CN112000470B - Memory data processing method, system and device - Google Patents
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- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
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Abstract
The application relates to a memory data processing method, a system and a device, wherein the memory data processing method comprises the following steps: acquiring target memory data, matching fields in the target memory data with preset fields in log classification, calculating the correlation between the target memory data and the log classification based on a matching result, and controlling a program corresponding to the target memory data to enter a dormant state if the calculated correlation is lower than a preset threshold value in a preset time. The problem of low efficiency of cleaning the memory resources is solved, and the efficient utilization of the memory resources is realized.
Description
Technical Field
The present disclosure relates to the field of computers, and in particular, to a method, system, and apparatus for processing memory data.
Background
The program of the network platform is manually started and stopped, and the program of one network platform can reach thousands or even tens of thousands, so that maintenance personnel can take effort and time to start and stop the network platform manually. In addition, many programs are in an enabled state, so that the probability of triggering an alarm is low, and the working efficiency of a server is very affected by the occupied resources of the memory of the programs.
In the related art, the method for reducing the machine memory is to clean the browsed history; turning off unused processes at the task process manager; methods such as a program for prohibiting the start-up are set at the start-up, however, the methods are very energy-consuming and are likely to be repeatedly started by the application in the system.
At present, an effective solution is not proposed for solving the problem of low efficiency of cleaning memory resources in the related art.
Disclosure of Invention
The embodiment of the application provides a memory data processing method, system and device, which are used for at least solving the problem of low efficiency of cleaning memory resources in the related technology.
In a first aspect, an embodiment of the present application provides a method for reducing a memory load, including: acquiring target memory data; matching the fields in the target memory data with preset fields in the log classification, and calculating the correlation degree between the target memory data and the log classification based on a matching result; and if the calculated correlation is lower than a preset threshold value in a preset time, controlling a program corresponding to the target memory data to enter a dormant state.
In some of these embodiments, the method further comprises: and if the calculated correlation degree reaches the preset threshold value, controlling a program corresponding to the target memory data to enter an on state.
In some embodiments, before the acquiring the target memory data, the method further includes:
setting a plurality of arrays, storing the preset fields of the log classification in the arrays, and setting corresponding weights for the preset fields.
In some embodiments, matching the field in the target memory data with a preset field in the log classification, and calculating the correlation between the target memory data and the log classification based on the matching result includes: matching the fields in the target memory data with preset fields in the log classification, and determining the corresponding positions of the fields in the target memory data in the log classification; acquiring the weight of the field in the target memory data according to the corresponding position of the field in the target memory data in the log classification; and weighting and averaging the fields in the target memory data to obtain the correlation degree.
In some embodiments, the preset threshold is a maximum value obtained by weighting and averaging weights of all fields in the target memory data in the time period.
In a second aspect, an embodiment of the present application provides a device for processing memory data, including: the device comprises an input module, a matching module and a control module; the input module is used for acquiring target memory data; the matching module is used for matching the fields in the target memory data with preset fields in the log classification, and calculating the correlation between the target memory data and the log classification based on a matching result; and the control module is used for controlling the program corresponding to the target memory data to enter a dormant state after the calculated correlation is lower than a preset threshold value in a preset time.
In some embodiments, the control module is further configured to control a program corresponding to the target memory data to enter an on state after the calculated correlation reaches the preset threshold.
In a third aspect, an embodiment of the present application provides a system for processing memory data, including: server equipment and terminals; the terminal is used for acquiring target memory data; the server equipment is used for matching the fields in the target memory data with preset fields in the log classification, and calculating the correlation between the target memory data and the log classification based on a matching result; and the server equipment is also used for controlling the program corresponding to the target memory data to enter a dormant state after judging that the calculated correlation is lower than a preset threshold value in preset time.
In a fourth aspect, an embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory and capable of running on the processor, where the processor implements the method for reducing memory load according to the first aspect when the processor executes the computer program.
In a fifth aspect, embodiments of the present application provide a storage medium having a computer program stored thereon, which when executed by a processor implements a method for reducing memory load as described in the first aspect above.
