CN113761908B - Method and device for processing stock user information - Google Patents
Method and device for processing stock user information Download PDFInfo
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Abstract
The invention discloses a device and a method for processing stock user information, and relates to the technical field of computers. One embodiment of the method comprises the following steps: responding to a processing request of stock user information, and training by adopting a natural language processing algorithm and a gradient lifting tree algorithm to obtain an identification model; acquiring stock user information, calling an identification model to detect the stock user information, obtaining target words and target word grades in the stock user information, and generating a grade set of the stock user information; and according to the highest grade in the grade set, invoking a corresponding flow in a processing flow library to process the stock user information. According to the embodiment, a natural language processing algorithm and a gradient lifting tree algorithm can be adopted for training to obtain an identification model, the identification model is called to detect stock user information, target words and target word grades existing in the stock user information are obtained, and then corresponding processes are called to process, so that safety control is conducted on the content of the stock user information.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a method and an apparatus for processing stock user information.
Background
The user information content in the internet system needs to be subjected to content security management from the system level, so that the user information content is prevented from violating national laws and regulations or infringing rights and interests of other people. In the prior art, a fixed target word stock is generally used for detecting and processing information newly registered or newly released by a user.
In the process of realizing the invention, the prior art has at least the following problems:
Because the target word stock is fixed, the target word stock has little effect when the information of the stock user is detected, and the information content of the stock user cannot be safely managed and controlled.
Disclosure of Invention
In view of this, the embodiment of the invention provides a method and a device for processing stock user information, which can adopt a natural language processing algorithm and a gradient lifting tree algorithm to perform model training to obtain an identification model capable of identifying target words and expanded words of the target words, and call the identification model to detect the stock user information to obtain the target words and target word levels existing in the stock user information, and call corresponding processes in a processing process library to perform processing to safely control the content of the stock user information.
To achieve the above object, according to an aspect of the embodiments of the present invention, there is provided a method for processing stock user information, including:
Responding to a processing request of stock user information, calling a user information sample, sample characteristics and a sample label, and training by adopting an algorithm and a gradient lifting tree algorithm to obtain an identification model;
acquiring stock user information, calling the identification model to detect the stock user information, obtaining target words and target word grades in the stock user information, and generating a grade set of the stock user information;
and according to the highest grade in the grade set, invoking a flow corresponding to the highest grade in a processing flow library, and updating the authority configuration information of the stock users.
Optionally, the retrieving the user information sample, the sample feature, the sample tag includes:
Invoking user information historical data of known processing results, and selecting a preset number of historical user information from the historical user information to serve as the user information sample;
Taking the characteristic of the historical user information as the sample characteristic; and taking the corresponding grade of the historical user information in the processing result and the grade of the historical target word in the historical user information as the sample label.
Optionally, training by using a natural language processing algorithm and a gradient lifting tree algorithm to obtain an identification model, including:
performing word segmentation processing on the historical user information by adopting a natural language processing algorithm, and performing voice and semantic expansion on words obtained by the word segmentation processing based on the called public opinion information to obtain a historical expansion word set;
And taking the historical user information and the historical expanded word set together as training samples, and training by adopting a gradient lifting tree algorithm according to the training samples, sample characteristics and sample labels to obtain the identification model.
Optionally, the level set of stock user information includes: target words, target word grades, stock user information corresponding to the target words, and user identifications of stock users corresponding to the target words;
And according to the highest level in the level set, invoking a flow corresponding to the highest level in a processing flow library to update the authority configuration information of the stock user, wherein the method comprises the following steps:
for the same user identification, taking the highest grade of the target word grades of the target words in the stock user information as the grade of the stock user information; taking the highest level in the levels of all the stock user information of the stock users as the level of the stock users;
and calling a flow corresponding to the level of the stock user in a processing flow library, and updating the authority configuration information of the stock user.
Optionally, the updating the authority configuration information of the stock user by calling a flow corresponding to the level of the stock user in the processing flow library includes:
When the level of the stock user is high, disabling the user account use permission of the stock user and notifying the stock user;
When the class of the stock user is the same, disabling the display authority of the user information in the class of the stock user and notifying the stock user;
when the level of the stock user is low, limiting and displaying the display authority of the target word with low level in the user information of the stock user, and informing the stock user;
Wherein, the user information at least comprises: user registration information and user release information.
