Intelligent carton production management system and method

Abstract

The embodiment of the invention provides an intelligent carton production management system and method, and the system comprises an obtaining module which is used for obtaining the production cost of a carton per unit, the production capacity of a time period, the storage cost of the carton per unit time, the stock of the current time period, and the transportation capacity of the current time period; the calculation module comprises a first calculation unit and is used for calculating the optimal total cost of the current time period based on the production cost of each unit of carton, the production capacity of the time period, the storage cost of each unit of carton and each unit of time, the inventory of the current time period and the transportation capacity of the current time period by combining a first preset algorithm; and the execution module is used for determining the optimal transportation volume corresponding to the optimal total cost so as to carry out transportation scheduling on the carton finished products based on the optimal transportation volume. According to the invention, the problem that the total cost of carton production is increased due to the fact that carton finished products cannot be transported in time in the prior art is solved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of intelligent management, and in particular, to an intelligent carton production management system and method. Background Art

[0002] With the rapid development of e-commerce and the rapid growth of the logistics industry, the production and application of cartons as an environmentally friendly and economical packaging material are receiving more and more attention. Cartons not only provide basic protection and transportation functions, but also achieve real-time tracking and monitoring of goods through the integration of intelligent technologies such as sensors and RFID tags, greatly improving logistics efficiency and cargo safety.

[0003] At present, the production of cartons has shifted from traditional manual manufacturing to automated and intelligent production. Modern production lines have significantly improved production efficiency and carton quality by introducing advanced robotic arms, automated cutting and folding equipment, and real-time monitoring systems. In addition, by integrating information technology such as ERP (Enterprise Resource Planning) and MES (Manufacturing Execution System), all aspects of the production process are effectively managed and optimized, thus achieving more flexible production scheduling and more precise inventory control.

[0004] Although carton production technology has made significant progress, manufacturers still face the challenge of total cost control in the process of implementing intelligent transformation. The continuous rise in production costs, including fluctuations in raw material prices, increased labor costs, and increasingly stringent environmental regulations, have put forward higher requirements on the cost control capabilities of enterprises. At the same time, the variability of market demand and the complexity of production have also made cost forecasting and control more difficult. In particular, with the improvement of production efficiency, if the finished cartons cannot be transported in time, the storage cost of the finished cartons will increase, and it is very easy to have a backlog of goods, which will lead to an increase in the total cost of carton production. Summary of the invention

[0005] The embodiments of the present invention provide an intelligent carton production management system and method, which at least solves the problem in the related art that the total cost of carton production increases because the finished carton products cannot be transported in time.

[0006] According to one embodiment of the present invention, there is provided an intelligent carton production management system, comprising: An acquisition module is used to acquire the production cost of each carton unit, the production volume of a time period, the storage cost of each carton unit per unit time, the inventory volume of the current time period, and the transportation volume of the current time period; The calculation module includes a first calculation unit, which is used to calculate the optimal total cost of the current time period based on the production cost of each unit carton, the production volume of the time period, the storage cost of each unit carton per unit time, the inventory volume of the current time period, the transportation volume of the current time period and a first preset algorithm; The execution module is used to determine the optimal transportation volume corresponding to the optimal total cost, so as to schedule the transportation of the finished cartons based on the optimal transportation volume.

[0007] According to another embodiment of the present invention, there is provided an intelligent carton production management method, comprising: Obtain the production cost per carton, the production volume in a time period, the storage cost per carton per unit time, the inventory volume in the current time period, and the transportation volume in the current time period; Calculate the optimal total cost for the current time period based on the production cost per unit carton, the production volume in the time period, the storage cost per unit carton per unit time, the inventory volume in the current time period, the transportation volume in the current time period and in combination with a first preset algorithm; An optimal transportation volume corresponding to the optimal total cost is determined to schedule transportation of finished cartons based on the optimal transportation volume.

