CN117913956A - Power supply safety management system and safety management method for unmanned aerial vehicle - Google Patents
Power supply safety management system and safety management method for unmanned aerial vehicle Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/0047—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with monitoring or indicating devices or circuits
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L3/00—Electric devices on electrically-propelled vehicles for safety purposes; Monitoring operating variables, e.g. speed, deceleration or energy consumption
- B60L3/0023—Detecting, eliminating, remedying or compensating for drive train abnormalities, e.g. failures within the drive train
- B60L3/0046—Detecting, eliminating, remedying or compensating for drive train abnormalities, e.g. failures within the drive train relating to electric energy storage systems, e.g. batteries or capacitors
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B64—AIRCRAFT; AVIATION; COSMONAUTICS
- B64U—UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
- B64U50/00—Propulsion; Power supply
- B64U50/30—Supply or distribution of electrical power
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/0029—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with safety or protection devices or circuits
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2200/00—Type of vehicles
- B60L2200/10—Air crafts
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Abstract
The application provides a power supply safety management system and a safety management method thereof for an unmanned aerial vehicle, wherein the power supply system comprises an energy supply module and a monitoring module, the energy supply module is used for supplying power to functional components of the unmanned aerial vehicle, and the monitoring module comprises an abnormal data acquisition module and a safety prediction module; the abnormal data acquisition module can acquire abnormal data at a preset frequency; the safety prediction module comprises a database module, an evaluation module and an adjustment module; the system comprises a database module, an unmanned aerial vehicle state safety evaluation threshold, an adjustment module and an unmanned aerial vehicle state safety evaluation threshold, wherein the database module records an abnormal class, a basic data threshold and a safety evaluation threshold of the abnormal class, a functional component associated with the abnormal class and the unmanned aerial vehicle state safety evaluation threshold, the evaluation module can evaluate the safety risk of the abnormal class according to the acquired abnormal data, and the adjustment module can adjust the energy supply of the functional component associated with the abnormal class according to the evaluation result of the safety risk. The application improves the accuracy of early warning.
Description
Technical Field
The application relates to the field of unmanned aerial vehicles, in particular to a power supply safety management system and a safety management method of an unmanned aerial vehicle.
Background
The energy supply of the unmanned aerial vehicle generally uses rechargeable batteries, and common types of rechargeable batteries include lithium polymer batteries, lithium ion batteries and nickel hydrogen batteries, and the rechargeable batteries are selected appropriately according to factors such as weight, flight time and power of the unmanned aerial vehicle. The power management of the unmanned aerial vehicle provides energy supply management and safety monitoring management of unmanned aerial vehicle functional units, and energy supply management is used for converting direct current electric energy provided by a battery into different voltages and currents required by each functional unit, wherein a converter or a voltage stabilizer is generally used for converting and adjusting the electric energy so as to ensure that each functional unit normally operates. The safety monitoring management is used for monitoring the state of the unmanned aerial vehicle and providing protection measures for the battery, wherein the monitoring abnormal data is one of the most important index data in the unmanned aerial vehicle task, and corresponding protection measures can be provided according to the result of the monitoring data. However, in the conventional power management method, characteristic data is generally obtained by using a data model, and after an abnormal situation is determined, a solution is assigned to the abnormal situation according to the generated abnormal situation. However, as the unmanned aerial vehicle often encounters comprehensive abnormal conditions in the task, that is, the situation that multiple types of abnormalities are combined together, and a maintenance scheme is adopted after the abnormal conditions occur, the possibility of occurrence of the risk of accidents is greatly increased, and therefore, a management method capable of judging the risk trend in advance, making risk interference in advance and sensitively adjusting the monitoring scheme is needed.
Disclosure of Invention
The following description is presented to enable one of ordinary skill in the art to make and use the invention. The preferred embodiments in the following description are by way of example only and other obvious variations will occur to those skilled in the art. The basic principles of the invention defined in the following description may be applied to other embodiments, variations, modifications, equivalents, and other technical solutions without departing from the spirit and scope of the invention.
