Data processing method and apparatus for target object, and computer device

Abstract

The present application relates to a data processing method and apparatus for a target object, and a computer device. The data processing method for a target object comprises: obtaining a standard estimation model and a layered estimation model for a target object, wherein the standard estimation model is used for calculating the amount of resources occupied by the target object in a standard mode, the layered estimation model is used for calculating the amount of resources occupied by the target object in a layered mode, the target object is stored in a high-frequency layer in the standard mode, and at least a portion of the target object is stored in a low-frequency layer in the layered mode; on the basis of the standard estimation model and the layered estimation model, calculating a balance point model, the balance point model being used for obtaining a balance function for the amount of resources occupied by the target object in the standard mode and the layered mode; and acquiring storage information of the target object, and, on the basis of the storage information and the balance point model, determining to use one of the standard estimation model and the layered estimation model to schedule the target object.

Description

Data processing methods, apparatus and computer equipment for target objects

[0001] Related applications

[0002] This application claims priority to Chinese patent application filed on November 28, 2024, application number 2024117286071, entitled "Data Processing Method, Apparatus and Computer Equipment for Target Object", the entire contents of which are incorporated herein by reference. Technical Field

[0003] This application relates to the field of computer technology, and in particular to a data processing method, apparatus, computer equipment, computer-readable storage medium, and computer program product for a target object. Background Technology

[0004] In recent years, cloud computing technology has developed rapidly, and a large number of cloud computing systems have been applied. Due to the accessibility, low cost, and high computing efficiency of cloud computing services, the number of individual, small business, and large-scale industrial users has grown at an unprecedented rate in the past few years.

[0005] In related technologies, data storage technology offers different levels of storage capacity and performance, achieving the best possible performance at the lowest cost. However, data access frequency is constantly changing, requiring real-time adjustments to data storage strategies. Existing scheduling algorithms, however, do not adequately consider users' varying resource demands and fail to provide resources effectively to meet those demands. Summary of the Invention

[0006] According to various embodiments of this application, a data processing method, apparatus, and computer device for a target object are provided.

[0007] Firstly, this application provides a data processing method for a target object, the method comprising:

[0008] Obtain a standard estimation model and a hierarchical estimation model for the target object. The standard estimation model is used to calculate the resource consumption of the target object in the standard mode, and the hierarchical estimation model is used to calculate the resource consumption of the target object in the hierarchical mode. In the standard mode, the target object is stored in the high-frequency layer, and in the hierarchical mode, at least a portion of the target object is stored in the low-frequency layer.

[0009] Based on the standard estimation model and the hierarchical estimation model, a balance point model is calculated. The balance point model is used to obtain a balance function of the amount of resources occupied by the target object in the standard mode and the hierarchical mode.

[0010] Obtain the storage information of the target object, and based on the storage information and the equilibrium point model, determine whether to use the standard estimation model or the hierarchical estimation model to schedule the target object;

[0011] The target object includes multiple sub-objects;

[0012] The standard estimation model and hierarchical estimation model for obtaining the target object include:

[0013] Obtain the hierarchical initial estimation model;

[0014] Determine the resource consumption of each of the sub-objects;

[0015] Based on the resource usage of the sub-objects and the preset average resource usage, the object monitoring parameters in the hierarchical initial estimation model are determined to obtain the hierarchical estimation model, including: when the resource usage of multiple sub-objects is not greater than the preset average resource usage, dividing the multiple sub-objects whose resource usage is not greater than the preset average resource usage into sub-object combinations; when the resource usage of the sub-object combination is greater than the preset average resource usage, calculating the object monitoring parameters in the hierarchical initial estimation model based on the resource usage of the sub-object combination to obtain the hierarchical estimation model.

[0016] In one embodiment, obtaining the storage information of the target object and determining, based on the storage information and the equilibrium point model, to schedule the target object using either the standard estimation model or the hierarchical estimation model includes:

[0017] When scheduling the target objects using the hierarchical estimation model, at least a portion of the target objects will be periodically scheduled to the low-frequency layer.

[0018] In one embodiment, obtaining the storage information of the target object and determining, based on the storage information and the equilibrium point model, to schedule the target object using either the standard estimation model or the hierarchical estimation model includes:

[0019] Obtain any two of the target object's average usage, access frequency, and storage time.

[0020] In one embodiment, determining the object monitoring parameters in the hierarchical initial estimation model based on the resource usage of the sub-object and a preset average resource usage includes:

[0021] If the resource usage of a sub-object is greater than the preset average usage, the object monitoring parameters in the hierarchical initial estimation model are calculated based on the preset average usage to obtain the hierarchical estimation model.

[0022] Secondly, this application also provides a data processing apparatus for a target object, the apparatus comprising:

[0023] The acquisition module is used to obtain a standard estimation model and a hierarchical estimation model of the target object. The standard estimation model is used to calculate the resource consumption of the target object in the standard mode and the resource consumption of the target object in the hierarchical mode. In the standard mode, the target object is stored in the high-frequency layer, and in the hierarchical mode, at least a portion of the target object is stored in the low-frequency layer.

[0024] The calculation module is used to calculate the equilibrium point model based on the standard estimation model and the hierarchical estimation model. The equilibrium point model is used to obtain the balance function of the resource consumption of the target object in the standard mode and the hierarchical mode.

