Food production traceability anti-counterfeiting method and system

Abstract

The application provides a food production traceability anti-counterfeiting method and system. The method comprises the following steps: fusing information of each transportation node in a transportation process corresponding to a sold food to obtain first-level traceability information; fusing disassembled video segments corresponding to each food part of the sold food based on a collection end to obtain second-level traceability information; and in response to selection and marking of the sold food by a user based on a VR end, displaying the first-level traceability information and the second-level traceability information of the sold food. The application at least improves traceability comprehensiveness.

Description

Technical Field

[0001] This invention relates to data processing technology, and more particularly to a method and system for tracing and preventing counterfeiting in food production. Background Technology

[0002] Driven by both the globalization of the food supply chain and consumption upgrades, consumers' demand for transparent and reliable food origins is becoming increasingly urgent. Food goes through multiple stages from production to retail, including planting / breeding, processing, warehousing, logistics, and distribution, each involving complex stakeholders and data interactions. Especially during transportation, key parameters such as cold chain temperature control, delivery time, and transit points directly impact food quality. In retail scenarios, consumers often want to intuitively understand the complete traceability information of food, such as the origin of the cut parts of meat products. However, traditional traceability systems often focus on recording data at a single stage, lacking the ability to integrate dynamic information across the entire chain, and their visualization methods are limited to text or two-dimensional images, failing to meet consumers' immersive perception needs regarding the food's journey.

[0003] Existing food traceability technologies mainly rely on RFID tags or QR codes to collect and store data at transportation nodes. However, these technologies have the following problems: First, node data is stored in an isolated form, without establishing a cross-stage correlation model, resulting in fragmented traceability information. Second, for the food dismantling stage, existing technologies mostly rely on manual recording or fixed camera monitoring, which has blind spots and the risk of data tampering. Moreover, video data is not linked to the individual food items, making it difficult to support accurate traceability. Third, the traceability information is displayed in a single way, requiring users to passively receive preset information and unable to actively query details of specific stages, thus limiting the credibility of the traceability system and the user experience. Summary of the Invention

[0004] Based on the above problems, the present invention is proposed to provide a food production traceability and anti-counterfeiting method and system that overcomes or at least partially solves the above problems.

[0005] According to one aspect of the present invention, a method for tracing and preventing counterfeiting in food production is provided, comprising the following steps:

[0006] By integrating information from each transportation node in the food sales process, primary traceability information is obtained.

[0007] The video segments of disassembly of each part of the food sold, obtained from the acquisition terminal, are fused together to obtain secondary traceability information;

[0008] In response to users selecting and marking the food items sold via VR devices, the system displays the primary and secondary traceability information of the food items sold.

[0009] Optionally, in the method according to the present invention, information from each transportation node in the transportation process corresponding to the sold food is fused to obtain primary traceability information, including:

[0010] Determine each transfer point in the transportation process corresponding to the food being sold, based on the order of transfer, and obtain the location of each transfer point.

[0011] When it is determined that the current location of the corresponding food being sold coincides with any other location, a transportation node corresponding to that transfer point is created, and the difference between the comprehensive evaluation value that has an evaluation correlation with that transfer point and the obtained node impact value is calculated to obtain the remaining evaluation value.

[0012] If the remaining value of the response evaluation is greater than the preset remaining value, the remaining value of the evaluation is determined as the comprehensive evaluation value that has an evaluation correlation with the next forwarding node;

[0013] When the evaluation remaining value corresponding to the transfer point located at the tail position is greater than the preset remaining value, each transportation node is sorted according to the transfer order, and each first blockchain node is established based on the obtained node sequence.

[0014] The location of each transportation node is stored in the corresponding first blockchain node, and an interactive retrieval relationship is established between each location and the corresponding evaluation residual value to obtain first-level traceability information.

[0015] Optionally, in the method according to the invention, the method further includes:

[0016] When it is determined that a transfer point located before any transfer point with a transport node does not have a transport node, the corresponding transfer point without a transport node is identified as a jump node, and jump nodes in adjacent positions are grouped into the same jump group.

[0017] Obtain the jump reason for each jump group and store the jump reason in the blockchain node corresponding to the transfer point adjacent to the jump node at the end of each jump group.

[0018] Optionally, in the method according to the present invention, establishing an interactive retrieval relationship between the location of each point and the corresponding evaluation residual value includes:

[0019] Establish evaluation traceability layers that have interactive retrieval relationships with each blockchain node, wherein the evaluation traceability layers include numerical slots for filling the remaining evaluation values ​​and evaluation slots for filling the evaluation reasons;

[0020] If the remaining value of the evaluation corresponding to any evaluation traceability layer is greater than the preset remaining value, the at least one standard evaluation reason selected by the management terminal from the preset reason library will be filled into the evaluation slot.

[0021] If the remaining evaluation value of the numerical slot corresponding to any evaluation traceability layer is less than or equal to a preset remaining threshold, obtain the customized evaluation reason composed of voice information and / or text information from the corresponding management terminal, and fill the customized evaluation reason into the evaluation slot.

[0022] Optionally, in the method according to the present invention, video fusion is performed on the disassembly video segments of each food part corresponding to any food sold, obtained from the acquisition end, to obtain secondary traceability information, including:

[0023] The response terminal performs a disassembly operation on the food being sold, and the control acquisition terminal, including the video terminal, captures video of the disassembly operation and determines the disassembly line based on the obtained total video of the operation.

