CN111950621A - Target data detection method, device, equipment and medium based on artificial intelligence - Google Patents
Target data detection method, device, equipment and medium based on artificial intelligence Download PDFInfo
- Publication number
- CN111950621A CN111950621A CN202010797367.6A CN202010797367A CN111950621A CN 111950621 A CN111950621 A CN 111950621A CN 202010797367 A CN202010797367 A CN 202010797367A CN 111950621 A CN111950621 A CN 111950621A
- Authority
- CN
- China
- Prior art keywords
- data
- target
- target data
- detection
- detected
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/018—Certifying business or products
- G06Q30/0185—Product, service or business identity fraud
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/08—Insurance
Abstract
The invention relates to artificial intelligence, and provides a target data detection method, a device, equipment and a medium based on artificial intelligence, which can expand a target data set to make a model more accurate, input the expanded data set into a self-encoder, output derivative characteristics, the method supports mass data, consumes short time, meets the requirements on characteristics under more prediction scenes, trains a preset neural network by using the derived characteristics to obtain a detection model for detection, integrates the data with the detection result as the target as target data, further, the target data can be automatically detected and screened in an artificial and intelligent way, the problems of low screening efficiency, high error rate and poor reliability are solved, meanwhile, the problems that the traditional classification model cannot process massive data, the feature extraction is difficult and time-consuming, and the time sequence or the joint features of multiple data sets are difficult to find are solved. The invention also relates to a block chain technology, and the detection model and the target data can be stored in the block chain.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a target data detection method, a target data detection device, target data detection equipment and a target data detection medium based on artificial intelligence.
Background
At present, with the rapid development of the insurance industry, the team of agents is getting stronger, and some agents have the situation of making or refreshing data instead (for example, making questions by using equipment of others) so as to generate a large amount of false data, which can cause larger data noise, reduce data quality, and further influence the usability of data due to lower authenticity, so that how to quickly and accurately detect target data becomes an urgent problem to be solved. In view of the above, a commonly adopted solution in the industry is to perform data screening according to preset rules, such as: and manually setting a screening principle, retaining data conforming to the principle, and deleting data not conforming to the principle. The method only depends on artificially defined rules to carry out data statistics, so that the screening efficiency is low, errors are easy to occur, and the reliability of the final detection result is poor.
In addition, screening only by using a traditional classification model is limited by the characteristics of the classification model, so that the problems that massive data cannot be processed, the feature extraction is difficult and time-consuming, time series or joint features of multiple data sets are difficult to find and the like can be caused.
Disclosure of Invention
In view of the above, it is necessary to provide a target data detection method, device, apparatus and medium based on artificial intelligence, which can automatically detect and screen target data in an artificial intelligence manner, solve the problems of low screening efficiency, high error rate and poor reliability, and also solve the problems that the conventional classification model cannot process massive data, the feature extraction is difficult and time-consuming, and it is difficult to find time series or joint features of multiple data sets.
An artificial intelligence based target data detection method, comprising:
determining a target tag in response to the received target data detection instruction;
acquiring data to be detected corresponding to a user to be detected;
selecting data from the data to be detected according to the target label to construct a target data set;
carrying out expansion processing on the target data set to obtain an expanded data set;
inputting the extended data set into a pre-trained self-encoder, and outputting derived features;
training a preset neural network by using the derived features to obtain a detection model;
inputting the data to be detected into the detection model, and outputting a detection result, wherein the detection result comprises a target and a non-target;
and integrating the data with the detection result as the target data.
According to a preferred embodiment of the present invention, the determining the target label includes one or more of the following ways:
analyzing the method body of the target data detection instruction to obtain data carried by the target data detection instruction, acquiring a preset label, matching the preset label in the data carried by the target data detection instruction, and determining the matched data as the target label; or
Acquiring historical target data, identifying keywords of the historical target data as first keywords, calculating the occurrence frequency of the first keywords, and acquiring the first keywords with the highest occurrence frequency as the target tags.
According to a preferred embodiment of the present invention, the selecting data from the data to be detected according to the target tag to construct a target data set includes:
identifying a keyword of each data in the data to be detected as a second keyword;
dividing the data with the same second keywords in the data to be detected into a class, and naming each class by the same second keywords;
configuring the weight of each category in the corresponding data;
determining the category with the weight greater than or equal to the configuration weight as a target category of the corresponding data;
defining a label of corresponding data by the target category;
and matching the target label in the defined labels, and acquiring data corresponding to the label matched with the target label to construct the target data set.
According to a preferred embodiment of the present invention, the expanding the target data set to obtain an expanded data set includes:
acquiring at least one predefined sub-tag, and selecting data corresponding to each sub-tag from the data to be detected to construct at least one sub-data set corresponding to each sub-tag;
calculating the intersection of the target data set and the at least one subdata set to obtain at least one sub-intersection;
and integrating the data in the at least one sub-intersection to obtain the extended data set.
According to a preferred embodiment of the present invention, before inputting the extended data set to a pre-trained self-encoder, the artificial intelligence based target data detection method further comprises:
inputting the data in the extended data set into an initial self-encoder for encoding processing to obtain an intermediate layer;
decoding the data of the middle layer, and transmitting the decoded data to an output layer;
comparing the input data and the output data of the initial self-encoder until the input data is the same as the output data, and stopping training to obtain the self-encoder;
acquiring data of an intermediate layer of the self-encoder as the derived feature.
According to the preferred embodiment of the present invention, after the preset neural network is trained by using the derived features to obtain a detection model, the target data detection method based on artificial intelligence further includes:
randomly acquiring data from the target data set to construct a verification set;
inputting the data in the verification set into the detection model, and outputting a first detection result;
acquiring data with the first detection result as a target, and calculating the proportion of the acquired data in the verification set;
when the proportion is larger than or equal to the configuration proportion, determining that the detection model passes verification, and storing the detection model to a block chain; or
And when the proportion is smaller than the configuration proportion, determining that the detection model is not verified, updating the at least one sub tag, updating the at least one sub data set according to the updated at least one sub tag, and performing optimization training on the detection model by using the at least one sub data set.