[0025] The determination module is used to obtain the storage information of the target object, and based on the storage information and the equilibrium point model, determine whether to use the standard estimation model or the hierarchical estimation model to schedule the target object;

[0026] The target object includes multiple sub-objects;

[0027] The acquisition module is further configured to acquire a hierarchical initial estimation model; determine the resource usage of each sub-object; and determine object monitoring parameters in the hierarchical initial estimation model based on the resource usage of the sub-object and a preset average usage, thereby obtaining the hierarchical estimation model. This includes: when the resource usage of multiple sub-objects is not greater than a preset average usage, dividing the multiple sub-objects with resource usage not greater than the preset average usage into sub-object combinations; and when the resource usage of a sub-object combination is greater than the preset average usage, calculating object monitoring parameters in the hierarchical initial estimation model based on the resource usage of the sub-object combination, thereby obtaining the hierarchical estimation model.

[0028] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0029] Obtain a standard estimation model and a hierarchical estimation model for the target object. The standard estimation model is used to calculate the resource consumption of the target object in the standard mode, and the hierarchical estimation model is used to calculate the resource consumption of the target object in the hierarchical mode. In the standard mode, the target object is stored in the high-frequency layer, and in the hierarchical mode, at least a portion of the target object is stored in the low-frequency layer.

[0030] Based on the standard estimation model and the hierarchical estimation model, a balance point model is calculated. The balance point model is used to obtain a balance function of the amount of resources occupied by the target object in the standard mode and the hierarchical mode.

[0031] Obtain the storage information of the target object, and based on the storage information and the equilibrium point model, determine whether to use the standard estimation model or the hierarchical estimation model to schedule the target object;

[0032] The target object includes multiple sub-objects;

[0033] The standard estimation model and hierarchical estimation model for obtaining the target object include:

[0034] Obtain the hierarchical initial estimation model;

[0035] Determine the resource consumption of each of the sub-objects;

[0036] Based on the resource usage of the sub-objects and the preset average resource usage, the object monitoring parameters in the hierarchical initial estimation model are determined to obtain the hierarchical estimation model, including: when the resource usage of multiple sub-objects is not greater than the preset average resource usage, dividing the multiple sub-objects whose resource usage is not greater than the preset average resource usage into sub-object combinations; when the resource usage of the sub-object combination is greater than the preset average resource usage, calculating the object monitoring parameters in the hierarchical initial estimation model based on the resource usage of the sub-object combination to obtain the hierarchical estimation model.

[0037] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0038] Obtain a standard estimation model and a hierarchical estimation model for the target object. The standard estimation model is used to calculate the resource consumption of the target object in the standard mode, and the hierarchical estimation model is used to calculate the resource consumption of the target object in the hierarchical mode. In the standard mode, the target object is stored in the high-frequency layer, and in the hierarchical mode, at least a portion of the target object is stored in the low-frequency layer.

[0039] Based on the standard estimation model and the hierarchical estimation model, a balance point model is calculated. The balance point model is used to obtain a balance function of the amount of resources occupied by the target object in the standard mode and the hierarchical mode.

[0040] Obtain the storage information of the target object, and based on the storage information and the equilibrium point model, determine whether to use the standard estimation model or the hierarchical estimation model to schedule the target object;

[0041] The target object includes multiple sub-objects;

[0042] The standard estimation model and hierarchical estimation model for obtaining the target object include:

[0043] Obtain the hierarchical initial estimation model;

[0044] Determine the resource consumption of each of the sub-objects;

[0045] Based on the resource usage of the sub-objects and the preset average resource usage, the object monitoring parameters in the hierarchical initial estimation model are determined to obtain the hierarchical estimation model, including: when the resource usage of multiple sub-objects is not greater than the preset average resource usage, dividing the multiple sub-objects whose resource usage is not greater than the preset average resource usage into sub-object combinations; when the resource usage of the sub-object combination is greater than the preset average resource usage, calculating the object monitoring parameters in the hierarchical initial estimation model based on the resource usage of the sub-object combination to obtain the hierarchical estimation model.

[0046] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0047] Obtain a standard estimation model and a hierarchical estimation model for the target object. The standard estimation model is used to calculate the resource consumption of the target object in the standard mode, and the hierarchical estimation model is used to calculate the resource consumption of the target object in the hierarchical mode. In the standard mode, the target object is stored in the high-frequency layer, and in the hierarchical mode, at least a portion of the target object is stored in the low-frequency layer.

[0048] Based on the standard estimation model and the hierarchical estimation model, a balance point model is calculated. The balance point model is used to obtain a balance function of the amount of resources occupied by the target object in the standard mode and the hierarchical mode.

[0049] Obtain the storage information of the target object, and based on the storage information and the equilibrium point model, determine whether to use the standard estimation model or the hierarchical estimation model to schedule the target object;

[0050] The target object includes multiple sub-objects;

[0051] The standard estimation model and hierarchical estimation model for obtaining the target object include:

[0052] Obtain the hierarchical initial estimation model;

[0053] Determine the resource consumption of each of the sub-objects;

[0054] Based on the resource usage of the sub-objects and the preset average resource usage, the object monitoring parameters in the hierarchical initial estimation model are determined to obtain the hierarchical estimation model, including: when the resource usage of multiple sub-objects is not greater than the preset average resource usage, dividing the multiple sub-objects whose resource usage is not greater than the preset average resource usage into sub-object combinations; when the resource usage of the sub-object combination is greater than the preset average resource usage, calculating the object monitoring parameters in the hierarchical initial estimation model based on the resource usage of the sub-object combination to obtain the hierarchical estimation model.

