CN116627853A

CN116627853A - Storage space recovery method, training device, training equipment and training medium for model

Info

Publication number: CN116627853A
Application number: CN202310396301.XA
Authority: CN
Inventors: 李金刚
Original assignee: Apollo Intelligent Connectivity Beijing Technology Co Ltd
Current assignee: Apollo Intelligent Connectivity Beijing Technology Co Ltd
Priority date: 2023-04-14
Filing date: 2023-04-14
Publication date: 2023-08-22

Abstract

The disclosure provides a storage space recovery method, a training method of a model, a training device of the model, equipment and a medium, and relates to the technical field of computers, in particular to the fields of storage resource management, artificial intelligence, data monitoring and the like. The specific implementation scheme comprises the following steps: determining an upper limit of the use times of the target program information in the code file of the application program in response to the starting operation of the application program; monitoring the number of uses of the target program information during the running of the application program; and recovering the storage space occupied by the target program information in response to the use times of the target program information reaching the upper limit of the use times. The method avoids the waste of storage resources caused by invalid occupation of the storage space, relieves the condition of shortage of the storage resources, and enables the equipment to more efficiently utilize the storage resources.

Description

Storage space recovery method, training device, training equipment and training medium for model

Technical Field

The present disclosure relates to the field of computer technology, and in particular, to the fields of storage resource management, artificial intelligence, data monitoring, and the like.

Background

In the related art, during the running of an application program, since some program information occupies a smaller memory space, after the use of the program information is finished, the occupied memory space of the program information is not generally recovered, which may cause waste of memory resources, so that some devices with relatively tight memory resources cannot efficiently utilize the memory resources.

Disclosure of Invention

The disclosure provides a storage space recycling method, a training device, training equipment and training media for models.

According to a first aspect of the present disclosure, there is provided a storage space reclamation method including:

determining an upper limit of the use times of the target program information in the code file of the application program in response to the starting operation of the application program;

monitoring the number of uses of the target program information during the running of the application program;

and recovering the storage space occupied by the target program information in response to the use times of the target program information reaching the upper limit of the use times.

In some embodiments of the present disclosure, determining an upper limit of usage of target program information in a code file of an application program includes:

determining the type of the target program information from the code file of the application program;

and acquiring the upper limit of the use times of the target program information from the code file in response to the target program information belonging to the first type information, wherein the first type information is the program information of which the upper limit of the use times is known.

determining running condition information of the application program in response to the target program information belonging to second type information, wherein the second type information is program information with unknown upper limit of using times;

and predicting the upper limit of the use times of the target program information based on the running condition information.

In some embodiments of the present disclosure, predicting the upper limit of the number of uses of the target program information based on the operating condition information includes:

inputting the running condition information into a pre-trained upper limit frequency prediction model;

and outputting the upper limit of the use times of the target program information by using the upper limit times prediction model.

In some embodiments of the present disclosure, the training data of the upper bound number of prediction model includes: the actual use times of the target program information in each historical operation period of the application program and the corresponding operation condition information in each historical operation period of the application program.

In some embodiments of the present disclosure, outputting the upper limit of the number of uses of the target program information using the upper limit number of times prediction model includes:

determining the running times of an upper limit times prediction model;

responding to the operation times of the upper limit times prediction model being smaller than a preset operation times threshold value, and outputting preliminary estimated use times of the target program information by using the upper limit times prediction model;

Determining the estimated increase use times corresponding to the upper limit times prediction model;

and taking the sum value of the preliminary estimated use times and the estimated increase use times as the upper limit of the use times of the target program information.

In some embodiments of the present disclosure, the estimated increase in usage times is a preset fixed number of times.

In some embodiments of the present disclosure, determining the estimated increased usage times corresponding to the upper bound times prediction model includes: and determining a difference value between the running times of the upper limit times prediction model and the running times threshold value, and determining the estimated increase use times corresponding to the upper limit times prediction model based on the difference value.

In some embodiments of the present disclosure, the storage space reclamation method further includes: after the application program finishes running, the actual use times of the target program information in the current running period of the application program and the running condition information in the current running period of the application program are used as new training data of the upper limit times prediction model.

In some embodiments of the present disclosure, after predicting the upper limit of the number of uses of the target program information based on the operating condition information, the storage space reclamation method further includes:

counting the actual use times and the predicted upper limit of the use times of the target program information in a plurality of running periods of the application program in a preset time period;

When it is determined that the target program information is equal to and unchanged from a plurality of actual usage times and a plurality of predicted usage times upper limits in a preset time period, the target program information is recorded as first type information, and one usage time upper limit predicted by the target program information in the preset time period is recorded in a code file.

