjusted in real time through the route feature, and the problem of insufficient adaptability of fixed weight is solved; The label of the target object is identified, the evaluation index of the target object is determined, the cost of manual judgment is eliminated, and the preparation time of the efficiency evaluation is greatly shortened; The index completion rate is calculated according to the dynamic weight; a corresponding processing strategy is generated based on a preset rule, the comprehensive index completion rate and the evaluation index, the efficiency evaluation result is seamlessly connected with the human resource system, the response speed of the efficiency evaluation result is greatly improved, and for the first time, the multi-dimensional index dynamic adjustment is combined with the efficiency automatic matching, which provides a standardized technical solution for efficiency improvement, such as revenue management post (RM) incentive assessment, and adapts to the high-frequency and multi-dimensional efficiency evaluation scene of aviation.

[0011] Through the present application, the technical problems of insufficient evaluation accuracy, low evaluation matching efficiency and evaluation linkage lag caused by index solidification in the process of efficiency evaluation of post efficiency in the prior art are solved.

[0012] Other features and advantages of the present application will be set forth in the descriptions that follow, and in part will be apparent from the descriptions or can be learned by practice of the present application. The purposes and other advantages of the present application will be realized and attained by the structures particularly pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly introduced in the following description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort based on these drawings.

[0014] Figure 1 The flowchart of the method provided by the embodiments of the present application is shown in the following table: Figure 2 The business process schematic diagram of the present application is shown in the following table: Figure 3 The data flow schematic diagram of the present application is shown in the following table: Figure 4 The corresponding relationship schematic diagram of an embodiment of the present application is shown in the following table: Figure 5 The function module schematic diagram of an embodiment of the performance analysis processing device based on dynamic weight of the present application is shown in the following table: Figure 6 The structural schematic diagram of an electronic device of the embodiments of the present application is shown in the following table: DETAILED DESCRIPTION

[0015] The technical solutions in the embodiments of the present application will be described clearly and completely in the following description with reference to the drawings of the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without any creative effort belong to the scope of protection of the present application.

[0016] To solve the problems in the prior art, with reference to Figure 1 The present application discloses a performance analysis processing method based on dynamic weight, which comprises the following steps: Step S10: obtaining the dynamic weight corresponding to the route feature based on the route feature and a dynamic weight model. In some specific embodiments, step S10 comprises: inputting the route feature into the dynamic weight model, and extracting a feature coefficient by the dynamic weight model; adjusting a preset initial weight corresponding to the route feature based on an adjustment coefficient and the feature coefficient, to obtain the dynamic weight output by the dynamic weight model.

[0017] In this embodiment, with reference to Figure 2 , data collection and preprocessing are first performed. Exemplarily, data collection: obtaining revenue management post (RM) personnel information from the human resource system, including post type (such as “domestic revenue analyst”), belonging area, whether to manage specific routes, etc.; obtaining budget data (such as “seat kilometer revenue budget KPI”) from the planning finance department; extracting data or information such as routes controlled by revenue management post (RM) personnel, belonging area, whether the area supervisor manages specific routes, etc. from the revenue management system; obtaining income or revenue data from the revenue data system, including seat kilometer revenue, seat occupancy rate, booking volume, supply volatility rate and demand volatility rate, etc.

[0018] Data preprocessing: cleaning abnormal values (such as records with negative income), standardizing formats (such as unifying the date format to YYYY-MM-DD), establishing a “personnel-route-index” association table, as shown in Figure 4 .

[0019] Based on the “personnel-route-index” association table, the route characteristics of the evaluation object are obtained, including route type and sailing season. After inputting the route characteristics into the dynamic weight model, based on the route type included in the route characteristics, the corresponding relationship table between the route type and the initial characteristic coefficient is queried to determine the first initial characteristic coefficient corresponding to the route type; based on the sailing season included in the route characteristics, the corresponding relationship table between the sailing season and the initial characteristic coefficient is queried to determine the second initial characteristic coefficient corresponding to the sailing season; then the first initial characteristic coefficient is multiplied by the second initial characteristic coefficient, and the product obtained is the characteristic coefficient.

