Method and device for determining productivity input, equipment and storage medium
By entering the data of capacity input into the prediction model and generating a capacity input report, the problems of high subjectivity and low efficiency of capacity input analysis in the existing technology are solved, and more accurate and efficient capacity input suggestions are achieved.
Patent Information
- Application Number
- CN202411603388.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-11
- Publication Date
- 2025-03-04
AI Technical Summary
When determining capacity investment in the prior art, human resource analysis and evaluation are subjective and inefficient, and it is impossible to timely and accurately obtain the capacity investment that meets actual needs.
By obtaining the data of the target capacity investment within the preset time, input it into the capacity input prediction model, obtaining the target decision information for the next time, and generating a capacity input report based on this information to achieve accurate suggestions for capacity input.
It improves the accuracy and efficiency of capacity investment suggestions and can meet the actual needs of enterprises more timely and accurately.
Smart Images
Figure CN119558679A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to artificial intelligence, and more particularly to a method, device, equipment, and storage medium for determining production capacity investment. Background Art
[0002] Capacity investment is of great significance in the operation and development of an enterprise, and directly affects the enterprise's production efficiency, market competitiveness, financial health and long-term development.
[0003] In the existing technology, relevant personnel often analyze various aspects of capacity investment, such as personnel and efficiency, and then adapt and configure the capacity investment for the next stage;
[0004] However, under the implementation of the above-mentioned existing technologies, human analysis and evaluation are too subjective and inefficient, and often cannot timely and accurately determine the actual needs of capacity investment. Summary of the invention
[0005] The embodiments of the present application provide a method, device, equipment, and storage medium for determining capacity investment, so as to provide more accurate recommendations on capacity investment.
[0006] In a first aspect, an embodiment of the present application provides a method for determining capacity investment, including:
[0007] Acquire first data of target capacity investment within a preset time period, wherein the first data includes: first suggested data, first actual data, and a first deviation value corresponding to the first suggested data and the first actual data;
[0008] Inputting the first data into a capacity input prediction model to obtain target decision information corresponding to a next time period after the preset time period, wherein the target decision information is used to indicate target correction data within the next time period;
[0009] A capacity input report for the next period of time is generated according to the target correction data.
[0010] In a possible implementation manner, before inputting the first data into the capacity input prediction model to obtain the target decision information corresponding to the next duration after the preset duration, the method further includes:
[0011] Acquire second data of multiple durations before the preset duration, wherein for each second data, the second data includes: second suggested data, second actual data, a second deviation value corresponding to the second suggested data and the second actual data, and decision information, wherein the decision information is used to indicate the correction data within the next duration of the second data;
[0012] The preset model is trained according to the second data of the multiple time periods to obtain the capacity input prediction model.
[0013] In a possible implementation manner, before training a preset model according to the second data of the plurality of durations to obtain the capacity input prediction model, the method further includes:
[0014] The second data of the plurality of durations are preprocessed, wherein the preprocessing includes: correcting erroneous data, filling in missing data, and unifying data formats.
[0015] In a possible implementation manner, generating the capacity input report for the next period of time according to the target correction data includes:
[0016] According to the target correction data, the third suggestion data corresponding to the next duration is corrected;
[0017] A capacity input report for the next period of time is generated according to the revised third suggestion data.
[0018] In a possible implementation, the method further includes:
[0019] Acquire third actual data corresponding to the next duration, and a third deviation value corresponding to the corrected third suggested data and the third actual data;
[0020] The capacity input forecasting model is updated according to the revised third recommended data, the third actual data, and the third deviation value.
[0021] In a possible implementation, the method further includes:
[0022] The capacity investment report for the next period of time is displayed in the interactive interface.
[0023] In a possible implementation, the method further includes:
[0024] The target correction data is adjusted in response to an adjustment operation by a user.
[0025] In a second aspect, an embodiment of the present application provides a device for determining capacity investment, including:
[0026] An acquisition module, configured to acquire first data of target capacity investment within a preset time period, wherein the first data includes: first suggested data, first actual data, and a first deviation value corresponding to the first suggested data and the first actual data;
[0027] A determination module, used for inputting the first data into a capacity input prediction model to obtain target decision information corresponding to a next time period after the preset time period, wherein the target decision information is used for indicating target correction data within the next time period;
[0028] A generation module is used to generate a production capacity input report for the next period of time according to the target correction data.