Optionally, after the identifying model is called to detect the stock user information, the method further includes:
Generating an expanded target word set according to the obtained target words and target word grades in the stock user information and the expanded words and expanded word grades generated in the detection process;
The extended target word set is used for detecting new user registration information or new user release information.
Optionally, the method for processing stock user information further includes:
obtaining model evaluation data, evaluating the identification model according to the model evaluation data, and generating a first evaluation result; optimizing the identification model according to the first evaluation result; and/or the number of the groups of groups,
After updating the authority configuration information of the stock users, receiving user feedback information sent by a client, and generating a second evaluation result according to the user feedback information; and optimizing the identification model according to the second evaluation result.
According to still another aspect of an embodiment of the present invention, there is provided a processing apparatus of stock user information, including:
The training module is used for responding to the processing request of the stock user information, calling the user information sample, the sample characteristics and the sample labels, and training by adopting a natural language processing algorithm and a gradient lifting tree algorithm to obtain an identification model;
the detection module is used for acquiring stock user information, calling the identification model to detect the stock user information, obtaining target words and target word grades in the stock user information, and generating a grade set of the stock user information;
and the processing module is used for calling a flow corresponding to the highest grade in the processing flow library according to the highest grade in the grade set, and updating the authority configuration information of the stock user.
Optionally, the retrieving the user information sample, the sample feature, the sample tag includes:
Invoking user information historical data of known processing results, and selecting a preset number of historical user information from the historical user information to serve as the user information sample;
Taking the characteristic of the historical user information as the sample characteristic; and taking the corresponding grade of the historical user information in the processing result and the grade of the historical target word in the historical user information as the sample label.
Optionally, training by using a natural language processing algorithm and a gradient lifting tree algorithm to obtain an identification model, including:
performing word segmentation processing on the historical user information by adopting a natural language processing algorithm, and performing voice and semantic expansion on words obtained by the word segmentation processing based on the called public opinion information to obtain a historical expansion word set;
And taking the historical user information and the historical expanded word set together as training samples, and training by adopting a gradient lifting tree algorithm according to the training samples, sample characteristics and sample labels to obtain the identification model.
Optionally, the level set of stock user information includes: target words, target word grades, stock user information corresponding to the target words, and user identifications of stock users corresponding to the target words;
And according to the highest level in the level set, invoking a flow corresponding to the highest level in a processing flow library to update the authority configuration information of the stock user, wherein the method comprises the following steps:
for the same user identification, taking the highest grade of the target word grades of the target words in the stock user information as the grade of the stock user information; taking the highest level in the levels of all the stock user information of the stock users as the level of the stock users;
and calling a flow corresponding to the level of the stock user in a processing flow library, and updating the authority configuration information of the stock user.
Optionally, the updating the authority configuration information of the stock user by calling a flow corresponding to the level of the stock user in the processing flow library includes:
When the level of the stock user is high, disabling the user account use permission of the stock user and notifying the stock user;
When the class of the stock user is the same, disabling the display authority of the user information in the class of the stock user and notifying the stock user;
when the level of the stock user is low, limiting and displaying the display authority of the target word with low level in the user information of the stock user, and informing the stock user;
Wherein, the user information at least comprises: user registration information and user release information.
Optionally, after the identifying model is called to detect the stock user information, the method further includes:
Generating an expanded target word set according to the obtained target words and target word grades in the stock user information and the expanded words and expanded word grades generated in the detection process;
The extended target word set is used for detecting new user registration information or new user release information.
Optionally, the above processing device for stock user information is further configured to:
obtaining model evaluation data, evaluating the identification model according to the model evaluation data, and generating a first evaluation result; optimizing the identification model according to the first evaluation result; and/or the number of the groups of groups,
After updating the authority configuration information of the stock users, receiving user feedback information sent by a client, and generating a second evaluation result according to the user feedback information; and optimizing the identification model according to the second evaluation result.