[0020] After obtaining the characteristic coefficient, the preset initial weight corresponding to the route characteristics is adjusted based on the adjustment coefficient and the characteristic coefficient, and the dynamic weight corresponding to the route characteristics output by the dynamic weight model is obtained.

[0021] Exemplarily, taking the route characteristic as the trunk route peak season as an example, when the trunk route is in the peak season, the sales index completion rate weight is adjusted from 35% to 40%, specifically, dynamic weight = benchmark weight x (1 + β x characteristic coefficient) = 35% x (1 + 0.1 x 0.15) ≈ 40%, wherein the characteristic coefficient is dynamically determined according to the route type and the sailing season, the route type includes trunk and branch, and the sailing season includes peak season and off-season, exemplarily, the trunk value is 0.1, the branch value is 0.1, the peak season value is 0.15, and the off-season value is 0.1; the benchmark weight is the preset initial weight (the revenue index preset initial weight is 30%, the revenue index preset initial weight is 35%, and the sales index preset initial weight is 35%); β is the adjustment coefficient, and the value range is preferably [-0.2, 0.2].

[0022] Further, the dynamic weight can also be calculated by a machine learning model (such as a random forest).

[0023] The blockchain technology is introduced to store the multi-dimensional dynamic evaluation indicators (income indicator completion rate, income index completion rate, and sales index completion rate), and the data tamper resistance is enhanced.

[0024] In step S20, the label of the target object is identified, and the evaluation indicator of the target object is determined. In some specific embodiments, step S20 includes: The label of the target object is identified, and a correspondence table between the label and the evaluation indicator is queried to determine the evaluation indicator of the target object. The label and the evaluation indicator are in one-to-one correspondence, and the evaluation indicator includes a first evaluation indicator, a second evaluation indicator, and a third evaluation indicator.

[0025] In this embodiment, continuing to refer to Figure 2 , the label of the target object includes a first preset label, a second preset label, and a third preset label. The first preset label is a route administrator, the second preset label is a regional responsible person, and the third preset label is a dual identity. For example, when the label of the target object is the first preset label (route administrator), the correspondence table between the label and the evaluation indicator is queried to determine that the evaluation indicator of the target object is the first evaluation indicator (single route indicator). When the label of the target object is the second preset label (regional responsible person), the correspondence table between the label and the evaluation indicator is queried to determine that the evaluation indicator of the target object is the second evaluation indicator (regional overall indicator). When the label of the target object is the third preset label (dual identity), the correspondence table between the label and the evaluation indicator is queried to determine that the evaluation indicator of the target object is the third evaluation indicator (single route indicator and regional overall indicator).

[0026] Further, the "post-indicator matrix table" can also be matched manually based on the "post-indicator matrix table", thereby reducing the difficulty of system development. The "post-indicator matrix table" is shown in Figure 4 .

[0027] In step S30, the comprehensive indicator completion rate is calculated according to the dynamic weight. In some specific embodiments, step S30 includes: The income indicator completion rate is calculated based on seat kilometer income and seat kilometer income budget. The income index completion rate is calculated based on the monthly airline route seat kilometer income, the monthly seat kilometer income of the route, the airline route seat kilometer income of the same period last year, and the seat kilometer income of the market of the route in the same period last year. The sales index completion rate is calculated based on the monthly airline route seat occupancy rate, the monthly seat occupancy rate of the route, the airline route seat occupancy rate of the last year, and the seat occupancy rate of the route of the last year; The comprehensive index completion rate is calculated based on the revenue index completion rate, the sales index completion rate, and the dynamic weight. The dynamic weight includes the dynamically adjusted weight corresponding to the revenue index completion rate, the dynamically adjusted weight corresponding to the sales index completion rate, and the dynamically adjusted weight corresponding to the sales index completion rate.