[0055] Details of one or more embodiments of this application are set forth in the following drawings and description. Other features, objects, and advantages of this application will become apparent from the specification, drawings, and claims. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in the embodiments of this application or the conventional technology, the drawings used in the description of the embodiments or the conventional technology will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the disclosed drawings without creative effort. The additional details or examples used to describe the drawings should not be considered as a limitation on the scope of any of the disclosed invention, the currently described embodiments and / or examples, and the best mode of these inventions as currently understood.

[0057] Figure 1 is a flowchart of a data processing method for a target object according to one or more embodiments.

[0058] Figure 2 is a flowchart of a data processing method for a target object provided according to one or more embodiments.

[0059] Figure 3 is a flowchart of a data processing method for a target object according to one or more embodiments.

[0060] Figure 4 is a flowchart of a data processing method for a target object provided according to one or more embodiments.

[0061] Figures 5 to 14 are schematic diagrams of the standard mode and the layered mode provided in different embodiments.

[0062] Figure 15 is a model diagram of a target object classification selector provided according to one or more embodiments.

[0063] Figure 16 is a model diagram of a scheduling center provided according to one or more embodiments.

[0064] Figure 17 is a flowchart of a hierarchical scheduling model provided according to one or more embodiments.

[0065] Figure 18 is a structural block diagram of a data processing apparatus for a target object provided according to one or more embodiments.

[0066] Figure 19 is an internal structural diagram of a computer device provided according to one or more embodiments. Detailed Implementation

[0067] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0068] Please refer to Figures 1 to 4. Several different embodiments of this application provide a data processing method for a target object. This method can also be applied to a server, and further to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. The terminal can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, etc. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0069] In one embodiment, as shown in Figure 1, a data processing method for a target object is provided. This embodiment illustrates the application of this method to a terminal. The data processing method for the target object includes the following steps:

[0070] Step S100: Obtain the standard estimation model and the hierarchical estimation model of the target object. The standard estimation model is used to calculate the resource consumption of the target object in the standard mode, and the hierarchical estimation model is used to calculate the resource consumption of the target object in the hierarchical mode. In the standard mode, the target object is stored in the high-frequency layer, and in the hierarchical mode, at least some of the target objects are stored in the low-frequency layer.

[0071] Step S200: Based on the standard estimation model and the hierarchical estimation model, calculate the equilibrium point model. The equilibrium point model is used to obtain the balance function of the amount of resources occupied by the target object in the standard mode and the hierarchical mode.

[0072] Step S300: Obtain the storage information of the target object, and based on the storage information and the balance point model, determine whether to use the standard estimation model or the hierarchical estimation model to schedule the target object.

[0073] In step S100, the target object may include stored data. The target object may include images, files, videos, etc. The resource usage may include the size of the target object's data, or the storage cost of the target object, etc.

[0074] The standard estimation model can be used to calculate the resource consumption of a target object in standard mode. It can also be used to estimate the standard storage cost of a target object. For example, in standard mode, the target object is stored in a high-frequency tier. In the high-frequency tier, the target object can have faster access speeds, but it also incurs higher storage costs.

[0075] The tiered estimation model can be used to calculate the resource consumption of a target object in a tiered mode. Specifically, it can be used to estimate the intelligent tiered storage cost of a target object. For example, in a tiered mode, some target objects are stored in a high-frequency tier, and some are stored in a low-frequency tier (sinking tier). Target objects in the high-frequency tier can have faster access speeds, while those in the low-frequency tier can have lower access speeds. Furthermore, some target objects can be periodically stored in the low-frequency tier. Naturally, the storage cost of the low-frequency tier can be lower than that of the high-frequency tier.

[0076] In step S200, the balance point model is used to obtain a balance function for the resource usage of the target object in standard mode and tiered mode. Specifically, it can combine the standard estimation model and the tiered estimation model to calculate the function when the standard storage cost obtained by the standard estimation model is consistent with the tiered storage cost obtained by the tiered estimation model, i.e., the balance point model.

[0077] In step S300, the storage information of the target object may include any two of the target object's average occupancy, access frequency, and storage time. Then, by substituting any two of the target object's average occupancy, access frequency, and storage time into the equilibrium point model, a third-party equilibrium value can be determined. Furthermore, based on this equilibrium value, it can be determined whether to use a standard estimation model or a hierarchical estimation model.

[0078] Afterwards, the obtained equilibrium point value can be sent to the client. When the standard mode is triggered, the target objects are stored using the standard mode. When the tiered mode is triggered, at least a portion of the target objects are periodically scheduled to the low-frequency tier. It can be understood that determining to use the standard estimation model means that storing the target objects in the high-frequency tier is less expensive. Determining to use the tiered estimation model means that storing the target objects in both the high-frequency and low-frequency tiers is less expensive. Furthermore, when scheduling target objects using the tiered estimation model, at least a portion of the target objects are periodically scheduled to the low-frequency tier. As an example, at least a portion of the target objects can be periodically scheduled to the low-frequency tier weekly or monthly.

