Data processing methods, commodity freight information processing methods, and data processing systems

Abstract

This specification provides a data processing method, a commodity freight information processing method, and a data processing system. The data processing method includes: determining the identification attribute data of the target object based on an object video; constructing an object model of the target object based on the object video, and determining the measurement attribute data of the target object based on the object model; updating the identification attribute data based on the object record data of the target object and the measurement attribute data to obtain object attribute data; and determining the resource data corresponding to the target object based on the object attribute data.

Description

Technical Field

[0001] The embodiments in this specification relate to the field of data processing technology, and in particular to data processing methods, commodity freight information processing methods, and data processing systems. Background Technology

[0002] In commodity trading applications, accurate modeling and resource consumption assessment of commodities are fundamental to resource optimization and task decision-making. Currently, commodity resource consumption typically relies on manual evaluation and calculation, leading to deficiencies in the accuracy and completeness of the determined resource consumption data. This results in significant deviations in resource prediction, impacting the scientific rigor and efficiency of subsequent task decisions. Therefore, a more effective data processing method is urgently needed to address these issues. Summary of the Invention

[0003] In view of this, embodiments of this specification provide a data processing method. One or more embodiments of this specification simultaneously relate to a method for processing commodity freight information, a data processing system, a data processing apparatus, a commodity freight information processing apparatus, a computing device, a computer-readable storage medium, and a computer program product, to address the technical deficiencies existing in the prior art.

[0004] According to a first aspect of the embodiments of this specification, a data processing method is provided, comprising: The identification attribute data of the target object are determined based on the object video of the target object; An object model of the target object is constructed based on the object video, and the measurement attribute data of the target object is determined according to the object model; The identification attribute data is updated based on the object record data of the target object and the measurement attribute data to obtain object attribute data; The resource data corresponding to the target object is determined based on the object attribute data.

[0005] According to a second aspect of the embodiments of this specification, a data processing system is provided, including a client and a server, comprising: The client is used to submit the object video of the target object to the server; The server is configured to: determine the identification attribute data of the target object based on the object video; construct an object model of the target object based on the object video, and determine the measurement attribute data of the target object based on the object model; update the identification attribute data based on the object record data of the target object and the measurement attribute data to obtain object attribute data; determine the resource data corresponding to the target object based on the object attribute data, and send the resource data to the client.

[0006] According to a third aspect of the embodiments of this specification, another data processing method is provided, including: Identify the target object's video; An object model of the target object is constructed based on the object video, and the measurement attribute data of the target object is determined according to the object model; Object attribute data is generated based on the object record data of the target object and the measurement attribute data; The resource data corresponding to the target object is determined based on the object attribute data.

[0007] According to a fourth aspect of the embodiments of this specification, a method for processing commodity freight information is provided, including: The identification attribute information of the target product is determined based on the product video of the target product; Based on the product video, a product measurement model of the target product is constructed, and the measurement attribute information of the target product is determined according to the product measurement model; The identification attribute information is updated based on the product record information and the measurement attribute information of the target product to obtain product attribute information; Based on the product attribute information, determine the shipping cost information corresponding to the target product.

[0008] According to a fifth aspect of the embodiments of this specification, a data processing apparatus is provided, comprising: The first determining module is configured to determine the identification attribute data of the target object based on the object video of the target object; The construction module is configured to construct an object model of the target object based on the object video, and determine the measurement attribute data of the target object based on the object model; The update module is configured to update the identification attribute data based on the object record data of the target object and the measurement attribute data to obtain object attribute data; The second determining module is configured to determine the resource data corresponding to the target object based on the object attribute data.

[0009] According to a sixth aspect of the embodiments of this specification, another data processing apparatus is provided, comprising: The first determining module is configured to determine the object video of the target object; The construction module is configured to construct an object model of the target object based on the object video, and determine the measurement attribute data of the target object based on the object model; The generation module is configured to generate object attribute data based on the object record data of the target object and the measurement attribute data; The second determining module is configured to determine the resource data corresponding to the target object based on the object attribute data.

[0010] According to a seventh aspect of the embodiments of this specification, a commodity freight information processing apparatus is provided, comprising: The first determining module is configured to determine the identification attribute information of the target product based on the product video of the target product; The construction module is configured to construct a product measurement model of the target product based on the product video, and determine the measurement attribute information of the target product according to the product measurement model; The update module is configured to update the identification attribute information based on the product record information and the measurement attribute information of the target product, thereby obtaining product attribute information; The second determining module is configured to determine the shipping cost information corresponding to the target product based on the product attribute information.

[0011] According to an eighth aspect of the embodiments of this specification, a computing device is provided, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, which, when executed by the processor, implement the steps of the above-described data processing method.

[0012] According to a ninth aspect of an embodiment of this specification, a computer-readable storage medium is provided that stores computer-executable instructions that, when executed by a processor, implement the steps of the data processing method described above.

[0013] According to a tenth aspect of the embodiments of this specification, a computer program product is provided, including a computer program or instructions that, when executed by a processor, implement the steps of the data processing method described above.

[0014] This specification provides a data processing method in one embodiment that determines the identification attribute data of a target object based on an object video of the target object, thereby determining the corresponding identification attribute data of the target object in the visual dimension. An object model of the target object is constructed based on the object video, and measurement attribute data of the target object is determined based on the object model. The attribute data of the object model of the target object is measured in the physical measurement dimension to obtain measurement attribute data. The identification attribute data is updated based on the object record data and measurement attribute data of the target object to obtain object attribute data that accurately expresses the physical parameters of the target object. Based on the object attribute data, resource data corresponding to the target object is determined, enabling accurate calculation of resource data and facilitating subsequent task decisions related to the target object, thereby improving task decision efficiency. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating a data processing method provided in one embodiment of this specification; Figure 2 This is an interactive schematic diagram of a data processing method provided in one embodiment of this specification; Figure 3 This is a flowchart of another data processing method provided in one embodiment of this specification; Figure 4 This is a schematic diagram of the structure of a data processing system provided in one embodiment of this specification; Figure 5 This is a flowchart illustrating a method for processing commodity freight information according to one embodiment of this specification; Figure 6 This is a schematic diagram of the structure of a data processing apparatus provided in one embodiment of this specification; Figure 7 This is a schematic diagram of the structure of another data processing device provided in one embodiment of this specification; Figure 8 This is a schematic diagram of the structure of a commodity freight information processing device provided in one embodiment of this specification; Figure 9 This is a structural block diagram of a computing device provided in one embodiment of this specification. Detailed Implementation

[0016] Many specific details are set forth in the following description to provide a full understanding of this specification. However, this specification can be implemented in many other ways than those described herein, and those skilled in the art can make similar extensions without departing from the spirit of this specification. Therefore, this specification is not limited to the specific implementations disclosed below.