[0028] In this embodiment, continuing to refer to Figure 2 , the multi-dimensional dynamic indicators include the revenue index completion rate, the sales index completion rate, and the dynamic weight. The dynamic weight includes the dynamically adjusted weight corresponding to the revenue index completion rate, the dynamically adjusted weight corresponding to the sales index completion rate, and the dynamically adjusted weight corresponding to the sales index completion rate. The weight can be dynamically adjusted according to the route type (such as trunk line / branch line) and the flight season (such as peak season / off-season), so as to realize the accurate adaptation of the examination.

[0029] The index definition of the revenue index completion rate is that the seat kilometer income budget is used as the examination index. The revenue index completion rate = (seat kilometer income ÷ seat kilometer income budget) × 100%.

[0030] The index definition of the revenue index completion rate is that the monthly airline route seat kilometer income is used as the examination index. The revenue index completion rate = (this month's airline route seat kilometer income ÷ this month's route seat kilometer income) ÷ (last year's same period airline route seat kilometer income ÷ last year's same period route seat kilometer income) × 100%.

[0031] The index definition of the sales index completion rate is that the monthly airline route seat occupancy rate is used as the examination index. The sales index completion rate = (this month's airline route seat occupancy rate ÷ this month's route seat occupancy rate) ÷ (last year's same period airline seat occupancy rate ÷ last year's same period route seat occupancy rate) × 100%).

[0032] Specifically, the revenue index completion rate is calculated. If the seat kilometer income of a route is 0.6 yuan and the budget is 5,000 yuan, then the revenue index completion rate = (0.6 ÷ 0.5) × 100% = 120%. The revenue index completion rate calculation: if the airline A route seat kilometer income is 0.5 yuan this month, the total income of A route is 1 yuan this month; the airline income of A route last year is 0.4 yuan, and the total income of A route last year is 0.9 yuan, then the revenue index completion rate = (0.5 ÷ 1) ÷ (0.4 ÷ 0.9) x 100% ≈ 112.5%; The sales index completion rate calculation: if the airline B route passenger seat rate is 85% this month, the total passenger seat rate of B route is 80% this month; the airline passenger seat rate of B route last year is 75%, and the total passenger seat rate of B route last year is 70%, then the sales index completion rate = (85% ÷ 80%) ÷ (75% ÷ 70%) x 100% ≈ 99.2%.

[0033] During the peak season of the trunk route, the weight of the sales index completion rate is dynamically adjusted from 35% to 40% (dynamic weight = 35% x (1 + 0.1 x 0.15) ≈ 40%), the revenue index completion rate is 25%, and the revenue index completion rate is 35%; The comprehensive index completion rate = 120% x 25% + 112.5% x 35% + 99.2% x 40% ≈ 108.3%.

[0034] Step S40, generating a corresponding processing strategy based on the preset rule, the comprehensive index completion rate and the evaluation index.

[0035] In some specific embodiments, step S40 includes: Classifying the target object based on the evaluation index of the target object to obtain a first class target object, a second class target object and a third class target object; Sorting the first class target object, the second class target object and the third class target object based on the comprehensive index completion rate respectively to obtain a sorting result; Generating a corresponding processing strategy based on the sorting result and the preset rule.

[0036] In this embodiment, based on the comprehensive index completion rate, the evaluation index of the evaluation object and the preset rule, the corresponding processing strategy is generated through the efficiency evaluation master database. Referring to Figure 3 , the data sources of the efficiency evaluation master database include: human resource system, financial system, revenue management system and revenue data system. The human resource system includes personnel post data such as post type, belonging area and management relationship; the financial system includes budget data such as seat kilometer income budget KPI; the revenue management system includes route control data such as controlled route and area attribution; the revenue data system includes real-time business data such as seat kilometer income, passenger seat rate and demand fluctuation rate.

[0037] The preset initial weight is dynamically adjusted based on a dynamic weight calculation module, and a dynamic weight is obtained. After the dynamic weight is obtained, the comprehensive index completion rate is calculated, and the evaluation index of the evaluation object obtained through the post matching module is combined. Through the reward and punishment decision module, the corresponding performance evaluation result is generated, that is, the processing strategy.

[0038] Specifically, the target objects are classified based on the evaluation indexes of the target objects, to obtain first-class target objects corresponding to the first evaluation index, second-class target objects corresponding to the second evaluation index, and third-class target objects corresponding to the third evaluation index.

