High-frequency material distribution processing method, device and equipment, storage medium and program product

Abstract

The invention relates to the technical field of material supply, provides a high-frequency material distribution processing method, device and equipment, a storage medium and a program product, and can realize pre-distribution of high-frequency materials and improve the distribution accuracy. According to historical production data, an actual use quantity sequence of a high-frequency material in a plurality of historical time windows is obtained; inputting the actual use quantity sequence of each historical time window into the prediction model to obtain a use demand quantity sequence of the high-frequency material in the future time window; and performing distribution processing on the high-frequency materials according to the use demand quantity sequence of the high-frequency materials in the future time window.

Description

Technical Field

[0001] The present application relates to the technical field of material supply, and in particular to a method, device, computer equipment, storage medium and computer program product for distributing and processing high-frequency materials. Background Art

[0002] High-frequency materials are an indispensable part of the production and operation process of an enterprise. The stability of their supply directly affects the continuous operation of the production line and the enterprise's operation. By allocating them in advance, it is possible to ensure that these high-frequency materials arrive on time before production, avoiding production interruptions caused by material shortages. Therefore, the allocation of high-frequency materials is a vital task. Reasonable material allocation can effectively improve the storage efficiency of the warehouse, reduce inventory costs, avoid material losses, and meet the work needs of employees.

[0003] In the production and operation of an enterprise, usually the asset manager allocates high-frequency materials based on inventory information only after receiving a replenishment request from the asset management specialists in each office. This approach may cause production operations to be interrupted due to the failure of high-frequency materials to arrive in advance during the production process, thus failing to ensure the continuity of production. Summary of the invention

[0004] Based on this, it is necessary to provide a method, device, computer equipment, storage medium and computer program product for distributing and processing high-frequency materials in response to the above-mentioned technical problems.

[0005] The present application provides a method for distributing and processing high-frequency materials, the method comprising:

[0006] Based on historical production data, the actual usage sequence of high-frequency materials in multiple historical time windows is obtained;

[0007] Input the actual usage sequence of each historical time window into the forecasting model to obtain the usage demand sequence of high-frequency materials in the future time window;

[0008] High-frequency materials are allocated and processed according to their usage demand sequence in future time windows.

[0009] In one embodiment, the actual usage sequence of each historical time window is input into the forecasting model to obtain the usage demand sequence of high-frequency materials in the future time window, including:

[0010] Input the actual usage sequence of each historical time window into the prediction model to obtain the prediction result corresponding to each historical time window;

[0011] Statistical processing is performed on multiple prediction results corresponding to each historical time window to obtain the usage demand sequence of high-frequency materials in the future time window.

[0012] In one embodiment, multiple prediction results corresponding to each historical time window are statistically processed to obtain a usage demand sequence of high-frequency materials in a future time window, including:

[0013] According to the difference between the historical prediction results of the prediction model for each historical time window and the actual usage sequence of the corresponding future time window, the weight corresponding to each historical time window is obtained;

[0014] According to the weights, a weighted summation process is performed on multiple prediction results corresponding to each historical time window to obtain a usage demand sequence of high-frequency materials in future time windows.

[0015] In one embodiment, the smaller the difference is, the greater the corresponding weight is.

[0016] In one embodiment, the method further comprises:

[0017] Based on historical production data, the usage frequency of each material is obtained;

[0018] According to the usage frequency, high-frequency materials are determined among various materials.

[0019] In one embodiment, high-frequency materials are determined among various materials according to the usage frequency, including:

[0020] Sort various materials according to the frequency of use; if they are sorted from high to low in frequency of use, the materials ranked first are determined as high-frequency materials; if they are sorted from low to high in frequency of use, the materials ranked last are determined as high-frequency materials;

[0021] or,

[0022] If the usage frequency is greater than or equal to the usage frequency threshold, the corresponding material is determined as a high-frequency material.

[0023] The present application provides a high-frequency material distribution and processing device, the device comprising:

[0024] The historical data acquisition module is used to obtain the actual usage quantity sequence of high-frequency materials in multiple historical time windows based on historical production data;

[0025] The forecasting module is used to input the actual usage quantity sequence of each historical time window into the forecasting model to obtain the usage demand quantity sequence of high-frequency materials in the future time window;

[0026] The allocation processing module is used to allocate and process high-frequency materials according to the usage demand sequence of high-frequency materials in the future time window.

[0027] The present application provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the above method.

