WO2024067135A1

WO2024067135A1 - Unmanned aircraft management method and system based on big data identification, and medium

Info

Publication number: WO2024067135A1
Application number: PCT/CN2023/118882
Authority: WO
Inventors: 胡华智; 刘畅; 陈皓东; 宋晨晖
Original assignee: 亿航智能设备（广州）有限公司
Priority date: 2022-09-29
Filing date: 2023-09-14
Publication date: 2024-04-04
Also published as: CN115602001A

Abstract

An unmanned aircraft management method and system based on big data identification, and a medium. The method comprises: obtaining feature identification information and flight data information of an unmanned aircraft; extracting feature identification data, and obtaining tracing source information and safety data, and a pre-stored flight data set; obtaining authorization level data, and displaying according to a warning threshold level on the basis of the authorization level data; generating a flight feature map according to the pre-stored flight data set, and predicting intended flight trajectory data; determining by means of the authorization level data and pre-stored flight data to obtain a flight permission correlation coefficient; and performing comparison on the basis of the coefficient and a preset authorization threshold, determining whether the unmanned aircraft is authorized to fly, and giving an alarm or interfering. Thus, feature information and flight data of an unmanned aircraft are subjected to flight authorization evaluation on the basis of big data identification technology, the technology of carrying out evaluation according to monitoring information data of the unmanned aircraft to obtain authorization parameters for authorization determining is implemented, and accurate identification of airspace safety management of the unmanned aircraft is improved.

Description

Unmanned aerial vehicle management method, system and medium based on big data identification

Technical Field

The present application relates to the field of big data security and unmanned aerial vehicle management technology, and more specifically, to an unmanned aerial vehicle management method, system and medium based on big data identification.

Background technique

As unmanned aerial vehicle technology matures, unmanned aerial vehicle applications become more frequent and widespread. In addition, as manufacturing costs and barriers to entry are reduced, the market for consumer-grade micro-UAVs and civil unmanned aerial vehicles has exploded. However, the country's current supervision of unmanned aerial vehicles lacks a systematic and comprehensive system and standards, and has not yet formed an authoritative standard management system. With the rapid development of the unmanned aerial vehicle market, the demand for urban unmanned aerial vehicle airspace control systems is becoming more and more urgent.

For regional protection and airspace limits, existing cities will designate restricted air zones or monitoring zones above urban areas, prohibiting entry of unmanned aerial vehicles except for authorized registered ones. In addition, there are intelligent means and smart systems for the supervision and identification of unmanned aerial vehicles, which rely too much on traditional systems or administrator-style management. Such conventional systems and means lack systematicity, mobility, flexibility and intelligence, which inhibits the beneficial use and rapid development of unmanned aerial vehicles, and reduces the driving force of unmanned aerial vehicles for the development of urban informatization and digitalization. In the process of determining and managing unmanned aerial vehicles, too many human factors interfere, affecting the sensitivity and accuracy of the determination, which hinders the scientific, reasonable and convenient use of unmanned aerial vehicles.

In view of the above problems, effective technical solutions are urgently needed.

Summary of the invention

The purpose of the embodiments of the present application is to provide an unmanned aerial vehicle management method, system and medium based on big data identification, which can improve the accuracy of identifying and managing the flight safety authorization status of unmanned aerial vehicles based on the collected unmanned aerial vehicle information data.

The present application also provides an unmanned aerial vehicle management method based on big data identification. The following steps are involved:

Monitor and obtain the characteristic identification information and flight data information of endangered or reported unmanned aerial vehicles;

Extracting feature identification data of the unmanned aerial vehicle according to the feature identification information and acquiring traceability source information and safety data, and extracting a pre-stored flight data set of the unmanned aerial vehicle according to the flight data information;

Acquiring authorization level data of the unmanned aerial vehicle according to the characteristic identification data, the traceability source information and the safety data, and performing a comparison display according to a preset warning threshold level according to the authorization level data;

Generating a flight characteristic map of the unmanned aerial vehicle according to the pre-stored flight data set, and predicting simulated flight trajectory data of the unmanned aerial vehicle according to a flight trajectory prediction model;

According to the proposed flight trajectory data combined with the authorization level data and the data of the pre-stored flight data set, authorization relevance judgment is performed to obtain a flight-qualified relevance coefficient of the unmanned aerial vehicle;

A threshold comparison is performed based on the flight authorization correlation coefficient and a preset authorization threshold, and whether the unmanned aerial vehicle is authorized to fly is determined based on the threshold comparison result. If it is determined to be an abnormal authorization, the unmanned aerial vehicle is warned or interfered with.

Optionally, in the unmanned aerial vehicle management method based on big data identification described in the embodiment of the present application, the monitoring and obtaining of feature identification information and flight data information of endangered or reported unmanned aerial vehicles includes:

Obtain the characteristic identification information of the unmanned aerial vehicle based on the transmission signals of the monitored unmanned aerial vehicle in the vicinity or the reported unmanned aerial vehicle, including type information, usage information, registration information and special certification information;

Performing identification of the unmanned aerial vehicle identity information based on the attribution registration information and the special certification information to obtain the operation purpose information and air traffic control declaration information of the unmanned aerial vehicle;

The flight data information is integrated according to the operation purpose information, air traffic control declaration information and special certification information.