According to the preferred embodiment of the present invention, after integrating the data of which the detection result is the target as the target data, the target data detection method based on artificial intelligence further includes:
calculating the proportion of the target data in the data to be detected;
when the ratio is lower than a configuration ratio, generating warning information according to the ratio;
determining the associated user of the user to be detected;
and sending the warning information to the terminal equipment of the user to be detected and the terminal equipment of the associated user.
An artificial intelligence based target data detection apparatus, the artificial intelligence based target data detection apparatus comprising:
the determining unit is used for responding to the received target data detection instruction and determining a target label;
the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring data to be detected corresponding to a user to be detected;
the selection unit is used for selecting data from the data to be detected according to the target label to construct a target data set;
the extension unit is used for carrying out extension processing on the target data set to obtain an extended data set;
the input unit is used for inputting the extended data set to a pre-trained self-encoder and outputting derived features;
the training unit is used for training a preset neural network by using the derived features to obtain a detection model;
the input unit is further configured to input the data to be detected to the detection model and output a detection result, where the detection result includes a target and a non-target;
and the integration unit is used for integrating the data with the detection result as the target data.
An electronic device, the electronic device comprising:
a memory storing at least one instruction; and
a processor executing instructions stored in the memory to implement the artificial intelligence based target data detection method.
A computer-readable storage medium having stored therein at least one instruction for execution by a processor in an electronic device to implement the artificial intelligence based target data detection method.
According to the technical scheme, the method can respond to a received target data detection instruction, determine a target label, acquire data to be detected corresponding to a user to be detected, select data from the data to be detected according to the target label to construct a target data set, perform expansion processing on the target data set to obtain an expanded data set, enable training of a subsequent model to be more accurate through data expansion, input the expanded data set to a pre-trained self-encoder, output derivative characteristics, extract characteristics through the self-encoder, not only can support massive data, but also is short in time consumption in a characteristic extraction process, can extract time sequences or combined characteristics of multiple data sets to meet requirements on the characteristics in more prediction scenes, train a preset neural network through the derivative characteristics to obtain a detection model, the data to be detected is input into the detection model, and a detection result is output, wherein the detection result comprises a target and a non-target, the data with the detection result as the target is integrated to serve as the target data, and then the target data can be automatically detected and screened in an artificial intelligent mode, so that the problems of low screening efficiency, high error rate and poor reliability caused by data statistics depending on artificially defined rules are solved, and meanwhile, the problems that the traditional classification model cannot process massive data, the feature extraction is difficult and time-consuming, and the time sequence or the joint features of multiple data sets are difficult to find are solved.
Drawings
FIG. 1 is a flow chart of a preferred embodiment of the target data detection method based on artificial intelligence of the present invention.
FIG. 2 is a functional block diagram of a preferred embodiment of the target data detection apparatus based on artificial intelligence according to the present invention.
Fig. 3 is a schematic structural diagram of an electronic device according to a preferred embodiment of the present invention for implementing an artificial intelligence-based target data detection method.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in detail with reference to the accompanying drawings and specific embodiments.
FIG. 1 is a flow chart of a preferred embodiment of the target data detection method based on artificial intelligence according to the present invention. The order of the steps in the flow chart may be changed and some steps may be omitted according to different needs.
The target data detection method based on artificial intelligence is applied to one or more electronic devices, which are devices capable of automatically performing numerical calculation and/or information processing according to preset or stored instructions, and the hardware of the electronic devices includes, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like.
The electronic device may be any electronic product capable of performing human-computer interaction with a user, for example, a Personal computer, a tablet computer, a smart phone, a Personal Digital Assistant (PDA), a game machine, an interactive Internet Protocol Television (IPTV), an intelligent wearable device, and the like.
The electronic device may also include a network device and/or a user device. The network device includes, but is not limited to, a single network server, a server group consisting of a plurality of network servers, or a Cloud Computing (Cloud Computing) based Cloud consisting of a large number of hosts or network servers.
The Network where the electronic device is located includes, but is not limited to, the internet, a wide area Network, a metropolitan area Network, a local area Network, a Virtual Private Network (VPN), and the like.
S10, determining a target tag in response to the received target data detection instruction.
In at least one embodiment of the invention, the target data detection instruction may be triggered by a designated person to be executed according to actual needs.
Of course, the target data detection instruction may also be configured to be triggered periodically, so as to filter the target data periodically, and avoid that when other related service scenarios execute a task by using data, the execution effect of the task is affected due to noise generated by non-target data.
In this embodiment, when a piece of data carries the target tag, the piece of data can be determined as target data. For example: the target tag may be: and executing the operation of importing the address book and the like.
In at least one embodiment of the present invention, the determining the target label includes, but is not limited to, one or more of the following combinations:
analyzing the method body of the target data detection instruction, obtaining data carried by the target data detection instruction, obtaining a preset label, matching the preset label in the data carried by the target data detection instruction, and determining the matched data as the target label.
Through the embodiment, when the target data detection instruction carries the target label, the target label can be directly extracted by using a preset label, so that the data processing time is saved, and the accuracy of the target label is ensured.
Or acquiring historical target data, identifying keywords of the historical target data as first keywords, calculating the occurrence frequency of the first keywords, and acquiring the first keywords with the highest occurrence frequency as the target tags.
Through the implementation manner, when the target data detection instruction does not carry the target tag, the target tag can be determined through historical target data.
Of course, in other embodiments, the target tag may be determined in other manners, and the present invention is not limited thereto.
For example: and receiving the label uploaded by the user as the target label.
And S11, acquiring the data to be detected corresponding to the user to be detected.
In this embodiment, the account of the user to be detected is acquired, all data corresponding to the account is acquired, and the acquired data is determined as the data to be detected.