Compared with the related art, the memory data processing method, system and device provided by the embodiment of the application are used for obtaining the target memory data, matching the fields in the target memory data with the preset fields in the log classification, calculating the correlation degree between the target memory data and the log classification based on the matching result, and controlling the program corresponding to the target memory data to enter the dormant state if the calculated correlation degree is lower than the preset threshold value within the preset time. The method solves the problem of low efficiency of cleaning the memory resources, and realizes the efficient utilization of the memory resources.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute an undue limitation to the application. In the drawings:
fig. 1 is a schematic diagram of an application scenario of a memory data processing method according to an embodiment of the present invention;
FIG. 2 is a flow chart of load reduction of a memory data processing method according to an embodiment of the present application;
FIG. 3 is a flow chart of an activation application of a memory data processing method according to an embodiment of the present application;
FIG. 4 is a flowchart of a practical application scenario of a memory data processing method according to an embodiment of the present application;
FIG. 5 is a schematic diagram of a memory data processing device according to an embodiment of the present application;
fig. 6 is a computer device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described and illustrated below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden on the person of ordinary skill in the art based on the embodiments provided herein, are intended to be within the scope of the present application. Moreover, it should be appreciated that while such a development effort might be complex and lengthy, it would nevertheless be a routine undertaking of design, fabrication, or manufacture for those of ordinary skill having the benefit of this disclosure, and thus should not be construed as having the benefit of this disclosure.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is to be expressly and implicitly understood by those of ordinary skill in the art that the embodiments described herein can be combined with other embodiments without conflict.
Unless defined otherwise, technical or scientific terms used herein should be given the ordinary meaning as understood by one of ordinary skill in the art to which this application belongs. Reference to "a," "an," "the," and similar terms herein do not denote a limitation of quantity, but rather denote the singular or plural. The terms "comprising," "including," "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, article, or apparatus that comprises a list of steps or modules (elements) is not limited to only those steps or elements but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. The terms "connected," "coupled," and the like in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The term "plurality" as used herein means greater than or equal to two. "and/or" describes an association relationship of an association object, meaning that there may be three relationships, e.g., "a and/or B" may mean: a exists alone, A and B exist together, and B exists alone. The terms "first," "second," "third," and the like, as used herein, are merely distinguishing between similar objects and not representing a particular ordering of objects.
The various techniques described herein may be used for various wireless communication systems such as 2G, 3G, 4G, 5G communication systems and next generation communication systems, as well as global system for mobile communications (Global System for Mobile communications, abbreviated GSM), code division multiple access (Code Division Multiple Access, abbreviated CDMA) systems, time division multiple access (Time Division Multiple Access, abbreviated TDMA) systems, wideband code division multiple access (Wideband Code Division Multiple Access Wireless, abbreviated WCDMA), frequency division multiple access (Frequency Division Multiple Addressing, abbreviated FDMA) systems, orthogonal frequency division multiple access (Orthogonal Frequency-Division Multiple Access, abbreviated OFDMA) systems, single carrier FDMA (SC-FDMA) systems, general packet Radio service (General Packet Radio Service, abbreviated GPRS) systems, long term evolution (Long Term Evolution, abbreviated LTE) systems, 5G New Radio (NR) systems, and other such communication systems.
Wherein the communication bus comprises hardware, software, or both, coupling components of the magnetic resonance system to each other. The communication bus includes, but is not limited to, at least one of: data Bus (Data Bus), address Bus (Address Bus), control Bus (Control Bus), expansion Bus (Expansion Bus), local Bus (Local Bus). By way of example, and not limitation, communication buses may include a graphics acceleration interface (Accelerated Graphics Port), abbreviated AGP, or other graphics Bus, an enhanced industry standard architecture (Extended Industry Standard Architecture, abbreviated EISA) Bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an industry standard architecture (Industry Standard Architecture, ISA) Bus, a wireless bandwidth (InfiniBand) interconnect, a Low Pin Count (LPC) Bus, a memory Bus, a micro channel architecture (Micro Channel Architecture, abbreviated MCa) Bus, a peripheral component interconnect (Peripheral Component Interconnect, abbreviated PCI) Bus, a PCI-Express (PCI-X) Bus, a serial advanced technology attachment (Serial Advanced Technology Attachment, abbreviated SATA) Bus, a video electronics standards association local (Video Electronics Standards Association Local Bus, abbreviated VLB) Bus, or other suitable Bus, or a combination of two or more of these. The communication bus may include one or more buses, where appropriate. Although embodiments of the present application describe and illustrate a particular bus, the present application contemplates any suitable bus or interconnect.