In some embodiments of the present disclosure, the operating condition information includes at least one of a start-up time point of the application program and an operating application scenario of the application program.

In some embodiments of the present disclosure, the object information is information of a method area that needs to be loaded into a memory when in use.

In some embodiments of the present disclosure, the object information includes class information and method information.

According to a second aspect of the present disclosure, there is provided a training method of an upper limit number of times prediction model for executing the storage space reclamation method provided in the first aspect of the present disclosure, the training method of the upper limit number of times prediction model including:

inputting the running condition information in the historical running period of the application program into an upper limit number prediction model, and outputting the upper limit of the using number of the target program information by using the upper limit number prediction model;

And adjusting parameters of the upper limit number prediction model based on the difference between the output upper limit number of times of use and the actual number of times of use of the target program information in the historical operating period.

According to a third aspect of the present disclosure, there is provided a storage space reclamation apparatus including an upper usage limit determination module, a usage number monitoring module, and a storage space reclamation module;

the upper use limit determining module is used for determining the upper use limit of the target program information in the code file of the application program in response to the starting operation of the application program;

the using number monitoring module is used for monitoring the using number of the target program information during the running of the application program;

the storage space recycling module is used for recycling the storage space occupied by the target program information in response to the use times of the target program information reach the upper limit of the use times.

In some embodiments of the present disclosure, the upper usage limit determining module is specifically configured to, when determining an upper usage limit of the target program information in the code file of the application program:

In some embodiments of the present disclosure, the upper usage limit determination module, when used to predict the upper usage limit of the target program information based on the operating condition information, is specifically configured to:

In some embodiments of the present disclosure, the upper usage limit determining module is specifically configured to, when configured to output the upper usage limit of the target program information using the upper usage limit number prediction model:

determining the running times of an upper limit times prediction model;

In some embodiments of the present disclosure, the storage space reclamation device further includes a training data acquisition module for: after the application program finishes running, the actual use times of the target program information in the current running period of the application program and the running condition information in the current running period of the application program are used as new training data of the upper limit times prediction model.

In some embodiments of the present disclosure, the storage space reclamation apparatus further includes an information type modification module for:

counting the actual use times and the predicted upper limit of the use times of the target program information in a plurality of running periods of the application program in a preset historical time;

when it is determined that the actual usage times of the target program information in the preset history time are equal to the predicted usage times, and the usage times are unchanged, the target program information is recorded as the first type information, and one usage times upper limit predicted by the target program information in the preset history time is recorded in the code file.

In some embodiments of the present disclosure, the object information is information of a device region that needs to be loaded into a memory when in use.

According to a fourth aspect of the present disclosure, there is provided a training apparatus of an upper limit number of times prediction model for performing the storage space reclamation method provided by some embodiments of the first aspect of the present disclosure, the training apparatus of the upper limit number of times prediction model including a sample data input module and a model parameter adjustment module;

The sample data input module is used for inputting the running condition information in the historical running period of the application program into the upper limit number prediction model, and outputting the upper limit of the using number of the target program information by using the upper limit number prediction model;

the model parameter adjustment module is used for adjusting parameters of the upper limit frequency prediction model based on the difference between the output upper limit frequency of use and the actual frequency of use of the target program information in the historical operation period.

According to a fifth aspect of the present disclosure, there is provided an electronic device comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method as provided in the first aspect or the method as provided in the second aspect.

According to a sixth aspect of the present disclosure, there is provided a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method provided in the first aspect or the method provided in the second aspect.

According to a seventh aspect of the present disclosure, there is provided a computer program item comprising a computer program which, when executed by a processor, implements the method provided by the first aspect or the method provided by the second aspect.

It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.

The beneficial effects that this disclosure provided technical scheme brought are:

according to the storage space recovery method provided by the embodiment of the invention, the upper limit of the use times of the target program information can be rapidly and accurately determined when the application program is started, and the occupied storage space is timely recovered when the use times of the target program information reach the upper limit of the use times, so that the waste of storage resources caused by invalid occupation of the storage space is avoided, the condition of shortage of the storage resources is relieved, and the equipment can more efficiently utilize the storage resources.

