Electrode preparation method, device and equipment based on artificial intelligence and storage medium

Abstract

The invention relates to the technical field of intelligent processing, in particular to an electrode material preparation method, device and equipment based on artificial intelligence and a storage medium. To-be-prepared materials and material information, to-be-stored position information and customer demand information corresponding to the to-be-prepared materials are obtained, the to-be-prepared materials are classified according to the material information, and material classification is obtained; and according to the material classification, the to-be-stored position information and the customer demand information, the priorities of the to-be-prepared materials are determined, after the priorities of all the to-be-prepared materials are determined, the material preparation sequence of all the to-be-prepared materials is sorted, a sorting result is obtained, and the to-be-prepared materials are prepared according to the sorting result. And the priority of the to-be-prepared materials is obtained through material classification, the to-be-stored position information and the customer demand information, the preparation sequence of the to-be-prepared materials is sorted according to the priority, a sorting result is obtained, and the to-be-prepared materials are prepared according to the sorting result. Therefore, the material preparation process is intelligently and dynamically optimized, and the intelligent level and the production efficiency of electrode material preparation are improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent processing technology, and in particular to an electrode preparation method, device, equipment and storage medium based on artificial intelligence. Background Art

[0002] In fields such as battery manufacturing, metallurgy, and chemical industry, the preparation process of electrode materials is an important link to ensure smooth production and on-time delivery of products. Traditional preparation methods usually rely on manual experience and lack systematic management and optimization.

[0003] Most of the current technical solutions rely on manual operations and lack the support of information and automation tools, making it difficult to achieve efficient management and scheduling. Manual material preparation is time-consuming, especially when dealing with a large number of different types of materials. It is difficult to locate and access them quickly and accurately, and material classification errors and access errors are prone to occur, affecting the subsequent production quality and progress. When customer needs are urgent, it is difficult to quickly adjust the preparation sequence, resulting in delivery delays, which may lead to improper preparation sequence of important or high-value materials and waste of resources.

[0004] Therefore, how to intelligently and dynamically optimize the material preparation process to improve the intelligence level and production efficiency of electrode preparation has become an urgent problem to be solved. Summary of the Invention

[0005] The embodiments of the present invention provide an artificial intelligence-based electrode preparation method, device, equipment and storage medium to solve the problem of intelligently and dynamically optimizing the material preparation process to improve the intelligence level and production efficiency of electrode preparation.

[0006] In a first aspect, an embodiment of the present invention provides an artificial intelligence-based electrode preparation method, comprising: Obtaining materials to be prepared and material information corresponding to the materials to be prepared, storage location information, and customer demand information; Classifying the materials to be prepared according to the material information to obtain a material classification; Determining the priority of the materials to be prepared according to the material classification, the storage location information and the customer demand information; After determining the priorities of all the materials to be prepared, the preparation order of all the materials to be prepared is sorted to obtain a sorting result, and the materials to be prepared are prepared according to the sorting result.

[0007] In a second aspect, an embodiment of the present invention provides an electrode preparation device based on artificial intelligence, comprising: An information acquisition module is used to acquire materials to be prepared and material information corresponding to the materials to be prepared, information on the storage location to be stored, and customer demand information; A material classification module is used to classify the materials to be prepared according to the material information to obtain a material classification; A priority determination module, configured to determine the priority of the materials to be prepared based on the material classification, the storage location information, and the customer demand information; The sorting module is used to sort the preparation order of all the materials to be prepared after determining the priorities of all the materials to be prepared, obtain the sorting results, and prepare the materials to be prepared according to the sorting results.

[0008] In a third aspect, an embodiment of the present invention provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned artificial intelligence-based electrode preparation method when executing the computer program.

[0009] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned artificial intelligence-based electrode preparation method is implemented.

