Starch inventory dynamic optimization and quality traceability system and method

Abstract

The invention relates to the technical field of starch inventory dynamic optimization, and discloses a starch inventory dynamic optimization and quality traceability system, which comprises a batch identification and data acquisition module for generating a unique RFID tag for each batch of starch and acquiring the production time, moisture value and detection report data of the batch of starch; the block chain evidence storage platform is used for receiving and storing data associated with the unique RFID tag generated for each batch of starch; and the inventory positioning and visualization module tracks the three-dimensional coordinates of the tray for storing starch in real time through the UWB positioning technology, and dynamically generates a visual inventory stockpiling thermodynamic diagram based on the coordinates. Each batch of starch is endowed with a unique RFID tag through the batch identification and data acquisition module, and key data is uploaded to the block chain evidence storage platform, so that the starch batch data cannot be tampered and full-link traceability is realized, and the accuracy and efficiency of quality traceability are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of starch inventory dynamic optimization, in particular to a starch inventory dynamic optimization and quality traceability system and method. BACKGROUND

[0002] In the field of starch warehouse management, traditional inventory management mainly relies on manual recording, barcode scanning or basic warehouse management system (WMS) for operation. Its core process includes the following aspects: in the warehouse link, the production information, quantity, etc. of the starch batch are manually recorded, and the data is input through paper documents or simple systems; inventory management relies on manual counting or regular barcode scanning to confirm the inventory location, and the out-of-stock sequence is determined by experience, and quality traceability is performed; when quality problems occur, paper records or scattered system data need to be traced back manually to check the source of raw materials, production batch and flow path; abnormal processing, when a deteriorated or unqualified batch is found, the inventory information is checked manually one by one, the related batches are located and recall is organized.

[0003] In the prior art, the production data and inventory flow records of the starch batch are scattered and stored, and are prone to errors caused by manual input, data tampering or system compatibility problems, resulting in information gaps. When it is necessary to trace the quality source of a batch, it is difficult to quickly associate the raw material suppliers, production links and downstream flow information, the traceability period is long and the accuracy is low, which is difficult to meet the requirements of food safety or production compliance SUMMARY

[0004] In view of the deficiencies of the prior art, the present application provides a starch inventory dynamic optimization and quality traceability system and method, which solves the problems of lack of batch quality traceability capability in traditional starch warehouse, high deviation rate of first-in first-out inventory, and difficulty in timely locating and recalling abnormal deteriorated batches.

[0005] To achieve the above purpose, the present application realizes the following technical scheme: a starch inventory dynamic optimization and quality traceability system, comprising: A batch identification and data acquisition module generates a unique RFID tag for each batch of starch, and acquires the production time, moisture value and detection report data of the batch of starch; A blockchain storage platform receives and stores the data associated with the unique RFID tag generated for each batch of starch; An inventory positioning and visualization module uses UWB positioning technology to track the three-dimensional coordinates of the tray storing the starch in real time, and dynamically generates a visual inventory stacking heat map based on the coordinates; An intelligent out-of-stock optimization module constructs a multi-objective optimization model, combines the remaining time of the shelf life of the starch batch, the inventory location moving cost, the order urgency and other parameters, and generates a priority sequence of the starch out-of-stock; The quality traceability and abnormality processing module can automatically freeze the associated inventory based on the smart contract when a batch of starch is detected to be abnormal, and can trace all associated batches of the same source along the blockchain node in reverse.

[0006] Through the above technical means: through the batch identification and data acquisition module, a unique RFID tag is generated for each batch of starch and key data such as production time is collected, and after encryption, the data is uploaded to the blockchain storage platform to realize non-tamperable storage, the UWB positioning technology of the inventory positioning and visualization module is used to obtain the three-dimensional coordinates of the tray and generate an inventory stacking heat map, which intuitively presents the inventory distribution, a multi-objective optimization model is constructed through the intelligent warehouse optimization module, and a warehouse priority sequence is generated in combination with parameters such as the remaining shelf life, and the quality traceability and abnormality processing module can automatically freeze the associated inventory based on the smart contract when an abnormal batch is detected, and can trace all associated batches of the same source along the blockchain in reverse, forming a whole process from data collection and storage, inventory positioning and visualization, intelligent warehouse optimization to abnormality traceability and processing.

[0007] Preferably, the workflow of the data acquisition module includes: generating a unique RFID tag according to the production information of the starch batch; binding the generated RFID tag with the corresponding batch of starch; encrypting the collected starch batch data; uploading the encrypted data to the blockchain storage platform.

