Barreled drinking water two-dimensional code whole life cycle traceability management method

Abstract

The application discloses a barreled drinking water two-dimensional code whole life cycle traceability management method and particularly relates to the technical field of traceability management. The application analyzes a recovery point backlog risk value, a load risk value and a cost loss value, combines a preset allowable value and a weighted logic calculation scheduling frequency coefficient, and then matches a differentiated scheduling interval through a mapping rule, so that high inventory point backlog and low inventory point invalid transportation are avoided, risks and costs are balanced, enterprise management efficiency is improved, and the problem that the prior art adopts fixed period scheduling without considering recovery point differences is solved.

Description

Technical Field

[0001] This invention relates to the field of traceability management technology, and more specifically, to a method for full lifecycle traceability management of bottled drinking water using QR codes. Background Technology

[0002] With the continuous expansion of the bottled drinking water market and the increasing health awareness of consumers, the market's demand for drinking water safety and full-chain transparency is becoming increasingly urgent. As a core recyclable resource in the industry, the management efficiency and traceability integrity of empty bottles directly affect the operating costs of enterprises and the trust of consumers.

[0003] However, the bottled drinking water industry still faces significant technical challenges in the full lifecycle management of empty bottles, making it difficult to meet the needs of actual operation and market supervision. These challenges are specifically reflected in the following aspects:

[0004] The rigid scheduling frequency of recycling is out of touch with reality. The industry generally adopts a fixed-cycle scheduling mode, which does not take into account the inventory growth rate of different recycling points and the differences in distance from the production end. Using the same scheduling frequency leads to the contradiction that some recycling points frequently accumulate empty barrels and occupy space, while other recycling points have multiple empty transports. This not only affects the consumer's experience of returning barrels, but also significantly increases the company's operating costs.

[0005] In addition, although existing technologies have enabled traceability of basic product information through QR codes, they have not deeply linked QR codes with dynamic data such as real-time inventory of empty barrels, production demand, and transportation trajectory. Furthermore, they lack route and frequency collaborative optimization schemes based on intelligent algorithms, making it difficult to solve the problems of high transportation costs, low scheduling efficiency, and non-closed-loop traceability.

[0006] To address this, a QR code-based full lifecycle traceability management method for bottled drinking water has been introduced. Summary of the Invention

[0007] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a method for full lifecycle traceability management of bottled drinking water using QR codes.

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] The method for full lifecycle traceability management of bottled drinking water using QR codes includes:

[0010] S1: Generate a unique QR code for each empty bucket. After the empty bucket is put into use for the first time, bind the information of each circulation of the empty bucket to the QR code.

[0011] S2: Obtain full-chain data for different empty barrel recycling points by collecting circulation information, including daily recycling volume, daily inventory volume, and transportation distance;

[0012] S3: Perform comprehensive analysis and processing of the entire chain data of different empty bucket recycling points, determine the scheduling frequency coefficient of each recycling point, and convert the scheduling frequency coefficient of each recycling point into a recycling scheduling interval based on the pre-built mapping rules between the scheduling frequency coefficient and the recycling scheduling interval, which serves as the recycling scheduling interval for each recycling point.

[0013] S4: Based on the recycling scheduling interval of each recycling point, select the recycling points that need to be scheduled for the current date as scheduling stations, calculate the estimated total recycling volume of each scheduling station to determine the number of vehicles to be scheduled. If the number of vehicles to be scheduled is greater than one, then formulate a combination plan for each group of routes based on the location of the production end and the scheduling station. After multi-dimensional evaluation of each group of route combination plans, determine the recycling route plan for the current date.

[0014] Specifically, in step S3, the data of the entire chain is comprehensively analyzed and processed;

[0015] Set the time zone for collecting data across the entire chain;

[0016] The average amount of recycling at each recycling point within the collection time zone is calculated and divided by the temporary storage capacity of the recycling point to determine the backlog risk value of each recycling point.

[0017] The average daily inventory at each collection point within the collection time zone is calculated and divided by the temporary storage capacity of the collection point to determine the load risk value of each collection point.

[0018] The distance between each recycling point and the production end is obtained. The distance is combined with the fuel consumption of the transport vehicle to calculate the fuel cost as the transportation cost. The transportation cost is divided by the average recycling volume of each recycling point to obtain the cost loss value.