[0079] In this embodiment, by calculating the equilibrium point model based on the standard estimation model and the hierarchical estimation model, a more suitable estimation model can be obtained. This also enables the data storage process to have more diverse and intelligent evaluation indicators, thereby allowing for a clear analysis of storage strategies and actual costs. Simultaneously, by using this hierarchical estimation model, at least some target objects can be periodically scheduled to the low-frequency layer, which can also reduce storage costs. Furthermore, in this embodiment, relevant information about the target objects can be collected during use, and then the parameters of the hierarchical estimation model can be recalculated, thereby adjusting the hierarchical estimation model in a timely manner and further saving storage costs.

[0080] In one embodiment, the target object includes multiple sub-objects. It can be understood that the target object may include multiple images, videos, files, databases, etc. In this case, as shown in Figure 2, step S100 may include:

[0081] Step S110: Obtain the hierarchical initial estimation model.

[0082] Step S120: Determine the resource usage of each sub-object.

[0083] Step S130: Based on the resource usage of the sub-object and the preset average usage, determine the object monitoring parameters in the hierarchical initial estimation model to obtain the hierarchical estimation model.

[0084] In step S110, the hierarchical initial estimation model can be a basic, unrefined hierarchical estimation model. The hierarchical initial estimation model may only define the basic logic and structure for hierarchical storage estimation of the target object.

[0085] In step S120, each sub-object may have a certain amount of resources occupied. This embodiment does not limit the range of resources occupied by sub-objects.

[0086] In step S130, the object monitoring parameters can be used to represent the cost required to monitor sub-objects. At this point, if there are many sub-objects, the cost of monitoring them may be higher. If there are fewer sub-objects, the cost of monitoring them may be lower.

[0087] In this embodiment, by analyzing the resource consumption of sub-objects in detail, an accurate and reliable hierarchical estimation model is obtained, thereby allowing for the selection of a more suitable storage model.

[0088] In one embodiment, as shown in Figures 3 and 4, step S130 may include:

[0089] Step S131: If the resource usage of multiple sub-objects is not greater than the preset average usage, divide the multiple sub-objects whose resource usage is not greater than the preset average usage into a sub-object group.

[0090] Step S132: If the resource usage of the sub-object combination is greater than the preset average usage, calculate the object monitoring parameters in the hierarchical initial estimation model based on the resource usage of the sub-object combination, and obtain the hierarchical estimation model.

[0091] Step S133: If the resource usage of a sub-object is greater than the preset average usage, calculate the object monitoring parameters in the hierarchical initial estimation model based on the preset average usage to obtain the hierarchical estimation model.

[0092] In steps S131 to S133, the preset average resource usage can be a predetermined amount. This embodiment does not limit the specific range of the preset average resource usage. When the resource usage of multiple sub-objects is not greater than the preset average resource usage, it indicates that the data volume of the sub-object is small. In this case, multiple sub-objects with resource usage not greater than the preset average resource usage can be grouped into sub-object combinations, thereby combining multiple sub-objects with smaller data volumes into a single sub-object combination with a larger data volume, thus reducing the cost required to monitor the sub-objects. Of course, when the resource usage of a sub-object is greater than the preset average resource usage, calculation can be performed directly.

[0093] As an example, if the resource consumption of the first sub-object is no greater than the preset average consumption, the first sub-object can be made to wait. Then, when a second sub-object appears with a resource consumption no greater than the preset average consumption, the first and second sub-objects can be grouped into a sub-object combination, and the resource consumption of the sub-object combination can be compared with the preset average consumption. If the resource consumption of the sub-object combination is no greater than the preset average consumption, the first and second sub-objects are kept waiting. If the resource consumption of the sub-object combination is greater than the preset average consumption, the object monitoring parameters in the hierarchical initial estimation model are calculated using this sub-object combination.

[0094] In this embodiment, by comparing the resource usage of a sub-object with the preset average usage, a lower cost for monitoring the sub-object is obtained, thereby reducing the cost of using the hierarchical estimation model.

[0095] The following examples illustrate specific implementations of this application. It should be understood that the functions and values ​​described below are merely illustrative and do not limit the scope of protection of this application.

[0096] The standard estimation model can be expressed as: F = Q × 1024 × a 标准 ×M=1024QaM.

[0097] The hierarchical initial estimation model can be expressed as:

[0098] Where Q represents the data volume of the target object as QTB. The average size of the sub-objects (files) is bGB. The storage cost for the target object is a yuan / GB / month. The tiered object monitoring cost is c yuan / 10,000 objects. Under the tiered model, the time for converting the storage type to low-frequency access is 30 days, and the data storage time is M×30 days, where M represents the number of months of storage.

[0099] Assuming there are no further access operations to the settled data, a fixed number of files each month (let's say this number represents 's' of the original number) will settle to the low-frequency layer and will not be accessed again, thus reverting to high-frequency data. Therefore, at most... If the settling of all data is completed within one month, then the stratified estimation model can be:

[0100] The 1024Qa standard can represent the cost for the first month, in which there is no data sinking.

[0101] By combining the joint standard estimation model and the hierarchical estimation model, we can obtain:

[0102] Among them, sM≤1 is to ensure that no more than 100% of the data is settled.

[0103] Assuming the standard storage cost is *astandard* = 0.118 yuan / GB / month, the low-frequency storage cost is *alowfrequency* = 0.08 yuan / GB / month, and the tiered object monitoring cost is *c* = 0.175 yuan / 10,000 objects, if there are Q = 1TB of files that need to be stored, the following can be obtained:

[0104] Based on the above model, the corresponding schematic diagrams are shown in Figures 5 to 14. In these figures, the blue surface represents the storage cost in the standard mode, and the red surface represents the storage cost in the tiered mode.

