CN116957453A - Distribution method and system based on high-speed image recognition - Google Patents
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Abstract
The application discloses a distribution method and a distribution system based on high-speed image recognition, which belong to the technical field of logistics distribution and comprise the following steps: the logistics distribution server distributes distribution tasks according to the order information and generates distribution scheme information; the delivery vehicle selects a corresponding route for delivery according to the delivery scheme information; collecting environmental data of a distribution route in the distribution process; according to the application, in the logistics distribution process, the problems of obstacles and other traffic jams on the distribution route are monitored in real time, the acquired image information is processed by adopting the cache processing unit, the processed environmental data can be analyzed and calculated based on the target detection algorithm of autonomous learning, the environmental defects are marked, and the distribution route is optimized according to the analysis and calculation result of the environmental data.
Description
Technical Field
The application belongs to the technical field of logistics distribution, and particularly relates to a distribution method and system based on high-speed image recognition.
Background
Modern logistics is an economic globalization product and is also an important service industry for promoting the economic globalization, and the China logistics industry is started later, but with the rapid development of national economy and the explosive growth of electronic commerce, the China logistics industry keeps a faster growth speed. However, modern logistics enterprises in China generally have high operation cost, and particularly, transportation scheduling and route planning are not suitable for development requirements of the modern logistics industry, so that logistics cost is high, and logistics distribution efficiency is low.
For example, chinese patent with the authority (bulletin) number CN109544070B discloses an automatic logistics distribution method, wherein a logistics distribution server generates a logistics distribution request according to dispatch order information sent by a first terminal device and sends the logistics distribution request to a vehicle management server; the vehicle management server performs task allocation processing according to the logistics distribution request, so that the logistics vehicle updates the driving path navigation data according to the path planning information, and drives according to the updated driving path navigation data, and simultaneously shares the position information with the vehicle management server and the logistics distribution server in real time; when the logistics vehicle runs to a delivery place, the delivery code input by a sender is identified and matched, and a delivery instruction is generated when matching is successful so as to perform a delivery operation; when the logistics vehicle runs to the dispatch location, the logistics vehicle monitors the pick-up code input by the receiver and performs matching identification, when matching is successful, the ID of the storage cabinet is determined according to the pick-up code which is matched successfully, and a pick-up instruction is generated for the receiver to pick up the express mail in the storage cabinet.
According to the logistics distribution method, the running route of the distribution vehicle cannot be monitored in real time in the logistics distribution process, so that when the situation that the running route is blocked or congestion is caused by other traffic problems is faced, the logistics distribution guiding route cannot be updated timely and effectively, and the logistics distribution efficiency is low; therefore, the distribution method and the distribution system based on the high-speed image recognition are provided for solving the problems in the prior art, improving the working efficiency, saving time and labor and reducing the production labor cost of enterprises.
Disclosure of Invention
The application aims to provide a distribution method and a distribution system based on high-speed image recognition, which are used for solving the problems that in the prior art, the running route of a distribution vehicle cannot be monitored in real time in the logistics distribution process, so that when the running route is blocked or congestion is caused by other traffic problems, the guiding route of logistics distribution cannot be updated timely and effectively, and the logistics distribution efficiency is low.
In order to achieve the above purpose, the application adopts the following technical scheme:
a distribution method based on high-speed image recognition comprises the following steps:
the logistics distribution server distributes distribution tasks according to the order information and generates distribution scheme information;
the delivery vehicle selects a corresponding route for delivery according to the delivery scheme information;
collecting environmental data of a delivery route by using a laser radar and a visible light monocular camera on a delivery vehicle in the delivery process;
processing the collected environmental data by adopting a plurality of cache processing units;
analyzing and calculating the processed environmental data based on an autonomous learning target detection algorithm, and marking environmental defects;
the logistics distribution server optimizes distribution routes based on the analysis and calculation results.
Preferably, the logistics distribution server distributes distribution tasks according to order information and generates distribution scheme information, wherein the order information comprises:
order ID, sender information, recipient information, courier size information, time of delivery, place of dispatch information.
Preferably, the logistics distribution server distributes distribution tasks according to order information and generates distribution scheme information, wherein the distribution scheme information comprises:
order ID, vehicle ID of the assigned delivery vehicle, path planning information, and predicted delivery time.