[0019] Specifically, in step S3, the scheduling frequency coefficient of each recycling point is determined;

[0020] The scheduling frequency coefficient of each recycling point is determined by comprehensively processing the backlog risk value, load risk value, and cost loss value using a weighted calculation logic in conjunction with a preset reference dataset.

[0021] Specifically, the reference dataset includes allowable backlog risk values, allowable load risk values, and expected cost loss values.

[0022] Specifically, the mapping rules in step S3;

[0023] By setting the scheduling frequency coefficients and corresponding coefficient intervals, each coefficient interval corresponds to a recycling scheduling interval.

[0024] Specifically, in step S4, the estimated total recovery amount for each dispatch station is calculated to determine the required number of dispatch vehicles;

[0025] For the current date's dispatch station, retrieve the average amount of data collected at each collection point within the collection time zone for each dispatch station, and use this as the average amount of data collected at each dispatch station.

[0026] The average recovery amount at each dispatch station is summed to obtain the estimated total recovery amount;

[0027] The estimated total amount of recycling is compared with the maximum number of bins that can be carried per vehicle. If the estimated total amount of recycling is greater than the maximum number of bins that can be carried per vehicle, then it is determined that the number of vehicles to be dispatched is greater than one.

[0028] Specifically, in step S4, a multi-dimensional evaluation process is performed on each group of route combination schemes;

[0029] Identify the recovery points with higher scheduling frequency coefficients in each route combination scheme as high-priority points;

[0030] Extract the estimated total time, completion time of high priority points, and total transportation cost from each group of route combination schemes;

[0031] The estimated total time, completion time of high priority points, and total transportation cost of each route combination scheme are standardized, and the scheme evaluation coefficient of each route combination scheme is obtained by comprehensive processing using weighted calculation logic.

[0032] Specifically, step S4 involves determining the recycling route plan for the current date;

[0033] The route combination scheme with the higher evaluation coefficient is selected as the recovery route scheme for the current date.

[0034] The technical effects and advantages of this invention are as follows:

[0035] (1) This invention analyzes the risk value of backlog at recycling points, the risk value of load, and the cost loss value, and calculates the scheduling frequency coefficient by combining the preset allowable value and weighted logic. Then, it matches the differentiated scheduling interval through mapping rules to avoid backlog at high inventory points and ineffective transportation at low inventory points, balances risk and cost, improves enterprise management efficiency, and solves the problem that the existing technology uses fixed-cycle scheduling and does not consider the differences of recycling points.

[0036] (2) The present invention first filters the stations for the day according to the scheduling interval, calculates the estimated total amount of recycling to determine the number of vehicles; in the multi-vehicle scenario, it combines the production end and the station location to generate route combinations, extracts three types of indicators: total time consumption, time consumption for high priority points to complete, and total transportation cost, calculates the scheme evaluation coefficient, selects the optimal route, and solves the problem that the existing technology relies on experience to plan routes and cannot combine the recycling scheduling frequency of different recycling points to plan the optimal route.

[0037] (3) This invention generates a unique QR code for each empty bucket, binds the factory information and each flow data, and enables full-process traceability through scanning or associated devices. This not only makes it easier for enterprises to locate abnormal links, but also shows consumers key traceability information and enhances consumer trust. Attached Figure Description

[0038] Figure 1 This is a flowchart of the method for full life-cycle traceability management of bottled drinking water using QR codes, as described in this invention. Detailed Implementation

[0039] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] like Figure 1 As shown, the QR code-based full lifecycle traceability management method for bottled drinking water includes:

[0041] S1: Generate a unique QR code for each empty can. The QR code is associated with the empty can's unique code and manufacturing information (including manufacturer, production date, and material specifications). After the empty can is used for the first time, the QR code is used to bind the information of each circulation of the empty can through IoT devices (such as barcode scanners and RFID readers) to ensure that the data of the empty can is traceable throughout its entire life cycle.

[0042] The QR code uses the MD5 encryption algorithm to ensure uniqueness, and the QR code label is made of PET waterproof and tear-resistant material with a wear-resistant coating on the surface.