[0105] First, please refer to Figures 5 and 6. On the b-axis, when the value of b (average file size) is relatively large (Figure 5), its impact on the curve is negligible; however, when the value of b is relatively small (Figure 6), its impact on cost is significant. Specifically, variations in b (average file size) within the range of 64KB to 0.05GB are more meaningful.

[0106] Secondly, please refer to Figures 7 and 8. In the s-axis direction, when the settlement rate is too high (e.g., above 20%), and the number of storage days is relatively large, the model will cause the cost reduction to become negative, which contradicts reality. Therefore, we set sM≤1 to ensure that no more than 100% of the data will be settled.

[0107] Furthermore, as the storage time M increases, the cost of the two storage modes is shown in Figures 9, 10, 11, 12, 13, and 14. Except for the first month, the cost of tiered storage becomes increasingly lower than that of standard storage as the storage time increases.

[0108] Solve the following equations:

[0109] The equilibrium point model can be obtained as follows:

[0110] The break-even model shows that if two of the following three factors are known: average file size (b), access frequency (s), and storage time (M), the cost break-even point between standard and tiered storage modes can be calculated. For example, in a fixed-number sinking mode, smart storage costs are inversely proportional to the average number of files, the sinking percentage, and the storage time.

[0111] In the first possible scenario, given an average file size of 0.005GB and a storage period of 12 months, we can calculate s = 0.0167 = 1.67%. This indicates that if more than 1.67% of the data decreases each month, the cost of tiered storage will be lower than standard storage within 12 months. In the second possible scenario, given an average file size of 0.005GB and a 10% decrease in data each month, we can calculate M = 2.84. This indicates that if the storage period is greater than 2.84 months in this scenario, the cost of tiered storage will be lower than standard storage. In the third possible scenario, given a 5% decrease in data each month and a storage period of 12 months, we can calculate the average file size b = 0.0016735GB (1.714MB). Therefore, as long as the average file size is greater than 1.714MB, the cost of tiered storage will be lower than standard storage for 12 months.

[0112] Furthermore, referring to Figures 15, 16, and 17, for the data processing system of the target object involved in this application, a classifier can be trained based on historical datasets. The classifier then receives target object data collected by the client, identifies the data type, and transmits the calculated results to form a hierarchical estimation model. The calculated parameters are then input into the hierarchical initial estimation model, and the scheduler calculates the optimal cost scheme and file scheduling rules based on the model, periodically executing the scheduling of target data. This application can also set up a monitoring center to monitor each sub-object, provide periodic feedback, recalculate the intelligent hierarchical model parameters, and adjust the hierarchical estimation model in a timely manner.

[0113] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0114] Based on the same inventive concept, this application also provides a data processing apparatus for implementing the data processing method for the target object described above. The solution provided by this apparatus is similar to the implementation scheme described in the above method; therefore, the specific limitations in one or more embodiments of the data processing apparatus for the target object provided below can be found in the limitations of the data processing method for the target object described above, and will not be repeated here.

[0115] In one embodiment, as shown in FIG18, a data processing apparatus for a target object is provided, comprising: an acquisition module, a calculation module, and a determination module, wherein:

[0116] The acquisition module is used to obtain the standard estimation model and the hierarchical estimation model of the target object. The standard estimation model is used to calculate the resource consumption of the target object in the standard mode and the resource consumption of the target object in the hierarchical mode. In the standard mode, the target object is stored in the high-frequency layer, and in the hierarchical mode, at least some of the target objects are stored in the low-frequency layer.

[0117] The calculation module is used to calculate the equilibrium point model based on the standard estimation model and the hierarchical estimation model. The equilibrium point model is used to obtain the balance function of the amount of resources occupied by the target object in the standard mode and the hierarchical mode.

[0118] The determination module is used to obtain the storage information of the target object and, based on the storage information and the equilibrium point model, determine whether to use the standard estimation model or the hierarchical estimation model to schedule the target object; wherein, the target object includes multiple sub-objects.

[0119] The acquisition module is also used to acquire the hierarchical initial estimation model; determine the resource consumption of each sub-object; and determine the object monitoring parameters in the hierarchical initial estimation model based on the resource consumption of the sub-objects and the preset average consumption, thereby obtaining the hierarchical estimation model. This includes: when the resource consumption of multiple sub-objects is not greater than the preset average consumption, dividing the multiple sub-objects with resource consumption not greater than the preset average consumption into sub-object combinations; and when the resource consumption of a sub-object combination is greater than the preset average consumption, calculating the object monitoring parameters in the hierarchical initial estimation model based on the resource consumption of the sub-object combination, thereby obtaining the hierarchical estimation model.

[0120] In one embodiment, the determining module is used to periodically schedule at least a portion of the target objects to a low-frequency layer when scheduling target objects using a hierarchical estimation model.

[0121] In one embodiment, the determining module is used to obtain any two of the target object's average occupancy, access frequency, and storage time.

[0122] In one embodiment, the acquisition module is used to calculate the object monitoring parameters in the hierarchical initial estimation model based on the preset average resource usage when the resource usage of the sub-object is greater than the preset average resource usage, thereby obtaining the hierarchical estimation model.

[0123] Each module in the aforementioned data processing device for the target object can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0124] In addition, the data processing apparatus for the target object of this application may also have other modules, such as a first module, a second module, etc., for processing other steps and methods provided in this application.