In the related art, when a security crisis is encountered on a network security platform, an alarm is usually given on a current large data platform by unmatching a security log from a file of a threat information base. Since each command is manually enabled or disabled, which may be thousands or even tens of thousands on a platform, relying solely on maintenance personnel to manually enable and disable can take a significant amount of effort and time. In addition, many commands are in an on state in the initial stage, almost no alarm triggering condition exists, and the probability of closing the commands is very low, but the commands occupy the resources of the system, and the working efficiency of the machine is also affected. Based on the above problems, an application scenario for reducing a memory load is provided in the embodiments of the present application, and fig. 1 is a schematic diagram of an application scenario of a memory data processing method according to an embodiment of the present invention. As shown in fig. 1, the system includes: a server device 12 and a terminal 10.
The terminal 10 is configured to obtain target memory data.
The server device 12 is configured to match a field in the target memory data with a preset field in the log classification, and calculate a correlation between the target memory data and the log classification based on a matching result; the category of the log classification selects the running software in the memory, such as terminal security software, firewall software and the like in common system software.
The server device 12 is further configured to control the program corresponding to the target memory data to enter a sleep state after determining that the calculated correlation is lower than a preset threshold value within a preset time.
It should be noted that, the server device 12 may be connected to the terminal 10, the connection manner is not limited to a wireless network and a limited network, and the result displayed by the terminal 10 may be transmitted to a mobile terminal such as a mobile phone or a tablet through other connection manners.
If the server device 12 has a high occupancy level, the server device may be down. When this occurs, the state of the server device 12 can only be restored by powering off or restarting the server device 12. However, the state of the server device 12 is recovered by turning off the power supply for a long time, which easily causes the problems of system destruction and system file loss of the server device 12. In the related technology, when the system memory occupation is large, a process management system is selected to be opened, whether an application occupying too much memory exists or not is monitored, the time and the proportion of the application occupying too much memory to the memory occupation are observed, software occupying too much memory is selected manually according to the actual use condition, a command for forcedly stopping the execution of the software is executed, and the process of the program is ended. However, the application scenario suitable for this situation has a great limitation, and in schools and office buildings with large machine rooms, many servers simultaneously run thousands of application programs, and the efficiency of manually closing the application process is very low. This situation requires an automatic matching method to shut down idle applications, and the following is a solution to the above problem according to the present invention. Those skilled in the art will appreciate that the mobile terminal 10 illustrated in fig. 1 is not limiting and may include more or fewer components than shown, or may combine certain components, or a different arrangement of components.
The present embodiment provides a method for reducing a memory load, and fig. 2 is a flowchart of a method for processing memory data according to an embodiment of the present application, as shown in fig. 2, where the flowchart includes the following steps:
step S202, obtaining target memory data.
The memory data can be acquired through executing a script, or the target data can be acquired by using a command statement in a command manager of the computer. The target memory data includes common fields of terminal security and firewall in the system memory log. The data is extracted to extract the frequency of occurrence of the network security and firewall software during the time period to determine the number of times the software is invoked during the time period.
Step S204, the field in the target memory data is matched with the preset field in the log classification, and the correlation degree between the target memory data and the log classification is calculated based on the matching result.
The preset field herein refers to a common field, where the common field in the terminal includes: data type, data subclass, device IP address, vendor name, product version, collector receive time, device receive time, start time, end time, event name, event message, threat level, destination process name, destination process command line, status, event result classification, destination address and destination hostname, etc. Common fields in firewalls include: data type, data subclass, device IP address, vendor name, product version, collector receive time, device receive time, start time, end time, module name, event message, threat level, policy ID, policy name, destination address, event result classification, etc.