[0010] Compared with the prior art, the present invention has the following beneficial effects: by obtaining materials to be prepared and material information, storage location information, and customer demand information of the corresponding materials to be prepared, the materials to be prepared are classified according to the material information to obtain a material classification, and the priority of the materials to be prepared is determined according to the material classification, storage location information, and customer demand information. After determining the priority of all materials to be prepared, the preparation order of all materials to be prepared is sorted to obtain a sorting result, and the materials to be prepared are prepared according to the sorting result. The priority of the materials to be prepared is obtained by material classification, storage location information, and customer demand information, and the preparation order of the materials to be prepared is sorted according to the priority to obtain a sorting result, and the materials to be prepared are prepared according to the sorting result. Thus, the material preparation process is intelligently and dynamically optimized to improve the intelligence level and production efficiency of electrode preparation. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0012] Figure 1 This is a schematic diagram of an application environment of an artificial intelligence-based electrode preparation method provided in Example 1 of the present invention; Figure 2This is a flow chart of an artificial intelligence-based electrode material preparation method provided in the second embodiment of the present invention; Figure 3 This is a flow chart of an artificial intelligence-based electrode preparation method provided in the third embodiment of the present invention; Figure 4 This is a flow chart of an artificial intelligence-based electrode material preparation method provided in the fourth embodiment of the present invention; Figure 5 This is a flow chart of an artificial intelligence-based electrode material preparation method provided in the fifth embodiment of the present invention; Figure 6 This is a flow chart of an artificial intelligence-based electrode preparation method provided in Example 6 of the present invention; Figure 7 This is a flow chart of an artificial intelligence-based electrode preparation method provided in Example 7 of the present invention; Figure 8 This is a structural diagram of an electrode preparation device based on artificial intelligence provided in Example 8 of the present invention; Figure 9 This is a structural diagram of a computer device provided in Example 9 of the present invention. DETAILED DESCRIPTION

[0013] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0014] like Figure 1 As shown, it is a schematic diagram of the application environment of an electrode preparation method based on artificial intelligence provided in Example 1 of the present invention, wherein the client and the server are connected for communication, and the user can provide the server with conditions, requirements and operating instructions for electrode preparation based on artificial intelligence by operating the client, and the server is used to execute the electrode preparation method based on artificial intelligence of the present invention according to the relevant content sent by the client. Among them, the client includes but is not limited to computer devices such as various personal computers, laptops, smart phones, tablet computers and portable wearable devices. The computer device corresponding to the server can be implemented with an independent server or a server cluster composed of multiple servers.

[0015] like Figure 2 FIG2 is a flow chart of an electrode preparation method based on artificial intelligence provided by the second embodiment of the present invention, wherein the electrode preparation method based on artificial intelligence is applied in Figure 1The artificial intelligence-based electrode preparation method may include the following steps: Step S201: obtaining materials to be prepared and material information, storage location information, and customer demand information corresponding to the materials to be prepared.

[0016] Among them, the list of materials to be prepared comes from customer orders, production plans or inventory management systems. The list should include basic information such as the name, quantity, specifications, model, etc. of each material. The enterprise resource planning system, manufacturing execution system or warehouse management system can be used to generate and manage this list. Material information includes but is not limited to material name, quantity, specifications, model, batch information, supplier information and material status. The storage location information includes but is not limited to warehouse number, shelf number, location number, capacity and environmental conditions. Customer demand information includes but is not limited to delivery date, delivery location, quality requirements, special requirements, order number and contact information.

[0017] Step S202: Classify the materials to be prepared according to the material information to obtain a material classification result.

[0018] Step S203: Determine the priority of the materials to be prepared based on the material classification results, the storage location information, and the customer demand information.

[0019] In step S202 and step S203, the material classification results may be general, urgent, and important. The material priority determination criteria include urgency, importance, customer demand, and storage location.

[0020] Urgency refers to the fact that the closer the customer's delivery date is, the higher the priority. If materials are in transit for a long time, they need to be prepared first to ensure on-time delivery. Importance refers to the fact that materials that have a greater impact on the production process or product quality, such as core components, have the highest priority. Materials that have a greater impact on the production process, such as general components, have a higher priority. Materials that have a smaller impact on the production process, such as packaging and auxiliary materials, have a lower priority. Customer demand refers to the fact that the needs of customers with high credibility have higher priority, orders with larger amounts have higher priority, orders with larger quantities have higher priority, and orders with special needs (such as customization requirements) have higher priority. Storage location refers to the fact that materials close to the material rack have higher priority for quick access, materials with easily accessible storage locations have higher priority, and materials that require special storage conditions have higher priority to ensure their quality.

[0021] Step S204: After determining the priorities of all the materials to be prepared, sorting the preparation order of all the materials to be prepared to obtain a sorting result, and preparing the materials to be prepared according to the sorting result.