[0024] The control acquisition terminal includes an image acquisition terminal to acquire images of the food being sold, and to build a food model corresponding to the food being sold based on the obtained food images;

[0025] Based on the food model, determine the part sub-models corresponding to different food parts and the baseline disassembly line corresponding to each part sub-model;

[0026] The operation disassembly line is compared with the baseline disassembly line based on the degree of overlap, and the disassembly video segment corresponding to each food part is obtained based on the comparison result.

[0027] The videos are sorted according to the order in which each segment is acquired, and second blockchain nodes are established based on the obtained video sequences.

[0028] Each part of the sub-model is stored in the corresponding second blockchain node, and an interactive retrieval relationship is established between each part of the sub-model and the corresponding disassembled video segment to obtain secondary traceability information.

[0029] Optionally, in the method according to the present invention, in response to the operation terminal performing a disassembly operation on the food for sale, the control acquisition terminal including the video terminal performs video acquisition of the disassembly operation, and determines the operation disassembly line based on the obtained total operation video, including:

[0030] The response terminal performs a disassembly operation on the food being sold, and the control acquisition terminal, including the video terminal, captures the video of the disassembly operation to obtain the total video of the operation, and performs image recognition on each image frame that makes up the total video of the operation.

[0031] Based on the recognition results, each operation image frame corresponding to the blade tip area of ​​the indicated operation terminal is aggregated into an operation image group, and the food area indicating the food being sold is determined for each operation image frame in the operation image group.

[0032] If the blade tip region and the food region are adjacent to each other in the same operation image frame, then the operation image frame is determined as the operation image frame.

[0033] Frame fusion is performed on each adjacent video frame based on the chronological order of the operation, and the operation decomposition line corresponding to the decomposition operation is determined based on each obtained video segment of the operation.

[0034] Optionally, in the method according to the present invention, determining the operation disassembly line corresponding to each disassembly operation based on each obtained operation video segment includes:

[0035] Pixel acquisition is performed on the operation image frames included in each operation video segment to obtain each blade tip pixel that makes up the blade tip region and each food pixel that makes up the food region.

[0036] All knife tip pixels adjacent to different food pixels are identified as disassembly pixels, and each operation image frame is stacked to obtain a stacked image.

[0037] Connect adjacent pixels of all the disassembled pixels in the stacked image to obtain all operation disassemble lines for the corresponding disassembled operation.

[0038] Optionally, in the method according to the present invention, the operation disassembly line and the reference disassembly line are compared based on the degree of overlap, and the disassembly video segment corresponding to each food part is obtained based on the comparison result, including:

[0039] Each operational disassembly line is compared with each baseline disassembly line based on the degree of overlap.

[0040] If the overlap between any segment of the operation disassembly line and the reference disassembly line is greater than a preset overlap value, the operation video segment corresponding to the operation disassembly line will be determined as the disassembly video segment of the food part of the corresponding reference disassembly line.

[0041] Optionally, in the method according to the invention, the method further includes:

[0042] If a user's gaze time on any second blockchain node included in the secondary traceability information exceeds a preset time on the VR device, the sub-model of the part stored in that second blockchain node will be displayed.

[0043] In response to a user's interaction of selecting a part sub-model on a VR device, the model selection area located in the part sub-model is determined based on the interaction trajectory of the selection interaction;

[0044] The selected area of ​​the model is divided into points to obtain the collection points of each model in an array, and the point elevation of each model collection point is obtained.

[0045] The unit price of the food part is retrieved based on the corresponding part sub-model, and the elevation base value obtained by averaging the elevation of the points collected for each model is multiplied with the unit price of the part to obtain the selected price.

[0046] Obtain the center point of the corresponding model selection area, and fill the selected price into the model selection area based on the center point.

[0047] According to another aspect of the present invention, a food production traceability and anti-counterfeiting system is provided, comprising:

[0048] The information fusion module is configured to fuse information from each transportation node in the transportation process of the food being sold to obtain primary traceability information.

[0049] The video fusion module is configured to fuse video segments obtained from the acquisition terminal for each part of the food sold to obtain secondary traceability information.

[0050] The information display module is configured to respond to users' selection and marking of the food for sale based on the VR terminal, and to display the primary traceability information and secondary traceability information of the food for sale.

[0051] According to the present invention, this invention can obtain primary traceability information by fusing information from various transportation nodes at which the food arrives during transportation. Furthermore, this invention also fuses video segments of disassembly of each food part corresponding to the food, obtained from the data collection terminal, to obtain secondary traceability information. Then, when a user selects and marks the food on a VR device, the server displays both the primary and secondary traceability information, transforming the traditional static presentation of traceability information into an immersive and interactive experience. This allows users to understand the detailed process of the food from transportation to disassembly, greatly enhancing their participation and understanding of food traceability information, optimizing the user experience, and increasing their trust in food safety. The primary traceability information of this invention integrates key node data from the food transportation process, while the secondary traceability information intuitively presents disassembly video segments of each food part through video fusion. The combination of these two provides rich and intuitive evidence for food safety supervision, constructing a multi-dimensional and multi-level traceability system. Significantly improves the accuracy and comprehensiveness of traceability, providing users with more reliable and detailed food traceability information. Attached Figure Description