[0028] The present application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to execute the above method.

[0029] The present application provides a computer program product, on which a computer program is stored, and the computer program is executed by a processor to perform the above method.

[0030] The above-mentioned high-frequency material allocation and processing method, device, computer equipment, storage medium and computer program product obtain the actual usage quantity sequence of high-frequency materials in multiple historical time windows based on historical production data; input the actual usage quantity sequence of each historical time window into the prediction model to obtain the usage demand quantity sequence of high-frequency materials in future time windows; and allocate and process high-frequency materials based on the usage demand quantity sequence of high-frequency materials in future time windows. With the solution provided by this application, asset managers do not need to allocate high-frequency materials only when they receive replenishment applications, and pre-allocation is achieved; and this application predicts the usage demand quantity sequence of high-frequency materials in future time windows based on the actual usage quantity sequence of high-frequency materials in multiple historical time windows, and can obtain more accurate results, thereby improving the accuracy of allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. 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 creative work.

[0032] Figure 1 It is a schematic flow chart of a method for distributing and processing high-frequency materials in one embodiment;

[0033] Figure 2 A schematic diagram of a prediction process in an embodiment;

[0034] Figure 3 It is a schematic flow chart of a method for distributing and processing high-frequency materials in another embodiment;

[0035] Figure 4 It is a structural block diagram of a high-frequency material distribution and processing device in one embodiment;

[0036] Figure 5 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0038] The high-frequency material distribution and processing method provided in the present application can be executed by a computer device. The method includes: Figure 1 Steps shown.

[0039] Step S101, obtaining the actual usage quantity sequence of high-frequency materials in multiple historical time windows based on historical production data.

[0040] The historical time window is a past time period starting from the current moment. Multiple historical time windows all belong to past time periods starting from the current moment, and different historical time windows correspond to different past time periods. For example, if the historical time window is the past month, the length of the past time period corresponding to the historical time window is one month; for another example, if the historical time window is the past two months, the length of the past time period corresponding to the historical time window is two months.

[0041] For example, based on historical production data, the actual usage quantity sequence of high-frequency materials in the past month, the actual usage quantity sequence of high-frequency materials in the past two months, and the actual usage quantity sequence of high-frequency materials in the past three months can be obtained. High-frequency materials can be materials with a usage frequency higher than a threshold in the production process, which can be set according to actual needs.

[0042] Step S102, input the actual usage quantity sequence of each historical time window into the prediction model to obtain the usage demand quantity sequence of the high-frequency material in the future time window.

[0043] Prediction models may include Long-Short Term Memory (LSTM) models.

[0044] The future time window is a future time period starting from the current moment.

[0045] Take the future time window of the next half month as an example. After obtaining the actual usage sequence of high-frequency materials in the past month, the actual usage sequence of high-frequency materials in the past month can be input into the forecasting model to obtain the forecast usage sequence of high-frequency materials in the next half month (recorded as sequence_1); after obtaining the actual usage sequence of high-frequency materials in the past two months, the actual usage sequence of high-frequency materials in the past two months can be input into the forecasting model to obtain the forecast usage sequence of high-frequency materials in the next half month (recorded as sequence_2); after obtaining the actual usage sequence of high-frequency materials in the past three months, the actual usage sequence of high-frequency materials in the past three months can be input into the forecasting model to obtain the forecast usage sequence of high-frequency materials in the next half month (recorded as sequence_3).

[0046] Statistics are collected for the predicted usage quantity sequences of high-frequency materials in the next half month, sequence_1, sequence_2 and sequence_3, and the statistical results are determined as the usage demand quantity sequence of high-frequency materials in the next half month.

[0047] Step S103, allocating and processing the high-frequency materials according to the usage demand sequence of the high-frequency materials in the future time window.

[0048] After obtaining the usage demand sequence of high-frequency materials in the next half month, high-frequency materials can be allocated according to the usage demand sequence and delivered to the production line in advance, thereby avoiding production interruptions due to the inability to extract and arrive high-frequency materials.

[0049] In the above embodiment, the actual usage quantity sequence of high-frequency materials in multiple historical time windows is obtained based on historical production data; the actual usage quantity sequence of each historical time window is input into the prediction model to obtain the usage demand quantity sequence of high-frequency materials in future time windows; and the high-frequency materials are allocated and processed based on the usage demand quantity sequence of high-frequency materials in future time windows. With the solution provided by the present application, asset managers do not need to allocate high-frequency materials upon receiving replenishment applications, and pre-allocation is achieved; and the present application predicts the usage demand quantity sequence of high-frequency materials in future time windows based on the actual usage quantity sequence of high-frequency materials in multiple historical time windows, and can obtain more accurate results, thereby improving the accuracy of allocation.