Optionally, in the unmanned aerial vehicle management method based on big data identification described in the embodiment of the present application, the feature identification information of the unmanned aerial vehicle is extracted according to the feature identification information. Identify data and obtain traceability source information and safety data, and extract a pre-stored flight data set of the unmanned aerial vehicle according to the flight data information, including:

According to the feature identification information, query a preset aircraft identification database to obtain corresponding feature identification data, including aircraft type data, operation purpose data, and control attribution data;

Obtaining traceability source information of the unmanned aerial vehicle according to the control attribution data query;

Extracting the safety data of the unmanned aerial vehicle from the aircraft identification database according to the traceability source information combined with the operation purpose information and the air traffic control declaration information;

A pre-stored flight data set of the unmanned aerial vehicle is extracted according to the flight data information, including flight destination data, airspace warning information data, mission instruction data and special operation data.

Optionally, in the unmanned aerial vehicle management method based on big data identification described in the embodiment of the present application, obtaining the authorization level data of the unmanned aerial vehicle according to the feature identification data, the traceability source information and the safety data, and performing a comparison display according to the preset warning threshold level according to the authorization level data, includes:

Perform risk value clustering according to the aircraft type data, operation purpose data and control attribution data combined with corresponding risk parameters to obtain a risk information value K ₁ ;

Obtain a safety identification value K ₂ by weighting the traceability source information and the safety degree data;

Calculate the authorization level data Y of the unmanned aerial vehicle according to the risk information value K ₁ and the safety identification value K _2: Y = (K ₁ + K ₂ ) / K ₂ ;

A threshold comparison is performed based on the authorization level data Y and a preset warning threshold to obtain a warning level corresponding to the warning threshold of the unmanned aerial vehicle, and the warning level is displayed according to the warning level.

Optionally, in the unmanned aerial vehicle management method based on big data identification described in an embodiment of the present application, generating a flight characteristic map of the unmanned aerial vehicle according to the pre-stored flight data set, and predicting the simulated flight trajectory data of the unmanned aerial vehicle according to a flight trajectory prediction model, includes:

Generate a flight characteristic map based on the flight destination data, airspace warning information data, mission instruction data and special operation data of the unmanned aerial vehicle;

Extract the flight mission feature information according to the flight feature map and input it into the preset flight trajectory prediction model to preset the flight trajectory, and obtain the simulated flight trajectory data of the unmanned aerial vehicle. Data, including route data, airspace multilateral data, and asymptote data.

Optionally, in the unmanned aerial vehicle management method based on big data identification described in an embodiment of the present application, the obtaining of the quasi-flight relevance coefficient of the unmanned aerial vehicle by performing authorization relevance judgment according to the proposed flight trajectory data combined with the authorization level data and the data of the pre-stored flight data set includes:

According to the route data, airspace multilateral data, asymptotic line data of the unmanned aerial vehicle, combined with the authorization level data Y and the flight destination data, airspace warning information data, mission instruction data and special operation data, authorization relevance judgment is performed to obtain a flight-permit relevance coefficient;

The calculation formula of the quasi-flight correlation coefficient is:

Among them, P is the quasi-flight correlation coefficient, s ₀ is the route data, t ₀ is the airspace multilateral data, h ₀ is the asymptotic line data, Y is the authorization level data, d is the flight destination data, c is the airspace warning information data, w is the mission instruction data, l is the special operation data, is the safety factor of the unmanned aircraft license, and ε _k is the credit index of the unmanned aircraft holder.

Optionally, in the unmanned aerial vehicle management method based on big data identification described in the embodiment of the present application, the threshold comparison is performed based on the flight permission correlation coefficient and a preset authorization threshold, and whether the unmanned aerial vehicle is authorized to fly is determined based on the threshold comparison result, and if it is determined to be abnormal authorization, the unmanned aerial vehicle is warned or interfered, including:

Obtaining a preset authorization threshold value based on the characteristic identification information of the unmanned aerial vehicle;

Performing a threshold comparison based on the flight-permitted correlation coefficient and a preset authorization threshold;

If the flight permission correlation coefficient is greater than the preset authorization threshold, the unmanned aerial vehicle is determined to be normally authorized; if the flight permission correlation coefficient is not greater than the preset authorization threshold, the unmanned aerial vehicle is determined to be abnormally authorized;

Conduct signal interference or command warnings to unmanned aerial vehicles with abnormal authorization.

In a second aspect, an embodiment of the present application provides an unmanned aerial vehicle management system based on big data identification, the system comprising: a memory and a processor, the memory comprising a program of an unmanned aerial vehicle management method based on big data identification, and the program of the unmanned aerial vehicle management method based on big data identification is executed by the processor to implement the following steps:

Optionally, in the unmanned aerial vehicle management system based on big data identification described in the embodiment of the present application, the monitoring and acquisition of feature identification information and flight data information of endangered or reported unmanned aerial vehicles includes:

In a third aspect, the embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium includes an unmanned aerial vehicle management method program based on big data identification, and when the unmanned aerial vehicle management method program based on big data identification is executed by a processor, The steps of the unmanned aerial vehicle management method based on big data identification are now described in any of the above items.

