Unmanned aerial vehicle intelligent logistics data rapid calculation method adaptive to reconfigurable photon calculation chip

Abstract

The invention discloses an unmanned aerial vehicle intelligent logistics data rapid calculation method adaptive to a reconfigurable photon calculation chip, and relates to the field of intelligent logistics. The method comprises the following steps: obtaining logistics data and a calculation demand; matching a preset calculation framework according to the calculation demand; configuring a reconfigurable photon chip according to the matched computing architecture, and enabling the reconfigurable photon chip to execute a computing task corresponding to the computing demand based on the logistics data; wherein the computing architecture is determined by the connection mode of the optical switch matrix in the reconfigurable photon chip and the layout of the optical waveguide structure. According to the invention, the reconfigurable photon calculation chip is utilized, the data processing speed is greatly improved, the energy consumption is reduced, the system adaptability is enhanced, the problems of slow data processing speed and high energy consumption of the existing unmanned aerial vehicle intelligent logistics are effectively solved, and the efficient development of the unmanned aerial vehicle intelligent logistics is powerfully promoted.

Description

Technical Field

[0001] The present application relates to the field of intelligent logistics technology, and particularly to a method for quickly calculating unmanned aerial vehicle (UAV) intelligent logistics data adapted to a reconfigurable photonic computing chip. Background Art

[0002] In the context of the booming e-commerce industry today, the efficiency of logistics distribution has become a key factor affecting user experience and industry competitiveness. As an innovative distribution model, UAV intelligent logistics is gradually moving from concept to practical application with its significant advantages of high efficiency, flexibility, and the ability to break through geographical restrictions. UAVs can quickly reach remote areas that are difficult to cover by traditional logistics, significantly shortening the distribution time, improving logistics efficiency, and meeting customers' demands for rapid delivery of goods.

[0003] During the operation of a UAV intelligent logistics system, it faces the severe challenge of massive data processing. Flight path planning needs to comprehensively consider various factors such as geographical information, weather conditions, and no-fly zones in real time; calculating the weight of goods not only requires accurate measurement but also needs to consider the load capacity of the UAV to ensure flight safety; matching delivery addresses involves rapid retrieval and analysis of a large amount of address data. Traditional data processing methods rely on electronic computing chips, which can complete data processing tasks to a certain extent. However, as the scale of logistics operations continues to expand and the amount of data grows rapidly, their processing speed gradually fails to meet real-time requirements. At the same time, electronic computing chips have high energy consumption, which further limits the flight duration and distribution range of UAVs with limited battery life. Summary of the Invention

[0004] Based on this, it is necessary to provide a method for quickly calculating UAV intelligent logistics data adapted to a reconfigurable photonic computing chip to address the above technical problems and improve data processing efficiency.

[0005] In a first aspect, the present application provides a method for quickly calculating UAV intelligent logistics data adapted to a reconfigurable photonic computing chip. The method includes:

[0006] Obtaining logistics data and calculation requirements;

[0007] Matching a preset calculation architecture according to the calculation requirements;

[0008] Configuring a reconfigurable photonic chip according to the matched calculation architecture, and enabling the reconfigurable photonic chip to execute the calculation task corresponding to the calculation requirements based on the logistics data;

[0009] Among them, the calculation architecture is determined by the connection mode of the optical switch matrix inside the reconfigurable photonic chip and the layout of the optical waveguide structure.

[0010] In one embodiment, the calculation requirements include flight path planning;

[0011] The computing architecture for flight path planning matching is a parallel search-based computing architecture. The optical switch matrix corresponding to the parallel search-based computing architecture is connected in a parallel transmission manner, and the corresponding optical waveguide structure is a mesh structure.

[0012] In one embodiment, the computing requirements include logistics distribution scheduling;

[0013] The computing architecture for logistics distribution scheduling matching is a computing architecture based on a task assignment model. The optical switch matrix corresponding to the computing architecture based on the task assignment model is connected according to the task assignment logic, and the corresponding optical waveguide structure is a hierarchical or partitioned structure.

[0014] In one embodiment, after configuring the reconfigurable photonic chip, the method further includes:

[0015] Verifying the execution of the computing tasks by the current reconfigurable photonic chip, and iteratively optimizing the computing architecture according to the verification result until the verification result meets the preset conditions.

[0016] In one embodiment, iteratively optimizing the computing architecture according to the verification result includes:

[0017] When the verification result does not meet the preset conditions, adjusting the optical switch matrix or the optical waveguide structure based on the computing architecture matched by the current reconfigurable photonic chip, reconfiguring the reconfigurable photonic chip based on the adjusted computing architecture, using the reconfigured reconfigurable photonic chip as the current reconfigurable photonic chip, updating the verification result, and determining whether the verification result meets the preset conditions.

[0018] In one embodiment, the logistics data is data screened according to the computing requirements and classified using a clustering algorithm.

[0019] In a second aspect, the present application also provides a drone intelligent logistics data fast calculation system adapted to a reconfigurable photonic computing chip. The system includes:

[0020] It includes a reconfigurable photonic computing module, and the reconfigurable photonic computing module includes a reconfigurable photonic computing chip and a driving circuit;

[0021] The driving circuit includes:

[0022] A data acquisition module for acquiring logistics data and computing requirements;

[0023] A matching module for matching a preset computing architecture according to the computing requirements;

[0024] A configuration module for configuring the reconfigurable photonic chip according to the matched computing architecture, and enabling the reconfigurable photonic chip to execute the computing tasks corresponding to the computing requirements based on the logistics data;

[0025] Among them, the computing architecture is determined by the connection mode of the optical switch matrix inside the reconfigurable photonic chip and the layout of the optical waveguide structure.

[0026] In one embodiment, the system further includes a drone terminal and a ground control center;

[0027] The drone terminal is equipped with a sensor component for collecting real-time sensing data and sends the real-time sensing data to the ground control center;

[0028] The ground control center is used to receive the real-time sensing data, extract relevant real-time sensing data according to the task requirements, determine the logistics data including the extracted real-time sensing data, and send the logistics data to the reconfigurable photonic computing module;

[0029] The ground control center is also used to receive the execution result corresponding to the computing task output by the reconfigurable photonic computing module and generate a task instruction according to the execution result and send it to the drone terminal.

[0030] In a third aspect, the present application also provides a computer device. The computer device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps in the above-mentioned method for quickly calculating drone intelligent logistics data adapted to a reconfigurable photonic computing chip.

[0031] In a fourth aspect, the present application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, and when the computer program is executed by a processor, it implements the steps in the above-mentioned method for quickly calculating drone intelligent logistics data adapted to a reconfigurable photonic computing chip.

[0032] In a fifth aspect, the present application also provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, it implements the steps in the above-mentioned method for quickly calculating drone intelligent logistics data adapted to a reconfigurable photonic computing chip.

