Sorting and conveying method, device and equipment for direct distribution equipment of power supply station and medium

Abstract

The invention discloses a sorting and conveying method, device and equipment for direct distribution equipment of a power supply station and a medium. The method comprises the following steps: acquiring a bar code collision probability model of each first transmission path; the bar code collision probability model is obtained according to statistics of historical equipment transmission data of all the first transmission paths; first equipment to be packaged is classified, and the first equipment is placed on a corresponding conveying belt according to a classification result; the classification result comprises a to-be-placed position of the first equipment; and searching an optimal transmission path of the first equipment from all the first transmission paths according to the bar code collision probability model by taking the conveyor belt as a starting point and the to-be-placed position as an end point. By means of the bar code sorting device, the probability that bar codes fall or are damaged due to collision can be reduced while the conveying efficiency of the device is improved, and the sorting accuracy of the device is improved.

Description

Technical Field

[0001] The present application relates to the field of logistics automation, and in particular to a method, device, equipment and medium for sorting and conveying equipment directly distributed from a power supply station. Background Art

[0002] In order to improve the delivery efficiency of direct distribution equipment from power supply stations and reduce labor costs, existing technologies have achieved a certain degree of automation.

[0003] However, in order to avoid barcode collision during the conveyor belt transportation process, the current direct distribution equipment of the power supply station needs to establish a serial outbound route for each type of equipment, or conduct real-time inspections by a small amount of manual labor. Among them, the serial assembly line not only wastes the utilization rate of the sorting and conveying equipment, but also a single equipment failure has a great impact on the normal operation of the assembly line, resulting in low sorting efficiency of the assembly line.

[0004] Therefore, how to plan parallel and cross-assembly lines to improve the equipment's conveying efficiency while reducing the risk of barcodes falling or being damaged due to collision has become a technical problem that needs to be solved. Summary of the invention

[0005] The present application provides a method, device, equipment and medium for sorting and conveying equipment directly distributed by a power supply station, so as to solve the technical problem of how to improve the equipment conveying efficiency while reducing the risk of barcodes falling or being damaged due to being touched by planning parallel and cross-flow production lines.

[0006] In order to solve the above technical problems, in a first aspect, an embodiment of the present application provides a method for sorting and conveying equipment directly distributed by a power supply station, comprising:

[0007] Obtaining a barcode collision probability model for each first transmission path; the barcode collision probability model is obtained based on historical device transmission data statistics of all the first transmission paths;

[0008] Classifying the first devices to be packaged, and placing the first devices on corresponding conveyor belts according to the classification results; the classification results include the positions of the first devices to be placed;

[0009] Taking the conveyor belt as the starting point and the position to be placed as the end point, the optimal transmission path of the first device is searched from all the first transmission paths according to the barcode collision probability model.

[0010] Compared with the prior art, the embodiments of the present application have the following beneficial effects: by using historical equipment transportation data, the probability of barcode collision in each first transmission path during the equipment assembly line transportation process is obtained, so as to predict and avoid high-risk paths where barcode collisions may occur, thereby greatly reducing the frequency of barcode collisions and ensuring that the information of the equipment is accurate during transportation; based on the classification results, the corresponding conveyor belt is used to transport the equipment, and the equipment is transported through the optimal transmission path, which improves the transportation efficiency while improving the accuracy of equipment sorting.

[0011] In some embodiments of the first aspect of the present application, the barcode collision probability model is obtained based on historical device transmission data statistics of all the first transmission paths, including:

[0012] According to the historical device transmission data, a first probability model of a first transmission path and another transmission path having overlapping sections is obtained, wherein the first transmission path is any feasible transmission path;

[0013] Acquire, based on the historical device transport data, a second probability model in which the barcode is on the side corresponding to the device when the device is on the first transmission path;

[0014] The barcode collision probability model of the first transmission path is constructed according to the first probability model and the second probability model.

[0015] Compared with the prior art, the above embodiment has the following beneficial effects: since the factors that affect the barcode collision include whether the surface corresponding to the barcode becomes sideways due to the bumpy transportation during the equipment transportation process, and the degree of congestion of the equipment transportation on the same transportation section, by constructing the probability of the first transmission path overlapping with other transmission paths and the probability of the surface corresponding to the barcode becoming sideways, the probability of the barcode being collided during the transportation process can be accurately estimated, and the probability of subsequent barcodes being collided can be effectively reduced.