Preferably, the delivery vehicle selects a corresponding route for delivery according to the delivery scheme information, and the method comprises the following steps:
the vehicle updates navigation data according to the path planning information in the dispatch scheme;
sending the express according to the navigation data;
and after the express delivery site is reached, updating the next delivery route according to the delivery scheme information.
Preferably, the collecting environmental data of the delivery route during the delivery process by using the laser radar and the visible light monocular camera on the delivery vehicle includes the following steps:
the visible light monocular camera is used for collecting environment images in real time, and the laser radar is used for detecting the position and the speed of object types in the environment;
processing the collected environment image and the position and speed data of the object category in the detection environment, and generating an environment data set;
and uploading the environment data set to a logistics distribution server.
Preferably, the processing of the collected environmental data using a plurality of cache processing units, wherein:
the cache processing unit is used for managing data in the logistics distribution server in page-to-page data units, each page of data comprises a plurality of block data, and each block data is used as an access unit of an upper system;
the cache processing unit processes input or output requests of the upper layer system with respect to a storage device.
Preferably, the processing of the collected environmental data by using a plurality of cache processing units, wherein the processing of the environmental data by the cache processing units comprises the following steps:
dividing a video image into a plurality of two-dimensional pixel blocks, and separating brightness information and chromaticity information of each pixel point in the video image;
converting a first address of chromaticity information of each pixel point in the two-dimensional pixel block into a second address in a main storage unit;
reading chromaticity information corresponding to a second address in the chromaticity caching unit, and taking the two-dimensional pixel block as a pixel point to carry out inaccurate processing;
and loading the brightness information of each pixel in the video image to a brightness buffer unit, and reading the brightness information in the brightness buffer unit.
Preferably, the target detection algorithm based on autonomous learning performs analysis and calculation on the processed environmental data, and marks environmental defects, and the target detection algorithm includes:
creating a label training set, wherein the training set is a properly sheared picture sample, so that a detection object is positioned at the center position and occupies a whole picture basically;
training a neural network, inputting a clipped image, and outputting whether an object is detected or not by the network;
applying the neural network to analyze and calculate the environmental data;
selecting the clipped picture, inputting the clipped small picture into the neural network, and classifying each position to judge whether an object to be detected exists in the clipped picture;
recording the position of the object to be detected, and marking that the position has defects.
Preferably, the logistics distribution server optimizes the distribution route based on the analysis and calculation result, and comprises the following steps:
uploading the defect position information searched based on the target detection algorithm to a logistics distribution server;
the logistics distribution server optimizes the distribution route based on the analysis and calculation result;
and transmitting the optimized delivery route to the delivery vehicle.
Based on the distribution method based on the image high-speed identification, the application also discloses a distribution system based on the image high-speed identification, which comprises the following steps:
the dispatch scheme generation module is used for dispatching a dispatch task according to the order information by the logistics distribution server and generating dispatch scheme information;
the dispatch route selection module is used for selecting a corresponding route for dispatch by the dispatch vehicle according to the dispatch scheme information;
the image information acquisition module is used for acquiring environmental data of the delivery route by utilizing a laser radar and a visible light monocular camera on the delivery vehicle in the delivery process;
the environment data processing module is used for processing the collected environment data by adopting a plurality of cache processing units;
the data analysis and calculation module is used for analyzing and calculating the processed environmental data based on an autonomous learning target detection algorithm and marking environmental defects;
and the distribution route optimization module is used for optimizing the distribution route based on the analysis and calculation result by the logistics distribution server.
The application has the technical effects and advantages that: compared with the prior art, the distribution method and system based on high-speed image recognition have the following advantages:
according to the application, in the logistics distribution process, the problems of obstacles and other traffic jams on the distribution route are monitored in real time, the acquired image information is processed by adopting the cache processing unit, the processed environmental data can be analyzed and calculated based on the target detection algorithm of autonomous learning, the environmental defects are marked, and the distribution route is optimized according to the analysis and calculation result of the environmental data.