[0052] Figure 1 A flowchart of a food production traceability and anti-counterfeiting method according to an embodiment of the present invention is shown;

[0053] Figure 2 A schematic diagram of an evaluation tracing layer according to an embodiment of the present invention is shown;

[0054] Figure 3 A schematic diagram of an operational disassembly line according to an embodiment of the present invention is shown;

[0055] Figure 4 A structural block diagram of a food production traceability and anti-counterfeiting system according to another embodiment of the present invention is shown. Detailed Implementation

[0056] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0057] To address the problems existing in the aforementioned background technology, the inventors proposed the solution of this invention. One embodiment of this invention provides a food production traceability and anti-counterfeiting method, which can be executed in a computing device, wherein the computing device can be understood as a terminal with data processing capabilities, such as a mobile phone or computer.

[0058] Figure 1 A flowchart of a food production traceability and anti-counterfeiting method according to an embodiment of the present invention is shown, such as... Figure 1 As shown, the food production traceability and anti-counterfeiting method proposed in this embodiment begins with step S102, which includes the following:

[0059] By integrating information from each transportation node in the food sales process, primary traceability information is obtained.

[0060] For example, in this embodiment, the food sold can be understood as pork, mutton, beef, etc. In order to ensure the quality of the food, it is necessary to use transportation tools such as refrigerated trucks to transport the food from the factory location to the sales location. In order to more accurately control the quality of the food during transportation, the server will establish various transportation nodes during transportation, so that the server can accurately determine the food status when the food arrives at each transportation node.

[0061] To ensure customers' (users') right to know about the food sold, and to guarantee the transparency and safety of the food's origin, the server will integrate information from each transportation node during the food's transportation process, such as the location of each transportation node, to obtain primary traceability information.

[0062] Furthermore, the aforementioned "integrating information from each transportation node in the food sales process to obtain primary traceability information" also includes the following steps:

[0063] Determine each transfer point in the transportation process corresponding to the food being sold, based on the order of transfer, and obtain the location of each transfer point.

[0064] When it is determined that the current location of the corresponding food being sold coincides with any other location, a transportation node corresponding to that transfer point is created, and the difference between the comprehensive evaluation value that has an evaluation correlation with that transfer point and the obtained node impact value is calculated to obtain the remaining evaluation value.

[0065] If the remaining value of the response evaluation is greater than the preset remaining value, the remaining value of the evaluation is determined as the comprehensive evaluation value that has an evaluation correlation with the next forwarding node;

[0066] When the evaluation remaining value corresponding to the transfer point located at the tail position is greater than the preset remaining value, each transportation node is sorted according to the transfer order, and each first blockchain node is established based on the obtained node sequence.

[0067] The location of each transportation node is stored in the corresponding first blockchain node, and an interactive retrieval relationship is established between each location and the corresponding evaluation residual value to obtain first-level traceability information.

[0068] For example, in this embodiment, since the transportation routes for different food items may be different, the transfer points corresponding to different food items may also be different. Therefore, the server first determines the various transfer points that the food items need to reach during transportation, and these transfer points are arranged in the order of transfer. Then, the server obtains the location of each transfer point.

[0069] Because there may be situations where food needs to be transported urgently, skipping one or more transfer nodes to reach the next transfer node, the server will obtain the current location of the food in real time during the transportation process. When it is determined that the current location of the food coincides with any other location, it means that the food has arrived at that transfer point. At this time, the server will create a transportation node corresponding to that transfer point.

[0070] When food arrives at any transfer point, staff will evaluate its freshness and other conditions, thus obtaining a node impact value. Then, the server will obtain the food's overall evaluation value before arriving at that transfer point—that is, the overall evaluation value that has an evaluation correlation with that transfer point—and calculate the difference between the overall evaluation value and the node impact value to obtain the remaining evaluation value.

[0071] Next, the server will compare the remaining evaluation value with the preset remaining value. When the remaining evaluation value is greater than the preset remaining value, it means that the food quality of the food being sold is in good condition. At this time, the server will determine the remaining evaluation value as a comprehensive evaluation value that has an evaluation correlation with the next forwarding node.

[0072] When the remaining evaluation value is less than or equal to the preset remaining value, it indicates that the food quality of the food being sold is in a poor state. The server will send the corresponding poor signal to the management terminal so that the management personnel can quickly know that the food being sold is in a poor state and take action accordingly.

[0073] When the food item reaches the last transfer point, it means that the food item has completed the transportation process. The server will compare the remaining evaluation value corresponding to the transfer point with the preset remaining value. When the remaining evaluation value is greater than the preset remaining value, it means that the food quality of the food item is in good condition and it can be sold.

[0074] At this point, the server first sorts the various transportation nodes according to the order of transfer, obtaining a node sequence, and then establishes each first blockchain node based on the node sequence. Next, the server stores the location of each transportation node into its corresponding first blockchain node, and then establishes an interactive retrieval relationship between each location and its corresponding remaining evaluation value to obtain primary traceability information.

[0075] This embodiment can accurately determine the food quality of the food being sold by calculating and evaluating the remaining value. Furthermore, by establishing each first blockchain node, the first-level traceability information not only has a high degree of openness and transparency, but also ensures the originality and authenticity of the first-level traceability information.

