Material supplement prediction method and device of intelligent manufacturing system and computer equipment

Abstract

The invention relates to a material supplement prediction method and device of an intelligent manufacturing system and computer equipment. The method comprises the steps of obtaining material monitoring information corresponding to order information of an intelligent manufacturing system and current environment information, and identifying order distribution information and order demand information of the intelligent manufacturing system based on the order information; and based on the order distribution information of the intelligent manufacturing system and the current environment information, predicting future order distribution information of the intelligent manufacturing system through an order quantity prediction model, and based on the order demand information and the material monitoring information corresponding to each piece of order information, determining the order quantity of the intelligent manufacturing system. Identifying a material deviation range and current material information of each material type of the intelligent manufacturing system; and through the material supplement prediction network, predicting the latest material supplement time point and the material supplement demand quantity of each material type of the intelligent manufacturing system, and obtaining a material supplement prediction result of the intelligent manufacturing system. By adopting the method, the analysis and prediction accuracy of the meal data can be improved.

Description

Technical Field

[0001] The present application relates to the fields of big data analysis and artificial intelligence technology, and in particular to a material replenishment prediction method, device and computer equipment for an intelligent manufacturing system. Background Art

[0002] In modern manufacturing and service industries, the application of intelligent manufacturing systems, automatic control systems, and logistics and warehousing management systems is becoming increasingly widespread. Intelligent manufacturing systems, through automated equipment and intelligent management, achieve automation and intelligentization of production processes, improving production efficiency and product quality. Automatic control systems, through various sensors and control algorithms, enable real-time monitoring and control of the production process, ensuring its stability and safety. Logistics and warehousing management systems, through intelligent management of logistics and warehousing activities, improve efficiency and accuracy. For intelligent production equipment, raw material monitoring and timely replenishment ensure efficient, safe, and stable production. Therefore, improving real-time monitoring and control of material replenishment is a current research focus.

[0003] Traditional solutions for intelligent manufacturing systems use sensors to monitor the current remaining material in each raw material barrel in real time and perform refill operations based on this data. However, this technology cannot predict refill needs. Therefore, when there is a large flow of people, refill operations may not be carried out in a timely manner, resulting in delayed refilling. This results in poor refill timeliness for intelligent manufacturing systems. Summary of the Invention

[0004] Based on this, it is necessary to provide a material replenishment prediction method, device, computer equipment, computer-readable storage medium and computer program product for an intelligent manufacturing system to address the above technical problems.

[0005] In a first aspect, the present application provides a material replenishment prediction method for an intelligent manufacturing system, comprising:

[0006] Obtaining order information of the intelligent manufacturing system, material monitoring information corresponding to each order information, and current environment information, and identifying order distribution information of the intelligent manufacturing system and order demand information corresponding to each order information based on each order information;

[0007] Based on the order distribution information of the intelligent manufacturing system and the current environment information, using an order quantity prediction model, predict the future order distribution information of the intelligent manufacturing system, and based on the order demand information corresponding to each order information and the material monitoring information corresponding to each order information, identify the material deviation range of each material type in the intelligent manufacturing system and the current material information of the intelligent manufacturing system;

[0008] Based on the future order distribution information, the material deviation range of each material type, and the current material information of the intelligent manufacturing system, the latest replenishment time point of each material type in the intelligent manufacturing system and the replenishment demand of each material type are predicted through the replenishment prediction network, and the latest replenishment time point of each material type and the replenishment demand of each material type are used as the material replenishment prediction results of the intelligent manufacturing system.

[0009] Optionally, the identifying, based on each of the order information, order distribution information of the intelligent manufacturing system and order demand information corresponding to each of the order information, includes:

[0010] Identify the order task corresponding to each order information and the customer demand information corresponding to each order information, and query the order type corresponding to each order information based on the order task corresponding to each order information;

[0011] Based on the customer demand information corresponding to each order information, identifying the material demand quantity of each material type corresponding to each order information, and using the material demand quantity of each material type corresponding to each order information as the order demand information corresponding to the order information;

[0012] For each order type, the order information corresponding to the order type is sorted in the time sequence corresponding to the order placement time point of each order information to obtain the sub-order distribution information of the order type, and the sub-order distribution information of all order types is used as the order distribution information of the intelligent manufacturing system.

[0013] Optionally, the predicting future order distribution information of the intelligent manufacturing system by using an order quantity prediction model based on the order distribution information of the intelligent manufacturing system and the current environment information includes:

[0014] Inquiring in the database the applicable scope of the environmental data corresponding to each order task of each order type, and identifying the environmental data change information of each environmental type and the pedestrian flow change information of each pedestrian flow type based on the current environmental information;

[0015] Based on the environmental data change information of each of the environmental types, querying the environmental trend information of each of the environmental types, and based on the human flow change information of the human flow type, identifying the human flow trend information of the human flow type through a linear trend recognition network;

[0016] For each order type, based on the sub-order distribution information corresponding to the order type, the linear trend recognition network is used to identify the order trend information of each order task of the order type; and based on the applicable scope of the environmental data corresponding to each order task, the environmental trend information of each environmental type, the pedestrian flow trend information of the pedestrian flow type, and the order trend information of each order task, the order quantity prediction model is used to predict the sub-future task distribution information of each order task;

[0017] The sub-future task distribution information of each order task of all order types is used as the future order distribution information of the intelligent manufacturing system.

[0018] Optionally, the identifying, based on the order demand information corresponding to each order information and the material monitoring information corresponding to each order information, the material deviation range of each material type of the intelligent manufacturing system and the current material information of the intelligent manufacturing system includes:

[0019] Based on the material monitoring information corresponding to each of the order information, identifying the actual material consumption value of each material type corresponding to each of the order information;

[0020] Calculating a material consumption deviation value for each material type corresponding to each order information based on the material demand quantity for each material type corresponding to each order information and the actual material consumption value for each material type corresponding to each order information, and identifying a material deviation range for each material type using a clustering algorithm based on the material consumption deviation value for each material type corresponding to each order information;

[0021] Obtain the refill record information corresponding to each material type, and based on the refill record information of each material type, identify the actual material value of each material type after the most recent refill;

[0022] Based on the actual material consumption value of each material type corresponding to each order information and the actual material value of each material type, the remaining material value of each material type is calculated, and the remaining material value of each material type is used as the current material information of the intelligent manufacturing system.