It can be understood that the data corresponding to the account is not necessarily data generated by the user to be detected, and if there is another person logging in the account of the user to be detected to perform an operation, the generated data is not actual data of the user to be detected, and may be regarded as data to be refreshed or replaced, that is, non-target data, and the actual data of the user to be detected is target data.
And S12, selecting data from the data to be detected according to the target label to construct a target data set.
In this embodiment, since all the data in the target data set have the target tag, all the data in the target data set are target data.
In at least one embodiment of the present invention, the selecting data from the data to be detected according to the target tag to construct a target data set includes:
identifying a keyword of each data in the data to be detected as a second keyword;
dividing the data with the same second keywords in the data to be detected into a class, and naming each class by the same second keywords;
configuring the weight of each category in the corresponding data;
determining the category with the weight greater than or equal to the configuration weight as a target category of the corresponding data;
defining a label of corresponding data by the target category;
and matching the target label in the defined labels, and acquiring data corresponding to the label matched with the target label to construct the target data set.
The configuration weight can be configured by self-definition according to actual requirements, and the invention is not limited.
After naming each category by the same keyword, each category needs to be given a certain weight to distinguish the importance degrees of different categories, and specifically, various data indexes can be considered comprehensively when determining the weight, such as: number of interviews, number of interview attractions, etc.
And S13, performing expansion processing on the target data set to obtain an expanded data set.
It can be understood that the more the data amount is, the more clear the relationship and the features between the data are, and the more beneficial the subsequent feature extraction and the training of the model are, therefore, the embodiment also needs to expand the target data.
Specifically, the expanding the target data set to obtain an expanded data set includes:
acquiring at least one predefined sub-tag, and selecting data corresponding to each sub-tag from the data to be detected to construct at least one sub-data set corresponding to each sub-tag;
calculating the intersection of the target data set and the at least one subdata set to obtain at least one sub-intersection;
and integrating the data in the at least one sub-intersection to obtain the extended data set.
Wherein the at least one sub-tag belongs to an extension to the target tag.
For example: the at least one sub-tag may include, but is not limited to: an AI interview is performed and attendance records are recorded.
It should be noted that the manner of constructing at least one sub data set corresponding to each sub tag is similar to the manner of constructing the target data set, and is not described herein again.
And, the data with the at least one sub-label and the target label may be determined as target data, so that the data in the at least one sub-intersection obtained after performing intersection processing all belong to the target data.
Through the embodiment, the data expansion can be further performed on the basis of the target data set to obtain sufficient data, so that a data base is provided for the subsequent feature extraction and the model training.
And S14, inputting the extended data set into a pre-trained self-encoder (Autoencoder) and outputting derivative features.
The derived features are features obtained after processing by the self-encoder on the basis of the at least one sub-intersection, and the features are extracted by the self-encoder, so that not only can mass data be supported, but also the time consumption of the feature extraction process is short, and the joint features of a time sequence or multiple data sets can be extracted to meet the requirements on the features in more prediction scenes.
In at least one embodiment of the present invention, before inputting the extended data set to a pre-trained self-encoder, the artificial intelligence based target data detection method further comprises:
inputting the data in the extended data set into an initial self-encoder for encoding processing to obtain an intermediate layer;
decoding the data of the middle layer, and transmitting the decoded data to an output layer;
comparing the input data and the output data of the initial self-encoder until the input data is the same as the output data, and stopping training to obtain the self-encoder;
acquiring data of an intermediate layer of the self-encoder as the derived feature.
Compared with the traditional characteristic processing mode, the embodiment utilizes the self-encoder to extract the characteristics, and the self-encoder is continuously trained, so that the data characteristics of the middle layer can fully express the data characteristics of the original input layer, and more characteristics can be obtained, and the model for subsequent training is more accurate and reliable.
And S15, training a preset neural network by using the derived features to obtain a detection model.
Wherein, the preset neural network refers to a network with a classification function, such as: support Vector Machine (SVM) networks, Multi-Layer neural networks (MLP), Radial Basis Function (RBF) networks, and the like.
In this embodiment, because the data volume of the derived features is sufficient and the feature expression is sufficient, the preset neural network is trained by the derived features, so that the training effect of the preset neural network can be better, and the accuracy of model detection is further improved.
It should be noted that, the present embodiment does not limit the training mode of the preset neural network.
In at least one embodiment of the present invention, after training a preset neural network with the derived features to obtain a detection model, the method for detecting target data based on artificial intelligence further includes:
randomly acquiring data from the target data set to construct a verification set;
inputting the data in the verification set into the detection model, and outputting a first detection result;
acquiring data with the first detection result as a target, and calculating the proportion of the acquired data in the verification set;
when the proportion is larger than or equal to the configuration proportion, determining that the detection model passes verification, and storing the detection model to a block chain;
the configuration proportion can be self-defined according to actual requirements, and the invention is not limited.
With the above embodiment, the inspection model can be verified with a small amount of target data to ensure the usability of the inspection model.
Meanwhile, the detection model is stored in a block chain, so that the safety of the detection model is further ensured.
Or when the proportion is smaller than the configuration proportion, determining that the detection model is not verified, updating the at least one sub tag, updating the at least one sub data set according to the updated at least one sub tag, and performing optimization training on the detection model by using the at least one sub data set.
Through the embodiment, when the detection model fails to be verified, the corresponding label is adjusted in time to optimize and train the detection model so as to enhance the adaptability of the detection model and improve the flexibility of the detection model.
And S16, inputting the data to be detected into the detection model, and outputting a detection result, wherein the detection result comprises a target and a non-target.
Specifically, when the detection result is a first identifier (such as 1 or Y), determining that the detection result is a target; or when the detection result is a second identifier (such as 0 or N), determining that the detection result is a non-target.
Through the embodiment, the target data can be automatically detected and screened in an artificial intelligent mode, and the problems of low screening efficiency, high error rate and poor reliability caused by data statistics depending on artificially defined rules in the prior art are solved.
S17, data targeted by the detection result is integrated as target data.