As can be seen from the above, the unmanned aerial vehicle management method, system and medium based on big data identification provided in the embodiments of the present application obtain the characteristic identification information and flight data information of the unmanned aerial vehicle to extract the characteristic identification data and obtain the traceability source information and safety data as well as the pre-stored flight data set and obtain the authorization level data, and display it according to the warning threshold level according to the authorization level data, generate a flight characteristic map according to the pre-stored flight data set and predict the intended flight trajectory data, and then combine the authorization level data with the pre-stored flight data to make a judgment to obtain the flight-permit correlation coefficient, and compare the coefficient with the preset authorization threshold to determine whether the unmanned aerial vehicle is authorized to fly and take warnings or interference; thereby, based on the big data identification technology, the unmanned aerial vehicle characteristic information and flight data are evaluated for authorization and flight permission, and the authorization judgment technology is realized by evaluating the authorization parameters obtained according to the monitoring information data of the unmanned aerial vehicle, thereby improving the accurate identification of the airspace safety management of unmanned aerial vehicles.

Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or be understood by practicing the embodiments of the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written description, claims, and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

FIG1 is a flow chart of an unmanned aerial vehicle management method based on big data identification provided by an embodiment of the present application;

FIG2 is a flow chart of obtaining unmanned aircraft characteristic identification information and flight data information of an unmanned aircraft management method based on big data identification provided by an embodiment of the present application;

FIG3 is a diagram of the acquisition of feature identification data, traceability source information and safety data, and pre-stored flight data sets in the unmanned aerial vehicle management method based on big data identification provided by an embodiment of the present application. flow chart;

FIG4 is a schematic diagram of the structure of an unmanned aerial vehicle management system based on big data identification provided in an embodiment of the present application.

Detailed ways

The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

Please refer to FIG. 1, which is a flow chart of an unmanned aerial vehicle management method based on big data identification in some embodiments of the present application. The unmanned aerial vehicle management method based on big data identification is used in a terminal device, such as a computer, a mobile phone terminal, etc. The unmanned aerial vehicle management method based on big data identification includes the following steps:

S101. Monitor and obtain the characteristic identification information and flight data information of endangered or reported unmanned aerial vehicles;

S102, extracting feature identification data of the unmanned aerial vehicle according to the feature identification information and acquiring traceability source information and safety data, and extracting a pre-stored flight data set of the unmanned aerial vehicle according to the flight data information;

S103, obtaining authorization level data of the unmanned aerial vehicle according to the characteristic identification data, the traceability source information and the safety data, and performing a comparison display according to a preset warning threshold level according to the authorization level data;

S104, generating a flight characteristic map of the unmanned aerial vehicle according to the pre-stored flight data set, and predicting simulated flight trajectory data of the unmanned aerial vehicle according to a flight trajectory prediction model;

S105, performing authorization relevance judgment according to the proposed flight trajectory data combined with the authorization level data and the data of the pre-stored flight data set to obtain a flight-qualified relevance coefficient of the unmanned aerial vehicle;

S106, performing a threshold comparison between the flight authorization correlation coefficient and a preset authorization threshold, and determining whether the unmanned aerial vehicle is authorized to fly based on the threshold comparison result; if it is determined to be abnormal authorization, warning or interfering with the unmanned aerial vehicle.

It should be noted that in order to monitor the situation of unmanned aerial vehicles flying close to the preset airspace or the unmanned aerial vehicles reported by temporary communications, and to determine whether airspace flight authorization or warning, interference or even forced landing can be carried out, the characteristic identification information of the unmanned aerial vehicle is obtained to extract the characteristic identification data of the unmanned aerial vehicle, and the traceability source information and safety data are obtained, and the pre-stored flight data set of the unmanned aerial vehicle is extracted based on the flight data information, and the authorization level data of the unmanned aerial vehicle is obtained based on the characteristic identification data, traceability source information and safety data, and the authorization level data is compared and displayed according to the preset warning threshold level, so that the system or supervisory personnel can clearly understand the authorization level of the unmanned aerial vehicle, and then generate a warning based on the pre-stored flight data set. A flight characteristic map of the unmanned aerial vehicle is formed and the simulated flight trajectory data of the unmanned aerial vehicle is predicted according to the flight trajectory prediction model. Finally, the authorization correlation judgment is performed based on the simulated flight trajectory data combined with the authorization level data and the data of the pre-stored flight data set to obtain the quasi-flight correlation coefficient of the unmanned aerial vehicle. The quasi-flight correlation coefficient is compared with the preset authorization threshold value. The comparison result determines whether the unmanned aerial vehicle is authorized to fly. If it is determined to be abnormal authorization, the unmanned aerial vehicle is warned or interfered. The authorization of the unmanned aerial vehicle is determined by obtaining and processing the identification and information data of the unmanned aerial vehicle to be determined, thereby realizing big data intelligent airspace authorization management of unmanned aerial vehicles and realizing airspace safety management.

