Emergency scheduling method and system based on digital twinning

Abstract

The invention discloses an emergency scheduling method and system based on digital twinning, and relates to the technical field of emergency scheduling. A gate position in an occupied state is marked as an occupied gate position, multi-source data of the occupied gate position is monitored regularly, whether the occupied gate position has an abnormal risk or not is predicted through an abnormal prediction model based on the multi-source data, and the abnormal risk of the occupied gate position is judged. When it is predicted that an abnormal risk exists in the occupied parking space, abnormal classification is conducted on the occupied parking space, then a corresponding scheduling strategy is generated in combination with the current operation state of the airport, the abnormal classification conditions of all the parking spaces in the airport are obtained regularly, and after the overall operation state of the airport is analyzed, whether the airport needs to be managed or not is judged; and generating a corresponding management strategy according to a judgment result. The scheduling system can perform scheduling optimization management on the parking space in advance in combination with the current operation condition of the airport after performing abnormal prediction on the parking space, so as to guarantee the safety and stability of the operation of the airport.

Description

Technical Field

[0001] The present invention relates to the technical field of emergency dispatch, and in particular to an emergency dispatch method and system based on digital twins. Background Art

[0002] Airport parking stand management involves multiple complex factors, including flight arrival and departure times, coordination between airlines and ground operations, aircraft types and sizes, passenger demand, and the availability of ground service equipment and resources. Since aircraft will involve refueling, cleaning, baggage loading, catering and other services during the arrival and departure process, any delay or abnormality in any link will affect the scheduling of parking stands, which may cause delays to other flights. Traditional parking stand scheduling usually adjusts scheduling strategies based on pre-arranged plans and real-time monitoring data. This scheduling method has the following defects:

[0003] The existing scheduling method usually starts scheduling only when an abnormality is detected at a parking space. When encountering special circumstances (such as multiple aircraft on the airport runway, aircraft at temporary parking spaces, or no vacant parking spaces), firstly, it will not only affect the airport itself, but will also trigger a delay chain effect at other connected airports, further exacerbating the overall flight scheduling chaos; secondly, it may increase the risk of ground traffic congestion, reduce the safety of airport ground operations, and may cause collisions or other ground accidents between aircraft; thirdly, when an abnormality occurs at a parking space where an aircraft is already parked, scheduling will be too late, causing damage to the aircraft and other problems.

[0004] Based on this, the present invention proposes an emergency dispatch method and system based on digital twins, which can predict abnormalities of parking spaces and then optimize the scheduling of parking spaces in advance based on the current operating conditions of the airport to ensure the safety and stability of airport operations. Summary of the invention

[0005] The purpose of the present invention is to provide an emergency dispatch method and system based on digital twins to address the deficiencies in the background technology.

[0006] In order to achieve the above object, the present invention provides the following technical solution: an emergency dispatch method based on digital twin, the dispatch method comprising the following steps:

[0007] The dispatch system obtains the occupied parking spaces in the airport based on the API interface of the airport management platform, marks the occupied parking spaces as occupied parking spaces, and regularly monitors the multi-source data of occupied parking spaces;

[0008] Based on multi-source data, the abnormal prediction model is used to predict whether there is an abnormal risk in occupying the parking space. The multi-source data includes the cumulative frequency of equipment failures and the collapse amplitude. The cumulative frequency of equipment failures and the collapse amplitude are brought into the abnormal prediction model. The model expression is: , where is the abnormal factor, is the cumulative frequency of equipment failures, is the collapse amplitude, , are the adjustment coefficients of the cumulative occurrence frequency of equipment failure and the collapse amplitude, respectively, and , are all greater than 0. When it is predicted that there is an abnormal risk in the occupied parking spaces, the occupied parking spaces are first classified as abnormal, and then the corresponding scheduling strategy is generated in combination with the current operation status of the airport;

[0009] After regularly obtaining the abnormal classification status of all parking spaces in the airport and analyzing the overall operation status of the airport, it is determined whether the airport needs to be managed and the corresponding management strategy is generated based on the judgment results.