[0039] The first-class target objects, the second-class target objects, and the third-class target objects are sorted (for example, sorted from large to small) based on the comprehensive index completion rate, to obtain a sorting result. Based on the sorting result and a preset rule, a corresponding processing strategy is generated. It is easy to understand that the sorting result includes a first sorting result of the first-class target objects, a second sorting result of the second-class target objects, and a third sorting result of the third-class target objects.

[0040] Exemplarily, taking the performance evaluation of a revenue management post (RM) incentive evaluation as an example, based on the first sorting result of the first-class target objects, the top 5 evaluation objects are determined as target objects that need to be rewarded, and the corresponding processing strategy is generated as: the next month's salary of the target objects that need to be rewarded is paid 1 times performance reward. The processing strategy is synchronized to the human resource system through the API interface, and the 1 times performance reward is automatically paid in the next month's salary through the human resource salary system.

[0041] Further, continuing to refer to Figure 3 After the corresponding processing strategy is generated, the performance evaluation data (including dynamic weight, matching result, and reward and punishment result) of this time is recorded to a real-time updating database, for iterative optimization of the dynamic weight model. Exemplarily, the sales index completion rate of the target object A increases from 99.2% to 110% after 3 months of reward, and the data is fed back to the dynamic weight model to optimize the characteristic coefficient of the sales index in the peak season.

[0042] The preset rule and the matching degree of team performance are continuously optimized (such as the promotion of the sales pulling effect of the peak season incentive by 12%) through the iterative model of the incentive effect analysis module.

[0043] Continuing to refer to Figure 3 The performance evaluation effect analysis module (Incentive Effect Analysis Module): records the correlation between historical processing data and performance changes, generates a "processing-performance curve", and is used for data analysis components for optimizing subsequent weight adjustment rules.

[0044] Real-time Linkage Interface: The technical interface connecting the performance evaluation system and the human resource salary module. For example, the reward and punishment results can be automatically synchronized to the salary calculation engine to trigger performance salary increase or decrease or post change warning.

[0045] In this embodiment, based on the route characteristics, the dynamic weight corresponding to the route characteristics is obtained through a dynamic weight model, and the index weight is adjusted in real time through the route characteristics, solving the problem of insufficient adaptability of fixed weight. The label of the target object is identified, the evaluation index of the target object is determined, the cost of manual judgment is eliminated, and the preparation time of performance evaluation is greatly shortened. According to the dynamic weight, the comprehensive index completion rate is calculated; based on the preset rule, the comprehensive index completion rate and the evaluation index, the corresponding processing strategy is generated, the performance evaluation result is seamlessly connected with the human resource system, the response speed of the performance evaluation result is greatly improved, and for the first time, the multi-dimensional index dynamic adjustment and the performance automatic matching are combined. The standardization technical scheme is provided for performance improvement, such as revenue management post (RM) incentive examination, which is suitable for high-frequency and multi-dimensional performance evaluation scene of aviation.

[0046] Through the embodiment, the technical problems of insufficient evaluation accuracy, low evaluation matching efficiency and evaluation linkage lag in the process of evaluating the performance of the post in the prior art are solved.

[0047] Based on the same inventive concept, the embodiment of the present application also provides a performance analysis processing device based on dynamic weight.

[0048] In an embodiment, refer to Figure 5 , Figure 5 The figure is a functional module schematic diagram of the performance analysis processing device based on dynamic weight of an embodiment of the present application. As shown in Figure 5 The performance analysis processing device based on dynamic weight includes: The weight adjustment module 10 is configured to obtain the dynamic weight corresponding to the route characteristics based on the route characteristics through a dynamic weight model; The label identification module 20 is configured to identify the label of the target object and determine the evaluation index of the target object; The index completion rate calculation module 30 is configured to calculate the comprehensive index completion rate according to the dynamic weight; The processing strategy generation module 40 is configured to generate the corresponding processing strategy based on the preset rule, the comprehensive index completion rate and the evaluation index.