Please refer to Figure 2, which is a flowchart of the unmanned aerial vehicle management method based on big data recognition in some embodiments of the present application for obtaining unmanned aerial vehicle feature identification information and flight data information. According to an embodiment of the present invention, the monitoring and acquisition of the characteristics of the endangered or reported unmanned aerial vehicle The identification information and flight data information are as follows:

S201. Obtain characteristic identification information of unmanned aerial vehicles based on the monitored unmanned aerial vehicles in the vicinity or the transmission signals of the reported unmanned aerial vehicles, including type information, usage information, registration information and special certification information;

S202, performing identification of the unmanned aerial vehicle identity information according to the attribution registration information and the special certification information to obtain the operation purpose information and air traffic control declaration information of the unmanned aerial vehicle;

S203, integrating flight data information according to the operation purpose information, air traffic control declaration information and special certification information.

It should be noted that for the unmanned aerial vehicles monitored in the airspace, it is necessary to determine the unmanned aerial vehicles to be evaluated through identity identification and aircraft data acquisition. The characteristic identification information of the unmanned aerial vehicle must be obtained based on the monitored unmanned aerial vehicle transmission signal, including type information, purpose information, ownership registration information and special certification information, which can reflect the category of the unmanned aerial vehicle, such as large, small or micro, the purpose such as military, civil, commercial, and the ownership registration status of the company, group or individual to which the unmanned aerial vehicle belongs, as well as special certification such as special model unmanned aerial vehicles, emergency rescue unmanned aerial vehicles, air defense unmanned aerial vehicles and other unmanned aerial vehicles with special purposes. The identity information of the unmanned aerial vehicle is identified through the ownership registration information and special certification information to obtain the operation purpose information and air traffic control declaration information of the unmanned aerial vehicle, such as the issuer of the flight instruction, the flight interval segment, the flight time and duration, the take-off weight, the details of the load items, etc. The flight data information is integrated according to the operation purpose information, air traffic control declaration information and special certification information.

Please refer to Figure 3, which is a flowchart of obtaining feature identification data, tracing source information, safety data, and pre-stored flight data sets in the unmanned aerial vehicle management method based on big data recognition in some embodiments of the present application. According to an embodiment of the present invention, the feature identification data of the unmanned aerial vehicle is extracted according to the feature identification information and the tracing source information and safety data are obtained, and the pre-stored flight data set of the unmanned aerial vehicle is extracted according to the flight data information, specifically:

S301, querying a preset aircraft identification database according to the characteristic identification information to obtain corresponding characteristic identification data, including aircraft type data, operation purpose data, and control attribution data;

S302: Obtain the traceability source of the unmanned aerial vehicle according to the control ownership data query information;

S303, extracting the safety data of the unmanned aerial vehicle from the aircraft identification database according to the traceability source information combined with the operation purpose information and the air traffic control declaration information;

S304, extracting a pre-stored flight data set of the unmanned aerial vehicle according to the flight data information, including flight destination data, airspace warning information data, mission instruction data and special operation data.

It should be noted that, according to the characteristic identification information of the unmanned aerial vehicle, the corresponding characteristic identification data is queried in the preset aircraft identification database to obtain the information data reflecting the type, functional use, purpose of use, flight instruction issuer, unmanned aerial vehicle ownership unit and flight controller of the unmanned aerial vehicle. According to the control ownership data, the traceability source information of the unmanned aerial vehicle is queried to reflect the data information source of the owner or controller. According to the traceability source information combined with the operation purpose information and the air traffic control declaration information, the safety data of the unmanned aerial vehicle is extracted from the aircraft identification database, that is, the safety data of the unmanned aerial vehicle can be queried and identified through the obtained unmanned aerial vehicle source information, flight operation information, instruction function information and air traffic control declaration data information. At the same time, according to the flight data information, the pre-stored flight data set of the unmanned aerial vehicle is extracted, including the flight destination data, airspace warning information data, mission instruction data and special operation data, that is, the flight purpose, flight range, passing airspace and airspace warning, airspace marking and instruction guidance, special operations, special tasks and other pre-stored flight data of the unmanned aerial vehicle are obtained.

According to an embodiment of the present invention, the obtaining of the authorization level data of the unmanned aerial vehicle according to the feature identification data, the traceability source information and the safety data, and performing a comparison display according to the preset warning threshold level according to the authorization level data, are specifically as follows:

It should be noted that, in order to obtain the authorization level of the unmanned aerial vehicle, the authorization level data of the unmanned aerial vehicle is calculated according to the risk information value and the safety identification value, and then the threshold is compared with the preset warning threshold. According to the threshold comparison result of the authorization level data and the warning threshold, the warning level corresponding to the warning threshold of the unmanned aerial vehicle is obtained. The warning level is divided into four levels according to the divided threshold range, which are level one to level four. Among them, the level one threshold range is (0.75, 1], the level two threshold range is (0.5, 0.75], the level three threshold range is (0.25, 0.5], and the level four threshold range is [0, 0.25]. For example, if the comparison threshold of a certain unmanned aerial vehicle A is 0.7, the warning level of the unmanned aerial vehicle A is level two, and it is displayed according to the corresponding warning level obtained, wherein, according to the aircraft type data _A1 , the operation purpose data _A2 , and the control attribution data _A3 combined with the corresponding risk parameters The calculation formula for risk information value obtained by risk value clustering is: The calculation formula for obtaining the security identification value by weighting the traceability source information τ ₀ and the security data S is K ₂ =τ ₀ ×S.