[0050] In one embodiment, the actual usage sequence of each historical time window is input into the forecasting model to obtain the usage demand sequence of high-frequency materials in the future time window, including Figure 2 The steps shown are:

[0051] Step S201, input the actual usage quantity sequence of each historical time window into the prediction model to obtain the prediction result corresponding to each historical time window; Step S202, perform statistical processing on multiple prediction results corresponding to each historical time window to obtain the usage demand quantity sequence of high-frequency materials in future time windows.

[0052] For example, after obtaining the actual usage sequence of high-frequency materials in the past month, the actual usage sequence of high-frequency materials in the past month can be input into the forecast model to obtain the usage prediction sequence of high-frequency materials in the next half month (recorded as sequence_1); after obtaining the actual usage sequence of high-frequency materials in the past two months, the actual usage sequence of high-frequency materials in the past two months can be input into the forecast model to obtain the usage prediction sequence of high-frequency materials in the next half month (recorded as sequence_2); after obtaining the actual usage sequence of high-frequency materials in the past three months, the actual usage sequence of high-frequency materials in the past three months can be input into the forecast model to obtain the usage prediction sequence of high-frequency materials in the next half month (recorded as sequence_3). Among them, the usage prediction sequence of high-frequency materials in the next half month, sequence_1, belongs to the forecast result corresponding to the past month; the usage prediction sequence of high-frequency materials in the next half month, sequence_2, belongs to the forecast result corresponding to the past two months; and the usage prediction sequence of high-frequency materials in the next half month, sequence_3, belongs to the forecast result corresponding to the past three months.

[0053] Statistics are collected for the predicted usage quantity sequences of high-frequency materials in the next half month, sequence_1, sequence_2 and sequence_3, and the statistical results are determined as the usage demand quantity sequence of high-frequency materials in the next half month.

[0054] This embodiment obtains corresponding prediction results based on actual usage quantity sequences in different historical time windows, and then performs statistics to obtain more accurate usage demand quantity sequences of high-frequency materials in future time windows.

[0055] In one embodiment, multiple prediction results corresponding to each historical time window are statistically processed to obtain a usage demand sequence of high-frequency materials in a future time window, including:

[0056] According to the difference between the historical prediction results of each historical time window and the actual usage sequence of the corresponding future time window, the weight corresponding to each historical time window is obtained; according to the weight, the multiple prediction results corresponding to each historical time window are weighted and summed to obtain the usage demand sequence of high-frequency materials in the future time window.

[0057] Multiple predictions can be made with the solution provided in this application. For example, a prediction is made with the solution provided in this application, and the current prediction belongs to the second prediction.

[0058] If the first forecast is January 1st, the actual usage quantity sequence of high-frequency materials in the past month before January 1st can be input into the forecast model, and the obtained forecast result (such as the forecast usage quantity sequence of high-frequency materials in the next half month after January 1st) can be called the historical forecast result of the forecast model for the past month before January 1st; the actual usage quantity sequence of high-frequency materials in the past two months before January 1st can also be input into the forecast model, and the obtained forecast result (such as the forecast usage quantity sequence of high-frequency materials in the next half month after January 1st) can be called the historical forecast result of the forecast model for the past two months before January 1st; the actual usage quantity sequence of high-frequency materials in the past three months before January 1st can also be input into the forecast model, and the obtained forecast result (such as the forecast usage quantity sequence of high-frequency materials in the next half month after January 1st) can be called the historical forecast result of the forecast model for the past three months before January 1st.

[0059] Afterwards, the actual usage quantity sequence of high-frequency materials from January 1 to January 15 can be obtained.

[0060] Based on the difference between the historical forecast results of the forecast model for the past month on January 1 and the actual usage sequence of high-frequency materials from January 1 to January 15, the weight corresponding to the past month can be obtained. The smaller the difference, the greater the corresponding weight.

[0061] Based on the difference between the historical forecast results of the forecast model for the past two months on January 1 and the actual usage sequence of high-frequency materials from January 1 to January 15, the weights corresponding to the past two months can be obtained. The smaller the difference, the greater the corresponding weight.