[0125] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram is shown in Figure 19. The computer device includes a processor, memory, input / output interfaces (I / O), and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is connected to the system bus via the I / O interfaces. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of the computer device stores data processing data of a target object. The I / O interfaces of the computer device are used for exchanging information between the processor and external devices. The communication interface of the computer device is used for communicating with external terminals via a network connection. When the computer program is executed by the processor, it implements a data processing method for a target object.

[0126] Those skilled in the art will understand that the structure shown in Figure 19 is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or may combine certain components, or may have different component arrangements.

[0127] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0128] Step S100: Obtain the standard estimation model and the hierarchical estimation model of the target object. The standard estimation model is used to calculate the resource consumption of the target object in the standard mode, and the hierarchical estimation model is used to calculate the resource consumption of the target object in the hierarchical mode. In the standard mode, the target object is stored in the high-frequency layer, and in the hierarchical mode, at least some of the target objects are stored in the low-frequency layer.

[0129] Step S200: Based on the standard estimation model and the hierarchical estimation model, calculate the equilibrium point model. The equilibrium point model is used to obtain the balance function of the amount of resources occupied by the target object in the standard mode and the hierarchical mode.

[0130] Step S300: Obtain the storage information of the target object, and based on the storage information and the balance point model, determine whether to use the standard estimation model or the hierarchical estimation model to schedule the target object.

[0131] In one embodiment, the processor further performs the following steps when executing the computer program:

[0132] When using a hierarchical estimation model to schedule target objects, at least some target objects will be periodically scheduled to a low-frequency layer.

[0133] In one embodiment, the processor further performs the following steps when executing the computer program:

[0134] Get any two of the target object's average usage, access frequency, or storage time.

[0135] In one embodiment, the processor further performs the following steps when executing the computer program:

[0136] Step S110: Obtain the hierarchical initial estimation model.

[0137] Step S120: Determine the resource usage of each sub-object.

[0138] Step S130: Based on the resource usage of the sub-object and the preset average usage, determine the object monitoring parameters in the hierarchical initial estimation model to obtain the hierarchical estimation model.

[0139] In one embodiment, the processor further performs the following steps when executing the computer program:

[0140] Step S131: If the resource usage of multiple sub-objects is not greater than the preset average usage, divide the multiple sub-objects whose resource usage is not greater than the preset average usage into a sub-object group.

[0141] Step S132: If the resource usage of the sub-object combination is greater than the preset average usage, calculate the object monitoring parameters in the hierarchical initial estimation model based on the resource usage of the sub-object combination, and obtain the hierarchical estimation model.

[0142] Step S133: If the resource usage of a sub-object is greater than the preset average usage, calculate the object monitoring parameters in the hierarchical initial estimation model based on the preset average usage to obtain the hierarchical estimation model.

[0143] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0144] Step S100: Obtain the standard estimation model and the hierarchical estimation model of the target object. The standard estimation model is used to calculate the resource consumption of the target object in the standard mode, and the hierarchical estimation model is used to calculate the resource consumption of the target object in the hierarchical mode. In the standard mode, the target object is stored in the high-frequency layer, and in the hierarchical mode, at least some of the target objects are stored in the low-frequency layer.

[0145] Step S200: Based on the standard estimation model and the hierarchical estimation model, calculate the equilibrium point model. The equilibrium point model is used to obtain the balance function of the amount of resources occupied by the target object in the standard mode and the hierarchical mode.

[0146] Step S300: Obtain the storage information of the target object, and based on the storage information and the balance point model, determine whether to use the standard estimation model or the hierarchical estimation model to schedule the target object.

[0147] In one embodiment, when the computer program is executed by the processor, it further performs the following steps:

[0148] When using a hierarchical estimation model to schedule target objects, at least some target objects will be periodically scheduled to a low-frequency layer.

[0149] In one embodiment, when the computer program is executed by the processor, it further performs the following steps:

[0150] Get any two of the target object's average usage, access frequency, or storage time.

[0151] In one embodiment, when the computer program is executed by the processor, it further performs the following steps:

[0152] Step S110: Obtain the hierarchical initial estimation model.

[0153] Step S120: Determine the resource usage of each sub-object.

[0154] Step S130: Based on the resource usage of the sub-object and the preset average usage, determine the object monitoring parameters in the hierarchical initial estimation model to obtain the hierarchical estimation model.

[0155] In one embodiment, when the computer program is executed by the processor, it further performs the following steps:

[0156] Step S131: If the resource usage of multiple sub-objects is not greater than the preset average usage, divide the multiple sub-objects whose resource usage is not greater than the preset average usage into a sub-object group.

[0157] Step S132: If the resource usage of the sub-object combination is greater than the preset average usage, calculate the object monitoring parameters in the hierarchical initial estimation model based on the resource usage of the sub-object combination, and obtain the hierarchical estimation model.

[0158] Step S133: If the resource usage of a sub-object is greater than the preset average usage, calculate the object monitoring parameters in the hierarchical initial estimation model based on the preset average usage to obtain the hierarchical estimation model.

[0159] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0160] Step S100: Obtain the standard estimation model and the hierarchical estimation model of the target object. The standard estimation model is used to calculate the resource consumption of the target object in the standard mode, and the hierarchical estimation model is used to calculate the resource consumption of the target object in the hierarchical mode. In the standard mode, the target object is stored in the high-frequency layer, and in the hierarchical mode, at least some of the target objects are stored in the low-frequency layer.

