Unmanned aerial vehicle processing method and device for power protection area and computer equipment

Abstract

The invention relates to an unmanned aerial vehicle processing method and device for an electric power protection area and computer equipment. The method comprises the following steps: detecting an unmanned aerial vehicle moving to a power protection area through non-contact detection equipment, and obtaining identification identity information and flight target information of a target unmanned aerial vehicle; the non-contact detection equipment is arranged on a power equipment check point in a power protection area; identifying the type of the target unmanned aerial vehicle based on the identification identity information and the flight target information of the target unmanned aerial vehicle; and if the type of the target unmanned aerial vehicle is a non-electric unmanned aerial vehicle, information collection and expelling are performed on the non-electric unmanned aerial vehicle through the electric unmanned aerial vehicle in the electric protection area. By adopting the method, the power unmanned aerial vehicle and the non-power unmanned aerial vehicle can be accurately identified, and the power equipment abnormity identification process is met.

Description

Technical Field

[0001] This application relates to the field of anomaly detection technology, and in particular to a method, apparatus and computer equipment for handling unmanned aerial vehicles (UAVs) in power protection areas. Background Technology

[0002] With the rapid development of drone technology, monitoring the operational status of power equipment in power protection zones using power drones can promptly detect anomalies and reduce the impact on the power system. However, this process may also result in non-power drones intruding into the power protection zone, which could affect anomaly detection.

[0003] The relevant technologies mainly involve detecting drones in key power protection areas. Once a drone is detected in a key power protection area, it can be countered or driven away.

[0004] However, the methods of related technologies cannot accurately distinguish between electric and non-electric drones, and cannot meet the requirements of the power equipment anomaly identification process. Summary of the Invention

[0005] Based on this, it is necessary to provide a method, apparatus, and computer equipment for processing drones in power protection areas to address the aforementioned technical problems. This method can accurately identify power-related drones and non-power-related drones, thus meeting the requirements of the power equipment anomaly identification process.

[0006] Firstly, this application provides a method for handling unmanned aerial vehicles (UAVs) in power protection zones, including:

[0007] The non-contact detection equipment is used to detect drones moving into the power protection area and obtain the identification information and flight target information of the target drones; the non-contact detection equipment is set up at the power equipment inspection points in the power protection area.

[0008] Based on the target drone's identification information and flight target information, the type of the target drone is identified;

[0009] If the target drone is a non-electric drone, information can be collected and the non-electric drone can be driven away by electric drones in the power protection zone.

[0010] In one embodiment, the type of the target drone is identified based on the target drone's identification information and flight target information, including:

[0011] The identification information and flight target information of the target drone are verified to obtain the identity verification result and the target verification result;

[0012] If both the identity verification result and the target verification result pass, the target drone type is determined to be an electric drone;

[0013] If at least one of the authentication results and the target authentication result fails, the target drone is determined to be a non-electric drone.

[0014] In one embodiment, the identification information and flight target information of the target drone are verified to obtain an identity verification result and a target verification result, including:

[0015] The identification information of the target drone is matched with the identification information of all electric drones in the database;

[0016] If the identification information of the target drone matches the identification information of any electric drone in the database, the identity verification result is determined to be successful.

[0017] If the flight target information is successfully matched with the target information of the matched electric drone, the target verification result is determined to be successful.

[0018] In one embodiment, the process of obtaining the identity information of the electric drone from the database includes:

[0019] For any given electric drone, its basic information is encrypted using a first-level encryption process based on the number of times it has been used, resulting in first-level encrypted information.

[0020] The primary encrypted information is then subjected to secondary encryption based on the current startup time of the power drone, resulting in secondary encrypted information.

[0021] The secondary encryption information is written to a designated location in a preset specification image, and the hash value of the preset specification image is used as the identity information of the electric drone.

[0022] In one embodiment, a non-contact detection device is used to detect a drone moving into a power protection area, and to obtain the target drone's identification information and flight target information, including:

[0023] Control non-contact detection equipment to send detection signals to the power protection area;

[0024] Based on the drone response signals received by non-contact detection equipment, the identification information and flight target information of the target drone are analyzed.

[0025] In one embodiment, information collection and dispersal of non-power drones using power-protected drones within a power protection zone includes:

[0026] Calculate the distance between each powered drone and a non-powered drone within the power protection zone;

[0027] Send control commands to the closest electric drone; the control commands are used to instruct the closest electric drone to collect information and drive away non-electric drones.

[0028] In one embodiment, the method further includes:

[0029] If the target drone is an electric drone, identify its functional type based on its identification information.

[0030] If the function type of the power drone is a power inspection drone, the operating status of power equipment in the power protection area can be determined based on the inspection information collected by the power inspection drone.

[0031] If the function type of the power drone is a power reconnaissance drone, the security risks of the power protection area can be determined based on the reconnaissance information collected by the power reconnaissance drone.

[0032] In one embodiment, the method further includes:

[0033] Given that the two target drones within a preset time range are a power line inspection drone and a power line reconnaissance drone, risk prediction is performed on the power protection area based on the inspection information collected by the power line inspection drone and the reconnaissance information collected by the power line reconnaissance drone, and the risk prediction result of the power protection area is obtained.

[0034] Secondly, this application also provides a drone processing device for a power protection area, comprising:

[0035] The acquisition module is used to detect drones moving into the power protection area using non-contact detection equipment, and to acquire the identification information and flight target information of the target drones; the non-contact detection equipment is set up at the power equipment inspection points in the power protection area;

[0036] The identification module is used to identify the type of the target drone based on its identification identity information and flight target information.

[0037] The processing module is used to collect information and drive away non-electric drones when the target drone is a non-electric drone, by using electric drones in the power protection zone.

[0038] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement any embodiment of the unmanned aerial vehicle (UAV) processing method for the power protection zone described in the first aspect above.

[0039] Fourthly, this application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements any embodiment of the unmanned aerial vehicle (UAV) processing method for the power protection zone described in the first aspect above.