[0017] The terminology used in one or more embodiments of this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the one or more embodiments of this specification. The singular forms “a,” “described,” and “the” as used in one or more embodiments of this specification and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in one or more embodiments of this specification refers to and includes any or all possible combinations of one or more associated listed items.

[0018] It should be understood that although the terms first, second, etc., may be used to describe various information in one or more embodiments of this specification, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first may also be referred to as second without departing from the scope of one or more embodiments of this specification, and similarly, second may also be referred to as first. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to a determination."

[0019] Furthermore, 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 one or more embodiments of this specification are all information and data authorized by the user or fully authorized by all parties. Moreover, the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0020] The technical solutions provided in this application can employ deep learning models with relatively large parameter scales. However, this large model is merely an example; this application does not limit the number of model parameters supported by the deep learning model used, aiming to meet actual needs. The deep learning models involved in this application can be artificial intelligence-based language models (LM) or multimodal models (MM).

[0021] First, the terms and concepts used in one or more embodiments of this specification will be explained.

[0022] Feature point matching algorithms are the process of identifying and establishing correspondences between local salient points (i.e., "feature points") in two or more images. These feature points typically possess invariance to rotation, scale, and illumination.

[0023] The PnP (Perspective-n-Point) algorithm is a classic geometric algorithm for estimating camera pose (i.e., rotation R and translation t) from 2D-3D point correspondences. Given n 3D points in space and their 2D projections into the image, PnP solves for the extrinsic parameters of the camera relative to these 3D points.

[0024] Direct Pose Regression Methods: These methods use deep neural networks to directly regress the 6D pose (3D rotation + 3D translation) of an object from an input image (or image features), without requiring explicit feature point extraction or geometric optimization.

[0025] PVNet (Pixel-wise Voting Network): A deep learning method for 6D object pose estimation. Instead of directly regressing the pose, it predicts the unit vector pointing from each pixel to the object's keypoints, then obtains the 2D positions of the keypoints through mean-shift clustering voting, and finally uses the PnP algorithm to solve for the 6D pose.

[0026] CNN (Convolutional Neural Network): A deep learning model specifically designed for processing data with a grid structure (such as images, videos, and speech spectrograms). Its core idea is to automatically extract spatially hierarchical features through convolutional operations with local receptive fields and weight sharing.

[0027] The Transformer model is a deep neural network architecture built on an attention mechanism, primarily used to process sequential data (such as text and time series) or data that can be transformed into a sequential form (such as image chunks). Its core characteristic is that it relies entirely on attention operations to model the relationships between elements, without using traditional recurrent structures (such as RNNs) or local convolution operations. The Transformer treats the input data as a set of unordered but positionally labeled elements (called tokens), dynamically aggregating contextual information by calculating the correlation (i.e., "attention") between each element and all other elements. This mechanism allows the model to simultaneously focus on any two positions in the sequence, effectively capturing long-distance dependencies.

[0028] To address the aforementioned technical problems, this specification provides a data processing method. This specification also relates to a commodity freight information processing method, a data processing system, a data processing device, a commodity freight information processing device, a computing device, a computer-readable storage medium, and a computer program product, which will be described in detail in the following embodiments.

[0029] When users associated with a target object require calculations of the target object's resource data, the server can determine the target object's identification attribute data based on the target object's video, thus determining the corresponding identification attribute data in the visual dimension. An object model of the target object is constructed based on the video, and measurement attribute data is determined based on the object model. The attribute data of the target object's object model is measured in the physical measurement dimension to obtain measurement attribute data. The identification attribute data is updated based on the target object's object record data and measurement attribute data to obtain object attribute data that accurately expresses the target object's physical parameters. Based on the object attribute data, the corresponding resource data for the target object is determined, enabling precise calculation of resource data. This facilitates subsequent task decisions related to the target object's resource data, improving task decision-making efficiency.

[0030] See Figure 1 , Figure 1 A flowchart of a data processing method according to an embodiment of this specification is shown, which specifically includes the following steps.

[0031] Step 102: Determine the identification attribute data of the target object based on the object video of the target object.

[0032] Specifically, the target object can be any object capable of being filmed, and it must be a physical object. The object video is the video obtained by filming the target object. The target video can be obtained by filming the target object from multiple angles. The angles from which the video of the target object is captured can be six views: front, back, top, bottom, left side, and right side. The attribute data is the attribute data related to the target object obtained by identifying each object video frame in the object video. The attribute data includes, but is not limited to, physical parameters such as the target object's volume, color, shape, and weight.

[0033] Based on this, video is captured of the target object to obtain the corresponding object video. The target object's recognition attribute data is determined based on the object video. By recognizing the target object in the object video, the target object's physical parameters are extracted, and these extracted physical parameters are used as the recognition attribute data.

[0034] In practical applications, the target object can be the product purchased by the user in a physical goods transaction scenario. The object video can be a video captured by the user through their terminal after receiving the product. Before the user captures the video of the target object, they can be prompted to capture the video from multiple angles. This facilitates subsequent identification of the target object's physical parameters based on the object video.

[0035] Furthermore, after determining the object video corresponding to the target object, since the object video is shot with the target object as the main subject and consists of continuous video frames, the recognition attribute data corresponding to the target object obtained through image recognition can be obtained by identifying at least one object video frame in the object video. The specific implementation is as follows: Extract at least one object video frame from the object video; input the at least one object video frame into an image recognition model to obtain the recognition attribute data of the target object.

[0036] Specifically, an object video frame refers to a video frame within an object video. The at least one object video frame extracted from the object video can be any of the video frames contained within the object video, or it can be a predetermined number of video frames extracted from the object video using a sampling method. Alternatively, it can be video frames extracted at preset intervals, such as extracting one object video frame after every two video frames. The image recognition model can be a large model with image recognition capabilities, used to identify the physical parameters of the target object within the object video frame. The attribute data can also be the physical parameters of the target object obtained by detecting at least one object video frame using AI algorithms.