Through above-mentioned embodiment, can combine the artificial intelligence means automatic follow wait to detect and select out all target data in the data, compare in traditional mode more high-efficient, and be difficult for makeing mistakes, the practicality is stronger.
Note that, in order to prevent data from being tampered, the target data may also be saved to the blockchain.
In at least one embodiment of the present invention, after integrating data whose detection result is a target as target data, the artificial intelligence-based target data detection method further includes:
calculating the proportion of the target data in the data to be detected;
when the ratio is lower than a configuration ratio, generating warning information according to the ratio;
determining the associated user of the user to be detected;
and sending the warning information to the terminal equipment of the user to be detected and the terminal equipment of the associated user.
The warning information is used for warning that the non-target data currently generated by the user to be detected is excessive.
The configuration ratio can be self-defined according to actual requirements, and the invention is not limited.
Wherein the associated user may include, but is not limited to: and the superior leader and attendance manager of the user to be detected.
Through the embodiment, the warning information is sent to the terminal equipment of the user to be detected, the warning effect of the user to be detected can be played, the phenomenon of violation can be avoided appearing again, meanwhile, the warning information is sent to the terminal equipment of the associated user, attention of related personnel can be aroused, the problem is timely handled when the problem occurs, the problem is prevented from being enlarged, and the effect of timely stopping damage is played.
According to the technical scheme, the method can respond to a received target data detection instruction, determine a target label, acquire data to be detected corresponding to a user to be detected, select data from the data to be detected according to the target label to construct a target data set, perform expansion processing on the target data set to obtain an expanded data set, enable training of a subsequent model to be more accurate through data expansion, input the expanded data set to a pre-trained self-encoder, output derivative characteristics, extract characteristics through the self-encoder, not only can support massive data, but also is short in time consumption in a characteristic extraction process, can extract time sequences or combined characteristics of multiple data sets to meet requirements on the characteristics in more prediction scenes, train a preset neural network through the derivative characteristics to obtain a detection model, the data to be detected is input into the detection model, and a detection result is output, wherein the detection result comprises a target and a non-target, the data with the detection result as the target is integrated to serve as the target data, and then the target data can be automatically detected and screened in an artificial intelligence mode, so that the problems of low screening efficiency, high error rate and poor reliability caused by data statistics depending on artificially defined rules in the prior art are solved, and meanwhile, the problems that a traditional classification model cannot process massive data, the feature extraction is difficult and time-consuming, time sequences or joint features of multiple data sets are difficult to find and the like are solved.
FIG. 2 is a functional block diagram of a preferred embodiment of the target data detection apparatus based on artificial intelligence according to the present invention. The artificial intelligence-based target data detection device 11 comprises a determination unit 110, an acquisition unit 111, a selection unit 112, an expansion unit 113, an input unit 114, a training unit 115 and an integration unit 116. The module/unit referred to in the present invention refers to a series of computer program segments that can be executed by the processor 13 and that can perform a fixed function, and that are stored in the memory 12. In the present embodiment, the functions of the modules/units will be described in detail in the following embodiments.
The determination unit 110 determines a target tag in response to the received target data detection instruction.
In at least one embodiment of the invention, the target data detection instruction may be triggered by a designated person to be executed according to actual needs.
Of course, the target data detection instruction may also be configured to be triggered periodically, so as to filter the target data periodically, and avoid that when other related service scenarios execute a task by using data, the execution effect of the task is affected due to noise generated by non-target data.
In this embodiment, when a piece of data carries the target tag, the piece of data can be determined as target data. For example: the target tag may be: and executing the operation of importing the address book and the like.
In at least one embodiment of the present invention, the determining unit 110 determines the target tag includes, but is not limited to, one or more of the following combinations:
analyzing the method body of the target data detection instruction, obtaining data carried by the target data detection instruction, obtaining a preset label, matching the preset label in the data carried by the target data detection instruction, and determining the matched data as the target label.
Through the embodiment, when the target data detection instruction carries the target label, the target label can be directly extracted by using a preset label, so that the data processing time is saved, and the accuracy of the target label is ensured.
Or acquiring historical target data, identifying keywords of the historical target data as first keywords, calculating the occurrence frequency of the first keywords, and acquiring the first keywords with the highest occurrence frequency as the target tags.
Through the implementation manner, when the target data detection instruction does not carry the target tag, the target tag can be determined through historical target data.
Of course, in other embodiments, the target tag may be determined in other manners, and the present invention is not limited thereto.
For example: and receiving the label uploaded by the user as the target label.
The acquisition unit 111 acquires data to be detected corresponding to a user to be detected.
In this embodiment, the account of the user to be detected is acquired, all data corresponding to the account is acquired, and the acquired data is determined as the data to be detected.
It can be understood that the data corresponding to the account is not necessarily data generated by the user to be detected, and if there is another person logging in the account of the user to be detected to perform an operation, the generated data is not actual data of the user to be detected, and may be regarded as data to be refreshed or replaced, that is, non-target data, and the actual data of the user to be detected is target data.
The selecting unit 112 selects data from the data to be detected according to the target label to construct a target data set.
In this embodiment, since all the data in the target data set have the target tag, all the data in the target data set are target data.
In at least one embodiment of the present invention, the selecting unit 112 selects data from the data to be detected according to the target tag to construct a target data set, including:
identifying a keyword of each data in the data to be detected as a second keyword;
dividing the data with the same second keywords in the data to be detected into a class, and naming each class by the same second keywords;
configuring the weight of each category in the corresponding data;
determining the category with the weight greater than or equal to the configuration weight as a target category of the corresponding data;
defining a label of corresponding data by the target category;
and matching the target label in the defined labels, and acquiring data corresponding to the label matched with the target label to construct the target data set.
The configuration weight can be configured by self-definition according to actual requirements, and the invention is not limited.
After naming each category by the same keyword, each category needs to be given a certain weight to distinguish the importance degrees of different categories, and specifically, various data indexes can be considered comprehensively when determining the weight, such as: number of interviews, number of interview attractions, etc.
