CN110717616A - Civil aviation unit human resource prediction method, electronic equipment and storage medium - Google Patents
Civil aviation unit human resource prediction method, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention provides a method for predicting human resources of a civil aviation set, which comprises the following steps: data preprocessing, building a prediction model, training the prediction model and predicting the manpower requirement of the unit. The invention relates to electronic equipment and a readable storage medium, which are used for executing a civil aviation set human resource prediction method. The invention establishes mathematical models of the unit manpower demand, flight quantities of various airports and flight quantities in various time periods, trains and verifies a prediction model based on a large amount of historical operating data and by applying a machine learning algorithm, predicts the unit manpower resources corresponding to a flight plan through the prediction model, realizes scientific, intelligent and fine prediction of the unit manpower demand, and provides scientific and effective decision data support for the unit management of an airline company.
Description
Technical Field
The invention relates to the technical field of civil aviation unit operation management and unit human resource management, in particular to a method for predicting human resources of a civil aviation unit, electronic equipment and a storage medium.
Background
The operation management of the airline company mainly comprises the processes of airplane introduction and transportation capacity layout planning, flight planning, unit human resource prediction, unit shift taking, unit shift arrangement, recovery and the like. The prediction of human resources of the unit is one of important links. However, the research on the aspect is few, most of the problems of unit shift taking and unit shift scheduling are researched, and mainly because the flight structure characteristics of each airline company are greatly different from the operation rules, a unified and effective model is difficult to establish. In the past, the manual prediction is mainly completed by means of manual rough calculation or brain-shooting, and the measuring and calculating effects are very poor. The rapid flowing production elements, the rapidly increased transport capacity network and the complicated aviation industry legislation make the prediction of the human resources of the unit become increasingly complicated, so that a scientific, intelligent and fine prediction method of the human resources of the civil aviation unit is urgently needed, and scientific and effective decision data support is provided for the unit management of an airline company.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide a method for predicting the human resources of a civil aviation set, and the problems that a unified and effective model cannot be established and the prediction effect is poor in the conventional method for predicting the human resources of the civil aviation set are solved.
The invention provides a method for predicting human resources of a civil aviation set, which comprises the following steps:
data preprocessing, namely calculating the number of historical daily unit demand persons according to historical unit shift taking data and historical unit shift scheduling data, and calculating the historical daily airport flight quantity of various airports according to historical flight data;
establishing a prediction model, classifying airports related to a flight plan, dividing one day into a plurality of time periods, and generating the prediction model according to the relation between the manpower demand of a unit and the flight quantity of various airports and the flight quantity of each time period;
training a prediction model, dividing the historical daily unit demand number and the historical daily airport flight quantity into a training sample and a test sample, training the prediction model through the training sample by adopting a machine learning algorithm, and inspecting the prediction model through the test sample;
predicting the manpower demand of the unit, calculating the daily flight quantity of various airports in the prediction time period of the flight plan to be predicted, and substituting the daily flight quantity of various airports in the prediction time period into the prediction model which passes the inspection to obtain the unit manpower resource prediction result.
Further, in the data preprocessing step, the historical flight group work data includes a task string start date, an end date, a fixed member, a base to which the fixed member belongs, and flight data, the historical flight group work data includes a member number, a task start date, an end date, and a task type, and the historical flight data includes a flight takeoff date, an end date, a flight hour, a flight fixed member, a flight type, and a base to which the fixed member belongs.
Further, in the step of establishing the prediction model, airports involved in the flight plan are divided into a special airport and a non-special airport, the special airport includes a class B airport, a class C airport and an international remote airport, and the non-special airport is specifically a common airport.
Further, in the step of establishing a prediction model, a specific formula of the prediction model is as follows:
y is the number of people required by the unit per day, X is a vector formed by flight quantities of various airports per day, the component X is composed of flight quantity which is not in the same day but in the same day as the landing moment, flight quantity which is not in the same day but in the special airport, flight quantity which is not in the same day but in n time periods divided by the same day as the flight taking moment and does not contain the special airport, flight quantity which is not in the same day as the landing moment but in the same day as the landing moment, flight quantity which is not in the special airport and in n time periods divided by the same day as the flight taking moment, delta hours is the length of the time periods, and n is the number of the time periods.
Further, in the step of training the prediction model, the machine learning algorithm includes a neural network algorithm and a support vector machine algorithm.
Further, in the step of training the prediction model, the historical daily unit demand number and the historical daily airport flight number are respectively calculated according to the following steps of 8: the ratio of 2 is divided into training samples and test samples.
