Capacity allocation method and device for file warehouse, equipment, medium and program product

Abstract

The invention provides a capacity allocation method and device for a file warehouse, equipment, a medium and a program product, and relates to the technical field of computers. The method comprises the following steps: acquiring use condition data of each warehouse in a target file warehouse, wherein a target file comprises a product warehouse or a mirror image warehouse; obtaining capacity trend data of the target file warehouse according to the use condition data; adjusting and optimizing warehouse capacity control parameters of each warehouse in the target file warehouse according to the capacity trend data to obtain adjusted and optimized warehouse capacity control parameters; according to the adjusted and optimized warehouse capacity control parameters, capacity allocation is carried out on each warehouse in the target file warehouse, and capacity planning, automatic allocation, adjustment and optimization of the file warehouse can be achieved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a method, apparatus, device, medium, and program product for allocating capacity of a file warehouse. Background Art

[0002] Existing computer technologies provide methods such as cloud platform capacity prediction, image space usage statistics, and rapid image deployment. However, these methods only provide predictions of the future capacity required for the cloud platform, accurate statistics of the space occupied by a single image, and quick image support for users. However, they cannot achieve capacity allocation for product warehouses and image warehouses, cannot provide effective intelligent planning and automatic allocation of warehouse capacity, do not dynamically monitor warehouse usage, and do not support intelligent tuning of intelligent planning and automatic allocation of warehouse capacity. Summary of the Invention

[0003] At least one embodiment of the present application provides a method, apparatus, device, medium, and program product for allocating capacity of a file repository, which is used to solve the problem in the prior art of being unable to implement capacity allocation for product repositories and image repositories.

[0004] In order to solve the above technical problems, this application is implemented as follows:

[0005] In a first aspect, an embodiment of the present application provides a method for allocating capacity of a file repository, including:

[0006] Obtaining usage data for each repository in a target file repository, wherein the target file repository includes a product repository or an image repository;

[0007] Obtaining capacity trend data of the target file warehouse based on the usage data;

[0008] Adjusting and optimizing the warehouse capacity control parameters of each warehouse in the target file warehouse according to the capacity trend data to obtain adjusted and optimized warehouse capacity control parameters;

[0009] Based on the adjusted and optimized warehouse capacity control parameters, capacity is allocated to each warehouse in the target file warehouse.

[0010] Optionally, the capacity allocation method for the file warehouse, wherein the warehouse capacity control parameters of each warehouse in the target file warehouse are adjusted and optimized according to the capacity trend data to obtain the adjusted and optimized warehouse capacity control parameters, includes:

[0011] Adjusting and optimizing a first parameter of each warehouse in the target file warehouse according to the capacity trend data to obtain an adjusted and optimized first parameter, wherein the first parameter includes at least one of the following: a single unit baseline capacity, a warehouse storage scale control value, and a warehouse complexity control value;

[0012] The warehouse capacity control parameter of each warehouse in the target file warehouse is adjusted and optimized according to the adjusted and optimized first parameter to obtain the adjusted and optimized warehouse capacity control parameter.

[0013] Optionally, in the capacity allocation method for the file warehouse, the adjusted and optimized warehouse capacity control parameter is equal to the product of the adjusted and optimized single unit benchmark capacity, the warehouse storage scale control value, and the warehouse complexity control value;

[0014] The adjusted and optimized warehouse complexity control value is equal to the sum of the products of the adjusted and optimized business complexity control value and the technical complexity control value and the corresponding weight coefficients respectively.

[0015] Optionally, the capacity allocation method for the file warehouse, wherein the warehouse storage scale control value of each warehouse in the target file warehouse is adjusted and optimized according to the capacity trend data to obtain the adjusted and optimized warehouse storage scale control value, includes:

[0016] Adjusting and optimizing the warehouse storage scale control information of each warehouse in the target file warehouse according to the capacity trend data to obtain adjusted and optimized warehouse storage scale control information, wherein the warehouse storage scale control information stores a correspondence between each warehouse storage scale level in a plurality of warehouse storage scale levels and a warehouse storage scale control value;

[0017] The warehouse storage scale control value of each warehouse in the target file is adjusted and optimized according to the adjusted and optimized warehouse storage scale control information to obtain the adjusted and optimized warehouse storage scale control value.

[0018] Optionally, the capacity allocation method for the file warehouse, wherein the warehouse complexity control value of each warehouse in the target file warehouse is adjusted and optimized according to the capacity trend data to obtain the adjusted and optimized warehouse complexity control value, includes:

[0019] Adjusting and optimizing the warehouse complexity control information of each warehouse in the target file warehouse according to the capacity trend data to obtain adjusted and optimized warehouse complexity control information, wherein the warehouse complexity control information stores a correspondence between each business complexity level in a plurality of business complexity levels and a business complexity control value, and stores a correspondence between each technical complexity level in a plurality of technical complexity levels and a technical complexity control value;

[0020] Adjusting and optimizing the business complexity control value and the technical complexity control value of each warehouse in the target file according to the adjusted and optimized warehouse complexity control information to obtain adjusted and optimized business complexity control values ​​and technical complexity control values;

[0021] According to the adjusted and optimized business complexity control value and technical complexity control value, the adjusted and optimized warehouse complexity control value is obtained.

[0022] Optionally, the method for allocating capacity of a file warehouse further includes:

[0023] Determining an initial warehouse capacity control parameter corresponding to each warehouse in the target file according to at least one of a type, a purpose, and an application scenario of the target file;

[0024] Capacity allocation is performed on the target file warehouse according to the initial warehouse capacity control parameter.

[0025] Optionally, the capacity allocation method of the file warehouse, wherein the target file warehouse is used to store target files, is constructed as a multi-level tree structure, each level of the multi-level tree structure corresponds to at least one warehouse, the categories of the warehouses on each level of the multi-level tree structure are different, the category of at least one warehouse on the first level of the multi-level tree structure belongs to the category range of the warehouse on the corresponding second level, and the first level structure is the next level structure of the second level structure.

[0026] Optionally, in the capacity allocation method for a file warehouse, the capacity trend data includes at least one of the following:

[0027] a usage growth rate of the target file warehouse;

[0028] The usage growth rate of each warehouse in the target file warehouse.