According to an embodiment of the present invention, generating the flight characteristic map of the unmanned aerial vehicle according to the pre-stored flight data set, and predicting the simulated flight trajectory data of the unmanned aerial vehicle according to the flight trajectory prediction model, specifically includes:

The flight mission characteristic information is extracted according to the flight characteristic map and input into a preset flight trajectory prediction model to preset the flight trajectory, thereby obtaining the simulated flight trajectory data of the unmanned aerial vehicle, including route data, airspace multilateral data, and asymptote data.

It should be noted that a flight characteristic map of an unmanned aerial vehicle is generated by extracting flight destination data, airspace warning information data, mission instruction data and special operation data from a pre-stored flight data set of an unmanned aerial vehicle. The flight characteristic map can reflect flight purpose, airspace warning, air traffic control signs, mission details, instruction lists and other flight mission information of the unmanned aerial vehicle in command flight operations. The flight mission characteristic information extracted from the flight characteristic map is input into a trained preset flight trajectory prediction model to preset the flight trajectory and obtain simulated flight trajectory data. In order to obtain accurate data on pre-flight trajectories of various types of unmanned aerial vehicles to perform various tasks, a preset flight trajectory prediction model is established. The preset flight trajectory prediction model is trained and obtained based on a large amount of flight mission sample data of various types of unmanned aerial vehicles in historical flight mission archive data. The larger the amount of data, the more accurate the result. The preset flight trajectory prediction model in this scheme is obtained through The flight mission feature information in the historical sample data and the actual flight trajectory data are input into the model as training data for training to obtain output values. When the output values meet the preset requirements, the training is stopped to obtain a trained preset flight trajectory prediction model.

According to an embodiment of the present invention, the authorization relevance judgment is performed based on the proposed flight trajectory data in combination with the authorization level data and the data of the pre-stored flight data set to obtain the quasi-flight relevance coefficient of the unmanned aerial vehicle, specifically:

The calculation formula of the quasi-flight correlation coefficient is:

Among them, P is the quasi-flight correlation coefficient, s ₀ is the route data, t ₀ is the airspace multilateral data, h ₀ is the asymptotic line data, Y is the authorization level data, d is the flight destination data, c is the airspace warning information data, w is the mission instruction data, l is the special operation data, is the safety factor of the unmanned aircraft license, and _εk is the credit index of the unmanned aircraft holder ( and ε _k are obtained by querying the characteristic identification information of the unmanned aerial vehicle in the aircraft identification database).

It should be noted that in order to evaluate the authorized flight status of an unmanned aerial vehicle, the unmanned aerial vehicle's flight permit correlation coefficient is obtained by processing and judging the unmanned aerial vehicle's simulated flight trajectory data in combination with the authorization level data and the data of the pre-stored flight data set. This coefficient can reflect the authorized flight status of the unmanned aerial vehicle.

According to an embodiment of the present invention, the threshold comparison is performed based on the flight permission correlation coefficient and a preset authorization threshold, and it is determined whether the unmanned aerial vehicle is authorized to fly based on the threshold comparison result. If it is determined to be abnormal authorization, the unmanned aerial vehicle is warned or interfered, specifically:

If the flight-permit correlation coefficient is greater than the preset authorization threshold, the unmanned aerial vehicle is determined to be normally authorized; if the flight-permit correlation coefficient is not greater than the preset authorization threshold, the unmanned aerial vehicle is determined to be normally authorized. Determine the unmanned aerial vehicle as an abnormal authorization;

It should be noted that in order to determine whether an unmanned aerial vehicle is authorized to fly, the preset authorization threshold is obtained by querying the unmanned aerial vehicle attribute information in the aircraft identification database through the characteristic identification information of the unmanned aerial vehicle, and the threshold is compared with the preset authorization threshold based on the unmanned aerial vehicle's flight authorization correlation coefficient. If the flight authorization correlation coefficient is greater than the preset authorization threshold, the unmanned aerial vehicle is determined to be normally authorized, that is, it obtains airspace authorization. If the flight authorization correlation coefficient is not greater than the preset authorization threshold, the unmanned aerial vehicle is determined to be abnormally authorized, that is, the unmanned aerial vehicle cannot obtain airspace authorization, and the unmanned aerial vehicle needs to be subjected to signal interference or command warning to achieve intelligent identification of unmanned aerial vehicles and release or warning processing.