[0008] Through one of the embodiments of the present invention, first, through the acquisition module, the system can obtain in real time the production cost per unit carton, the production volume of the time period, the storage cost per unit carton per unit time, the inventory volume of the current time period, and the transportation volume of the current time period. The acquisition of such real-time data is the basis for optimizing production and transportation scheduling. Secondly, the calculation module uses the acquired data and combines the preset algorithm (the first preset algorithm) to calculate the optimal total cost of the current time period. This algorithm may take into account multiple factors such as production efficiency, storage cost, and transportation cost to ensure that the total cost is minimized. Secondly, the execution module determines the optimal transportation volume based on the calculated optimal total cost and performs transportation scheduling accordingly. This means that the system can dynamically adjust the transportation plan based on real-time data and cost calculation results to reduce inventory backlogs and storage costs. Secondly, by optimizing transportation scheduling, the system can reduce the inventory backlog of finished cartons, because the optimal transportation volume is calculated based on the current inventory and production volume, which can ensure that the produced cartons can be transported out in time, reduce storage time, and thus reduce storage costs. Secondly, through real-time monitoring and calculation, the system can more accurately predict costs and make dynamic adjustments during the production process to cope with the variability of market demand and the complexity of production. Furthermore, through the intelligent management system, enterprises can more effectively utilize resources such as raw materials, labor and transportation tools, reduce waste, and improve overall production and transportation efficiency.

[0009] In summary, the embodiment of the present invention realizes real-time monitoring and optimization of the carton production and transportation process through an intelligent management system, thereby reducing the problem of increased total costs due to the inability to transport finished products in a timely manner, and realizing effective control and reduction of costs. This method not only improves the economic benefits of the enterprise, but also meets the current requirements for environmental protection and efficient use of resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 is a schematic diagram of the structure of an intelligent carton production management system according to an embodiment of the present invention; Figure 2 is a schematic diagram of the structure of a calculation module according to an embodiment of the present invention; Figure 3 is a schematic diagram of the structure of an acquisition module according to an embodiment of the present invention; Figure 4 is a schematic diagram of the structure of a first computing unit according to an embodiment of the present invention; Figure 5 is a schematic diagram of the structure of a second computing unit according to an embodiment of the present invention; Figure 6 is a flow chart of an intelligent carton production management method according to an embodiment of the present invention; Figure 7 is a flow chart of a method for calculating an optimal transportation volume according to an embodiment of the present invention; Figure 8 is a flow chart of a method for automatically obtaining quotation data of a transportation company according to an embodiment of the present invention; Fig. 9 is a flow chart of a method for determining an optimal transportation company according to an embodiment of the present invention.

[0011] Explanation of the accompanying drawings: 1. Acquisition module; 11. First data interface unit; 12. Data storage unit; 2. Calculation module; 21. First calculation unit; 211. First calculation subunit; 212. Second calculation subunit; 22. Second calculation unit; 221. Third calculation subunit; 222. Fourth calculation subunit; 223. Fifth calculation subunit; 3. Execution module. DETAILED DESCRIPTION

[0012] Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings and in combination with the embodiments.

[0013] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0014] In this embodiment, an intelligent carton production management system is provided. Figure 1is a schematic diagram of the structure of an intelligent carton production management system according to an embodiment of the present invention, Figure 2 is a schematic diagram of the structure of a computing module according to an embodiment of the present invention. Figure 1 and Figure 2 As shown, the system includes: an acquisition module 1, a calculation module 2, and an execution module 3. Among them, Acquisition module 1, used to acquire the production cost of each carton unit, the production volume of a time period, the storage cost of each carton unit per unit time, the inventory volume of the current time period, and the transportation volume of the current time period; In an exemplary implementation, the following method may be used: Sensors and automation equipment: Install sensors and automation equipment such as weight sensors, counters, barcode or RFID scanners on the production line to automatically collect data such as production volume and inventory level.

[0015] Data interface: Automatically obtain data such as production cost, inventory and transportation volume through the interface with information systems such as enterprise resource planning (ERP) system and manufacturing execution system (MES).

[0016] Data validation: To ensure the accuracy of the data, the acquisition module 1 may include data validation mechanisms, such as data anomaly detection and error prompts, to ensure that the input data conforms to the expected range and logic.

[0017] Data storage: The collected data needs to be stored in a database for historical data analysis and real-time data processing.

