CN111723226A - Information management method based on big data and Internet and artificial intelligence cloud server - Google Patents

Information management method based on big data and Internet and artificial intelligence cloud server Download PDF

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CN111723226A
CN111723226A CN202010539130.8A CN202010539130A CN111723226A CN 111723226 A CN111723226 A CN 111723226A CN 202010539130 A CN202010539130 A CN 202010539130A CN 111723226 A CN111723226 A CN 111723226A
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CN111723226B (en
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黄雨勤
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Guangzhou Jingzhuan Duoying Investment Consultation Co ltd
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    • G06F16/532—Query formulation, e.g. graphical querying
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
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Abstract

The disclosed embodiment provides an information management method based on big data and internet and an artificial intelligence cloud server, which classifies image classification labels under each artificial intelligence recognition result based on preset big data collection classification, thereby taking the difference of different big data collection classifications into consideration, improving the condition of multi-classification conflict in the classification process, in addition, by combining the user search behavior information and the historical classification information of the target graph search object, respectively performing big data analysis on each graph unit library in the target graph search object based on each corresponding graph classification grade bookmark of the artificial intelligence recognition result after comparing the information management index sequences of the two graph classification grade bookmarks, being convenient for the historical classification condition based on the previous big data analysis, thereby performing more accurate and rapid big data processing on the graph unit library based on some key graph classification grade bookmarks, the efficiency of big data analysis is improved, reduce buffering time.

Description

Information management method based on big data and Internet and artificial intelligence cloud server
Technical Field
The disclosure relates to the technical field of big data and the Internet, in particular to an information management method based on big data and the Internet and an artificial intelligence cloud server.
Background
With the development of big data and internet, a user can perform label classification search (for example, label search for each classifiable object in each video frame) on a graphic search object (for example, a video frame sequence with a large amount of information to be extracted) through the internet, and in the process, big data analysis can be performed on each graphic unit library in the graphic search object in combination with a related graphic classification hierarchical bookmark (for example, a big data analysis algorithm or strategy in combination with a related graphic classification hierarchical bookmark, and the like).
Generally speaking, a plurality of different graph classification grading bookmarks exist in an artificial intelligence recognition result, and the inventor finds, through creative research, that in a conventional scheme, differences of different big data collection classifications are not generally considered, so that a situation that multi-classification conflicts occur in a classification process is easily caused, and in the classification process, a user may need to perform more accurate and rapid big data processing on an image unit library based on a history classification situation during previous big data analysis and based on some key graph classification grading bookmarks based on the history classification situation, but the conventional scheme cannot meet the requirement, and further, for the user, a buffering time per time may be long in an actual big data classification process.
Disclosure of Invention
In order to overcome at least the above-mentioned deficiencies in the prior art, the present disclosure provides an information management method based on big data and internet and an artificial intelligence cloud server, which classifies image classification tags under each artificial intelligence recognition result based on a predetermined big data collection classification, thereby improving the situation of multi-classification conflict during classification in consideration of the difference between different big data collection classifications, and further, by combining user search behavior information and history classification information of a target graph search object, respectively performing big data analysis on each graph cell library in the target graph search object based on each corresponding graph classification bookmark of the artificial intelligence recognition result after comparing information management index sequences of the two graph classification hierarchical bookmarks, thereby facilitating the history classification situation based on the previous big data analysis, therefore, the image unit library is subjected to more accurate and rapid big data processing based on some key graph classification and grading bookmarks, the big data analysis efficiency is improved, and the buffering time is shortened.
In a first aspect, the present disclosure provides an information management method based on big data and the internet, which is applied to an artificial intelligence cloud server, where the artificial intelligence cloud server is in communication connection with a plurality of internet access devices, and the method includes:
acquiring image classification labels of a target graph search object under the artificial intelligence recognition result of each artificial intelligence recognition model from each Internet access device, classifying the image classification labels under each artificial intelligence recognition result according to preset big data collection classification, and respectively generating an image classification label sequence of each big data collection classification;
determining a target graphic classification grade bookmark associated with each artificial intelligence identification result according to user search behavior information of the target graphic search object, and respectively determining grade label information of a first indexable grade bookmark of the target graphic classification grade bookmark in an image classification label sequence of corresponding big data collection classification aiming at the target graphic classification grade bookmark associated with each artificial intelligence identification result to obtain a first information management index sequence of the target graphic classification grade bookmark, wherein the target graphic classification grade bookmark is a graphic classification grade bookmark matched with the user search behavior information of the target graphic search object in advance;
determining the key graph classification grade bookmark associated with each artificial intelligence identification result according to the historical classification grade information of the target graph search object, respectively acquiring a second indexable classification bookmark of the key graph classification grade bookmark aiming at the key graph classification grade bookmark associated with each artificial intelligence identification result, and determining the hierarchical label information of the second indexable classification bookmark in the image classification label sequence of the corresponding big data collection classification to obtain a second information management index sequence of the key graph classification hierarchical bookmark, the key graphic classification grading bookmark is a graphic classification grading bookmark with the classification frequency greater than a set frequency threshold value in the historical classification grading information of the target graphic search object, the classification frequency is used for representing the search classification times of the graph classification grading bookmark in unit time;
and respectively carrying out big data analysis on each graphic unit library in the target graphic search object based on each corresponding graphic classification grading bookmark of the artificial intelligence recognition result according to the matching relation between the first information management index sequence and the second information management index sequence.
In a possible implementation manner of the first aspect, the step of classifying the image classification labels under each artificial intelligence recognition result according to a predetermined big data collection classification, and generating an image classification label sequence of each big data collection classification respectively includes:
acquiring a classification target corresponding to each preset big data collection classification, forming a classification target sequence of each preset big data collection classification, and acquiring associated classification target information of each target classification target of each artificial intelligence identification result and a classification target of the classification target sequence;
calculating the density of key classification targets of each type of target big data collection classification according to the associated classification target information of the target classification targets and the classification targets of the classification target sequence, and selecting the classification targets from the classification target sequence according to the density of the key classification targets of each type of target big data collection classification to obtain the arrangement distribution of the initial classification targets;
if the total classification target distribution density of the initial classification target arrangement distribution is greater than the maximum total classification target distribution density required by the total classification target distribution density, dispersing a first key classification target in the initial classification target arrangement distribution to a first distribution density, and aggregating a second key classification target in the initial classification target arrangement distribution to the first distribution density, wherein the second key classification target is a key classification target of which the label density of the label classification where the key classification target is located is less than a set degree, and the first key classification target is a key classification target of which the label density of the label classification where the key classification target is located is not less than the set degree;
calculating the total classification target distribution density of the initial classification target arrangement distribution after the updating;
if the total classification target distribution density of the initial classification target arrangement distribution after the updating is greater than the maximum total classification target distribution density, performing the above processing on the initial classification target arrangement distribution after the updating again;
if the total classification target distribution density of the initial classification target arrangement distribution after the updating is less than or equal to the maximum total classification target distribution density, taking the initial classification target arrangement distribution before the updating as a first updating arrangement distribution, and sorting the target big data collection classifications according to the sequence from the low priority to the high priority of the big data collection classifications to obtain a target big data collection classification sequence;
and classifying the image classification labels under each artificial intelligence recognition result according to the target big data collection classification sequence, and respectively generating an image classification label sequence of each big data collection classification.
In a possible implementation manner of the first aspect, the user search behavior information includes classification scene type information, and the step of determining the target graphic classification hierarchical bookmark associated with each artificial intelligence recognition result according to the user search behavior information of the target graphic search object includes:
and obtaining the classification scene type information of the target graph search object, and obtaining the target graph classification grade bookmark associated with each artificial intelligence identification result according to the classification scene type information and the corresponding relation between each preset classification scene type information and the target graph classification grade bookmark in each label grade.