[0023] Optionally, the predicting, based on the future order distribution information, the material deviation range of each material type, and the current material information of the intelligent manufacturing system, the latest refueling time point of each material type in the intelligent manufacturing system and the refueling demand of each material type through a refueling prediction network includes:

[0024] Obtain the material consumption deviation value of each material type corresponding to each order information, and based on the material consumption deviation value of each material type corresponding to each order information and the order task corresponding to each order information, calculate the average material consumption deviation value of each order task for each material type;

[0025] Based on the sub-future task distribution information of each order task, the average material consumption deviation value of each order task for each material type, and the average material consumption value of each order task for each material type, generating actual consumption distribution information of each material type for each order task;

[0026] Based on the actual consumption distribution information of each material type for each order task, the future consumption distribution information of each material type is predicted through the replenishment prediction network. Based on the future consumption distribution information of each material type and the remaining material value of each material type, the latest replenishment time point of each material type and the replenishment demand of each material type are calculated.

[0027] Optionally, the generating of actual consumption distribution information of each material type for each order task based on the sub-future task distribution information of each order task, the average material consumption deviation value of each order task for each material type, and the average material consumption value of each order task for each material type includes:

[0028] For each order task, based on the sub-future task distribution information of the order task and the average material consumption value of each material type of the order task, calculate the actual consumption distribution information of each material type of the order task;

[0029] Based on the average material consumption deviation value of each material type for the order task, the actual consumption distribution information of each material type is subjected to distribution adjustment processing to obtain the actual consumption distribution information of each material type for each order task.

[0030] In a second aspect, the present application also provides a material replenishment prediction device for an intelligent manufacturing system, comprising:

[0031] An acquisition module is used to obtain each order information of the intelligent manufacturing system, the material monitoring information corresponding to each order information, and the current environment information, and based on each order information, identify the order distribution information of the intelligent manufacturing system and the order demand information corresponding to each order information;

[0032] an identification module configured to predict future order distribution information of the intelligent manufacturing system using an order quantity prediction model based on the order distribution information of the intelligent manufacturing system and the current environment information, and to identify a material deviation range for each material type of the intelligent manufacturing system and current material information of the intelligent manufacturing system based on order demand information corresponding to each order information and material monitoring information corresponding to each order information;

[0033] The prediction module is used to predict the latest replenishment time point of each material type in the intelligent manufacturing system and the replenishment demand of each material type through the replenishment prediction network based on the future order distribution information, the material deviation range of each material type, and the current material information of the intelligent manufacturing system, and use the latest replenishment time point of each material type and the replenishment demand of each material type as the material replenishment prediction result of the intelligent manufacturing system.

[0034] Optionally, the acquisition module is specifically configured to:

[0035] Identify the order task corresponding to each order information and the customer demand information corresponding to each order information, and query the order type corresponding to each order information based on the order task corresponding to each order information;

[0036] Based on the customer demand information corresponding to each order information, identifying the material demand quantity of each material type corresponding to each order information, and using the material demand quantity of each material type corresponding to each order information as the order demand information corresponding to the order information;

[0037] For each order type, the order information corresponding to the order type is sorted in the time sequence corresponding to the order placement time point of each order information to obtain the sub-order distribution information of the order type, and the sub-order distribution information of all order types is used as the order distribution information of the intelligent manufacturing system.

[0038] Optionally, the identification module is specifically configured to:

[0039] Inquiring in the database the applicable scope of the environmental data corresponding to each order task of each order type, and identifying the environmental data change information of each environmental type and the pedestrian flow change information of each pedestrian flow type based on the current environmental information;

[0040] Based on the environmental data change information of each of the environmental types, querying the environmental trend information of each of the environmental types, and based on the human flow change information of the human flow type, identifying the human flow trend information of the human flow type through a linear trend recognition network;

[0041] For each order type, based on the sub-order distribution information corresponding to the order type, the linear trend recognition network is used to identify the order trend information of each order task of the order type; and based on the applicable scope of the environmental data corresponding to each order task, the environmental trend information of each environmental type, the pedestrian flow trend information of the pedestrian flow type, and the order trend information of each order task, the order quantity prediction model is used to predict the sub-future task distribution information of each order task;

[0042] The sub-future task distribution information of each order task of all order types is used as the future order distribution information of the intelligent manufacturing system.

[0043] Optionally, the identification module is specifically configured to:

[0044] Based on the material monitoring information corresponding to each of the order information, identifying the actual material consumption value of each material type corresponding to each of the order information;

[0045] Calculating a material consumption deviation value for each material type corresponding to each order information based on the material demand quantity for each material type corresponding to each order information and the actual material consumption value for each material type corresponding to each order information, and identifying a material deviation range for each material type using a clustering algorithm based on the material consumption deviation value for each material type corresponding to each order information;

[0046] Obtain the refill record information corresponding to each material type, and based on the refill record information of each material type, identify the actual material value of each material type after the most recent refill;

[0047] Based on the actual material consumption value of each material type corresponding to each order information and the actual material value of each material type, the remaining material value of each material type is calculated, and the remaining material value of each material type is used as the current material information of the intelligent manufacturing system.

[0048] Optionally, the prediction module is specifically used to:

[0049] Obtain the material consumption deviation value of each material type corresponding to each order information, and based on the material consumption deviation value of each material type corresponding to each order information and the order task corresponding to each order information, calculate the average material consumption deviation value of each order task for each material type;

[0050] Based on the sub-future task distribution information of each order task, the average material consumption deviation value of each order task for each material type, and the average material consumption value of each order task for each material type, generating actual consumption distribution information of each material type for each order task;

[0051] Based on the actual consumption distribution information of each material type for each order task, the future consumption distribution information of each material type is predicted through the replenishment prediction network. Based on the future consumption distribution information of each material type and the remaining material value of each material type, the latest replenishment time point of each material type and the replenishment demand of each material type are calculated.