[0062] Based on the difference between the historical forecast results of the forecast model for the past three months on January 1 and the actual usage sequence of high-frequency materials from January 1 to January 15, the weights corresponding to the past three months can be obtained. The smaller the difference, the greater the corresponding weight.

[0063] If the current forecast is March 1st, input the actual usage sequence of high-frequency materials in the past month before March 1st into the forecast model to obtain the forecast result (that is, the forecast usage sequence of high-frequency materials in the next half month can be recorded as sequence_4); input the actual usage sequence of high-frequency materials in the past two months before March 1st into the forecast model to obtain the forecast result (that is, the forecast usage sequence of high-frequency materials in the next half month can be recorded as sequence_6); input the actual usage sequence of high-frequency materials in the past three months before March 1st into the forecast model to obtain the forecast result (that is, the forecast usage sequence of high-frequency materials in the next half month can be recorded as sequence_6).

[0064] Multiply the weight corresponding to the past month and the forecast result sequence_4, multiply the weight corresponding to the past two months and the forecast result sequence_5, and multiply the weight corresponding to the past three months and the forecast result sequence_6. Sum the three multiplication results and use the result as the usage demand sequence of high-frequency materials in the next half month.

[0065] In one embodiment, the method provided in the present application further includes: obtaining the usage frequency of each material based on historical production data; and determining high-frequency materials among various materials based on the usage frequency.

[0066] In this embodiment, the usage frequency of each material can be obtained based on historical production data, and the material used as the high-frequency material can be determined based on the usage frequency, so that the high-frequency material can undergo adaptive changes as production proceeds.

[0067] In one embodiment, high-frequency materials are determined among various materials according to the usage frequency, including:

[0068] Various materials are sorted according to the frequency of use; if they are sorted from large to small by frequency of use, the materials ranked first are determined as high-frequency materials; if they are sorted from small to large by frequency of use, the materials ranked last are determined as high-frequency materials; or, if the frequency of use is greater than or equal to the frequency of use threshold, the corresponding material is determined as a high-frequency material.

[0069] After obtaining the usage frequency of each material, you can sort them from high to low according to the usage frequency to obtain a material sequence, and determine the materials ranked in the first N in the material sequence as high-frequency materials. N is an integer greater than or equal to 1. Alternatively, you can sort them from low to high according to the usage frequency to obtain a material sequence, and determine the materials ranked in the last M in the material sequence as high-frequency materials. M is an integer greater than or equal to 1.

[0070] In addition, the relative size between the usage frequency of each material and the usage frequency threshold can also be compared; if the usage frequency of a material is greater than or equal to the usage frequency threshold, the material can be determined as a high-frequency material.

[0071] This application also provides an embodiment, referring to Figure 3 In this embodiment, through the comprehensive use of multiple links such as demand assessment, bill of materials, allocation strategy, inventory monitoring, allocation execution and feedback adjustment, the scientific, reasonable and efficient allocation of high-frequency materials in office assets is achieved. By continuously optimizing and adjusting the plan, it will provide strong support for the development of the enterprise.

[0072] The needs assessment includes the following aspects:

[0073] 1. Collect and analyze historical data to determine the specific types and usage frequencies of high-frequency materials.

[0074] 2. Through investigation, collect the needs and opinions of each office on high-frequency materials.

[0075] 3. Taking into account development factors, predict the changes in demand for high-frequency materials in the future.

[0076] The bill of materials includes the following aspects:

[0077] 1. Based on the results of demand assessment, prepare a list of high-frequency materials, clearly specifying the name, specification, number, quantity and other information of each material.

[0078] 2. The list should be updated regularly to reflect changes in material requirements and actual inventory status.

[0079] The allocation strategy includes the following aspects:

[0080] 1. Adopt a quantitative allocation strategy and determine the allocation amount based on historical usage and predicted demand.

[0081] 2. Establish emergency reserves to deal with emergencies or special needs.

[0082] 3. Encourage resource sharing and coordination to optimize resource allocation.

[0083] Priority setting includes the following aspects:

[0084] 1. Set different priorities based on factors such as task urgency and importance.

[0085] 2. Tasks with high priority will be given higher priority in material allocation to ensure their work proceeds smoothly.

[0086] Planning ahead includes the following aspects:

[0087] 1. Develop a timetable for advance planning and clarify the work content and goals of each stage.