[0022] Among them, the system can automatically sort the preparation of all materials to be prepared according to the preset sorting rules, and automatically determine the sorting results based on the storage location and status information of the materials and combined with customer needs. Through AI and machine learning algorithms, the sorting results are determined based on historical data and current information, and a detailed sorting report is generated, listing the sorting results of each material, including the preparation order and related information. An electronic label is attached to each material to facilitate subsequent operations and management. According to the sorting results, a preparation list for each material is prepared, and the preparation operations are carried out in sequence according to the order of the sorting list. During the preparation process, the preparation status of each material is recorded, such as preparation time, preparation personnel, etc.

[0023] Preferably, a priority scoring model can be constructed to extract material features from the material information, extract storage location features from the storage location information, and extract demand features from the customer demand information. Feature fusion is performed in the scoring model based on the extracted material features, storage location features, and demand features, thereby obtaining a priority ranking of the materials to be prepared. The priority ranking can be composed of immediate processing, fast processing, regular processing, etc., and corresponds to the emergency channel, priority channel, and regular channel of the processing channel respectively.

[0024] Among them, material characteristics may include type code, size specifications, and process requirements; storage location characteristics may include the remaining capacity of the target shelf and the current regional logistics flow; demand characteristics may include the order delivery countdown and customer rating score.

[0025] Among them, the scoring model can adopt a weighted scoring method, where priority score = (material weight × 40%) + (storage location weight × 30%) + (customer demand weight × 30%).

[0026] Among them, the process requirements in the above material characteristics can be rated as process complexity from 1 to 5 levels. When the process complexity is level 5, it is set to 0.2 points, and 0.2 points are added on the basis of 0.2 for each level reduction in process complexity. The target shelf saturation assessment of the above storage location characteristics can be set to 0% to 100%. When the target shelf saturation assessment is less than 50%, the score is set to 1 point. When it is greater than or equal to 50% and less than 80%, it is set to 0.5 points. When it is greater than or equal to 80%, it is set to 0 points. The order delivery countdown in the above customer demand characteristics can be used to assess the delivery urgency. When the remaining delivery time is greater than or equal to 15 days, it is set to 0 points. When the remaining delivery time is less than 15 days and greater than or equal to 5 days, 0.01 points are added for each day reduction. The remaining delivery time is 15 days. When the time is less than 5 days and greater than or equal to 1 day, 0.025 points will be added on the basis of 0.1 points for each day reduced. The part of the remaining delivery time that is greater than 1 day and less than 24 hours will be treated as 1 day. When the remaining delivery time is less than 24 hours and greater than or equal to 3 hours, 1 / 30 point will be added on the basis of 0.2 points for each hour reduced. When the remaining delivery time is less than 3 hours and has not yet been exceeded, 0.2 point will be added on the basis of 0.9 points for each hour reduced. When the order is overdue, it will be set to 1.5 points. The above priority score can be corresponded to the processing channel according to the score range. The priority score greater than or equal to 0.6 points is set as the emergency channel, the priority score less than 0.6 points and greater than or equal to 0.4 points is set as the priority channel, and the priority score less than 0.4 points is set as the regular channel.

[0027] Among them, a dynamic adjustment mechanism can be set up. When the remaining delivery time is less than 12 hours, the customer demand weight is automatically increased to 40%. When the target shelf saturation of the above target area is greater than 75%, the weight of the storage location is reduced to 20%.

[0028] For example, the material parameters of an electrode are: process complexity = level 3, the material score is 0.6×40% = 0.24, the target shelf saturation = 80%, the storage location score is 0×30% = 0, the remaining delivery time = 18 hours, and the customer demand score is 0.4×30% = 0.12. In summary, the total score of the material to be prepared is 0.36, which is the regular processing in the priority sorting corresponding to the above regular channel.

[0029] Among them, when the remaining delivery time is reduced to 10 hours, the delivery warning is triggered and the demand weight is increased to 40%. At this time, the priority score of the pending material is 0.24+0+[(24-10) / 30+0.2]×40%=0.24+0.267=0.507. At this time, the priority is increased by 15%, that is, it enters the quick processing in the priority sorting corresponding to the above priority channel.