Further, before the data preprocessing step, a step of loading historical data is also included, and the step of loading historical data is to load the historical crew work data, the historical crew shift data and the historical flight data.
Further, the step of predicting the crew manpower requirement further comprises loading the flight plan to be predicted.
An electronic device, comprising: a processor;
a memory; and a program, wherein the program is stored in the memory and configured to be executed by the processor, the program comprising instructions for performing one of the above-described civil aircraft crew human resources prediction methods.
A computer-readable storage medium, on which a computer program is stored, the computer program being executed by a processor to perform the above-mentioned method for predicting human resources of civil aviation aircraft.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a method for predicting human resources of a civil aviation set, which comprises the following steps: data preprocessing, namely calculating the number of historical daily unit demand persons according to historical unit shift taking data and historical unit shift scheduling data, and calculating the historical daily airport flight quantity of various airports according to historical flight data; establishing a prediction model, classifying airports related to a flight plan, dividing one day into a plurality of time periods, and generating the prediction model according to the relation between the manpower demand of a unit and the flight quantity of various airports and the flight quantity of each time period; training a prediction model, dividing the number of the required units per day and the flight quantity of various airports per day into a training sample and a test sample, training the prediction model through the training sample by adopting a machine learning algorithm, and inspecting the prediction model through the test sample; and predicting the manpower demand of the unit, calculating the daily flight quantity of various airports in the prediction time period of the flight plan to be predicted, and substituting the daily flight quantity of various airports in the prediction time period into the prediction model which passes the inspection to obtain the unit manpower resource prediction result. The invention relates to electronic equipment and a readable storage medium, which are used for executing a civil aviation set human resource prediction method. The invention establishes mathematical models of the unit manpower demand, flight quantities of various airports and flight quantities in various time periods, trains and verifies a prediction model based on a large amount of historical operating data and by applying a machine learning algorithm, predicts the unit manpower resources corresponding to a flight plan through the prediction model, realizes scientific, intelligent and fine prediction of the unit manpower demand, and provides scientific and effective decision data support for the unit management of an airline company.
The foregoing description is only an overview of the technical solutions of the present invention, and in order to make the technical solutions of the present invention more clearly understood and to implement them in accordance with the contents of the description, the following detailed description is given with reference to the preferred embodiments of the present invention and the accompanying drawings. The detailed description of the present invention is given in detail by the following examples and the accompanying drawings.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
FIG. 1 is a flow chart of a method for predicting human resources of a civil aviation unit.
Detailed Description
The present invention will be further described with reference to the accompanying drawings and the detailed description, and it should be noted that any combination of the embodiments or technical features described below can be used to form a new embodiment without conflict.
A method for predicting human resources of a civil aviation unit is shown in figure 1 and comprises the following steps:
in an embodiment, before the data preprocessing step, the historical data is loaded, specifically, the historical crew shift data, and the historical flight data are loaded.
And (3) data preprocessing, namely calculating the number of the historical daily unit demand according to the historical unit shift taking data and the historical unit shift scheduling data, and calculating the historical daily airport flight quantity of various airports according to the historical flight data. The historical flight scheduling data of the unit comprises a task string starting date, a task ending date, a fixed member, a base to which the historical flight scheduling data belongs and flight data, the historical flight scheduling data of the unit comprises a member number, a task starting date, a task ending date and a task type, and the historical flight data comprises a flight takeoff date, a flight ending date, a flight hour, a flight fixed member, a flight type and a base to which the historical flight scheduling data belongs.
And (3) establishing a prediction model, wherein the number of people required by the unit is positively correlated with the flight quantity on the whole, namely the larger the flight quantity is, the larger the manpower requirement of the unit is. Since the flight structure characteristics and the operation rules of each airline company are different, the correlation is influenced by certain factors, such as: the flying C-type airport needs double machine lengthes, the international long flight line needs double sets, and the machine group takes two and three sections of different requirements on manpower in one day. Therefore, in the present embodiment, feature quantities of the flight plan are extracted for modeling, and airport dimensions and time dimensions are mainly considered. Specifically, the airport dimensions are: classifying airports related to the flight plan, including B-type airports, C-type airports, international remote airports and other common airports, and searching the mathematical relationship between the manpower demand of the unit and the flight quantity of various airports; the time dimension is: dividing a day into a plurality of time periods, such as 12 periods: 0-2 points, 2-4 points, 4-6 points, … points, 22-24 points, and finding the mathematical relationship between the manpower demand of the unit and the flight quantity in each time period. In order to better depict the flight structure and more comprehensively reflect the flight structure, the embodiment combines the two dimensions, specifically, the airport is divided into a special airport (a class B airport, a class C airport, an international remote airport) and a non-special airport (other ordinary airports), the combined flight time is used as input, the number of people required by a unit is used as output, and the established prediction model has the following specific formula:
y is the number of people required by the unit per day, X is a vector formed by flight quantities of various airports per day, f is a function of X → Y, the component X is composed of flight quantities of special airports, the flight takeoff time is not on the day, the landing time is on the day, the flight quantities of the special airports are included, the flight takeoff time falls in n time periods divided into the day, the flight quantities of the special airports are included, the flight takeoff time is not on the day, the landing time is on the day and does not include the flight quantities of the special airports, the flight takeoff time falls in n time periods divided into the day and does not include the flight quantities of the special airports, delta hours is the time period length, and n is the time period number.
Training a prediction model, dividing the historical daily unit demand number and the historical daily airport flight quantity into a training sample and a testing sample, specifically, respectively dividing the historical daily unit demand number and the historical daily airport flight quantity into 8: 2, dividing the training samples and the test samples into training samples and testing samples, wherein the dividing proportion of the training samples and the testing samples can be determined according to actual data volume, training the prediction model through the training samples by adopting a machine learning algorithm, and inspecting the prediction model through the testing samples; preferably, the machine learning algorithm includes a neural network algorithm and a support vector machine algorithm. And (3) determining a final unit demand and number prediction model, namely the mathematical relation between the unit manpower demand and the flight plan, through training and inspection, and issuing the prediction model.
Predicting the manpower requirement of the unit, loading a flight plan to be predicted, calculating various airport flight quantities each day in the prediction time period of the flight plan to be predicted, substituting the various airport flight quantities each day in the prediction time period into a prediction model which passes the inspection to obtain a unit manpower resource prediction result, and generating a report from the unit manpower resource prediction result.
An electronic device, comprising: a processor;
a memory; and a program, wherein the program is stored in the memory and configured to be executed by the processor, the program comprising instructions for performing one of the above-described methods of civil aviation crew human resources prediction.
A computer-readable storage medium, on which a computer program is stored, the computer program being executed by a processor to perform the above-mentioned method for predicting human resources of civil aviation aircrafts.
The invention provides a method for predicting human resources of a civil aviation set, which comprises the following steps: data preprocessing, namely calculating the number of historical daily unit demand persons according to historical unit shift taking data and historical unit shift scheduling data, and calculating the historical daily airport flight quantity of various airports according to historical flight data; establishing a prediction model, classifying airports related to a flight plan, dividing one day into a plurality of time periods, and generating the prediction model according to the relation between the manpower demand of a unit and the flight quantity of various airports and the flight quantity of each time period; training a prediction model, dividing the number of the required units per day and the flight quantity of various airports per day into a training sample and a test sample, training the prediction model through the training sample by adopting a machine learning algorithm, and inspecting the prediction model through the test sample; and predicting the manpower demand of the unit, calculating the daily flight quantity of various airports in the prediction time period of the flight plan to be predicted, and substituting the daily flight quantity of various airports in the prediction time period into the prediction model which passes the inspection to obtain the unit manpower resource prediction result. The invention relates to electronic equipment and a readable storage medium, which are used for executing a civil aviation set human resource prediction method. The invention establishes mathematical models of the unit manpower demand, flight quantities of various airports and flight quantities in various time periods, trains and verifies a prediction model based on a large amount of historical operating data and by applying a machine learning algorithm, predicts the unit manpower resources corresponding to a flight plan through the prediction model, realizes scientific, intelligent and fine prediction of the unit manpower demand, and provides scientific and effective decision data support for the unit management of an airline company.
The foregoing is merely a preferred embodiment of the invention and is not intended to limit the invention in any manner; those skilled in the art can readily practice the invention as shown and described in the drawings and detailed description herein; however, those skilled in the art should appreciate that they can readily use the disclosed conception and specific embodiments as a basis for designing or modifying other structures for carrying out the same purposes of the present invention without departing from the scope of the invention as defined by the appended claims; meanwhile, any changes, modifications, and evolutions of the equivalent changes of the above embodiments according to the actual techniques of the present invention are still within the protection scope of the technical solution of the present invention.
