Kitchen food safety traceability method and system

Abstract

This application provides a method and system for tracing food safety in the kitchen, applied in the field of food safety technology. By acquiring the original traceability identifier and data of the food to be processed, and combining this with processing intent information and real-time collected physical quantity change data, a logical association is established between the internal traceability identifier and the original traceability identifier. This seamlessly connects the processing data of intermediate materials to the original traceability chain, and records a traceability dataset containing key information such as processing time, operators, and processing type, thus forming a complete and traceable data chain covering the material transformation path. This effectively solves the problem of fragmented or ambiguous traceability information during the internal material transformation process in the kitchen in existing technologies, achieving the beneficial effect of complete and accurate traceability from raw materials to intermediate products and then to the final dish.

Description

Technical Field

[0001] This application relates to the field of food safety technology, and in particular to a method and system for tracing food safety in the kitchen. Background Technology

[0002] In the modern food supply chain, establishing a comprehensive information recording system is crucial for ensuring safety and efficiency. However, existing traceability systems face challenges when dealing with the complex material transformations within the kitchen: when a single raw ingredient is broken down, processed into various intermediate products, or mixed with other ingredients in the kitchen, the original traceability information chain is easily broken.

[0003] Specifically, existing models are highly effective at handling linear information chains for whole, individually packaged items. However, the situation is far more complex in central kitchens or restaurant kitchens. For example, once a batch of half a beef shank enters the kitchen, it may be cut into steaks, ground into minced meat, diced for soup, generating various intermediate products, or even mixed with other ingredients. The initial total batch identification code can no longer accurately correspond to the final dish, leading to an ambiguous traceability chain.

[0004] New mechanisms need to be established within the kitchen to track these transitions, such as recording the processing personnel, time, raw material batches, and output weight, and generating internal identification codes for new materials while linking them to the original batches. However, the busy kitchen environment often makes data entry an additional burden, leading to omissions or errors, such as mixing different batches of raw materials but only recording one batch, or forgetting to create an internal code for new materials.

[0005] Such internal management chaos has serious consequences in food safety incidents. Suppose a customer falls ill after eating a hamburger. While the traceability system may show that the original batch of beef for the patty met standards before entering the kitchen, missing internal records may prevent the system from tracing the patty's specific preparation time, storage conditions, or the personnel handling it. If problematic batches were mixed in but not recorded, the investigation will be led in the wrong direction, failing to find the true cause and implicating innocent suppliers, thus undermining the credibility of the traceability system.

[0006] Therefore, existing technologies urgently need to be improved to address the aforementioned problems. Summary of the Invention

[0007] In view of the shortcomings of the prior art, this application provides a kitchen food safety traceability method and system, which solves the problem of broken or ambiguous traceability information during the material transformation process in the kitchen, and has the advantage of realizing complete and accurate traceability from raw materials to intermediate products and then to the final dish.

[0008] Firstly, a method for tracing food safety in the kitchen, the method comprising the following steps: S1: Obtain the original traceability identifier of the food to be processed, and retrieve the traceability data corresponding to the original traceability identifier; S2: Obtain processing intention information for the food to be processed, wherein the processing intention information is used to characterize the processing type of material transformation; S3: Real-time acquisition of physical quantity change data of the food to be processed during the processing process; S4: Based on the physical quantity change data and the processing intention information, determine the quantitative attributes of the intermediate materials generated during processing, and generate a unique internal traceability identifier for the intermediate materials; S5: Establish a logical association between the internal traceability identifier and the original traceability identifier, so as to connect the processing data of the intermediate material to the traceability chain corresponding to the original traceability identifier; S6: Record a traceability dataset containing processing time information, operator identity information, processing type information, and the logical relationships mentioned above, forming a traceable data chain covering the material conversion path.

[0009] Furthermore, step S1 includes: S11: Obtain real-time image information of the processing station; S12: Perform feature recognition on the real-time image information to identify the action characteristics of the operator towards the food to be processed; S13: Match the corresponding processing type from the preset processing action library according to the action characteristics to obtain the processing intention information.

[0010] Furthermore, the physical quantity change data includes the weight reduction of the food to be processed on the processing equipment; step S3 includes: S31: Obtain the preset conversion coefficient corresponding to the processing type; S32: Calculate the output weight of the intermediate material based on the weight reduction and the preset conversion coefficient, and use the output weight as the quantitative attribute.