[0030] Among them, for the above preferred method, a multi-dimensional reward function can be set, and a weighted combination reward function can be adopted, which can include the following sub-items: Total reward function term: ; Storage efficiency bonus for storage locations: ; Reward items for the urgency of delivery of customer requirements: ; Penalty item for material process complexity: ; ; In the formula, R is the total reward value, α is the storage efficiency weight, is the storage efficiency reward value, β is the delivery urgency weight, is the delivery urgency reward value, γ is the process complexity weight, is the process complexity bonus value, is the maximum capacity of the target area, is the used capacity of the current target area, k is the adjustment coefficient, is the remaining delivery time, is the total delivery time, is the process complexity level (5 is the most complex).

[0031] For example, a material: =820, =18h, =4, we get: ; ; ; Then, the total reward value is R=0.4*0.858+0.35+1.25-0.25*0.336=0.734.

[0032] It can be understood that in the process of preparing materials to be prepared, when a new material is detected, it is determined whether the remaining delivery time of the new material is less than 24 hours. If the remaining delivery time of the new material is less than 24 hours, the above-mentioned quick processing is triggered. If the remaining delivery time is not less than 24 hours, it is determined whether the process complexity of the new material is greater than level 3. If it is greater than level 3, the preset high-precision equipment is assigned for processing. If the process complexity is greater than level 3, the above-mentioned conventional processing is performed.

[0033] It can be understood that a dynamic priority adjustment mechanism can be set up to update the priority weight matrix once an hour, or the priority weights can be updated according to different time periods. For example, during the morning shift from 8 a.m. to 12 p.m., the delivery urgency is 0.6, the process complexity is 0.25, and the warehousing efficiency is 0.15. During the afternoon shift from 13 p.m. to 17 p.m., the delivery urgency is 0.5, the process complexity is 0.3, and the warehousing efficiency is 0.2. During the night shift from 18 p.m. to 10 p.m., the delivery urgency is 0.4, the process complexity is 0.35, and the warehousing efficiency is 0.25.

[0034] Among them, event-driven adjustments can be made according to actual conditions, for example, there are urgent order insertions (such as sudden shortening of delivery urgency), equipment abnormality warnings (such as AGV failures), and material abnormalities (such as defects discovered by visual inspection).

[0035] For example, consider a scenario where an urgent order is received at 2:30 PM (originally scheduled to start at 3:00 PM, with the aforementioned lunch shift weight). When the system detects that there are only 8 hours left before delivery (i.e., less than the 24-hour threshold), an urgent adjustment protocol is triggered: the delivery urgency is increased from 0.5 to 0.7; the process complexity weight is reduced from 0.3 to 0.2; and the warehouse efficiency weight remains at 0.2. This results in an increase in the original priority score from 0.69 to 0.69, raising the order's priority from 5th to 1st.

[0036] A specific case description is that the event chain that occurred on a certain day was: 09:00: Order A was received (the remaining delivery time was 36 hours, and the process complexity was level 2); 11:30: Order B was urgently inserted (the remaining delivery time was 12 hours, and the process complexity was level 4); 14:00: Equipment failure was detected in storage area C.

[0037] The system is adjusted accordingly, triggering the expedited processing of order B. The path weights are recalculated. The original path cost is shorter but takes longer to deliver. The new path bypasses the fault zone, is slightly longer but takes less time (for example, by activating a high-speed conveyor belt), and the priority weight matrix is ​​updated.

[0038] Among them, the purpose of the above-mentioned preferred embodiment is to provide a logical processing method of the electrode preparation method based on artificial intelligence, and the specific data examples involved are used as examples and references to assist understanding. The setting of specific data can be changed and adjusted according to the actual situation in the actual working process of the electrode preparation, and is not a specific limitation.

[0039] In an embodiment of the present application, by obtaining materials to be prepared and material information, storage location information, and customer demand information corresponding to the materials to be prepared, the materials to be prepared are classified according to the material information to obtain a material classification, and the priority of the materials to be prepared is determined according to the material classification, storage location information, and customer demand information. After determining the priority of all materials to be prepared, the preparation order of all materials to be prepared is sorted to obtain a sorting result, and the materials to be prepared are prepared according to the sorting result. The priority of the materials to be prepared is obtained by material classification, storage location information, and customer demand information, and the preparation order of the materials to be prepared is sorted according to the priority to obtain a sorting result, and the materials to be prepared are prepared according to the sorting result. In this way, the material preparation process is intelligently and dynamically optimized to improve the intelligence level and production efficiency of electrode preparation.

