CN111950621B - Target data detection method, device, equipment and medium based on artificial intelligence - Google Patents
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Abstract
The invention relates to artificial intelligence, and provides a target data detection method, device, equipment and medium based on artificial intelligence, which can expand a target data set, enable a model to be more accurate, input the expanded data set into a self-encoder, output derivative features, support massive data and have short time consumption, meet the requirements on features in more prediction scenes, train a preset neural network by the derivative features to obtain a detection model for detection, integrate data with detection results as target data, further automatically detect and screen the target data in an artificial intelligence mode, solve the problems of low screening efficiency, high error rate and poor reliability, and solve the problems that the traditional classification model cannot process massive data, the feature extraction is difficult and time-consuming, and the time sequence or multi-data set joint feature is difficult to find. The invention also relates to a blockchain technology, and a detection model and target data can be stored in the blockchain.
Description
Technical Field
The present invention relates to the field of artificial intelligence technologies, and in particular, to a target data detection method, apparatus, device, and medium based on artificial intelligence.
Background
At present, with the rapid development of insurance industry, teams of agents are also growing, and partial agents have situations of replacing or brushing data (such as using equipment of other people to do questions) so as to generate a large amount of false data, and the false data can cause larger data noise, reduce data quality and influence the usability of the data due to lower authenticity, so that how to quickly and accurately detect target data becomes a problem to be solved. For the above-mentioned cases, a solution generally adopted in the industry is to screen data with rules set in advance, for example: and manually setting a screening principle, reserving data conforming to the principle, and deleting data not conforming to the principle. The method only relies on artificially defined rules to carry out data statistics, so that the screening efficiency is low, mistakes are easy to occur, and the reliability of the final detection result is poor.
In addition, the screening by using the 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, the time sequence or the joint features of multiple data sets are difficult to find and the like are possibly caused.
Disclosure of Invention
In view of the above, it is necessary to provide a method, a device and a medium for detecting target data 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 solve the problems that the traditional classification model cannot process mass data, the feature extraction is difficult and time-consuming, and the time sequence or the joint feature of multiple data sets is difficult to find.
An artificial intelligence based target data detection method, the 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 tag to construct a target data set;
performing expansion processing on the target data set to obtain an expanded data set;
Inputting the extended data set to a pre-trained self-encoder, outputting derived features;
training a preset neural network by using the derivative characteristics 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;
Integrating the data with the detection result as the target as target data.
According to a preferred embodiment of the present invention, the determining the target tag includes 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 tag, matching the preset tag with the data carried by the target data detection instruction, and determining the matched data as the target tag; or alternatively
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 highest occurrence frequency as the target labels.
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 datum 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 one category, and naming each category 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 the target category of the corresponding data;
defining a label of the corresponding data according to the target category;
And matching the target label in the defined label, 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 performing expansion processing on the target data set to obtain an expanded data set includes:
acquiring at least one pre-defined sub-label, selecting data corresponding to each sub-label from the data to be detected, and constructing at least one sub-data set corresponding to each sub-label;
calculating the intersection of the target data set and the at least one sub data 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, the artificial intelligence based target data detection method further comprises, before inputting the extended data set to a pre-trained self-encoder:
Inputting the data in the extended data set to 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 and the output data are the same, and stopping training to obtain the self-encoder;
Data of an intermediate layer of the self-encoder is acquired as the derived feature.
According to a preferred 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 of which the first detection result is a target, and calculating the proportion of the acquired data in the verification set;
when the proportion is greater than or equal to the configuration proportion, determining that the detection model passes verification, and storing the detection model into a blockchain; or alternatively
And when the proportion is smaller than the configuration proportion, determining that the detection model fails to pass verification, updating the at least one sub-label, updating the at least one sub-data set according to the updated at least one sub-label, and optimally training the detection model by using the at least one sub-data set.