[0043] S2: Collect full-chain data from different empty barrel recycling points, including daily recycling volume, daily inventory volume, and transportation distance;

[0044] After the empty barrels are put into use for the first time, each time they are transferred (such as from the manufacturer to the filling plant, or from the recycling point to the warehouse), the QR code must be scanned by a barcode scanner or RFID reader to record dynamic information such as the transfer node (location), transfer time, operator, and associated order number, and updated to the database in real time to form the transfer log of the QR code.

[0045] The recycling, inventory, and transportation data collected by IoT devices must be accurately matched with empty bins by "scanning QR codes" or "associating QR codes with devices".

[0046] Recycling volume statistics: Staff at recycling points use smart terminals to scan the QR codes on empty bins. The system automatically records "the empty bin with this QR code has been recycled" and adds it up by recycling point and recycling date to generate the daily recycling volume (e.g., recycling point A recycles 50 bins on a certain day, meaning 50 different QR codes were scanned and marked "recycled").

[0047] Inventory statistics: Empty barrel inventory sensors in the warehouse are linked to QR codes. Each storage location corresponds to the scanned and entered empty barrel QR code. The system automatically calculates the daily inventory by reading the number of "inbound but not outbound" QR codes in real time (e.g., if there are 80 QR codes in the warehouse with the status "in stock", the inventory is 80).

[0048] By default, the entire data chain of empty bucket recycling is preprocessed, specifically: abnormal data such as "processing time is 0" and "recycling volume is negative" caused by equipment failure or human input errors are deleted; data from temporarily closed recycling points are removed to ensure the accuracy and validity of the dataset.

[0049] S3: Perform comprehensive analysis and processing of the entire chain data of different empty bucket recycling points, determine the scheduling frequency coefficient of each recycling point, and convert the scheduling frequency coefficient of each recycling point into a recycling scheduling interval based on the pre-built mapping rules between the scheduling frequency coefficient and the recycling scheduling interval, which serves as the recycling scheduling interval for each recycling point.

[0050] Mapping rules: This involves setting a set of coefficient intervals corresponding to the scheduling frequency coefficient, with each set of coefficient intervals corresponding to a recycling scheduling interval. The recycling scheduling interval is set between 1 and 5 days, and the higher the scheduling frequency coefficient, the higher the probability of matching a 1-day interval.

[0051] For example, the scheduling frequency coefficient is denoted as K;

[0052] 1.0 < K ≤ 1.2 corresponds to 1 day, the coefficient is high, and daily scheduling is required to avoid backlog;

[0053] 0.8 < K ≤ 1.0 corresponds to 2 days, which is a relatively high coefficient. The scheduling is carried out every 2 days to balance risk and cost.

[0054] 0.6 < K ≤ 0.8 corresponds to 3 days, with a moderate coefficient, and scheduling occurs every 3 days;

[0055] 0.4 < K ≤ 0.6 corresponds to 4 days, which is a relatively low coefficient. Scheduling every 4 days reduces ineffective transportation.

[0056] 0 < K ≤ 0.4 corresponds to 5 days, the coefficient is extremely low, and scheduling can be carried out every 5 days (such as remote recycling points with low recycling volume).

[0057] Specifically:

[0058] Set the time zone for collecting data across the entire chain;

[0059] The average amount of recycling at each recycling point within the collection time zone is calculated and divided by the temporary storage capacity of the recycling point to determine the backlog risk value of each recycling point.

[0060] The average daily inventory at each collection point within the collection time zone is calculated and divided by the temporary storage capacity of the collection point to determine the load risk value of each collection point.

[0061] The distance between each recycling point and the production end is obtained. The distance is combined with the fuel consumption of the transport vehicle to calculate the fuel cost as the transportation cost. The transportation cost is divided by the average recycling volume of each recycling point to obtain the cost loss value.

[0062] Additional explanation: Formula: Fuel cost = (Distance ÷ 100) × Fuel consumption per 100 kilometers × Fuel price. If road tolls and driver fees are included in the transportation cost, they are directly summed to obtain the final transportation cost.

