Intelligent scheduling method and system based on rule base

Abstract

The present application relates to an intelligent scheduling method and system based on a rule base. The method includes: measuring the reference signal receiving power value of the drone and outputting a signal attenuation spectrum; obtaining the signal attenuation value based on the signal attenuation spectrum and inverting the effective width and effective height of the segment based on the signal attenuation value, and calculating the effective space of the segment based on the segment length, effective width and effective length; adopting the corresponding minimum safety distance based on a preset scenario library; and dynamically adjusting the drone queue based on the minimum safety distance. This method outputs an attenuated signal spectrum through the reference signal receiving power value of the drone, obtains the signal attenuation value based on the attenuated signal spectrum, inverts the effective width and effective height of the segment through the signal attenuation value and calculates the effective space of the segment, and selects the corresponding minimum safety distance based on the scenario of the segment, thereby improving capacity utilization efficiency.

Description

Technical Field

[0001] The present application relates to the technical field of drone scheduling, and in particular to an intelligent scheduling method and system based on a rule base. Background Art

[0002] As time goes by, drones have been used in various fields due to their flexibility and unique aerial perspective. As the number of drones increases, the airspace in which drones fly also needs to be managed and guided to ensure their orderly operation.

[0003] Existing drone airspace management technologies primarily rely on integrated management systems based on fixed route capacity allocation and time-window scheduling. These solutions primarily use static grid division to preset fixed capacity thresholds.

[0004] However, although the static division method simplifies the complexity of airspace management, it fails to fully consider the differences in dynamic traffic capacity between different flight segments, resulting in inefficient capacity utilization. Summary of the Invention

[0005] 1. Problem to be solved

[0006] Based on this, it is necessary to provide a rule-based intelligent scheduling method and system that can improve capacity utilization efficiency in response to the above technical problems.

[0007] 2. Technical solution

[0008] In a first aspect, the present application provides an intelligent scheduling method based on a rule base. The method includes:

[0009] measuring a reference signal received power value of the UAV and outputting a signal attenuation spectrum based on the reference signal received power value;

[0010] Acquire a signal attenuation value based on the signal attenuation map and invert the effective width and effective height of the flight segment based on the signal attenuation value, wherein the effective width is the width of the flight segment that is actually available under the signal attenuation condition, and the effective height is the height of the flight segment that is actually available under the signal attenuation condition;

[0011] Calculate the effective space of the flight segment based on the flight segment length, effective width and effective length;

[0012] Adopting a corresponding minimum safety distance based on a preset scene library, wherein the scene library stores at least scene information and a corresponding minimum safety distance;

[0013] Dynamically adjust the drone queue based on the minimum safe distance.

[0014] In one embodiment, obtaining a signal attenuation value based on a signal attenuation map and inverting an effective width and an effective height of a flight segment based on the signal attenuation value includes:

[0015] Get the preset basic width of the segment;

[0016] Invert the effective width of the segment based on the preset width attenuation coefficient, basic width and signal attenuation value;

[0017] Get the preset base altitude of the flight segment;

[0018] The effective altitude of the flight segment is inverted based on the altitude attenuation coefficient of the flight segment, the basic altitude and the signal attenuation value.

[0019] In one embodiment, adopting the corresponding minimum safety distance based on the preset scenario library includes:

[0020] The scenario library includes at least conservative scenarios, flexible scenarios and partitioned scenarios;

[0021] When the conservative scenario is selected, the minimum safety distance with the largest value among different types of drones in the flight segment is set as the minimum safety distance;

[0022] When the flexible scenario is selected, the corresponding minimum safety interval is calculated based on the ratio of different types of drones in the flight segment and set as the minimum safety distance;

[0023] When the zoning scenario is selected, different types of drones are divided based on different areas and the minimum safety distance of the corresponding drones is set based on the area.

[0024] In one embodiment, dynamically adjusting the drone queue based on the minimum safety distance includes:

[0025] Count the actual distance between drones and count the types of drones;

[0026] Send flow limiting instructions to drones that do not match the drone type in the flight segment and drones whose actual distance value is less than the minimum safety distance;

[0027] The drone queue is dynamically adjusted until the drone type matches the flight segment and the actual distance value of the drone is not less than the minimum safety distance.

[0028] In one embodiment, when a drone is detected to have entered a preset buffer zone, a deceleration instruction and an altitude lock instruction are sent to the drone;

[0029] If the actual speed of the drone is greater than the deceleration speed sent, a drone route adjustment command is sent to the drone;

[0030] If the drone does not respond to the route adjustment command, a forced hovering command is sent to the drone until the drone performs the hovering operation.

[0031] In one embodiment, when the scenario is an emergency scenario, a weighted evaluation model is constructed and a priority ranking queue is generated, where the weight indicators include at least the urgency of the task, the power status, and the range factor;

[0032] Set the task urgency to be proportional to the task waiting time.

[0033] In a second aspect, the present application also provides an intelligent scheduling system based on a rule base. The system includes:

[0034] A signal attenuation spectrum output module is used to measure the reference signal received power value of the UAV and output a signal attenuation spectrum based on the reference signal received power value;

[0035] An effective width and effective height calculation module, configured to obtain a signal attenuation value based on a signal attenuation map and invert the effective width and effective height of the flight segment based on the signal attenuation value. The effective width is the width of the flight segment that is actually available under the signal attenuation condition, and the effective height is the height of the flight segment that is actually available under the signal attenuation condition.

[0036] An effective space calculation module is used to calculate the effective space of a flight segment based on the flight segment length, effective width and effective length;

[0037] A minimum safety distance selection module, configured to adopt a corresponding minimum safety distance based on a preset scenario library, wherein the scenario library stores at least scenario information and corresponding minimum safety distances;

[0038] The drone dynamic adjustment module is used to dynamically adjust the drone queue based on the minimum safety distance.

[0039] In a third aspect, the present application further provides a computer system. The computer system includes a memory and a processor. The memory stores a computer program. When the processor executes the computer program, the following steps are performed:

[0040] measuring a reference signal received power value of the UAV and outputting a signal attenuation spectrum based on the reference signal received power value;

[0041] Acquire a signal attenuation value based on the signal attenuation map and invert the effective width and effective height of the flight segment based on the signal attenuation value, wherein the effective width is the width of the flight segment that is actually available under the signal attenuation condition, and the effective height is the height of the flight segment that is actually available under the signal attenuation condition;

[0042] Calculate the effective space of the flight segment based on the flight segment length, effective width and effective length;

[0043] Adopting a corresponding minimum safety distance based on a preset scene library, wherein the scene library stores at least scene information and a corresponding minimum safety distance;

[0044] Dynamically adjust the drone queue based on the minimum safe distance.

[0045] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the following steps:

[0046] measuring a reference signal received power value of the UAV and outputting a signal attenuation spectrum based on the reference signal received power value;

[0047] Acquire a signal attenuation value based on the signal attenuation map and invert the effective width and effective height of the flight segment based on the signal attenuation value, wherein the effective width is the width of the flight segment that is actually available under the signal attenuation condition, and the effective height is the height of the flight segment that is actually available under the signal attenuation condition;

[0048] Calculate the effective space of the flight segment based on the flight segment length, effective width and effective length;

[0049] Adopting a corresponding minimum safety distance based on a preset scene library, wherein the scene library stores at least scene information and a corresponding minimum safety distance;

[0050] Dynamically adjust the drone queue based on the minimum safe distance.

[0051] In a fifth aspect, the present application further provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the following steps:

[0052] measuring a reference signal received power value of the UAV and outputting a signal attenuation spectrum based on the reference signal received power value;

[0053] Acquire a signal attenuation value based on the signal attenuation map and invert the effective width and effective height of the flight segment based on the signal attenuation value, wherein the effective width is the width of the flight segment that is actually available under the signal attenuation condition, and the effective height is the height of the flight segment that is actually available under the signal attenuation condition;

[0054] Calculate the effective space of the flight segment based on the flight segment length, effective width and effective length;

[0055] Adopting a corresponding minimum safety distance based on a preset scene library, wherein the scene library stores at least scene information and a corresponding minimum safety distance;

[0056] Dynamically adjust the drone queue based on the minimum safe distance.

[0057] 3. Beneficial effects

[0058] The present application adopts the above method, outputs an attenuation signal spectrum through the reference signal receiving power value of the drone, obtains the signal attenuation value based on the attenuation signal spectrum, inverts the effective width and effective height of the flight segment through the signal attenuation value and calculates the effective space of the flight segment, and selects the corresponding minimum safety distance based on the scene of the flight segment, so that the actual distance value between each drone in the flight segment conforms to the dynamic traffic capacity under the scene, thereby improving the capacity utilization efficiency; at the same time, instead of adopting a static preset capacity method, the effective space and capacity of the flight segment are calculated according to the actual scene; when the signal quality is reduced, it is necessary to consider the influence of factors such as command transmission reliability and communication capability. When the signal attenuates, the system will reduce the effective width and height of the flight segment. By reducing the available space, the drone is restricted to fly in a smaller central area, and more safety margins are preset to deal with communication risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 FIG1 is an application environment diagram of an intelligent scheduling method based on a rule base in one embodiment;

[0060] Figure 2 is a flow chart of an intelligent scheduling method based on a rule base in one embodiment;

[0061] Figure 3 is a structural block diagram of an intelligent scheduling system based on a rule base in one embodiment;

[0062] Figure 4 FIG. 1 is a diagram showing the internal structure of a computer system in one embodiment. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0064] Under the preset capacity of static grids, the actual traffic capacity in plain areas with wide and unobstructed views may be higher than the preset threshold; in complex scenarios such as canyon terrain, congestion or safety hazards may occur due to the influence of many factors such as space, signals, and weather.

[0065] The intelligent scheduling method based on rule base provided in the embodiment of the present application can be applied to Figure 1In the application environment shown, the drone intelligent scheduling system specifically consists of a multi-source data perception layer, a dynamic rule base, an intelligent scheduling algorithm engine, and an actuator. The multi-source data perception layer inputs data into the dynamic rule base, and the rules in the dynamic rule base drive the intelligent scheduling algorithm engine, which then controls the output signals to the actuator. The multi-source data perception layer includes a 5G-A base station for acquiring signal attenuation data, a Remote ID receiver for acquiring drone location or density, and an ADS-B processor for acquiring civil aircraft trajectories. The dynamic rule base includes a segment rule table containing dynamic parameters such as width or altitude, a drone model spacing matrix for different safety distance categories, and a priority strategy for medical or logistics weight configuration. The intelligent scheduling algorithm engine includes a capacity calculation module for time flow monitoring, a priority decision module for mission feature analysis, and an emergency fuse controller for airspace conflict detection. The actuator includes a flow regulator for outputting access control commands, a route distributor for altitude or speed adjustment, and a fuse execution unit containing emergency shutdown protocols.

[0066] In one embodiment, Figure 2 As shown, the method includes the following steps:

[0067] Step 202: Measure the reference signal receiving power value of the UAV and output a signal attenuation spectrum based on the reference signal receiving power value.

[0068] Among them, the RSRP (reference signal received power) value of the drone terminal is measured through the 5G-A base station, and the signal attenuation spectrum (resolution 10m×10m) is output.

[0069] Step 204 : obtaining a signal attenuation value based on the signal attenuation map and inverting the effective width and effective height of the flight segment based on the signal attenuation value.

[0070] Among them, the effective width is the width of the flight segment that is actually available under the condition of signal attenuation, and the effective altitude is the altitude that is actually available under the condition of signal attenuation.

[0071] Step 206 : Calculate the effective space of the flight segment based on the flight segment length, the effective width, and the effective length.

[0072] The calculation formula for the effective space of a flight segment is as follows:

[0073] ;

[0074] In the above formula, is the actual available space of the segment under the current signal attenuation condition, is the effective width of the segment (dynamic width value), which indicates the actual available width of the segment under the current signal attenuation conditions. It is the effective altitude of the flight segment (dynamic altitude value), indicating the actual available altitude of the flight segment under the current signal attenuation conditions.

[0075] Step 208: adopt the corresponding minimum safety distance based on the preset scene library.

[0076] The scene library at least stores scene information and a corresponding minimum safety distance calculation algorithm.

[0077] Step 210: Dynamically adjust the drone queue based on the minimum safety distance.

[0078] In the above-mentioned intelligent scheduling method based on the rule base, the attenuation signal spectrum is output through the reference signal receiving power value of the drone, the signal attenuation value is obtained based on the attenuation signal spectrum, the effective width and effective height of the flight segment are inverted through the signal attenuation value, and the effective space of the flight segment is calculated. The corresponding minimum safety distance is selected based on the scenario of the flight segment, so that the actual distance value between each drone in the flight segment conforms to the dynamic traffic capacity under the scenario, thereby improving the capacity utilization efficiency.

[0079] In one embodiment, the rule engine inverts the effective width and effective height of the flight segment based on the attenuation value, and the signal attenuation value is obtained based on the signal attenuation map and the effective width and effective height of the flight segment is inverted based on the signal attenuation value.

[0080] Get the preset basic width of the segment; invert the effective width of the segment based on the preset width attenuation coefficient, basic width and signal attenuation value; get the preset basic height of the segment; invert the effective height of the segment based on the height attenuation coefficient, basic height and signal attenuation value of the segment.

[0081] The greater the signal attenuation (the smaller the ΔRSRP), the smaller the effective width and height of the flight segment, to ensure that the drone can still fly safely in weak signal conditions. The formula for calculating the effective width of the flight segment is as follows:

[0082] ;

[0083] in, is the effective width of the segment (dynamic width value), which indicates the actual available width of the segment under the current signal attenuation conditions. is the basic width of the segment, i.e. the preset width under ideal signal conditions, is the attenuation coefficient, which is a proportional constant used to convert the signal attenuation value (ΔRSRP) into the ratio of the width adjustment. The attenuation value of the Reference Signal Received Power (RSRP) indicates the change in signal strength (usually a negative value indicates a weakening signal).

[0084] The calculation formula for the effective altitude of the flight segment is as follows:

[0085] ;

[0086] in, is the effective altitude of the flight segment (dynamic altitude value), which indicates the actual available altitude of the flight segment under the current signal attenuation conditions. is the basic altitude of the flight segment, that is, the preset altitude under ideal signal conditions. is the height attenuation coefficient, which is used to convert the signal attenuation value (ΔRSRP) into the ratio of height adjustment. It is the attenuation value of the Reference Signal Received Power (RSRP), which indicates the change in signal strength (usually a negative value, indicating a weakening signal).

[0087] In one embodiment, in actual use, different types of drones often fly together in a flight segment. Considering the characteristics of different scenarios, different solutions can be used to set the minimum safety interval between drones. The corresponding minimum safety distances based on the preset scenario library include:

[0088] The scenario library includes at least conservative scenarios, flexible scenarios and partitioned scenarios;

[0089] When the conservative scenario is selected, the minimum safety distance with the largest value among different types of drones in the flight segment is set as the minimum safety distance;

[0090] Among them, the conservative method based on the maximum safety interval: taking the largest minimum safety interval of all drone models as the unified safety interval of the flight segment, which is suitable for low-density, single-model-dominated flight segments.

[0091] When the flexible scenario is selected, the corresponding minimum safety interval is calculated based on the ratio of different types of drones in the flight segment and set as the minimum safety distance;

[0092] Among them, the flexible method based on weighted average: calculate the weighted average minimum safety distance according to the proportion of each aircraft type in the flight segment.

[0093] When the zoning scenario is selected, different types of drones are divided based on different areas and the minimum safety distance of the corresponding drones is set based on the area.

[0094] Among them, the zoning method based on hierarchical management: the flight segments are layered according to altitude or area, different layers or areas allow different types of drones to fly, and each layer or area uses the minimum safety distance of the corresponding model.

[0095] In one embodiment, the execution control layer broadcasts a current limiting instruction through the 5G-A URLLC channel to dynamically adjust the minimum safe distance of the drone queue, including:

[0096] Count the actual distance values ​​between drones and count the drone types; send flow limiting instructions to drones that do not match the drone type in the flight segment and drones whose actual distance value is less than the minimum safety distance; dynamically adjust the drone queue until the drone type matches the flight segment and the actual distance value of the drone is not less than the minimum safety distance.

[0097] A throttling command is broadcast over the 5G-A URLLC (Ultra-Reliable Low Latency Communication) channel, notifying the fleet of drones that can enter a particular flight segment. The throttling command includes a list of permitted drone IDs, ensuring the maximum number of drones in the flight segment does not exceed capacity. The fleet is dynamically adjusted to ensure the number and distribution of drones within the flight segment meet safety requirements.

[0098] It's worth noting that when the 5G-A signal is unstable or unable to provide reliable signal attenuation data, the system can switch to Remote ID density estimation mode. This mode relies on the Remote ID information broadcast by drones to estimate the density of drones within the flight segment and calculate the maximum capacity based on the safety spacing of drones. Based on the maximum capacity of the flight segment, the system generates flow control instructions and determines the list of drone IDs allowed to enter the flight segment. The flow control instructions are broadcast via direct Remote ID communication or alternative communication channels (such as LoRa and Wi-Fi). Drones that receive the flow control instructions adjust their flight status accordingly.

[0099] In one embodiment, a multi-level emergency fuse mechanism is used when a drone intrudes into a flight segment, specifically including:

[0100] When the drone is detected entering the preset buffer area, a deceleration command and an altitude lock command are sent to the drone; if the actual speed of the drone is greater than the sent deceleration speed, a drone route adjustment command is sent to the drone; if the drone does not respond to the route adjustment command, a forced hovering command is sent to the drone until the drone performs a hovering operation.

[0101] Among them, the hierarchical response process: Level 1 response: The system sends instructions to the drone through the 5G-A eMBB network, requiring it to decelerate to 3m / s and activate the altitude lock function to avoid conflicts in the vertical direction. After receiving the instructions, the drone automatically performs deceleration and altitude lock operations. The system continuously monitors the flight status of the drone. If the intrusion behavior continues, it will automatically upgrade to the level 2 response.

[0102] Level 2 Response: The system automatically assigns new routes to drones based on a dynamic capacity rule base to avoid conflict zones. New route instructions are sent to the drones via the 5G-A URLLC network, and the drones automatically adjust their flight paths upon receiving them. If conflict warnings continue, the system upgrades to Level 3 Response.

[0103] Level 3 Response: The system sends an EFC emergency stop command to the drone via Remote ID direct communication, forcing the drone to enter a hovering evasive state. Upon receiving the command, the drone immediately executes a hovering maneuver until the conflict is resolved.

[0104] The communication method is as follows:

[0105]

[0106] In one embodiment, the multi-dimensional task priority evaluation specifically includes:

[0107] When the scenario is an emergency scenario, a weighted evaluation model is constructed and a priority sorting queue is generated. The weight indicators include at least the task urgency, power status and range factors; the task urgency is set to be proportional to the task waiting time.

[0108] A weighted evaluation model based on task attributes (including task urgency classification, remaining vehicle battery percentage, and uncompleted flight distance) is established to generate a time-varying priority queue. By default, task urgency accounts for 60%, battery status for 30%, and flight distance for 10% (P = 0.6T + 0.3E + 0.1D). This solution stores these weights as hot-update rules in a rule base. In emergency scenarios, these default rules can be overridden to create a dynamically updated task scheduling sequence. For example, in a meteorological disaster emergency response scenario, P can be adjusted to 0.2T + 0.5E + 0.3D. Furthermore, a time factor is supported, so if a delay occurs, the priority level increases over time. For example, if T(t) = T0 × (1 + λt), then P = 0.6T(t) + 0.3E + 0.1D.

[0109] Among them, λ is the growth rate of emergency tasks (for example, the medical emergency task can be set to 0.05, which means an increase of 5% per minute); t is the task waiting time.

[0110] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0111] Based on the same inventive concept, the present application also provides a rule-based intelligent scheduling system for implementing the rule-based intelligent scheduling method described above. The solution provided by this system is similar to the solution described in the method described above. Therefore, the specific limitations of one or more rule-based intelligent scheduling system embodiments provided below can be found in the limitations of the rule-based intelligent scheduling method described above and will not be repeated here.

[0112] In one embodiment, Figure 3 As shown, a rule-based intelligent scheduling system is provided, including: a signal attenuation map output module, an effective width and effective height calculation module, an effective space calculation module, a minimum safety distance selection module, a UAV dynamic adjustment module, a multi-level fuse module and an emergency priority assessment module, wherein:

[0113] A signal attenuation spectrum output module is used to measure the reference signal received power value of the UAV and output a signal attenuation spectrum based on the reference signal received power value;

[0114] An effective width and effective height calculation module, configured to obtain a signal attenuation value based on a signal attenuation map and invert the effective width and effective height of the flight segment based on the signal attenuation value. The effective width is the width of the flight segment that is actually available under the signal attenuation condition, and the effective height is the height of the flight segment that is actually available under the signal attenuation condition.

[0115] An effective space calculation module is used to calculate the effective space of a flight segment based on the flight segment length, effective width and effective length;

[0116] A minimum safety distance selection module, configured to adopt a corresponding minimum safety distance based on a preset scenario library, wherein the scenario library stores at least scenario information and corresponding minimum safety distances;

[0117] The drone dynamic adjustment module is used to dynamically adjust the drone queue based on the minimum safety distance.

[0118] In one embodiment, the effective width and effective height calculation module is also used to: obtain the preset basic width of the flight segment; invert the effective width of the flight segment based on the preset width attenuation coefficient, basic width and signal attenuation value; obtain the preset basic height of the flight segment; invert the effective height of the flight segment based on the height attenuation coefficient, basic height and signal attenuation value of the flight segment.

[0119] In one embodiment, the minimum safety distance selection module is also used to: the scenario library includes at least conservative scenarios, flexible scenarios and partition scenarios; when the conservative scenario is selected, the minimum safety interval with the largest value among different types of drones in the flight segment is set as the minimum safety distance; when the flexible scenario is selected, the corresponding minimum safety interval is calculated based on the proportion of different types of drones in the flight segment and set as the minimum safety distance; when the partition scenario is selected, different types of drones are divided based on different areas and the minimum safety distance of the corresponding drones is set based on the area.

[0120] In one embodiment, the minimum safety distance selection module is also used to: count the actual distance values ​​between drones and count the types of drones; send flow limiting instructions to drones that do not match the drone type in the flight segment and drones whose actual distance values ​​are less than the minimum safety distance; dynamically adjust the drone queue until the drone type matches the flight segment and the actual distance value of the drone is not less than the minimum safety distance.

[0121] In one embodiment, the multi-stage fuse module is also used to: when it is detected that the drone enters a preset buffer area, send a deceleration instruction and an altitude lock instruction to the drone; if the actual speed of the drone is greater than the deceleration speed sent, send a drone route adjustment instruction to the drone; if the drone does not respond to the route adjustment instruction, send a forced hovering instruction to the drone until the drone performs a hovering operation.

[0122] In one embodiment, the emergency priority assessment module is also used to: when the scenario is an emergency scenario, construct a weighted assessment model and generate a priority sorting queue, where the weight indicators include at least task urgency, power status and range factors; and set the task urgency to be proportional to the task waiting time.

[0123] Each module in the aforementioned rule-based intelligent scheduling system can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer system in the form of hardware, or can be stored in a computer system memory in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0124] In one embodiment, a computer system is provided. The computer system may be a server, and its internal structure diagram may be as follows: Figure 4As shown. The computer system includes a processor, a memory, and a network interface connected via a system bus. The processor of the computer system is used to provide computing and control capabilities. The memory of the computer system includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer system is used to store data. The network interface of the computer system is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, it implements an intelligent scheduling method based on a rule base.

[0125] In one embodiment, a computer system is provided. The computer system may be a terminal, and its internal structure diagram may be as follows: Figure 4 As shown. The computer system includes a processor, memory, communication interface, display screen and input system connected via a system bus. The processor of the computer system is used to provide computing and control capabilities. The memory of the computer system includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The communication interface of the computer system is used to communicate with an external terminal in a wired or wireless manner. The wireless manner can be implemented through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, an intelligent scheduling method based on a rule base is implemented.

[0126] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer system to which the solution of the present application is applied. The specific computer system may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0127] In one embodiment, a computer system is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0128] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0129] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0130] It should be noted that the user information (including but not limited to user system information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0131] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile 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 various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), data processing logic devices based on quantum computing, and the like.

[0132] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, 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 specification.

[0133] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. An intelligent scheduling method based on a rule base, characterized in that: The method comprises: measuring a reference signal received power value of the UAV and outputting a signal attenuation spectrum based on the reference signal received power value; Acquire a signal attenuation value based on the signal attenuation map and invert the effective width and effective height of the flight segment based on the signal attenuation value, wherein the effective width is the width of the flight segment that is actually available under the signal attenuation condition, and the effective height is the height of the flight segment that is actually available under the signal attenuation condition; Calculate the effective space of the flight segment based on the flight segment length, effective width and effective length; Adopting a corresponding minimum safety distance based on a preset scene library, wherein the scene library stores at least scene information and a corresponding minimum safety distance; Dynamically adjust the drone queue based on the effective space of the flight segment and the minimum safety distance.

2. The intelligent scheduling method based on rule base according to claim 1 is characterized in that: The method of obtaining the signal attenuation value based on the signal attenuation map and inverting the effective width and effective height of the flight segment based on the signal attenuation value includes: Get the preset basic width of the segment; Invert the effective width of the segment based on the preset width attenuation coefficient, basic width and signal attenuation value; Get the preset base altitude of the flight segment; The effective altitude of the flight segment is inverted based on the altitude attenuation coefficient of the flight segment, the basic altitude and the signal attenuation value.

3. The intelligent scheduling method based on rule base according to claim 1 is characterized in that: The corresponding minimum safety distance based on the preset scene library includes: The scenario library includes at least conservative scenarios, flexible scenarios and partitioned scenarios; When the conservative scenario is selected, the minimum safety intervals corresponding to different types of drones in the flight segment are calculated, and the minimum safety interval with the largest value is selected as the minimum safety distance; When the flexible scenario is selected, the corresponding minimum safety interval is calculated based on the ratio of different types of drones in the flight segment and the preset weighted coefficient and set as the minimum safety distance; When the zoning scenario is selected, different types of drones are divided based on different areas and the minimum safety distance of the corresponding drones is set based on the area.

4. The intelligent scheduling method based on rule base according to claim 3 is characterized in that: The dynamic adjustment of the drone queue based on the effective space of the flight segment and the minimum safety distance includes: Count the actual distance between drones and count the types of drones; Send flow limiting instructions to drones that do not match the drone type in the flight segment and drones whose actual distance value is less than the minimum safety distance; The drone queue is dynamically adjusted until the drone type matches the flight segment and the actual distance value of the drone is not less than the minimum safety distance.

5. The intelligent scheduling method based on rule base according to claim 1 is characterized in that: The method further comprises: When the drone is detected entering the preset buffer area, a deceleration command and an altitude lock command are sent to the drone; If the actual speed of the drone is greater than the deceleration speed sent, a drone route adjustment command is sent to the drone; If the drone does not respond to the route adjustment command, a forced hovering command is sent to the drone until the drone performs the hovering operation.

6. The intelligent scheduling method based on rule base according to claim 1, characterized in that: The method further comprises: When the scenario is an emergency scenario, a weighted evaluation model is constructed and a priority ranking queue is generated. The weight indicators include at least the urgency of the task, the power status and the range factor. Set the task urgency to be proportional to the task waiting time.

7. An intelligent scheduling system based on a rule base, characterized in that: The system comprises: A signal attenuation spectrum output module is used to measure the reference signal received power value of the UAV and output a signal attenuation spectrum based on the reference signal received power value; An effective width and effective height calculation module, configured to obtain a signal attenuation value based on a signal attenuation map and invert the effective width and effective height of the flight segment based on the signal attenuation value. The effective width is the width of the flight segment that is actually available under the signal attenuation condition, and the effective height is the height of the flight segment that is actually available under the signal attenuation condition. An effective space calculation module is used to calculate the effective space of a flight segment based on the flight segment length, effective width and effective length; A minimum safety distance selection module, configured to adopt a corresponding minimum safety distance based on a preset scenario library, wherein the scenario library stores at least scenario information and corresponding minimum safety distances; The UAV dynamic adjustment module is used to dynamically adjust the UAV fleet based on the effective space of the flight segment and the minimum safety distance.

8. A computer system comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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