[0010] In a preferred embodiment, predicting whether there is an abnormal risk of occupying a parking space by using an abnormal prediction model based on multi-source data includes the following steps:

[0011] Multi-source data is introduced into the abnormal prediction model, and the abnormal prediction model calculates and obtains the abnormal factors of occupied parking spaces;

[0012] After the dispatch system obtains the abnormal factors of all occupied parking spaces, it obtains the current number of free parking spaces and aircraft traffic frequency at the airport through the airport management platform;

[0013] The obtained number of free parking stands and the aircraft passage frequency are normalized so that the value range of the number of free parking stands and the aircraft passage frequency is mapped to [0,1], and the normalized value of the number of free parking stands and the normalized value of the aircraft passage frequency are obtained. The normalized value of the number of free parking stands is subtracted from the normalized value of the aircraft passage frequency to obtain an adjustment value. The first abnormal threshold is dynamically adjusted according to the obtained adjustment value. The adjustment algorithm is:

[0014] , where is the first abnormal threshold after dynamic adjustment, is the first abnormal threshold before dynamic adjustment, is the adjustment value;

[0015] After obtaining the first abnormal threshold after dynamic adjustment, compare the abnormal factor with the first abnormal threshold after dynamic adjustment. If the abnormal factor is greater than the first abnormal threshold after dynamic adjustment, it is predicted that there is an abnormal risk in the parking space and scheduling management is required in advance. If the abnormal factor is less than or equal to the first abnormal threshold after dynamic adjustment, it is predicted that there is no abnormal risk in the parking space and scheduling management is not required in advance.

[0016] In a preferred embodiment, predicting whether there is an abnormal risk of occupying a parking space by using an abnormal prediction model based on multi-source data also includes the following steps:

[0017] Multi-source data is introduced into the abnormal prediction model, and the abnormal prediction model calculates and obtains the abnormal factors of occupied parking spaces;

[0018] The acquired abnormal factor is compared with the preset first abnormal threshold. If the abnormal factor is greater than the preset first abnormal threshold, it is predicted that there is an abnormal risk in the parking space. If the abnormal factor is less than or equal to the preset first abnormal threshold, it is predicted that there is no abnormal risk in the parking space.

[0019] In a preferred embodiment, when it is predicted that there is an abnormal risk in occupying a parking space, abnormal classification of the occupied parking space includes the following steps:

[0020] When it is predicted that there is an abnormal risk in occupying the parking space, the abnormal factor of occupying the parking space is compared with the preset second abnormal threshold, and the second abnormal threshold is dynamically adjusted by the obtained adjustment value. The adjustment algorithm is: , where is the second abnormal threshold after dynamic adjustment, is the second abnormal threshold before dynamic adjustment, is the adjustment value;

[0021] If the abnormality factor is less than or equal to the second abnormality threshold after dynamic adjustment, it is predicted that the occupation of the parking space is a minor abnormality and its use is still supported. The occupation of the parking space is classified as a Class I abnormality. If the abnormality factor is greater than the second abnormality threshold after dynamic adjustment, it is predicted that the occupation of the parking space is a severe abnormality and its use is not supported. The occupation of the parking space is classified as a Class II abnormality.

[0022] In a preferred embodiment, when it is predicted that there is an abnormal risk in occupying a parking space, abnormal classification of the occupied parking space includes the following steps:

[0023] When it is predicted that there is an abnormal risk in occupying the parking space, the abnormal factor of the occupied parking space is compared with a preset second abnormal threshold. The second abnormal threshold is used to predict the severity of the abnormality in occupying the parking space. If the abnormal factor is less than or equal to the second abnormal threshold, it is predicted that the occupation of the parking space is a minor abnormality and its use is still supported. The occupied parking space is classified as a Class I abnormality. If the abnormal factor is greater than the second abnormal threshold, it is predicted that the occupation of the parking space is a serious abnormality and its use is not supported. The occupied parking space is classified as a Class II abnormality.

[0024] In a preferred embodiment, generating a corresponding scheduling strategy in combination with the current operation status of the airport includes the following steps:

[0025] Obtain the number of first-class abnormal and second-class abnormal occupied parking stands at the airport, add the number of first-class abnormal occupied parking stands to the number of second-class abnormal occupied parking stands to obtain the total number of abnormal occupied parking stands, and obtain the number of free parking stands at the airport;

[0026] If the number of vacant parking stands is greater than or equal to the total number of abnormally occupied parking stands, aircraft on Class I and Class II abnormally occupied parking stands will be dispatched to vacant parking stands for parking;

[0027] If the number of free parking stands is less than the total number of abnormally occupied parking stands, a secondary analysis is performed. If the number of free parking stands is greater than or equal to the number of Class II abnormally occupied parking stands, aircraft on Class II abnormally occupied parking stands are dispatched to free parking stands, and the parking time of aircraft on Class I abnormally occupied parking stands is limited.