[0063] For example, the daily recycling volume at recycling point A is: 420, 450, 430, 460, 440, 470, 450;

[0064] Inventory quantities are: 380, 410, 390, 420, 400, 430, 410;

[0065] The daily recycling volume at recycling point B is: 280, 260, 290, 270, 250, 280, 260.

[0066] Inventory quantities are: 220, 200, 230, 210, 190, 220, 200;

[0067] Calculate the "backlog risk coefficient" for each recycling point (to determine whether the recycling volume is likely to lead to backlog).

[0068] Formula: Backlog Risk Coefficient = (Average Daily Recycling Amount in Collection Time Zone) ÷ Temporary Storage Capacity at Recycling Point (The closer the coefficient is to 1, the closer the average daily recycling amount is to the capacity limit, and the higher the backlog risk).

[0069] Calculation for recycling point A:

[0070] The average number of items collected over 7 days is calculated as follows: (420 + 450 + 430 + 460 + 440 + 470 + 450) ÷ 7 = 3120 ÷ 7 ≈ 445.71.

[0071] Backlog risk coefficient = 445.71 ÷ 500 ≈ 0.89;

[0072] Calculation for recycling point B:

[0073] The average number of items collected over 7 days is calculated as follows: (280 + 260 + 290 + 270 + 250 + 280 + 260) ÷ 7 = 1890 ÷ 7 = 270 items.

[0074] Backlog risk coefficient = 270 ÷ 800 = 0.34 (< 0.5, low backlog risk)

[0075] Calculate the "load risk coefficient" for each recovery point;

[0076] Formula: Load risk coefficient = (average daily inventory in the collection time zone) ÷ temporary storage capacity at the recycling point (the higher the coefficient, the more temporary storage space the inventory occupies, and the greater the load).

[0077] Calculation for recycling point A:

[0078] The average inventory level over 7 days is calculated as (380 + 410 + 390 + 420 + 400 + 430 + 410) ÷ 7 = 405.71 units; the load risk coefficient is approximately 405.71 ÷ 500 ≈ 0.81.

[0079] Calculation for recycling point B:

[0080] The average inventory level over 7 days is calculated as (220 + 200 + 230 + 210 + 190 + 220 + 200) ÷ 7 = 210 units; the load risk coefficient is calculated as 210 ÷ 800 = 0.26.

[0081] If the distance between recycling point A and the production end is 15km, and the distance between recycling point B and the production end is 30km;

[0082] Therefore, the fuel cost at recycling point A is calculated as follows: Fuel cost = (15 ÷ 100) × 12 × 8.5 = 0.15 × 12 × 8.5 = 15.3 yuan;

[0083] Total transportation cost = 15.3 yuan (fuel) + 5 yuan (road toll) + 200 yuan (driver) = 220.3 yuan;

[0084] Estimated unit cost = 220.3 ÷ 445.71 ≈ 0.49 yuan / unit;

[0085] Recycling point B calculates: Fuel cost = (30 ÷ 100) × 12 × 8.5 = 0.3 × 12 × 8.5 = 30.6 yuan;

[0086] Total transportation cost = 30.6 yuan (fuel) + 18 yuan (road toll) + 200 yuan (driver) = 248.6 yuan;

[0087] Estimated unit cost = 248.6 ÷ 270 ≈ 0.92 yuan / unit.

[0088] For each recycling point, the backlog risk value, load risk value, and cost loss value are combined with the reference dataset preset by the management personnel and a weighted calculation logic is used to determine the scheduling frequency coefficient of each recycling point; the reference dataset includes the allowable backlog risk value, the allowable load risk value, and the expected cost loss value.

[0089] Additional explanation,

[0090] The allowable backlog risk value represents the maximum risk that a company can tolerate at its recycling points due to "recycling volume > dispatch volume" under normal operation.

[0091] The permissible load risk value represents the maximum level of risk that a company can tolerate in terms of "inventory occupancy temporary storage capacity" at recycling points under normal operating conditions;

[0092] This represents the ideal cost threshold set by the company for "transporting each empty barrel from the recycling point to the production end" while ensuring transportation quality (such as timely delivery and no damage to empty barrels).