According to a preferred embodiment of the present invention, after integrating the data with the detection result as the target data, the target data detection method based on artificial intelligence further includes:
calculating the duty ratio of the target data in the data to be detected;
When the duty ratio is lower than the configuration duty ratio, generating warning information according to the duty 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:
A determining unit for determining a target tag in response to the received target data detection instruction;
the acquisition unit is used for acquiring to-be-detected data corresponding to the to-be-detected user;
The selection unit is used for selecting data from the data to be detected according to the target tag to construct a target data set;
The expansion unit is used for carrying out expansion processing on the target data set to obtain an expanded data set;
an input unit for inputting the extended data set to a pre-trained self-encoder, outputting derived features;
the training unit is used for training a preset neural network by the derivative characteristics 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
And the processor executes the instructions stored in the memory to realize the target data detection method based on the artificial intelligence.
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 target tag can be determined in response to the received target data detection instruction, the data to be detected corresponding to the user to be detected is obtained, the target data set is constructed by selecting the data from the data to be detected according to the target tag, the target data set is subjected to expansion processing, the expanded data set is obtained, the training of a subsequent model is enabled to be more accurate through data expansion, the expanded data set is input into a pre-trained self-encoder, derived features are output, the features are extracted through the self-encoder, massive data can be supported, the time consumption of the feature extraction process is short, the time sequence or the combined features of multiple data sets can be extracted, the requirements for the features under more prediction scenes are met, the neural network is further trained by the derived features, the detection model is obtained, the data to be detected is input into the detection model, and the detection result is output.
Drawings
FIG. 1 is a flow chart of a preferred embodiment of the artificial intelligence based target data detection method of the present invention.
FIG. 2 is a functional block diagram of a preferred embodiment of the artificial intelligence based target data detection apparatus of the present invention.
FIG. 3 is a schematic diagram of an electronic device implementing a preferred embodiment of an artificial intelligence based target data detection method according to the present invention.
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 artificial intelligence based target data detection method of the present invention. The order of the steps in the flowchart may be changed and some steps may be omitted according to various needs.
The target data detection method based on artificial intelligence is applied to one or more electronic devices, wherein the electronic devices 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 comprises, but is not limited to, microprocessors, application SPECIFIC INTEGRATED Circuits (ASICs), programmable gate arrays (Field-Programmable GATE ARRAY, FPGA), digital processors (DIGITAL SIGNAL processors, DSPs), embedded devices and the like.
The electronic device may be any electronic product that can interact with a user in a human-computer manner, such as a Personal computer, a tablet computer, a smart phone, a Personal digital assistant (Personal DIGITAL ASSISTANT, PDA), a game console, an interactive internet protocol television (Internet Protocol Television, IPTV), a smart wearable device, etc.
The electronic device may also include a network device and/or a user device. Wherein the network device includes, but is not limited to, a single network server, a server group composed of a plurality of network servers, or a Cloud based Cloud Computing (Cloud Computing) composed of a large number of hosts or network servers.
The network in which 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 (Virtual Private Network, VPN), and the like.
S10, determining a target label in response to the received target data detection instruction.
In at least one embodiment of the present invention, the target data detection instructions may be triggered by a designated person to execute according to actual needs.
Of course, the target data detection instruction may also be configured to trigger periodically, so as to screen target data periodically, and avoid that other related service scenarios influence the execution effect of the task due to noise generated by non-target data when the task is executed by using the 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: an operation of importing an address book, etc. is performed.
In at least one embodiment of the present invention, the determining the target tag includes, but is not limited to, one or more of the following:
Analyzing the method body of the target data detection instruction to obtain the data carried by the target data detection instruction, acquiring a preset tag, matching the preset tag with the data carried by the target data detection instruction, and determining the matched data as the target tag.
Through the implementation mode, when the target label is carried in the target data detection instruction, the target label can be directly extracted by the preset label, so that the time for data processing 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 highest occurrence frequency as the target labels.
By the embodiment, when the target tag is not carried in the target data detection instruction, the target tag can be determined through historical target data.
Of course, in other embodiments, the target tag may be determined in other ways, and the invention is not limited.
For example: and receiving the label uploaded by the user as the target label.
S11, obtaining to-be-detected data corresponding to the to-be-detected user.