[0018] User Interface: Provide a user interface that allows the operator to monitor the data collection process and make manual adjustments or interventions when necessary.

[0019] For example, for production cost data, the production cost data of each carton can be automatically obtained from the financial module through integration with the ERP system. For production volume data, the number of cartons produced in each time period can be automatically recorded through sensors and counters on the production line. For storage cost data, the storage cost per carton per unit time can be automatically calculated according to the occupancy of storage space and storage time through the rate set by the system. For inventory volume data, the inventory volume of the current time period can be obtained in real time through the warehouse management system (WMS). For transportation volume data, the transportation volume that has been scheduled or completed in the current time period can be obtained through the transportation management system (TMS).

[0020] The calculation module 2 includes a first calculation unit 21, which is used to calculate the optimal total cost of the current time period based on the production cost of each unit carton, the production volume of the time period, the storage cost of each unit carton per unit time, the inventory volume of the current time period, the transportation volume of the current time period and the first preset algorithm; The execution module 3 is used to determine the optimal transportation volume corresponding to the optimal total cost, so as to schedule the transportation of the finished cartons based on the optimal transportation volume.

[0021] By adopting the above technical solution, firstly, through the acquisition module 1, the system can obtain in real time the production cost per unit carton, the production volume of the time period, the storage cost per unit carton per unit time, the inventory volume of the current time period and the transportation volume of the current time period. The acquisition of such real-time data is the basis for optimizing production and transportation scheduling.

[0022] Secondly, the calculation module 2 uses the acquired data and combines it with a preset algorithm (first preset algorithm) to calculate the optimal total cost for the current time period. This algorithm may take into account multiple factors such as production efficiency, storage cost, and transportation cost to ensure that the total cost is minimized.

[0023] Secondly, the execution module 3 determines the optimal transportation volume based on the calculated optimal total cost and performs transportation scheduling accordingly. This means that the system can dynamically adjust the transportation plan based on real-time data and cost calculation results to reduce inventory backlogs and storage costs.

[0024] Secondly, by optimizing transportation scheduling, the system can reduce the inventory backlog of finished cartons, because the optimal transportation volume is calculated based on the current inventory and production volume, which can ensure that the produced cartons can be transported out in time, reducing storage time and thus reducing storage costs.

[0025] Secondly, through real-time monitoring and calculation, the system can more accurately predict costs and make dynamic adjustments during the production process to cope with the variability of market demand and the complexity of production.

[0026] Furthermore, through intelligent management systems, enterprises can more effectively utilize resources such as raw materials, labor and transportation tools, reduce waste, and improve overall production and transportation efficiency.

[0027] In summary, the embodiment of the present invention realizes real-time monitoring and optimization of the carton production and transportation process through an intelligent management system, thereby reducing the problem of increased total costs due to the inability to transport finished products in a timely manner, and realizing effective control and reduction of costs. This method not only improves the economic benefits of the enterprise, but also meets the current requirements for environmental protection and efficient use of resources.

[0028] Figure 3is a schematic diagram of the structure of an acquisition module according to an embodiment of the present invention. In one implementation, Figure 3 As shown, the acquisition module 1 includes: a first data interface unit 11 and a data storage unit 12. Among them, The first data interface unit 11 is used to call the API of the continuously cooperating transportation company to regularly obtain quotation data, wherein the quotation data includes: price, service level, and transportation time; In an exemplary implementation, the main function of the first data interface unit 11 is to interact with the API of an external transportation company and obtain transportation quotation data regularly.

[0029] The specific implementation methods can be: API call: The first data interface unit 11 is designed to be able to interact with the APIs of multiple transport companies. This is usually achieved through HTTP requests, such as using GET or POST methods.

[0030] Data acquisition: Call these APIs regularly (for example, every day or every time the production plan is updated) to obtain the latest transportation quotation data.

[0031] Data parsing: Parse the quote data obtained from the API into a format that the system can process, such as JSON or XML, and extract key information, including price, service level, and shipping time.

