CN117076775A - Information data processing method, information data processing device, computer equipment and storage medium - Google Patents
Information data processing method, information data processing device, computer equipment and storage medium Download PDFInfo
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- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
Abstract
The application belongs to the field of artificial intelligence and financial science and technology, and relates to a processing method of information data, which comprises the following steps: acquiring a label keyword of an enterprise dimension label; extracting keywords from the acquired initial enterprise information to obtain information keywords; performing similarity calculation on the tag keywords and the information keywords to obtain similarity; determining target tag keywords matched with the initial information from all tag keywords based on the similarity; labeling the initial enterprise information by using the target label keyword to obtain information containing the target enterprise; storing the target enterprise information. The application also provides a processing device of the information data, a computer device and a storage medium. In addition, the application also relates to a block chain technology, and error positioning information can be stored in the block chain. The method and the device can be applied to the label labeling scene of the enterprise information in the financial field, improve the processing efficiency of label labeling of the enterprise information, and ensure the accuracy of the enterprise label of the generated enterprise information.
Description
Technical Field
The application relates to the technical field of artificial intelligence development and the technical field of finance, in particular to a method, a device, computer equipment and a storage medium for processing information data.
Background
With the rapid development of the mobile internet, the way in which people acquire information has also changed greatly, and more people acquire information using networks. As an enterprise-oriented ToB application, information recommendation policies are different from personal-oriented applications. The ToC application is personal, generates a user image by collecting basic information of a user, generates a mapping relation with information labels, and completes thousands of people recommendation, and has a mature scheme and recommendation model. However, for enterprise-oriented ToB applications, enterprise information provided by information kiosks/third party vendors often has no enterprise-related tags. In the prior art, related label labeling personnel are generally required to be arranged to execute labeling processing on various enterprise information in an application, so that the label labeling mode applied to the enterprise information needs to consume more manpower resources, has low processing efficiency and cannot guarantee the accuracy of the enterprise labels of the generated enterprise information.
Disclosure of Invention
The embodiment of the application aims to provide a processing method, a device, computer equipment and a storage medium of information data, which are used for solving the technical problems that more manpower resources are required to be consumed, the processing efficiency is low and the accuracy of an enterprise label of generated enterprise information cannot be ensured in the existing label marking mode applied to the enterprise information.
In order to solve the above technical problems, an embodiment of the present application provides a method for processing information data, which adopts the following technical scheme:
acquiring label keywords of a pre-constructed enterprise dimension label;
acquiring initial enterprise information acquired in advance;
extracting keywords from the initial enterprise information to obtain corresponding information keywords;
performing similarity calculation on the tag keywords and the information keywords based on a preset similarity algorithm to obtain corresponding similarity;
determining target tag keywords matched with the initial information from all the tag keywords based on the similarity;
performing label labeling processing on the initial enterprise information by using the target label keywords to obtain target enterprise information containing enterprise labels;
And storing the target enterprise information based on the data type of the target enterprise information.
Further, the step of determining, based on the similarity, a target tag keyword that matches the initial information from among the tag keywords specifically includes:
comparing the values of all the similarities, and screening out the appointed similarity with the maximum value from all the similarities;
judging whether the appointed similarity is larger than a preset similarity threshold value or not;
if the specified similarity is greater than the similarity threshold, acquiring specified tag keywords corresponding to the specified similarity from all the tag keywords;
and taking the appointed tag keyword as the target tag keyword.
Further, after the step of determining whether the specified similarity is greater than a preset similarity threshold, the method further includes:
if the specified similarity is smaller than the similarity threshold, acquiring a preset attribute-free label;
performing label labeling processing on the initial enterprise information by using the non-attribute label to obtain labeled initial enterprise information;
and storing the marked initial enterprise information based on a preset storage medium.
Further, the step of storing the target enterprise information based on the data type of the target enterprise information specifically includes:
acquiring the data type of the target enterprise information;
determining a target data storage mode corresponding to the data type;
and storing the target enterprise information based on the target data storage mode.
Further, after the step of storing the target enterprise information based on the data type of the target enterprise information, the method further comprises:
acquiring enterprise information to be pushed; the enterprise information to be pushed carries corresponding enterprise labels;
acquiring a target enterprise portrayal tag associated with a target user;
inputting the target enterprise portrait tag and the enterprise information to be pushed into a preset information recommendation model;
and analyzing and processing the enterprise information to be pushed and the target enterprise portrait tag through the information recommendation model to generate a target enterprise information list corresponding to the target user.
Further, before the step of inputting the target enterprise portrait tag and the enterprise information to be pushed into a preset information recommendation model, the method further comprises:
Acquiring pre-acquired training sample data; wherein the training sample data comprises a business portrait tag and specified business information of the same business tag as the business portrait tag;
calling a preset initial learning model;
and carrying out iterative training on the initial learning model based on the training sample data to obtain the information recommendation model.