[0011] Furthermore, in step S4, generating a unique internal traceability identifier for the intermediate material includes the following steps: S41: Obtain the device identifier and current timestamp information for the device performing the processing operation; S42: Combine and encode the original traceability identifier, the device identifier, and the timestamp information to generate the internal traceability identifier.

[0012] Furthermore, step S5 includes: S51: Using the original traceability identifier as an index key, create an associated entry in the traceability database that corresponds to the internal traceability identifier; S52: Write the processing data into the associated entry and establish a logical pointer to the traceability data corresponding to the original traceability identifier to form a chain data structure from raw materials to intermediate materials.

[0013] Furthermore, step S6 includes the following: S7: Monitor the warehousing trigger signal of the intermediate material; S8: In response to the warehousing trigger signal, synchronize the traceability dataset to the cloud traceability platform and update the current inventory status of the intermediate materials.

[0014] Furthermore, in step S8, updating the current inventory status of the intermediate material includes the following steps: S81: Write the quantitative attributes of the intermediate materials into the inventory management database and bind them with the internal traceability identifier; S82: Establish an association index between inventory data and the traceability dataset through the internal traceability identifier, so as to synchronously display the material conversion path of the intermediate material on the inventory query interface.

[0015] Furthermore, step S2 is followed by: S21: Monitor the allocation trigger command for the material to be processed, wherein the allocation trigger command includes the target number of allocation portions and the corresponding allocation weight of each portion; S22: Based on the allocation trigger instruction, generate a unique sub-traceability identifier for each allocated material, and establish an inheritance association between the sub-traceability identifier and the original traceability identifier.

[0016] Furthermore, step S6 includes: S61: Based on the allocated weight, the traceability data of the material to be processed is diverted to generate a diversion traceability record that corresponds one-to-one with each of the sub-traceability identifiers; S62: The processing data of the intermediate materials are respectively attached to the corresponding diversion traceability records to form a multi-branch parallel traceability path.

[0017] Secondly, a kitchen food safety traceability system, the system being used to implement the steps of any of the above methods, the system comprising: Raw data acquisition module: acquires the original traceability identifier of the food to be processed, and retrieves the traceability data corresponding to the original traceability identifier; Processing intent acquisition module: Acquires processing intent information for the food to be processed, wherein the processing intent information is used to characterize the processing type of material transformation; Physical quantity acquisition module: collects real-time data on changes in the physical quantities of the food to be processed during the processing process; Quantitative attribute determination module: Based on the physical quantity change data and the processing intention information, determine the quantitative attributes of the intermediate materials generated during processing, and generate a unique internal traceability identifier for the intermediate materials; Logical association module: Establishes a logical association between the internal traceability identifier and the original traceability identifier, so as to connect the processing data of the intermediate material to the traceability chain corresponding to the original traceability identifier; Traceability Record Module: Records a traceability dataset containing processing time information, operator identity information, processing type information, and the logical relationships mentioned above, forming a traceable data chain covering the material conversion path.

[0018] Beneficial Effects: The kitchen food safety traceability method and system proposed in this application can accurately determine the quantitative attributes of intermediate materials and generate unique internal traceability identifiers by acquiring the original traceability identifiers and traceability data of the food to be processed, combined with processing intention information and real-time collected physical quantity change data. More importantly, by establishing a logical association between the internal traceability identifier and the original traceability identifier, the processing data of intermediate materials is seamlessly connected to the original traceability chain, and a traceability dataset containing key information such as processing time, operators, and processing type is recorded, thereby forming a complete traceable data chain covering the material transformation path. This effectively solves the problem of broken or ambiguous traceability information in the internal material transformation process of the kitchen in the prior art, and achieves the beneficial effect of complete and accurate traceability from raw materials to intermediate products and then to the final dish. Attached Figure Description

[0019] Figure 1 This is a flowchart of a kitchen food safety traceability method proposed in this application.

[0020] Figure 2 This is a structural diagram of a kitchen food safety traceability system proposed in this application.

[0021] Figure 3 This is a schematic diagram of a kitchen food safety traceability system proposed in this application.

[0022] Labeling Explanation: 201, Raw Data Acquisition Module; 202, Processing Intent Acquisition Module; 203, Physical Quantity Acquisition Module; 204, Quantitative Attribute Determination Module; 205, Logical Association Module; 206, Traceability Record Module. Detailed Implementation

[0023] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. The components of the embodiments of this application described and marked in the accompanying drawings can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0024] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0025] To effectively connect the linear traceability information of the external supply chain with the non-linear material transformation process within the kitchen—a complex internal production unit—and avoid information gaps in the traceability chain at the critical final link, this application provides a kitchen food safety traceability method. The core of this method lies in deeply integrating the data capture process into the physical processing flow. Intelligent equipment automatically senses material transformation and establishes data associations, thereby forming a traceable data chain covering the entire material transformation path.