[0161] Step S200: Based on the standard estimation model and the hierarchical estimation model, calculate the equilibrium point model. The equilibrium point model is used to obtain the balance function of the amount of resources occupied by the target object in the standard mode and the hierarchical mode.

[0162] Step S300: Obtain the storage information of the target object, and based on the storage information and the balance point model, determine whether to use the standard estimation model or the hierarchical estimation model to schedule the target object.

[0163] In one embodiment, when the computer program is executed by the processor, it further performs the following steps:

[0164] When using a hierarchical estimation model to schedule target objects, at least some target objects will be periodically scheduled to a low-frequency layer.

[0165] In one embodiment, when the computer program is executed by the processor, it further performs the following steps:

[0166] Get any two of the target object's average usage, access frequency, or storage time.

[0167] In one embodiment, when the computer program is executed by the processor, it further performs the following steps:

[0168] Step S110: Obtain the hierarchical initial estimation model.

[0169] Step S120: Determine the resource usage of each sub-object.

[0170] Step S130: Based on the resource usage of the sub-object and the preset average usage, determine the object monitoring parameters in the hierarchical initial estimation model to obtain the hierarchical estimation model.

[0171] In one embodiment, when the computer program is executed by the processor, it further performs the following steps:

[0172] Step S131: If the resource usage of multiple sub-objects is not greater than the preset average usage, divide the multiple sub-objects whose resource usage is not greater than the preset average usage into a sub-object group.

[0173] Step S132: If the resource usage of the sub-object combination is greater than the preset average usage, calculate the object monitoring parameters in the hierarchical initial estimation model based on the resource usage of the sub-object combination, and obtain the hierarchical estimation model.

[0174] Step S133: If the resource usage of a sub-object exceeds the preset average usage, calculate the object monitoring parameters in the hierarchical initial estimation model based on the preset average usage to obtain the hierarchical estimation model. It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of related data must comply with relevant regulations.

[0175] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0176] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0177] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A data processing method for a target object, the method comprising: Obtain a standard estimation model and a hierarchical estimation model for the target object. The standard estimation model is used to calculate the resource consumption of the target object in the standard mode, and the hierarchical estimation model is used to calculate the resource consumption of the target object in the hierarchical mode. In the standard mode, the target object is stored in the high-frequency layer, and in the hierarchical mode, at least a portion of the target object is stored in the low-frequency layer. Based on the standard estimation model and the hierarchical estimation model, a balance point model is calculated. The balance point model is used to obtain a balance function of the amount of resources occupied by the target object in the standard mode and the hierarchical mode. Obtain the storage information of the target object, and based on the storage information and the equilibrium point model, determine whether to use the standard estimation model or the hierarchical estimation model to schedule the target object; The target object includes multiple sub-objects; The standard estimation model and hierarchical estimation model for obtaining the target object include: Obtain the hierarchical initial estimation model; Determine the resource consumption of each of the sub-objects; and Based on the resource usage of the sub-objects and the preset average resource usage, the object monitoring parameters in the hierarchical initial estimation model are determined to obtain the hierarchical estimation model, including: when the resource usage of multiple sub-objects is not greater than the preset average resource usage, dividing the multiple sub-objects whose resource usage is not greater than the preset average resource usage into sub-object combinations; when the resource usage of the sub-object combination is greater than the preset average resource usage, calculating the object monitoring parameters in the hierarchical initial estimation model based on the resource usage of the sub-object combination to obtain the hierarchical estimation model.

2. The data processing method for a target object according to claim 1, wherein obtaining the storage information of the target object and determining, based on the storage information and the equilibrium point model, to schedule the target object using either the standard estimation model or the hierarchical estimation model, comprises: When scheduling the target objects using the hierarchical estimation model, at least a portion of the target objects will be periodically scheduled to the low-frequency layer.

3. The data processing method for a target object according to claim 1, wherein obtaining the storage information of the target object and determining, based on the storage information and the equilibrium point model, to schedule the target object using either the standard estimation model or the hierarchical estimation model, comprises: Obtain any two of the target object's average usage, access frequency, and storage time.

4. The data processing method for the target object according to claim 1, wherein determining the object monitoring parameters in the hierarchical initial estimation model based on the resource occupancy of the sub-object and the preset average occupancy includes: If the resource usage of a sub-object is greater than the preset average usage, the object monitoring parameters in the hierarchical initial estimation model are calculated based on the preset average usage to obtain the hierarchical estimation model.

5. A data processing apparatus for a target object, the apparatus comprising: The acquisition module is used to obtain a standard estimation model and a hierarchical estimation model of the target object. The standard estimation model is used to calculate the resource consumption of the target object in the standard mode and the resource consumption of the target object in the hierarchical mode. In the standard mode, the target object is stored in the high-frequency layer, and in the hierarchical mode, at least a portion of the target object is stored in the low-frequency layer. The calculation module is used to calculate the equilibrium point model based on the standard estimation model and the hierarchical estimation model. The equilibrium point model is used to obtain the balance function of the resource consumption of the target object in the standard mode and the hierarchical mode. The determination module is used to obtain the storage information of the target object, and based on the storage information and the equilibrium point model, determine whether to use the standard estimation model or the hierarchical estimation model to schedule the target object; The target object includes multiple sub-objects; and The acquisition module is further configured to acquire a hierarchical initial estimation model; determine the resource usage of each sub-object; and determine object monitoring parameters in the hierarchical initial estimation model based on the resource usage of the sub-object and a preset average usage, thereby obtaining the hierarchical estimation model. This includes: when the resource usage of multiple sub-objects is not greater than a preset average usage, dividing the multiple sub-objects with resource usage not greater than the preset average usage into sub-object combinations; and when the resource usage of a sub-object combination is greater than the preset average usage, calculating object monitoring parameters in the hierarchical initial estimation model based on the resource usage of the sub-object combination, thereby obtaining the hierarchical estimation model.