[0076] Furthermore, the above method also includes the following steps:

[0077] When it is determined that a transfer point located before any transfer point with a transport node does not have a transport node, the corresponding transfer point without a transport node is identified as a jump node, and jump nodes in adjacent positions are grouped into the same jump group.

[0078] Obtain the jump reason for each jump group and store the jump reason in the blockchain node corresponding to the transfer point adjacent to the jump node at the end of each jump group.

[0079] For example, in this embodiment, when the server determines that a transfer point located before any transfer point with a transportation node does not have a transportation node, it means that the food being sold did not reach this transfer point, but skipped the transfer node and went directly to the transportation node.

[0080] To ensure the integrity of primary traceability information, the server first identifies transfer points without transportation nodes as skip nodes. Since skip nodes in adjacent locations typically have the same skip reason, to reduce the time spent filling in skip reasons on the management side, thereby improving work efficiency and reducing the corresponding data processing load on the server, the server will group skip nodes in adjacent locations into the same skip group.

[0081] Then, the server will obtain the jump reason for each jump group through the management terminal. In order to make it easier for users to understand more intuitively which transfer node the food for sale started from and what jump reason it was based on, the server will store the jump reason in the blockchain node corresponding to the transfer point adjacent to the jump node at the end of each jump group.

[0082] Furthermore, the aforementioned "establishing the interactive retrieval relationship between each location and its corresponding remaining evaluation value" also includes the following steps:

[0083] Establish evaluation traceability layers that have interactive retrieval relationships with each blockchain node, wherein the evaluation traceability layers include numerical slots for filling the remaining evaluation values ​​and evaluation slots for filling the evaluation reasons;

[0084] If the remaining value of the evaluation corresponding to any evaluation traceability layer is greater than the preset remaining value, the at least one standard evaluation reason selected by the management terminal from the preset reason library will be filled into the evaluation slot.

[0085] If the remaining evaluation value of the numerical slot corresponding to any evaluation traceability layer is less than or equal to a preset remaining threshold, obtain the customized evaluation reason composed of voice information and / or text information from the corresponding management terminal, and fill the customized evaluation reason into the evaluation slot.

[0086] For example, in this embodiment, the server establishes an interactive retrieval relationship between each location and its corresponding remaining evaluation value. First, the server establishes various evaluation traceability layers that have interactive retrieval relationships with each blockchain node. These evaluation traceability layers include numerical slots for filling the remaining evaluation value and evaluation slots for filling the evaluation reason, such as... Figure 2 As shown, this allows the server to display the evaluation traceability layer that has an interactive relationship with any blockchain node after the user interacts with it. This makes it easier for users to understand the remaining evaluation value of the food being sold at each transportation node and the reasons for the evaluation.

[0087] Specifically, when the remaining evaluation value corresponding to any evaluation traceability layer is greater than a preset remaining value, it indicates that the food quality of the food being sold is in good condition. This means that the food quality has not significantly changed upon arrival at the transportation node corresponding to that evaluation traceability layer. Therefore, the server provides the management end with a preset reason library containing multiple standard evaluation reasons, such as weather reasons or distance reasons. The management end can select at least one standard evaluation reason from the preset reason library. The server then fills the evaluation slot with the standard evaluation reason selected by the management end.

[0088] When the remaining evaluation value of the numerical slot corresponding to any evaluation traceability layer is less than or equal to the preset remaining threshold, it indicates that the food quality of the food being sold is in a poor state. That is, the food quality of the food has changed significantly when it arrives at the transportation node corresponding to the evaluation traceability layer. Therefore, the management needs to fill in the evaluation reason in a personalized way. At this time, the server will obtain the customized evaluation reason composed of voice information and / or text information from the management and then fill the customized evaluation reason into the evaluation slot.

[0089] This embodiment can obtain the evaluation reasons in different ways based on different comparison results between the evaluation remaining value and the preset remaining threshold. This can not only enhance the authenticity of the evaluation source traceability layer, but also improve the filling efficiency of the management end.

[0090] Step S104 includes the following:

[0091] The video segments of disassembly of each part of the food product obtained from the acquisition terminal are fused together to obtain secondary traceability information.

[0092] For example, in this embodiment, the acquisition terminal can be understood as a port that can capture video and images of food during the disassembly process.

[0093] Since a food product may have multiple parts, such as pork, the corresponding parts may include the foreleg, hind leg, etc., the acquisition device can capture video segments of the different parts of the food product.

[0094] The server will merge the disassembly video segments of each part of the food product obtained from the acquisition terminal to obtain secondary traceability information. This will allow users to more accurately trace the source of a specific food part through the secondary traceability information.

[0095] Furthermore, the aforementioned "merging of video segments from the disassembly of each food part corresponding to the food being sold, obtained from the acquisition terminal, to obtain secondary traceability information" also includes the following steps:

[0096] The response terminal performs a disassembly operation on the food being sold, and the control acquisition terminal, including the video terminal, captures video of the disassembly operation and determines the disassembly line based on the obtained total video of the operation.

[0097] The control acquisition terminal includes an image acquisition terminal to acquire images of the food being sold, and to build a food model corresponding to the food being sold based on the obtained food images;

[0098] Based on the food model, determine the part sub-models corresponding to different food parts and the baseline disassembly line corresponding to each part sub-model;

[0099] The operation disassembly line is compared with the baseline disassembly line based on the degree of overlap, and the disassembly video segment corresponding to each food part is obtained based on the comparison result.