[0032] For example, the system needs to obtain quotes from three different transport companies. The first data interface unit 11 configures three API endpoints and sends requests at predetermined time intervals. The data returned by each request may include prices, service levels (such as standard, expedited, etc.) and estimated shipping times for multiple transport options. These data are parsed and prepared for subsequent cost calculations and transport scheduling decisions.

[0033] The data storage unit 12 is used to store quotation data.

[0034] In an exemplary implementation, the main function of the data storage unit 12 is to store the transportation quotation data obtained from the first data interface unit 11 .

[0035] The specific implementation methods can be: Database design: Design a database table to store the quote data of the transportation company, including fields such as price, service level, and transportation time.

[0036] Data writing: Whenever the first data interface unit 11 obtains new quotation data, these data will be written into the database.

[0037] Data management: Implement data creation, deletion, query and modification (CRUD) operations to ensure data integrity and consistency.

[0038] Data access: Provides an interface for other modules (such as calculation module 2) to access these stored quotation data for cost calculation and transportation scheduling.

[0039] For example, the data storage unit 12 may use a relational database management system (RDBMS) such as MySQL or PostgreSQL to store data. The database table may be designed to include fields such as transport company ID, price, service level, transport time, data update time, etc. Whenever there is new quotation data, the system will automatically update the corresponding record in the database to ensure the real-time and accuracy of the data.

[0040] Figure 4 is a schematic diagram of the structure of a first computing unit according to an embodiment of the present invention. In one implementation, Figure 4 As shown, the first calculation unit 21 includes: a first calculation subunit 211 and a second calculation subunit 212. Among them, The first calculation subunit 211 is used to reversely recursively calculate the first optimal total cost and the first optimal transportation volume of each previous time period starting from the last time period; The second calculation subunit 212 is used to positively update the inventory quantity of the next time period, the second optimal transportation quantity of the current time period, and the second optimal total cost of the current time period based on the first optimal transportation quantity starting from the first time period; The execution module 3 is also used for: Starting from the first time period, the second optimal transportation volume and the second optimal total cost of each time period are output to schedule the transportation of finished cartons based on the optimal transportation volume.

[0041] In one implementation, the first preset algorithm is: V t (I t )=min Tt (C p *P t +C s *I t *Δt+k*T t +V t+1 (I t+1 )); I t+1 =I t +P t −T t ; Among them, C p is the production cost per unit of carton, P t is the production volume of cartons in time period t, C sis the storage cost per unit carton per unit time, I t is the inventory of cartons in the current time period t, I t+1 is the inventory of cartons in the next time period t+1, k is the transportation cost per unit of carton, T t is the transportation volume of cartons in time period t, V t (I t ) is the time period t and inventory status I t The optimal total cost under V t+1 (I t+1 ) is the optimal total cost for the next time period t+1.

[0042] The following is an example to explain the above content: The first calculation subunit 211 implements the following functions: reversely recursively derive the optimal total cost and optimal transportation volume for each time period.

[0043] The specific implementation methods can be: Recursive process: Starting from the last time period, use the reverse recursive method of dynamic programming to calculate the optimal total cost V for each time period t (I t ) and the optimal transport volume (T t ). This process is based on the recursive formula of the algorithm, calculating from the back to the front until the first time period.

[0044] Recursive formula: Use the given recursive formula, combined with the production cost (C p ), production volume (P t ), storage cost (C s )、Current Inventory(I t ), transportation cost (k), and inventory quantity for the next time period (I t+1 ) to calculate the optimal total cost for each time period.

[0045] Therefore, through backward recursion, the system is able to determine the optimal transportation strategy that should be adopted to achieve the global optimal total cost at the end of each time period.

[0046] The second calculation subunit 212 implements the following functions: positively updating the inventory quantity, optimal transportation quantity and optimal total cost in each time period.

[0047] The specific implementation methods can be: Update process: Starting from the first time period, the inventory quantity (I t+1 ), and calculate the optimal total cost for the current time period (V t (It )) and the optimal transport volume (T t ).

[0048] Inventory Update: Using Formula I t+1 =I t +P t −T t To update the inventory level for each time period.