[0040] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements any embodiment of the unmanned aerial vehicle (UAV) processing method for the power protection zone described in the first aspect above.

[0041] The aforementioned method, apparatus, and computer equipment for handling drones in power protection zones utilize non-contact detection devices to detect drones moving into the power protection zone, acquiring the target drone's identification and flight target information. These non-contact detection devices are installed at power equipment checkpoints within the power protection zone. Based on the target drone's identification and flight target information, the type of the target drone is identified. If the target drone is a non-power drone, information is collected and the non-power drone is driven away using power drones within the power protection zone. This method, by deploying non-contact detection devices at power equipment checkpoints within the power protection zone, achieves accurate detection, identification, and flight target information capture of drones intruding into the power protection zone. This allows for accurate differentiation between power drones and non-power drones. Furthermore, by leveraging power drones within the area to conduct targeted information collection and drive away of non-power drones, it avoids the inefficiency and safety risks of manual inspections and significantly improves the speed and accuracy of the power protection zone's response to illegal drones, meeting the requirements of power equipment anomaly identification. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 This is an application environment diagram of a drone processing method for a power protection area in one embodiment;

[0044] Figure 2 This is a flowchart illustrating a method for handling drones in a power protection zone, as shown in one embodiment.

[0045] Figure 3 This is a flowchart illustrating a method for handling drones in a power protection zone, as shown in one embodiment.

[0046] Figure 4 This is a flowchart illustrating a method for handling drones in a power protection zone, as shown in one embodiment.

[0047] Figure 5 This is a flowchart illustrating a method for handling drones in a power protection zone, as shown in one embodiment.

[0048] Figure 6 This is a flowchart illustrating a method for handling drones in a power protection zone, as shown in one embodiment.

[0049] Figure 7 This is a flowchart illustrating a method for handling drones in a power protection zone, as shown in one embodiment.

[0050] Figure 8 This is a flowchart illustrating a method for handling drones in a power protection zone, as shown in one embodiment.

[0051] Figure 9 This is a flowchart illustrating a method for handling drones in a power protection zone, as shown in one embodiment.

[0052] Figure 10 This is a structural block diagram of a drone processing device in a power protection area according to one embodiment. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0054] Before providing a detailed description of the technical solution of this application, a brief explanation of the background technology of this application will be given first.

[0055] With the rapid development of drone technology, its application in production activities and safety management is becoming increasingly widespread. In power grid inspection, drones are used to monitor the operational status of power equipment, promptly identifying any anomalies and reducing the impact on the power system. However, due to the increasing number of drones, a one-size-fits-all approach is often used in key areas. This involves installing drone detection and countermeasures equipment, initiating countermeasures as soon as a drone is detected. This approach is not only unfriendly to drones operating on the power grid, but also lacks effective evidence collection and retention.

[0056] To address the aforementioned issues, this application provides a method, apparatus, and computer device for processing drones in power protection zones, capable of accurately identifying power-related drones and non-power-related drones, thus fulfilling the requirements for power equipment anomaly identification. The technical solution of this application will now be described in detail.

[0057] The UAV processing method for power protection areas provided in this application embodiment can be applied to, for example... Figure 1 The application environment shown is illustrated. For example, the computer device can be a server, personal computer, laptop, smartphone, tablet, mobile phone, etc. The computer device may include a processor, memory, and network interface connected via a system bus or wirelessly. The processor provides computing and control capabilities. The memory may include non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores data from the drone processing in the power protection area. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements a drone processing method for the power protection area. The computer device can be implemented using a standalone computer or a cluster of multiple computer devices. It should be noted that the memory of the computer device is not limited to the above-mentioned memory and may also include high-speed random access memory, volatile solid-state memory, etc. Furthermore, the architecture of the computer device is not limited to the above-described cases; some components may be added or omitted.

[0058] In one exemplary embodiment, such as Figure 2 As shown, a method for handling unmanned aerial vehicles (UAVs) in power protection zones is provided, which can be applied to... Figure 1 Taking a computer device as an example, the explanation includes the following steps S101 to S103. Wherein:

[0059] S101 uses non-contact detection equipment to detect drones moving into the power protection area and obtain the identification information and flight target information of the target drones; the non-contact detection equipment is set up at the power equipment inspection point in the power protection area.

[0060] A power protection zone refers to a specific protected area around or within a power facility to ensure the safe and stable operation of the power system, prevent external damage, and ensure public safety. Non-contact detection equipment is installed at power equipment inspection points within the power protection zone. A power protection zone may include multiple power equipment inspection points, each equipped with a non-contact detection device. Alternatively, a power protection zone may include only one power equipment inspection point, with a non-contact detection device installed at that point.

[0061] In this embodiment, a computer device can control a non-contact detection device to send detection signals to a power protection area according to a preset time period. When a new drone is present in the power protection area, the non-contact detection device receives the drone's response signal and sends it to the computer device. The computer device can determine the target drone's identification information and flight target information by analyzing the drone's response signal. The identification information may include the drone's serial number, Media Access Control (MAC) address, manufacturer code, and other inherent identification information.

[0062] Alternatively, non-contact detection equipment can collect image data of the power protection area at a preset cycle and send the collected image data to a computer. The computer uses a target detection algorithm to detect the image data and determine whether a drone exists in the image data. If it exists, and the drone has recently moved into the power protection area, it is identified as the target drone. The computer then uses this image information to obtain the target drone's identification information and flight target information.

[0063] S102 identifies the type of target drone based on its identification information and flight target information.

[0064] The target drone can be either an electric drone or a non-electric drone.

[0065] In this embodiment, after obtaining the identification information and flight target information of the target drone, the computer device can directly input the identification information and flight target information of the target drone into a preset drone type recognition model. The drone type recognition model extracts feature information from the identification information and flight target information, and identifies the type of the target drone based on this feature information. Alternatively, the computer device can match the identification information of the target drone with the identification information of all electric drones in the database, and match the flight target information of the target drone with the flight target information of all electric drones in the database. If a matching electric drone is found, the type of the target drone is determined to be an electric drone; otherwise, the type of the target drone is determined to be a non-electric drone.