[0033] The above-mentioned method for quickly calculating drone intelligent logistics data adapted to a reconfigurable photonic computing chip includes: obtaining logistics data and computing requirements; matching a preset computing architecture according to the computing requirements; configuring a reconfigurable photonic chip according to the matched computing architecture, and enabling the reconfigurable photonic chip to execute the computing task corresponding to the computing requirements based on the logistics data; among them, the computing architecture is determined by the connection mode of the optical switch matrix inside the reconfigurable photonic chip and the layout of the optical waveguide structure. Through innovative design and technology integration, it strives to achieve efficient, stable and low-power operation of drone intelligent logistics in data processing, and promote the entire industry to move towards intelligence and high efficiency. Description of the Drawings

[0034] Figure 1Schematic diagram of the process of a method for quickly calculating UAV intelligent logistics data adapted to a reconfigurable photonic computing chip in an embodiment;

[0035] Figure 2 Workflow diagram of a system for quickly calculating UAV intelligent logistics data adapted to a reconfigurable photonic computing chip in an embodiment;

[0036] Figure 3 Block diagram of the structure of a system for quickly calculating UAV intelligent logistics data adapted to a reconfigurable photonic computing chip in an embodiment. Detailed implementation manners

[0037] In order to make the objectives, technical solutions and advantages of the present application clearer and more understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0038] The embodiment of the present application provides a method for quickly calculating UAV intelligent logistics data adapted to a reconfigurable photonic computing chip, which is applicable to the drive circuit of the reconfigurable photonic computing chip, such as Figure 1 shown, and includes the following steps:

[0039] Step 102, obtain logistics data and calculation requirements.

[0040] Among them, the logistics data includes order information (recipient, contact information, delivery address, etc.), commodity information (commodity specifications, commodity quantity, commodity weight, commodity volume, etc.), order time (departure time, delivery time, etc.), and road condition information (road congestion situation, weather conditions, etc.). The calculation requirements include flight path planning, logistics distribution scheduling, flight attitude and stability calculation, load and endurance calculation, resource allocation, etc.

[0041] In one embodiment, the calculation requirements can be determined by analyzing the type of logistics data. That is, when the reconfigurable photonic computing module receives the data, the drive circuit first analyzes the data in detail to determine the calculation requirements. For example, if the data contains a large amount of geographical location information, real-time traffic data, and the position and attitude data of the UAV itself, it can be basically judged as flight path planning data, corresponding to the calculation requirements of flight path planning; if it mainly involves information such as the weight, volume, delivery address, and order quantity of the goods, it is more likely to be logistics distribution scheduling data, corresponding to the calculation requirements of logistics distribution scheduling.

[0042] Step 104, match the preset calculation architecture according to the calculation requirements; among them, the calculation architecture is determined by the connection mode of the optical switch matrix inside the reconfigurable photonic chip and the layout of the optical waveguide structure.

[0043] The adaptable reconfigurable photonic computing chip can achieve at least five different computing architectures by changing the connection mode of the internal optical switch matrix and the layout of the optical waveguide structure according to different data computing requirements. These preset architectures are written in the driving circuit in the form of programs and are matched according to the one-to-one, one-to-many, or many-to-one mapping relationships pre-constructed with the computing requirements. When the matching is successful, the internal of the chip is reconfigured by the optical switch matrix and the optical waveguide structure.

[0044] In the UAV intelligent logistics system, the reconfigurable photonic computing chip module plays a key role. It can change the connection mode of the internal optical switch matrix and the layout of the optical waveguide structure according to different data computing requirements to adapt to diverse computing tasks such as flight path planning and logistics distribution scheduling.

[0045] For example, when performing flight path planning calculations, by adjusting the connection mode of the optical switch matrix, a computing architecture suitable for path search is constructed; when performing logistics distribution scheduling calculations, the layout of the optical waveguide structure is reconfigured to optimize the execution efficiency of the algorithm. Through this flexible architecture adjustment and algorithm adaptation, efficient data processing is achieved.

[0046] Step 106, configure the reconfigurable photonic chip according to the matched computing architecture, and make the reconfigurable photonic chip execute the computing task corresponding to the computing requirement based on the logistics data.

[0047] After determining the computing architecture applicable to the computing requirement, the driving circuit starts to configure the chip. By controlling the connection mode of the optical switch matrix and adjusting the layout of the optical waveguide structure, the chip is set to the corresponding architecture. Based on this architecture, complex computing tasks are completed, and the computing results are transmitted back to the data processing center for further analysis and to guide the UAV to execute logistics tasks.

[0048] In one embodiment, the computing requirement includes flight path planning; the computing architecture matched for flight path planning is a computing architecture based on parallel search. The optical switch matrix corresponding to the computing architecture based on parallel search is connected in a parallel transmission mode, and the corresponding optical waveguide structure is a mesh structure.

[0049] In terms of flight path planning, a computing architecture based on parallel search is adopted. The optical switch matrix is composed of micro-electro-mechanical system (MEMS) optical switches, and the switching time does not exceed 1 microsecond. When performing path planning, the optical switch matrix constructs a special connection mode, enabling optical signals to be transmitted simultaneously in multiple channels representing different possible paths. This is like opening up multiple parallel "roads" for optical signals, allowing them to explore different routes simultaneously. The optical waveguide structure uses silicon-based materials with an optical transmission loss lower than 0.1 dB / cm, and its layout is compact and networked, with all branches connected to each other.

[0050] Such a layout design can ensure the efficient transmission and interaction of optical signals between different paths, reduce signal transmission delay and loss, make full use of the high-speed parallel transmission characteristics of optical signals, and quickly find the optimal path. In a complex geographical environment, it may take several minutes or even longer for a traditional electronic chip combined with a single algorithm to plan a path, while this parallel architecture can complete it within seconds, greatly improving the efficiency of path planning and meeting the time-sensitive requirements of logistics and distribution.

[0051] In one embodiment, the computing requirements include logistics and distribution scheduling; the computing architecture matching logistics and distribution scheduling is a computing architecture based on a task assignment model. The optical switch matrix corresponding to the computing architecture based on the task assignment model is connected according to the task assignment logic, and the corresponding optical waveguide structure is a hierarchical or partitioned structure.

[0052] The logistics and distribution scheduling adopts a computing architecture based on a task assignment model. The optical switch matrix is also composed of MEMS optical switches with a switching time of no more than 1 microsecond, but the connection method is adjusted according to factors such as the weight, volume, and delivery address of the goods. It will reasonably connect the data processing paths related to different goods attributes, allowing optical signals to be transmitted between different processing units according to specific task assignment logic. For example, the attribute information and delivery information of the same item are on the same transmission path. The optical waveguide structure is also made of silicon-based materials, and its layout focuses on integrating data on different goods attributes and delivery task information. For example, it is designed as a hierarchical or partitioned structure, with different layers or regions respectively processing data such as weight, volume, and delivery address, and then summarizing and comprehensively analyzing these data through specific optical waveguide connection methods, and then formulating an optimal allocation plan for more than 100 delivery tasks within 1 second, greatly improving the efficiency of logistics and distribution.