[0026] Please refer to Figure 1 A method for tracing food safety in the kitchen, the method includes the following steps: S1: Obtain the original traceability identifier of the food to be processed and retrieve the traceability data corresponding to the original traceability identifier; S2: Obtain processing intent information for the food to be processed. The processing intent information is used to characterize the processing type of material transformation. S3: Real-time collection of physical quantity changes in the food to be processed during the processing process; S4: Based on the physical quantity change data and processing intention information, determine the quantitative attributes of the intermediate materials generated during processing, and generate a unique internal traceability identifier for the intermediate materials; S5: Establish a logical relationship between the internal traceability identifier and the original traceability identifier to connect the processing data of intermediate materials to the traceability chain corresponding to the original traceability identifier; S6: Record a traceability dataset containing processing time information, operator identity information, processing type information, and logical relationships to form a traceable data chain covering the material conversion path.

[0027] The method works by using a processing station that integrates intelligent identification, sensing, and interaction functions to transform the data recording process, which originally required manual intervention, into an embedded process that is automatically completed along with the physical processing actions. When a raw material enters the processing stage inside the kitchen, its identity information, physical transformation process, and information on newly generated intermediate materials are captured and correlated in real time and accurately, thereby seamlessly extending the external linear traceability chain to every minute branch inside the kitchen.

[0028] The following is a detailed explanation of the method's execution process through a specific application scenario. Suppose a restaurant kitchen receives a batch of half a scallop weighing fifty kilograms. The packaging has a QR code as the original traceability identifier, which encodes a unique batch number, such as SUPPLIER-BEEF-20231027-001.

[0029] First, the process involves obtaining the original traceability identifier for the food to be processed and retrieving its traceability data. An operator, such as a chef, places the half-steak on a specially designed smart processing table. An industrial-grade image acquisition device, such as a fixed barcode scanner, is integrated beneath or to the side of this table. When the beef is placed in the designated area, the image acquisition device is automatically triggered, scanning and decoding the QR code on the packaging to obtain the original traceability identifier SUPPLIER-BEEF-20231027-001. Upon receiving this identifier, the microcontroller inside the processing table initiates a data request to the restaurant's central data management platform via the kitchen's wireless or wired network. The central data management platform then accesses an external supply chain traceability cloud platform through an application programming interface (API) to retrieve all upstream traceability data associated with this identifier. This data may include information about the cattle farm, feed records, quarantine certificates, slaughter date, cutting workshop information, and temperature monitoring records throughout the cold chain transportation process. The retrieved traceability data will be clearly displayed on the integrated screen of the processing station for the chef to check and confirm that the source of the ingredients is reliable and meets the receiving standards.

[0030] Next, it is necessary to obtain the processing intent information for the food to be processed. Processing intent information refers to the preset logical tags for physically altering the physical form of the food. During execution, the specific type of material transformation is determined by matching the extracted voice feature vectors or screen click commands with a preset processing logic mapping table. The mapping table is pre-stored in local memory and contains estimated loss rates for different processing types, as well as prefix rules for subsequent identifier generation. Through this mapping, unstructured operational actions are transformed into structured business logic that can be recognized by a computer, providing a decision-making basis for subsequent quantitative calculations.

[0031] In one specific embodiment, this step is completed through a human-machine interface. The display screen of the intelligent processing station is an industrial-grade capacitive touchscreen with a specially designed user interface featuring large buttons for easy operation by chefs wearing gloves. The interface presets various common processing options, such as cutting steak, mincing meat, and deboning. Before starting processing, the chef simply touches the corresponding button on the screen, for example, cutting steak. The microcontroller then records this selection as the processing intention information for this operation.

[0032] In another embodiment, to further free up the chef's hands, voice interaction can be used. The processing station integrates a high signal-to-noise ratio microphone array and a localized offline voice recognition module. This offline design effectively copes with the noisy environment of a kitchen and avoids recognition lag caused by network latency. The chef can directly issue voice commands, such as "start cutting the steak." The voice recognition module converts the captured voice signal into a text command, which is then parsed by the microcontroller to determine the processing intention as cutting the steak. After confirming the intention, a confirmation message pops up on the display screen, and the chef can initiate the processing process by confirming again with voice or by tapping the screen.