[0028] If the parking time of an aircraft in a type of abnormally occupied parking stand is equal to the time threshold and there is an empty parking stand at the airport, the aircraft in the type of abnormally occupied parking stand will be dispatched to the empty parking stand for parking;

[0029] If the parking time of an aircraft in a type of abnormally occupied parking position is equal to the time threshold and there is no vacant parking position at the airport, at least one runway will be temporarily closed and the aircraft in the type of abnormally occupied parking position will be dispatched to the closed runway for parking.

[0030] In a preferred embodiment, after regularly obtaining the abnormal classification status of all parking spaces in the airport and analyzing the overall operation status of the airport, it is determined whether the airport needs to be managed, including the following steps:

[0031] Regularly obtain the number of Class I abnormal parking spaces and Class II abnormal parking spaces of all parking spaces at the airport, and calculate the management value. The expression is:

[0032] , where is the management value, is the number of abnormal parking spaces of type I, is the number of Class II abnormal parking spaces, is the total number of parking spaces at the airport;

[0033] The larger the management value is, the more it is necessary to carry out overall management of the airport in advance. If the management value is greater than or equal to the management threshold, it is judged that the airport needs to be managed. If the management value is less than the management threshold, it is judged that the airport does not need to be managed.

[0034] An emergency dispatch system based on digital twins, including a parking space marking module, an abnormality prediction module, and a management module;

[0035] Parking stand marking module: obtains the occupied parking stands in the airport based on the API interface of the airport management platform, and marks the occupied parking stands as occupied parking stands;

[0036] Abnormal prediction module: Regularly monitor the multi-source data of occupied parking spaces, and use the abnormal prediction model to predict whether there is an abnormal risk in the occupied parking spaces based on the multi-source data. When it is predicted that there is an abnormal risk in the occupied parking spaces, the occupied parking spaces are first classified as abnormal, and then the corresponding scheduling strategy is generated in combination with the current operation status of the airport;

[0037] Management module: Regularly obtain the abnormal classification status of all parking spaces in the airport, analyze the overall operation status of the airport, determine whether the airport needs to be managed, and generate corresponding management strategies based on the judgment results.

[0038] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0039] 1. The present invention obtains the occupied parking spaces in the airport based on the API interface of the airport management platform, marks the occupied parking spaces as occupied parking spaces, regularly monitors the multi-source data of occupied parking spaces, and predicts whether there is an abnormal risk in occupying parking spaces through an abnormal prediction model based on the multi-source data. When it is predicted that there is an abnormal risk in occupying parking spaces, the occupied parking spaces are first classified as abnormal, and then a corresponding scheduling strategy is generated in combination with the current operating status of the airport. After the scheduling system can predict the abnormality of the parking spaces, it can optimize the scheduling management of the parking spaces in advance in combination with the current operating status of the airport to ensure the safety and stability of the airport operation.

[0040] 2. When predicting that there is an abnormal risk in occupying a parking space, the present invention first classifies the occupied parking space abnormally, then generates a corresponding dispatching strategy in combination with the current operation status of the airport, regularly obtains the abnormal classification status of all parking spaces in the airport, analyzes the overall operation status of the airport, determines whether the airport needs to be managed, and generates a corresponding management strategy based on the judgment result, thereby improving the overall management efficiency of the airport. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0042] Figure 1 The present invention is a flow chart of the method. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] Example 1: Please refer to Figure 1 As shown, the emergency dispatch method based on digital twin described in this embodiment includes the following steps:

[0045] The dispatch system obtains the occupied parking spaces in the airport based on the API interface of the airport management platform, marks the occupied parking spaces as occupied parking spaces, regularly monitors the multi-source data of occupied parking spaces, and predicts whether there is an abnormal risk in occupying parking spaces through an abnormal prediction model based on the multi-source data. When it is predicted that there is an abnormal risk in occupying parking spaces, the occupied parking spaces are first classified as abnormal, and then the corresponding dispatch strategy is generated in combination with the current operating status of the airport. The abnormal classification status of all parking spaces in the airport is regularly obtained to analyze the overall operating status of the airport, and then it is determined whether the airport needs to be managed, and the corresponding management strategy is generated based on the judgment result.