[0029] In a possible implementation manner, before inputting the first data into the capacity input prediction model to obtain the target decision information corresponding to the next duration after the preset duration, the determination module is further used to:
[0030] Acquire second data of multiple durations before the preset duration, wherein for each second data, the second data includes: second suggested data, second actual data, a second deviation value corresponding to the second suggested data and the second actual data, and decision information, wherein the decision information is used to indicate the correction data within the next duration of the second data;
[0031] The preset model is trained according to the second data of the multiple time periods to obtain the capacity input prediction model.
[0032] In a possible implementation manner, before training a preset model according to the second data of the plurality of durations to obtain the capacity input prediction model, the determination module is further configured to:
[0033] The second data of the plurality of durations are preprocessed, wherein the preprocessing includes: correcting erroneous data, filling in missing data, and unifying data formats.
[0034] In a possible implementation manner, the generating module is specifically used to:
[0035] According to the target correction data, the third suggestion data corresponding to the next duration is corrected;
[0036] A capacity input report for the next period of time is generated according to the revised third suggestion data.
[0037] In a possible implementation manner, the determining module is further configured to:
[0038] Acquire third actual data corresponding to the next duration, and a third deviation value corresponding to the corrected third suggested data and the third actual data;
[0039] The capacity input forecasting model is updated according to the revised third recommended data, the third actual data, and the third deviation value.
[0040] In a possible implementation manner, the determining module is further configured to:
[0041] The capacity investment report for the next period of time is displayed in the interactive interface.
[0042] In a possible implementation manner, the determining module is further configured to:
[0043] The target correction data is adjusted in response to an adjustment operation by a user.
[0044] In a third aspect, an embodiment of the present application provides an electronic device, including: a memory, a processor;
[0045] The memory stores computer-executable instructions;
[0046] The processor executes the computer-executable instructions stored in the memory, so that the processor executes the above first aspect and / or various possible implementations of the first aspect.
[0047] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the first aspect above and / or various possible implementations of the first aspect.
[0048] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above first aspect and / or various possible implementation methods of the first aspect.
[0049] The method, device, equipment, and storage medium for determining capacity investment provided in the embodiments of the present application obtain first data of target capacity investment within a preset time period, the first data including: first recommended data, first actual data, and a first deviation value corresponding to the first recommended data and the first actual data; input the first data into a capacity investment prediction model to obtain target decision information corresponding to the next time period after the preset time period, the target decision information is used to indicate target correction data within the next time period; and generate a capacity investment report for the next time period based on the target correction data, so as to achieve the effect of more accurately giving recommendations on the capacity investment amount. BRIEF DESCRIPTION OF THE DRAWINGS
[0050] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.
[0051] Figure 1 Schematic diagram of the process of determining the capacity investment provided for this application Figure 1 ;
[0052] Figure 2 Schematic diagram of the process of determining the capacity investment provided for this application Figure 2 ;
[0053] Figure 3 Schematic diagram of the process of determining the capacity investment provided for this application Figure 3 ;
[0054] Figure 4 A schematic diagram of the structure of a device for determining production capacity input provided in this application;
[0055] Figure 5 A schematic diagram of the structure of the electronic device provided in this application.
[0056] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION
[0057] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.
[0058] First, the terms involved in this application are explained:
[0059] Data analysis: It is to collect a large amount of data, analyze it using appropriate statistical analysis methods, classify and summarize it using efficient analysis tools, and extract valuable information from it, summarize and conclude to form effective conclusions. Therefore, by analyzing the data summarized by the enterprise or the massive amount of real data obtained from the market, a reasonable capacity reference value for the current situation of the enterprise can be obtained. This process can be supplemented by visualization tools to help decision makers see the trend and development of actual capacity investment, so as to obtain valuable conclusions and further make inferences with predictive significance.
[0060] Secondly, the technical background involved in this application is described:
[0061] Capacity investment is of great significance in the operation and development of an enterprise, and directly affects the enterprise's production efficiency, market competitiveness, financial health and long-term development.
