Transport device dispatching method and apparatus, computer device, and storage medium

Abstract

The present application relates to a transport device dispatching method and apparatus, a computer device, a storage medium, and a computer program product, which can be used in the technical field of computers. The method comprises: acquiring an image to be recognized (101), said image comprising an object to be recognized, and an identification code being set on said object; recognizing the identification code in said image to obtain identification code information of said object (102); determining the current position of said object on the basis of the identification code information (103); recognizing the current position and a target position of said object to obtain a position recognition result of said object (104), the position recognition result being used for indicating whether said object is located in the target position; and dispatching a transport device on the basis of the position recognition result (105), the transport device being used for transporting said object.

Description

Transport equipment scheduling method, device, computer equipment and storage medium

[0001] Related applications

[0002] This application claims priority to Chinese patent application number 2023118655558, filed on December 30, 2023, entitled “Position Identification Method, Device, Computer Equipment and Storage Medium”. In addition, this application also claims priority to Chinese patent application number 2023118723112, filed on December 30, 2023, entitled “Position Identification Method, Device, Computer Equipment and Storage Medium”, the entire text of which is hereby incorporated by reference. Technical Field

[0003] The present application relates to the field of computer technology, and in particular to a transportation equipment scheduling method, apparatus, computer equipment, storage medium, and computer program product. Background Art

[0004] With the development of intelligent technology, location recognition has important applications in numerous fields. By identifying the location of objects, we can understand their exact position in space, which is crucial for resource optimization, path planning, inventory management, and other aspects. Therefore, how to efficiently perform location recognition has become a key research direction.

[0005] Traditional technology usually uses manual visual observation to perform location recognition; however, this method requires a lot of manual processing time, resulting in low efficiency of location recognition. Since location recognition is very important for the scheduling of transportation equipment, low efficiency of location recognition will lead to low scheduling efficiency of transportation equipment. Summary of the Invention

[0006] According to various embodiments provided in the present application, a transportation equipment scheduling method, apparatus, computer equipment, computer-readable storage medium, and computer program product are provided.

[0007] In a first aspect, the present application provides a method for transport equipment scheduling, which is applied to a terminal. The method includes:

[0008] Acquire an image to be identified; the image to be identified includes an object to be identified, and the object to be identified is provided with an identification code;

[0009] Identifying the identification code in the image to be identified to obtain identification code information of the object to be identified;

[0010] Determining the current location of the object to be identified based on the identification code information;

[0011] Identifying the current position and the target position of the object to be identified to obtain a position identification result of the object to be identified; the position identification result is used to indicate whether the object to be identified is located within the target position;

[0012] The transport equipment is scheduled based on the position recognition result; the transport equipment is used to transport the object to be recognized.

[0013] In one embodiment, determining the current location of the object to be identified based on the identification code information includes:

[0014] Determining coordinate information of the identification code according to the identification code information;

[0015] The current position of the object to be identified is determined according to the coordinate information of the identification code.

[0016] In one embodiment, determining the current position of the object to be identified based on the coordinate information of the identification code includes:

[0017] performing a position boundary fitting process on the object to be identified according to the coordinate information of the identification code to obtain a fitting position boundary of the object to be identified;

[0018] The fitted position boundary is identified as the current position of the object to be identified.

[0019] In one embodiment, identifying the current position and the target position of the object to be identified to obtain a position identification result of the object to be identified includes:

[0020] Determining the vertex position of the object to be identified according to the fitted position boundary;

[0021] The vertex position of the object to be identified and the target position are identified to obtain the position identification result.

[0022] In one embodiment, identifying the vertex position of the object to be identified and the target position to obtain the position identification result includes:

[0023] In the case where it is recognized that all vertex positions of the object to be identified are located within the target position, confirming that the object to be identified is located within the target position;

[0024] When it is recognized that not all of the vertex positions of the object to be recognized are located within the target position, it is confirmed that the object to be recognized is not located within the target position.

[0025] In one embodiment, identifying the current position and the target position of the object to be identified to obtain a position identification result of the object to be identified includes:

[0026] In the case where the current position and the target position of the object to be identified overlap, cutting out a region image corresponding to the object to be identified from the image to be identified;

[0027] Performing vertex position recognition on the object to be recognized in the region image using a vertex position recognition model to obtain the vertex position of the object to be recognized;

[0028] The vertex position and the target position are identified to obtain a position recognition result of the object to be identified.

[0029] In one embodiment, identifying the vertex position and the target position to obtain a position recognition result of the object to be identified includes:

[0030] Identifying the vertex position and the target position to obtain a vertex position recognition result of the object to be identified; the vertex position recognition result is used to indicate whether the vertex of the object to be identified is located within the target position;

[0031] determining the current number of the vertices located within the target position according to the vertex position recognition result;

[0032] In a case where the current number is greater than or equal to the preset number, confirming the position recognition result of the object to be recognized indicates that the object to be recognized is located within the target position.

[0033] In one embodiment, when the current position overlaps with the target position of the object to be identified, cutting out the area image corresponding to the object to be identified from the image to be identified includes:

[0034] In the case where the current position and the candidate position of the object to be identified overlap, cutting out the regional image from the image to be identified;

[0035] A candidate position that overlaps with the current position is used as the target position.

[0036] In one embodiment, the method further comprises:

[0037] Identifying a candidate region from the image to be identified;

[0038] The position of the candidate area is determined as the candidate position of the object to be identified.

[0039] In one embodiment, scheduling the transport equipment based on the position identification result includes:

[0040] Obtaining a first historical location recognition result corresponding to a first recognized image; the first recognized image is an image captured within a first preset time period before the time when the image to be recognized was captured;

[0041] When the first historical location recognition result and the location recognition result are consistent, obtaining a second historical location recognition result corresponding to a second recognized image; the second recognized image is an image captured within a second preset time period before the time when the image to be recognized is captured;

[0042] Determining an object state recognition result corresponding to the target location based on the location recognition result, the first historical location recognition result, and the second historical location recognition result; the object state recognition result is used to indicate whether the object to be recognized is within the target location;

[0043] generating a transport instruction corresponding to the object to be identified when the object state identification result indicates that the object to be identified is present in the target location;

[0044] The transport instruction is sent to a transport device; the transport device is used to transport the object to be identified from the target location to the transport location of the object to be identified.

[0045] In one embodiment, scheduling the transport equipment based on the position identification result includes:

[0046] When the position recognition result indicates that the object to be recognized is located within the target position, obtaining a historical position recognition result corresponding to a recognized image; the recognized image is an image of the object to be recognized taken within a preset time period before the time when the image to be recognized was taken;

[0047] If the historical position recognition results corresponding to the recognized images all indicate that the object to be recognized is located within the target position, confirming that the object to be recognized is successfully located within the target position;

[0048] Determining the identification of the object to be identified according to the identification code information of the object to be identified;

[0049] Determining a transport location of the object to be identified based on the identifier of the object to be identified;

[0050] The transport location is sent to a transport device; the transport device is used to transport the object to be identified to the transport location.