[0040] like Figure 3 FIG. 1 is a flow chart of an electrode material preparation method based on artificial intelligence provided in Embodiment 3 of the present invention. Before classifying the materials to be prepared and obtaining the material classification results in step S202, the electrode material preparation method based on artificial intelligence may further include the following steps: Step S301: Obtain preset historical material preparation data, obtain the target usage frequency of the material to be prepared based on the historical material preparation data, and determine the target inventory of the material to be prepared based on the target usage frequency.

[0041] Among them, historical material preparation data within the past year is usually selected because recent data can better reflect the current production and demand situation. The historical material preparation data is sorted and cleaned, invalid data and outliers are removed, and the accuracy and consistency of the data are ensured. The average number of times each material is used within the selected time range is calculated, and the frequency of use is weighted according to different time points or the importance of the order. The highest frequency of use in the historical records is selected as a reference. According to the target frequency of use and the current customer's order needs, the target inventory is adjusted. If there are large orders in the near future, the target inventory can be appropriately increased. If the demand for a certain material in the production plan is large, the target inventory can be appropriately increased. Consider the procurement cycle of the material to ensure that the target inventory can meet production needs. If the procurement cycle of a certain material is long, the target inventory can be appropriately increased.

[0042] Step S302: obtaining the current inventory of the material to be prepared according to the material information of the material to be prepared.

[0043] Step S303: determine whether the current inventory is less than the target inventory.

[0044] Step S304: If the current inventory is less than the target inventory, the step of classifying the materials to be prepared according to the material information and obtaining the material classification results is executed until the material preparation is completed.

[0045] Among them, according to the inventory data of the material information of the materials to be prepared, including the material name, current inventory, storage location, etc., the material information is sorted and cleaned to obtain the current inventory of the materials to be prepared, and the target inventory of each material is obtained from the target inventory in step S301. The current inventory is compared with the target inventory to generate a comparison report. According to the comparison results, a list of materials that need to be replenished is generated. If the current inventory of the materials to be prepared is less than the target inventory, the operations of the above steps S201 to S204 are executed. If the current inventory of the materials to be prepared is greater than the target inventory, the material preparation operation is stopped.

[0046] In this embodiment, the target inventory of the material to be prepared and the current inventory of the material to be prepared are obtained, and the material preparation task is executed based on the comparison result of the target inventory of the material to be prepared and the current inventory of the material to be prepared, thereby ensuring the efficiency and accuracy of the material preparation process.

[0047] like Figure 4 FIG. 1 is a flow chart of an electrode material preparation method based on artificial intelligence provided in a fourth embodiment of the present invention. Before classifying the materials to be prepared and obtaining the material classification results in step S202, the electrode material preparation method based on artificial intelligence may further include the following steps: Step S401: Determine the target storage time of the materials to be prepared based on customer demand information.

[0048] Step S402: determining the current storage time of the material to be prepared according to the material information of the material to be prepared.

[0049] Step S403: determine whether the current storage time is longer than the target storage time.

[0050] Step S404: If the current storage time is longer than the target storage time, the materials to be prepared are prepared.

[0051] Among them, customer demand information can be obtained through the sales order system, customer management system or customer contract. Customer demand information includes customer name, order quantity, delivery date, special needs, etc. The target storage time can be calculated based on the delivery date of the customer order, and the time period from material preparation to delivery is taken into account to ensure that materials can be in place in time. For customer orders with special needs, the target storage time is appropriately adjusted to ensure that the needs are met.

[0052] The current storage time of the materials to be prepared can be obtained from the material information of the materials to be prepared. The current storage time of each material is compared with the target storage time to determine whether it is higher than the target storage time. If the current storage time is greater than the target storage time, it is necessary to prepare the materials. Alternatively, a threshold value, such as 120%, is set. If the current storage time exceeds 120% of the target storage time, it is determined that preparation is required, and the materials to be prepared are prepared based on the judgment result.

[0053] Optionally, before classifying the materials to be prepared according to the material information in step S202 and obtaining the material classification result, the electrode material preparation method based on artificial intelligence may further include the following steps: Determine the location of the materials to be prepared based on the information to be stored.

[0054] Prepare the materials to be prepared according to their locations.

[0055] Among them, the storage information of the materials to be prepared includes the material name, storage conditions, distance from the production area, etc. According to the storage conditions of the materials, the appropriate storage location is determined, and according to the distance between the storage location and the production area, the preparation priority of the materials to be prepared is determined. The closer the distance, the higher the priority, and the prepared materials are transported to the target storage location.