Drawings

The drawings are for a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:

fig. 1 is a schematic flow chart of a storage space recycling method provided in the present disclosure;

FIG. 2 is a flow chart illustrating another method of reclaiming storage space provided by the present disclosure;

FIG. 3 is a flow chart of a training method of an upper bound number of times prediction model provided by the present disclosure;

FIG. 4 illustrates a schematic diagram of a first storage space reclamation apparatus provided by the present disclosure;

FIG. 5 illustrates a schematic diagram of a second storage space reclamation apparatus provided by the present disclosure;

FIG. 6 illustrates a schematic diagram of a third storage space reclamation apparatus provided by the present disclosure;

FIG. 7 shows a schematic diagram of a training apparatus of the upper bound number of times prediction model provided by the present disclosure;

fig. 8 shows a schematic block diagram of an example electronic device that may be used to implement the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

It should be appreciated that in embodiments of the present disclosure, the character "/" generally indicates that the context associated object is an "or" relationship. The terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated.

For example, for an application program running in a virtual machine, during the running of the application program, because the memory space occupied by the program information loaded into the method area of the memory is small, and in consideration of that the program information may be used multiple times in the application program, in order to avoid the influence of the repeated loading of the program information on the running efficiency of the program, the program information is not deleted after being loaded into the method area, so that the memory space is occupied by the program information, and the program information is not deleted in the method area until the application program finishes running. It can be appreciated that the constant occupation of the storage space by the program information in the method area may cause a waste of storage resources, and particularly, for some devices (such as vehicle devices) with relatively tight storage resources, the constant occupation of the storage resources in the method area may cause the devices to fail to efficiently utilize the limited storage resources. However, the related art lacks a method capable of timely recycling the storage space occupied by the above-mentioned program information.

The method may be performed by a terminal device, or by a computer, or by a server, or by other devices having data processing capabilities. The subject of execution of the method is not limited herein. In some embodiments, the execution body of the storage space reclaiming method provided in the embodiments of the present disclosure may be a terminal device (such as a vehicle-mounted computer) on a host vehicle.

Optionally, the terminal device may be a mobile phone, a tablet computer, a wearable device, a vehicle-mounted device, an augmented reality (augmented reality, AR)/Virtual Reality (VR) device, a notebook computer, an ultra-mobile personal computer (UMPC), a netbook, a personal digital assistant (personal digital assistant, PDA), or the like, and the specific type of the terminal device is not limited in the embodiments of the present disclosure.

In some embodiments, the server may be a single server, or may be a server cluster formed by a plurality of servers. In some implementations, the server cluster may also be a distributed cluster. The present disclosure is not limited to a specific implementation of the server.

The storage space reclamation method provided in the present disclosure is exemplified below.

Fig. 1 shows a flow chart of a storage space recycling method provided by the present disclosure, as shown in fig. 1, the method may mainly include the following steps:

s110: in response to the application program starting operation, an upper limit of the number of times of use of the target program information in the code file of the application program is determined.

Here, the target program information refers to a part of program information that is not recovered in the storage space occupied during the running of the application program, and the storage space recovery method provided by the present disclosure is mainly used for recovering the storage space occupied by the program information.

Alternatively, for an application program running in a virtual machine formation, the target program information is a part of information of a Method Area (Method Area) that needs to be loaded into the memory when in use. Here, the method section is a concept defined in the virtual machine specification for storing data such as class information, method information, constant pool, static variables, JIT-compiled code, and the like. Specifically, the object information in the embodiments of the present disclosure may include class information, method information, and the like.

In S110, after the application is started to run, an upper limit of the number of uses thereof may be determined for at least one target program information in the code file of the application. Here, the upper limit of the number of uses refers to the number of times that the target program information estimated in advance is used at most in the running period of the application program.

Alternatively, some of the target program information is used a fixed number of times in the running period of the application program, and thus the fixed number of times of use may be recorded in the code file as an upper limit of the number of times of use of the target program information, it being understood that the upper limit of the number of times of use is known for this kind of target program information. Specifically, program information whose upper limit of the number of uses is known may be defined as first type information, and in S110, after the application program is started to run, the type of the target program information may be determined from the code file of the application program, and then the upper limit of the number of uses of the target program information is acquired from the code file in response to the target program information belonging to the first type information. In the embodiment of the disclosure, a field for recording the upper limit of the number of uses of the first type information may be added to the code file, and when it is determined that the target program information belongs to the first type information, the field may be searched and identified in the code file, so as to determine the upper limit of the number of uses of the target program information.

