CN116225338A - Data processing method and device based on time sequence information and storage information - Google Patents

Data processing method and device based on time sequence information and storage information Download PDF

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CN116225338A
CN116225338A CN202310239168.7A CN202310239168A CN116225338A CN 116225338 A CN116225338 A CN 116225338A CN 202310239168 A CN202310239168 A CN 202310239168A CN 116225338 A CN116225338 A CN 116225338A
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data
target
information
storage space
storage
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CN116225338B (en
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李光辉
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Hubei Central China Technology Development Of Electric Power Co ltd
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Guangzhou Chaohui Intelligent Technology Co ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/06—Digital input from, or digital output to, record carriers, e.g. RAID, emulated record carriers or networked record carriers
    • G06F3/0601—Interfaces specially adapted for storage systems
    • G06F3/0602—Interfaces specially adapted for storage systems specifically adapted to achieve a particular effect
    • G06F3/0604—Improving or facilitating administration, e.g. storage management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/06—Digital input from, or digital output to, record carriers, e.g. RAID, emulated record carriers or networked record carriers
    • G06F3/0601—Interfaces specially adapted for storage systems
    • G06F3/0628—Interfaces specially adapted for storage systems making use of a particular technique
    • G06F3/0638—Organizing or formatting or addressing of data

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  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
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Abstract

The invention discloses a data processing method and a device based on time sequence information and storage information, wherein the method comprises the following steps: determining time sequence information of each target data to be processed to obtain a time sequence data result set, determining equipment storage information by determining the data storage condition of the target intelligent equipment, performing classification operation on each target data based on the time sequence data result set and the equipment storage information to obtain at least one data class group, performing compression processing operation on each data class group to obtain a corresponding compressed data group, analyzing each compressed data group to obtain a compression analysis result, determining a target storage space of each compressed data group according to the compression analysis result, and further performing preset target processing operation on the target data. Therefore, the method and the device can release the storage space of the target device, and are beneficial to improving the efficiency and the intelligence of the intelligent device for data processing and improving the effectiveness and the accuracy of the intelligent device for data processing.

Description

Data processing method and device based on time sequence information and storage information
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a data processing method and apparatus based on time sequence information and storage information.
Background
At present, the data are not available in daily work, life and study of people in the big data era. With the increasing amount of data, there is a higher pursuit of quality of data processing. Most of the intelligent devices can process data, and the data processing is already in all the fields of the current social production and social life and is an indispensable part of the life of people. However, the space where the current intelligent device can store data is limited, and as the time length for people to use the intelligent device increases, the capacity of the data storage space of the intelligent device becomes smaller and smaller, which not only causes the inefficiency of the intelligent device on data processing, but also causes the inefficiency of the intelligent device on data processing and even errors in data processing. It is important to provide a new data processing method to improve the efficiency and effectiveness of data processing of the intelligent device.
Disclosure of Invention
The technical problem to be solved by the invention is to provide the data processing method and the device based on the time sequence information and the storage information, which can be beneficial to improving the efficiency and the intelligence of the intelligent equipment for data processing, improving the effectiveness and the accuracy of the intelligent equipment for data processing, releasing the data storage space of the intelligent equipment and improving the experience and the comfort of a user using the intelligent equipment.
In order to solve the technical problem, a first aspect of the present invention discloses a data processing method based on time sequence information and storage information, the method comprising:
analyzing at least one target data to be processed in target intelligent equipment to obtain data information of each target data, wherein the data information comprises data type information, data storage information and data quantity information;
for each target data, determining time sequence information of the target data, analyzing the time sequence information of each target data to obtain a time sequence analysis result of each target data, and summarizing the time sequence analysis results of all the target data to obtain a time sequence data result set;
determining the data storage condition in the target intelligent equipment, and determining the equipment storage information of the target intelligent equipment according to the first storage information of all the data storage spaces; the data storage condition comprises first storage information of each data storage space included in the target intelligent device, wherein the first storage information comprises one or more of data types stored in each data storage space, data time sequence information stored in each data storage space and data quantity information stored in each data storage space;
Generating a data set to be processed according to all the target data, and executing classification operation on each target data included in the data set to be processed based on the time sequence data result set and the equipment storage information to obtain at least one data category group, wherein each data category group at least comprises one target data;
for each data category group, executing preset compression processing operation on each target data included in the data category group to obtain a compressed data group corresponding to the data category group;
for each compressed data set, performing an analysis operation on the compressed data set to obtain a compression analysis result of the compressed data set, determining a target storage space of the compressed data set based on the compression analysis result of the compressed data set, and performing a preset target processing operation on the compressed data set according to the target storage space of the compressed data set; the preset target processing operation comprises a storage operation; and the target storage space is the storage space in the target intelligent equipment.
As an alternative embodiment, in the first aspect of the present invention, the method further includes:
Determining second storage information of each data storage space in the target intelligent device;
for each data storage space, judging whether the second storage information of all the data storage spaces meets the preset space storage condition according to the second storage information of the data storage space;
when the fact that second storage information of all the data storage spaces unevenly meets the preset space storage conditions is judged, determining the data storage spaces which do not meet the preset space storage conditions as first data storage spaces;
for each first data storage space, determining at least one first data according to a time sequence analysis result of each target data included in the first data storage space, and determining a second data storage space corresponding to each first data according to target data information of each first data; the target data information comprises data type information of the first data, time sequence information of the first data and data abstract information of the first data;
and for each first data, executing a preset data moving operation on the first data according to the second data storage space corresponding to the first data so as to move the first data to the second data storage space corresponding to the first data.
As an alternative embodiment, in the first aspect of the present invention, the method further includes:
when judging that the second storage information of all the data storage spaces meets the preset space storage condition, determining storage attribute information of each data storage space, wherein the storage attribute information comprises one or more of maximum data capacity information of each data storage space, data storage type information of each data storage space, data storage time sequence information of each data storage space and data quantity information contained in each data storage space;
determining the storage attribute similarity between each data storage space and each remaining data storage space except the data storage space according to the storage attribute information of each data storage space, and obtaining a storage attribute similarity set;
judging whether a data storage space combination with storage attribute similarity greater than or equal to a preset storage attribute similarity threshold exists in the storage attribute similarity set, wherein the data storage space combination comprises at least two data storage spaces, and the storage attribute similarity between all the data storage spaces included in the data storage space combination is greater than or equal to the preset storage attribute similarity threshold;
When judging that the storage attribute similarity set has the data storage space combination with the storage attribute similarity greater than or equal to a preset storage attribute similarity threshold value, for each data storage space combination, determining storage space combination information of the data storage space combination according to second storage information of each data storage space included in the data storage space combination, judging whether the storage space combination information of the data storage space combination meets preset storage space combination conditions, and executing merging operation on all the data storage spaces included in the data storage space combination to merge all data included in all the data storage spaces included in the data storage space combination when judging that the storage space combination information of the data storage space combination meets the preset storage space combination conditions; the storage space combination information of each data storage space combination comprises the number of used data storage spaces of each data storage space and the number of free data storage spaces of each data storage space.
As an optional implementation manner, in the first aspect of the present invention, before analyzing at least one target data to be processed in the target smart device to obtain data information of each target data, the method further includes:
determining at least one target data to be processed in target intelligent equipment, and acquiring data source information of each target data, wherein the data source information comprises one or more of source address information of each target data, source equipment information of each target data, source user information of each target data and source scene information of each target data;
for each target data, determining the data security parameters of the target data according to the data source information of the target data, and generating a data security parameter set according to the data security parameters of all the target data; the data security parameter set comprises data security parameters of all the target data;
judging whether all the data security parameters included in the data security parameter set meet preset data security conditions or not;
when all the data security parameters included in the data security parameter set are judged to meet the preset data security conditions, triggering and executing at least one target data to be processed in the analysis target intelligent device to obtain the operation of the data information of each target data;
When all the data security parameters included in the data security parameter set are judged to unevenly meet the preset data security conditions, determining the data security parameters which do not meet the preset data security conditions as target data security parameters, and determining target data corresponding to each target data security parameter as first target data;
for each first target data, determining a data safety reason that the data safety parameter of the first target data does not meet a preset data safety condition, updating the target data to be processed according to the data safety reason, and triggering and executing at least one target data to be processed in the analysis target intelligent device to obtain the data information of each target data.
As an optional implementation manner, in the first aspect of the present invention, the time sequence analysis result of each target data includes a collection time of the target data;
the step of performing a classification operation on each target data included in the data set to be processed based on the time-series data result set and the device storage information to obtain at least one data category group, including:
For each target data included in the time sequence data result set, determining the acquisition time interval duration between the acquisition time of the target data and each residual target data except the target data in the time sequence data result set according to the time sequence analysis result of the target data, and obtaining an acquisition time interval duration set;
judging whether a target interval duration exists in the collection time interval duration set, wherein the target interval duration is smaller than or equal to a preset interval duration threshold;
when judging that the target interval duration exists in the collection time interval duration set, determining the data type and the data quantity of target data corresponding to the target interval duration for each target interval duration, judging whether the data type and the data quantity of the target data corresponding to the target interval duration are the same, and determining all the target data corresponding to the target interval duration as a data class group when judging that the data type and the data quantity of the target data corresponding to the target interval duration are the same and the data quantity of the target data corresponding to the target interval duration meet the preset equipment storage condition.