[0056] In this embodiment, the target storage time of the materials to be prepared and the current storage time of the materials to be prepared are used for judgment, and the materials to be prepared are prepared according to the judgment result, thereby ensuring the efficiency and accuracy of the material preparation process.

[0057] like Figure 5 FIG. 1 is a flow chart of an artificial intelligence-based electrode material preparation method according to a fifth embodiment of the present invention. In step S203, the priority of the materials to be prepared is determined according to the material classification results, the storage location information, and the customer demand information. The steps may include: Step S501: If the material classification result is common, then according to the material information, the processing type parameter of the material to be prepared, the completion time parameter of the material to be prepared, and the priority parameter of the material to be prepared are obtained.

[0058] Step S502: Calculate the priority of the materials to be prepared based on the processing type parameter, the completion time parameter, and the priority parameter.

[0059] If the material classification result is ordinary, the calculation formula for the priority of the material on standby is as follows: ; Where T is the priority of the material to be prepared, A is the processing type parameter, B is the completion time parameter, C is the priority parameter, and D is the arrangement constant.

[0060] like Figure 6 FIG. 1 is a flow chart of an artificial intelligence-based electrode material preparation method according to a sixth embodiment of the present invention. In step S203, the priority of the materials to be prepared is determined according to the material classification results, the storage location information, and the customer demand information. The steps may include: Step S601: If the material classification result is urgent, the first current remaining quantity of the material to be prepared is obtained.

[0061] Step S602: If the first current remaining quantity is lower than the preset first additional target reserved value, it is determined that the priority of the to-be-prepared material is higher than the priorities of other to-be-prepared materials.

[0062] Among them, urgent materials refer to those materials that have a greater impact on production and are in very urgent customer demand. The current inventory data of each urgent material is exported, including the material name, current inventory quantity, etc. Based on production experience and historical data, a first additional target reserved value is set. The first additional target reserved value is an additional reserved value set after meeting the target inventory in the above step S301. If the first current remaining quantity is lower than the first additional target reserved value, the priority of the material is marked as the highest priority.

[0063] In this embodiment, when a material is classified as urgent, the first current remaining quantity of the material to be prepared is obtained. When the first current remaining quantity is compared with the preset first additional target reserve value, the priority of the material to be prepared is determined to be higher than that of other materials to be prepared. This further improves the priority setting of the materials to be prepared and enhances the comprehensiveness of the electrode preparation based on artificial intelligence.

[0064] like Figure 7 FIG. 1 is a flow chart of an artificial intelligence-based electrode material preparation method according to a seventh embodiment of the present invention. In step S203, the priority of the materials to be prepared is determined according to the material classification result, the storage location information, and the customer demand information. The steps may include: Step S701: If the material classification result is an important class, the importance of the material to be prepared is obtained according to the customer demand information.

[0065] Step S702: obtaining a second current remaining quantity of the material to be prepared, and determining that the priority of the material to be prepared is higher than the priorities of other materials to be prepared when the importance reaches a preset level and the second current remaining quantity is less than a preset second additional target reserved value.

[0066] Among them, important materials refer to those materials that have a greater impact on production and have high customer demand but are not necessarily urgent. From the material classification results obtained in step S202, important materials are screened out. According to customer demand information, different weights can be assigned according to the customer demand information of each order, including delivery date, order amount, special needs, etc., as well as the customer's level and historical cooperation. Different weights can be assigned according to the order amount, the urgency of the delivery date, whether there are special needs, and the level of the customer.

[0067] Production experience and historical data can pre-set a reasonable second additional target reserved value to ensure that the inventory of important materials can meet the needs of important customers. The second current remaining quantity is compared with the second additional target reserved value, and a comprehensive judgment is made based on the importance. If the second current remaining quantity is less than the second additional target reserved value and the importance is greater than or equal to the preset importance threshold, the priority of the material is determined to be the highest priority.

[0068] In this embodiment, when the material classification result is important, the importance of the material to be prepared is determined based on customer demand information, and the priority of the material to be prepared is determined based on the importance and the second current remaining quantity. This further improves the setting of the priority of the material to be prepared and enhances the comprehensiveness of the electrode material preparation based on artificial intelligence.