Drawings
Fig. 1 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present application;
FIG. 2 is a flow chart of a distribution method based on high-speed image recognition in an embodiment of the application;
FIG. 3 is a system block diagram of a distribution system based on high-speed image recognition according to the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. The specific embodiments described herein are merely illustrative of the application and are not intended to limit the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The distribution method based on the image high-speed recognition, which is related to the embodiment of the application, is mainly applied to electronic equipment, and the electronic equipment can be equipment with display and processing functions such as a PC (personal computer), a portable computer, a mobile terminal and the like.
Referring to fig. 1, fig. 1 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the application. In an embodiment of the present application, an electronic device may include a processor and a memory, where the processor and the memory are connected by a communication bus; the processor is used for calling and executing the program stored in the memory; the memory is used for storing a program for realizing a distribution method based on high-speed image recognition.
By way of example, the electronic devices in the disclosed embodiments of the application may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), car terminals (e.g., car navigation terminals), and the like, as well as stationary terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 1 is only an example and should not be construed as limiting the functionality and scope of use of the disclosed embodiments of the application.
As shown in fig. 1, the electronic device may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 101 that may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 102 or a program loaded from a storage means 108 into a Random Access Memory (RAM) 103. In the RAM103, various programs and data required for the operation of the electronic device are also stored. The processing device 101, ROM102, and RAM103 are connected to each other by a bus 104. An input/output (I/O) interface 105 is also connected to bus 104.
In general, the following devices may be connected to the I/O interface 105: input devices 106 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 107 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage devices 108 including, for example, magnetic tape, hard disk, etc.; and a communication device 109. The communication means 109 may allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. While fig. 1 shows an electronic device having various means, it is to be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead.
In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a non-transitory computer readable medium, the computer program comprising program code for performing a high-speed image recognition-based delivery method as shown in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means 109, or from the storage means 108, or from the ROM 102. When the computer program is executed by the processing device 101, the above-described functions defined in the distribution method based on the high-speed recognition of images according to the disclosed embodiment of the present application are executed.
Those skilled in the art will appreciate that the hardware architecture shown in fig. 1 is not limiting of the electronic device and may include more or fewer components than shown, or may combine certain components, or a different arrangement of components.
With continued reference to fig. 1, the memory of fig. 1, which is a computer readable storage medium, may include an operating system, a network communication module, and a distribution method program based on high-speed recognition of images.
In fig. 1, the network communication module is mainly used for connecting with a server and performing data communication with the server; the processor may call the distribution method program based on the image high-speed identification stored in the memory, and execute the distribution method based on the image high-speed identification provided by the embodiment of the application.
The application provides a distribution method based on high-speed image recognition as shown in fig. 2, which comprises the following steps:
s1, a logistics distribution server distributes distribution tasks according to order information and generates distribution scheme information;
specifically, the logistics distribution server distributes distribution tasks according to order information and generates distribution scheme information, wherein the order information comprises:
order ID, sender information, recipient information, courier size information, time of delivery, place of dispatch information.
Further, the logistics distribution server distributes distribution tasks according to the order information and generates distribution scheme information, wherein the distribution scheme information comprises the following steps:
order ID, vehicle ID of the assigned delivery vehicle, path planning information, and predicted delivery time.
S2, the delivery vehicle selects a corresponding route for delivery according to the delivery scheme information;
specifically, the delivery vehicle selects a corresponding route to deliver according to the delivery scheme information, and the delivery vehicle comprises the following steps:
the vehicle updates navigation data according to the path planning information in the dispatch scheme;
sending the express according to the navigation data;
and after the express delivery site is reached, updating the next delivery route according to the delivery scheme information.
S3, acquiring environmental data of a delivery route by using a laser radar and a visible light monocular camera on a delivery vehicle in the delivery process;
specifically, the method for collecting environmental data of a delivery route by using a laser radar and a visible light monocular camera on a delivery vehicle in the delivery process comprises the following steps:
the visible light monocular camera is used for collecting environment images in real time, and the laser radar is used for detecting the position and the speed of object types in the environment;
processing the collected environment image and the position and speed data of the object category in the detection environment, and generating an environment data set;
and uploading the environment data set to a logistics distribution server.