As shown in FIG4 , the present invention further discloses an unmanned aerial vehicle management system based on big data identification, including a memory 41 and a processor 42, wherein the memory includes an unmanned aerial vehicle management method program based on big data identification, and when the unmanned aerial vehicle management method program based on big data identification is executed by the processor, the following steps are implemented:

A threshold comparison is performed based on the flight permission correlation coefficient and a preset authorization threshold, and a determination is made based on the threshold comparison result whether the unmanned aerial vehicle is authorized to fly. If it is determined to be abnormal authorization, the unmanned aerial vehicle is authorized to fly. Warning or interference with the unmanned aerial vehicle.

According to an embodiment of the present invention, the monitoring and acquisition of characteristic identification information and flight data information of an endangered or declared unmanned aerial vehicle is specifically as follows:

It should be noted that the unmanned aerial vehicles for airspace monitoring need to be identified through identification and aircraft data acquisition to determine whether they are unmanned aerial vehicles to be evaluated. The manned aircraft transmits signals to obtain the characteristic identification information of the unmanned aerial vehicle, including type information, purpose information, registration information and special certification information, which can reflect the category of the unmanned aerial vehicle such as large, small or micro, the purpose such as military, civil, commercial, and the registration status of the company, group or individual to which the unmanned aerial vehicle belongs, as well as special certification such as special model unmanned aerial vehicles, emergency rescue unmanned aerial vehicles, air defense unmanned aerial vehicles and other unmanned aerial vehicle information with special purposes. The unmanned aerial vehicle identity information is identified through the registration information and special certification information to obtain the unmanned aerial vehicle's operating purpose information and air traffic control declaration information, such as the issuer of the flight instruction, the flight interval segment, the flight time and duration, the take-off weight, the details of the load items and other information; the flight data information is integrated according to the operating purpose information, air traffic control declaration information and special certification information.

According to an embodiment of the present invention, extracting the feature identification data of the unmanned aerial vehicle according to the feature identification information and acquiring the traceability source information and the safety data, and extracting the pre-stored flight data set of the unmanned aerial vehicle according to the flight data information, specifically comprises:

It should be noted that, according to the characteristic identification information of the unmanned aerial vehicle, the corresponding characteristic identification data is queried in the preset aircraft identification database to obtain the information data reflecting the type of the unmanned aerial vehicle, functional use, purpose of use, the issuer of the flight instruction, the unit to which the unmanned aerial vehicle belongs, and the flight controller. According to the control ownership data, the traceability source information of the unmanned aerial vehicle is queried to reflect the data information source of the owner or controller. According to the traceability source information, the operation purpose information and the air traffic control declaration information, the safety data of the unmanned aerial vehicle is extracted from the aircraft identification database. That is, the safety data of the unmanned aerial vehicle can be queried and identified by obtaining the unmanned aerial vehicle source information, flight operation information, instruction function information and air traffic control declaration data information. The pre-stored flight data set of the unmanned aerial vehicle is extracted according to the flight data information, including the flight destination data, airspace warning information data, mission instruction data and special operation data, that is, the pre-stored flight data such as the flight purpose, flight range, passing airspace and airspace warning, airspace marking and instruction guidance, special operations, special tasks, etc. of the unmanned aerial vehicle are obtained.

According to the flight destination data, airspace warning information data, mission Command data and special operation data generate flight characteristic maps;

It should be noted that the flight characteristic map of the unmanned aerial vehicle is generated by extracting the flight destination data, airspace warning information data, mission instruction data and special operation data of the pre-stored flight data set of the unmanned aerial vehicle. The flight characteristic map can reflect the flight purpose, airspace warning, air traffic control identification, mission details, instruction list and other flight mission information of the unmanned aerial vehicle in the command flight operation. The flight mission characteristic information extracted according to the flight characteristic map is input into the trained preset flight trajectory prediction model to preset the flight trajectory and obtain the simulated flight trajectory data. Among them, in order to obtain accurate data of the pre-flight trajectory of various types of unmanned aerial vehicles to perform various tasks, a preset flight trajectory prediction model is established. The preset flight trajectory prediction model is obtained by training based on a large amount of flight mission sample data of various types of unmanned aerial vehicles in historical flight mission archive data. The larger the data volume, the more accurate the result. The preset flight trajectory prediction model in this scheme uses the flight mission characteristic information in the historical sample data and the actual flight trajectory data as training data to input into the model for training to obtain the output value. When the output value meets the preset requirements, the training is stopped to obtain the trained preset flight trajectory prediction model.

The calculation formula of the quasi-flight correlation coefficient is:

Among them, P is the flight relevance coefficient, s ₀ is the route data, t ₀ is the airspace multilateral data, h ₀ is the asymptotic line data, Y is the authorization level data, d is the flight destination data, and c is the airspace warning information Data, w is task instruction data, l is special operation data, is the safety factor of the unmanned aircraft license, and _εk is the credit index of the unmanned aircraft holder ( and ε _k are obtained by querying the characteristic identification information of the unmanned aerial vehicle in the aircraft identification database).

A third aspect of the present invention provides a readable storage medium, which includes an unmanned aerial vehicle management method program based on big data identification. When the unmanned aerial vehicle management method program based on big data identification is executed by a processor, the steps of the unmanned aerial vehicle management method based on big data identification as described in any one of the above items are implemented.