[0066] S103, if the target drone is a non-electric drone, information is collected and the non-electric drone is driven away by electric drones in the power protection zone.

[0067] In this embodiment, if the target drone is determined to be a non-electric drone, it is necessary to collect evidence and drive it away. A computer device can control any electric drone within the power protection zone to collect information about the non-electric drone and drive it away by outputting a warning voice message. Alternatively, the computer device can also select the electric drone closest to the non-electric drone from all electric drones within the power protection zone and control that electric drone to collect information and drive away the non-electric drone.

[0068] In the aforementioned method for handling drones in power protection zones, non-contact detection equipment is used to detect drones moving into the power protection zone, obtaining the target drone's identification information and flight target information. This non-contact detection equipment is installed at power equipment checkpoints within the power protection zone. Based on the target drone's identification information and flight target information, the type of the target drone is identified. If the target drone is a non-power drone, information is collected and the non-power drone is driven away using power drones within the power protection zone. This method, by deploying non-contact detection equipment at power equipment checkpoints within the power protection zone, achieves accurate detection, identification information, and flight target information capture of drones intruding into the power protection zone. This allows for accurate differentiation between power drones and non-power drones. Furthermore, by utilizing power drones within the area, targeted information collection and removal of non-power drones are carried out. This avoids the inefficiency and safety risks of manual inspections and significantly improves the speed and accuracy of the response to illegal drones in power protection zones, meeting the requirements of power equipment anomaly identification.

[0069] In one embodiment, such as Figure 3 As shown, the specific content of identifying the type of target drone based on the target drone's identification information and flight target information includes:

[0070] S201 verifies the identification information and flight target information of the target UAV, and obtains the identity verification result and the target verification result.

[0071] In this embodiment, the computer device can match the identification information of the target drone with the identification information of each electric drone in the database. If a match is successful, the identity verification result is determined to be successful. Simultaneously, the computer device can also match the flight target information of the target drone with the target information of each electric drone in the database. If a match is successful, the target verification result is determined to be successful.

[0072] Alternatively, after the identity information is matched, the computer device can match the flight target information with the target information of the successfully matched electric drone. If the match is successful, the target verification result is determined to be verified.

[0073] S202, If both the identity verification result and the target verification result are verified, then the type of the target drone is determined to be an electric drone.

[0074] In this embodiment of the application, if both the identity verification result and the target verification result pass the verification, it means that the electric drones corresponding to the two matching processes are the same electric drone. In this case, it is highly likely that the target drone is an electric drone, and the type of the target drone can be directly determined to be an electric drone.

[0075] S203, if at least one of the authentication results and the target authentication results fails, the target drone type is determined to be a non-electric drone.

[0076] In this embodiment of the application, if at least one of the identity verification result and the target verification result fails, it means that the electric drones corresponding to the two matching processes may not be the same electric drone, or there may be no successfully matched identity information or no successfully matched target information. In this case, it indicates that the probability of the target drone being an electric drone is small, and the type of the target drone can be directly determined to be a non-electric drone.

[0077] In the aforementioned method for handling drones within power protection zones, the identification information and flight target information of the target drone are verified to obtain an identity verification result and a target verification result. If both the identity verification result and the target verification result pass, the target drone is determined to be a power-powered drone. If at least one of the identity verification result and the target verification result fails, the target drone is determined to be a non-power-powered drone. This method significantly improves the accuracy and rigor of drone type determination by performing dual verification of the target drone's identification information and flight target information, effectively avoiding misjudgments caused by a single verification dimension.

[0078] The following example will detail the verification of the target drone's identification information and flight target information to obtain the identity verification result and target verification result. Figure 4 As shown, it includes:

[0079] S301 matches the identification information of the target drone with the identification information of all electric drones in the database.

[0080] In this embodiment of the application, the computer device can calculate the similarity between the identification information of the target drone and the identification information of all electric drones in the database. If any similarity is greater than a preset similarity threshold, it is determined that the identification information of the target drone and the identification information of the electric drone are successfully matched.

[0081] S302, if the identification information of the target drone matches the identification information of any electric drone in the database, the identity verification result is determined to be successful.

[0082] In this embodiment of the application, when it is determined that the identification information of the target drone matches the identification information of any electric drone in the database, it indicates that the probability of the target drone being an electric drone is relatively high, and the identity verification result of the target drone can be determined as successful.

[0083] S303 If the flight target information is successfully matched with the target information of the matched electric drone, the target verification result is determined to be verified successfully.

[0084] In this embodiment of the application, to further verify whether the target drone is an electric drone, it is necessary to calculate the similarity between the flight target information of the target drone and the target information of the successfully matched electric drone. If the similarity between the two is greater than a preset threshold, it is determined that the two are successfully matched, and the target verification result can be determined as verification passed.

[0085] In the aforementioned method for handling drones within power protection zones, the identification information of the target drone is matched against the identification information of all power-related drones in the database. If the identification information of the target drone successfully matches the identification information of any power-related drone in the database, the identity verification result is determined to be successful. Similarly, if the flight target information is successfully matched against the target information of the matched power-related drone, the target verification result is determined to be successful. This method first performs a precise match between the identification information of the target drone and the identification information of power-related drones in the database. Then, it performs a secondary matching of the flight target information for the successfully matched power-related drones. Through this dual verification logic, it quickly identifies the range of legitimate power-related drones by matching the database identification information, and further eliminates anomalies such as identity fraud and illegal modification through flight target information verification, significantly improving the accuracy of identity verification and target verification.

[0086] The above embodiments all describe the process of determining the type of target drone, which requires the use of the identity information of electric drones in the database. The following embodiment will then illustrate the use of the identity information of electric drones in the database. Figure 5 As shown, the process of obtaining the identity information of electric drones in this database includes:

[0087] S401: For any given electric drone, perform first-level encryption on the basic information of the electric drone according to the number of times it has been used, and obtain first-level encrypted information.