Further, after the step of analyzing the to-be-pushed enterprise information and the target enterprise portrait tag through the information recommendation model to generate a target enterprise information list corresponding to the target user, the method further includes:
acquiring user information of the target user;
information inquiry is carried out based on the user information so as to obtain the communication information and the work rest information of the target user;
and pushing the target enterprise information list to the target user based on the communication information and the work rest information.
In order to solve the above technical problems, the embodiment of the present application further provides a processing device for information data, which adopts the following technical scheme:
the first acquisition module is used for acquiring label keywords of the pre-constructed enterprise dimension labels;
The second acquisition module is used for acquiring initial enterprise information acquired in advance;
the extraction module is used for extracting keywords from the initial enterprise information to obtain corresponding information keywords;
the calculation module is used for carrying out similarity calculation on the tag keywords and the information keywords based on a preset similarity algorithm to obtain corresponding similarity;
the determining module is used for determining target tag keywords matched with the initial information from all the tag keywords based on the similarity;
the first processing module is used for carrying out label marking processing on the initial enterprise information by using the target label keyword to obtain target enterprise information containing enterprise labels;
and the second processing module is used for storing and processing the target enterprise information based on the data type of the target enterprise information.
In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical schemes:
acquiring label keywords of a pre-constructed enterprise dimension label;
acquiring initial enterprise information acquired in advance;
extracting keywords from the initial enterprise information to obtain corresponding information keywords;
Performing similarity calculation on the tag keywords and the information keywords based on a preset similarity algorithm to obtain corresponding similarity;
determining target tag keywords matched with the initial information from all the tag keywords based on the similarity;
performing label labeling processing on the initial enterprise information by using the target label keywords to obtain target enterprise information containing enterprise labels;
and storing the target enterprise information based on the data type of the target enterprise information.
In order to solve the above technical problems, an embodiment of the present application further provides a computer readable storage medium, which adopts the following technical schemes:
acquiring label keywords of a pre-constructed enterprise dimension label;
acquiring initial enterprise information acquired in advance;
extracting keywords from the initial enterprise information to obtain corresponding information keywords;
performing similarity calculation on the tag keywords and the information keywords based on a preset similarity algorithm to obtain corresponding similarity;
determining target tag keywords matched with the initial information from all the tag keywords based on the similarity;
Performing label labeling processing on the initial enterprise information by using the target label keywords to obtain target enterprise information containing enterprise labels;
and storing the target enterprise information based on the data type of the target enterprise information.
Compared with the prior art, the embodiment of the application has the following main beneficial effects:
firstly, acquiring label keywords of a pre-constructed enterprise dimension label; then acquiring initial enterprise information acquired in advance; extracting keywords from the initial enterprise information to obtain corresponding information keywords, and calculating the similarity between the tag keywords and the information keywords based on a preset similarity algorithm to obtain corresponding similarity; subsequently, determining target tag keywords matched with the initial information from all the tag keywords based on the similarity; further performing label labeling processing on the initial enterprise information by using the target label keywords to obtain target enterprise information containing enterprise labels; and finally, storing the target enterprise information based on the data type of the target enterprise information. According to the embodiment of the application, through the use of a similarity algorithm, similarity analysis is carried out on the label keywords of the pre-constructed enterprise dimension labels and the information keywords, and then label marking processing of initial enterprise information is realized according to the obtained target enterprise information, so that the target enterprise information containing the enterprise labels is obtained, automatic marking processing of the enterprise information is realized, the workload required by label marking of the enterprise information is effectively reduced, the processing efficiency of label marking of the enterprise information is improved, and the accuracy of the generated enterprise labels of the enterprise information is ensured.
Drawings
In order to more clearly illustrate the solution of the present application, a brief description will be given below of the drawings required for the description of the embodiments of the present application, it being apparent that the drawings in the following description are some embodiments of the present application, and that other drawings may be obtained from these drawings without the exercise of inventive effort for a person of ordinary skill in the art.
FIG. 1 is an exemplary system architecture diagram in which the present application may be applied;
FIG. 2 is a flow chart of one embodiment of a method of processing information data according to the present application;
FIG. 3 is a schematic diagram of an embodiment of an information data processing apparatus according to the present application;
FIG. 4 is a schematic structural diagram of one embodiment of a computer device in accordance with the present application.
Detailed Description
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the applications herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having" and any variations thereof in the description of the application and the claims and the description of the drawings above are intended to cover a non-exclusive inclusion. The terms first, second and the like in the description and in the claims or in the above-described figures, are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
In order to make the person skilled in the art better understand the solution of the present application, the technical solution of the embodiment of the present application will be clearly and completely described below with reference to the accompanying drawings.
As shown in fig. 1, a system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
The user may interact with the server 105 via the network 104 using the terminal devices 101, 102, 103 to receive or send messages or the like. Various communication client applications, such as a web browser application, a shopping class application, a search class application, an instant messaging tool, a mailbox client, social platform software, etc., may be installed on the terminal devices 101, 102, 103.