[0029] In a second aspect, an embodiment of the present application further provides a capacity allocation device for a file warehouse, comprising:

[0030] An acquisition module is used to acquire usage data of each repository in a target file repository, where the target file repository includes a product repository or an image repository;

[0031] A first obtaining module, configured to obtain capacity trend data of the target file warehouse based on the usage data;

[0032] A second obtaining module is configured to adjust and optimize the warehouse capacity control parameters of each warehouse in the target file warehouse according to the capacity trend data, and obtain adjusted and optimized warehouse capacity control parameters;

[0033] The allocation module is used to allocate capacity to each warehouse in the target file warehouse according to the adjusted and optimized warehouse capacity control parameters.

[0034] In a third aspect, an embodiment of the present application also provides a capacity allocation device for a file warehouse, comprising: a processor, a memory, and a program or instruction stored on the memory and executable on the processor, wherein when the processor executes the program or instruction, the capacity allocation method for the file warehouse as described in the first aspect is implemented.

[0035] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the capacity allocation method for the file warehouse as described in the first aspect is implemented.

[0036] In a fifth aspect, an embodiment of the present application further provides a computer program product, comprising computer instructions, which, when executed by a processor, implement the capacity allocation method for the file warehouse as described in the first aspect.

[0037] Compared with the prior art, the embodiments of the present application provide a method, apparatus, equipment, medium and program product for capacity allocation of a file warehouse. By dynamically monitoring the target file warehouse, usage data of each warehouse in the target file warehouse is obtained. Based on the usage data, capacity trend data of the target file warehouse is obtained to achieve trend prediction and provide support data for subsequent capacity allocation. The warehouse capacity control parameters of each warehouse in the target file warehouse are adjusted and optimized according to the capacity trend data to achieve intelligent tuning of the warehouse capacity control parameters. Capacity is allocated to each warehouse in the target file warehouse according to the adjusted and optimized warehouse capacity control parameters to support intelligent capacity planning and automatic allocation. The target file warehouse includes a product warehouse and a mirror warehouse, thereby achieving capacity allocation for the product warehouse and the mirror warehouse. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present application. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0039] Figure 1 This is a schematic diagram of the architecture of the capacity allocation system for the file warehouse described in an embodiment of the present application;

[0040] Figure 2 This is a flow chart of one implementation method of the capacity allocation method of the file warehouse described in an embodiment of the present application;

[0041] Figure 3 This is a schematic diagram of an example of classification of a product warehouse according to an embodiment of the present application;

[0042] Figure 4 This is a schematic diagram of an example of the classification of the image repository described in an embodiment of the present application;

[0043] Figure 5 This is a flow chart of a method for allocating capacity of a file warehouse according to an embodiment of the present application;

[0044] Figure 6 This is a flow chart of the second implementation method of the capacity allocation method for the file warehouse described in the embodiment of the present application;

[0045] Figure 7 This is a flowchart of the third implementation method of the capacity allocation method for the file warehouse described in the embodiment of the present application;

[0046] Figure 8 This is a schematic diagram of the structure of the capacity allocation device of the file warehouse according to the embodiment of the present application;

[0047] Figure 9 This is a hardware block diagram of the capacity allocation device for the file warehouse described in an embodiment of the present application.

[0048] Description of reference numerals: 801 - acquisition module; 802 - first acquisition module; 803 - second acquisition module; 804 - allocation module; 901 - processor; 902 - memory; 903 - transceiver; 904 - user interface. DETAILED DESCRIPTION

[0049] The terms "first", "second", etc. in this application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the terms used in this way are interchangeable where appropriate, so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first" and "second" are generally of the same type, and do not limit the number of objects, for example, the first object can be one or more. In addition, "or" in this application represents at least one of the connected objects. For example, "A or B" covers three options, namely, Option 1: including A but not including B; Option 2: including B but not including A; Option 3: including both A and B. The character " / " generally indicates that the objects associated before and after are in an "or" relationship.

[0050] Figure 1 This is a schematic diagram of the architecture of the capacity allocation system of the file warehouse described in the embodiment of this application. Figure 1An embodiment of the present application provides a capacity allocation system for a file warehouse, including a warehouse capacity planning and allocation module and a warehouse capacity monitoring center. The warehouse capacity planning and allocation module is used to call a warehouse capacity intelligent planning and automatic allocation algorithm (i.e., a file warehouse capacity allocation method provided in an embodiment of the present application) according to the type, purpose, and application scenario of products and images. The algorithm realizes intelligent planning and automatic allocation of warehouse capacity by selecting warehouse capacity control parameters and strategies, setting warehouse capacity control parameters, and calculating warehouse capacity control parameters.

[0051] The warehouse capacity monitoring center is used to dynamically monitor the usage of each product warehouse and mirror warehouse in the product warehouse and the mirror warehouse, and obtain usage data such as warehouse occupancy rate in real time. On this basis, the usage data is statistically analyzed and trended to obtain capacity trend data such as warehouse usage growth rate, thereby providing data and decision-making support for intelligent planning of warehouse capacity and optimization of automatic allocation algorithms.

[0052] The Warehouse Capacity Planning and Allocation Module intelligently adjusts and optimizes warehouse capacity control parameters and strategies based on capacity trend data. It then recalculates warehouse capacity based on these optimized and adjusted warehouse capacity control parameters, enabling intelligent capacity planning and automatic allocation algorithms. Furthermore, the module utilizes intelligent warehouse capacity planning and automatic allocation algorithms to achieve unified planning and automatic allocation of product and image warehouse capacity based on product and image type, purpose, and application scenario.

[0053] Figure 2 A flow chart of one implementation method of the capacity allocation method of the file warehouse described in the embodiment of the present application. Figure 2 As shown, the warehouse capacity planning and allocation module is used to classify and manage the product warehouse and the mirror warehouse according to information such as the type, purpose, and application scenario of the products and images. The product warehouse and the mirror warehouse are divided into a multi-level tree structure, and each level of the structure contains multiple independent product libraries or mirror libraries. The targets of capacity planning, allocation, control, adjustment, and optimization are these independent product libraries and mirror libraries. In the embodiments of the present application, these independent product libraries and mirror libraries are collectively referred to as warehouses.

[0054] Optional uses include the application of various segmented artifacts and images to support basic functionality and business development in specific areas. Different categories of segmented artifacts and images may have different characteristics in terms of size, frequency of use, and concurrency. Optional application scenarios include the upload and distributed storage of segmented artifacts and images with different uses, categories, and sizes, as well as providing fast downloading of segmented artifacts and images according to different user requirements.

[0055] Generally, the capacity and storage scale of the basic image repository and the public dependency repository are larger than those of the business repository.