[0052] Optionally, the prediction module is specifically used to:

[0053] For each order task, based on the sub-future task distribution information of the order task and the average material consumption value of each material type of the order task, calculate the actual consumption distribution information of each material type of the order task;

[0054] Based on the average material consumption deviation value of each material type for the order task, the actual consumption distribution information of each material type is subjected to distribution adjustment processing to obtain the actual consumption distribution information of each material type for each order task.

[0055] In a third aspect, the present application provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the methods described in the first aspect when executing the computer program.

[0056] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of any one of the methods in the first aspect.

[0057] In a fifth aspect, the present application provides a computer program product, wherein the computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of any one of the methods in the first aspect are implemented.

[0058] The material replenishment prediction method, device and computer equipment of the above-mentioned intelligent manufacturing system obtain each order information of the intelligent manufacturing system, the material monitoring information corresponding to each order information, and the current environmental information, and identify the order distribution information of the intelligent manufacturing system and the order demand information corresponding to each order information based on each order information; based on the order distribution information of the intelligent manufacturing system and the current environmental information, predict the future order distribution information of the intelligent manufacturing system through an order quantity prediction model, and based on the order demand information corresponding to each order information and the material monitoring information corresponding to each order information, identify the material deviation range of each material type of the intelligent manufacturing system and the current material information of the intelligent manufacturing system; based on the future order distribution information, the material deviation range of each material type and the current material information of the intelligent manufacturing system, predict the latest replenishment time point of each material type of the intelligent manufacturing system and the replenishment demand of each material type through a replenishment prediction network, and use the latest replenishment time point of each material type and the replenishment demand of each material type as the material replenishment prediction result of the intelligent manufacturing system. In this embodiment, by combining order information, material monitoring information corresponding to each order information collected by sensor detection equipment set in the intelligent manufacturing system, and current environmental information, the order distribution information and order demand information of the intelligent manufacturing system are analyzed, and then combined with the current environmental information, the future order distribution information of the intelligent manufacturing system is analyzed. By combining environmental information such as human flow, weather, and time, the future order distribution information of the intelligent manufacturing system is comprehensively analyzed, thereby improving the accuracy and comprehensiveness of identifying future order distribution, ensuring that replenishment operations can be carried out in time when the human flow is large. Then, this solution also starts from the actual material consumption of each material type, which is not limited to the demand for each material type for each order information, but also includes the actual consumption of each material type during production for each order information, thereby comprehensively predicting the latest replenishment time point of each material type in the intelligent manufacturing system and the replenishment demand for each material type, thereby ensuring the accuracy and comprehensiveness of replenishment. Finally, this solution combines environmental analysis to predict order quantities and actual material consumption data for material replenishment analysis. This eliminates the need for manual analysis and the potential for errors in early warning based solely on remaining material levels. This ensures efficient replenishment and timely replenishment even when personnel are busy. This comprehensively improves the timeliness of replenishment for the intelligent manufacturing system. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0060] Figure 1 1 is a flow chart of a material replenishment prediction method for an intelligent manufacturing system in one embodiment;

[0061] Figure 2 A schematic diagram of a process flow of a material replenishment prediction example of an intelligent manufacturing system in one embodiment;

[0062] Figure 3 This is a structural block diagram of a material replenishment prediction device for an intelligent manufacturing system in one embodiment;

[0063] Figure 4 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0065] The material replenishment prediction method for the intelligent manufacturing system provided in the embodiment of the present application can be applied to the intelligent control system for material demand prediction. The intelligent control system includes a central processing unit, a data storage module, an output control module, and a user interaction module. The central processing unit uses an ARM Cortex-A7 processor, the data storage module uses a 128GB flash memory, the output control module uses a solid-state relay, and the user interaction module uses a 7-inch touch screen. The central processing unit of the system can be applied to a terminal, which can be, but is not limited to, various personal computers, laptops, mid-range computers, etc. Among them, the terminal analyzes the order distribution information and order demand information of the intelligent manufacturing system by combining the order information, the material monitoring information corresponding to each order information collected by the sensor detection equipment set in the intelligent manufacturing system, and the current environmental information, and then combines the current environmental information to analyze the future order distribution information of the intelligent manufacturing system, so that by combining the environmental information such as human flow, weather, time, etc., the future order distribution information of the intelligent manufacturing system is comprehensively analyzed, thereby improving the accuracy and comprehensiveness of the recognition of future order distribution, ensuring that the replenishment operation can be carried out in time when the human flow is large. Then, this solution also starts from the actual material consumption of each material type, not only limited to the demand for each material type for each order information, but also includes the actual consumption of each material type for each order information during production, thereby comprehensively predicting the latest replenishment time point of each material type in the intelligent manufacturing system and the replenishment demand for each material type, ensuring the accuracy and comprehensiveness of replenishment. Finally, this solution combines environmental analysis to predict order quantities and actual material consumption data for material replenishment analysis. This eliminates the need for manual analysis and the potential for errors in early warning based solely on remaining material levels. This ensures efficient replenishment and timely replenishment even when personnel are busy. This comprehensively improves the timeliness of replenishment for the intelligent manufacturing system.

[0066] In an exemplary embodiment, Figure 1 As shown, a material replenishment prediction method for an intelligent manufacturing system is provided, which is described by taking the application of the method to a terminal as an example, and includes the following steps S101 to S103.

[0067] Step S101: Obtain order information of the intelligent manufacturing system, material monitoring information corresponding to each order information, and current environment information, and identify order distribution information of the intelligent manufacturing system and order demand information corresponding to each order information based on each order information.