[0049] Optionally, in an embodiment, the weight adjustment module 10 is configured to: input the route feature into the dynamic weight model, and extract a feature coefficient through the dynamic weight model; adjust a preset initial weight corresponding to the route feature based on the adjustment coefficient and the feature coefficient, to obtain a dynamic weight corresponding to the route feature output by the dynamic weight model.

[0050] Optionally, in an embodiment, the route feature includes a route type and a navigation season, and the weight adjustment module 10 is configured to: query a correspondence table between a route type and an initial feature coefficient based on the route type included in the route feature, to determine a first initial feature coefficient corresponding to the route type; query a correspondence table between a navigation season and an initial feature coefficient based on the navigation season included in the route feature, to determine a second initial feature coefficient corresponding to the navigation season; multiply the first initial feature coefficient by the second initial feature coefficient, and use a product obtained as the feature coefficient.

[0051] Optionally, in an embodiment, the label includes a first preset label, a second preset label, and a third preset label, and the label identification module 20 is configured to: identify a label of a target object, and query a correspondence table between a label and an evaluation index to determine an evaluation index of the target object; wherein the label and the evaluation index correspond to each other one by one, and the evaluation index includes a first evaluation index, a second evaluation index, and a third evaluation index.

[0052] Optionally, in an embodiment, the dynamic weight includes a dynamically adjusted weight corresponding to a yield index completion rate, a dynamically adjusted weight corresponding to the yield index completion rate, and a dynamically adjusted weight corresponding to a sales index completion rate.

[0053] Optionally, in an embodiment, the index completion rate calculation module 30 is configured to: calculate a yield index completion rate based on seat kilometer income and seat kilometer income budget; calculate a yield index completion rate based on monthly airline route seat kilometer income, monthly route seat kilometer income, last year's same period airline route seat kilometer income, and last year's same period route market seat kilometer income; calculate a sales index completion rate based on monthly airline route passenger seat rate, monthly route passenger seat rate, last year's same period airline route passenger seat rate, and last year's same period route passenger seat rate; Based on the revenue index completion rate, the revenue index completion rate, the sales index completion rate, and the dynamic weight, a comprehensive index completion rate is calculated.

[0054] Optionally, in an embodiment, the processing strategy generation module 40 is configured to: Based on the evaluation index of the target object, the target object is classified to obtain a first type of target object, a second type of target object, and a third type of target object. Based on the comprehensive index completion rate, the first type of target object, the second type of target object, and the third type of target object are sorted respectively to obtain a sorting result. Based on the sorting result and the preset rule, a corresponding processing strategy is generated.

[0055] Corresponding to each step in the above-mentioned embodiment of the performance analysis processing method based on dynamic weight, the function implementation of each module in the above-mentioned performance analysis processing device based on dynamic weight is corresponding, and the function and implementation process will not be repeated here.

[0056] Based on the same inventive concept, the embodiments of the present application also provide an electronic device, the structure of which is shown in Figure 6 The processor is used to read and execute the computer program stored in the memory to realize the above-mentioned method of performance analysis processing based on dynamic weight.

[0057] Based on the same inventive concept, the embodiments of the present application also provide a computer storage medium, which stores computer executable instructions, and the computer executable instructions realize the above-mentioned method of performance analysis processing based on dynamic weight when executed.

[0058] Finally, it should be noted that: in some of the processes described in the embodiments of the present application, a plurality of operations or steps appear in a specific order, but it should be understood that these operations or steps can be executed or performed in parallel or in a different order from that in the embodiments of the present application, and the serial number of the operation only distinguishes different operations, and the serial number itself does not represent any execution order. In addition, these processes can include more or fewer operations, and these operations or steps can be executed or performed in sequence or in parallel, and these operations or steps can be combined.