[0056] Figure 3 This is a schematic diagram of an example of the classification of the product warehouse described in an embodiment of the present application. Figure 4 This is a schematic diagram of an example of the classification of the image warehouse described in the embodiment of the present application. According to the business type of the product, the product warehouse can be divided into a public dependency library and a business product library, and the public dependency library will have a larger capacity and storage scale than the business product library. According to the business type of the mirror, the image warehouse can be divided into a basic image library and a business image library, and the basic image library will have a larger capacity and storage scale than the business image library. Further, according to the application scenario, the public dependency library, the basic image library, the business product library and the business image library are respectively divided into Java libraries, Python libraries, etc. And, according to the technical field, the Java libraries and Python libraries under the public dependency library and the basic image library are respectively divided into technical field 1 library, technical field 2 library, ..., technical field n library; according to the business field, the Java libraries and Python libraries under the business product library and the business image library are respectively divided into business field 1 library, business field 2 library, ..., business field n library.

[0057] The above examples of product warehouses and image warehouses demonstrate that factors such as warehouse baseline capacity, storage scale, technical complexity, and business complexity are key indicators influencing warehouse capacity planning and allocation. Next, we will design an algorithm for intelligent warehouse capacity planning and automatic allocation based on these parameters.

[0058] Intelligent warehouse capacity planning and automatic allocation algorithms, including:

[0059] (1) Determine multiple warehouse capacity control parameters based on factors affecting warehouse capacity allocation.

[0060] Among them, the factors affecting warehouse capacity allocation include: single unit benchmark capacity (B) factor, warehouse storage scale (S) factor, and warehouse complexity (C) factor.

[0061] Through research and analysis of warehouse usage scenarios in the current market and during project development, the factors affecting warehouse capacity allocation are determined.

[0062] Regarding the single unit base capacity (B) factor, the warehouse capacity control parameters include the single unit base capacity (B), which includes:

[0063] Product base capacity (B A ), used to set the basic capacity value for a single product;

[0064] Image base capacity (B I), used to set the base capacity value for a single image.

[0065] For the warehouse storage scale (S) factor, the warehouse capacity control parameters include the warehouse storage scale control value (S V ), the warehouse storage scale control value (S V ) is used to indicate the number of individuals that each warehouse is expected to store.

[0066] For the warehouse complexity (C) factor, the warehouse capacity control parameters include warehouse complexity control value (C V ), the warehouse complexity control value (C V )include:

[0067] Business complexity control value (C BV ), used to indicate the business complexity parameters corresponding to each warehouse;

[0068] Technical complexity control value (C TV ), which is used to indicate the technical complexity parameters corresponding to each warehouse.

[0069] (2) Formulate a warehouse capacity control strategy for each of the warehouse capacity control parameters.

[0070] Among them, based on the warehouse capacity control parameters, a warehouse capacity control strategy is formulated, including:

[0071] According to the product base capacity (B A ) and image base capacity (B I ), combined with the warehouse storage scale control value (S V ) and warehouse complexity control value (C V ), calculate the warehouse capacity control parameter (V) of each warehouse.

[0072] For the single base capacity (B), determine the product base capacity (B according to the empirical value A ) and image base capacity (B I ), and based on the product base capacity (B A ) and image base capacity (B I ), determine the warehouse capacity control parameter (V) of each warehouse.

[0073] For warehouse storage scale control value (S V ) Set warehouse storage scale tiers and standards based on the purpose and application scenarios of artifacts and images, combined with the specific business scale of the enterprise or organization. For each warehouse, estimate the number of units likely to be reached within a specific time period and determine the warehouse storage scale tier based on the warehouse storage scale tier standards.

[0074] For warehouse complexity control value (CV ) Based on the purpose and application scenarios of artifacts and images, combined with the specific business or technical characteristics of the enterprise or organization, set warehouse complexity levels and standards. Warehouses are graded according to both business and technical complexity. Based on the complexity of each warehouse category and the set complexity standards, the corresponding business and technical complexity level is determined.

[0075] (3) According to the warehouse capacity control strategy, a warehouse capacity control value is set for each of the warehouse capacity control parameters.

[0076] For the single base capacity (B), set a common base capacity (B) for each product and image. A ) and image base capacity (B I ). For example, you can set B A =0.5G, B I =0.8G.

[0077] Here, the determination of the single base capacity (B) is only an example reference value. The setting may vary depending on the application scenario, business scale and technical characteristics. V ), warehouse complexity control value (C V ) is set in a similar way and will not be described in detail.

[0078] For warehouse storage scale control value (S V ), determine the warehouse storage scale control value (S V ) corresponding to the control level (S L ), that is, the warehouse storage scale level. According to the purpose and application scenario of products and images, combined with the specific business scale of the enterprise or organization, and the precision of control and management, the warehouse storage scale level (S L ). For example, you can set S L =5.

[0079] According to the warehouse storage scale level (S L ) of the grading number, combined with the business scale, design the control standard of warehouse storage scale level. For example: corresponding to S L =5, set the control standards for each level, that is, the warehouse storage scale control value (S V ), as shown in Table 1 below:

[0080] Warehouse storage scale level Control standard (number of individuals) Control standard (reference value) <![CDATA[S L 1]]> [0,50) 30 <![CDATA[S L 2]]> [50,150) 100 <![CDATA[S L 3]]> [150,300) 250 <![CDATA[S L 4]]> [300,500) 380 <![CDATA[S L 5]]> [500,+∞) 600

[0081] Table 1

[0082] It should be noted that the warehouse storage scale level, control standard (number of units) and control standard (reference value) in Table 1 are only for reference. The settings will vary depending on the application scenario, business scale and technical characteristics. BL ), namely the business complexity level, and the control level corresponding to the technical complexity control value (C TL ), that is, the determination of the technical complexity level adopts a similar method. No further details will be given.

[0083] Determine the warehouse storage scale control value (S V ), analyze the business scale of a specific warehouse classification, determine the warehouse storage scale level corresponding to each warehouse according to the warehouse storage scale level control standard, and set the control standard for each warehouse. For example: after analysis, it is determined that the warehouse storage scale level corresponding to the warehouse is S L 4, then the warehouse storage scale control value S of the warehouse V =380.

[0084] For warehouse complexity control value (C V ), determine the weight coefficient, and determine the business complexity control value (C BV ) corresponding weight coefficient (W BC ) and technical complexity control value (C TV ) corresponding weight coefficient (W TC ). You can set W for each warehouse separately. BC and W TC For each group W BC and W TC , satisfying W BC +W TC = 1. For example, for a certain warehouse classification, according to the purpose and application scenario of the products and images in the warehouse, you can set W BC =0.7, W TC =0.3.