In order to solve the problem, the application provides a power supply safety management system for an unmanned aerial vehicle, which comprises an energy supply module and a monitoring module; the energy supply module is used for providing power for functional components of the unmanned aerial vehicle, and the monitoring module comprises an abnormal data acquisition module and a safety prediction module; the abnormal data acquisition module can acquire abnormal data at a preset frequency; the safety prediction module comprises a database module, an evaluation module and an adjustment module; the system comprises a database module, an unmanned aerial vehicle state safety evaluation threshold, an adjustment module and an unmanned aerial vehicle state safety evaluation threshold, wherein the database module records an abnormal class, a basic data threshold and a safety evaluation threshold of the abnormal class, a functional component associated with the abnormal class and the unmanned aerial vehicle state safety evaluation threshold, the evaluation module can evaluate the safety risk of the abnormal class according to the acquired abnormal data, and the adjustment module can adjust the energy supply of the functional component associated with the abnormal class according to the evaluation result of the safety risk.
The adjusting module comprises a low risk monitoring module and a high risk monitoring module.
The low risk monitoring module collects abnormal data at a first frequency, evaluates safety risks of corresponding abnormal categories according to the collected abnormal data, and judges whether to switch into the high risk monitoring module for monitoring according to an evaluation result.
The high risk monitoring module increases the frequency of abnormal data acquisition from a first frequency to a second frequency, evaluates the safety risk of the corresponding abnormal category according to the acquired abnormal data, and judges whether to limit the application power consumption of the functional component according to the evaluation result.
The application also provides a safety management method using the power supply safety management system for the unmanned aerial vehicle, which comprises the following steps:
S1, setting a task starting time as Ts and a predicted ending time as Te in an R-th inspection task executed by an unmanned plane P;
Setting exception data corresponding to m exception categories ND, nd= [ ND 1,ND2,ND3,…,NDm ], wherein the risk weight of the z-type exception ND z is NW z, and the list of functional components associated with the z-type exception ND z is PG;
S2, monitoring at a first frequency K 1, and setting an abnormal data Qi z of a z-type abnormal ND z obtained at Ti time, wherein an abnormal data difference DeltaQi z= Qiz- Q0z between the abnormal data Qi z of the z-type abnormal ND z and a basic data threshold Q0 z is obtained according to a basic data threshold Q0 z of the z-type abnormal ND z;
Obtaining a security assessment score for class z exception ND z for Ti time ；
A security assessment threshold C0 z according to class z exception ND z;
when Ci z＜C0z is found, go to step S3;
when Ci z≥C0z is found, go to step S4;
S3, continuing to monitor at a first frequency K 1, and setting an initial evaluation coefficient eta 1, wherein eta 1 is more than 1 and less than 1.5;
Obtaining abnormal data Q (i+1) z of the z-th type abnormal ND z at a time T (i+1) after the time Ti, and obtaining an abnormal data difference DeltaQ (i+1) z=Q(i+1)z-Q0z between abnormal data Q (i+1) z of the z-th type abnormal at the time T (i+1) and a basic data threshold Q0 z according to a basic data threshold Q0 z of the z-th type abnormal ND z;
Obtaining security assessment score of class z exception class ND z of unmanned aerial vehicle at time T (i+1) ；
A security assessment threshold C0 z according to class z exception ND z;
when C (i+1) z≤Ciz, the present step is cycled;
When C (i+1) z≥C0z, the process proceeds to step S4;
When Ci z＜C (i+1)z＜C0z is executed, a state security assessment score containing all abnormal categories, which is obtained by unmanned plane P at time T (i+1), is obtained ；
A threshold C0 is evaluated according to unmanned aerial vehicle state safety;
When C (i+1) is not less than C0, the step S4 is carried out:
when C (i+1) < C0, cycling through this step;
s4, increasing the monitoring frequency from a first frequency K 1 to a second frequency K 2, wherein K 2＞K1;
Obtaining a second evaluation coefficient eta 2 on the basis of the first evaluation coefficient eta 1, wherein eta 2＞η1;
Obtaining abnormal data Q (i+1) z 'of the z-th type abnormal ND z at a time T (i+1)' after the time Ti, and obtaining an abnormal data difference delta Q (i+1) z'= Q(i+1)z'-Q0z between the abnormal data Q (i+1) z 'of the z-th type abnormal in the time T (i+1)' and the basic data threshold Q0 z according to the basic data threshold Q0 z of the z-th type abnormal ND z in the database;
obtaining security assessment score of unmanned plane z-type anomaly ND z at time T (i+1) ；
A security assessment threshold C0 z according to class z exception ND z;
When C (i+1) z'＜ C0z, switching to the step S3;
When C (i+1) z'≥ C0z, switching to S5;
S5, obtaining a function component list PG, PG= [ PG 1,PG2,PG3,…,PGn ] associated with a z-th type abnormal ND z, setting an importance level coefficient of a j-th function component PG j as PW j, and limiting the application power consumption of the function components in the list according to the order of the importance level coefficient from low to high.