[0049] Therefore, through forward updating, the system can ensure that the inventory and transportation volumes in each time period are adjusted based on the global optimal strategy to minimize costs.

[0050] For the execution module 3, its functions are: outputting the optimal transportation volume and the optimal total cost for each time period, and performing transportation scheduling based on this information.

[0051] The specific implementation methods can be: Output results: Starting from the first time period, the optimal transportation volume and optimal total cost of each time period are output. This information will be used to guide the actual transportation scheduling.

[0052] Guide transportation scheduling: Based on the calculated optimal transportation volume, the execution module 3 will schedule the corresponding transportation resources, such as vehicles and routes, to ensure that the cartons can be transported according to the optimal strategy.

[0053] Therefore, the execution module 3 ensures that the transportation scheduling of cartons matches the calculated optimal strategy, thereby minimizing costs and maximizing efficiency throughout the entire production and transportation cycle.

[0054] In summary, through the reverse recursion of the first calculation subunit 211 and the forward update of the second calculation subunit 212, the system can determine the optimal transportation strategy for each time period. The execution module 3 performs actual transportation scheduling according to these strategies to ensure the cost efficiency of the entire production and logistics process is optimized. This method not only improves resource utilization, but also reduces unnecessary storage and transportation costs, and ultimately realizes the intelligence and automation of the production and transportation process.

[0055] In one embodiment, Figure 2 As shown, the calculation module 2 also includes: The second calculation unit 22 is used to determine the optimal transportation company based on the quotation data and in combination with a second preset algorithm.

[0056] Figure 5 is a schematic diagram of the structure of a second computing unit according to an embodiment of the present invention. In one implementation, Figure 5 As shown, in one embodiment, the second computing unit 22 includes: The third calculation subunit 221 is used to determine a comprehensive scoring function based on price, service level, and transportation time to obtain a scoring result for each transportation company; A fourth calculation subunit 222, for determining a weight factor of each transport company based on the scoring result; The fifth calculation subunit 223 is used to determine the optimal transportation company based on the weight factor.

[0057] In one embodiment, the comprehensive scoring function is: F i =α* +β*S i +γ* ; Among them, P i is the price of the i-th transport company, S i is the service level of the i-th transport company, T i is the transportation time of the i-th transportation company, α is the weight of the price, β is the weight of the service level, γ is the weight of the transportation time, and F i is the scoring result of the i-th transportation company.

[0058] In one embodiment, the formula for calculating the weight factor is: W i = ; The calculation formula for determining the best transport company based on the weight factor is: i * =argmax i W i ; Among them, W i is the weight factor of the i-th transportation company, i * It is the transportation company with the largest weight factor.

[0059] In summary, the second calculation unit 22 uses mathematical models and algorithms to select the best transport company by comprehensively considering price, service level and transportation time. The third calculation subunit 221 is responsible for calculating the comprehensive score of each transport company, the fourth calculation subunit 222 determines the weight factor according to the score, and the fifth calculation subunit 223 selects the best transport company based on these weight factors. This method not only improves the objectivity and scientificity of transportation decisions, but also helps to reduce transportation costs, improve service quality, and ultimately optimize the entire logistics process.

[0060] The following example explains the above process: For example, there are three transportation companies A, B, and C, and you need to decide which company to work with based on their prices, service levels, and shipping times. Here are some data for each company: Company A: Price: P A =100 yuan, service level: S A =8, transportation time: T A =3 days; Company B: Price: P B =120 yuan, service level: S B =7, transportation time: T B =2 days; Company C: Price: P C =90 yuan, service level: S C =9, transportation time: T C =4 days; Set the weight coefficients: α=0.5, β=1.0, γ=0.5.

[0061] The third calculation subunit 221 calculates the comprehensive score of each transportation company in the following manner: Rating for Company A: F A =0.5* +1.0*8+0.5* ; Rating for Company B: F B =0.5* +1.0*7+0.5* ; Rating for Company C: F C =0.5* +1.0*9+0.5* ; The fourth calculation subunit 222 determines the weight factor of each transportation company according to the scoring result in the following manner: For company A, the weight factor W A = ; The weight factor W for company B B = ; The weight factor W for company C C = ; The fifth computing subunit 223 implements the comparison based on W A , W B , W C , the way to select the company with the largest weight factor as the optimal transportation company is: F A =0.005+8+0.1667=8.1717, W A = ; F B=0.00417+7+0.5=7.50417, W B = ; F C =0.00556+9+0.25=9.25556, W C = .