In an optional implementation manner, in the first aspect of the present invention, for each of the compressed data sets, performing an analysis operation on the compressed data set to obtain a compression analysis result of the compressed data set, and determining a target storage space of the compressed data set based on the compression analysis result of the compressed data set includes:
for each compressed data set, determining a data tag of each target data included in the compressed data set, extracting semantic information of the data tag of each target data included in the compressed data set, and determining a target semantic tag of the compressed data set according to the semantic information of each target data included in the compressed data set;
for each compressed data set, determining the semantic matching degree between the target semantic tag of the compressed data set and the storage attribute tag of each storage space according to the target semantic tag of the compressed data set and the storage attribute tag of each storage space included in the target intelligent device, obtaining a semantic matching degree set, determining the highest semantic matching degree from the semantic matching degree set, and determining the storage space corresponding to the highest semantic matching degree as the target storage space of the compressed data set.
In a first aspect of the present invention, for each of the first data storage spaces, the determining at least one first data according to a time sequence analysis result of each of the target data included in the first data storage space includes:
according to the time sequence analysis result of each target data included in the first data storage space, determining the time sequence result similarity between the target data and each other target data except the target data in the first data storage space for each target data in the first data storage space, obtaining a time sequence result similarity set, and determining at least one first data according to the time sequence result similarity set, wherein the time sequence result similarity of the first data is smaller than or equal to a preset time sequence similarity threshold;
and determining a second data storage space corresponding to each first data according to the target data information of each first data, including:
for each first data, determining a data category parameter of the first data according to the data type information of the first data and the data abstract information of the first data;
And for each first data, determining at least one target data storage space matched with the data category parameter of the first data in all idle data storage spaces of the target intelligent equipment according to the data category parameter of the first data, determining a data storage space matched with the time sequence information of the first data in all target data storage spaces according to the time sequence information of the first data, and determining the data storage space matched with the time sequence information of the first data as a second data storage space corresponding to the first data.
The second aspect of the present invention discloses a data processing apparatus based on time series information and storage information, the apparatus comprising:
the analysis module is used for analyzing at least one target data to be processed in the target intelligent equipment to obtain data information of each target data, wherein the data information comprises data type information, data storage information and data quantity information;
a determining module, configured to determine, for each of the target data, timing information of the target data;
the analysis module is further used for analyzing the time sequence information of each target data to obtain a time sequence analysis result of each target data;
The summarizing module is used for summarizing time sequence analysis results of all the target data to obtain a time sequence data result set;
the determining module is further used for determining the data storage condition in the target intelligent device and determining the device storage information of the target intelligent device according to the first storage information of all the data storage spaces; the data storage condition comprises first storage information of each data storage space included in the target intelligent device, wherein the first storage information comprises one or more of data types stored in each data storage space, data time sequence information stored in each data storage space and data quantity information stored in each data storage space;
the generation module is used for generating a data set to be processed according to all the target data;
the classification module is used for performing classification operation on each target data included in the data set to be processed based on the time sequence data result set and the equipment storage information to obtain at least one data category group, wherein each data category group at least comprises one target data;
The determining module is further configured to perform an analysis operation on each compressed data set to obtain a compression analysis result of the compressed data set, and determine a target storage space of the compressed data set based on the compression analysis result of the compressed data set;
the processing module is used for executing preset target processing operation on the compressed data set according to the target storage space of the compressed data set; the preset target processing operation comprises a storage operation; and the target storage space is the storage space in the target intelligent equipment.
As an optional implementation manner, in the second aspect of the present invention, the determining module is further configured to determine second storage information of each of the data storage spaces in the target smart device;
the apparatus further comprises:
the judging module is used for judging whether the second storage information of all the data storage spaces meets the preset space storage conditions or not according to the second storage information of the data storage spaces for each data storage space;
the determining module is further configured to determine, as a first data storage space, a data storage space that does not meet the preset space storage condition when the judging module judges that second storage information of all the data storage spaces unevenly meet the preset space storage condition;
The determining module is further configured to determine, for each first data storage space, at least one first data according to a time sequence analysis result of each target data included in the first data storage space, and determine, according to target data information of each first data, a second data storage space corresponding to each first data; the target data information comprises data type information of the first data, time sequence information of the first data and data abstract information of the first data;
and the moving module is used for executing preset data moving operation on the first data according to the second data storage space corresponding to the first data for each first data so as to move the first data to the second data storage space corresponding to the first data.
As an optional implementation manner, in the second aspect of the present invention, the determining module is further configured to determine, when the judging module judges that the second storage information of all the data storage spaces meets the preset space storage condition, one or more of storage attribute information of each data storage space, maximum data capacity information of each data storage space, data storage type information of each data storage space, data storage timing information of each data storage space, and data quantity information included in each data storage space;
The determining module is further configured to determine, according to storage attribute information of each data storage space, storage attribute similarity between each data storage space and each remaining data storage space except the data storage space, so as to obtain a storage attribute similarity set;
the judging module is further configured to judge whether a data storage space combination with a storage attribute similarity greater than or equal to a preset storage attribute similarity threshold exists in the storage attribute similarity set, where the data storage space combination includes at least two data storage spaces, and the storage attribute similarity between all the data storage spaces included in the data storage space combination is greater than or equal to the preset storage attribute similarity threshold;
the determining module is further configured to determine, when the determining module determines that a data storage space combination with a storage attribute similarity greater than or equal to a preset storage attribute similarity threshold exists in the storage attribute similarity set, for each data storage space combination, storage space combination information of the data storage space combination according to second storage information of each data storage space included in the data storage space combination;
The judging module is further used for judging whether the storage space combination information of the data storage space combination meets preset storage space combination conditions;
the apparatus further comprises:
the merging module is used for executing merging operation on all the data storage spaces included in the data storage space combination when the judging module judges that the storage space combination information of the data storage space combination meets the preset storage space combination condition so as to merge all the data included in all the data storage spaces included in the data storage space combination; the storage space combination information of each data storage space combination comprises the number of used data storage spaces of each data storage space and the number of free data storage spaces of each data storage space.
As an optional implementation manner, in the second aspect of the present invention, the determining module is further configured to determine, before the analyzing module analyzes at least one target data to be processed in the target smart device to obtain data information of each target data, at least one target data to be processed in the target smart device;
The apparatus further comprises:
the acquisition module is used for acquiring data source information of each target data, wherein the data source information comprises one or more of source address information of each target data, source equipment information of each target data, source user information of each target data and source scene information of each target data;
the determining module is further used for determining the data security parameters of the target data according to the data source information of the target data for each target data;
the generation module is further used for generating a data security parameter set according to the data security parameters of all the target data; the data security parameter set comprises data security parameters of all the target data;
the judging module is further configured to judge whether all the data security parameters included in the data security parameter set meet a preset data security condition; when all the data security parameters included in the data security parameter set are judged to meet the preset data security conditions, triggering the analysis module to execute at least one target data to be processed in the analysis target intelligent device, and obtaining the operation of the data information of each target data;
The determining module is further configured to determine, when the judging module judges that all the data security parameters included in the data security parameter set unevenly meet the preset data security conditions, data security parameters that do not meet the preset data security conditions as target data security parameters, and determine target data corresponding to each of the target data security parameters as first target data;
the determining module is further configured to determine, for each first target data, a data security reason that a data security parameter of the first target data does not meet a preset data security condition, update the target data to be processed according to the data security reason, and trigger the analyzing module to execute at least one target data to be processed in the target intelligent device to obtain an operation of data information of each target data.
As an optional implementation manner, in the second aspect of the present invention, the time sequence analysis result of each target data includes a collection time of the target data;
the specific way that the classification module performs classification operation on each target data included in the data set to be processed based on the time sequence data result set and the equipment storage information to obtain at least one data category group includes:
For each target data included in the time sequence data result set, determining the acquisition time interval duration between the acquisition time of the target data and each residual target data except the target data in the time sequence data result set according to the time sequence analysis result of the target data, and obtaining an acquisition time interval duration set;
judging whether a target interval duration exists in the collection time interval duration set, wherein the target interval duration is smaller than or equal to a preset interval duration threshold;
when judging that the target interval duration exists in the collection time interval duration set, determining the data type and the data quantity of target data corresponding to the target interval duration for each target interval duration, judging whether the data type and the data quantity of the target data corresponding to the target interval duration are the same, and determining all the target data corresponding to the target interval duration as a data class group when judging that the data type and the data quantity of the target data corresponding to the target interval duration are the same and the data quantity of the target data corresponding to the target interval duration meet the preset equipment storage condition.
In a second aspect of the present invention, the determining module performs, for each of the compressed data sets, an analysis operation on the compressed data set to obtain a compression analysis result of the compressed data set, and determines, based on the compression analysis result of the compressed data set, a specific manner of the target storage space of the compressed data set includes:
for each compressed data set, determining a data tag of each target data included in the compressed data set, extracting semantic information of the data tag of each target data included in the compressed data set, and determining a target semantic tag of the compressed data set according to the semantic information of each target data included in the compressed data set;
for each compressed data set, determining the semantic matching degree between the target semantic tag of the compressed data set and the storage attribute tag of each storage space according to the target semantic tag of the compressed data set and the storage attribute tag of each storage space included in the target intelligent device, obtaining a semantic matching degree set, determining the highest semantic matching degree from the semantic matching degree set, and determining the storage space corresponding to the highest semantic matching degree as the target storage space of the compressed data set.