[0033] Furthermore, in some more automated implementations, the process of acquiring processing intent information can be completed without the operator actively declaring it. In this case, the steps for acquiring processing intent information include: S11: Obtain real-time image information of the processing station; S12: Perform feature recognition on real-time image information to identify the action characteristics of operators towards the food to be processed; S13: Match the corresponding processing type from the preset processing action library based on the action characteristics to obtain processing intention information.

[0034] In one specific implementation, a depth camera is mounted above the intelligent processing table to acquire real-time image information of the processing station, including 3D point cloud data and color information. The processing unit performs feature recognition on the real-time image information and analyzes the operator's hand joint movement trajectory using a skeleton extraction algorithm. When it detects that the operator is holding a knife with a specific geometric shape and performing up-and-down cutting movements at a specific frequency, the operator's action features regarding the food to be processed are extracted. Based on the action features, the corresponding processing type is matched from a preset processing action library. If the feature matching degree exceeds a preset threshold, it is automatically identified as a slicing processing logic, and the processing intention information is obtained. By using environmental perception technology to obtain the material transformation direction in real time, the processing intention is captured seamlessly while ensuring food hygiene.

[0035] After obtaining the processing intention, the method enters the stage of real-time acquisition of physical quantity changes in the food to be processed during processing. A high-precision weighing sensor array is embedded beneath the surface of the intelligent processing table, employing, for example, multiple resistance strain gauge weighing modules, to ensure uniform and accurate measurement of the weight of materials across the entire table, with an accuracy down to the gram level. Before the chef begins processing, the weighing sensors record the initial weight of half a slab of beef, for example, 50 kilograms. When the chef begins cutting, removing a steak from the half slab and moving it out of the weighing area, the weighing sensors monitor the real-time decrease in the total weight of the materials on the table. The microcontroller continuously reads the output data from the weighing sensors at a high frequency and calculates the real-time weight reduction. For example, when the first steak is cut, the table weight becomes 49.75 kilograms, and the physical quantity change data is a weight reduction of 0.25 kilograms.

[0036] Subsequently, based on the collected data on changes in physical quantities and the acquired processing intent information, the quantitative attributes of the intermediate material generated are determined, and a unique internal traceability identifier is generated for this intermediate material. In this example, combining the weight reduction of 0.25 kg and the processing intent of cutting the steak, it is determined that a steak with a quantitative attribute of 250 grams has been generated. This 250 grams is recorded as the core quantitative attribute of this intermediate material. Next, this newly generated steak needs to be assigned a completely new, unique identifier within the kitchen. The generation rules for this internal traceability identifier are designed to include rich contextual information to enhance its traceability.

[0037] To improve accuracy when determining the quantitative properties of intermediate materials, more refined computational models can be introduced. In some processing steps, material transformation is not lossless. For example, deboning or mincing meat results in some loss. In this case, the physical quantity change data can specifically refer to the amount of weight reduction of the food being processed on the processing equipment, and the steps to determine the quantitative properties include: S31: Obtain the preset conversion coefficient corresponding to the processing type; S32: Calculate the output weight of intermediate materials based on the weight reduction and the preset conversion coefficient, and use the output weight as a quantitative attribute. For example, a conversion factor is pre-set for each processing type. Cutting steak results in minimal waste, so its conversion factor might be set at 99.5%. However, for mincing meat, since some meat remains in the equipment, the conversion factor might be set at 98%. When a chef selects mincing as the processing intention, and the weighing sensor detects a one-kilogram reduction in the original beef weight, the weight of the intermediate material (minced meat) is not simply recorded as one kilogram. Instead, one kilogram is multiplied by the 98% conversion factor to calculate 0.98 kilograms as the final output weight of this batch of minced meat. This output weight is recorded as a quantitative attribute of the intermediate material, making inventory and cost accounting more accurate.

[0038] Specifically, the process of generating a unique internal traceability identifier for intermediate materials includes: S41: Obtain the device identifier and current timestamp information for the device performing the processing operation; S42: Combine and encode the original traceability identifier, device identifier, and timestamp information to generate an internal traceability identifier.

[0039] The equipment identifier is a unique number for this intelligent processing station, such as A01. The timestamp information is provided by the processing station's internal real-time clock module, accurate to the second, such as 10:00:05 AM on October 27, 2023. Then, the original traceability identifier, equipment identifier, and timestamp information are combined and encoded to generate an internal traceability identifier. For example, the original identifier SUPPLIER-BEEF-20231027-001, equipment identifier A01, and timestamp 20231027100005 can be concatenated, possibly with a serial number added to prevent multiple materials from being generated within the same second. The final result is an internal traceability identifier such as A01-SUPPLIER-BEEF-20231027-001-20231027100005-01. This newly generated identifier is not only unique but also includes the material source, processing location, and processing time.