[0008] Preferably, the encryption process includes: classify the collected starch batch data, eliminate invalid information, unify the data format, and ensure data integrity; generate public and private keys using an asymmetric encryption algorithm, the public key is used for data encryption, and the private key is used for decryption and permission management; use the generated public key to perform encryption operation on the preprocessed starch batch data, and convert the original data into ciphertext that cannot be directly read; perform integrity check on the encrypted ciphertext, generate a hash value of the ciphertext through a hash algorithm, and ensure that the data has not been tampered with during encryption; upload the encrypted ciphertext and the corresponding hash value to the blockchain storage platform, and further ensure data security by using the distributed storage characteristics of the blockchain.

[0009] Preferably, the workflow of the inventory positioning and visualization module includes: The UWB base station deployed in the warehouse communicates with the UWB tag on the tray to obtain the position signal of the tray in real time; the real-time three-dimensional coordinates of the tray are calculated by a triangular positioning algorithm based on the position signal obtained by the UWB positioning unit; and the inventory stacking heat map is dynamically generated and updated based on the three-dimensional coordinates of the tray and the inventory quantity information of each batch of starch to show the inventory distribution.

[0010] Preferably, the calculation process of the three-dimensional coordinates comprises: Based on the position signal, the straight-line distance from the UWB tag to each UWB base station participating in positioning is calculated; A three-dimensional coordinate system is established with the warehouse space as a reference, the fixed coordinates of each UWB base station are substituted into the triangular positioning algorithm, and the corresponding spatial distance equation is constructed based on the distance from the tray to each base station; The real-time coordinate value of the tray in the three-dimensional coordinate system is obtained by solving the equation set, and the real-time three-dimensional coordinates of the tray are calculated.

[0011] Preferably, the generation process of the heat map comprises: The real-time three-dimensional coordinates of each tray output by the three-dimensional coordinate calculation unit are received to determine the specific position of each tray in the warehouse space; the starch batch information corresponding to each tray is associated, and the inventory quantity data of each batch is extracted; Based on the actual warehouse space, the virtual grid units are divided according to the preset accuracy, the three-dimensional coordinates are corresponded to the specific grid, and the warehouse space is digitally segmented; For each grid unit, the total amount of starch inventory carried by all trays in the grid is summarized, and the inventory quantity is converted into a heat value; According to the heat value of the grid unit and the corresponding visual identifier, an initial inventory stacking heat map is rendered in the three-dimensional space model.

[0012] Preferably, the working process of the intelligent outbound optimization module comprises: The remaining time of the shelf life of each batch of starch, the moving cost from the current inventory location to the outbound port, and the urgency parameter of each order are obtained in real time; A multi-objective optimization mathematical model containing starch parameters is constructed to reduce the first-in-first-out execution deviation rate, reduce the inventory moving cost, and improve the order response speed; The multi-objective optimization mathematical model is solved, and the outbound sequence of each batch of starch is sorted to generate an outbound priority sequence.

[0013] Preferably, the construction process of the mathematical model comprises: According to the three objectives of reducing the first-in-first-out execution deviation rate, reducing the inventory moving cost, and improving the order response speed, a quantifiable mathematical function expression is constructed respectively; Extracting starch batch parameters from the system and standardizing the same dimension; Explicit boundary constraints of the model ensure the feasibility of the solution; The three objective functions are integrated into a single comprehensive objective function by using the weighted summation method.

[0014] Preferably, the workflow of the quality traceability and abnormality processing module comprises: Receiving the quality detection results of the starch batch, and triggering the abnormality response mechanism when an abnormal batch is detected; After the abnormality response mechanism is triggered, all associated starch batches with the same origin as the abnormal batch are traced back along the node information of the raw material source and the production process stored in the blockchain, and the tracing results are fed back to the system.

[0015] Preferably, a starch inventory dynamic optimization and quality traceability method comprises: A unique RFID tag is generated for each batch of starch, and the production time, moisture value and detection report data of the batch of starch are collected; Receiving and storing the data associated with the unique RFID tag generated for each batch of starch; The three-dimensional coordinates of the tray storing the starch are tracked in real time through the UWB positioning technology, and a visual inventory stacking heat map is dynamically generated based on the coordinates; A multi-objective optimization model is constructed, and the remaining time of the shelf life of the starch batch, the inventory location moving cost, the order urgency and other parameters are combined to generate a starch out-of-stock priority sequence; When a batch of starch is detected to be abnormal, the associated inventory is automatically frozen based on the smart contract, and all associated batches of the same raw material are traced back along the blockchain nodes.

[0016] The present application provides a starch inventory dynamic optimization and quality traceability system and method. The following beneficial effects are provided: 1、In the present application, the batch identification and data acquisition module assigns a unique RFID tag to each batch of starch and uploads the key data to the blockchain storage platform, realizing the non-tamperability and full-link traceability of the starch batch data, and improving the accuracy and efficiency of quality traceability.

[0017] 2、In the present application, the UWB positioning unit, three-dimensional coordinate calculation unit and stacking heat map generation unit of the inventory positioning and visualization module track the tray position in real time and dynamically generate a heat map, and the multi-objective optimization model and out-of-stock priority sorting unit of the intelligent out-of-stock optimization module are combined to accurately plan the out-of-stock sequence, greatly reducing the execution deviation rate of the first-in-first-out inventory.