[0088] 2. Combine the bill of materials and allocation strategy to develop a specific allocation plan to ensure the feasibility and effectiveness of the plan.

[0089] 3. Purchase in time to ensure timely supply of materials.

[0090] Inventory monitoring includes the following aspects:

[0091] 1. Monitor the inventory system, grasp the inventory status in real time, and ensure the continuity of material supply.

[0092] 2. Set an inventory warning line, and take replenishment measures in time when the inventory level is lower than the warning line.

[0093] 3. Regularly count and check inventory to ensure the accuracy of inventory data.

[0094] The allocation execution includes the following aspects:

[0095] 1. Strictly implement distribution work according to advance planning and distribution strategies to ensure that each office can obtain the required materials in a timely manner.

[0096] 3. Establish an allocation record system to record and compile statistics on allocations to facilitate subsequent analysis and adjustments.

[0097] Feedback adjustment includes the following aspects:

[0098] 1. Establish a feedback mechanism to collect opinions and suggestions from each office on assigned work.

[0099] 2. Adjust allocation strategies and plans in a timely manner based on feedback to ensure continuous optimization of material allocation.

[0100] 3. Track and evaluate the adjusted plan to ensure its effectiveness and sustainability.

[0101] The solution of this embodiment has the following effects:

[0102] 1. Ensure production continuity: The stability of high-frequency material supply directly affects the continuous operation of the production line. By allocating them in advance, it can be ensured that these materials arrive on time before production, avoiding production interruptions caused by material shortages.

[0103] 2. Optimize inventory management: Allocating high-frequency materials in advance helps companies predict inventory needs more accurately, thereby avoiding inventory backlogs or shortages. Through reasonable inventory planning, inventory costs can be reduced and capital turnover can be improved.

[0104] 3. Improve production efficiency: When high-frequency materials are supplied in a timely manner, the production line can maintain efficient operation and reduce the time and resources wasted waiting for materials. This will help improve production efficiency and reduce production costs.

[0105] 4. Enhance the ability to cope with risks: There are certain uncertainties in market environment, supply chain and other factors. By allocating high-frequency materials in advance, enterprises can reduce the risks brought by these uncertainties to a certain extent, such as sudden shortage of suppliers and logistics delays.

[0106] 5. Optimize resource allocation: Allocating high-frequency materials in advance helps enterprises allocate resources more reasonably, such as human, material, and financial resources. This helps to ensure that all links in the production process can be fully supported, thereby improving overall production efficiency.

[0107] 6. Meeting customer needs: The timely supply of high-frequency materials helps companies deliver products on time and meet customer needs. This will help maintain good customer relationships and improve customer satisfaction and loyalty.

[0108] 7. Support flexible production planning: By allocating high-frequency materials in advance, enterprises can adjust production plans more flexibly to respond to market changes or changes in customer demand. This will help enterprises maintain their competitive advantage and achieve sustainable development.

[0109] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed 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 part of the steps or stages in other steps.

[0110] Based on the same inventive concept, the embodiment of the present application also provides a high-frequency material distribution and processing device for implementing the high-frequency material distribution and processing method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of one or more high-frequency material distribution and processing devices provided below can refer to the limitations of the high-frequency material distribution and processing method above, and will not be repeated here.

[0111] In one embodiment, Figure 4As shown, a high-frequency material distribution and processing device is provided, comprising:

[0112] The historical data acquisition module 401 is used to obtain the actual usage quantity sequence of high-frequency materials in multiple historical time windows according to historical production data;

[0113] The prediction module 402 is used to input the actual usage quantity sequence of each historical time window into the prediction model to obtain the usage demand quantity sequence of the high-frequency material in the future time window;

[0114] The allocation processing module 403 is used to allocate the high-frequency materials according to the usage demand sequence of the high-frequency materials in the future time window.

[0115] In one embodiment, the prediction module 402 is further configured to:

[0116] The actual usage quantity sequence of each historical time window is input into the prediction model to obtain the prediction result corresponding to each historical time window; the multiple prediction results corresponding to each historical time window are statistically processed to obtain the usage demand quantity sequence of high-frequency materials in the future time window.

[0117] In one embodiment, the prediction module 402 is further configured to:

[0118] According to the difference between the historical prediction results of each historical time window and the actual usage sequence of the corresponding future time window, the weight corresponding to each historical time window is obtained; according to the weight, the multiple prediction results corresponding to each historical time window are weighted and summed to obtain the usage demand sequence of high-frequency materials in the future time window.