In this embodiment, the account number of the user to be detected is obtained, all data corresponding to the account number is obtained, and the obtained data is determined to be the data to be detected.
It can be understood that the data corresponding to the account number is not necessarily the data generated by the user to be detected, if other people log in the account number of the user to be detected to operate, the generated data is not the actual data of the user to be detected, and can be regarded as data to be brushed or substituted, namely, non-target data, and the actual data of the user to be detected is the target data.
S12, selecting data from the data to be detected according to the target label to construct a target data set.
In this embodiment, since the data in the target data set all carry the target tag, the data in the target data set are all 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 datum 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 one category, and naming each category 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 the target category of the corresponding data;
defining a label of the corresponding data according to the target category;
And matching the target label in the defined label, and acquiring data corresponding to the label matched with the target label to construct the target data set.
The configuration weights can be configured in a self-defined manner according to actual requirements, and the invention is not limited.
After naming each category by the same keyword, a certain weight needs to be assigned to each category to distinguish importance degrees of different categories, and in particular, multiple data indexes can be comprehensively considered when determining the weight, such as: number of interviews, number of attractive interviews, etc.
S13, performing expansion processing on the target data set to obtain an expanded data set.
It can be understood that the more the data volume is, the more definite the relationship and characteristics between the data are, which is more beneficial to the subsequent feature extraction and model training, so the present embodiment also needs to expand the target data.
Specifically, the performing expansion processing on the target data set to obtain an expanded data set includes:
acquiring at least one pre-defined sub-label, selecting data corresponding to each sub-label from the data to be detected, and constructing at least one sub-data set corresponding to each sub-label;
calculating the intersection of the target data set and the at least one sub data 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: AI interviews were performed with attendance records.
It should be noted that, the manner of constructing at least one sub-data set corresponding to each sub-label is similar to the manner of constructing the target data set, and is not described herein.
And, the data with the at least one sub-tag and the target tag at the same time may be determined as target data, so that the data in the at least one sub-intersection obtained after the intersection processing is performed all belong to the target data.
By the implementation mode, the data expansion can be further executed on the basis of the target data set so as to acquire sufficient data, and a data basis is provided for the extraction of subsequent features and the training of the model.
S14, inputting the extended data set to a pre-trained self-encoder (Autoencoder) and outputting derivative features.
The derived features are features obtained by processing the features 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 massive data can be supported, the time consumption of the feature extraction process is short, and the combined features of time sequences or multiple data sets can be extracted, so that the requirements on the features under more prediction scenes are met.
In at least one embodiment of the present invention, the artificial intelligence based target data detection method further comprises, prior to inputting the extended data set to a pre-trained self-encoder:
Inputting the data in the extended data set to 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 and the output data are the same, and stopping training to obtain the self-encoder;
Data of an intermediate layer of the self-encoder is acquired as the derived feature.
Compared with the traditional feature processing mode, the embodiment utilizes the self-encoder to extract the features, and the self-encoder is continuously trained, so that the data features of the middle layer not only can fully express the data features of the original input layer, but also can obtain more features, and a model for subsequent training is more accurate and reliable.
And S15, training a preset neural network by using the derivative characteristics to obtain a detection model.
The preset neural network refers to a network with a classification function, for example: support vector machine networks (Support Vector Machine, SVM), multi-Layer persistence (MLP), radial basis function (Radial Basis Function, RBF), and the like.
In this embodiment, since the data amount of the derived features is sufficient and the feature expression is sufficient, training the preset neural network with the derived features can make the training effect of the preset neural network better, and further improve the accuracy of model detection.
It should be noted that, in this embodiment, the training manner of the preset neural network is not limited.
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 of which the first detection result is a target, and calculating the proportion of the acquired data in the verification set;
When the proportion is greater than or equal to the configuration proportion, determining that the detection model passes verification, and storing the detection model into a blockchain;
The configuration proportion can be configured in a self-defined manner according to actual requirements, and the invention is not limited.