[0053] The present invention designs a corresponding computing architecture for the reconfigurable photonic computing chip according to different computing requirements to adapt to the characteristics of different computing requirements. For example, in flight path planning, a computing architecture based on parallel search is constructed, using the high-speed parallel transmission characteristics of optical signals to search multiple possible paths simultaneously and quickly find the optimal path; in logistics and distribution scheduling, a computing architecture based on a task assignment model is designed to reasonably allocate delivery tasks according to factors such as the weight, volume, and delivery address of the goods. The present invention only discloses the matching principles of two computing scenarios and computing frameworks, and matching computing architectures are also designed according to the characteristics of other computing requirements, which will not be elaborated here.

[0054] Through this flexible architecture adjustment, it can adapt to diverse computing tasks such as flight path planning and logistics distribution scheduling. For different logistics data calculation scenarios, the reconfigurable photonic computing chip module can quickly switch and optimize algorithms. When processing large-scale logistics order data, a parallel computing algorithm is adopted to divide the data into multiple subtasks according to different logics and process them in parallel inside the chip, realizing high-speed parallel processing of data and greatly improving the computing efficiency.

[0055] In one embodiment, after configuring the reconfigurable photonic chip, the method further includes: verifying the execution of the computing task by the current reconfigurable photonic chip, and iteratively optimizing the computing architecture according to the verification result. When the verification result does not meet the preset conditions, the optical switch matrix or the optical waveguide structure is adjusted based on the computing architecture matched by the current reconfigurable photonic chip, and the reconfigurable photonic chip is reconfigured based on the adjusted computing architecture. Taking the reconfigured reconfigurable photonic chip as the current reconfigurable photonic chip, the verification result is updated, and it is determined whether the verification result meets the preset conditions. This process continues until the verification result meets the preset conditions.

[0056] After configuration, the chip performs preliminary calculations and compares the results with preset standards or reference data for verification. For example, in path planning calculations, check whether the calculated path avoids no-fly zones and takes into account real-time traffic conditions; in distribution scheduling calculations, check whether the distribution task allocation is reasonable, whether it meets the requirements of cargo weight, volume limits, and distribution time requirements, etc. If the verification result is not satisfactory, the drive circuit will dynamically optimize and adjust the computing architecture according to the feedback information. It may slightly adjust the connection method of the optical switch matrix or modify some parameters of the optical waveguide structure, and then perform calculations and verifications again. After multiple optimization adjustments, when the calculation result meets the requirements, this computing architecture is determined as the optimal architecture for the current data calculation, and the chip then completes subsequent data processing tasks based on this architecture, thus ensuring the efficient operation of the entire unmanned aerial vehicle intelligent logistics system.

[0057] This embodiment optimizes the algorithm parameters in real time according to the actual calculation results and feedback information, further improving the performance of the algorithm.

[0058] In one embodiment, the logistics data is data screened according to computing requirements and classified using a clustering algorithm.

[0059] Before sending the logistics data to the reconfigurable photonic computing module, it is also necessary to perform preprocessing, classification, screening and other operations on it. First, classify the received data, identify at least 10 different types of data formats through pattern recognition algorithms, then perform denoising processing, use wavelet denoising algorithms to remove noise interference in the data, and then perform normalization operations to unify data in different ranges into the same numerical range for convenient subsequent analysis and processing. In addition, clustering algorithms such as K-Means are used to classify the cargo information. This algorithm clusters the cargo according to characteristics such as weight, volume, and delivery priority, so that similar cargoes are grouped together for convenient subsequent processing and scheduling. Use path planning algorithms such as Dijkstra to plan the flight path in combination with real-time traffic data and dynamic factors such as weather conditions. Among them, the Dijkstra algorithm is a classic shortest path algorithm. By continuously searching for the adjacent nodes of the current node, it finds the shortest path from the starting point to the ending point. In practical applications, in combination with real-time traffic data (road congestion conditions, no-fly zones) and weather conditions (factors such as strong winds and heavy rains that affect flight safety), the path planning can be dynamically adjusted to ensure the optimality of the UAV flight path. Through comprehensive and efficient classification, preprocessing and analysis of the data, the logistics data to be sent to the reconfigurable photonic computing chip module is accurately determined.

[0060] In one embodiment, the reconfigurable photonic computing module is communicatively connected to the ground control center, and the ground control center is communicatively connected to the UAV terminal.

[0061] Among them, the UAV terminal is equipped with a high-precision MEMS weight sensor, which is the core component for obtaining cargo weight data. Based on microelectromechanical system technology, it senses the change in cargo gravity through the internal micro-sensing structure and converts it into an accurate electrical signal output. The measurement accuracy can reach ±0.1 grams, which is sufficient to meet the stringent requirements of most logistics cargo weight measurements. In actual logistics scenarios, whether it is the delivery of light and small commodities or heavier industrial parts, it can accurately measure. Its temperature compensation function is to monitor the ambient temperature in real time through the built-in temperature sensor, and then use the pre-calibrated temperature compensation algorithm to correct the measurement data to ensure that the measurement accuracy is not affected within the extreme ambient temperature range of -20°C to 60°C, ensuring the reliability of the data. The UAV terminal is also equipped with a GNSS position sensor, which integrates the receiving modules of the world's four major satellite navigation systems and can simultaneously receive signals from different satellite systems. Through a multi-system fusion positioning algorithm, centimeter-level positioning accuracy can be achieved. During cold start, since it is necessary to search for and lock the satellite signal, the time does not exceed 30 seconds; while during hot start, because the system retains some previous positioning information, it can quickly obtain accurate geographical location data within no more than 1 second.

[0062] This data is crucial for the flight path planning of drones in complex geographical environments. Whether in areas with high-rise buildings in the city or remote mountainous areas, it can provide accurate position references for drones. The attitude sensor based on MEMS technology contains a micro-mechanical gyroscope and an accelerometer inside. By detecting the angular velocity and acceleration changes of the drone in three axes, it can monitor the flight attitude in real time. The sampling frequency is not less than 100Hz, which means it can collect at least 100 data points per second, ensuring real-time tracking of the drone's flight attitude. During the flight of the drone, whether it is turning, ascending / descending or hovering, the attitude sensor can promptly capture the attitude changes and provide key data for the flight control system to ensure flight safety and stability. The high-speed wireless communication module supports the 5G communication standard and adopts advanced modulation and demodulation technologies and multi-antenna technologies, with a transmission rate of over 1Gbps. In practical applications, it can quickly transmit a large amount of logistics data and flight data collected by the drone to the ground control center. Even in urban environments with strong signal interference and long-distance transmission scenarios, it can ensure the stability and efficiency of data transmission.