[0040] After assigning identity and quantifiable attributes to intermediate materials, a logical association needs to be established between the internal traceability identifier and the original traceability identifier. This logical association is achieved by constructing node connections in a non-relational database. The original traceability identifier serves as the root node, and the newly generated internal traceability identifier is attached as a child node. Each node stores a unique identifier pointing to its parent node, thus forming a logical chain for reverse tracing. When full-chain traceability is required, by traversing the parent node identifiers in the child nodes, it is possible to trace back to the original supply source level by level. This tree-like data structure not only supports one-to-many material decomposition scenarios but also supports many-to-one mixed processing scenarios through multiple parent node identifiers, ensuring the integrity of the traceability path during complex transformation processes.

[0041] Specifically, the process of establishing logical relationships includes: S51: Use the original traceability identifier as the index key to create an associated entry in the traceability database that corresponds to the internal traceability identifier; S52: Write the processing data into the associated entries and establish a logical pointer to the traceability data corresponding to the original traceability identifier to form a chain data structure from raw materials to intermediate materials.

[0042] The database can be deployed locally on a kitchen server or in the cloud. Using SUPPLIER-BEEF-20231027-001 as the lookup key will create a new sub-entry or related table entry under its corresponding record. Then, the newly generated internal traceability identifier and other processing data are written into this related entry, and a logical pointer is established pointing to the traceability data corresponding to the original traceability identifier. In this way, a clear chain-like data structure from raw materials to intermediate materials is formed. During a query, the internal identifier of the new steak can be used to find its related entry, and then the logical pointer can be used to trace back to the complete traceability information of the original half of beef. This data structure naturally supports a one-to-many decomposition relationship, that is, the same original beef can be associated with multiple intermediate materials of different times, types, and weights, such as steaks, minced meat, etc., each with its own independent internal traceability identifier and processing record.

[0043] Finally, all relevant information from the entire processing event is integrated and recorded into a complete traceability dataset. This dataset includes processing time information accurate to the second, operator identification information obtained through chefs swiping employee cards or fingerprint recognition, previously determined processing type information for cutting the steak, and, crucially, the logical relationship between the internal traceability identifier and the original traceability identifier. All this information is solidified into a single data record and stored in the traceability database, thus forming an immutable, traceable data chain covering the entire transformation path from raw materials to intermediate materials.

[0044] Through the aforementioned steps, the method proposed in this application transforms the complex material transformation process within the kitchen into a series of automated, structured data records. In the event of a food safety issue, investigators can trace back from the final consumed dish, such as a steak. By using the steak's internal traceability identifier associated with the dish, they can immediately determine when, by which chef, on which equipment, from which batch of raw beef it was cut, and its precise weight. Combined with the original traceability identifier, they can further trace back to all upstream links in the beef supply chain. This eliminates the black box of traceability information within the kitchen, achieving true end-to-end food safety traceability.

[0045] Once intermediate materials have been processed and an internal traceability identifier has been generated, their lifecycle does not end; they are typically stored in a warehouse or refrigerated container. To link traceability information with inventory management, after recording the traceability dataset, this method may further include: S7: Monitors the trigger signal for intermediate materials entering the warehouse; S8: In response to the inbound trigger signal, synchronize the traceability dataset to the cloud traceability platform and update the current inventory status of intermediate materials.

[0046] Inbound trigger signals can be generated in several ways. For example, a chef divides prepared steaks into plates with blank QR code labels, prints labels containing internal traceability identifiers for each steak on a printer at the smart processing station, and affixes them. When the chef places the plates into the designated refrigerated cabinet, scanning the barcode reader at the cabinet door constitutes an inbound trigger signal. Alternatively, the plates themselves may have reusable RFID tags embedded, and an RFID reader is installed at the refrigerated cabinet door. When a plate passes through, its tag is automatically read, also generating an inbound trigger signal. Once this signal is detected, the local system immediately uploads the newly generated complete traceability dataset for this batch of steaks to the cloud-based traceability platform via the network, enabling data backup and sharing. Simultaneously, the local or cloud-based inventory management system receives the instruction and updates the inventory status.

[0047] Specifically, updating the current inventory status of intermediate materials includes the following steps: S81: Write the quantitative attributes of intermediate materials into the inventory management database and bind them with the internal traceability identifier; S82: Establish an association index between inventory data and traceability dataset through internal traceability identifiers to synchronously display the material conversion path of intermediate materials on the inventory query interface.