[0051] In a second aspect, the present application further provides a transportation equipment scheduling device. The device comprises:

[0052] An image acquisition module is used to acquire an image to be identified; the image to be identified includes an object to be identified, and the object to be identified is provided with an identification code;

[0053] An image recognition module, configured to recognize the identification code in the image to be recognized and obtain identification code information of the object to be recognized;

[0054] A position determination module, configured to determine the current position of the object to be identified based on the identification code information;

[0055] a target recognition module, configured to recognize the current position and the target position of the object to be recognized, and obtain a position recognition result of the object to be recognized; the position recognition result is used to indicate whether the object to be recognized is located within the target position;

[0056] The equipment scheduling module is used to schedule transportation equipment based on the position recognition result; the transportation equipment is used to transport the object to be recognized.

[0057] In a third aspect, the present application further provides a computer device. The computer device includes a memory and one or more processors, wherein the memory stores computer-readable instructions, and when the one or more processors execute the computer-readable instructions, the following steps are implemented:

[0058] Acquire an image to be identified; the image to be identified includes an object to be identified, and the object to be identified is provided with an identification code;

[0059] Identifying the identification code in the image to be identified to obtain identification code information of the object to be identified;

[0060] Determining the current location of the object to be identified based on the identification code information;

[0061] Identifying the current position and the target position of the object to be identified to obtain a position identification result of the object to be identified; the position identification result is used to indicate whether the object to be identified is located within the target position;

[0062] The transport equipment is scheduled based on the position recognition result; the transport equipment is used to transport the object to be recognized.

[0063] In a fourth aspect, the present application further provides one or more computer-readable storage media. The computer-readable storage media stores computer-readable instructions thereon, and when the computer-readable instructions are executed by one or more processors, the following steps are implemented:

[0064] Acquire an image to be identified; the image to be identified includes an object to be identified, and the object to be identified is provided with an identification code;

[0065] Identifying the identification code in the image to be identified to obtain identification code information of the object to be identified;

[0066] Determining the current location of the object to be identified based on the identification code information;

[0067] Identifying the current position and the target position of the object to be identified to obtain a position identification result of the object to be identified; the position identification result is used to indicate whether the object to be identified is located within the target position;

[0068] The transport equipment is scheduled based on the position recognition result; the transport equipment is used to transport the object to be recognized.

[0069] In a fifth aspect, the present application further provides a computer program product. The computer program product includes computer-readable instructions, which, when executed by one or more processors, implement the following steps:

[0070] Acquire an image to be identified; the image to be identified includes an object to be identified, and the object to be identified is provided with an identification code;

[0071] Identifying the identification code in the image to be identified to obtain identification code information of the object to be identified;

[0072] Determining the current location of the object to be identified based on the identification code information;

[0073] Identifying the current position and the target position of the object to be identified to obtain a position identification result of the object to be identified; the position identification result is used to indicate whether the object to be identified is located within the target position;

[0074] The transport equipment is scheduled based on the position recognition result; the transport equipment is used to transport the object to be recognized.

[0075] The details of one or more embodiments of the present application are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the present application will become apparent from the description, drawings, and claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0077] FIG1 is a schematic flow chart of a method for scheduling transportation equipment according to an embodiment;

[0078] FIG2 is a schematic flow chart of steps for determining a current location in one embodiment;

[0079] FIG3 is a schematic flow chart of the steps of determining the current position in another embodiment;

[0080] FIG4 is a flow chart showing steps for determining a location recognition result in one embodiment;

[0081] FIG5 is a flow chart of steps for determining a location recognition result in one embodiment;

[0082] FIG6 is a schematic flow chart of steps for determining a target location in one embodiment;

[0083] FIG7 is a schematic flow chart of the steps of determining a candidate location in one embodiment;

[0084] FIG8 is a schematic diagram of an image to be recognized in one embodiment;

[0085] FIG9 is a block diagram of a transport equipment scheduling device according to an embodiment;

[0086] FIG10 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0087] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0088] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0089] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0090] The transfer yard currently uses cage trucks to transfer goods, but because cage trucks have a certain weight, the transfer efficiency within the yard is not high. Therefore, AGV (automatic guided vehicle) can be introduced to transfer cage trucks within the yard; by issuing tasks in the task management system of the site, and then the AGV transports the designated cage truck from the designated starting point to the designated end point, and then completes these tasks. The AGV task issuance process can be manual or automatic. The core points of the system for automatically calling AGV to transfer cage trucks provided in this application are: 1. Cage truck ID (identification number) recognition method based on code scanning; 2. Cage truck arrival recognition method based on the result of code scanning on the top of the cage truck; 3. AGV calling method based on state machine; 4. Cage truck identification method.

[0091] Based on this, the present application provides a transportation equipment scheduling method, apparatus, computer equipment, storage medium and computer program product. First, the transportation equipment scheduling method provided by the present application is described.

[0092] In an exemplary embodiment, as shown in FIG1 , a method for dispatching transport equipment is provided. This embodiment uses the method applied to a terminal as an example for illustration; it is understood that the method can also be applied to a server, or to a system including a terminal and a server, and implemented through interaction between the terminal and the server. The terminal can be, but is not limited to, various personal computers, laptops, smartphones, tablet computers, etc.; the server can be implemented as an independent server or a server cluster consisting of multiple servers. In this embodiment, the method includes the following steps:

[0093] Step S101 , obtaining an image to be identified; the image to be identified includes an object to be identified, and an identification code is set on the object to be identified.

[0094] The image to be identified may be an image to be identified taken by a camera. For example, the image to be identified may be an image taken by a camera at the top of a storage location, and the image contains an object to be identified.

[0095] The object to be identified may be an object whose position needs to be identified, for example, the object to be identified may be a cage truck. For example, in a logistics scenario, the object to be identified may be a cage truck used in the logistics scenario.

[0096] The identification code may be a barcode (such as a one-dimensional code), for example, the identification code may be a one-dimensional code affixed to the top of the cage vehicle.

[0097] Optionally, the terminal obtains image data of the top of the storage location through a camera or other device, and the image data contains an object to be identified (such as a cage truck) whose position needs to be identified.

[0098] Step S102: Identify the identification code in the image to be identified to obtain identification code information of the object to be identified.

[0099] The identification code information may be a number corresponding to each identification code obtained through identification.

[0100] Optionally, the terminal processes the image data to identify an identification code set on the surface of the object to be identified; and parses the identified identification code to obtain information such as a number as identification code information of the object to be identified.

[0101] Step S103: determining the current position of the object to be identified according to the identification code information.

[0102] The current position may be the contour position of the object to be identified in the image to be identified.

[0103] Optionally, the terminal identifies the contour position of the object to be identified in the image to be identified based on the obtained position information of the multiple identification codes, as the current position of the object to be identified.

[0104] Step S104 , identifying the current position and the target position of the object to be identified, and obtaining a position identification result of the object to be identified; the position identification result is used to indicate whether the object to be identified is located within the target position.