[0062] In the existing technology, relevant personnel often analyze various aspects of capacity investment, such as personnel and efficiency, and then adapt and configure the capacity investment for the next stage;
[0063] However, under the implementation of the above-mentioned existing technologies, human analysis and evaluation are too subjective and inefficient, and often cannot timely and accurately determine the actual needs of capacity investment.
[0064] The method for determining capacity investment provided in the present application obtains first data of target capacity investment within a preset time period, the first data including: first suggested data, first actual data, and a first deviation value corresponding to the first suggested data and the first actual data; the first data is input into a capacity investment prediction model to obtain target decision information corresponding to the next time period after the preset time period, the target decision information is used to indicate target correction data within the next time period; a method for generating a capacity investment report for the next time period according to the target correction data solves the technical problem of being unable to accurately give recommendations on the capacity investment amount.
[0065] It should be noted that the method for determining the capacity investment and the device for determining the capacity investment of the present application can be used in the field of artificial intelligence, and can also be used in any field other than the field of artificial intelligence. The application field of the method for determining the capacity investment and the device for determining the capacity investment of the present application is not limited.
[0066] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.
[0067] Figure 1 Schematic diagram of the process of determining the capacity investment provided for this application Figure 1 ,like Figure 1 As shown, the method for determining the capacity investment includes the following steps:
[0068] Step 11, obtaining first data of target capacity investment within a preset time period;
[0069] The first data includes: first suggested data, first actual data, and a first deviation value corresponding to the first suggested data and the first actual data;
[0070] In this step, the preset duration may be the duration of a current period of time.
[0071] In a possible implementation, the entire month of October is taken as an example for explanation.
[0072] The first suggested data may be the planned capacity input data for the target capacity input within October, such as 100; the first actual data may be the actual capacity input data for the target capacity input within October, such as 90; the first deviation value corresponding to the first suggested data and the first actual data may be the difference between the planned capacity input data for the target capacity input within October and the actual capacity input data, that is, 10.
[0073] Among them, the first deviation value reflects the gap between the plan and the actual input, that is, the difference between the first recommended data and the first actual data.
[0074] It should be understood that the recommended data and actual data involved in this application are only illustrated in terms of quantity, such as 100, 90, etc., and are not limited. In fact, the data may include: personnel ratio, efficiency, working hours, equipment ratio, capital investment, etc.
[0075] Step 12: input the first data into the capacity input prediction model to obtain target decision information corresponding to the next time period after the preset time period;
[0076] The target decision information is used to indicate the target correction data within the next time period;
[0077] In this step, after the first suggested data, the first actual data, and the first deviation value corresponding to the first suggested data and the first actual data are obtained, the above data can be input into a pre-trained capacity input prediction model to obtain corrected data;
[0078] Furthermore, the correction data is the target correction data for the next time period after the preset time period, and is used to correct the recommended data for the next time period after the preset time period.
[0079] Optionally, the following may also be performed: adjusting the target correction data in response to an adjustment operation by the user.
[0080] In this implementation, the user can adjust the target correction data output by the capacity input forecasting model in real time based on experience and / or actual conditions within the next period after the preset period.
[0081] Step 13: Generate a capacity input report for the next period based on the target correction data.
[0082] In this step, after the target correction data is obtained, the capacity input report for the next time period can be obtained by combining the recommended data corresponding to the next time period after the preset time period.
[0083] In this implementation, in order to compensate for the insufficient or excessive production capacity within the preset time period and ensure production continuity, the production capacity target for the next time period is adjusted based on the target correction data obtained above to generate a production capacity investment report for the next time period for reference by relevant personnel.
[0084] In a possible implementation, the next time period may be the next month in the above example, such as November.
[0085] Furthermore, it is also possible to perform: displaying the capacity investment report for the next period in the interactive interface.
[0086] In this implementation, the capacity investment report for the next period of time can be displayed through the interactive interface of the electronic device, so as to assist relevant personnel in understanding the recommendations for capacity investment at the next moment in a visual manner.