[0046] This application obtains the occupied parking spaces in the airport based on the API interface of the airport management platform, marks the occupied parking spaces as occupied parking spaces, regularly monitors the multi-source data of occupied parking spaces, and predicts whether there is an abnormal risk of occupying parking spaces through an abnormal prediction model based on the multi-source data. When it is predicted that there is an abnormal risk of occupying parking spaces, the occupied parking spaces are first classified as abnormal, and then the corresponding scheduling strategy is generated in combination with the current operating status of the airport. After the scheduling system can predict the abnormality of the parking spaces, it can optimize the scheduling management of the parking spaces in advance in combination with the current operating status of the airport to ensure the safety and stability of the airport operation.

[0047] When predicting that there is an abnormal risk in occupying a parking space, this application first classifies the occupied parking space abnormally, then generates a corresponding scheduling strategy based on the current operation status of the airport, and regularly obtains the abnormal classification status of all parking spaces in the airport to analyze the overall operation status of the airport, and then determines whether the airport needs to be managed, and generates a corresponding management strategy based on the judgment result, thereby improving the overall management efficiency of the airport.

[0048] Embodiment 2: The dispatch system obtains the occupied parking spaces in the airport based on the API interface of the airport management platform. In the present application, the occupied parking spaces are defined as parking spaces where aircraft are parked or where aircraft will enter in the future, and the occupied parking spaces are marked as occupied parking spaces, including the following steps:

[0049] Utilize the API interface of the airport management platform to obtain real-time information on all parking spaces, including the current status, occupancy, and upcoming flight information of the parking spaces.

[0050] Determine the status of each parking position:

[0051] Occupied: If there is an aircraft parked at the parking stand, it will be marked as "Occupied".

[0052] About to be occupied: Check flight information to determine whether there is an aircraft scheduled to park at the parking space in the next period of time (for example, 30 minutes or 1 hour). If so, mark it as "about to be occupied".

[0053] Integrate the judgment results and mark all "occupied" or "to be occupied" parking spaces as occupied parking spaces. Create an occupied parking space list for easy use in the subsequent dispatch system.

[0054] Set up a scheduled task or use an event-driven mechanism to update the occupied parking space information regularly or when the flight status changes. When an aircraft leaves the parking space or a flight is cancelled, update the parking space status to "free" in a timely manner.

[0055] The status and information of occupied parking spaces are stored in the database to ensure data persistence and queryability, and support subsequent operations of the dispatch system. An API interface feedback function is provided to return the current occupied parking space information to other systems or modules, and support the call of other business logic.

[0056] Regularly monitor the multi-source data of occupied parking spaces, and predict whether there is an abnormal risk of occupying parking spaces through an abnormal prediction model based on the multi-source data, including the following steps:

[0057] Multi-source data on occupied parking spaces include the cumulative frequency of equipment failures and collapse amplitude;

[0058] Substituting the cumulative occurrence frequency of equipment failures and collapse amplitude into the abnormal prediction model, the model expression is: , where is the abnormal factor, is the cumulative frequency of equipment failures, is the collapse amplitude, , are the adjustment coefficients of the cumulative occurrence frequency of equipment failure and the collapse amplitude, respectively, and , Both are greater than 0.

[0059] The logic for obtaining the cumulative frequency of equipment failures is as follows: obtain the cumulative number of equipment failures that occupy the parking space during the monitoring period, and divide the cumulative number of equipment failures by the monitoring period to obtain the cumulative frequency of equipment failures. The greater the cumulative frequency of equipment failures, the more serious the abnormality of occupying the parking space.

[0060] The calculation logic of the collapse amplitude is as follows: multiple monitoring points are set on the parking space, the collapse depth at each monitoring point is obtained, the mean collapse depth and the standard deviation of the collapse depth are calculated based on the collapse depths at multiple monitoring points, and the collapse amplitude is obtained by dividing the mean collapse depth by the standard deviation of the collapse depth. The larger the collapse amplitude, the more serious the abnormality of occupying the parking lot.

[0061] Multi-source data is introduced into the abnormal prediction model, and the abnormal prediction model calculates and obtains the abnormal factor of the occupied parking space. The larger the abnormal factor, the greater the possibility of abnormal risk of the parking space, and the more serious the abnormality;

[0062] The acquired abnormal factor is compared with a preset first abnormal threshold value. If the abnormal factor is greater than the preset first abnormal threshold value, it is predicted that the parking position has an abnormal risk. If the abnormal factor is less than or equal to the preset first abnormal threshold value, it is predicted that the parking position does not have an abnormal risk.