[0085] Determine the business complexity control value (C BV ) corresponding to the control level (C BL ) is the business complexity level, and the technical complexity control value (C TV ) corresponding to the control level (C TL ), that is, the level of technical complexity. According to the purpose and application scenario of the product and image, combined with the sophistication of control and management, C BL 、C TL For example, you can set C BL =5, C TL =3.

[0086] According to CBL 、C TL The number of levels, combined with business and technical characteristics, determines the control standard of complexity level. For example: corresponding to C BL =5, set the business complexity control standards for each level and determine the business complexity control value (C BV ) are shown in Table 2 below:

[0087] Business complexity level Control standard (complexity) Control standard (reference value) <![CDATA[C BL 1]]> The business is simple 0.6 <![CDATA[C BL 2]]> The business is relatively simple 0.9 <![CDATA[C BL 3]]> Average business complexity 1.2 <![CDATA[C BL 4]]> The business is relatively complex 1.5 <![CDATA[C BL 5]]> The business is complex 1.8

[0088] Table 2

[0089] Corresponding to C TL =3, set the technical complexity control standards for each level and determine the technical complexity control value (C TV ), as shown in Table 3 below:

[0090] Technical complexity level Control standard (complexity) Control standard (reference value) <![CDATA[C TL 1]]> Simple technology 0.6 <![CDATA[C TL 2]]> The technology is relatively complex 1.2 <![CDATA[C TL 3]]> The technology is complex 1.8

[0091] Table 3

[0092] Determine the warehouse complexity control value (C V ), analyze the business complexity of the warehouse (C B ), according to the business complexity level control standard, determine the business complexity level corresponding to the warehouse, and determine the business complexity control value (C BV ).

[0093] For example: After analysis, it is determined that the business complexity level corresponding to the warehouse is C BL 2, then the business complexity control value C of the warehouse classification BV =0.9.

[0094] Analyze the technical complexity of the warehouse (C T ), according to the technical complexity level control standard, determine the technical complexity level corresponding to the warehouse and determine the technical complexity control value (C TV ).

[0095] For example: After analysis, it is determined that the technical complexity level of the warehouse is C TL 3, then the business complexity control value C of the warehouse classification TV =1.8.

[0096] Calculate warehouse capacity control parameters (V), including:

[0097] Warehouse capacity control parameters of product warehouse (V A ), V A =B A *S V *(W BC *C BV +W TC *CTV );

[0098] Among them, W BC +W TC =1; For example, corresponding to the example value determined above, we can calculate:

[0099] V A =0.5G*380*(0.7*0.9+0.3*1.8)=222.3G;

[0100] The warehouse capacity control parameter of the mirror warehouse (V I ), V I =B I *S V *(W BC *C BV +W TC *C TV );

[0101] Among them, W BC +W TC =1; For example, corresponding to the example value determined above, we can calculate:

[0102] V I =0.8G*380*(0.7*0.9+0.3*1.8)=355.68G.

[0103] It should be noted that all the above control parameters need to determine the initial values, namely the initial warehouse capacity control parameters, based on experience, and then execute the following file warehouse capacity allocation method to achieve adjustment and optimization of the file warehouse.

[0104] Please refer to Figure 5 , an embodiment of the present application provides a capacity allocation method for a file warehouse, comprising:

[0105] Step 501: Obtain usage data of each repository in a target file repository, where the target file repository includes a product repository or an image repository; and the target file includes a product or an image.

[0106] Optionally, the usage data may be usage data of each warehouse in the target file warehouse at a first moment. In this embodiment of the present application, the warehouse capacity monitoring center may monitor the usage data of each warehouse in the target file warehouse at a first moment through a monitoring program, where the first moment is the current moment, thereby obtaining the usage data of each warehouse in real time.

[0107] Specifically, the warehouse capacity monitoring center aggregates and calculates the individual usage data of each warehouse in real time to generate usage data. The usage data includes at least one of the following:

[0108] Overall warehouse occupancy rate (U G ), the capacity occupancy rate of the target file warehouse, that is, the percentage of the capacity usage of all warehouses of the target file warehouse;

[0109] Warehouse classification occupancy rate (U T ), the capacity usage percentage of each category in the target file warehouse;

[0110] Warehouse individual occupancy rate (U S ), the capacity usage percentage of each warehouse in the target file warehouse.

[0111] By determining the occupancy rate monitoring index U of the warehouse as a whole, category, and individual G 、U T 、U S , to monitor the usage of different dimensions of warehouse capacity. The monitoring period can be daily, weekly, monthly, quarterly, annual, etc.

[0112] Optionally, the target file warehouse is used to store target files and is constructed as a multi-level tree structure. Each level of the multi-level tree structure corresponds to at least one warehouse. The categories of the warehouses on each level of the multi-level tree structure are different. The category of at least one warehouse on the first level of the multi-level tree structure belongs to the category range of the warehouse on the corresponding second level. The first level structure is the next level structure of the second level structure.

[0113] In one embodiment, optionally, before obtaining usage data of each repository in the target file repository, the method further includes:

[0114] determining, based on at least one of the type, purpose, and application scenario of the target file, an initial warehouse capacity control parameter corresponding to each warehouse in the target file, and dividing the target file warehouse into a multi-level tree structure;

[0115] Capacity allocation is performed on the target file warehouse according to the initial warehouse capacity control parameter.

[0116] It should be noted that prior to step 501, the specific steps for determining the initial warehouse capacity control parameters corresponding to each warehouse in the target file based on at least one of the target file's type, purpose, and application scenario are described above and will not be repeated here. Then, based on the initial capacity control parameters, capacity planning and automatic allocation are performed according to a multi-level tree structure to obtain the target file warehouse. This allows for real-time monitoring of the target file warehouse in step 501, obtaining usage data for each warehouse.

[0117] Step 502: Obtain capacity trend data of the target file warehouse based on the usage data.

[0118] Optionally, the capacity trend data is the capacity trend data of the target file warehouse in a first period or a second moment, the first period is the period after the first moment, the second moment is the moment after the first moment, and the first moment is the moment of obtaining the situation usage data.