[0100] The videos are sorted according to the order in which each segment is acquired, and second blockchain nodes are established based on the obtained video sequences.

[0101] Each part of the sub-model is stored in the corresponding second blockchain node, and an interactive retrieval relationship is established between each part of the sub-model and the corresponding disassembled video segment to obtain secondary traceability information.

[0102] For example, in this embodiment, when the operating terminal starts to disassemble the food for sale, the server controls the video terminal included in the acquisition terminal to acquire video of the disassembly operation, thereby obtaining the total video of the operation, and then determines the operation disassembly line based on the total video of the operation. The operation disassembly line can be understood as the disassembly trajectory generated by the operating terminal when disassembling the food for sale.

[0103] Then, the server controls the image acquisition end, including the image acquisition end, to acquire images of the food being sold, thereby obtaining food images. Then, based on the food images, a food model corresponding to the food being sold is built. Then, based on the food model, the sub-models of different parts of the food and the baseline disassembly line corresponding to each sub-model of the food part are determined. This can be understood as obtaining the complete food parts by disassembling them according to the baseline disassembly line.

[0104] Since the disassembly line operated by the operator may deviate from the baseline disassembly line, the server will compare the disassembly line and the baseline disassembly line based on the degree of overlap, and then obtain the disassembly video segment corresponding to each food part based on the comparison result.

[0105] The server then sorts the videos according to the order in which each disassembled video segment was acquired, thus obtaining a video sequence. It then establishes secondary blockchain nodes based on the video sequence and stores each part's sub-model in its corresponding secondary blockchain node. Finally, the server establishes an interactive retrieval relationship between each part's sub-model and its corresponding disassembled video segment, thereby obtaining secondary traceability information. This allows users to quickly understand the various parts of the food being sold through secondary traceability information, and after interacting with the part's sub-model corresponding to any food part, they can trace and view the disassembled video segment of that food part.

[0106] Furthermore, the aforementioned "responding to the operation terminal performing a disassembly operation on the food being sold, controlling the video terminal included in the acquisition terminal to acquire video of the disassembly operation, and determining the operation disassembly line based on the obtained total operation video" also includes the following steps:

[0107] The response terminal performs a disassembly operation on the food being sold, and the control acquisition terminal, including the video terminal, captures the video of the disassembly operation to obtain the total video of the operation, and performs image recognition on each image frame that makes up the total video of the operation.

[0108] Based on the recognition results, each operation image frame corresponding to the blade tip area of ​​the indicated operation terminal is aggregated into an operation image group, and the food area indicating the food being sold is determined for each operation image frame in the operation image group.

[0109] If the blade tip region and the food region are adjacent to each other in the same operation image frame, then the operation image frame is determined as the operation image frame.

[0110] Frame fusion is performed on each adjacent video frame based on the chronological order of the operation, and the operation decomposition line corresponding to the decomposition operation is determined based on each obtained video segment of the operation.

[0111] For example, in this embodiment, when the operator performs a disassembly operation on the food being sold, the server controls the video terminal included in the acquisition terminal to capture video of the disassembly operation, thereby obtaining the total video of the operation.

[0112] Since the operation terminal usually uses knives to disassemble the food being sold, in order to more accurately determine the operation disassembly line, the server will first perform image recognition on each operation image frame that makes up the total operation video, and then, based on the recognition results, summarize each operation image frame that exists in the blade tip area of ​​the corresponding operation terminal into the operation image group.

[0113] Then, the server will determine the food area of ​​the food being sold indicated by each operation image frame in the operation image group. When the knife tip area and the food area are adjacent in the same operation image frame, that is, the knife tip area is close to the food area, it can be understood that the operator is performing a disassembly operation on the food being sold at the time corresponding to the operation image frame. Therefore, the server will determine the operation image frame as the operation image frame.

[0114] Since the operation of disassembling the same part of food is usually smooth and continuous, the server can obtain a complete operation video segment by fusing each adjacent operation video frame in chronological order. Then, the server will determine the operation disassembly line corresponding to each operation video segment.

[0115] Furthermore, the aforementioned "determining the operation disassembly line corresponding to each obtained operation video segment" also includes the following steps:

[0116] Pixel acquisition is performed on the operation image frames included in each operation video segment to obtain each blade tip pixel that makes up the blade tip region and each food pixel that makes up the food region.

[0117] All knife tip pixels adjacent to different food pixels are identified as disassembly pixels, and each operation image frame is stacked to obtain a stacked image.

[0118] Connect adjacent pixels of all the disassembled pixels in the stacked image to obtain all operation disassemble lines for the corresponding disassembled operation.

[0119] For example, in this embodiment, the server will acquire pixels of the operation image frames included in each operation video segment, thereby obtaining each blade tip pixel that makes up the blade tip area and each food pixel that makes up the food area.

[0120] Then, the server will identify all the knife tip pixels adjacent to different food pixels as disassembly pixels. This can also be understood as the operation being performed by the operating end based on the disassembly pixels.

[0121] Next, the server stacks each operation image frame to obtain a stacked image. Then, it connects adjacent pixels in all the decomposed pixels in the stacked image to obtain the operation decomposition lines corresponding to the decomposition operation, for example... Figure 3 As shown.