[0059] The above merely describes the preferred embodiments of the present application, and is not intended to limit the present application. Although the present application is described in detail with reference to the foregoing embodiments, modifications to the foregoing embodiments or equivalent replacements to some technical features thereof can be made by those skilled in the art, and any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for performance analysis processing based on dynamic weights, characterized in that, The method comprises: Based on the route characteristics, the dynamic weight model is used to obtain the dynamic weight corresponding to the route characteristics; Identify the label of the target object and determine the evaluation index of the target object; According to the dynamic weight, the comprehensive index completion rate is calculated; Based on the preset rule, the comprehensive index completion rate and the evaluation index, the corresponding processing strategy is generated.

2. The method of claim 1, wherein, Based on the route characteristics, the dynamic weight model is used to obtain the dynamic weight corresponding to the route characteristics, comprising: The route characteristics are input into the dynamic weight model, and the characteristic coefficients are extracted by the dynamic weight model; Based on the adjustment coefficient and the characteristic coefficient, the preset initial weight corresponding to the route characteristics is adjusted to obtain the dynamic weight output by the dynamic weight model.

3. The method of claim 2, wherein, The route characteristics include route type and navigation season, and the characteristic coefficients are extracted, comprising: Based on the route type included in the route characteristics, the correspondence table between the route type and the initial characteristic coefficient is queried to determine the first initial characteristic coefficient corresponding to the route type; Based on the navigation season included in the route characteristics, the correspondence table between the navigation season and the initial characteristic coefficient is queried to determine the second initial characteristic coefficient corresponding to the navigation season; The first initial characteristic coefficient is multiplied by the second initial characteristic coefficient, and the product obtained is used as the characteristic coefficient.

4. The method of claim 1, wherein, The label includes a first preset label, a second preset label and a third preset label, and the label of the target object is identified to determine the evaluation index of the target object, comprising: Identify the label of the target object and query the correspondence table between the label and the evaluation index to determine the evaluation index of the target object; Wherein, the label and the evaluation index are one-to-one correspondence, and the evaluation index includes first evaluation index, second evaluation index and third evaluation index.

5. The method of claim 1, wherein, The dynamic weight includes the dynamic adjusted weight corresponding to the revenue index completion rate, the dynamic adjusted weight corresponding to the revenue index completion rate and the dynamic adjusted weight corresponding to the sales index completion rate.

6. The method of claim 1, wherein, According to the dynamic weight, the comprehensive index completion rate is calculated, comprising: Based on seat kilometer income and seat kilometer income budget, the revenue index completion rate is calculated; Based on the monthly airline route seat kilometer income, the monthly seat kilometer income of the route, the airline route seat kilometer income of the same period last year and the seat kilometer income of the same period last year, the revenue index completion rate is calculated; Based on the monthly airline route seat occupancy rate and the monthly seat occupancy rate of the route, the airline route seat occupancy rate of the same period last year and the seat occupancy rate of the same period last year, the sales index completion rate is calculated; Based on the dynamic weight, the revenue index completion rate, the revenue index completion rate and the sales index completion rate, the comprehensive index completion rate is calculated.

7. The method of claim 1, wherein, Based on the preset rule, the comprehensive index completion rate and the evaluation index, the corresponding processing strategy is generated, comprising: Based on the evaluation index of the target object, the target object is classified to obtain the first type target object, the second type target object and the third type target object; The first type of target object, the second type of target object and the third type of target object are sorted based on the comprehensive index completion rates, and a sorting result is obtained; A corresponding processing strategy is generated based on the sorting result and the preset rule.

8. A dynamic weight-based performance analysis processing apparatus, characterized by comprising: The device comprises: A weight adjustment module configured to obtain a dynamic weight corresponding to the route feature based on the route feature through a dynamic weight model; A label identification module configured to identify a label of a target object and determine an evaluation index of the target object; An index completion rate calculation module configured to calculate a comprehensive index completion rate according to the dynamic weight; A processing strategy generation module configured to generate a corresponding processing strategy based on a preset rule, the comprehensive index completion rate and the evaluation index.

9. An electronic device, comprising: Comprise: A memory and a processor; The processor is configured to read and execute a computer program stored in the memory to implement the dynamic weight-based performance analysis processing method of any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer executable instructions, and the computer executable instructions execute the dynamic weight-based performance analysis processing method of any one of claims 1-7.

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