[0062] Compare W A , W B , W, for example W C The value of is the largest, then company C is determined to be the best choice.

[0063] In summary, in the above-mentioned intelligent carton production management system, by comprehensively considering the price, service level and transportation time of the transportation company, and using mathematical models and algorithms to determine the optimal transportation company, it has made a significant contribution to solving the problem of increased total costs caused by the inability to transport finished cartons in a timely manner. For example: Contribution 1: Accurate cost-benefit analysis.

[0064] Through the comprehensive scoring function, the system can accurately evaluate the cost-effectiveness of each transport company. This evaluation not only takes into account the price factor, but also includes the service level and transportation time, so that the selected transport company is not only reasonably priced, but also guarantees service quality and transportation efficiency.

[0065] Contribution 2: Optimizing transportation scheduling.

[0066] By determining the best transport company, the system can ensure that finished cartons can be transported at the lowest cost, highest service level and fastest shipping time. This directly reduces inventory backlogs caused by transportation delays, thereby reducing storage costs and avoiding additional costs due to cargo backlogs.

[0067] Contribution 3: Dynamically adjust transportation strategies.

[0068] The system not only selects the best transport company at the initial stage, but also dynamically adjusts the transport strategy based on real-time data and market changes. For example, if the service level of a transport company declines or the transport time increases, the system can re-evaluate and select a new best transport company to ensure that transport efficiency and cost control are always in the best state.

[0069] Contribution 4: Improve overall logistics efficiency.

[0070] By selecting the best transport company, the system can improve overall logistics efficiency, including reducing transportation time and improving the reliability of cargo delivery. This not only reduces the time cost in the logistics process, but also enhances customer satisfaction, helping to establish long-term customer relationships and market competitiveness.

[0071] Contribution 5: Reduce operational risks.

[0072] Choosing a transport company with a high service level and short transportation time can reduce the risks during transportation, such as the risk of damage or loss of goods. This not only reduces potential compensation costs, but also maintains the reputation of the company.

[0073] In short, through the comprehensive scoring and weight factor calculation in the above intelligent carton production management system, enterprises can choose the most suitable transportation company, thereby optimizing transportation costs, improving transportation efficiency and service quality. This method not only solves the problem of the inability to transport finished cartons in a timely manner, but also reduces the overall production cost and improves the market competitiveness and economic benefits of enterprises.

[0074] In this embodiment, an intelligent carton production management method is also provided. Figure 6 is a flow chart of an intelligent carton production management method according to an embodiment of the present invention. Figure 6 As shown, the method includes: Step S601, obtaining the production cost per unit carton, the production volume in a time period, the storage cost per unit carton per unit time, the inventory volume in the current time period, and the transportation volume in the current time period; By adopting the above technical solution, in the background technology, due to the lack of accurate data support, it is difficult for enterprises to accurately predict and control costs. Step S601 obtains these key data in real time, providing a basis for subsequent cost calculation and optimization.

[0075] Step S602, calculating the optimal total cost of the current time period based on the production cost of each unit carton, the production volume of the time period, the storage cost of each unit carton per unit time, the inventory volume of the current time period, the transportation volume of the current time period and the first preset algorithm; By adopting the above technical solution, the difficulties in cost prediction and control mentioned in the background technology are solved through the application of algorithms. The algorithms can help enterprises choose the lowest cost solution among multiple production and transportation solutions, thereby achieving cost optimization.

[0076] Step S603, determining the optimal transportation volume corresponding to the optimal total cost, so as to schedule transportation of the finished cartons based on the optimal transportation volume.

[0077] By adopting the above technical solution, the problem of the inability to transport finished cartons in time and the backlog of goods mentioned in the background technology is effectively solved through step S603. Through accurate transportation scheduling, it can be ensured that the produced cartons can be transported out in time, reducing inventory backlogs, thereby reducing storage costs and avoiding additional costs caused by backlogs.

