Intelligent re-decision method and system for unmanned aerial vehicle traffic patrol scheme

[0061]In order to make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are described clearly and completely. Obviously, the described embodiments are part of the embodiments of the present invention, not all of them. example. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present invention.

[0062]The embodiment of the present application solves the problem of poor adaptability of the prior art by providing an intelligent decision-making method, system and storage medium for the UAV traffic patrol solution, and improves the adaptability of the UAV traffic patrol solution.

[0063]The technical solutions in the embodiments of the present application are to solve the above technical problems, and the general idea is as follows:

[0064]The embodiment of the present invention obtains the UAV traffic patrol data when the UAV is executing the UAV traffic patrol plan; performs the associated processing on the UAV traffic patrol data; based on the traffic incidents and presets that occur during the UAV traffic patrol The cycle of determining whether the event triggers heavy decision-making, and analyzes the type of event-triggered heavy decision-making; based on the associated UAV traffic patrol data, judges whether the reasoning triggers the heavy-decision decision, and analyzes the type of reasoning-triggered decision-making; among them, the type of heavy decision-making Both include: heavy decision-making for traffic patrol tasks and heavy decision-making for UAV flight missions; conflict resolution processing on the type of event-triggered decision-making and the type of reasoning-triggered decision-making, to obtain the type of heavy decision-making for the UAV traffic patrol plan; The aircraft traffic patrol plan carries out corresponding types of heavy decision-making. The embodiment of the present invention triggers and judges heavy decision-making from both active and passive levels, and resolves conflicts between the two types of heavy decision-making, so as to implement heavy decision-making on the UAV traffic patrol scheme, which can enhance the UAV traffic patrol scheme. Adaptability during execution.

[0065]In order to better understand the above technical solutions, the above technical solutions will be described in detail below in conjunction with the accompanying drawings of the specification and specific implementations.

[0066]The embodiment of the present invention provides an intelligent decision-making method for a UAV traffic patrol scheme. The method is executed by a computer, such asfigure 1 Shown is an overall flowchart of an embodiment of the present invention,figure 2 It is a process framework diagram of an embodiment of the present invention. The method includes the following steps:

[0067]S1. Obtain UAV traffic patrol data when the UAV executes the UAV traffic patrol plan; the UAV traffic patrol data includes: UAV data, pilot data and UAV mission data;

[0068]S2. Perform correlation processing on the UAV traffic patrol data;

[0069]S3. Judging whether the event triggers the re-decision based on the traffic incidents that occur during the UAV traffic patrol and the preset cycle, and analyze the type of the event triggering the re-decision; based on the associated UAV traffic patrol data, determine whether the reasoning triggers the re-decision , And analyze the types of heavy decision-making triggered by reasoning; among them, the types of heavy decision-making include: heavy decision-making for traffic patrol missions and heavy decision-making for UAV flight missions;

[0070]S4. Perform conflict resolution processing on the type of event-triggered decision-making and the type of reasoning-triggering decision-making, to obtain the decision-making type of the UAV traffic patrol plan;

[0071]S5. Make corresponding types of heavy decisions on the UAV traffic patrol plan.

[0072]The embodiment of the present invention obtains the UAV traffic patrol data when the UAV is executing the UAV traffic patrol plan; performs the associated processing on the UAV traffic patrol data; based on the traffic incidents and presets that occur during the UAV traffic patrol Cycle to determine whether an event triggers heavy decision-making, and analyze the type of event-triggered heavy-decision decision; determine whether to reason to trigger heavy decision-making based on the associated UAV traffic patrol data, and analyze the type of reasoning-triggered decision-making; among them, the type of heavy decision-making Both include: heavy decision-making for traffic patrol tasks and heavy decision-making for UAV flight missions; conflict resolution processing on the type of event-triggered heavy-decision decision and the type of reasoning-triggered heavy decision-making, to obtain the heavy decision-making type of the UAV traffic patrol plan; The aircraft traffic patrol plan carries out corresponding types of heavy decision-making. The embodiment of the present invention triggers and judges heavy decision-making from both active and passive levels, and resolves conflicts between the two types of heavy decision-making, so as to implement heavy decision-making on the UAV traffic patrol scheme, which can enhance the UAV traffic patrol scheme. Adaptability during execution.

[0073]The following is a detailed analysis of each step.

[0074]In step S1, the UAV traffic patrol data when the UAV executes the UAV traffic patrol plan is obtained; the UAV traffic patrol data includes: UAV data, pilot data and UAV mission data.