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablet computers, electronic book readers, MP3 players (Moving Picture Experts Group Audio Layer III, dynamic video expert compression standard audio plane 3), MP4 (Moving Picture Experts Group Audio Layer IV, dynamic video expert compression standard audio plane 4) players, laptop and desktop computers, and the like.
The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the terminal devices 101, 102, 103.
It should be noted that, the method for processing information data provided in the embodiment of the present application is generally executed by a server/terminal device, and accordingly, the device for processing information data is generally disposed in the server/terminal device.
The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.
Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.
It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
With continued reference to FIG. 2, a flow chart of one embodiment of a method of processing information data according to the present application is shown. The order of the steps in the flowchart may be changed and some steps may be omitted according to various needs. The processing method of the information data provided by the embodiment of the application can be applied to any scene needing to carry out label construction of enterprise information, and the processing method of the information data can be applied to products of the scenes, such as label construction of insurance enterprise information in the field of financial insurance. The information data processing method comprises the following steps:
Step S201, obtaining label keywords of a pre-constructed enterprise dimension label.
In this embodiment, the electronic device (e.g., the server/terminal device shown in fig. 1) on which the information data processing method operates may acquire the tag key words through a wired connection manner or a wireless connection manner. It should be noted that the wireless connection may include, but is not limited to, 3G/4G/5G connection, wiFi connection, bluetooth connection, wiMAX connection, zigbee connection, UWB (ultra wideband) connection, and other now known or later developed wireless connection. The enterprise dimension tags may include enterprise type, industry, scale, and enterprise user behavior tags, among others. The enterprise types may include small, individual business, personal business, etc. Industry types may include financial types, medical types, IT types, and so forth. The scale types may include extra large, medium, small scale, etc. The weights can be further distributed to various enterprise dimension labels, and the greater the influence degree of the enterprise dimension labels on enterprises is, the greater the corresponding weights of the enterprise dimension labels are. By way of example, if the business dimension label is an industry, the label keywords of the corresponding industry dimension label may include financial, medical, IT, and the like.
Step S202, acquiring pre-acquired initial enterprise information.
In this embodiment, in the application scenario in the field of financial technology, the enterprise information may be the enterprise information to be recommended of the captured financial technology company, such as insurance company, bank, etc.
Step S203, extracting keywords from the initial enterprise information to obtain corresponding information keywords.
In this embodiment, a keyword extraction algorithm may be used to extract keywords from the initial enterprise information, so as to obtain corresponding information keywords. The selection of the keyword extraction algorithm is not limited, and can be determined according to actual use requirements.
Step S204, performing similarity calculation on the tag keywords and the information keywords based on a preset similarity algorithm to obtain corresponding similarity.
In this embodiment, the selection of the similarity algorithm is not limited, and may be determined according to actual use requirements, for example, a cosine similarity algorithm may be used.
Step S205, determining target tag keywords matched with the initial information from all the tag keywords based on the similarity.
In this embodiment, the specific implementation process of determining the target tag keyword matched with the initial information from all the tag keywords based on the similarity is described in further detail in the following specific embodiments, which will not be described herein.
And S206, performing label labeling processing on the initial enterprise information by using the target label keywords to obtain target enterprise information containing enterprise labels.
In this embodiment, the target label keyword is used to label the initial enterprise information, so as to construct an association relationship between the target label keyword and the initial enterprise information, thereby obtaining target enterprise information including an enterprise label, where the enterprise label may refer to a corresponding target label keyword.
Step S207, storing the target enterprise information based on the data type of the target enterprise information.
In this embodiment, the foregoing specific implementation process of storing the target enterprise information based on the data type of the target enterprise information will be described in further detail in the following specific embodiments, which will not be described herein.
Firstly, acquiring label keywords of a pre-constructed enterprise dimension label; then acquiring initial enterprise information acquired in advance; extracting keywords from the initial enterprise information to obtain corresponding information keywords, and calculating the similarity between the tag keywords and the information keywords based on a preset similarity algorithm to obtain corresponding similarity; subsequently, determining target tag keywords matched with the initial information from all the tag keywords based on the similarity; further performing label labeling processing on the initial enterprise information by using the target label keywords to obtain target enterprise information containing enterprise labels; and finally, storing the target enterprise information based on the data type of the target enterprise information. According to the method, the similarity analysis is carried out on the label keywords of the pre-constructed enterprise dimension labels and the information keywords based on the similarity algorithm, so that label marking processing of initial enterprise information is achieved according to the obtained target enterprise information, and therefore the target enterprise information containing the enterprise labels is obtained, automatic marking processing of the enterprise information is achieved, workload required by label marking of the enterprise information is effectively reduced, processing efficiency of label marking of the enterprise information is improved, and accuracy of the generated enterprise labels of the enterprise information is guaranteed.