6. The apparatus according to claim 5, wherein the determining module is further configured to, when scheduling the target objects using the hierarchical estimation model, periodically schedule at least a portion of the target objects to a low-frequency layer.

7. The apparatus according to claim 5, wherein the determining module is further configured to obtain any two of the average occupancy, access frequency, and storage time of the target object.

8. The apparatus according to claim 5, wherein the acquisition module is further configured to, when the resource occupancy of the sub-object is greater than the preset average occupancy, calculate the object monitoring parameters in the hierarchical initial estimation model based on the preset average occupancy, and obtain the hierarchical estimation model.

9. A computer device comprising a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the following steps: Obtain a standard estimation model and a hierarchical estimation model for the target object. The standard estimation model is used to calculate the resource consumption of the target object in the standard mode, and the hierarchical estimation model is used to calculate the resource consumption of the target object in the hierarchical mode. In the standard mode, the target object is stored in the high-frequency layer, and in the hierarchical mode, at least a portion of the target object is stored in the low-frequency layer. Based on the standard estimation model and the hierarchical estimation model, a balance point model is calculated. The balance point model is used to obtain a balance function of the amount of resources occupied by the target object in the standard mode and the hierarchical mode. Obtain the storage information of the target object, and based on the storage information and the equilibrium point model, determine whether to use the standard estimation model or the hierarchical estimation model to schedule the target object; in, The target object includes multiple sub-objects; The standard estimation model and hierarchical estimation model for obtaining the target object include: Obtain the hierarchical initial estimation model; Determine the resource consumption of each of the sub-objects; and Based on the resource usage of the sub-objects and the preset average resource usage, the object monitoring parameters in the hierarchical initial estimation model are determined to obtain the hierarchical estimation model, including: when the resource usage of multiple sub-objects is not greater than the preset average resource usage, dividing the multiple sub-objects whose resource usage is not greater than the preset average resource usage into sub-object combinations; when the resource usage of the sub-object combination is greater than the preset average resource usage, calculating the object monitoring parameters in the hierarchical initial estimation model based on the resource usage of the sub-object combination to obtain the hierarchical estimation model.

10. The computer device according to claim 9, wherein when the processor executes the computer program, it further performs the following steps: When scheduling the target objects using the hierarchical estimation model, at least a portion of the target objects will be periodically scheduled to the low-frequency layer.

11. The computer device according to claim 9, wherein when the processor executes the computer program, it further performs the following steps: Obtain any two of the target object's average usage, access frequency, and storage time.

12. The computer device according to claim 9, wherein when the processor executes the computer program, it further performs the following steps: If the resource usage of a sub-object is greater than the preset average usage, the object monitoring parameters in the hierarchical initial estimation model are calculated based on the preset average usage to obtain the hierarchical estimation model.

13. A computer-readable storage medium having a computer program stored thereon, the computer program, when executed by a processor, causing the processor to perform the following steps: Obtain a standard estimation model and a hierarchical estimation model for the target object. The standard estimation model is used to calculate the resource consumption of the target object in the standard mode, and the hierarchical estimation model is used to calculate the resource consumption of the target object in the hierarchical mode. In the standard mode, the target object is stored in the high-frequency layer, and in the hierarchical mode, at least a portion of the target object is stored in the low-frequency layer. Based on the standard estimation model and the hierarchical estimation model, a balance point model is calculated. The balance point model is used to obtain a balance function of the amount of resources occupied by the target object in the standard mode and the hierarchical mode. Obtain the storage information of the target object, and based on the storage information and the equilibrium point model, determine whether to use the standard estimation model or the hierarchical estimation model to schedule the target object; in, The target object includes multiple sub-objects; The standard estimation model and hierarchical estimation model for obtaining the target object include: Obtain the hierarchical initial estimation model; Determine the resource consumption of each of the sub-objects; and Based on the resource usage of the sub-objects and the preset average resource usage, the object monitoring parameters in the hierarchical initial estimation model are determined to obtain the hierarchical estimation model, including: when the resource usage of multiple sub-objects is not greater than the preset average resource usage, dividing the multiple sub-objects whose resource usage is not greater than the preset average resource usage into sub-object combinations; when the resource usage of the sub-object combination is greater than the preset average resource usage, calculating the object monitoring parameters in the hierarchical initial estimation model based on the resource usage of the sub-object combination to obtain the hierarchical estimation model.

14. The computer-readable storage medium of claim 13, wherein when the processor executes the computer program, it further performs the following steps: When scheduling the target objects using the hierarchical estimation model, at least a portion of the target objects will be periodically scheduled to the low-frequency layer.

15. A computer program product comprising a computer program that, when executed by a processor, implements the steps of the method according to any one of claims 1 to 4.

Images