Wherein, the step S5 comprises:
setting the functional components in a functional component list PG= [ PG 1,PG2,PG3,…,PGn ] associated with a z-th type exception ND z to be arranged from low to high in importance level coefficient, and setting the importance level coefficient of a j-th functional component PG j to be PW j;
Setting the early warning index as Fmax, wherein when the jth functional component PG j is limited by application power consumption, if And if the value is greater than Fmax, a safety early warning is sent out.
Wherein, in step S4, the second frequency K 2=η1K1.
Wherein in step S4, the second evaluation coefficientWhere e is a natural constant.
The beneficial effects achieved by the application are as follows:
According to the application, for the comprehensive abnormal situation of the unmanned aerial vehicle in the task, the targeted abnormal type can be analyzed in the abnormal combination of various types, the maintenance scheme is adopted before the abnormal situation is generated, the risk trend is predicted in advance, the risk of accidents is greatly reduced, meanwhile, the monitoring scheme can be sensitively adjusted, the energy supply distribution mode is adjusted, the power supply application of the functional component with higher importance level in the flight task is ensured to the greatest extent, and the flight task can be stably completed.
The application overcomes the defect that most mechanism models are only simplified linear systems, can judge complex conditions of nonlinearity, higher degree of freedom and multivariable coupling, has high triggering speed, lower cost, very quick start and recovery of early warning and wide application range.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present application, and other drawings may be obtained according to these drawings to those skilled in the art.
Fig. 1 is a flow chart of a security management method for a power security management system of an unmanned aerial vehicle.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The types of flight tasks executed by unmanned aerial vehicles are more and more widely, functional components to be started are more and more according to different task demands, power consumption application control of the power supply system for supplying energy to the functional components is more and more complex, in some specific cases, such as security tasks of urban parks, transmission line inspection tasks with severe areas, water quality monitoring tasks, line calibration inspection tasks, rescue tasks and the like, the functional applications generally comprise shooting applications requiring video shooting through cameras mounted on unmanned aerial vehicle bodies, particularly comprise applications of various sensors such as high-definition zoom cameras, infrared cameras, night vision cameras, laser radars and the like, and applications for transmitting high-definition pictures to a platform, and control applications requiring a control center to transmit control instructions to the unmanned aerial vehicle to control the steering of unmanned aerial vehicle cameras, the flight state and route of the unmanned aerial vehicle and the like, the application discloses a power safety management system and a safety management method for an unmanned aerial vehicle, wherein the power safety management system comprises an energy supply module and a monitoring module, wherein the energy supply module is used for providing power for the functional module started by the unmanned aerial vehicle, the monitoring module comprises an abnormal data acquisition module and a safety prediction module, wherein the abnormal data acquisition module can acquire abnormal data of the unmanned aerial vehicle in a flight task, and the safety prediction module comprises a database module, an evaluation module and an adjustment module; the database module records an abnormal category, a basic data threshold value and a safety evaluation threshold value of the abnormal category, a functional component associated with the abnormal category and a unmanned aerial vehicle state safety evaluation threshold value; the evaluation module can evaluate the safety risk according to the acquired abnormal data, and the adjustment module can adjust the energy supply of the functional components associated with the abnormal category according to the evaluation result.