In some alternative implementations, step S205 includes the steps of:
and comparing the values of all the similarities, and screening the designated similarity with the largest value from all the similarities.
In this embodiment, the sorting result may be obtained by comparing the values of all the similarities and sorting the values in order from large to small, so that the first-order similarity in the sorting result may be used as the specified similarity.
And judging whether the appointed similarity is larger than a preset similarity threshold value.
In this embodiment, the value of the similarity threshold is not specifically limited, and may be set according to the actual requirement for similarity confirmation.
And if the specified similarity is greater than the similarity threshold, acquiring specified tag keywords corresponding to the specified similarity from all the tag keywords.
And taking the appointed tag keyword as the target tag keyword.
The application screens out the appointed similarity with the largest value from all the similarities by comparing the values of all the similarities; then judging whether the appointed similarity is larger than a preset similarity threshold value or not; if the specified similarity is greater than the similarity threshold, acquiring specified tag keywords corresponding to the specified similarity from all the tag keywords; and taking the appointed tag keyword as the target tag keyword. According to the method, the specified similarity with the largest value is obtained by comparing all the calculated similarities, the specified similarity is further compared with the preset similarity threshold, if the specified similarity is larger than the similarity threshold, the specified label keyword corresponding to the specified similarity is used as the target label keyword, and the accuracy of the target label keyword is improved.
In some optional implementations of this embodiment, after the step of determining whether the specified similarity is greater than a preset similarity threshold, the electronic device may further perform the following steps:
and if the specified similarity is smaller than the similarity threshold, acquiring a preset attribute-free label.
In this embodiment, the above-mentioned attribute-free label is a label that is pre-built to indicate that there is no attribute of the enterprise dimension.
And performing label labeling processing on the initial enterprise information by using the non-attribute label to obtain labeled initial enterprise information.
In this embodiment, for example, if the business dimension label is industry and the business information is information without industry attribute, the business label of the information without industry attribute is classified as no industry, that is, the label labeling process is performed on the business information by using the label without industry.
And storing the marked initial enterprise information based on a preset storage medium.
In this embodiment, for the special enterprise information including the non-attribute tag, a storage medium dedicated for storing the special enterprise information is pre-configured, so as to implement specialized storage of the special enterprise information, which is beneficial to quickly searching for the required enterprise information with the non-attribute tag based on the use of the storage medium.
When the appointed similarity is detected to be smaller than the similarity threshold value, acquiring a preset attribute-free label; then, labeling the initial enterprise information by using the non-attribute label to obtain labeled initial enterprise information; and storing the marked initial enterprise information based on a preset storage medium. According to the method and the device, when the appointed similarity is detected to be smaller than the similarity threshold, label marking processing is intelligently carried out on the initial enterprise information by adopting the attribute-free labels, so that the marked initial enterprise information is generated, and the accuracy of the generated marked initial enterprise information is ensured.
In some alternative implementations, step S207 includes the steps of:
and acquiring the data type of the target enterprise information.
In this embodiment, the data type refers to an information data type.
And determining a target data storage mode corresponding to the data type.
In this embodiment, for the data of different data types, the data storage modes corresponding to the data types one to one are allocated in advance, so as to realize the standard storage of the data of different data types, thereby being beneficial to the rapid acquisition of the subsequent data and improving the data acquisition efficiency. Specifically, the storage mode of the data of the information data type can be determined according to the actual service requirement. For example, for the enterprise information belonging to the information data type, the storage of the enterprise information by using a blockchain manner may be preset.
And storing the target enterprise information based on the target data storage mode.
The application obtains the data type of the target enterprise information; then determining a target data storage mode corresponding to the data type; and storing the target enterprise information based on the target data storage mode. According to the application, the target enterprise information is stored by adopting the target data storage mode corresponding to the data type of the target enterprise information, so that the storage intelligence and the storage standardization of the enterprise information are improved.
In some alternative implementations, after step S207, the electronic device may further perform the following steps:
acquiring enterprise information to be pushed;
in this embodiment, the number of the enterprise information includes a plurality of pieces, and the enterprise information to be pushed carries a corresponding enterprise tag. In the application scenario in the field of financial technology, the enterprise information may be the enterprise information of a captured financial technology company, such as an insurance company, a bank, etc.
A target enterprise portrayal tag associated with a target user is obtained.
In this embodiment, the target enterprise portrait tag refers to an enterprise portrait tag of an enterprise where a target user is located. Enterprise portrayal tags can include, but are not limited to, enterprise name, enterprise type, industry, scale, and the like.
And inputting the target enterprise portrait tag and the enterprise information to be pushed into a preset information recommendation model.
In this embodiment, for the training generation process of the information recommendation model, the present application will be described in further detail in the following specific embodiments, which will not be described here.