[0037] Based on this, at least one object video frame is extracted from the object video. This at least one object video frame is then input into an image recognition model, which identifies the target object in each object video frame, obtaining the target object's attribute data. Alternatively, computer vision algorithms can be used to identify the target object in at least one object video frame and obtain the target object's attribute data.

[0038] For example, in a product transaction scenario, the target object can be a physical product that the user (consumer) has already purchased and received. If the user purchased a mobile phone and needs to return it, the return shipping cost is difficult to estimate accurately due to the phone's weight. The user can record a video of the phone, which serves as the object video. After submitting the object video, the client or server can use an image recognition model to identify the phone in at least one frame of the object video, obtaining physical identification parameters such as weight distribution, size, and color—that is, identification attribute data. Alternatively, computer recognition algorithms can be used to identify at least one object video frame; these can be feature point matching algorithms, such as the PnP (Perspective-n-Point) algorithm, or direct pose regression algorithms, such as PVNet. The identification attribute data is then extracted using computer recognition algorithms.

[0039] In summary, by inputting at least one object video frame into the image recognition model, the recognition attribute data of the target object can be obtained, thereby improving the accuracy of the recognition attribute data.

[0040] Furthermore, when using an image recognition model to identify at least one object video frame, considering that the target object in the video is a three-dimensional physical object, we can first identify the multi-dimensional information of the target object, and then extract data from the multi-dimensional information in the dimension of physical parameters. The specific implementation is as follows: The image recognition model is used to identify at least one object video frame to obtain multidimensional information of the target object, and data extraction is performed on the multidimensional information in the physical parameter dimension to obtain the recognition attribute data of the target object.

[0041] Specifically, the multidimensional information of a target object can be its three-dimensional shape, as well as attribute data such as size and color determined based on its three-dimensional shape. The physical parameter dimension represents the target object's physical attributes such as size, color, volume, and weight.

[0042] Based on this, an image recognition model is used to identify at least one video frame containing an object, obtaining the three-dimensional shape of the target object, and determining its multidimensional information based on the three-dimensional shape. Data extraction is performed on the target object's physical parameters such as size, color, volume, and weight to obtain its identification attribute data.

[0043] In summary, by extracting multidimensional information from the physical parameter dimension, we can obtain the identification attribute data of the target object and improve the accuracy of the identification attribute data.

[0044] Furthermore, considering that the target object is a physical object, video of the target object can be captured according to the user's needs, as specifically implemented as follows: In response to a user's touch operation on an object record associated with the target object, a video capture page is displayed to the user; the video of the target object captured by the user through the video capture page is received.

[0045] Specifically, when the target object is a product, the object record can be a detailed description of the product edited by the user (merchant) on the trading platform, or it can be a product order placed by a user (consumer). Touch operations on the object record can include the user recording a video of the target object. When the user is a consumer, the purpose of recording the video is to calculate return shipping costs; when the user is a merchant, the purpose is to calculate shipping costs and, based on these costs, to set or adjust the price of the target object.

[0046] Based on this, in response to the user's touch operation on the object record associated with the target object, a video capture page is displayed to the user; the object video captured by the user through the video capture page is received.

[0047] Continuing with the previous example, when the target object is a product and the user of the trading platform is a consumer, the consumer can return the product. Before returning the product, the consumer can access the order by clicking on the product's order details, then click the shipping cost estimation camera control to be redirected to a video capture page. On this page, the consumer can capture a video of the product in real-time using their mobile device. This video is used to calculate the estimated return shipping cost. If the user is a merchant, the purpose of capturing the video is to calculate shipping costs and, based on these costs, to set or adjust the price of the target product.

[0048] In summary, the system displays a video capture page in response to a user's touch operation on an object record associated with a target object. The user then uses this video capture page to capture video of the target object, thus improving the efficiency of video capture.

[0049] Step 104: Construct an object model of the target object based on the object video, and determine the measurement attribute data of the target object according to the object model.

[0050] Specifically, after determining the target object's recognition attribute data based on the target object's video, an object model of the target object can be constructed based on the video, and the target object's measurement attribute data can be determined based on the object model. The object model can be a virtual model corresponding to the target object. The object model can be a three-dimensional model or a virtual model of other dimensions. The measurement attribute data of the target object are the physical parameters of the target object obtained by actually measuring the object model. The physical parameters obtained by measuring the object model include, but are not limited to, the target object's volume, weight distribution, color, shape, and center of mass position.

[0051] Based on this, after determining the target object's recognition attribute data from the target object's video, an object model of the target object is constructed based on the video. This object model can be a three-dimensional virtual model. The measurement attribute data of the target object is then determined based on the object model; that is, the object model is measured to obtain the target object's physical parameters.

[0052] Furthermore, considering that the key parameters for measuring a target object are its weight distribution and volume, we can measure the object model in the volume dimension to obtain volume measurement data, and measure the object model in the weight distribution dimension to obtain weight distribution measurement data. Then, we can use the volume measurement data and weight distribution measurement data as the target object's measurement attribute data. The specific implementation is as follows: The object model is measured in the volume dimension to obtain volume measurement data, and the object model is measured in the weight distribution dimension to obtain weight distribution measurement data; the volume measurement data and the weight distribution measurement data are used as the measurement attribute data of the target object.

[0053] Based on this, the object model is measured in terms of volume to obtain volume measurement data, and the object model is also measured in terms of weight distribution to obtain weight distribution measurement data. The object model can be generated by capturing parameters in at least one object video frame using AI technology. The volume measurement data and weight distribution measurement data are used as the measurement attribute data of the target object.

[0054] Following the previous example, after determining the object model, the 3D object model can be post-processed to obtain a clean, closed mesh model suitable for volume calculation. For the triangular mesh in the mesh model, the divergence theorem is used to calculate the closed volume, obtaining volume measurement data. A CNN or Transformer model is then trained, taking an image and a 3D shape as input, and outputting a local density map or total mass to obtain weight distribution measurement data.

[0055] In summary, using volume measurement data and weight distribution measurement data as the target object's measurement attribute data improves the richness of the measurement attribute data and ensures the accuracy of subsequent object attribute data.