[0075]Obtain UAV data through UAV ground station, including: UAV real-time flight control data, UAV trajectory data and UAV equipment basic data;

[0076]Obtain pilot data through the traffic police command center, including: on-the-job information data and pilot basic data;

[0077]Obtain UAV mission data through the traffic police command center, including: patrol mission data and flight mission data.

[0078]In step S2, correlation processing is performed on the UAV traffic patrol data. include:

[0079]Performing data cleaning on the UAV traffic patrol data based on the data cleaning method;

[0080]Perform correlation analysis on the data after data cleaning based on correlation algorithm.

[0081]In the embodiment of the present invention, the data cleaning method and the associated algorithm are all existing technologies, and will not be described here.

[0082]In step S3, determine whether the event triggers the re-decision based on the traffic incidents that occurred during the UAV traffic patrol and the preset period, and analyze the type of the event-triggered re-decision; determine whether to reason based on the associated UAV traffic patrol data Trigger re-decision, and analyze the type of reasoning that triggers re-decision.

[0083]Among them, the type of event-triggered decision-making and the type of reasoning-triggered decision-making include: traffic patrol mission decision-making and UAV flight mission decision-making.

[0084]The heavy decision-making of traffic patrol tasks refers to the fact that when the command center receives a new patrol task, the heavy-duty decision-making of the traffic patrol task will be carried out when the UAV is performing a patrol task. For example: emergencies in the road network, the command center receives an alarm, and a certain section of the road network has severe weather.

[0085]The heavy decision-making of the drone flight mission refers to: when an unexpected situation occurs during the flight mission of the drone, and the current flight task cannot be completed, the remaining flight tasks need to be reassigned from the command center to other drones. Will make important decisions about UAV flight missions. .

[0086]It should be noted that, in the embodiment of the present invention, according to the severity and urgency of the traffic incident that affects the UAV's implementation of the traffic plan, expert evaluation and other methods can be used to pre-set three traffic incidents, which are respectively recorded as: Traffic incidents, secondary traffic incidents, and tertiary traffic incidents. Among them, first-level traffic incidents have the highest urgency and need to be executed first, second-level traffic incidents have the second highest urgency, and third-level traffic incidents have the weakest urgency. At the same time, in order to avoid excessive repetitive decision-making, the first period is preset to Tmin , And the second period is T.

[0087]Specifically, judging whether an event triggers a re-decision, including:

[0088]When the drone is conducting traffic patrols, it always detects whether there is a traffic incident.

[0089]For traffic event K, determine the type of traffic event K, including first-level traffic events, second-level traffic events, and third-level traffic events.

[0090]If the traffic event K is a first-level traffic event, it is immediately determined as an event triggering a re-decision, that is, when a traffic event K occurs, it is determined as an event triggering a re-decision. The type of event-triggered decision-making is the type of decision-making corresponding to traffic event K, and the next decision-making event trigger will continue to be judged.

[0091]If the traffic event K is a secondary traffic event, it is determined whether the time interval Δt from the moment when the last event triggers the re-decision to the moment when the traffic event K occurs is greater than or equal to the preset first cycle Tmin; If it is, it is immediately determined that the event triggers the re-decision, the type of the event-triggered re-decision is the type of the re-decision corresponding to the traffic event K, and continues to judge the event trigger of the next re-decision. If not, add the traffic event K to the list of secondary traffic events. At the end of the preset first period, it is determined that the event triggers re-decision, that is, the first period T after the occurrence of traffic event Kmin After the corresponding time, it is determined that the event triggers re-decision; among them, the type of event-triggered re-decision is: the highest-priority re-decision type among all the traffic incidents in the second-level traffic event list; and continue to judge the next time. The decision-making event is triggered.

[0092]In the embodiment of the present invention, the priority of the heavy decision-making type is: heavy decision-making for traffic patrol task>UAV flight missions are critical to decision-making.

[0093]Specifically, the decision-making types corresponding to all traffic events in the second-level traffic event list are summarized, and the decision-making type with the highest trigger priority is determined.

[0094]If the traffic event K is a three-level traffic event, it is determined whether the time interval Δt from the time when the last event triggers the re-decision to the moment when the traffic event K occurs is greater than or equal to the preset first cycle; if it is, it is immediately determined as the event triggers the re-decision , The type of event-triggered decision-making is the type of decision-making corresponding to traffic event K, and the next decision-making event trigger will continue to be judged. If not, add the traffic event K to the three-level traffic event list; at the end of the preset second cycle, it is determined that the event triggers re-decision, that is, after the traffic event K occurs after the time corresponding to the second cycle T, Determined as event-triggered decision-making; among them, the type of event-triggered decision-making is: the decision-making type with the highest priority among all the traffic incidents in the three-level traffic event list; and continue to judge the event trigger of the next decision-making .