In a second aspect of the present invention, the determining module determines, for each of the first data storage spaces, at least one first data according to a time sequence analysis result of each of the target data included in the first data storage space, where the specific manner includes:
according to the time sequence analysis result of each target data included in the first data storage space, determining the time sequence result similarity between the target data and each other target data except the target data in the first data storage space for each target data in the first data storage space, obtaining a time sequence result similarity set, and determining at least one first data according to the time sequence result similarity set, wherein the time sequence result similarity of the first data is smaller than or equal to a preset time sequence similarity threshold;
and the determining module determines the specific mode of the second data storage space corresponding to each first data according to the target data information of each first data, wherein the specific mode comprises the following steps:
for each first data, determining a data category parameter of the first data according to the data type information of the first data and the data abstract information of the first data;
And for each first data, determining at least one target data storage space matched with the data category parameter of the first data in all idle data storage spaces of the target intelligent equipment according to the data category parameter of the first data, determining a data storage space matched with the time sequence information of the first data in all target data storage spaces according to the time sequence information of the first data, and determining the data storage space matched with the time sequence information of the first data as a second data storage space corresponding to the first data.
A third aspect of the present invention discloses another data processing apparatus based on timing information and storage information, the apparatus comprising:
a memory storing executable program code;
a processor coupled to the memory;
the processor invokes the executable program code stored in the memory to execute the data processing method based on the time sequence information and the stored information disclosed in the first aspect of the invention.
A fourth aspect of the present invention discloses a computer-readable storage medium storing computer instructions which, when called, are used to perform the data processing method based on timing information and storage information disclosed in the first aspect of the present invention.
Compared with the prior art, the embodiment of the invention has the following beneficial effects:
in an embodiment of the invention. Therefore, the method and the device can be beneficial to improving the efficiency and the intelligence of the intelligent equipment for data processing, improving the effectiveness and the accuracy of the intelligent equipment for data processing, releasing the data storage space of the intelligent equipment and improving the experience and the comfort of a user using the intelligent equipment.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a data processing method based on time sequence information and storage information according to an embodiment of the invention;
FIG. 2 is a flow chart of another data processing method based on time sequence information and storage information according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a data processing apparatus according to an embodiment of the present invention based on timing information and storage information;
FIG. 4 is a schematic diagram of another data processing apparatus according to an embodiment of the present invention based on timing information and storage information;
fig. 5 is a schematic structural diagram of a data processing apparatus according to another embodiment of the present invention.
Description of the embodiments
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The terms first, second and the like in the description and in the claims and in the above-described figures are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, apparatus, article, or article that comprises a list of steps or elements is not limited to only those listed but may optionally include other steps or elements not listed or inherent to such process, method, article, or article.
Reference herein 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 invention. 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. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
The invention discloses a data processing method and a data processing device based on time sequence information and storage information, which can be beneficial to improving the efficiency and the intelligence of data processing of intelligent equipment, improving the effectiveness and the accuracy of data processing of the intelligent equipment, releasing the data storage space of the intelligent equipment and improving the experience and the comfort of a user using the intelligent equipment. The following will describe in detail.
Examples
Referring to fig. 1, fig. 1 is a flow chart of a data processing method based on time sequence information and storage information according to an embodiment of the invention. The data processing method based on the time sequence information and the storage information described in fig. 1 can be applied to a data processing device based on the time sequence information and the storage information, and also can be applied to a cloud server or a local server for data processing based on the time sequence information and the storage information, and the embodiment of the invention is not limited. As shown in fig. 1, the data processing method based on the timing information and the storage information may include the following operations:
101. And analyzing at least one target data to be processed in the target intelligent equipment to obtain the data information of each target data.
In the embodiment of the invention, the data information comprises data type information, data storage information and data quantity information.
In the embodiment of the present invention, further, the data information further includes data source information. Wherein the data type information comprises one or more of picture type, text type, audio type, video type and the like; the data storage information comprises storage information which is stored to the target intelligent device and storage information which is not stored to the target intelligent device; the data quantity information comprises one or more of data total quantity information and data quantity information of each type; the data source information comprises one or more of a source ip address of the data, source time information of the data and source equipment information of the data.
102. For each target data, determining the time sequence information of the target data, analyzing the time sequence information of each target data to obtain the time sequence analysis result of each target data, and summarizing the time sequence analysis results of all the target data to obtain a time sequence data result set.
In this embodiment of the present invention, optionally, the time sequence information of the target data includes one or more of acquisition time information of the target data, storage time information of the target data, and movement time information of the target data.
103. And determining the data storage condition in the target intelligent device, and determining the device storage information of the target intelligent device according to the first storage information of all the data storage spaces.
In the embodiment of the invention, the data storage condition comprises first storage information of each data storage space included in the target intelligent equipment; the first storage information includes one or more of a data type stored in each data storage space, data timing information stored in each data storage space, and data amount information stored in each data storage space.
104. And generating a data set to be processed according to all the target data, and executing classification operation on each target data included in the data set to be processed based on the time sequence data result set and the equipment storage information to obtain at least one data category group.
In the embodiment of the invention, each data category group at least comprises one target data.
In the embodiment of the present invention, optionally, generating the data set to be processed according to all the target data includes: summarizing all target data, and determining all target data as a data set to be processed.
105. And for each data category group, executing preset compression processing operation on each target data included in the data category group to obtain a compressed data group corresponding to the data category group.
In the embodiment of the present invention, the data size corresponding to the compressed data set is smaller than or equal to the sum of the data sizes of all the target data included in the compressed data set.
106. For each compressed data set, performing an analysis operation on the compressed data set to obtain a compression analysis result of the compressed data set, determining a target storage space of the compressed data set based on the compression analysis result of the compressed data set, and performing a preset target processing operation on the compressed data set according to the target storage space of the compressed data set.
In the embodiment of the invention, the preset target processing operation comprises a storage operation; the target storage space is the storage space in the target intelligent device.
It can be seen that, implementing the data processing method based on time sequence information and storage information described in fig. 1 can determine the time sequence information of each target data to be processed to obtain a time sequence data result set, determine the storage information of the device by determining the data storage condition of the target intelligent device, perform a classification operation on each target data based on the time sequence data result set and the storage information of the device to obtain at least one data class group, perform a compression processing operation on each data class group to obtain a corresponding compressed data group, analyze each compressed data group to obtain a compression analysis result, determine the target storage space of each compressed data group according to the compression analysis result, and further perform a preset target processing operation on the target data, which is beneficial to improving the efficiency and intelligence of data processing of the intelligent device, improving the effectiveness and accuracy of data processing of the intelligent device, releasing the data storage space of the intelligent device, and further improving the experience and comfort of using the intelligent device by a user.
Examples
Referring to fig. 2, fig. 2 is a flow chart of another data processing method based on time sequence information and storage information according to an embodiment of the invention. The data processing method based on the time sequence information and the storage information described in fig. 2 may be applied to a data processing device based on the time sequence information and the storage information, and may also be applied to a cloud server or a local server for data processing based on the time sequence information and the storage information, which is not limited in the embodiment of the present invention. As shown in fig. 2, the data processing method based on the timing information and the storage information may include the following operations:
201. and analyzing at least one target data to be processed in the target intelligent equipment to obtain the data information of each target data.
202. For each target data, determining the time sequence information of the target data, analyzing the time sequence information of each target data to obtain the time sequence analysis result of each target data, and summarizing the time sequence analysis results of all the target data to obtain a time sequence data result set.
203. And determining the data storage condition in the target intelligent device, and determining the device storage information of the target intelligent device according to the first storage information of all the data storage spaces.
204. And generating a data set to be processed according to all the target data, and executing classification operation on each target data included in the data set to be processed based on the time sequence data result set and the equipment storage information to obtain at least one data category group.
205. And for each data category group, executing preset compression processing operation on each target data included in the data category group to obtain a compressed data group corresponding to the data category group.
206. For each compressed data set, performing an analysis operation on the compressed data set to obtain a compression analysis result of the compressed data set, determining a target storage space of the compressed data set based on the compression analysis result of the compressed data set, and performing a preset target processing operation on the compressed data set according to the target storage space of the compressed data set.
In the embodiment of the present invention, for the detailed description of steps 201-206, please refer to other descriptions of steps 101-106 in the first embodiment, and the detailed description of the embodiment of the present invention is omitted.
207. Determining second storage information of each data storage space in the target intelligent device, and judging whether the second storage information of all the data storage spaces meets preset space storage conditions or not according to the second storage information of the data storage space for each data storage space.
In an embodiment of the present invention, optionally, the second storage information of each data storage space includes stored data quantity information of the data storage space, and free storage quantity information available for storing data in the data storage space.
In the embodiment of the present invention, the second storage information includes stored data quantity information of the data storage space, and determining whether the second storage information of all the data storage spaces meets a preset space storage condition includes: judging whether the stored data quantity of the data storage space is larger than or equal to a preset quantity upper limit threshold according to the stored data quantity information of the data storage space, and determining that the second storage information of the data storage space meets preset space storage conditions when the judgment result is yes; and if not, determining that the second storage information of the data storage space does not meet the preset space storage condition.
208. And when the second storage information of all the data storage spaces is judged to be uneven to meet the preset space storage condition, determining the data storage space which does not meet the preset space storage condition as the first data storage space.