[0048] As a specific implementation method, when materials enter the storage stage, inventory management data is updated synchronously by monitoring the entry trigger signal of intermediate materials. The quantitative attributes of intermediate materials are written into the inventory management database and bound to internal traceability identifiers. An association index is established between inventory data and the traceability dataset through the internal traceability identifiers, allowing the material conversion path of intermediate materials to be displayed synchronously on the inventory query interface. When managers click on a specific batch of materials on the inventory terminal, the interface dynamically loads the entire process flow from raw material procurement to kitchen processing based on the internal association index. The material conversion path is displayed in the form of a timeline, detailing the responsible person, processing equipment, and weight change at each processing node.

[0049] This approach achieves deep integration of physical asset management and digital traceability information, providing real-time data support for food safety early warning. Specifically, a new inventory record is added to the inventory management system, containing a steak with an internal traceability identifier of A01-SUPPLIER-BEEF-20231027-001-20231027100005-01, weighing 250 grams, stored in refrigerator number one. Furthermore, this inventory record is linked or indexed to the previously recorded complete traceability dataset via the internal traceability identifier. Thus, when warehouse staff query the inventory in refrigerator number one on the inventory management interface, they can not only see the quantity of this batch of steaks but also click on details to directly view all historical information, such as which batch of beef shank it originated from, who processed it, and when, achieving deep integration of physical inventory and traceability information.

[0050] Besides scenarios involving changes in material form, another common operation in kitchens is the simple allocation of materials. For example, a large box of 50 kilograms of potatoes needs to be divided into ten portions of 5 kilograms each, and distributed to ten different preparation areas. In this scenario, the material itself does not undergo a qualitative change, but the whole is being broken down. To independently track each allocated portion of material, after obtaining the processing intention information, the method may also include: S21: Monitor the allocation trigger command for the material to be processed. The allocation trigger command includes the target number of allocation portions and the corresponding allocation weight for each portion. S22: Based on the allocation trigger instruction, generate a unique sub-traceability identifier for each allocated material and establish an inheritance relationship between the sub-traceability identifier and the original traceability identifier.

[0051] When a chef needs to portion ingredients, they can select the portioning operation on the smart processing station interface and input the target number of portions and the weight of each portion. In a specific embodiment, suppose the chef needs to portion 50 kilograms of potatoes into ten portions, each weighing 5 kilograms. The input target number of portions would be 10, and the weight of each portion would be 5 kilograms. Upon receiving this portioning trigger command, a unique sub-traceability identifier will be generated for each of the ten portions of potatoes, such as POTATO-BATCH-001-01, POTATO-BATCH-001-02, and so on, up to POTATO-BATCH-001-10. Simultaneously, an inheritance relationship is established in the database between these ten sub-traceability identifiers and the original traceability identifier POTATO-BATCH-001 of the original whole box of potatoes.

[0052] Accordingly, special handling is required for this distribution situation when recording the source tracing dataset. The steps for recording the source tracing dataset include: S61: Based on the allocated weight, the traceability data of the materials to be processed is diverted to generate diversion traceability records that correspond one-to-one with each sub-traceability identifier; S62: Load the processing data of intermediate materials into the corresponding diversion traceability records to form a multi-branch parallel traceability path.

[0053] This means that the traceability information of the original ingredients to be processed will be copied or linked to a new branch traceability record corresponding to the target number of portions, with each record corresponding to a sub-traceability identifier. Subsequently, if the POTATO-BATCH-001-01 potato in preparation area one is processed into mashed potatoes, then the processing data for the mashed potatoes and the newly generated internal traceability identifier will be attached to the POTATO-BATCH-001-01 branch path, without affecting the traceability records of the other nine portions of potatoes. In this way, multiple parallel and independently traceable traceability paths are formed starting from a single source, greatly improving the precision of traceability management.

[0054] Please refer to Figure 2 , Figure 3 This application also provides a kitchen food safety traceability system, which is used to implement the steps of any of the above methods, and the system includes: Raw data acquisition module 201: Acquires the original traceability identifier of the food to be processed and retrieves the traceability data corresponding to the original traceability identifier; Processing Intent Acquisition Module 202: Acquires processing intent information for the food to be processed. The processing intent information is used to characterize the processing type of material transformation. Physical quantity acquisition module 203: Real-time acquisition of physical quantity changes in the food to be processed during the processing process; Quantitative attribute determination module 204: Based on the physical quantity change data and processing intention information, determine the quantitative attributes of the intermediate materials generated during processing, and generate a unique internal traceability identifier for the intermediate materials; Logical association module 205: Establishes a logical association between the internal traceability identifier and the original traceability identifier, so as to connect the processing data of intermediate materials to the traceability chain corresponding to the original traceability identifier; Traceability Record Module 206: Records traceability datasets containing processing time information, operator identity information, processing type information, and logical relationships, forming a traceable data chain covering the material conversion path.