And analyzing and processing the enterprise information to be pushed and the target enterprise portrait tag through the information recommendation model to generate a target enterprise information list corresponding to the target user.
In this embodiment, the information recommendation model is used to analyze the to-be-pushed enterprise information and the target enterprise portrait label, so as to calculate the matching score of the keyword in each to-be-pushed enterprise information and the target enterprise portrait label, and according to the matching score, the target enterprise information list corresponding to the target user is selected from all to-be-pushed enterprise information. Wherein the target enterprise information list includes a plurality of pieces of enterprise information arranged from high to low in a matching score. The plurality of pieces of enterprise information refer to enterprise information with matching scores greater than a preset score threshold value in the enterprise information to be pushed, the value of the score threshold value is not particularly limited, and the enterprise information can be set according to actual use requirements.
The method and the device acquire enterprise information to be pushed; then, a target enterprise portrait tag associated with a target user is obtained; inputting the target enterprise portrait tag and the enterprise information to be pushed into a preset information recommendation model; and then analyzing and processing the enterprise information to be pushed and the target enterprise portrait tag through the information recommendation model to generate a target enterprise information list corresponding to the target user. The method and the device for processing the target enterprise portrait labels and the enterprise information to be pushed, which are associated with the target users, are based on the use of the information recommendation model, so that a target enterprise information list for pushing the target users can be quickly and accurately generated, the recommendation efficiency of the enterprise information list is improved, and the intelligence and the accuracy of enterprise information recommendation are improved.
In some optional implementations of this embodiment, before the step of inputting the target enterprise portrait tag and the enterprise information to be pushed into a preset information recommendation model, the electronic device may further perform the following steps:
pre-acquired training sample data is acquired.
In this embodiment, the training sample data includes a business portrait tag and specified business information having the same business tag as the business portrait tag.
And calling a preset initial learning model.
In this embodiment, the number of the initial learning models includes a plurality of models, specifically, a multi-model fusion network employing deep learning. The deep learning multi-model fusion network is composed of a form of Attention mechanism (Attention) +convolutional neural network (CNN) model.
And carrying out iterative training on the initial learning model based on the training sample data to obtain the information recommendation model.
In this embodiment, by using a plurality of initial learning models, training is performed according to different enterprise dimensions by using sample data corresponding to the enterprise dimensions in the training sample data, and the training sample data corresponding to the learning by the initial learning models includes enterprise portrait tags and data association relations between specified enterprise information having the same enterprise tag as the enterprise portrait tags. And adding the scores output by the initial learning models into weights, and using a weighted average loss correction model to obtain a final information recommendation model. In the use process of the information recommendation model, enterprise user behavior data can be further collected, the enterprise user behavior is used as a sample to perform model optimization on the information recommendation model, namely, the enterprise user behavior data is used as data similar to enterprise labels to perform model training, corresponding factor weights are adjusted, so that the information recommendation model performs analysis on the enterprise user behavior data, the factor weights of the recommendation model are consulted in a back feeding mode, and accuracy and precision of a recommendation algorithm are improved.
The application acquires the pre-acquired training sample data; then calling a preset initial learning model; and performing iterative training on the initial learning model based on the training sample data to obtain the information recommendation model. According to the method, the initial learning model is trained by adopting the pre-collected training sample data, so that the information recommendation model applied to enterprise information recommendation can be quickly constructed, and the creation efficiency of the information recommendation model is improved. The method is beneficial to realizing the purpose of recommending matched enterprise information for users rapidly and accurately through the use of the information recommendation model, thereby improving the recommendation intelligence and recommendation accuracy of the enterprise information.
In some optional implementations of this embodiment, after the step of analyzing the to-be-pushed enterprise information and the target enterprise portrait tag by the information recommendation model to generate a target enterprise information list corresponding to the target user, the electronic device may further execute the following steps:
and acquiring the user information of the target user.
In this embodiment, the user information may include a user name.
And inquiring information based on the user information to acquire the communication information and the work rest information of the target user.
In this embodiment, information inquiry may be performed on a client information system constructed in advance based on the user information, so as to inquire out the communication information and the work and rest information of the target user. The communication information may include a telephone number or a mail address, among others. The work rest information refers to a rest period of the target user on a work day, such as a noon break period.
And pushing the target enterprise information list to the target user based on the communication information and the work rest information.
In this embodiment, the information pushing time may be set according to the work rest information, so as to implement information pushing processing for pushing the target enterprise information list to the target user in a rest segment corresponding to the work rest information of the target user, thereby avoiding affecting the work of the target user and improving the sending intelligence of the information.
The application obtains the user information of the target user; then, carrying out information inquiry based on the user information to acquire the communication information and the work and rest information of the target user; and pushing the target enterprise information list to the target user based on the communication information and the work rest information, so as to realize information pushing processing of pushing the target enterprise information list to the target user in a rest section corresponding to the work rest information of the target user, thereby avoiding influencing the work of the target user and improving the sending intelligence of the information.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present application.