[0119] Optionally, the capacity trend data includes at least one of the following:

[0120] Overall warehouse utilization growth rate (I G ), the usage growth rate of the target file warehouse, that is, the growth percentage of the capacity usage of all warehouses of the target file warehouse;

[0121] Warehouse classification usage growth rate (I T ), the capacity usage growth percentage of each category in the target file warehouse;

[0122] Warehouse individual usage growth rate (I S ), the capacity usage growth percentage of each warehouse in the target file warehouse.

[0123] In an embodiment of the present application, by performing statistical analysis on usage data and combining it with a machine learning algorithm to perform model training, the trend of the first time period or the second moment in the future is predicted, and capacity trend data is generated, thereby providing optimized support data for warehouse intelligent planning and automatic allocation algorithms.

[0124] Step 503 : adjusting and optimizing the warehouse capacity control parameters of each warehouse in the target file warehouse according to the capacity trend data to obtain adjusted and optimized warehouse capacity control parameters.

[0125] Step 504: Allocate capacity to each warehouse in the target file warehouse according to the adjusted and optimized warehouse capacity control parameters.

[0126] In the embodiment of the present application, capacity planning and automatic allocation are performed for each warehouse based on the adjusted and optimized warehouse capacity control parameters and the multi-level tree structure.

[0127] In one embodiment, optionally, adjusting and optimizing the warehouse capacity control parameters of each warehouse in the target file warehouse according to the capacity trend data to obtain the adjusted and optimized warehouse capacity control parameters includes: adjusting and optimizing a first parameter of each warehouse in the target file warehouse at a first moment according to the capacity trend data to obtain the adjusted and optimized first parameter, wherein the first parameter includes at least one of the following: a single unit baseline capacity, a warehouse storage scale control value, and a warehouse complexity control value;

[0128] The warehouse capacity control parameter of each warehouse in the target file warehouse at the first moment is adjusted and optimized according to the adjusted and optimized first parameter to obtain the adjusted and optimized warehouse capacity control parameter.

[0129] In the embodiment of the present application, for the single base capacity, according to the capacity trend data, combined with the purpose, application scenario and future business development of the product and the image, the adjusted and optimized single base capacity is obtained, including the product base capacity (B A ) and image base capacity (B I ).

[0130] For example, based on the capacity trend data calculated by the machine learning algorithm model, it is found that the warehouse usage growth rate (I) will increase, and the warehouse occupancy rate (U) at the second moment will increase to a certain value. Then, based on the degree of increase in the warehouse usage growth rate (I) and the value that the warehouse occupancy rate (U) will reach, and in combination with the development and change requirements of product and image usage and future application scenarios, the product baseline capacity (B A ) and image base capacity (B I ). The adjustment and optimization methods of other control parameters are the same as above and will not be described in detail.

[0131] Therefore, the warehouse capacity intelligent planning and automatic allocation algorithm in the embodiment of the present application is based on the adjusted and optimized warehouse capacity control parameter (V), including the warehouse capacity control parameter (V) of each warehouse in the product warehouse. A ) and the warehouse capacity control parameter (V I ), automatically adjust the warehouse capacity planning and allocation.

[0132] In one embodiment, optionally, the adjusted and optimized warehouse capacity control parameter is equal to the product of the adjusted and optimized single unit benchmark capacity, the warehouse storage scale control value, and the warehouse complexity control value;

[0133] The adjusted and optimized warehouse complexity control value is equal to the sum of the products of the adjusted and optimized business complexity control value and the technical complexity control value and the corresponding weight coefficients respectively.

[0134] In the embodiment of the present application, for the product warehouse, the optimized warehouse capacity control parameter (V A ) is based on the product base capacity (B A ), warehouse storage scale control value (S V ), business complexity control value (C BV ), technical complexity control value (C TV ), the weight coefficient corresponding to the business complexity control value (W BC) and the weight coefficient corresponding to the technical complexity control value (W TC ) is determined, and the specific formula is:

[0135] V A =B A *S V *(W BC *C BV +W TC *C TV )

[0136] For the mirror warehouse, adjust the optimized warehouse capacity control parameters (V I ) is based on the image base capacity (B I ), warehouse storage scale control value (S V ), business complexity control value (C BV ), technical complexity control value (C TV ), the weight coefficient corresponding to the business complexity control value (W BC ) and the weight coefficient corresponding to the technical complexity control value (W TC ) is determined, and the specific formula is:

[0137] V I =B I *S V *(W BC *C BV +W TC *C TV )

[0138] In one embodiment, optionally, adjusting and optimizing the warehouse storage scale control value of each warehouse in the target file warehouse at a first moment based on the capacity trend data to obtain the adjusted and optimized warehouse storage scale control value includes:

[0139] adjusting and optimizing the warehouse storage scale control information of each warehouse in the target file warehouse at a first moment according to the capacity trend data to obtain adjusted and optimized warehouse storage scale control information, wherein the warehouse storage scale control information stores a correspondence between each warehouse storage scale level in a plurality of warehouse storage scale levels and a warehouse storage scale control value;

[0140] The warehouse storage scale control value of each warehouse in the target file at the first moment is adjusted and optimized according to the adjusted and optimized warehouse storage scale control information to obtain the adjusted and optimized warehouse storage scale control value.

[0141] In the embodiment of the present application, based on the capacity trend data and in combination with the business scale development, the warehouse storage scale (S) control information of each warehouse in the target file warehouse at the first moment is adjusted and optimized, wherein the warehouse storage scale (S) control information includes:

[0142] Warehouse storage scale level;

[0143] Control standards (number of units), set values ​​for each warehouse storage size level;

[0144] Control standard (reference value), i.e. warehouse storage scale control value, set value for each warehouse storage scale level;

[0145] Control interval value for each warehouse storage scale level.

[0146] In one embodiment, optionally, adjusting and optimizing the warehouse complexity control value of each warehouse in the target file warehouse at a first moment according to the capacity trend data to obtain the adjusted and optimized warehouse complexity control value includes:

[0147] Adjusting and optimizing warehouse complexity control information of each warehouse in the target file warehouse at a first moment according to the capacity trend data to obtain adjusted and optimized warehouse complexity control information, wherein the warehouse complexity control information stores a correspondence between each business complexity level in a plurality of business complexity levels and a business complexity control value, and stores a correspondence between each technical complexity level in a plurality of technical complexity levels and a technical complexity control value;

[0148] Adjusting and optimizing the business complexity control value and the technical complexity control value of each warehouse in the target file at the first moment according to the adjusted and optimized warehouse complexity control information to obtain adjusted and optimized business complexity control values ​​and technical complexity control values;

[0149] According to the adjusted and optimized business complexity control value and technical complexity control value, the adjusted and optimized warehouse complexity control value is obtained.