In the embodiment of the present invention, the number of the first data storage spaces may be one or more, and the embodiment of the present invention is not limited specifically.
209. And for each first data storage space, determining at least one first data according to the time sequence analysis result of each target data included in the first data storage space, and determining a second data storage space corresponding to each first data according to the target data information of each first data.
In the embodiment of the invention, the target data information comprises data type information of the first data, time sequence information of the first data and data abstract information of the first data.
In the embodiment of the present invention, optionally, the second data storage spaces corresponding to the different first data may be the same or different, and the embodiment of the present invention is not limited.
In an embodiment of the present invention, optionally, the data summary information of the first data is used to represent the content of the first data; for example, if the first data is of a picture type and the first data is a landscape picture, the data summary information of the first data may be the landscape picture.
210. And for each first data, executing a preset data moving operation on the first data according to the second data storage space corresponding to the first data so as to move the first data to the second data storage space corresponding to the first data.
In an embodiment of the present invention, optionally, a preset data movement operation is performed on the first data, so that before the first data is moved to a second data storage space corresponding to the first data, the method further includes: and executing compression operation on the first data to obtain first compressed data, and moving the first compressed data to a second data storage space corresponding to the first data. Therefore, the first data can be compressed before being moved to the second data storage space corresponding to the first data, the memory occupation amount of the first data can be reduced, the efficiency of moving the first data to the second data storage space corresponding to the first data can be improved, and the data storage amount of the second data storage space corresponding to the first data can be saved.
Therefore, the data processing method based on the time sequence information and the storage information described in fig. 2 can determine the first data storage space in the target intelligent device by determining the second storage information of each data storage space in the target intelligent device, determining the first data according to the time sequence analysis result of each target data, further determining the second data storage space corresponding to each first data, and moving each first data to the second data storage space corresponding to the first data, so that the data can be moved according to the storage information of the target intelligent device and the time sequence information of each target data, the data storage space of each data storage space of the target intelligent device can be reasonably utilized, the data storage space utilization rate of each data storage space of the target intelligent device can be saved, the intelligence and the accuracy of processing the data can be improved, and the efficiency of processing each data stored in the target intelligent device can be improved.
In an alternative embodiment, the method further comprises:
when the second storage information of all the data storage spaces meets the preset space storage condition, determining storage attribute information of each data storage space, wherein the storage attribute information comprises one or more of maximum data capacity information of each data storage space, data storage type information of each data storage space, data storage time sequence information of each data storage space and data quantity information contained in each data storage space;
determining the storage attribute similarity between each data storage space and each remaining data storage space except the data storage space according to the storage attribute information of each data storage space, and obtaining a storage attribute similarity set;
judging whether a data storage space combination with the storage attribute similarity greater than or equal to a preset storage attribute similarity threshold exists in the storage attribute similarity set, wherein the data storage space combination comprises at least two data storage spaces, and the storage attribute similarity between all the data storage spaces included in the data storage space combination is greater than or equal to the preset storage attribute similarity threshold;
When judging that the storage attribute similarity exists in the storage attribute similarity set, determining storage space combination information of each data storage space combination according to second storage information of each data storage space included in the data storage space combination, judging whether the storage space combination information of the data storage space combination meets preset storage space combination conditions, and executing merging operation on all data storage spaces included in the data storage space combination when judging that the storage space combination information of the data storage space combination meets the preset storage space combination conditions so as to merge all data included in all data storage spaces included in the data storage space combination; wherein the storage space combination information of each data storage space combination includes used data storage space information of each data storage space included in the data storage space combination.
In this alternative embodiment, for example, when the target smart device includes A, B, C data storage spaces, storage attribute similarities between a and B, A and C and between B and C are determined, resulting in a storage attribute similarity set that includes storage attribute similarities between a and B, storage attribute similarities between a and C, and storage attribute similarities between B and C.
In this optional embodiment, optionally, when it is determined that there is no data storage space combination in the storage attribute similarity set, where the storage attribute similarity is greater than or equal to a preset storage attribute similarity threshold, the process may be ended.
In this optional embodiment, optionally, determining whether the storage space combination information of the data storage space combination meets a preset storage space combination condition includes:
judging whether the sum of the number of idle data storage spaces of the data storage spaces in the data storage space combination is larger than or equal to a preset idle number threshold value;
when the sum of the number of the free data storage spaces of the data storage spaces in the data storage space combination is larger than or equal to a preset free number threshold value, determining that the storage space combination information of the data storage space combination meets the preset storage space combination condition; when the sum of the free data storage space numbers of the data storage spaces in the data storage space combination is judged to be smaller than a preset free number threshold, determining that the storage space combination information of the data storage space combination does not meet the preset storage space combination condition.
It can be seen that, implementing this optional embodiment, when the second storage information of all the data storage spaces meets the preset space storage condition, storage attribute information of each data storage space is determined, storage attribute similarity between each data storage space and each remaining data storage space except for the data storage space is determined to obtain a storage attribute similarity set, and whether a data storage space combination exists is judged based on the storage attribute similarity set, if so, whether the storage space combination information of the data storage space combination meets the preset storage space combination condition is judged, if so, merging operation is performed on all the data storage spaces included in the data storage space combination, so that all the data included in all the data storage spaces included in the data storage space combination are merged, storage attribute similarity of each data storage space of the target intelligent device and the data storage space with larger storage space can be merged, each data storage space of the target intelligent device can be reasonably utilized, data of each data storage space of the target intelligent device can be saved, and the corresponding data storage space of the target intelligent device can be improved, and the target intelligent device can be more easily processed, and the user comfort of the target intelligent device can be improved.
In another optional embodiment, before analyzing at least one target data to be processed in the target intelligent device to obtain the data information of each target data, the method further includes:
determining at least one target data to be processed in target intelligent equipment, and acquiring data source information of each target data, wherein the data source information comprises one or more of source address information of each target data, source equipment information of each target data, source user information of each target data and source scene information of each target data;
for each target data, determining the data security parameters of the target data according to the data source information of the target data, and generating a data security parameter set according to the data security parameters of all the target data; the data security parameter set comprises data security parameters of all target data;
judging whether all data security parameters included in the data security parameter set meet preset data security conditions or not;
when all the data security parameters included in the data security parameter set are judged to meet the preset data security conditions, triggering and executing at least one target data to be processed in the target intelligent equipment to obtain the data information of each target data;
When all data security parameters included in the data security parameter set are judged to be uneven to meet preset data security conditions, determining the data security parameters which do not meet the preset data security conditions as target data security parameters, and determining target data corresponding to each target data security parameter as first target data;
for each first target data, determining a data safety reason that the data safety parameter of the first target data does not meet a preset data safety condition, updating the target data to be processed according to the data safety reason, and triggering and executing at least one target data to be processed in the target intelligent equipment to obtain the data information operation of each target data.
In this alternative embodiment, for each target data, determining a data security parameter of the target data according to data source information of the target data includes:
acquiring historical data transmission records of target intelligent equipment, for each target data, determining the historical data matching degree between the data source information of the target data and the historical data transmission records of the target intelligent equipment according to the data source information of the target data, and determining the data security parameters of the target data according to the historical data matching degree; the data security parameters comprise the data security degree of the target data, and if the matching degree between the historical data transmission record and the data source information of the target data is higher, the data security parameters of the target data represent that the data security degree of the target data is higher; if the matching degree between the historical data transmission record and the data source information of the target data is lower, the data security parameter of the target data indicates that the data security degree of the target data is lower.
In this optional embodiment, optionally, determining whether all data security parameters included in the data security parameter set meet a preset data security condition includes:
judging whether all data security degrees included in the data security parameter set are larger than or equal to a preset data security degree threshold value;
when all the data security degrees included in the data security parameter set are judged to be greater than or equal to a preset data security degree threshold value, determining that all the data security parameters included in the data security parameter set meet preset data security conditions;
when judging that the data security degree included in the data security parameter set is smaller than the preset data security degree threshold, determining that all data security parameter non-uniformity included in the data security parameter set meets the preset data security condition.
In this alternative embodiment, updating the target data to be processed according to a data security cause includes:
when the target data to be processed is encrypted data included in the data security reasons, a masking operation and/or an encryption operation is performed on the target data to be processed to update the target data.
It can be seen that, by implementing this alternative embodiment, before analyzing the target data, the target data can be determined first, the data source information of the target data is obtained, and then the data security parameter of each target data is determined to obtain a data security parameter set, whether all the data security parameters included in the data security set meet the preset data security conditions is determined, if yes, the operation of analyzing at least one target data in the target intelligent device to obtain the data information of each target data is performed, if not, the data security parameter that does not meet the data security conditions is determined to be the target data security parameter, the target data corresponding to each target data security parameter is determined to be the first target data, and the data security reason that each first target data does not meet the preset data security conditions is determined, the target data is updated according to the data safety reasons and the operation of analyzing at least one target data in the target intelligent device to obtain the data information of each target data is triggered and executed, the target data can be analyzed to obtain the data information of each target data when the data safety parameters of the target data meet the preset data safety conditions, the safety and the intelligence of the target intelligent device for processing the target data can be improved, the condition that the target intelligent device is damaged or lost due to the fact that the target intelligent device processes the data with lower safety degree can be prevented, the safety and the reliability of the data processing by using the target intelligent device can be ensured, the convenience of the target intelligent device or the corresponding user of the target intelligent device for processing the data can be improved, and further, the experience and the comfort level of the user for data processing by using the target intelligent equipment are improved.