The adjusting module comprises a low risk monitoring module and a high risk monitoring module, wherein the low risk monitoring module collects abnormal data at a first frequency, evaluates the safety risk of the corresponding abnormal category according to the collected abnormal data, judges whether to switch into the high risk monitoring module to monitor according to an evaluation result, the high risk monitoring module increases the frequency of the abnormal data collection from the first frequency to a second frequency, evaluates the safety risk of the corresponding abnormal category according to the collected abnormal data, and judges whether to limit the application power consumption of the functional component according to the evaluation result. Through the arrangement, the risk trend can be judged in advance, the proper monitoring step is selected according to the risk trend condition, and the energy supply of the functional component is adjusted and interfered before the abnormality occurs.
Specifically, in one embodiment, the starting time of the R-th inspection task executed by the unmanned plane P is Ts, the predicted ending time is Te, and the predicted inspection duration of the R-th inspection task Δtr=te-Ts;
The method comprises the steps of setting abnormal data to be acquired by an abnormal data acquisition module to correspond to m abnormal categories ND, ND= [ ND 1,ND2,ND3,…,NDm ], wherein the risk weight of a z-th abnormal ND z is NW z;
After the R-th inspection task starts, the monitoring module enters a basic monitoring step;
The basic monitoring step comprises the following steps: during the process of executing an R-th inspection task, a monitoring module monitors at a first frequency K 1, and an abnormal data acquisition module is set to acquire abnormal data Q1 z of a z-th type abnormal ND z at a time T1, wherein an abnormal data difference delta Q1 z= Q1z- Q0z between the abnormal data Q1 z of the z-th type abnormal ND z and a basic data threshold Q0 z is acquired according to a basic data threshold Q0 z of the z-th type abnormal ND z in a database;
Obtaining a security assessment score C1 z=△Q1z (Te-Ts)/(T1-Ts) for the z-th class anomaly ND z at time T1;
A security assessment threshold C0 z according to a z-type exception ND z stored in the database;
When C1 z＜C0z represents that the risk trend of the z-th type abnormality in the current monitoring is smaller, recording the abnormal data Q1 z, the acquisition time T1 and the abnormal data difference DeltaQ 1 z in a risk abnormality list, and entering a low risk monitoring step;
When C1 z≥C0z represents that the security risk trend of the z-type abnormal ND z is larger, recording the abnormal data Q1 z, the acquisition time T1 and the abnormal data difference delta Q1 z in a risk abnormal list, and entering a high risk monitoring step;
the low risk monitoring step includes:
The monitoring module continues to monitor at a first frequency K 1, and the monitoring module sets an initial evaluation coefficient eta 1, wherein eta 1 is more than 1 and less than 1.5 in the embodiment; setting a second abnormal data Q2 z which acquires a z-th abnormal ND z at a time T2 (T2=T1+omega), and acquiring an abnormal data difference delta Q2 z=Q2z-Q0z between abnormal data Q2 z of the z-th abnormal class and a basic data threshold Q0 z according to a basic data threshold Q0 z of the z-th abnormal ND z in the database;
Recording the difference value delta Q2 z of the abnormal data in a risk abnormal list to obtain a security assessment score C2 z=η1△Q2z (Te-Ts)/(T2-Ts) of the z-th class abnormal class ND z of the unmanned aerial vehicle at the time of T2;
A security assessment threshold C0 z according to a z-type anomaly ND z in the database;
when C2 z≤C1z is carried out, judging that the risk is low, and continuing to monitor by a low risk monitoring step;
when C2 z≥C0z is carried out, judging that the risk is continuously increased, and entering a high risk monitoring step;
When C1 z＜C2z＜C0z, a state safety evaluation score c2=including all anomaly categories obtained by the unmanned plane P at time T2 is obtained ；
According to unmanned plane state safety evaluation threshold C0 in the database;
When C2 is more than or equal to C0, switching to a high risk monitoring step;
when C2 is less than C0, the method proceeds to a low risk monitoring step.