[0018] 3、The quality traceability and abnormality processing module in the application has an abnormality detection unit and a blockchain reverse tracing unit, when a batch is detected to be abnormal, the associated inventory is automatically frozen by the smart contract, and all associated batches of the same source raw material are quickly traced, which significantly shortens the positioning and recall response time of the abnormal deteriorated batch and reduces the abnormal influence range.

[0019] 4、The application realizes automation and intelligentization of starch inventory management through the cooperative operation of each module, reduces manual intervention in manual counting, data entry and warehouse decision-making, effectively reduces manual management cost, and improves the accuracy of inventory turnover and order response speed. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 It is a system architecture diagram of the application; Figure 2 It is a data acquisition module workflow diagram of the application; Figure 3 It is a warehouse positioning and visualization module workflow diagram of the application; Figure 4 It is a smart warehouse optimization module workflow diagram of the application; Figure 5 It is a quality traceability and abnormality processing module workflow diagram of the application; Figure 6 It is a method flow diagram of the application. DETAILED DESCRIPTION

[0021] The technical solutions of the application will be described clearly and completely in combination with the drawings in the specification. Obviously, the described embodiments are only part of the embodiments of the application, not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor belong to the protection scope of the application.

[0022] Embodiment one: Please refer to the attached Figure 1 -attached Figure 6 The starch inventory dynamic optimization and quality traceability system provided by the embodiment of the application comprises: a batch identification and data acquisition module, which generates a unique RFID tag for each batch of starch, and acquires production time, moisture value and detection report data of the batch of starch; A blockchain storage platform receives and stores the data associated with the unique RFID tag generated for each batch of starch; A warehouse positioning and visualization module, which dynamically generates a visual inventory stacking heat map based on the three-dimensional coordinates of the tray storing the starch in real time through UWB positioning technology; The intelligent warehouse-out optimization module constructs a multi-objective optimization model, combines parameters such as the remaining time of the shelf life of the starch batch, the inventory location movement cost, and the order urgency, and generates a starch warehouse-out priority sequence. The quality traceability and abnormality processing module, when a batch of starch is detected to be abnormal, automatically freezes the associated inventory based on the smart contract, and reverses the traceability of all associated batches of the same source material along the blockchain node.

[0023] Specifically, according to the layout of the starch warehouse, a specified number of UWB base stations are preset in the starch warehouse, UWB tags and RFID tags are installed on the pallets of each batch of starch, a local storage platform is built by deploying a blockchain node server, which is used to store the data associated with the unique RFID tag of the starch, and the system also configures a warehouse management terminal including an RFID card reader, a data entry interface and a visual display screen. The basic parameters such as the three-dimensional space coordinate range of the warehouse, the location of the warehouse outlet, the unit moving cost, the order emergency quantification standard and the starch shelf life threshold are entered into the system. When the batch of starch is completed and ready to be warehoused, the system automatically generates a unique RFID tag (including batch code, production line number, etc. Unique identifier), the tag is physically bound to the batch of starch through the RFID card reader, and the production time, moisture detection value, third-party quality detection report and other data of the batch are manually entered through the warehouse management terminal or automatically obtained from the production system. The system checks the data format, such as whether the moisture value is within the standard range, to ensure integrity. UWB positioning receives real-time signal propagation time data through wireless communication between the base station and the pallet UWB tag, and converts the signal time into real-time coordinates of the pallet in the three-dimensional coordinate system of the warehouse based on the triangular positioning algorithm. At the same time, the pallet coordinates are mapped to the preset warehouse grid model, the inventory quantity in each grid is summarized, and color identification is given according to the quantity gradient to dynamically generate a visual stacking heat map. When the pallet coordinates change due to warehousing, unloading or shifting, the system updates the heat map in real time. The intelligent unloading optimization module extracts the production time of each batch of starch from the blockchain storage platform, obtains the pallet coordinates from the inventory positioning visualization module, and obtains the order urgency from the order system to reduce the FIFO deviation rate, reduce the moving cost and improve the order response speed. As the goal, a mathematical model containing the above parameters is constructed, the model is solved by genetic algorithm, the unloading sequence of each batch is generated, and the priority sequence is pushed to the warehouse management terminal. The warehouse personnel executes the unloading operation according to the sequence, and the system synchronously updates the inventory data of the blockchain storage platform. The quality traceability and abnormal handling module receives the quality abnormal signal from the laboratory or the sampling link, triggers the smart contract, and the system freezes the associated inventory data of the batch and the same raw material and the same production batch in the blockchain based on the contract rules. Marked as unloading, and push the warning to the management terminal. Trace the raw material supplier, production time, detection record and warehousing and circulation path of the abnormal batch along the blockchain node to generate a list of associated batches, and organize targeted recall according to the list and the heat map positioning of the physical inventory. At the same time, the recall record is written into the blockchain. Through the above technical means, the system realizes the intelligentization of the whole process from warehousing to unloading and from normal management to abnormal handling of the starch inventory, and enhances the traceability of the warehouse.