[0150] In an embodiment of the present application, based on capacity trend data, combined with the purpose and application scenarios of products and images, and the development of business and technical complexity, the warehouse complexity control value of each warehouse in the target file warehouse at the first moment is adjusted and optimized. The warehouse complexity control information includes:

[0151] Business complexity level;

[0152] Control standards (number of individuals), set values ​​for each business complexity level;

[0153] Control standard (reference value), i.e., business complexity control value, the set value for each business complexity level;

[0154] Control interval values ​​for each business complexity level;

[0155] The weight coefficient corresponding to the business complexity control value;

[0156] Technical complexity level;

[0157] Control criteria (number of individuals), set values ​​for each level of technical complexity;

[0158] Control standards (reference values), i.e., technical complexity control values, set values ​​for each technical complexity level;

[0159] Control interval values ​​for each technical complexity level;

[0160] The weight coefficient corresponding to the technical complexity control value.

[0161] In one embodiment, optionally, obtaining capacity trend data of the target file warehouse at a first time period or a second time instant based on the usage data includes:

[0162] Capacity trend data of the target file warehouse at a first time period or a second time is obtained based on the usage data and a linear regression machine learning algorithm model.

[0163] In an embodiment of the present application, model training is performed based on usage data in combination with a machine learning algorithm to generate capacity trend data for the first time period or the second moment.

[0164] Among them, the machine learning algorithm model and training parameters of warehouse occupancy rate (U) are:

[0165] A simple linear regression machine learning algorithm model is used to train and predict warehouse occupancy (U), including the overall warehouse occupancy (U G ), warehouse classification occupancy rate (U T ), warehouse individual occupancy rate (U S ).

[0166] U=β0+β1T

[0167] U corresponds to the object of model training and prediction, including the overall occupancy rate of the warehouse (U G ), warehouse classification occupancy rate (U T ), warehouse individual occupancy rate (U S ); β0 is the intercept of the linear regression machine learning algorithm model, which is set to the warehouse occupancy rate at the beginning of training; β1 is the model parameter that needs to be trained; T is the training time independent variable.

[0168] Based on training needs, model training can be performed hourly or daily. The model parameter β1 is calculated and trained based on the actual monitored U value, the β0 constant, and the time T. Based on the trained β1, the U value for the first period or the second moment in the future is calculated.

[0169] Warehouse usage growth rate (I) machine learning algorithm model and training parameters:

[0170] A simple linear regression machine learning algorithm model is used to train and predict the warehouse usage growth rate (I), including the overall warehouse usage growth rate (I G ), warehouse classification usage growth rate (I T ), warehouse individual usage growth rate (I S ).

[0171] I=βT

[0172] I corresponds to the objects of model training and prediction, including the overall utilization growth rate of the warehouse (I G ), warehouse classification usage growth rate (I T ), warehouse individual usage growth rate (I S ), β is the model parameter that needs to be trained, and T is the training time independent variable.

[0173] Model training can be performed on an hourly or daily basis, depending on training needs. The model parameter β is calculated and trained using the actual monitored I value combined with time T. Based on the trained β, the I value for the first or second time period in the future is calculated and compared with the I value at the first time period. This allows us to determine whether the warehouse usage growth rate is increasing or decreasing.

[0174] Therefore, based on the trained model, we can predict the future development trend of warehouse indicators such as warehouse occupancy rate (U) and warehouse utilization growth rate (I), obtain capacity trend data, and derive the warehouse occupancy rate (U) and warehouse utilization growth rate (I) in the first period or second moment in the future, thereby providing a basis for intelligent planning of warehouse capacity and parameter optimization and adjustment of automatic allocation algorithms.

[0175] Figure 6 This is a flow chart of the second implementation method of the capacity allocation method of the file warehouse described in the embodiment of this application. Figure 6 As shown, an embodiment of the present application provides a design and implementation method for monitoring the usage of product warehouses and image warehouses. By adopting this design and implementation method, the usage of product warehouses and image warehouses can be dynamically monitored, usage data can be obtained in real time, real-time calculation and statistical analysis can be performed, and capacity trend data can be output to provide optimization support data for intelligent planning and automatic allocation algorithms of warehouse capacity.

[0176] Figure 7This is a flow chart of the third implementation method of the capacity allocation method of the file warehouse described in the embodiment of this application. Figure 7 The paper describes a design and implementation method for intelligently optimizing the automatic allocation algorithm for product and image warehouse capacity. This design and implementation method can be used to perform intelligent statistical analysis of warehouse capacity usage based on product and image warehouse usage data, predict future trends, optimize intelligent warehouse capacity planning and automatic allocation algorithms, and automatically adjust warehouse capacity planning and allocation.

[0177] Based on the types, uses, and application scenarios of products and images, we design intelligent capacity planning and automatic allocation algorithms for product warehouses and image warehouses, thereby solving the problem of existing product warehouses and image warehouses having no storage capacity for hardware, and the problem of planning and allocating warehouse capacity based on the types, uses, and application scenarios of products and images.

[0178] Design a dynamic monitoring solution for the usage data of the product warehouse and image warehouse, and perform trend analysis on the warehouse usage data, so as to solve the problem that the existing product warehouse and image warehouse do not monitor the usage of products and images and cannot perform capacity trend analysis.

[0179] By analyzing the warehouse's capacity trend data, the intelligent planning and automatic allocation algorithms for the product warehouse and mirror warehouse are intelligently tuned, thus solving the existing problem of being unable to optimize and adjust the algorithms based on the product warehouse and mirror warehouse usage data and capacity trend data.

[0180] In summary, the capacity allocation method of the file warehouse described in the embodiment of the present application includes a design and implementation method for intelligent planning and automatic allocation of product and image warehouse capacity, a design and implementation method for intelligent tuning of the automatic allocation algorithm of product and image warehouse capacity, and a design and implementation method for monitoring the usage of product and image warehouses.

[0181] The design and implementation method for intelligent planning and automatic allocation of product image warehouse capacity uses a warehouse capacity planning and automatic allocation algorithm based on the type, purpose, and application scenario of products and images. This allows for unified planning and automatic allocation of product image warehouse capacity. This intelligent warehouse capacity planning and automatic allocation algorithm selects warehouse capacity control parameters and policies, and sets these control parameters to achieve intelligent planning and automatic allocation of warehouse capacity.