[0063] In this application, all parking spaces in the airport are marked as occupied parking spaces and free parking spaces, which can avoid monitoring of free parking spaces. First, it can reduce the data monitoring and calculation burden of the dispatching system, and second, it can reduce the energy consumption of the dispatching system.

[0064] Specifically, since the operation status of the airport is different at each time, if a fixed first abnormal threshold is adopted, it may lead to over-prediction and waste scheduling resources, or it may lead to under-prediction and affect the timeliness of scheduling;

[0065] Therefore, in this application, the first abnormal threshold can also be dynamically adjusted in combination with the current operation status of the airport to ensure scheduling efficiency;

[0066] After obtaining the abnormal factors of all occupied parking spaces, the dispatch system obtains the current number of free parking spaces and the frequency of aircraft traffic at the airport through the airport management platform. The smaller the number of free parking spaces, the more scheduling management is needed in advance; the greater the frequency of aircraft traffic, the more scheduling management is needed in advance;

[0067] The calculation logic of aircraft passage frequency is as follows: according to the airport management platform, the number of take-offs (the number of times the aircraft flies away from the airport) and landings (the number of times the aircraft enters the airport) of aircraft in the future period are obtained, and the number of take-offs and landings of the aircraft are added to the number of aircraft passages, and the aircraft passage frequency is obtained by dividing the number of passages by the monitoring time.

[0068] The obtained number of free parking stands and the aircraft passage frequency are normalized so that the value range of the number of free parking stands and the aircraft passage frequency is mapped to [0,1], and the normalized value of the number of free parking stands and the normalized value of the aircraft passage frequency are obtained. The normalized value of the aircraft passage frequency is subtracted from the normalized value of the number of free parking stands to obtain the adjustment value. The larger the adjustment value, the more it is necessary to reduce the first abnormal threshold, so that the abnormal prediction can be advanced, which can ensure the timeliness of scheduling. The first abnormal threshold is dynamically adjusted through the obtained adjustment value. The adjustment algorithm is:

[0069] , where is the first abnormal threshold after dynamic adjustment, is the first abnormal threshold before dynamic adjustment, is the adjustment value;

[0070] After obtaining the first abnormal threshold after dynamic adjustment, compare the abnormal factor with the first abnormal threshold after dynamic adjustment. If the abnormal factor is greater than the first abnormal threshold after dynamic adjustment, it is predicted that there is an abnormal risk in the parking space and scheduling management is required in advance. If the abnormal factor is less than or equal to the first abnormal threshold after dynamic adjustment, it is predicted that there is no abnormal risk in the parking space and scheduling management is not required in advance.

[0071] When it is predicted that there is an abnormal risk in occupying a parking space, abnormal classification is performed on the occupied parking space, including the following steps:

[0072] When it is predicted that there is an abnormal risk in occupying the parking space, the abnormal factor of the occupied parking space is compared with a preset second abnormal threshold, and the second abnormal threshold is used to predict the severity of the abnormality of occupying the parking space. If the abnormal factor is less than or equal to the second abnormal threshold, it is predicted that the occupied parking space is slightly abnormal and still supported for use, and the occupied parking space is classified as a Class I abnormality. If the abnormal factor is greater than the second abnormal threshold, it is predicted that the occupied parking space is severely abnormal and is not supported for use, and the occupied parking space is classified as a Class II abnormality.

[0073] Specifically, in this application, since it is necessary to make a prediction in combination with the current operation status of the airport, it is necessary to dynamically adjust the second abnormal threshold value through the obtained adjustment value, and the adjustment algorithm is:

[0074] , where is the second abnormal threshold after dynamic adjustment, is the second abnormal threshold before dynamic adjustment, is the adjustment value;

[0075] If the abnormality factor is less than or equal to the second abnormality threshold after dynamic adjustment, it is predicted that the occupation of the parking space is a minor abnormality and its use is still supported. The occupation of the parking space is classified as a Class I abnormality. If the abnormality factor is greater than the second abnormality threshold after dynamic adjustment, it is predicted that the occupation of the parking space is a severe abnormality and its use is not supported. The occupation of the parking space is classified as a Class II abnormality.