[0087] The method for determining capacity investment provided in the embodiment of the present application can be used in the field of artificial intelligence. The method includes: obtaining first data of target capacity investment within a preset duration, the first data including: first suggested data, first actual data, and a first deviation value corresponding to the first suggested data and the first actual data; inputting the first data into the capacity investment prediction model to obtain the target decision information corresponding to the next duration after the preset duration, the target decision information is used to indicate the target correction data within the next duration; based on the target correction data, generating a capacity investment report for the next duration. This method is used to achieve the effect of giving more accurate recommendations on the amount of capacity investment.
[0088] Based on the above embodiments, Figure 2 Schematic diagram of the process of determining the capacity investment provided for this application Figure 2 ,like Figure 2 As shown, before step 12, the method for determining the capacity investment also includes the following steps:
[0089] Step 21, obtaining second data of multiple time periods before a preset time period;
[0090] Wherein, for each second data, the second data includes: second suggested data, second actual data, a second deviation value corresponding to the second suggested data and the second actual data, and decision information, where the decision information is used to indicate the correction data within the next time period of the second data;
[0091] In this step, second data within multiple time periods can be obtained from historical data for use in subsequent training of a capacity investment prediction model.
[0092] The historical data can be obtained by screening from a large amount of business data generated during the operation process, or it can be public business data obtained through various legal channels, and the recommended data and actual data are selected from them and used as the second recommended data and second actual data, and the difference between the second recommended data and the second actual data is determined as the second deviation value.
[0093] Furthermore, for each pair of second recommended data and second actual data, the actual data for the next duration and the recommended data for the next duration can be read from the corresponding business data. Based on the actual data for the next duration and the recommended data for the next duration, the corrected data for the next duration can be obtained as decision-making information.
[0094] Furthermore, the second data of multiple durations are preprocessed, and the preprocessing includes: correcting erroneous data, filling in missing data, and unifying data formats.
[0095] Step 22: Train the preset model based on the second data of multiple time periods to obtain a capacity input prediction model.
[0096] In this step, after the multiple second data for training are obtained as mentioned above, these second data will be used as input features and output features of the preset model for training to obtain a capacity investment prediction model.
[0097] Optional, in the following Figure 3 After the example, you can also modify the capacity input forecast model by performing the following steps:
[0098] Step 1: Obtain third actual data corresponding to the next duration, and a third deviation value corresponding to the corrected third suggested data and the third actual data;
[0099] In this implementation, the capacity input forecasting model can be updated in real time to obtain a more accurate forecast, and the actual data corresponding to the next period of time, that is, the third actual data and the revised third recommended data, can be obtained.
[0100] In a possible implementation, based on the above example, the third actual data in November and the third recommended data in November are obtained, and then the third deviation value corresponding to the third recommended data and the third actual data can be obtained.
[0101] Step 2: Update the capacity input forecasting model according to the revised third recommended data, the third actual data, and the third deviation value.
[0102] In this implementation, the revised third recommended data, the third actual data, and the third deviation value are used as new training data for training the capacity input prediction model to update the parameters of the capacity input prediction model and realize the update of the capacity input prediction model.
[0103] The method for determining capacity investment provided in the embodiment of the present application is to obtain the second data of multiple time periods before the preset time period; wherein, for each second data, the second data includes: second recommended data, second actual data, second deviation value corresponding to the second recommended data and the second actual data, and decision information, and the decision information is used to indicate the correction data within the next time period of the second data; according to the second data of multiple time periods, the preset model is trained to obtain the capacity investment prediction model. The method is used to train a capacity investment prediction model that can output more accurate target correction data.
[0104] Based on the above embodiments, Figure 3 Schematic diagram of the process of determining the capacity investment provided for this application Figure 3 ,like Figure 3 As shown, the above step 13 may include the following steps:
[0105] Step 31: Correct the third suggestion data corresponding to the next duration according to the target correction data;
[0106] In this step, after obtaining the target correction data, the third suggestion data corresponding to the next duration of the preset duration is obtained, and the third suggestion data is corrected.
[0107] In a possible implementation, according to the above analysis, the target correction data is 5, and the recommended data corresponding to the next time period, that is, the third recommended data is 100, then the revised target capacity is 105.
[0108] Step 32: Generate a capacity input report for the next period of time according to the revised third suggestion data.