By the above embodiment, the detection model can be verified with a small amount of target data to ensure usability of the detection model.
Meanwhile, the detection model is stored in a blockchain so as to further ensure the safety of the detection model.
Or when the proportion is smaller than the configuration proportion, determining that the detection model is not verified, updating the at least one sub-label, updating the at least one sub-data set according to the updated at least one sub-label, and carrying out optimization training on the detection model by using the at least one sub-data set.
Through the embodiment, when the detection model fails to pass verification, the corresponding label optimization training can be timely adjusted to train the detection model, so that the adaptability of the detection model is enhanced, and meanwhile, the flexibility of the detection model is improved.
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 implementation mode, the detection and screening of the target data can be automatically performed in an artificial intelligence mode, and the problems of low screening efficiency, high error rate and poor reliability caused by data statistics according to the artificially defined rules in the prior art are solved.
S17, integrating the data with the detection result as the target as target data.
Through the implementation mode, all target data can be automatically screened from the data to be detected by combining an artificial intelligence means, and compared with a traditional mode, the method is more efficient, is not easy to make mistakes, and has stronger practicability.
In order to prevent the 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 the data with the detection result as the target data, the target data detection method based on artificial intelligence further includes:
calculating the duty ratio of the target data in the data to be detected;
When the duty ratio is lower than the configuration duty ratio, generating warning information according to the duty 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 non-target data generated by the user to be detected are excessive currently.
The configuration duty ratio can be configured in a self-defined way according to actual requirements, and the invention is not limited.
Wherein the associated user may include, but is not limited to: and the superior leadership and attendance management personnel of the user to be detected.
Through the embodiment, the warning information is sent to the terminal equipment of the user to be detected, so that the warning effect on the user to be detected can be achieved, the illegal behavior is avoided, meanwhile, the warning information is sent to the terminal equipment of the associated user, the attention of related personnel can be drawn, the problem is timely processed when the problem occurs, the problem is avoided from being enlarged, and the effect of timely stopping the damage is achieved.
According to the technical scheme, the target tag can be determined in response to the received target data detection instruction, the data to be detected corresponding to the user to be detected is obtained, the target data set is constructed by selecting the data from the data to be detected according to the target tag, the target data set is subjected to expansion processing, the expanded data set is obtained, the training of a subsequent model is enabled to be more accurate through data expansion, the expanded data set is input into a pre-trained self-encoder, derived features are output, the features are extracted through the self-encoder, massive data can be supported, the time consumption of the feature extraction process is short, the time sequence or the joint features of multiple data sets can be extracted, the requirements for the features under more prediction scenes are met, the neural network is further trained by the derived features, the detection model is obtained, the data to be detected is input into the detection model, and the detection result is output.
FIG. 2 is a functional block diagram of a preferred embodiment of the artificial intelligence based object data detection device of the present invention. The artificial intelligence based target data detecting apparatus 11 includes a determining unit 110, an acquiring unit 111, a selecting unit 112, an expanding unit 113, an input unit 114, a training unit 115, and an integrating unit 116. The module/unit referred to in the present invention refers to a series of computer program segments capable of being executed by the processor 13 and of performing a fixed function, which are stored in the memory 12. In the present embodiment, the functions of the respective modules/units will be described in detail in the following embodiments.
The determining unit 110 determines the target tag in response to the received target data detection instruction.
In at least one embodiment of the present invention, the target data detection instructions may be triggered by a designated person to execute according to actual needs.
Of course, the target data detection instruction may also be configured to trigger periodically, so as to screen target data periodically, and avoid that other related service scenarios influence the execution effect of the task due to noise generated by non-target data when the task is executed by using the 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: an operation of importing an address book, etc. is performed.
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:
Analyzing the method body of the target data detection instruction to obtain the data carried by the target data detection instruction, acquiring a preset tag, matching the preset tag with the data carried by the target data detection instruction, and determining the matched data as the target tag.
Through the implementation mode, when the target label is carried in the target data detection instruction, the target label can be directly extracted by the preset label, so that the time for data processing 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 highest occurrence frequency as the target labels.