The expansion unit 113 performs expansion processing on the target data set to obtain an expanded data set.
It can be understood that the more the data amount is, the more clear the relationship and the features between the data are, and the more beneficial the subsequent feature extraction and the training of the model are, therefore, the embodiment also needs to expand the target data.
Specifically, the expanding unit 113 performs expansion processing on the target data set, and obtaining an expanded data set includes:
acquiring at least one predefined sub-tag, and selecting data corresponding to each sub-tag from the data to be detected to construct at least one sub-data set corresponding to each sub-tag;
calculating the intersection of the target data set and the at least one subdata set to obtain at least one sub-intersection;
and integrating the data in the at least one sub-intersection to obtain the extended data set.
Wherein the at least one sub-tag belongs to an extension to the target tag.
For example: the at least one sub-tag may include, but is not limited to: an AI interview is performed and attendance records are recorded.
It should be noted that the manner of constructing at least one sub data set corresponding to each sub tag is similar to the manner of constructing the target data set, and is not described herein again.
And, the data with the at least one sub-label and the target label may be determined as target data, so that the data in the at least one sub-intersection obtained after performing intersection processing all belong to the target data.
Through the embodiment, the data expansion can be further performed on the basis of the target data set to obtain sufficient data, so that a data base is provided for the subsequent feature extraction and the model training.
The input unit 114 inputs the extended data set to a pre-trained self-encoder (Autoencoder) and outputs a derived feature.
The derived features are features obtained after processing by the self-encoder on the basis of the at least one sub-intersection, and the features are extracted by the self-encoder, so that not only can mass data be supported, but also the time consumption of the feature extraction process is short, and the joint features of a time sequence or multiple data sets can be extracted to meet the requirements on the features in more prediction scenes.
In at least one embodiment of the present invention, before the extended data set is input to a pre-trained self-encoder, the data in the extended data set is input to an initial self-encoder for encoding processing, so as to obtain an intermediate layer;
decoding the data of the middle layer, and transmitting the decoded data to an output layer;
comparing the input data and the output data of the initial self-encoder until the input data is the same as the output data, and stopping training to obtain the self-encoder;
acquiring data of an intermediate layer of the self-encoder as the derived feature.
Compared with the traditional characteristic processing mode, the embodiment utilizes the self-encoder to extract the characteristics, and the self-encoder is continuously trained, so that the data characteristics of the middle layer can fully express the data characteristics of the original input layer, and more characteristics can be obtained, and the model for subsequent training is more accurate and reliable.
The training unit 115 trains a preset neural network with the derived features to obtain a detection model.
Wherein, the preset neural network refers to a network with a classification function, such as: support Vector Machine (SVM) networks, Multi-Layer neural networks (MLP), Radial Basis Function (RBF) networks, and the like.
In this embodiment, because the data volume of the derived features is sufficient and the feature expression is sufficient, the preset neural network is trained by the derived features, so that the training effect of the preset neural network can be better, and the accuracy of model detection is further improved.
It should be noted that, the present embodiment does not limit the training mode of the preset neural network.
In at least one embodiment of the invention, after a preset neural network is trained by the derived features to obtain a detection model, data are randomly acquired from the target data set to construct a verification set;
inputting the data in the verification set into the detection model, and outputting a first detection result;
acquiring data with the first detection result as a target, and calculating the proportion of the acquired data in the verification set;
when the proportion is larger than or equal to the configuration proportion, determining that the detection model passes verification, and storing the detection model to a block chain;
the configuration proportion can be self-defined according to actual requirements, and the invention is not limited.
With the above embodiment, the inspection model can be verified with a small amount of target data to ensure the usability of the inspection model.
Meanwhile, the detection model is stored in a block chain, so that the safety of the detection model is further ensured.
Or when the proportion is smaller than the configuration proportion, determining that the detection model is not verified, updating the at least one sub tag, updating the at least one sub data set according to the updated at least one sub tag, and performing optimization training on the detection model by using the at least one sub data set.
Through the embodiment, when the detection model fails to be verified, the corresponding label is adjusted in time to optimize and train the detection model so as to enhance the adaptability of the detection model and improve the flexibility of the detection model.
The input unit 114 inputs the data to be detected to the detection model, and outputs a detection result, where the detection result includes a target and a non-target.
Specifically, when the detection result is a first identifier (such as 1 or Y), determining that the detection result is a target; or when the detection result is a second identifier (such as 0 or N), determining that the detection result is a non-target.
Through the embodiment, the target data can be automatically detected and screened in an artificial intelligent mode, and the problems of low screening efficiency, high error rate and poor reliability caused by data statistics depending on artificially defined rules in the prior art are solved.
The integration unit 116 integrates data targeted by the detection result as target data.
Through above-mentioned embodiment, can combine the artificial intelligence means automatic follow wait to detect and select out all target data in the data, compare in traditional mode more high-efficient, and be difficult for makeing mistakes, the practicality is stronger.
Note that, in order to prevent data from being tampered, the target data may also be saved to the blockchain.
In at least one embodiment of the present invention, after integrating data whose detection result is a target as target data, calculating a ratio of the target data in the data to be detected;
when the ratio is lower than a configuration ratio, generating warning information according to the ratio;
determining the associated user of the user to be detected;
and sending the warning information to the terminal equipment of the user to be detected and the terminal equipment of the associated user.
The warning information is used for warning that the non-target data currently generated by the user to be detected is excessive.
The configuration ratio can be self-defined according to actual requirements, and the invention is not limited.
Wherein the associated user may include, but is not limited to: and the superior leader and attendance manager of the user to be detected.
Through the embodiment, the warning information is sent to the terminal equipment of the user to be detected, the warning effect of the user to be detected can be played, the phenomenon of violation can be avoided appearing again, meanwhile, the warning information is sent to the terminal equipment of the associated user, attention of related personnel can be aroused, the problem is timely handled when the problem occurs, the problem is prevented from being enlarged, and the effect of timely stopping damage is played.