[0068] In this embodiment, the terminal queries the intelligent manufacturing system's database to collect all order information from the current moment to a preset time period, thereby obtaining order information for each item in the intelligent manufacturing system. The preset time period can be 3 hours, 2 hours, 1 hour, etc., and the intelligent manufacturing system can be the control system of an intelligent liquid dispensing machine. The terminal then receives gravity change information collected by gravity sensors located at the material barrels of each material type, obtaining material monitoring information corresponding to each order information. Finally, the terminal obtains current environmental information from multiple third-party platforms and user records of the intelligent liquid dispensing machine. This current environmental information includes weather information, temperature information, time information, and traffic flow information. These multiple third-party platforms include, but are not limited to, meteorological platforms and time platforms. Finally, based on each item of order information, the terminal identifies order distribution information for the intelligent manufacturing system and the order demand information corresponding to each item of order information. The order distribution information includes sub-order distribution information for different order types, while the order demand information indicates the material demand for each material type corresponding to each order information. Material types include, but are not limited to, main material types, auxiliary material types, and additive types. For example, main ingredient types include water, tea, milk, and fruit puree, while auxiliary ingredient types include flavor auxiliary ingredients (syrup, raw materials of different flavors), temperature auxiliary ingredients (ice cubes), and texture auxiliary ingredients (red beans, milk cap, tapioca pearls, pudding, and coconut flakes). The specific recognition process will be explained in detail later.

[0069] Step S102: Based on the order distribution information of the intelligent manufacturing system and the current environmental information, the future order distribution information of the intelligent manufacturing system is predicted through the order quantity prediction model, and based on the order demand information corresponding to each order information and the material monitoring information corresponding to each order information, the material deviation range of each material type of the intelligent manufacturing system and the current material information of the intelligent manufacturing system are identified.

[0070] In this embodiment, the terminal uses an order quantity prediction model based on the intelligent manufacturing system's order distribution information and current environmental information to predict the system's future order distribution information. It also identifies the material deviation range for each material type in the intelligent manufacturing system, as well as the current material information of the intelligent manufacturing system, based on the order demand information and material monitoring information corresponding to each order information. The order quantity prediction model is a linear trend prediction model based on a time series prediction method. The future order distribution information includes the distribution information of sub-future tasks for each order task. Order tasks for different order information may be the same, while each order type includes one or more order tasks. Different order information corresponding to the same order task may originate from different users and at different order time points. The order information corresponding to each order task differs in information such as user origin, order time point, and order quantity. The material deviation range for each material type is the range corresponding to the deviation between the actual usage of each material type and the standard usage of each material type. The current material information summary includes the remaining amount of each material type.

[0071] Step S103, based on the future order distribution information, the material deviation range of each material type, and the current material information of the intelligent manufacturing system, the latest replenishment time point of each material type in the intelligent manufacturing system and the replenishment demand of each material type are predicted through the replenishment prediction network, and the latest replenishment time point of each material type and the replenishment demand of each material type are used as the material replenishment prediction results of the intelligent manufacturing system.

[0072] In this embodiment, based on future order distribution information, the material deviation range for each material type, and the current material information of the intelligent manufacturing system, the terminal uses a material replenishment prediction network to predict the latest restocking time and restocking requirement for each material type in the intelligent manufacturing system. These latest restocking time and restocking requirement are used as the material restocking prediction results for the intelligent manufacturing system. The material replenishment prediction network is also a linear trend prediction model based on time series prediction methods. The difference between this material replenishment prediction network and the order quantity prediction model lies in the different sample data used for their training.

[0073] Based on the above scheme, by combining the order information, the material monitoring information corresponding to each order information collected by the sensor detection equipment set in the intelligent manufacturing system, and the current environmental information, the order distribution information and order demand information of the intelligent manufacturing system are analyzed, and then combined with the current environmental information, the future order distribution information of the intelligent manufacturing system is analyzed. By combining environmental information such as human flow, weather, and time, the future order distribution information of the intelligent manufacturing system is comprehensively analyzed, thereby improving the accuracy and comprehensiveness of the recognition of future order distribution, ensuring that replenishment operations can be carried out in time when the human flow is large. Then, this scheme also starts from the actual material consumption of each material type, not only limited to the demand for each material type for each order information, but also includes the actual consumption of each material type for each order information during production, thereby comprehensively predicting the latest replenishment time point of each material type in the intelligent manufacturing system and the replenishment demand for each material type, ensuring the accuracy and comprehensiveness of replenishment. Finally, this solution combines environmental analysis to predict order quantities and actual material consumption data for material replenishment analysis. This eliminates the need for manual analysis and the potential for errors in early warning based solely on remaining material levels. This ensures efficient replenishment and timely replenishment even when personnel are busy. This comprehensively improves the timeliness of replenishment for the intelligent manufacturing system.

[0074] Optionally, based on each order information, the order distribution information of the intelligent manufacturing system and the order demand information corresponding to each order information are identified, including: identifying the order task corresponding to each order information and the customer demand information corresponding to each order information, and querying the order type corresponding to each order information based on the order task corresponding to each order information; identifying the material demand quantity of each material type corresponding to each order information based on the customer demand information corresponding to each order information, and using the material demand quantity of each material type corresponding to each order information as the order demand information corresponding to the order information; for each order type, sorting and processing each order information corresponding to the order type according to the time sequence corresponding to the order placement time point of each order information to obtain sub-order distribution information of the order type, and using the sub-order distribution information of all order types as the order distribution information of the intelligent manufacturing system.

[0075] In this embodiment, the terminal identifies the order task corresponding to each order information and the customer requirement information corresponding to each order information, and queries the order type corresponding to each order information based on the order task corresponding to each order information. The customer requirement information corresponding to each order information is the customer's material requirement information corresponding to each order information, such as temperature requirement information, sweetness requirement information, volume requirement information, auxiliary material requirement information, additive requirement information, etc.

[0076] Then, the terminal identifies the material demand quantity of each material type corresponding to each order information based on the customer demand information corresponding to each order information, and uses the material demand quantity of each material type corresponding to each order information as the order demand information corresponding to the order information.

[0077] For each order type, the terminal sorts the order information corresponding to the order type in the time sequence corresponding to the order placement time of each order information, obtains the sub-order distribution information of the order type, and uses the sub-order distribution information of all order types as the order distribution information of the intelligent manufacturing system.