[0076] Generate a corresponding dispatch strategy based on the current operation status of the airport, including the following steps:

[0077] Obtain the number of Class I and Class II abnormal occupied parking stands at the airport, add the number of Class I abnormal occupied parking stands to the number of Class II abnormal occupied parking stands to obtain the total number of abnormal occupied parking stands, and obtain the number of free parking stands at the airport. If the number of free parking stands is greater than or equal to the total number of abnormal occupied parking stands, dispatch the aircraft on Class I and Class II abnormal occupied parking stands to the free parking stands. If the number of free parking stands is less than the total number of abnormal occupied parking stands, perform a secondary analysis. If the number of free parking stands is greater than or equal to the number of Class II abnormal occupied parking stands, dispatch the aircraft on the Class I and Class II abnormal occupied parking stands to the free parking stands. Aircraft at Class II abnormally occupied parking stands are dispatched to vacant parking stands, and the parking time of aircraft at Class I abnormally occupied parking stands is limited. If the parking time of aircraft at Class I abnormally occupied parking stands is equal to the time threshold, and there are vacant parking stands at the airport, aircraft at Class I abnormally occupied parking stands are dispatched to vacant parking stands. If the parking time of aircraft at Class I abnormally occupied parking stands is equal to the time threshold, and there are no vacant parking stands at the airport, at least one runway is temporarily closed, and aircraft at Class I abnormally occupied parking stands are dispatched to the closed runway for parking.

[0078] It should be noted that when there are Class I or Class II abnormal occupied parking stands at the airport, the dispatch system will obtain the abnormal factors of all vacant parking stands, and screen out the vacant parking stands with abnormal factors greater than the first abnormal threshold, and retain the vacant parking stands with abnormal factors less than or equal to the first abnormal threshold for use. This can avoid aircraft entering abnormal parking stands for the second time.

[0079] After regularly obtaining the abnormal classification status of all parking spaces in the airport and analyzing the overall operation status of the airport, it is determined whether the airport needs to be managed. Based on the judgment results, a corresponding management strategy is generated, including the following steps:

[0080] Regularly obtain the number of Class I abnormal parking spaces and Class II abnormal parking spaces of all parking spaces at the airport, and calculate the management value. The expression is:

[0081] , where is the management value, is the number of abnormal parking spaces of type I, is the number of Class II abnormal parking spaces, is the total number of parking spaces at the airport;

[0082] The larger the management value, the more it is necessary to manage the airport in advance. If the management value is greater than or equal to the management threshold, it is determined that the airport needs to be managed. If the management value is less than the management threshold, it is determined that the airport does not need to be managed.

[0083] It is determined that the airport needs to be managed, and the corresponding management strategy is generated based on the judgment result:

[0084] Conduct a comprehensive inspection of all parking stands to assess the status of equipment, the integrity of facilities and operational efficiency to identify potential problems. Develop a priority maintenance and update plan for parking stands with frequent abnormalities to ensure that key facilities such as ground power supply, guide lights and sign clarity remain in good condition. Review the configuration of parking stands and optimize the allocation of parking stands according to aircraft types and flight requirements to ensure efficient use of resources. Develop a regular maintenance and inspection plan to ensure that parking stands and related facilities are in good condition and reduce sudden failures. Use data analysis tools to continuously monitor the use of parking stands, analyze the causes of abnormalities, and propose improvement measures to reduce future abnormalities. Establish and improve the emergency response system to ensure rapid response when abnormalities occur, and develop clear operating procedures and division of responsibilities. Strengthen training for ground service personnel and dispatchers to improve their ability to handle abnormal situations and emergency response speed. Optimize the information integration capability of the airport management system to ensure real-time acquisition of parking stand status information and enhance decision support. Strengthen communication and coordination between airport management departments and airlines, ground service companies, etc. to ensure joint response in abnormal situations. Conduct environmental and safety impact assessments to ensure that abnormal parking space management strategies meet safety standards and environmental protection requirements.

[0085] Embodiment 3: The emergency dispatch system based on digital twin described in this embodiment includes a parking space marking module, an abnormality prediction module, and a management module;

[0086] Parking stand marking module: Based on the API interface of the airport management platform, the occupied parking stands in the airport are obtained, and the occupied parking stands are marked as occupied parking stands. The occupied parking stand information is sent to the abnormal prediction module;

[0087] Abnormal prediction module: Regularly monitor the multi-source data of occupied parking spaces, and use the abnormal prediction model to predict whether there is an abnormal risk in occupying parking spaces based on the multi-source data. When it is predicted that there is an abnormal risk in occupying parking spaces, the occupied parking spaces are first classified as abnormal, and then the corresponding scheduling strategy is generated in combination with the current operating status of the airport. The abnormal classification results are sent to the management module;

[0088] Management module: Regularly obtain the abnormal classification status of all parking spaces in the airport, analyze the overall operation status of the airport, determine whether the airport needs to be managed, and generate corresponding management strategies based on the judgment results.