[0055] The kitchen food safety traceability system provided in this application is based on the fact that through the collaborative work of various functional modules, it can realize the automated and refined management of the material transformation process in the kitchen and the seamless connection of the data chain.

[0056] Specifically, the raw data acquisition module 201 can be configured to interact with food packaging or labels via scanning devices (such as barcode / QR code scanners, RFID readers) to automatically read raw traceability identifiers. In a preferred embodiment, this module can be a standalone hardware device or a software component integrated into the processing equipment, responsible for communicating with external data sources (such as supplier databases, cloud-based traceability platforms) to acquire and verify raw traceability data.

[0057] The processing intent acquisition module 202 can be implemented as a user interface, allowing operators to manually select the processing type; alternatively, it can interface with the production planning system to automatically receive preset processing instructions. Furthermore, this module can automatically recognize the operator's processing actions or verbal commands through image or voice recognition technology, thereby determining the processing intent information.

[0058] The physical quantity acquisition module 203 may include various sensors, such as high-precision weighing sensors, temperature sensors, and humidity sensors, which are integrated on or near processing equipment (such as cutting boards, containers, ovens, etc.). This module is responsible for converting the analog signals acquired by the sensors into digital signals and transmitting them to the central processing unit for further analysis via wired or wireless means.

[0059] The quantification attribute determination module 204 can be a software program running on a processor. It receives data from the physical quantity acquisition module and information from the processing intent acquisition module, and executes a preset algorithm (e.g., calculating the output weight based on the weight reduction and conversion factor). Simultaneously, this module is responsible for generating a unique internal traceability identifier according to predetermined encoding rules (e.g., combining timestamps, device IDs, and original traceability identifier fragments).

[0060] The logical association module 205 can be a specific functional module in a database management system, responsible for creating and maintaining association records between different traceability identifiers in the traceability database. This module ensures that the traceability chain from raw ingredients to intermediate materials can be seamlessly connected by defining primary key-foreign key relationships or establishing logical pointers, forming a clear parent-child or inheritance relationship.

[0061] The traceability record module 206 can be a data storage and management unit responsible for the structured storage of all key processing information (including time, personnel, type, material associations, etc.) generated by other modules. This module can be configured to store data on a local server or synchronize data to a cloud-based traceability platform via a network interface to achieve long-term data preservation and remote access.

[0062] Traditional food traceability systems, when dealing with the complex material transformation processes within the kitchen, suffer from a lack of refined management and automated recording methods for material decomposition and mixing. This leads to a tendency for the original traceability information chain to break down, making it difficult to accurately track the material transformation path. For example, existing technologies often rely on manual recording, which is susceptible to operator negligence or errors, resulting in the loss of critical transformation information and consequently affecting the efficiency and accuracy of traceability in the event of a food safety incident.

[0063] To address this, the kitchen food safety traceability system of this application introduces a series of functional modules to achieve automated, real-time, and refined management of the material transformation process within the kitchen, significantly improving the continuity and reliability of traceability. Compared to traditional solutions that rely on manual recording or can only track linear supply chains, this system can automatically acquire original traceability information, collect real-time data on changes in physical quantities during processing, intelligently identify processing intentions, and automatically generate unique internal traceability identifiers for newly generated intermediate materials. More importantly, through the logical association module 205, the system can automatically establish a correlation between intermediate materials and original ingredients, seamlessly connecting processing data to the original traceability chain, thereby forming a complete, continuous, and detailed traceable data chain for material transformation. This systematic solution effectively avoids information omissions and errors caused by manual operation, ensures the integrity of the traceability chain during material transformation, greatly improves the efficiency and accuracy of food safety management, and provides consumers with more reliable food safety guarantees.