Taking the initialization class in the code file as an example, the initialization class is used only once when a process in the starting process of the application program is created, and then is not used during the running of the application program, so that the initialization class belongs to the first type of information. In the code file, a field for recording the upper limit of the number of uses of the initialization class may be added, and the value of the field is 1, that is, the upper limit of the number of uses of the initialization class is 1. When the application program starts to run, the initialization class is determined to belong to the first type information, and then a field '1' corresponding to the initialization class is searched and identified in the code file, so that the upper limit of the using times of the target program information is determined to be 1.

In the embodiment of the disclosure, the known upper limit of the use times of the program information is defined as the first type information, and the upper limit of the use times of the program information is recorded in the code file, so that when an application program is started, the upper limit of the use times of the target program information can be quickly acquired from the code file under the condition that the target program information is determined to be the first type information, and the efficiency of determining the upper limit of the use times is improved.

Optionally, the number of times of use of some object program information in the operation period of the application program is not completely fixed, and the number of times of use of the object program information in the operation period of the application program is associated with the operation condition information of the application program. That is, the number of uses of the target program information in the operating cycle of the application may also be different for different operating condition information, that is, the upper limit of the number of uses of the target program information is generally unknown here. For this kind of information, the upper limit of the number of uses of the target program information may be predicted based on the running condition information of the application program.

Specifically, program information whose upper limit of the number of uses is unknown may be defined as the second type information. In S110, after the application program is started to run, the type of the target program information may be determined from the code file of the application program, the running condition information of the application program is determined in response to the target program information belonging to the second type information, and then the upper limit of the number of uses of the target program information is predicted based on the running condition information. In the case of the target program information whose number of uses is not fixed, in which there is a correlation between the number of uses of the target program information in the operation cycle of the application program and the operation condition information of the application program, the upper limit of the number of uses of the target program information can be accurately predicted based on the operation condition information.

Here, a specific process of predicting the upper limit of the number of uses of the target program information based on the running condition information will be further described in the following, and will not be described herein.

S120: the number of uses of the target program information is monitored during the running of the application program.

Optionally, in S120, a remaining usage number of the target program information may be set, where an initial value of the remaining usage number is an upper limit of the usage number of the target program information. Each time the target program information is monitored to be used, the remaining use times can be subtracted by 1.

Alternatively, in S120, the number of times of use of one piece of target program information may be set, the initial value of the number of times of use being 0. The number of times of use may be accumulated by 1 each time the target program information is monitored to be used.

S130: and recovering the storage space occupied by the target program information in response to the use times of the target program information reaching the upper limit of the use times.

Alternatively, when the remaining number of uses set in S120 becomes 0, it is indicated that the number of uses of the target program information reaches the upper limit of the number of uses. Or when the number of used times set in S120 is equal to the upper limit of the number of used times, that is, it is indicated that the number of used times of the target program information reaches the upper limit of the number of used times.

When the use times of the target program information reach the upper limit of the use times, the storage space occupied by the target program information can be recovered. Specifically, the target program information may be deleted from the memory into which it is loaded, thereby reclaiming the memory space occupied by the target program information. For example, for an application program running in virtual machine formation, when it is determined that the number of times of use of the target program information reaches the upper limit of the number of times of use, the target program information may be deleted in a method area of the memory, thereby reclaiming the storage space occupied by the target program information.

Alternatively, in S130, when the target program information is deleted from the memory into which it is loaded, other information that is only relied upon by the target program information may also be deleted from the memory.

It should be noted that, after S130, if the target program information still needs to be used continuously, the target program information may also be reloaded into the memory, and other information only relied on by the target program information may also be reloaded into the memory.

Alternatively, in the embodiment of the present disclosure, when the upper limit of the number of times of use of the target program information is predicted based on the running condition information, the running condition information may be input to a pre-trained upper limit number of times prediction model, and the upper limit of the number of times of use of the target program information may be output using the upper limit number of times prediction model.

Here, the upper limit number of times prediction model is an artificial intelligence model trained based on the operation condition information during the historical operation of the application program and the actual number of times of use of the target program information during the historical operation of the application program. After model training is completed, the upper limit number of times prediction model can obtain the upper limit of the use number of the target program information based on the running condition information of the application program in the current running period. According to the method and the device for predicting the use frequency upper limit of the target program information based on the running condition information by utilizing the pre-trained artificial intelligent model, the efficiency of the prediction process and the accuracy of the prediction result can be remarkably improved. In addition, the upper limit number of times prediction model is obtained by training based on the running condition information of the application program in the historical running period and the actual use number of the target program information in the historical running period of the application program, so that the result predicted by the upper limit number of times prediction model is more consistent with the actual use condition of the user, and a more targeted prediction result can be provided for each user.