[0095]During specific implementation of the embodiment of the present invention, if no traffic incident occurs, it is always determined that no incident triggers a re-decision.

[0096]In the embodiment of the present invention, the first period T can be setmin Is 5s, and the second period T is 8s.

[0097]The first-level traffic incidents include: collision incidents of drones, incidents of unmanned aerial vehicles discovering traffic accidents and insufficient power of drones, new patrol mission incidents and incidents of mechanical failure of the drone itself.

[0098]When a UAV collision event occurs, the UAV flight mission is triggered to make a re-decision.

[0099]When the drone detects a traffic accident and the drone battery is insufficient, it triggers the traffic patrol task to make a heavy decision.

[0100]When a new patrol task event occurs, the traffic patrol task is triggered to make a heavy decision.

[0101]When the unmanned aerial vehicle's own mechanical failure event occurs, the drone's flight mission is triggered to make a re-decision.

[0102]Secondary traffic incidents include: incidents where the drone detects a traffic accident and the drone has sufficient power, and a new flight mission incident.

[0103]When a UAV discovers a traffic accident and the UAV has sufficient power, it triggers the UAV flight mission to re-decision.

[0104]When a new flight task event occurs, the UAV flight task is triggered to make a re-decision.

[0105]Three-level traffic incidents include: UAV encounters severe weather events.

[0106]When a UAV encounters a severe weather event, the traffic patrol task is triggered to make a heavy decision.

[0107]Based on the associated UAV traffic patrol data, it is judged whether the reasoning triggers heavy decision-making, including:

[0108]S321. Start timing from the time when the reasoning triggers the re-decision last time, and determine whether the timing time Δt is greater than or equal to the first period, and if not, wait.

[0109]If yes, then:

[0110]S322. Search the preset case library to confirm each case in the case library; compare the associated UAV traffic patrol data with each case, and calculate the associated UAV traffic patrol data and cases The global similarity of the library; determine whether the global similarity exceeds the preset global similarity threshold, if so, it is determined that case reasoning triggers heavy decision-making, and the case reasoning score is calculated.

[0111]In the embodiment of the present invention, the case library includes several pre-acquired cases and the heavy decision type corresponding to each case. Specifically, the cases in the case library can be obtained based on the mission records processed in the past, or they can be manually written by the traffic command center. After each re-decision is completed, the case library will also be based on the situation of the re-decision. Update.

[0112]Specifically, the method for obtaining the global similarity includes:

[0113]S3221, extract characteristic attributes of the associated UAV traffic patrol data.

[0114]Specifically, there are two types of attributes: one is to determine symbol attributes, which are usually used in situations where attribute values ​​are discrete. The second is to determine the attributes of numbers. The difference between the attributes of numbers can be reflected by the distance between points.

[0115]S3222, search the preset case library, and confirm each case in the case library.

[0116]S3223. Compare the extracted feature attributes with each case, and calculate the attribute similarity. The calculation method of attribute similarity is as follows:

[0117]Determine symbol attributes:

[0118]

[0119] Indicates the i-th characteristic attribute of the problem case, Represents the i-th characteristic attribute of the j-th source case in the case library, Represents the similarity between the i-th feature attribute of the problem case and the i-th feature attribute of the j-th source case. When the value of the problem case attribute is equal to the value of the source case attribute, then In other cases

[0120]Determine the number of attributes:

[0121]

[0122] Represents the Euclidean distance between the i-th feature attribute of the j-th source case in the case library and the i-th feature attribute of the problem case, ziIndicates the value range of the i-th characteristic attribute.

[0123]S3224. Calculate a global similarity based on the attribute similarity.

[0124]When calculating the global similarity, it can only be calculated based on the common attributes. Therefore, the weight of each common attribute needs to be re-normalized. The calculation method is as follows:

[0125]

[0126]In the above formula, the global similarity is determined by Ssim (Q, C) represents, where Q represents the feature attribute set of the problem case, C represents the feature attribute set of the source case, m is the number of feature attributes in the intersection of Q and C, ωiRepresents the weight of the i-th feature attribute in the intersection of Q and C, WQ∩CRepresents the sum of the weights of all feature attributes in the intersection of Q and C.

[0127]It is determined whether the global similarity exceeds a preset global similarity threshold, and if so, it is determined that case reasoning triggers a re-decision.