According to another aspect of an embodiment of the present invention, there is provided an electronic device for processing stock user information, including:
One or more processors;
Storage means for storing one or more programs,
When the one or more programs are executed by the one or more processors, the one or more processors are enabled to implement the method for processing stock user information provided by the present invention.
According to still another aspect of the embodiments of the present invention, there is provided a computer-readable medium having stored thereon a computer program which, when executed by a processor, implements the method for processing stock user information provided by the present invention.
One embodiment of the above invention has the following advantages or benefits: because a natural language processing algorithm and a gradient lifting tree algorithm are adopted for model training, an identification model capable of identifying target words and expansion words of the target words is obtained, and the identification model is called to detect stock user information so as to perform corresponding processing; after the stock user information is detected, an expanded word set can be obtained for detecting new user information; the method solves the technical problems that the prior art cannot safely control the information content of the stock users and the user information is not well safely controlled by using a fixed word stock; and further, the method and the device can safely control the information content of the stored user, and achieve the technical effect of better controlling the new user information based on the original fixed word stock and the obtained expanded word set.
Further effects of the above-described non-conventional alternatives are described below in connection with the embodiments.
Drawings
The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
fig. 1 is a schematic diagram of the main flow of a method for processing stock user information according to a first embodiment of the present invention;
fig. 2 is a schematic diagram of a flow of a method for processing stock user information according to a second embodiment of the present invention;
FIG. 3 is an exemplary system architecture diagram in which embodiments of the present invention may be applied;
Fig. 4 is a schematic diagram of a computer system suitable for use in implementing an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, in which various details of the embodiments of the present invention are included to facilitate understanding, and are to be considered merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 is a schematic diagram of main flow of a method for processing stock user information according to a first embodiment of the present invention, as shown in fig. 1, including:
Step 101, responding to a processing request of stock user information, calling a user information sample, a sample feature and a sample label, and training by adopting a natural language processing algorithm and a gradient lifting tree algorithm to obtain an identification model;
102, acquiring stock user information, calling the identification model to detect the stock user information, obtaining target words and target word grades in the stock user information, and generating a grade set of the stock user information;
And 103, according to the highest grade in the grade set, invoking a flow corresponding to the highest grade in a processing flow library, and updating the authority configuration information of the stock user.
The user information may be information that the user sends to the internet through the client, such as: information registered by the user (such as a user name, address information filled by the user and the like), information published by the user on the platform (such as short sentence information and article information published by the user in a forum, comment information and message information published in a shopping website, exchange information in a net friend group and the like); the stock user information may be user information currently resident in the internet; the target word may be a word that violates national laws and regulations or violates the interests of others, such as a "sensitive word" that is generally considered, or a word that cannot be revealed based on platform specifications; the target word level can be preset, can be represented by numbers (such as 0, 1, 2 and 3), can also be represented by language description (such as none, low, medium and high, and zero, one and two levels), and can be set to be more safely controlled as the level number is higher; the process library may store preset process methods corresponding to each level, and a process method with a higher corresponding level may be set as a stricter process method.
The embodiment of the invention provides a method and a device for processing stock user information, which can adopt a natural language processing algorithm and a gradient lifting tree algorithm to carry out model training to obtain an identification model capable of identifying target words and expansion words of the target words, and call the identification model to detect the stock user information to obtain the target words and the target word grades in the stock user information, and call corresponding processes in a processing process library to carry out processing to safely manage and control the content of the stock user information.
In some embodiments, the retrieving the user information sample, sample feature, sample tag includes:
Invoking user information historical data of known processing results, and selecting a preset number of historical user information from the historical user information to serve as the user information sample; taking the characteristic of the historical user information as the sample characteristic; and taking the corresponding grade of the historical user information in the processing result and the grade of the historical target word in the historical user information as the sample label.
Further, in some embodiments, training with a natural language processing algorithm and a gradient-lifting tree algorithm to obtain the recognition model includes:
Performing word segmentation processing on the historical user information by adopting a natural language processing algorithm, and performing voice and semantic expansion on words obtained by the word segmentation processing based on the called public opinion information to obtain a historical expansion word set; and taking the historical user information and the historical expanded word set together as training samples, and training by adopting a gradient lifting tree algorithm according to the training samples, sample characteristics and sample labels to obtain the identification model.