[0024] Further, the workflow of the data acquisition module includes: Generating a unique RFID tag according to the production information of the batch of starch; and the generated RFID tag is bound to the corresponding batch of starch; The collected starch batch data is encrypted; The encrypted data is uploaded to the blockchain storage platform.

[0025] Specifically, based on the production information of the starch batch such as production date, shift, and production line number, a unique identifier ID is generated through a hash algorithm, and the ID is written into the RFID tag to ensure that the tag information corresponds to the batch. When warehousing, the tag is scanned through an RFID reader, and the quantity information of the batch is also recorded. The system automatically associates the tag ID with the batch production data and stores it, forming a tag-batch mapping relationship table. The collected batch data such as production time, moisture value, and detection report number is standardized in format, and repeated or invalid fields are removed. An asymmetric encryption algorithm is used to encrypt the data. The encrypted ciphertext and data hash value are uploaded to the blockchain storage platform through HTTPS protocol. After the platform node verifies that the hash value is correct, the data is written into the block and synchronized to all nodes, completing the storage.

[0026] Further, the encryption process includes: The collected starch batch data is classified, invalid information is removed, the data format is unified, and the data integrity is ensured. Public and private keys are generated using an asymmetric encryption algorithm. The public key is used for data encryption, and the private key is used for decryption and permission management. The generated public key is used to encrypt the preprocessed starch batch data, converting the original data into ciphertext that cannot be directly read. The integrity of the encrypted ciphertext is checked. The hash value of the ciphertext is generated through a hash algorithm to ensure that the data has not been tampered with during encryption. The encrypted ciphertext and the corresponding hash value are uploaded to the blockchain storage platform, and the distributed storage characteristics of the blockchain are used to further ensure data security.

[0027] Specifically, the data is divided into basic information such as production time, batch number and quality information such as moisture value, test report and management information such as storage time, operator, invalid information such as empty value, format error data is removed, the test report number format is checked by regular expression, all data is converted to JSON format, the field is unified, the asymmetric key pair is generated by the key management system, the public key is stored in the data acquisition module for encryption, the private key is stored in the offline encryption device, only authorized administrators can call, the public key Pub is used to encrypt the preprocessed JSON data, and the ciphertext is generated, for large capacity data such as test report PDF, symmetric encryption + public key encryption symmetric key is adopted, which is specifically to encrypt PDF with AES-256 algorithm first, then encrypt AES key with Pub, finally upload ciphertext + encrypted AES key, calculate SHA-256 hash value of encrypted ciphertext, upload hash value and ciphertext to blockchain, the blockchain node recalculates the hash value after receiving, if it is consistent with the uploaded value, it is confirmed that the data has not been tampered with, and the verified ciphertext and hash value are written into the blockchain block, the block contains the hash value of the previous block, forming a chain structure, which is distributed and stored in each node, ensuring that single point data damage does not affect the integrity of the whole data, and the above technical means ensures the confidentiality, integrity and tamper resistance of the data, providing a reliable data security foundation for the whole inventory optimization and traceability system.

[0028] Further, the workflow of the inventory positioning and visualization module includes: The UWB base station deployed in the warehouse communicates with the UWB tag on the tray to obtain the position signal of the tray in real time; the real-time three-dimensional coordinates of the tray are calculated by the triangular positioning algorithm based on the position signal of the tray obtained by the UWB positioning unit; and the inventory stacking heat map is dynamically generated and updated based on the three-dimensional coordinates of the tray and the inventory quantity information of each batch of starch to show the inventory distribution.

[0029] Specifically, the UWB tag on the starch tray periodically sends pulse signals, the UWB base station in the warehouse receives the signals and records the signal arrival time, the straight-line distance from the tag to each base station is calculated based on the signal propagation time between the UWB base station and the tag, the triangular positioning algorithm is used to construct a system of three linear equations combined with the fixed coordinates of the base station, and the real-time three-dimensional coordinates of the tray are obtained by solving, the total inventory quantity in each grid is calculated by dividing the warehouse space into 1m x 1m x 0.5m virtual grids, and the color is assigned according to the quantity interval, such as blue for 0-200kg, yellow for 201-500kg, and red for more than 501kg, of course, the colors corresponding to different intervals can be adjusted according to the set threshold, and the heat map is rendered in the three-dimensional model, and when the tray moves and the coordinates change, the quantity and color of the corresponding grid are updated in real time.

[0030] Further, the calculation process of three-dimensional coordinates includes: Based on the position signal, the straight-line distance from the UWB tag to each UWB base station participating in positioning is calculated; A three-dimensional coordinate system is established with the warehouse space as a reference, the fixed coordinates of each UWB base station are substituted into the triangular positioning algorithm, and the corresponding spatial distance equations are constructed according to the distances of the pallets to each base station; The real-time coordinate values of the pallets in the three-dimensional coordinate system are obtained by solving the equation set, and the real-time three-dimensional coordinates of the pallets are calculated.

[0031] Specifically, three UWB base stations (denoted as base station 1, base station 2, and base station 3) are fixedly installed in the warehouse space, and their three-dimensional coordinates are pre-measured by a calibration tool and input into the system, such as the coordinates of base station 1 (x1, y1, z1) = (0, 0, 0), the coordinates of base station 2 (x2, y2, z2) = (50, 0, 0), and the coordinates of base station 3 (x3, y3, z3) = (0, 50, 0). The positioning is in meters. In this way, a three-dimensional space with the base stations as the space can be constructed. One UWB tag is installed on each pallet storing starch. The tag periodically sends pulse signals at a pre-set frequency. When the UWB tag sends a signal, the time stamp of the sending time is recorded synchronously. The three base stations receive the pulse signals sent by the tag and record the respective receiving time stamps. The UWB positioning unit obtains the receiving time stamps from the base stations through a wired interface, calculates the signal propagation time, and since the propagation speed of the UWB signal in the air is close to the speed of light, the system pre-sets the signal propagation speed as 3×10 8 m / s. According to the distance = speed × time, the straight-line distances of the tag to the three base stations are calculated respectively, the distance of the tag to base station 1 d1 = 3×10 8 m / s×t1, the distance of the tag to base station 2 d2 = 3×10 8 m / s×t2, and the distance of the tag to base station 3 d3 = 3×10 8 m / s×t3. Among them, the distance value needs to be positive and not exceed the maximum communication distance of the UWB tag. The three distances need to satisfy the spatial geometric constraints, such as the sum of any two distances being greater than the third distance, to ensure compliance with the triangular inequality and exclude measurement errors. If a certain distance is invalid, such as exceeding the range or violating the geometric constraints, the system automatically discards the data and uses the average value of the last three valid measurements to replace it to ensure the accuracy of subsequent coordinate calculation. The lower left corner of the warehouse floor is taken as the origin (0, 0, 0) to establish a three-dimensional rectangular coordinate system, the X-axis is the length direction of the warehouse, the Y-axis is the width direction, and the Z-axis is the height direction. The coordinates of the three base stations are (x1, y1, z1), (x2, y2, z2), and (x3, y3, z3) respectively. According to the spatial two-point distance formula, the equation set is constructed as (x-x1) 2 +(y-y1) 2 +(z-z1) 2= d1 2 , (x - x2) 2 + (y - y2) 2 + (z - z2) 2 = d2 2 , (x - x3) 2 + (y - y3) 2 + (z - z3) 2 = d3 2 , the equation group is simplified by elimination method to obtain a linear equation, and the base station coordinates and distance values are substituted to obtain the tray coordinates (x, y, z).

[0032] Further, the generation process of the heat map includes: Receiving the real-time three-dimensional coordinates of each tray output by the three-dimensional coordinate calculation unit, and determining the specific position of each tray in the warehouse space; associating the starch batch information corresponding to each tray, and extracting the inventory quantity data of each batch; Based on the actual warehouse space, the virtual grid unit is divided according to the preset accuracy, and the three-dimensional coordinates are mapped to the specific grid to digitally segment the warehouse space; For each grid unit, the total amount of starch inventory carried by all trays in the grid is summarized, and the inventory quantity is converted into a heat value; According to the heat value of the grid unit and the corresponding visual identifier, an initial inventory stacking heat map is rendered in the three-dimensional space model.

[0033] Receiving the tray coordinates output by the three-dimensional coordinate calculation unit such as (15.3, 8.7, 2.1), mapping the physical location description such as A zone 1 column 4 layer through the warehouse space partitioning rules such as X∈[0,20) for A zone, Y∈[0,10) for column 1, and Z∈[2,3) for the 4th layer, querying the blockchain storage platform through the RFID tag ID to obtain the inventory quantity of the corresponding batch, establishing a coordinate-quantity mapping table, and dividing the virtual grid according to the preset accuracy, such as 1m in the X direction, 1m in the Y direction, and 0.5m in the Z direction, each grid is assigned a unique identifier, such as X 15-16 , Y 8-9 , Z 2.0-2.5 , wherein X 15-16 represents the X-axis of the grid in the three-dimensional coordinate system, which usually corresponds to the length direction of the warehouse, covering the coordinate range of 15m to 16m, Y 8-9 represents the Y-axis of the grid, which usually corresponds to the width direction of the warehouse, covering the coordinate range of 8m to 9m, and Z 2.0-2.5 represents the Z-axis of the grid, which usually corresponds to the height direction such as the number of shelves, covering the coordinate range of 2.0m to 2.5m, and according to all tray coordinates, it is classified into the corresponding grid, and the total inventory quantity in each grid is summarized, such as X 15-16 , Y8-9 , Z 2.0-2.5 , X 15-16 The total quantity in the grid is 300 kg, and the quantity is converted into a thermal value, such as quantity / maximum inventory threshold x 100, and the threshold is set to 1000 kg, then 300 kg corresponds to a thermal value of 30, in the three-dimensional warehouse model, each grid is assigned a color according to the thermal value, such as thermal value 0-30 for blue, 31-60 for yellow, and 61-100 for red, forming an initial stacking thermal map, and realizing interactive viewing on the management terminal.

[0034] Further, the workflow of the intelligent outbound optimization module includes: Real-time acquisition of the remaining shelf life of each starch batch, the moving cost from the current inventory location to the outbound port, and the urgency parameter of each order; To reduce the first-in-first-out execution deviation rate, reduce inventory moving cost, and improve order response speed, a multi-objective optimization mathematical model containing starch parameters is constructed; Solving the multi-objective optimization mathematical model, sorting the outbound sequence of each starch batch, and generating an outbound priority sequence.

[0035] Specifically, the production time is extracted from the blockchain, the current time-production time is calculated, such as production time 2023-10-01 and current time 2023-10-10, the remaining shelf life is 20 days, the total shelf life is 30 days, based on the tray coordinates and the outbound port coordinates, such as (0, 0, 0), the straight-line distance is calculated, such as 15m, multiplied by the unit moving cost, such as 2 yuan / m, 30 yuan, according to the order required delivery time division, such as 5 levels within 24 hours, 3 levels within 48 hours, and 1 level within 72 hours, to reduce the first-in-first-out deviation rate, reduce the moving cost, and improve the order response speed, a mathematical model is constructed, a genetic algorithm is used to solve the model, and the comprehensive score of each batch is output, the higher the score, the higher the priority, the sequence is sorted from high to low, and an outbound priority sequence is generated, which is pushed to the warehouse terminal in synchronization to guide the outbound operation.

[0036] Further, the construction process of the mathematical model includes: According to the three targets of reducing the first-in-first-out execution deviation rate, reducing the inventory moving cost, and improving the order response speed, respectively, a quantifiable mathematical function expression is constructed; The starch batch parameters are extracted from the system and standardized with the same dimension; The boundary constraints of the model are clearly defined to ensure the feasibility of the solution; The three objective functions are integrated into a single comprehensive objective function using the weighted summation method.

[0037] Specifically, a first-in, first-out (FIFO) execution deviation rate objective function is constructed, which is the proportion of the production time difference between a certain batch and the earliest batch to the total shelf life. The lower the proportion, the smaller the deviation. The formula is as follows: Among them, T i S represents the production time of a certain batch of starch. i This refers to the total shelf life of this batch, T min The production time of the earliest production batch in the inventory; If T i =T min If this batch is the earliest produced batch, then f1 = 0, indicating no deviation. If T i >T min If the production time is later than the earliest batch, then f1>0, indicating a deviation. The larger the value, the more severe the deviation. Construct an objective function for inventory movement costs, where movement cost is the product of the straight-line distance from the pallet to the exit and the cost per unit distance. Lower costs are preferred. The formula is as follows: Where C is the cost per unit distance. This is the straight-line distance from the starch pallet to the outlet. Construct an objective function for order response speed, where response speed is defined as the ratio of urgency level to travel time; the higher the ratio, the faster the response speed. The formula is as follows: Where V is the transport speed, t i The urgency level is K, which represents the time required for the order and the customer's attributes. Since the three objective functions have different dimensions (f1 = proportion, f2 = currency, f3 = ratio), they need to be standardized to the [0,1] interval to eliminate the difference in dimensions. Specifically: For f1, calculate the f1 values ​​for all batches and take the maximum value f. 1ax For f2, calculate the f2 values ​​for all batches and take the maximum value f. 2ax For f3, calculate the f3 values ​​for all batches and take the maximum value f. 3ax Specifically; Among them, the higher the standardized response speed of f3, the better. It is inversely mapped to 1-standardized value and transformed into the minimization objective. To prevent optimization results from exceeding the range of actual inventory and order demand, constraints need to be set: the outbound quantity Qi of a certain batch must not exceed its actual inventory quantity I. i That is, 0≤Q i ≤I i The sum of the outbound quantities of all batches must meet the total order demand D, i.e. n is the number of all batches in stock, the same batch can only be assigned to the current order once, and cannot be repeated, that is, Q i ∈{0}∪[a,I i ]; By assigning different weights to the three objectives, the normalized objective function is integrated into a single function. The weights are set according to the business needs of the starch warehouse, and the sum of the weights is 1. If priority is given to first-in, first-out and reduction of expired loss, f1 ′ weight, w1=0.4, if the operation cost needs to be controlled, f2 ′ weight, w2=0.3, if the order response efficiency needs to be improved, f3 ′ weight, w3=0.3, based on the above data, the single comprehensive objective function is: F=w1×f1 ′ +w2×f2 ′ +w3×f3 ′ ; Through the above process, the model can output the comprehensive objective function value F of each starch batch, and the optimal warehouse-out priority sequence can be generated by sorting F from small to large.

[0038] Further, the workflow of the quality traceability and abnormal handling module includes: Receiving the quality detection results of the starch batches, when an abnormal batch is detected, triggering the abnormal response mechanism; After the abnormal response mechanism is triggered, all associated starch batches with the same origin as the abnormal batch are traced back along the node information of the raw material source and the production process stored in the blockchain, and the tracing results are fed back to the system.

[0039] Specifically, the quality inspection end uploads the detection results through the system interface, the quality traceability and abnormal handling module automatically identifies the abnormal batch, triggers the abnormal response mechanism, and sends warning information to the management terminal, including batch ID, detection value, and locks the warehouse-out permission of the batch. Marked as frozen state in the blockchain, extract the raw material information of the abnormal batch from the blockchain, such as raw material supply ID and raw material batch, trace all production batches of the same raw material batch, query the current inventory location of these production batches, generate an associated batch list, and push the tracing results to the management terminal. The administrator can initiate a freeze instruction through the terminal and generate a recall task sheet for warehouse personnel to execute the recall operation. After the recall is completed, the system writes the processing results to the blockchain to form a closed loop record.

[0040] Embodiment two: A starch inventory dynamic optimization and quality traceability method, comprising: Generating a unique RFID tag for each batch of starch, and collecting the production time, moisture value and detection report data of the batch of starch; Receiving and storing data associated with a unique RFID tag generated for each batch of starch; Real-time tracking the three-dimensional coordinates of the tray storing the starch through UWB positioning technology, and dynamically generating a visual inventory stacking heat map based on the coordinates; Constructing a multi-objective optimization model, combining the remaining shelf life of the starch batch, the inventory location moving cost, the order urgency, and other parameters to generate a starch out-of-warehouse priority sequence; When a batch of starch is detected abnormally, automatically freezing the associated inventory based on the smart contract, and tracing all associated batches of the same source material in reverse along the blockchain nodes.

[0041] Specifically, a unique RFID tag is generated for each batch of starch, and production time, moisture value, and detection report data are collected. The tag is bound to the batch, encrypted, and uploaded to the blockchain storage platform. The blockchain storage platform receives the encrypted data, writes it into the block through the consensus mechanism, forms an unalterable batch data record, communicates with the tag through the UWB base station to obtain the location signal, calculates the three-dimensional coordinates of the tray, associates the batch inventory quantity, dynamically generates and updates the stacking heat map, takes the shelf life remaining time, moving cost, and order urgency parameters of each batch, constructs a multi-objective optimization model, solves and generates an out-of-warehouse priority sequence, receives the quality detection result, when an abnormal batch is detected, triggers an abnormal response mechanism, automatically freezes the associated inventory, traces the same source associated batches in reverse along the blockchain, and feeds back the results.

[0042] Although embodiments of the present application have been shown and described, it is to be understood that various modifications, substitutions, replacements and changes can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A starch inventory dynamic optimization and quality traceability system, characterized in that, Comprise: Batch identification and data collection module, generate a unique RFID tag for each batch of starch, and collect the production time, moisture value and detection report data of the batch of starch; Blockchain storage platform, receive and store the data associated with the unique RFID tag generated for each batch of starch; Inventory positioning and visualization module, real-time track the three-dimensional coordinates of the tray storing starch through UWB positioning technology, and dynamically generate a visual inventory stacking heat map based on the coordinates; Intelligent outbound optimization module, construct a multi-objective optimization model, combine the remaining time of the shelf life of the starch batch, the inventory location moving cost, the order urgency and other parameters to generate a starch outbound priority sequence; Quality traceability and abnormal processing module, when a batch of starch is detected abnormally, automatically freeze the associated inventory based on the smart contract, and trace all associated batches of the same source raw material in reverse along the blockchain nodes.

2. The starch inventory dynamic optimization and quality traceability system of claim 1, wherein, The workflow of the data collection module includes: Generate a unique RFID tag according to the production information of the starch batch; Bind the generated RFID tag with the corresponding batch of starch; Encrypt the collected starch batch data; Upload the encrypted data to the blockchain storage platform.

3. A starch inventory dynamic optimization and quality traceability system according to claim 2, wherein, The encryption process includes: Classify the collected starch batch data, eliminate invalid information, unify the data format, and ensure data integrity; Use asymmetric encryption algorithm to generate public key and private key, public key for data encryption, private key for decryption and permission management; Use the generated public key to perform encryption operation on the preprocessed starch batch data, converting the original data into ciphertext that cannot be directly read; Perform integrity check on the encrypted ciphertext, generate a hash value of the ciphertext through a hash algorithm, and ensure that the data has not been tampered with during encryption; Upload the encrypted ciphertext and the corresponding hash value to the blockchain storage platform, and further ensure data security by using the distributed storage feature of the blockchain.

4. The starch inventory dynamic optimization and quality traceability system of claim 1, wherein, The workflow of the inventory positioning and visualization module includes: Communicate with the UWB tag on the tray through the UWB base station deployed in the warehouse to obtain the position signal of the tray in real time; According to the tray position signal obtained by the UWB positioning unit, calculate the real-time three-dimensional coordinates of the tray through the triangular positioning algorithm; Based on the three-dimensional coordinates of the tray and the inventory quantity information of each batch of starch, dynamically generate and update the inventory stacking heat map to show the inventory distribution.

5. The starch inventory dynamic optimization and quality traceability system of claim 4, wherein, The calculation process of the three-dimensional coordinates includes: Based on the position signal, calculate the straight-line distance from the UWB tag to each participating UWB base station; Establish a three-dimensional coordinate system with the warehouse space as a reference, substitute the fixed coordinates of each UWB base station into the triangular positioning algorithm, and construct the corresponding spatial distance equation according to the distance from the tray to each base station; Solve the equation set to obtain the real-time coordinate value of the tray in the three-dimensional coordinate system, and calculate the real-time three-dimensional coordinates of the tray.

6. The starch inventory dynamic optimization and quality traceability system of claim 1, wherein, The generation process of the heat map includes: Receive the real-time three-dimensional coordinates of each tray output by the three-dimensional coordinate calculation unit to determine the specific position of each tray in the warehouse space; Associate the starch batch information corresponding to each tray, and extract the inventory quantity data of each batch; Based on the actual space of the warehouse, the virtual grid units are divided according to the preset precision, the three-dimensional coordinates are corresponded to the specific grid, and the warehouse space is digitally segmented; For each grid unit, the total amount of starch inventory carried by all the pallets in the grid is summarized, and the inventory amount is converted into a heat value; According to the heat value of the grid unit and the corresponding visual identifier, an initial inventory stacking heat map is rendered in the three-dimensional space model.

7. The starch inventory dynamic optimization and quality traceability system of claim 1, wherein, The workflow of the intelligent outbound optimization module includes: Real-time acquisition of the shelf life remaining time of each starch batch, the moving cost from the current inventory location to the outbound port, and the urgency parameter of each order; To reduce the first-in-first-out execution deviation rate, reduce the inventory moving cost, and improve the order response speed, a multi-objective optimization mathematical model containing starch parameters is constructed; Solving the multi-objective optimization mathematical model, the outbound sequence of each starch batch is sorted, and the outbound priority sequence is generated.

8. The starch inventory dynamic optimization and quality traceability system of claim 7, wherein, The construction process of the mathematical model includes: According to the three targets of reducing the first-in-first-out execution deviation rate, reducing the inventory moving cost, and improving the order response speed, respectively, a quantifiable mathematical function expression is constructed; The starch batch parameters are extracted from the system and standardized with the same dimension; The boundary constraints of the model are clarified to ensure the feasibility of the solution; The three objective functions are integrated into a single comprehensive objective function by using the weighted summation method.

9. The starch inventory dynamic optimization and quality traceability system of claim 1, wherein, The workflow of the quality traceability and abnormality processing module includes: Receive the quality detection results of the starch batch, and when an abnormal batch is detected, trigger the abnormal response mechanism; After the abnormal response mechanism is triggered, all associated starch batches with the same origin as the abnormal batch are traced back along the node information of the raw material source and production process stored in the blockchain, and the tracing results are fed back to the system.

10. A method for dynamic optimization of starch inventory and quality traceability, for use in a system for dynamic optimization of starch inventory and quality traceability according to any one of claims 1-9, characterized in that, It includes: Generate a unique RFID tag for each batch of starch, and collect the production time, moisture value, and detection report data of the batch of starch; Receive and store the data associated with the unique RFID tag generated for each batch of starch; Real-time tracking of the three-dimensional coordinates of the pallets storing the starch through UWB positioning technology, and dynamically generating a visual inventory stacking heat map based on the coordinates; Construct a multi-objective optimization model, combine the shelf life remaining time of the starch batch, the inventory location moving cost, and the order urgency parameter, and generate a starch outbound priority sequence; When a batch of starch is detected to be abnormal, automatically freeze the associated inventory based on the smart contract, and trace back all associated batches of the same raw material along the blockchain nodes.

Images