It is emphasized that the target enterprise information may also be stored in a blockchain node in order to further ensure privacy and security of the target enterprise information.
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), which is essentially a decentralised database, is a string of data blocks that are generated by cryptographic means in association, each data block containing a batch of information of network transactions for verifying the validity of the information (anti-counterfeiting) 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 embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.
Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by computer readable instructions stored in a computer readable storage medium that, when executed, may comprise the steps of the embodiments of the methods described above. The storage medium may be a nonvolatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a random access Memory (Random Access Memory, RAM).
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited in order and may be performed in other orders, unless explicitly stated herein. Moreover, at least some of the steps in the flowcharts of the figures may include a plurality of sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, the order of their execution not necessarily being sequential, but may be performed in turn or alternately with other steps or at least a portion of the other steps or stages.
With further reference to fig. 3, as an implementation of the method shown in fig. 2, the present application provides an embodiment of an information data processing apparatus, which corresponds to the embodiment of the method shown in fig. 2, and the apparatus is particularly applicable to various electronic devices.
As shown in fig. 3, the information data processing apparatus 300 according to the present embodiment includes: a first acquisition module 301, a second acquisition module 302, an extraction module 303, a calculation module 304, a determination module 305, a first processing module 306, and a second processing module 307. Wherein:
a first obtaining module 301, configured to obtain a label keyword of a pre-constructed enterprise dimension label;
a second acquiring module 302, configured to acquire initial enterprise information acquired in advance;
the extracting module 303 is configured to extract keywords from the initial enterprise information to obtain corresponding information keywords;
the calculating module 304 is configured to perform similarity calculation on the tag keyword and the information keyword based on a preset similarity algorithm, so as to obtain a corresponding similarity;
a determining module 305, configured to determine, based on the similarity, a target tag keyword that matches the initial information from all the tag keywords;
A first processing module 306, configured to perform label labeling processing on the initial enterprise information by using the target label keyword, so as to obtain target enterprise information including an enterprise label;
and a second processing module 307, configured to store and process the target enterprise information based on the data type of the target enterprise information.
In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the information data processing method in the foregoing embodiment one by one, and are not described herein again.
In some alternative implementations of the present embodiment, the determining module 305 includes:
the screening submodule is used for carrying out numerical comparison on all the similarities and screening out the appointed similarity with the largest numerical value from all the similarities;
the judging submodule is used for judging whether the appointed similarity is larger than a preset similarity threshold value or not;
the first obtaining submodule is used for obtaining specified tag keywords corresponding to the specified similarity from all the tag keywords if the specified similarity is larger than the similarity threshold;
and the first determining submodule is used for taking the appointed tag keyword as the target tag keyword.
In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the information data processing method in the foregoing embodiment one by one, and are not described herein again.
In some optional implementations of this embodiment, the determining module 305 further includes:
the second obtaining submodule is used for obtaining a preset attribute-free label if the appointed similarity is smaller than the similarity threshold value;
the processing sub-module is used for carrying out label marking processing on the initial enterprise information by using the non-attribute label to obtain marked initial enterprise information;
the first storage sub-module is used for storing the marked initial enterprise information based on a preset storage medium.
In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the information data processing method in the foregoing embodiment one by one, and are not described herein again.
In some alternative implementations of the present embodiment, the second processing module 307 includes:
a third obtaining sub-module, configured to obtain a data type of the target enterprise information;
the second determining submodule is used for determining a target data storage mode corresponding to the data type;
And the second storage sub-module is used for storing the target enterprise information based on the target data storage mode.
In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the information data processing method in the foregoing embodiment one by one, and are not described herein again.
In some optional implementations of this embodiment, the information data processing apparatus further includes:
the third acquisition module is used for acquiring enterprise information to be pushed; the enterprise information to be pushed carries corresponding enterprise labels;
a fourth acquisition module, configured to acquire a target enterprise portrayal tag associated with a target user;
the input module is used for inputting the target enterprise portrait tag and the enterprise information to be pushed into a preset information recommendation model;
and the analysis module is used for analyzing and processing the enterprise information to be pushed and the target enterprise portrait tag through the information recommendation model and generating a target enterprise information list corresponding to the target user.
In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the information data processing method in the foregoing embodiment one by one, and are not described herein again.
In some optional implementations of this embodiment, the information data processing apparatus further includes:
a fifth acquisition module for acquiring training sample data acquired in advance; wherein the training sample data comprises a business portrait tag and specified business information of the same business tag as the business portrait tag;
the calling module is used for calling a preset initial learning model;
and the training module is used for carrying out iterative training on the initial learning model based on the training sample data to obtain the information recommendation model.
In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the information data processing method in the foregoing embodiment one by one, and are not described herein again.