By the embodiment, when the target tag is not carried in the target data detection instruction, the target tag can be determined through historical target data.
Of course, in other embodiments, the target tag may be determined in other ways, and the invention is not limited.
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 number of the user to be detected is obtained, all data corresponding to the account number is obtained, and the obtained data is determined to be the data to be detected.
It can be understood that the data corresponding to the account number is not necessarily the data generated by the user to be detected, if other people log in the account number of the user to be detected to operate, the generated data is not the actual data of the user to be detected, and can be regarded as data to be brushed or substituted, namely, non-target data, and the actual data of the user to be detected is the target data.
The selection unit 112 selects data from the data to be detected according to the target tag to construct a target data set.
In this embodiment, since the data in the target data set all carry the target tag, the data in the target data set are all target data.
In at least one embodiment of the present invention, the selecting unit 112 selects a data construction target data set from the data to be detected according to the target tag includes:
Identifying a keyword of each datum 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 one category, and naming each category 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 the target category of the corresponding data;
defining a label of the corresponding data according to the target category;
And matching the target label in the defined label, and acquiring data corresponding to the label matched with the target label to construct the target data set.
The configuration weights can be configured in a self-defined manner according to actual requirements, and the invention is not limited.
After naming each category by the same keyword, a certain weight needs to be assigned to each category to distinguish importance degrees of different categories, and in particular, multiple data indexes can be comprehensively considered when determining the weight, such as: number of interviews, number of attractive interviews, 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 volume is, the more definite the relationship and characteristics between the data are, which is more beneficial to the subsequent feature extraction and model training, so the present embodiment also needs to expand the target data.
Specifically, the expanding unit 113 performs expanding processing on the target data set, to obtain an expanded data set, including:
acquiring at least one pre-defined sub-label, selecting data corresponding to each sub-label from the data to be detected, and constructing at least one sub-data set corresponding to each sub-label;
calculating the intersection of the target data set and the at least one sub data 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: AI interviews were performed with attendance records.
It should be noted that, the manner of constructing at least one sub-data set corresponding to each sub-label is similar to the manner of constructing the target data set, and is not described herein.
And, the data with the at least one sub-tag and the target tag at the same time may be determined as target data, so that the data in the at least one sub-intersection obtained after the intersection processing is performed all belong to the target data.
By the implementation mode, the data expansion can be further executed on the basis of the target data set so as to acquire sufficient data, and a data basis is provided for the extraction of subsequent features and the training of the model.
The input unit 114 inputs the extended data set to a pre-trained self-encoder (Autoencoder) and outputs derived features.
The derived features are features obtained by processing the features 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 massive data can be supported, the time consumption of the feature extraction process is short, and the combined features of time sequences or multiple data sets can be extracted, so that the requirements on the features under more prediction scenes are met.
In at least one embodiment of the present invention, before the extended data set is input to a pre-trained self-encoder, 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 and the output data are the same, and stopping training to obtain the self-encoder;
Data of an intermediate layer of the self-encoder is acquired as the derived feature.
Compared with the traditional feature processing mode, the embodiment utilizes the self-encoder to extract the features, and the self-encoder is continuously trained, so that the data features of the middle layer not only can fully express the data features of the original input layer, but also can obtain more features, and a 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.
The preset neural network refers to a network with a classification function, for example: support vector machine networks (Support Vector Machine, SVM), multi-Layer persistence (MLP), radial basis function (Radial Basis Function, RBF), and the like.
In this embodiment, since the data amount of the derived features is sufficient and the feature expression is sufficient, training the preset neural network with the derived features can make the training effect of the preset neural network better, and further improve the accuracy of model detection.
It should be noted that, in this embodiment, the training manner of the preset neural network is not limited.
In at least one embodiment of the present invention, after training a preset neural network with the derived features to obtain a detection model, 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 of which the first detection result is a target, and calculating the proportion of the acquired data in the verification set;
When the proportion is greater than or equal to the configuration proportion, determining that the detection model passes verification, and storing the detection model into a blockchain;
The configuration proportion can be configured in a self-defined manner according to actual requirements, and the invention is not limited.