[0069] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0070] like Figure 8 , which is a schematic diagram of an artificial intelligence-based electrode preparation device provided in Example 8 of the present invention. The artificial intelligence-based electrode preparation device corresponds one-to-one with the artificial intelligence-based electrode preparation method in the above-mentioned embodiment. The artificial intelligence-based electrode preparation device includes an information acquisition module 81, a material classification module 82, a priority determination module 83, and a sorting module 84. The functional modules are described in detail as follows: The information acquisition module 81 is used to obtain the materials to be prepared and the material information of the corresponding materials to be prepared, the storage location information and the customer demand information; The material classification module 82 is used to classify the materials to be prepared according to the material information to obtain the material classification; Priority determination module 83, used to determine the priority of materials to be prepared based on material classification, storage location information and customer demand information; The sorting module 84 is used to sort the preparation order of all the materials to be prepared after determining the priorities of all the materials to be prepared, obtain the sorting results, and prepare the materials to be prepared according to the sorting results.

[0071] Optionally, the artificial intelligence-based electrode preparation device further includes: The target inventory determination module is used to obtain preset historical material preparation data before classifying the materials to be prepared and obtaining the material classification results. Based on the historical material preparation data, the target usage frequency of the materials to be prepared is obtained, and the target inventory of the materials to be prepared is determined based on the target usage frequency. The current inventory determination module is used to obtain the current inventory of the materials to be prepared based on the material information of the materials to be prepared; The inventory judgment module is used to judge whether the current inventory is less than the target inventory; The step execution module is used to classify the materials to be prepared according to the material information and obtain the material classification results if the current inventory is less than the target inventory, until the material preparation is completed.

[0072] Optionally, the artificial intelligence-based electrode preparation device further includes: The target storage time determination module is used to determine the target storage time of the materials to be prepared based on customer demand information before classifying the materials to be prepared and obtaining the material classification results; The current storage time determination module is used to determine the current storage time of the materials to be prepared based on the material information of the materials to be prepared; A storage time judgment module is used to judge whether the current storage time is higher than the target storage time; The material preparation module is used to prepare the materials to be prepared if the current storage time is higher than the target storage time.

[0073] Optionally, the artificial intelligence-based electrode preparation device further includes: A storage location determination module is used to determine the location of the materials to be prepared based on the storage information before classifying the materials to be prepared and obtaining the material classification results; The location preparation module is used to prepare the materials to be prepared according to their locations.

[0074] Optionally, the priority determination module 83 includes: A parameter acquisition unit is used to obtain the processing type parameter, completion time parameter and priority parameter of the material to be prepared based on the material information if the material classification result is ordinary; The priority calculation unit is used to calculate the priority of the materials to be prepared according to the processing type parameter, the completion time parameter and the priority parameter.

[0075] Optionally, the priority determination module 83 includes: A first quantity acquisition unit is used to acquire a first current remaining quantity of the material to be prepared if the material classification result is an urgent class; The reserve value judgment unit is used to determine that the priority of the to-be-prepared material is higher than the priorities of other to-be-prepared materials if the first current remaining quantity is lower than a preset first additional target reserve value.

[0076] Optionally, the priority determination module 83 includes: The importance determination unit is used to obtain the importance of the material to be prepared based on customer demand information if the material classification result is an important category; The important category priority determination unit is used to obtain the second current remaining quantity of the material to be prepared, and when the importance reaches a preset level and the second current remaining quantity is less than the preset second additional target reserved value, determine that the priority of the material to be prepared is higher than the priority of other materials to be prepared.

[0077] For the specific definition of the electrode preparation device based on artificial intelligence, please refer to the definition of the electrode preparation method based on artificial intelligence above, which will not be repeated here. The various modules in the above-mentioned electrode preparation device based on artificial intelligence can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0078] like Figure 9 The figure shows a schematic diagram of the structure of a computer device provided in Example 9 of the present invention. The computer device includes a processor, a memory, a network interface and a database connected via a system bus. 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, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, an electrode preparation method based on artificial intelligence is realized.

[0079] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the electrode preparation method based on artificial intelligence in the above embodiment is implemented, for example Figures 2 to 7Alternatively, when the processor executes the computer program, the functions of each module / unit in the embodiment of the electrode preparation device based on artificial intelligence are realized, for example Figure 8 The functions of the information acquisition module 81, material classification module 82, priority determination module 83, and sorting module 84 are not described here in detail to avoid repetition.