[0128]When the global similarity exceeds the preset global similarity threshold, it is determined that case reasoning triggers heavy decision-making. And extract the case with the greatest attribute similarity, and use the type corresponding to the case as the decision-making type of case-based reasoning.

[0129]In the embodiment of the present invention, the threshold value is set to 0.8.

[0130]In the embodiment of the present invention, the case-based reasoning score refers to the product of the global similarity and the weight coefficient of the corresponding type of case-based reasoning decision-making.

[0131]Specifically, the two types of heavy decision-making are assigned weight coefficients in advance: traffic patrol mission heavy decision is a, UAV flight mission heavy decision is b, and a>b. For example, it can be: the heavy decision of traffic patrol mission is 2, and the heavy decision of drone flight mission is 1.

[0132]S323: Perform rule reasoning on the associated UAV traffic patrol data, and determine whether the preset rule reasoning condition is met, and if so, it is determined that the rule reasoning triggers heavy decision-making, and the rule reasoning score is calculated.

[0133]Specifically, the preset rule reasoning conditions include:

[0134]Rule 1: The drone's speed is lower than 0.5m/s or higher than 5m/s, at this time, the drone's flight mission is triggered to make a decision.

[0135]Rule 2: When traffic jam occurs, the traffic patrol task is triggered to make heavy decisions.

[0136]Rule 3: The UAV's battery is insufficient. At this time, the UAV flight mission is triggered to make a decision.

[0137]Rule 4: The UAV's power is abnormal, and the UAV's flight mission is triggered to make a decision at this time.

[0138]Rule 5: The UAV's communication signal is unstable. At this time, the UAV's flight mission is triggered to make heavy decisions.

[0139]Rule reasoning score refers to the weight coefficient of the corresponding type of rule reasoning heavy decision.

[0140]S324. Obtain the type of reasoning-triggered decision-making based on the case reasoning score and the rule-based reasoning score, and determine it as reasoning-triggered decision-making; and continue to judge the next decision-making reasoning after waiting for the preset first period of time trigger.

[0141]The embodiment of the present invention performs conflict resolution on case-based reasoning heavy decision-making and rule-based reasoning heavy decision-making, so as to determine the heavy decision-making type of reasoning heavy decision-making. The specific conflict resolution method is:

[0142]Comparing the case-based reasoning score and the rule-based reasoning score, the type of reasoning triggering heavy decision-making is the type corresponding to the high-scoring heavy decision-making.

[0143]When case-based reasoning triggers heavy decision-making, it means that the rule-based reasoning score is 0. At this time, it is judged as the type of heavy-decision decision-making triggered by case-based reasoning.

[0144]When rule-based reasoning triggers heavy decision-making, it means that the case-based reasoning score is 0. At this time, it is judged as the type of heavy-decision decision-making triggered by rule reasoning.

[0145]When case-based reasoning triggers heavy decision-making and rule-based reasoning triggers heavy decision-making, compare the size of case-based reasoning score and rule-based reasoning score. The type of reasoning-triggered decision-making is the type corresponding to the high-scoring decision-making.

[0146]Specifically, when the case-based reasoning score is greater than the rule-based reasoning score, it is determined as the type of case-based reasoning triggering heavy decision-making; when the rule-based reasoning score is greater than or equal to the case-based reasoning score, it is determined as the type of rule-based reasoning triggering heavy decision-making.

[0147]In step S4, conflict resolution processing is performed on the type of event-triggered decision-making and the type of reasoning-triggering decision-making, and the decision-making type of the UAV traffic patrol plan is obtained.

[0148]Specifically, if the type of the event-triggered decision-making is the same as the type of the reasoning-triggered decision-making, it is determined to execute the corresponding decision-making type;

[0149]If the type of the event-triggered decision-making is different from the type of the reasoning-triggered decision-making, the execution is based on a preset priority: the type of the event-triggered decision-making and the type of the reasoning-triggered decision-making have a higher priority The type of heavy decision-making.

[0150]The preset priority is: heavy decision-making for traffic patrol tasks>UAV flight missions are critical to decision-making.

[0151]In step S5, a corresponding type of heavy decision is made on the UAV traffic patrol plan. include:

[0152]A decision-making method is selected based on the preset method library, and the UAV traffic patrol plan is processed, and several decision-making UAV traffic patrol plans are obtained.

[0153]Among them, the heavy decision-making methods of traffic patrol tasks include:

[0154]Genetic algorithm, simulated annealing algorithm, particle swarm algorithm, tabu search algorithm

[0155]The key decisions of drone flight missions include:

[0156]A* algorithm, Dijskra algorithm, D* path search algorithm, ant colony algorithm

[0157]Based on the preset method library, extract a decision-making plan selection method, select a UAV traffic patrol plan after decision-making, and execute it.