[0093] The specific process of calculating the scheduling frequency coefficient using weighted calculation logic is as follows:

[0094] The backlog risk value, load risk value, and cost loss value are respectively labeled as... The allowable backlog risk value, allowable load risk value, and expected cost loss value are respectively marked as follows: ;

[0095] According to the formula Calculations are performed to obtain the scheduling frequency coefficient; where These are the preset weighting coefficients.

[0096] S4: Based on the recycling scheduling interval of each recycling point, select the recycling points that need to be scheduled for the current date as scheduling stations, calculate the estimated total recycling volume of each scheduling station to determine the number of vehicles to be scheduled. If the number of vehicles to be scheduled is greater than one, then formulate a combination plan for each group of routes based on the location of the production end and the scheduling station. After multi-dimensional evaluation of each group of route combination plans, determine the recycling route plan for the current date.

[0097] For example, based on the "recycling scheduling interval" of each recycling point, filter the sites whose "current date is within the scheduling period":

[0098] Recycling Point A (1-day interval): Daily scheduling required, scheduling required on the 4th day;

[0099] Collection point B (interval 3 days): Scheduling days are 1 / 4 / 7 / 10, scheduling is required on the 4th day;

[0100] Collection point C (interval 2 days): Scheduling days are 2 / 4 / 6 / 8, scheduling is required on the 4th day;

[0101] →Sites requiring scheduling on this day: A, B, C.

[0102] Specifically:

[0103] For the current date's dispatch station, retrieve the average amount of data collected at each collection point within the collection time zone for each dispatch station, and use this as the average amount of data collected at each dispatch station.

[0104] The average recovery amount at each dispatch station is summed to obtain the estimated total recovery amount;

[0105] The estimated total amount of recycling is compared with the maximum number of bins that can be carried by a single vehicle. If the estimated total amount of recycling is greater than the maximum number of bins that can be carried by a single vehicle, it is determined that the number of vehicles to be dispatched is greater than one.

[0106] For example, sum up the "average amount of recycling" of the dispatch stations on the same day: Estimated total recycling amount = A (445.71) + B (270) + C (320) + D (180) ≈ 445.71 + 270 = 715.71 + 320 = 1035.71 + 180 ≈ 1215.71 (rounded to 1216).

[0107] Assuming the company's transport vehicles have a "maximum capacity of 500 bins per vehicle" (the standard loading capacity of a medium-sized truck), then: Number of vehicles required = Estimated total recycling volume ÷ Maximum capacity of bins per vehicle (rounded up) → 1216 ÷ 500 ≈ 2.43 → 3 vehicles are needed.

[0108] Identify the recovery points with higher scheduling frequency coefficients in each route combination scheme as high-priority points;

[0109] Extract the estimated total time, completion time of high priority points, and total transportation cost from each group of route combination schemes;

[0110] Additional information: Estimated total time required for the plan:

[0111] Total time = "driving time per vehicle + loading and unloading time", where:

[0112] Travel time = (outbound distance + return distance) ÷ average speed (calculated at 40km / h for urban roads);

[0113] Loading and unloading time = number of stations × loading and unloading time per station (0.5 hours / station).

[0114] For example, the time calculation for each vehicle in Scheme 1:

[0115] Vehicle 1 (responsible for point A): Travel distance: production end → A (15km) + A → production end (15km) = 30km, travel time: 30km ÷ 40km / h = 0.75 hours, loading and unloading time: 1 station × 0.5 hours = 0.5 hours, total time: 0.75 + 0.5 = 1.25 hours;

[0116] Vehicle 2 (responsible for point C): Travel distance: production end → C (20km) + C → production end (20km) = 40km, travel time: 40km ÷ 40km / h = 1 hour, loading and unloading time: 1 station × 0.5 hours = 0.5 hours, total time: 1 + 0.5 = 1.5 hours;

[0117] Vehicle 3 (responsible for point B): Travel distance: production end → B (30km) + B → production end (30km) = 60km, travel time: 60km ÷ 40km / h = 1.5 hours, loading and unloading time: 1 station × 0.5 hours = 0.5 hours, total time: 1.5 + 0.5 = 2.0 hours.

[0118] Estimated total time for the plan: The longest time among the 3 vehicles is 2.0 hours (determined by vehicle 3; since all vehicles depart at the same time, the overall completion time is based on the latest finish).