Optionally, the running condition information includes at least one of a starting time point of the application program and a running application scenario of the application program. It should be noted that, at different starting time points, the user may have different usage requirements for some program information, so that the starting time point of the application program may be used as a basis for predicting the upper limit of the number of times of usage of the target program information. The running application scenes of the application program can comprise a front-end running scene and a background running scene, and the use requirements of the running application scenes are different in different running application scenes for certain program information, so that the running application scenes of the application program can be used as the basis for predicting the upper limit of the use times of the target program information.

Optionally, when the upper limit number of times of use of the target program information is output by using the upper limit number of times prediction model, the embodiment of the disclosure specifically includes: determining the running times of an upper limit times prediction model; responding to the operation times of the upper limit times prediction model being smaller than a preset operation times threshold value, and outputting preliminary estimated use times of the target program information by using the upper limit times prediction model; determining the estimated increase use times corresponding to the upper limit times prediction model; and taking the sum value of the preliminary estimated use times and the estimated increase use times as the upper limit of the use times of the target program information. Here, the operation frequency threshold may be determined according to actual design needs, when the operation frequency of the upper limit frequency prediction model is smaller than the preset operation frequency threshold, which indicates that the number of times the upper limit frequency prediction model is trained is smaller, the accuracy of the prediction result of the upper limit frequency prediction model is poor, so that the use frequency output by the upper limit frequency prediction model cannot be directly used as the upper limit of the use frequency of the target program information, but the use frequency output by the upper limit frequency prediction model needs to be used as the initial estimation use frequency, and the sum of the initial estimation use frequency and the estimated increase use frequency is used as the final upper limit of the use frequency, so that the target program information is prevented from being deleted in the memory too early due to the fact that the upper limit of the use frequency is too small, and further, the target program information is prevented from being repeatedly loaded when the target program information needs to be reused, which may ensure that the application program has higher operation efficiency.

In some embodiments of the present disclosure, the estimated increase in use is a predetermined fixed number of times, for example, the estimated increase in use is fixed at 2. Under the condition that the running times of the upper limit times prediction model are smaller than a preset running times threshold value, the primary estimated using times of outputting the target program information by using the upper limit times prediction model are summed with 2 each time, so that the upper limit of the using times of the target program information is obtained.

In some embodiments of the present disclosure, determining the estimated increased usage times corresponding to the upper bound times prediction model includes: and determining a difference value between the running times of the upper limit times prediction model and the running times threshold value, and determining the estimated increase use times corresponding to the upper limit times prediction model based on the difference value. Here, the estimated increase use number may be approximately proportional to the difference value, and the smaller the difference value is, the smaller the estimated increase use number is. As the number of operations of the upper limit number of operations prediction model increases, the difference between the number of operations of the upper limit number of operations prediction model and the threshold of the number of operations decreases, and the number of times the upper limit number of operations prediction model is trained increases, and the accuracy of the prediction result output by the upper limit number of operations prediction model increases gradually, so that the estimated increase number of operations needs to be dynamically adjusted based on the change of the difference, so that the sum of the preliminary estimated increase number of operations and the estimated increase number of operations is closer to the actual upper limit of the number of operations.

Optionally, after predicting the upper limit of the number of uses of the target program information based on the operation condition information, the storage space reclamation method further includes: counting the actual use times and the predicted upper limit of the use times of the target program information in a plurality of running periods of the application program in a preset time period; when it is determined that the target program information is equal to and unchanged from a plurality of actual usage times and a plurality of predicted usage times upper limits in a preset time period, the target program information is recorded as first type information, and one usage time upper limit predicted by the target program information in the preset time period is recorded in a code file. It can be understood that the number of actual uses of the target program information is equal to and unchanged from the number of predicted upper limits of the number of uses in the preset time period, which means that the number of uses of the target program information during the running of the application program is also fixed, so that the target program information can be subdivided into the first type information and the upper limits of the number of uses thereof can be recorded in the code file, and in the subsequent use process of the application program, the upper limits of the number of uses of the target program information can be obtained directly and quickly in the code file without predicting the upper limits of the number of uses of the target program information based on the running condition information, which can promote the efficiency of determining the upper limits of the number of uses.

Fig. 2 is a schematic flow chart of another storage space recycling method provided by the present disclosure, and as shown in fig. 2, the method may mainly include the following steps:

s210: in response to the application program starting operation, an upper limit of the number of times of use of the target program information in the code file of the application program is determined.

S220: the number of uses of the target program information is monitored during the running of the application program.

S230: and recovering the storage space occupied by the target program information in response to the use times of the target program information reaching the upper limit of the use times.

Here, the specific execution steps of S210 to S230 may refer to the corresponding contents in S110 to S130, and will not be described herein.

S240: after the application program finishes running, the actual use times of the target program information in the current running period of the application program and the running condition information in the current running period of the application program are used as new training data of the upper limit times prediction model.

It can be understood that the actual usage times of the running condition information and the target program information corresponding to each running period of the application program can be used as new training data to retrain the upper limit number of times prediction model, so that the prediction result of the upper limit number of times prediction model is more accurate, and the upper limit number of times of use can be predicted more accurately.

Fig. 3 is a schematic flow chart of a training method of an upper limit number of times prediction model provided by the present disclosure, where the upper limit number of times prediction model is used in the storage space recovery method provided in the foregoing some embodiments, as shown in fig. 3, the method may mainly include the following steps:

s310: the operation condition information in the history operation period of the application program is input into an upper limit number prediction model, and an upper limit number of use of the target program information is output by using the upper limit number prediction model.

S320: and adjusting parameters of the upper limit number prediction model based on the difference between the output upper limit number of times of use and the actual number of times of use of the target program information in the historical operating period.

Alternatively, the function loss may be determined based on a difference between the output upper limit of the number of uses and the actual number of uses of the target program information during the history of operation; and adjusting parameters of the upper limit number prediction model based on the determined function loss.

Based on the same principle as the storage space recycling method described above, an embodiment of the present disclosure provides a storage space recycling apparatus, and fig. 4 shows a schematic diagram of a first storage space recycling apparatus provided by the present disclosure, and as shown in fig. 4, a storage space recycling apparatus 400 includes an upper usage limit determining module 410, a usage number monitoring module 420, and a storage space recycling module 430.

The upper usage limit determining module 410 is configured to determine an upper usage limit of the target program information in the code file of the application program in response to the application program starting operation.

The usage monitoring module 420 is used for monitoring the usage of the target program information during the running of the application program.

The storage space recycling module 430 is configured to recycle the storage space occupied by the target program information in response to the number of uses of the target program information reaching the upper limit of the number of uses.

According to the storage space recovery device provided by the embodiment of the disclosure, the upper limit of the use times of the target program information can be rapidly and accurately determined when the application program is started, and the occupied storage space is timely recovered when the use times of the target program information reach the upper limit of the use times, so that the waste of storage resources caused by invalid occupation of the storage space is avoided, the condition of shortage of the storage resources is relieved, and the storage resources can be more efficiently utilized by the device.

In some embodiments of the present disclosure, the upper usage limit determining module 410 is specifically configured to, when determining an upper usage limit of the target program information in the code file of the application program:

In some embodiments of the present disclosure, the upper usage limit determination module 410, when used to predict an upper usage limit for the target program information based on the operating condition information, is specifically configured to:

In some embodiments of the present disclosure, the upper usage limit determining module 410 is specifically configured to, when configured to output the upper usage limit of the target program information using the upper usage limit number prediction model:

Determining the running times of an upper limit times prediction model;

Fig. 5 illustrates a schematic diagram of a second storage space reclamation device provided by the present disclosure, and as illustrated in fig. 5, the storage space reclamation device 400 further includes a training data acquisition module 440 on the basis of including an upper usage limit determination module 410, a usage number monitoring module 420, and a storage space reclamation module 430.

The training data acquisition module 440 is configured to: after the application program finishes running, the actual use times of the target program information in the current running period of the application program and the running condition information in the current running period of the application program are used as new training data of the upper limit times prediction model.

Fig. 6 illustrates a schematic diagram of a third storage space reclamation device provided by the present disclosure, and as illustrated in fig. 6, the storage space reclamation device 400 further includes an information type modification module 450 on the basis of including an upper usage limit determination module 410, a usage number monitoring module 420, and a storage space reclamation module 430.

The information type changing module 450 is configured to: counting the actual use times and the predicted upper limit of the use times of the target program information in a plurality of running periods of the application program in a preset historical time; when it is determined that the actual usage times of the target program information in the preset history time are equal to the predicted usage times, and the usage times are unchanged, the target program information is recorded as the first type information, and one usage times upper limit predicted by the target program information in the preset history time is recorded in the code file.

It is understood that each of the above modules of the storage space reclamation apparatus 400 in the embodiment of the present disclosure has a function of implementing the corresponding steps of the storage space reclamation method described above. The functions can be realized by hardware, and can also be realized by executing corresponding software by hardware. The hardware or software includes one or more modules corresponding to the functions described above. The modules may be software and/or hardware, and each module may be implemented separately or may be implemented by integrating multiple modules. For the functional description of each module of the storage space recycling apparatus 400, reference may be made to the corresponding description of the storage space recycling method, which is not repeated herein.

Based on the same principle as the storage space reclamation method described above, the embodiments of the present disclosure provide a training apparatus of an upper limit number of times prediction model for performing the storage space reclamation method provided by some embodiments of the first aspect of the present disclosure. Fig. 7 shows a schematic diagram of a training apparatus for an upper-limit number of times prediction model provided in the present disclosure, and as shown in fig. 7, the training apparatus 700 for an upper-limit number of times prediction model includes a sample data input module 710 and a model parameter adjustment module 720.

The sample data input module 710 is configured to input the operation status information during the historical operation of the application program into the upper limit number prediction model, and output an upper limit number of use of the target program information using the upper limit number prediction model.

The model parameter adjustment module 720 is configured to adjust parameters of the upper limit number prediction model based on a difference between the output upper limit number of times of use and the actual number of times of use of the target program information in the historical operating period.

It can be understood that the above modules of the training device for the upper limit number of times prediction model in the embodiments of the present disclosure have functions of implementing the corresponding steps of the training method for the upper limit number of times prediction model. The functions can be realized by hardware, and can also be realized by executing corresponding software by hardware. The hardware or software includes one or more modules corresponding to the functions described above. The modules may be software and/or hardware, and each module may be implemented separately or may be implemented by integrating multiple modules. For the functional description of each module of the storage space recovery device, reference may be specifically made to the corresponding description of the training method of the upper limit number prediction model, which is not described herein.

In the technical scheme of the upper limit number prediction model, the acquisition, storage, application and the like of the related user personal information all accord with the regulations of related laws and regulations, and the public order welcome is not violated.

According to an embodiment of the disclosure, the disclosure further provides an electronic device, a readable storage medium, a computer program item.

In an exemplary embodiment, an electronic device includes: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method as described in the above embodiments. The electronic device may be the computer or server described above.

In an exemplary embodiment, the readable storage medium may be a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method according to the above embodiment.

In an exemplary embodiment, the computer program item comprises a computer program which, when executed by a processor, implements a method according to the above embodiments.

Fig. 8 illustrates a schematic block diagram of an example electronic device 800 that may be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 8, the electronic device 800 includes a computing unit 801 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 802 or a computer program loaded from a storage unit 808 into a Random Access Memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the device 800 can also be stored. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other by a bus 804. An input/output (I/O) interface 805 is also connected to the bus 804.

Various components in electronic device 800 are connected to I/O interface 805, including: an input unit 806 such as a keyboard, mouse, etc.; an output unit 807 such as various types of displays, speakers, and the like; a storage unit 808, such as a magnetic disk, optical disk, etc.; and a communication unit 809, such as a network card, modem, wireless communication transceiver, or the like. The communication unit 809 allows the electronic device 800 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunication networks.

The computing unit 801 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 801 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The calculation unit 801 performs the respective methods and processes described above, for example, a memory space reclamation method or a training method of an upper limit number of times prediction model. For example, in some embodiments, the memory reclamation method or the training method of the upper bound number prediction model may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as the storage unit 808. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 800 via the ROM 802 and/or the communication unit 809. When the computer program is loaded into the RAM 803 and executed by the computing unit 801, one or more steps of the above-described storage space reclamation method or training method of the upper limit number of times prediction model may be performed. Alternatively, in other embodiments, the computing unit 801 may be configured to perform the memory reclamation method or the training method of the predictive model in any other suitable manner (e.g., by means of firmware).

Various implementations of the systems and techniques described here above can be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Programming (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data reader), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.