[0088] In this embodiment, a first-level encryption can be achieved by multiplying the numerical part of the device model number of the electric drone by the number of uses, and adding the alphabetical part to the number of uses sequentially. The computer device can then multiply the number of uses and the basic information of the electric drone according to the encryption logic of the first-level encryption to obtain the first-level encrypted information. For example, if the device model in the basic information is fly305 and the number of uses is 3, the corresponding first-level encrypted information is iob915, where 'y' is in the sequence + 3, and the sequence can be looped to 'b' according to the alphabet. Alternatively, the alphabet can be custom-made to further enhance the encryption strength.

[0089] S402, according to the current start time of the power drone, the primary encrypted information is processed into secondary encrypted information.

[0090] In this embodiment, secondary encryption can directly add the current startup time to the primary encryption information. This is because the drone startup time is not the same as the time the drone leaves the drone bay, making it more concealed and impossible to observe directly. In other words, the computer device can add the current startup time of the electric drone to the primary encryption information to obtain secondary encryption information. For example, if the current startup time is 9:30:15, the corresponding secondary encryption information is iob915-093015.

[0091] S403, writes secondary encryption information to a designated location in a preset specification image, and uses the hash value of the preset specification image as the identity information of the electric drone.

[0092] In this embodiment, after obtaining the secondary encryption information, the computer device can write the secondary encryption information into a designated location of a preset-specification image, and then determine the hash value of the preset-specification image through hash calculation to obtain the identity information of the power drone. For example, the secondary encryption information iob915-093015 can be embedded in a rectangular blank image of size 900*900 in a pixel-based correspondence manner. 'i' can correspond to 30 black pixels, 'o' can correspond to 40 white pixels, the first '9' corresponds to 9 black pixels, the second '9' corresponds to 9 white pixels, and so on. The correspondence between characters and pixel counts is preset in advance. The alternating black and white pixels constitute the information embedded in the image. The designated embedding location can be temporarily and randomly changed before each deployment. Thus, any slight change in the image will lead to a change in the hash value, which can fully guarantee the identification key's ability to resist cracking.

[0093] In the aforementioned method for handling drones within power protection zones, for any given power drone, its basic information is first-level encrypted based on the number of times it has been used, resulting in first-level encrypted information. Then, the first-level encrypted information is second-level encrypted based on the drone's current startup time, resulting in second-level encrypted information. This second-level encrypted information is then written into a designated location within a pre-defined image, and the hash value of the image is used as the drone's identity information. This method processes the drone's basic information using a dual dynamic encryption approach, embedding the second-level encrypted information into a designated location within a pre-defined image, using the image's hash value as its unique identity information, and leveraging dynamic encryption factors to achieve real-time dynamic updates of the identity information. This fundamentally prevents the risk of identity information being stolen, tampered with, or misused.

[0094] In one embodiment, such as Figure 6 As shown, the specific content of obtaining the identification information and flight target information of the target drone by detecting the drone moving into the power protection area through the above-mentioned non-contact detection equipment includes:

[0095] S501 controls non-contact detection equipment to send detection signals to the power protection area.

[0096] In this embodiment of the application, the computer device can send control commands to the non-contact detection device according to a preset detection cycle. The control commands are used to instruct the non-contact detection device to send detection signals to the power protection area.

[0097] S502 analyzes the target drone's identification information and flight target information based on the drone response signals received by non-contact detection equipment.

[0098] In this embodiment, when a new drone is present in the power protection zone, the new drone will send a drone response signal to the non-contact detection device. The non-contact detection device will then send the drone response signal to a computer device, which can analyze the drone response signal to obtain the target drone's identification information and flight target information.

[0099] In the aforementioned method for handling drones within power protection zones, a non-contact detection device is controlled to send detection signals to the power protection zone. Based on the drone response signals received by the non-contact detection device, the identification information and flight target information of the target drone are analyzed. This method actively sends detection signals by controlling a non-contact detection device within the power protection zone, and then analyzes the identification information and flight target information based on the response signals fed back by the drone. This achieves proactive and non-contact information collection from drones, avoiding the response delay and incomplete coverage problems caused by traditional detection methods that rely on passive signal reception, and also improving the real-time performance and completeness of information collection.

[0100] The following example will be used to describe in detail the above-mentioned method of using power-protected drones in power protection zones to collect information and drive away non-power-protected drones. Figure 7 As shown, the content includes:

[0101] S601 calculates the distance between each power-enabled drone and a non-power-enabled drone within the power protection zone.

[0102] In this embodiment, the computer device can send a location acquisition command to each power-powered drone within the power protection area. Upon receiving the command, each power-powered drone feeds back its own location information to the computer device. Simultaneously, the computer device can predict the location of non-powered drones based on flight target information collected by non-contact detection equipment. Then, it calculates the position difference information between each non-powered drone and each power-powered drone, and uses this position difference information as the distance.

[0103] S602 sends control commands to the closest electric drone; the control commands are used to instruct the closest electric drone to collect information and drive away non-electric drones.

[0104] In this embodiment, after obtaining the distances between all electric drones and non-electric drones, the computer device can select the electric drone with the shortest distance from all drones and send a control command to that electric drone. Upon receiving the control command, the electric drone collects information from the non-electric drones and then drives them away after the information collection is complete.

[0105] In the aforementioned method for handling drones within power protection zones, the distance between each powered drone and a non-powered drone within the power protection zone is calculated. A control command is then sent to the powered drone with the shortest distance. This control command instructs the powered drone with the shortest distance to collect information and drive away the non-powered drone. By calculating the real-time distance between each powered drone and a non-powered drone within the power protection zone, and prioritizing sending control commands to the powered drone with the shortest distance, this method instructs it to perform information collection and drive-away operations. This optimal distance selection enables rapid response and significantly reduces the time non-powered drones remain illegally.

[0106] In one embodiment, such as Figure 8 As shown, the method also includes:

[0107] S701, if the target drone is an electric drone, identify the functional type of the electric drone based on the identification information of the electric drone.