The features may be used to describe user information such as: the frequency of changing the piece of user information, whether the piece of user information contains known target words, the number of the contained known target words or the target word grade, whether the user account corresponding to the piece of user information has a security management record, whether the user account corresponding to the piece of user information never initiates a complaint request and the like. The sample characteristics can be learned by other modes, can be obtained according to the regulation of a network platform, and can be updated through subsequent optimization; the public opinion information can contain public opinion information, and can also comprise information types required to be safely controlled and specified by national related regulations or network platforms;
The target word can be expanded in voice and semanteme by adopting a natural language processing algorithm to obtain an expanded word of the target word, and the expanded word can be regarded as a word which has certain association with the target word in voice and/or semanteme; based on this association, in the daily language use of people, the corresponding target word can be usually associated by the expansion word; therefore, since the target word needs to be securely controlled, the expansion word needs to be securely controlled, and the prior art lacks the security control for the expansion word. By training the recognition model through the method, the recognition model can be made to recognize according to the input user information based on the existing sample, sample characteristics, sample labels and public opinion information, and target words and target word grades in the user information are output; in the recognized target words in the user information, words expanded by the known target words can exist, so that the effect of more comprehensively and safely managing the user information is achieved.
In some embodiments, the hierarchical set of stock user information comprises: target words, target word grades, stock user information corresponding to the target words, and user identifications of stock users corresponding to the target words;
And according to the highest level in the level set, invoking a flow corresponding to the highest level in a processing flow library to update the authority configuration information of the stock user, wherein the method comprises the following steps:
For the same user identification, taking the highest grade of the target word grades of the target words in the stock user information as the grade of the stock user information; taking the highest level in the levels of all the stock user information of the stock users as the level of the stock users; and calling a flow corresponding to the level of the stock user in a processing flow library, and updating the authority configuration information of the stock user.
Further, in some embodiments, the invoking the process corresponding to the level of the stock user in the process flow library updates the authority configuration information of the stock user, including:
When the level of the stock user is high, disabling the user account use permission of the stock user and notifying the stock user; when the class of the stock user is the same, disabling the display authority of the user information in the class of the stock user and notifying the stock user; when the level of the stock user is low, limiting and displaying the display authority of the target word with low level in the user information of the stock user, and informing the stock user;
Wherein, the user information at least comprises: user registration information and user release information.
After the stock user information is detected by using the recognition model, target words and target word grades in the stock user information can be obtained, so that a grade set of the stock user information is generated, and the grade of the stock user information and the grade of the stock user can be determined according to the grade set by the method; such as:
For user identification a, it registers user account A1; if the stock user A1 issues the information B1 and the information B2, detecting to determine that the information B1 contains the target word c and the target word d, wherein the grade of the target word c is 1, the grade of the target word d is 3, and detecting that the information B2 does not contain the target word; from the above information, the following information can be derived: the level of the stock user information B1 is determined to be 3 by the level of the target word d, and the level of the stock user information B2 is 0; the rank of the stock user A1 is determined to be 3 by the rank of the stock user information B1.
In the processing flow, when the level of the stock users is high, the account numbers of the high-level users can be frozen after manual auditing; when the class of the stock users is the middle, the corresponding user information can be reset for the middle class account; when the level of the stock users is low, the corresponding user information can be displayed by using a mask.
In some practical applications, the same actual user (natural person) may have multiple user identities (e.g. multiple accounts are registered through different authentication information), where the natural person may be rated: by identifying the natural person, the level of the natural person is the same as the highest level in all user levels, and the authority configuration information of all users registered by the natural person is updated according to the flow corresponding to the level of the natural person.
In some embodiments, after invoking the identification model to detect the stock user information, further comprising:
Generating an expanded target word set according to the obtained target words and target word grades in the stock user information and the expanded words and expanded word grades generated in the detection process;
The extended target word set is used for detecting new user registration information or new user release information.