[0056] Step 106: Update the identification attribute data based on the object record data of the target object and the measurement attribute data to obtain object attribute data.

[0057] Specifically, after constructing an object model of the target object based on the object video and determining the target object's measurement attribute data according to the object model, the recognition attribute data can be updated based on the target object's object record data and measurement attribute data to obtain object attribute data. The target object's object record data can be the actual data of the target object recorded in the object database, including but not limited to the target object's material, shape, and weight. In the case of a product, the object record data is the product data associated with the product order. The object attribute data is the comprehensive data corresponding to the target object obtained by analyzing and integrating the object record data, the recognition attribute data obtained through recognition methods, and the measurement attribute data obtained through 3D measurement methods.

[0058] Based on this, after constructing an object model of the target object based on the object video and determining the measurement attribute data of the target object according to the object model, the recognition attribute data is updated based on the object record data and measurement attribute data of the target object to obtain object attribute data. The object record data, measurement attribute data, and recognition attribute data are then analyzed and integrated to obtain comprehensive data corresponding to the target object.

[0059] Furthermore, considering that the identified attribute data and the measured attribute data are attribute data corresponding to the target object obtained through different methods, in order to ensure the accuracy of the target object's attribute data, the object attribute data can be generated by comparing the identified attribute data and the measured attribute data. The specific implementation is as follows: The identification attribute data and the measurement attribute data are compared; if it is determined from the comparison result that there is a data difference between the identification attribute data and the measurement attribute data, the object attribute data is generated based on the measurement attribute data and the object record data of the target object, or, historical object attribute data associated with the object record data is determined and the historical object attribute data is used as the object attribute data; If, based on the comparison results, it is determined that there is no data difference between the identification attribute data and the measurement attribute data, the identification attribute data and / or the measurement attribute data are supplemented based on the object record data of the target object to obtain the object attribute data.

[0060] Specifically, comparing identification attribute data and measurement attribute data can be done by using measurement attribute data as a benchmark and comparing it with identification attribute data. Data differences between identification attribute data and measurement attribute data indicate a difference in at least one physical parameter dimension, such as weight difference, volume difference, or other physical parameter differences. The historical object attribute data associated with the object record data refers to the object attribute data of historical objects similar to the target object category corresponding to the object record data; that is, the object attribute data of historically calculated objects.

[0061] Based on this, the identification attribute data and measurement attribute data are compared. If the comparison results indicate that there is a data difference between the identification attribute data and the measurement attribute data, it means that there is a parameter difference between the identification attribute data and the measurement attribute data in at least one physical parameter dimension. Object attribute data is generated based on the measurement attribute data and the object record data of the target object; that is, the measurement attribute data and the object record data of the target object are integrated to generate object attribute data. Alternatively, historical object attribute data of historical objects associated with the object record data are determined and used as object attribute data. The historical objects are of the same or similar category to the target object. If the comparison results indicate that there is no data difference between the identification attribute data and the measurement attribute data, it means that there is no parameter difference between the identification attribute data and the measurement attribute data in any one of the at least one physical parameter dimensions. Object attribute data is obtained by supplementing the identification attribute data and / or measurement attribute data based on the object record data of the target object.

[0062] In summary, by comparing and identifying attribute data and measuring attribute data, object attribute data is generated, ensuring the accuracy of the target object's object attribute data.

[0063] Step 108: Determine the resource data corresponding to the target object based on the object attribute data.

[0064] Specifically, after updating the identification attribute data based on the object record data and measurement attribute data of the target object to obtain the object attribute data, the resource data corresponding to the target object can be determined based on the object attribute data. The resource data corresponding to the target object can be the resource consumption data of the target object. In the case that the target object is a commodity, the resource data can be the transportation cost required to transport the target object. The transportation cost is the transportation cost from region A to region B. The commodity can be a consumer's goods to be returned, a merchant's goods to be shipped, or a merchant's goods to be sold.

[0065] Based on this, after updating the identification attribute data using the object record data and measurement attribute data of the target object to obtain object attribute data, the resource data corresponding to the target object is determined based on the object attribute data. When the object attribute data is accurate, the accuracy of the resource data determined based on the object attribute data is also greatly improved.

[0066] Furthermore, the resource processing task includes at least one resource processing subtask. Each resource processing subtask corresponds to a set of parameters to be calculated. When calculating resource data, it is necessary to calculate each set of parameters one by one, and integrate at least one set of resource sub-data obtained from the calculation to obtain the resource data. The specific implementation is as follows: The resource processing task associated with the target object is determined, and at least one resource processing subtask is determined to be included in the resource processing task; the at least one resource processing subtask is executed based on the object attribute data to obtain at least one set of resource sub-data; the at least one set of resource sub-data is integrated to obtain the resource data of the target object.

[0067] Specifically, resource processing tasks can be predefined resource calculation rules or resource calculation strategies. Resource processing tasks are used to calculate the amount of resources consumed when transporting a target object, i.e., resource data. In the case of a commodity, the resource processing task is the freight calculation rule. Resource data can be the return shipping cost required for a consumer who has purchased and received the commodity to return it. A resource processing task contains at least one resource processing subtask, each corresponding to a set of parameters to be calculated. This set of parameters can include resource consumption items and the corresponding resource calculation methods. Resource sub-data can be data calculated based on a set of parameters, corresponding to resource consumption items and the corresponding resource consumption amounts. Integrating at least one set of resource sub-data can involve summing the resource consumption amounts contained in at least one set of resource sub-data to obtain the total resource consumption. The resource data contains the total resource consumption.

[0068] Based on this, the resource processing tasks associated with the target object are determined, and at least one resource processing subtask is identified within each resource processing task. Each resource processing task contains at least one resource processing subtask, and each subtask corresponds to a set of parameters to be calculated. At least one resource processing subtask is executed based on the object attribute data, and resource data calculations are performed based on at least one set of parameters to be calculated and the object attribute data to obtain at least one set of resource sub-data. The at least one set of resource sub-data is then integrated to obtain the resource data of the target object.

[0069] Continuing with the previous example, a resource processing task includes at least one resource processing subtask, each corresponding to a set of parameters to be calculated. This set of parameters includes, but is not limited to, various resource consumption items such as basic freight, additional weight fees, cross-border transportation fees, and surcharges. Resource consumption needs to be calculated item by item, combining the weight and transportation distance from the object's attribute data, to obtain the resource consumption amount corresponding to each item, i.e., the resource sub-data. Summing at least one resource consumption amount yields the total resource consumption in the resource data, i.e., the resource data. The resource data could be: Origin: Region A; Destination: Region B; Product Volume: 0.2 cubic meters; Product Weight: 0.5 kg; Cold Chain Required: No; Estimated Total Freight: 15 yuan.

[0070] In summary, by integrating at least one set of resource sub-data, the resource data of the target object can be obtained, thereby improving the accuracy of resource data calculation.

[0071] Furthermore, after calculating and obtaining resource data, representing it in data form is not conducive to obtaining key information from the resource data. Therefore, after calculating and obtaining resource data, visualization processing can be performed on the resource data, as specifically implemented as follows: A resource data chart is generated based on the at least one set of resource sub-data contained in the resource data.

[0072] Specifically, resource sub-data can be data calculated based on a set of parameters corresponding to at least one resource processing sub-task. Resource sub-data includes resource consumption items and the corresponding resource consumption amounts. Resource data charts can be either resource consumption distribution maps or resource consumption distribution tables.

[0073] Based on this, a resource data chart is generated from at least one set of resource sub-data included in the resource data. This chart can be a resource consumption distribution map or a resource consumption distribution table. When the resource data chart is a resource consumption distribution map, the resource consumption corresponding to each set of resource sub-data occupies a portion of the map. The size of the area corresponding to each set of resource sub-data is directly proportional to the amount of resource consumption; the higher the resource consumption, the larger the area corresponding to the resource sub-data. The resource consumption distribution map can be any form of statistical chart, such as a pie chart or a bar chart.

[0074] Following the previous example, after integrating at least one set of resource sub-data to obtain the resource data for the target object, the resource data can be visualized to generate resource data charts. Each set of resource sub-data corresponds to a resource consumption item, i.e., a billing item. Billing items include, but are not limited to, basic shipping costs, additional weight fees, cross-border shipping costs, surcharges, etc. Resource data charts can be drawn according to the calculated costs corresponding to each billing item. Displaying the resource data charts to users allows them to clearly understand the shipping items and their corresponding shipping costs. When the user is a merchant, determining the resource data chart for the goods to be sold facilitates the merchant's pricing or price adjustments for the goods.

[0075] In summary, resource data charts are generated based on at least one set of resource sub-data contained in the resource data. By visualizing the resource data, these charts are presented to users, allowing them to clearly understand the shipping items and their corresponding shipping costs. This facilitates users' decisions regarding returns or product pricing (price adjustments).

[0076] This specification provides a data processing method in one embodiment that determines the identification attribute data of a target object based on an object video of the target object, thereby determining the corresponding identification attribute data of the target object in the visual dimension. An object model of the target object is constructed based on the object video, and measurement attribute data of the target object is determined based on the object model. The attribute data of the object model of the target object is measured in the physical measurement dimension to obtain measurement attribute data. The identification attribute data is updated based on the object record data and measurement attribute data of the target object to obtain object attribute data that accurately expresses the physical parameters of the target object. Based on the object attribute data, resource data corresponding to the target object is determined, enabling accurate calculation of resource data and facilitating subsequent task decisions related to the target object, thereby improving task decision efficiency.

[0077] The following is in conjunction with the appendix Figure 2 Taking the application of the data processing method provided in this specification in calculating return shipping costs as an example, the data processing method will be further explained. Among other things, Figure 2 An interactive schematic diagram of a data processing method provided in one embodiment of this specification is shown, which specifically includes the following steps.

[0078] Step 202: The user on the user terminal clicks on the estimated shipping cost for shooting.

[0079] Step 204: Initialize the module to render order details.

[0080] Step 206: The user on the user terminal selects the target order.

[0081] Step 208: Initialize the module and turn on the camera.

[0082] Before starting the recording, the user selects the order requiring after-sales service. The user clicks on the estimated shipping cost to start the recording, the order details are displayed on the user's device, the user selects a specific order, and then turns on the camera.

[0083] Step 210: The user on the user terminal takes and uploads real-time video of the product to the real-time video analysis module.

[0084] Step 212: The real-time video analysis module inputs video frames into the image recognition engine.

[0085] The real-time video analytics module captures live video streams taken by the user and dynamically detects the physical parameters of objects in the scene using image recognition and AI algorithms. Users capture live video of products via mobile devices and obtain the live video stream through the device interface. Video frames are fed into the image recognition engine, which analyzes each frame using a pre-trained deep learning model.

[0086] Step 214: The dynamic physical measurement module extracts the three-dimensional shape and key physical parameters of the product.

[0087] AR technology is used to capture the three-dimensional shape of goods, and key physical parameters (such as volume and weight distribution) are extracted through computer vision algorithms.

[0088] Step 216: The dynamic physical measurement module generates a virtual 3D model based on AI algorithms.

[0089] Step 218: The dynamic physical measurement module calculates the volume and weight distribution of the goods.

[0090] Step 220: The dynamic physical measurement module optimizes the weight estimation accuracy by combining order information.

[0091] Step 222: The dynamic physical measurement module sends the physical characteristic data of the goods to the freight rule matching module.

[0092] Step 224: The freight rule matching module calls the logistics company's fee rules.

[0093] Step 226: The freight rule matching module dynamically calculates the freight details.

[0094] The freight rate matching module calls upon the logistics company's fee rules (such as initial weight, additional weight prices, regional differences, etc.) and dynamically calculates freight rates based on the physical characteristics of the goods. For international logistics scenarios, factors such as tariffs and cross-border transportation surcharges can also be considered to provide a more comprehensive freight rate estimate.

[0095] Step 228: The user interaction and decision support module displays the shipping cost details.

[0096] The shipping details are displayed to users in chart form, including basic shipping costs, additional weight fees, and other possible surcharges.

[0097] Step 230: The user on the user terminal decides whether to initiate a return or adjust the pricing.

[0098] Users can decide whether to initiate a return or adjust the product price based on the shipping details.

[0099] This specification provides a data processing method in one embodiment that uses AR technology and AI algorithms for real-time video analysis to capture the three-dimensional shape of goods and dynamically calculate their physical properties. This eliminates the need for users to manually input product information, improving efficiency and accuracy and overcoming the shortcomings of traditional manual input methods. By automating the analysis of product physical characteristics, it reduces human intervention and provides more accurate shipping cost estimates. Multi-view video acquisition combined with AR technology comprehensively captures the three-dimensional shape of goods, improving measurement accuracy. By integrating AR, AI, and logistics company fee rules, a complete shipping cost assessment system is constructed. Real-time, transparent shipping cost estimates reduce consumer decision-making costs and transaction disputes. It can also help non-professional sellers optimize their product pricing strategies, reducing transaction friction and disputes.

[0100] See Figure 3 , Figure 3 A flowchart of another data processing method provided according to an embodiment of this specification is shown, which specifically includes the following steps.

[0101] Step 302: Determine the target object's video; Step 304: Construct an object model of the target object based on the object video, and determine the measurement attribute data of the target object according to the object model; Step 306: Generate object attribute data based on the object record data of the target object and the measurement attribute data; Step 308: Determine the resource data corresponding to the target object based on the object attribute data.

[0102] This specification provides another data processing method based on an embodiment. When a user associated with a target object needs to calculate the target object's resource data, the server or client can determine the target object's object video. An object model of the target object is constructed based on the object video, and measurement attribute data of the target object is determined according to the object model. The attribute data of the target object's object model is measured in the physical measurement dimension to obtain the measurement attribute data. Object attribute data is generated based on the target object's object record data and measurement attribute data to obtain object attribute data that accurately expresses the target object's physical parameters. Based on the object attribute data, the resource data corresponding to the target object is determined, enabling accurate calculation of the resource data. This facilitates subsequent task decisions related to the target object's associated resource data, improving task decision efficiency.

[0103] Figure 4 A schematic diagram of a data processing system according to an embodiment of this specification is shown. The data processing system 400 includes a client 410 and a server 420. The client 410 is used to submit an object video of a target object to the server 420. The server 420 is used to determine the identification attribute data of the target object based on the object video; construct an object model of the target object based on the object video, and determine the measurement attribute data of the target object based on the object model; update the identification attribute data based on the object record data of the target object and the measurement attribute data to obtain object attribute data; determine the resource data corresponding to the target object based on the object attribute data, and send the resource data to the client 410.

[0104] In practical applications, when a client needs to calculate resource data for a target object, it can collect an object video of the target object and send it to the server. The server determines the target object's identification attribute data based on the object video, identifying the corresponding identification attribute data in the visual dimension. An object model of the target object is constructed based on the object video, and the measurement attribute data of the target object is determined based on the object model. The attribute data of the object model is measured in the physical measurement dimension to obtain the measurement attribute data. The identification attribute data is updated based on the target object's object record data and measurement attribute data to obtain object attribute data that accurately expresses the physical parameters of the target object. Based on the object attribute data, the corresponding resource data for the target object is determined and sent to the client. The server can perform precise calculations of resource data, facilitating subsequent task decisions by the client user regarding the associated resource data for the target object, thus improving task decision-making efficiency.

[0105] See Figure 5 , Figure 5 A flowchart of a commodity freight information processing method according to an embodiment of this specification is shown.

[0106] Step 502: Determine the identification attribute information of the target product based on the product video of the target product; Step 504: Construct a product measurement model for the target product based on the product video, and determine the measurement attribute information of the target product according to the product measurement model; Step 506: Update the identification attribute information based on the product record information and the measurement attribute information of the target product to obtain product attribute information; Step 508: Determine the shipping cost information corresponding to the target product based on the product attribute information.

[0107] In practical applications, in the context of goods transportation, freight costs can be calculated based on the target product's video and record information. The process involves: capturing a video of the target product; determining its visual attributes based on the video; constructing a measurement model for the product; measuring its physical attributes; updating the identification attributes based on the record information and measurement attributes to accurately represent its physical parameters; and determining the freight costs based on these attributes, facilitating subsequent task decisions related to freight costs and improving efficiency. When determining freight costs based on attribute information, a freight information processing task can be executed. Once this task is completed, the freight costs are calculated.

[0108] Corresponding to the above method embodiments, this specification also provides data processing apparatus embodiments. Figure 6 A schematic diagram of the structure of a data processing apparatus according to one embodiment of this specification is shown. Figure 6 As shown, the device includes: The first determining module 602 is configured to determine the identification attribute data of the target object based on the object video of the target object; The construction module 604 is configured to construct an object model of the target object based on the object video, and determine the measurement attribute data of the target object according to the object model; The update module 606 is configured to update the identification attribute data based on the object record data of the target object and the measurement attribute data to obtain object attribute data; The second determining module 608 is configured to determine the resource data corresponding to the target object based on the object attribute data.

[0109] In an optional embodiment, the first determining module 602 is further configured to: Extract at least one object video frame from the object video; The at least one object video frame is input into the image recognition model to obtain the recognition attribute data of the target object.

[0110] In an optional embodiment, the first determining module 602 is further configured to: The image recognition model is used to identify at least one object video frame to obtain multidimensional information of the target object, and data extraction is performed on the multidimensional information in the physical parameter dimension to obtain the recognition attribute data of the target object.

[0111] In an optional embodiment, the first determining module 602 is further configured to: In response to a user's touch operation on an object record associated with the target object, a video capture page is displayed to the user; Receive the video of the target object captured by the user through the video capture page.

[0112] In an optional embodiment, the building module 604 is further configured to: The object model is measured in the volume dimension to obtain volume measurement data, and the object model is measured in the weight distribution dimension to obtain weight distribution measurement data; The volume measurement data and the weight distribution measurement data are used as the measurement attribute data of the target object.

[0113] In an optional embodiment, the update module 606 is further configured to: The identification attribute data and the measurement attribute data are compared; If a data difference is found between the identification attribute data and the measurement attribute data based on the comparison results, the object attribute data is generated based on the measurement attribute data and the object record data of the target object; or, historical object attribute data associated with the object record data is determined and the historical object attribute data is used as the object attribute data. If, based on the comparison results, it is determined that there is no data difference between the identification attribute data and the measurement attribute data, the identification attribute data and / or the measurement attribute data are supplemented based on the object record data of the target object to obtain the object attribute data.

[0114] In an optional embodiment, the second determining module 608 is further configured to: Determine at least one set of parameters to be calculated for the resource processing task; Calculate at least one set of resource sub-data based on the at least one set of parameters to be calculated and the object attribute data; Integrate at least one set of resource sub-data to obtain the resource data of the target object.

[0115] In an optional embodiment, the second determining module 608 is further configured to: A resource data chart is generated based on the at least one set of resource sub-data contained in the resource data.

[0116] This specification provides a data processing apparatus in one embodiment that determines the identification attribute data of a target object based on an object video of the target object, and determines the corresponding identification attribute data of the target object in the visual dimension. An object model of the target object is constructed based on the object video, and measurement attribute data of the target object is determined based on the object model. The attribute data of the object model of the target object is measured in the physical measurement dimension to obtain measurement attribute data. The identification attribute data is updated based on the object record data and measurement attribute data of the target object to obtain object attribute data that accurately expresses the physical parameters of the target object. Based on the object attribute data, resource data corresponding to the target object is determined, realizing accurate calculation of resource data, facilitating subsequent task decisions related to the resource data of the target object, and improving task decision efficiency.

[0117] The above is an illustrative scheme of a data processing apparatus according to this embodiment. It should be noted that the technical solution of this data processing apparatus and the technical solution of the data processing method described above belong to the same concept. For details not described in detail in the technical solution of the data processing apparatus, please refer to the description of the technical solution of the data processing method described above.

[0118] Corresponding to the above method embodiments, this specification also provides another data processing apparatus embodiment. Figure 7 A schematic diagram of another data processing apparatus provided in one embodiment of this specification is shown. Figure 7 As shown, the device includes: The first determining module 702 is configured to determine the object video of the target object; The construction module 704 is configured to construct an object model of the target object based on the object video, and determine the measurement attribute data of the target object according to the object model; The generation module 706 is configured to generate object attribute data based on the object record data of the target object and the measurement attribute data; The second determining module 708 is configured to determine the resource data corresponding to the target object based on the object attribute data.

[0119] This specification provides another data processing apparatus according to one embodiment. When a user associated with a target object needs to calculate the resource data of the target object, the server or client can determine the object video of the target object. An object model of the target object is constructed based on the object video, and measurement attribute data of the target object is determined according to the object model. The attribute data of the object model of the target object is measured in the physical measurement dimension to obtain the measurement attribute data. Object attribute data is generated based on the object record data and measurement attribute data of the target object, obtaining object attribute data that accurately expresses the physical parameters of the target object. Based on the object attribute data, the resource data corresponding to the target object is determined, realizing accurate calculation of the resource data, facilitating subsequent task decisions related to the resource data of the target object, and improving task decision efficiency.

[0120] The above is an illustrative scheme of another data processing device according to this embodiment. It should be noted that the technical solution of this data processing device and the technical solution of the other data processing method described above belong to the same concept. For details not described in detail in the technical solution of the data processing device, please refer to the description of the technical solution of the data processing method described above.

[0121] Corresponding to the above method embodiments, this specification also provides an embodiment of a commodity freight information processing device. Figure 8 A schematic diagram of a commodity freight information processing device according to one embodiment of this specification is shown. Figure 8 As shown, the device includes: The first determining module 802 is configured to determine the identification attribute information of the target product based on the product video of the target product; The construction module 804 is configured to construct a product measurement model of the target product based on the product video, and determine the measurement attribute information of the target product according to the product measurement model; The update module 806 is configured to update the identification attribute information based on the product record information and the measurement attribute information of the target product, thereby obtaining product attribute information; The second determining module 808 is configured to determine the shipping information corresponding to the target product based on the product attribute information.

[0122] This specification provides another embodiment of a commodity shipping information processing device, which acquires video of a target commodity to obtain a commodity video. Based on the commodity video, it determines the target commodity's identification attribute information, specifically the visual identification attribute information. A commodity measurement model of the target commodity is constructed based on the commodity video, and the measurement attribute information of the target commodity is determined according to the commodity measurement model. The attribute information of the commodity measurement model is measured in the physical measurement dimension to obtain the measurement attribute information. The identification attribute information is updated based on the commodity record information and measurement attribute information of the target commodity to obtain commodity attribute information that accurately expresses the physical parameters of the target commodity. Based on the commodity attribute information, the corresponding commodity shipping information is determined, enabling accurate calculation of the commodity shipping information. This facilitates subsequent task decisions related to the commodity shipping information for the target commodity, improving task decision-making efficiency.

[0123] The above is an illustrative scheme of another commodity freight information processing device according to this embodiment. It should be noted that the technical solution of this commodity freight information processing device and the technical solution of the other commodity freight information processing method described above belong to the same concept. For details not described in detail in the technical solution of the commodity freight information processing device, please refer to the description of the technical solution of the commodity freight information processing method described above.

[0124] Figure 9 A structural block diagram of a computing device 900 according to one embodiment of this specification is shown. The components of the computing device 900 include, but are not limited to, a memory 910 and a processor 920. The processor 920 is connected to the memory 910 via a bus 930, and a database 950 is used to store data.

[0125] The computing device 900 also includes an access device 940, which enables the computing device 900 to communicate via one or more networks 960. Examples of these networks include Public Switched Telephone Network (PSTN), Local Area Network (LAN), Wide Area Network (WAN), Personal Area Network (PAN), or combinations of communication networks such as the Internet. The access device 940 may include one or more of any type of wired or wireless network interface (e.g., a network interface card (NIC)), such as an IEEE 802.11 Wireless Local Area Network (WLAN) wireless interface, a Wi-MAX (Worldwide Interoperability for Microwave Access) interface, an Ethernet interface, a Universal Serial Bus (USB) interface, a cellular network interface, a Bluetooth interface, or a Near Field Communication (NFC) interface.

[0126] In one embodiment of this specification, the above-described components of the computing device 900 and Figure 9 Other components, not shown, can also be connected to each other, for example, via a bus. It should be understood that... Figure 9 The block diagram of the computing device shown is for illustrative purposes only and is not intended to limit the scope of this specification. Those skilled in the art can add or replace other components as needed.

[0127] The computing device 900 can be any type of stationary or mobile computing device, including mobile computers or mobile computing devices (e.g., tablet computers, personal digital assistants, laptop computers, notebook computers, netbooks, etc.), mobile phones (e.g., smartphones), wearable computing devices (e.g., smartwatches, smart glasses, etc.) or other types of mobile devices, or stationary computing devices such as desktop computers or personal computers (PCs). The computing device 900 can also be a mobile or stationary server.