[0119] High priority point completion time = "Travel time from production to the site + loading and unloading time at the site" (no need to wait for the return trip, only the time of "arrival → completion of loading and unloading" needs to be calculated to ensure timely clearance of inventory.

[0120] If the high priority point is point A (K=1.101), the completion time of the high priority point in Scheme 1 is calculated as follows: the travel time of vehicle 1 from the production end to A is 15km ÷ 40km / h = 0.375 hours, the loading and unloading time is 0.5 hours, and the completion time is 0.375 + 0.5 = 0.875 hours.

[0121] Cost per vehicle = fuel cost + toll fees + driver's fee, where:

[0122] Fuel cost = (total driving distance ÷ 100) × fuel consumption per 100 kilometers (12L) × fuel price (8.5 yuan / L);

[0123] Road and bridge tolls: charged according to the actual route (5 yuan for point A, 18 yuan for point B, 10 yuan for point C). Driver fee: fixed at 200 yuan per vehicle per trip.

[0124] Cost calculation for each vehicle in Option 1:

[0125] Vehicle 1 (Point A): Fuel cost: (30km ÷ 100) × 12 × 8.5 = 0.3 × 102 = 30.6 yuan; Road toll: 5 yuan; Driver's fee: 200 yuan; Total cost: 30.6 + 5 + 200 = 235.6 yuan;

[0126] Vehicle 2 (Point C): Fuel cost: (40km ÷ 100) × 12 × 8.5 = 0.4 × 102 = 40.8 yuan;

[0127] Road and bridge toll: 10 yuan; driver's fee: 200 yuan; total cost: 40.8 + 10 + 200 = 250.8 yuan;

[0128] Vehicle 3 (Point B): Fuel cost: (60km ÷ 100) × 12 × 8.5 = 0.6 × 102 = 61.2 yuan;

[0129] Road and bridge toll: 18 yuan; driver's fee: 200 yuan; total cost: 61.2 + 18 + 200 = 279.2 yuan;

[0130] Total transportation cost of the plan: The total cost of 3 vehicles is 235.6 + 250.8 + 279.2 = 765.6 yuan.

[0131] The estimated total time, completion time of high priority points, and total transportation cost of each route combination scheme are standardized. The scheme evaluation coefficient of each route combination scheme is obtained by comprehensive processing using weighted calculation logic. The route combination scheme with the higher scheme evaluation coefficient is selected as the recovery route scheme for the current date.

[0132] The specific process of standardization is as follows:

[0133] Using the "linear normalization method", based on the characteristic that "the lower the better" of the indicator, the following formula is used: Standardized score = (maximum value of indicator - indicator value of the scheme) ÷ (maximum value of indicator - minimum value of indicator).

[0134] The evaluation coefficients of the scheme are obtained by comprehensive processing using weighted calculation logic, specifically:

[0135] The estimated total time, completion time of high-priority points, and total transportation cost of each route combination scheme are respectively marked as follows: Substitute into the formula The evaluation coefficient P of the scheme is calculated; where All are preset weighting coefficients;

[0136] S5: Implement the determined recycling scheduling interval and recycling route plan as the implementation plan, and send the full-chain data of the execution process to the management personnel in real time;

[0137] Additional information: Consumers can obtain the following information via the QR code:

[0138] Consumer side: Consumers open the dedicated APP and use the "scan" function to scan the QR code on the bottle. After the system parses the QR code, it will redirect to the preset consumer page. The page displays key traceability information after anonymization, including the name of the water source, production time, and main circulation nodes (such as "water plant - XX service station - consumer"). Detailed data involving corporate privacy or sensitivity will not be displayed.

[0139] The above formulas are all dimensionless calculations. Dimensionless calculations can be performed using various methods such as standardization, which will not be elaborated here. The formulas are derived from software simulations based on a large amount of collected data, and the preset parameters in the formulas can be set by those skilled in the art according to the actual situation.

[0140] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, ATA hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium can be a solid-state ATA hard disk.

[0141] It should be understood that in the various embodiments of this application, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0142] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0143] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0144] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0145] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0146] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable ATA hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0147] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for full lifecycle traceability management of bottled drinking water using QR codes, characterized in that: include: S1: Generate a unique QR code for each empty bucket. After the empty bucket is put into use for the first time, bind the information of each circulation of the empty bucket to the QR code. S2: Obtain full-chain data for different empty barrel recycling points by collecting circulation information, including daily recycling volume, daily inventory volume, and transportation distance; S3: Perform comprehensive analysis and processing of the entire chain data of different empty bucket recycling points, determine the scheduling frequency coefficient of each recycling point, and convert the scheduling frequency coefficient of each recycling point into a recycling scheduling interval based on the pre-built mapping rules between the scheduling frequency coefficient and the recycling scheduling interval, which serves as the recycling scheduling interval for each recycling point. S4: Based on the recycling scheduling interval of each recycling point, select the recycling points that need to be scheduled for the current date as scheduling stations, calculate the estimated total recycling volume of each scheduling station to determine the number of vehicles to be scheduled. If the number of vehicles to be scheduled is greater than one, then formulate a combination plan for each group of routes based on the location of the production end and the scheduling station. After multi-dimensional evaluation of each group of route combination plans, determine the recycling route plan for the current date.

2. The method for full lifecycle traceability management of bottled drinking water using QR codes according to claim 1, characterized in that: In step S3, the data of the entire chain is comprehensively analyzed and processed; Set the time zone for collecting data across the entire chain; The average amount of recycling at each recycling point within the collection time zone is calculated and divided by the temporary storage capacity of the recycling point to determine the backlog risk value of each recycling point. The average daily inventory at each collection point within the collection time zone is calculated and divided by the temporary storage capacity of the collection point to determine the load risk value of each collection point. The distance between each recycling point and the production end is obtained. The distance is combined with the fuel consumption of the transport vehicle to calculate the fuel cost as the transportation cost. The transportation cost is divided by the average recycling volume of each recycling point to obtain the cost loss value.

3. The method for full lifecycle traceability management of bottled drinking water using QR codes according to claim 2, characterized in that: In step S3, the scheduling frequency coefficient of each recycling point is determined. The scheduling frequency coefficient of each recycling point is determined by combining the backlog risk value, load risk value, and cost loss value with the preset reference dataset and using weighted calculation logic.

4. The method for full lifecycle traceability management of bottled drinking water using QR codes according to claim 3, characterized in that: The reference dataset includes allowable backlog risk values, allowable load risk values, and expected cost loss values.

5. The method for full lifecycle traceability management of bottled drinking water using QR codes according to claim 1, characterized in that: The mapping rules in step S3; By setting the scheduling frequency coefficients and corresponding coefficient intervals, each coefficient interval corresponds to a recycling scheduling interval.

6. The method for full lifecycle traceability management of bottled drinking water using QR codes according to claim 1, characterized in that: In step S4, the estimated total recovery volume of each dispatch station is calculated to determine the required number of dispatch vehicles. For the current date's dispatch station, retrieve the average amount of data collected at each collection point within the collection time zone for each dispatch station, and use this as the average amount of data collected at each dispatch station. The average recovery amount at each dispatch station is summed to obtain the estimated total recovery amount; The estimated total amount of recycling is compared with the maximum number of bins that can be carried per vehicle. If the estimated total amount of recycling is greater than the maximum number of bins that can be carried per vehicle, then it is determined that the number of vehicles to be dispatched is greater than one.

7. The method for full lifecycle traceability management of bottled drinking water using QR codes according to claim 6, characterized in that: In step S4, a multi-dimensional evaluation process is performed on each group of route combination schemes. The recovery point with the highest scheduling frequency coefficient in each route combination scheme is identified as a high-priority point; Extract the estimated total time, completion time of high priority points, and total transportation cost from each group of route combination schemes; The estimated total time, completion time of high priority points, and total transportation cost of each route combination scheme are standardized, and the scheme evaluation coefficient of each route combination scheme is obtained by comprehensive processing using weighted calculation logic.

8. The method for full lifecycle traceability management of bottled drinking water using QR codes according to claim 7, characterized in that: In step S4, the recycling route plan for the current date is determined; The route combination scheme with the highest evaluation coefficient is selected as the recovery route scheme for the current date.

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