[0063] The hardware platform of the ground control center selects a high-performance computer with a multi-core processor. The multi-core processor can process multiple tasks in parallel, greatly improving the data processing efficiency. The memory is not less than 32GB, providing sufficient space for running complex data processing software and storing a large amount of temporary data, meeting the requirements of large-scale data processing and complex operations. When processing large-scale logistics order data, it can respond quickly without jamming. The software of the ground control center can complete the data processing process before the data is input into the reconfigurable photonic computing module, such as preprocessing processes like denoising, clustering algorithm processing, and path planning algorithm processing. The ground control center also includes communication management software based on the TCP / IP protocol stack, which is the basic protocol stack for Internet communication and has wide compatibility and stability. Through communication links such as wired gigabit Ethernet interfaces and 5G, it can achieve stable and efficient communication with the drone terminal and the reconfigurable photonic computing module. In addition, the communication uses the AES-256 encryption algorithm to encrypt the data. The AES-256 encryption algorithm is a symmetric encryption algorithm with a key length of 256 bits and extremely high encryption strength, which can effectively protect the security of data during transmission. The size of the data buffer can be dynamically adjusted within the range of 1GB to 10GB. According to the real-time rate of data transmission and processing requirements, it can automatically adjust the buffer size to ensure that data is not lost. The retransmission mechanism adopts the Automatic Repeat reQuest (ARQ) protocol. When detecting data transmission errors or losses, it can automatically request the sender to retransmit the data to ensure reliable data transmission.

[0064] The reconfigurable photonic computing chip is fabricated using nanoscale lithography technology, which can create extremely fine circuit structures on the chip, improving the chip's integration and performance. The internal optical waveguide structure uses silicon-based materials, which have good optical properties and stability, with an optical transmission loss lower than 0.1 dB / cm, ensuring low loss during the transmission of optical signals, enabling efficient transmission of optical signals within the chip, and improving the computing efficiency. The drive circuit based on field-effect transistor (FET) technology precisely controls the chip's operating state and parameter configuration by controlling the on and off states of the FET. The bias voltage of the optical modulator is precisely controlled. The optical modulator is a key component for converting electrical signals into optical signals. By adjusting the bias voltage, precise modulation of the optical signal intensity and phase can be achieved, with a modulation accuracy of up to 0.1 dB and 0.1 radian, thus meeting the requirements of different computing tasks for optical signals. The sensitivity of the photodetector can be adjusted in the range of -50 dBm to -20 dBm. The photodetector is responsible for converting optical signals into electrical signals. By adjusting its sensitivity, it can adapt to optical signal inputs of different intensities, ensuring stable operation of the chip under different computing tasks. Through the interface circuit supporting the PCIe 4.0 interface standard, the computing results are returned to the ground control center at a transmission rate of not less than 16 GB / s. The PCIe 4.0 interface has high-speed data transmission capabilities and can quickly transmit the computing results of the chip. The interface circuit uses transformer isolation technology and can withstand an electrical isolation voltage of 1000 V, effectively preventing electrical interference between different circuits and ensuring the stability of data transmission. The signal shaping circuit can control the rise and fall times of the input signal within 1 nanosecond, shape the signal, improve the signal quality, and ensure the reliability of the communication connection.

[0065] Based on the above-mentioned method for rapid calculation of UAV intelligent logistics data adapted to the reconfigurable photonic computing chip, a UAV intelligent logistics management method is as Figure 2 shown, including:

[0066] S201. Data acquisition. During the flight of the UAV, the weight sensor, position sensor, and attitude sensor work simultaneously. The weight sensor continuously monitors the weight of the goods, converts the weight change into an electrical signal through the internal sensing element, and outputs it after amplification and filtering processing; the position sensor continuously receives satellite signals and obtains geographical location data after calculation and processing; the attitude sensor continuously detects the angular velocity and acceleration of the UAV and converts these data into attitude information through the internal microprocessor. These data are synchronously collected and summarized to provide comprehensive raw data for subsequent data processing.

[0067] S202. Data Transmission: The collected logistics data and flight data are packaged and modulated according to the 5G communication standard through a high-speed wireless communication module, and stably transmitted to the ground control center at a transmission rate of not less than 1 Gbps. During the transmission process, forward error correction coding technology is adopted to improve the transmission reliability of data in the wireless channel, ensuring the integrity of data even in the face of signal fading and interference.

[0068] S203. Ground Pretreatment: The data processing software of the ground control center first classifies the received data, identifies at least 10 different types of data formats through pattern recognition algorithms, then performs denoising processing, uses wavelet denoising algorithms to remove noise interference in the data, and then performs normalization operations to unify data in different ranges into the same numerical range for convenient subsequent analysis and processing. Then, specific algorithms are used to analyze in combination with dynamic factors. When analyzing flight path planning data, real-time traffic data and weather conditions are combined as dynamic factors, and the optimized Dijkstra algorithm is used to determine the data that requires complex calculations, and these data are sent to the reconfigurable photonic computing chip module through the communication management software.

[0069] S204. High-Speed Computing: After receiving the data, the reconfigurable photonic computing chip module's drive circuit responds quickly. Based on the data type and calculation requirements, the optical switch matrix and optical waveguide structure inside the chip are reconfigurably designed based on the computing architecture that matches the calculation requirements to complete the calculation tasks corresponding to the calculation requirements.

[0070] S205. Result Return and Application: After the calculation is completed, the reconfigurable photonic computing chip module returns the calculation results to the ground control center through the interface circuit at a transmission rate of not less than 16 GB / s. Based on the calculation results, the ground control center generates flight control instructions and logistics distribution plans through the communication management software. The flight control instructions control the flight attitude and speed of the unmanned aerial vehicle according to the current position, attitude, and planned flight path of the unmanned aerial vehicle; the logistics distribution plan determines the distribution tasks and order of each unmanned aerial vehicle according to the classification of goods and delivery address information. These instructions and plans are sent to the unmanned aerial vehicle terminal through the communication link to guide the unmanned aerial vehicle to complete the logistics distribution task.

[0071] In one embodiment, the system composed of the unmanned aerial vehicle terminal, the ground control center, and the reconfigurable photonic computing chip module has a fault diagnosis and recovery mechanism. When faults occur in the unmanned aerial vehicle terminal, the ground control center, and the reconfigurable photonic computing chip module, the system can automatically detect the fault point within 5 seconds and switch data transmission and calculation tasks through backup links and redundant devices; at the same time, the system can record the time, type, and relevant data of the fault occurrence for subsequent fault analysis and system optimization.

[0072] The present invention is dedicated to constructing a drone intelligent logistics data quick calculation system adapted to a reconfigurable photonic computing chip, aiming to fundamentally solve the bottleneck problems faced by current drone intelligent logistics in data processing. On the one hand, the electronic computing chips relied on by traditional drone intelligent logistics have slow processing speeds and are difficult to meet the time-sensitive requirements of logistics distribution. In the face of sudden orders and urgent delivery tasks, they are unable to plan the optimal path in a timely manner, resulting in delivery delays. On the other hand, their high energy consumption severely limits the endurance of drones, restricting the delivery range and making it difficult to achieve long-distance and large-scale logistics distribution. At the same time, there have always been technical barriers in the integration of reconfigurable photonic computing chips and drone systems, including mismatched hardware interfaces and difficult-to-adapt software algorithms. Through innovative designs and technology integration, the present invention strives to achieve efficient, stable, and low-energy consumption operation of drone intelligent logistics in data processing, promoting the entire industry towards intelligence and high efficiency.