According to the recognition model obtained through training, the invention can detect the stock user information and can also generate an expanded target word set for detecting new user registration information or new user release information according to the target words and the target word grades in the stock user information and the expanded words and the expanded word grades generated in the detection process. In some practical applications, the expanded target word set can be called by other system interfaces or other network platforms, and is directly used for detecting security control of other information.
In some embodiments, the method for processing stock user information further includes:
obtaining model evaluation data, evaluating the identification model according to the model evaluation data, and generating a first evaluation result; optimizing the identification model according to the first evaluation result; and/or the number of the groups of groups,
After updating the authority configuration information of the stock users, receiving user feedback information sent by a client, and generating a second evaluation result according to the user feedback information; and optimizing the identification model according to the second evaluation result.
The method can optimize the identification model in time so as to achieve more accurate identification and better use effect.
Fig. 2 is a schematic diagram of main modules of an apparatus 200 for processing stock user information according to a third embodiment of the present invention, and as shown in fig. 2, the apparatus 200 for processing stock user information includes:
The training module 201 is configured to, in response to a processing request for the stock user information, invoke a user information sample, a sample feature, and a sample tag, and train by using a natural language processing algorithm and a gradient lifting tree algorithm to obtain an identification model;
The detection module 202 is configured to obtain stock user information, invoke the recognition model to detect the stock user information, obtain a target word and a target word level existing in the stock user information, and generate a level set of the stock user information;
and the processing module 203 is configured to invoke a flow corresponding to the highest level in the processing flow library according to the highest level in the level set, and update the authority configuration information of the stock user.
The user information may be information that the user sends to the internet through the client, such as: information registered by the user (such as a user name, address information filled by the user and the like), information published by the user on the platform (such as short sentence information and article information published by the user in a forum, comment information and message information published in a shopping website, exchange information in a net friend group and the like); the stock user information may be user information currently resident in the internet; the target word may be a word that violates national laws and regulations or violates the interests of others, such as a "sensitive word" that is generally considered, or a word that cannot be revealed based on platform specifications; the target word level can be preset, can be represented by numbers (such as 0, 1, 2 and 3), can also be represented by language description (such as none, low, medium and high, and zero, one and two levels), and can be set to be more safely controlled as the level number is higher; the process library may store preset process methods corresponding to each level, and a process method with a higher corresponding level may be set as a stricter process method.
The embodiment of the invention provides a method and a device for processing stock user information, which can adopt a natural language processing algorithm and a gradient lifting tree algorithm to carry out model training to obtain an identification model capable of identifying target words and expansion words of the target words, and call the identification model to detect the stock user information to obtain the target words and the target word grades in the stock user information, and call corresponding processes in a processing process library to carry out processing to safely manage and control the content of the stock user information.
In some embodiments, the retrieving the user information sample, sample feature, sample tag includes:
Invoking user information historical data of known processing results, and selecting a preset number of historical user information from the historical user information to serve as the user information sample;
Taking the characteristic of the historical user information as the sample characteristic; and taking the corresponding grade of the historical user information in the processing result and the grade of the historical target word in the historical user information as the sample label.
Further, in some embodiments, training with a natural language processing algorithm and a gradient-lifting tree algorithm to obtain the recognition model includes:
performing word segmentation processing on the historical user information by adopting a natural language processing algorithm, and performing voice and semantic expansion on words obtained by the word segmentation processing based on the called public opinion information to obtain a historical expansion word set;
And taking the historical user information and the historical expanded word set together as training samples, and training by adopting a gradient lifting tree algorithm according to the training samples, sample characteristics and sample labels to obtain the identification model.
The features may be used to describe user information such as: the frequency of changing the piece of user information, whether the piece of user information contains known target words, the number of the contained known target words or the target word grade, whether the user account corresponding to the piece of user information has a security management record, whether the user account corresponding to the piece of user information never initiates a complaint request and the like. The sample characteristics can be learned by other modes, can be obtained according to the regulation of a network platform, and can be updated through subsequent optimization; the public opinion information can contain public opinion information, and can also comprise information types required to be safely controlled and specified by national related regulations or network platforms;
The target word can be expanded in voice and semanteme by adopting a natural language processing algorithm to obtain an expanded word of the target word, and the expanded word can be regarded as a word which has certain association with the target word in voice and/or semanteme; based on this association, in the daily language use of people, the corresponding target word can be usually associated by the expansion word; therefore, since the target word needs to be securely controlled, the expansion word needs to be securely controlled, and the prior art lacks the security control for the expansion word. By training the recognition model through the method, the recognition model can be made to recognize according to the input user information based on the existing sample, sample characteristics, sample labels and public opinion information, and target words and target word grades in the user information are output; in the recognized target words in the user information, words expanded by the known target words can exist, so that the effect of more comprehensively and safely managing the user information is achieved.