[0158]Specifically, the selection method of the heavy decision-making scheme is a selection method, and the specific selection method can adopt the existing technology, which is not limited in this embodiment.

[0159]The embodiment of the present invention further includes: evaluating the execution process of the selected heavy-duty decision-making UAV traffic patrol plan and the heavy-duty decision-making plan; and updating the preset method library according to the evaluation result.

[0160]The embodiment of the present invention also provides an intelligent decision-making system for a UAV traffic patrol scheme. The above-mentioned system includes a computer, and the computer includes:

[0161]At least one storage unit;

[0162]At least one processing unit;

[0163]Wherein, at least one instruction is stored in the at least one storage unit, and the at least one instruction is loaded and executed by the at least one processing unit to implement the following steps:

[0164]Obtain UAV traffic patrol data when the UAV executes the UAV traffic patrol plan; the UAV traffic patrol data includes: UAV data, pilot data and UAV mission data;

[0165]Perform correlation processing on the UAV traffic patrol data;

[0166]Based on the traffic incidents that occurred during UAV traffic patrols and the preset period, judge whether the event triggers the re-decision, and analyze the type of event triggering the re-decision; based on the associated UAV traffic patrol data, judge whether the reasoning triggers the re-decision, and Analysis and reasoning trigger the types of heavy decision-making; among them, the types of heavy decision-making include: heavy decision-making for traffic patrol missions and heavy decision-making for UAV flight missions;

[0167]Perform conflict resolution processing on the type of event-triggered decision-making and the type of reasoning-triggered decision-making, to obtain the type of decision-making for the UAV traffic patrol plan;

[0168]Make corresponding types of heavy decisions on the UAV traffic patrol plan.

[0169]It is understandable that the above-mentioned decision-making system provided by the embodiment of the present invention corresponds to the above-mentioned decision-making method, and the relevant content explanation, examples, beneficial effects and other parts can refer to the intelligent decision-making method of the UAV traffic patrol scheme The corresponding content will not be repeated here.

[0170]In summary, compared with the prior art, it has the following beneficial effects:

[0171]The embodiment of the present invention obtains the UAV traffic patrol data when the UAV is executing the UAV traffic patrol plan; performs the associated processing on the UAV traffic patrol data; based on the traffic incidents and presets that occur during the UAV traffic patrol Cycle to determine whether an event triggers heavy decision-making, and analyze the type of event-triggered heavy-decision decision; determine whether to reason to trigger heavy decision-making based on the associated UAV traffic patrol data, and analyze the type of reasoning-triggered decision-making; among them, the type of heavy decision-making Both include: heavy decision-making for traffic patrol tasks and heavy decision-making for UAV flight missions; conflict resolution processing on the type of event-triggered heavy-decision decision and the type of reasoning-triggered heavy decision-making, to obtain the heavy decision-making type of the UAV traffic patrol plan; The aircraft traffic patrol plan carries out corresponding types of heavy decision-making. The embodiment of the present invention triggers and judges heavy decision-making from both active and passive levels, and resolves conflicts between the two types of heavy decision-making, so as to implement heavy decision-making on the UAV traffic patrol scheme, which can enhance the UAV traffic patrol scheme. Adaptability during execution.

[0172]It should be noted that, through the description of the above implementation manners, those skilled in the art can clearly understand that each implementation manner can be implemented by means of software plus a necessary general hardware platform. Based on this understanding, the above technical solution essentially or the part that contributes to the existing technology can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM/RAM, magnetic A disc, an optical disc, etc., include a number of instructions to make a computer device (which may be a personal computer, a server, or a network device, etc.) execute the methods described in each embodiment or some parts of the embodiment. In the instructions provided here, a lot of specific details are explained. However, it can be understood that the embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures, and technologies are not shown in detail, so as not to obscure the understanding of this specification.

[0173]In this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such existence between these entities or operations. The actual relationship or order. Moreover, the terms "include", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes those that are not explicitly listed Other elements of, or also include elements inherent to this process, method, article or equipment. If there are no more restrictions, the element defined by the sentence "including a..." does not exclude the existence of other identical elements in the process, method, article, or equipment that includes the element.

[0174]The above embodiments are only used to illustrate the technical solutions of the present invention, but not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, a person of ordinary skill in the art should understand that: The recorded technical solutions are modified, or some of the technical features are equivalently replaced; these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.