According to the technical scheme, the method can respond to a received target data detection instruction, determine a target label, acquire data to be detected corresponding to a user to be detected, select data from the data to be detected according to the target label to construct a target data set, perform expansion processing on the target data set to obtain an expanded data set, enable training of a subsequent model to be more accurate through data expansion, input the expanded data set to a pre-trained self-encoder, output derivative characteristics, extract characteristics through the self-encoder, not only can support massive data, but also is short in time consumption in a characteristic extraction process, can extract time sequences or combined characteristics of multiple data sets to meet requirements on the characteristics in more prediction scenes, train a preset neural network through the derivative characteristics to obtain a detection model, the data to be detected is input into the detection model, and a detection result is output, wherein the detection result comprises a target and a non-target, the data with the detection result as the target is integrated to serve as the target data, and then the target data can be automatically detected and screened in an artificial intelligence mode, so that the problems of low screening efficiency, high error rate and poor reliability caused by data statistics depending on artificially defined rules in the prior art are solved, and meanwhile, the problems that a traditional classification model cannot process massive data, the feature extraction is difficult and time-consuming, time sequences or joint features of multiple data sets are difficult to find and the like are solved.
Fig. 3 is a schematic structural diagram of an electronic device according to a preferred embodiment of the present invention, which implements a target data detection method based on artificial intelligence.
The electronic device 1 may comprise a memory 12, a processor 13 and a bus, and may further comprise a computer program, such as an artificial intelligence based object data detection program, stored in the memory 12 and executable on the processor 13.
It will be understood by those skilled in the art that the schematic diagram is merely an example of the electronic device 1, and does not constitute a limitation to the electronic device 1, the electronic device 1 may have a bus-type structure or a star-type structure, the electronic device 1 may further include more or less hardware or software than those shown in the figures, or different component arrangements, for example, the electronic device 1 may further include an input and output device, a network access device, and the like.
It should be noted that the electronic device 1 is only an example, and other existing or future electronic products, such as those that can be adapted to the present invention, should also be included in the scope of the present invention, and are included herein by reference.
The memory 12 includes at least one type of readable storage medium, which includes flash memory, removable hard disks, multimedia cards, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disks, optical disks, etc. The memory 12 may in some embodiments be an internal storage unit of the electronic device 1, for example a removable hard disk of the electronic device 1. The memory 12 may also be an external storage device of the electronic device 1 in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the electronic device 1. Further, the memory 12 may also include both an internal storage unit and an external storage device of the electronic device 1. The memory 12 may be used not only to store application software installed in the electronic device 1 and various types of data, such as codes of an object data detection program based on artificial intelligence, etc., but also to temporarily store data that has been output or is to be output.
The processor 13 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same or different functions, including one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips. The processor 13 is a Control Unit (Control Unit) of the electronic device 1, connects various components of the electronic device 1 by using various interfaces and lines, and executes various functions and processes data of the electronic device 1 by running or executing programs or modules (for example, executing an artificial intelligence-based target data detection program and the like) stored in the memory 12 and calling data stored in the memory 12.
The processor 13 executes an operating system of the electronic device 1 and various installed application programs. The processor 13 executes the application program to implement the steps in each of the artificial intelligence based target data detection method embodiments described above, such as the steps shown in fig. 1.
Illustratively, the computer program may be divided into one or more modules/units, which are stored in the memory 12 and executed by the processor 13 to accomplish the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program in the electronic device 1. For example, the computer program may be divided into a determination unit 110, an acquisition unit 111, a selection unit 112, an extension unit 113, an input unit 114, a training unit 115, an integration unit 116.
The integrated unit implemented in the form of a software functional module may be stored in a computer-readable storage medium. The software functional module is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, a computer device, or a network device) or a processor (processor) to execute the portions of the artificial intelligence based target data detection method according to the embodiments of the present invention.
The integrated modules/units of the electronic device 1 may be stored in a computer-readable storage medium if they are implemented in the form of software functional units and sold or used as separate products. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented.
Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM).
Further, the computer-usable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the blockchain node, and the like.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, and is used for verifying the information's targeting (anti-counterfeiting) and generating a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
The bus may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one arrow is shown in FIG. 3, but this does not indicate only one bus or one type of bus. The bus is arranged to enable connection communication between the memory 12 and at least one processor 13 or the like.
Although not shown, the electronic device 1 may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 13 through a power management device, so as to implement functions of charge management, discharge management, power consumption management, and the like through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device 1 may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
Further, the electronic device 1 may further include a network interface, and optionally, the network interface may include a wired interface and/or a wireless interface (such as a WI-FI interface, a bluetooth interface, etc.), which are generally used for establishing a communication connection between the electronic device 1 and other electronic devices.
Optionally, the electronic device 1 may further comprise a user interface, which may be a Display (Display), an input unit (such as a Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable for displaying information processed in the electronic device 1 and for displaying a visualized user interface, among other things.
It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.
Fig. 3 only shows the electronic device 1 with components 12-13, and it will be understood by a person skilled in the art that the structure shown in fig. 3 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or a combination of certain components, or a different arrangement of components.
With reference to fig. 1, the memory 12 of the electronic device 1 stores a plurality of instructions to implement an artificial intelligence based target data detection method, and the processor 13 can execute the plurality of instructions to implement:
determining a target tag in response to the received target data detection instruction;
acquiring data to be detected corresponding to a user to be detected;
selecting data from the data to be detected according to the target label to construct a target data set;
carrying out expansion processing on the target data set to obtain an expanded data set;
inputting the extended data set into a pre-trained self-encoder, and outputting derived features;
training a preset neural network by using the derived features to obtain a detection model;
inputting the data to be detected into the detection model, and outputting a detection result, wherein the detection result comprises a target and a non-target;
and integrating the data with the detection result as the target data.
Specifically, the processor 13 may refer to the description of the relevant steps in the embodiment corresponding to fig. 1 for a specific implementation method of the instruction, which is not described herein again.