[0105] The target location may be a target location area where the object to be identified needs to be located, for example, the target location may be a storage location.

[0106] The position recognition result can be used to indicate whether the cage car has reached the target position.

[0107] Optionally, the terminal reads a preset target position of the object to be identified from a database; matches the calculated current position with the target position to determine whether the current position and the target position coincide with each other; if the current position matches the target position, it indicates that the object to be identified has reached the target position, and the position recognition result of the object to be identified is output as the object to be identified is located within the target position; if they do not match, the position recognition result of the object to be identified is output as the object to be identified is not located within the target position.

[0108] Step S105 , scheduling transportation equipment based on the position recognition result; the transportation equipment is used to transport the object to be recognized.

[0109] In the above-mentioned transportation equipment scheduling method, an image to be identified is obtained; the image to be identified includes an object to be identified, and an identification code is set on the object to be identified; the identification code in the image to be identified is identified to obtain identification code information of the object to be identified; the current position of the object to be identified is determined based on the identification code information; the current position and the target position of the object to be identified are identified to obtain a position identification result of the object to be identified; the position identification result is used to indicate whether the object to be identified is located within the target position; and the transportation equipment is scheduled based on the position identification result. This scheme identifies the identification code set on the object to be identified in the image to be identified to obtain identification code information, determines the current position of the object to be identified based on the identification code information, identifies the current position and the target position of the object to be identified to obtain a position identification result of whether the object to be identified is located within the target position; in this way, the scheme automatically identifies whether the object is located within the target position through the identification code and position information, thereby improving the efficiency and accuracy of position identification, thereby improving the scheduling efficiency and accuracy of transportation equipment.

[0110] In an exemplary embodiment, as shown in FIG2 , in step S103 , the current position of the object to be identified is determined according to the identification code information, specifically including the following:

[0111] Step S201, determining the coordinate information of the identification code according to the identification code information;

[0112] Step S202: determining the current position of the object to be identified based on the coordinate information of the identification code.

[0113] The coordinate information of the identification code may be the pixel coordinate position of each identification code in the image to be identified obtained through image recognition.

[0114] Optionally, the terminal identifies each identification code from the image to be identified; for each identified identification code, the identification code information belonging to the same cage car is obtained according to the corresponding identification code information; for each cage car, the coordinate information of the identification code is determined according to the pixel coordinate position of the corresponding identification code in the image to be identified; according to the distribution pattern of the coordinate information of the identification code of the same cage car, the outline shape or bounding box of the cage car in the image to be identified is fitted; for example, the long axis distribution of the identification code coordinate points in the image to be identified can be analyzed to fit a rectangular outline that matches the storage location; or a deep learning network can be used to take the one-dimensional code coordinate points as input to directly identify the outline of the cage car; no matter which algorithm is used, the position of the cage car in the image to be identified can be calculated according to the coordinate information of the identification code; the calculated outline or bounding box position of the cage car is used as the current position of the cage car to be identified.

[0115] The technical solution provided in this embodiment automatically predicts the current position of the object to be identified through the coordinate information of the identification code, which is conducive to efficiently and accurately determining the current position of the object to be identified, thereby helping to improve the efficiency and accuracy of position recognition.

[0116] In an exemplary embodiment, as shown in FIG3 , determining the current position of the object to be identified based on the coordinate information of the identification code specifically includes the following:

[0117] Step S301, performing position boundary fitting processing on the object to be identified based on the coordinate information of the identification code to obtain the fitting position boundary of the object to be identified;

[0118] Step S302: identifying the fitted position boundary as the current position of the object to be identified.

[0119] Among them, the fitting processing of the position boundary can be a process of fitting the outline or boundary shape of the object to be identified in the image to be identified based on the distribution law of the identification code coordinate points. For example, the long axis distribution of the one-dimensional code coordinates is used to fit a rectangular outline that matches the storage location.

[0120] The fitted position boundary may be the outline or boundary shape of the cage vehicle in the image to be identified, which is calculated through the position boundary fitting process. For example, the fitted position boundary may be a rectangular outline (rectangular frame).

[0121] Optionally, the terminal fits the position boundary of the object to be identified based on the distribution pattern of the coordinates of the identification code. For example, the long axis distribution of the identification code coordinate points in the image can be used to fit a contour that matches the shape of the object to be identified as the fitted position boundary of the object to be identified; the fitted rectangular contour is used as the current position of the object to be identified in the image to be identified.

[0122] The technical solution provided in this embodiment fits the position boundary shape of the object to be identified through the identification code coordinates, and uses the fitted position boundary as the current position of the object to be identified, which is conducive to efficiently and accurately determining the current position of the object to be identified, thereby helping to improve the efficiency and accuracy of position recognition.

[0123] In an exemplary embodiment, as shown in FIG4 , in step S104 , the current position and the target position of the object to be identified are identified to obtain a position identification result of the object to be identified, which specifically includes the following:

[0124] Step S401, determining the vertex position of the object to be identified based on the fitted position boundary;

[0125] Step S402 : Identify the vertex positions and target positions of the object to be identified to obtain a position identification result.

[0126] The vertex position may be the coordinate position of each vertex in the fitted position boundary (such as a rectangular outline) in the image to be recognized.

[0127] Optionally, the terminal identifies the coordinate position of each vertex in the fitting position boundary in the image to be identified based on the fitting position boundary, as the vertex position of the object to be identified; matches and identifies the vertex position of the object to be identified with the target position, determines whether the vertex position matches the target position, and obtains the position recognition result.

[0128] The technical solution provided in this embodiment first determines the vertex position of the object to be identified, and then determines the position recognition result by identifying the vertex position and the target position, so that position recognition can be performed using vertex positions with less data volume but higher accuracy, which is conducive to further improving the efficiency and accuracy of position recognition.

[0129] In an exemplary embodiment, the vertex positions and target positions of the object to be identified are identified to obtain a position identification result, which specifically includes the following contents: when it is identified that the vertex positions of the object to be identified are all located within the target position, it is confirmed that the object to be identified is located within the target position; when it is identified that the vertex positions of the object to be identified are not all located within the target position, it is confirmed that the object to be identified is not located within the target position.

[0130] Optionally, the terminal determines whether the vertex positions of the object to be identified are all within the range of the target position; when it is identified that the vertex positions of the object to be identified are all within the range of the target position, the position recognition result of the object to be identified is confirmed as the object to be identified is within the range of the target position; when it is identified that the vertex positions of the object to be identified are not all within the range of the target position, the position recognition result of the object to be identified is confirmed as the object to be identified is not within the range of the target position.

[0131] The technical solution provided in this embodiment automatically determines the position recognition result directly based on whether the vertex position is within the range of the target position, thereby facilitating further improving the efficiency of position recognition.