[0078] Based on the above solution, the comprehensiveness and accuracy of the analysis of each order type are improved by splitting and identifying each order information and analyzing the materials.

[0079] Optionally, based on the order distribution information of the intelligent manufacturing system and the current environmental information, the future order distribution information of the intelligent manufacturing system is predicted through an order quantity prediction model, including: querying the applicable scope of environmental data corresponding to each order task of each order type in the database, and identifying the environmental data change information of each environmental type and the human flow change information of the human flow type based on the current environmental information; querying the environmental trend information of each environmental type based on the environmental data change information of each environmental type, and identifying the human flow trend information of the human flow type based on the human flow change information of the human flow type through a linear trend recognition network; for each order type, based on the sub-order distribution information corresponding to the order type, identifying the order trend information of each order task of the order type through a linear trend recognition network, and based on the applicable scope of environmental data corresponding to each order task, the environmental trend information of each environmental type, the human flow trend information of the human flow type, and the order trend information of each order task, predicting the sub-future task distribution information of each order task through an order quantity prediction model; using the sub-future task distribution information of each order task of all order types as the future order distribution information of the intelligent manufacturing system.

[0080] In this embodiment, the terminal queries the database for the applicable scope of environmental data corresponding to each order task of each order type. Based on the current environmental information, it identifies changes in environmental data for each environmental type, as well as changes in pedestrian flow for each pedestrian flow type. Environmental types include, but are not limited to, weather types, temperature types, and time types. The reason why pedestrian flow types need to be separated separately is that they are acquired differently from the previous three types, and pedestrian flow predictions cannot be directly obtained, but can only be identified based on the linear trend recognition network designed in this solution.

[0081] The terminal queries environmental trend information for each environment type based on environmental data change information for each environment type, and identifies pedestrian flow trend information for each pedestrian flow type based on pedestrian flow change information using a linear trend recognition network. The linear trend recognition network is a linear neural network based on a linear regression method.

[0082] For each order type, the terminal identifies the order trend information of each order task of the order type based on the sub-order distribution information corresponding to the order type through a linear trend recognition network, and predicts the sub-future task distribution information of each order task through an order quantity prediction model based on the applicable scope of the environmental data corresponding to each order task, the environmental trend information of each environmental type, the pedestrian flow trend information of the pedestrian flow type, and the order trend information of each order task.

[0083] Finally, the terminal uses the sub-future task distribution information of each order task of all order types as the future order distribution information of the intelligent manufacturing system.

[0084] Based on the above solution, by combining environmental information to predict the future order distribution information of different order tasks, the prediction accuracy of the future order distribution information of each order task is improved.

[0085] Optionally, based on the order demand information corresponding to each order information and the material monitoring information corresponding to each order information, the material deviation range of each material type of the intelligent manufacturing system and the current material information of the intelligent manufacturing system are identified, including: based on the material monitoring information corresponding to each order information, the actual material consumption value of each material type corresponding to each order information is identified; based on the material demand of each material type corresponding to each order information and the actual material consumption value of each material type corresponding to each order information, the material consumption deviation value of each material type corresponding to each order information is calculated, and based on the material consumption deviation value of each material type corresponding to each order information, the material deviation range of each material type is identified through a clustering algorithm; the replenishment record information corresponding to each material type is obtained, and based on the replenishment record information of each material type, the actual material value of each material type after the most recent replenishment is identified; based on the actual material consumption value of each material type corresponding to each order information and the actual material value of each material type, the remaining material value of each material type is calculated, and the remaining material value of each material type is used as the current material information of the intelligent manufacturing system.

[0086] In this embodiment, the terminal identifies the actual material consumption value of each material type corresponding to each order information based on the material monitoring information corresponding to each order information. The terminal then calculates a material consumption deviation value for each material type corresponding to each order information based on the material requirement and the actual material consumption value of each material type corresponding to each order information. This material consumption deviation value is the material requirement minus the actual material consumption value. This material consumption deviation value can be positive or negative. A positive value indicates that the material requirement is greater than the actual material consumption value, while a negative value indicates that the material requirement is less than the actual material consumption value.

[0087] Based on the material consumption deviation values for each material type corresponding to each order, the terminal uses a clustering algorithm to identify the material deviation range for each material type. The terminal then obtains the corresponding refill records for each material type and, based on these records, identifies the actual material value of each material type after the most recent refill.

[0088] Finally, the terminal calculates the remaining material value of each material type based on the actual material consumption value of each material type corresponding to each order information and the actual material value of each material type, and uses the remaining material value of each material type as the current material information of the intelligent manufacturing system.

[0089] Based on the above solution, by calculating the consumption deviation value of each material, the material deviation range of each material type is identified, thereby improving the accuracy of the actual material consumption analysis of the material type.

[0090] Optionally, based on future order distribution information, material deviation ranges of each material type, and current material information of the intelligent manufacturing system, the latest replenishment time point of each material type in the intelligent manufacturing system and the replenishment demand of each material type are predicted through the replenishment prediction network, including: obtaining the material consumption deviation value of each material type corresponding to each order information, and calculating the average material consumption deviation value of each order task for each material type based on the material consumption deviation value of each material type corresponding to each order information and the order task corresponding to each order information; generating the actual consumption distribution information of each material type for each order task based on the sub-future task distribution information of each order task, the average material consumption deviation value of each order task for each material type, and the average material consumption value of each order task for each material type; based on the actual consumption distribution information of each material type for each order task, predicting the future consumption distribution information of each material type through the replenishment prediction network, and calculating the latest replenishment time point of each material type and the replenishment demand of each material type based on the future consumption distribution information of each material type and the remaining material value of each material type.

[0091] In this embodiment, the terminal obtains the material consumption deviation value of each material type corresponding to each order information, and based on the material consumption deviation value of each material type corresponding to each order information and the order task corresponding to each order information, calculates the average material consumption deviation value of each order task for each material type.

[0092] The terminal then generates actual consumption distribution information for each material type for each order task based on the distribution information of each future task's sub-tasks, the average material consumption deviation for each material type, and the average material consumption value for each material type. This actual consumption distribution information represents the consumption distribution information for each material type during the actual production process for each order task.

[0093] Next, based on the actual consumption distribution information of each material type for each order task, the terminal uses the replenishment prediction network to predict the future consumption distribution information of each material type. Based on the future consumption distribution information of each material type and the remaining material value of each material type, the terminal calculates the latest replenishment time point for each material type and the replenishment requirement for each material type. This calculation method is to use the future consumption distribution information of each material type to calculate the remaining material value of each material type that can meet the consumption duration of the material type, and use the time point from the current time point to the end of the consumption duration as the latest replenishment time point, and use the total material consumption value in the future consumption distribution information after this latest time point as the replenishment requirement for the material type.

[0094] Based on the above solution, the consumption of each material type is predicted by analyzing and predicting the trends of different order tasks, which improves the comprehensiveness of the consumption analysis of each material type and avoids the prediction bias problem caused by simply predicting the material consumption.

[0095] Optionally, based on the sub-future task distribution information of each order task, the average material consumption deviation value of each order task for each material type, and the average material consumption value of each order task for each material type, the actual consumption distribution information of each material type for each order task is generated, including: for each order task, based on the sub-future task distribution information of the order task and the average material consumption value of the order task for each material type, calculating the actual consumption distribution information of each material type of the order task; based on the average material consumption deviation value of the order task for each material type, performing distribution adjustment processing on the actual consumption distribution information of each material type to obtain the actual consumption distribution information of each material type for each order task.

[0096] In this embodiment, for each order task, the terminal calculates the actual consumption distribution information for each material type in the order task based on the order task's sub-future task distribution information and the order task's average material consumption value for each material type. The terminal then performs a distribution adjustment on the actual consumption distribution information for each material type based on the order task's average material consumption deviation value for each material type, thereby obtaining the actual consumption distribution information for each material type in the order task. This partial adjustment is performed by adding the average material consumption deviation value for each material type to the consumption amount at each moment in the actual consumption distribution information to obtain the actual consumption distribution information.

[0097] Based on the above solution, by starting from different order tasks, the actual consumption distribution information of different material types is calculated respectively, so that the actual consumption distribution information obtained is more accurate.

[0098] The application also provides an example of material replenishment prediction for an intelligent manufacturing system, such as Figure 2 As shown, the specific processing process includes the following steps:

[0099] Step S201: Acquire order information of the intelligent manufacturing system, material monitoring information corresponding to each order information, and current environment information.

[0100] Step S202 , identifying the order task corresponding to each order information and the customer demand information corresponding to each order information, and querying the order type corresponding to each order information based on the order task corresponding to each order information.

[0101] Step S203 , based on the customer demand information corresponding to each order information, identifying the material demand quantity of each material type corresponding to each order information, and using the material demand quantity of each material type corresponding to each order information as the order demand information corresponding to the order information.

[0102] In step S204, for each order type, the order information corresponding to the order type is sorted in the time sequence corresponding to the order placement time point of each order information to obtain the sub-order distribution information of the order type, and the sub-order distribution information of all order types is used as the order distribution information of the intelligent manufacturing system.

[0103] Step S205 , querying the database for applicable scope of environmental data corresponding to each order task of each order type, and identifying environmental data change information of each environmental type and pedestrian flow change information of each pedestrian flow type based on current environmental information.

[0104] Step S206 , based on the environmental data change information of each environmental type, query the environmental trend information of each environmental type, and based on the human flow change information of the human flow type, identify the human flow trend information of the human flow type through a linear trend recognition network.

[0105] In step S207, for each order type, based on the sub-order distribution information corresponding to the order type, the order trend information of each order task of the order type is identified through a linear trend recognition network, and based on the applicable scope of the environmental data corresponding to each order task, the environmental trend information of each environmental type, the human flow trend information of the human flow type, and the order trend information of each order task, the sub-future task distribution information of each order task is predicted through an order quantity prediction model.

[0106] Step S208: The sub-future task distribution information of each order task of all order types is used as the future order distribution information of the intelligent manufacturing system.

[0107] Step S209 : Based on the material monitoring information corresponding to each order information, the actual material consumption value of each material type corresponding to each order information is identified.

[0108] Step S210, based on the material demand of each material type corresponding to each order information and the actual material consumption value of each material type corresponding to each order information, calculate the material consumption deviation value of each material type corresponding to each order information, and based on the material consumption deviation value of each material type corresponding to each order information, identify the material deviation range of each material type through a clustering algorithm.

[0109] Step S211 : acquiring the refill record information corresponding to each material type, and identifying the actual material value of each material type after the most recent refill based on the refill record information of each material type.

[0110] Step S212: Calculate the remaining material value of each material type based on the actual material consumption value of each material type corresponding to each order information and the actual material value of each material type, and use the remaining material value of each material type as the current material information of the intelligent manufacturing system.

[0111] Step S213: Obtain the material consumption deviation value of each material type corresponding to each order information, and based on the material consumption deviation value of each material type corresponding to each order information and the order task corresponding to each order information, calculate the average material consumption deviation value of each order task for each material type.

[0112] Step S214 , for each order task, based on the sub-future task distribution information of the order task and the average material consumption value of the order task for each material type, calculate the actual consumption distribution information of each material type of the order task.

[0113] Step S215 , based on the average material consumption deviation value of each material type for each order task, the actual consumption distribution information of each material type is adjusted to obtain the actual consumption distribution information of each material type for each order task.

[0114] Step S216, based on the actual consumption distribution information of each material type of each order task, predict the future consumption distribution information of each material type through the replenishment prediction network, and based on the future consumption distribution information of each material type and the remaining material value of each material type, calculate the latest replenishment time point of each material type and the replenishment demand of each material type.

[0115] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0116] Based on the same inventive concept, the present application also provides a material replenishment prediction device for an intelligent manufacturing system for implementing the aforementioned material replenishment prediction method for an intelligent manufacturing system. The implementation solution provided by this device is similar to the implementation solution described in the aforementioned method. Therefore, the specific limitations of the material replenishment prediction device for one or more intelligent manufacturing systems provided below can be found in the limitations of the material replenishment prediction method for an intelligent manufacturing system described above, and will not be repeated here.

[0117] In an exemplary embodiment, Figure 3 As shown, a material replenishment prediction device for an intelligent manufacturing system is provided, comprising: an acquisition module 310, an identification module 320 and a prediction module 330, wherein:

[0118] An acquisition module 310 is configured to acquire order information of the intelligent manufacturing system, material monitoring information corresponding to each order information, and current environment information, and identify order distribution information of the intelligent manufacturing system and order demand information corresponding to each order information based on each order information;

[0119] Identification module 320 is configured to predict future order distribution information of the intelligent manufacturing system using an order quantity prediction model based on the order distribution information of the intelligent manufacturing system and the current environment information, and to identify material deviation ranges for various material types in the intelligent manufacturing system and current material information of the intelligent manufacturing system based on order demand information corresponding to each order information and material monitoring information corresponding to each order information;

[0120] The prediction module 330 is used to predict the latest replenishment time point of each material type in the intelligent manufacturing system and the replenishment demand of each material type through the replenishment prediction network based on the future order distribution information, the material deviation range of each material type, and the current material information of the intelligent manufacturing system, and use the latest replenishment time point of each material type and the replenishment demand of each material type as the material replenishment prediction result of the intelligent manufacturing system.

[0121] Optionally, the acquisition module 310 is specifically configured to:

[0122] Identify the order task corresponding to each order information and the customer demand information corresponding to each order information, and query the order type corresponding to each order information based on the order task corresponding to each order information;

[0123] Based on the customer demand information corresponding to each order information, identifying the material demand quantity of each material type corresponding to each order information, and using the material demand quantity of each material type corresponding to each order information as the order demand information corresponding to the order information;

[0124] For each order type, the order information corresponding to the order type is sorted in the time sequence corresponding to the order placement time point of each order information to obtain the sub-order distribution information of the order type, and the sub-order distribution information of all order types is used as the order distribution information of the intelligent manufacturing system.

[0125] Optionally, the identification module 320 is specifically configured to:

[0126] Inquiring in the database the applicable scope of the environmental data corresponding to each order task of each order type, and identifying the environmental data change information of each environmental type and the pedestrian flow change information of each pedestrian flow type based on the current environmental information;

[0127] Based on the environmental data change information of each of the environmental types, querying the environmental trend information of each of the environmental types, and based on the human flow change information of the human flow type, identifying the human flow trend information of the human flow type through a linear trend recognition network;

[0128] For each order type, based on the sub-order distribution information corresponding to the order type, the linear trend recognition network is used to identify the order trend information of each order task of the order type; and based on the applicable scope of the environmental data corresponding to each order task, the environmental trend information of each environmental type, the pedestrian flow trend information of the pedestrian flow type, and the order trend information of each order task, the order quantity prediction model is used to predict the sub-future task distribution information of each order task;

[0129] The sub-future task distribution information of each order task of all order types is used as the future order distribution information of the intelligent manufacturing system.

[0130] Optionally, the identification module 320 is specifically configured to:

[0131] Based on the material monitoring information corresponding to each of the order information, identifying the actual material consumption value of each material type corresponding to each of the order information;

[0132] Calculating a material consumption deviation value for each material type corresponding to each order information based on the material demand quantity for each material type corresponding to each order information and the actual material consumption value for each material type corresponding to each order information, and identifying a material deviation range for each material type using a clustering algorithm based on the material consumption deviation value for each material type corresponding to each order information;

[0133] Obtain the refill record information corresponding to each material type, and based on the refill record information of each material type, identify the actual material value of each material type after the most recent refill;

[0134] Based on the actual material consumption value of each material type corresponding to each order information and the actual material value of each material type, the remaining material value of each material type is calculated, and the remaining material value of each material type is used as the current material information of the intelligent manufacturing system.

[0135] Optionally, the prediction module 330 is specifically configured to:

[0136] Obtain the material consumption deviation value of each material type corresponding to each order information, and based on the material consumption deviation value of each material type corresponding to each order information and the order task corresponding to each order information, calculate the average material consumption deviation value of each order task for each material type;

[0137] Based on the sub-future task distribution information of each order task, the average material consumption deviation value of each order task for each material type, and the average material consumption value of each order task for each material type, generating actual consumption distribution information of each material type for each order task;

[0138] Based on the actual consumption distribution information of each material type for each order task, the future consumption distribution information of each material type is predicted through the replenishment prediction network. Based on the future consumption distribution information of each material type and the remaining material value of each material type, the latest replenishment time point of each material type and the replenishment demand of each material type are calculated.

[0139] Optionally, the prediction module 330 is specifically configured to:

[0140] For each order task, based on the sub-future task distribution information of the order task and the average material consumption value of each material type of the order task, calculate the actual consumption distribution information of each material type of the order task;

[0141] Based on the average material consumption deviation value of each material type for the order task, the actual consumption distribution information of each material type is subjected to distribution adjustment processing to obtain the actual consumption distribution information of each material type for each order task.

[0142] Each module in the material replenishment prediction device of the intelligent manufacturing system described above can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in the computer device in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0143] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 4As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface, the display unit and the input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a material replenishment prediction method for an intelligent manufacturing system is implemented. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse.

[0144] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0145] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, steps of a material replenishment prediction method for an intelligent manufacturing system are implemented.

[0146] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the material replenishment prediction method of the intelligent manufacturing system are implemented.

[0147] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the steps of a material replenishment prediction method for an intelligent manufacturing system.

[0148] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0149] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processor involved in the various embodiments provided herein may be, but are not limited to, a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, and the like.

[0150] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0151] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A material replenishment prediction method for an intelligent manufacturing system, characterized in that: The method comprises: Obtaining order information of the intelligent manufacturing system, material monitoring information corresponding to each order information, and current environment information, and identifying order distribution information of the intelligent manufacturing system and order demand information corresponding to each order information based on each order information; Based on the order distribution information of the intelligent manufacturing system and the current environment information, using an order quantity prediction model, predict the future order distribution information of the intelligent manufacturing system, and based on the order demand information corresponding to each order information and the material monitoring information corresponding to each order information, identify the material deviation range of each material type in the intelligent manufacturing system and the current material information of the intelligent manufacturing system; Based on the future order distribution information, the material deviation range of each material type, and the current material information of the intelligent manufacturing system, the latest replenishment time point of each material type in the intelligent manufacturing system and the replenishment demand of each material type are predicted through the replenishment prediction network, and the latest replenishment time point of each material type and the replenishment demand of each material type are used as the material replenishment prediction results of the intelligent manufacturing system.

2. The method according to claim 1, characterized in that The identifying, based on each of the order information, order distribution information of the intelligent manufacturing system and order demand information corresponding to each of the order information, includes: Identify the order task corresponding to each order information and the customer demand information corresponding to each order information, and query the order type corresponding to each order information based on the order task corresponding to each order information; Based on the customer demand information corresponding to each order information, identifying the material demand quantity of each material type corresponding to each order information, and using the material demand quantity of each material type corresponding to each order information as the order demand information corresponding to the order information; For each order type, the order information corresponding to the order type is sorted in the time sequence corresponding to the order placement time point of each order information to obtain the sub-order distribution information of the order type, and the sub-order distribution information of all order types is used as the order distribution information of the intelligent manufacturing system.

3. The method according to claim 2, characterized in that The predicting of future order distribution information of the intelligent manufacturing system based on the order distribution information of the intelligent manufacturing system and the current environment information by using an order quantity prediction model includes: In the database, query the applicable scope of the environmental data corresponding to each order task of each order type, and identify the environmental data change information of each environmental type and the pedestrian flow change information of the pedestrian flow type based on the current environmental information; Based on the environmental data change information of each of the environmental types, querying the environmental trend information of each of the environmental types, and based on the human flow change information of the human flow type, identifying the human flow trend information of the human flow type through a linear trend recognition network; For each order type, based on the sub-order distribution information corresponding to the order type, the linear trend recognition network is used to identify the order trend information of each order task of the order type; and based on the applicable scope of the environmental data corresponding to each order task, the environmental trend information of each environmental type, the pedestrian flow trend information of the pedestrian flow type, and the order trend information of each order task, the order quantity prediction model is used to predict the sub-future task distribution information of each order task; The sub-future task distribution information of each order task of all order types is used as the future order distribution information of the intelligent manufacturing system.

4. The method according to claim 2, characterized in that The identifying, based on the order demand information corresponding to each order information and the material monitoring information corresponding to each order information, the material deviation range of each material type of the intelligent manufacturing system and the current material information of the intelligent manufacturing system includes: Based on the material monitoring information corresponding to each of the order information, identifying the actual material consumption value of each material type corresponding to each of the order information; Calculating a material consumption deviation value for each material type corresponding to each order information based on the material demand quantity for each material type corresponding to each order information and the actual material consumption value for each material type corresponding to each order information, and identifying a material deviation range for each material type using a clustering algorithm based on the material consumption deviation value for each material type corresponding to each order information; Obtain the refill record information corresponding to each material type, and based on the refill record information of each material type, identify the actual material value of each material type after the most recent refill; Based on the actual material consumption value of each material type corresponding to each order information and the actual material value of each material type, the remaining material value of each material type is calculated, and the remaining material value of each material type is used as the current material information of the intelligent manufacturing system.

5. The method according to claim 3, characterized in that The method includes predicting the latest refill time point and refill demand of each material type of the intelligent manufacturing system based on the future order distribution information, the material deviation range of each material type, and the current material information of the intelligent manufacturing system through a refill prediction network, including: Obtain the material consumption deviation value of each material type corresponding to each order information, and based on the material consumption deviation value of each material type corresponding to each order information and the order task corresponding to each order information, calculate the average material consumption deviation value of each order task for each material type; Based on the sub-future task distribution information of each order task, the average material consumption deviation value of each order task for each material type, and the average material consumption value of each order task for each material type, generating actual consumption distribution information of each material type for each order task; Based on the actual consumption distribution information of each material type for each order task, the future consumption distribution information of each material type is predicted through the replenishment prediction network. Based on the future consumption distribution information of each material type and the remaining material value of each material type, the latest replenishment time point of each material type and the replenishment demand of each material type are calculated.

6. The method according to claim 5, characterized in that Generating actual consumption distribution information of each material type for each order task based on the sub-future task distribution information of each order task, the average material consumption deviation value of each order task for each material type, and the average material consumption value of each order task for each material type includes: For each order task, based on the sub-future task distribution information of the order task and the average material consumption value of each material type of the order task, calculate the actual consumption distribution information of each material type of the order task; Based on the average material consumption deviation value of each material type for the order task, the actual consumption distribution information of each material type is subjected to distribution adjustment processing to obtain the actual consumption distribution information of each material type for each order task.

7. A material replenishment prediction device for an intelligent manufacturing system, characterized in that: The device comprises: An acquisition module is used to obtain each order information of the intelligent manufacturing system, the material monitoring information corresponding to each order information, and the current environment information, and based on each order information, identify the order distribution information of the intelligent manufacturing system and the order demand information corresponding to each order information; an identification module configured to predict future order distribution information of the intelligent manufacturing system using an order quantity prediction model based on the order distribution information of the intelligent manufacturing system and the current environment information, and to identify a material deviation range for each material type of the intelligent manufacturing system and current material information of the intelligent manufacturing system based on order demand information corresponding to each order information and material monitoring information corresponding to each order information; The prediction module is used to predict the latest replenishment time point of each material type in the intelligent manufacturing system and the replenishment demand of each material type through the replenishment prediction network based on the future order distribution information, the material deviation range of each material type, and the current material information of the intelligent manufacturing system, and use the latest replenishment time point of each material type and the replenishment demand of each material type as the material replenishment prediction result of the intelligent manufacturing system.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

Images