[0089] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0090] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0091] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. An emergency dispatch method based on digital twins, characterized by: The scheduling method comprises the following steps: The dispatch system obtains the occupied parking spaces in the airport based on the API interface of the airport management platform, marks the occupied parking spaces as occupied parking spaces, and regularly monitors the multi-source data of occupied parking spaces; Based on multi-source data, the abnormal prediction model is used to predict whether there is an abnormal risk in occupying the parking space. The multi-source data includes the cumulative frequency of equipment failures and the collapse amplitude. The cumulative frequency of equipment failures and the collapse amplitude are brought into the abnormal prediction model. The model expression is: , where is the abnormal factor, is the cumulative frequency of equipment failures, is the collapse amplitude, , are the adjustment coefficients of the cumulative occurrence frequency of equipment failure and the collapse amplitude, respectively, and , are all greater than 0. When it is predicted that there is an abnormal risk in the occupied parking spaces, the occupied parking spaces are first classified as abnormal, and then the corresponding scheduling strategy is generated in combination with the current operation status of the airport; After regularly obtaining the abnormal classification status of all parking spaces in the airport and analyzing the overall operation status of the airport, it is determined whether the airport needs to be managed and the corresponding management strategy is generated based on the judgment results.

2. The emergency dispatch method based on digital twin according to claim 1, characterized in that: Based on multi-source data, the abnormal prediction model is used to predict whether there is an abnormal risk in occupying the parking space, including the following steps: Multi-source data is introduced into the abnormal prediction model, and the abnormal prediction model calculates and obtains the abnormal factors of occupied parking spaces; After the dispatch system obtains the abnormal factors of all occupied parking spaces, it obtains the current number of free parking spaces and aircraft traffic frequency at the airport through the airport management platform; The obtained number of free parking stands and the aircraft passage frequency are normalized so that the value range of the number of free parking stands and the aircraft passage frequency is mapped to [0,1], and the normalized value of the number of free parking stands and the normalized value of the aircraft passage frequency are obtained. The normalized value of the number of free parking stands is subtracted from the normalized value of the aircraft passage frequency to obtain an adjustment value. The first abnormal threshold is dynamically adjusted according to the obtained adjustment value. The adjustment algorithm is: , where is the first abnormal threshold after dynamic adjustment, is the first abnormal threshold before dynamic adjustment, is the adjustment value; After obtaining the first abnormal threshold after dynamic adjustment, compare the abnormal factor with the first abnormal threshold after dynamic adjustment. If the abnormal factor is greater than the first abnormal threshold after dynamic adjustment, it is predicted that there is an abnormal risk in the parking space and scheduling management is required in advance. If the abnormal factor is less than or equal to the first abnormal threshold after dynamic adjustment, it is predicted that there is no abnormal risk in the parking space and scheduling management is not required in advance.

3. The emergency dispatch method based on digital twin according to claim 1, characterized in that: Based on multi-source data, the abnormal prediction model is used to predict whether there is an abnormal risk in occupying the parking space, and the following steps are also included: Multi-source data is introduced into the abnormal prediction model, and the abnormal prediction model calculates and obtains the abnormal factors of occupied parking spaces; The acquired abnormal factor is compared with the preset first abnormal threshold. If the abnormal factor is greater than the preset first abnormal threshold, it is predicted that there is an abnormal risk in the parking space. If the abnormal factor is less than or equal to the preset first abnormal threshold, it is predicted that there is no abnormal risk in the parking space.