[0182] The design and implementation method of intelligent tuning of the automatic allocation algorithm for product and image warehouse capacity intelligently adjusts and optimizes warehouse capacity control parameters and control strategies, and recalculates warehouse capacity control parameters according to the optimized and adjusted parameters to achieve intelligent planning of warehouse capacity and intelligent optimization of the automatic allocation algorithm, and automatically adjust the planning and allocation of warehouse capacity.

[0183] The design and implementation method of monitoring the usage of product and image warehouses can dynamically monitor the usage of product and image warehouses, and obtain usage data such as image warehouse occupancy rate and capacity adjustment frequency in real time. On this basis, capacity trend data such as warehouse usage growth rate data is calculated and generated, thereby providing data and decision support for intelligent planning of warehouse capacity and optimization of automatic allocation algorithms.

[0184] Please refer to Figure 8 , an embodiment of the present application further provides a capacity allocation device for a file warehouse, comprising:

[0185] An acquisition module 801 is configured to acquire usage data of each repository in a target file repository, where the target file repository includes a product repository or an image repository;

[0186] A first obtaining module 802 is configured to obtain capacity trend data of the target file warehouse based on the usage data;

[0187] A second obtaining module 803 is configured to adjust and optimize the warehouse capacity control parameters of each warehouse in the target file warehouse according to the capacity trend data, and obtain adjusted and optimized warehouse capacity control parameters;

[0188] The allocation module 804 is configured to allocate capacity to each warehouse in the target file warehouse according to the adjusted and optimized warehouse capacity control parameters.

[0189] Optionally, in the capacity allocation device for the file warehouse, the second obtaining module 803 is specifically configured to:

[0190] Adjusting and optimizing a first parameter of each warehouse in the target file warehouse according to the capacity trend data to obtain an adjusted and optimized first parameter, wherein the first parameter includes at least one of the following: a single unit baseline capacity, a warehouse storage scale control value, and a warehouse complexity control value;

[0191] The warehouse capacity control parameter of each warehouse in the target file warehouse is adjusted and optimized according to the adjusted and optimized first parameter to obtain the adjusted and optimized warehouse capacity control parameter.

[0192] Optionally, in the capacity allocation device for the file warehouse, the adjusted and optimized warehouse capacity control parameter is equal to the product of the adjusted and optimized single unit benchmark capacity, the warehouse storage scale control value, and the warehouse complexity control value;

[0193] The adjusted and optimized warehouse complexity control value is equal to the sum of the products of the adjusted and optimized business complexity control value and the technical complexity control value and the corresponding weight coefficients respectively.

[0194] Optionally, in the capacity allocation device for the file warehouse, the second obtaining module 803 is specifically configured to:

[0195] Adjusting and optimizing the warehouse storage scale control information of each warehouse in the target file warehouse according to the capacity trend data to obtain adjusted and optimized warehouse storage scale control information, wherein the warehouse storage scale control information stores a correspondence between each warehouse storage scale level in a plurality of warehouse storage scale levels and a warehouse storage scale control value;

[0196] The warehouse storage scale control value of each warehouse in the target file is adjusted and optimized according to the adjusted and optimized warehouse storage scale control information to obtain the adjusted and optimized warehouse storage scale control value.

[0197] Optionally, in the capacity allocation device for the file warehouse, the second obtaining module 803 is specifically configured to:

[0198] Adjusting and optimizing the warehouse complexity control information of each warehouse in the target file warehouse according to the capacity trend data to obtain adjusted and optimized warehouse complexity control information, wherein the warehouse complexity control information stores a correspondence between each business complexity level in a plurality of business complexity levels and a business complexity control value, and stores a correspondence between each technical complexity level in a plurality of technical complexity levels and a technical complexity control value;

[0199] Adjusting and optimizing the business complexity control value and the technical complexity control value of each warehouse in the target file according to the adjusted and optimized warehouse complexity control information to obtain adjusted and optimized business complexity control values ​​and technical complexity control values;

[0200] According to the adjusted and optimized business complexity control value and technical complexity control value, the adjusted and optimized warehouse complexity control value is obtained.

[0201] Optionally, the capacity allocation device for the file warehouse further comprises:

[0202] a first partitioning module, configured to determine an initial warehouse capacity control parameter corresponding to each warehouse in the target file according to at least one of the type, purpose, and application scenario of the target file, and to partition the target file warehouse into a multi-level tree structure;

[0203] The second partitioning module is used to allocate capacity to the target file warehouse according to the initial warehouse capacity control parameter.

[0204] Optionally, the capacity allocation device of the file warehouse, wherein the target file warehouse is used to store target files, is constructed as a multi-level tree structure, each level of the multi-level tree structure corresponds to at least one warehouse, the categories of the warehouses on each level of the multi-level tree structure are different, the category of at least one warehouse on the first level of the multi-level tree structure belongs to the category range of the warehouse on the corresponding second level, and the first level structure is the next level structure of the second level structure.

[0205] Optionally, in the capacity allocation device for a file warehouse, the capacity trend data includes at least one of the following:

[0206] a usage growth rate of the target file warehouse;

[0207] The usage growth rate of each warehouse in the target file warehouse.

[0208] It should be noted that the above-mentioned device provided in the embodiment of the present application can implement all the method steps implemented in the above-mentioned method embodiment and can achieve the same technical effect. The parts and beneficial effects of this embodiment that are the same as those in the method embodiment will not be described in detail here.

[0209] The present application also provides a capacity allocation device for a file warehouse, such as Figure 9 As shown, including:

[0210] Processor 901, memory 902, transceiver 903 and programs or instructions stored in the memory 902 and executable on the processor 901; when the processor 901 executes the programs or instructions, each process of the above-mentioned file warehouse capacity allocation method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, they will not be described here.

[0211] The transceiver 903 is configured to receive and send data under the control of the processor 901 .

[0212] Among them, Figure 9In the embodiment, the bus architecture may include any number of interconnected buses and bridges, specifically connecting various circuits of one or more processors represented by processor 901 and memory represented by memory 902. The bus architecture may also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and are therefore not further described herein. The bus interface provides an interface. The transceiver 903 may be a plurality of components, i.e., a transmitter and a receiver, providing a unit for communicating with various other devices on a transmission medium. For different user devices, the user interface 904 may also be an interface capable of connecting external or internal devices as required, and the connected devices include but are not limited to a keypad, a display, a speaker, a microphone, a joystick, etc.