By the above embodiment, the detection model can be verified with a small amount of target data to ensure usability of the detection model.
Meanwhile, the detection model is stored in a blockchain so as to further ensure the safety of the detection model.
Or when the proportion is smaller than the configuration proportion, determining that the detection model is not verified, updating the at least one sub-label, updating the at least one sub-data set according to the updated at least one sub-label, and carrying out optimization training on the detection model by using the at least one sub-data set.
Through the embodiment, when the detection model fails to pass verification, the corresponding label optimization training can be timely adjusted to train the detection model, so that the adaptability of the detection model is enhanced, and meanwhile, the flexibility of the detection model is improved.
The input unit 114 inputs the data to be detected to the detection model, and outputs a detection result, wherein 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 implementation mode, the detection and screening of the target data can be automatically performed in an artificial intelligence mode, and the problems of low screening efficiency, high error rate and poor reliability caused by data statistics according to the artificially defined rules in the prior art are solved.
The integrating unit 116 integrates the data whose detection result is the target as target data.
Through the implementation mode, all target data can be automatically screened from the data to be detected by combining an artificial intelligence means, and compared with a traditional mode, the method is more efficient, is not easy to make mistakes, and has stronger practicability.
In order to prevent the 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 with a detection result as a target as target data, a duty ratio of the target data in the data to be detected is calculated;
When the duty ratio is lower than the configuration duty ratio, generating warning information according to the duty 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 non-target data generated by the user to be detected are excessive currently.
The configuration duty ratio can be configured in a self-defined way according to actual requirements, and the invention is not limited.
Wherein the associated user may include, but is not limited to: and the superior leadership and attendance management personnel of the user to be detected.
Through the embodiment, the warning information is sent to the terminal equipment of the user to be detected, so that the warning effect on the user to be detected can be achieved, the illegal behavior is avoided, meanwhile, the warning information is sent to the terminal equipment of the associated user, the attention of related personnel can be drawn, the problem is timely processed when the problem occurs, the problem is avoided from being enlarged, and the effect of timely stopping the damage is achieved.
According to the technical scheme, the target tag can be determined in response to the received target data detection instruction, the data to be detected corresponding to the user to be detected is obtained, the target data set is constructed by selecting the data from the data to be detected according to the target tag, the target data set is subjected to expansion processing, the expanded data set is obtained, the training of a subsequent model is enabled to be more accurate through data expansion, the expanded data set is input into a pre-trained self-encoder, derived features are output, the features are extracted through the self-encoder, massive data can be supported, the time consumption of the feature extraction process is short, the time sequence or the joint features of multiple data sets can be extracted, the requirements for the features under more prediction scenes are met, the neural network is further trained by the derived features, the detection model is obtained, the data to be detected is input into the detection model, and the detection result is output.
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.
The electronic device 1 may comprise a memory 12, a processor 13 and a bus, and may further comprise a computer program stored in the memory 12 and executable on the processor 13, such as an artificial intelligence based target data detection program.
It will be appreciated 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 of the electronic device 1, the electronic device 1 may be a bus type structure, a star type structure, the electronic device 1 may further comprise more or less other hardware or software than illustrated, or a different arrangement of components, for example, the electronic device 1 may further comprise an input-output device, a network access device, etc.
It should be noted that the electronic device 1 is only used as an example, and other electronic products that may be present in the present invention or may be present in the future are also included in the scope of the present invention by way of reference.
The memory 12 includes at least one type of readable storage medium including flash memory, a removable hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 12 may in some embodiments be an internal storage unit of the electronic device 1, such as a mobile 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 memory card (SMART MEDIA CARD, SMC), a Secure Digital (SD) card, a flash memory card (FLASH CARD) or the like, which are 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 for storing application software installed in the electronic device 1 and various types of data, such as code of an artificial intelligence-based target data detection program, but also for temporarily storing data that has been output or is to be output.