In yet another alternative embodiment, the time sequence analysis result of each target data includes the acquisition time of the target data;
based on the time sequence data result set and the equipment storage information, performing classification operation on each target data included in the data set to be processed to obtain at least one data category group, wherein the classification operation comprises the following steps:
for each target data included in the time sequence data result set, determining the acquisition time interval duration between the acquisition time of the target data and each residual target data except the target data in the time sequence data result set according to the time sequence analysis result of the target data, and obtaining an acquisition time interval duration set;
judging whether a target interval duration exists in the acquisition time interval duration set, wherein the target interval duration is smaller than or equal to a preset interval duration threshold;
when judging that the target interval duration exists in the collection time interval duration set, determining the data type and the data quantity of target data corresponding to the target interval duration for each target interval duration, judging whether the data type and the data quantity of the target data corresponding to the target interval duration are the same, and determining all the target data corresponding to the target interval duration as a data class group when judging that the data type and the data quantity of the target data corresponding to the target interval duration are the same and the data quantity of the target data meet the preset equipment storage condition.
In this optional embodiment, optionally, the collection time interval duration set includes a collection time interval duration between the target data and each remaining target data; for example, if the target data is a and the remaining target data is B and C, the collection time interval duration set includes collection time interval durations of a and B and collection time interval durations of a and C; further, when the target data is B and the remaining target data is a and C, the collection time interval duration set includes collection time interval durations of B and C.
In this optional embodiment, optionally, each target interval duration corresponds to at least two target data; for each target interval duration, determining the data type and the data quantity of the target data corresponding to the target interval duration includes: and for each target interval duration, determining the data type of each target data corresponding to the target interval duration and the data quantity of each target data.
In this optional embodiment, optionally, determining whether the data types of the target data corresponding to the target interval duration are the same and the number of data meets a preset device storage condition includes:
Judging whether the data types of all the target data corresponding to the target interval duration are the same or not, and judging whether the sum of the data quantity of all the target data corresponding to the target interval duration is smaller than or equal to a preset data quantity threshold value or not;
when the data types of all the target data corresponding to the target interval duration are the same and the sum of the data amounts of all the target data corresponding to the target interval duration is less than or equal to a preset data amount threshold value, determining that the data types of the target data corresponding to the target interval duration are the same and the data amounts meet preset equipment storage conditions;
when the data types of all the target data corresponding to the target interval duration are identical and/or the sum of the data amounts of all the target data corresponding to the target interval duration is larger than a preset data amount threshold value, determining that the data types of the target data corresponding to the target interval duration are identical and the data amounts do not meet preset equipment storage conditions.
It can be seen that, implementing the embodiment capable of tracking can determine, according to the time sequence analysis result of each target data, the acquisition time interval duration between the acquisition time of the target data and each remaining target data except the target data in the time sequence data result set, obtain the acquisition time interval duration set, determine whether there is a target interval duration, if so, determine the data type and the data quantity of the target data corresponding to the target interval duration, and determine whether the corresponding condition is met, if so, determine all the target data corresponding to the target interval duration as a data class group, determine the data class group based on the acquisition time of the target data and the data type of the target data, and can improve the intelligence of determining the data class group, thereby being beneficial to improving the accuracy and reliability of determining the data class group, and subsequently being capable of executing compression processing operation on each data class group, improving the efficiency of executing compression processing operation so as to improve the efficiency and the convenience of processing data of the target intelligent device, and being capable of saving the data storage quantity of each data storage space of the target intelligent device, being beneficial to improving the data storage space utilization rate of the target intelligent device or improving the user experience of the intelligent device, thereby being beneficial to improving the user experience of the intelligent device.
In yet another alternative embodiment, for each compressed data set, performing an analysis operation on the compressed data set to obtain a compressed analysis result of the compressed data set, and determining a target storage space of the compressed data set based on the compressed analysis result of the compressed data set, includes:
for each compressed data set, determining a data tag of each target data included in the compressed data set, extracting semantic information of the data tag of each target data included in the compressed data set, and determining a target semantic tag of the compressed data set according to the semantic information of each target data included in the compressed data set;
for each compressed data set, determining the semantic matching degree between the target semantic tag of the compressed data set and the storage attribute tag of each storage space according to the target semantic tag of the compressed data set and the storage attribute tag of each storage space included in the target intelligent device, obtaining a semantic matching degree set, determining the highest semantic matching degree from the semantic matching degree set, and determining the storage space corresponding to the highest semantic matching degree as the target storage space of the compressed data set.
In this alternative embodiment, optionally, the target semantic tag of each compressed data set includes summary semantic information of the compressed data set, and the target semantic tag of each compressed data set can be used to represent summary information of the compressed data set. For example, when the target data included in one compressed data set is text data and the text data is the names of all students in one class, the target semantic tag of the compressed data set is the name of the student.
In this alternative embodiment, optionally, a storage attribute tag for each storage space is used to indicate what data the storage space is used to store. For example, when the storage attribute tag of one storage space is a landscape picture, the storage space is used for storing landscape picture data; when the storage attribute tag of one storage space is a character video, then that storage space is used to store character video data.
It can be seen that, implementing this alternative embodiment can determine semantic information according to the data tag of each data included in each compressed data group, and then determine the target semantic tag of the compressed data group, and according to the target semantic tag of each compressed data group and the storage attribute tag of each storage space in the target intelligent device, determine the semantic matching degree, and determine the storage space corresponding to the highest semantic matching degree as the target storage space of the compressed data group, can determine the target storage space based on the semantic information of the compressed data group and the storage attribute information of each storage space, can improve accuracy and reliability of determining the target storage space, and can determine the target storage space based on the semantic matching degree, can improve the intelligence of determining the target storage space, and can throw improvement of the matching degree between the target storage space and the target data, thereby being beneficial to improving the accuracy and reliability of executing corresponding operations on the target data according to the target storage space, avoiding multiple movements of the target data, and being beneficial to improving the efficiency and convenience of executing corresponding operations on the target data, and being beneficial to improving the comfort of users of the target intelligent device or the intelligent device to the target intelligent device.
In yet another alternative embodiment, for each first data storage space, determining at least one first data according to a result of the time sequence analysis of each target data included in the first data storage space includes:
according to the time sequence analysis result of each target data included in the first data storage space, determining the time sequence result similarity between the target data and each other target data except the target data in the first data storage space for each target data in the first data storage space, obtaining a time sequence result similarity set, and determining at least one first data according to the time sequence result similarity set, wherein the time sequence result similarity of the first data is smaller than or equal to a preset time sequence similarity threshold;
and determining a second data storage space corresponding to each first data according to the target data information of each first data, including:
for each first data, determining a data type parameter of the first data according to the data type information of the first data and the data abstract information of the first data;
for each first data, determining at least one target data storage space matched with the data category parameter of the first data in all idle data storage spaces of the target intelligent device according to the data category parameter of the first data, determining a data storage space matched with the time sequence information of the first data in all target data storage spaces according to the time sequence information of the first data, and determining the data storage space matched with the time sequence information of the first data as a second data storage space corresponding to the first data.
In this optional embodiment, optionally, determining at least one first data according to the set of similarity of time sequence results includes:
and performing low-to-high sequencing operation on all the time sequence result similarities included in the time sequence result similarity set to obtain a similarity sequence table, sequentially selecting a target number of target similarities from the similarity sequence table, and determining target data corresponding to each target similarity as first data. Optionally, the number of targets may be one or more, and embodiments of the present invention are not limited specifically.
In this alternative embodiment, it should be noted that the data type of the target data storage space capable of storing data matches the data type parameter of the first data; the timing information of the data that can be stored in the second data storage space matches the timing information of the first data.
In this optional embodiment, optionally, the data class parameter of the first data includes data type information of the first data and data summary information of the first data; further, the data summary information of the first data comprises one or more of data content and data quantity of the first data; the data class parameter of the first data comprises a data type of the first data.
In this optional embodiment, optionally, determining a data storage space matching the timing information of the first data from all the target data storage spaces includes: determining the space time sequence information of each target data storage space, determining the time sequence information similarity between the space time sequence information of each target data storage space and the time sequence information of the first data, and determining the target data storage space corresponding to the highest time sequence information similarity as the data storage space matched with the time sequence information of the first data.
Therefore, by implementing the alternative embodiment, the time sequence result similarity between each target data and each other target data can be determined according to the time sequence analysis result of each target data to obtain a time sequence result similarity set, and the first data can be determined according to the time sequence result similarity set, so that the intelligence of determining the first data can be improved, the accuracy and reliability of determining the first data can be improved, and the accuracy and reliability of moving the first data to the second data storage space corresponding to the first data can be improved; and determining the data category parameter of the first data according to the data type information and the data summary information of each first data, determining a target data storage space in all idle data storage spaces of the target intelligent device, and determining a second data storage space corresponding to the first data from all target data storage spaces according to the time sequence information of the first data, so that the accuracy and the intelligence of determining the second data storage space can be improved, the data can be moved according to the storage information of the target intelligent device and the time sequence information of each target data, the reasonable utilization of each data storage space of the target intelligent device can be realized, the data storage capacity of each data storage space of the target intelligent device can be saved, and the improvement of the storage space utilization rate of each data storage space of the target intelligent device and the improvement of the processing efficiency of each data stored in the target intelligent device are facilitated.