The unmanned aerial vehicle management method, system and medium based on big data identification disclosed in the present invention extract feature identification data by acquiring feature identification information and flight data information of the unmanned aerial vehicle, obtain traceability source information and safety data, and pre-stored flight data set and obtain authorization level data, display according to the warning threshold level based on the authorization level data, generate a flight feature map according to the pre-stored flight data set and predict the simulated flight trajectory data, and then combine the authorization level data with the pre-stored flight data to judge and obtain the flight-permit correlation coefficient, compare the coefficient with the preset authorization threshold value to determine whether the unmanned aerial vehicle is authorized to fly and take warning or interference; thereby, based on the big data identification technology, the unmanned aerial vehicle feature information and flight data are evaluated for authorization and flight permission, and the authorization judgment technology is realized by evaluating the authorization parameters obtained according to the monitoring information data of the unmanned aerial vehicle, thereby improving the accurate recognition of the airspace safety management of the unmanned aerial vehicle.

In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

Those skilled in the art will appreciate that all or part of the steps of the above method embodiments may be implemented by hardware associated with program instructions, and the above program may be stored in a readable storage medium, which, when executed, executes the steps of the above method embodiments; and the above storage medium may be used to store the program instructions in a readable storage medium. Storage media include: mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks or optical disks, and other media that can store program codes.

Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

Claims

The unmanned aerial vehicle management method based on big data identification is characterized by comprising the following steps:

Monitor and obtain the characteristic identification information and flight data information of endangered or reported unmanned aerial vehicles;

Extracting feature identification data of the unmanned aerial vehicle according to the feature identification information and acquiring traceability source information and safety data, and extracting a pre-stored flight data set of the unmanned aerial vehicle according to the flight data information;

Acquiring authorization level data of the unmanned aerial vehicle according to the characteristic identification data, the traceability source information and the safety data, and performing a comparison display according to a preset warning threshold level according to the authorization level data;

Generating a flight characteristic map of the unmanned aerial vehicle according to the pre-stored flight data set, and predicting simulated flight trajectory data of the unmanned aerial vehicle according to a flight trajectory prediction model;

According to the proposed flight trajectory data combined with the authorization level data and the data of the pre-stored flight data set, authorization relevance judgment is performed to obtain a flight-qualified relevance coefficient of the unmanned aerial vehicle;

A threshold comparison is performed based on the flight authorization correlation coefficient and a preset authorization threshold, and whether the unmanned aerial vehicle is authorized to fly is determined based on the threshold comparison result. If it is determined to be an abnormal authorization, the unmanned aerial vehicle is warned or interfered with.
The unmanned aerial vehicle management method based on big data identification according to claim 1 is characterized in that the monitoring and acquisition of characteristic identification information and flight data information of endangered or reported unmanned aerial vehicles includes:

Obtain the characteristic identification information of the unmanned aerial vehicle based on the transmission signals of the monitored unmanned aerial vehicle in the vicinity or the reported unmanned aerial vehicle, including type information, usage information, registration information and special certification information;

Performing identification of the unmanned aerial vehicle identity information based on the attribution registration information and the special certification information to obtain the operation purpose information and air traffic control declaration information of the unmanned aerial vehicle;

Integrate flight data based on the operation purpose information, air traffic control declaration information and special certification information According to information.
The unmanned aerial vehicle management method based on big data identification according to claim 2 is characterized in that the extracting the feature identification data of the unmanned aerial vehicle according to the feature identification information and obtaining the traceability source information and safety data, and extracting the pre-stored flight data set of the unmanned aerial vehicle according to the flight data information, comprises:

According to the feature identification information, query a preset aircraft identification database to obtain corresponding feature identification data, including aircraft type data, operation purpose data, and control attribution data;

Obtaining traceability source information of the unmanned aerial vehicle according to the control attribution data query;

Extracting the safety data of the unmanned aerial vehicle from the aircraft identification database according to the traceability source information combined with the operation purpose information and the air traffic control declaration information;

A pre-stored flight data set of the unmanned aerial vehicle is extracted according to the flight data information, including flight destination data, airspace warning information data, mission instruction data and special operation data.
The unmanned aerial vehicle management method based on big data identification according to claim 3 is characterized in that the obtaining of the authorization level data of the unmanned aerial vehicle according to the feature identification data, the traceability source information and the safety data, and performing a comparison display according to the preset warning threshold level according to the authorization level data, comprises:

Perform risk value clustering according to the aircraft type data, operation purpose data and control attribution data combined with corresponding risk parameters to obtain a risk information value K 1 ;

Obtain a safety identification value K 2 by weighting the traceability source information and the safety degree data;

Calculate the authorization level data Y of the unmanned aerial vehicle according to the risk information value K 1 and the safety identification value K 2: Y = (K 1 + K 2 ) / K 2 ;