In some optional implementations of this embodiment, the information data processing apparatus further includes:
a sixth acquisition module, configured to acquire user information of the target user;
the query module is used for carrying out information query based on the user information so as to acquire the communication information and the work rest information of the target user;
and the pushing module is used for pushing the target enterprise information list to the target user based on the communication information and the work rest information.
In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the information data processing method in the foregoing embodiment one by one, and are not described herein again.
In order to solve the technical problems, the embodiment of the application also provides computer equipment. Referring specifically to fig. 4, fig. 4 is a basic structural block diagram of a computer device according to the present embodiment.
The computer device 4 comprises a memory 41, a processor 42, a network interface 43 communicatively connected to each other via a system bus. It should be noted that only computer device 4 having components 41-43 is shown in the figures, but it should be understood that not all of the illustrated components are required to be implemented and that more or fewer components may be implemented instead. It will be appreciated by those skilled in the art that the computer device herein is a device capable of automatically performing numerical calculations and/or information processing in accordance with predetermined or stored instructions, the hardware of which includes, but is not limited to, microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASICs), programmable gate arrays (fields-Programmable Gate Array, FPGAs), digital processors (Digital Signal Processor, DSPs), embedded devices, etc.
The computer equipment can be a desktop computer, a notebook computer, a palm computer, a cloud server and other computing equipment. The computer equipment can perform man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch pad or voice control equipment and the like.
The memory 41 includes at least one type of readable storage medium including flash memory, hard disk, multimedia card, card memory (e.g., SD or DX memory, etc.), random Access Memory (RAM), static Random Access Memory (SRAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), programmable Read Only Memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the storage 41 may be an internal storage unit of the computer device 4, such as a hard disk or a memory of the computer device 4. In other embodiments, the memory 41 may also be an external storage device of the computer device 4, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card) or the like, which are provided on the computer device 4. Of course, the memory 41 may also comprise both an internal memory unit of the computer device 4 and an external memory device. In this embodiment, the memory 41 is typically used for storing an operating system installed on the computer device 4 and various application software, such as computer readable instructions of a processing method of information data. Further, the memory 41 may be used to temporarily store various types of data that have been output or are to be output.
The processor 42 may be a central processing unit (Central Processing Unit, CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments. The processor 42 is typically used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is configured to execute computer readable instructions stored in the memory 41 or process data, such as computer readable instructions for executing a processing method of the information data.
The network interface 43 may comprise a wireless network interface or a wired network interface, which network interface 43 is typically used for establishing a communication connection between the computer device 4 and other electronic devices.
Compared with the prior art, the embodiment of the application has the following main beneficial effects:
in the embodiment of the application, firstly, the label keywords of the pre-constructed enterprise dimension labels are obtained; then acquiring initial enterprise information acquired in advance; extracting keywords from the initial enterprise information to obtain corresponding information keywords, and calculating the similarity between the tag keywords and the information keywords based on a preset similarity algorithm to obtain corresponding similarity; subsequently, determining target tag keywords matched with the initial information from all the tag keywords based on the similarity; further performing label labeling processing on the initial enterprise information by using the target label keywords to obtain target enterprise information containing enterprise labels; and finally, storing the target enterprise information based on the data type of the target enterprise information. According to the embodiment of the application, through the use of a similarity algorithm, similarity analysis is carried out on the label keywords of the pre-constructed enterprise dimension labels and the information keywords, and then label marking processing of initial enterprise information is realized according to the obtained target enterprise information, so that the target enterprise information containing the enterprise labels is obtained, automatic marking processing of the enterprise information is realized, the workload required by label marking of the enterprise information is effectively reduced, the processing efficiency of label marking of the enterprise information is improved, and the accuracy of the generated enterprise labels of the enterprise information is ensured.
The present application also provides another embodiment, namely, a computer-readable storage medium storing computer-readable instructions executable by at least one processor to cause the at least one processor to perform the steps of the information data processing method as described above.
Compared with the prior art, the embodiment of the application has the following main beneficial effects:
in the embodiment of the application, firstly, the label keywords of the pre-constructed enterprise dimension labels are obtained; then acquiring initial enterprise information acquired in advance; extracting keywords from the initial enterprise information to obtain corresponding information keywords, and calculating the similarity between the tag keywords and the information keywords based on a preset similarity algorithm to obtain corresponding similarity; subsequently, determining target tag keywords matched with the initial information from all the tag keywords based on the similarity; further performing label labeling processing on the initial enterprise information by using the target label keywords to obtain target enterprise information containing enterprise labels; and finally, storing the target enterprise information based on the data type of the target enterprise information. According to the embodiment of the application, through the use of a similarity algorithm, similarity analysis is carried out on the label keywords of the pre-constructed enterprise dimension labels and the information keywords, and then label marking processing of initial enterprise information is realized according to the obtained target enterprise information, so that the target enterprise information containing the enterprise labels is obtained, automatic marking processing of the enterprise information is realized, the workload required by label marking of the enterprise information is effectively reduced, the processing efficiency of label marking of the enterprise information is improved, and the accuracy of the generated enterprise labels of the enterprise information is ensured.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present application.