Examples
Referring to fig. 3, fig. 3 is a schematic structural diagram of a data processing apparatus based on time sequence information and storage information according to an embodiment of the present invention. As shown in fig. 3, the data processing apparatus based on the timing information and the storage information may include:
the analysis module 301 is configured to analyze at least one target data to be processed in the target intelligent device, so as to obtain data information of each target data, where the data information includes data type information, data storage information and data quantity information;
a determining module 302, configured to determine, for each target data, timing information of the target data;
an analysis module 301, configured to analyze the timing information of each target data to obtain a timing analysis result of each target data;
the summarizing module 303 is configured to summarize time sequence analysis results of all the target data to obtain a time sequence data result set;
the determining module 302 is further configured to determine a data storage condition in the target intelligent device, and determine device storage information of the target intelligent device according to the first storage information of all the data storage spaces; the data storage condition comprises first storage information of each data storage space included in the target intelligent device, wherein the first storage information comprises one or more of data types stored in each data storage space, data time sequence information stored in each data storage space and data quantity information stored in each data storage space;
A generating module 304, configured to generate a data set to be processed according to all the target data;
a classification module 305, configured to perform a classification operation on each target data included in the data set to be processed based on the time-series data result set and the device storage information, to obtain at least one data class group, where each data class group includes at least one target data;
the determining module 302 is further configured to perform an analysis operation on each compressed data set to obtain a compression analysis result of the compressed data set, and determine a target storage space of the compressed data set based on the compression analysis result of the compressed data set;
a processing module 306, configured to execute a preset target processing operation on the compressed data set according to the target storage space of the compressed data set; the preset target processing operation comprises a storage operation; the target storage space is the storage space in the target intelligent device.
As can be seen, implementing the apparatus described in fig. 3 can determine the time sequence information of each target data to be processed to obtain a time sequence data result set, determine the device storage information by determining the data storage condition of the target intelligent device, perform the classification operation on each target data based on the time sequence data result set and the device storage information to obtain at least one data class group, perform the compression processing operation on each data class group to obtain a corresponding compressed data group, analyze each compressed data group to obtain a compression analysis result, determine the target storage space of each compressed data group according to the compression analysis result, and further perform the preset target processing operation on the target data, which is beneficial to improving the efficiency and the intelligence of the data processing of the intelligent device, improving the effectiveness and the accuracy of the data processing of the intelligent device, releasing the data storage space of the intelligent device, and further improving the experience and comfort of using the intelligent device by the user.
In an alternative embodiment, as shown in fig. 4, the determining module 302 is further configured to determine second storage information of each data storage space in the target smart device;
the apparatus further comprises:
a judging module 307, configured to judge, for each data storage space, whether the second storage information of all the data storage spaces meets a preset space storage condition according to the second storage information of the data storage space;
the determining module 302 is further configured to determine, as the first data storage space, a data storage space that does not satisfy the preset space storage condition when the judging module 307 judges that the second storage information of all the data storage spaces does not satisfy the preset space storage condition;
the determining module 302 is further configured to determine, for each first data storage space, at least one first data according to a time sequence analysis result of each target data included in the first data storage space, and determine, according to target data information of each first data, a second data storage space corresponding to each first data; the target data information comprises data type information of the first data, time sequence information of the first data and data abstract information of the first data;
And the moving module 308 is configured to perform, for each first data, a preset data moving operation on the first data according to the second data storage space corresponding to the first data, so that the first data moves to the second data storage space corresponding to the first data.
Therefore, the device described in fig. 4 can determine the first data storage space in the target intelligent device by determining the second storage information of each data storage space in the target intelligent device, determine the first data according to the time sequence analysis result of each target data, further determine the second data storage space corresponding to each first data, and move each first data to the second data storage space corresponding to the first data, so that the data can be moved according to the storage information of the target intelligent device and the time sequence information of each target data, the reasonable utilization of each data storage space of the target intelligent device can be realized, the data storage amount of each data storage space of the target intelligent device can be saved, the storage space utilization rate of each data storage space of the target intelligent device can be improved, the intelligence and the accuracy of processing the data can be improved, and the efficiency of processing each data stored in the target intelligent device can be improved.
In another alternative embodiment, as shown in fig. 4, the determining module 302 is further configured to determine, when the judging module 307 judges that the second storage information of all the data storage spaces meets the preset space storage condition, one or more of maximum data capacity information of each data storage space, data storage type information of each data storage space, data storage timing information of each data storage space, and data amount information included in each data storage space of the storage attribute information packet;
the determining module 302 is further configured to determine, according to the storage attribute information of each data storage space, a storage attribute similarity between each data storage space and each remaining data storage space except the data storage space, so as to obtain a storage attribute similarity set;
the judging module 307 is further configured to judge whether a data storage space combination with a storage attribute similarity greater than or equal to a preset storage attribute similarity threshold exists in the storage attribute similarity set, where the data storage space combination includes at least two data storage spaces, and the storage attribute similarity between all the data storage spaces included in the data storage space combination is greater than or equal to the preset storage attribute similarity threshold;
The determining module 302 is further configured to determine, when the determining module 307 determines that there are data storage space combinations in the storage attribute similarity set, where the storage attribute similarity is greater than or equal to a preset storage attribute similarity threshold, for each data storage space combination, storage space combination information of the data storage space combination according to second storage information of each data storage space included in the data storage space combination;
the judging module 307 is further configured to judge whether the storage space combination information of the data storage space combination meets a preset storage space combination condition;
the apparatus further comprises:
a merging module 309, configured to, when the determining module 307 determines that the storage space combination information of the data storage space combination meets a preset storage space combination condition, perform a merging operation on all data storage spaces included in the data storage space combination, so as to merge all data included in all data storage spaces included in the data storage space combination; the storage space combination information of each data storage space combination includes used data storage space number information of each data storage space included in the data storage space combination and free data storage space number information of each data storage space.
It can be seen that, implementing the apparatus described in fig. 4 can determine storage attribute information of each data storage space when the second storage information of all the data storage spaces satisfies the preset space storage condition, determine storage attribute similarity between each data storage space and each remaining data storage space except for the data storage space to obtain a storage attribute similarity set, and determine whether a data storage space combination exists based on the storage attribute similarity set, if so, determine whether the storage space combination information of the data storage space combination satisfies the preset storage space combination condition, if so, perform merging operation on all the data storage spaces included in the data storage space combination, so as to merge all the data included in all the data storage spaces included in the data storage space combination, and can merge the data storage spaces with similar storage attributes and larger storage spaces in the target intelligent device, thereby realizing reasonable utilization of each data storage space of the target intelligent device, saving data of each data storage space of the target intelligent device, being beneficial to improving the utilization rate of each data storage space of the target intelligent device or improving the target intelligent device and the comfort of the user's intelligent device.
In yet another alternative embodiment, as shown in fig. 4, the determining module 302 is further configured to determine at least one target data to be processed in the target smart device before the analyzing module 301 analyzes the at least one target data to be processed in the target smart device to obtain data information of each target data;
the apparatus further comprises:
an acquisition module 310, configured to acquire data source information of each target data, where the data source information includes one or more of source address information of each target data, source device information of each target data, source user information of each target data, and source scene information of each target data;
the determining module 302 is further configured to determine, for each target data, a data security parameter of the target data according to data source information of the target data;
the generating module 304 is further configured to generate a data security parameter set according to the data security parameters of all the target data; the data security parameter set comprises data security parameters of all target data;
the judging module 307 is further configured to judge whether all data security parameters included in the data security parameter set meet a preset data security condition; when all the data security parameters included in the data security parameter set are judged to meet the preset data security conditions, triggering the analysis module 301 to execute the operation of analyzing at least one target data to be processed in the target intelligent device to obtain the data information of each target data;
The determining module 302 is further configured to determine, when the judging module 307 judges that all data security parameters included in the data security parameter set do not satisfy the preset data security conditions, the data security parameters that do not satisfy the preset data security conditions as target data security parameters, and determine target data corresponding to each target data security parameter as first target data;
the determining module 302 is further configured to determine, for each first target data, a data security reason that the data security parameter of the first target data does not meet a preset data security condition, update target data to be processed according to the data security reason, and trigger the analyzing module 301 to perform an operation of analyzing at least one target data to be processed in the target intelligent device to obtain data information of each target data.
It can be seen that the apparatus described in fig. 4 is implemented to determine the target data and acquire the data source information of the target data before analyzing the target data, and further determine the data security parameter of each target data to obtain a data security parameter set, determine whether all the data security parameters included in the data security set meet the preset data security conditions, if yes, perform the operation of analyzing at least one target data in the target intelligent device to obtain the data information of each target data, if not, determine the data security parameter that does not meet the data security conditions as the target data security parameter, determine the target data corresponding to each target data security parameter as the first target data, determine the data security reason that does not meet the preset data security conditions for each first target data, update the target data according to the data security reason, and trigger the operation of performing the analysis on at least one target data in the target intelligent device to obtain the data information of each target data, and analyze the target data when the data security parameters of the target data are met the preset data security conditions, and thus obtain the data information of each target data, and improve the safety of the target intelligent device, and prevent the intelligent device from damaging the target intelligent device due to the safety of the target intelligent device or the intelligent device being damaged by the user, and the safety of the intelligent device being more convenient to process the target data, and further, the experience and the comfort level of the user for data processing by using the target intelligent equipment are improved.