[0109] In this step, the capacity investment report can include the following parts:
[0110] 1. Report title and date
[0111] 2. Data Overview:
[0112] Main data for the current period: equipment status, number of personnel, raw material inventory, production efficiency, etc.
[0113] Target capacity: revised third recommended data;
[0114] Actual production capacity: the current ratio of various equipment, personnel, etc. involved.
[0115] 3. Improvement suggestions:
[0116] Propose how to adjust the production plan to increase production capacity (such as increasing staff, optimizing equipment scheduling, etc.).
[0117] In one possible implementation, the conclusion in the capacity investment report for the next period is 105. To ensure that this goal is achieved, the following measures can be taken:
[0118] 1. Resource allocation: Ensure that production resources (such as equipment, personnel, and raw materials) are adequately prepared to support the revised third recommended data.
[0119] 2. Production scheduling optimization: adjust production plans and work time allocation according to revised targets to ensure a smooth transition of production links.
[0120] 3. Equipment and personnel inspection: Check the equipment status to ensure the normal operation of the equipment, solve the fault problems in time, and provide appropriate production training to employees to improve production efficiency.
[0121] The method for determining capacity investment provided in the embodiment of the present application amends the third suggested data corresponding to the next time period according to the target correction data, and generates a capacity investment report for the next time period according to the amended third suggested data. In this technical solution, it is realized how to generate a capacity investment report that can be directly used by users to assist in the allocation of capacity.
[0122] The following are device embodiments involved in this application:
[0123] Figure 4 A schematic diagram of the structure of the device for determining the capacity input provided in this application, such as Figure 4 As shown, the device comprises:
[0124] An acquisition module 41 is used to acquire first data of target capacity investment within a preset time period, wherein the first data includes: first suggested data, first actual data, and a first deviation value corresponding to the first suggested data and the first actual data;
[0125] A determination module 42, configured to input the first data into a capacity input prediction model, and obtain target decision information corresponding to a next time period after a preset time period, wherein the target decision information is used to indicate target correction data within the next time period;
[0126] The generating module 43 is used to generate a capacity input report for the next period of time according to the target correction data.
[0127] In a possible implementation, before inputting the first data into the capacity input prediction model to obtain the target decision information corresponding to the next duration after the preset duration, the determination module 42 is further used to:
[0128] Acquire second data of multiple durations before a preset duration, wherein for each second data, the second data includes: second suggested data, second actual data, a second deviation value corresponding to the second suggested data and the second actual data, and decision information, wherein the decision information is used to indicate the correction data within the next duration of the second data;
[0129] The preset model is trained according to the second data of multiple time periods to obtain a capacity investment prediction model.
[0130] In a possible implementation, before training a preset model according to the second data of multiple durations to obtain a capacity input prediction model, the determination module 42 is further used to:
[0131] Preprocessing is performed on the second data of multiple time lengths, and the preprocessing includes: correcting erroneous data, filling in missing data, and unifying data formats.
[0132] In a possible implementation, the generating module 43 is specifically configured to:
[0133] According to the target correction data, the third suggested data corresponding to the next duration is corrected;
[0134] Generate the capacity input report for the next period based on the revised third recommended data.
[0135] In a possible implementation, the determination module 42 is further configured to:
[0136] Acquire third actual data corresponding to the next duration, and a third deviation value corresponding to the corrected third suggested data and the third actual data;
[0137] The capacity input forecasting model is updated according to the revised third recommended data, the third actual data, and the third deviation value.
[0138] In a possible implementation, the determination module 42 is further configured to:
[0139] The capacity investment report for the next period of time is displayed in the interactive interface.
[0140] In a possible implementation, the determination module 42 is further configured to:
[0141] In response to the user's adjustment operation, the target correction data is adjusted.
[0142] The device for determining capacity investment provided in this embodiment can execute the method provided in the above method embodiment, and its implementation principle and technical effect are similar, which will not be described in detail in this embodiment.
[0143] Figure 5This is a schematic diagram of the structure of the electronic device provided in this application. Figure 5 As shown, the electronic device provided in this embodiment includes: at least one processor 51 and a memory 52. Optionally, the device also includes a communication component 53. The processor 51, the memory 52 and the communication component 53 are connected via a bus 54.