The processor 13 may be comprised of integrated circuits in some embodiments, for example, a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functions, including one or more central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors, various control chips, and the like. The processor 13 is a Control Unit (Control Unit) of the electronic device 1, connects the respective components of the entire electronic device 1 using various interfaces and lines, executes or executes programs or modules stored in the memory 12 (for example, executes an artificial intelligence-based target data detection program or the like), and invokes data stored in the memory 12 to perform various functions of the electronic device 1 and process the data.
The processor 13 executes the operating system of the electronic device 1 and various types of applications installed. The processor 13 executes the application program to implement the steps of the various embodiments of the artificial intelligence based target data detection method described above, such as the steps shown in fig. 1.
Illustratively, the computer program may be partitioned into one or more modules/units that are stored in the memory 12 and executed by the processor 13 to complete the present invention. The one or more modules/units may be a series of instruction segments of a computer program capable of performing a specific function for describing the execution of the computer program in the electronic device 1. For example, the computer program may be divided into a determining unit 110, an acquiring unit 111, a selecting unit 112, an expanding unit 113, an input unit 114, a training unit 115, an integrating unit 116.
The integrated units implemented in the form of software functional modules described above may be stored in a computer readable storage medium. The software functional modules are stored in a storage medium and include instructions for causing a computer device (which may be a personal computer, a computer device, or a network device, etc.) or a processor (processor) to perform portions of the artificial intelligence-based target data detection method according to various embodiments of the invention.
The integrated modules/units of the electronic device 1 may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as separate products. Based on this understanding, the present invention may also be implemented by a computer program for instructing a relevant hardware device to implement all or part of the procedures of the above-mentioned embodiment method, where the computer program may be stored in a computer readable storage medium and the computer program may be executed by a processor to implement the steps of each of the above-mentioned method embodiments.
Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a 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 from the use of blockchain nodes, and the like.
The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The blockchain (Blockchain), essentially a de-centralized database, is a string of data blocks that are generated in association using cryptographic methods, each of which contains information from a batch of network transactions for verifying the targeting (anti-counterfeiting) of the information and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.
The bus may be a peripheral component interconnect standard (PERIPHERAL COMPONENT INTERCONNECT, PCI) bus, or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. For ease of illustration, only one arrow is shown in FIG. 3, but only one bus or one type of bus is not shown. The bus is arranged to enable a connection communication between the memory 12 and at least one processor 13 or the like.
Although not shown, the electronic device 1 may further comprise a power source (such as a battery) for powering the various components, which may preferably be logically connected to the at least one processor 13 via a power management means, so as to perform functions such as charge management, discharge management, and power consumption management via the power management means. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device 1 may further include various sensors, bluetooth modules, wi-Fi modules, etc., which will not be described herein.
Further, the electronic device 1 may also comprise a network interface, optionally the network interface may comprise a wired interface and/or a wireless interface (e.g. WI-FI interface, bluetooth interface, etc.), typically used for establishing a communication connection between the electronic device 1 and other electronic devices.
The electronic device 1 may optionally further comprise a user interface, which may be a Display, an input unit, such as a Keyboard (Keyboard), or 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, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device 1 and for displaying a visual user interface.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
Fig. 3 shows only an electronic device 1 with components 12-13, it being 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 may combine certain components, or a different arrangement of components.
In connection with fig. 1, the memory 12 in the electronic device 1 stores a plurality of instructions to implement an artificial intelligence based target data detection method, the processor 13 being executable 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 tag to construct a target data set;
performing expansion processing on the target data set to obtain an expanded data set;
Inputting the extended data set to a pre-trained self-encoder, outputting derived features;
training a preset neural network by using the derivative characteristics 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;
Integrating the data with the detection result as the target as target data.
Specifically, the specific implementation method of the above instructions by the processor 13 may refer to the description of the relevant steps in the corresponding embodiment of fig. 1, which is not repeated herein.