Step S206, if the calculated correlation is lower than a preset threshold value in a preset time, controlling the program corresponding to the target memory data to enter a dormant state.
Through the steps S202 to S206, the input data (i.e. the target memory data) is first found in the corresponding log classification in the computer, and then the weights of these classifications are extracted to determine whether the input data exists in the array of the log classification. For example, a flag existing in the array is 1, a flag not existing is 0, a threshold is set, the existing field is multiplied by the weight of the field, and if the result of the multiplication is smaller than a preset threshold, the program is closed.
By the method, the effect of quickly releasing the memory can be achieved, the problem of low efficiency of cleaning the memory resources is solved, and efficient utilization of the memory resources is realized.
The present embodiment provides a method for processing memory data, and fig. 3 is a flowchart of an activation application of the method for processing memory data according to an embodiment of the present application, as shown in fig. 3, where the flowchart includes the following steps:
step S302, if the calculated correlation reaches the preset threshold, the program corresponding to the target memory data is controlled to enter an on state.
Through step S302, it is first determined whether the input data exists in the array of log classifications by finding the corresponding log classifications in the computer and then extracting the weights of the fields. For example, the existing flag is 1, the non-existing flag is 0, the existing field is multiplied by the weight of the field, and if the multiplication result reaches the preset threshold value, the control program is started. By doing so, manual opening of applications can be avoided, thousands of applications, even tens of thousands of applications, which need to be manually opened, and a large amount of time is consumed. Meanwhile, the problem of low efficiency of manual opening application is solved, and the application with low relevance is quickly closed.
In some embodiments, before the fetching the target memory data, the method further comprises: setting a plurality of arrays, storing the preset field of the log classification in the arrays, and setting corresponding weights for the preset field, wherein the preset field corresponds to the weights one by one.
Wherein the preset field may be a common field in the software (application or program) manually summarized. In addition, a field in this application is an identification of each column in a two-dimensional table of a data structure in a relational model. When data is stored in a computer, the data must be stored in a certain structure and a certain organization format. The relationship model is currently most widely used in reality, and requires that data be stored in a two-dimensional table containing a limited number of different rows and specific relationships. Each column in the two-dimensional table is a field, the field name is used to represent the field, and the field is specified by the user, and a certain naming rule is followed in different systems. For example, if a common field array for a class is [ A1, A2, a 3..an ], then the array of weights is [ λ1, λ2, λ3..λn ], which are one-to-one corresponding, A1 corresponds to λ1, an corresponds to λn, as shown in table one below. These fields are commonly used fields, such as those commonly found in firewall software: the method comprises the steps of classifying data types, data subclasses, equipment IP addresses, manufacturer names, product versions, acquisition receiving time, equipment receiving time, starting time, ending time, module names, event messages, threat levels, strategy IDs, strategy names, destination addresses and event results, forming an array by the fields, storing the array, setting a weight value for each field, setting the weight values into an array, establishing a one-to-one mapping relation between the arrays of the fields and the arrays of the weight values, knowing the weight value of the field when extracting the field of the array, and realizing efficient matching of the fields of software and the weight values of the fields.
List one
| Fields | Field name |
| dataType | Data type |
| dataSubType | Data subclass |
| deviceAddress | Device IP address |
| productVendorName | Vendor name |
| deviceSendProductName | Product name |
| deviceSendProductVersion | Product version |
| collectorReceiptTime | Collector reception time |
| deviceReceiptTime | Device reception time |
| startTime | Start time |
| endTime | End time |
| modelName | Module name |
| name | Event name |
| message | Event message |
| severity | Threat level |
| policyId | Policy ID |
| policyName | Policy name |
| destAddress | Destination address |
| catOutcome | Event outcome classification |
In some embodiments, matching the field in the target memory data with a preset field in the log classification, and calculating the correlation between the target memory data and the log classification based on the matching result includes: matching the field in the target memory data with a preset field in the log classification, and determining the corresponding position of the field in the target memory data in the log classification; acquiring the weight of the field in the target memory data according to the corresponding position of the field in the target memory data in the log classification; the correlation is obtained by weighting and averaging the fields in the target memory data. The method can quickly know the relativity of the data in the system.
