A commodity transaction information sharing method and system based on blockchain technology

Abstract

The present invention relates to the field of blockchain technology, and in particular to a method and system for sharing commodity transaction information based on blockchain technology. The method comprises the following steps: obtaining commodity transaction information and commodity location data; obtaining commodity environment video in real time through a camera; performing motion state analysis on commodity location data according to the commodity environment video to generate commodity motion state data; performing position time series analysis on commodity motion state data through commodity location data to generate commodity location time series data; performing dynamic position point mapping on commodity transaction information through commodity location time series data to construct a commodity dynamic location map; performing dynamic environment change recognition on commodity environment video to generate dynamic environment change data; performing dynamic environment rendering on the commodity dynamic location map through dynamic environment change data to generate environment rendering texture data. The present invention realizes efficient and accurate sharing of commodity transaction information.

Description

Technical Field

[0001] The present invention relates to the field of blockchain technology, and in particular to a commodity transaction information sharing method and system based on blockchain technology. Background Art

[0002] With the rapid development of e-commerce, commodity transactions have become more frequent and complex on a global scale. However, there are some problems in the current commodity trading process, such as inefficient and inaccurate information sharing. In order to solve these problems and improve the transparency and efficiency of commodity transactions, the introduction of blockchain technology has become a promising solution. Blockchain is a decentralized, distributed ledger technology that ensures the security and immutability of transaction data through encryption and consensus algorithms. In traditional commodity transactions, transaction information is usually managed and verified by centralized institutions or third-party intermediaries. However, this centralized model is prone to information leakage, data tampering and trust issues. Therefore, a commodity transaction information sharing method and system based on blockchain technology is proposed to solve these problems and improve the transaction process. Summary of the invention

[0003] In order to solve the above technical problems, the present invention proposes a commodity transaction information sharing method and system based on blockchain technology to solve at least one of the above technical problems.

[0004] To achieve the above object, the present invention provides a commodity transaction information sharing method based on blockchain technology, comprising the following steps:

[0005] Step S1: Obtain commodity transaction information and commodity location data; obtain commodity environment video in real time through a camera; perform motion state analysis on the commodity location data according to the commodity environment video to generate commodity motion state data;

[0006] Step S2: Performing a position time series analysis on the commodity movement state data through the commodity position data to generate commodity position time series data; performing dynamic position point mapping on the commodity transaction information through the commodity position time series data to construct a commodity dynamic position map;

[0007] Step S3: performing dynamic environment change recognition on the product environment video to generate dynamic environment change data; performing dynamic environment rendering on the product dynamic location map based on the dynamic environment change data to generate environment rendering texture data; performing environment-driven perception processing on the product dynamic location map based on the environment rendering texture data to generate an environment perception location map;

[0008] Step S4: Perform sub-granular rendering on the environment perception location map to construct a super-pixel environment perception image; perform dynamic digital twin scene fusion on the super-pixel environment perception image according to the environment rendering map data to construct a 3D real-time location scene model;

[0009] Step S5: Divide the commodity transaction information into nodes to generate commodity transaction data nodes; perform smart contract analysis on the commodity transaction information to generate commodity smart contracts; construct a decentralized blockchain network for the commodity transaction data nodes through commodity smart contracts to build a commodity blockchain network;

[0010] Step S6: Use the commodity transaction data node to perform real-time node mapping on the 3D real-time location scene model to obtain commodity transaction tag data; perform dynamic expansion convolution processing on the commodity blockchain network and the 3D real-time location scene model through the commodity transaction tag data to construct a commodity blockchain scene model to perform commodity transaction information sharing operations.

[0011] The present invention can understand the transaction details of the commodity and its position in space by acquiring commodity transaction information and commodity location data. Real-time acquisition of commodity environment video can provide visual information about the environment in which the commodity is located. Motion state analysis can identify the motion state of the commodity in the video, such as still, moving, rotating, etc., so as to obtain dynamic information of the commodity. Commodity position time series analysis can reveal the position change of the commodity at different time points and provide time series information of the commodity position. Dynamic position point mapping combines the commodity position information with the transaction information to construct a commodity dynamic position map, which can display the movement trajectory and transaction details of the commodity in space. Dynamic environment change recognition can detect environmental changes in the commodity environment video, such as lighting changes, shadow changes, etc., and provide dynamic information of the environment. Dynamic environment rendering uses environmental change data to render the commodity dynamic position map and generates environmental rendering texture data reflecting environmental changes. Environment-driven perception processing applies the environmental rendering texture data to the commodity dynamic position map, so that it has the ability to perceive the environment and generates an environment-aware position map. Sub-fine-grained rendering can The environment perception location map is rendered more finely, providing a high-resolution environment perception image. The dynamic digital twin scene fusion combines the environment rendering map data with the super-pixel environment perception image to generate a 3D real-time location scene model with dynamic scene information. The commodity transaction data node division divides the commodity transaction information into different nodes, providing the organizational structure of the transaction data. The smart contract analysis can perform intelligent processing of commodity transaction information and generate commodity smart contracts for standardizing and executing transactions. The decentralized blockchain network construction uses commodity smart contracts to construct a decentralized blockchain network for commodity transaction data nodes, ensuring the security and credibility of commodity transaction data. The commodity transaction data nodes are used to perform real-time node mapping on the 3D real-time location scene model, which can associate the commodity transaction information with the real-time location scene model and obtain the commodity transaction tag data. The dynamic expansion convolution processing uses the commodity transaction tag data to process the commodity blockchain network and the 3D real-time location scene model, constructs the commodity blockchain scene model, and realizes the sharing of commodity transaction information.

[0012] Preferably, step S1 comprises the following steps:

[0013] Step S11: Obtain commodity transaction information and commodity location data;

[0014] Step S12: Obtaining a video of the commodity environment in real time through a camera;

[0015] Step S13: performing location trajectory analysis on the commodity location data to generate commodity movement trajectory data;

[0016] Step S14: Perform motion optical flow vector analysis on the product environment video to generate product optical flow vector data;

[0017] Step S15: performing motion state analysis on the motion trajectory data of the commodity according to the commodity optical flow vector data to generate commodity motion state data.

[0018] The present invention can understand the transaction details of the commodity and its position in space by acquiring commodity transaction information and commodity location data. The commodity transaction information includes important information such as commodity attributes, prices, and transaction time, which is helpful to understand the characteristics and historical transaction status of the commodity. Real-time acquisition of commodity environment video can provide visual information about the environment in which the commodity is located. The commodity environment video can contain information such as scenes, other objects, and crowds around the commodity, which is helpful to understand the contextual environment in which the commodity is located. Position trajectory analysis can reveal the position changes of the commodity at different time points and provide movement path information of the commodity position. The commodity movement trajectory data records the movement path of the commodity in space and can be used to analyze the movement law and activity range of the commodity. Motion optical flow vector analysis can detect the movement information of objects in the video, including speed and direction. The commodity optical flow vector data records the movement speed and direction of the commodity in the video and can be used to analyze the movement characteristics and trends of the commodity. Motion state analysis can identify the movement state of the commodity in the video, such as stillness, movement, rotation, etc., so as to obtain dynamic information of the commodity. The commodity movement state data provides the movement state of the commodity at different time points and can be used to analyze the activity mode and behavior characteristics of the commodity.

[0019] Preferably, step S2 comprises the following steps:

[0020] Step S21: Calculate the speed distribution of the commodity position data to generate commodity speed distribution data;

[0021] Step S22: performing sliding window segmentation on the commodity speed distribution data based on the commodity movement trajectory data to generate sliding window data;

[0022] Step S23: using the sliding window data to perform a position time series analysis on the commodity movement state data to generate commodity position time series data;

[0023] Step S24: constructing a commodity location map based on commodity transaction information;

[0024] Step S25: Dynamically map the commodity location map to the location points using the commodity location time series data to construct a commodity dynamic location map.

[0025] The present invention can understand the movement speed of goods at different positions by calculating the speed distribution of commodity position data. The commodity speed distribution data provides statistical information on the movement speed of the commodity, which can be used to analyze the speed, stability and other characteristics of the commodity. The sliding window segmentation can divide the commodity speed distribution data according to the time window to obtain the speed distribution in different time periods. The sliding window data provides the change of the commodity speed distribution in different time periods, which is helpful to observe the dynamic change of the movement speed of the commodity. The position time series analysis is combined with the sliding window data to associate the position and time of the commodity to obtain the position time series data of the commodity. The commodity position time series data records the position information of the commodity at different time points, which can be used to analyze the movement path and position change trend of the commodity. The commodity location map is a spatial reference map constructed based on commodity transaction information, marking the location information of different commodities. The commodity location map provides the distribution of commodities in space, which can be used to locate and find specific commodities. The commodity dynamic location map is constructed based on the combination of commodity location time series data and commodity location map, reflecting the change of commodity location over time. The commodity dynamic location map can display the location of the commodity at different time points, and update the location information over time, providing a dynamic visual display of the commodity location.

[0026] Preferably, the specific steps of step S3 are:

[0027] Step S31: Analyze the visual features of the product environment video using computer vision technology to generate environmental visual feature data;

[0028] Step S32: performing dynamic environmental change recognition on the environmental visual feature data to generate dynamic environmental change data;

[0029] Step S33: Perform dynamic environment rendering on the dynamic location map of the commodity through the dynamic environment change data to generate environment rendering map data;

[0030] Step S34: relocating the environment rendering map data to generate map position data;

[0031] Step S35: dynamically optimizing the environment rendering map data using the map position data to generate dynamic detail optimized rendering map data;

[0032] Step S36: performing environment-driven perception processing on the dynamic location map of the commodity according to the dynamic detail optimized rendering map data to generate an environment-aware location map;

[0033] The present invention uses computer vision technology to perform feature analysis on commodity environment videos and can extract visual features in the environment, such as color, texture, shape, etc. The environmental visual feature data provides a visual description of the environment in which the commodity is located, which helps to understand the characteristics and special attributes of the environment. Dynamic environmental change recognition can analyze changes in environmental visual feature data and identify dynamic changes in the environment, such as moving objects, lighting changes, etc. Dynamic environmental change data records the dynamic changes in the environment and can be used to analyze the changing trends and dynamic features of the environment. Dynamic environmental rendering uses dynamic environmental change data to render a dynamic location map of the commodity and combines the dynamic change information of the environment with the location map. Environmental rendering map data provides the impact of environmental changes on the commodity location map and presents the visual effects of the environment at different time points. Position relocation is achieved by reproducing environmental rendering map data. The data is processed to align the rendered map data with the product location data to ensure the accuracy of the map position. The map position data provides the accurate position information corresponding to each pixel in the environment rendering map data, providing a basis for subsequent rendering and processing. Dynamic detail optimization uses the map position data to adjust and optimize the environment rendering map data to enhance the details and realism of the rendered map. The dynamic detail optimization rendering map data presents high-quality visual effects of environment rendering and enhances the environmental perception of the product location map. The environment-aware location map is generated based on the combination of dynamic detail optimization rendering map data and the product dynamic location map, reflecting the perceptual impact of the environment on the location map. The environment-aware location map provides environmental perception information in the product location map, including lighting, shadows, reflections, etc., making the location map more realistic and environmentally aware.

[0034] Preferably, the specific steps of step S32 are:

[0035] Step S321: Calculate the ambient brightness of the product environment video using the ambient brightness reduction calculation formula to generate an ambient brightness curve;

[0036] Step S322: extracting image texture features from the environmental visual feature data according to the environmental brightness curve to generate environmental texture feature data;

[0037] Step S323: performing optical flow analysis on the commodity environment video according to the environment texture feature data to generate motion vector feature data;

[0038] Step S324: performing dynamic background structure analysis on the motion vector feature data to generate dynamic background structure data;

[0039] Step S325: performing dynamic environment change recognition on the environment visual feature data based on the dynamic background structure data to generate dynamic environment change data;

[0040] The present invention can obtain the brightness value of each frame in the commodity environment video by calculating the environment brightness. The environment brightness curve provides the brightness change in the commodity environment video, which can be used to analyze the brightness and darkness of the environment and the trend of illumination change. The image texture feature extraction can extract texture information from each frame of the commodity environment video, including features such as color and texture structure. The environment texture feature data provides a description of the texture features in the commodity environment video, which can be used to analyze the texture changes and features of the environment. The optical flow analysis can detect the motion information of the object in the commodity environment video, including speed and direction. The motion vector feature data records the motion speed and direction of the object in the commodity environment video, which can be used to analyze the motion changes and trends of the object in the environment. The dynamic background structure analysis can identify the dynamic background structure in the commodity environment video, such as moving trees, flowing water, etc. The dynamic background structure data provides a description of the dynamic background structure in the commodity environment video, which can be used to analyze the dynamic background changes and structural features in the environment. The dynamic environment change recognition combines the dynamic background structure data and the environment visual feature data to identify the dynamic environment changes in the commodity environment video, such as wind blowing leaves, rippling water, etc. The dynamic environment change data provides a description of the dynamic environment changes in the commodity environment video, which is helpful to analyze the dynamic changes and features in the environment.

[0041] Preferably, the ambient brightness loss calculation formula in step S321 is specifically:

[0042]

[0043] Wherein, d is the ambient brightness loss index, α is the average brightness in the ambient video, Δt is the calculated time range, x is the ambient light reflectivity, y is the ambient light refractive index, L is the optical parameter correction factor, and G is the ambient color difference value.

[0044] The present invention is achieved by The average ambient brightness α is multiplied by the time range Δt and divided by the square root of the sum of the squares of the ambient light reflectance x and the refractive index y, taking into account the proportional relationship between the average ambient brightness α and the ambient light reflectance x and the refractive index y. This section adjusts the value of the brightness reduction index to reflect the degree of ambient brightness reduction. The brightness loss index is adjusted by considering the change rate of the optical parameter correction factor L to the light reflectivity x and the refractive index y. The optical parameter correction factor L may change with the change of the light reflectivity x and the refractive index y. Therefore, by calculating the partial derivatives of the optical parameter correction factor L with respect to x and y And multiply it by the time range Δt, the brightness reduction index can be adjusted to consider the impact of changes in lighting parameters on brightness reduction. ln(G+1) The environmental color difference value G is used to describe the degree of color difference in different areas of the environment. By calculating the natural logarithm of G, the brightness reduction index can be adjusted to consider the impact of color differences on brightness reduction. The square root of the sum of the squares of the ambient light reflectivity x, refractive index y and time factor t is used as the denominator to adjust the brightness loss index. This can take into account the relationship between lighting parameters and time to more accurately calculate brightness loss.

[0045] Preferably, step S33 includes the following steps:

[0046] Step S331: performing dynamic light source rendering on the dynamic location map of the commodity according to the ambient brightness curve to generate a dynamic brightness change scene;

[0047] Step S332: performing a color tone difference analysis on the scene with dynamic brightness change according to the environmental texture feature data to generate color tone difference data;

[0048] Step S333: using the dynamic background structure data to fit the regional environment structure details of the dynamic brightness change scene to generate a dynamic background structure map;

[0049] Step S334: assigning a motion trajectory to the dynamic background structure map through motion vector feature data to generate a dynamic trajectory background map;

[0050] Step S335: dynamically adjust the rendering parameters of the dynamic trajectory background map according to the color tone difference data to generate environment rendering map data.

[0051] The present invention adjusts the brightness and direction of the light source according to the ambient brightness curve through dynamic light source rendering, so as to simulate the change of light in the commodity environment. The generated dynamic brightness change scene can reflect the change of light in the commodity environment and provide basic data for subsequent processing steps. The hue difference analysis can identify the color difference in the dynamic brightness change scene, compare the hue changes in different areas, and the generated hue difference data provides the hue characteristics of different areas in the commodity environment, which is helpful to analyze the color changes and characteristics in the environment. The regional environment structure detail fitting uses the dynamic background structure data to adjust the dynamic brightness change scene, so that the scene is more in line with the actual environment. The generated dynamic background structure map provides the detailed information of the background structure in the commodity environment, which can be used to enhance the realism and fidelity of the environment. The motion trajectory is given by using the motion vector feature data to merge the dynamic background structure map with the motion trajectory of the object, so as to increase the dynamic sense of the scene. The generated dynamic trajectory background map provides the combination effect of the background structure and the object movement in the commodity environment, so that the scene is richer and more vivid. The rendering parameter dynamic adjustment processing adjusts the rendering parameters according to the hue difference data, so that the rendering effect is more in line with the hue change of the environment. The generated environment rendering map data provides the scene image processed by rendering in the commodity environment, which has better visual effect and realism.

[0052] Preferably, the specific steps of step S4 are:

[0053] Step S41: performing particle sampling on the environment perception position map to generate particle data;

[0054] Step S42: performing sub-pixel rendering parameter calculation on the particle data to generate sub-pixel rendering parameters;

[0055] Step S43: performing sub-granular rendering on the environment perception location map using sub-pixel rendering parameters to construct a super-pixel environment perception image;

[0056] Step S44: dynamically fusion the super-pixel environment perception image with the digital twin scene according to the environment rendering map data to construct a 3D real-time location scene model;

[0057] The present invention can extract key information from the environment perception location map through particle sampling and represent it as discrete particle data. The generated particle data can better describe the characteristics and structure of the environment, and provide a high-quality data basis for subsequent steps. The sub-pixel rendering parameter calculation uses particle data to improve the accuracy and detail of the rendering. The generated sub-pixel rendering parameters can more accurately describe the rendering characteristics of the environment, and provide more accurate rendering parameters for subsequent steps. Sub-fine-grained rendering uses sub-pixel rendering parameters to render the environment perception location map with high precision. The constructed super-pixel environment perception image can provide a more delicate and realistic environment image containing more detailed information. The dynamic digital twin scene fusion uses the environment rendering map data to fuse the super-pixel environment perception image with the real scene. The constructed 3D real-time location scene model can provide an environment model with high realism and interactivity, which is used for the display and communication of commodity information sharing.

[0058] Preferably, the specific steps of step S5 are:

[0059] Step S51: dividing commodity transaction information into nodes to generate commodity transaction data nodes;

[0060] Step S52: Perform smart contract analysis on commodity transaction information and generate smart contract logic;

[0061] Step S53: Using smart contract logic to perform smart contract integration editing on the commodity transaction data node to generate a commodity smart contract;

[0062] Step S54: Decentralize the commodity transaction data nodes through commodity smart contracts to build a commodity blockchain network.

[0063] The present invention can classify and divide commodity transaction information according to certain rules and standards through node division to form independent transaction data nodes. The generated commodity transaction data nodes can better organize and manage commodity transaction information and improve the readability and operability of data. Smart contract analysis can conduct in-depth logical analysis of commodity transaction information and identify the rules and conditions therein. The generated smart contract logic can describe the specific rules and constraints of commodity transactions and provide a basis for smart contracts for subsequent steps. Smart contract integrated editing uses smart contract logic to edit and assemble commodity transaction data nodes to generate executable commodity smart contracts. The generated commodity smart contracts can automatically execute transaction rules and conditions to ensure the legality and reliability of transactions. The decentralized blockchain network uses commodity smart contracts to build a distributed transaction network that does not rely on central institutions. The constructed commodity blockchain network can ensure the transparency, security and traceability of transactions and improve the credibility and efficiency of commodity information sharing.

[0064] Preferably, the specific steps of step S6 are:

[0065] Step S61: using commodity transaction data nodes to perform real-time node mapping on the 3D real-time location scene model to obtain commodity transaction tag data;

[0066] Step S62: performing convolution preprocessing on the commodity blockchain network and the 3D real-time location scene model through the commodity transaction tag data to generate a blockchain convolution sample set and a scene model convolution sample set;

[0067] Step S63: Perform dynamic dilation convolution processing on the blockchain convolution sample set and the scene model convolution sample set to construct a commodity blockchain scene model;

[0068] Step S64: Upload the commodity blockchain scenario model to the cloud server to perform commodity transaction information sharing operations.

[0069] The present invention can determine the position and marking information of the commodity in the scene by mapping the commodity transaction data node with the 3D real-time location scene model through real-time node mapping. The obtained commodity transaction marking data can provide the specific position and attribute information of the commodity in the real-time scene model, providing a data basis for subsequent steps. The convolution preprocessing uses the commodity transaction marking data to process and convert the commodity blockchain network and the 3D real-time location scene model. The generated blockchain convolution sample set and scene model convolution sample set can better adapt to subsequent convolution processing and analysis. The dynamic expansion convolution processing can extract richer features and information by processing the blockchain convolution sample set and the scene model convolution sample set. The constructed commodity blockchain scene model can more comprehensively describe the position, attributes and transaction information of the commodity in the scene. Uploading the commodity blockchain scene model to the cloud server can realize remote storage and sharing of data. By executing the commodity transaction information sharing operation, the wide sharing and accessibility of commodity information can be realized, and the reliability and convenience of transactions can be promoted.

[0070] In this specification, a commodity transaction information sharing system based on blockchain technology is provided, including:

[0071] The information collection module is used to obtain commodity transaction information and commodity location data; obtain commodity environment video in real time through the camera; perform motion state analysis on commodity location data based on commodity environment video to generate commodity motion state data;

[0072] The dynamic location module is used to perform location time series analysis on the commodity movement state data through the commodity location data to generate commodity location time series data; dynamically map the commodity transaction information through the commodity location time series data to construct a commodity dynamic location map;

[0073] The environment rendering module is used to identify dynamic environment changes of the product environment video to generate dynamic environment change data; perform dynamic environment rendering on the dynamic location map of the product through the dynamic environment change data to generate environment rendering texture data; perform environment-driven perception processing on the dynamic location map of the product according to the environment rendering texture data to generate an environment perception location map;

[0074] The location scene model module is used to perform sub-granular rendering of the environment perception location map to construct a super-pixel environment perception image; the super-pixel environment perception image is dynamically fused with a digital twin scene based on the environment rendering map data to construct a 3D real-time location scene model;

[0075] The blockchain network module is used to divide commodity transaction information into nodes to generate commodity transaction data nodes; perform smart contract analysis on commodity transaction information to generate commodity smart contracts; and construct a decentralized blockchain network for commodity transaction data nodes through commodity smart contracts to build a commodity blockchain network.

[0076] The dilated convolution module is used to use the commodity transaction data nodes to perform real-time node mapping on the 3D real-time location scene model to obtain commodity transaction tag data; the commodity blockchain network and the 3D real-time location scene model are dynamically dilated and convoluted through the commodity transaction tag data to construct a commodity blockchain scene model to perform commodity transaction information sharing operations.

[0077] The present invention constructs a commodity transaction information sharing system based on blockchain technology, and through an information collection module, the transaction information and location data of the commodity can be obtained, including the attributes, price, transaction time, etc. of the commodity, as well as the location coordinates of the commodity in the scene. By performing time series analysis on the commodity location data, the motion state of the commodity can be understood, including information such as movement trajectory, speed, acceleration, etc., and the motion state data of the commodity can be generated. According to the commodity location time series data, it is mapped to the commodity transaction information, and a dynamic location map of the commodity is constructed, that is, the location information of the commodity is associated with the transaction information, and the binding of the location and the transaction is realized. By analyzing the commodity environment video, dynamic changes in the environment, such as changes in lighting, movement of objects, etc., can be identified, and dynamic environment change data can be generated. The dynamic environment change data is applied to the dynamic location map of the commodity, and the environment is rendered, that is, the environment changes are reflected on the location map of the commodity, and the environment rendering map data is generated. According to the environment rendering map data, the dynamic location map of the commodity is sensed and processed, and the influencing factors of the environment are integrated into the location map to generate an environment perception location map, thereby more accurately describing the location of the commodity in different environments. Sub-granular rendering is performed on the environment perception location map, and the location map is refined into a super-pixel environment perception image to provide more detailed environment perception information. According to the environment rendering map data, the super-pixel environment perception image is fused with the real-time location scene model to generate a 3D real-time location scene model, realize a dynamic digital twin scene, and accurately present the location and environment of the goods in the model. The commodity transaction information is divided into nodes to generate independent commodity transaction data nodes to improve the efficiency of data organization and management. By performing smart contract analysis on commodity transaction information, the smart contract of the commodity is generated, that is, the description of transaction rules and conditions, to ensure the legality and reliability of the transaction. The commodity smart contract is used to build a decentralized blockchain network for the commodity transaction data nodes to establish a distributed and reliable commodity transaction network. Through real-time node mapping, the commodity transaction tag data, that is, the location and tag information of the commodity in the real-time scene model, is obtained. By performing dynamic expansion convolution processing on the commodity transaction tag data, a commodity blockchain scene model is constructed to describe the location, attributes and transaction information of the commodity in the scene. Upload the commodity blockchain scenario model to the cloud server, execute the commodity transaction information sharing operation, realize the wide sharing and accessibility of commodity information, and promote the reliability and convenience of transactions. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 This is a flowchart of the steps of a commodity transaction information sharing method and system based on blockchain technology of the present invention;

[0079] Figure 2 Detailed implementation flow chart of step S1;

[0080] Figure 3 Detailed implementation flow chart of step S2;

[0081] Figure 4 Detailed implementation flow chart of step S3. DETAILED DESCRIPTION

[0082] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0083] This application example provides a commodity transaction information sharing method and system based on blockchain technology. The execution subject of the commodity transaction information sharing method and system based on blockchain technology includes but is not limited to: mechanical equipment, data processing platform, cloud server node, network upload device, etc. equipped with the system can be regarded as the general computing node of this application, and the data processing platform includes but is not limited to: at least one of an audio and image management system, an information management system, and a cloud data management system.

[0084] See also Figures 1 to 4 The present invention provides a commodity transaction information sharing method based on blockchain technology, and the commodity transaction information sharing method based on blockchain technology comprises the following steps:

[0085] Step S1: Obtain commodity transaction information and commodity location data; obtain commodity environment video in real time through a camera; perform motion state analysis on the commodity location data according to the commodity environment video to generate commodity motion state data;

[0086] Step S2: Performing a position time series analysis on the commodity movement state data through the commodity position data to generate commodity position time series data; performing dynamic position point mapping on the commodity transaction information through the commodity position time series data to construct a commodity dynamic position map;

[0087] Step S3: performing dynamic environment change recognition on the product environment video to generate dynamic environment change data; performing dynamic environment rendering on the product dynamic location map based on the dynamic environment change data to generate environment rendering texture data; performing environment-driven perception processing on the product dynamic location map based on the environment rendering texture data to generate an environment perception location map;

[0088] Step S4: Perform sub-granular rendering on the environment perception location map to construct a super-pixel environment perception image; perform dynamic digital twin scene fusion on the super-pixel environment perception image according to the environment rendering map data to construct a 3D real-time location scene model;

[0089] Step S5: Divide the commodity transaction information into nodes to generate commodity transaction data nodes; perform smart contract analysis on the commodity transaction information to generate commodity smart contracts; construct a decentralized blockchain network for the commodity transaction data nodes through commodity smart contracts to build a commodity blockchain network;

[0090] Step S6: Use the commodity transaction data node to perform real-time node mapping on the 3D real-time location scene model to obtain commodity transaction tag data; perform dynamic expansion convolution processing on the commodity blockchain network and the 3D real-time location scene model through the commodity transaction tag data to construct a commodity blockchain scene model to perform commodity transaction information sharing operations.

[0091] The present invention can understand the transaction details of the commodity and its position in space by acquiring commodity transaction information and commodity location data. Real-time acquisition of commodity environment video can provide visual information about the environment in which the commodity is located. Motion state analysis can identify the motion state of the commodity in the video, such as still, moving, rotating, etc., so as to obtain dynamic information of the commodity. Commodity position time series analysis can reveal the position change of the commodity at different time points and provide time series information of the commodity position. Dynamic position point mapping combines the commodity position information with the transaction information to construct a commodity dynamic position map, which can display the movement trajectory and transaction details of the commodity in space. Dynamic environment change recognition can detect environmental changes in the commodity environment video, such as lighting changes, shadow changes, etc., and provide dynamic information of the environment. Dynamic environment rendering uses environmental change data to render the commodity dynamic position map and generates environmental rendering texture data reflecting environmental changes. Environment-driven perception processing applies the environmental rendering texture data to the commodity dynamic position map, so that it has the ability to perceive the environment and generates an environment-aware position map. Sub-fine-grained rendering can The environment perception location map is rendered more finely, providing a high-resolution environment perception image. The dynamic digital twin scene fusion combines the environment rendering map data with the super-pixel environment perception image to generate a 3D real-time location scene model with dynamic scene information. The commodity transaction data node division divides the commodity transaction information into different nodes, providing the organizational structure of the transaction data. The smart contract analysis can perform intelligent processing of commodity transaction information and generate commodity smart contracts for standardizing and executing transactions. The decentralized blockchain network construction uses commodity smart contracts to construct a decentralized blockchain network for commodity transaction data nodes, ensuring the security and credibility of commodity transaction data. The commodity transaction data nodes are used to perform real-time node mapping on the 3D real-time location scene model, which can associate the commodity transaction information with the real-time location scene model and obtain the commodity transaction tag data. The dynamic expansion convolution processing uses the commodity transaction tag data to process the commodity blockchain network and the 3D real-time location scene model, constructs the commodity blockchain scene model, and realizes the sharing of commodity transaction information.

[0092] In the embodiment of the present invention, reference Figure 1 The above is a flowchart of a method and system for sharing commodity transaction information based on blockchain technology of the present invention. In this example, the steps of the method for sharing commodity transaction information based on blockchain technology include:

[0093] Step S1: Obtain commodity transaction information and commodity location data; obtain commodity environment video in real time through a camera; perform motion state analysis on the commodity location data according to the commodity environment video to generate commodity motion state data;

[0094] In this embodiment, the transaction information and location data of the commodity are obtained through a suitable data source (such as a database, API, etc.). These data may include the unique identifier of the commodity, the transaction time, the transaction location, and the location coordinates of the commodity at the time of the transaction, etc. A camera or other appropriate device is used to obtain a video of the environment in which the commodity is located in real time through a video acquisition function. Ensure that the position and angle of the camera can cover the area where the commodity is located, and process and analyze the obtained commodity environment video to extract useful information. This can include video frame decoding, image recognition, target tracking and other technologies. In the commodity environment video, the motion state data of the commodity can be obtained by tracking and analyzing the location of the commodity. This can be achieved by calculating the change, speed, acceleration, etc. of the commodity position. Common methods include optical flow method, Kalman filtering, etc., and the analyzed commodity motion state data is sorted and encoded to generate a data structure describing the motion state of the commodity. This can be a data object or data file containing information such as timestamp, location coordinates, speed, acceleration, etc.

[0095] Step S2: Performing a position time series analysis on the commodity movement state data through the commodity position data to generate commodity position time series data; performing dynamic position point mapping on the commodity transaction information through the commodity position time series data to construct a commodity dynamic position map;

[0096] In this embodiment, the location time series analysis is performed on the commodity motion state data. This may include calculating the change, trajectory, dwell time and other information of the commodity position. For discrete location data, interpolation or smoothing algorithms may be used to fill in missing data points to obtain more continuous location time series data, and the commodity location data after the location time series analysis may be sorted and encoded to generate the location time series data of the commodity. This may be a data object or data file containing time series, location coordinates, and motion state, and the commodity location time series data may be associated with the commodity transaction information to achieve the mapping of dynamic location points. The commodity transaction information may be matched with the corresponding location data, and the relationship between the two may be established. Appropriate visualization tools or techniques may be selected to clearly and intuitively display the dynamic location point mapping of the commodity location time series data and commodity transaction information at different time points by the location and transaction information of the commodity, and construct a dynamic location map of the commodity. This may be a visualized map showing the location and transaction information of the commodity at different time points.

[0097] Step S3: performing dynamic environment change recognition on the product environment video to generate dynamic environment change data; performing dynamic environment rendering on the product dynamic location map based on the dynamic environment change data to generate environment rendering texture data; performing environment-driven perception processing on the product dynamic location map based on the environment rendering texture data to generate an environment perception location map;

[0098] In this embodiment, computer vision and image processing techniques, such as target detection, optical flow method, background modeling, etc., are used to analyze dynamic environmental changes in the video. Computer vision and image processing techniques, such as target detection, optical flow method, background modeling, etc., are used to analyze dynamic environmental changes in the video, and the video is identified by dynamic environmental changes. This can include information such as object movement, illumination change, scene change, etc. in the detection environment. The results of dynamic environmental change identification are sorted and encoded to generate dynamic environmental change data. This can be a data object or data file containing time series, environmental change type, and location information. According to the type and location information of environmental change, the corresponding environmental rendering effect is applied to the corresponding position in the map, and the dynamic location map of the commodity rendered by the dynamic environment is sorted and encoded to generate environmental rendering map data. This can be a data object or data file containing time series and location map rendering map, and the dynamic location map of the commodity is subjected to environment-driven perception processing. According to the rendering map data, the area of ​​environmental change is marked on the map or the enhanced effect of environmental perception is provided.

[0099] Step S4: Perform sub-granular rendering on the environment perception location map to construct a super-pixel environment perception image; perform dynamic digital twin scene fusion on the super-pixel environment perception image according to the environment rendering map data to construct a 3D real-time location scene model;

[0100] In this embodiment, the map is rendered at a sub-granularity, which can be achieved by dividing the map into superpixels (superpixels are a method of dividing an image into continuous areas with similar features), using image processing and rendering techniques, such as a superpixel segmentation algorithm, to perform fine-grained division and rendering of the environment-aware location map, generating a superpixel environment-aware image, combining the environment rendering map data, and constructing a 3D real-time location scene model, which can be achieved by fusing the superpixel environment-aware image with the dynamic environment changes in the environment rendering map data, using computer graphics and rendering techniques, such as a digital twin scene fusion algorithm, to fuse the superpixel environment-aware image with the dynamic environment changes in the environment rendering map data, and generating a model with a 3D real-time location scene, using a superpixel segmentation algorithm to perform sub-granular division of the environment-aware location map to ensure that the rendering result has a more detailed visual effect, combining the dynamic environment changes in the environment rendering map data, using a digital twin scene fusion algorithm to fuse the superpixel environment-aware image with the dynamic environment changes, selecting appropriate computer graphics and rendering techniques to achieve the fusion of the superpixel environment-aware image with the dynamic environment changes, and constructing a model with a 3D real-time location scene.

[0101] Step S5: Divide the commodity transaction information into nodes to generate commodity transaction data nodes; perform smart contract analysis on the commodity transaction information to generate commodity smart contracts; construct a decentralized blockchain network for the commodity transaction data nodes through commodity smart contracts to build a commodity blockchain network;

[0102] In this embodiment, the commodity transaction information is used to divide the transaction data into nodes. This can be done according to different transaction attributes, participants, or other relevant factors. The node division can divide the commodity transaction information into different data nodes. Each node represents an independent transaction entity or participant. The commodity transaction information is analyzed by smart contracts to generate commodity smart contracts. Smart contracts are contracts that automatically execute and execute programmable conditions. They can be implemented on a blockchain network. In smart contracts, the rules, conditions, and operations of commodity transactions are defined to ensure the reliability and security of transactions. Commodity smart contracts are used to build a decentralized blockchain network for commodity transaction data nodes. Blockchain is a distributed ledger technology that can record and verify the immutability and transparency of transaction data. Each commodity transaction data node can be used as a participant in the blockchain network to ensure the consistency and credibility of transaction data through a consensus mechanism.

[0103] Step S6: Use the commodity transaction data node to perform real-time node mapping on the 3D real-time location scene model to obtain commodity transaction tag data; perform dynamic expansion convolution processing on the commodity blockchain network and the 3D real-time location scene model through the commodity transaction tag data to construct a commodity blockchain scene model to perform commodity transaction information sharing operations.

[0104] In this embodiment, the commodity transaction data node is associated with the corresponding position in the 3D real-time location scene model. The real-time node mapping can map the commodity transaction data node to the corresponding position in the 3D real-time location scene model according to the attribute or identifier of the commodity transaction data node to obtain the commodity transaction tag data. The commodity transaction tag data is obtained from the 3D real-time location scene model through the real-time node mapping. The commodity transaction tag data may include information related to commodity transactions, such as the attributes of the commodity, the transaction status, the participants, etc. The commodity transaction tag data can be used to identify and describe the location and objects related to the commodity transaction in the 3D real-time location scene model. Through dynamic expansion convolution processing, the commodity transaction tag data can be fused and processed with the commodity blockchain network and the 3D real-time location scene model to construct the commodity blockchain scene model. Dynamic expansion convolution is an image processing and analysis technology that can be used to extract features from the tag data and process the scene model. The commodity blockchain scene model can provide a comprehensive view to show the association and influence of commodity transaction information in the blockchain network and the 3D real-time location scene, and associate the commodity transaction tag data with the commodity blockchain network and the corresponding location and objects in the 3D real-time location scene model.

[0105] In this embodiment, reference Figure 2 The above is a schematic flow chart of the detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include:

[0106] Step S11: Obtain commodity transaction information and commodity location data;

[0107] Step S12: Obtaining a video of the commodity environment in real time through a camera;

[0108] Step S13: performing location trajectory analysis on the commodity location data to generate commodity movement trajectory data;

[0109] Step S14: Perform motion optical flow vector analysis on the product environment video to generate product optical flow vector data;

[0110] Step S15: performing motion state analysis on the motion trajectory data of the commodity according to the commodity optical flow vector data to generate commodity motion state data.

[0111] The present invention can understand the transaction details of the commodity and its position in space by acquiring commodity transaction information and commodity location data. The commodity transaction information includes important information such as commodity attributes, prices, and transaction time, which is helpful to understand the characteristics and historical transaction status of the commodity. Real-time acquisition of commodity environment video can provide visual information about the environment in which the commodity is located. The commodity environment video can contain information such as scenes, other objects, and crowds around the commodity, which is helpful to understand the contextual environment in which the commodity is located. Position trajectory analysis can reveal the position changes of the commodity at different time points and provide movement path information of the commodity position. The commodity movement trajectory data records the movement path of the commodity in space and can be used to analyze the movement law and activity range of the commodity. Motion optical flow vector analysis can detect the movement information of objects in the video, including speed and direction. The commodity optical flow vector data records the movement speed and direction of the commodity in the video and can be used to analyze the movement characteristics and trends of the commodity. Motion state analysis can identify the movement state of the commodity in the video, such as stillness, movement, rotation, etc., so as to obtain dynamic information of the commodity. The commodity movement state data provides the movement state of the commodity at different time points and can be used to analyze the activity mode and behavior characteristics of the commodity.

[0112] In this embodiment, commodity transaction information including commodity attributes, transaction status, participants, etc. is obtained, and commodity location data is obtained. The location information of the commodity in space can be obtained through sensors, GPS positioning or other location tracking technologies. A camera device is used to collect video data of the environment in which the commodity is located in real time. The visual information of the commodity environment is captured in the form of video streams or frames. The commodity location data is used to analyze and process the location trajectory of the commodity in space. The location tracking algorithm or trajectory analysis technology can be used to calculate and infer the movement path, speed, acceleration, etc. of the commodity to generate commodity motion trajectory data. The motion optical flow analysis technology is used to analyze the pixel movement in the commodity environment video. The motion optical flow vector represents the movement direction and speed information of each pixel in the image, which can be calculated by the optical flow algorithm. The commodity optical flow vector data and the commodity motion trajectory data are used to analyze and infer the movement state of the commodity. The consistency of the motion trajectory and the optical flow vector can be compared to determine whether the commodity is stationary, in uniform motion, accelerated motion or in other motion states to generate commodity motion state data.

[0113] In this embodiment, reference Figure 3 The above is a schematic flow chart of the detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include:

[0114] Step S21: Calculate the speed distribution of the commodity position data to generate commodity speed distribution data;

[0115] Step S22: performing sliding window segmentation on the commodity speed distribution data based on the commodity movement trajectory data to generate sliding window data;

[0116] Step S23: using the sliding window data to perform a position time series analysis on the commodity movement state data to generate commodity position time series data;

[0117] Step S24: constructing a commodity location map based on commodity transaction information;

[0118] Step S25: Dynamically map the commodity location map to the location points using the commodity location time series data to construct a commodity dynamic location map.

[0119] The present invention can understand the movement speed of goods at different positions by calculating the speed distribution of commodity position data. The commodity speed distribution data provides statistical information on the movement speed of the commodity, which can be used to analyze the speed, stability and other characteristics of the commodity. The sliding window segmentation can divide the commodity speed distribution data according to the time window to obtain the speed distribution in different time periods. The sliding window data provides the change of the commodity speed distribution in different time periods, which is helpful to observe the dynamic change of the movement speed of the commodity. The position time series analysis is combined with the sliding window data to associate the position and time of the commodity to obtain the position time series data of the commodity. The commodity position time series data records the position information of the commodity at different time points, which can be used to analyze the movement path and position change trend of the commodity. The commodity location map is a spatial reference map constructed based on commodity transaction information, marking the location information of different commodities. The commodity location map provides the distribution of commodities in space, which can be used to locate and find specific commodities. The commodity dynamic location map is constructed based on the combination of commodity location time series data and commodity location map, reflecting the change of commodity location over time. The commodity dynamic location map can display the location of the commodity at different time points, and update the location information over time, providing a dynamic visual display of the commodity location.

[0120] In this embodiment, the commodity position data is used to calculate the speed of the commodity at different time points. The speed value of the commodity at each time point is calculated by differentiating the position data or using a mathematical model. These speed values ​​are statistically analyzed and distributed to generate commodity speed distribution data. The commodity motion trajectory data is used to segment the commodity speed distribution data in a sliding window manner. The sliding window is a window of a fixed size that slides sequentially on the commodity motion trajectory to extract the speed distribution data within the window. The size of the sliding window and the sliding step size can be used to control the granularity and coverage of the segmentation. The position timing analysis can include statistical analysis of the speed distribution data within the sliding window. The location changes of the goods, such as the average, variance, minimum, maximum, etc. of the location, generate the time series data of the location of the goods, describe the location characteristics and change trends of the goods in different time periods, and the product location map can be a virtual scene or plane to display the location and distribution of the goods. According to the location data in the commodity transaction information, the goods are marked or drawn on the map to establish the commodity location map. According to the location information in the commodity location time series data, the location points of the goods on the map are updated and adjusted to reflect the dynamic location changes of the goods. A dynamic location map of the goods is constructed to display the location status of the goods at different time points.

[0121] In this embodiment, reference Figure 4 The above is a schematic flow chart of the detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of step S3 include:

[0122] Step S31: Analyze the visual features of the product environment video using computer vision technology to generate environmental visual feature data;

[0123] Step S32: performing dynamic environmental change recognition on the environmental visual feature data to generate dynamic environmental change data;

[0124] Step S33: Perform dynamic environment rendering on the dynamic location map of the commodity through the dynamic environment change data to generate environment rendering map data;

[0125] Step S34: relocating the environment rendering map data to generate map position data;

[0126] Step S35: dynamically optimizing the environment rendering map data using the map position data to generate dynamic detail optimized rendering map data;

[0127] Step S36: performing environment-driven perception processing on the dynamic location map of the commodity according to the dynamic detail optimized rendering map data to generate an environment-aware location map;

[0128] The present invention uses computer vision technology to perform feature analysis on commodity environment videos and can extract visual features in the environment, such as color, texture, shape, etc. The environmental visual feature data provides a visual description of the environment in which the commodity is located, which helps to understand the characteristics and special attributes of the environment. Dynamic environmental change recognition can analyze changes in environmental visual feature data and identify dynamic changes in the environment, such as moving objects, lighting changes, etc. Dynamic environmental change data records the dynamic changes in the environment and can be used to analyze the changing trends and dynamic features of the environment. Dynamic environmental rendering uses dynamic environmental change data to render a dynamic location map of the commodity and combines the dynamic change information of the environment with the location map. Environmental rendering map data provides the impact of environmental changes on the commodity location map and presents the visual effects of the environment at different time points. Position relocation is achieved by reproducing environmental rendering map data. The data is processed to align the rendered map data with the product location data to ensure the accuracy of the map position. The map position data provides the accurate position information corresponding to each pixel in the environment rendering map data, providing a basis for subsequent rendering and processing. Dynamic detail optimization uses the map position data to adjust and optimize the environment rendering map data to enhance the details and realism of the rendered map. The dynamic detail optimization rendering map data presents high-quality visual effects of environment rendering and enhances the environmental perception of the product location map. The environment-aware location map is generated based on the combination of dynamic detail optimization rendering map data and the product dynamic location map, reflecting the perceptual impact of the environment on the location map. The environment-aware location map provides environmental perception information in the product location map, including lighting, shadows, reflections, etc., making the location map more realistic and environmentally aware.

[0129] In this embodiment, computer vision technology is used to extract and analyze features of the commodity environment video. A pre-trained visual model, such as a convolutional neural network (CNN), can be used to extract features from video frames. The dynamic environmental changes between video frames are identified using environmental visual feature data. Machine learning or deep learning algorithms are used to train and classify the visual feature data to determine whether there are environmental changes between video frames, generate dynamic environmental change data to describe the dynamic changes of the environment, and apply corresponding environmental rendering effects to corresponding positions on the map according to the position and intensity of the environmental changes to reflect the impact of the environmental changes on the map. Environmental rendering map data is generated to describe the dynamic changes on the map. The environmental rendering effect of each location is achieved by matching the feature points on the map or using image registration technology to correspond the environmental rendering map data to the actual location on the map, and determining the location of the map on the map for subsequent rendering and processing. According to the changes in the map position and the characteristics of the environmental rendering map data, the map data is adjusted and optimized to improve the rendering effect and realism, and dynamic detail optimization rendering map data is generated to describe the dynamic rendering details of each location on the map. According to the environmental detail information in the rendering map data, the presentation method of the product location points on the map is adjusted to match the environmental rendering effect, and an environmentally aware location map is generated, which provides comprehensive perception and presentation of the product location and environment.

[0130] In this embodiment, the specific steps of step S32 are:

[0131] Step S321: Calculate the ambient brightness of the product environment video using the ambient brightness reduction calculation formula to generate an ambient brightness curve;

[0132] Step S322: extracting image texture features from the environmental visual feature data according to the environmental brightness curve to generate environmental texture feature data;

[0133] Step S323: performing optical flow analysis on the commodity environment video according to the environment texture feature data to generate motion vector feature data;

[0134] Step S324: performing dynamic background structure analysis on the motion vector feature data to generate dynamic background structure data;

[0135] Step S325: performing dynamic environment change recognition on the environment visual feature data based on the dynamic background structure data to generate dynamic environment change data;

[0136] The present invention can obtain the brightness value of each frame in the commodity environment video by calculating the environment brightness. The environment brightness curve provides the brightness change in the commodity environment video, which can be used to analyze the brightness and darkness of the environment and the trend of illumination change. The image texture feature extraction can extract texture information from each frame of the commodity environment video, including features such as color and texture structure. The environment texture feature data provides a description of the texture features in the commodity environment video, which can be used to analyze the texture changes and features of the environment. The optical flow analysis can detect the motion information of the object in the commodity environment video, including speed and direction. The motion vector feature data records the motion speed and direction of the object in the commodity environment video, which can be used to analyze the motion changes and trends of the object in the environment. The dynamic background structure analysis can identify the dynamic background structure in the commodity environment video, such as moving trees, flowing water, etc. The dynamic background structure data provides a description of the dynamic background structure in the commodity environment video, which can be used to analyze the dynamic background changes and structural features in the environment. The dynamic environment change recognition combines the dynamic background structure data and the environment visual feature data to identify the dynamic environment changes in the commodity environment video, such as wind blowing leaves, rippling water, etc. The dynamic environment change data provides a description of the dynamic environment changes in the commodity environment video, which is helpful to analyze the dynamic changes and features in the environment.

[0137] In this embodiment, the video is decomposed into a series of continuous video frames, and the ambient brightness is calculated for each video frame. The brightness loss calculation formula can be used to calculate the brightness value according to the RGB value of each pixel. Common brightness calculation formulas include grayscale, YUV conversion, etc. The ambient brightness values ​​calculated for each video frame are organized in time sequence to form an ambient brightness curve. The ambient brightness curve is used to extract image texture features for each video frame. Common image texture feature extraction methods include gray level co-occurrence matrix (GLCM), wavelet transform, etc. The texture features extracted from each video frame are organized in time sequence to form environmental texture feature data. The environmental texture feature data is used to perform optical flow calculation on adjacent video frames. Optical flow is a vector that describes the direction and speed of pixel movement in an image, and motion vector features are extracted from the optical flow calculation results. Various optical flow algorithms, such as Lucas-Kanade, Horn-Schunck, etc., can be used to perform dynamic background structure analysis using motion vector feature data. The dynamic background structure can be identified by detecting the stability and consistency in the motion vector. According to the results of the dynamic background structure analysis, each video frame is marked as a static background or a dynamic background to form dynamic background structure data. The dynamic background structure data is used to identify dynamic environmental changes in the environmental visual feature data. According to the changes in the dynamic background structure, it can be determined whether the environment has changed. According to the results of the dynamic environmental change identification, each video frame is marked as an environmental change or a non-environmental change to form dynamic environmental change data.

[0138] In this embodiment, the ambient brightness loss calculation formula in step S321 is specifically:

[0139]

[0140] Wherein, d is the ambient brightness loss index, α is the average brightness in the ambient video, Δt is the calculated time range, x is the ambient light reflectivity, y is the ambient light refractive index, L is the optical parameter correction factor, and G is the ambient color difference value.

[0141] The present invention is achieved by The average ambient brightness α is multiplied by the time range Δt and divided by the square root of the sum of the squares of the ambient light reflectance x and the refractive index y, taking into account the proportional relationship between the average ambient brightness α and the ambient light reflectance x and the refractive index y. This section adjusts the value of the brightness reduction index to reflect the degree of ambient brightness reduction. The brightness loss index is adjusted by considering the change rate of the optical parameter correction factor L to the light reflectivity x and the refractive index y. The optical parameter correction factor L may change with the change of the light reflectivity x and the refractive index y. Therefore, by calculating the partial derivatives of the optical parameter correction factor L with respect to x and y And multiply it by the time range Δt, the brightness reduction index can be adjusted to consider the impact of changes in lighting parameters on brightness reduction. ln(G+1) The environmental color difference value G is used to describe the degree of color difference in different areas of the environment. By calculating the natural logarithm of G, the brightness reduction index can be adjusted to consider the impact of color differences on brightness reduction. The square root of the sum of the squares of the ambient light reflectivity x, refractive index y and time factor t is used as the denominator to adjust the brightness loss index. This can take into account the relationship between lighting parameters and time to more accurately calculate brightness loss.

[0142] In this embodiment, step S33 includes the following steps:

[0143] Step S331: performing dynamic light source rendering on the dynamic location map of the commodity according to the ambient brightness curve to generate a dynamic brightness change scene;

[0144] Step S332: performing a color tone difference analysis on the scene with dynamic brightness change according to the environmental texture feature data to generate color tone difference data;

[0145] Step S333: using the dynamic background structure data to fit the regional environment structure details of the dynamic brightness change scene to generate a dynamic background structure map;

[0146] Step S334: assigning a motion trajectory to the dynamic background structure map through motion vector feature data to generate a dynamic trajectory background map;

[0147] Step S335: dynamically adjust the rendering parameters of the dynamic trajectory background map according to the color tone difference data to generate environment rendering map data.

[0148] The present invention adjusts the brightness and direction of the light source according to the ambient brightness curve through dynamic light source rendering, so as to simulate the change of light in the commodity environment. The generated dynamic brightness change scene can reflect the change of light in the commodity environment and provide basic data for subsequent processing steps. The hue difference analysis can identify the color difference in the dynamic brightness change scene, compare the hue changes in different areas, and the generated hue difference data provides the hue characteristics of different areas in the commodity environment, which is helpful to analyze the color changes and characteristics in the environment. The regional environment structure detail fitting uses the dynamic background structure data to adjust the dynamic brightness change scene, so that the scene is more in line with the actual environment. The generated dynamic background structure map provides the detailed information of the background structure in the commodity environment, which can be used to enhance the realism and fidelity of the environment. The motion trajectory is given by using the motion vector feature data to merge the dynamic background structure map with the motion trajectory of the object, so as to increase the dynamic sense of the scene. The generated dynamic trajectory background map provides the combination effect of the background structure and the object movement in the commodity environment, so that the scene is richer and more vivid. The rendering parameter dynamic adjustment processing adjusts the rendering parameters according to the hue difference data, so that the rendering effect is more in line with the hue change of the environment. The generated environment rendering map data provides the scene image processed by rendering in the commodity environment, which has better visual effect and realism.

[0149] In this embodiment, the environment brightness curve is used as the light source control parameter to render the dynamic position map with dynamic light source. According to the environment brightness value at different time points, the illumination intensity and color are adjusted to simulate the change of the light source in the environment, and the dynamic position map rendered with dynamic light source is converted into a dynamic brightness change scene, wherein the brightness of different positions will change with the passage of time, and the environment texture feature data is used to extract the hue feature of the dynamic brightness change scene. The hue information of each position can be extracted by color space conversion, histogram and other methods, and the hue features at different time points are compared to calculate the difference between them. Various color difference calculation methods such as Euclidean distance and Bhattacharyya distance can be used to generate hue difference data according to the results of hue difference analysis, record the hue changes at different positions, and perform regional environment structure detail fitting on the dynamic brightness change scene according to the dynamic background structure data. According to the background structure information of each position, the brightness change of the corresponding position is adjusted to make it consistent with the environment background structure, and the dynamic brightness change scene after the regional environment structure detail fitting is converted into a dynamic background structure map. Each pixel in the map corresponds to a position, and the brightness change information of the position is recorded. The motion vector feature data is used to assign the motion trajectory to the dynamic background structure map. According to the direction and size of the motion vector, the background structure map at the corresponding position is displaced and deformed to simulate the motion trajectory of the object. The dynamic background structure map assigned with the motion trajectory is converted into a dynamic trajectory background map. Each pixel in the map corresponds to a position, and the dynamic background structure information and the motion trajectory of the object at that position are recorded. The rendering parameters of the dynamic trajectory background map are dynamically adjusted using the hue difference data. According to the hue difference of different positions, the rendering parameters such as brightness, contrast, saturation, etc. are adjusted to make the hue change of the image more realistic, and the dynamic trajectory background map that has been dynamically adjusted with the rendering parameters is converted into the environment rendering map data. Each pixel in the map corresponds to a position, and the rendering parameter information of the position is recorded for subsequent environment rendering and display.

[0150] In this embodiment, the specific steps of step S4 are:

[0151] Step S41: performing particle sampling on the environment perception position map to generate particle data;

[0152] Step S42: performing sub-pixel rendering parameter calculation on the particle data to generate sub-pixel rendering parameters;

[0153] Step S43: performing sub-granular rendering on the environment perception location map using sub-pixel rendering parameters to construct a super-pixel environment perception image;

[0154] Step S44: dynamically fusion the super-pixel environment perception image with the digital twin scene according to the environment rendering map data to construct a 3D real-time location scene model;

[0155] The present invention can extract key information from the environment perception location map through particle sampling and represent it as discrete particle data. The generated particle data can better describe the characteristics and structure of the environment, and provide a high-quality data basis for subsequent steps. The sub-pixel rendering parameter calculation uses particle data to improve the accuracy and detail of the rendering. The generated sub-pixel rendering parameters can more accurately describe the rendering characteristics of the environment, and provide more accurate rendering parameters for subsequent steps. Sub-fine-grained rendering uses sub-pixel rendering parameters to render the environment perception location map with high precision. The constructed super-pixel environment perception image can provide a more delicate and realistic environment image containing more detailed information. The dynamic digital twin scene fusion uses the environment rendering map data to fuse the super-pixel environment perception image with the real scene. The constructed 3D real-time location scene model can provide an environment model with high realism and interactivity, which is used for the display and communication of commodity information sharing.

[0156] In this embodiment, particle sampling is performed on the environment perception location map, and random sampling or other sampling methods are used to generate a group of particles representing the location of the object in the location map. The sampled particle location information is organized into particle data, and the position, size, direction and other related attributes of each particle are recorded. Sub-pixel rendering parameters are calculated for each particle, and sub-pixel rendering parameters such as brightness, color, transparency, etc. are calculated based on the location, size and other attributes of the particle. The calculated sub-pixel rendering parameters are associated with the particle data to form sub-pixel rendering parameter data, and the sub-pixel rendering parameter information of each particle is recorded. Sub-fine-grained rendering is performed on the environment perception location map using the sub-pixel rendering parameter data, and the sub-pixel rendering parameters of each particle are calculated based on the sub-pixel rendering parameters of each particle. The rendering parameters are calculated and rendered to the corresponding position to form a super-pixel environment perception image. The environment perception location map after sub-granular rendering is converted into super-pixel environment perception image data, which contains the detailed information of the object and takes into account the rendering parameters at the sub-pixel level. The super-pixel environment perception image and the environment rendering map are dynamically fused with the digital twin scene using the environment rendering map data. The scene model is made more realistic by fusing the color, lighting and other information in the rendering map into the super-pixel environment perception image. The super-pixel environment perception image fused with the dynamic digital twin scene is converted into 3D real-time location scene model data, which can be displayed and interacted in a real-time environment, presenting information such as the position, shape and appearance of the object.

[0157] In this embodiment, the specific steps of step S5 are:

[0158] Step S51: dividing commodity transaction information into nodes to generate commodity transaction data nodes;

[0159] Step S52: Perform smart contract analysis on commodity transaction information and generate smart contract logic;

[0160] Step S53: Using smart contract logic to perform smart contract integration editing on the commodity transaction data node to generate a commodity smart contract;

[0161] Step S54: Decentralize the commodity transaction data nodes through commodity smart contracts to build a commodity blockchain network.

[0162] The present invention can classify and divide commodity transaction information according to certain rules and standards through node division to form independent transaction data nodes. The generated commodity transaction data nodes can better organize and manage commodity transaction information and improve the readability and operability of data. Smart contract analysis can conduct in-depth logical analysis of commodity transaction information and identify the rules and conditions therein. The generated smart contract logic can describe the specific rules and constraints of commodity transactions and provide a basis for smart contracts for subsequent steps. Smart contract integrated editing uses smart contract logic to edit and assemble commodity transaction data nodes to generate executable commodity smart contracts. The generated commodity smart contracts can automatically execute transaction rules and conditions to ensure the legality and reliability of transactions. The decentralized blockchain network uses commodity smart contracts to build a distributed transaction network that does not rely on central institutions. The constructed commodity blockchain network can ensure the transparency, security and traceability of transactions and improve the credibility and efficiency of commodity information sharing.

[0163] In this embodiment, commodity transaction data nodes are divided as needed. Node division can be performed according to different standards, such as time period division, commodity category division, etc. Similar transaction data are grouped into one node to form a data node set, and commodity transaction data nodes are generated according to the results of node division. Each node contains a set of commodity transaction data with similar characteristics, and the commodity transaction data nodes are subjected to smart contract analysis. This includes checking the legality of transactions, verifying the integrity of transactions, analyzing transaction patterns, etc., and generating smart contract logic based on the analysis results of commodity transaction information. Smart contract logic defines the rules, conditions, and operations executed in commodity transactions, and the generated smart contract logic is applied to commodity transaction data nodes. According to the smart contract logic, the smart contract code is edited and integrated, including defining the state variables, functions, and events of the contract, and the edited smart contract code is compiled and deployed. Compilation converts the source code into bytecode that can be executed on the blockchain network, and deployment deploys the smart contract to the contract address on the blockchain network. After completing the integrated editing, compilation, and deployment, a commodity smart contract is generated. The contract contains the smart contract code of the commodity transaction data node and can be called and executed on the blockchain network, and the generated commodity smart contract is deployed to the blockchain network. By calling the interface of the blockchain platform, publishing the smart contract on the network, obtaining the contract address, and connecting the commodity transaction data node with the commodity smart contract. The data node can interact with the blockchain network by calling the smart contract method, write the relevant transaction data into the blockchain, and realize the decentralized storage and verification of data. By connecting multiple commodity transaction data nodes to the commodity smart contract on the blockchain network, a commodity blockchain network is constructed. In this network, the verification and recording of commodity transaction data are executed by smart contracts, realizing a decentralized trading environment.

[0164] In this embodiment, the specific steps of step S6 are:

[0165] Step S61: using commodity transaction data nodes to perform real-time node mapping on the 3D real-time location scene model to obtain commodity transaction tag data;

[0166] Step S62: performing convolution preprocessing on the commodity blockchain network and the 3D real-time location scene model through the commodity transaction tag data to generate a blockchain convolution sample set and a scene model convolution sample set;

[0167] Step S63: Perform dynamic dilation convolution processing on the blockchain convolution sample set and the scene model convolution sample set to construct a commodity blockchain scene model;

[0168] Step S64: Upload the commodity blockchain scenario model to the cloud server to perform commodity transaction information sharing operations.

[0169] The present invention can determine the position and marking information of the commodity in the scene by mapping the commodity transaction data node with the 3D real-time location scene model through real-time node mapping. The obtained commodity transaction marking data can provide the specific position and attribute information of the commodity in the real-time scene model, providing a data basis for subsequent steps. The convolution preprocessing uses the commodity transaction marking data to process and convert the commodity blockchain network and the 3D real-time location scene model. The generated blockchain convolution sample set and scene model convolution sample set can better adapt to subsequent convolution processing and analysis. The dynamic expansion convolution processing can extract richer features and information by processing the blockchain convolution sample set and the scene model convolution sample set. The constructed commodity blockchain scene model can more comprehensively describe the position, attributes and transaction information of the commodity in the scene. Uploading the commodity blockchain scene model to the cloud server can realize remote storage and sharing of data. By executing the commodity transaction information sharing operation, the wide sharing and accessibility of commodity information can be realized, and the reliability and convenience of transactions can be promoted.

[0170] In this embodiment, the commodity transaction data nodes are mapped to the corresponding positions in the 3D real-time location scene model. This can be done by matching the commodity transaction data with the scene model to find the corresponding position or area. Based on the real-time node mapping, the commodity transaction marking data is obtained. The marking data can be the location coordinates, identifiers or other related information of the commodity in the scene model, which are used to mark the presence of the commodity in the scene. The 3D real-time location scene model is convolutionally preprocessed using the commodity transaction marking data. This may involve mapping the commodity transaction marking data to the coordinate system of the scene model, extracting relevant features or performing other preprocessing operations. Based on the preprocessed commodity transaction marking data, a convolution sample set for the blockchain network is generated. These samples can be used as input to the blockchain network for training, verification or other related tasks. Based on the preprocessed commodity transaction marking data, a convolution sample set for the 3D real-time location scene model is generated. These samples can be used as input to the blockchain network for training, verification or other related tasks. For training, inference or other related tasks of the scene model, dynamic expansion convolution processing is applied to the blockchain convolution sample set and the scene model convolution sample set. This processing method can dynamically adjust the size and shape of the convolution kernel during the convolution process to adapt to different commodity transaction data and scene model features. Based on the dynamic expansion convolution processing, feature extraction and fusion operations are performed, which can include extracting shared features of commodity transaction data and scene models, and fusing them into a representation of the commodity blockchain scene model. Through dynamic expansion convolution processing and feature fusion, a commodity blockchain scene model is constructed, which can capture the association and features between commodity transaction data and 3D real-time location scene models for subsequent analysis, prediction or other tasks. The commodity blockchain scene model is run on a cloud server to perform commodity transaction information sharing operations, which can include combining commodity transaction data with scene models to share, query, and visualize information.

[0171] In this embodiment, a commodity transaction information sharing system based on blockchain technology is provided, including:

[0172] The information collection module is used to obtain commodity transaction information and commodity location data; obtain commodity environment video in real time through the camera; perform motion state analysis on commodity location data based on commodity environment video to generate commodity motion state data;

[0173] The dynamic location module is used to perform location time series analysis on the commodity movement state data through the commodity location data to generate commodity location time series data; dynamically map the commodity transaction information through the commodity location time series data to construct a commodity dynamic location map;

[0174] The environment rendering module is used to identify dynamic environment changes of the product environment video to generate dynamic environment change data; perform dynamic environment rendering on the dynamic location map of the product through the dynamic environment change data to generate environment rendering texture data; perform environment-driven perception processing on the dynamic location map of the product according to the environment rendering texture data to generate an environment perception location map;

[0175] The location scene model module is used to perform sub-granular rendering of the environment perception location map to construct a super-pixel environment perception image; the super-pixel environment perception image is dynamically fused with a digital twin scene based on the environment rendering map data to construct a 3D real-time location scene model;

[0176] The blockchain network module is used to divide commodity transaction information into nodes to generate commodity transaction data nodes; perform smart contract analysis on commodity transaction information to generate commodity smart contracts; and construct a decentralized blockchain network for commodity transaction data nodes through commodity smart contracts to build a commodity blockchain network.

[0177] The dilated convolution module is used to use the commodity transaction data nodes to perform real-time node mapping on the 3D real-time location scene model to obtain commodity transaction tag data; the commodity blockchain network and the 3D real-time location scene model are dynamically dilated and convoluted through the commodity transaction tag data to construct a commodity blockchain scene model to perform commodity transaction information sharing operations.

[0178] The present invention constructs a commodity transaction information sharing system based on blockchain technology, and through an information collection module, the transaction information and location data of the commodity can be obtained, including the attributes, price, transaction time, etc. of the commodity, as well as the location coordinates of the commodity in the scene. By performing time series analysis on the commodity location data, the motion state of the commodity can be understood, including information such as movement trajectory, speed, acceleration, etc., and the motion state data of the commodity can be generated. According to the commodity location time series data, it is mapped to the commodity transaction information, and a dynamic location map of the commodity is constructed, that is, the location information of the commodity is associated with the transaction information, and the binding of the location and the transaction is realized. By analyzing the commodity environment video, dynamic changes in the environment, such as changes in lighting, movement of objects, etc., can be identified, and dynamic environment change data can be generated. The dynamic environment change data is applied to the dynamic location map of the commodity, and the environment is rendered, that is, the environment changes are reflected on the location map of the commodity, and the environment rendering map data is generated. According to the environment rendering map data, the dynamic location map of the commodity is sensed and processed, and the influencing factors of the environment are integrated into the location map to generate an environment perception location map, thereby more accurately describing the location of the commodity in different environments. Sub-granular rendering is performed on the environment perception location map, and the location map is refined into a super-pixel environment perception image to provide more detailed environment perception information. According to the environment rendering map data, the super-pixel environment perception image is fused with the real-time location scene model to generate a 3D real-time location scene model, realize a dynamic digital twin scene, and accurately present the location and environment of the goods in the model. The commodity transaction information is divided into nodes to generate independent commodity transaction data nodes to improve the efficiency of data organization and management. By performing smart contract analysis on commodity transaction information, the smart contract of the commodity is generated, that is, the description of transaction rules and conditions, to ensure the legality and reliability of the transaction. The commodity smart contract is used to build a decentralized blockchain network for the commodity transaction data nodes to establish a distributed and reliable commodity transaction network. Through real-time node mapping, the commodity transaction tag data, that is, the location and tag information of the commodity in the real-time scene model, is obtained. By performing dynamic expansion convolution processing on the commodity transaction tag data, a commodity blockchain scene model is constructed to describe the location, attributes and transaction information of the commodity in the scene. Upload the commodity blockchain scenario model to the cloud server, execute the commodity transaction information sharing operation, realize the wide sharing and accessibility of commodity information, and promote the reliability and convenience of transactions.

[0179] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0180] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0181] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A commodity transaction information sharing method based on blockchain technology, characterized in that: The following steps are involved: Step S1: Obtain commodity transaction information and commodity location data; obtain commodity environment video in real time through a camera; perform motion state analysis on the commodity location data according to the commodity environment video to generate commodity motion state data; Step S2: Performing a position time series analysis on the commodity movement state data through the commodity position data to generate commodity position time series data; performing dynamic position point mapping on the commodity transaction information through the commodity position time series data to construct a commodity dynamic position map; Step S3: performing dynamic environment change recognition on the product environment video to generate dynamic environment change data; performing dynamic environment rendering on the product dynamic location map based on the dynamic environment change data to generate environment rendering texture data; performing environment-driven perception processing on the product dynamic location map based on the environment rendering texture data to generate an environment perception location map; Step S4: Perform sub-granular rendering on the environment perception location map to construct a super-pixel environment perception image; perform dynamic digital twin scene fusion on the super-pixel environment perception image according to the environment rendering map data to construct a 3D real-time location scene model; Step S5: Divide the commodity transaction information into nodes to generate commodity transaction data nodes; perform smart contract analysis on the commodity transaction information to generate commodity smart contracts; construct a decentralized blockchain network for the commodity transaction data nodes through commodity smart contracts to build a commodity blockchain network; Step S6: Use the commodity transaction data node to perform real-time node mapping on the 3D real-time location scene model to obtain commodity transaction tag data; perform dynamic expansion convolution processing on the commodity blockchain network and the 3D real-time location scene model through the commodity transaction tag data to construct a commodity blockchain scene model to perform commodity transaction information sharing operations.

2. The method according to claim 1, characterized in that The specific steps of step S1 are: Step S11: Obtain commodity transaction information and commodity location data; Step S12: Obtaining a video of the commodity environment in real time through a camera; Step S13: performing location trajectory analysis on the commodity location data to generate commodity movement trajectory data; Step S14: Perform motion optical flow vector analysis on the product environment video to generate product optical flow vector data; Step S15: performing motion state analysis on the motion trajectory data of the commodity according to the commodity optical flow vector data to generate commodity motion state data.

3. The method according to claim 1, characterized in that: The specific steps of step S2 are: Step S21: Calculate the speed distribution of the commodity position data to generate commodity speed distribution data; Step S22: performing sliding window segmentation on the commodity speed distribution data based on the commodity movement trajectory data to generate sliding window data; Step S23: using the sliding window data to perform a position time series analysis on the commodity movement state data to generate commodity position time series data; Step S24: constructing a commodity location map based on commodity transaction information; Step S25: Dynamically map the commodity location map to the location points using the commodity location time series data to construct a commodity dynamic location map.

4. The method according to claim 1, characterized in that: The specific steps of step S3 are: Step S31: Analyze the visual features of the product environment video using computer vision technology to generate environmental visual feature data; Step S32: performing dynamic environmental change recognition on the environmental visual feature data to generate dynamic environmental change data; Step S33: Perform dynamic environment rendering on the dynamic location map of the commodity through the dynamic environment change data to generate environment rendering map data; Step S34: relocating the environment rendering map data to generate map position data; Step S35: dynamically optimizing the environment rendering map data using the map position data to generate dynamic detail optimized rendering map data; Step S36: Performing environment-driven perception processing on the dynamic location map of the commodity according to the dynamic detail optimized rendering texture data to generate an environment-aware location map.

5. The method according to claim 4, characterized in that The specific steps of step S32 are: Step S321: Calculate the ambient brightness of the product environment video using the ambient brightness reduction calculation formula to generate an ambient brightness curve; Step S322: extracting image texture features from the environmental visual feature data according to the environmental brightness curve to generate environmental texture feature data; Step S323: performing optical flow analysis on the commodity environment video according to the environment texture feature data to generate motion vector feature data; Step S324: performing dynamic background structure analysis on the motion vector feature data to generate dynamic background structure data; Step S325: performing dynamic environment change recognition on the environment visual feature data based on the dynamic background structure data to generate dynamic environment change data; The ambient brightness loss calculation formula in step S321 is specifically: ； in, is the ambient brightness impairment index, is the average brightness in the environment video, is the time range for calculation, is the ambient light reflectivity, is the ambient light refractive index, is the optical parameter correction factor, is the environmental color difference value, time factor t; The average value of the ambient brightness α is multiplied by the time range Δt and divided by the square root of the sum of the squares of the ambient light reflectivity x and the refractive index y, taking into account the proportional relationship between the average value of the ambient brightness α and the ambient light reflectivity x and the refractive index y; by multiplying the time range Δt and dividing by , the value of the brightness reduction index is adjusted to reflect the degree of reduction of ambient brightness; The brightness loss index is adjusted by considering the change rate of the optical parameter correction factor L to the light reflectivity x and the refractive index y; the optical parameter correction factor L changes with the change of the light reflectivity x and the refractive index y; therefore, by calculating the partial derivatives of the optical parameter correction factor L with respect to x and y And multiply it by the time range Δt to adjust the brightness reduction index to consider the impact of changes in lighting parameters on brightness reduction; the environmental color difference value G is used to describe the degree of color difference in different areas of the environment; , the brightness loss index is adjusted to take into account the effect of color difference on brightness loss; The square root of the sum of the squares of the ambient light reflectivity x, the refractive index y and the time factor t is used as the denominator to adjust the brightness loss index.

6. The method according to claim 4, characterized in that The specific steps of step S33 are: Step S331: performing dynamic light source rendering on the dynamic location map of the commodity according to the ambient brightness curve to generate a dynamic brightness change scene; Step S332: performing a color tone difference analysis on the scene with dynamic brightness change according to the environmental texture feature data to generate color tone difference data; Step S333: using the dynamic background structure data to fit the regional environment structure details of the dynamic brightness change scene to generate a dynamic background structure map; Step S334: assigning a motion trajectory to the dynamic background structure map through motion vector feature data to generate a dynamic trajectory background map; Step S335: dynamically adjust the rendering parameters of the dynamic trajectory background map according to the color tone difference data to generate environment rendering map data.

7. The method according to claim 1, characterized in that The specific steps of step S4 are: Step S41: performing particle sampling on the environment perception position map to generate particle data; Step S42: performing sub-pixel rendering parameter calculation on the particle data to generate sub-pixel rendering parameters; Step S43: performing sub-granular rendering on the environment perception location map using sub-pixel rendering parameters to construct a super-pixel environment perception image; Step S44: Dynamically fuse the super-pixel environment perception image with the digital twin scene according to the environment rendering map data to build a 3D real-time location scene model.

8. The method according to claim 1, characterized in that: The specific steps of step S5 are: Step S51: dividing commodity transaction information into nodes to generate commodity transaction data nodes; Step S52: Perform smart contract analysis on commodity transaction information and generate smart contract logic; Step S53: Using smart contract logic to perform smart contract integration editing on the commodity transaction data node to generate a commodity smart contract; Step S54: Decentralize the commodity transaction data nodes through commodity smart contracts to build a commodity blockchain network.

9. The method according to claim 1, characterized in that: The specific steps of step S6 are: Step S61: using commodity transaction data nodes to perform real-time node mapping on the 3D real-time location scene model to obtain commodity transaction tag data; Step S62: performing convolution preprocessing on the commodity blockchain network and the 3D real-time location scene model through the commodity transaction tag data to generate a blockchain convolution sample set and a scene model convolution sample set; Step S63: Perform dynamic dilation convolution processing on the blockchain convolution sample set and the scene model convolution sample set to construct a commodity blockchain scene model; Step S64: Upload the commodity blockchain scenario model to the cloud server to perform commodity transaction information sharing operations.

10. A commodity transaction information sharing system based on blockchain technology, characterized in that: The method for sharing commodity transaction information based on blockchain technology as claimed in claim 1 comprises: The information collection module is used to obtain commodity transaction information and commodity location data; obtain commodity environment video in real time through the camera; perform motion state analysis on commodity location data based on commodity environment video to generate commodity motion state data; The dynamic location module is used to perform location time series analysis on the commodity movement state data through the commodity location data to generate commodity location time series data; dynamically map the commodity transaction information through the commodity location time series data to construct a commodity dynamic location map; The environment rendering module is used to identify dynamic environment changes of the product environment video to generate dynamic environment change data; perform dynamic environment rendering on the dynamic location map of the product through the dynamic environment change data to generate environment rendering texture data; perform environment-driven perception processing on the dynamic location map of the product according to the environment rendering texture data to generate an environment perception location map; The location scene model module is used to perform sub-granular rendering of the environment perception location map to construct a super-pixel environment perception image; the super-pixel environment perception image is dynamically fused with a digital twin scene based on the environment rendering map data to construct a 3D real-time location scene model; The blockchain network module is used to divide commodity transaction information into nodes to generate commodity transaction data nodes; perform smart contract analysis on commodity transaction information to generate commodity smart contracts; and construct a decentralized blockchain network for commodity transaction data nodes through commodity smart contracts to build a commodity blockchain network. The dilated convolution module is used to use the commodity transaction data nodes to perform real-time node mapping on the 3D real-time location scene model to obtain commodity transaction tag data; the commodity blockchain network and the 3D real-time location scene model are dynamically dilated and convoluted through the commodity transaction tag data to construct a commodity blockchain scene model to perform commodity transaction information sharing operations.

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