In yet another alternative embodiment, as shown in fig. 4, the time sequence analysis result of each target data includes the acquisition time of the target data;
the classifying module 305 performs a classifying operation on each target data included in the data set to be processed based on the time-series data result set and the device storage information, and the specific manner of obtaining at least one data category group includes:
for each target data included in the time sequence data result set, determining the acquisition time interval duration between the acquisition time of the target data and each residual target data except the target data in the time sequence data result set according to the time sequence analysis result of the target data, and obtaining an acquisition time interval duration set;
judging whether a target interval duration exists in the acquisition time interval duration set, wherein the target interval duration is smaller than or equal to a preset interval duration threshold;
when judging that the target interval duration exists in the collection time interval duration set, determining the data type and the data quantity of target data corresponding to the target interval duration for each target interval duration, judging whether the data type and the data quantity of the target data corresponding to the target interval duration are the same, and determining all the target data corresponding to the target interval duration as a data class group when judging that the data type and the data quantity of the target data corresponding to the target interval duration are the same and the data quantity of the target data meet the preset equipment storage condition.
As can be seen, implementing the apparatus described in fig. 4 can determine, according to the time sequence analysis result of each target data, the acquisition time interval duration between the acquisition time of the target data and each remaining target data except the target data in the time sequence data result set, obtain the acquisition time interval duration set, determine whether there is a target interval duration, if so, determine the data type and the data number of the target data corresponding to the target interval duration, and determine whether the corresponding condition is met, if so, determine all the target data corresponding to the target interval duration as one data category set, determine the data category set based on the acquisition time of the target data and the data type of the target data, thereby being capable of improving the intelligence of determining the data category set, being beneficial to improving the accuracy and reliability of determining the data category set, and subsequently being capable of executing a compression processing operation on each data category set, being capable of improving the efficiency of executing the compression processing operation so as to improve the efficiency and the convenience of processing data of the target intelligent device, and being capable of saving the data storage amount of each data storage space of the target intelligent device, being beneficial to improving the data storage space utilization rate of the target intelligent device or being beneficial to improving the user experience of the intelligent device.
In yet another alternative embodiment, as shown in fig. 4, the determining module 302 performs, for each compressed data set, an analysis operation on the compressed data set to obtain a compression analysis result of the compressed data set, and determines, based on the compression analysis result of the compressed data set, a specific manner of determining the target storage space of the compressed data set includes:
for each compressed data set, determining a data tag of each target data included in the compressed data set, extracting semantic information of the data tag of each target data included in the compressed data set, and determining a target semantic tag of the compressed data set according to the semantic information of each target data included in the compressed data set;
for each compressed data set, determining the semantic matching degree between the target semantic tag of the compressed data set and the storage attribute tag of each storage space according to the target semantic tag of the compressed data set and the storage attribute tag of each storage space included in the target intelligent device, obtaining a semantic matching degree set, determining the highest semantic matching degree from the semantic matching degree set, and determining the storage space corresponding to the highest semantic matching degree as the target storage space of the compressed data set.
It can be seen that the implementation of the apparatus described in fig. 4 can determine semantic information according to the data tag of each data included in each compressed data set, further determine the target semantic tag of the compressed data set, determine the semantic matching degree according to the target semantic tag of each compressed data set and the storage attribute tag of each storage space in the target intelligent device, determine the storage space corresponding to the highest semantic matching degree as the target storage space of the compressed data set, determine the target storage space based on the semantic information of the compressed data set and the storage attribute information of each storage space, determine the target storage space based on the semantic matching degree, and further determine the target storage space, and can improve the intelligence of determining the target storage space, and can throw and improve the matching degree between the target storage space and the target data, thereby being beneficial to improving the accuracy and reliability of executing corresponding operations on the target data according to the target storage space, avoiding multiple movements of the target data, and being beneficial to improving the efficiency and convenience of executing corresponding operations on the target data, and improving the comfort of the target intelligent device or the user experience of the target intelligent device to the target intelligent device.
In yet another alternative embodiment, as shown in fig. 4, the determining module 302 determines, for each first data storage space, a specific manner of at least one first data according to a time sequence analysis result of each target data included in the first data storage space, including:
according to the time sequence analysis result of each target data included in the first data storage space, determining the time sequence result similarity between the target data and each other target data except the target data in the first data storage space for each target data in the first data storage space, obtaining a time sequence result similarity set, and determining at least one first data according to the time sequence result similarity set, wherein the time sequence result similarity of the first data is smaller than or equal to a preset time sequence similarity threshold;
and, the determining module 302 determines, according to the target data information of each first data, a specific manner of the second data storage space corresponding to each first data, including:
for each first data, determining a data type parameter of the first data according to the data type information of the first data and the data abstract information of the first data;
For each first data, determining at least one target data storage space matched with the data category parameter of the first data in all idle data storage spaces of the target intelligent device according to the data category parameter of the first data, determining a data storage space matched with the time sequence information of the first data in all target data storage spaces according to the time sequence information of the first data, and determining the data storage space matched with the time sequence information of the first data as a second data storage space corresponding to the first data.
As can be seen, the device described in fig. 4 can determine the time sequence result similarity between each target data and each other target data according to the time sequence analysis result of each target data to obtain a time sequence result similarity set, and determine the first data according to the time sequence result similarity set, so that the intelligence of determining the first data can be improved, the accuracy and reliability of determining the first data can be improved, and further the accuracy and reliability of moving the first data to the second data storage space corresponding to the first data can be improved; and determining the data category parameter of the first data according to the data type information and the data summary information of each first data, determining a target data storage space in all idle data storage spaces of the target intelligent device, and determining a second data storage space corresponding to the first data from all target data storage spaces according to the time sequence information of the first data, so that the accuracy and the intelligence of determining the second data storage space can be improved, the data can be moved according to the storage information of the target intelligent device and the time sequence information of each target data, the reasonable utilization of each data storage space of the target intelligent device can be realized, the data storage capacity of each data storage space of the target intelligent device can be saved, and the improvement of the storage space utilization rate of each data storage space of the target intelligent device and the improvement of the processing efficiency of each data stored in the target intelligent device are facilitated.
Examples
Referring to fig. 5, fig. 5 is a schematic diagram of a data processing apparatus based on timing information and storage information according to an embodiment of the invention. As shown in fig. 5, the data processing apparatus based on the timing information and the storage information may include:
a memory 401 storing executable program codes;
a processor 402 coupled with the memory 401;
the processor 402 invokes executable program codes stored in the memory 401 to perform the steps in the data processing method based on the timing information and the storage information described in the first embodiment or the second embodiment of the present invention.
Examples
The embodiment of the invention discloses a computer storage medium which stores computer instructions for executing the steps in the data processing method based on time sequence information and storage information described in the first embodiment or the second embodiment of the invention when the computer instructions are called.
Examples
An embodiment of the present invention discloses a computer program product comprising a non-transitory computer readable storage medium storing a computer program, and the computer program is operable to cause a computer to perform the steps of the data processing method described in the first or second embodiment based on the timing information and the storage information.
The apparatus embodiments described above are merely illustrative, wherein the modules illustrated as separate components may or may not be physically separate, and the components shown as modules may or may not be physical, i.e., may be located in one place, or may be distributed over a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above detailed description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course by means of hardware. Based on such understanding, the foregoing technical solutions may be embodied essentially or in part in the form of a software product that may be stored in a computer-readable storage medium including Read-Only Memory (ROM), random-access Memory (Random Access Memory, RAM), programmable Read-Only Memory (Programmable Read-Only Memory, PROM), erasable programmable Read-Only Memory (Erasable Programmable Read Only Memory, EPROM), one-time programmable Read-Only Memory (OTPROM), electrically erasable programmable Read-Only Memory (EEPROM), compact disc Read-Only Memory (Compact Disc Read-Only Memory, CD-ROM) or other optical disc Memory, magnetic disc Memory, tape Memory, or any other medium that can be used for computer-readable carrying or storing data.
Finally, it should be noted that: the embodiment of the invention discloses a data processing method and device based on time sequence information and storage information, which is disclosed by the embodiment of the invention only and is only used for illustrating the technical scheme of the invention, but not limiting the technical scheme; although the invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art will understand that; the technical scheme recorded in the various embodiments can be modified or part of technical features in the technical scheme can be replaced equivalently; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.

Claims (10)

1. A data processing method based on timing information and stored information, the method comprising:
analyzing at least one target data to be processed in target intelligent equipment to obtain data information of each target data, wherein the data information comprises data type information, data storage information and data quantity information;
for each target data, determining time sequence information of the target data, analyzing the time sequence information of each target data to obtain a time sequence analysis result of each target data, and summarizing the time sequence analysis results of all the target data to obtain a time sequence data result set;
Determining the data storage condition in the target intelligent equipment, and determining the equipment storage information of the target intelligent equipment according to the first storage information of all the data storage spaces; the data storage condition comprises first storage information of each data storage space included in the target intelligent device; the first storage information comprises one or more of data types stored in each data storage space, data time sequence information stored in each data storage space and data quantity information stored in each data storage space;
generating a data set to be processed according to all the target data, and executing classification operation on each target data included in the data set to be processed based on the time sequence data result set and the equipment storage information to obtain at least one data category group, wherein each data category group at least comprises one target data;
for each data category group, executing preset compression processing operation on each target data included in the data category group to obtain a compressed data group corresponding to the data category group;
For each compressed data set, performing an analysis operation on the compressed data set to obtain a compression analysis result of the compressed data set, determining a target storage space of the compressed data set based on the compression analysis result of the compressed data set, and performing a preset target processing operation on the compressed data set according to the target storage space of the compressed data set; the preset target processing operation comprises a storage operation; and the target storage space is the storage space in the target intelligent equipment.