In the calculation, a function may be set to solve the correlation problem, and as a specific application example, fig. 4 is a flowchart of an actual application scenario of the memory data processing method according to the embodiment of the present application, as shown in fig. 4, where the flowchart includes the following steps:
step S401, the data fields in the soc log are analyzed and summarized to obtain common fields in each category, namely preset fields. Common fields for terminal security and firewall are as follows; taking a firewall as an example, the preset fields include: data type, data subclass, device IP address, vendor name, product version, collector receive time, device receive time, start time, end time, module name, event message, threat level, policy ID, policy name, destination address, and event result classification.
Step S402, obtaining k field arrays F according to the positions of the fields in each category, and fixing the field arrays F according to the sequence;
step S403, a unique weight is set for each field in each category to obtain k weight arrays V, which is equivalent to a type of field corresponding weight for each weight array, and the arrays are fixed according to the order of the weights.
In step S404, n fields are assumed in the data, and a key array is assumed, which executes a judgment statement for each type of array F and array V.
Step S405, determining whether a certain field is contained in the data, and using a determination statement: fi=isexted (a, b) for return, b is present in array a, is 1 (representing that the subsequent weight will work), and is not 0 (the subsequent weight will not work).
In step S406, the position of a field in the data in a certain class is determined, the corresponding weight is extracted from the weight array V by the position, and the sentence vi=pos (a, b) is used for returning, b represents the position in the array a, and the value is from 1 to n.
Step S407, performing a formula calculation for each type of F, V according to the D1, D2, key array, as follows:
obtained from fi obtained from D1 and D2The two results are multiplied, the calculated results of all fields of the data are accumulated and averaged, and the correlation strength of the data and a certain class can be obtained, the larger the numerical value is, the stronger the correlation is represented, and the smaller the numerical value is, the lower the correlation is represented.
In some embodiments, the preset threshold is a maximum value obtained by weighting and averaging weights of all fields in the target memory data in a time period, and whether the software is started in the time period can be known by judging whether the maximum value is reached, so that the starting condition of the software in the time period can be accurately judged.
The embodiment also provides a device for processing the memory data. FIG. 5 is a schematic diagram of a memory data processing device according to an embodiment of the present application, as shown in FIG. 5, the device includes: an input module 50, a matching module 52, and a control module 54; wherein,
the input module 50 is used for acquiring target memory data; the matching module 52 is configured to match a field in the target memory data with a preset field in the log classification, and calculate a correlation between the target memory data and the log classification based on a matching result; the control module 54 is configured to control the program corresponding to the target memory data to enter a sleep state after the calculated correlation is lower than a preset threshold value in a preset time. The module realizes rapid reduction of the memory load of the computer equipment.
The present embodiment also provides an electronic device comprising a memory having stored therein a computer program and a processor arranged to run the computer program to perform the steps of any of the method embodiments described above.
Optionally, the electronic apparatus may further include a transmission device and an input/output device, where the transmission device is connected to the processor, and the input/output device is connected to the processor.
Alternatively, in the present embodiment, the above-described processor may be configured to execute the following steps by a computer program:
step S1, acquiring target memory data;
step S2, matching the fields in the target memory data with preset fields in the log classification, and calculating the correlation degree between the target memory data and the log classification based on a matching result;
and step S3, if the calculated correlation is lower than a preset threshold value in a preset time, controlling a program corresponding to the target memory data to enter a dormant state. The problem of low efficiency of cleaning the memory resources is solved, and efficient utilization of the memory resources is realized.
The above-described respective modules may be functional modules or program modules, and may be implemented by software or hardware. For modules implemented in hardware, the various modules described above may be located in the same processor; or the above modules may be located in different processors in any combination.