[0064] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for tracing food safety in the kitchen, characterized in that, The method includes the following steps: S1: Obtain the original traceability identifier of the food to be processed, and retrieve the traceability data corresponding to the original traceability identifier; S2: Obtain processing intention information for the food to be processed, wherein the processing intention information is used to characterize the processing type of material transformation; S3: Real-time acquisition of physical quantity change data of the food to be processed during the processing process; S4: Based on the physical quantity change data and the processing intention information, determine the quantitative attributes of the intermediate materials generated during processing, and generate a unique internal traceability identifier for the intermediate materials; S5: Establish a logical association between the internal traceability identifier and the original traceability identifier, so as to connect the processing data of the intermediate material to the traceability chain corresponding to the original traceability identifier; S6: Record a traceability dataset containing processing time information, operator identity information, processing type information, and the logical relationships mentioned above, forming a traceable data chain covering the material conversion path.

2. The method for tracing food safety in a kitchen according to claim 1, characterized in that, Step S1 includes: S11: Obtain real-time image information of the processing station; S12: Perform feature recognition on the real-time image information to identify the action characteristics of the operator towards the food to be processed; S13: Match the corresponding processing type from the preset processing action library according to the action characteristics to obtain the processing intention information.

3. The kitchen food safety traceability method according to claim 1, characterized in that, The physical quantity change data includes the weight reduction of the food to be processed on the processing equipment; step S3 includes: S31: Obtain the preset conversion coefficient corresponding to the processing type; S32: Calculate the output weight of the intermediate material based on the weight reduction and the preset conversion coefficient, and use the output weight as the quantitative attribute.

4. The kitchen food safety traceability method according to claim 1, characterized in that, In step S4, generating a unique internal traceability identifier for the intermediate material includes the following steps: S41: Obtain the device identifier and current timestamp information for the device performing the processing operation; S42: Combine and encode the original traceability identifier, the device identifier, and the timestamp information to generate the internal traceability identifier.

5. A method for tracing food safety in the kitchen according to claim 1, characterized in that, Step S5 includes: S51: Using the original traceability identifier as an index key, create an associated entry in the traceability database that corresponds to the internal traceability identifier; S52: Write the processing data into the associated entry and establish a logical pointer to the traceability data corresponding to the original traceability identifier to form a chain data structure from raw materials to intermediate materials.

6. The kitchen food safety traceability method according to claim 1, characterized in that, Step S6 and following it include: S7: Monitor the warehousing trigger signal of the intermediate material; S8: In response to the warehousing trigger signal, synchronize the traceability dataset to the cloud traceability platform and update the current inventory status of the intermediate materials.

7. A method for tracing food safety in the kitchen according to claim 1, characterized in that, In step S8, updating the current inventory status of the intermediate material includes the following steps: S81: Write the quantitative attributes of the intermediate materials into the inventory management database and bind them with the internal traceability identifier; S82: Establish an association index between inventory data and the traceability dataset through the internal traceability identifier, so as to synchronously display the material conversion path of the intermediate material on the inventory query interface.

8. A method for tracing food safety in a kitchen according to claim 1, characterized in that, Step S2 is followed by: S21: Monitor the allocation trigger command for the material to be processed, wherein the allocation trigger command includes the target number of allocation portions and the corresponding allocation weight of each portion; S22: Based on the allocation trigger instruction, generate a unique sub-traceability identifier for each allocated material, and establish an inheritance association between the sub-traceability identifier and the original traceability identifier.

9. A method for tracing food safety in a kitchen according to claim 8, characterized in that, Step S6 includes: S61: Based on the allocated weight, the traceability data of the material to be processed is diverted to generate a diversion traceability record that corresponds one-to-one with each of the sub-traceability identifiers; S62: The processing data of the intermediate materials are respectively attached to the corresponding diversion traceability records to form a multi-branch parallel traceability path.

10. A kitchen food safety traceability system, characterized in that, The system includes: Raw data acquisition module: acquires the original traceability identifier of the food to be processed, and retrieves the traceability data corresponding to the original traceability identifier; Processing intent acquisition module: Acquires processing intent information for the food to be processed, wherein the processing intent information is used to characterize the processing type of material transformation; Physical quantity acquisition module: collects real-time data on changes in the physical quantities of the food to be processed during the processing process; Quantitative attribute determination module: Based on the physical quantity change data and the processing intention information, determine the quantitative attributes of the intermediate materials generated during processing, and generate a unique internal traceability identifier for the intermediate materials; Logical association module: Establishes a logical association between the internal traceability identifier and the original traceability identifier, so as to connect the processing data of the intermediate material to the traceability chain corresponding to the original traceability identifier; Traceability Record Module: Records a traceability dataset containing processing time information, operator identity information, processing type information, and the logical relationships mentioned above, forming a traceable data chain covering the material conversion path.

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