2. The method for processing data based on time series information and stored information according to claim 1, wherein the method further comprises:
determining second storage information of each data storage space in the target intelligent device; for each data storage space, judging whether the second storage information of all the data storage spaces meets the preset space storage condition according to the second storage information of the data storage space;
when the fact that second storage information of all the data storage spaces unevenly meets the preset space storage conditions is judged, determining the data storage spaces which do not meet the preset space storage conditions as first data storage spaces;
For each first data storage space, determining at least one first data according to a time sequence analysis result of each target data included in the first data storage space, and determining a second data storage space corresponding to each first data according to target data information of each first data; the target data information comprises data type information of the first data, time sequence information of the first data and data abstract information of the first data;
and for each first data, executing a preset data moving operation on the first data according to the second data storage space corresponding to the first data so as to move the first data to the second data storage space corresponding to the first data.
3. The method for processing data based on time series information and stored information according to claim 2, wherein the method further comprises:
when judging that the second storage information of all the data storage spaces meets the preset space storage condition, determining storage attribute information of each data storage space, wherein the storage attribute information comprises one or more of maximum data capacity information of each data storage space, data storage type information of each data storage space, data storage time sequence information of each data storage space and data quantity information contained in each data storage space;
Determining the storage attribute similarity between each data storage space and each remaining data storage space except the data storage space according to the storage attribute information of each data storage space, and obtaining a storage attribute similarity set;
judging whether a data storage space combination with storage attribute similarity greater than or equal to a preset storage attribute similarity threshold exists in the storage attribute similarity set, wherein the data storage space combination comprises at least two data storage spaces, and the storage attribute similarity between all the data storage spaces included in the data storage space combination is greater than or equal to the preset storage attribute similarity threshold;
when judging that the storage attribute similarity set has the data storage space combination with the storage attribute similarity greater than or equal to a preset storage attribute similarity threshold value, for each data storage space combination, determining storage space combination information of the data storage space combination according to second storage information of each data storage space included in the data storage space combination, judging whether the storage space combination information of the data storage space combination meets preset storage space combination conditions, and executing merging operation on all the data storage spaces included in the data storage space combination to merge all data included in all the data storage spaces included in the data storage space combination when judging that the storage space combination information of the data storage space combination meets the preset storage space combination conditions; the storage space combination information of each data storage space combination comprises the number of used data storage spaces of each data storage space and the number of free data storage spaces of each data storage space.
4. The method for processing data based on time sequence information and stored information according to claim 3, wherein before analyzing at least one target data to be processed in the target intelligent device to obtain the data information of each target data, the method further comprises:
determining at least one target data to be processed in target intelligent equipment, and acquiring data source information of each target data, wherein the data source information comprises one or more of source address information of each target data, source equipment information of each target data, source user information of each target data and source scene information of each target data;
for each target data, determining the data security parameters of the target data according to the data source information of the target data, and generating a data security parameter set according to the data security parameters of all the target data; the data security parameter set comprises data security parameters of all the target data;
judging whether all the data security parameters included in the data security parameter set meet preset data security conditions or not;
When all the data security parameters included in the data security parameter set are judged to meet the preset data security conditions, triggering and executing at least one target data to be processed in the analysis target intelligent device to obtain the operation of the data information of each target data;
when all the data security parameters included in the data security parameter set are judged to unevenly meet the preset data security conditions, determining the data security parameters which do not meet the preset data security conditions as target data security parameters, and determining target data corresponding to each target data security parameter as first target data;
for each first target data, determining a data safety reason that the data safety parameter of the first target data does not meet a preset data safety condition, updating the target data to be processed according to the data safety reason, and triggering and executing at least one target data to be processed in the analysis target intelligent device to obtain the data information of each target data.
5. The method for processing data based on time series information and stored information according to claim 4, wherein the time series analysis result of each target data includes the acquisition time of the target data;
The step of performing a classification operation on each target data included in the data set to be processed based on the time-series data result set and the device storage information to obtain at least one data category group, including:
for each target data included in the time sequence data result set, determining the acquisition time interval duration between the acquisition time of the target data and each residual target data except the target data in the time sequence data result set according to the time sequence analysis result of the target data, and obtaining an acquisition time interval duration set;
judging whether a target interval duration exists in the collection time interval duration set, wherein the target interval duration is smaller than or equal to a preset interval duration threshold;
when judging that the target interval duration exists in the collection time interval duration set, determining the data type and the data quantity of target data corresponding to the target interval duration for each target interval duration, judging whether the data type and the data quantity of the target data corresponding to the target interval duration are the same, and determining all the target data corresponding to the target interval duration as a data class group when judging that the data type and the data quantity of the target data corresponding to the target interval duration are the same and the data quantity of the target data corresponding to the target interval duration meet the preset equipment storage condition.
6. The method according to claim 5, wherein for each of the compressed data sets, performing an analysis operation on the compressed data set to obtain a compression analysis result of the compressed data set, and determining a target storage space of the compressed data set based on the compression analysis result of the compressed data set, comprises:
for each compressed data set, determining a data tag of each target data included in the compressed data set, extracting semantic information of the data tag of each target data included in the compressed data set, and determining a target semantic tag of the compressed data set according to the semantic information of each target data included in the compressed data set;
for each compressed data set, determining the semantic matching degree between the target semantic tag of the compressed data set and the storage attribute tag of each storage space according to the target semantic tag of the compressed data set and the storage attribute tag of each storage space included in the target intelligent device, obtaining a semantic matching degree set, determining the highest semantic matching degree from the semantic matching degree set, and determining the storage space corresponding to the highest semantic matching degree as the target storage space of the compressed data set.
7. The method according to claim 6, wherein for each of the first data storage spaces, the determining at least one first data based on a result of the time series analysis of each of the target data included in the first data storage space, comprises:
according to the time sequence analysis result of each target data included in the first data storage space, determining the time sequence result similarity between the target data and each other target data except the target data in the first data storage space for each target data in the first data storage space, obtaining a time sequence result similarity set, and determining at least one first data according to the time sequence result similarity set, wherein the time sequence result similarity of the first data is smaller than or equal to a preset time sequence similarity threshold;
and determining a second data storage space corresponding to each first data according to the target data information of each first data, including:
for each first data, determining a data category parameter of the first data according to the data type information of the first data and the data abstract information of the first data;
And for each first data, determining at least one target data storage space matched with the data category parameter of the first data in all idle data storage spaces of the target intelligent equipment according to the data category parameter of the first data, determining a data storage space matched with the time sequence information of the first data in all target data storage spaces according to the time sequence information of the first data, and determining the data storage space matched with the time sequence information of the first data as a second data storage space corresponding to the first data.
8. A data processing apparatus based on timing information and stored information, the apparatus comprising:
the analysis module is used for analyzing at least one target data to be processed in the target intelligent equipment to obtain data information of each target data, wherein the data information comprises data type information, data storage information and data quantity information;
a determining module, configured to determine, for each of the target data, timing information of the target data;
the analysis module is further used for analyzing the time sequence information of each target data to obtain a time sequence analysis result of each target data;
The summarizing module is used for summarizing time sequence analysis results of all the target data to obtain a time sequence data result set;
the determining module is further used for determining the data storage condition in the target intelligent device and determining the device storage information of the target intelligent device according to the first storage information of all the data storage spaces; the data storage condition comprises first storage information of each data storage space included in the target intelligent device, wherein the first storage information comprises one or more of data types stored in each data storage space, data time sequence information stored in each data storage space and data quantity information stored in each data storage space;
the generation module is used for generating a data set to be processed according to all the target data;
the classification module is used for performing classification operation on each target data included in the data set to be processed based on the time sequence data result set and the equipment storage information to obtain at least one data category group, wherein each data category group at least comprises one target data;
The determining module is further configured to perform an analysis operation on each compressed data set to obtain a compression analysis result of the compressed data set, and determine a target storage space of the compressed data set based on the compression analysis result of the compressed data set;
the processing module is used for executing preset target processing operation on the compressed data set according to the target storage space of the compressed data set; the preset target processing operation comprises a storage operation; and the target storage space is the storage space in the target intelligent equipment.
9. A data processing apparatus based on timing information and stored information, the apparatus comprising:
a memory storing executable program code;
a processor coupled to the memory;
the processor invokes the executable program code stored in the memory to perform the data processing method based on timing information and stored information as claimed in any one of claims 1 to 7.
10. A computer storage medium storing computer instructions which, when invoked, are adapted to perform the data processing method based on timing information and stored information according to any one of claims 1-7.
CN202310239168.7A 2023-03-13 2023-03-13 Data processing method and device based on time sequence information and storage information Active CN116225338B (en)

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