The high risk monitoring step includes:
increasing from the initial acquisition frequency K 1 to a second frequency K 2=η1K1; the monitoring module continues to monitor at a second frequency K 2;
a second evaluation coefficient eta 2=e(C1z- C0z)/ C0zη1 is obtained on the basis of the first evaluation coefficient eta 1,
Obtaining second abnormal data Q2 z of a z-th abnormal class ND z at a time T2 (T2=T1+omega), and obtaining an abnormal data difference delta Q2 z=Q2z-Q0z between abnormal data Q2 z of the z-th abnormal class ND z and a basic data threshold Q0 z at the time T2 according to a basic data threshold Q0 z of the z-th abnormal class ND z in the database;
Recording the difference delta Q2 z of the abnormal data in a risk abnormality list to obtain a security assessment score C2 z'=η2△Q2z (Te-Ts)/(T2-Ts) of the z-th type abnormality ND z of the unmanned plane at the time T2;
A security assessment threshold C0 z according to a z-type anomaly ND z in the database;
when C2 z'＜ C0z is carried out, the monitoring is carried out in a low risk monitoring step;
When C2 z'≥ C0z, the power consumption limiting step is shifted.
The limiting power consumption step includes: acquiring a functional component list PG associated with a z-type exception ND z, and limiting the application power consumption of the functional component list PG according to the order of importance level coefficients in the functional component list PG from low to high; specifically, in the present embodiment, a function component list pg= [ PG 1,PG2,PG3,…,PGn ] associated with ND z is set, the function components in the list are arranged in order of importance level coefficients from low to high, and the importance level coefficient of the jth function component PG j is set as PW j;
Setting an early warning index fmax=0.5, setting an importance level coefficient PW j =0.2 of a function component PG 1 with the lowest importance level in the function component list PG, closing the function component PG 1 and then transferring to a high risk monitoring step because PW j is smaller than Fmax, setting a third abnormal data Q3 z of a z-th abnormal class ND z obtained at the time of T3, and obtaining an importance level coefficient PW 2 =0.4 of PG 2 when the safety evaluation score C3 z≥ C0z of the z-th abnormal class ND z of the unmanned aerial vehicle at the time of T3, wherein the sum of the weights of the function components required to be limited is larger than the early warning index Fmax because 0.4+0.2 is larger than 0.5, and sending early warning to a system at the moment.
While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the application. It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (8)
1. A power supply safety management system for an unmanned aerial vehicle, which is characterized by comprising an energy supply module and a monitoring module; the energy supply module is used for providing power for functional components of the unmanned aerial vehicle, and the monitoring module comprises an abnormal data acquisition module and a safety prediction module; the abnormal data acquisition module can acquire abnormal data at a preset frequency; the safety prediction module comprises a database module, an evaluation module and an adjustment module; the system comprises a database module, an unmanned aerial vehicle state safety evaluation threshold, an adjustment module and an unmanned aerial vehicle state safety evaluation threshold, wherein the database module records an abnormal class, a basic data threshold and a safety evaluation threshold of the abnormal class, a functional component associated with the abnormal class and the unmanned aerial vehicle state safety evaluation threshold, the evaluation module can evaluate the safety risk of the abnormal class according to the acquired abnormal data, and the adjustment module can adjust the energy supply of the functional component associated with the abnormal class according to the evaluation result of the safety risk.
2. The power security management system for a drone of claim 1, wherein the adjustment module includes a low risk monitoring module and a high risk monitoring module.
3. The power safety management system for the unmanned aerial vehicle according to claim 2, wherein the low risk monitoring module collects abnormal data at a first frequency, evaluates safety risks of corresponding abnormal categories according to the collected abnormal data, and judges whether to switch to the high risk monitoring module for monitoring according to an evaluation result.
4. The power safety management system for the unmanned aerial vehicle according to claim 3, wherein the frequency of the abnormal data collection by the high risk monitoring module is increased from the first frequency to the second frequency, the safety risk of the corresponding abnormal category is evaluated according to the collected abnormal data, and whether the application power consumption of the functional component is limited is judged according to the evaluation result.