The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server incorporating a blockchain.

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel, sequentially, or in a different order, provided that the desired results of the disclosed aspects are achieved, and are not limited herein.

The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.

Claims

1. A storage space reclamation method, the method comprising:

determining the upper limit of the use times of target program information in a code file of an application program in response to the starting operation of the application program;

and responding to the using times of the target program information reaching the upper limit of the using times, and recovering the storage space occupied by the target program information.

2. The method of claim 1, wherein the determining the upper limit of the number of uses of the target program information in the code file of the application program comprises:

and acquiring the upper limit of the use times of the target program information from the code file in response to the target program information belonging to first type information, wherein the first type information is program information with the known upper limit of the use times.

3. The method of claim 1, wherein the determining the upper limit of the number of uses of the target program information in the code file of the application program comprises:

and predicting an upper limit of the number of uses of the target program information based on the running condition information.

4. The method of claim 3, wherein predicting the upper limit of the number of uses of the target program information based on the operating condition information comprises:

5. The method of claim 4, wherein the training data of the upper bound number of prediction model comprises: the actual use times of the target program information in each historical operation period of the application program and the corresponding operation condition information in each historical operation period of the application program.

6. The method of claim 4, wherein the outputting the upper limit of the number of uses of the target program information using the upper limit number of times prediction model comprises:

determining the operation times of the upper limit times prediction model;

7. The method of claim 6, wherein the estimated increased usage frequency is a preset fixed frequency;

or determining the estimated increase usage frequency corresponding to the upper limit frequency prediction model includes: and determining a difference value between the running times of the upper limit times prediction model and the running times threshold value, and determining the estimated increase use times corresponding to the upper limit times prediction model based on the difference value.

8. The method of claim 4, further comprising: and after the application program finishes running, taking the actual use times of the target program information in the current running period of the application program and the running condition information of the application program in the current running period as new training data of the upper limit times prediction model.

9. The method according to claim 3, wherein after said predicting the upper limit of the number of uses of the target program information based on the operating condition information, further comprising:

and when the target program information is determined to be the same as and unchanged from a plurality of actual use times and a plurality of predicted use times upper limits in the preset time period, recording the target program information as the first type information, and recording one use time upper limit predicted by the target program information in the preset time period in the code file.

10. A method according to claim 3, wherein the operating condition information comprises at least one of a start-up time point of the application program and an operating application scenario of the application program.

11. The method of any of claims 1-10, wherein the target program information is information of a method area that needs to be loaded into memory when in use.

12. The method of claim 11, wherein the object information includes class information and method information.

13. A training method of an upper bound number of times prediction model for performing the storage space reclamation method of any of claims 4-12, the training method comprising:

inputting the running condition information in the historical running period of the application program into the upper limit number prediction model, and outputting the upper limit of the using number of the target program information by using the upper limit number prediction model;

and adjusting parameters of the upper limit number prediction model based on the output upper limit number of times of use and the difference of the actual number of times of use of the target program information in the historical operating period.

14. A storage space reclamation apparatus, the apparatus comprising:

the use number monitoring module is used for monitoring the use number of the target program information during the running of the application program;

and the storage space recycling module is used for recycling the storage space occupied by the target program information in response to the use times of the target program information reaching the upper limit of the use times.

15. The apparatus of claim 14, wherein the upper usage limit determining module, when configured to determine an upper usage limit of the target program information in the code file of the application program, is specifically configured to:

16. The apparatus of claim 14, wherein the upper usage limit determining module, when configured to determine an upper usage limit of the target program information in the code file of the application program, is specifically configured to:

17. A training apparatus of an upper bound times prediction model for performing the storage space reclamation method of any of claims 4-12, the training apparatus comprising:

the sample data input module is used for inputting the running condition information in the historical running period of the application program into the upper limit frequency prediction model, and outputting the upper limit of the using frequency of the target program information by utilizing the upper limit frequency prediction model;

and the model parameter adjustment module is used for adjusting parameters of the upper limit frequency prediction model based on the difference between the output upper limit frequency of use and the actual frequency of use of the target program information in the historical operation period.

18. An electronic device, comprising: at least one processor; and a memory communicatively coupled to the at least one processor;

wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-12 or to perform the method of claim 13.

19. A non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method of any one of claims 1-12 or to perform the method of claim 13.

20. A computer program item comprising a computer program which, when executed by a processor, implements the method of any one of claims 1-12 or implements the method of claim 13.