[0073] Specifically, the technical effects brought by the present invention are as follows:

[0074] 1. Significantly improve data processing timeliness: The reconfigurable photonic computing chip adopted by the system, combined with an optimized algorithm, has achieved a qualitative leap in data processing. When planning the flight path, based on the parallel search computing architecture that utilizes the high-speed parallel transmission characteristics of optical signals, multiple paths are searched simultaneously. Compared with traditional electronic chips combined with a single algorithm, it can complete the optimal path planning in complex geographical environments within seconds, while the traditional method may take several minutes or even longer, greatly shortening the decision-making time. This ensures that drones can quickly plan a reasonable path in the face of sudden orders and urgent delivery tasks, meeting the time-sensitive requirements of logistics distribution and effectively avoiding delivery delays. In the logistics distribution scheduling calculation, the reconfigurable photonic computing chip can formulate the optimal allocation plan for more than 100 delivery tasks within 1 second, while traditional processing methods are difficult to handle such a large amount of data in a short time, significantly improving the logistics distribution efficiency.

[0075] 2. Greatly reduce energy consumption and extend endurance: The internal optical waveguide structure of the reconfigurable photonic computing chip uses silicon-based materials, and the optical transmission loss is lower than 0.1 dB / cm. The drive circuit based on field-effect transistor (FET) technology precisely controls the working state of the chip to achieve low-power operation. Compared with traditional electronic computing chips, the energy consumption can be reduced by more than 50%. For drones with limited battery endurance, this greatly extends the endurance time, enabling drones to perform delivery tasks over longer distances, and the delivery range can be expanded to more than 1.5 times the original, effectively solving the problem of limited delivery range caused by high energy consumption of traditional drones and providing the possibility for long-distance and large-scale logistics distribution.

[0076] 3. Highly adaptable to complex application scenarios: From the hardware level, the drone terminal integrates a variety of high-precision sensors, including a MEMS weight sensor with temperature compensation function, a GNSS position sensor supporting multi-satellite systems, and an attitude sensor with a high sampling frequency, ensuring stable and accurate data collection in extreme environmental temperatures from -20°C to 60°C, urban high-rise building occlusion, and complex geographical environments in remote mountainous areas. From the software level, the data processing software uses the K-Means clustering algorithm and the Dijkstra algorithm combined with dynamic factors, the communication management software is based on the TCP / IP protocol stack and adopts a variety of data guarantee mechanisms, and the reconfigurable photonic computing chip module can flexibly adjust the architecture and algorithm, enabling the entire system to adaptively adjust the working mode and processing strategy according to different logistics data calculation requirements, real-time traffic data, and dynamic factors such as weather conditions, and being highly adaptable to diverse logistics application scenarios.

[0077] To fully illustrate the specific implementation of the method of the present invention and at the same time demonstrate the superiority of the method of the present invention, the following six specific implementation scenarios are given for detailed description.

[0078] In the implementation scenario of urban express delivery, a high-precision GNSS position sensor is adopted, combined with a multi-system fusion positioning algorithm, to ensure centimeter-level positioning in the urban high-rise building occlusion environment. It is equipped with a high-speed wireless communication module supporting the 5G communication standard with a transmission rate of over 1 Gbps to collect real-time data on the weight, position, and attitude of the goods and transmit them quickly. The MEMS weight sensor equipped has an accuracy of up to ±0.1 grams, and the attitude sensor is based on MEMS technology with an angular velocity measurement range of ±2000° / s and a sampling frequency of not less than 100 Hz. A high-performance computer with a multi-core processor and a memory of not less than 32 GB is used as the hardware platform. The installed data processing software uses the K-Means clustering algorithm to classify express parcels by delivery area and uses the Dijkstra algorithm combined with real-time traffic and no-fly zone data to plan the route. The communication management software is based on the TCP / IP protocol stack to achieve communication with the drone terminal and the reconfigurable photonic computing chip module. The chip manufactured by nanoscale lithography technology has an internal optical waveguide structure made of silicon-based materials with an optical transmission loss of less than 0.1 dB / cm. A drive circuit based on field-effect transistor (FET) technology is configured to respond to the working state of the control chip within the microsecond level. Through an interface circuit supporting the PCIe 4.0 interface standard, it communicates with the ground control center at a transmission rate of not less than 16 GB / s.

[0079] During the flight of the drone in the city, the weight sensor collects the weight of the goods in real time, the position sensor obtains the geographical location, and the attitude sensor monitors the flight attitude, and the data is synchronously summarized. Through the high-speed wireless communication module, according to the 5G communication standard, the collected data is stably transmitted to the ground control center. The data processing software classifies the received data, identifies various data formats, and after denoising and normalization processing, combines the real-time traffic and no-fly zone data, and uses algorithms to analyze and determine complex calculation data, and sends it to the reconfigurable photonic computing chip module through the communication management software. After receiving the data, the chip module drives the circuit to adjust the optical switch matrix (the switching time does not exceed 1 microsecond) and the optical waveguide structure composed of microelectromechanical system (MEMS) optical switches according to the data type and calculation requirements, constructs a parallel search calculation architecture, and quickly plans the optimal delivery route. The calculation result is returned to the ground control center through the interface circuit, generates flight control instructions and logistics distribution plans, and sends them to the drone terminal to guide the distribution task.

[0080] The high-speed computing ability of the reconfigurable photonic computing chip reduces the path planning time from several minutes of traditional electronic chips to several seconds, greatly improving the distribution efficiency. At the same time, the low energy consumption characteristics of the chip reduce the energy consumption of the drone, extend the endurance time, and reduce the charging frequency during frequent takeoff and landing distribution in the city.

[0081] In the implementation scenario of material transportation in remote mountainous areas, an MEMS weight sensor with temperature compensation function is equipped to maintain a measurement accuracy of ±0.1 grams in the environment of -20°C to 60°C. The attitude sensor is based on MEMS technology, with an angular velocity measurement range of ±2000° / s and a sampling frequency of not less than 100Hz, monitoring the flight attitude at a high sampling frequency to ensure stable flight under the complex airflow in the mountains. A high-speed wireless communication module supporting the 5G communication standard with a transmission rate of over 1Gbps is carried to transmit data. Even in mountainous areas where the signal is easily affected by terrain, the stable transmission of data can be guaranteed. A high-performance computer with a multi-core processor and a memory of not less than 32GB is used as the hardware platform to ensure the computing power when processing complex geographical and meteorological data in the mountains. The installed data processing software uses the K-Means clustering algorithm to classify materials by destination, and uses the improved Dijkstra algorithm, combined with mountain terrain data (altitude, slope, valley orientation) and real-time weather conditions (strong wind, heavy rain, fog) to plan the route. The communication management software is based on the TCP / IP protocol stack and stably communicates with the drone terminal and the reconfigurable photonic computing chip module through signal enhancement devices. The chip manufactured by nanoscale lithography technology has an internal optical waveguide structure made of silicon-based materials, and the optical transmission loss is lower than 0.1dB / cm. A drive circuit based on field effect transistor (FET) technology is configured to respond to the working state of the control chip within microseconds. Through the interface circuit supporting the PCIe 4.0 interface standard, it communicates with the ground control center at a transmission rate of not less than 16GB / s. To meet the long-distance transportation requirements in mountainous areas, the internal computing architecture of the chip is optimized, and a cache area is increased to handle the processing of a large amount of terrain data.