[0144] In a specific implementation process, at least one processor 51 executes the computer-executable instructions stored in the memory 52, so that at least one processor 51 executes the above method.
[0145] The specific implementation process of the processor 51 can be found in the above method embodiment, and its implementation principle and technical effect are similar, so this embodiment will not be repeated here.
[0146] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the invention may be directly implemented as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.
[0147] The memory may include a high-speed memory (Random Access Memory, RAM), and may also include a non-volatile memory (Non-volatile Memory, NVM), such as at least one disk memory.
[0148] The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of the present application is not limited to only one bus or one type of bus.
[0149] The present application also provides a computer program product, including a computer program, which implements the above method when executed by a processor.
[0150] The present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the above method is implemented.
[0151] The above-mentioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special-purpose computer.
[0152] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (Application Specific Integrated Circuits, referred to as: ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.
[0153] The division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.
[0154] Another point is that the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, device or unit, which may be electrical, mechanical or other forms.
[0155] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, may be located in one place or may be distributed over multiple network units.
[0156] Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
[0157] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.
[0158] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program codes.
[0159] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.
[0160] Finally, it should be noted that those skilled in the art will readily conceive of other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any variations, uses or adaptations of the present application, which follow the general principles of the present application and include common knowledge or customary technical means in the art that are not disclosed in the present application, are not limited to the precise structures described above and shown in the drawings, and may be modified and changed in various ways without departing from the scope thereof. The scope of the present application is limited only by the appended claims.
Claims
1. A method for determining capacity investment, characterized in that: include: Acquire first data of target capacity investment within a preset time period, wherein the first data includes: first suggested data, first actual data, and a first deviation value corresponding to the first suggested data and the first actual data; Inputting the first data into a capacity input prediction model to obtain target decision information corresponding to a next time period after the preset time period, wherein the target decision information is used to indicate target correction data within the next time period; A capacity input report for the next period of time is generated according to the target correction data.
2. The method according to claim 1, characterized in that Before inputting the first data into the capacity input prediction model to obtain the target decision information corresponding to the next time period after the preset time period, the method further includes: Acquire second data of multiple durations before the preset duration, wherein for each second data, the second data includes: second suggested data, second actual data, a second deviation value corresponding to the second suggested data and the second actual data, and decision information, wherein the decision information is used to indicate the correction data within the next duration of the second data; The preset model is trained according to the second data of the multiple time periods to obtain the capacity input prediction model.
3. The method according to claim 2, characterized in that Before training a preset model according to the second data of the plurality of durations to obtain the capacity input prediction model, the method further includes: The second data of the plurality of durations are preprocessed, wherein the preprocessing includes: correcting erroneous data, filling in missing data, and unifying data formats.
4. The method according to any one of claims 1 to 3, characterized in that: Generating the capacity input report for the next period of time according to the target correction data includes: According to the target correction data, the third suggestion data corresponding to the next duration is corrected; A capacity input report for the next period of time is generated according to the revised third suggestion data.
5. The method according to claim 4, characterized in that The method further comprises: Acquire third actual data corresponding to the next duration, and a third deviation value corresponding to the corrected third suggested data and the third actual data; The capacity input forecasting model is updated according to the revised third recommended data, the third actual data, and the third deviation value.
6. The method according to any one of claims 1 to 3, characterized in that: The method further comprises: The capacity investment report for the next period of time is displayed in the interactive interface.
7. The method according to any one of claims 1 to 3, characterized in that: The method further comprises: The target correction data is adjusted in response to an adjustment operation by a user.
8. A device for determining production capacity input, characterized in that: include: An acquisition module, configured to acquire first data of target capacity investment within a preset time period, wherein the first data includes: first suggested data, first actual data, and a first deviation value corresponding to the first suggested data and the first actual data; A determination module, used for inputting the first data into a capacity input prediction model to obtain target decision information corresponding to a next time period after the preset time period, wherein the target decision information is used for indicating target correction data within the next time period; A generation module is used to generate a production capacity input report for the next period of time according to the target correction data.
9. An electronic device, characterized in that: include: Memory, processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory, so that the processor performs the method according to any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 7 when executed by a processor.
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