In some embodiments, the hierarchical set of stock user information comprises: target words, target word grades, stock user information corresponding to the target words, and user identifications of stock users corresponding to the target words;
And according to the highest level in the level set, invoking a flow corresponding to the highest level in a processing flow library to update the authority configuration information of the stock user, wherein the method comprises the following steps:
for the same user identification, taking the highest grade of the target word grades of the target words in the stock user information as the grade of the stock user information; taking the highest level in the levels of all the stock user information of the stock users as the level of the stock users;
and calling a flow corresponding to the level of the stock user in a processing flow library, and updating the authority configuration information of the stock user.
Further, in some embodiments, the invoking the process corresponding to the level of the stock user in the process flow library updates the authority configuration information of the stock user, including:
When the level of the stock user is high, disabling the user account use permission of the stock user and notifying the stock user;
When the class of the stock user is the same, disabling the display authority of the user information in the class of the stock user and notifying the stock user;
when the level of the stock user is low, limiting and displaying the display authority of the target word with low level in the user information of the stock user, and informing the stock user;
Wherein, the user information at least comprises: user registration information and user release information.
After the stock user information is detected by using the recognition model, target words and target word grades in the stock user information can be obtained, so that a grade set of the stock user information is generated, and the grade of the stock user information and the grade of the stock user can be determined according to the grade set by the method; such as:
For user identification a, it registers user account A1; if the stock user A1 issues the information B1 and the information B2, detecting to determine that the information B1 contains the target word c and the target word d, wherein the grade of the target word c is 1, the grade of the target word d is 3, and detecting that the information B2 does not contain the target word; from the above information, the following information can be derived: the level of the stock user information B1 is determined to be 3 by the level of the target word d, and the level of the stock user information B2 is 0; the rank of the stock user A1 is determined to be 3 by the rank of the stock user information B1.
In the processing flow, when the level of the stock users is high, the account numbers of the high-level users can be frozen after manual auditing; when the class of the stock users is the middle, the corresponding user information can be reset for the middle class account; when the level of the stock users is low, the corresponding user information can be displayed by using a mask.
In some practical applications, the same actual user (natural person) may have multiple user identities (e.g. multiple accounts are registered through different authentication information), where the natural person may be rated: by identifying the natural person, the level of the natural person is the same as the highest level in all user levels, and the authority configuration information of all users registered by the natural person is updated according to the flow corresponding to the level of the natural person.
In some embodiments, after invoking the identification model to detect the stock user information, further comprising:
Generating an expanded target word set according to the obtained target words and target word grades in the stock user information and the expanded words and expanded word grades generated in the detection process;
The extended target word set is used for detecting new user registration information or new user release information.
According to the recognition model obtained by training, the invention can detect the stock user information and can also generate an expanded target word set for detecting new user registration information or new user release information according to the obtained target words and target word grades in the stock user information and the expanded words and expanded word grades generated in the detection process. In some practical applications, the expanded target word set can be called by other system interfaces or other network platforms, and is directly used for detecting security control of other information.
In some embodiments, the above processing device for stock user information is further configured to:
obtaining model evaluation data, evaluating the identification model according to the model evaluation data, and generating a first evaluation result; optimizing the identification model according to the first evaluation result; and/or the number of the groups of groups,
After updating the authority configuration information of the stock users, receiving user feedback information sent by a client, and generating a second evaluation result according to the user feedback information; and optimizing the identification model according to the second evaluation result.
The method can optimize the identification model in time so as to achieve more accurate identification and better use effect.