4. The emergency dispatch method based on digital twin according to claim 2 is characterized in that: When it is predicted that there is an abnormal risk in occupying a parking space, abnormal classification is performed on the occupied parking space, including the following steps: When it is predicted that there is an abnormal risk in occupying the parking space, the abnormal factor of occupying the parking space is compared with the preset second abnormal threshold, and the second abnormal threshold is dynamically adjusted by the obtained adjustment value. The adjustment algorithm is: , where is the second abnormal threshold after dynamic adjustment, is the second abnormal threshold before dynamic adjustment, is the adjustment value; If the abnormality factor is less than or equal to the second abnormality threshold after dynamic adjustment, it is predicted that the occupation of the parking space is a minor abnormality and its use is still supported. The occupation of the parking space is classified as a Class I abnormality. If the abnormality factor is greater than the second abnormality threshold after dynamic adjustment, it is predicted that the occupation of the parking space is a severe abnormality and its use is not supported. The occupation of the parking space is classified as a Class II abnormality.

5. The emergency dispatch method based on digital twin according to claim 3 is characterized in that: When it is predicted that there is an abnormal risk in occupying a parking space, abnormal classification is performed on the occupied parking space, including the following steps: The abnormal factor of the occupied parking space is compared with the preset second abnormal threshold. The second abnormal threshold is used to predict the severity of the abnormality of occupying the parking space. If the abnormal factor is less than or equal to the second abnormal threshold, it is predicted that the occupied parking space is a minor abnormality and its use is still supported. The occupied parking space is classified as a Class I abnormality. If the abnormal factor is greater than the second abnormal threshold, it is predicted that the occupied parking space is a serious abnormality and its use is not supported. The occupied parking space is classified as a Class II abnormality.

6. The emergency dispatch method based on digital twin according to claim 4 is characterized in that: Generate a corresponding dispatch strategy based on the current operation status of the airport, including the following steps: Obtain the number of first-class abnormal and second-class abnormal occupied parking stands at the airport, add the number of first-class abnormal occupied parking stands to the number of second-class abnormal occupied parking stands to obtain the total number of abnormal occupied parking stands, and obtain the number of free parking stands at the airport; If the number of vacant parking stands is greater than or equal to the total number of abnormally occupied parking stands, aircraft on Class I and Class II abnormally occupied parking stands will be dispatched to vacant parking stands for parking; If the number of free parking stands is less than the total number of abnormally occupied parking stands, a secondary analysis is performed. If the number of free parking stands is greater than or equal to the number of Class II abnormally occupied parking stands, aircraft on Class II abnormally occupied parking stands are dispatched to free parking stands, and the parking time of aircraft on Class I abnormally occupied parking stands is limited. If the parking time of an aircraft in a type of abnormally occupied parking stand is equal to the time threshold and there is an empty parking stand at the airport, the aircraft in the type of abnormally occupied parking stand will be dispatched to the empty parking stand for parking; If the parking time of an aircraft in a type of abnormally occupied parking position is equal to the time threshold and there is no vacant parking position at the airport, at least one runway will be temporarily closed and the aircraft in the type of abnormally occupied parking position will be dispatched to the closed runway for parking.

7. The emergency dispatch method based on digital twin according to claim 6 is characterized in that: Regularly obtain the abnormal classification status of all parking spaces in the airport and analyze the overall operation status of the airport to determine whether the airport needs to be managed, including the following steps: Regularly obtain the number of Class I abnormal parking spaces and Class II abnormal parking spaces of all parking spaces at the airport, and calculate the management value. The expression is: , where is the management value, is the number of abnormal parking spaces of type I, is the number of Class II abnormal parking spaces, is the total number of parking spaces at the airport; The larger the management value is, the more it is necessary to carry out overall management of the airport in advance. If the management value is greater than or equal to the management threshold, it is judged that the airport needs to be managed. If the management value is less than the management threshold, it is judged that the airport does not need to be managed.

8. An emergency dispatch system based on digital twin, used to implement the dispatch method according to any one of claims 1 to 7, characterized in that: It includes parking space marking module, abnormality prediction module and management module; Parking stand marking module: obtains the occupied parking stands in the airport based on the API interface of the airport management platform, and marks the occupied parking stands as occupied parking stands; Abnormal prediction module: Regularly monitor the multi-source data of occupied parking spaces, and use the abnormal prediction model to predict whether there is an abnormal risk in the occupied parking spaces based on the multi-source data. When it is predicted that there is an abnormal risk in the occupied parking spaces, the occupied parking spaces are first classified as abnormal, and then the corresponding scheduling strategy is generated in combination with the current operation status of the airport; Management module: Regularly obtain the abnormal classification status of all parking spaces in the airport, analyze the overall operation status of the airport, determine whether the airport needs to be managed, and generate corresponding management strategies based on the judgment results.

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