[0082] During the flight of the drone in the mountainous area, the weight sensor continuously collects the weight of the materials. The position sensor uses a multi-system fusion positioning algorithm to overcome the problem of satellite signal occlusion in the mountains and obtain the geographical location. The attitude sensor monitors the flight attitude and synchronously summarizes the data. Through the high-speed wireless communication module, in accordance with the 5G communication standard and with the help of signal relay devices, the collected data is stably transmitted to the ground control center. The data processing software classifies the received data, identifies various data formats, performs denoising and normalization processing, and then combines the mountain terrain and weather data to determine complex calculation data through algorithm analysis, and sends it to the reconfigurable photonic computing chip module through the communication management software. After receiving the data, the chip module adjusts the optical switch matrix (the switching time does not exceed 1 microsecond) composed of microelectromechanical system (MEMS) optical switches and the optical waveguide structure according to the transportation requirements in the mountains, optimizes the logistics distribution scheduling algorithm, and considers the special risks of mountain flight (unstable airflow, obstacle avoidance requirements due to complex terrain), and plans a safe and efficient transportation route. The calculation result is returned to the ground control center through the interface circuit, generating flight control instructions and a material transportation plan, and sending them to the drone terminal to guide the drone to transport materials.

[0083] The system can adapt to the complex geographical and climatic conditions in remote mountainous areas. The reconfigurable photonic computing chip can quickly process a large amount of data, plan safe and efficient routes for mountainous area material transportation, solve the problem that traditional solutions are difficult to handle complex environmental data processing, and ensure the timely delivery of materials. Compared with traditional solutions, the transportation time can be shortened by 20%-30%, and flight accidents caused by unreasonable route planning can be effectively avoided.

[0084] In the implementation scenario of fresh food cold chain distribution, the accuracy of the weight sensor can reach ±0.1 grams, accurately measuring the weight of fresh food goods to prevent overloading from affecting flight safety and goods preservation. It is equipped with a high-speed wireless communication module that supports the 5G communication standard and has a transmission rate of over 1 Gbps, which can collect data on the weight, position, and attitude of goods in real time and transmit it quickly. The attitude sensor is based on MEMS technology, with an angular velocity measurement range of ±2000° / s and a sampling frequency of not less than 100 Hz. A temperature sensor is equipped to monitor the cold chain environment temperature in real time to ensure that fresh food is transported at an appropriate temperature. A high-performance computer with a multi-core processor and a memory of not less than 32 GB is used as the hardware platform. The installed data processing software uses the K-Means clustering algorithm to classify fresh food orders according to the distribution area and preservation requirements, and uses the Dijkstra algorithm to plan routes in combination with real-time traffic and cold chain distribution time window requirements. The communication management software is based on the TCP / IP protocol stack to achieve communication with the drone terminal and the reconfigurable photonic computing chip module. The chip manufactured by nanoscale lithography technology has an internal optical waveguide structure made of silicon-based materials, and the optical transmission loss is lower than 0.1 dB / cm. A drive circuit based on field effect transistor (FET) technology is configured to respond to the working state of the control chip within the microsecond level. Through an interface circuit that supports the PCIe 4.0 interface standard, it communicates with the ground control center at a transmission rate of not less than 16 GB / s. In response to the special computing requirements of cold chain distribution, the internal computing architecture of the chip is optimized, and a real-time analysis module for temperature data is added.

[0085] During the flight of the drone, the weight sensor collects the weight of the goods in real time, the position sensor obtains the geographical location, the attitude sensor monitors the flight attitude, the temperature sensor monitors the temperature of the cold chain environment, and the data is synchronously summarized. Through the high-speed wireless communication module, according to the 5G communication standard, the collected data is stably transmitted to the ground control center. The data processing software classifies the received data, identifies various data formats, performs denoising and normalization processing, and then combines the real-time traffic and cold chain delivery time window requirements. Using algorithms to analyze and determine complex calculation data, it is sent to the reconfigurable photonic computing chip module through the communication management software. After receiving the data, the chip module drives the circuit to adjust the optical switch matrix composed of microelectromechanical system (MEMS) optical switches (switching time does not exceed 1 microsecond) and the optical waveguide structure according to the data type and cold chain delivery calculation requirements, optimizes the delivery sequence, and ensures that the fresh food is delivered within the shortest time and the temperature always meets the fresh-keeping requirements. The calculation results are returned to the ground control center through the interface circuit, generating flight control instructions and logistics delivery plans, which are sent to the drone terminal to guide the drone to deliver fresh food.

[0086] The reconfigurable photonic computing chip quickly processes the delivery tasks, reduces the delivery time, and ensures the quality of fresh food. At the same time, the low energy consumption characteristics of the entire system reduce the power consumption of the cold chain equipment and extend the working hours of the drone and the cold chain equipment. Compared with the traditional solution, it can increase the freshness retention rate of fresh food by 10%-15% and shorten the delivery time by 15%-25%.

[0087] In the implementation scenario of rapid emergency material delivery, a high-speed wireless communication module supporting the 5G communication standard with a transmission rate of over 1 Gbps is quickly activated to rapidly transmit on-site situation and material data. The weight sensor has an accuracy of up to ±0.1 grams and can collect the weight of materials in real-time. The attitude sensor is based on MEMS technology, with an angular velocity measurement range of ±2000° / s and a sampling frequency of not less than 100 Hz, enabling rapid acquisition of flight attitude data. The position sensor combines a multi-system fusion positioning algorithm to accurately locate in complex rescue environments. Using a high-performance computer with a multi-core processor and a memory of not less than 32 GB as the hardware platform to ensure rapid response in emergencies. The installed data processing software uses the K-Means clustering algorithm to classify rescue materials according to the degree of urgency, and uses the Dijkstra algorithm combined with real-time disaster information (affected area range, road damage situation, personnel distribution) to plan the delivery route. The communication management software is based on the TCP / IP protocol stack and communicates with the drone terminal and the reconfigurable photonic computing chip module through the emergency communication network. The chip manufactured using nanoscale lithography technology has an internal optical waveguide structure made of silicon-based materials, with an optical transmission loss of less than 0.1 dB / cm. A drive circuit based on field effect transistor (FET) technology is configured to respond to the working state of the control chip within the microsecond level. Through an interface circuit supporting the PCIe 4.0 interface standard, it communicates with the ground control center at a transmission rate of not less than 16 GB / s. To meet the urgent computing requirements of rapid emergency material delivery, the internal computing architecture of the chip is optimized, the operation priority is increased, and key data is ensured to be processed first.

[0088] During the emergency rescue flight of the drone, the weight sensor collects the weight of materials in real-time, the position sensor obtains the geographical location, and the attitude sensor monitors the flight attitude, quickly collecting on-site situation and material data and synchronously summarizing the data. Through the high-speed wireless communication module, in accordance with the 5G communication standard, through the emergency communication network, the collected data is quickly transmitted to the ground control center. The data processing software classifies the received data, identifies various data formats, performs denoising and normalization processing, and then combines real-time disaster information to use algorithms to analyze and determine complex calculation data, and sends it to the reconfigurable photonic computing chip module through the communication management software. After receiving the data, the chip module's drive circuit adjusts the optical switch matrix (with a switching time of no more than 1 microsecond) composed of microelectromechanical system (MEMS) optical switches and the optical waveguide structure according to the emergency delivery requirements, quickly completes complex calculations, determines the optimal delivery plan, and plans a safe and efficient delivery route considering the complex terrain of the affected area and the emergency rescue requirements. The calculation results are returned to the ground control center through the interface circuit, generating flight control instructions and an emergency material delivery plan, and sending them to the drone terminal to guide the drone to deliver emergency materials.

[0089] The high-speed computing ability of the reconfigurable photonic computing chip enables the emergency material delivery plan to be determined within seconds, significantly shortening the decision-making time compared with traditional plans, winning precious time for rescue, and meeting the extremely high requirements for timeliness in emergency scenarios. Compared with traditional plans, the decision-making time can be shortened by 80%-90%, greatly improving the rescue efficiency.

[0090] In the implementation scenario of large-scale e-commerce promotion logistics, a large amount of cargo and flight data are continuously collected. It is equipped with a high-speed wireless communication module that supports the 5G communication standard and has a transmission rate of more than 1 Gbps, and transmits quickly through 5G communication. The accuracy of the weight sensor can reach ±0.1 grams, and it collects the weight of the cargo in real time. The attitude sensor is based on MEMS technology, with an angular velocity measurement range of ±2000° / s and a sampling frequency of not less than 100 Hz. The position sensor combines multi-system fusion positioning algorithms for precise positioning. Using a high-performance computer with a multi-core processor and a memory of not less than 64 GB as the hardware platform to cope with the pressure of processing massive order data. The installed data processing software uses the K-Means clustering algorithm to classify massive orders in multiple dimensions such as delivery area and commodity type, and uses the Dijkstra algorithm to plan the path in combination with real-time traffic and delivery timeliness requirements. The communication management software is based on the TCP / IP protocol stack to achieve communication with the drone terminal and the reconfigurable photonic computing chip module. The chip manufactured by nanoscale lithography technology has an internal optical waveguide structure made of silicon-based materials, and the optical transmission loss is less than 0.1 dB / cm. A drive circuit based on field effect transistor (FET) technology is configured to respond to the working state of the control chip within the microsecond level. Through an interface circuit that supports the PCIe 4.0 interface standard, it communicates with the ground control center at a transmission rate of not less than 16 GB / s. Using a parallel computing algorithm, it divides and processes large-scale logistics order data, and optimizes the computing architecture inside the chip according to the characteristics of large-scale e-commerce promotion logistics, increasing the number of parallel computing cores.

[0091] During the flight of the drone, a large amount of data on the weight, position, and attitude of the ordered goods are collected and the data is synchronously summarized. Through the high-speed wireless communication module, according to the 5G communication standard, the collected data is quickly transmitted to the ground control center. The data processing software classifies the massive data, identifies various data formats, and after denoising and normalization processing, combines real-time traffic and delivery timeliness requirements, uses algorithms to analyze and determine complex calculation data, and sends it to the reconfigurable photonic computing chip module through the communication management software. After receiving the data, the drive circuit adjusts the optical switch matrix (the switching time does not exceed 1 microsecond) composed of microelectromechanical system (MEMS) optical switches and the optical waveguide structure according to the data type and the calculation requirements of large-scale e-commerce promotion logistics, and uses a parallel computing algorithm to quickly complete the distribution scheduling calculation and improve the calculation efficiency. The calculation results are returned to the ground control center through the interface circuit, generating flight control instructions and logistics distribution plans, and sending them to the drone terminal to guide drone delivery.

[0092] The parallel computing and flexible architecture adjustment capabilities of the reconfigurable photonic computing chip enable it to efficiently process massive amounts of data. During large-scale e-commerce promotions, it can ensure the efficient and orderly progress of logistics distribution, solving the problem of insufficient processing capacity in traditional solutions when the data volume explodes. Compared with traditional solutions, the order processing efficiency can be increased by 30%-50%, effectively reducing delivery delays.

[0093] In the implementation scenario of emergency medical delivery, in addition to being equipped with high-precision weight, position, and attitude sensors and high-speed wireless communication modules, vibration sensors and humidity sensors are additionally installed. The vibration sensors are used to monitor the vibration of the drone during transportation to prevent the drugs from being damaged due to vibration; the humidity sensors continuously monitor the humidity of the transportation environment to ensure that humidity-sensitive drugs are in suitable storage conditions. The accuracy of each sensor is consistent with the previous embodiments, and the 5G communication module stably transmits data. Based on a high-performance computer as the hardware foundation, the data processing software in operation classifies orders meticulously according to the expiration date, urgency, and special storage requirements of the drugs. When using the Dijkstra algorithm to plan the path, it combines the location of the hospital, traffic conditions, and the timeliness requirements of the drugs to ensure that the drugs can be delivered on time. The communication management software safeguards the security of drug delivery information through an encrypted communication channel. The chip is manufactured based on nanolithography technology and has a low-loss optical waveguide structure. In response to the strict requirements for safety and timeliness in medical delivery, the drive circuit optimizes the control logic to ensure the stable operation of the chip. The interface circuit ensures high-speed data transmission, and the internal computing architecture of the chip is optimized to add a processing module for the special attribute data of drugs, and dynamically adjusts the delivery priority according to the expiration date of the drugs.

[0094] When the drone is flying, the weight, position, attitude, vibration, and humidity sensors work simultaneously to collect data comprehensively. The weight sensor monitors the weight of the medicine to ensure transportation safety; the position sensor accurately locates; the attitude sensor ensures stable flight; the vibration and humidity sensors monitor the transportation environment, and these data are collected synchronously. Through the 5G communication module, the collected data is transmitted to the ground control center according to the encryption protocol to ensure the security and integrity during data transmission. After receiving the data, the data processing software first performs format recognition and denoising processing, and then combines the special requirements of medicine distribution, such as the expiration date and storage conditions, to analyze and classify the data, screen out the data that requires complex calculations, and send it to the reconfigurable photonic computing chip module through the communication management software. After receiving the data, the chip module drives the circuit to adjust the optical switch matrix and optical waveguide structure according to the requirements of medical distribution to construct a dedicated computing model. On the basis of considering the expiration date and urgency of the medicine, the distribution route and time plan are optimized to ensure that the medicine is delivered quickly and safely within the expiration date. The calculation results are returned to the ground control center through the interface circuit to generate detailed distribution instructions, including flight speed, altitude, path, and special precautions, and sent to the drone terminal to guide the drone to complete the medical distribution task.