In the embodiments provided in the present invention, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. An artificial intelligence based target data detection method, characterized in that the artificial intelligence based target data detection method comprises:
determining a target tag in response to the received target data detection instruction;
acquiring data to be detected corresponding to a user to be detected;
selecting data from the data to be detected according to the target label to construct a target data set;
carrying out expansion processing on the target data set to obtain an expanded data set;
inputting the extended data set into a pre-trained self-encoder, and outputting derived features;
training a preset neural network by using the derived features to obtain a detection model;
inputting the data to be detected into the detection model, and outputting a detection result, wherein the detection result comprises a target and a non-target;
and integrating the data with the detection result as the target data.
2. The artificial intelligence based object data detection method of claim 1, wherein the determining the object label comprises one or more of the following combinations:
analyzing the method body of the target data detection instruction to obtain data carried by the target data detection instruction, acquiring a preset label, matching the preset label in the data carried by the target data detection instruction, and determining the matched data as the target label; or
Acquiring historical target data, identifying keywords of the historical target data as first keywords, calculating the occurrence frequency of the first keywords, and acquiring the first keywords with the highest occurrence frequency as the target tags.
3. The artificial intelligence based target data detection method of claim 1, wherein the selecting data from the data to be detected according to the target tag to construct a target data set comprises:
identifying a keyword of each data in the data to be detected as a second keyword;
dividing the data with the same second keywords in the data to be detected into a class, and naming each class by the same second keywords;
configuring the weight of each category in the corresponding data;
determining the category with the weight greater than or equal to the configuration weight as a target category of the corresponding data;
defining a label of corresponding data by the target category;
and matching the target label in the defined labels, and acquiring data corresponding to the label matched with the target label to construct the target data set.
4. The artificial intelligence based target data detection method of claim 1, wherein the expanding the target data set to obtain an expanded data set comprises:
acquiring at least one predefined sub-tag, and selecting data corresponding to each sub-tag from the data to be detected to construct at least one sub-data set corresponding to each sub-tag;
calculating the intersection of the target data set and the at least one subdata set to obtain at least one sub-intersection;
and integrating the data in the at least one sub-intersection to obtain the extended data set.
5. The artificial intelligence based target data detection method of claim 1, wherein prior to inputting the extended data set to a pre-trained self-encoder, the artificial intelligence based target data detection method further comprises:
inputting the data in the extended data set into an initial self-encoder for encoding processing to obtain an intermediate layer;
decoding the data of the middle layer, and transmitting the decoded data to an output layer;
comparing the input data and the output data of the initial self-encoder until the input data is the same as the output data, and stopping training to obtain the self-encoder;
acquiring data of an intermediate layer of the self-encoder as the derived feature.
6. The method for detecting target data based on artificial intelligence of claim 1, wherein after training a predetermined neural network with the derived features to obtain a detection model, the method for detecting target data based on artificial intelligence further comprises:
randomly acquiring data from the target data set to construct a verification set;
inputting the data in the verification set into the detection model, and outputting a first detection result;
acquiring data with the first detection result as a target, and calculating the proportion of the acquired data in the verification set;
when the proportion is larger than or equal to the configuration proportion, determining that the detection model passes verification, and storing the detection model to a block chain; or
And when the proportion is smaller than the configuration proportion, determining that the detection model is not verified, updating the at least one sub tag, updating the at least one sub data set according to the updated at least one sub tag, and performing optimization training on the detection model by using the at least one sub data set.
7. The artificial intelligence based target data detection method according to claim 1, wherein after integrating data targeted by the detection result as target data, the artificial intelligence based target data detection method further comprises:
calculating the proportion of the target data in the data to be detected;
when the ratio is lower than a configuration ratio, generating warning information according to the ratio;
determining the associated user of the user to be detected;
and sending the warning information to the terminal equipment of the user to be detected and the terminal equipment of the associated user.
8. An artificial intelligence based object data detecting apparatus, comprising:
the determining unit is used for responding to the received target data detection instruction and determining a target label;
the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring data to be detected corresponding to a user to be detected;
the selection unit is used for selecting data from the data to be detected according to the target label to construct a target data set;
the extension unit is used for carrying out extension processing on the target data set to obtain an extended data set;
the input unit is used for inputting the extended data set to a pre-trained self-encoder and outputting derived features;
the training unit is used for training a preset neural network by using the derived features to obtain a detection model;
the input unit is further configured to input the data to be detected to the detection model and output a detection result, where the detection result includes a target and a non-target;
and the integration unit is used for integrating the data with the detection result as the target data.
9. An electronic device, characterized in that the electronic device comprises:
a memory storing at least one instruction; and
a processor executing instructions stored in the memory to implement the artificial intelligence based target data detection method of any one of claims 1 to 7.