[0128] The processor 920 is used to execute the following computer-executable instructions, which, when executed by the processor, implement the steps of the above-described data processing method.

[0129] The above is an illustrative scheme of a computing device according to this embodiment. It should be noted that the technical solution of this computing device and the technical solution of the data processing method described above belong to the same concept. For details not described in detail in the technical solution of the computing device, please refer to the description of the technical solution of the data processing method described above.

[0130] An embodiment of this specification also provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the above-described data processing method.

[0131] The above is an illustrative scheme of a computer-readable storage medium according to this embodiment. It should be noted that the technical solution of this storage medium and the technical solution of the data processing method described above belong to the same concept. For details not described in detail in the technical solution of the storage medium, please refer to the description of the technical solution of the data processing method described above.

[0132] An embodiment of this specification also provides a computer program product, including a computer program or instructions that, when executed by a processor, implement the steps of the above-described data processing method.

[0133] The above is an illustrative scheme of a computer program product according to this embodiment. It should be noted that the technical solution of this computer program product and the technical solution of the data processing method described above belong to the same concept. For details not described in detail in the technical solution of the computer program product, please refer to the description of the technical solution of the data processing method described above.

[0134] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0135] The computer program / instructions include computer program code, which may be in the form of source code, object code, executable file, or certain intermediate forms. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium may be appropriately added or removed according to the requirements of patent practice. For example, in some regions, according to patent practice, computer-readable media may not include electrical carrier signals and telecommunication signals.

[0136] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments in this specification are not limited to the described order of actions, because according to the embodiments in this specification, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the embodiments in this specification.

[0137] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0138] The preferred embodiments disclosed above are merely illustrative of this specification. The optional embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the embodiments described herein. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the embodiments, thereby enabling those skilled in the art to better understand and utilize this specification. This specification is limited only by the claims and their full scope and equivalents.

Claims

1. A data processing method, comprising: determining identification attribute data of a target object according to object video of the target object; constructing an object model of the target object based on the object video, and determining measurement attribute data of the target object according to the object model; updating the identification attribute data based on object record data of the target object and the measurement attribute data, to obtain object attribute data; determining resource data corresponding to the target object based on the object attribute data.

2. The data processing method of claim 1, wherein the determining identification attribute data of a target object according to object video of the target object comprises: extracting at least one object video frame from the object video; inputting the at least one object video frame into an image recognition model to obtain the identification attribute data of the target object.

3. The data processing method of claim 2, wherein the inputting the at least one object video frame into an image recognition model to obtain the identification attribute data of the target object comprises: recognizing the at least one object video frame by using the image recognition model to obtain multi-dimensional information of the target object, and performing data extraction on the multi-dimensional information in a physical parameter dimension to obtain the identification attribute data of the target object.

4. The data processing method of claim 1, wherein the determining of the object video comprises: in response to a touch operation of a user on an object record associated with the target object, displaying a video collection page to the user; receiving the object video collected by the user on the target object through the video collection page.

5. The data processing method of claim 1, wherein the determining measurement attribute data of the target object according to the object model comprises: measuring the object model in a volume dimension to obtain volume measurement data, and measuring the object model in a weight distribution dimension to obtain weight distribution measurement data; using the volume measurement data and the weight distribution measurement data as the measurement attribute data of the target object.

6. The data processing method of claim 1, wherein the updating the identification attribute data based on object record data of the target object and the measurement attribute data to obtain object attribute data comprises: comparing the identification attribute data and the measurement attribute data; in a case where it is determined according to a comparison result that there is a data difference between the identification attribute data and the measurement attribute data, generating the object attribute data based on the measurement attribute data and the object record data of the target object, or determining historical object attribute data associated with the object record data, and using the historical object attribute data as the object attribute data; in a case where it is determined according to a comparison result that there is no data difference between the identification attribute data and the measurement attribute data, supplementing the identification attribute data and / or the measurement attribute data based on the object record data of the target object to obtain the object attribute data.

7. The data processing method of claim 1, wherein the determining the resource data corresponding to the target object based on the object attribute data comprises: determining a resource processing task associated with the target object, and determining at least one resource processing subtask contained in the resource processing task; executing the at least one resource processing subtask based on the object attribute data to obtain at least one set of resource subdata; and integrating the at least one set of resource subdata to obtain the resource data of the target object.

8. The data processing method of claim 7, wherein after the determining the resource data corresponding to the target object based on the object attribute data, the method further comprises: generating a resource data chart based on the at least one set of resource subdata contained in the resource data.

9. A commodity shipping information processing method, comprising: determining identification attribute information of a target commodity based on a commodity video of the target commodity; constructing a commodity measurement model of the target commodity based on the commodity video, and determining measurement attribute information of the target commodity based on the commodity measurement model; updating the identification attribute information based on commodity record information and the measurement attribute information of the target commodity to obtain commodity attribute information; and determining commodity shipping information corresponding to the target commodity based on the commodity attribute information.

10. A data processing method, comprising: determining an object video of a target object; constructing an object model of the target object based on the object video, and determining measurement attribute data of the target object based on the object model; generating object attribute data based on object record data and the measurement attribute data of the target object; and determining resource data corresponding to the target object based on the object attribute data.

11. A data processing system comprising a client and a server, comprising: the client configured to submit an object video of a target object to the server; the server configured to determine identification attribute data of the target object based on the object video; construct an object model of the target object based on the object video, and determine measurement attribute data of the target object based on the object model; update the identification attribute data based on object record data and the measurement attribute data of the target object to obtain object attribute data; determine resource data corresponding to the target object based on the object attribute data, and send the resource data to the client.

12. A computing device, comprising: a memory and a processor; the memory configured to store computer executable instructions, and the processor configured to execute the computer executable instructions, wherein the computer executable instructions, when executed by the processor, implement the steps of the method of any one of claims 1 to 10.

13. A computer readable storage medium storing computer executable instructions, wherein the computer executable instructions, when executed by a processor, implement the steps of the method of any one of claims 1 to 10.

14. A computer program product comprising a computer program or instructions, wherein the computer program or instructions, when executed by a processor, implement the steps of the method of any one of claims 1 to 10. ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​

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