In one embodiment, fig. 6 is a computer device according to an embodiment of the present application, where a computer program is stored, and the computer program when executed by a processor implements the steps in the node switching method provided in the foregoing embodiments. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, 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 media. The database of the computer device is for storing real-time video stream data. The network interface of the computer device is used for communicating with an external terminal through a network connection.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed may comprise the steps of the embodiments of the methods described above. The various techniques described herein may be used for various wireless communication systems such as 2G, 3G, 4G, 5G communication systems and next generation communication systems, as well as global system for mobile communications (Global System for Mobile communications, abbreviated GSM), code division multiple access (Code Division Multiple Access, abbreviated CDMA) systems, time division multiple access (Time Division Multiple Access, abbreviated TDMA) systems, wideband code division multiple access (Wideband Code Division Multiple Access Wireless, abbreviated WCDMA), frequency division multiple access (Frequency Division Multiple Addressing, abbreviated FDMA) systems, orthogonal frequency division multiple access (Orthogonal Frequency-Division Multiple Access, abbreviated OFDMA) systems, single carrier FDMA (SC-FDMA) systems, general packet Radio service (General Packet Radio Service, abbreviated GPRS) systems, long term evolution (Long Term Evolution, abbreviated LTE) systems, 5G New Radio (NR) systems, and other such communication systems.
Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples represent only a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application is to be determined by the claims appended hereto.
Claims (10)
1. A memory data processing method, comprising:
acquiring target memory data;
matching the fields in the target memory data with preset fields in the log classification, and calculating the correlation degree between the target memory data and the log classification based on a matching result;
and if the calculated correlation is lower than a preset threshold value in a preset time, controlling a program corresponding to the target memory data to enter a dormant state.
2. The method according to claim 1, wherein the method further comprises:
and if the calculated correlation degree reaches the preset threshold value, controlling a program corresponding to the target memory data to enter an on state.
3. The method of claim 1, wherein prior to the retrieving the target memory data, the method further comprises:
setting a plurality of arrays, storing the preset fields of the log classification in the arrays, and setting corresponding weights for the preset fields.
4. The method of claim 3, wherein matching the fields in the target memory data with the predetermined fields in the log class and calculating the correlation of the target memory data with the log class based on the matching result comprises:
matching the fields in the target memory data with the preset fields in the log classification, and determining the corresponding positions of the fields in the target memory data in the log classification;
acquiring the weight of the field in the target memory data according to the corresponding position of the field in the target memory data in the log classification;
and weighting and averaging the fields in the target memory data to obtain the correlation degree.
5. The method of claim 4, wherein the predetermined threshold is a maximum value obtained by weighting and averaging weights of all fields in the target memory data in a time period.
6. A memory data processing apparatus, comprising: the device comprises an input module, a matching module and a control module;
the input module is used for acquiring target memory data;
the matching module is used for matching the fields in the target memory data with preset fields in the log classification, and calculating the correlation between the target memory data and the log classification based on a matching result;
and the control module is used for controlling the program corresponding to the target memory data to enter a dormant state after the calculated correlation is lower than a preset threshold value in a preset time.
7. The apparatus of claim 6, wherein the control module is further configured to control the program corresponding to the target memory data to enter an on state after the calculated correlation reaches the preset threshold.
8. A memory data processing system, comprising: server equipment and terminals; wherein,
the terminal is used for acquiring target memory data;
the server equipment is used for matching the fields in the target memory data with preset fields in the log classification, and calculating the correlation between the target memory data and the log classification based on a matching result;
and the server equipment is also used for controlling the program corresponding to the target memory data to enter a dormant state after judging that the calculated correlation is lower than a preset threshold value in preset time.
9. An electronic device comprising a memory and a processor, wherein the memory has stored therein a computer program, the processor being arranged to run the computer program to perform the memory data processing method of any of claims 1 to 5.
10. A storage medium having a computer program stored therein, wherein the computer program is arranged to perform the memory data processing method of any one of claims 1 to 5 when run.
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