10. A computer-readable storage medium characterized by: the computer-readable storage medium has stored therein at least one instruction that is executable by a processor in an electronic device to implement the artificial intelligence based target data detection method of any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010797367.6A CN111950621A (en) | 2020-08-10 | 2020-08-10 | Target data detection method, device, equipment and medium based on artificial intelligence |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010797367.6A CN111950621A (en) | 2020-08-10 | 2020-08-10 | Target data detection method, device, equipment and medium based on artificial intelligence |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111950621A true CN111950621A (en) | 2020-11-17 |
Family
ID=73333183
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010797367.6A Pending CN111950621A (en) | 2020-08-10 | 2020-08-10 | Target data detection method, device, equipment and medium based on artificial intelligence |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111950621A (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112232606A (en) * | 2020-12-16 | 2021-01-15 | 平安科技（深圳）有限公司 | Service data prediction method, device, equipment and storage medium |
| CN112417007A (en) * | 2020-12-03 | 2021-02-26 | 恒安嘉新（北京）科技股份公司 | Data analysis method and device, electronic equipment and storage medium |
| CN112686762A (en) * | 2020-12-25 | 2021-04-20 | 中国平安人寿保险股份有限公司 | Policy data violation detection method and device, computer equipment and storage medium |
| WO2023029704A1 (en) * | 2021-08-31 | 2023-03-09 | 华为技术有限公司 | Data processing method, apparatus and system |
| CN116542565A (en) * | 2023-05-09 | 2023-08-04 | 上海依蕴宠物用品有限公司 | Pet puffed food management method and system based on proportion detection technology |
| CN116542565B (en) * | 2023-05-09 | 2024-04-16 | 上海依蕴宠物用品有限公司 | Pet puffed food management method and system based on proportion detection technology |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110222554A (en) * | 2019-04-16 | 2019-09-10 | 深圳壹账通智能科技有限公司 | Cheat recognition methods, device, electronic equipment and storage medium |
| CN110348490A (en) * | 2019-06-20 | 2019-10-18 | 宜通世纪科技股份有限公司 | A kind of soil quality prediction technique and device based on algorithm of support vector machine |
| CN110619423A (en) * | 2019-08-06 | 2019-12-27 | 平安科技（深圳）有限公司 | Multitask prediction method and device, electronic equipment and storage medium |
| CN111325069A (en) * | 2018-12-14 | 2020-06-23 | 珠海格力电器股份有限公司 | Production line data processing method and device, computer equipment and storage medium |
| CN111476276A (en) * | 2020-03-17 | 2020-07-31 | 中国平安人寿保险股份有限公司 | Intelligent data classification method and device, electronic equipment and readable storage medium |
-
2020
- 2020-08-10 CN CN202010797367.6A patent/CN111950621A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111325069A (en) * | 2018-12-14 | 2020-06-23 | 珠海格力电器股份有限公司 | Production line data processing method and device, computer equipment and storage medium |
| CN110222554A (en) * | 2019-04-16 | 2019-09-10 | 深圳壹账通智能科技有限公司 | Cheat recognition methods, device, electronic equipment and storage medium |
| CN110348490A (en) * | 2019-06-20 | 2019-10-18 | 宜通世纪科技股份有限公司 | A kind of soil quality prediction technique and device based on algorithm of support vector machine |
| CN110619423A (en) * | 2019-08-06 | 2019-12-27 | 平安科技（深圳）有限公司 | Multitask prediction method and device, electronic equipment and storage medium |
| CN111476276A (en) * | 2020-03-17 | 2020-07-31 | 中国平安人寿保险股份有限公司 | Intelligent data classification method and device, electronic equipment and readable storage medium |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112417007A (en) * | 2020-12-03 | 2021-02-26 | 恒安嘉新（北京）科技股份公司 | Data analysis method and device, electronic equipment and storage medium |
| CN112232606A (en) * | 2020-12-16 | 2021-01-15 | 平安科技（深圳）有限公司 | Service data prediction method, device, equipment and storage medium |
| CN112232606B (en) * | 2020-12-16 | 2021-04-02 | 平安科技（深圳）有限公司 | Service data prediction method, device, equipment and storage medium |
| CN112686762A (en) * | 2020-12-25 | 2021-04-20 | 中国平安人寿保险股份有限公司 | Policy data violation detection method and device, computer equipment and storage medium |
| WO2023029704A1 (en) * | 2021-08-31 | 2023-03-09 | 华为技术有限公司 | Data processing method, apparatus and system |
| CN116542565A (en) * | 2023-05-09 | 2023-08-04 | 上海依蕴宠物用品有限公司 | Pet puffed food management method and system based on proportion detection technology |
| CN116542565B (en) * | 2023-05-09 | 2024-04-16 | 上海依蕴宠物用品有限公司 | Pet puffed food management method and system based on proportion detection technology |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| WO2021189899A1 (en) | Link state tracking method and apparatus, and electronic device and computer storage medium | |
| CN111950621A (en) | Target data detection method, device, equipment and medium based on artificial intelligence | |
| CN112446025A (en) | Federal learning defense method and device, electronic equipment and storage medium | |
| CN112801718B (en) | User behavior prediction method, device, equipment and medium | |
| CN111949708B (en) | Multi-task prediction method, device, equipment and medium based on time sequence feature extraction | |
| CN112231586A (en) | Course recommendation method, device, equipment and medium based on transfer learning | |
| CN113780691A (en) | Data testing method and device, electronic equipment and storage medium | |
| CN111666415A (en) | Topic clustering method and device, electronic equipment and storage medium | |
| CN115081538A (en) | Customer relationship identification method, device, equipment and medium based on machine learning | |
| CN113806434A (en) | Big data processing method, device, equipment and medium | |
| CN111985545A (en) | Target data detection method, device, equipment and medium based on artificial intelligence | |
| CN114862140A (en) | Behavior analysis-based potential evaluation method, device, equipment and storage medium | |
| CN114612194A (en) | Product recommendation method and device, electronic equipment and storage medium | |
| CN114003704A (en) | Method and device for creating designated tag guest group, electronic equipment and storage medium | |
| CN112017763B (en) | Medical image data transmission method, device, equipment and medium | |
| CN111950707A (en) | Behavior prediction method, apparatus, device and medium based on behavior co-occurrence network | |
| CN111651452A (en) | Data storage method and device, computer equipment and storage medium | |
| CN112396547B (en) | Course recommendation method, device, equipment and medium based on unsupervised learning | |
| CN113434542B (en) | Data relationship identification method and device, electronic equipment and storage medium | |
| CN114996386A (en) | Business role identification method, device, equipment and storage medium | |
| CN114185776A (en) | Big data point burying method, device, equipment and medium for application program | |
| CN112115890A (en) | Drunk driving identification method, device, equipment and medium based on artificial intelligence | |
| CN112330080A (en) | Factor screening method, device, equipment and medium based on connectivity graph | |
| CN112860851A (en) | Course recommendation method, device, equipment and medium based on root cause analysis | |
| CN112699285B (en) | Data classification method and device, computer equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |