CN112749754B - Method and device for early warning of abnormal calculation of gear withdrawal time - Google Patents
Method and device for early warning of abnormal calculation of gear withdrawal time Download PDFInfo
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Abstract
According to the method and the device for early warning of abnormal calculation of the withdrawal time, the calculated withdrawal time of the current flight is calculated according to the flight state information by acquiring the flight state information of the current flight; checking whether the calculated withdrawal time needs to send out early warning according to a preset decision tree, wherein the preset decision tree is generated through state information of historical flights; and when the calculated gear withdrawal time is in an incorrect state, generating early warning information. According to the application, the calculated withdrawal time of the flight can be accurately predicted, and effective early warning is carried out on the calculated withdrawal time in an incorrect state, so that prompt is realized, airport management personnel can timely adjust the operation of the airport flight, and the passenger satisfaction is improved.
Description
Technical Field
The application relates to the field of airport operation management, in particular to a method and a device for early warning abnormal calculation of gear withdrawal time.
Background
The current civil aviation uses the withdrawal time as a statistical standard for the flight punctual take-off, and the withdrawal time is used as a standard, so that the situations that passengers wait for a long time on the aircraft for guaranteeing the flight punctual rate and the like are effectively reduced, and the service satisfaction of the passengers is improved. Therefore, the flight withdrawal during airport operation is one of the key factors affecting the flight alignment.
In the running process of the airport, the calculated gear removing time (COBT, calculated off Block Time) of the flight is calculated mainly through an airport collaborative decision-making system, and the coordinated gear removing time of the flight is determined according to the calculated gear removing time, so that the airport can complete the flight guarantee task within the specified time. Therefore, incorrect calculation of the gear removing time can lead to waste of airport flight guarantee resources, flight delay and reduction of flight operation indexes, so that the punctuation rate of an airport is reduced.
Therefore, how to judge whether the calculated wheel stop removing time of the flight is normal in time, and send out early warning to the abnormal calculated wheel stop removing time, and remind the abnormal calculated wheel stop removing time in time, so that airport management personnel can adjust the operation of the airport flight in time, and the quasi point rate of the airport is ensured, thereby improving the satisfaction degree of passengers.
Disclosure of Invention
The application provides a method and a device for early warning of abnormal calculation of wheel withdrawal time, which aim to prompt the abnormal calculation of the wheel withdrawal time in time, so that airport management personnel can adjust the operation of airport flights in time, the quasi point rate of an airport is ensured, and the satisfaction degree of passengers is improved.
In order to achieve the above object, the present application provides the following technical solutions:
a method for early warning of abnormal calculation of shift withdrawal time, comprising:
acquiring flight status information of a current flight, and calculating calculated gear removing time of the current flight according to the flight status information;
Checking whether the calculated withdrawal time needs to send out early warning according to a preset decision tree, wherein the preset decision tree is generated through state information of historical flights;
And when the calculated gear withdrawal time is in an incorrect state, generating early warning information.
Optionally, the method for generating the preset decision tree includes:
acquiring historical flight state information of flight operation, and calculating corresponding calculated gear removing time according to the historical flight state information;
According to the calculated withdrawal time as a training set, carrying out data analysis processing on the training set to generate a decision tree;
checking the decision tree through the flight status information of the current flight operation, cutting branches influencing the prediction accuracy on the decision tree, and generating the preset decision tree.
Optionally, the calculating the withdrawal time is used as a training set, and the training set is subjected to data analysis processing to generate a decision tree, specifically:
and according to the calculated withdrawal time serving as a training set, carrying out data analysis processing on the training set by adopting an ID3 algorithm to generate the decision tree.
Optionally, the calculating the withdrawal time is used as a training set, and the data analysis processing is performed on the training set by adopting an ID3 algorithm to generate the decision tree, which specifically includes:
Setting the information entropy with k total training sets as Wherein P i represents the proportion of the current j types of samples;
Setting a training set with a discrete attribute a having V preset values { a1, a2, & gt, av }, wherein in the training set, the training set with the value av on the attribute a is denoted as D v, and the information increment obtained by dividing the training set by the attribute a is as follows:
The calculated wheel withdrawal time is used as a training set according to Calculating the attribute with the maximum current gain as a node of the decision tree;
Sequentially calculating information increment according to each attribute in the discrete attributes, and sequentially determining nodes of the decision tree;
And generating the decision tree according to the nodes of the decision tree.
An apparatus for early warning of abnormal calculation of shift withdrawal time, comprising:
The first processing unit is used for acquiring the flight status information of the current flight and calculating the calculated gear removing time of the current flight according to the flight status information;
The second processing unit is used for checking whether the calculated withdrawal time needs to send out early warning according to a preset decision tree, wherein the preset decision tree is generated through the state information of the historical flights;
and the third processing unit is used for generating early warning information when the calculated gear withdrawal time is in an incorrect state.
Optionally, the second processing unit is configured to:
acquiring historical flight state information of flight operation, and calculating corresponding calculated gear removing time according to the historical flight state information;
According to the calculated withdrawal time as a training set, carrying out data analysis processing on the training set to generate a decision tree;
checking the decision tree through the flight status information of the current flight operation, cutting branches influencing the prediction accuracy on the decision tree, and generating the preset decision tree.
Optionally, the second processing unit is specifically configured to:
and according to the calculated withdrawal time serving as a training set, carrying out data analysis processing on the training set by adopting an ID3 algorithm to generate the decision tree.
Optionally, the second processing unit is specifically configured to:
Setting the information entropy with k total training sets as Wherein P i represents the proportion of the current j types of samples;
Setting a training set with a discrete attribute a having V preset values { a1, a2, & gt, av }, wherein in the training set, the training set with the value av on the attribute a is denoted as D v, and the information increment obtained by dividing the training set by the attribute a is as follows:
The calculated wheel withdrawal time is used as a training set according to Calculating the attribute with the maximum current gain as a node of the decision tree;
Sequentially calculating information increment according to each attribute in the discrete attributes, and sequentially determining nodes of the decision tree;
And generating the decision tree according to the nodes of the decision tree.
A storage medium comprising a stored program, wherein the program, when run, controls a device in which the storage medium is located to perform the method of early warning of abnormal calculation of shift withdrawal time as described above.
An electronic device comprising at least one processor, and at least one memory, bus connected to the processor; the processor and the memory complete communication with each other through the bus; the processor is used for calling the program instructions in the memory to execute the method for early warning of abnormal calculation of the gear withdrawal time.
According to the method and the device for early warning of abnormal calculation of the withdrawal time, the calculated withdrawal time of the current flight is calculated according to the flight state information by acquiring the flight state information of the current flight; checking whether the calculated withdrawal time needs to send out early warning according to a preset decision tree, wherein the preset decision tree is generated through state information of historical flights; and when the calculated gear withdrawal time is in an incorrect state, generating early warning information. According to the application, the calculated withdrawal time of the flight can be accurately predicted, and effective early warning is carried out on the calculated withdrawal time in an incorrect state, so that prompt is realized, airport management personnel can timely adjust the operation of the airport flight, and the passenger satisfaction is improved.
Drawings
In order to more clearly illustrate the embodiments of the application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a method for early warning of abnormal calculation of shift withdrawal time according to an embodiment of the present application;
FIG. 2 is a schematic diagram of an early warning display effect of an airport collaborative decision-making system provided by an embodiment of the application;
FIG. 3 is a schematic diagram of a device for early warning of abnormal calculation of a gear withdrawal time according to an embodiment of the present application;
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The applicant has found that the calculated withdrawal time of a flight is mainly limited by the number of aircraft seats, the position of the flight stop, the flight status of the flight, the departure time of the flight, etc. At present, with the increasing number of flights, the requirement of an airline company cannot be met by manually judging and calculating the gear removing time. Therefore, the application provides a method and a device for early warning of abnormal calculation of the withdrawal time, which are used for judging whether the calculated withdrawal time of the flights is normal or not through a decision tree generated by the existing flight data information of the airports, and sending early warning of the abnormal calculation of the withdrawal time, so that the method and the device can prompt in time, enable airport managers to adjust the operation of the flights of the airports in time, and improve the satisfaction of passengers.
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Referring to fig. 1, a flowchart of a method for early warning of abnormal calculation of a gear withdrawal time is provided in an embodiment of the present application. As shown in fig. 1, the method for early warning of abnormal calculation of the gear withdrawal time provided by the embodiment of the application specifically includes the following steps:
S11: and acquiring the flight status information of the current flight, and calculating the calculated gear removing time of the current flight according to the flight status information.
In practical application, in the airport running process, the calculated gear removing time of the flights is calculated mainly through an airport collaborative decision-making system according to the flight state information, and the coordinated gear removing time of the flights is determined according to the calculated gear removing time, so that the airports complete the flight guarantee tasks within the specified time.
S12: and checking whether the calculated withdrawal time needs to send out early warning according to a preset decision tree, wherein the preset decision tree is generated through the state information of the historical flights.
It should be noted that the calculated gear removal time of a flight is affected by various factors, such as the number of seats of the aircraft, the position of the stop of the flight, the flight status of the flight, the arrival time of the flight, and the like. In the traditional mode, whether the calculated gear removing time of the flight is correct or not is checked through parameter configuration according to the information of the flight, so that effective early warning cannot be timely made on the incorrect calculated gear removing time.
In the embodiment of the application, the unique decision tree of the airport is generated through the existing flight status information of the airport and is used for judging whether the calculated withdrawal time of the current flight is correct or not. Namely: the method mainly comprises the steps of generating a corresponding decision tree through flight data existing in an airport, and checking whether the calculated gear removing time currently calculated needs to send out early warning through the generated decision tree when the calculated gear removing time of the flight is acquired, so that the problem that early warning cannot be sent out in time when the calculated gear removing time of the flight influenced by a plurality of factors is incorrect is solved.
In practical application, the decision tree algorithm is a common machine learning algorithm, and can well classify examples through a series of rules, similar to a process of making decisions by selection in specific applications, in the embodiment of the application, the core of the early warning for removing gear COBT for abnormal calculation is through adopting the decision tree algorithm. Specifically, a decision tree is created by adopting the existing ID3 algorithm (the ID3 algorithm is based on an information theory, and the information entropy and the information increment are taken as measurement standards to realize the induction classification of data, wherein the attribute with the maximum information gain value in the current sample set is selected as a test attribute, the sample set is divided according to the value of the test attribute, the sample set is divided into a plurality of sub-sample sets by a plurality of different values of the test attribute, and meanwhile, a new leaf node grows out from a node corresponding to the sample set on the decision tree). The creation of the decision tree is roughly divided into two steps. Firstly, generating a decision tree: and taking the real data currently operated by a large number of airports as a training set, and carrying out data analysis processing on the training set to generate a decision tree. Second, correcting the decision tree: the method mainly uses a new sample (new flight operation data generated by airport operation) to check the preliminary rules generated in the decision tree generation process, and cuts out branches affecting the prediction accuracy.
It should be noted that, the method for generating the preset decision tree may specifically include the following steps:
acquiring historical flight state information of flight operation, and calculating corresponding calculated gear removing time according to the historical flight state information;
According to the calculated withdrawal time as a training set, carrying out data analysis processing on the training set to generate a decision tree;
checking the decision tree through the flight status information of the current flight operation, cutting branches influencing the prediction accuracy on the decision tree, and generating the preset decision tree.
In the embodiment of the present application, the calculating the withdrawal time according to the above is used as a training set, and the training set is subjected to data analysis processing to generate a decision tree, specifically: and according to the calculated withdrawal time serving as a training set, carrying out data analysis processing on the training set by adopting an ID3 algorithm to generate the decision tree.
Further, the step of calculating the withdrawal time according to the calculation is used as a training set, and the step of performing data analysis processing on the training set by adopting an ID3 algorithm to generate the decision tree specifically includes the following steps:
Setting the information entropy with k total training sets as Wherein P i represents the proportion of the current j types of samples;
Setting a training set with a discrete attribute a having V preset values { a1, a2, & gt, av }, wherein in the training set, the training set with the value av on the attribute a is denoted as D v, and the information increment obtained by dividing the training set by the attribute a is as follows:
the calculated wheel withdrawal time is used as a training set and is according to the formula Calculating the attribute with the maximum current gain as a node of the decision tree;
Sequentially calculating information increment according to each attribute in the discrete attributes, and sequentially determining nodes of the decision tree;
And generating the decision tree according to the nodes of the decision tree.
It should be noted that, the airport collaborative decision-making system can configure whether the early warning of incorrectly calculating the gear-withdrawal time is needed currently according to the decision tree on the configuration page, and can also generate different decision trees according to the flight running states of different airports to realize the early warning of calculating the gear-withdrawal time.
In practical applications, the decision tree may be constructed by a number set of airports, for example, as shown in table 1:
TABLE 1
According to the data given in Table 1, according to the formulaAnd calculating the attribute with the maximum current gain as a node of the decision tree, and if the gain of the COBT and the arrival time difference value is the maximum, selecting the COBT and the arrival time difference value as the node of the decision tree. However, in practical applications, it is not possible to determine whether or not an early warning is required only by the difference between the COBT and the arrival time, and then it is necessary to recursively recalculate the information gain from the remaining attributes such as arrival/departure, arrival/arrival, location, and the like, and the calculated gain is also calculated using the formula/>Calculating the maximum gain under the residual condition as the next node of the decision tree; the maximum gain is obtained as the next node of the decision tree until the entire event can be determined.
S13: and when the calculated gear withdrawal time is in an incorrect state, generating early warning information.
In the embodiment of the application, if the calculated gear removing time of the current flight is judged to need to send out early warning according to a preset decision tree, the calculated gear removing time is determined to be in an incorrect state, early warning information is generated, and the early warning information is sent to an airport collaborative decision system, and then the optional airport collaborative decision system has the display effect shown in figure 2.
According to the early warning method for the abnormal calculation of the withdrawal time, the calculated withdrawal time of the current flight is calculated according to the flight state information by acquiring the flight state information of the current flight; checking whether the calculated withdrawal time needs to send out early warning according to a preset decision tree, wherein the preset decision tree is generated through state information of historical flights; and when the calculated gear withdrawal time is in an incorrect state, generating early warning information. According to the embodiment of the application, the calculated withdrawal time of the flight can be accurately predicted, and effective early warning is carried out on the calculated withdrawal time in an incorrect state, so that prompt is realized, airport management personnel can timely adjust the operation of the airport flight, and the passenger satisfaction is improved.
Referring to fig. 3, based on the method for early warning of abnormal gear withdrawal time according to the foregoing embodiment, the present embodiment correspondingly discloses a device for early warning of abnormal gear withdrawal time, where the device specifically includes:
The first processing unit 31 is configured to obtain flight status information of a current flight, and calculate a calculated gear removal time of the current flight according to the flight status information;
The second processing unit 32 is configured to check whether the calculated withdrawal time needs to send out an early warning according to a preset decision tree, where the preset decision tree is generated by status information of historical flights;
And the third processing unit 33 is configured to generate early warning information when the calculated gear withdrawal time is in an incorrect state.
Further, the second processing unit 32 is configured to:
acquiring historical flight state information of flight operation, and calculating corresponding calculated gear removing time according to the historical flight state information;
According to the calculated withdrawal time as a training set, carrying out data analysis processing on the training set to generate a decision tree;
checking the decision tree through the flight status information of the current flight operation, cutting branches influencing the prediction accuracy on the decision tree, and generating the preset decision tree.
Further, the second processing unit 32 is specifically configured to:
and according to the calculated withdrawal time serving as a training set, carrying out data analysis processing on the training set by adopting an ID3 algorithm to generate the decision tree.
Further, the second processing unit 32 is specifically configured to:
Setting the information entropy with k total training sets as Wherein P i represents the proportion of the current j types of samples;
Setting a training set with a discrete attribute a having V preset values { a1, a2, & gt, av }, wherein in the training set, the training set with the value av on the attribute a is denoted as D v, and the information increment obtained by dividing the training set by the attribute a is as follows:
The calculated wheel withdrawal time is used as a training set according to Calculating the attribute with the maximum current gain as a node of the decision tree;
Sequentially calculating information increment according to each attribute in the discrete attributes, and sequentially determining nodes of the decision tree;
And generating the decision tree according to the nodes of the decision tree.
The device for early warning the abnormal calculation of the gear withdrawal time comprises a processor and a memory, wherein the first processing unit, the second processing unit, the third processing unit and the like are stored in the memory as program units, and the processor executes the program units stored in the memory to realize corresponding functions.
The processor includes a kernel, and the kernel fetches the corresponding program unit from the memory. The kernel can be set with one or more than one, and the abnormal calculation of the withdrawal time is timely reminded by adjusting the kernel parameters, so that airport management personnel can timely adjust the operation of airport flights, the punctuation rate of the airport is ensured, and the passenger satisfaction is improved.
The embodiment of the application provides a storage medium, wherein a program is stored on the storage medium, and the program is executed by a processor to realize the method for early warning of abnormal calculation of the gear withdrawal time.
The embodiment of the application provides a processor which is used for running a program, wherein the method for early warning abnormal calculation of the gear withdrawal time is executed when the program runs.
An embodiment of the present application provides an electronic device, as shown in fig. 4, where the electronic device 40 includes at least one processor 401, and at least one memory 402 and a bus 403 connected to the processor; wherein the processor 401 and the memory 402 complete communication with each other through the bus 403; the processor 401 is configured to call the program instructions in the memory 402 to execute the above-mentioned method for early warning of abnormal calculation of the gear withdrawal time.
The electronic device herein may be a server, a PC, a PAD, a mobile phone, etc.
The application also provides a computer program product adapted to perform, when executed on a data processing device, a program initialized with the method steps of:
acquiring flight status information of a current flight, and calculating calculated gear removing time of the current flight according to the flight status information;
Checking whether the calculated withdrawal time needs to send out early warning according to a preset decision tree, wherein the preset decision tree is generated through state information of historical flights;
And when the calculated gear withdrawal time is in an incorrect state, generating early warning information.
Preferably, the method for generating the preset decision tree includes:
acquiring historical flight state information of flight operation, and calculating corresponding calculated gear removing time according to the historical flight state information;
According to the calculated withdrawal time as a training set, carrying out data analysis processing on the training set to generate a decision tree;
checking the decision tree through the flight status information of the current flight operation, cutting branches influencing the prediction accuracy on the decision tree, and generating the preset decision tree.
Preferably, the calculating the withdrawal time is used as a training set, and the training set is subjected to data analysis processing to generate a decision tree, specifically:
and according to the calculated withdrawal time serving as a training set, carrying out data analysis processing on the training set by adopting an ID3 algorithm to generate the decision tree.
Preferably, the calculating the withdrawal time is used as a training set, and the ID3 algorithm is adopted to perform data analysis processing on the training set to generate the decision tree, specifically:
Setting the information entropy with k total training sets as Wherein P i represents the proportion of the current j types of samples;
Setting a training set with a discrete attribute a having V preset values { a1, a2, & gt, av }, wherein in the training set, the training set with the value av on the attribute a is denoted as D v, and the information increment obtained by dividing the training set by the attribute a is as follows:
The calculated wheel withdrawal time is used as a training set according to Calculating the attribute with the maximum current gain as a node of the decision tree;
Sequentially calculating information increment according to each attribute in the discrete attributes, and sequentially determining nodes of the decision tree;
And generating the decision tree according to the nodes of the decision tree.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, the device includes one or more processors (CPUs), memory, and a bus. The device may also include input/output interfaces, network interfaces, and the like.
The memory may include volatile memory, random Access Memory (RAM), and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM), among other forms in computer readable media, the memory including at least one memory chip. Memory is an example of a computer-readable medium.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.
Claims (4)
1. The method for early warning of abnormal calculation of the gear withdrawal time is characterized by comprising the following steps:
acquiring flight status information of a current flight, and calculating calculated gear removing time of the current flight according to the flight status information;
Checking whether the calculated withdrawal time needs to send out early warning according to a preset decision tree, wherein the preset decision tree is generated through state information of historical flights;
When the calculated gear withdrawal time is in an incorrect state, generating early warning information;
the method for generating the preset decision tree comprises the following steps:
acquiring historical flight state information of flight operation, and calculating corresponding calculated gear removing time according to the historical flight state information;
According to the calculated withdrawal time as a training set, adopting an ID3 algorithm to perform data analysis processing on the training set to generate a decision tree;
checking the decision tree through the flight status information of the current flight operation, cutting branches influencing the prediction accuracy on the decision tree, and generating the preset decision tree;
the method comprises the steps of calculating the withdrawal time as a training set, and adopting an ID3 algorithm to perform data analysis processing on the training set to generate the decision tree, wherein the decision tree is specifically:
Setting the information entropy with k total training sets as Wherein P j represents the proportion of the current j types of samples;
Setting a training set with a discrete attribute a having V preset values { a1, a2, & gt, av }, wherein in the training set, the training set with the value av on the attribute a is denoted as D v, and the information increment obtained by dividing the training set by the attribute a is as follows:
The calculated wheel withdrawal time is used as a training set according to Calculating the attribute with the maximum current gain as a node of the decision tree;
Sequentially calculating information increment according to each attribute in the discrete attributes, and sequentially determining nodes of the decision tree;
And generating the decision tree according to the nodes of the decision tree.
2. The utility model provides a device for removing gear time early warning to improper calculation which characterized in that includes:
The first processing unit is used for acquiring the flight status information of the current flight and calculating the calculated gear removing time of the current flight according to the flight status information;
The second processing unit is used for checking whether the calculated withdrawal time needs to send out early warning according to a preset decision tree, wherein the preset decision tree is generated through the state information of the historical flights;
the third processing unit is used for generating early warning information when the calculated gear withdrawal time is in an incorrect state;
Wherein the second processing unit is configured to:
acquiring historical flight state information of flight operation, and calculating corresponding calculated gear removing time according to the historical flight state information;
According to the calculated withdrawal time as a training set, adopting an ID3 algorithm to perform data analysis processing on the training set to generate a decision tree;
checking the decision tree through the flight status information of the current flight operation, cutting branches influencing the prediction accuracy on the decision tree, and generating the preset decision tree;
The second processing unit is specifically configured to:
Setting the information entropy with k total training sets as Wherein P j represents the proportion of the current j types of samples;
Setting a training set with a discrete attribute a having V preset values { a1, a2, & gt, av }, wherein in the training set, the training set with the value av on the attribute a is denoted as D v, and the information increment obtained by dividing the training set by the attribute a is as follows:
The calculated wheel withdrawal time is used as a training set according to Calculating the attribute with the maximum current gain as a node of the decision tree;
Sequentially calculating information increment according to each attribute in the discrete attributes, and sequentially determining nodes of the decision tree;
And generating the decision tree according to the nodes of the decision tree.
3. A storage medium comprising a stored program, wherein the program, when run, controls a device in which the storage medium is located to perform the method for early warning of an abnormal calculation of a shift withdrawal time as claimed in claim 1.
4. An electronic device comprising at least one processor, and at least one memory, bus coupled to the processor; the processor and the memory complete communication with each other through the bus; the processor is configured to invoke program instructions in the memory to perform the method of early warning of abnormal calculation of shift withdrawal time as set forth in claim 1.
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