[0108] Among them, power industry drones include power inspection drones and power reconnaissance drones. Since many forest fires are related to power equipment failures, power reconnaissance drones can analyze images and infrared data of the area for early warning during high-altitude reconnaissance. Power inspection drones mainly assess the heating status and insulation terminal damage of equipment; therefore, they fly at a lower altitude (30-50 meters) to get relatively close to the equipment. Power reconnaissance drones are primarily tasked with environmental threat perception, identifying safety risks such as wildfires and foreign object intrusion in the area; therefore, they fly at a higher altitude (80-120 meters) to conduct large-scale monitoring.

[0109] In this embodiment of the application, when the target drone is determined to be a power drone, the computer device can obtain the types of power drones in the database that match the identification information of the power drone, and use the type of the power drone as the functional type of the power drone. Alternatively, the computer device can match the identification information of the power drone with the information of power inspection drones and power reconnaissance drones respectively, and use the drone type with the higher similarity as the functional type of the power drone.

[0110] S702, if the function type of the power drone is a power inspection drone, the operating status of power equipment in the power protection area is determined based on the inspection information collected by the power inspection drone.

[0111] The pre-installed detection equipment group for the power line inspection drone includes an infrared thermal imager and an ultraviolet corona detector.

[0112] In this embodiment, when the function type of the power drone is determined to be a power inspection drone, the computer equipment does not need to intervene. At this time, the power inspection drone, through its inspection process, can collect data on the drone's load percentage, temperature, and electrical discharge sparks, and transmit the collected data to the computer equipment. Through analysis of this data, the computer equipment can accurately determine the operating status of the power equipment within the power protection zone.

[0113] It should be noted that the equipment temperature data and equipment discharge spark data are obtained through the infrared thermal imager and ultraviolet corona detector integrated on the power inspection drone, and the equipment load percentage is obtained through communication between the drone and the power system.

[0114] Understandably, non-contact detection devices can also be installed on power inspection drones within power protection zones. Specifically, Near Field Communication (NFC) identification chips can be pre-installed on these drones, enabling long-range identification via non-contact detection. The identification process includes: receiving the power inspection drone's response to the detection signal in real time; identifying the type of target drone based on the response signal; quickly determining whether the target drone is a power-related drone; and facilitating efficient subsequent removal and evidence collection. If identification is successful, no removal is required; otherwise, a power reconnaissance drone is directly invoked to collect evidence against the target drone (such as its actions that damage the power grid or pose a potential risk of damage). Once evidence is collected, the drone is removed.

[0115] S703, if the function type of the power drone is a power reconnaissance drone, the security risks of the power protection area are determined based on the reconnaissance information collected by the power reconnaissance drone.

[0116] The pre-installed detection equipment group of the power reconnaissance drone includes an infrared thermal imager, an ultraviolet corona detector, a multispectral imager, and a sensor group. The infrared thermal imager mainly acquires temperature data of the power equipment; the ultraviolet corona detector mainly acquires discharge data of the power equipment; high-temperature areas within the region can be monitored through infrared image information, and environmental information can be obtained directly by the drone or through communication with the meteorological center.

[0117] In this embodiment, when the function type of the power reconnaissance drone is determined to be a power reconnaissance drone, the computer equipment does not need to interfere. At this time, the power reconnaissance drone, through the reconnaissance process, can acquire infrared image information and environmental information within the area, and transmit the collected infrared image information and environmental information to the computer equipment. For example, environmental information may include ambient temperature, ambient humidity, vegetation dryness, and wind speed.

[0118] Computer equipment can monitor regional fire risks based on infrared image information and environmental information within the area. When the monitoring result indicates a high risk, it will notify the fire department to coordinate. When the monitoring result indicates a low risk, it will predict and analyze regional fire risks based on the operating status information of electrical equipment within the area and environmental information, and adjust the inspection strategy according to the prediction and analysis results.

[0119] In the above-mentioned method for handling drones in power protection areas, if the target drone is a power drone, the functional type of the power drone is identified based on its identification information; if the functional type of the power drone is a power inspection drone, the operating status of the power equipment in the power protection area is determined based on the inspection information collected by the power inspection drone; if the functional type of the power drone is a power reconnaissance drone, the safety risks of the power protection area are determined based on the reconnaissance information collected by the power reconnaissance drone.

[0120] Based on this, when drones, including power inspection drones and power reconnaissance drones, enter the power protection zone within a preset time range, the computer equipment can simultaneously acquire infrared imagery and environmental information collected by the power reconnaissance drone, and equipment load percentage, equipment temperature data, and equipment discharge spark data collected by the power inspection drone. In the process of assessing risks in the power protection zone, a single data point can affect the accuracy of anomaly detection. Therefore, information collected by both types of drones can be combined to improve the accuracy of anomaly detection results.

[0121] Therefore, in one embodiment, the process of risk prediction for power protection zones includes:

[0122] Given that the two target drones within a preset time range are a power line inspection drone and a power line reconnaissance drone, risk prediction is performed on the power protection area based on the inspection information collected by the power line inspection drone and the reconnaissance information collected by the power line reconnaissance drone, and the risk prediction result of the power protection area is obtained.

[0123] Assuming the inspection information collected by power line inspection drones includes equipment temperature data and electrical discharge spark data, and the reconnaissance information collected by power line reconnaissance drones includes vegetation dryness and real-time ambient temperature, humidity, and wind speed in the power protection area, then the specific content includes:

[0124] S11: Based on the equipment temperature data and equipment discharge spark data of the power equipment within the power protection area, obtain the real-time operating risk value of the power equipment; and based on the real-time operating risk value and the real-time load data of the power equipment, obtain the dynamic operating risk curve R1(t) of the power equipment.

[0125] S12: Based on the vegetation dryness and real-time ambient temperature, humidity and wind speed in the power protection area, obtain the dynamic environmental risk curve R2(t) of the power protection area;

[0126] S13: Dimensionlessly calculate and weighted summation of the dynamic operation risk curve R1(t) and the dynamic environmental risk curve R2(t) to obtain the dynamic risk curve R(t); Based on the time interval corresponding to the dynamic risk curve R(t) exceeding the preset threshold, determine the power protection area and perform risk prediction.