Claims (10)
1. A method for predicting human resources of civil aviation sets is characterized by comprising the following steps:
data preprocessing, namely calculating the number of historical daily unit demand persons according to historical unit shift taking data and historical unit shift scheduling data, and calculating the historical daily airport flight quantity of various airports according to historical flight data;
establishing a prediction model, classifying airports related to a flight plan, dividing one day into a plurality of time periods, and generating the prediction model according to the relation between the manpower demand of a unit and the flight quantity of various airports and the flight quantity of each time period;
training a prediction model, dividing the historical daily unit demand number and the historical daily airport flight quantity into a training sample and a test sample, training the prediction model through the training sample by adopting a machine learning algorithm, and inspecting the prediction model through the test sample;
predicting the manpower demand of the unit, calculating the daily flight quantity of various airports in the prediction time period of the flight plan to be predicted, and substituting the daily flight quantity of various airports in the prediction time period into the prediction model which passes the inspection to obtain the unit manpower resource prediction result.
2. The method for predicting the human resources of the civil aviation aircrew according to claim 1, wherein: in the data preprocessing step, the historical flight scheduling data of the unit comprises a task string starting date, an ending date, a fixed member, a base to which the unit belongs and flight data, the historical flight scheduling data of the unit comprises a member number, a task starting date, an ending date and a task type, and the historical flight data comprises a flight takeoff date, an ending date, a flight hour, a flight fixed member, a flight type and a base to which the flight type belongs.
3. The method for predicting the human resources of the civil aviation aircrew according to claim 1, wherein: in the step of establishing the prediction model, airports related to the flight plan are divided into a special airport and a non-special airport, the special airport comprises a B-type airport, a C-type airport and an international remote airport, and the non-special airport is a common airport.
4. The method for predicting the human resources of the civil aviation aircrew according to claim 3, wherein: in the step of establishing the prediction model, a specific formula of the prediction model is as follows:
y is the number of people required by the unit per day, X is a vector formed by flight quantities of various airports per day, the component X is composed of flight quantity which is not in the same day but in the same day as the landing moment, flight quantity which is not in the same day but in the special airport, flight quantity which is not in the same day but in n time periods divided by the same day as the flight taking moment and does not contain the special airport, flight quantity which is not in the same day as the landing moment but in the same day as the landing moment, flight quantity which is not in the special airport and in n time periods divided by the same day as the flight taking moment, delta hours is the length of the time periods, and n is the number of the time periods.
5. The method for predicting the human resources of the civil aviation aircrew according to claim 1, wherein: in the step of training the prediction model, the machine learning algorithm comprises a neural network algorithm and a support vector machine algorithm.
6. The method for predicting the human resources of the civil aviation aircrew according to claim 1, wherein: in the step of training the prediction model, the number of the historical daily unit demands and the historical daily flight quantity of various airports are respectively 8: the ratio of 2 is divided into training samples and test samples.
7. The method for predicting the human resources of the civil aviation aircrew according to claim 1, wherein: the method comprises a data preprocessing step, a historical data loading step and a historical flight data loading step, wherein the historical data loading step is to load the historical unit shift taking data, the historical unit shift scheduling data and the historical flight data.
8. The method for predicting the human resources of the civil aviation aircrew according to claim 1, wherein: the step of predicting the manpower requirement of the unit further comprises loading the flight plan to be predicted.
9. An electronic device, characterized by comprising: a processor;
a memory; and a program, wherein the program is stored in the memory and configured to be executed by the processor, the program comprising instructions for carrying out the method of any one of claims 1-8.
10. A computer-readable storage medium having stored thereon a computer program, characterized in that: the computer program is executed by a processor for performing the method according to any of claims 1-8.
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| CN110930771A (en) * | 2019-11-19 | 2020-03-27 | 中国民航信息网络股份有限公司 | Control method and device for airline inventory |
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| CN112967530A (en) * | 2021-03-02 | 2021-06-15 | 携程旅游网络技术（上海）有限公司 | Method, system, equipment and medium for determining flight idle resources |
| CN113706044A (en) * | 2021-09-02 | 2021-11-26 | 广东工业大学 | Airport ground service personnel operation scheduling method, system, computer equipment and storage medium |
| CN114741133A (en) * | 2022-04-21 | 2022-07-12 | 中国航空无线电电子研究所 | Comprehensive modularized avionics system resource allocation and evaluation method based on model |
| CN115759386A (en) * | 2022-11-11 | 2023-03-07 | 中国民航科学技术研究院 | Method and device for predicting flight-taking result of civil aviation flight and electronic equipment |
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Application publication date: 20200121 |