[0122] It can be noted that this embodiment can obtain all the operation disassembly lines of the corresponding disassembly operation through the overlay image, which has a certain degree of completeness.

[0123] Furthermore, the aforementioned "compare the operational disassembly line with the baseline disassembly line based on the degree of overlap, and obtain the disassembly video segment corresponding to each food part based on the comparison result" also includes the following steps:

[0124] Each operational disassembly line is compared with each baseline disassembly line based on the degree of overlap.

[0125] If the overlap between any segment of the operation disassembly line and the reference disassembly line is greater than a preset overlap value, the operation video segment corresponding to the operation disassembly line will be determined as the disassembly video segment of the food part of the corresponding reference disassembly line.

[0126] For example, in this embodiment, the server will compare each operation disassembly line with each reference disassembly line based on the degree of overlap. When the degree of overlap between any operation disassembly line and the reference disassembly line is greater than a preset overlap value, it indicates that the operation disassembly line and the reference disassembly line are relatively consistent. Therefore, the server can determine the operation video segment corresponding to the operation disassembly line as the disassembly video segment of the food part of the corresponding reference disassembly line.

[0127] Step S106 includes the following:

[0128] In response to users selecting and marking the food items sold via VR devices, the system displays the primary and secondary traceability information of the food items sold.

[0129] For example, in this embodiment, when a user selects and marks the food being sold through the VR terminal, the server will display the primary and secondary traceability information of the food, making it easier for the user to understand the various traceability information of the food more clearly.

[0130] Furthermore, the above method also includes the following steps:

[0131] If a user's gaze time on any second blockchain node included in the secondary traceability information exceeds a preset time on the VR device, the sub-model of the part stored in that second blockchain node will be displayed.

[0132] In response to a user's interaction of selecting a part sub-model on a VR device, the model selection area located in the part sub-model is determined based on the interaction trajectory of the selection interaction;

[0133] The selected area of ​​the model is divided into points to obtain the collection points of each model in an array, and the point elevation of each model collection point is obtained.

[0134] The unit price of the food part is retrieved based on the corresponding part sub-model, and the elevation base value obtained by averaging the elevation of the points collected for each model is multiplied with the unit price of the part to obtain the selected price.

[0135] Obtain the center point of the corresponding model selection area, and fill the selected price into the model selection area based on the center point.

[0136] For example, in this embodiment, when a user gazes at any of the second blockchain nodes included in the secondary traceability information for a longer than a preset time through the VR terminal, it can be assumed that the user wants to further understand the traceability information corresponding to the second blockchain node. At this time, the server will display the part sub-model stored in the second blockchain node.

[0137] Since food parts may be large, users may only want to buy a portion of the food. To meet the purchasing needs of different users, the server supports users to select the part sub-model based on their own needs, that is, to select the area they want to buy.

[0138] When a user interacts with a sub-model of a part through the VR device, the server determines the model selection area within the sub-model based on the interaction trajectory corresponding to the user's selection. Then, the server divides the model selection area into points, resulting in an array of model acquisition points. Next, the server obtains the elevation of each model acquisition point.

[0139] The server calculates the average elevation of each data collection point in the model to obtain the elevation baseline. Since the unit price of different food parts is different, the server retrieves the unit price corresponding to the food part based on the sub-model, and then multiplies the elevation baseline by the unit price to obtain the selected price.

[0140] Finally, the server will obtain the center point of the model selection area, and then fill the selection price into the model selection area according to the position of the center point, so that users can quickly know the selection price corresponding to the model selection area and decide whether to purchase it.

[0141] According to the present invention, this invention can obtain primary traceability information by fusing information from various transportation nodes at which the food arrives during transportation. Furthermore, this invention also fuses video segments of disassembly of each food part corresponding to the food, obtained from the data collection terminal, to obtain secondary traceability information. Then, when a user selects and marks the food on a VR device, the server displays both the primary and secondary traceability information, transforming the traditional static presentation of traceability information into an immersive and interactive experience. This allows users to understand the detailed process of the food from transportation to disassembly, greatly enhancing their participation and understanding of food traceability information, optimizing the user experience, and increasing their trust in food safety. The primary traceability information of this invention integrates key node data from the food transportation process, while the secondary traceability information intuitively presents disassembly video segments of each food part through video fusion. The combination of these two provides rich and intuitive evidence for food safety supervision, constructing a multi-dimensional and multi-level traceability system. Significantly improves the accuracy and comprehensiveness of traceability, providing users with more reliable and detailed food traceability information.

[0142] Another embodiment of the present invention provides a food production traceability and anti-counterfeiting system. Figure 4 Its corresponding system block diagram includes:

[0143] The information fusion module is configured to fuse information from each transportation node in the transportation process of the food being sold to obtain primary traceability information.

[0144] The video fusion module is configured to fuse video segments obtained from the acquisition terminal for each part of the food sold to obtain secondary traceability information.

[0145] The information display module is configured to respond to users' selection and marking of the food for sale based on the VR terminal, and to display the primary traceability information and secondary traceability information of the food for sale.

[0146] In the specification provided herein, the algorithms and displays are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used with the examples of this invention. The required structure for constructing such systems is apparent from the above description. Furthermore, this invention is not directed to any particular programming language. It should be understood that the contents of the invention described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing preferred embodiments of the invention.