[0078] Through the above steps S601 to S603, the carton production and transportation process is fully optimized. Through accurate data acquisition, advanced cost calculation and intelligent transportation scheduling, the total cost control problem mentioned in the background technology is effectively solved. This method not only improves production efficiency and logistics efficiency, but also reduces the cost increase caused by inventory backlog and transportation delays, and realizes the reduction of the total cost of carton production.

[0079] Figure 7 is a flow chart of a method for calculating the optimal transportation volume according to an embodiment of the present invention. In one implementation, Figure 7 As shown, the method also includes: Step S701, starting from the last time period, reversely recursively derive the first optimal total cost and the first optimal transportation volume of each previous time period; Step S702, starting from the first time period, positively updating the inventory quantity of the next time period, the second optimal transportation quantity of the current time period, and the second optimal total cost of the current time period according to the first optimal transportation quantity; Step S703, starting from the first time period, output the second optimal transportation volume and the second optimal total cost of each time period to schedule the transportation of finished cartons based on the optimal transportation volume.

[0080] In one implementation, the first preset algorithm is: V t (I t )=min Tt (C p *P t +C s *I t *Δt+k*T t +V t+1 (I t+1 )); I t+1 =I t +P t −T t ; Among them, C p is the production cost per unit of carton, P t is the production volume of cartons in time period t, C s is the storage cost per unit carton per unit time, I t is the inventory of cartons in the current time period t, I t+1 is the inventory of cartons in the next time period t+1, k is the transportation cost per unit of carton, T t is the transportation volume of cartons in time period t, V t (I t ) is the time period t and inventory status It The optimal total cost under V t+1 (I t+1 ) is the optimal total cost for the next time period t+1.

[0081] Figure 8 is a flow chart of a method for automatically obtaining quotation data of a transportation company according to an embodiment of the present invention. In one implementation, Figure 8 Said method further comprises: Step S801, calling the API of the continuously cooperating transportation company to regularly obtain quotation data, wherein the quotation data includes: price, service level, and transportation time; Step S802, storing quotation data.

[0082] In one embodiment, the method further includes: determining an optimal transportation company based on the quotation data in combination with a second preset algorithm.

[0083] Fig. 9 is a flow chart of a method for determining an optimal transportation company according to an embodiment of the present invention. In one implementation, Fig. 9 The method of determining the best transportation company based on the quotation data and in combination with the second preset algorithm includes: Step S901, determining a comprehensive scoring function based on price, service level, and transportation time to obtain a scoring result for each transportation company; In one embodiment, the comprehensive scoring function is: Fi = α* +β*Si+γ* ; Among them, Pi is the price of the i-th transportation company, Si is the service level of the i-th transportation company, Ti is the transportation time of the i-th transportation company, α is the weight of the price, β is the weight of the service level, γ is the weight of the transportation time, and Fi is the score result of the i-th transportation company.

[0084] Step S902, determining a weight factor for each transport company based on the scoring result; Step S903, determining the optimal transportation company based on the weight factors.

[0085] In one embodiment, the formula for calculating the weight factor is: Wi= ; The calculation formula for determining the best transport company based on the weight factor is: i*=argmaxiWi; Among them, Wi is the weight factor of the i-th transportation company, and i* is the transportation company with the largest weight factor.

[0086] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by adding the necessary general hardware platform with the help of software, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present invention.

[0087] An embodiment of the present invention further provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0088] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0089] An embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0090] In an exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0091] An embodiment of the present invention further provides a computer program product, including computer instructions, which, when executed by a processor, implement the steps of the methods described in various embodiments of the present application.

[0092] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0093] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0094] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An intelligent carton production management system, characterized in that: include: An acquisition module is used to acquire the production cost of each carton unit, the production volume of a time period, the storage cost of each carton unit per unit time, the inventory volume of the current time period, and the transportation volume of the current time period; The calculation module includes a first calculation unit, which is used to calculate the optimal total cost of the current time period based on the production cost of each unit carton, the production volume of the time period, the storage cost of each unit carton per unit time, the inventory volume of the current time period, the transportation volume of the current time period and a first preset algorithm; The execution module is used to determine the optimal transportation volume corresponding to the optimal total cost, so as to schedule the transportation of the finished cartons based on the optimal transportation volume.

2. The system according to claim 1, characterized in that The first computing unit comprises: A first calculation subunit is used to reversely recursively calculate the first optimal total cost and the first optimal transportation volume of each previous time period starting from the last time period; A second calculation subunit is used to positively update the inventory quantity of the next time period, the second optimal transportation quantity of the current time period, and the second optimal total cost of the current time period based on the first optimal transportation quantity starting from the first time period; The execution module is also used for: Starting from the first time period, the second optimal transportation volume and the second optimal total cost of each time period are output to schedule transportation of finished cartons based on the optimal transportation volume.

3. The system according to claim 1, characterized in that The first preset algorithm is: V t (I t )=min Tt (C p *P t +C s *I t *Δt+k*T t +V t+1 (I t+1 )); I t+1 =I t +P t −T t ; Among them, C p is the production cost per unit of carton, P t is the production volume of cartons in time period t, C s is the storage cost per unit carton per unit time, I t is the inventory of cartons in the current time period t, I t+1 is the inventory of cartons in the next time period t+1, k is the transportation cost per unit of carton, T t is the transportation volume of cartons in time period t, V t (I t ) is the time period t and inventory status I t The optimal total cost under V t+1 (I t+1 ) is the optimal total cost for the next time period t+1.

4. The system according to claim 1, characterized in that The acquisition module comprises: A first data interface unit is used to call the API of the continuously cooperating transportation company to periodically obtain quotation data, wherein the quotation data includes: price, service level, and transportation time; The data storage unit is used to store the quotation data.

5. The system according to claim 4, characterized in that The calculation module also includes: The second calculation unit is used to determine the optimal transportation company based on the quotation data and in combination with a second preset algorithm.

6. The system according to claim 5, characterized in that The second computing unit comprises: A third calculation subunit is used to determine a comprehensive scoring function based on the price, service level, and transportation time to obtain a scoring result for each of the transportation companies; A fourth calculation subunit, configured to determine a weight factor of each of the transportation companies based on the scoring result; The fifth calculation subunit is used to determine the optimal transportation company based on the weight factor.

7. The system according to claim 6, characterized in that The comprehensive scoring function is: F i =a* +β*S i +c* ; Among them, P i is the price of the i-th transport company, S i is the service level of the i-th transport company, T i is the transportation time of the i-th transportation company, α is the weight of the price, β is the weight of the service level, γ is the weight of the transportation time, and F i is the scoring result of the i-th transportation company.

8. The system according to claim 6, characterized in that The formula for calculating the weight factor is: W i = ; The calculation formula for determining the optimal transportation company based on the weight factor is: i * =argmax i W i ; Among them, W i is the weight factor of the i-th transportation company, i * It is the transportation company with the largest weight factor.

9. An intelligent carton production management method, characterized in that: include: Obtain the production cost per carton, the production volume in a time period, the storage cost per carton per unit time, the inventory volume in the current time period, and the transportation volume in the current time period; Calculate the optimal total cost for the current time period based on the production cost per unit carton, the production volume in the time period, the storage cost per unit carton per unit time, the inventory volume in the current time period, the transportation volume in the current time period and in combination with a first preset algorithm; An optimal transportation volume corresponding to the optimal total cost is determined to schedule transportation of finished cartons based on the optimal transportation volume.

10. The method according to claim 9, characterized in that Also includes: Starting from the last time period, reversely recursively deduce the first optimal total cost and the first optimal transportation volume of each previous time period; Starting from the first time period, the inventory quantity of the next time period, the second optimal transportation quantity of the current time period, and the second optimal total cost of the current time period are positively updated according to the first optimal transportation quantity; Starting from the first time period, the second optimal transportation volume and the second optimal total cost of each time period are output to schedule transportation of finished cartons based on the optimal transportation volume.

Images