Fig. 3 illustrates an exemplary system architecture 300 of a method of processing stock user information or a processing apparatus of stock user information to which embodiments of the present invention may be applied.
As shown in fig. 3, the system architecture 300 may include terminal devices 301, 302, 303, a network 304, and a server 305. The network 304 is used as a medium to provide communication links between the terminal devices 301, 302, 303 and the server 305. The network 304 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
A user may interact with the server 305 via the network 304 using the terminal devices 301, 302, 303 to receive or send messages or the like. Various client applications may be installed on the terminal devices 301, 302, 303, such as shopping class applications, blog class applications, search class applications, instant messaging tools, mailbox clients, social platform software, and the like.
The terminal devices 301, 302, 303 may be a variety of electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablets, laptop and desktop computers, and the like.
The server 305 may be a server providing various services, such as a background management server providing support for shopping-type websites browsed by the user using the terminal devices 301, 302, 303. The background management server can process the received data such as the user information detection request and the like, and feed back the processing result to the terminal equipment.
It should be noted that, the method for processing the stock user information provided in the embodiment of the present invention is generally executed by the server 305, and accordingly, the device for processing the stock user information is generally disposed in the server 305.
It should be understood that the number of terminal devices, networks and servers in fig. 3 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Referring now to FIG. 4, there is illustrated a schematic diagram of a computer system 400 suitable for use in implementing an embodiment of the present invention. The terminal device shown in fig. 4 is only an example, and should not impose any limitation on the functions and the scope of use of the embodiment of the present invention.
As shown in fig. 4, the computer system 400 includes a Central Processing Unit (CPU) 401, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 402 or a program loaded from a storage section 408 into a Random Access Memory (RAM) 403. In RAM 403, various programs and data required for the operation of system 400 are also stored. The CPU 401, ROM 402, and RAM 403 are connected to each other by a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
The following components are connected to the I/O interface 405: an input section 406 including a keyboard, a mouse, and the like; an output portion 407 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker, and the like; a storage section 408 including a hard disk or the like; and a communication section 409 including a network interface card such as a LAN card, a modem, or the like. The communication section 409 performs communication processing via a network such as the internet. The drive 410 is also connected to the I/O interface 405 as needed. A removable medium 411 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed on the drive 410 as needed, so that a computer program read therefrom is installed into the storage section 408 as needed.
In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 409 and/or installed from the removable medium 411. The above-described functions defined in the system of the present invention are performed when the computer program is executed by a Central Processing Unit (CPU) 401.
The computer readable medium shown in the present invention may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules involved in the embodiments of the present invention may be implemented in software or in hardware. The described modules may also be provided in a processor, for example, as: a processor comprises a training module, a detection module and a processing module. The names of these modules do not constitute a limitation on the module itself in some cases.
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be present alone without being fitted into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to include: step 101, responding to a processing request of stock user information, calling a user information sample, a sample feature and a sample label, and training by adopting a natural language processing algorithm and a gradient lifting tree algorithm to obtain an identification model; 102, acquiring stock user information, calling the identification model to detect the stock user information, obtaining target words and target word grades in the stock user information, and generating a grade set of the stock user information; and 103, according to the highest grade in the grade set, invoking a flow corresponding to the highest grade in a processing flow library, and updating the authority configuration information of the stock user.
According to the technical scheme of the embodiment of the invention, the model training is performed by adopting a natural language processing algorithm and a gradient lifting tree algorithm to obtain the recognition model capable of recognizing the target word and the expansion word of the target word, and the recognition model is called to detect the stock user information so as to perform corresponding processing; after the stock user information is detected, an expanded word set can be obtained for detecting new user information; the method solves the technical problems that the prior art cannot safely control the information content of the stock users and the user information is not well safely controlled by using a fixed word stock; and further, the method and the device can safely control the information content of the stored user, and achieve the technical effect of better controlling the new user information based on the original fixed word stock and the obtained expanded word set.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives can occur depending upon design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (9)
1. A method for processing stock user information, comprising:
Responding to a processing request of stock user information, calling historical data of the user information to obtain a user information sample, sample characteristics and a sample label, and training by adopting a natural language processing algorithm and a gradient lifting tree algorithm to obtain an identification model;
Acquiring stock user information, calling the identification model to detect the stock user information, obtaining target words and target word grades in the stock user information, and generating a grade set of the stock user information; the hierarchical set of stock user information includes: target words, target word grades, stock user information corresponding to the target words, and user identifications of stock users corresponding to the target words;
According to the highest level in the level set, invoking a flow corresponding to the highest level in a processing flow library to update the authority configuration information of the stock user, including: for the same user identification, taking the highest grade of the target word grades of the target words in the stock user information as the grade of the stock user information; taking the highest level in the levels of all the stock user information of the stock users as the level of the stock users; and calling a flow corresponding to the level of the stock user in a processing flow library, and updating the authority configuration information of the stock user.