[0095] The fast computing ability of the reconfigurable photonic computing chip enables the path planning and task scheduling of medical distribution to be completed in a short time, meeting the urgent medical needs. Through the real-time monitoring and processing of transportation environment data, the quality and safety of the medicine are guaranteed. Compared with the traditional distribution scheme, the distribution time can be shortened by 30%-40%, and the medicine damage rate is reduced by more than 50%, greatly improving the efficiency and reliability of medical distribution.

[0096] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are shown sequentially according to the indication of the arrows, these steps do not necessarily have to be executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps does not have a strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages, and these steps or stages do not necessarily have to be executed at the same moment, but can be executed at different moments, and the execution order of these steps or stages does not necessarily have to be sequential, but can be executed alternately or alternately with at least a part of other steps or steps or stages in other steps.

[0097] Based on the same inventive concept, an embodiment of the present application further provides a drone intelligent logistics data rapid calculation system for a reconfigurable photonic computing chip adapted to implement the above-mentioned drone intelligent logistics data rapid calculation method. The solution provided by this system to solve the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the drone intelligent logistics data rapid calculation system for a reconfigurable photonic computing chip provided below can refer to the limitations on the drone intelligent logistics data rapid calculation method for a reconfigurable photonic computing chip in the above text, and will not be repeated here.

[0098] In one embodiment, as Figure 3 shown, a drone intelligent logistics data rapid calculation system for a reconfigurable photonic computing chip is provided, including a reconfigurable photonic computing module. The reconfigurable photonic computing module includes a reconfigurable photonic computing chip and a driving circuit. The driving circuit includes: a data acquisition module for acquiring logistics data and calculation requirements; a matching module for matching a preset calculation architecture according to the calculation requirements; a configuration module for configuring the reconfigurable photonic chip according to the matched calculation architecture, so that the reconfigurable photonic chip executes the calculation task corresponding to the calculation requirements based on the logistics data; wherein, the calculation architecture is determined by the connection mode of the optical switch matrix inside the reconfigurable photonic chip and the layout of the optical waveguide structure.

[0099] In one embodiment, the system further includes a drone terminal and a ground control center; the drone terminal is equipped with a sensor component for collecting real-time sensing data and sends the real-time sensing data to the ground control center; the ground control center is used to receive the real-time sensing data, extract relevant real-time sensing data according to the task requirements, determine the logistics data including the extracted real-time sensing data, and send the logistics data to the reconfigurable photonic computing module; the ground control center is further used to receive the execution result corresponding to the calculation task output by the reconfigurable photonic computing module and generate a task instruction according to the execution result and send it to the drone terminal.

[0100] Each module in the above-mentioned drone intelligent logistics data rapid calculation system for a reconfigurable photonic computing chip can be implemented in whole or in part by software, hardware, and their combination. The above-mentioned modules can be embedded in the processor in the computer device in hardware form or be independent of the processor, or can be stored in the memory in the computer device in software form, so that the processor can call and execute the operations corresponding to the above-mentioned modules.

[0101] In one embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in all the above method embodiments are implemented.

[0102] 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 all the above method embodiments are implemented.

[0103] In one embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in all the above method embodiments are implemented.

[0104] 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 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. Moreover, the collection, use, and processing of relevant data need to comply with the relevant laws, regulations, and standards of relevant countries and regions.

[0105] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing 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 embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. 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), magnetoresistive 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 be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0106] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, 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, it should be considered as the scope described in this specification.

[0107] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A method for rapid calculation of intelligent logistics data of unmanned aerial vehicles adapted to a reconfigurable photonic computing chip, characterized in that: The method comprises: Obtain logistics data and computing requirements; Matching a preset computing architecture according to the computing requirements; Configuring a reconfigurable photonic chip according to the matched computing architecture, so that the reconfigurable photonic chip performs a computing task corresponding to the computing demand based on the logistics data; The computing architecture is determined by the connection mode of the optical switch matrix inside the reconfigurable photonic chip and the layout of the optical waveguide structure.

2. The method according to claim 1, characterized in that: The computational requirements include flight path planning; The computing architecture for the flight path planning and matching is a computing architecture based on parallel search. The optical switch matrix corresponding to the computing architecture based on parallel search is connected in a parallel transmission manner, and the corresponding optical waveguide structure is a mesh structure.

3. The method according to claim 1, characterized in that The computing requirements include logistics distribution scheduling; The computing architecture for the logistics distribution scheduling matching is a computing architecture based on a task allocation model. The optical switch matrix corresponding to the computing architecture based on the task allocation model is connected according to the task allocation logic, and the corresponding optical waveguide structure is a layered or partitioned structure.

4. The method according to claim 1, characterized in that: After configuring the reconfigurable photonic chip, the method further includes: Verify the execution of the computing task by the current reconfigurable photonic chip, and iteratively optimize the computing architecture according to the verification result until the verification result meets the preset condition.

5. The method according to claim 4, characterized in that The iterative optimization of the computing architecture according to the verification result comprises: When the verification result does not meet the preset conditions, the optical switch matrix or the optical waveguide structure is adjusted based on the computing architecture that matches the current reconfigurable photonic chip, and the reconfigurable photonic chip is reconfigured based on the adjusted computing architecture. The reconfigured reconfigurable photonic chip is used as the current reconfigurable photonic chip, the verification result is updated, and it is determined whether the verification result meets the preset conditions.

6. The method according to claim 1, characterized in that The logistics data is data that is screened according to the computing requirements and classified using a clustering algorithm.

7. An intelligent logistics data rapid calculation system for unmanned aerial vehicles adapted to a reconfigurable photonic computing chip, characterized in that: The system includes a reconfigurable photon computing module, and the reconfigurable photon computing module includes a reconfigurable photon computing chip and a driving circuit; The driving circuit comprises: Data acquisition module, used to obtain logistics data and calculate requirements; A matching module, used to match a preset computing architecture according to the computing requirements; A configuration module, configured to configure a reconfigurable photonic chip according to the matched computing architecture, so that the reconfigurable photonic chip performs a computing task corresponding to the computing demand based on the logistics data; The computing architecture is determined by the connection mode of the optical switch matrix inside the reconfigurable photonic chip and the layout of the optical waveguide structure.

8. The system according to claim 7, characterized in that: The system also includes a UAV terminal and a ground control center; The drone terminal is equipped with a sensor component for collecting real-time sensor data, and sends the real-time sensor data to the ground control center; The ground control center is used to receive the real-time sensor data, extract relevant real-time sensor data according to task requirements, determine the logistics data including the extracted real-time sensor data, and send the logistics data to the reconfigurable photonic computing module; The ground control center is also used to receive the execution result corresponding to the computing task output by the reconfigurable photonic computing module, and generate a task instruction according to the execution result and send it to the UAV terminal.

9. A computer device 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.

10. 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.

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