In a possible implementation manner of the first aspect, the step of obtaining a first information management index sequence of the target graph classification hierarchical bookmark by determining, for the target graph classification hierarchical bookmark associated with each artificial intelligence recognition result, hierarchical tag information of a first indexable classification bookmark of the target graph classification hierarchical bookmark in an image classification tag sequence of a corresponding big data collection classification, respectively, includes:
aiming at the target graphic classification grading bookmark associated with each artificial intelligence recognition result, respectively acquiring an index running script matched with the target graphic classification grading bookmark, and acquiring a label classification object corresponding to the index running script when the index running script continuously indexes and searches for an object entity corresponding to one label classification object in the artificial intelligence recognition result in a preset time period as a target label classification object;
judging whether the classification index searching features of the target label classification object are matched with the classification index searching features of the index nodes of a preset information management index unit or not, if the classification index searching features are not matched, adjusting the classification index searching features of the target label classification object to the label classification object matched with the classification index searching features of the index nodes of the information management index unit, and inputting the label classification object to the information management index unit;
calculating an input label classification object by using the information management index unit, acquiring hierarchical label information corresponding to the input label classification object, expanding each label marking information of the target graph classification hierarchical bookmark in the target label classification object, and acquiring label expansion information of each label marking information in the target label classification object;
determining a hierarchical label with the frequency degree of label labeling information being greater than a preset frequency degree in hierarchical label information corresponding to the input label classification object as a first indexable classification bookmark, and converting a label feature vector of each label labeling information in the input label classification object to obtain label extension information of each label labeling information in the input label classification object;
determining a first tag extension information sequence of the whole tag classification object according to tag extension information of each tag labeling information in the target tag classification object, and determining a second tag extension information sequence of the first indexable classification bookmark according to tag extension information of each tag labeling information in the first indexable classification bookmark;
and determining a label extension information sequence of the first indexable classification bookmark according to the first label extension information sequence, the second label extension information sequence and a preset proportion, and determining the hierarchical label information of the first indexable classification bookmark of the target graphic classification hierarchical bookmark in the corresponding image classification label sequence of the big data collection classification according to the label extension information of each label marking information in the target label classification object and the label extension information sequence to obtain a first information management index sequence of the target graphic classification hierarchical bookmark.
In a possible implementation manner of the first aspect, the step of determining, according to tag extension information of each tag label information in the target tag classification object and the tag extension information sequence, hierarchical tag information of a first indexable classification bookmark of the target graphic classification hierarchical bookmark in an image classification tag sequence of a corresponding big data collection classification, to obtain a first information management index sequence of the target graphic classification hierarchical bookmark includes:
determining tag extension information of each tag marking information in the target tag classification object and a matching extension information node of the tag extension information sequence, acquiring a first marking item node of each tag marking information in the target tag classification object according to the matching extension information node, and acquiring a marking item node of each tag marking information in the target tag classification object according to the first marking item node of each tag marking information in the target tag classification object and the hierarchical tag information;
or, calculating the tag extension information of each tag label information in the target tag classification object and the matching extension information node of the tag extension information sequence to obtain the first label item node of each tag label information in the target tag classification object, and calculating a first labeling item node of each label labeling information in the target label classification object according to a preset coverage area to obtain a second labeling item node of each label labeling information in the target label classification object, wherein the node coverage difference between the second annotation item node and the first annotation item node is less than the preset coverage interval, acquiring a second labeling item node of each label labeling information in the target label classification object according to the second labeling item node of each label labeling information in the target label classification object and the hierarchical label information;
and determining to obtain hierarchical label information of the first indexable classification bookmark in the image classification label sequence of the corresponding big data collection classification according to the labeling item node of each label labeling information in the target label classification object so as to obtain a first information management index sequence of the target graphic classification hierarchical bookmark.
In a possible implementation manner of the first aspect, the step of determining, according to the historical hierarchical classification information of the target graph search object, a key graph classification hierarchical bookmark associated with each artificial intelligence recognition result includes:
obtaining historical hierarchical classification information of the target graph search object, wherein the historical hierarchical classification information comprises a plurality of historical classified updating change information respectively corresponding to a plurality of graph classified hierarchical bookmarks;
when determining that a plurality of historical classification updating change information corresponding to any one graph classification grading bookmark meets a preset classification dynamic condition, determining an initial classification area to be determined of a first preset classification dynamic label range matched with the preset classification dynamic condition according to the historical classification updating change information of the graph classification grading bookmark and the range content of the preset classification dynamic label range, wherein the preset classification dynamic condition comprises the following steps: presetting the range content of the classification dynamic label range to be larger than the range content of the set range;
updating change information, range contents of the preset classification dynamic label range, an initial pending classification area of the first preset classification dynamic label range and the density of the preset classification dynamic label range according to historical classification of the graph classification grading bookmark, and determining that a plurality of preset classification dynamic label ranges matched with the preset classification dynamic conditions correspond to the initial pending classification area of the graph classification grading bookmark;
if the position of a grading label corresponding to the graph classification grading bookmark in the graph classification grading bookmark is matched with the initial undetermined classification area of a classification grading change interval, and if the grading label is the first grading label of the classification grading change interval, obtaining the graph classification grading bookmark matched with the previous preset classification dynamic label range adjacent to the classification grading change interval as a screened graph classification grading bookmark, and identifying one graph classification grading bookmark without the screened graph classification grading bookmark in the grading label as a target graph classification grading bookmark matched with the classification grading change interval;
if the hierarchical label is not the first hierarchical label of the classification hierarchical change interval, acquiring a target graphic classification hierarchical bookmark matched with the classification hierarchical change interval, identifying the target graphic classification hierarchical bookmark in the hierarchical label, and identifying at least one key object to be classified of the target graphic classification hierarchical bookmark, wherein the graphic classification hierarchical bookmark corresponds to a plurality of preset classification dynamic label ranges;
in the preset classification dynamic label range, calculating a classification level difference between any two adjacent classification labels of at least one key undetermined classification object of the target graph classification grade bookmark in the preset classification dynamic label range and a dynamic feature vector of the at least one key undetermined classification object of the target graph classification grade bookmark in the preset classification dynamic label range according to the classification scene information of the at least one key undetermined classification object of the target graph classification grade bookmark in the plurality of classification labels;
counting the classification times of the preset classification dynamic label range, determining the average classification frequency and the classification frequency variance of the target graphic classification hierarchical bookmark in the preset classification dynamic label range according to the classification level difference and the dynamic feature vector, and calculating the key feature parameter of the target graphic classification hierarchical bookmark in the preset classification dynamic label range according to the average classification frequency and the classification frequency variance;
and calculating the key evaluation degree of each graph classification grading bookmark according to the key characteristic parameters of each graph classification grading bookmark in the range of the matched preset classification dynamic labels, and determining the graph classification grading bookmarks with the key evaluation degrees larger than the set scores as the key graph classification grading bookmarks.
In a possible implementation manner of the first aspect, the step of performing big data analysis on each graphic unit library in the target graphic search object based on each corresponding graphic classification hierarchical bookmark of the artificial intelligence recognition result according to a matching relationship between the first information management index sequence and the second information management index sequence includes:
matching the information management index sequence of each target graphic classification grading bookmark in the first information management index sequence with the information management index sequence of each matched key graphic classification grading bookmark in the second information management index sequence to obtain a plurality of matching degrees, wherein each matched key graphic classification grading bookmark in the second information management index sequence is matched with the arrangement sequence of the corresponding target graphic classification grading bookmark in the respective information management index sequence, and the matching degree is determined according to the contact ratio between the information management index sequence of the target graphic classification grading bookmark and the information management index sequence of the matched key graphic classification grading bookmark;
and respectively carrying out big data analysis on each graphic unit library in the target graphic search object according to the matching degrees and based on each corresponding graphic classification grading bookmark of the artificial intelligence recognition result.
In a possible implementation manner of the first aspect, the step of performing big data analysis on each graphic unit library in the target graphic search object according to the plurality of matching degrees based on each corresponding graphic classification hierarchical bookmark of the artificial intelligence recognition result includes:
when the matching degree between the information management index sequence of any one target graphic classification grading bookmark and the information management index sequence of the matched key graphic classification grading bookmark is greater than the set matching degree, taking the target graphic classification grading bookmark and the key graphic classification grading bookmark as a big data analysis combined object;
when the matching degree between the information management index sequence of any one target graphic classification grading bookmark and the information management index sequence of the matched key graphic classification grading bookmark is not more than the set matching degree, the target graphic classification grading bookmark and the key graphic classification grading bookmark are independently used as a big data analysis independent object;
in the process of big data analysis of each graphic unit library in the target graphic search object, when the graphic classification grading bookmark corresponding to the graphic unit library exists in the big data analysis combined object, big data analysis of the graphic unit library is synchronously completed according to the big data analysis combined object, and when the graphic classification grading bookmark corresponding to the graphic unit library exists in the big data analysis independent object, big data analysis of the graphic unit library is completed according to the big data analysis independent object.
In a second aspect, an embodiment of the present disclosure further provides an information management apparatus based on big data and the internet, which is applied to an artificial intelligence cloud server, where the artificial intelligence cloud server is in communication connection with a plurality of internet access devices, and the apparatus includes:
the classification module is used for acquiring image classification labels of the target graph search object under the artificial intelligence recognition result of each artificial intelligence recognition model from each Internet access device, classifying the image classification labels under each artificial intelligence recognition result according to preset big data collection classification, and respectively generating an image classification label sequence of each big data collection classification;
a first determining module, configured to determine, according to user search behavior information of the target graphic search object, a target graphic classification hierarchical bookmark associated with each artificial intelligence recognition result, and for the target graphic classification hierarchical bookmark associated with each artificial intelligence recognition result, respectively determine hierarchical tag information of a first indexable classification bookmark of the target graphic classification hierarchical bookmark in a corresponding image classification tag sequence of big data collection classification, to obtain a first information management index sequence of the target graphic classification hierarchical bookmark, where the target graphic classification hierarchical bookmark is a graphic classification hierarchical bookmark that is pre-matched with the user search behavior information of the target graphic search object;
a second determining module, configured to determine, according to the historical hierarchical classification information of the target graph search object, a key graph classification hierarchical bookmark associated with each artificial intelligence recognition result, and obtain, for the key graph classification hierarchical bookmark associated with each artificial intelligence recognition result, a second indexable classification bookmark of the key graph classification hierarchical bookmark respectively, and determining the hierarchical label information of the second indexable classification bookmark in the image classification label sequence of the corresponding big data collection classification to obtain a second information management index sequence of the key graph classification hierarchical bookmark, the key graphic classification grading bookmark is a graphic classification grading bookmark with the classification frequency greater than a set frequency threshold value in the historical classification grading information of the target graphic search object, the classification frequency is used for representing the search classification times of the graph classification grading bookmark in unit time;
and the big data analysis module is used for respectively carrying out big data analysis on each graphic unit library in the target graphic search object based on each corresponding graphic classification hierarchical bookmark of the artificial intelligence recognition result according to the matching relation between the first information management index sequence and the second information management index sequence.
In a third aspect, an embodiment of the present disclosure further provides an information management system based on big data and the internet, where the information management system based on big data and the internet includes an artificial intelligence cloud server and a plurality of internet access devices in communication connection with the artificial intelligence cloud server, and the method includes:
the Internet access equipment is used for sending an image classification label of a target graphic search object under the artificial intelligence recognition result of each artificial intelligence recognition model to the artificial intelligence cloud server;
the artificial intelligence cloud server is used for acquiring image classification labels of target graph search objects under the artificial intelligence recognition results of each artificial intelligence recognition model from each Internet access device, classifying the image classification labels under the artificial intelligence recognition results according to preset big data collection classification, and respectively generating an image classification label sequence of each big data collection classification;
the artificial intelligence cloud server is used for determining a target graphic classification grading bookmark associated with each artificial intelligence identification result according to user search behavior information of the target graphic search object, respectively determining grading label information of a first indexable classification bookmark of the target graphic classification grading bookmark in a corresponding big data collection and classification image classification label sequence aiming at the target graphic classification grading bookmark associated with each artificial intelligence identification result, and obtaining a first information management index sequence of the target graphic classification grading bookmark, wherein the target graphic classification grading bookmark is a graphic classification grading bookmark matched with the user search behavior information of the target graphic search object in advance;
the artificial intelligence cloud server is used for determining the key graph classification and classification bookmarks associated with the artificial intelligence recognition results according to the historical classification and classification information of the target graph search object, respectively acquiring second indexable classification bookmarks of the key graph classification and classification bookmarks for the key graph classification and classification bookmarks associated with the artificial intelligence recognition results, and determining the hierarchical label information of the second indexable classification bookmark in the image classification label sequence of the corresponding big data collection classification to obtain a second information management index sequence of the key graph classification hierarchical bookmark, the key graphic classification grading bookmark is a graphic classification grading bookmark with the classification frequency greater than a set frequency threshold value in the historical classification grading information of the target graphic search object, the classification frequency is used for representing the search classification times of the graph classification grading bookmark in unit time;
and the artificial intelligence cloud server is used for respectively carrying out big data analysis on each graphic unit library in the target graphic search object based on each corresponding graphic classification hierarchical bookmark of the artificial intelligence recognition result according to the matching relation between the first information management index sequence and the second information management index sequence.
In a fourth aspect, an embodiment of the present disclosure further provides an artificial intelligence cloud server, where the artificial intelligence cloud server includes a processor, a machine-readable storage medium, and a network interface, where the machine-readable storage medium, the network interface, and the processor are connected through a bus system, the network interface is configured to be communicatively connected to at least one internet access device, the machine-readable storage medium is configured to store a program, an instruction, or a code, and the processor is configured to execute the program, the instruction, or the code in the machine-readable storage medium, so as to execute the big data and internet based information management method in any one of the possible designs of the first aspect or the first aspect.
In a fifth aspect, an embodiment of the present disclosure provides a computer-readable storage medium, where instructions are stored, and when executed, cause a computer to perform the big data and internet based information management method in the first aspect or any one of the possible designs of the first aspect.
Based on any one of the above aspects, the present disclosure classifies the image classification labels under each artificial intelligence recognition result based on the predetermined big data collection classification, thereby taking into account the difference of different big data collection classifications, improving the situation of multi-classification conflict during the classification, and moreover, by combining the user search behavior information and the historical hierarchical classification information of the target graph search object, after comparing the information management index sequences of the two graph classification hierarchical bookmarks, respectively performing big data analysis on each graph unit library in the target graph search object based on each corresponding graph classification hierarchical bookmark of the artificial intelligence recognition result, thereby facilitating the historical classification situation based on the previous big data analysis, and further performing more accurate and rapid big data processing on the image unit libraries based on some key graph classification hierarchical bookmarks, and improving the big data analysis efficiency, reducing the buffering time.
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To more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present disclosure and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings may be obtained from the drawings without inventive effort.
Fig. 1 is a schematic view of an application scenario of an information management system based on big data and the internet according to an embodiment of the present disclosure;
fig. 2 is a schematic flowchart of an information management method based on big data and the internet according to an embodiment of the present disclosure;
FIG. 3 is a functional module diagram of an information management device based on big data and Internet according to an embodiment of the disclosure;
fig. 4 is a block diagram illustrating a structure of an artificial intelligence cloud server for implementing the above-mentioned big data and internet-based information management method according to an embodiment of the present disclosure.
Detailed Description
The present disclosure is described in detail below with reference to the drawings, and the specific operation methods in the method embodiments can also be applied to the device embodiments or the system embodiments.
Fig. 1 is an interaction diagram of an information management system 10 based on big data and internet according to an embodiment of the present disclosure. The big data and internet based information management system 10 may include an artificial intelligence cloud server 100 and an internet access device 200 communicatively connected to the internet of things cloud artificial intelligence cloud server 100. The big data and internet based information management system 10 shown in fig. 1 is only one possible example, and in other possible embodiments, the big data and internet based information management system 10 may also include only a portion of the components shown in fig. 1 or may also include other components.
In this embodiment, the internet access device 200 may comprise a mobile device, a tablet computer, a laptop computer, etc., or any combination thereof. In some embodiments, the mobile device may include a smart home device, a wearable device, a smart mobile device, a virtual reality device, an augmented reality device, or the like, or any combination thereof. In some embodiments, the smart home devices may include control devices of smart electrical devices, smart monitoring devices, smart televisions, smart cameras, and the like, or any combination thereof. In some embodiments, the wearable device may include a smart bracelet, a smart lace, smart glass, a smart helmet, a smart watch, a smart garment, a smart backpack, a smart accessory, or the like, or any combination thereof. In some embodiments, the smart mobile device may include a smartphone, a personal digital assistant, a gaming device, and the like, or any combination thereof. In some embodiments, the virtual reality device and/or the augmented reality device may include a virtual reality helmet, virtual reality glass, a virtual reality patch, an augmented reality helmet, augmented reality glass, an augmented reality patch, or the like, or any combination thereof. For example, the virtual reality device and/or augmented reality device may include various virtual reality products and the like.
In this embodiment, the internet of things cloud artificial intelligence cloud server 100 and the internet access device 200 in the big data and internet based information management system 10 may execute the network security protection method of the internet of things mobile base station described in the following method embodiment in a matching manner, and the detailed description of the method embodiment below may be referred to in the specific steps executed by the artificial intelligence cloud server 100 and the internet access device 200.
In this embodiment, the information management system 10 based on big data and internet can be implemented in various application scenarios, such as a blockchain application scenario, an intelligent home application scenario, and an intelligent control application scenario.
In order to solve the technical problem in the foregoing background, fig. 2 is a schematic flowchart of an information management method based on big data and the internet according to an embodiment of the present disclosure, where the information management method based on big data and the internet according to the embodiment of the present disclosure may be executed by the artificial intelligence cloud server 100 shown in fig. 1, and it should be further understood that, in various embodiments of the present disclosure, the size of the sequence number of each process does not mean the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiment of the present disclosure. The information management method based on big data and internet will be described in detail below.
Step S110, obtaining the image classification label of the target graph search object under the artificial intelligence recognition result of each artificial intelligence recognition model from each internet access device 200, classifying the image classification labels under each artificial intelligence recognition result according to the predetermined big data collection classification, and respectively generating the image classification label sequence of each big data collection classification.
Step S120, determining a target graphic classification grade bookmark associated with each artificial intelligence identification result according to user search behavior information of a target graphic search object, and respectively determining grade label information of a first indexable grade bookmark of the target graphic classification grade bookmark in an image classification label sequence of corresponding big data collection classification aiming at the target graphic classification grade bookmark associated with each artificial intelligence identification result to obtain a first information management index sequence of the target graphic classification grade bookmark.
Step S130, determining key graph classification and classification bookmarks associated with each artificial intelligence recognition result according to historical classification and classification information of the target graph search object, respectively obtaining second indexable classification bookmarks of the key graph classification and classification bookmarks aiming at the key graph classification and classification bookmarks associated with each artificial intelligence recognition result, and determining classification label information of the second indexable classification bookmarks in the corresponding image classification label sequence of big data collection classification, so as to obtain a second information management index sequence of the key graph classification and classification bookmarks.
Step S140, according to the matching relationship between the first information management index sequence and the second information management index sequence, respectively performing big data analysis on each graphic unit library in the target graphic search object based on each corresponding graphic classification hierarchical bookmark of the artificial intelligence recognition result.
In this embodiment, the target graphic classification hierarchical bookmark is a graphic classification hierarchical bookmark that can be matched with user search behavior information of the target graphic search object in advance, and in detail, for different target graphic search objects (for example, a scan photographing type graphic search object, an image uploading type graphic search object, and the like), different corresponding graphic classification hierarchical bookmarks can be preset according to different service use requirements of the respective objects. For example, a graphical category rating bookmark may include a category label and a rating label to which the category label is located.
In this embodiment, the key graphic classification hierarchical bookmark may be a graphic classification hierarchical bookmark whose classification frequency in the history hierarchical classification information of the target graphic search object is greater than a set frequency threshold, and the classification frequency may be used to represent the number of search classifications of the graphic classification hierarchical bookmark in a unit time. The service usage requirement may be determined according to actual requirements, and may include information retrieval, information analysis, information filling, and the like, for example, and is not limited in detail herein.
In this embodiment, the artificial intelligence recognition model may be obtained by training samples of various pattern search objects in advance, and the artificial intelligence recognition model that recognizes different types of pattern objects may be trained, so that the image classification label under the artificial intelligence recognition result of the target pattern search object may be recognized.
Based on the above steps, the present embodiment classifies the image classification tags under each artificial intelligence recognition result based on the predetermined big data collection classification, thereby taking into account the difference of different big data collection classifications, improving the situation of multi-classification conflict during the classification, and furthermore, by combining the user search behavior information and the historical classification information of the target graph search object, and after comparing the information management index sequences of the two graph classification bookmarks, respectively performing big data analysis on each graph unit library in the target graph search object based on each corresponding graph classification bookmark of the artificial intelligence recognition result, thereby facilitating the historical classification situation based on the previous big data analysis, and further performing more accurate and rapid big data processing on the graph unit library based on some key graph classification bookmarks, and improving the big data analysis efficiency, reducing the buffering time.
In one possible implementation, for step S110, in order to improve the accuracy of the division and reduce redundant information to improve the classification accuracy, the following exemplary sub-steps may be further implemented, which are described in detail below.
And a substep S111, obtaining a classification target corresponding to each preset big data collection classification, forming a classification target sequence of each preset big data collection classification, and obtaining related classification target information of each target classification target of each artificial intelligence recognition result and the classification target of the classification target sequence.
And a substep S112, calculating the density of the key classification targets of each kind of target big data collection classification according to the related classification target information of the target classification targets and the classification targets of the classification target sequence, and selecting the classification targets from the classification target sequence according to the density of the key classification targets of each kind of target big data collection classification to obtain the initial classification target arrangement distribution.
In the sub-step S113, if the total classification target distribution density of the initial classification target arrangement distribution is greater than the maximum total classification target distribution density required by the total classification target distribution density, the first key classification targets in the initial classification target arrangement distribution are distributed to the first distribution density, and the second key classification targets in the initial classification target arrangement distribution are aggregated to the first distribution density.
For example, in one possible example, the second key classification target may refer to a key classification target in which the label density (e.g., the number of labels in a unit area) of the label hierarchy in which the key classification target is located is less than a set level, and the first key classification target refers to a key classification target in which the label density of the label hierarchy in which the key classification target is located is not less than the set level.
And a substep S114 of calculating the total classification target distribution density of the initial classification target arrangement distribution after the updating.
In the substep S115, if the total classification target distribution density of the initial classification target arrangement distribution after the current update is greater than the maximum total classification target distribution density, the above processing is performed again on the initial classification target arrangement distribution after the current update.
And a substep S116, if the total classification target distribution density of the initial classification target arrangement distribution after the updating is less than or equal to the maximum total classification target distribution density, taking the initial classification target arrangement distribution before the updating as a first updating arrangement distribution, and sorting the target big data collection classifications according to the sequence from the low priority to the high priority of the big data collection classification to obtain a target big data collection classification sequence.
And a substep S117, classifying the image classification labels under each artificial intelligence recognition result according to the target big data collection classification sequence, and respectively generating an image classification label sequence of each big data collection classification.
For example, in detail, in sub-step S117, the target big data collection classifications may be grouped according to a target big data collection classification sequence, each group including a first big data collection classification and a second big data collection classification that are related to the function level of the target big data collection classification sequence and are consistent with the level difference of the function level, the first big data collection classification having a lower priority than the second big data collection classification.
Then, each packet is sequentially taken as a target packet in the order from low priority to high priority in the hierarchy difference from the function hierarchy, and the target packet is subjected to the following second update processing: the critical classification targets of the first big data collection category of the target group in the first updated permutation distribution are increased by a set number, and the critical classification targets of the second big data collection category of the target group in the first updated permutation distribution are decreased by the set number.
On this basis, it can be judged whether the total classification target distribution density of the first updated arrangement distribution after the current update is greater than the total classification target distribution density requirement, and if the total classification target distribution density of the first updated arrangement distribution after the current update is greater than the total classification target distribution density requirement, the first updated arrangement distribution after the current update is taken as the final classification target arrangement distribution. And if the total classification target distribution density of the updated first updating arrangement distribution is not greater than the total classification target distribution density requirement, taking the next group as a new target group, and performing second updating processing on the new target group.
For another example, if the total classification target distribution density of the initial classification target arrangement distribution is less than the minimum total classification target distribution density that is greater than the total classification target distribution density requirement, the following third update process is performed on the initial classification target arrangement distribution: the first key classification target in the initial classification target arrangement distribution is increased by a first distribution density, and the second key classification target in the initial classification target arrangement distribution is decreased by the first distribution density.
On the basis, the total classification target distribution density of the initial classification target arrangement distribution after the updating is calculated, and if the total classification target distribution density of the initial classification target arrangement distribution after the updating is smaller than the minimum total classification target distribution density, the third updating processing is performed on the initial classification target arrangement distribution after the updating. Or if the total classification target distribution density of the initial classification target arrangement distribution after the update is greater than or equal to the minimum total classification target distribution density, taking the initial classification target arrangement distribution before the update as a second update arrangement distribution, and sorting the target big data collection classifications according to the sequence from the low priority to the high priority of the big data collection classifications to obtain a target big data collection classification sequence.
Thus, the target big data collection classifications can be grouped according to the target big data collection classification sequence, each group comprises a first big data collection classification and a second big data collection classification which are related to the function level of the target big data collection classification sequence and are consistent with the level difference of the function level, and the priority of the first big data collection classification is lower than that of the second big data collection classification.
Then, each packet is sequentially taken as a target packet in the order from low priority to high priority in the hierarchy difference from the function hierarchy, and the following fourth update processing is performed on the target packet: the key classification targets of the first big data collection classification of the target group in the second updated permutation distribution are decreased by a set number, and the key classification targets of the second big data collection classification of the target group in the second updated permutation distribution are increased by the set number.
Further, this embodiment may determine whether the total classification target distribution density of the second updated arrangement distribution after the current update is greater than the total classification target distribution density requirement, if the total classification target distribution density of the second updated arrangement distribution after the current update is greater than the total classification target distribution density requirement, take the second updated arrangement distribution after the current update as the final classification target arrangement distribution, and if the total classification target distribution density of the second updated arrangement distribution after the current update is not greater than the total classification target distribution density requirement, take the next group as the new target group, and perform the fourth update processing on the new target group.
Thus, the image classification label of each classification target in the final classification target arrangement distribution of each target big data collection classification can be classified into the image classification label sequence of the big data collection classification.
In a possible implementation manner, the user search behavior information may include classification scene type information, and for step S120, the present embodiment may obtain classification scene type information of the target graphic search object, and obtain a target graphic classification grade bookmark associated with each artificial intelligence recognition result according to the classification scene type information and a preset corresponding relationship between each classification scene type information and a target graphic classification grade bookmark in each label grade.
In one possible implementation, still referring to step S120, in order to accurately obtain the first information management index sequence of the target graphic classification hierarchical bookmark, the following exemplary implementation can be implemented, which is described in detail below.
And a substep S121, aiming at the target graphic classification and classification bookmarks associated with the artificial intelligence recognition results, respectively obtaining an index running script matched with the target graphic classification and classification bookmarks, and obtaining a label classification object corresponding to the index running script when the index running script continuously indexes and searches for an object entity corresponding to one label classification object in the artificial intelligence recognition results in a preset time period as a target label classification object.
And a substep S122 of judging whether the classification index searching feature of the target label classification object is matched with the classification index searching feature of the index node of the preset information management index unit, if the classification index searching feature is not matched, adjusting the classification index searching feature of the target label classification object to the label classification object matched with the classification index searching feature of the index node of the information management index unit, and inputting the label classification object to the information management index unit.
And a substep S123 of calculating the input label classification object by using the information management index unit, acquiring hierarchical label information corresponding to the input label classification object, expanding each label marking information of the target graph classification hierarchical bookmark in the target label classification object, and acquiring label expansion information of each label marking information in the target label classification object.
And a substep S124, determining a hierarchical label with a frequency of label labeling information greater than a preset frequency in the hierarchical label information corresponding to the input label classification object as a first indexable classification bookmark, and converting the label feature vector of each label labeling information in the input label classification object to obtain the label extension information of each label labeling information in the input label classification object.
And a substep S125, determining a first tag expansion information sequence of the whole tag classification object according to the tag expansion information of each tag label information in the target tag classification object, and determining a second tag expansion information sequence of the first indexable classification bookmark according to the tag expansion information of each tag label information in the first indexable classification bookmark.
And a substep S126, determining a label extension information sequence of the first indexable classification bookmark according to the first label extension information sequence, the second label extension information sequence and a preset proportion, and determining the hierarchical label information of the first indexable classification bookmark of the target graph classification hierarchical bookmark in the corresponding image classification label sequence of the big data collection classification according to the label extension information and the label extension information sequence of each label marking information in the target label classification object to obtain a first information management index sequence of the target graph classification hierarchical bookmark.
For example, in one possible example, in sub-step S126, a matching extension information node of the tag extension information and the tag extension information sequence of each tag label information in the target tag classification object may be determined, and the first labeled item node of each tag label information in the target tag classification object is obtained according to the matching extension information node, so as to obtain the labeled item node of each tag label information in the target tag classification object according to the first labeled item node and the hierarchical tag information of each tag label information in the target tag classification object.
For another example, in the sub-step S126, in another possible example, a matching extended information node of the tag extended information and the tag extended information sequence of each tag label information in the target tag classification object may be calculated to obtain a first labeled item node of each tag label information in the target tag classification object, and the first labeled item node of each tag label information in the target tag classification object may be calculated according to a preset coverage interval to obtain a second labeled item node of each tag label information in the target tag classification object.
It is worth to be noted that a node coverage range difference between the second labeling item node and the first labeling item node is smaller than a preset coverage interval, so that the labeling item node of each label labeling information in the target label classification object is obtained according to the second labeling item node and the hierarchical label information of each label labeling information in the target label classification object.
Therefore, the hierarchical label information of the first indexable classification bookmark in the image classification label sequence of the corresponding big data collection classification can be determined and obtained according to the labeling item node of each label labeling information in the target label classification object, so as to obtain the first information management index sequence of the target graph classification hierarchical bookmark.
In one possible implementation manner, for step S130, in order to further determine the key graph classification hierarchical bookmark associated with each artificial intelligence recognition result, the following exemplary embodiments may be implemented, which are described in detail below.
For example, the present embodiment may acquire history hierarchical classification information of the target graphic search object, and the history hierarchical classification information may specifically include a plurality of history hierarchical update change information respectively corresponding to a plurality of graphic hierarchical bookmarks. And then when determining that the plurality of historical classification updating change information corresponding to any one of the graphic classification grading bookmarks all meet the preset classification dynamic condition, determining an initial classification area to be determined of a first preset classification dynamic label range matched with the preset classification dynamic condition according to the historical classification updating change information of the graphic classification grading bookmarks and the range content of the preset classification dynamic label range. Wherein, the preset classification dynamic condition may include: the range content of the preset classification dynamic label range is larger than the range content of the set range.
And then, according to the historical classification updating change information of the graph classification grading bookmark, the range content of the preset classification dynamic label range, the initial pending classification area of the first preset classification dynamic label range and the density of the preset classification dynamic label range, determining that a plurality of preset classification dynamic label ranges matched with the preset classification dynamic conditions correspond to the initial pending classification area of the graph classification grading bookmark. If the position of the graph classification grading bookmark of the grading label corresponding to the graph classification grading bookmark in the graph classification grading bookmark is matched with an initial undetermined classification area of a classification grading change interval, and if the grading label is the first grading label of the classification grading change interval, the graph classification grading bookmark matched with the range of a previous preset classification dynamic label adjacent to the classification grading change interval is obtained as a screened graph classification grading bookmark, and one graph classification grading bookmark without the screened graph classification grading bookmark is identified in the grading label as a target graph classification grading bookmark matched with the classification grading change interval.
For another example, if the hierarchical tag is not the first hierarchical tag of the classification hierarchical variation interval, a target graphic classification hierarchical bookmark matching the classification hierarchical variation interval is obtained, the target graphic classification hierarchical bookmark is identified in the hierarchical tag, and at least one key object to be classified of the target graphic classification hierarchical bookmark is identified, wherein each graphic classification hierarchical bookmark corresponds to a plurality of preset classification dynamic tag ranges.
Therefore, in the range of the preset classification dynamic labels, according to the classification scene information of at least one key undetermined classification object of the target graph classification bookmark in a plurality of classification labels, the classification level difference of at least one key undetermined classification object of the target graph classification bookmark between any two adjacent classification labels in the range of the preset classification dynamic labels and the dynamic feature vector of at least one key undetermined classification object of the target graph classification bookmark in the range of the preset classification dynamic labels can be calculated.
Then, the classification frequency of the preset classification dynamic label range may be counted, and according to the classification level difference and the dynamic feature vector, the average classification frequency and the classification frequency variance of the target graphic classification hierarchical bookmark in the preset classification dynamic label range may be determined (for example, the average classification frequency may be obtained by multiplying the classification frequency by the classification level difference and the dynamic feature vector, so as to obtain a corresponding classification frequency variance according to the average classification frequency), and according to the average classification frequency and the classification frequency variance, the key feature parameter of the target graphic classification hierarchical bookmark in the preset classification dynamic label range may be calculated, for example, the key feature parameter of the target graphic classification hierarchical bookmark in the preset classification dynamic label range may be obtained by multiplying the average classification frequency and the classification frequency variance.
Therefore, the key evaluation degree of each graphic classification grading bookmark can be calculated according to the key characteristic parameters of each graphic classification grading bookmark in the range of the matched preset classification dynamic tags, and the graphic classification grading bookmark with the key evaluation degree larger than the set score is determined as the key graphic classification grading bookmark, so that the key graphic classification grading bookmark can be accurately positioned, and then, the user searching behavior information and the historical classification grading information of the target graphic searching object are combined, the information management index sequence of the two graphic classification grading bookmarks is compared, and then, the big data analysis is respectively carried out on each graphic unit library in the target graphic searching object based on each corresponding graphic classification grading bookmark of the artificial intelligent recognition result.
In one possible implementation, step S140 may be implemented in the following exemplary embodiments, which are described in detail below.
And a substep S141 of matching the information management index sequence of each target graphic classification hierarchical bookmark in the first information management index sequence with the information management index sequence of each matched key graphic classification hierarchical bookmark in the second information management index sequence to obtain a plurality of matching degrees.
For example, it should be noted that each matched key graph classification hierarchical bookmark in the second information management index sequence is matched with the arrangement sequence of the corresponding target graph classification hierarchical bookmark in the respective information management index sequence, and the matching degree is determined according to the contact degree between the information management index sequence of the target graph classification hierarchical bookmark and the information management index sequence of the matched key graph classification hierarchical bookmark.
And a substep S142, respectively carrying out big data analysis on each graphic unit library in the target graphic search object according to each corresponding graphic classification grading bookmark based on the artificial intelligence recognition result according to the matching degrees.
For example, in the sub-step S142, when the matching degree between the information management index sequence of any one target graphic classification hierarchical bookmark and the information management index sequence of the matched key graphic classification hierarchical bookmark is greater than the set matching degree, the target graphic classification hierarchical bookmark and the key graphic classification hierarchical bookmark are used as a big data analysis combined object.
For another example, when the matching degree between the information management index sequence of any one target graphic classification hierarchical bookmark and the information management index sequence of the matched key graphic classification hierarchical bookmark is not greater than the set matching degree, the target graphic classification hierarchical bookmark and the key graphic classification hierarchical bookmark are separately used as a big data analysis independent object.
Therefore, in the process of analyzing big data of each graphic unit library in the target graphic search object, when the graphic classification grading bookmark corresponding to the graphic unit library exists in the big data analysis combined object, the big data analysis of the graphic unit library is synchronously completed according to the big data analysis combined object, and when the graphic classification grading bookmark corresponding to the graphic unit library exists in the big data analysis independent object, the big data analysis of the graphic unit library is completed according to the big data analysis independent object.
Therefore, by combining the user searching behavior information and the historical hierarchical classification information of the target graphic searching object, after the information management index sequences of the two graphic classification hierarchical bookmarks are compared, each corresponding graphic classification hierarchical bookmark based on the artificial intelligence recognition result respectively carries out big data analysis on each graphic unit library in the target graphic searching object, the historical classification condition during the analysis based on the previous big data can be conveniently carried out, the more accurate and rapid big data processing can be carried out on the graphic unit libraries based on some key graphic classification hierarchical bookmarks, the big data analysis efficiency is improved, and the buffering time is shortened.
It should be particularly noted that after determining the key graph classification hierarchical bookmark associated with each artificial intelligence recognition result, the present embodiment may further obtain the second information management index sequence of the key graph classification hierarchical bookmark according to a similar operation manner of obtaining the first information management index sequence of the target graph classification hierarchical bookmark in the foregoing embodiment, which has been described in detail before, and thus is not described herein again.
Fig. 3 is a schematic diagram of functional modules of an information management apparatus 300 based on big data and the internet according to an embodiment of the present disclosure, and this embodiment may divide the functional modules of the information management apparatus 300 based on big data and the internet according to a method embodiment executed by the artificial intelligence cloud server 100, that is, the following functional modules corresponding to the information management apparatus 300 based on big data and the internet may be used to execute each method embodiment executed by the artificial intelligence cloud server 100. The big data and internet based information management apparatus 300 may include a classification module 310, a first determination module 320, a second determination module 330, and a big data analysis module 340, wherein the functions of the functional modules of the big data and internet based information management apparatus 300 are described in detail below.
The classifying module 310 is configured to obtain an image classification label of the target graph search object under the artificial intelligence recognition result of each artificial intelligence recognition model from each internet access device 200, classify the image classification labels under the artificial intelligence recognition results according to a predetermined big data collection classification, and generate an image classification label sequence of each big data collection classification. The classifying module 310 may be configured to perform the step S110, and the detailed implementation of the classifying module 310 may refer to the detailed description of the step S110.
The first determining module 320 is configured to determine a target graphic classification grade bookmark associated with each artificial intelligence recognition result according to user search behavior information of the target graphic search object, and determine, for the target graphic classification grade bookmark associated with each artificial intelligence recognition result, grade label information of a first indexable classification bookmark of the target graphic classification grade bookmark in a corresponding big data collection classified image classification label sequence, to obtain a first information management index sequence of the target graphic classification grade bookmark, where the target graphic classification grade bookmark is a graphic classification grade bookmark that is pre-matched with the user search behavior information of the target graphic search object. The first determining module 320 may be configured to perform the step S120, and for a detailed implementation of the first determining module 320, reference may be made to the detailed description of the step S120.
A second determining module 330, configured to determine, according to the historical hierarchical classification information of the target graph search object, a key graph classification hierarchical bookmark associated with each artificial intelligence recognition result, and for the key graph classification hierarchical bookmark associated with each artificial intelligence recognition result, respectively obtain second indexable classification bookmarks of the key graph classification hierarchical bookmarks, and determining hierarchical label information of a second indexable hierarchical bookmark in the corresponding image classification label sequence of the big data collection and classification to obtain a second information management index sequence of the key graph classification hierarchical bookmark, wherein the key graph classification hierarchical bookmark is a graph classification hierarchical bookmark of which the classification frequency in the historical hierarchical classification information of the target graph search object is greater than a set frequency threshold, and the classification frequency is used for representing the search classification frequency of the graph classification hierarchical bookmark in unit time. The second determining module 330 may be configured to perform the step S130, and the detailed implementation of the second determining module 330 may refer to the detailed description of the step S130.
And the big data analysis module 340 is configured to perform big data analysis on each graphic unit library in the target graphic search object based on each corresponding graphic classification hierarchical bookmark of the artificial intelligence recognition result according to the matching relationship between the first information management index sequence and the second information management index sequence. The big data analysis module 340 may be configured to perform the step S140, and the detailed implementation manner of the big data analysis module 340 may refer to the detailed description of the step S140.
It should be noted that the division of the modules of the above apparatus is only a logical division, and the actual implementation may be wholly or partially integrated into one physical entity, or may be physically separated. And these modules may all be implemented in software invoked by a processing element. Or may be implemented entirely in hardware. And part of the modules can be realized in the form of calling software by the processing element, and part of the modules can be realized in the form of hardware. For example, the classification module 310 may be a separate processing element, or may be integrated into a chip of the apparatus, or may be stored in a memory of the apparatus in the form of program code, and a processing element of the apparatus calls and executes the functions of the classification module 310. Other modules are implemented similarly. In addition, all or part of the modules can be integrated together or can be independently realized. The processing element described herein may be an integrated circuit having signal processing capabilities. In implementation, each step of the above method or each module above may be implemented by an integrated logic circuit of hardware in a processor element or an instruction in the form of software.
For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as: one or more Application Specific Integrated Circuits (ASICs), or one or more microprocessors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs), among others. For another example, when some of the above modules are implemented in the form of a processing element scheduler code, the processing element may be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor that can call program code. As another example, these modules may be integrated together, implemented in the form of a system-on-a-chip (SOC).
Fig. 4 illustrates a hardware structure diagram of an artificial intelligence cloud server 100 for implementing the big data and internet-based information management method, provided by the embodiment of the present disclosure, and as shown in fig. 4, the artificial intelligence cloud server 100 may include a processor 110, a machine-readable storage medium 120, a bus 130, and a transceiver 140.
In a specific implementation process, the at least one processor 110 executes computer-executable instructions stored in the machine-readable storage medium 120 (for example, the classification module 310, the first determination module 320, the second determination module 330, and the big data analysis module 340 included in the big data and internet-based information management apparatus 300 shown in fig. 3), so that the processor 110 may execute the big data and internet-based information management method according to the above method embodiment, where the processor 110, the machine-readable storage medium 120, and the transceiver 140 are connected via the bus 130, and the processor 110 may be configured to control the transceiver 140 to perform a transceiving action, so as to perform data transceiving with the aforementioned internet access device 200.
For a specific implementation process of the processor 110, reference may be made to the above-mentioned method embodiments executed by the artificial intelligence cloud server 100, which implement principles and technical effects similar to each other, and details of this embodiment are not described herein again.
In the embodiment shown in fig. 4, it should be understood that the Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present invention may be embodied directly in a hardware processor, or in a combination of the hardware and software modules within the processor.
The machine-readable storage medium 120 may comprise high-speed RAM memory and may also include non-volatile storage NVM, such as at least one disk memory.
The bus 130 may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus 130 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, the buses in the figures of the present disclosure are not limited to only one bus or one type of bus.
In addition, the embodiment of the disclosure also provides a readable storage medium, in which a computer executing instruction is stored, and when a processor executes the computer executing instruction, the information management method based on big data and internet is realized.
The readable storage medium described above may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The non-volatile memory may be a Read-only memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), or a flash memory. Volatile Memory can be Random Access Memory (RAM), which acts as external cache Memory. By way of example, but not limitation, many forms of RAM are available, such as Static random access memory (Static RAM, SRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic random access memory (Synchronous DRAM, SDRAM), Double Data rate Synchronous Dynamic random access memory (DDR SDRAM), Enhanced Synchronous SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), and direct memory bus RAM (DR RAM). It should be noted that the memory of the systems and methods described herein is intended to comprise, without being limited to, these and any other suitable types of memory.
It is also to be understood that the terminology used in the embodiments of the disclosure and the appended claims is for the purpose of describing particular embodiments only, and is not intended to be limiting of the embodiments of the disclosure. For example, as used in the disclosed embodiments and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present disclosure, and not for limiting the same; while the present disclosure has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art will understand that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present disclosure.

Claims (10)

1. An information management method based on big data and the Internet is applied to an artificial intelligence cloud server which is in communication connection with a plurality of Internet access devices, and the method comprises the following steps:
acquiring image classification labels of a target graph search object under the artificial intelligence recognition result of each artificial intelligence recognition model from each Internet access device, classifying the image classification labels under each artificial intelligence recognition result according to preset big data collection classification, and respectively generating an image classification label sequence of each big data collection classification;
determining a target graphic classification grade bookmark associated with each artificial intelligence identification result according to user search behavior information of the target graphic search object, and respectively determining grade label information of a first indexable grade bookmark of the target graphic classification grade bookmark in an image classification label sequence of corresponding big data collection classification aiming at the target graphic classification grade bookmark associated with each artificial intelligence identification result to obtain a first information management index sequence of the target graphic classification grade bookmark, wherein the target graphic classification grade bookmark is a graphic classification grade bookmark matched with the user search behavior information of the target graphic search object in advance;
determining the key graph classification grade bookmark associated with each artificial intelligence identification result according to the historical classification grade information of the target graph search object, respectively acquiring a second indexable classification bookmark of the key graph classification grade bookmark aiming at the key graph classification grade bookmark associated with each artificial intelligence identification result, and determining the hierarchical label information of the second indexable classification bookmark in the image classification label sequence of the corresponding big data collection classification to obtain a second information management index sequence of the key graph classification hierarchical bookmark, the key graphic classification grading bookmark is a graphic classification grading bookmark with the classification frequency greater than a set frequency threshold value in the historical classification grading information of the target graphic search object, the classification frequency is used for representing the search classification times of the graph classification grading bookmark in unit time;
and respectively carrying out big data analysis on each graphic unit library in the target graphic search object based on each corresponding graphic classification grading bookmark of the artificial intelligence recognition result according to the matching relation between the first information management index sequence and the second information management index sequence.
2. The big data and internet based information management method according to claim 1, wherein the step of classifying the image classification tags under the respective artificial intelligence recognition results according to the predetermined big data collection classification and generating the image classification tag sequence of each big data collection classification respectively comprises:
acquiring a classification target corresponding to each preset big data collection classification, forming a classification target sequence of each preset big data collection classification, and acquiring associated classification target information of each target classification target of each artificial intelligence identification result and a classification target of the classification target sequence;
calculating the density of key classification targets of each type of target big data collection classification according to the associated classification target information of the target classification targets and the classification targets of the classification target sequence, and selecting the classification targets from the classification target sequence according to the density of the key classification targets of each type of target big data collection classification to obtain the arrangement distribution of the initial classification targets;
if the total classification target distribution density of the initial classification target arrangement distribution is greater than the maximum total classification target distribution density required by the total classification target distribution density, dispersing a first key classification target in the initial classification target arrangement distribution to a first distribution density, and aggregating a second key classification target in the initial classification target arrangement distribution to the first distribution density, wherein the second key classification target is a key classification target of which the label density of the label classification where the key classification target is located is less than a set degree, and the first key classification target is a key classification target of which the label density of the label classification where the key classification target is located is not less than the set degree;
calculating the total classification target distribution density of the initial classification target arrangement distribution after the updating;
if the total classification target distribution density of the initial classification target arrangement distribution after the updating is greater than the maximum total classification target distribution density, performing the above processing on the initial classification target arrangement distribution after the updating again;
if the total classification target distribution density of the initial classification target arrangement distribution after the updating is less than or equal to the maximum total classification target distribution density, taking the initial classification target arrangement distribution before the updating as a first updating arrangement distribution, and sorting the target big data collection classifications according to the sequence from the low priority to the high priority of the big data collection classifications to obtain a target big data collection classification sequence;
and classifying the image classification labels under each artificial intelligence recognition result according to the target big data collection classification sequence, and respectively generating an image classification label sequence of each big data collection classification.
3. The method for managing information based on big data and internet as claimed in claim 1, wherein the user search behavior information includes classification scene type information, and the step of determining the target graphic classification hierarchical bookmark associated with each artificial intelligence recognition result according to the user search behavior information of the target graphic search object comprises:
and obtaining the classification scene type information of the target graph search object, and obtaining the target graph classification grade bookmark associated with each artificial intelligence identification result according to the classification scene type information and the corresponding relation between each preset classification scene type information and the target graph classification grade bookmark in each label grade.
4. The information management method based on big data and internet according to claim 1, wherein the step of obtaining the first information management index sequence of the target graphic classification hierarchical bookmark by determining the hierarchical label information of the first indexable classification bookmark of the target graphic classification hierarchical bookmark in the image classification label sequence of the corresponding big data collection classification for the target graphic classification hierarchical bookmark associated with each artificial intelligence recognition result comprises:
aiming at the target graphic classification grading bookmark associated with each artificial intelligence recognition result, respectively acquiring an index running script matched with the target graphic classification grading bookmark, and acquiring a label classification object corresponding to the index running script when the index running script continuously indexes and searches for an object entity corresponding to one label classification object in the artificial intelligence recognition result in a preset time period as a target label classification object;
judging whether the classification index searching features of the target label classification object are matched with the classification index searching features of the index nodes of a preset information management index unit or not, if the classification index searching features are not matched, adjusting the classification index searching features of the target label classification object to the label classification object matched with the classification index searching features of the index nodes of the information management index unit, and inputting the label classification object to the information management index unit;
calculating an input label classification object by using the information management index unit, acquiring hierarchical label information corresponding to the input label classification object, expanding each label marking information of the target graph classification hierarchical bookmark in the target label classification object, and acquiring label expansion information of each label marking information in the target label classification object;
determining a hierarchical label with the frequency degree of label labeling information being greater than a preset frequency degree in hierarchical label information corresponding to the input label classification object as a first indexable classification bookmark, and converting a label feature vector of each label labeling information in the input label classification object to obtain label extension information of each label labeling information in the input label classification object;
determining a first tag extension information sequence of the whole tag classification object according to tag extension information of each tag labeling information in the target tag classification object, and determining a second tag extension information sequence of the first indexable classification bookmark according to tag extension information of each tag labeling information in the first indexable classification bookmark;
and determining a label extension information sequence of the first indexable classification bookmark according to the first label extension information sequence, the second label extension information sequence and a preset proportion, and determining the hierarchical label information of the first indexable classification bookmark of the target graphic classification hierarchical bookmark in the corresponding image classification label sequence of the big data collection classification according to the label extension information of each label marking information in the target label classification object and the label extension information sequence to obtain a first information management index sequence of the target graphic classification hierarchical bookmark.
5. The method for information management based on big data and internet as claimed in claim 1, wherein the step of determining the hierarchical tag information of the first indexable classification bookmark of the target graphic classification hierarchical bookmark in the image classification tag sequence of the corresponding big data collection classification according to the tag extension information and the tag extension information sequence of each tag label information in the target tag classification object to obtain the first information management index sequence of the target graphic classification hierarchical bookmark comprises:
determining tag extension information of each tag marking information in the target tag classification object and a matching extension information node of the tag extension information sequence, acquiring a first marking item node of each tag marking information in the target tag classification object according to the matching extension information node, and acquiring a marking item node of each tag marking information in the target tag classification object according to the first marking item node of each tag marking information in the target tag classification object and the hierarchical tag information;
or, calculating the tag extension information of each tag label information in the target tag classification object and the matching extension information node of the tag extension information sequence to obtain the first label item node of each tag label information in the target tag classification object, and calculating a first labeling item node of each label labeling information in the target label classification object according to a preset coverage area to obtain a second labeling item node of each label labeling information in the target label classification object, wherein the node coverage difference between the second annotation item node and the first annotation item node is less than the preset coverage interval, acquiring a second labeling item node of each label labeling information in the target label classification object according to the second labeling item node of each label labeling information in the target label classification object and the hierarchical label information;
and determining to obtain hierarchical label information of the first indexable classification bookmark in the image classification label sequence of the corresponding big data collection classification according to the labeling item node of each label labeling information in the target label classification object so as to obtain a first information management index sequence of the target graphic classification hierarchical bookmark.
6. The big data and Internet based information management method according to any one of claims 1 to 5, wherein the step of determining the key graph classification hierarchical bookmark associated with each artificial intelligence recognition result according to the historical hierarchical classification information of the target graph search object comprises:
obtaining historical hierarchical classification information of the target graph search object, wherein the historical hierarchical classification information comprises a plurality of historical classified updating change information respectively corresponding to a plurality of graph classified hierarchical bookmarks;
when determining that a plurality of historical classification updating change information corresponding to any one graph classification grading bookmark meets a preset classification dynamic condition, determining an initial classification area to be determined of a first preset classification dynamic label range matched with the preset classification dynamic condition according to the historical classification updating change information of the graph classification grading bookmark and the range content of the preset classification dynamic label range, wherein the preset classification dynamic condition comprises the following steps: presetting the range content of the classification dynamic label range to be larger than the range content of the set range;
updating change information, range contents of the preset classification dynamic label range, an initial pending classification area of the first preset classification dynamic label range and the density of the preset classification dynamic label range according to historical classification of the graph classification grading bookmark, and determining that a plurality of preset classification dynamic label ranges matched with the preset classification dynamic conditions correspond to the initial pending classification area of the graph classification grading bookmark;
if the position of a grading label corresponding to the graph classification grading bookmark in the graph classification grading bookmark is matched with the initial undetermined classification area of a classification grading change interval, and if the grading label is the first grading label of the classification grading change interval, obtaining the graph classification grading bookmark matched with the previous preset classification dynamic label range adjacent to the classification grading change interval as a screened graph classification grading bookmark, and identifying one graph classification grading bookmark without the screened graph classification grading bookmark in the grading label as a target graph classification grading bookmark matched with the classification grading change interval;
if the hierarchical label is not the first hierarchical label of the classification hierarchical change interval, acquiring a target graphic classification hierarchical bookmark matched with the classification hierarchical change interval, identifying the target graphic classification hierarchical bookmark in the hierarchical label, and identifying at least one key object to be classified of the target graphic classification hierarchical bookmark, wherein the graphic classification hierarchical bookmark corresponds to a plurality of preset classification dynamic label ranges;
in the preset classification dynamic label range, calculating a classification level difference between any two adjacent classification labels of at least one key undetermined classification object of the target graph classification grade bookmark in the preset classification dynamic label range and a dynamic feature vector of the at least one key undetermined classification object of the target graph classification grade bookmark in the preset classification dynamic label range according to the classification scene information of the at least one key undetermined classification object of the target graph classification grade bookmark in the plurality of classification labels;
counting the classification times of the preset classification dynamic label range, determining the average classification frequency and the classification frequency variance of the target graphic classification hierarchical bookmark in the preset classification dynamic label range according to the classification level difference and the dynamic feature vector, and calculating the key feature parameter of the target graphic classification hierarchical bookmark in the preset classification dynamic label range according to the average classification frequency and the classification frequency variance;
and calculating the key evaluation degree of each graph classification grading bookmark according to the key characteristic parameters of each graph classification grading bookmark in the range of the matched preset classification dynamic labels, and determining the graph classification grading bookmarks with the key evaluation degrees larger than the set scores as the key graph classification grading bookmarks.
7. The big data and internet based information management method according to any one of claims 1 to 6, wherein the step of respectively performing big data analysis on each graphic unit library in the target graphic search object based on each corresponding graphic classification hierarchical bookmark of the artificial intelligence recognition result according to the matching relationship between the first information management index sequence and the second information management index sequence comprises:
matching the information management index sequence of each target graphic classification grading bookmark in the first information management index sequence with the information management index sequence of each matched key graphic classification grading bookmark in the second information management index sequence to obtain a plurality of matching degrees, wherein each matched key graphic classification grading bookmark in the second information management index sequence is matched with the arrangement sequence of the corresponding target graphic classification grading bookmark in the respective information management index sequence, and the matching degree is determined according to the contact ratio between the information management index sequence of the target graphic classification grading bookmark and the information management index sequence of the matched key graphic classification grading bookmark;
and respectively carrying out big data analysis on each graphic unit library in the target graphic search object according to the matching degrees and based on each corresponding graphic classification grading bookmark of the artificial intelligence recognition result.
8. The method for managing big data and internet-based information according to claim 7, wherein the step of performing big data analysis on each graphic cell library in the target graphic search object according to the plurality of matching degrees based on each corresponding graphic classification hierarchical bookmark of the artificial intelligence recognition result comprises:
when the matching degree between the information management index sequence of any one target graphic classification grading bookmark and the information management index sequence of the matched key graphic classification grading bookmark is greater than the set matching degree, taking the target graphic classification grading bookmark and the key graphic classification grading bookmark as a big data analysis combined object;
when the matching degree between the information management index sequence of any one target graphic classification grading bookmark and the information management index sequence of the matched key graphic classification grading bookmark is not more than the set matching degree, the target graphic classification grading bookmark and the key graphic classification grading bookmark are independently used as a big data analysis independent object;
in the process of big data analysis of each graphic unit library in the target graphic search object, when the graphic classification grading bookmark corresponding to the graphic unit library exists in the big data analysis combined object, big data analysis of the graphic unit library is synchronously completed according to the big data analysis combined object, and when the graphic classification grading bookmark corresponding to the graphic unit library exists in the big data analysis independent object, big data analysis of the graphic unit library is completed according to the big data analysis independent object.
9. An artificial intelligence cloud server, wherein the artificial intelligence cloud server comprises a processor, a machine-readable storage medium and a network interface, the machine-readable storage medium, the network interface and the processor are connected through a bus system, the network interface is used for being connected with at least one internet access device in a communication mode, the machine-readable storage medium is used for storing programs, instructions or codes, and the processor is used for executing the programs, the instructions or the codes in the machine-readable storage medium so as to execute the big data and internet based information management method of any one of claims 1 to 8.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium is configured with a program, instructions or code, which when executed, implements the big data and internet based information management method of any one of claims 1 to 8.
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