2. The method of claim 1, wherein the retrieving the user information samples, sample characteristics, sample tags, comprises:
Invoking user information historical data of known processing results, and selecting a preset number of historical user information from the historical user information to serve as the user information sample;
Taking the characteristic of the historical user information as the sample characteristic; and taking the corresponding grade of the historical user information in the processing result and the grade of the historical target word in the historical user information as the sample label.
3. The method of claim 2, wherein training to obtain the recognition model using a natural language processing algorithm and a gradient-lifted tree algorithm comprises:
performing word segmentation processing on the historical user information by adopting a natural language processing algorithm, and performing voice and semantic expansion on words obtained by the word segmentation processing based on the called public opinion information to obtain a historical expansion word set;
And taking the historical user information and the historical expanded word set together as training samples, and training by adopting a gradient lifting tree algorithm according to the training samples, sample characteristics and sample labels to obtain the identification model.
4. The method of claim 1, wherein the invoking the process flow corresponding to the level of the stock user in the process flow library updates the rights configuration information of the stock user, comprising:
When the level of the stock user is high, disabling the user account use permission of the stock user and notifying the stock user;
When the class of the stock user is the same, disabling the display authority of the user information in the class of the stock user and notifying the stock user;
when the level of the stock user is low, limiting and displaying the display authority of the target word with low level in the user information of the stock user, and informing the stock user;
Wherein, the user information at least comprises: user registration information and user release information.
5. A method according to any one of claims 1-3, further comprising, after invoking the identification model to detect the stock user information:
Generating an expanded target word set according to the obtained target words and target word grades in the stock user information and the expanded words and expanded word grades generated in the detection process;
The extended target word set is used for detecting new user registration information or new user release information.
6. A method according to any one of claims 1-3, further comprising:
obtaining model evaluation data, evaluating the identification model according to the model evaluation data, and generating a first evaluation result; optimizing the identification model according to the first evaluation result; and/or the number of the groups of groups,
After updating the authority configuration information of the stock users, receiving user feedback information sent by a client, and generating a second evaluation result according to the user feedback information; and optimizing the identification model according to the second evaluation result.
7. A device for processing stock user information, comprising:
The training module is used for responding to the processing request of the stock user information, calling the user information history data to obtain a user information sample, sample characteristics and sample labels, and training by adopting a natural language processing algorithm and a gradient lifting tree algorithm to obtain an identification model;
The detection module is used for acquiring stock user information, calling the identification model to detect the stock user information, obtaining target words and target word grades in the stock user information, and generating a grade set of the stock user information; the hierarchical set of stock user information includes: target words, target word grades, stock user information corresponding to the target words, and user identifications of stock users corresponding to the target words;
The processing module is used for calling a flow corresponding to the highest level in the processing flow library according to the highest level in the level set, updating the authority configuration information of the stock user, and comprises the following steps: for the same user identification, taking the highest grade of the target word grades of the target words in the stock user information as the grade of the stock user information; taking the highest level in the levels of all the stock user information of the stock users as the level of the stock users; and calling a flow corresponding to the level of the stock user in a processing flow library, and updating the authority configuration information of the stock user.
8. A processing electronic device for storing user information, comprising:
One or more processors;
Storage means for storing one or more programs,
When executed by the one or more processors, causes the one or more processors to implement the method of any of claims 1-6.
9. A computer readable medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any of claims 1-6.
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