[0016] In some embodiments of the first aspect of the present application, constructing the barcode collision probability model of the first transmission path according to the first probability model and the second probability model includes:

[0017] The barcode collision probability model is specifically:

[0018] P(collision) = P(overlap) * P(side)

[0019] Among them, P (collision) is a barcode collision probability model in which the barcode of the device on the first transmission path is collided; P (overlap) is a first probability model; and P (side) is a second probability model.

[0020] In some embodiments of the first aspect of the present application, classifying the first devices to be packaged and placing the first devices on corresponding conveyor belts according to the classification results includes:

[0021] Acquire first image data of the first device through an image acquisition unit;

[0022] Inputting the first image data into a pre-trained graph convolutional network to obtain the classification result; the classification result also includes the category of the first device;

[0023] According to the category, the first device is placed corresponding to the conveyor belt.

[0024] Compared with the prior art, the above embodiment has the following beneficial effects: the first device is automatically classified through image recognition technology, and the first device is placed on the corresponding conveyor belt according to the classification result, thereby improving the degree of automation of the device sorting process and reducing the manual utilization rate.

[0025] In some embodiments of the first aspect of the present application, placing the first device before the corresponding conveyor belt according to the category further includes:

[0026] The classification result also includes the position where the first device corresponds to the barcode to be pasted;

[0027] generating a barcode of the first device according to the category;

[0028] The barcode is placed at the to-be-attached position of the first device.

[0029] Compared with the prior art, the above embodiment has the following beneficial effects: after obtaining the classification result of the first device through image recognition technology, a barcode of the corresponding device is generated, and the specific location of the barcode in the device is identified, thereby realizing automatic barcode generation and barcode sticking operations, thereby improving the degree of automation of the equipment sorting process and reducing the manual utilization rate.

[0030] In some embodiments of the first aspect of the present application, taking the conveyor belt as a starting point and the position to be placed as an end point, searching for an optimal transmission path of the first device from all the first transmission paths according to the barcode collision probability model includes:

[0031] According to the barcode collision probability model, a cost function is constructed, and a path network graph consisting of all the first transmission paths is obtained;

[0032] The path network graph is recursively searched starting from the starting point by using the A* algorithm in combination with the cost function until the optimal transmission path is obtained from all the first transmission paths.

[0033] Compared with the prior art, the above embodiment has the following beneficial effects: by constructing a cost function through a barcode collision probability model, the influence of barcode collision on path selection can be considered in the process of optimizing the transportation path, thereby reducing the barcode collision probability while ensuring the transportation efficiency.

[0034] In some embodiments of the first aspect of the present application, constructing a cost function according to the barcode collision probability model includes:

[0035] The cost function is specifically:

[0036] f(n)=g(n)+α×P(n)+h(n)

[0037] Among them, f(n) is the total cost value corresponding to the current recursive node n; g(n) is the cost value from the starting point to the current recursive node n; α is the weight coefficient; P(n) is the barcode collision probability model of the first transmission path from the previous recursive node to the current recursive node n; h(n) is the cost value from the current recursive node n to the end point.

[0038] In a second aspect, the embodiment of the present application further provides a sorting and conveying device for equipment directly distributed from a power supply station, including: a barcode collision probability model acquisition module, an equipment classification module, and a path optimization module;

[0039] The barcode collision probability model acquisition module is used to acquire the barcode collision probability model of each first transmission path; the barcode collision probability model is acquired based on the historical device transmission data statistics of all the first transmission paths;

[0040] The device classification module is used to classify the first devices to be packaged and place the first devices on the corresponding conveyor belt according to the classification results; the classification results include the positions of the first devices to be placed;

[0041] The path optimization module is used to use the conveyor belt as a starting point and the position to be placed as an end point, and to search for an optimal transmission path for the first device from all the first transmission paths according to the barcode collision probability model.

[0042] In a third aspect, the present application also provides a terminal device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the above-mentioned method for sorting and conveying direct distribution equipment of a power supply station when executing the computer program.

[0043] In a fourth aspect, the present application also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the above-mentioned method for sorting and conveying direct distribution equipment of a power supply station. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 A schematic flow chart of a method for sorting and conveying equipment directly distributed from a power supply station provided in some embodiments of the present application;

[0045] Figure 2 This is a schematic structural diagram of a sorting and conveying device for direct distribution equipment of a power supply station provided in some embodiments of the present application. DETAILED DESCRIPTION

[0046] In order to avoid barcode collision during the conveyor belt transportation process, the current direct distribution equipment of the power supply station needs to establish a serial outbound route for each type of equipment, or conduct real-time inspections by a small amount of manual labor. Among them, the serial assembly line not only wastes the utilization rate of the sorting and conveying equipment, but also a single equipment failure has a great impact on the normal operation of the assembly line, resulting in low sorting efficiency of the assembly line.

[0047] In order to solve the above technical problems, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0048] Embodiment 1

[0049] Please refer to Figure 1 , a method for sorting and conveying equipment directly distributed from a power supply station provided in an embodiment of the present application, including S10 to S30, specifically:

[0050] S10: Obtain a barcode collision probability model of each first transmission path; the barcode collision probability model is obtained based on historical device transmission data statistics of all the first transmission paths.

[0051] Furthermore, in some embodiments of the present application, the barcode collision probability model is obtained based on statistics of historical device transmission data of all the first transmission paths, including:

[0052] According to the historical device transmission data, a first probability model of a first transmission path and another transmission path having overlapping sections is obtained, wherein the first transmission path is any feasible transmission path;

[0053] Acquire, based on the historical device transport data, a second probability model in which the barcode is on the side corresponding to the device when the device is on the first transmission path;

[0054] The barcode collision probability model of the first transmission path is constructed according to the first probability model and the second probability model.

[0055] Factors that affect barcode collisions include whether the surface corresponding to the barcode becomes sideways due to bumps during transportation, and the degree of congestion of equipment transportation on the same transportation section. Therefore, by constructing the probability of overlap between the first transmission path and other transmission paths and the probability of the barcode corresponding surface becoming sideways, the probability of barcodes being collided during transportation can be accurately estimated, effectively reducing the probability of subsequent barcodes being collided.

[0056] Further, in some embodiments of the present application, constructing the barcode collision probability model of the first transmission path according to the first probability model and the second probability model includes:

[0057] The barcode collision probability model is specifically:

[0058] P(collision) = P(overlap) * P(side)

[0059] Among them, P (collision) is a barcode collision probability model in which the barcode of the device on the first transmission path is collided; P (overlap) is a first probability model; and P (side) is a second probability model.

[0060] Preferably, in some embodiments of the present application, the first probability model and the second probability model are obtained by the following preferred implementations:

[0061] During the transportation process, by real-time monitoring the average transportation density of equipment between every two nodes in the first transportation path (i.e., the total number of equipment on the transportation section between the two nodes at any time), when the transportation density of a certain transportation section suddenly increases relative to the previous transportation section, the current transportation section is determined to be an overlapping section, thereby obtaining the number of all overlapping sections in the entire first transportation path, further obtaining the probability of the first transportation path overlapping with other transportation paths, and obtaining a first probability model.

[0062] During the transportation process, the real-time detection device detects the position of the barcode during the transportation process. Specifically, the barcode target can be identified through a barcode scanner or an image recognition device. Furthermore, the statistical device can detect the frequency of the barcode being on the side in the overlapping part with other conveying paths during the transportation of the first conveying path, thereby obtaining a second probability model.

[0063] S20: Classifying the first devices to be packaged, and placing the first devices on corresponding conveyor belts according to the classification results; the classification results include the positions where the first devices are to be placed.

[0064] Preferably, in some embodiments of the present application, the first device includes but is not limited to: electric energy meters, smart terminals, low-voltage transformers and other devices.

[0065] Further, in some embodiments of the present application, classifying the first devices to be packaged and placing the first devices on corresponding conveyor belts according to the classification results includes:

[0066] Acquire first image data of the first device through an image acquisition unit;

[0067] Inputting the first image data into a pre-trained graph convolutional network to obtain the classification result; the classification result also includes the category of the first device;

[0068] According to the category, the first device is placed corresponding to the conveyor belt.

[0069] Through image recognition technology, the first device is automatically classified and placed on the corresponding conveyor belt according to the classification result, which improves the automation level of the equipment sorting process and reduces the manual utilization rate.

[0070] Preferably, in some embodiments of the present application, an image acquisition unit is provided on a manipulator, and after the first image data of the first device to be packaged is collected by the image acquisition unit, it is necessary to combine the convolutional network to classify the metering assets that need to be sorted. Constructing a convolutional network includes but is not limited to: TensorFlow or PyTorch, designing a network structure, including a convolutional layer, a pooling layer, and a fully connected layer, etc., to extract image features. Further combined with sensors such as weight sensors and other equipment, characteristic data such as the weight, color, identification, attributes, specifications, and shape of the equipment are obtained, and the image features and the above-mentioned other characteristic data are input into the convolutional network to obtain classification results. According to the classification results, a control instruction is sent to the manipulator to place the first device on the corresponding conveyor belt, thereby avoiding interference of metering assets in the multi-channel transportation process and improving the efficiency and accuracy of classification.

[0071] Preferably, in some embodiments of the present application, after the classification results are obtained, a corresponding number of conveyor belts are started or set according to the classification results.

[0072] Preferably, in some embodiments of the present application, when training the convolutional network, by setting the confidence judgment and optimization (such as stochastic gradient descent or cross entropy loss to optimize the performance of the model), even if any image is input into the recognition model, the recognition result within the limited category will be given, and then the characteristics and classification rules of different metering assets can be learned, so as to realize the category recognition of metering assets including electric energy meters (in boxes), smart terminals (in boxes), low-voltage transformers (in pieces), etc. Among them, the training data set can be constructed by shooting the actual first device or obtaining images from an existing database, or using synthetic images.

[0073] Further, in some embodiments of the present application, before placing the first device to the corresponding conveyor belt according to the category, it also includes:

[0074] The classification result also includes the position where the first device corresponds to the barcode to be pasted;

[0075] generating a barcode of the first device according to the category;

[0076] The barcode is placed at the to-be-attached position of the first device.

[0077] Through image recognition technology, after obtaining the classification result of the first device, the barcode of the corresponding device is generated, and the specific location of the barcode in the device is identified to realize automatic barcode generation and code sticking operations, thereby improving the degree of automation of the equipment sorting process and reducing the manual utilization rate.

[0078] Preferably, in some embodiments of the present application, the classification results output by the convolutional network include but are not limited to: the type of device, the position where the barcode corresponding to the device is to be affixed, and the position where the first device is to be placed.

[0079] Preferably, in some embodiments of the present application, the generated barcode can be printed and pasted to the location to be pasted by, for example, a robotic arm or other automated labeling equipment.

[0080] S30: Taking the conveyor belt as a starting point and the position to be placed as an end point, searching for an optimal transmission path of the first device from all the first transmission paths according to the barcode collision probability model.

[0081] Further, in some embodiments of the present application, taking the conveyor belt as the starting point and the position to be placed as the end point, searching for the optimal transmission path of the first device from all the first transmission paths according to the barcode collision probability model includes:

[0082] According to the barcode collision probability model, a cost function is constructed, and a path network graph consisting of all the first transmission paths is obtained;

[0083] The path network graph is recursively searched starting from the starting point by using the A* algorithm in combination with the cost function until the optimal transmission path is obtained from all the first transmission paths.

[0084] By constructing a cost function based on the barcode collision probability model, the impact of barcode collision on path selection can be considered in the process of optimizing the transportation path, thereby reducing the barcode collision probability while ensuring the transportation efficiency.

[0085] Furthermore, in some embodiments of the present application, constructing a cost function according to the barcode collision probability model includes:

[0086] The cost function is specifically:

[0087] f(n)=g(n)+α×P(n)+h(n)

[0088] Among them, f(n) is the total cost corresponding to the current recursive node n; g(n) is the cost from the starting point to the current recursive node n; α is the weight coefficient; P(n) is the barcode collision probability model of the first transmission path from the previous recursive node to the current recursive node n, that is, P(collision); h(n) is the cost from the current recursive node n to the end point.

[0089] Preferably, in some embodiments of the present application, the cost value h(n) from the current recursive node n to the end point can be obtained by calculating the Euclidean distance or Manhattan distance from the current node to the end point, preferably by using Manhattan distance calculation.

[0090] Preferably, in some embodiments of the present application, the optimal transmission path is planned according to the A* algorithm through the following preferred implementations:

[0091] S31: Initialize an open list and a closed list, wherein the open list is used to store nodes to be processed, and the closed list is used to store processed nodes, and the nodes are all nodes in the path network graph;

[0092] S32: taking the conveyor belt where the first device currently planned is placed as the starting point, taking the position to be placed in the classification result as the end point, and adding the starting point to the open list;

[0093] S33: Determine whether there is an end point in the closed list. If so, terminate the recursion and return the optimal transmission path. Otherwise, select the node with the smallest f(n) from the open list as the current recursive node n.

[0094] S34: Generate all neighbor nodes n' of the current recursive node n, and recalculate f(n') and g(n')+α×P(n') for each neighbor node. For the convenience of description, g(n')'=g(n')+α×P(n') is used later;

[0095] S35: If n' is in the open list, and the g(n')' corresponding to the current starting node reaching n' is smaller, then update f(n') and the parent node of n', where the parent node is the current recursive node n; otherwise, execute S36;

[0096] S36: If n' is not in the open list, add n' to the open list, and record the currently calculated f(n') and the currently recursive node n as its parent node;

[0097] S37: Return to S33.

[0098] From the above, it can be seen that the method for sorting and conveying equipment directly distributed from a power supply station provided in an embodiment of the present application has the following beneficial effects: through historical equipment transportation data, the probability of barcode collision of the equipment in each first transmission path during the assembly line transportation process is obtained, so as to predict and avoid high-risk paths where barcode collisions may occur, thereby greatly reducing the frequency of barcode collisions and ensuring that the information of the equipment is accurate during the transportation process; based on the classification results, the corresponding conveyor belt is used to transport the equipment, and it is transported through the optimal transmission path, which improves the transportation efficiency while improving the accuracy of equipment sorting.

[0099] Embodiment 2

[0100] refer to Figure 2 , a power supply station direct distribution equipment sorting and conveying device provided in an embodiment of the present application, comprising: a barcode collision probability model acquisition module 11, an equipment classification module 12 and a path optimization module 13;

[0101] Furthermore, in some embodiments of the present application, the barcode collision probability model acquisition module 11 is used to obtain the barcode collision probability model of each first transmission path; the barcode collision probability model is acquired based on the historical device transportation data statistics of all the first transmission paths; the device classification module 12 is used to classify the first devices to be packaged, and place the first devices on the corresponding conveyor belt according to the classification results; the classification results include the position to be placed of the first devices; the path optimization module 13 is used to take the conveyor belt as the starting point and the position to be placed as the end point, and search for the optimal transmission path for the first device from all the first transmission paths according to the barcode collision probability model.

[0102] Furthermore, in some embodiments of the present application, the barcode collision probability model is obtained based on statistics of historical device transmission data of all the first transmission paths, including:

[0103] According to the historical device transmission data, a first probability model of a first transmission path and another transmission path having overlapping sections is obtained, wherein the first transmission path is any feasible transmission path;

[0104] Acquire, based on the historical device transport data, a second probability model in which the barcode is on the side corresponding to the device when the device is on the first transmission path;

[0105] The barcode collision probability model of the first transmission path is constructed according to the first probability model and the second probability model.

[0106] Further, in some embodiments of the present application, constructing the barcode collision probability model of the first transmission path according to the first probability model and the second probability model includes:

[0107] The barcode collision probability model is specifically:

[0108] P(collision) = P(overlap) * P(side)

[0109] Among them, P (collision) is a barcode collision probability model in which the barcode of the device on the first transmission path is collided; P (overlap) is a first probability model; and P (side) is a second probability model.

[0110] Further, in some embodiments of the present application, classifying the first devices to be packaged and placing the first devices on corresponding conveyor belts according to the classification results includes:

[0111] Acquire first image data of the first device through an image acquisition unit;

[0112] Inputting the first image data into a pre-trained graph convolutional network to obtain the classification result; the classification result also includes the category of the first device;

[0113] According to the category, the first device is placed corresponding to the conveyor belt.

[0114] Further, in some embodiments of the present application, before placing the first device to the corresponding conveyor belt according to the category, it also includes:

[0115] The classification result also includes the position where the first device corresponds to the barcode to be pasted;

[0116] generating a barcode of the first device according to the category;

[0117] The barcode is placed at the to-be-attached position of the first device.

[0118] Further, in some embodiments of the present application, taking the conveyor belt as the starting point and the position to be placed as the end point, searching for the optimal transmission path of the first device from all the first transmission paths according to the barcode collision probability model includes:

[0119] According to the barcode collision probability model, a cost function is constructed, and a path network graph consisting of all the first transmission paths is obtained;

[0120] The path network graph is recursively searched starting from the starting point by using the A* algorithm in combination with the cost function until the optimal transmission path is obtained from all the first transmission paths.

[0121] Furthermore, in some embodiments of the present application, constructing a cost function according to the barcode collision probability model includes:

[0122] The cost function is specifically:

[0123] f(n)=g(n)+α×P(n)+h(n)

[0124] Among them, f(n) is the total cost value corresponding to the current recursive node n; g(n) is the cost value from the starting point to the current recursive node n; α is the weight coefficient; P(n) is the barcode collision probability model of the first transmission path from the previous recursive node to the current recursive node n; h(n) is the cost value from the current recursive node n to the end point.

[0125] It can be understood that the above-mentioned device embodiment corresponds to the method embodiment of the present invention. The power supply station direct distribution equipment sorting and conveying device provided by the embodiment of the present invention can implement any method embodiment of the present invention, that is, the power supply station direct distribution equipment sorting and conveying method provided in embodiment one.

[0126] From the above, it can be seen that the sorting and conveying device for equipment directly distributed from a power supply station provided in the embodiment of the present application has the following beneficial effects: through historical equipment transportation data, the probability of barcode collision of the equipment in each first transmission path during the assembly line transportation process is obtained, so as to predict and avoid high-risk paths where barcode collisions may occur, thereby greatly reducing the frequency of barcode collisions and ensuring that the information of the equipment is accurate during the transportation process; based on the classification results, the corresponding conveyor belt is used to transport the equipment, and it is transported through the optimal transmission path, which improves the transportation efficiency while improving the accuracy of equipment sorting.

[0127] Embodiment 3

[0128] Based on the above-mentioned embodiment of the method for sorting and conveying direct-distribution equipment of a power station, another embodiment of the present application provides a terminal device for sorting and conveying direct-distribution equipment of a power station, and the terminal device for sorting and conveying direct-distribution equipment of a power station comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the method for sorting and conveying direct-distribution equipment of a power station of any embodiment of the present application is implemented.

[0129] Exemplarily, in this embodiment, the computer program can be divided into one or more modules, and the one or more modules are stored in the memory and executed by the processor to complete the present application. The one or more modules can be a series of computer program instruction segments that can complete specific functions, and the instruction segments are used to describe the execution process of the computer program in the sorting and conveying equipment of the direct distribution equipment of the power supply station.

[0130] The power supply station direct distribution equipment sorting and conveying equipment can be a computing device such as a desktop computer, a notebook, a palm computer, a cloud server, etc. The power supply station direct distribution equipment sorting and conveying terminal equipment can include, but is not limited to, a processor and a memory.

[0131] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc. The processor is the control center of the sorting and conveying equipment of the direct distribution equipment of the power supply station, and uses various interfaces and lines to connect the various parts of the sorting and conveying equipment of the direct distribution equipment of the power supply station. The memory may be used to store the computer program and / or module, and the processor realizes various functions of the sorting and conveying equipment of the direct distribution equipment of the power supply station by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function, etc.; the data storage area may store data created according to the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory and may also include non-volatile memory, such as a hard disk, an internal memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card (FlashCard), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0132] Embodiment 4

[0133] Based on the above-mentioned embodiment of the method for sorting and conveying equipment directly distributed to power stations, another embodiment of the present application provides a storage medium, wherein the storage medium includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the method for sorting and conveying equipment directly distributed to power stations of any embodiment of the present application.

[0134] In this embodiment, the storage medium is a computer-readable storage medium, and the computer program includes computer program code, which may be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0135] The specific embodiments described above further describe the purpose, technical solutions and beneficial effects of the present application in detail. It should be understood that the above description is only a specific embodiment of the present application and is not intended to limit the scope of protection of the present application. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of protection of the present application.

Claims

1. A method for sorting and conveying equipment directly distributed by a power supply station, characterized in that: include: Obtaining a barcode collision probability model for each first transmission path; the barcode collision probability model is obtained based on historical device transmission data statistics of all the first transmission paths; Classifying the first devices to be packaged, and placing the first devices on corresponding conveyor belts according to the classification results; the classification results include the positions of the first devices to be placed; Taking the conveyor belt as the starting point and the position to be placed as the end point, the optimal transmission path of the first device is searched from all the first transmission paths according to the barcode collision probability model.

2. A method for sorting and conveying equipment directly distributed by a power supply station as claimed in claim 1, characterized in that: The barcode collision probability model is obtained based on the historical device transmission data statistics of all the first transmission paths, including: According to the historical device transmission data, a first probability model of a first transmission path and another transmission path having overlapping sections is obtained, wherein the first transmission path is any feasible transmission path; Acquire, based on the historical device transport data, a second probability model in which the barcode is on the side corresponding to the device when the device is on the first transmission path; The barcode collision probability model of the first transmission path is constructed according to the first probability model and the second probability model.

3. A method for sorting and conveying equipment directly distributed by a power supply station as described in claim 2, characterized in that: The step of constructing the barcode collision probability model of the first transmission path according to the first probability model and the second probability model includes: The barcode collision probability model is specifically: P(collision) = P(overlap) * P(side) Among them, P (collision) is a barcode collision probability model in which the barcode of the device on the first transmission path is collided; P (overlap) is a first probability model; and P (side) is a second probability model.

4. A method for sorting and conveying equipment directly distributed by a power supply station as claimed in claim 1, characterized in that: The step of classifying the first devices to be packaged and placing the first devices on corresponding conveyor belts according to the classification results includes: Acquire first image data of the first device through an image acquisition unit; Inputting the first image data into a pre-trained graph convolutional network to obtain the classification result; the classification result also includes the category of the first device; According to the category, the first device is placed corresponding to the conveyor belt.

5. A method for sorting and conveying equipment directly distributed by a power supply station as claimed in claim 4, characterized in that: Placing the first device in front of the corresponding conveyor belt according to the category further includes: The classification result also includes the position where the first device corresponds to the barcode to be pasted; generating a barcode of the first device according to the category; The barcode is placed at the to-be-attached position of the first device.

6. A method for sorting and conveying equipment directly distributed by a power supply station as claimed in claim 1, characterized in that: The method takes the conveyor belt as a starting point and the position to be placed as an end point, and searches for an optimal transmission path of the first device from all the first transmission paths according to the barcode collision probability model, including: According to the barcode collision probability model, a cost function is constructed, and a path network graph consisting of all the first transmission paths is obtained; The path network graph is recursively searched starting from the starting point by using the A* algorithm in combination with the cost function until the optimal transmission path is obtained from all the first transmission paths.

7. A method for sorting and conveying equipment directly distributed by a power supply station as claimed in claim 6, characterized in that: The cost function is constructed according to the barcode collision probability model, comprising: The cost function is specifically: f(n)=g(n)+α×P(n)+h(n) Among them, f(n) is the total cost value corresponding to the current recursive node n; g(n) is the cost value from the starting point to the current recursive node n; α is the weight coefficient; P(n) is the barcode collision probability model of the first transmission path from the previous recursive node to the current recursive node n; h(n) is the cost value from the current recursive node n to the end point.

8. A sorting and conveying device for direct distribution equipment of a power supply station, characterized in that: include: Barcode collision probability model acquisition module, equipment classification module and path optimization module; The barcode collision probability model acquisition module is used to acquire the barcode collision probability model of each first transmission path; the barcode collision probability model is acquired based on the historical device transmission data statistics of all the first transmission paths; The device classification module is used to classify the first devices to be packaged and place the first devices on the corresponding conveyor belt according to the classification results; the classification results include the positions of the first devices to be placed; The path optimization module is used to use the conveyor belt as a starting point and the position to be placed as an end point, and to search for an optimal transmission path for the first device from all the first transmission paths according to the barcode collision probability model.

9. A terminal device, characterized in that: It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements a method for sorting and conveying direct distribution equipment of a power supply station as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute a method for sorting and conveying direct distribution equipment of a power supply station as described in any one of claims 1 to 7.

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