[0080] 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 electrode preparation method based on artificial intelligence in the above embodiment is implemented, such as Figures 2 to 7 Alternatively, when the computer program is executed by the processor, the functions of each module / unit in the embodiment of the electrode preparation device based on artificial intelligence are realized, for example Figure 8 The functions of the information acquisition module 81, material classification module 82, priority determination module 83, and sorting module 84 are not described here in detail to avoid repetition. The computer-readable storage medium may be non-volatile or volatile.

[0081] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. 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, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0082] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0083] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. An electrode preparation method based on artificial intelligence, characterized in that: include: Obtaining materials to be prepared and material information corresponding to the materials to be prepared, storage location information, and customer demand information; Classify the materials to be prepared according to the material information to obtain a material classification result; Determining the priority of the materials to be prepared according to the material classification results, the storage location information and the customer demand information; After determining the priorities of all the materials to be prepared, the preparation order of all the materials to be prepared is sorted to obtain a sorting result, and the materials to be prepared are prepared according to the sorting result.

2. The electrode preparation method based on artificial intelligence according to claim 1, characterized in that: Before classifying the materials to be prepared and obtaining the material classification results, the method further includes: Obtaining preset historical material preparation data, obtaining a target usage frequency of the material to be prepared based on the historical material preparation data, and determining a target inventory of the material to be prepared based on the target usage frequency; Obtaining the current inventory of the material to be prepared according to the material information of the material to be prepared; Determining whether the current inventory is less than the target inventory; If the current inventory is less than the target inventory, the step of classifying the materials to be prepared according to the material information to obtain a material classification result is performed until the material preparation is completed.

3. The electrode preparation method based on artificial intelligence according to claim 1, characterized in that: Before classifying the materials to be prepared and obtaining the material classification results, the method further includes: Determine the target storage time of the materials to be prepared based on the customer demand information; Determining the current storage time of the material to be prepared according to the material information of the material to be prepared; Determining whether the current storage time is greater than the target storage time; If the current storage time is longer than the target storage time, the material to be prepared is prepared.

4. The electrode preparation method based on artificial intelligence according to claim 1, characterized in that: Classifying the materials to be prepared and obtaining the material classification results includes: Determine the location of the materials to be prepared according to the information to be stored; The materials to be prepared are prepared according to the locations of the materials to be prepared.

5. The electrode preparation method based on artificial intelligence according to claim 1, characterized in that: Determining the priority of the materials to be prepared according to the material classification result, the to-be-storage location information, and the customer demand information includes: If the material classification result is common, then according to the material information, obtain the processing type parameter of the material to be prepared, the completion time parameter of the material to be prepared, and the priority parameter of the material to be prepared; The priority of the material to be prepared is calculated according to the processing type parameter, the completion time parameter and the priority parameter.

6. The electrode preparation method based on artificial intelligence according to claim 5, characterized in that: Determining the priority of the materials to be prepared according to the material classification result, the to-be-storage location information, and the customer demand information includes: If the material classification result is urgent, obtaining the first current remaining quantity of the material to be prepared; If the first current remaining quantity is lower than a preset first additional target reserved value, it is determined that the priority of the to-be-prepared material is higher than the priorities of other to-be-prepared materials.

7. The electrode preparation method based on artificial intelligence according to claim 5, characterized in that: Determining the priority of the materials to be prepared according to the material classification result, the to-be-storage location information, and the customer demand information includes: If the material classification result is an important class, then the importance of the material to be prepared is obtained according to the customer demand information; Obtain a second current remaining quantity of the to-be-prepared material, and when the importance reaches a preset level and the second current remaining quantity is less than a preset second additional target reserved value, determine that the priority of the to-be-prepared material is higher than that of other to-be-prepared materials.

8. An electrode preparation device based on artificial intelligence, characterized in that: include: An information acquisition module is used to acquire materials to be prepared and material information corresponding to the materials to be prepared, information on the storage location to be stored, and customer demand information; A material classification module is used to classify the materials to be prepared according to the material information to obtain a material classification; A priority determination module, configured to determine the priority of the materials to be prepared based on the material classification, the storage location information, and the customer demand information; The sorting module is used to sort the preparation order of all the materials to be prepared after determining the priorities of all the materials to be prepared, obtain the sorting results, and prepare the materials to be prepared according to the sorting results.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the artificial intelligence-based electrode preparation method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the artificial intelligence-based electrode preparation method according to any one of claims 1 to 7 is implemented.

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