[0127] S14: Adjust the inspection strategy based on the risk prediction results of the power protection area.

[0128] The above-mentioned method uses power line inspection drones and power line reconnaissance drones to acquire data for predictive analysis of regional fire risks, which can improve the accuracy of fire risk assessment. At the same time, by adjusting the inspection strategy based on the predictive analysis results, the frequency of drone patrols can be increased during periods of high fire risk. Therefore, it is possible to identify existing fire risks in a timely manner, thereby improving the timeliness of fire assessment.

[0129] In one embodiment, the process of obtaining the real-time operational risk value of the power equipment in step S11 above includes:

[0130] S111: Obtain the area of ​​different temperature steps in the equipment temperature data;

[0131] S112: Obtain the number of discharges and the intensity value of each discharge from the equipment discharge spark data under a fixed detection duration;

[0132] S113: Obtain the real-time operational risk value R1 at the detection time point by running the risk model. The operational risk model is expressed as follows:

[0133]

[0134] Where n is the number of temperature steps divided according to a preset interval, i∈[1,n]; This represents the mean value of the i-th temperature step at the detection time point; The ambient temperature value at the time of detection; Let be the area corresponding to the i-th temperature step; The sum of the areas of all temperature steps; m is the number of discharges occurring within a fixed period before the detection time; j∈[1,m], Let be the intensity value of the j-th discharge; The average intensity value across all discharge cycles; The maximum intensity value across all discharge cycles; u is the proportional coefficient, which is set based on the test data and used to adjust the weight between the maximum and the mean. This is the baseline value for discharge intensity, which is set based on empirical data; These are normalization coefficients used to adjust... The range of values ​​facilitates comprehensive analysis in conjunction with equipment temperature factors, based on the test data. The maximum range of values ​​is set.

[0135] The above-mentioned operational risk model enables the assessment of operational risk status of power equipment in real time by using the acquired operational risk value when the temperature or discharge status of the power equipment is abnormal.

[0136] In one embodiment, the process of obtaining the dynamic risk curve R1(t) in step S11 above includes:

[0137] Through formula The dynamic operational risk curve R1(t) is calculated and obtained. In this formula, The percentage of equipment load at the detection time point; This represents the real-time load percentage of power equipment. This is a load percentage impact comparison function. Calculated using the above formula, the real-time operational risk of power equipment can be inferred from the operational risk value over a period of time and the real-time load percentage of the power equipment. This improves the accuracy of the input data in subsequent judgment processes, thereby enhancing the accuracy of the judgment results.

[0138] In one embodiment, the process of obtaining the dynamic environmental risk curve R2(t) in step S12 above includes:

[0139] S121: Periodically acquire regional vegetation aridity using a multispectral imager;

[0140] S122: Obtain the dynamic environmental risk curve R2(t) through the environmental risk model, which is expressed as follows:

[0141]

[0142] Where dr is the vegetation dryness obtained from the most recent detection at the current time point; This is the real-time temperature value; This is a reference temperature value; This is the real-time humidity value; The reference humidity value is x1, x2 are preset weighting coefficients; This is the real-time wind speed value; This is a reference value for wind speed. Reference temperature value. Reference humidity value and wind force reference value All settings are based on empirical data, and the preset weighting coefficients are set according to the degree of influence of the test data.

[0143] In one embodiment, the process of obtaining regional vegetation aridity in step S121 above includes:

[0144] S1211, Identify vegetation within the region based on a neural network model to obtain the vegetation coverage area;

[0145] Among them, the neural network model can adopt existing image AI recognition models to identify and judge regional buildings, vegetation, bare land, roads, etc.

[0146] S1212 uses a multispectral imager in a power reconnaissance drone to emit visible green light, visible red light, near-infrared light, and short-infrared light into the vegetation-covered area, and obtains the reflectivity of each light.

[0147] Among them, under visible light (400-700nm), the proportion of xanthophyll increases and green light reflectance increases when water decreases. Under near-infrared (NIR, 700-1300nm), water decrease leads to cell wilting and NIR reflectance decreases. Under short-wave infrared (SWIR, 1500-2500nm), water content is positively correlated with absorption depth.

[0148] S1213, through formula The dryness degree dr is calculated.

[0149] Where q is the number of different wavelengths of light emitted; k∈[1,q]; The reflectivity is in the k-th band; This is the aridity function corresponding to the band range of the k-th band, which is set by fitting the measurement data of different aridity plants under different bands in the laboratory. This is a weighting adjustment factor, which is set according to the accuracy adaptability of different bands for different scenarios; the weighting adjustment factor is seasonally adjusted and meets the following requirements. .

[0150] In one embodiment, the process of determining the risk prediction results for the power protection zone in step S13 above includes:

[0151] S131: By using a power reconnaissance drone to monitor the temperature radiation of the area at the monitoring location, real-time temperature radiation images are obtained. The maximum temperature Tm in the real-time temperature radiation images is compared with the first threshold Th1 and the second threshold Th2 respectively.

[0152] The first threshold Th1 and the second threshold Th2 can be set based on empirical data. Threshold Th1 reflects the critical situation when a fire occurs, and Th1 > Th2.

[0153] S132: When Tm > Th1, the monitoring result is judged as high risk and the fire department is notified.

[0154] S133: When Th1≥Tm≥Th2, use power reconnaissance drones to track and analyze the area where Tm≥Th2, and determine whether the monitoring results are high-risk based on the tracking and analysis.

[0155] S134: When Tm < Th2, the monitoring result is judged to be low risk;

[0156] In one embodiment, the tracking analysis process in step S133 above includes:

[0157] S1331: Based on the coordinates of the power reconnaissance UAV, determine the coordinates of the center of the region Tm≥Th2, obtain the current wind strength and direction, and control the power reconnaissance UAV to fly to a position point at the center of the region Tm≥Th2 at a distance L in the wind direction. ,in, It is a fixed distance value, based on The distance between the selected location point for obtaining effective air quality parameters and the center of the region where Tm≥Th2 is set under wind force; w is the current wind speed, which is selected and set within a reasonable range; This is a reference value for wind force.

[0158] S1332: Acquire several sets of air quality parameters using the sensor array on the power reconnaissance drone;

[0159] S1333: Control the power reconnaissance drone to return to the monitoring location and acquire real-time temperature radiation images again;

[0160] S1334: The monitoring risk value is obtained by using several sets of air quality parameters and two sets of real-time temperature and radiation images. The monitoring risk value is used to determine whether the monitoring result is high risk.

[0161] In one embodiment, the calculation process for the monitoring risk value in step S1334 above includes:

[0162] The formula for calculating the monitoring risk value can be expressed as:

[0163]

[0164] Where V is the number of air quality parameters, z∈[1, z]; The z-th air quality monitoring value includes carbon dioxide concentration, carbon monoxide concentration, and PM2.5 concentration. The standard value for the z-th air quality item is set based on the average data of the area under non-fire conditions. Let be the correlation coefficient for the z-th air quality factor. This coefficient is set based on the numerical range and degree of influence of each air quality factor. It should be noted that... The values ​​have corresponding units, and the dimensions of the corresponding air quality parameters can be removed. As a calibration constant, it is set based on the numerical range and degree of influence of two factors obtained from the test data; The distance between the center points of the regions Tm≥Th2 in the two sets of real-time temperature radiation images; The rate is affected by the movement of the solar angle; This refers to the time point when the temperature and radiation images were acquired during the second monitoring. S1 represents the time point at which the temperature radiation image was acquired during the first monitoring; S2 represents the area of ​​the region where Tm ≥ Th2 in the temperature radiation image during the second monitoring; S1 represents the area of ​​the region where Tm ≥ Th2 in the temperature radiation image during the first monitoring. This is a reference value for area error, obtained based on empirical data; it is calculated using the formula... This can reflect the degree to which the environment exceeds normal conditions, through the formula... This method can reflect the location and degree of change in the diffusion state of areas where Tm ≥ Th2, eliminating interference from fixed heat sources such as heat pipes and industrial heat sources, and also reducing interference from sunlight reflection. Fire risk is assessed by comparing the acquired monitoring risk value Rm with a preset threshold Rth, which is set based on relevant data under critical conditions during testing. Therefore, when the monitoring risk value Rm ≥ Rth, the monitoring result is judged as high risk. This process reduces the probability of false judgments and improves the accuracy of fire risk assessment.

[0165] The following detailed embodiment will illustrate the method for handling drones in the aforementioned power protection area. Figure 9 As shown, the method includes:

[0166] S801 controls non-contact detection equipment to send detection signals to the power protection area;

[0167] S802 analyzes the identification information and flight target information of the target drone based on the drone response signal received by the non-contact detection device.

[0168] S803 matches the identification information of the target drone with the identification information of all electric drones in the database;

[0169] S804, if the identification information of the target UAV matches the identification information of any electric UAV in the database, the identity verification result is determined to be successful.

[0170] S805 If the flight target information is successfully matched with the target information of the successfully matched electric drone, the target verification result is determined to be verified and the type of the target drone is determined to be an electric drone.

[0171] S806 identifies the functional type of electric drones based on their identification information.

[0172] S807, If the function type of the power drone is a power inspection drone, the operating status of the power equipment in the power protection area is determined based on the inspection information collected by the power inspection drone.

[0173] S808, If the function type of the power drone is a power reconnaissance drone, the security risks of the power protection area are determined based on the reconnaissance information collected by the power reconnaissance drone;

[0174] S809, if at least one of the authentication result and the target authentication result fails, the target drone type is determined to be a non-electric drone;

[0175] S810 calculates the distance between each power-enabled drone and a non-power-enabled drone within the power protection zone;

[0176] S811 sends control commands to the closest electric drone; the control commands are used to instruct the closest electric drone to collect information and drive away non-electric drones.

[0177] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0178] Based on the same inventive concept, this application also provides a drone processing device for power protection areas to implement the drone processing method for power protection areas described above. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more drone processing device embodiments for power protection areas provided below can be found in the limitations of the drone processing method for power protection areas described above, and will not be repeated here.

[0179] In one exemplary embodiment, such as Figure 10As shown, a drone processing device for a power protection area is provided, comprising: an acquisition module 11, an identification module 12, and a processing module 13, wherein:

[0180] The acquisition module 11 is used to detect drones moving into the power protection area using a non-contact detection device, and to acquire the identification information and flight target information of the target drone; the non-contact detection device is set at the power equipment inspection point in the power protection area;

[0181] The identification module 12 is used to identify the type of the target drone based on the target drone's identification identity information and flight target information;

[0182] The processing module 13 is used to collect information and drive away non-electric drones when the target drone is a non-electric drone, by using electric drones in the power protection zone.

[0183] In an exemplary embodiment, the identification module includes: a verification unit, a first determination unit, and a second determination unit, wherein:

[0184] The verification unit is used to verify the identification information and flight target information of the target UAV, and obtain the identity verification result and the target verification result.

[0185] The first determining unit is used to determine the type of the target drone as an electric drone if both the identity verification result and the target verification result are verified.

[0186] The second determining unit is used to determine that the target drone is a non-electric drone if at least one of the authentication results and the target authentication result fails.

[0187] In an exemplary embodiment, the verification unit is further configured to match the identification information of the target UAV with the identification information of all electric UAVs in the database; if the identification information of the target UAV is successfully matched with the identification information of any electric UAV in the database, the identity verification result is determined to be verified successfully; if the flight target information is successfully matched with the target information of the successfully matched electric UAV, the target verification result is determined to be verified successfully.

[0188] In an exemplary embodiment, the verification unit is further configured to perform first-level encryption on the basic information of any electric drone according to the number of times the electric drone has been used, to obtain first-level encrypted information; perform second-level encryption on the first-level encrypted information according to the current start time of the electric drone, to obtain second-level encrypted information; write the second-level encrypted information into a specified position of a preset specification image, and use the hash value of the preset specification image as the identity information of the electric drone.

[0189] In an exemplary embodiment, the acquisition module includes: a control unit and a parsing unit, wherein:

[0190] The control unit is used to control the non-contact detection equipment to send detection signals to the power protection area;

[0191] The analysis unit is used to analyze the identification information and flight target information of the target drone based on the drone response signal received by the non-contact detection device.

[0192] In an exemplary embodiment, the above-described processing module includes: a calculation unit and a transmission unit, wherein:

[0193] The calculation unit is used to calculate the distance between each power-powered drone and a non-power-powered drone within the power protection zone;

[0194] The transmitting unit is used to send control commands to the electric drone with the shortest distance; the control commands are used to instruct the electric drone with the shortest distance to collect information and drive away non-electric drones.

[0195] In an exemplary embodiment, the UAV processing device for the aforementioned power protection area further includes: a function identification module, a status determination module, and a risk determination module, wherein:

[0196] The function identification module is used to identify the function type of the electric drone based on its identification information when the target drone is an electric drone.

[0197] The status determination module is used to determine the operating status of power equipment in the power protection area based on the inspection information collected by the power inspection drone when the function type of the power drone is a power inspection drone.

[0198] The risk assessment module is used to determine the security risks of power protection areas based on the reconnaissance information collected by the power reconnaissance drone when the function type of the power drone is a power reconnaissance drone.

[0199] In an exemplary embodiment, the drone processing device for the aforementioned power protection area further includes: a risk prediction module, wherein:

[0200] The risk prediction module is used to predict the risk of a power protection area based on the inspection information collected by the power inspection drone and the reconnaissance information collected by the power reconnaissance drone when two target drones are of different types within a preset time range. The module then obtains the risk prediction result for the power protection area.

[0201] Each module in the UAV processing device for the aforementioned power protection area can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0202] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement any one of the above-described methods for handling unmanned aerial vehicles in power protection zones.

[0203] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements any one of the contents of the above-described method for handling unmanned aerial vehicles in the power protection zone.

[0204] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements any one of the above-described methods for handling unmanned aerial vehicles in the power protection zone.

[0205] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0206] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0207] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0208] The above embodiments merely illustrate several implementation methods of this application, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of this application's patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for handling unmanned aerial vehicles (UAVs) in a power protection zone, characterized in that, The method includes: The non-contact detection device detects drones moving into the power protection area and obtains the identification information and flight target information of the target drones; the non-contact detection device is installed at the power equipment inspection point in the power protection area. Based on the identification information and flight target information of the target drone, the type of the target drone is identified; If the target drone is a non-electric drone, information is collected and the non-electric drone is driven away by an electric drone in the power protection zone.

2. The method according to claim 1, characterized in that, The process of identifying the type of the target drone based on its identification information and flight target information includes: The identification information and flight target information of the target UAV are verified to obtain the identity verification result and the target verification result; If both the identity verification result and the target verification result pass the verification, then the type of the target drone is determined to be the electric drone; If at least one of the authentication results and the target authentication results fails, the target drone is determined to be a non-electric drone.

3. The method according to claim 2, characterized in that, The verification of the identification information and flight target information of the target UAV to obtain the identity verification result and the target verification result includes: The identification information of the target drone is matched with the identification information of all electric drones in the database; If the identification information of the target drone matches the identification information of any power drone in the database, then the identity verification result is determined to be successful. If the flight target information is successfully matched with the target information of the matched electric drone, then the target verification result is determined to be verified successfully.

4. The method according to claim 3, characterized in that, The process of obtaining the identity information of the power drone in the database includes: For any given electric drone, its basic information is encrypted using a first-level encryption process based on the number of times the electric drone has been used, resulting in first-level encrypted information. The primary encryption information is then subjected to secondary encryption based on the current startup time of the electric drone to obtain secondary encryption information; The secondary encryption information is written to a designated location in a preset specification image, and the hash value of the preset specification image is used as the identity information of the electric drone.

5. The method according to any one of claims 1-4, characterized in that, The method of detecting drones moving into the power protection area using non-contact detection equipment to obtain the target drone's identification information and flight target information includes: Control the non-contact detection device to send a detection signal to the power protection area; Based on the drone response signal received by the non-contact detection device, the identification information and flight target information of the target drone are analyzed.

6. The method according to any one of claims 1-4, characterized in that, The method of collecting information and driving away non-power drones through power-protected drones in the power protection zone includes: Calculate the distance between each electric drone and the non-electric drone in the power protection zone; Send control commands to the electric drone with the shortest distance; the control commands are used to instruct the electric drone with the shortest distance to collect information and drive away the non-electric drone.

7. The method according to any one of claims 1-4, characterized in that, The method further includes: If the target drone is an electric drone, the functional type of the electric drone is identified based on its identification information. If the function type of the power drone is a power inspection drone, the operating status of the power equipment in the power protection area is determined based on the inspection information collected by the power inspection drone. If the function type of the power drone is a power reconnaissance drone, the security risks of the power protection area are determined based on the reconnaissance information collected by the power reconnaissance drone.

8. The method according to any one of claims 1-4, characterized in that, The method further includes: If, within a preset time range, the two target drones are a power line inspection drone and a power line reconnaissance drone, risk prediction is performed on the power protection area based on the inspection information collected by the power line inspection drone and the reconnaissance information collected by the power line reconnaissance drone, and the risk prediction result of the power protection area is obtained.

9. A drone processing device for a power protection zone, characterized in that, The device includes: The acquisition module is used to detect drones moving into the power protection area using a non-contact detection device, and to acquire the identification information and flight target information of the target drone; the non-contact detection device is installed at the power equipment inspection point in the power protection area; The identification module is used to identify the type of the target drone based on its identification information and flight target information. The processing module is used to collect information on and drive away non-electric drones when the target drone is a non-electric drone, using electric drones in the power protection zone.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

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