5. A security management method using the power security management system for a drone of any one of claims 1-4, the steps comprising:
S1, setting a task starting time as Ts and a predicted ending time as Te in an R-th inspection task executed by an unmanned plane P;
Setting exception data corresponding to m exception categories ND, nd= [ ND 1,ND2,ND3,…,NDm ], wherein the risk weight of the z-type exception ND z is NW z, and the list of functional components associated with the z-type exception ND z is PG;
S2, monitoring at a first frequency K 1, and setting an abnormal data Qi z of a z-type abnormal ND z obtained at Ti time, wherein an abnormal data difference DeltaQi z= Qiz- Q0z between the abnormal data Qi z of the z-type abnormal ND z and a basic data threshold Q0 z is obtained according to a basic data threshold Q0 z of the z-type abnormal ND z;
Obtaining a security assessment score for class z exception ND z for Ti time ；
A security assessment threshold C0 z according to class z exception ND z;
when Ci z＜C0z is found, go to step S3;
when Ci z≥C0z is found, go to step S4;
S3, continuing to monitor at a first frequency K 1, and setting an initial evaluation coefficient eta 1, wherein eta 1 is more than 1 and less than 1.5;
Obtaining abnormal data Q (i+1) z of the z-th type abnormal ND z at a time T (i+1) after the time Ti, and obtaining an abnormal data difference DeltaQ (i+1) z =Q(i+1)z-Q0z between abnormal data Q (i+1) z of the z-th type abnormal at the time T (i+1) and a basic data threshold Q0 z according to a basic data threshold Q0 z of the z-th type abnormal ND z;
Obtaining security assessment score of class z exception class ND z of unmanned aerial vehicle at time T (i+1) ；
A security assessment threshold C0 z according to class z exception ND z;
when C (i+1) z≤Ciz, the present step is cycled;
When C (i+1) z≥C0z, the process proceeds to step S4;
When Ci z＜C (i+1)z＜C0z is executed, a state security assessment score containing all abnormal categories, which is obtained by unmanned plane P at time T (i+1), is obtained ；
A threshold C0 is evaluated according to unmanned aerial vehicle state safety;
When C (i+1) is not less than C0, the step S4 is carried out:
when C (i+1) < C0, cycling through this step;
s4, increasing the monitoring frequency from a first frequency K 1 to a second frequency K 2, wherein K 2＞K1;
Obtaining a second evaluation coefficient eta 2 on the basis of the first evaluation coefficient eta 1, wherein eta 2＞η1;
Obtaining abnormal data Q (i+1) z 'of the z-th type abnormal ND z at a time T (i+1)' after the time Ti, and obtaining an abnormal data difference delta Q (i+1) z'= Q(i+1) z'-Q0z between the abnormal data Q (i+1) z 'of the z-th type abnormal in the time T (i+1)' and the basic data threshold Q0 z according to the basic data threshold Q0 z of the z-th type abnormal ND z in the database;
obtaining security assessment score of unmanned plane z-type anomaly ND z at time T (i+1) ；
A security assessment threshold C0 z according to class z exception ND z;
When C (i+1) z'＜ C0z, switching to the step S3;
When C (i+1) z'≥ C0z, switching to S5;
S5, obtaining a function component list PG, PG= [ PG 1,PG2,PG3,…,PGn ] associated with a z-th type abnormal ND z, setting an importance level coefficient of a j-th function component PG j as PW j, and limiting the application power consumption of the function components in the list PG according to the order of the importance level coefficient from low to high.
6. The security management method for a power security management system of an unmanned aerial vehicle of claim 5, wherein the S5 step comprises:
setting the functional components in a functional component list PG= [ PG 1,PG2,PG3,…,PGn ] associated with a z-th type exception ND z to be arranged from low to high in importance level coefficient, and setting the importance level coefficient of a j-th functional component PG j to be PW j;
Setting the early warning index as Fmax, wherein when the jth functional component PG j is limited by application power consumption, if And if the value is greater than Fmax, a safety early warning is sent out.
7. The security management method for a power security management system of a drone of claim 5, wherein in step S4, the second frequency K 2=η1 K1.
8. The security management method for the power security management system of the unmanned aerial vehicle of claim 5, wherein, in step S4, the second evaluation coefficientWhere e is a natural constant.
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