The laser radar is a radar system that detects a characteristic quantity such as a position and a speed of a target by emitting a laser beam. The working principle is that a detection signal (laser beam) is emitted to a target, then a received signal (target echo) reflected from the target is compared with the emission signal, and after proper processing, the related information of the target, such as parameters of the distance, the azimuth, the altitude, the speed, the gesture, the even the shape and the like of the target, can be obtained. The laser changes the electric pulse into the light pulse to be emitted, and the light receiver restores the light pulse reflected from the target into the electric pulse to be sent to the display.
It should be further noted that, the visible light monocular camera includes a lens, an image sensor, an ISP, a serializer, and other main structures, where the lens collects the reflected light of the object to be illuminated and gathers the reflected light onto the image sensor, similar to the aperture in aperture imaging, and the focal length is a measurement parameter for measuring the light gathering or divergence of the lens. When parallel rays are incident on the convex lens, the ideal lens will collect all the incident rays to a point, which is the focal point. The focal length refers to the distance from the optical center of the lens to the focal point.
There are mainly two types of image sensors, CMOS type and CCD type. CMOS image sensors are currently absolutely dominant in the field of vehicle cameras, CMOS being a semiconductor made of two elements, silicon and germanium. On the scale of a chip, millions and millions of pixels are integrated, and each pixel is integrated with circuits such as a light-sensitive diode, a transmission electrode gate, an amplifying electrode gate and the like, and mainly plays three roles of photoelectric conversion, charge-voltage conversion and analog-digital conversion.
ISP (Image Signal Processor ) for processing the front-end image sensor output signals.
The CSI is one of serial interfaces of cameras, and is particularly suitable for data transmission of cameras of mobile equipment due to the characteristics of high speed, low transmission delay and low power consumption of the CSI, but the transmission distance is short, the signal integrity cannot be guaranteed by long-distance transmission, two long-distance high-speed transmission buses which are mainstream in the current vehicle-mounted field are FPD-Link and GMSL based on LVDS protocols, and 15m long-distance transmission can be supported by adopting coaxial cables/shielded twisted pair lines at present.
S4, processing the collected environmental data by adopting a plurality of cache processing units;
specifically, the method uses a plurality of cache processing units to process the collected environmental data, wherein:
the cache processing unit is used for managing data in the logistics distribution server in page-to-page data units, each page of data comprises a plurality of block data, and each block data is used as an access unit of an upper system;
the cache processing unit processes input or output requests of the upper layer system with respect to a storage device.
In still other embodiments, the cache processing unit includes:
a cache array for storing data, wherein the cache array is for storing a duplicate copy of an L1 cache of the computing system;
a hardware decoder to decode at least one instruction that needs to cycle over a memory area that is larger than a cache line size and to be offloaded from execution by an execution cluster of a computing system;
loop control hardware for controlling loops through the memory regions in the cache array;
cache locking hardware for locking the memory region in the cache array being operated on; and
operating hardware coupled to the hardware decoder and the cache array for performing a plurality of operations on the cache array according to the decoded instructions.
The operating hardware also includes a set of one or more buffers for temporarily storing data being operated on.
The operating hardware is to read a cache region from the L1 cache, to store a duplicate cache region in the cache array, to lock the duplicate cache region, and to operate on the duplicate cache region. The operating hardware is to invalidate the cache region in the L1 cache and unlock the copy cache region after operating on the copy cache region. The hardware decoder is further to decode load and store requests received from an execution cluster of a computing system, and the operating hardware is to process the load and store requests. The plurality of operations performed by the operating hardware for the decoded instruction include store operations and load operations. At least one of the instructions offloaded from execution of the execution clusters by the computing system requires execution of a computation, and the operating hardware includes a set of one or more execution hardware for performing the computation of the at least one of the instructions.
The embodiment describes a cache processing unit executing instructions that have been offloaded from execution by an execution cluster of a computing system. For example, certain memory management functions (e.g., memory flushing, memory copying, transposing, etc.) are offloaded from execution by an execution cluster of a computing system and directly executed by a cache co-processing unit (which may include the data being operated on). As another example, instructions that result in performing constant computing operations on successive regions of a cache array within a cache co-processing unit may be offloaded to and executed by the cache co-processing unit (e.g., matrix dot product, array summation, etc.). Offloading these instructions to the cache co-processing unit reduces the number of load and store operations between the cache processing unit and the execution clusters of the computing system, thereby reducing instruction counts, freeing up the resources (e.g., reservation Stations (RSs), reorder buffers (ROBs), fill buffers, etc.) of the execution clusters, which allows the execution clusters to use those resources to process other instructions.
Still further, the processing of the collected environmental data using a plurality of cache processing units, wherein the processing of the environmental data by the cache processing units comprises the steps of:
dividing a video image into a plurality of two-dimensional pixel blocks, and separating brightness information and chromaticity information of each pixel point in the video image;
converting a first address of chromaticity information of each pixel point in the two-dimensional pixel block into a second address in a main storage unit;
reading chromaticity information corresponding to a second address in the chromaticity caching unit, and taking the two-dimensional pixel block as a pixel point to carry out inaccurate processing;
and loading the brightness information of each pixel in the video image to a brightness buffer unit, and reading the brightness information in the brightness buffer unit.
S5, analyzing and calculating the processed environmental data based on an autonomous learning target detection algorithm, and marking environmental defects;
specifically, the target detection algorithm based on autonomous learning performs analysis and calculation on the processed environmental data, and marks environmental defects, and the target detection algorithm includes:
creating a label training set, wherein the training set is a properly sheared picture sample, so that a detection object is positioned at the center position and occupies a whole picture basically;
training a neural network, inputting a clipped image, and outputting whether an object is detected or not by the network;
applying the neural network to analyze and calculate the environmental data;
selecting the clipped picture, inputting the clipped small picture into the neural network, and classifying each position to judge whether an object to be detected exists in the clipped picture;
recording the position of the object to be detected, and marking that the position has defects.
S6, the logistics distribution server optimizes the distribution route based on the analysis and calculation result.
Specifically, the logistics distribution server optimizes a distribution route based on an analysis and calculation result, and comprises the following steps:
uploading the defect position information searched based on the target detection algorithm to a logistics distribution server;
the logistics distribution server optimizes the distribution route based on the analysis and calculation result;
and transmitting the optimized delivery route to the delivery vehicle.
In still other embodiments, the dispensing method may further comprise the steps of:
an operator inputs order information by using an input module of an intelligent client of the intelligent device, so that logistics order information is displayed on a display screen of the intelligent client of the operator;
designing the most reasonable route by utilizing an operation module of the intelligent client;
the intelligent client exchanges information with the cloud end, and the information is communicated with an intelligent APP of a dispatcher;
after distribution is completed, payment is completed by using a payment module of the intelligent APP and reflected on the intelligent APP and the intelligent client.
The login module in the intelligent terminal and the intelligent APP means that a user performs user name and password login in the intelligent client and the intelligent APP by using the intelligent equipment, the login module comprises a registration unit and a login unit, the registration unit is used for the user to perform real name registration login without using recommended software, the login unit comprises user name login and user name storage, and if the user login user name and corresponding password are consistent with information in the user name storage, the login is successful; if the user login user name and the corresponding password are inconsistent with the information in the user name storage, the login fails.
In some other embodiments, the system further includes an operation module, the operation module calculates an optimal route, a vehicle speed and a driving time according to the route in the map and the congestion condition of the road, the remaining delivery vehicles are larger than vehicles of the delivery vehicles required by the goods to be delivered, matches the logistics vehicles according to the optimal route, and generates a goods delivery order, the system includes: vehicles for which other delivery orders have been matched during the delivery time and the delivery time, vehicles for which the route of the matched order is the same as the optimal route of the goods to be delivered and the time is matched with the delivery time and the delivery time of the goods to be delivered, and vehicles for which other delivery orders have not been matched during the delivery time and the delivery time are selected, and vehicles for which the current positioning position information is closest to the delivery site of the goods to be delivered are selected.
Based on the above-mentioned distribution method based on high-speed image recognition, as shown in fig. 3, the present embodiment further discloses a distribution system based on high-speed image recognition, including:
the dispatch scheme generation module is used for dispatching a dispatch task according to the order information by the logistics distribution server and generating dispatch scheme information;
specifically, the order information includes: order ID, sender information, recipient information, courier size information, time of delivery, place of dispatch information. Further, the logistics distribution server distributes distribution tasks according to the order information and generates distribution scheme information, wherein the distribution scheme information comprises the following steps: order ID, vehicle ID of the assigned delivery vehicle, path planning information, and predicted delivery time.
The dispatch route selection module is used for selecting a corresponding route for dispatch by the dispatch vehicle according to the dispatch scheme information;
specifically, the vehicle updates navigation data according to the path planning information in the dispatch scheme; sending the express according to the navigation data; and after the express delivery site is reached, updating the next delivery route according to the delivery scheme information.
The image information acquisition module is used for acquiring environmental data of the delivery route by utilizing a laser radar and a visible light monocular camera on the delivery vehicle in the delivery process;
specifically, the visible light monocular camera is used for collecting environmental images in real time, and the laser radar is used for detecting the position and the speed of the object type in the environment; processing the collected environment image and the position and speed data of the object category in the detection environment, and generating an environment data set; and uploading the environment data set to a logistics distribution server.
The environment data processing module is used for processing the collected environment data by adopting a plurality of cache processing units;
specifically, the cache processing unit is used for managing data in the logistics distribution server in page-to-page data units, each page of data comprises a plurality of block data, and each block data is used as an access unit of an upper system; the cache processing unit processes input or output requests of the upper layer system with respect to a storage device.
The data analysis and calculation module is used for analyzing and calculating the processed environmental data based on an autonomous learning target detection algorithm and marking environmental defects;
specifically, a label training set is created, and the training set is a properly sheared picture sample, so that a detection object is positioned at the center position and occupies a whole picture basically; training a neural network, inputting a clipped image, and outputting whether an object is detected or not by the network; applying the neural network to analyze and calculate the environmental data; selecting the clipped picture, inputting the clipped small picture into the neural network, and classifying each position to judge whether an object to be detected exists in the clipped picture; recording the position of the object to be detected, and marking that the position has defects.
And the distribution route optimization module is used for optimizing the distribution route based on the analysis and calculation result by the logistics distribution server. Uploading the defect position information searched based on the target detection algorithm to a logistics distribution server; the logistics distribution server optimizes the distribution route based on the analysis and calculation result; and transmitting the optimized delivery route to the delivery vehicle.
According to the application, in the logistics distribution process, the problems of obstacles and other traffic jams on the distribution route are monitored in real time, the acquired image information is processed by adopting the cache processing unit, the processed environmental data can be analyzed and calculated based on the target detection algorithm of autonomous learning, the environmental defects are marked, and the distribution route is optimized according to the analysis and calculation result of the environmental data.
In addition, the embodiment of the application also provides a computer readable storage medium.
The computer readable storage medium of the present application stores an identification program, wherein the identification program, when executed by a processor, implements the steps of the delivery method as described above.
The method implemented when the identification program is executed may refer to various embodiments of the distribution method of the present application, and will not be described herein.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The foregoing embodiment numbers of the present application are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
The application is operational with numerous general purpose or special purpose computer system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) as described above, comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present application.
Finally, it should be noted that: the foregoing description is only illustrative of the preferred embodiments of the present application, and although the present application has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described, or equivalents may be substituted for elements thereof, and any modifications, equivalents, improvements or changes may be made without departing from the spirit and principles of the present application.
Claims (10)
1. The distribution method based on the high-speed image recognition is characterized by comprising the following steps of:
the logistics distribution server distributes distribution tasks according to the order information and generates distribution scheme information;
the delivery vehicle selects a corresponding route for delivery according to the delivery scheme information;
collecting environmental data of a delivery route by using a laser radar and a visible light monocular camera on a delivery vehicle in the delivery process;
processing the collected environmental data by adopting a plurality of cache processing units;
analyzing and calculating the processed environmental data based on an autonomous learning target detection algorithm, and marking environmental defects;
the logistics distribution server optimizes distribution routes based on the analysis and calculation results.
2. The image high-speed recognition-based distribution method according to claim 1, wherein: the logistics distribution server distributes distribution tasks according to order information and generates distribution scheme information, wherein the order information comprises the following components:
order ID, sender information, recipient information, courier size information, time of delivery, place of dispatch information.
3. The image high-speed recognition-based distribution method according to claim 2, wherein: the logistics distribution server distributes distribution tasks according to the order information and generates distribution scheme information, wherein the distribution scheme information comprises the following steps:
order ID, vehicle ID of the assigned delivery vehicle, path planning information, and predicted delivery time.
4. A delivery method based on high-speed recognition of images as set forth in claim 3, wherein: the delivery vehicle selects a corresponding route to deliver according to the delivery scheme information, and the delivery vehicle comprises the following steps:
the vehicle updates navigation data according to the path planning information in the dispatch scheme;
sending the express according to the navigation data;
and after the express delivery site is reached, updating the next delivery route according to the delivery scheme information.
5. The image high-speed recognition-based distribution method according to claim 4, wherein: the method for acquiring the environmental data of the delivery route by using the laser radar and the visible light monocular camera on the delivery vehicle in the delivery process comprises the following steps:
the visible light monocular camera is used for collecting environment images in real time, and the laser radar is used for detecting the position and the speed of object types in the environment;
processing the collected environment image and the position and speed data of the object category in the detection environment, and generating an environment data set;
and uploading the environment data set to a logistics distribution server.
6. The image high-speed recognition-based distribution method according to claim 5, wherein: the method comprises the steps of adopting a plurality of cache processing units to process collected environment data, wherein:
the cache processing unit is used for managing data in the logistics distribution server in page-to-page data units, each page of data comprises a plurality of block data, and each block data is used as an access unit of an upper system;
the cache processing unit processes input or output requests of the upper layer system with respect to a storage device.
7. The image high-speed recognition-based distribution method according to claim 6, wherein: the method adopts a plurality of cache processing units to process the collected environmental data, wherein the cache processing units process the environmental data and comprise the following steps:
dividing a video image into a plurality of two-dimensional pixel blocks, and separating brightness information and chromaticity information of each pixel point in the video image;
converting a first address of chromaticity information of each pixel point in the two-dimensional pixel block into a second address in a main storage unit;
reading chromaticity information corresponding to a second address in the chromaticity caching unit, and taking the two-dimensional pixel block as a pixel point to carry out inaccurate processing;
and loading the brightness information of each pixel in the video image to a brightness buffer unit, and reading the brightness information in the brightness buffer unit.
8. The image high-speed recognition-based distribution method according to claim 7, wherein: the target detection algorithm based on autonomous learning analyzes and calculates the processed environmental data and marks the environmental defects, and the target detection algorithm comprises:
creating a label training set, wherein the training set is a properly sheared picture sample, so that a detection object is positioned at the center position and occupies a whole picture basically;
training a neural network, inputting a clipped image, and outputting whether an object is detected or not by the network;
applying the neural network to analyze and calculate the environmental data;
selecting the clipped picture, inputting the clipped small picture into the neural network, and classifying each position to judge whether an object to be detected exists in the clipped picture;
recording the position of the object to be detected, and marking that the position has defects.
9. The image high-speed recognition-based distribution method according to claim 8, wherein: the logistics distribution server optimizes a distribution route based on the analysis and calculation result, and comprises the following steps:
uploading the defect position information searched based on the target detection algorithm to a logistics distribution server;
the logistics distribution server optimizes the distribution route based on the analysis and calculation result;
and transmitting the optimized delivery route to the delivery vehicle.
10. A distribution system based on high-speed recognition of images, comprising a distribution method based on high-speed recognition of images according to any one of claims 1 to 9, characterized by comprising:
the dispatch scheme generation module is used for dispatching a dispatch task according to the order information by the logistics distribution server and generating dispatch scheme information;
the dispatch route selection module is used for selecting a corresponding route for dispatch by the dispatch vehicle according to the dispatch scheme information;
the image information acquisition module is used for acquiring environmental data of the delivery route by utilizing a laser radar and a visible light monocular camera on the delivery vehicle in the delivery process;
the environment data processing module is used for processing the collected environment data by adopting a plurality of cache processing units;
the data analysis and calculation module is used for analyzing and calculating the processed environmental data based on an autonomous learning target detection algorithm and marking environmental defects;
and the distribution route optimization module is used for optimizing the distribution route based on the analysis and calculation result by the logistics distribution server.
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