In the several embodiments provided in the present invention, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
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 characteristics 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 evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. A plurality of units or means recited in the system claims can also be implemented by means of software or hardware by means of one unit or means. The terms second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (9)
1. The target data detection method based on the artificial intelligence is characterized by comprising the following steps of:
Determining a target tag in response to the received target data detection instruction;
the obtaining the data to be detected corresponding to the user to be detected comprises the following steps: acquiring an account number of the user to be detected, acquiring all data corresponding to the account number, and determining all data as the data to be detected, wherein the all data comprise actual data generated by the user to be detected;
Selecting data from the data to be detected according to the target tag to construct a target data set;
Performing expansion processing on the target data set to obtain an expanded data set, wherein the step of obtaining the expanded data set comprises the following steps: acquiring at least one pre-defined sub-label, selecting data corresponding to each sub-label from the data to be detected, and constructing at least one sub-data set corresponding to each sub-label; calculating the intersection of the target data set and the at least one sub data set to obtain at least one sub intersection; integrating the data in the at least one sub-intersection to obtain the extended data set;
Inputting the extended data set to a pre-trained self-encoder, outputting derived features;
training a preset neural network by using the derivative characteristics 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;
Integrating the data with the detection result as the target as target data.
2. The artificial intelligence based target data detection method of claim 1, wherein the determining target tags comprises a combination of one or more of:
analyzing the method body of the target data detection instruction to obtain data carried by the target data detection instruction, acquiring a preset tag, matching the preset tag with the data carried by the target data detection instruction, and determining the matched data as the target tag; or alternatively
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 highest occurrence frequency as the target labels.
3. The artificial intelligence based target data detection method of claim 1, wherein the selecting data from the data to be detected to construct a target data set according to the target tag comprises:
Identifying a keyword of each datum 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 one category, and naming each category 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 the target category of the corresponding data;
defining a label of the corresponding data according to the target category;
And matching the target label in the defined label, 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 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 to 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 and the output data are the same, and stopping training to obtain the self-encoder;
Data of an intermediate layer of the self-encoder is acquired as the derived feature.
5. The method for detecting target data based on artificial intelligence according to 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 of which the first detection result is a target, and calculating the proportion of the acquired data in the verification set;
when the proportion is greater than or equal to the configuration proportion, determining that the detection model passes verification, and storing the detection model into a blockchain; or alternatively
And when the proportion is smaller than the configuration proportion, determining that the detection model fails to pass verification, updating the at least one sub-label, updating the at least one sub-data set according to the updated at least one sub-label, and optimally training the detection model by using the at least one sub-data set.
6. The artificial intelligence-based target data detection method according to claim 1, wherein after integrating the data whose detection result is the target as the target data, the artificial intelligence-based target data detection method further comprises:
calculating the duty ratio of the target data in the data to be detected;
When the duty ratio is lower than the configuration duty ratio, generating warning information according to the duty 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.
7. An artificial intelligence based target data detection apparatus, comprising:
A determining unit for determining a target tag in response to the received target data detection instruction;
the obtaining unit is configured to obtain to-be-detected data corresponding to a to-be-detected user, including: acquiring an account number of the user to be detected, acquiring all data corresponding to the account number, and determining all data as the data to be detected, wherein the all data comprise actual data generated by the user to be detected;
The selection unit is used for selecting data from the data to be detected according to the target tag to construct a target data set;
An expansion unit, configured to perform expansion processing on the target data set, to obtain an expanded data set, including: acquiring at least one pre-defined sub-label, selecting data corresponding to each sub-label from the data to be detected, and constructing at least one sub-data set corresponding to each sub-label; calculating the intersection of the target data set and the at least one sub data set to obtain at least one sub intersection; integrating the data in the at least one sub-intersection to obtain the extended data set;
an input unit for inputting the extended data set to a pre-trained self-encoder, outputting derived features;
the training unit is used for training a preset neural network by the derivative characteristics 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.
8. 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 of any one of claims 1 to 6.
9. A computer-readable storage medium, characterized by: the computer-readable storage medium has stored therein at least one instruction that is executed by a processor in an electronic device to implement the artificial intelligence based target data detection method of any one of claims 1 to 6.
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