[0132] In an exemplary embodiment, the current position and the target position of the object to be identified are identified to obtain a position recognition result of the object to be identified, including: when there is an overlap between the current position and the target position of the object to be identified, cutting out a regional image corresponding to the object to be identified from the image to be identified; performing vertex position recognition on the object to be identified in the regional image through a vertex position recognition model to obtain the vertex position of the object to be identified; and identifying the vertex position and the target position to obtain a position recognition result of the object to be identified.

[0133] The target position may be the final position to which the object to be identified needs to reach, for example, the target position may be a storage location. The region image may be a sub-image corresponding to the object to be identified that is cut out from the image to be identified.

[0134] Optionally, the terminal determines whether there is any overlap between the current position of the object to be identified and the target position of the object to be identified (such as a storage location). If there is an overlap between the current position and the target position, the image area corresponding to the object to be identified is cut out from the image to be identified according to the coordinates of the current position to obtain the area image corresponding to the object to be identified.

[0135] The vertex position recognition model can be a deep learning model used to identify the specific outline or structural feature point locations of an object to be recognized in a regional image. For example, the vertex position recognition model is used to identify the coordinates of the four vertices of a cage car. The vertex positions can be the coordinates of feature points of the object to be recognized identified by the vertex position recognition model, such as the coordinates of the four vertices of the object to be recognized.

[0136] Optionally, the terminal inputs the area image into the vertex position recognition model, and performs vertex position recognition on the object to be identified in the area image through the vertex position recognition model to obtain the vertex position of the object to be identified output by the vertex position recognition model, such as the specific coordinates of the four vertices of the object to be identified (cage car).

[0137] The position recognition result may be a recognition result of determining whether the object to be recognized is completely located within the target position based on a comparison relationship between the vertex position and the target position.

[0138] Optionally, the terminal compares the identified vertex position with the target position (such as a storage location). If at least three points in the vertex position are within the target position range, it is determined that the object to be identified is within the target position; otherwise, it is determined that the object to be identified is not within the target position. Based on whether the object to be identified is within the target position, the position identification result of the object to be identified is generated and output.

[0139] The technical solution provided in this embodiment is to cut out the regional image corresponding to the object to be identified from the image to be identified when there is an overlap between the current position and the target position of the object to be identified; perform vertex position recognition on the object to be identified in the regional image through a vertex position recognition model to obtain the vertex position of the object to be identified; identify the vertex position and the target position to obtain a position recognition result of the object to be identified; the position recognition result is used to indicate whether the object to be identified is located within the target position. This solution identifies the current position of the object to be identified from the image to be identified, and when there is an overlap between the current position and the target position of the object to be identified, cut out the regional image corresponding to the object to be identified, identify the vertex position of the object to be identified in the regional image through a vertex position recognition model, identify the vertex position and the target position to obtain a position recognition result of whether the object to be identified is located within the target position; in this way, this solution determines the position state of the object to be identified by identifying the preliminary position and precise position of the object to be identified and comparing them with the target position, which is conducive to improving the efficiency and accuracy of position recognition.

[0140] In an exemplary embodiment, as shown in FIG5 , identifying the vertex position and the target position to obtain a position recognition result of the object to be identified includes:

[0141] Step 501: Identify the vertex position and the target position to obtain a vertex position recognition result of the object to be identified; the vertex position recognition result is used to indicate whether the vertex of the object to be identified is located within the target position;

[0142] Step 502, determining the current number of vertices located within the target position based on the vertex position recognition result;

[0143] Step 503 : When the current number is greater than or equal to the preset number, confirm that the position recognition result of the object to be recognized indicates that the object to be recognized is located within the target position.

[0144] The vertex position recognition result may be a recognition result obtained by comparing the vertex position with the target position coordinates to determine whether each vertex of the object to be recognized is within the target position range.

[0145] The current number may be the number of vertices currently confirmed to be located within the target position obtained through statistics of vertex position recognition results.

[0146] The preset number may be a set number threshold, which is used to determine whether the object to be identified has completely entered the target position.

[0147] Optionally, the terminal compares the identified vertex position coordinates with the target position coordinates to determine whether each vertex is within the target position range; based on the comparison results of the vertex position and the target position, obtains the vertex position recognition result of each vertex of the object to be identified, which indicates whether the vertex is within the target position; based on all the vertex position recognition results obtained, counts the number of vertices currently confirmed to be within the target position; compares the current number with a preset number, wherein the preset number can be a pre-set minimum number of vertices that can be used to determine that the object to be identified has completely entered the target position; if the current number is greater than or equal to the preset number, it indicates that enough vertices of the object to be identified have entered the target position, indicating that the object to be identified is within the target position, and then confirms that the position recognition result of the object to be identified indicates that the object to be identified is within the target position.

[0148] The technical solution provided in this embodiment further determines whether the entire object to be identified has entered the target position by counting the number of vertices entering the target position, thereby obtaining a more accurate position recognition result, which is conducive to improving the accuracy of position recognition.

[0149] In an exemplary embodiment, as shown in FIG6 , when the current position overlaps with the target position of the object to be identified, extracting a region image corresponding to the object to be identified from the image to be identified includes:

[0150] Step 601: when the current position overlaps with the candidate position of the object to be identified, extract a region image from the image to be identified;

[0151] Step 602: The candidate position that overlaps with the current position is used as the target position.

[0152] The candidate locations may be candidate locations that the object to be identified can reach, for example, the candidate locations may be location coordinate ranges of different storage locations.

[0153] Optionally, the terminal determines whether there is overlap between the current position and the candidate position of the object to be identified; if there is overlap between the current position and the candidate position, a regional image is cut out from the image to be identified; and the candidate position that overlaps with the current position is used as the target position of the object to be identified.

[0154] The technical solution provided in this embodiment automatically extracts the regional image from the image to be identified and identifies the target position of the object to be identified based on the overlap between the current position and the candidate position, which is conducive to accurately determining the regional image and the target position, thereby helping to improve the accuracy of position recognition.

[0155] In an exemplary embodiment, as shown in FIG7 , the method further includes:

[0156] Step 701, identifying candidate regions from the image to be identified;

[0157] Step 702: Determine the position of the candidate area as the candidate position of the object to be identified.

[0158] The candidate area may be a candidate arrival area of ​​the object to be identified contained in the image to be identified. For example, the candidate area may be an area of ​​each storage location.

[0159] The position of the candidate area may be a coordinate point of the candidate area or a coordinate range of the candidate area.

[0160] Optionally, the terminal identifies candidate areas containing the object to be identified from the image to be identified; locates each identified candidate area in the image coordinate system to determine its position coordinates; and uses the position coordinates of each candidate area as a set of candidate positions of the object to be identified. This completes the process of identifying candidate areas from the original image and determining their positions as candidate positions.

[0161] The technical solution provided in this embodiment automatically identifies the candidate area in the image to be identified and determines its position as the candidate position of the object to be identified, which is conducive to accurately determining the candidate position, thereby helping to improve the accuracy of position recognition.

[0162] In an exemplary embodiment, scheduling transportation equipment based on position recognition results includes: obtaining a first historical position recognition result corresponding to a first recognized image; the first recognized image is an image captured within a first preset time period before the shooting time of the image to be recognized; when the first historical position recognition result and the position recognition result are consistent, obtaining a second historical position recognition result corresponding to a second recognized image; the second recognized image is an image captured within a second preset time period before the shooting time of the image to be recognized; determining an object state recognition result corresponding to the target position based on the position recognition result, the first historical position recognition result, and the second historical position recognition result; the object state recognition result is used to indicate whether there is an object to be recognized in the target position; when the object state recognition result indicates that there is an object to be recognized in the target position, generating a transportation instruction corresponding to the object to be recognized; sending the transportation instruction to the transportation equipment; the transportation equipment is used to transport the object to be recognized from the target position to the transportation position of the object to be recognized.

[0163] .

[0164] Among them, the first recognized image can be a series of historical images taken within a first preset time period before the time when the image to be recognized is taken, such as three consecutive frames of images. The first historical position recognition result can be the position recognition result of the first recognized image, which is used to indicate whether the object to be recognized is located within the target position. The second recognized image can be a series of images taken within a second preset time period before the time when the image to be recognized is taken, for example, the second preset time includes the first preset time, and the second recognized image includes the first recognized image. For example, the second recognized image can be ten consecutive frames of images. The second historical position recognition result can be the position recognition result of the second recognized image, which is used to indicate whether the object to be recognized is located within the target position. The object state recognition result can be the result of judging whether the object to be recognized is within the target position based on the position recognition results of multiple time points.

[0165] Optionally, the terminal obtains a first historical location recognition result corresponding to the first recognized image from the historical data; determines whether the first historical location recognition result and the location recognition result are consistent (for example, whether both indicate that the object to be recognized is located within the target location); if the first historical location recognition result and the location recognition result are consistent, obtains a second historical location recognition result corresponding to the second recognized image from the historical data; determines whether the location recognition result, the first historical location recognition result, and the second historical location recognition result are consistent (for example, whether both indicate that the object to be recognized is located within the target location), and obtains a comparison result of whether they are consistent; based on the comparison result, determines the object state recognition result corresponding to the target location. For example, if the comparison result is a location recognition result, the first historical location recognition result, and the second historical location recognition result both indicate that the object to be recognized is located within the target location, confirming that the object state recognition result indicates that there is an object to be recognized within the target location, and confirming that the object to be recognized has been successfully located within the target location. The transport instruction may be an instruction for controlling a transport device to transport the object to be recognized.

[0166] The transport equipment may be a device responsible for transporting the object to be identified, for example, the transport equipment may be an AGV (Automatic Guided Vehicle).

[0167] The transport location may be the transport destination location of the object to be identified.

[0168] Optionally, the terminal determines whether there is an object to be identified in the target position based on the current position identification result and the historical position identification result, and obtains the object status identification result; if the object status identification result indicates that there is an object to be identified in the target position, a corresponding transportation instruction is generated for the object to be identified, wherein the instruction content may include the location coordinates of the object to be identified, the transportation destination (transportation location) and other information; the generated transportation instruction is sent to the transportation equipment (such as AGV), so that after receiving the transportation instruction, the transportation equipment autonomously navigates to the target position according to the instruction content, and transports the object to be identified from the target position to the transportation destination.

[0169] The technical solution provided by this embodiment utilizes historical location recognition results from images at multiple time points and compares them with the current location recognition results, thereby more reliably determining whether the target location contains an object to be identified. This facilitates accurate determination of whether the target location contains an object to be identified. By controlling the transportation of the object to be identified based on image recognition, the process of automating the transportation of the object to be identified from one location to another is achieved, thereby improving transportation efficiency.

[0170] The following is an example of a method for scheduling transportation equipment provided by the present application. This example uses the method applied to a terminal as an example. The main steps include:

[0171] The first step is to obtain an image to be identified; the image to be identified includes an object to be identified, and an identification code is set on the object to be identified; the identification code in the image to be identified is identified to obtain the identification code information of the object to be identified; and the current position of the object to be identified is determined based on the identification code information.

[0172] In the second step, the terminal identifies a candidate area from the image to be identified and determines the position of the candidate area as the candidate position of the object to be identified.

[0173] In the third step, when the current position of the terminal overlaps with the candidate position of the object to be identified, the terminal cuts out the regional image from the image to be identified; and uses the candidate position that overlaps with the current position as the target position.

[0174] In the fourth step, the terminal uses the vertex position recognition model to identify the vertex position of the object to be identified in the regional image to obtain the vertex position of the object to be identified.

[0175] In the fifth step, the terminal identifies the vertex position and the target position to obtain the vertex position recognition result of the object to be identified; the vertex position recognition result is used to indicate whether the vertex of the object to be identified is located within the target position; based on the vertex position recognition result, the current number of vertices located within the target position is determined; when the current number is greater than or equal to the preset number, it is confirmed that the position recognition result of the object to be identified indicates that the object to be identified is located within the target position.

[0176] In the sixth step, the terminal obtains the first historical location recognition result corresponding to the first recognized image; the first recognized image is an image taken within a first preset time period before the shooting time of the image to be recognized; when the first historical location recognition result and the position recognition result are consistent, the second historical location recognition result corresponding to the second recognized image is obtained; the second recognized image is an image taken within a second preset time period before the shooting time of the image to be recognized; based on the position recognition result, the first historical location recognition result and the second historical location recognition result, the object state recognition result corresponding to the target location is determined; the object state recognition result is used to indicate whether there is an object to be recognized in the target location.

[0177] In the seventh step, when the object status recognition result indicates that there is an object to be identified in the target location, the terminal generates a transportation instruction corresponding to the object to be identified; sends the transportation instruction to the transportation equipment; and the transportation equipment is used to transport the object to be identified from the target location to the transportation location of the object to be identified.

[0178] The technical solution provided in this embodiment identifies the current position of the object to be identified from the image to be identified. When there is an overlap between the current position and the target position of the object to be identified, the regional image corresponding to the object to be identified is cut out, the vertex position of the object to be identified in the regional image is identified through a vertex position recognition model, the vertex position and the target position are identified, and a position recognition result of whether the object to be identified is located within the target position is obtained. In this way, the solution determines the position state of the object to be identified by identifying the preliminary position and the precise position of the object to be identified and comparing them with the target position, which is conducive to improving the efficiency and accuracy of position recognition.

[0179] In an exemplary embodiment, transportation equipment is scheduled based on position recognition results, including the following: when the position recognition result indicates that the object to be identified is located in the target position, a historical position recognition result corresponding to the identified image is obtained; the identified image is an image of the object to be identified taken within a preset time period before the shooting time of the image to be identified; when the historical position recognition results corresponding to the identified images all indicate that the object to be identified is located in the target position, confirming that the object to be identified is successfully located in the target position; determining the identification of the object to be identified based on the identification code information of the object to be identified; determining the transportation position of the object to be identified based on the identification of the object to be identified; sending the transportation position to the transportation equipment; the transportation equipment is used to transport the object to be identified to the transportation position.

[0180] The recognized image may be an image captured within a preset period of time before the image to be recognized.

[0181] The preset time period may be a pre-set period of time.

[0182] The historical position recognition result may be a result obtained by performing position recognition on a recognized image, and is used to indicate whether the object to be recognized is located within the target position.

[0183] Optionally, the terminal determines whether the position recognition result indicates that the object to be recognized is located within the target position; if the position recognition result indicates that the object to be recognized is located within the target position, obtains a recognized image taken within a preset time period before the time when the image to be recognized is taken; performs position recognition on the recognized image to obtain a corresponding historical position recognition result; determines whether the historical position recognition results all indicate that the object to be recognized is located within the target position; if the historical position recognition results all indicate that the object to be recognized is located within the target position, then confirms that the object to be recognized is successfully located within the target position, for example, this indicates that the object to be recognized has successfully stayed within the range of the target position. The identifier of the object to be recognized can be a unique mark of the object to be recognized, such as the ID (identification number) of the cage car.

[0184] The transport location may be a target location to which the object to be identified needs to be transported.

[0185] The transport equipment may be equipment for transporting objects to be identified, such as an AGV (Automated Guided Vehicle).

[0186] Optionally, the terminal determines the unique identification of the object to be identified based on the identification code information on the object to be identified; based on the identified identification of the object to be identified, queries and determines the next transportation location corresponding to the object to be identified from the system; sends the determined transportation location to the transportation equipment, so that the transportation equipment autonomously navigates to the location of the object to be identified based on the received transportation location, grabs the object to be identified and loads it onto the vehicle, drives and transports the object to be identified to the transportation location, and unloads the object to be identified at the transportation location.

[0187] The technical solution provided by this embodiment improves the accuracy of location recognition by combining historical location recognition results over a period of time with a single location recognition result indicating that the object to be identified is at the target location. If the historical results also indicate that the object is within the target location, the object is ultimately confirmed to be successfully located or remaining at the target location. The identification code information is used to determine the identity of the object to be identified, and the corresponding next transportation task is queried, completing the automated location recognition and transportation process, thereby improving transportation efficiency.

[0188] The following is an example of a method for scheduling transportation equipment provided by the present application. This example uses the method applied to a terminal as an example. The main steps include:

[0189] In the first step, the terminal obtains an image to be identified; the image to be identified includes an object to be identified, and an identification code is set on the object to be identified.

[0190] In the second step, the terminal identifies the identification code in the image to be identified and obtains the identification code information of the object to be identified.

[0191] In the third step, the terminal determines the coordinate information of the identification code based on the identification code information; based on the coordinate information of the identification code, the terminal performs position boundary fitting processing on the object to be identified to obtain the fitting position boundary of the object to be identified; and identifies the fitting position boundary as the current position of the object to be identified.

[0192] In the fourth step, the terminal determines the vertex positions of the object to be identified based on the fitted position boundary; when it is identified that the vertex positions of the object to be identified are all located within the target position, it is confirmed that the object to be identified is located within the target position; when it is identified that the vertex positions of the object to be identified are not all located within the target position, it is confirmed that the object to be identified is not located within the target position.

[0193] In the fifth step, when the position recognition result indicates that the object to be identified is located in the target position, the terminal obtains the historical position recognition result corresponding to the identified image; the identified image is an image of the object to be identified taken within a preset time period before the shooting time of the image to be identified; when the historical position recognition results corresponding to the identified images all indicate that the object to be identified is located in the target position, it is confirmed that the object to be identified is successfully located in the target position.

[0194] In the sixth step, the terminal determines the identification of the object to be identified based on the identification code information of the object to be identified; determines the transportation location of the object to be identified based on the identification of the object to be identified; sends the transportation location to the transportation equipment; and the transportation equipment is used to transport the object to be identified to the transportation location.

[0195] The position recognition result is used to indicate whether the object to be recognized is located within the target position.

[0196] The technical solution provided by this embodiment obtains identification code information by identifying the identification code set on the object to be identified in the image to be identified, determines the current position of the object to be identified based on the identification code information, identifies the current position and the target position of the object to be identified, and obtains a position identification result of whether the object to be identified is located within the target position; in this way, the solution automatically identifies whether the object is located within the target position through the identification code and position information, which is conducive to improving the efficiency and accuracy of position identification.

[0197] The following is an application example to illustrate the transportation equipment scheduling method provided by this application. This application example uses the method applied to a terminal as an example. The main steps include:

[0198] In the first step, the terminal obtains the cage car ID through a scanner.

[0199] An industrial barcode scanner or fisheye camera can be installed on the top of the storage space (depending on the installation height. Because the site will have a steel platform, the first floor will not be very high, perhaps less than 2.5 meters, making it suitable for a fisheye camera to capture the entire top of the caged car). A laser scanner can also be installed to serve as a trigger for the barcode scanner or fisheye camera. When the laser scanner detects a caged car approaching, it triggers the barcode scanner to obtain the caged car's ID (the ID information can include the specific coordinates of the 1D code).

[0200] In the second step, when the cage car is pushed into the storage location, the terminal sends a trigger signal through the laser scanner based on the detected information, triggering the barcode scanner to work. The barcode scanner obtains the image (image to be identified) based on the acquired signal and decodes the one-dimensional code in the image and outputs the coordinates of the one-dimensional code and the actual content of the one-dimensional code.

[0201] In the third step, the terminal obtains the captured image, one-dimensional code coordinates and specific recognition results through the barcode scanner.

[0202] In the fourth step, the terminal obtains the result and coordinates of the one-dimensional code, analyzes the coordinates to determine the initial posture of the cage car, which can be determined by multiple one-dimensional codes (refer to Figure 8, a circle of one-dimensional codes can be pre-attached to the top of the cage car, and the top of the cage car is a rectangular shape, which can be affixed with 7 one-dimensional codes). When the rectangle approximately enclosed by the long axis straight lines of multiple one-dimensional codes is basically similar to the area marked by the storage location, it can be determined that the cage car is basically in place.

[0203] With reference to FIG8 , the image to be identified in FIG8 includes an object to be identified (a cage truck), wherein seven identification codes are attached around the top of the object to be identified.

[0204] In the fifth step, the terminal inputs the image into the cage car vertex recognition network (such as RTMPose, real-time key point detection model) to determine the top vertex of the cage car, and then determines whether the vertex is within the warehouse location calibration area; through multiple images, it is determined that the cage car has not moved, thereby confirming that the cage car is in place.

[0205] In the sixth step, after the terminal confirms that the cage car is in place, it obtains the corresponding transportation task of the cage car in the system according to the identified cage car ID, and automatically calls the nearest AGV to transport the cage car to the corresponding destination.

[0206] For example, the scanner captures an image based on the acquired signal and decodes the one-dimensional code in the image to obtain the one-dimensional code information, and sends the one-dimensional code information to the terminal, which obtains the one-dimensional code information sent by the scanner; for the same cage car, the one-dimensional code information is the same (has the same ID), so it can be determined whether the acquired one-dimensional code information belongs to the one-dimensional code of the same cage car or the one-dimensional codes of multiple cage cars; after identifying the one-dimensional code information, the terminal can identify the coordinates of the one-dimensional code of the same cage car; based on the coordinates of the one-dimensional code of the same cage car, the actual posture of the cage car can be determined, for example, a rectangular frame of the cage car can be fitted through the coordinates of the one-dimensional code; based on the vertex coordinates of the rectangular frame of the cage car, it is determined whether the cage car is in the storage area; through multiple frames of images, the time the cage car has been in the storage area is identified. If the time is greater than the preset time threshold, it means that the cage car has been successfully put into storage; subsequently, by calling the AGV system, the cage car is transported to the target location based on the identified cage car ID.

[0207] The technical solution provided by this application example automatically identifies whether an object is located within a target location through identification codes and location information, thereby improving the efficiency and accuracy of location recognition.

[0208] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0209] Based on the same inventive concept, embodiments of the present application also provide a transport equipment scheduling device for implementing the aforementioned transport equipment scheduling method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more of the following embodiments of the transport equipment scheduling device can be found in the aforementioned limitations of the transport equipment scheduling method and will not be further elaborated here.

[0210] In an exemplary embodiment, as shown in FIG9 , a transportation equipment scheduling device is provided. The device 900 may include:

[0211] The image acquisition module 901 is used to acquire an image to be identified; the image to be identified includes an object to be identified, and an identification code is set on the object to be identified;

[0212] The image recognition module 902 is used to recognize the identification code in the image to be recognized and obtain the identification code information of the object to be recognized;

[0213] A position determination module 903 is used to determine the current position of the object to be identified based on the identification code information;

[0214] The target recognition module 904 is used to identify the current position and the target position of the object to be recognized, and obtain a position recognition result of the object to be recognized; the position recognition result is used to indicate whether the object to be recognized is located within the target position;

[0215] The equipment scheduling module 905 is used to schedule transportation equipment based on the position recognition result; the transportation equipment is used to transport the object to be recognized.

[0216] In an exemplary embodiment, the position determination module 903 is further configured to determine the coordinate information of the identification code according to the identification code information; and determine the current position of the object to be identified according to the coordinate information of the identification code.

[0217] In an exemplary embodiment, the position determination module 903 is further configured to perform position boundary fitting processing on the object to be identified based on the coordinate information of the identification code to obtain the fitted position boundary of the object to be identified; and identify the fitted position boundary as the current position of the object to be identified.

[0218] In an exemplary embodiment, the target recognition module 904 is further configured to determine the vertex positions of the object to be recognized based on the fitted position boundary; and recognize the vertex positions of the object to be recognized and the target position to obtain a position recognition result.

[0219] In an exemplary embodiment, the target recognition module 904 is also used to confirm that the object to be recognized is located within the target position when it is identified that the vertex positions of the object to be recognized are all located within the target position; and to confirm that the object to be recognized is not located within the target position when it is identified that not all vertex positions of the object to be recognized are located within the target position.

[0220] In an exemplary embodiment, the target recognition module 904 is also used to, when there is an overlap between the current position and the target position of the object to be recognized, cut out the area image corresponding to the object to be recognized from the image to be recognized; perform vertex position recognition on the object to be recognized in the area image through a vertex position recognition model to obtain the vertex position of the object to be recognized; and recognize the vertex position and the target position to obtain a position recognition result of the object to be recognized.

[0221] In an exemplary embodiment, the target recognition module 904 is also used to identify the vertex position and the target position to obtain a vertex position recognition result of the object to be identified; the vertex position recognition result is used to indicate whether the vertex of the object to be identified is located within the target position; based on the vertex position recognition result, the current number of vertices located within the target position is determined; when the current number is greater than or equal to a preset number, it is confirmed that the position recognition result of the object to be identified indicates that the object to be identified is located within the target position.

[0222] In an exemplary embodiment, the target recognition module 904 is also used to cut out a regional image from the image to be recognized when there is an overlap between the current position and the candidate position of the object to be recognized; and use the candidate position that overlaps with the current position as the target position.

[0223] In an exemplary embodiment, the target recognition module 904 is further configured to identify a candidate region from the image to be recognized; and determine a position of the candidate region as a candidate position of the object to be recognized.

[0224] In an exemplary embodiment, the device scheduling module 905 is also used to obtain a first historical location recognition result corresponding to a first recognized image; the first recognized image is an image taken within a first preset time period before the shooting time of the image to be recognized; when the first historical location recognition result and the location recognition result are consistent, obtain a second historical location recognition result corresponding to the second recognized image; the second recognized image is an image taken within a second preset time period before the shooting time of the image to be recognized; determine the object state recognition result corresponding to the target location based on the location recognition result, the first historical location recognition result and the second historical location recognition result; the object state recognition result is used to indicate whether there is an object to be recognized in the target location; when the object state recognition result indicates that there is an object to be recognized in the target location, generate a transportation instruction corresponding to the object to be recognized; send the transportation instruction to the transportation equipment; the transportation equipment is used to transport the object to be recognized from the target location to the transportation location of the object to be recognized.

[0225] In an exemplary embodiment, the equipment scheduling module 905 is also used to obtain the historical position recognition result corresponding to the identified image when the position recognition result indicates that the object to be identified is located within the target position; the identified image is an image of the object to be identified taken within a preset time period before the shooting time of the image to be identified; when the historical position recognition results corresponding to the identified images all indicate that the object to be identified is located within the target position, confirm that the object to be identified is successfully located within the target position; determine the identification of the object to be identified based on the identification code information of the object to be identified; determine the transportation location of the object to be identified based on the identification of the object to be identified; send the transportation location to the transportation equipment; the transportation equipment is used to transport the object to be identified to the transportation location.

[0226] Each module in the aforementioned transportation equipment scheduling device may be implemented in whole or in part through software, hardware, or a combination thereof. Each module may be embedded in or independent of one or more processors in a computer device in the form of hardware, or may be stored in a computer device memory in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0227] In an exemplary embodiment, a computer device is provided, which may be a terminal. Its internal structure may be as shown in FIG10 . The computer device includes one or more processors, memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are connected to the system bus via the input / output interface. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer-readable instructions. The internal memory provides an environment for the operation of the operating system and computer-readable instructions in the non-volatile storage medium. The input / output interface of the computer device is configured to exchange information between the processor and external devices. The communication interface of the computer device is configured to communicate with external terminals via wired or wireless communication, where the wireless communication may be achieved via Wi-Fi, a mobile cellular network, NFC (near-field communication), or other technologies. When executed by the processor, the computer-readable instructions implement a method for dispatching transportation equipment. The display unit of the computer device is configured to produce a visually visible image and may be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse.

[0228] Those skilled in the art will understand that the structure shown in FIG10 is merely a block diagram of a portion of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0229] In an exemplary embodiment, a computer device is also provided, including a memory and one or more processors, wherein the memory stores computer-readable instructions, and the one or more processors implement the steps in the above-mentioned method embodiments when executing the computer-readable instructions.

[0230] In an exemplary embodiment, one or more computer-readable storage media are provided, on which computer-readable instructions are stored. When the computer-readable instructions are executed by one or more processors, the steps in the above-mentioned method embodiments are implemented.

[0231] In an exemplary embodiment, a computer program product is provided, including computer-readable instructions, which implement the steps of the above-mentioned method embodiments when executed by one or more processors.

[0232] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through computer-readable instructions. The computer-readable instructions can be stored in a non-volatile computer-readable storage medium. When the computer-readable instructions are executed, they can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processor involved in the various embodiments provided herein may be, but are not limited to, a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, and the like.

[0233] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0234] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A transportation equipment scheduling method, characterized in that, Applied to a terminal; the method includes: Obtain an image to be recognized; the image to be recognized includes an object to be recognized, and an identification code is set on the object to be recognized; Recognize the identification code in the image to be recognized to obtain the identification code information of the object to be recognized; Determine the current position of the object to be recognized according to the identification code information; Recognize the current position and the target position of the object to be recognized to obtain a position recognition result of the object to be recognized; the position recognition result is used to indicate whether the object to be recognized is within the target position; Schedule a transportation device based on the position recognition result; the transportation device is used to transport the object to be recognized.

2. The method according to claim 1, wherein The determining the current position of the object to be recognized according to the identification code information includes: Determine the coordinate information of the identification code according to the identification code information; Determine the current position of the object to be recognized according to the coordinate information of the identification code.

3. The method according to claim 2, wherein The determining the current position of the object to be recognized according to the coordinate information of the identification code includes: Perform fitting processing on the position boundary of the object to be recognized according to the coordinate information of the identification code to obtain a fitted position boundary of the object to be recognized; Identify the fitted position boundary as the current position of the object to be recognized.

4. The method according to claim 3, wherein The recognizing the current position and the target position of the object to be recognized to obtain a position recognition result of the object to be recognized includes: Determine the vertex positions of the object to be recognized according to the fitted position boundary; Recognize the vertex positions of the object to be recognized and the target position to obtain the position recognition result.

5. The method according to claim 4, wherein The recognizing the vertex positions of the object to be recognized and the target position to obtain a position recognition result of the object to be recognized includes: When it is recognized that all the vertex positions of the object to be recognized are within the target position, confirm that the object to be recognized is within the target position; When it is recognized that not all the vertex positions of the object to be recognized are within the target position, confirm that the object to be recognized is not within the target position.

6. The method according to claim 1, characterized in that The recognizing the current position and the target position of the object to be recognized to obtain a position recognition result of the object to be recognized includes: When there is an overlap between the current position and the target position of the object to be recognized, extract the region image corresponding to the object to be recognized from the image to be recognized; Perform vertex position recognition on the object to be recognized in the region image through a vertex position recognition model to obtain the vertex positions of the object to be recognized; Recognize the vertex positions and the target position to obtain a position recognition result of the object to be recognized.

7. The method according to claim 6, wherein The recognizing the vertex positions and the target position to obtain a position recognition result of the object to be recognized includes: Recognize the vertex positions and the target position to obtain a vertex position recognition result of the object to be recognized; the vertex position recognition result is used to indicate whether the vertices of the object to be recognized are within the target position; Determine the current quantity of the vertices located within the target position according to the vertex position recognition result; In the case where the current quantity is greater than or equal to a preset quantity, confirm that the position recognition result of the object to be recognized indicates that the object to be recognized is located within the target position.

8. The method according to claim 6, wherein In the case where there is an overlap between the current position and the target position of the object to be recognized, extract the region image corresponding to the object to be recognized from the image to be recognized, including: In the case where there is an overlap between the current position and the candidate position of the object to be recognized, extract the region image from the image to be recognized; Take the candidate position that overlaps with the current position as the target position.

9. The method according to claim 8, wherein The method further includes: Identify a candidate region from the image to be recognized; Determine the position of the candidate region as the candidate position of the object to be recognized.

10. The method according to claim 6, characterized in that The scheduling of the transportation device based on the position recognition result includes: Obtain the first historical position recognition result corresponding to the first recognized image; the first recognized image is an image captured within a first preset time period before the capture time of the image to be recognized; In the case where the first historical position recognition result and the position recognition result are both consistent, obtain the second historical position recognition result corresponding to the second recognized image; the second recognized image is an image captured within a second preset time period before the capture time of the image to be recognized; Determine the object state recognition result corresponding to the target position according to the position recognition result, the first historical position recognition result, and the second historical position recognition result; the object state recognition result is used to indicate whether there is the object to be recognized within the target position; Generate a transportation instruction corresponding to the object to be recognized in the case where the object state recognition result indicates that there is the object to be recognized within the target position; Send the transportation instruction to the transportation device; the transportation device is used to transport the object to be recognized from the target position to the transportation position of the object to be recognized.

11. The method according to claim 1, characterized in that, The scheduling of the transportation device based on the position recognition result includes: In the case where the position recognition result indicates that the object to be recognized is located within the target position, obtain the historical position recognition result corresponding to the recognized image; the recognized image is an image captured for the object to be recognized within a preset time period before the capture time of the image to be recognized; In the case where the historical position recognition results corresponding to the recognized images all indicate that the object to be recognized is located within the target position, confirm that the object to be recognized is successfully located within the target position; Determine the identifier of the object to be recognized according to the identifier information of the object to be recognized; Determine the transportation position of the object to be recognized according to the identifier of the object to be recognized; Send the transportation position to the transportation device; the transportation device is used to transport the object to be recognized to the transportation position.

12. A transportation equipment scheduling device, characterized in that, The device includes: An image acquisition module, configured to acquire an image to be recognized; the image to be recognized includes an object to be recognized, and an identifier is set on the object to be recognized; An image recognition module for recognizing the identification code in the image to be recognized to obtain the identification code information of the object to be recognized; A position determination module for determining the current position of the object to be recognized according to the identification code information; A target recognition module for recognizing the current position and the target position of the object to be recognized to obtain a position recognition result of the object to be recognized; the position recognition result is used to indicate whether the object to be recognized is located within the target position; A device scheduling module for scheduling a transportation device based on the position recognition result; the transportation device is used to transport the object to be recognized.

13. A computer device, comprising a memory and one or more processors, the memory storing computer-readable instructions, characterized in that, When the one or more processors execute the computer-readable instructions, the steps of the method according to any one of claims 1 to 11 are implemented.

14. One or more computer-readable storage media having computer-readable instructions stored thereon, wherein When the computer-readable instructions are executed by one or more processors, the steps of the method according to any one of claims 1 to 11 are implemented.

15. A computer program product comprising computer-readable instructions, characterized in that, When the computer-readable instructions are executed by one or more processors, the steps of the method according to any one of claims 1 to 11 are implemented.

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