It is apparent that the above-described embodiments are only some embodiments of the present application, but not all embodiments, and the preferred embodiments of the present application are shown in the drawings, which do not limit the scope of the patent claims. This application may be embodied in many different forms, but rather, embodiments are provided in order to provide a thorough and complete understanding of the present disclosure. Although the application has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described in the foregoing description, or equivalents may be substituted for elements thereof. All equivalent structures made by the content of the specification and the drawings of the application are directly or indirectly applied to other related technical fields, and are also within the scope of the application.
Claims (10)
1. A method for processing information data, comprising the steps of:
acquiring label keywords of a pre-constructed enterprise dimension label;
acquiring initial enterprise information acquired in advance;
extracting keywords from the initial enterprise information to obtain corresponding information keywords;
performing similarity calculation on the tag keywords and the information keywords based on a preset similarity algorithm to obtain corresponding similarity;
determining target tag keywords matched with the initial information from all the tag keywords based on the similarity;
performing label labeling processing on the initial enterprise information by using the target label keywords to obtain target enterprise information containing enterprise labels;
and storing the target enterprise information based on the data type of the target enterprise information.
2. The method according to claim 1, wherein the step of determining a target tag keyword matching the initial information from among the tag keywords based on the similarity, comprises:
comparing the values of all the similarities, and screening out the appointed similarity with the maximum value from all the similarities;
Judging whether the appointed similarity is larger than a preset similarity threshold value or not;
if the specified similarity is greater than the similarity threshold, acquiring specified tag keywords corresponding to the specified similarity from all the tag keywords;
and taking the appointed tag keyword as the target tag keyword.
3. The method of claim 2, further comprising, after the step of determining whether the specified similarity is greater than a predetermined similarity threshold:
if the specified similarity is smaller than the similarity threshold, acquiring a preset attribute-free label;
performing label labeling processing on the initial enterprise information by using the non-attribute label to obtain labeled initial enterprise information;
and storing the marked initial enterprise information based on a preset storage medium.
4. The method for processing information data according to claim 1, wherein the step of storing the target enterprise information based on the data type of the target enterprise information comprises:
acquiring the data type of the target enterprise information;
determining a target data storage mode corresponding to the data type;
And storing the target enterprise information based on the target data storage mode.
5. The method according to claim 1, characterized by further comprising, after the step of storing the target business information based on the data type of the target business information:
acquiring enterprise information to be pushed; the enterprise information to be pushed carries corresponding enterprise labels;
acquiring a target enterprise portrayal tag associated with a target user;
inputting the target enterprise portrait tag and the enterprise information to be pushed into a preset information recommendation model;
and analyzing and processing the enterprise information to be pushed and the target enterprise portrait tag through the information recommendation model to generate a target enterprise information list corresponding to the target user.
6. The method according to claim 5, further comprising, before the step of inputting the target enterprise portrait tag and the enterprise information to be pushed into a preset information recommendation model:
acquiring pre-acquired training sample data; wherein the training sample data comprises a business portrait tag and specified business information of the same business tag as the business portrait tag;
Calling a preset initial learning model;
and carrying out iterative training on the initial learning model based on the training sample data to obtain the information recommendation model.
7. The method according to claim 5, wherein after the step of generating a target enterprise information list corresponding to the target user by analyzing the enterprise information to be pushed and the target enterprise portrait tag by the information recommendation model, the method further comprises:
acquiring user information of the target user;
information inquiry is carried out based on the user information so as to obtain the communication information and the work rest information of the target user;
and pushing the target enterprise information list to the target user based on the communication information and the work rest information.
8. An information data processing apparatus, comprising:
the first acquisition module is used for acquiring label keywords of the pre-constructed enterprise dimension labels;
the second acquisition module is used for acquiring initial enterprise information acquired in advance;
the extraction module is used for extracting keywords from the initial enterprise information to obtain corresponding information keywords;
The calculation module is used for carrying out similarity calculation on the tag keywords and the information keywords based on a preset similarity algorithm to obtain corresponding similarity;
the determining module is used for determining target tag keywords matched with the initial information from all the tag keywords based on the similarity;
the first processing module is used for carrying out label marking processing on the initial enterprise information by using the target label keyword to obtain target enterprise information containing enterprise labels;
and the second processing module is used for storing and processing the target enterprise information based on the data type of the target enterprise information.
9. A computer device comprising a memory and a processor, the memory having stored therein computer readable instructions which when executed by the processor implement the steps of the method of processing information data as claimed in any one of claims 1 to 7.
10. A computer-readable storage medium, wherein computer-readable instructions are stored on the computer-readable storage medium, which when executed by a processor, implement the steps of the information data processing method according to any one of claims 1 to 7.
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