A threshold comparison is performed based on the authorization level data Y and a preset warning threshold to obtain a warning level corresponding to the warning threshold of the unmanned aerial vehicle, and the warning level is displayed according to the warning level.
The unmanned aerial vehicle management method based on big data identification according to claim 4 is characterized in that the generating of the flight characteristic map of the unmanned aerial vehicle according to the pre-stored flight data set and predicting the simulated flight trajectory data of the unmanned aerial vehicle according to the flight trajectory prediction model comprises:

According to the flight destination data, airspace warning information data, mission Command data and special operation data generate flight characteristic maps;

The flight mission characteristic information is extracted according to the flight characteristic map and input into a preset flight trajectory prediction model to preset the flight trajectory, thereby obtaining the simulated flight trajectory data of the unmanned aerial vehicle, including route data, airspace multilateral data, and asymptote data.
The unmanned aerial vehicle management method based on big data identification according to claim 5 is characterized in that the step of performing authorization relevance judgment based on the proposed flight trajectory data combined with the authorization level data and the data of the pre-stored flight data set to obtain the quasi-flight relevance coefficient of the unmanned aerial vehicle comprises:

According to the route data, airspace multilateral data, asymptotic line data of the unmanned aerial vehicle, combined with the authorization level data Y and the flight destination data, airspace warning information data, mission instruction data and special operation data, authorization relevance judgment is performed to obtain a flight-permit relevance coefficient;

The calculation formula of the quasi-flight correlation coefficient is:

Among them, P is the quasi-flight correlation coefficient, s 0 is the route data, t 0 is the airspace multilateral data, h 0 is the asymptotic line data, Y is the authorization level data, d is the flight destination data, c is the airspace warning information data, w is the mission instruction data, l is the special operation data, is the safety factor of the unmanned aircraft license, and ε k is the credit index of the unmanned aircraft holder.
The unmanned aerial vehicle management method based on big data identification according to claim 6 is characterized in that the threshold comparison is performed based on the flight permission correlation coefficient and the preset authorization threshold, and whether the unmanned aerial vehicle is authorized to fly is determined based on the threshold comparison result, and if it is determined to be abnormal authorization, the unmanned aerial vehicle is warned or interfered, including:

Obtaining a preset authorization threshold value based on the characteristic identification information of the unmanned aerial vehicle;

Performing a threshold comparison based on the flight-permitted correlation coefficient and a preset authorization threshold;

If the flight permission correlation coefficient is greater than the preset authorization threshold, the unmanned aerial vehicle is determined to be normally authorized; if the flight permission correlation coefficient is not greater than the preset authorization threshold, the unmanned aerial vehicle is determined to be abnormally authorized;

Conduct signal interference or command warnings to unmanned aerial vehicles with abnormal authorization.
The unmanned aerial vehicle management system based on big data identification is characterized in that the system comprises: a memory and a processor, wherein the memory comprises a program of an unmanned aerial vehicle management method based on big data identification, and when the program of the unmanned aerial vehicle management method based on big data identification is executed by the processor, the following steps are implemented:

Monitor and obtain the characteristic identification information and flight data information of endangered or reported unmanned aerial vehicles;

Extracting feature identification data of the unmanned aerial vehicle according to the feature identification information and acquiring traceability source information and safety data, and extracting a pre-stored flight data set of the unmanned aerial vehicle according to the flight data information;

Acquiring authorization level data of the unmanned aerial vehicle according to the characteristic identification data, the traceability source information and the safety data, and performing a comparison display according to a preset warning threshold level according to the authorization level data;

Generating a flight characteristic map of the unmanned aerial vehicle according to the pre-stored flight data set, and predicting simulated flight trajectory data of the unmanned aerial vehicle according to a flight trajectory prediction model;

According to the proposed flight trajectory data combined with the authorization level data and the data of the pre-stored flight data set, authorization relevance judgment is performed to obtain a flight-qualified relevance coefficient of the unmanned aerial vehicle;

A threshold comparison is performed based on the flight authorization correlation coefficient and a preset authorization threshold, and whether the unmanned aerial vehicle is authorized to fly is determined based on the threshold comparison result. If it is determined to be an abnormal authorization, the unmanned aerial vehicle is warned or interfered with.
The unmanned aerial vehicle management system based on big data identification according to claim 8 is characterized in that the monitoring and acquisition of characteristic identification information and flight data information of endangered or reported unmanned aerial vehicles includes:

Obtain the characteristic identification information of the unmanned aerial vehicle based on the transmission signals of the monitored unmanned aerial vehicle in the vicinity or the reported unmanned aerial vehicle, including type information, usage information, registration information and special certification information;

Performing identification of the unmanned aerial vehicle identity information based on the attribution registration information and the special certification information to obtain the operation purpose information and air traffic control declaration information of the unmanned aerial vehicle;

Integrate flight data based on the operation purpose information, air traffic control declaration information and special certification information According to information.
A computer-readable storage medium, characterized in that the computer-readable storage medium includes an unmanned aerial vehicle management method program based on big data identification, and when the unmanned aerial vehicle management method program based on big data identification is executed by a processor, the steps of the unmanned aerial vehicle management method based on big data identification as described in any one of claims 1 to 7 are implemented.