[0213] The processor 901 is responsible for managing the bus architecture and general processing, and the memory 902 can store data used by the processor 901 when performing operations.

[0214] The present application also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the computer program implements the various processes of the above-mentioned file warehouse capacity allocation method embodiment and can achieve the same technical effect. To avoid repetition, the description is not repeated here. The computer-readable storage medium is, for example, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0215] An embodiment of the present application also provides a computer program product, including computer instructions. When the computer instructions are executed by a processor, the various processes of the above-mentioned file warehouse capacity allocation method embodiment are implemented, and the same technical effect can be achieved. To avoid repetition, they are not repeated here.

[0216] It should be noted that, in this document, the terms "include," "comprising," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0217] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, and the computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), including a number of instructions for enabling a terminal (which can be a mobile phone, computer, server, air conditioner, or network equipment, etc.) to execute the methods described in each embodiment of the present application.

[0218] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of this application, ordinary technicians in this field can also make many forms without departing from the purpose of this application and the scope of protection of the claims, all of which are within the protection of this application.

Claims

1. A capacity allocation method for a file warehouse, characterized in that: include: Obtaining usage data for each repository in a target file repository, wherein the target file repository includes a product repository or an image repository; Obtaining capacity trend data of the target file warehouse based on the usage data; Adjusting and optimizing the warehouse capacity control parameters of each warehouse in the target file warehouse according to the capacity trend data to obtain adjusted and optimized warehouse capacity control parameters; Based on the adjusted and optimized warehouse capacity control parameters, capacity is allocated to each warehouse in the target file warehouse.

2. The capacity allocation method of the file warehouse according to claim 1, characterized in that: Adjusting and optimizing the warehouse capacity control parameters of each warehouse in the target file warehouse according to the capacity trend data to obtain adjusted and optimized warehouse capacity control parameters includes: Adjusting and optimizing a first parameter of each warehouse in the target file warehouse according to the capacity trend data to obtain an adjusted and optimized first parameter, wherein the first parameter includes at least one of the following: a single unit baseline capacity, a warehouse storage scale control value, and a warehouse complexity control value; The warehouse capacity control parameter of each warehouse in the target file warehouse is adjusted and optimized according to the adjusted and optimized first parameter to obtain the adjusted and optimized warehouse capacity control parameter.

3. The capacity allocation method for a file warehouse according to claim 2, characterized in that: The adjusted and optimized warehouse capacity control parameter is equal to the product of the adjusted and optimized single unit benchmark capacity, the warehouse storage scale control value and the warehouse complexity control value; The adjusted and optimized warehouse complexity control value is equal to the sum of the products of the adjusted and optimized business complexity control value and the technical complexity control value and the corresponding weight coefficients respectively.

4. The capacity allocation method for a file warehouse according to claim 2, characterized in that: Adjusting and optimizing the warehouse storage scale control value of each warehouse in the target file warehouse according to the capacity trend data to obtain the adjusted and optimized warehouse storage scale control value includes: Adjusting and optimizing the warehouse storage scale control information of each warehouse in the target file warehouse according to the capacity trend data to obtain adjusted and optimized warehouse storage scale control information, wherein the warehouse storage scale control information stores a correspondence between each warehouse storage scale level in a plurality of warehouse storage scale levels and a warehouse storage scale control value; The warehouse storage scale control value of each warehouse in the target file is adjusted and optimized according to the adjusted and optimized warehouse storage scale control information to obtain the adjusted and optimized warehouse storage scale control value.

5. The capacity allocation method of the file warehouse according to claim 2, characterized in that: Adjusting and optimizing the warehouse complexity control value of each warehouse in the target file warehouse according to the capacity trend data to obtain an adjusted and optimized warehouse complexity control value includes: Adjusting and optimizing the warehouse complexity control information of each warehouse in the target file warehouse according to the capacity trend data to obtain adjusted and optimized warehouse complexity control information, wherein the warehouse complexity control information stores a correspondence between each business complexity level in a plurality of business complexity levels and a business complexity control value, and stores a correspondence between each technical complexity level in a plurality of technical complexity levels and a technical complexity control value; Adjusting and optimizing the business complexity control value and the technical complexity control value of each warehouse in the target file according to the adjusted and optimized warehouse complexity control information to obtain adjusted and optimized business complexity control values ​​and technical complexity control values; According to the adjusted and optimized business complexity control value and technical complexity control value, the adjusted and optimized warehouse complexity control value is obtained.

6. The capacity allocation method of a file warehouse according to claim 1, characterized in that: The method further comprises: Determining an initial warehouse capacity control parameter corresponding to each warehouse in the target file according to at least one of a type, a purpose, and an application scenario of the target file; Capacity allocation is performed on the target file warehouse according to the initial warehouse capacity control parameter.

7. The capacity allocation method of a file warehouse according to claim 1, characterized in that: The target file warehouse is used to store target files and is constructed as a multi-level tree structure. Each level of the multi-level tree structure corresponds to at least one warehouse. The categories of the warehouses on each level of the multi-level tree structure are different. The category of at least one warehouse on the first level of the multi-level tree structure belongs to the category range of the warehouse on the corresponding second level. The first level structure is the next level structure of the second level structure.

8. The capacity allocation method of a file warehouse according to claim 1, characterized in that: The capacity trend data includes at least one of the following: a usage growth rate of the target file warehouse; The usage growth rate of each warehouse in the target file warehouse.

9. A capacity allocation device for a file warehouse, characterized in that: include: An acquisition module is used to acquire usage data of each repository in a target file repository, where the target file repository includes a product repository or an image repository; A first obtaining module, configured to obtain capacity trend data of the target file warehouse based on the usage data; A second obtaining module is configured to adjust and optimize the warehouse capacity control parameters of each warehouse in the target file warehouse according to the capacity trend data, and obtain adjusted and optimized warehouse capacity control parameters; The allocation module is used to allocate capacity to each warehouse in the target file warehouse according to the adjusted and optimized warehouse capacity control parameters.

10. A capacity allocation device for a file warehouse, characterized in that: include: A processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein the processor implements the capacity allocation method for a file warehouse as described in any one of claims 1 to 8 when executing the program or instruction.

11. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the capacity allocation method for a file repository according to any one of claims 1 to 8.

12. A computer program product, characterized in that The method comprises computer instructions, which, when executed by a processor, implements the capacity allocation method for a file warehouse as described in any one of claims 1 to 8.

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