[0119] In one embodiment, the smaller the difference is, the greater the corresponding weight is.

[0120] In one embodiment, the device further comprises a high-frequency material determination module, which is used to:

[0121] According to the historical production data, the usage frequency of each material is obtained; according to the usage frequency, high-frequency materials are determined among various materials.

[0122] In one embodiment, the high-frequency material determination module is further used to:

[0123] Various materials are sorted according to the frequency of use; if they are sorted from large to small by frequency of use, the materials ranked first are determined as high-frequency materials; if they are sorted from small to large by frequency of use, the materials ranked last are determined as high-frequency materials; or, if the frequency of use is greater than or equal to the frequency of use threshold, the corresponding material is determined as a high-frequency material.

[0124] Each module in the high-frequency material distribution and processing device can be implemented in whole or in part by 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 a computer device in the form of software, so that the processor can call and execute the operations corresponding to each module.

[0125] In an exemplary embodiment, a computer device is provided, the internal structure of which can be shown as follows: Figure 5 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through 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, 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 database of the computer device is used to store data involved in the above method. 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 through a network connection. When the computer program is executed by the processor, a method for distributing and processing high-frequency materials is implemented.

[0126] Those skilled in the art will understand that Figure 5 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 those shown in the figure, or combine certain components, or have a different arrangement of components.

[0127] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps in the above-mentioned various method embodiments when executing the computer program.

[0128] 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 in the above-mentioned method embodiments are implemented.

[0129] In one embodiment, a computer program product is provided, on which a computer program is stored, and the computer program is used by a processor to execute the steps in the above-mentioned various method embodiments.

[0130] 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.

[0131] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed 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 the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can 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 can include random access memory (RAM) or external cache memory, etc. As an 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 each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0132] The technical features of the above embodiments may 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 application.

[0133] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for distributing and processing high-frequency materials, characterized in that: The method comprises: Based on historical production data, the actual usage sequence of high-frequency materials in multiple historical time windows is obtained; Input the actual usage sequence of each historical time window into the forecasting model to obtain the usage demand sequence of high-frequency materials in the future time window; High-frequency materials are allocated and processed according to their usage demand sequence in future time windows.

2. The method according to claim 1, characterized in that Input the actual usage sequence of each historical time window into the forecasting model to obtain the usage demand sequence of high-frequency materials in the future time window, including: Input the actual usage sequence of each historical time window into the prediction model to obtain the prediction result corresponding to each historical time window; Statistical processing is performed on multiple prediction results corresponding to each historical time window to obtain the usage demand sequence of high-frequency materials in the future time window.

3. The method according to claim 2, characterized in that Perform statistical processing on multiple forecast results corresponding to each historical time window to obtain the usage demand sequence of high-frequency materials in the future time window, including: According to the difference between the historical prediction results of the prediction model for each historical time window and the actual usage sequence of the corresponding future time window, the weight corresponding to each historical time window is obtained; According to the weights, a weighted summation process is performed on multiple prediction results corresponding to each historical time window to obtain a usage demand sequence of high-frequency materials in future time windows.

4. The method according to claim 3, characterized in that The smaller the difference, the greater the corresponding weight.

5. The method according to claim 1, characterized in that The method further comprises: Based on historical production data, the usage frequency of each material is obtained; According to the usage frequency, high-frequency materials are determined among various materials.

6. The method according to claim 5, characterized in that Based on the frequency of use, high-frequency materials are identified among various materials, including: Sort various materials according to the frequency of use; if they are sorted from high to low in frequency of use, the materials ranked first are determined as high-frequency materials; if they are sorted from low to high in frequency of use, the materials ranked last are determined as high-frequency materials; or, If the usage frequency is greater than or equal to the usage frequency threshold, the corresponding material is determined as a high-frequency material.

7. A high-frequency material distribution and processing device, characterized in that: The device comprises: The historical data acquisition module is used to obtain the actual usage quantity sequence of high-frequency materials in multiple historical time windows based on historical production data; The forecasting module is used to input the actual usage sequence of each historical time window into the forecasting model to obtain the usage demand sequence of high-frequency materials in the future time window; The allocation processing module is used to allocate and process high-frequency materials according to the usage demand sequence of high-frequency materials in the future time window.

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 method according to any one of claims 1 to 6 is 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 method according to any one of claims 1 to 6 is implemented.

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

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