[0147] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0148] Similarly, it should be understood that, in order to streamline this disclosure and aid in understanding one or more of the various aspects of the invention, in the description of exemplary embodiments of the invention above, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof.

[0149] Those skilled in the art will understand that modules, units, or components of the devices disclosed in the examples herein can be arranged in the devices described in this embodiment, or alternatively, can be located in one or more devices different from the devices in this example. The modules in the foregoing examples can be combined into a single module or, in addition, can be divided into multiple sub-modules.

[0150] Those skilled in the art will understand that the modules in the device of the embodiment can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiment can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components.

[0151] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features included in other embodiments but not others, combinations of features from different embodiments are meant to be within the scope of the invention and form different embodiments.

[0152] Furthermore, some of the embodiments described herein are methods or combinations of method elements that can be implemented by a processor of a computer system or by other means of performing the functions. Therefore, a processor having the necessary instructions for implementing the methods or method elements forms means for implementing the methods or method elements. Furthermore, the elements described herein in the apparatus embodiments are examples of means for implementing the functions performed by elements for the purposes of carrying out the invention.

[0153] As used herein, unless otherwise specified, the use of ordinal numbers such as “first,” “second,” “third,” etc., to describe ordinary objects merely indicates different instances of similar objects and is not intended to imply that the objects being described must have a given order in time, space, ordering, or any other manner.

[0154] Although the invention has been described with respect to a limited number of embodiments, those skilled in the art will understand from the foregoing description that other embodiments are conceivable within the scope of the invention described herein. Furthermore, it should be noted that the language used in this specification has been chosen primarily for readability and edibility purposes, and not for the purpose of explaining or limiting the subject matter of the invention.

Claims

1. A method for tracing and preventing counterfeiting in food production, characterized in that, Includes the following steps: By integrating information from each transportation node in the food sales process, primary traceability information is obtained. The video segments of disassembly of each part of the food sold, obtained from the acquisition terminal, are fused together to obtain secondary traceability information; In response to users selecting and marking the food items for sale via VR devices, the primary and secondary traceability information of the food items for sale will be displayed. By integrating information from each transportation node in the food sales process, primary traceability information is obtained, including: Determine each transfer point in the transportation process corresponding to the food being sold, based on the order of transfer, and obtain the location of each transfer point. When it is determined that the current location of the corresponding food being sold coincides with any other location, a transportation node corresponding to that transfer point is created, and the difference between the comprehensive evaluation value that has an evaluation correlation with that transfer point and the obtained node impact value is calculated to obtain the remaining evaluation value. If the remaining value of the response evaluation is greater than the preset remaining value, the remaining value of the evaluation is determined as the comprehensive evaluation value that has an evaluation correlation with the next forwarding node; When the evaluation remaining value corresponding to the transfer point located at the tail position is greater than the preset remaining value, each transportation node is sorted according to the transfer order, and each first blockchain node is established based on the obtained node sequence. The location of each transportation node is stored in the corresponding first blockchain node, and an interactive retrieval relationship is established between each location and the corresponding evaluation residual value to obtain primary traceability information. The video segments of each disassembly of a food part corresponding to any food sold are obtained from the acquisition terminal and then fused together to obtain secondary traceability information, including: The response terminal performs a disassembly operation on the food being sold, and the control acquisition terminal, including the video terminal, captures video of the disassembly operation and determines the disassembly line based on the obtained total video of the operation. The control acquisition terminal includes an image acquisition terminal to acquire images of the food being sold, and to build a food model corresponding to the food being sold based on the obtained food images; Based on the food model, determine the part sub-models corresponding to different food parts and the baseline disassembly line corresponding to each part sub-model; The operation disassembly line is compared with the baseline disassembly line based on the degree of overlap, and the disassembly video segment corresponding to each food part is obtained based on the comparison result. The videos are sorted according to the order in which each segment is acquired, and second blockchain nodes are established based on the obtained video sequences. Each part of the sub-model is stored in the corresponding second blockchain node, and an interactive retrieval relationship is established between each part of the sub-model and the corresponding disassembled video segment to obtain secondary traceability information.

2. The method according to claim 1, characterized in that, The method further includes: When it is determined that a transfer point located before any transfer point with a transport node does not have a transport node, the corresponding transfer point without a transport node is identified as a jump node, and jump nodes in adjacent positions are grouped into the same jump group. Obtain the jump reason for each jump group and store the jump reason in the blockchain node corresponding to the transfer point adjacent to the jump node at the end of each jump group.

3. The method according to claim 1, characterized in that, Establish an interactive retrieval relationship between the location of each point and its corresponding remaining evaluation value, including: Establish evaluation traceability layers that have interactive retrieval relationships with each blockchain node, wherein the evaluation traceability layers include numerical slots for filling the remaining evaluation values ​​and evaluation slots for filling the evaluation reasons; If the remaining value of the evaluation corresponding to any evaluation traceability layer is greater than the preset remaining value, the at least one standard evaluation reason selected by the management terminal from the preset reason library will be filled into the evaluation slot. If the remaining evaluation value of the numerical slot corresponding to any evaluation traceability layer is less than or equal to a preset remaining threshold, obtain the customized evaluation reason composed of voice information and / or text information from the corresponding management terminal, and fill the customized evaluation reason into the evaluation slot.

4. The method according to claim 1, characterized in that, The response terminal performs a disassembly operation on the food being sold, and the control acquisition terminal, including the video terminal, captures video of the disassembly operation. Based on the obtained total video of the operation, the operation disassembly line is determined, including: The response terminal performs a disassembly operation on the food being sold, and the control acquisition terminal, including the video terminal, captures the video of the disassembly operation to obtain the total video of the operation, and performs image recognition on each image frame that makes up the total video of the operation. Based on the recognition results, each operation image frame corresponding to the blade tip area of ​​the indicated operation terminal is aggregated into an operation image group, and the food area indicating the food being sold is determined for each operation image frame in the operation image group. If the blade tip region and the food region are adjacent to each other in the same operation image frame, then the operation image frame is determined as the operation image frame. Frame fusion is performed on each adjacent video frame based on the chronological order of the operation, and the operation decomposition line corresponding to the decomposition operation is determined based on each obtained video segment of the operation.

5. The method according to claim 4, characterized in that, Based on each obtained operation video segment, determine the corresponding operation decomposition line for the decomposition operation, including: Pixel acquisition is performed on the operation image frames included in each operation video segment to obtain each blade tip pixel that makes up the blade tip region and each food pixel that makes up the food region. All knife tip pixels adjacent to different food pixels are identified as disassembly pixels, and each operation image frame is stacked to obtain a stacked image. Connect adjacent pixels of all the disassembled pixels in the stacked image to obtain all operation disassemble lines for the corresponding disassembled operation.

6. The method according to claim 4, characterized in that, The disassembly line is compared with the baseline disassembly line based on the degree of overlap, and the disassembly video segment corresponding to each food part is obtained based on the comparison result, including: Each operational disassembly line is compared with each baseline disassembly line based on the degree of overlap. If the overlap between any segment of the operation disassembly line and the reference disassembly line is greater than a preset overlap value, the operation video segment corresponding to the operation disassembly line will be determined as the disassembly video segment of the food part of the corresponding reference disassembly line.

7. The method according to claim 1, characterized in that, The method further includes: If a user's gaze time on any second blockchain node included in the secondary traceability information exceeds a preset time on the VR device, the sub-model of the part stored in that second blockchain node will be displayed. In response to a user's interaction of selecting a part sub-model on a VR device, the model selection area located in the part sub-model is determined based on the interaction trajectory of the selection interaction. The selected area of ​​the model is divided into points to obtain the collection points of each model in an array, and the point elevation of each collection point is obtained. The unit price of the food part is retrieved based on the corresponding part sub-model, and the elevation base value obtained by averaging the elevation of the points collected for each model is multiplied with the unit price of the part to obtain the selected price. Obtain the center point of the corresponding model selection area, and fill the selected price into the model selection area based on the center point.

8. A food production traceability and anti-counterfeiting system, characterized in that, include: The information fusion module is configured to fuse information from each transportation node in the transportation process of the food being sold to obtain primary traceability information. The video fusion module is configured to fuse video segments obtained from the acquisition terminal for each part of the food sold to obtain secondary traceability information. The information display module is configured to respond to users' selection and marking of the food for sale based on the VR terminal, and to display the primary traceability information and secondary traceability information of the food for sale; By integrating information from each transportation node in the food sales process, primary traceability information is obtained, including: Determine each transfer point in the transportation process corresponding to the food being sold, based on the order of transfer, and obtain the location of the point corresponding to each transfer point; When it is determined that the current location of the corresponding food being sold coincides with any other location, a transportation node corresponding to that transfer point is created, and the difference between the comprehensive evaluation value that has an evaluation correlation with that transfer point and the obtained node impact value is calculated to obtain the remaining evaluation value. If the remaining value of the response evaluation is greater than the preset remaining value, the remaining value of the evaluation is determined as the comprehensive evaluation value that has an evaluation correlation with the next forwarding node; When the evaluation remaining value corresponding to the transfer point located at the tail position is greater than the preset remaining value, each transportation node is sorted according to the transfer order, and each first blockchain node is established based on the obtained node sequence. The location of each transportation node is stored in the corresponding first blockchain node, and an interactive retrieval relationship is established between each location and the corresponding evaluation residual value to obtain primary traceability information. The video segments of each disassembly of a food part corresponding to any food sold are obtained from the acquisition terminal and then fused together to obtain secondary traceability information, including: The response terminal performs a disassembly operation on the food being sold, and the control acquisition terminal, including the video terminal, captures video of the disassembly operation and determines the disassembly line based on the obtained total video of the operation. The control acquisition terminal includes an image acquisition terminal to acquire images of the food being sold, and to build a food model corresponding to the food being sold based on the obtained food images; Based on the food model, determine the part sub-models corresponding to different food parts and the baseline disassembly line corresponding to each part sub-model; The operation disassembly line is compared with the baseline disassembly line based on the degree of overlap, and the disassembly video segment corresponding to each food part is obtained based on the comparison result. The videos are sorted according to the order in which each segment is acquired, and second blockchain nodes are established based on the obtained video sequences. Each part of the sub-model is stored in the corresponding second blockchain node, and an interactive retrieval relationship is established between each part of the sub-model and the corresponding disassembled video segment to obtain secondary traceability information.

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